Articles | Volume 12, issue 3
https://doi.org/10.5194/esd-12-899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review
| 
23 Aug 2021
Review |
| 23 Aug 2021
| 23 Aug 2021
The potential for structural errors in emergent constraints
Benjamin M. Sanderson
CORRESPONDING AUTHOR
Climate Modeling and Global Change, CERFACS, Toulouse, France
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Angeline G. Pendergrass
Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Charles D. Koven
Earth and Environmental Sciences, Lawrence Berkeley National Lab, Berkeley CA, USA
Florent Brient
Dynamical Meteorology Department, LMD/IPSL, Sorbonne Université, Paris, France
Ben B. B. Booth
Hadley Centre for Climate Prediction and Research, Met Office, Exeter, UK
Rosie A. Fisher
Climate Modeling and Global Change, CERFACS, Toulouse, France
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Reto Knutti
Dep. of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Related authors
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
Short summary
Short summary
This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Benjamin Sanderson
Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020, https://doi.org/10.5194/esd-11-721-2020, 2020
Short summary
Short summary
Here, we assess the degree to which the idealized responses to transient forcing increase and step change forcing increase relate to warming under future scenarios. We find a possible explanation for the poor performance of transient metrics (relative to equilibrium response) as a metric of high-emission future warming in terms of their sensitivity to non-equilibrated initial conditions, and propose alternative metrics which better describe warming under high mitigation scenarios.
Benjamin Sanderson
Earth Syst. Dynam., 11, 563–577, https://doi.org/10.5194/esd-11-563-2020, https://doi.org/10.5194/esd-11-563-2020, 2020
Short summary
Short summary
Levels of future temperature change are often used interchangeably with carbon budget allowances in climate policy, a relatively robust relationship on the timescale of this century. However, recent advances in understanding underline that continued warming after net-zero emissions have been achieved cannot be ruled out by observations of warming to date. We consider here how such behavior could be constrained and how policy can be framed in the context of these uncertainties.
Michael Wehner, Dáithí Stone, Dann Mitchell, Hideo Shiogama, Erich Fischer, Lise S. Graff, Viatcheslav V. Kharin, Ludwig Lierhammer, Benjamin Sanderson, and Harinarayan Krishnan
Earth Syst. Dynam., 9, 299–311, https://doi.org/10.5194/esd-9-299-2018, https://doi.org/10.5194/esd-9-299-2018, 2018
Short summary
Short summary
The United Nations Framework Convention on Climate Change challenged the scientific community to describe the impacts of stabilizing the global temperature at its 21st Conference of Parties. A specific target of 1.5 °C above preindustrial levels had not been seriously considered by the climate modeling community prior to the Paris Agreement. This paper analyzes heat waves in simulations designed for this target. We find there are reductions in extreme temperature compared to a 2 °C target.
Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson
Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, https://doi.org/10.5194/esd-9-135-2018, 2018
Short summary
Short summary
Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Benjamin M. Sanderson, Yangyang Xu, Claudia Tebaldi, Michael Wehner, Brian O'Neill, Alexandra Jahn, Angeline G. Pendergrass, Flavio Lehner, Warren G. Strand, Lei Lin, Reto Knutti, and Jean Francois Lamarque
Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, https://doi.org/10.5194/esd-8-827-2017, 2017
Short summary
Short summary
We present the results of a set of climate simulations designed to simulate futures in which the Earth's temperature is stabilized at the levels referred to in the 2015 Paris Agreement. We consider the necessary future emissions reductions and the aspects of extreme weather which differ significantly between the 2 and 1.5 °C climate in the simulations.
Benjamin M. Sanderson, Michael Wehner, and Reto Knutti
Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, https://doi.org/10.5194/gmd-10-2379-2017, 2017
Short summary
Short summary
How should climate model simulations be combined to produce an overall assessment that reflects both their performance and their interdependencies? This paper presents a strategy for weighting climate model output such that models that are replicated or models that perform poorly in a chosen set of metrics are appropriately weighted. We perform sensitivity tests to show how the method results depend on variables and parameter values.
Allison H. Baker, Dorit M. Hammerling, Sheri A. Mickelson, Haiying Xu, Martin B. Stolpe, Phillipe Naveau, Ben Sanderson, Imme Ebert-Uphoff, Savini Samarasinghe, Francesco De Simone, Francesco Carbone, Christian N. Gencarelli, John M. Dennis, Jennifer E. Kay, and Peter Lindstrom
Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016, https://doi.org/10.5194/gmd-9-4381-2016, 2016
Short summary
Short summary
We apply lossy data compression to output from the Community Earth System Model Large Ensemble Community Project. We challenge climate scientists to examine features of the data relevant to their interests and identify which of the ensemble members have been compressed, and we perform direct comparisons on features critical to climate science. We find that applying lossy data compression to climate model data effectively reduces data volumes with minimal effect on scientific results.
Brian C. O'Neill, Claudia Tebaldi, Detlef P. van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti, Elmar Kriegler, Jean-Francois Lamarque, Jason Lowe, Gerald A. Meehl, Richard Moss, Keywan Riahi, and Benjamin M. Sanderson
Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, https://doi.org/10.5194/gmd-9-3461-2016, 2016
Short summary
Short summary
The Scenario Model Intercomparison Project (ScenarioMIP) will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. The design consists of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions. Climate model projections will facilitate integrated studies of climate change as well as address targeted scientific questions.
Colin Gareth Jones, Fanny Adloff, Ben Booth, Peter Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene Hewitt, Hazel Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm Roberts, Benjamin Sanderson, Roland Séférian, Samuel Somot, Pier-Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco J. Doblas-Reyes, Markus G. Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Froelicher, Neven Fuckar, Matthew Gidden, Helge Goessling, Rune Grand Graversen, Silvio Gualdi, Jose Manuel Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason A. Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Melia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard Wood, Shuting Yang, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2024-453, https://doi.org/10.5194/egusphere-2024-453, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming ~6 years (i.e. to 2030). Advances in these areas will increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, as well as deliver significantly improved scientific support to international climate policy, such as the 7th IPCC Assessment Report and the UNFCCC Global Stocktake under the Paris Climate Agreement.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-17, https://doi.org/10.5194/gmd-2024-17, 2024
Preprint under review for GMD
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate; yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes; but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We adapt a fire-behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
EGUsphere, https://doi.org/10.5194/egusphere-2023-2720, https://doi.org/10.5194/egusphere-2023-2720, 2023
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth System Models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Benjamin Mark Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Seferian, Bjørn Hallvard Samset, Detlef van Vuuren, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2023-2127, https://doi.org/10.5194/egusphere-2023-2127, 2023
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth System Models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches which have largely focussed on experiments with prescribed atmospheric carbon dioxide concentrations. We highlight the technical feasibility of achieving these simulations in coming years.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
Short summary
Short summary
We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Nina Raoult, Tim Jupp, Ben Booth, and Peter Cox
Earth Syst. Dynam., 14, 723–731, https://doi.org/10.5194/esd-14-723-2023, https://doi.org/10.5194/esd-14-723-2023, 2023
Short summary
Short summary
Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023, https://doi.org/10.5194/esd-14-457-2023, 2023
Short summary
Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti
Earth Syst. Dynam., 14, 81–100, https://doi.org/10.5194/esd-14-81-2023, https://doi.org/10.5194/esd-14-81-2023, 2023
Short summary
Short summary
Precipitation change is an important consequence of climate change, but it is hard to detect and quantify. Our intuitive method yields robust and interpretable detection of forced precipitation change in three observational datasets for global mean and extreme precipitation, but the different observational datasets show different magnitudes of forced change. Assessment and reduction of uncertainties surrounding forced precipitation change are important for future projections and adaptation.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
Short summary
Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Amy H. Peace, Ben B. B. Booth, Leighton A. Regayre, Ken S. Carslaw, David M. H. Sexton, Céline J. W. Bonfils, and John W. Rostron
Earth Syst. Dynam., 13, 1215–1232, https://doi.org/10.5194/esd-13-1215-2022, https://doi.org/10.5194/esd-13-1215-2022, 2022
Short summary
Short summary
Anthropogenic aerosol emissions have been linked to driving climate responses such as shifts in the location of tropical rainfall. However, the interaction of aerosols with climate remains one of the most uncertain aspects of climate modelling and limits our ability to predict future climate change. We use an ensemble of climate model simulations to investigate what impact the large uncertainty in how aerosols interact with climate has on predicting future tropical rainfall shifts.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
Short summary
Short summary
We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Jie Zhang, Kalli Furtado, Steven T. Turnock, Jane P. Mulcahy, Laura J. Wilcox, Ben B. Booth, David Sexton, Tongwen Wu, Fang Zhang, and Qianxia Liu
Atmos. Chem. Phys., 21, 18609–18627, https://doi.org/10.5194/acp-21-18609-2021, https://doi.org/10.5194/acp-21-18609-2021, 2021
Short summary
Short summary
The CMIP6 ESMs systematically underestimate TAS anomalies in the NH midlatitudes, especially from 1960 to 1990. The anomalous cooling is concurrent in time and space with anthropogenic SO2 emissions. The spurious drop in TAS is attributed to the overestimated aerosol concentrations. The aerosol forcing sensitivity cannot well explain the inter-model spread of PHC biases. And the cloud-amount term accounts for most of the inter-model spread in aerosol forcing sensitivity.
Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen
Geosci. Model Dev., 14, 4977–4999, https://doi.org/10.5194/gmd-14-4977-2021, https://doi.org/10.5194/gmd-14-4977-2021, 2021
Short summary
Short summary
Quantifying dynamical and thermodynamical components of regional precipitation change is a key challenge in climate science. We introduce a novel statistical model (Latent Linear Adjustment Autoencoder) that combines the flexibility of deep neural networks with the robustness advantages of linear regression. The method enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
Short summary
Short summary
This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Polly C. Buotte, Charles D. Koven, Chonggang Xu, Jacquelyn K. Shuman, Michael L. Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, and Lara M. Kueppers
Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, https://doi.org/10.5194/bg-18-4473-2021, 2021
Short summary
Short summary
We present an approach for ensuring the definitions of plant types in dynamic vegetation models are connected to the underlying ecological processes controlling community composition. Our approach can be applied regionally or globally. Robust resolution of community composition will allow us to use these models to address important questions related to future climate and management effects on plant community composition, structure, carbon storage, and feedbacks within the Earth system.
Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu
Biogeosciences, 18, 4005–4020, https://doi.org/10.5194/bg-18-4005-2021, https://doi.org/10.5194/bg-18-4005-2021, 2021
Short summary
Short summary
We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences, 17, 6185–6205, https://doi.org/10.5194/bg-17-6185-2020, https://doi.org/10.5194/bg-17-6185-2020, 2020
Short summary
Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti
Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, https://doi.org/10.5194/esd-11-995-2020, 2020
Short summary
Short summary
In this study, we weight climate models by their performance with respect to simulating aspects of historical climate and their degree of interdependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of-century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared with the unweighted mean.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023, https://doi.org/10.5194/bg-17-4999-2020, https://doi.org/10.5194/bg-17-4999-2020, 2020
Short summary
Short summary
The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
Short summary
Short summary
Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
Vivek K. Arora, Anna Katavouta, Richard G. Williams, Chris D. Jones, Victor Brovkin, Pierre Friedlingstein, Jörg Schwinger, Laurent Bopp, Olivier Boucher, Patricia Cadule, Matthew A. Chamberlain, James R. Christian, Christine Delire, Rosie A. Fisher, Tomohiro Hajima, Tatiana Ilyina, Emilie Joetzjer, Michio Kawamiya, Charles D. Koven, John P. Krasting, Rachel M. Law, David M. Lawrence, Andrew Lenton, Keith Lindsay, Julia Pongratz, Thomas Raddatz, Roland Séférian, Kaoru Tachiiri, Jerry F. Tjiputra, Andy Wiltshire, Tongwen Wu, and Tilo Ziehn
Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, https://doi.org/10.5194/bg-17-4173-2020, 2020
Short summary
Short summary
Since the preindustrial period, land and ocean have taken up about half of the carbon emitted into the atmosphere by humans. Comparison of different earth system models with the carbon cycle allows us to assess how carbon uptake by land and ocean differs among models. This yields an estimate of uncertainty in our understanding of how land and ocean respond to increasing atmospheric CO2. This paper summarizes results from two such model intercomparison projects that use an idealized scenario.
Benjamin Sanderson
Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020, https://doi.org/10.5194/esd-11-721-2020, 2020
Short summary
Short summary
Here, we assess the degree to which the idealized responses to transient forcing increase and step change forcing increase relate to warming under future scenarios. We find a possible explanation for the poor performance of transient metrics (relative to equilibrium response) as a metric of high-emission future warming in terms of their sensitivity to non-equilibrated initial conditions, and propose alternative metrics which better describe warming under high mitigation scenarios.
Benjamin Sanderson
Earth Syst. Dynam., 11, 563–577, https://doi.org/10.5194/esd-11-563-2020, https://doi.org/10.5194/esd-11-563-2020, 2020
Short summary
Short summary
Levels of future temperature change are often used interchangeably with carbon budget allowances in climate policy, a relatively robust relationship on the timescale of this century. However, recent advances in understanding underline that continued warming after net-zero emissions have been achieved cannot be ruled out by observations of warming to date. We consider here how such behavior could be constrained and how policy can be framed in the context of these uncertainties.
Charles D. Koven, Ryan G. Knox, Rosie A. Fisher, Jeffrey Q. Chambers, Bradley O. Christoffersen, Stuart J. Davies, Matteo Detto, Michael C. Dietze, Boris Faybishenko, Jennifer Holm, Maoyi Huang, Marlies Kovenock, Lara M. Kueppers, Gregory Lemieux, Elias Massoud, Nathan G. McDowell, Helene C. Muller-Landau, Jessica F. Needham, Richard J. Norby, Thomas Powell, Alistair Rogers, Shawn P. Serbin, Jacquelyn K. Shuman, Abigail L. S. Swann, Charuleka Varadharajan, Anthony P. Walker, S. Joseph Wright, and Chonggang Xu
Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, https://doi.org/10.5194/bg-17-3017-2020, 2020
Short summary
Short summary
Tropical forests play a crucial role in governing climate feedbacks, and are incredibly diverse ecosystems, yet most Earth system models do not take into account the diversity of plant traits in these forests and how this diversity may govern feedbacks. We present an approach to represent diverse competing plant types within Earth system models, test this approach at a tropical forest site, and explore how the representation of disturbance and competition governs traits of the forest community.
Andrew H. MacDougall, Thomas L. Frölicher, Chris D. Jones, Joeri Rogelj, H. Damon Matthews, Kirsten Zickfeld, Vivek K. Arora, Noah J. Barrett, Victor Brovkin, Friedrich A. Burger, Micheal Eby, Alexey V. Eliseev, Tomohiro Hajima, Philip B. Holden, Aurich Jeltsch-Thömmes, Charles Koven, Nadine Mengis, Laurie Menviel, Martine Michou, Igor I. Mokhov, Akira Oka, Jörg Schwinger, Roland Séférian, Gary Shaffer, Andrei Sokolov, Kaoru Tachiiri, Jerry Tjiputra, Andrew Wiltshire, and Tilo Ziehn
Biogeosciences, 17, 2987–3016, https://doi.org/10.5194/bg-17-2987-2020, https://doi.org/10.5194/bg-17-2987-2020, 2020
Short summary
Short summary
The Zero Emissions Commitment (ZEC) is the change in global temperature expected to occur following the complete cessation of CO2 emissions. Here we use 18 climate models to assess the value of ZEC. For our experiment we find that ZEC 50 years after emissions cease is between −0.36 to +0.29 °C. The most likely value of ZEC is assessed to be close to zero. However, substantial continued warming for decades or centuries following cessation of CO2 emission cannot be ruled out.
Doug McNeall, Jonny Williams, Richard Betts, Ben Booth, Peter Challenor, Peter Good, and Andy Wiltshire
Geosci. Model Dev., 13, 2487–2509, https://doi.org/10.5194/gmd-13-2487-2020, https://doi.org/10.5194/gmd-13-2487-2020, 2020
Short summary
Short summary
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land surface parameter settings than for other forests. It was unclear if this discrepancy was due to a bias in the modelled climate or an error in the land surface component of the model. Correcting the climate of the model with a statistical model corrects the simulation of the Amazon forest, suggesting that the land surface component of the model is not the source of the discrepancy.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
Short summary
Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Christian G. Andresen, David M. Lawrence, Cathy J. Wilson, A. David McGuire, Charles Koven, Kevin Schaefer, Elchin Jafarov, Shushi Peng, Xiaodong Chen, Isabelle Gouttevin, Eleanor Burke, Sarah Chadburn, Duoying Ji, Guangsheng Chen, Daniel Hayes, and Wenxin Zhang
The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, https://doi.org/10.5194/tc-14-445-2020, 2020
Short summary
Short summary
Widely-used land models project near-surface drying of the terrestrial Arctic despite increases in the net water balance driven by climate change. Drying was generally associated with increases of active-layer depth and permafrost thaw in a warming climate. However, models lack important mechanisms such as thermokarst and soil subsidence that will change the hydrological regime and add to the large uncertainty in the future Arctic hydrological state and the associated permafrost carbon feedback.
Chris D. Jones, Thomas L. Frölicher, Charles Koven, Andrew H. MacDougall, H. Damon Matthews, Kirsten Zickfeld, Joeri Rogelj, Katarzyna B. Tokarska, Nathan P. Gillett, Tatiana Ilyina, Malte Meinshausen, Nadine Mengis, Roland Séférian, Michael Eby, and Friedrich A. Burger
Geosci. Model Dev., 12, 4375–4385, https://doi.org/10.5194/gmd-12-4375-2019, https://doi.org/10.5194/gmd-12-4375-2019, 2019
Short summary
Short summary
Global warming is simply related to the total emission of CO2 allowing us to define a carbon budget. However, information on the Zero Emissions Commitment is a key missing link to assess remaining carbon budgets to achieve the climate targets of the Paris Agreement. It was therefore decided that a small targeted MIP activity to fill this knowledge gap would be extremely valuable. This article formalises the experimental design alongside the other CMIP6 documentation papers.
Camille Risi, Joseph Galewsky, Gilles Reverdin, and Florent Brient
Atmos. Chem. Phys., 19, 12235–12260, https://doi.org/10.5194/acp-19-12235-2019, https://doi.org/10.5194/acp-19-12235-2019, 2019
Short summary
Short summary
Water molecules can be light (one oxygen atom and two hydrogen atoms) or heavy (one hydrogen atom is replaced by a deuterium atom). These different molecules are called water isotopes. The isotopic composition of water vapor can potentially provide information about physical processes along the water cycle, but the factors controlling it are complex. As a first step, we propose an equation to predict the water vapor isotopic composition near the surface of tropical oceans.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
Short summary
Short summary
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Christoph Heinze, Veronika Eyring, Pierre Friedlingstein, Colin Jones, Yves Balkanski, William Collins, Thierry Fichefet, Shuang Gao, Alex Hall, Detelina Ivanova, Wolfgang Knorr, Reto Knutti, Alexander Löw, Michael Ponater, Martin G. Schultz, Michael Schulz, Pier Siebesma, Joao Teixeira, George Tselioudis, and Martin Vancoppenolle
Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, https://doi.org/10.5194/esd-10-379-2019, 2019
Short summary
Short summary
Earth system models for producing climate projections under given forcings include additional processes and feedbacks that traditional physical climate models do not consider. We present an overview of climate feedbacks for key Earth system components and discuss the evaluation of these feedbacks. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research.
Gab Abramowitz, Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Robert Pincus, and Gavin A. Schmidt
Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, https://doi.org/10.5194/esd-10-91-2019, 2019
Short summary
Short summary
Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
Jennifer W. Harden, Jonathan A. O'Donnell, Katherine A. Heckman, Benjamin N. Sulman, Charles D. Koven, Chien-Lu Ping, and Gary J. Michaelson
SOIL Discuss., https://doi.org/10.5194/soil-2018-41, https://doi.org/10.5194/soil-2018-41, 2019
Revised manuscript not accepted
Short summary
Short summary
We examined changes in soil carbon (C) associated with permafrost thaw, warming, and ecosystem shifts using a space-for-time study. Soil C turnover was estimated for soil C fractions using soil C and radiocarbon data. Observations informed a simple model to track soil C change over time. Both losses and gains of soil C occur in the profile due to shifts in C among density-separated fractions. Thawing initially resulted in C gains to mineral soil and eventually C losses as warming persists.
Ashehad A. Ali, Yuanchao Fan, Marife D. Corre, Martyna M. Kotowska, Evelyn Hassler, Fernando E. Moyano, Christian Stiegler, Alexander Röll, Ana Meijide, Andre Ringeler, Christoph Leuschner, Tania June, Suria Tarigan, Holger Kreft, Dirk Hölscher, Chonggang Xu, Charles D. Koven, Rosie Fisher, Edzo Veldkamp, and Alexander Knohl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-236, https://doi.org/10.5194/gmd-2018-236, 2018
Revised manuscript not accepted
Short summary
Short summary
We used carbon-use and water-use related datasets of small-holder rubber plantations from Jambi province, Indonesia to develop and calibrate a rubber plant functional type for the Community Land Model (CLM-rubber). Increased sensitivity of stomata to soil water stress and enhanced respiration costs enabled the model to capture the magnitude of transpiration and leaf area index. Including temporal variations in leaf life span enabled the model to better capture the seasonality of leaf litterfall.
Jill S. Johnson, Leighton A. Regayre, Masaru Yoshioka, Kirsty J. Pringle, Lindsay A. Lee, David M. H. Sexton, John W. Rostron, Ben B. B. Booth, and Kenneth S. Carslaw
Atmos. Chem. Phys., 18, 13031–13053, https://doi.org/10.5194/acp-18-13031-2018, https://doi.org/10.5194/acp-18-13031-2018, 2018
Short summary
Short summary
We estimate the uncertainty in an aerosol–climate model that has been tuned to match several common types of observations. We used a large set of model simulations and built emulators so that we could generate 4 million “variants” of our climate model. Even after using nine aerosol and cloud observations to constrain the model, the uncertainty remains large. We conclude that estimates of aerosol forcing from multi-model studies are likely to be more uncertain than currently estimated.
Leighton A. Regayre, Jill S. Johnson, Masaru Yoshioka, Kirsty J. Pringle, David M. H. Sexton, Ben B. B. Booth, Lindsay A. Lee, Nicolas Bellouin, and Kenneth S. Carslaw
Atmos. Chem. Phys., 18, 9975–10006, https://doi.org/10.5194/acp-18-9975-2018, https://doi.org/10.5194/acp-18-9975-2018, 2018
Short summary
Short summary
We sample uncertainty in one climate model by perturbing aerosol and physical atmosphere parameters. Our uncertainty is comparable to multi-model studies. Atmospheric parameters cause most of the top-of-atmosphere flux uncertainty; uncertainty in aerosol forcing is mostly caused by aerosols: both are important. The strongest aerosol forcings are inconsistent with top-of-atmosphere flux observations. Better constraint requires observations that share causes of uncertainty with aerosol forcing.
Xiyan Xu, William J. Riley, Charles D. Koven, and Gensuo Jia
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-257, https://doi.org/10.5194/bg-2018-257, 2018
Preprint withdrawn
Michael Wehner, Dáithí Stone, Dann Mitchell, Hideo Shiogama, Erich Fischer, Lise S. Graff, Viatcheslav V. Kharin, Ludwig Lierhammer, Benjamin Sanderson, and Harinarayan Krishnan
Earth Syst. Dynam., 9, 299–311, https://doi.org/10.5194/esd-9-299-2018, https://doi.org/10.5194/esd-9-299-2018, 2018
Short summary
Short summary
The United Nations Framework Convention on Climate Change challenged the scientific community to describe the impacts of stabilizing the global temperature at its 21st Conference of Parties. A specific target of 1.5 °C above preindustrial levels had not been seriously considered by the climate modeling community prior to the Paris Agreement. This paper analyzes heat waves in simulations designed for this target. We find there are reductions in extreme temperature compared to a 2 °C target.
Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson
Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, https://doi.org/10.5194/esd-9-135-2018, 2018
Short summary
Short summary
Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Angeline G. Pendergrass, Andrew Conley, and Francis M. Vitt
Earth Syst. Sci. Data, 10, 317–324, https://doi.org/10.5194/essd-10-317-2018, https://doi.org/10.5194/essd-10-317-2018, 2018
Short summary
Short summary
We document and validate radiative kernels for the surface and top-of-atmosphere calculated with NCAR's CESM1 climate model. A radiative kernel is the change in radiation in response to a small change in a property of the atmosphere or surface, essentially a partial derivative. They are used to quantify temperature, water vapor, surface albedo, and cloud feedbacks. We made these kernels because few are available for the surface. We also validate the kernels against the expected model responses.
Nicholas C. Parazoo, Charles D. Koven, David M. Lawrence, Vladimir Romanovsky, and Charles E. Miller
The Cryosphere, 12, 123–144, https://doi.org/10.5194/tc-12-123-2018, https://doi.org/10.5194/tc-12-123-2018, 2018
Short summary
Short summary
Carbon models suggest the permafrost carbon feedback (soil carbon emissions from permafrost thaw) acts as a slow, unobservable leak. We investigate if permafrost temperature provides an observable signal to detect feedbacks. We find a slow carbon feedback in warm sub-Arctic permafrost soils, but potentially rapid feedback in cold Arctic permafrost. This is surprising since the cold permafrost region is dominated by tundra and underlain by deep, cold permafrost thought impervious to such changes.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
Short summary
Short summary
Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Wei Li, Philippe Ciais, Shushi Peng, Chao Yue, Yilong Wang, Martin Thurner, Sassan S. Saatchi, Almut Arneth, Valerio Avitabile, Nuno Carvalhais, Anna B. Harper, Etsushi Kato, Charles Koven, Yi Y. Liu, Julia E.M.S. Nabel, Yude Pan, Julia Pongratz, Benjamin Poulter, Thomas A. M. Pugh, Maurizio Santoro, Stephen Sitch, Benjamin D. Stocker, Nicolas Viovy, Andy Wiltshire, Rasoul Yousefpour, and Sönke Zaehle
Biogeosciences, 14, 5053–5067, https://doi.org/10.5194/bg-14-5053-2017, https://doi.org/10.5194/bg-14-5053-2017, 2017
Short summary
Short summary
We used several observation-based biomass datasets to constrain the historical land-use change carbon emissions simulated by models. Compared to the range of the original modeled emissions (from 94 to 273 Pg C), the observationally constrained global cumulative emission estimate is 155 ± 50 Pg C (1σ Gaussian error) from 1901 to 2012. Our approach can also be applied to evaluate the LULCC impact of land-based climate mitigation policies.
Deborah A. Clark, Shinichi Asao, Rosie Fisher, Sasha Reed, Peter B. Reich, Michael G. Ryan, Tana E. Wood, and Xiaojuan Yang
Biogeosciences, 14, 4663–4690, https://doi.org/10.5194/bg-14-4663-2017, https://doi.org/10.5194/bg-14-4663-2017, 2017
Short summary
Short summary
Improved modeling of tropical-forest carbon cycling is urgently needed to project future climate and to guide global policy for greenhouse gases. Tropical forests store and process immense amounts of carbon, and their carbon cycling may be responding to climate change. Our goal with this paper, a multidisciplinary collaboration between modelers and field ecologists, is to identify reference-level field data from tropical forests that can be used to guide the models for these key ecosystems.
Henrique F. Duarte, Brett M. Raczka, Daniel M. Ricciuto, John C. Lin, Charles D. Koven, Peter E. Thornton, David R. Bowling, Chun-Ta Lai, Kenneth J. Bible, and James R. Ehleringer
Biogeosciences, 14, 4315–4340, https://doi.org/10.5194/bg-14-4315-2017, https://doi.org/10.5194/bg-14-4315-2017, 2017
Short summary
Short summary
We evaluate the Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site that is subjected to water stress each summer. We found that, after calibration, CLM was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress. This study demonstrates that carbon isotopes can expose structural weaknesses in CLM and provide a key constraint that may guide future model development.
Benjamin M. Sanderson, Yangyang Xu, Claudia Tebaldi, Michael Wehner, Brian O'Neill, Alexandra Jahn, Angeline G. Pendergrass, Flavio Lehner, Warren G. Strand, Lei Lin, Reto Knutti, and Jean Francois Lamarque
Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, https://doi.org/10.5194/esd-8-827-2017, 2017
Short summary
Short summary
We present the results of a set of climate simulations designed to simulate futures in which the Earth's temperature is stabilized at the levels referred to in the 2015 Paris Agreement. We consider the necessary future emissions reductions and the aspects of extreme weather which differ significantly between the 2 and 1.5 °C climate in the simulations.
Hendrik Andersen, Jan Cermak, Julia Fuchs, Reto Knutti, and Ulrike Lohmann
Atmos. Chem. Phys., 17, 9535–9546, https://doi.org/10.5194/acp-17-9535-2017, https://doi.org/10.5194/acp-17-9535-2017, 2017
Short summary
Short summary
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system understanding. In this study, we use near-global satellite and reanalysis data sets to predict marine liquid-water clouds by means of artificial neural networks. We show that on the system scale, lower-tropospheric stability and boundary layer height are the main determinants of liquid-water clouds. Aerosols show the expected impact on clouds but are less relevant than some meteorological factors.
Benjamin M. Sanderson, Michael Wehner, and Reto Knutti
Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, https://doi.org/10.5194/gmd-10-2379-2017, 2017
Short summary
Short summary
How should climate model simulations be combined to produce an overall assessment that reflects both their performance and their interdependencies? This paper presents a strategy for weighting climate model output such that models that are replicated or models that perform poorly in a chosen set of metrics are appropriately weighted. We perform sensitivity tests to show how the method results depend on variables and parameter values.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
Short summary
Short summary
Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Kathrin M. Keller, Sebastian Lienert, Anil Bozbiyik, Thomas F. Stocker, Olga V. Churakova (Sidorova), David C. Frank, Stefan Klesse, Charles D. Koven, Markus Leuenberger, William J. Riley, Matthias Saurer, Rolf Siegwolf, Rosemarie B. Weigt, and Fortunat Joos
Biogeosciences, 14, 2641–2673, https://doi.org/10.5194/bg-14-2641-2017, https://doi.org/10.5194/bg-14-2641-2017, 2017
Andrew G. Slater, David M. Lawrence, and Charles D. Koven
The Cryosphere, 11, 989–996, https://doi.org/10.5194/tc-11-989-2017, https://doi.org/10.5194/tc-11-989-2017, 2017
Short summary
Short summary
This work defines a metric for evaluation of a specific model snow process, namely, heat transfer through snow into soil. Heat transfer through snow regulates the difference in air temperature versus soil temperature. Accurate representation of the snow heat transfer process is critically important for accurate representation of the current and future state of permafrost. Utilizing this metric, we can clearly identify models that can and cannot reasonably represent snow heat transfer.
Allison H. Baker, Dorit M. Hammerling, Sheri A. Mickelson, Haiying Xu, Martin B. Stolpe, Phillipe Naveau, Ben Sanderson, Imme Ebert-Uphoff, Savini Samarasinghe, Francesco De Simone, Francesco Carbone, Christian N. Gencarelli, John M. Dennis, Jennifer E. Kay, and Peter Lindstrom
Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016, https://doi.org/10.5194/gmd-9-4381-2016, 2016
Short summary
Short summary
We apply lossy data compression to output from the Community Earth System Model Large Ensemble Community Project. We challenge climate scientists to examine features of the data relevant to their interests and identify which of the ensemble members have been compressed, and we perform direct comparisons on features critical to climate science. We find that applying lossy data compression to climate model data effectively reduces data volumes with minimal effect on scientific results.
Doug McNeall, Jonny Williams, Ben Booth, Richard Betts, Peter Challenor, Andy Wiltshire, and David Sexton
Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, https://doi.org/10.5194/esd-7-917-2016, 2016
Short summary
Short summary
We compare simulated with observed forests to constrain uncertain input parameters of the land surface component of a climate model.
We find that the model is unlikely to be able to simulate the Amazon and other major forests simultaneously at any one parameter set, suggesting a bias in the model's representation of the Amazon.
We find we cannot constrain parameters individually, but we can rule out large areas of joint parameter space.
Nathan P. Gillett, Hideo Shiogama, Bernd Funke, Gabriele Hegerl, Reto Knutti, Katja Matthes, Benjamin D. Santer, Daithi Stone, and Claudia Tebaldi
Geosci. Model Dev., 9, 3685–3697, https://doi.org/10.5194/gmd-9-3685-2016, https://doi.org/10.5194/gmd-9-3685-2016, 2016
Short summary
Short summary
Detection and attribution of climate change is the process of determining the causes of observed climate changes, which has underpinned key conclusions on the role of human influence on climate in the reports of the Intergovernmental Panel on Climate Change (IPCC). This paper describes a coordinated set of climate model experiments that will form part of the Sixth Coupled Model Intercomparison Project and will support improved attribution of climate change in the next IPCC report.
Brian C. O'Neill, Claudia Tebaldi, Detlef P. van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti, Elmar Kriegler, Jean-Francois Lamarque, Jason Lowe, Gerald A. Meehl, Richard Moss, Keywan Riahi, and Benjamin M. Sanderson
Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, https://doi.org/10.5194/gmd-9-3461-2016, 2016
Short summary
Short summary
The Scenario Model Intercomparison Project (ScenarioMIP) will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. The design consists of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions. Climate model projections will facilitate integrated studies of climate change as well as address targeted scientific questions.
Brett Raczka, Henrique F. Duarte, Charles D. Koven, Daniel Ricciuto, Peter E. Thornton, John C. Lin, and David R. Bowling
Biogeosciences, 13, 5183–5204, https://doi.org/10.5194/bg-13-5183-2016, https://doi.org/10.5194/bg-13-5183-2016, 2016
Short summary
Short summary
We use carbon isotopes of CO2 to improve the performance of a land surface model, a component with earth system climate models. We found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and nitrogen limitation. It follows that isotopes have a unique potential to improve model performance and provide insight into land surface model development.
Fang Zhao, Ning Zeng, Ghassem Asrar, Pierre Friedlingstein, Akihiko Ito, Atul Jain, Eugenia Kalnay, Etsushi Kato, Charles D. Koven, Ben Poulter, Rashid Rafique, Stephen Sitch, Shijie Shu, Beni Stocker, Nicolas Viovy, Andy Wiltshire, and Sonke Zaehle
Biogeosciences, 13, 5121–5137, https://doi.org/10.5194/bg-13-5121-2016, https://doi.org/10.5194/bg-13-5121-2016, 2016
Short summary
Short summary
The increasing seasonality of atmospheric CO2 is strongly linked with enhanced land vegetation activities in the last 5 decades, for which the importance of increasing CO2, climate and land use/cover change was evaluated in single model studies (Zeng et al., 2014; Forkel et al., 2016). Here we examine the relative importance of these factors in multiple models. Our results highlight models can show similar results in some benchmarks with different underlying regional dynamics.
Xiyan Xu, William J. Riley, Charles D. Koven, Dave P. Billesbach, Rachel Y.-W. Chang, Róisín Commane, Eugénie S. Euskirchen, Sean Hartery, Yoshinobu Harazono, Hiroki Iwata, Kyle C. McDonald, Charles E. Miller, Walter C. Oechel, Benjamin Poulter, Naama Raz-Yaseef, Colm Sweeney, Margaret Torn, Steven C. Wofsy, Zhen Zhang, and Donatella Zona
Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, https://doi.org/10.5194/bg-13-5043-2016, 2016
Short summary
Short summary
Wetlands are the largest global natural methane source. Peat-rich bogs and fens lying between 50°N and 70°N contribute 10–30% to this source. The predictive capability of the seasonal methane cycle can directly affect the estimation of global methane budget. We present multiscale methane seasonal emission by observations and modeling and find that the uncertainties in predicting the seasonal methane emissions are from the wetland extent, cold-season CH4 production and CH4 transport processes.
Chris D. Jones, Vivek Arora, Pierre Friedlingstein, Laurent Bopp, Victor Brovkin, John Dunne, Heather Graven, Forrest Hoffman, Tatiana Ilyina, Jasmin G. John, Martin Jung, Michio Kawamiya, Charlie Koven, Julia Pongratz, Thomas Raddatz, James T. Randerson, and Sönke Zaehle
Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, https://doi.org/10.5194/gmd-9-2853-2016, 2016
Short summary
Short summary
How the carbon cycle interacts with climate will affect future climate change and how society plans emissions reductions to achieve climate targets. The Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) is an endorsed activity of CMIP6 and aims to quantify these interactions and feedbacks in state-of-the-art climate models. This paper lays out the experimental protocol for modelling groups to follow to contribute to C4MIP. It is a contribution to the CMIP6 GMD Special Issue.
Wenli Wang, Annette Rinke, John C. Moore, Duoying Ji, Xuefeng Cui, Shushi Peng, David M. Lawrence, A. David McGuire, Eleanor J. Burke, Xiaodong Chen, Bertrand Decharme, Charles Koven, Andrew MacDougall, Kazuyuki Saito, Wenxin Zhang, Ramdane Alkama, Theodore J. Bohn, Philippe Ciais, Christine Delire, Isabelle Gouttevin, Tomohiro Hajima, Gerhard Krinner, Dennis P. Lettenmaier, Paul A. Miller, Benjamin Smith, Tetsuo Sueyoshi, and Artem B. Sherstiukov
The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, https://doi.org/10.5194/tc-10-1721-2016, 2016
Short summary
Short summary
The winter snow insulation is a key process for air–soil temperature coupling and is relevant for permafrost simulations. Differences in simulated air–soil temperature relationships and their modulation by climate conditions are found to be related to the snow model physics. Generally, models with better performance apply multilayer snow schemes.
Stefan C. Dekker, Margriet Groenendijk, Ben B. B. Booth, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 7, 525–533, https://doi.org/10.5194/esd-7-525-2016, https://doi.org/10.5194/esd-7-525-2016, 2016
Short summary
Short summary
Our analysis allows us to infer maps of changing plant water-use efficiency (WUE) for 1901–2010, using atmospheric observations of temperature, humidity and CO2. Our estimated increase in global WUE is consistent with the tree-ring and eddy covariance data, but much larger than the historical WUE increases simulated by Earth System Models (ESMs). We therefore conclude that the effects of increasing CO2 on plant WUE are significantly underestimated in the latest climate projections.
Andrew H. MacDougall and Reto Knutti
Biogeosciences, 13, 2123–2136, https://doi.org/10.5194/bg-13-2123-2016, https://doi.org/10.5194/bg-13-2123-2016, 2016
Short summary
Short summary
The soils of the permafrost region are estimated to hold 1100 to 1500 billion tonnes of carbon. As climate change progresses much of this permafrost is expected to thaw and the carbon within it decay. Here we conduct numerical experiments with a climate model to estimate with formal uncertainty bounds the release of carbon from permafrost soils. Our simulations suggest that the permafrost carbon will make a significant but not cataclysmic contribution to climate change over the next centuries.
W. Wang, A. Rinke, J. C. Moore, X. Cui, D. Ji, Q. Li, N. Zhang, C. Wang, S. Zhang, D. M. Lawrence, A. D. McGuire, W. Zhang, C. Delire, C. Koven, K. Saito, A. MacDougall, E. Burke, and B. Decharme
The Cryosphere, 10, 287–306, https://doi.org/10.5194/tc-10-287-2016, https://doi.org/10.5194/tc-10-287-2016, 2016
Short summary
Short summary
We use a model-ensemble approach for simulating permafrost on the Tibetan Plateau. We identify the uncertainties across models (state-of-the-art land surface models) and across methods (most commonly used methods to define permafrost).
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
S. Peng, P. Ciais, G. Krinner, T. Wang, I. Gouttevin, A. D. McGuire, D. Lawrence, E. Burke, X. Chen, B. Decharme, C. Koven, A. MacDougall, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, C. Delire, T. Hajima, D. Ji, D. P. Lettenmaier, P. A. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
The Cryosphere, 10, 179–192, https://doi.org/10.5194/tc-10-179-2016, https://doi.org/10.5194/tc-10-179-2016, 2016
Short summary
Short summary
Soil temperature change is a key indicator of the dynamics of permafrost. Using nine process-based ecosystem models with permafrost processes, a large spread of soil temperature trends across the models. Air temperature and longwave downward radiation are the main drivers of soil temperature trends. Based on an emerging observation constraint method, the total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000.
Q. Zhu, W. J. Riley, J. Tang, and C. D. Koven
Biogeosciences, 13, 341–363, https://doi.org/10.5194/bg-13-341-2016, https://doi.org/10.5194/bg-13-341-2016, 2016
Short summary
Short summary
Here we develop, calibrate, and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers based on enzyme kinetics theory. Our model provides an ecologically consistent representation of nutrient competition appropriate for land biogeochemical models integrated in Earth system models.
G. Murray-Tortarolo, P. Friedlingstein, S. Sitch, V. J. Jaramillo, F. Murguía-Flores, A. Anav, Y. Liu, A. Arneth, A. Arvanitis, A. Harper, A. Jain, E. Kato, C. Koven, B. Poulter, B. D. Stocker, A. Wiltshire, S. Zaehle, and N. Zeng
Biogeosciences, 13, 223–238, https://doi.org/10.5194/bg-13-223-2016, https://doi.org/10.5194/bg-13-223-2016, 2016
Short summary
Short summary
We modelled the carbon (C) cycle in Mexico for three different time periods: past (20th century), present (2000-2005) and future (2006-2100). We used different available products to estimate C stocks and fluxes in the country. Contrary to other current estimates, our results showed that Mexico was a C sink and this is likely to continue in the next century (unless the most extreme climate-change scenarios are reached).
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
Short summary
Short summary
Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
R. A. Fisher, S. Muszala, M. Verteinstein, P. Lawrence, C. Xu, N. G. McDowell, R. G. Knox, C. Koven, J. Holm, B. M. Rogers, A. Spessa, D. Lawrence, and G. Bonan
Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, https://doi.org/10.5194/gmd-8-3593-2015, 2015
Short summary
Short summary
Predicting the distribution of vegetation under novel climates is important, both to understand how climate change will impact ecosystem services, but also to understand how vegetation changes might affect the carbon, energy and water cycles. Historically, predictions have been heavily dependent upon observations of existing vegetation boundaries. In this paper, we attempt to predict ecosystem boundaries from the ``bottom up'', and illustrate the complexities and promise of this approach.
C. D. Koven, J. Q. Chambers, K. Georgiou, R. Knox, R. Negron-Juarez, W. J. Riley, V. K. Arora, V. Brovkin, P. Friedlingstein, and C. D. Jones
Biogeosciences, 12, 5211–5228, https://doi.org/10.5194/bg-12-5211-2015, https://doi.org/10.5194/bg-12-5211-2015, 2015
Short summary
Short summary
Terrestrial carbon feedbacks are a large uncertainty in climate change. We separate modeled feedback responses into those governed by changed carbon inputs (productivity) and changed outputs (turnover). The disaggregated responses show that both are important in controlling inter-model uncertainty. Interactions between productivity and turnover are also important, and research must focus on these interactions for more accurate projections of carbon cycle feedbacks.
P. R. Halloran, B. B. B. Booth, C. D. Jones, F. H. Lambert, D. J. McNeall, I. J. Totterdell, and C. Völker
Biogeosciences, 12, 4497–4508, https://doi.org/10.5194/bg-12-4497-2015, https://doi.org/10.5194/bg-12-4497-2015, 2015
Short summary
Short summary
The oceans currently take up around a quarter of the carbon dioxide (CO2) emitted by human activity. While stored in the ocean, this CO2 is not causing global warming. Here we explore high latitude North Atlantic CO2 uptake across a set of climate model simulations, and find that the models show a peak in ocean CO2 uptake around the middle of the century after which time CO2 uptake begins to decline. We identify the causes of this long-term change and interannual variability in the models.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
Short summary
Short summary
Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
D. E. Keller, A. M. Fischer, C. Frei, M. A. Liniger, C. Appenzeller, and R. Knutti
Hydrol. Earth Syst. Sci., 19, 2163–2177, https://doi.org/10.5194/hess-19-2163-2015, https://doi.org/10.5194/hess-19-2163-2015, 2015
G. Hugelius, J. Strauss, S. Zubrzycki, J. W. Harden, E. A. G. Schuur, C.-L. Ping, L. Schirrmeister, G. Grosse, G. J. Michaelson, C. D. Koven, J. A. O'Donnell, B. Elberling, U. Mishra, P. Camill, Z. Yu, J. Palmtag, and P. Kuhry
Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, https://doi.org/10.5194/bg-11-6573-2014, 2014
Short summary
Short summary
This study provides an updated estimate of organic carbon stored in the northern permafrost region. The study includes estimates for carbon in soils (0 to 3 m depth) and deeper sediments in river deltas and the Yedoma region. We find that field data is still scarce from many regions. Total estimated carbon storage is ~1300 Pg with an uncertainty range of between 1100 and 1500 Pg. Around 800 Pg carbon is perennially frozen, equivalent to all carbon dioxide currently in the Earth's atmosphere.
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
G. Hugelius, J. G. Bockheim, P. Camill, B. Elberling, G. Grosse, J. W. Harden, K. Johnson, T. Jorgenson, C. D. Koven, P. Kuhry, G. Michaelson, U. Mishra, J. Palmtag, C.-L. Ping, J. O'Donnell, L. Schirrmeister, E. A. G. Schuur, Y. Sheng, L. C. Smith, J. Strauss, and Z. Yu
Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, https://doi.org/10.5194/essd-5-393-2013, 2013
C. D. Koven, W. J. Riley, Z. M. Subin, J. Y. Tang, M. S. Torn, W. D. Collins, G. B. Bonan, D. M. Lawrence, and S. C. Swenson
Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, https://doi.org/10.5194/bg-10-7109-2013, 2013
N. Schaller, J. Cermak, M. Wild, and R. Knutti
Earth Syst. Dynam., 4, 253–266, https://doi.org/10.5194/esd-4-253-2013, https://doi.org/10.5194/esd-4-253-2013, 2013
B. B. B. Booth, D. Bernie, D. McNeall, E. Hawkins, J. Caesar, C. Boulton, P. Friedlingstein, and D. M. H. Sexton
Earth Syst. Dynam., 4, 95–108, https://doi.org/10.5194/esd-4-95-2013, https://doi.org/10.5194/esd-4-95-2013, 2013
J. H. T. Williams, R. S. Smith, P. J. Valdes, B. B. B. Booth, and A. Osprey
Geosci. Model Dev., 6, 141–160, https://doi.org/10.5194/gmd-6-141-2013, https://doi.org/10.5194/gmd-6-141-2013, 2013
J. Y. Tang, W. J. Riley, C. D. Koven, and Z. M. Subin
Geosci. Model Dev., 6, 127–140, https://doi.org/10.5194/gmd-6-127-2013, https://doi.org/10.5194/gmd-6-127-2013, 2013
Related subject area
Dynamics of the Earth system: concepts
Rate-induced tipping in natural and human systems
Tracing the Snowball bifurcation of aquaplanets through time reveals a fundamental shift in critical-state dynamics
Multi-million-year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes
Reliability of resilience estimation based on multi-instrument time series
The ExtremeX global climate model experiment: investigating thermodynamic and dynamic processes contributing to weather and climate extremes
ESD Ideas: planetary antifragility: a new dimension in the definition of the safe operating space for humanity
Glacial runoff buffers droughts through the 21st century
Inarticulate past: similarity properties of the ice–climate system and their implications for paleo-record attribution
Extreme weather and societal impacts in the eastern Mediterranean
Sedimentary microplankton distributions are shaped by oceanographically connected areas
Natural hazards and extreme events in the Baltic Sea region
Taxonomies for structuring models for World–Earth systems analysis of the Anthropocene: subsystems, their interactions and social–ecological feedback loops
ESD Ideas: A weak positive feedback between sea level and the planetary albedo
Sea level dynamics and coastal erosion in the Baltic Sea region
Earth system economics: a biophysical approach to the human component of the Earth system
The half-order energy balance equation – Part 1: The homogeneous HEBE and long memories
The half-order energy balance equation – Part 2: The inhomogeneous HEBE and 2D energy balance models
A dynamical systems characterization of atmospheric jet regimes
Synchronized spatial shifts of Hadley and Walker circulations
ESD Ideas: The Peclet number is a cornerstone of the orbital and millennial Pleistocene variability
Temperatures from energy balance models: the effective heat capacity matters
Relating climate sensitivity indices to projection uncertainty
The role of prior assumptions in carbon budget calculations
Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World–Earth modeling framework
π-theorem generalization of the ice-age theory
Earth system data cubes unravel global multivariate dynamics
ESD Ideas: Why are glaciations slower than deglaciations?
Fractional governing equations of transient groundwater flow in unconfined aquifers with multi-fractional dimensions in fractional time
Climate system response to stratospheric sulfate aerosols: sensitivity to altitude of aerosol layer
Minimal dynamical systems model of the Northern Hemisphere jet stream via embedding of climate data
Millennium-length precipitation reconstruction over south-eastern Asia: a pseudo-proxy approach
Including the efficacy of land ice changes in deriving climate sensitivity from paleodata
The role of moisture transport for precipitation in the inter-annual and inter-daily fluctuations of the Arctic sea ice extension
On the assessment of the moisture transport by the Great Plains low-level jet
ESD Ideas: The stochastic climate model shows that underestimated Holocene trends and variability represent two sides of the same coin
Cascading transitions in the climate system
The climate of a retrograde rotating Earth
Diurnal land surface energy balance partitioning estimated from the thermodynamic limit of a cold heat engine
How intermittency affects the rate at which rainfall extremes respond to changes in temperature
Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data
On deeper human dimensions in Earth system analysis and modelling
Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset
Estimating sowing and harvest dates based on the Asian summer monsoon
Quantifying changes in spatial patterns of surface air temperature dynamics over several decades
Systematic Correlation Matrix Evaluation (SCoMaE) – a bottom–up, science-led approach to identifying indicators
Climate indices for the Baltic states from principal component analysis
Fractal scaling analysis of groundwater dynamics in confined aquifers
An explanation for the different climate sensitivities of land and ocean surfaces based on the diurnal cycle
Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques
Young people's burden: requirement of negative CO2 emissions
Paul D. L. Ritchie, Hassan Alkhayuon, Peter M. Cox, and Sebastian Wieczorek
Earth Syst. Dynam., 14, 669–683, https://doi.org/10.5194/esd-14-669-2023, https://doi.org/10.5194/esd-14-669-2023, 2023
Short summary
Short summary
Complex systems can undergo abrupt changes or tipping points when external forcing crosses a critical level and are of increasing concern because of their severe impacts. However, tipping points can also occur when the external forcing changes too quickly without crossing any critical levels, which is very relevant for Earth’s systems and contemporary climate. We give an intuitive explanation of such rate-induced tipping and provide illustrative examples from natural and human systems.
Georg Feulner, Mona Bukenberger, and Stefan Petri
Earth Syst. Dynam., 14, 533–547, https://doi.org/10.5194/esd-14-533-2023, https://doi.org/10.5194/esd-14-533-2023, 2023
Short summary
Short summary
One limit of planetary habitability is defined by the threshold of global glaciation. If Earth cools, growing ice cover makes it brighter, leading to further cooling, since more sunlight is reflected, eventually leading to global ice cover (Snowball Earth). We study how much carbon dioxide is needed to prevent global glaciation in Earth's history given the slow increase in the Sun's brightness. We find an unexpected change in the characteristics of climate states close to the Snowball limit.
Gaëlle Leloup and Didier Paillard
Earth Syst. Dynam., 14, 291–307, https://doi.org/10.5194/esd-14-291-2023, https://doi.org/10.5194/esd-14-291-2023, 2023
Short summary
Short summary
Records of past carbon isotopes exhibit oscillations. It is clear over very different time periods that oscillations of 400 kyr take place. Also, strong oscillations of approximately 8–9 Myr are seen over different time periods. While earlier modelling studies have been able to produce 400 kyr oscillations, none of them produced 8–9 Myr cycles. Here, we propose a simple model for the carbon cycle that is able to produce 8–9 Myr oscillations in the modelled carbon isotopes.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
Short summary
Short summary
The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Oliver López-Corona, Melanie Kolb, Elvia Ramírez-Carrillo, and Jon Lovett
Earth Syst. Dynam., 13, 1145–1155, https://doi.org/10.5194/esd-13-1145-2022, https://doi.org/10.5194/esd-13-1145-2022, 2022
Short summary
Short summary
Climate change, the loss of biodiversity and land-use change, among others, have been recognized as main human perturbations to Earth system dynamics, the so-called planetary boundaries. Effort has been made to understand how to define a safe operating space for humanity (accepted levels of these perturbations). In this work we address the problem by assessing the Earth's capacity to respond to these perturbations, a capacity that the planet is losing.
Lizz Ultee, Sloan Coats, and Jonathan Mackay
Earth Syst. Dynam., 13, 935–959, https://doi.org/10.5194/esd-13-935-2022, https://doi.org/10.5194/esd-13-935-2022, 2022
Short summary
Short summary
Global climate models suggest that droughts could worsen over the coming century. In mountain basins with glaciers, glacial runoff can ease droughts, but glaciers are retreating worldwide. We analyzed how one measure of drought conditions changes when accounting for glacial runoff that changes over time. Surprisingly, we found that glacial runoff can continue to buffer drought throughout the 21st century in most cases, even as the total amount of runoff declines.
Mikhail Y. Verbitsky
Earth Syst. Dynam., 13, 879–884, https://doi.org/10.5194/esd-13-879-2022, https://doi.org/10.5194/esd-13-879-2022, 2022
Short summary
Short summary
Reconstruction and explanation of past climate evolution using proxy records is the essence of paleoclimatology. In this study, we use dimensional analysis of a dynamical model on orbital timescales to recognize theoretical limits of such forensic inquiries. Specifically, we demonstrate that major past events could have been produced by physically dissimilar processes making the task of paleo-record attribution to a particular phenomenon fundamentally difficult if not impossible.
Assaf Hochman, Francesco Marra, Gabriele Messori, Joaquim G. Pinto, Shira Raveh-Rubin, Yizhak Yosef, and Georgios Zittis
Earth Syst. Dynam., 13, 749–777, https://doi.org/10.5194/esd-13-749-2022, https://doi.org/10.5194/esd-13-749-2022, 2022
Short summary
Short summary
Gaining a complete understanding of extreme weather, from its physical drivers to its impacts on society, is important in supporting future risk reduction and adaptation measures. Here, we provide a review of the available scientific literature, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean region.
Peter D. Nooteboom, Peter K. Bijl, Christian Kehl, Erik van Sebille, Martin Ziegler, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 357–371, https://doi.org/10.5194/esd-13-357-2022, https://doi.org/10.5194/esd-13-357-2022, 2022
Short summary
Short summary
Having descended through the water column, microplankton in ocean sediments represents the ocean surface environment and is used as an archive of past and present surface oceanographic conditions. However, this microplankton is advected by turbulent ocean currents during its sinking journey. We use simulations of sinking particles to define ocean bottom provinces and detect these provinces in datasets of sedimentary microplankton, which has implications for palaeoclimate reconstructions.
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
Short summary
Short summary
A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Jonathan F. Donges, Wolfgang Lucht, Sarah E. Cornell, Jobst Heitzig, Wolfram Barfuss, Steven J. Lade, and Maja Schlüter
Earth Syst. Dynam., 12, 1115–1137, https://doi.org/10.5194/esd-12-1115-2021, https://doi.org/10.5194/esd-12-1115-2021, 2021
Ben Marzeion
Earth Syst. Dynam., 12, 1057–1060, https://doi.org/10.5194/esd-12-1057-2021, https://doi.org/10.5194/esd-12-1057-2021, 2021
Short summary
Short summary
The oceans are typically darker than land surfaces. Expanding oceans through sea-level rise may thus lead to a darker planet Earth, reflecting less sunlight. The additionally absorbed sunlight may heat planet Earth, leading to further sea-level rise. Here, we provide a rough estimate of the strength of this feedback: it turns out to be very weak, but clearly positive, thereby destabilizing the Earth system.
Ralf Weisse, Inga Dailidienė, Birgit Hünicke, Kimmo Kahma, Kristine Madsen, Anders Omstedt, Kevin Parnell, Tilo Schöne, Tarmo Soomere, Wenyan Zhang, and Eduardo Zorita
Earth Syst. Dynam., 12, 871–898, https://doi.org/10.5194/esd-12-871-2021, https://doi.org/10.5194/esd-12-871-2021, 2021
Short summary
Short summary
The study is part of the thematic Baltic Earth Assessment Reports – a series of review papers summarizing the knowledge around major Baltic Earth science topics. It concentrates on sea level dynamics and coastal erosion (its variability and change). Many of the driving processes are relevant in the Baltic Sea. Contributions vary over short distances and across timescales. Progress and research gaps are described in both understanding details in the region and in extending general concepts.
Eric D. Galbraith
Earth Syst. Dynam., 12, 671–687, https://doi.org/10.5194/esd-12-671-2021, https://doi.org/10.5194/esd-12-671-2021, 2021
Short summary
Short summary
Scientific tradition has left a gap between the study of humans and the rest of the Earth system. Here, a holistic approach to the global human system is proposed, intended to provide seamless integration with natural sciences. At the core, this focuses on what humans are doing with their time, what the bio-physical outcomes of those activities are, and what the lived experience is. The quantitative approach can facilitate data analysis across scales and integrated human–Earth system modeling.
Shaun Lovejoy
Earth Syst. Dynam., 12, 469–487, https://doi.org/10.5194/esd-12-469-2021, https://doi.org/10.5194/esd-12-469-2021, 2021
Short summary
Short summary
Monthly scale, seasonal-scale, and decadal-scale modeling of the atmosphere is possible using the principle of energy balance. Yet the scope of classical approaches is limited because they do not adequately deal with energy storage in the Earth system. We show that the introduction of a vertical coordinate implies that the storage has a huge memory. This memory can be used for macroweather (long-range) forecasts and climate projections.
Shaun Lovejoy
Earth Syst. Dynam., 12, 489–511, https://doi.org/10.5194/esd-12-489-2021, https://doi.org/10.5194/esd-12-489-2021, 2021
Short summary
Short summary
Radiant energy is exchanged between the Earth's surface and outer space. Some of the local imbalances are stored in the subsurface, and some are transported horizontally. In Part 1 I showed how – in a horizontally homogeneous Earth – these classical approaches imply long-memory storage useful for seasonal forecasting and multidecadal projections. In this Part 2, I show how to apply these results to the heterogeneous real Earth.
Gabriele Messori, Nili Harnik, Erica Madonna, Orli Lachmy, and Davide Faranda
Earth Syst. Dynam., 12, 233–251, https://doi.org/10.5194/esd-12-233-2021, https://doi.org/10.5194/esd-12-233-2021, 2021
Short summary
Short summary
Atmospheric jets are a key component of the climate system and of our everyday lives. Indeed, they affect human activities by influencing the weather in many mid-latitude regions. However, we still lack a complete understanding of their dynamical properties. In this study, we try to relate the understanding gained in idealized computer simulations of the jets to our knowledge from observations of the real atmosphere.
Kyung-Sook Yun, Axel Timmermann, and Malte F. Stuecker
Earth Syst. Dynam., 12, 121–132, https://doi.org/10.5194/esd-12-121-2021, https://doi.org/10.5194/esd-12-121-2021, 2021
Short summary
Short summary
Changes in the Hadley and Walker cells cause major climate disruptions across our planet. What has been overlooked so far is the question of whether these two circulations can shift their positions in a synchronized manner. We here show the synchronized spatial shifts between Walker and Hadley cells and further highlight a novel aspect of how tropical sea surface temperature anomalies can couple these two circulations. The re-positioning has important implications for extratropical rainfall.
Mikhail Y. Verbitsky and Michel Crucifix
Earth Syst. Dynam., 12, 63–67, https://doi.org/10.5194/esd-12-63-2021, https://doi.org/10.5194/esd-12-63-2021, 2021
Short summary
Short summary
We demonstrate here that a single physical phenomenon, specifically, a naturally changing balance between intensities of temperature advection and diffusion in the viscous ice media, may influence the entire spectrum of the Pleistocene variability from orbital to millennial timescales.
Gerrit Lohmann
Earth Syst. Dynam., 11, 1195–1208, https://doi.org/10.5194/esd-11-1195-2020, https://doi.org/10.5194/esd-11-1195-2020, 2020
Short summary
Short summary
With the development of computer capacities, simpler models like energy balance models have not disappeared, and a stronger emphasis has been given to the concept of a hierarchy of models. The global temperature is calculated by the radiation budget through the incoming energy from the Sun and the outgoing energy from the Earth. The argument that the temperature can be calculated by a simple radiation budget is revisited, and it is found that the effective heat capacity matters.
Benjamin Sanderson
Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020, https://doi.org/10.5194/esd-11-721-2020, 2020
Short summary
Short summary
Here, we assess the degree to which the idealized responses to transient forcing increase and step change forcing increase relate to warming under future scenarios. We find a possible explanation for the poor performance of transient metrics (relative to equilibrium response) as a metric of high-emission future warming in terms of their sensitivity to non-equilibrated initial conditions, and propose alternative metrics which better describe warming under high mitigation scenarios.
Benjamin Sanderson
Earth Syst. Dynam., 11, 563–577, https://doi.org/10.5194/esd-11-563-2020, https://doi.org/10.5194/esd-11-563-2020, 2020
Short summary
Short summary
Levels of future temperature change are often used interchangeably with carbon budget allowances in climate policy, a relatively robust relationship on the timescale of this century. However, recent advances in understanding underline that continued warming after net-zero emissions have been achieved cannot be ruled out by observations of warming to date. We consider here how such behavior could be constrained and how policy can be framed in the context of these uncertainties.
Jonathan F. Donges, Jobst Heitzig, Wolfram Barfuss, Marc Wiedermann, Johannes A. Kassel, Tim Kittel, Jakob J. Kolb, Till Kolster, Finn Müller-Hansen, Ilona M. Otto, Kilian B. Zimmerer, and Wolfgang Lucht
Earth Syst. Dynam., 11, 395–413, https://doi.org/10.5194/esd-11-395-2020, https://doi.org/10.5194/esd-11-395-2020, 2020
Short summary
Short summary
We present an open-source software framework for developing so-called
world–Earth modelsthat link physical, chemical and biological processes with social, economic and cultural processes to study the Earth system's future trajectories in the Anthropocene. Due to its modular structure, the software allows interdisciplinary studies of global change and sustainable development that combine stylized model components from Earth system science, climatology, economics, ecology and sociology.
Mikhail Y. Verbitsky and Michel Crucifix
Earth Syst. Dynam., 11, 281–289, https://doi.org/10.5194/esd-11-281-2020, https://doi.org/10.5194/esd-11-281-2020, 2020
Short summary
Short summary
Using the central theorem of dimensional analysis, the π theorem, we show that the relationship between the amplitude and duration of glacial cycles is governed by a property of scale invariance that does not depend on the physical nature of the underlying positive and negative feedbacks incorporated by the system. It thus turns out to be one of the most fundamental properties of the Pleistocene climate.
Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo, Lina M. Estupinan-Suarez, Victor H. Gutierrez-Velez, Martin Gutwin, Martin Jung, Maria C. Londoño, Diego G. Miralles, Phillip Papastefanou, and Markus Reichstein
Earth Syst. Dynam., 11, 201–234, https://doi.org/10.5194/esd-11-201-2020, https://doi.org/10.5194/esd-11-201-2020, 2020
Short summary
Short summary
The ever-growing availability of data streams on different subsystems of the Earth brings unprecedented scientific opportunities. However, researching a data-rich world brings novel challenges. We present the concept of
Earth system data cubesto study the complex dynamics of multiple climate and ecosystem variables across space and time. Using a series of example studies, we highlight the potential of effectively considering the full multivariate nature of processes in the Earth system.
Christine Ramadhin and Chuixiang Yi
Earth Syst. Dynam., 11, 13–16, https://doi.org/10.5194/esd-11-13-2020, https://doi.org/10.5194/esd-11-13-2020, 2020
Short summary
Short summary
Here we explore ancient climate transitions from warm periods to ice ages and from ice ages to warm periods of the last 400 000 years. The changeovers from warm to ice age conditions are slower than those from ice age to warm conditions. We propose the presence of strong negative sea–ice feedbacks may be responsible for slowing the transition from warm to full ice age conditions. By improving understanding of past abrupt changes, we may have improved knowledge of future system behavior.
M. Levent Kavvas, Tongbi Tu, Ali Ercan, and James Polsinelli
Earth Syst. Dynam., 11, 1–12, https://doi.org/10.5194/esd-11-1-2020, https://doi.org/10.5194/esd-11-1-2020, 2020
Short summary
Short summary
After deriving a fractional continuity equation, a previously-developed equation for water flux in porous media was combined with the Dupuit approximation to obtain an equation for groundwater motion in multi-fractional space in unconfined aquifers. As demonstrated in the numerical application, the orders of the fractional space and time derivatives modulate the speed of groundwater table evolution, slowing the process with the decrease in the powers of the fractional derivatives from 1.
Krishna-Pillai Sukumara-Pillai Krishnamohan, Govindasamy Bala, Long Cao, Lei Duan, and Ken Caldeira
Earth Syst. Dynam., 10, 885–900, https://doi.org/10.5194/esd-10-885-2019, https://doi.org/10.5194/esd-10-885-2019, 2019
Short summary
Short summary
We find that sulfate aerosols are more effective in cooling the climate system when they reside higher in the stratosphere. We explain this sensitivity in terms of radiative forcing at the top of the atmosphere. Sulfate aerosols heat the stratospheric layers, causing an increase in stratospheric water vapor content and a reduction in high clouds. These changes are larger when aerosols are prescribed near the tropopause, offsetting part of the aerosol-induced negative radiative forcing/cooling.
Davide Faranda, Yuzuru Sato, Gabriele Messori, Nicholas R. Moloney, and Pascal Yiou
Earth Syst. Dynam., 10, 555–567, https://doi.org/10.5194/esd-10-555-2019, https://doi.org/10.5194/esd-10-555-2019, 2019
Short summary
Short summary
We show how the complex dynamics of the jet stream at midlatitude can be described by a simple mathematical model. We match the properties of the model to those obtained by the jet data derived from observations.
Stefanie Talento, Lea Schneider, Johannes Werner, and Jürg Luterbacher
Earth Syst. Dynam., 10, 347–364, https://doi.org/10.5194/esd-10-347-2019, https://doi.org/10.5194/esd-10-347-2019, 2019
Short summary
Short summary
Quantifying hydroclimate variability beyond the instrumental period is essential for putting fluctuations into long-term perspective and providing a validation for climate models. We evaluate, in a virtual setup, the potential for generating millennium-long summer precipitation reconstructions over south-eastern Asia.
We find that performing a real-world reconstruction with the current available proxy network is indeed feasible, as virtual-world reconstructions are skilful in most areas.
Lennert B. Stap, Peter Köhler, and Gerrit Lohmann
Earth Syst. Dynam., 10, 333–345, https://doi.org/10.5194/esd-10-333-2019, https://doi.org/10.5194/esd-10-333-2019, 2019
Short summary
Short summary
Processes causing the same global-average radiative forcing might lead to different global temperature changes. We expand the theoretical framework by which we calculate paleoclimate sensitivity with an efficacy factor. Applying the revised approach to radiative forcing caused by CO2 and land ice albedo perturbations, inferred from data of the past 800 000 years, gives a new paleo-based estimate of climate sensitivity.
Luis Gimeno-Sotelo, Raquel Nieto, Marta Vázquez, and Luis Gimeno
Earth Syst. Dynam., 10, 121–133, https://doi.org/10.5194/esd-10-121-2019, https://doi.org/10.5194/esd-10-121-2019, 2019
Short summary
Short summary
Ice melting at the scale of inter-annual fluctuations against the trend is favoured by an increase in moisture transport in summer, autumn, and winter and a decrease in spring. On a daily basis extreme humidity transport increases the formation of ice in winter and decreases it in spring, summer, and autumn; in these three seasons it thus contributes to Arctic sea ice melting. These patterns differ sharply from that linked to decline, especially in summer when the opposite trend applies.
Iago Algarra, Jorge Eiras-Barca, Gonzalo Miguez-Macho, Raquel Nieto, and Luis Gimeno
Earth Syst. Dynam., 10, 107–119, https://doi.org/10.5194/esd-10-107-2019, https://doi.org/10.5194/esd-10-107-2019, 2019
Short summary
Short summary
We analyse moisture transport triggered by the Great Plains low-level jet (GPLLJ), a maximum in wind speed fields located within the first kilometre of the US Great Plain's troposphere, through the innovative Eulerian Weather Research and Forecasting Model tracer tool. Much moisture associated with this low-level jet has been found in northern regions located in a vast extension of the continent, highlighting the key role played by the GPLLJ in North America's advective transport of moisture.
Gerrit Lohmann
Earth Syst. Dynam., 9, 1279–1281, https://doi.org/10.5194/esd-9-1279-2018, https://doi.org/10.5194/esd-9-1279-2018, 2018
Short summary
Short summary
Long-term sea surface temperature trends and variability are underestimated in models compared to paleoclimate data. The idea is presented that the trends and variability are related, which is elaborated in a conceptual model framework. The temperature spectrum can be used to estimate the timescale-dependent climate sensitivity.
Mark M. Dekker, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 9, 1243–1260, https://doi.org/10.5194/esd-9-1243-2018, https://doi.org/10.5194/esd-9-1243-2018, 2018
Short summary
Short summary
We introduce a framework of cascading tipping, i.e. a sequence of abrupt transitions occurring because a transition in one system affects the background conditions of another system. Using bifurcation theory, various types of these events are considered and early warning indicators are suggested. An illustration of such an event is found in a conceptual model, coupling the North Atlantic Ocean with the equatorial Pacific. This demonstrates the possibility of events such as this in nature.
Uwe Mikolajewicz, Florian Ziemen, Guido Cioni, Martin Claussen, Klaus Fraedrich, Marvin Heidkamp, Cathy Hohenegger, Diego Jimenez de la Cuesta, Marie-Luise Kapsch, Alexander Lemburg, Thorsten Mauritsen, Katharina Meraner, Niklas Röber, Hauke Schmidt, Katharina D. Six, Irene Stemmler, Talia Tamarin-Brodsky, Alexander Winkler, Xiuhua Zhu, and Bjorn Stevens
Earth Syst. Dynam., 9, 1191–1215, https://doi.org/10.5194/esd-9-1191-2018, https://doi.org/10.5194/esd-9-1191-2018, 2018
Short summary
Short summary
Model experiments show that changing the sense of Earth's rotation has relatively little impact on the globally and zonally averaged energy budgets but leads to large shifts in continental climates and patterns of precipitation. The retrograde world is greener as the desert area shrinks. Deep water formation shifts from the North Atlantic to the North Pacific with subsequent changes in ocean overturning. Over large areas of the Indian Ocean, cyanobacteria dominate over bulk phytoplankton.
Axel Kleidon and Maik Renner
Earth Syst. Dynam., 9, 1127–1140, https://doi.org/10.5194/esd-9-1127-2018, https://doi.org/10.5194/esd-9-1127-2018, 2018
Short summary
Short summary
Turbulent fluxes represent an efficient way to transport heat and moisture from the surface into the atmosphere. Due to their inherently highly complex nature, they are commonly described by semiempirical relationships. What we show here is that these fluxes can also be predicted by viewing them as the outcome of a heat engine that operates between the warm surface and the cooler atmosphere and that works at its limit.
Marc Schleiss
Earth Syst. Dynam., 9, 955–968, https://doi.org/10.5194/esd-9-955-2018, https://doi.org/10.5194/esd-9-955-2018, 2018
Short summary
Short summary
The present study aims at explaining how intermittency (i.e., the alternation of dry and rainy periods) affects the rate at which precipitation extremes increase with temperature. Using high-resolution rainfall data from 99 stations in the United States, we show that at scales beyond a few hours, intermittency causes rainfall extremes to deviate substantially from Clausius–Clapeyron. A new model is proposed to better represent and predict these changes across scales.
Ragnhild Bieltvedt Skeie, Terje Berntsen, Magne Aldrin, Marit Holden, and Gunnar Myhre
Earth Syst. Dynam., 9, 879–894, https://doi.org/10.5194/esd-9-879-2018, https://doi.org/10.5194/esd-9-879-2018, 2018
Short summary
Short summary
A key question in climate science is how the global mean surface temperature responds to changes in greenhouse gases. This dependency is quantified by the climate sensitivity, which is determined by the complex feedbacks in the climate system. In this study observations of past climate change are used to estimate this sensitivity. Our estimate is consistent with values for the equilibrium climate sensitivity estimated by complex climate models but sensitive to the use of uncertain input data.
Dieter Gerten, Martin Schönfeld, and Bernhard Schauberger
Earth Syst. Dynam., 9, 849–863, https://doi.org/10.5194/esd-9-849-2018, https://doi.org/10.5194/esd-9-849-2018, 2018
Short summary
Short summary
Cultural processes are underrepresented in Earth system models, although they decisively shape humanity’s planetary imprint. We set forth ideas on how Earth system analysis can be enriched by formalising aspects of religion (understood broadly as a collective belief in things held sacred). We sketch possible modelling avenues (extensions of existing Earth system models and new co-evolutionary models) and suggest research primers to explicate and quantify mental aspects of the Anthropocene.
Stefan Lange
Earth Syst. Dynam., 9, 627–645, https://doi.org/10.5194/esd-9-627-2018, https://doi.org/10.5194/esd-9-627-2018, 2018
Short summary
Short summary
The bias correction of surface downwelling longwave and shortwave radiation using parametric quantile mapping methods is shown to be more effective (i) at the daily than at the monthly timescale, (ii) if the spatial resolution gap between the reference data and the data to be corrected is bridged in a more suitable manner than by bilinear interpolation, and (iii) if physical upper limits are taken into account during the adjustment of either radiation component.
Camilla Mathison, Chetan Deva, Pete Falloon, and Andrew J. Challinor
Earth Syst. Dynam., 9, 563–592, https://doi.org/10.5194/esd-9-563-2018, https://doi.org/10.5194/esd-9-563-2018, 2018
Short summary
Short summary
Sowing and harvest dates are a significant source of uncertainty within crop models. South Asia is one region with a large uncertainty. We aim to provide more accurate sowing and harvest dates than currently available and that are relevant for climate impact assessments. This method reproduces the present day sowing and harvest dates for most parts of India and when applied to two future periods provides a useful way of modelling potential growing season adaptations to changes in future climate.
Dario A. Zappalà, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 9, 383–391, https://doi.org/10.5194/esd-9-383-2018, https://doi.org/10.5194/esd-9-383-2018, 2018
Short summary
Short summary
The dynamics of our climate involves multiple timescales, and while a lot of work has been devoted to quantifying variations in time-averaged variables or variations in their seasonal cycles, variations in daily variability that occur over several decades still remain poorly understood. Here we analyse daily surface air temperature and demonstrate that inter-decadal changes can be precisely identified and quantified with the Hilbert analysis tool.
Nadine Mengis, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 9, 15–31, https://doi.org/10.5194/esd-9-15-2018, https://doi.org/10.5194/esd-9-15-2018, 2018
Short summary
Short summary
The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select, transparent, nonredundant indicators for a comprehensive assessment of the Earth system state. We show that due to changing climate forcing, such as anthropogenic climate change, the ad hoc assessment indicators might need to be reevaluated. Within an iterative process, this method would allow us to select scientifically consistent and societally relevant assessment indicators.
Liga Bethere, Juris Sennikovs, and Uldis Bethers
Earth Syst. Dynam., 8, 951–962, https://doi.org/10.5194/esd-8-951-2017, https://doi.org/10.5194/esd-8-951-2017, 2017
Short summary
Short summary
We define three new climate indices based on monthly mean temperature and total precipitation values that describe the main features of the climate in the Baltic states. Higher values in each index correspond to (1) less distinct seasonality and (2) warmer and (3) wetter climate. It was calculated that in the future all three indices will increase. Such indices summarize and illustrate the spatial features of the Baltic climate, and they can be used in further analysis of climate change impact.
Tongbi Tu, Ali Ercan, and M. Levent Kavvas
Earth Syst. Dynam., 8, 931–949, https://doi.org/10.5194/esd-8-931-2017, https://doi.org/10.5194/esd-8-931-2017, 2017
Short summary
Short summary
Groundwater level fluctuations in confined aquifer wells with long observations exhibit site-specific fractal scaling behavior, and the underlying distribution exhibits either non-Gaussian characteristics, which may be fitted by the Lévy stable distribution, or Gaussian characteristics. The estimated Hurst exponent is highly dependent on the length and the specific time interval of the time series. The MF-DFA and MMA analyses showed that different levels of multifractality exist.
Axel Kleidon and Maik Renner
Earth Syst. Dynam., 8, 849–864, https://doi.org/10.5194/esd-8-849-2017, https://doi.org/10.5194/esd-8-849-2017, 2017
Short summary
Short summary
We provide an explanation why land temperatures respond more strongly to global warming than ocean temperatures, a robust finding in observations and models that has so far not been understood well. We explain it by the different ways by which ocean and land surfaces buffer the strong variation in solar radiation and demonstrate this with a simple, physically based model. Our explanation also illustrates why nighttime temperatures warm more strongly, another robust finding of global warming.
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, and Miguel D. Mahecha
Earth Syst. Dynam., 8, 677–696, https://doi.org/10.5194/esd-8-677-2017, https://doi.org/10.5194/esd-8-677-2017, 2017
Short summary
Short summary
Anomalies and extremes are often detected using univariate peak-over-threshold approaches in the geoscience community. The Earth system is highly multivariate. We compare eight multivariate anomaly detection algorithms and combinations of data preprocessing. We identify three anomaly detection algorithms that outperform univariate extreme event detection approaches. The workflows have the potential to reveal novelties in data. Remarks on their application to real Earth observations are provided.
James Hansen, Makiko Sato, Pushker Kharecha, Karina von Schuckmann, David J. Beerling, Junji Cao, Shaun Marcott, Valerie Masson-Delmotte, Michael J. Prather, Eelco J. Rohling, Jeremy Shakun, Pete Smith, Andrew Lacis, Gary Russell, and Reto Ruedy
Earth Syst. Dynam., 8, 577–616, https://doi.org/10.5194/esd-8-577-2017, https://doi.org/10.5194/esd-8-577-2017, 2017
Short summary
Short summary
Global temperature now exceeds +1.25 °C relative to 1880–1920, similar to warmth of the Eemian period. Keeping warming less than 1.5 °C or CO2 below 350 ppm now requires extraction of CO2 from the air. If rapid phaseout of fossil fuel emissions begins soon, most extraction can be via improved agricultural and forestry practices. In contrast, continued high emissions places a burden on young people of massive technological CO2 extraction with large risks, high costs and uncertain feasibility.
Cited articles
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092,
2002.
Allen, M. R. and Stott, P. A.: Estimating signal amplitudes in optimal
fingerprinting, part I: theory, Clim. Dynam., 21, 477–491,
https://doi.org/10.1007/s00382-003-0313-9, 2003.
Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing,
feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate
models, Geophys. Res. Lett., 39, L09712, https://doi.org/10.1029/2012gl051607, 2012.
Annan, J. D., Hargreaves, J. C., Mauritsen, T., and Stevens, B.: What could we learn about climate sensitivity from variability in the surface temperature record?, Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, 2020.
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Baker, N. C. and Taylor, P. C.: A Framework for Evaluating Climate Model
Performance Metrics, J. Climate, 29, 1773–1782,
https://doi.org/10.1175/JCLI-D-15-0114.1, 2016.
Boé, J., Hall, A., and Qu, X.: September sea-ice cover in the Arctic
Ocean projected to vanish by 2100, Nat. Geosci., 2, 341–343,
https://doi.org/10.1038/ngeo467, 2009.
Boer, G. J., Stowasser, M., and Hamilton, K.: Inferring climate sensitivity
from volcanic events, Clim. Dynam., 28, 481–502,
https://doi.org/10.1007/s00382-006-0193-x, 2007.
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018.
Bony, S., Schulz, H., Vial, J., and Stevens, B.: Sugar, Gravel, Fish, and
Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on
Environmental Conditions, Geophys. Res. Lett., 47, e2019GL085988,
https://doi.org/10.1029/2019GL085988, 2020.
Bretherton, C. and Caldwell, P.: Combining Emergent Constraints for Climate
Sensitivity, J. Climate, 33, 7413–7430,
https://doi.org/10.1175/JCLI-D-19-0911.1, 2020.
Brienen, R. J. W., Caldwell, L., Duchesne, L., Voelker, S., Barichivich, J.,
Baliva, M., Ceccantini, G., Di Filippo, A., Helama, S., Locosselli, G. M.,
Lopez, L., Piovesan, G., Schöngart, J., Villalba, R., and Gloor, E.:
Forest carbon sink neutralized by pervasive growth-lifespan trade-offs, Nat.
Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-17966-z, 2020.
Brient, F.: Reducing uncertainties in climate projections with emergent
constraints: Concepts, Examples and Prospects, Adv. Atmos.
Sci., 37, 1–15,
https://doi.org/10.1007/s00376-019-9140-8, 2019.
Brient, F. and Schneider, T.: Constraints on Climate Sensitivity from
Space-Based Measurements of Low-Cloud Reflection, J. Climate,
29, 5821–5835, https://doi.org/10.1175/jcli-d-15-0897.1, 2016.
Brient, F., Schneider, T., Tan, Z., Bony, S., Qu, X., and Hall, A.:
Shallowness of tropical low clouds as a predictor of climate models'
response to warming, Clim. Dynam., 47, 433–449,
https://doi.org/10.1007/s00382-015-2846-0, 2016.
Brown, P. T., Stolpe, M. B., and Caldeira, K.: Assumptions for emergent
constraints, Nature, 563, E1–E3, https://doi.org/10.1038/s41586-018-0638-5, 2018.
Brunner, L., Lorenz, R., Zumwald, M., and Knutti, R.: Quantifying uncertainty
in European climate projections using combined performance-independence
weighting, Environ. Res. Lett., 14, 124010,
https://doi.org/10.1088/1748-9326/ab492f, 2019.
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020.
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B.
D., and Sanderson, B. M.: Statistical significance of climate sensitivity
predictors obtained by data mining, Geophys. Res. Lett., 41,
1803–1808, https://doi.org/10.1002/2014gl059205, 2014.
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating Emergent
Constraints on Equilibrium Climate Sensitivity, J. Climate, 31,
3921–3942, https://doi.org/10.1175/jcli-d-17-0631.1, 2018.
Chadburn, S. E., Burke, E. J., Cox, P. M., Friedlingstein, P., Hugelius, G.,
and Westermann, S.: An observation-based constraint on permafrost loss as a
function of global warming, Nat. Clim. Change, 7, 340–344,
https://doi.org/10.1038/nclimate3262, 2017.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model
Benchmarking (ILAMB) System: Design, Theory, and Implementation, J.
Adv. Model. Earth Sy., 10, 2731–2754,
https://doi.org/10.1029/2018ms001354, 2018.
Covey, C., Abe-Ouchi, A., Boer, G. J., Boville, B. A., Cubasch, U.,
Fairhead, L., Flato, G. M., Gordon, H., Guilyardi, E., Jiang, X., Johns, T.
C., Le Treut, H., Madec, G., Meehl, G. A., Miller, R., Noda, A., Power, S.
B., Roeckner, E., Russell, G., Schneider, E. K., Stouffer, R. J., Terray, L.,
and von Storch, J.-S.: The seasonal cycle in coupled ocean-atmosphere
general circulation models, Clim. Dynam., 16, 775–787,
https://doi.org/10.1007/s003820000081, 2000.
Cox, P. M.: Emergent Constraints on Climate-Carbon Cycle Feedbacks, Curr.
Clim. Change Rep., 5, 275–281, https://doi.org/10.1007/s40641-019-00141-y, 2019.
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C.,
Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate
change constrained by carbon dioxide variability, Nature, 494,
341–344, https://doi.org/10.1038/nature11882, 2013.
Cox, P. M., Williamson, M. S., Nijsse, F. J. M. M., and Huntingford, C.: Cox
et al. reply, Nature, 563, E10–E15, https://doi.org/10.1038/s41586-018-0641-x,
2018a.
Cox, P. M., Huntingford, C., and Williamson, M. S.: Emergent constraint on
equilibrium climate sensitivity from global temperature variability, Nature,
553, 319–322, https://doi.org/10.1038/nature25450, 2018b.
Davies-Barnard, T., Meyerholt, J., Zaehle, S., Friedlingstein, P., Brovkin, V., Fan, Y., Fisher, R. A., Jones, C. D., Lee, H., Peano, D., Smith, B., Wårlind, D., and Wiltshire, A. J.: Nitrogen cycling in CMIP6 land surface models: progress and limitations, Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, 2020.
de Wilde, P. and Tian, W.: : Towards probabilistic performance
metrics for climate change impact studies, Energy and Buildings, 43,
3013–3018, https://doi.org/10.1016/j.enbuild.2011.07.014, 2011.
Douville, H. and Plazzotta, M.: Midlatitude Summer Drying: An Underestimated
Threat in CMIP5 Models?, Geophys. Res. Lett., 44, 9967–9975,
https://doi.org/10.1002/2017gl075353, 2017.
Edwards, J. M., Beljaars, A. C. M., Holtslag, A. A. M., and Lock, A. P.:
Representation of Boundary-Layer Processes in Numerical Weather Prediction
and Climate Models, Bound.-Lay. Meteorol., 177, 511–539,
https://doi.org/10.1007/s10546-020-00530-z, 2020.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G.,
Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M.,
Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P.,
Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G.,
Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D.,
Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.:
Taking climate model evaluation to the next level, Nat. Clim. Change,
9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P., Huntingford, C., Meir, P., and Ian Woodward, F.:
Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological
scale limitations, New Phytol., 187, 666–681, https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F.,
Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd,
V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E.,
Mercado, L. M., Norby, R. J., Pak, B., von Randow, C., Quesada, C. A.,
Schaap, K. J., Valverde-Barrantes, O. J., Wang, Y.-P., Yang, X., Zaehle, S.,
Zhu, Q., and Lapola, D. M.: Amazon forest response to CO2 fertilization
dependent on plant phosphorus acquisition, Nat. Geosci., 12,
736–741, https://doi.org/10.1038/s41561-019-0404-9, 2019.
Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., and Webster, M.
D.: Quantifying uncertainties in climate system properties with the use of
recent climate observations, Science, 295, 113–117,
https://doi.org/10.1126/science.1064419, 2002.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections
due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/jcli-d-12-00579.1, 2014.
Geoffroy, O., Saint-Martin, D., Bellon, G., Voldoire, A., Olivié, D. J.
L., and Tytéca, S.: Transient Climate Response in a Two-Layer
Energy-Balance Model. Part II: Representation of the Efficacy of Deep-Ocean
Heat Uptake and Validation for CMIP5 AOGCMs, J. Climate, 26,
1859–1876, https://doi.org/10.1175/jcli-d-12-00196.1, 2013.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res., 113, D06104,
https://doi.org/10.1029/2007jd008972, 2008.
Golaz, J., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W.,
Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H., Lin, W.,
Lipscomb, W. H., Ma, P., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K.,
Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model
Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model.
Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Gordon, N. D. and Klein, S. A.: Low-cloud optical depth feedback in climate
models, J. Geophys. Res.-Atmos., 119, 6052–6065,
https://doi.org/10.1002/2013JD021052, 2014.
Goris, N., Tjiputra, J. F., Olsen, A., Schwinger, J., Lauvset, S. K., and
Jeansson, E.: Constraining projection-based estimates of the future North
Atlantic carbon uptake, J. Climate, 31, 3959–3978,
https://doi.org/10.1175/jcli-d-17-0564.1, 2018.
Gregory, J. M., Andrews, T., and Good, P.: The inconstancy of the transient
climate response parameter under increasing CO2, Philos. T. R. Soc. A, 373, 20140417, https://doi.org/10.1098/rsta.2014.0417, 2015.
Hall, A., Cox, P., Huntingford, C., and Klein, S.: Progressing emergent
constraints on future climate change, Nat. Clim. Change, 9, 269–278,
https://doi.org/10.1038/s41558-019-0436-6, 2019.
Hargreaves, J. C., Annan, J. D., Yoshimori, M., and Abe-Ouchi, A.: Can the
Last Glacial Maximum constrain climate sensitivity?, Geophys. Res.
Lett., 39, L24702, https://doi.org/10.1029/2012gl053872, 2012.
Hegerl, G. and Zwiers, F.: Use of models in detection and attribution of
climate change, WIRES Clim. Change, 2,
570–591, https://doi.org/10.1002/wcc.121, 2011.
Hegerl, G. C., Stott, P. A., Allen, M. R., Mitchell, J. F. B., Tett, S. F.
B., and Cubasch, U.: Optimal detection and attribution of climate change:
sensitivity of results to climate model differences, Clim. Dynam., 16,
737–754, https://doi.org/10.1007/s003820000071, 2000.
Hegerl, G. C., Crowley, T. J., Hyde, W. T., and Frame, D. J.: Climate
sensitivity constrained by temperature reconstructions over the past seven
centuries, Nature, 440, 1029–1032, https://doi.org/10.1038/nature04679, 2006.
Hoffman, F. M., Randerson, J. T., Arora, V. K., Bao, Q., Cadule, P., Ji, D.,
Jones, C. D., Kawamiya, M., Khatiwala, S., Lindsay, K., Obata, A.,
Shevliakova, E., Six, K. D., Tjiputra, J. F., Volodin, E. M., and Wu, T.:
Causes and implications of persistent atmospheric carbon dioxide biases in
Earth System Models, J. Geophys. Res.-Biogeo.,
119, 141–162, https://doi.org/10.1002/2013jg002381, 2014.
Holtslag, A. A. M., Steeneveld, G. J., and van de Wiel, B. J. H.: Role of land-surface temperature
feedback on model performance for the stable boundary layer, in:
Atmospheric Boundary Layers, edited by: Baklanov, A. and Grisogono, B., Springer, New York, NY, 205–220,
https://doi.org/10.1007/978-0-387-74321-9_14, 2007.
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan,
Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C.,
Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of
Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017.
Huber, M., Mahlstein, I., Wild, M., Fasullo, J., and Knutti, R.: Constraints
on Climate Sensitivity from Radiation Patterns in Climate Models, J. Climate,
24, 1034–1052, https://doi.org/10.1175/2010JCLI3403.1, 2011.
Jiménez-de-la-Cuesta, D. and Mauritsen, T.: Emergent constraints on Earth’s transient and
equilibrium response to doubled CO2 from post-1970s global warming, Nat. Geosci., 12,
902–905, https://doi.org/10.1038/s41561-019-0463-y,
2019.
Kamae, Y., Shiogama, H., Watanabe, M., Ogura, T., Yokohata, T., and Kimoto,
M.: Lower-Tropospheric Mixing as a Constraint on Cloud Feedback in a
Multiparameter Multiphysics Ensemble, J. Climate, 29, 6259–6275,
https://doi.org/10.1175/JCLI-D-16-0042.1, 2016.
Karpechko, A. Y., Maraun, D., and Eyring, V.: Improving Antarctic total ozone
projections by a process-oriented multiple diagnostic ensemble regression,
J. Atmos. Sci., 70, 3959–3976, https://doi.org/10.1175/jas-d-13-071.1, 2013.
Kessler, A. and Tjiputra, J.: The Southern Ocean as a constraint to reduce uncertainty in future ocean carbon sinks, Earth Syst. Dynam., 7, 295–312, https://doi.org/10.5194/esd-7-295-2016, 2016.
Kettleborough, J. A., Booth, B. B. B., Stott, P. A., and Allen, M. R.:
Estimates of uncertainty in predictions of global mean surface temperature,
J. Climate, 20, 843–855, https://doi.org/10.1175/jcli4012.1, 2007.
Kiehl, J. T.: Twentieth century climate model response and climate
sensitivity, Geophys. Res. Lett., 34, L22710, https://doi.org/10.1029/2007gl031383, 2007.
Klein, S. A. and Hall, A.: Emergent Constraints for Cloud Feedbacks, Current
Climate Change Reports, 1, 276–287, https://doi.org/10.1007/s40641-015-0027-1, 2015.
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-cloud feedbacks
from cloud-controlling factors: A review, Surv. Geophys., 38, 1307–1329,
https://doi.org/10.1007/s10712-017-9433-3, 2017.
Knutti, R.: Why are climate models reproducing the observed global surface
warming so well?, Geophys. Res. Lett., 35, L18704, https://doi.org/10.1029/2008gl034932,
2008.
Knutti, R. and Tomassini, L.: Constraints on the transient climate response
from observed global temperature and ocean heat uptake, Geophys. Res.
Lett., 35, L09701, https://doi.org/10.1029/2007gl032904, 2008.
Knutti, R., Stocker, T. F., Joos, F., and Plattner, G.-K.: Constraints on
radiative forcing and future climate change from observations and climate
model ensembles, Nature, 416, 719–723, https://doi.org/10.1038/416719a, 2002.
Knutti, R., Meehl, G. A., Allen, M. R., and Stainforth, D. A.: Constraining
Climate Sensitivity from the Seasonal Cycle in Surface Temperature, J.
Climate, 19, 4224–4233, https://doi.org/10.1175/jcli3865.1, 2006.
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy:
Generation CMIP5 and how we got there, Geophys. Res. Lett., 40,
1194–1199, https://doi.org/10.1002/grl.50256, 2013.
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E.
M., and Eyring, V.: A climate model projection weighting scheme accounting
for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918,
https://doi.org/10.1002/2016gl072012, 2017.
Koven, C., Arora, V. K., Cadule, P., Fisher, R. A., Jones, C. D., Lawrence, D. M., Lewis, J., Lindsey, K., Mathesius, S., Meinshausen, M., Mills, M., Nicholls, Z., Sanderson, B. M., Swart, N. C., Wieder, W. R., and Zickfeld, K.: 23rd Century surprises: Long-term dynamics of the climate and carbon cycle under both high and net negative emissions scenarios, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-23, in review, 2021.
Kubo, R.: The fluctuation-dissipation theorem, Rep. Prog. Phys., 29, 255,
https://doi.org/10.1088/0034-4885/29/1/306, 1966.
Kwiatkowski, L., Bopp, L., Aumont, O., Ciais, P., Cox, P. M.,
Laufkötter, C., Li, Y., and Séférian, R.: Emergent constraints on
projections of declining primary production in the tropical oceans, Nat.
Clim. Change, 7, 355–358, https://doi.org/10.1038/nclimate3265, 2017.
Leith, C. E.: Climate Response and Fluctuation Dissipation, J.
Atmos. Sci., 32, 2022–2026,
https://doi.org/10.1175/1520-0469(1975)032<2022:crafd>2.0.co;2,
1975.
Lenton, T. M., Rockström, J., Gaffney, O., Rahmstorf, S., Richardson,
K., Steffen, W., and Schellnhuber, H. J.: Climate tipping points – too
risky to bet against, Nature, 575, 592–595,
https://doi.org/10.1038/d41586-019-03595-0, 2019.
Levine, N. M., Zhang, K., Longo, M., Baccini, A., Phillips, O. L., Lewis, S.
L., Alvarez-Dávila, E., de Andrade, A. C. S., Brienen, R. J. W., Erwin,
T. L., Feldpausch, T. R., Mendoza, A. L. M., Vargas, P. N., Prieto, A.,
Silva-Espejo, J. E., Malhi, Y., and Moorcroft, P. R.: Ecosystem heterogeneity
determines the ecological resilience of the Amazon to climate change,
P. Natl. Acad. Sci. USA, 113, 793–797,
https://doi.org/10.1073/pnas.1511344112, 2016.
Lipat, B. R., Tselioudis, G., Grise, K. M., and Polvani, L. M.: CMIP5 models'
shortwave cloud radiative response and climate sensitivity linked to the
climatological Hadley cell extent, Geophys. Res. Lett., 44,
5739–5748, https://doi.org/10.1002/2017gl073151, 2017.
Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P.
B., Fitzjarrald, D. R., Hayek, M. N., Restrepo-Coupe, N., Saleska, S. R., da
Silva, R., Stark, S. C., Tapajós, R. P., Wiedemann, K. T., Zhang, K.,
Wofsy, S. C., and Moorcroft, P. R.: Ecosystem heterogeneity and diversity
mitigate Amazon forest resilience to frequent extreme droughts, New Phytol.,
219, 914–931, https://doi.org/10.1111/nph.15185, 2018.
Lorenz, R., Herger, N., Sedláček, J., Eyring, V., Fischer, E. M., and
Knutti, R.: Prospects and Caveats of Weighting Climate Models for Summer
Maximum Temperature Projections Over North America, J. Geophys. Res.-Atmos., 123, 4509–4526, https://doi.org/10.1029/2017JD027992, 2018.
Mahlstein, I. and Knutti, R.: September Arctic sea ice predicted to
disappear near 2 ∘C global warming above present, J. Geophys.
Res., 117, D06104, https://doi.org/10.1029/2011jd016709, 2012.
Masson, D. and Knutti, R.: Climate model genealogy, Geophys. Res. Lett.,
38, L08703, https://doi.org/10.1029/2011gl046864, 2011.
Masson, D. and Knutti, R.: Predictor screening, calibration, and
observational constraints in climate model ensembles: An illustration using
climate sensitivity, J. Climate, 26, 887–898,
https://doi.org/10.1175/jcli-d-11-00540.1, 2013a.
Masson, D. and Knutti, R.: Predictor Screening, Calibration, and
Observational Constraints in Climate Model Ensembles: An Illustration Using
Climate Sensitivity, J. Climate, 26, 887–898,
https://doi.org/10.1175/JCLI-D-11-00540.1, 2013b.
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta,
M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz,
D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a
global model, J. Adv. Model. Earth Sy., 4, M00A01, https://doi.org/10.1029/2012MS000154,
2012.
McDowell, N., Allen, C. D., Anderson-Teixeira, K., Brando, P., Brienen, R.,
Chambers, J., Christoffersen, B., Davies, S., Doughty, C., Duque, A.,
Espirito-Santo, F., Fisher, R., Fontes, C. G., Galbraith, D., Goodsman, D.,
Grossiord, C., Hartmann, H., Holm, J., Johnson, D. J., Kassim, A. R.,
Keller, M., Koven, C., Kueppers, L., Kumagai, T., Malhi, Y., McMahon, S. M.,
Mencuccini, M., Meir, P., Moorcroft, P., Muller-Landau, H. C., Phillips, O.
L., Powell, T., Sierra, C. A., Sperry, J., Warren, J., Xu, C., and Xu, X.:
Drivers and mechanisms of tree mortality in moist tropical forests, New
Phytol., 219, 851–869, https://doi.org/10.1111/nph.15027, 2018.
McKiver, W. J., Vichi, M., Lovato, T., Storto, A., and Masina, S.: Impact of
increased grid resolution on global marine biogeochemistry, J. Marine Syst.,
147, 153–168, https://doi.org/10.1016/j.jmarsys.2014.10.003, 2015.
McNeall, D., Williams, J., Booth, B., Betts, R., Challenor, P., Wiltshire, A., and Sexton, D.: The impact of structural error on parameter constraint in a climate model, Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, 2016.
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F.,
Stouffer, R. J., Taylor, K. E., and Schlund, M.: Context for interpreting
equilibrium climate sensitivity and transient climate response from the
CMIP6 Earth system models, Sci. Adv., 6, eaba1981,
https://doi.org/10.1126/sciadv.aba1981, 2020.
Mongwe, N. P., Chang, N., and Monteiro, P. M. S.: The seasonal cycle as a
mode to diagnose biases in modelled CO2 fluxes in the Southern Ocean, Ocean Model. (Oxf.), 106, 90–103, https://doi.org/10.1016/j.ocemod.2016.09.006, 2016.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the
atmosphere, Tr. Akad. Nauk. SSSR Geophiz. Inst., 24, 163–187, 1954.
Needham, J. F., Chambers, J., Fisher, R., Knox, R., and Koven, C. D.: Forest
responses to simulated elevated CO under alternate hypotheses of size- and
age-dependent mortality, Glob. Change Biol., 26, 5734–5753, https://doi.org/10.1111/gcb.15254, 2020.
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020.
O'Gorman, P. A.: Sensitivity of tropical precipitation extremes to climate
change, Nat. Geosci., 5, 697–700, https://doi.org/10.1038/ngeo1568, 2012.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W.,
Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., Dubash, N.
K., Edenhofer, O., Elgizouli, I., Field, C. B., Forster, P., Friedlingstein,
P., Fuglestvedt, J., Gomez-Echeverri, L., Hallegatte, S., Hegerl, G.,
Howden, M., Jiang, K., Jimenez Cisneroz, B., Kattsov, V., Lee, H., Mach, K.
J., Marotzke, J., Mastrandrea, M. D., Meyer, L., Minx, J., Mulugetta, Y.,
O'Brien, K., Oppenheimer, M., Pereira, J. J., Pichs-Madruga, R., Plattner,
G.-K., Pörtner, H.-O., Power, S. B., Preston, B., Ravindranath, N. H.,
Reisinger, A., Riahi, K., Rusticucci, M., Scholes, R., Seyboth, K., Sokona,
Y., Stavins, R., Stocker, T. F., Tschakert, P., van Vuuren, D., and van
Ypserle, J.-P.: Climate Change 2014: Synthesis Report. Contribution of
Working Groups I, II and III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Pachauri, R. K., and
Meyer, L., IPCC, Geneva, Switzerland, available at:
https://epic.awi.de/id/eprint/37530/ (last access: 30 January 2020), 2014.
Palmer, T.: Short-term tests validate long-term estimates of climate change,
Nature, 582, 185–186, https://doi.org/10.1038/d41586-020-01484-5, 2020.
Piani, C., Frame, D. J., Stainforth, D. A., and Allen, M. R.: Constraints on
climate change from a multi-thousand member ensemble of simulations,
Geophys. Res. Lett., 32, L23825, https://doi.org/10.1029/2005gl024452, 2005.
Pincus, R., Winker, D., Bony, S., and Stevens, B. (Eds.): Shallow Clouds, Water Vapor,
Circulation, and Climate Sensitivity, Springer International Publishing,
https://doi.org/10.1007/978-3-319-77273-8, 2018.
Plazzotta, M., Séférian, R., Douville, H., Kravitz, B., and Tjiputra,
J.: Land Surface Cooling Induced by Sulfate Geoengineering Constrained by
Major Volcanic Eruptions, Geophys. Res. Lett., 45, 5663–5671,
https://doi.org/10.1029/2018GL077583, 2018.
Po-Chedley, S., Proistosescu, C., Armour, K. C., and Santer, B. D.: Climate
constraint reflects forced signal, Nature, 563, E6–E9,
https://doi.org/10.1038/s41586-018-0640-y, 2018.
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?,
J. Climate, 20, 3971–3981, https://doi.org/10.1175/jcli4186.1, 2007.
Qu, X. and Hall, A.: On the persistent spread in snow-albedo feedback,
Clim. Dynam., 42, 69–81, https://doi.org/10.1007/s00382-013-1774-0, 2014.
Qu, X., Hall, A., Klein, S. A., and Caldwell, P. M.: On the spread of changes
in marine low cloud cover in climate model simulations of the 21st century,
Clim. Dynam., 42, 2603–2626, https://doi.org/10.1007/s00382-013-1945-z,
2014.
Renoult, M., Annan, J. D., Hargreaves, J. C., Sagoo, N., Flynn, C., Kapsch, M.-L., Li, Q., Lohmann, G., Mikolajewicz, U., Ohgaito, R., Shi, X., Zhang, Q., and Mauritsen, T.: A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP, Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, 2020.
Ribes, A., Zwiers, F. W., Azaïs, J.-M., and Naveau, P.: A new
statistical approach to climate change detection and attribution, Clim.
Dynam., 48, 367–386, https://doi.org/10.1007/s00382-016-3079-6, 2017.
Rodwell, M. J. and Palmer, T. N.: Using numerical weather prediction to
assess climate models, Q. J. Roy. Meteor.
Soc., 133, 129–146, https://doi.org/10.1002/qj.23, 2007.
Rose, B. E. J. and Rayborn, L.: The effects of ocean heat uptake on
transient climate sensitivity, Curr. Clim. Change Rep., 2, 190–201,
https://doi.org/10.1007/s40641-016-0048-4, 2016.
Rougier, J.: Probabilistic Inference for Future Climate Using an Ensemble of
Climate Model Evaluations, Climatic Change, 81, 247–264,
https://doi.org/10.1007/s10584-006-9156-9, 2007.
Royer, D. L., Berner, R. A., and Park, J.: Climate sensitivity constrained by
CO2 concentrations over the past 420 million years, Nature, 446,
530–532, https://doi.org/10.1038/nature05699, 2007.
Rugenstein, M., Bloch-Johnson, J., Gregory, J., Andrews, T., Mauritsen, T.,
Li, C., Frölicher, T. L., Paynter, D., Danabasoglu, G., Yang, S.,
Dufresne, J., Cao, L., Schmidt, G. A., Abe-Ouchi, A., Geoffroy, O., and
Knutti, R.: Equilibrium Climate Sensitivity Estimated by Equilibrating
Climate Models, Geophys. Res. Lett., 47, 1029, https://doi.org/10.1029/2019GL083898,
2020.
Rypdal, M., Fredriksen, H.-B., Rypdal, K., and Steene, R. J.: Emergent
constraints on climate sensitivity, Nature, 563, E4–E5,
https://doi.org/10.1038/s41586-018-0639-4, 2018.
Sakschewski, B., von Bloh, W., Boit, A., Poorter, L., Peña-Claros, M.,
Heinke, J., Joshi, J., and Thonicke, K.: Resilience of Amazon forests emerges
from plant trait diversity, Nat. Clim. Change, 6, 1032–1036,
https://doi.org/10.1038/nclimate3109, 2016.
Sallée, J.-B., Shuckburgh, E., Bruneau, N., Meijers, A. J. S.,
Bracegirdle, T. J., Wang, Z., and Roy, T.: Assessment of Southern Ocean water
mass circulation and characteristics in CMIP5 models: Historical bias and
forcing response, J. Geophys. Res.-Oceans, 118, 1830–1844,
https://doi.org/10.1002/jgrc.20135, 2013.
Sanderson, B.: Relating climate sensitivity indices to projection uncertainty, Earth Syst. Dynam., 11, 721–735, https://doi.org/10.5194/esd-11-721-2020, 2020.
Sanderson, B.: benmsanderson/structure_ec: (1.1), Zenodo [code],
https://doi.org/10.5281/zenodo.5093130, 2021.
Sanderson, B. M.: A Multimodel Study of Parametric Uncertainty in
Predictions of Climate Response to Rising Greenhouse Gas Concentrations,
J. Climate, 24, 1362–1377, https://doi.org/10.1175/2010jcli3498.1, 2011.
Sanderson, B. M.: On the estimation of systematic error in regression-based
predictions of climate sensitivity, Climatic Change, 118, 757–770,
https://doi.org/10.1007/s10584-012-0671-6, 2013.
Sanderson, B. M., Knutti, R., Aina, T., Christensen, C., Faull, N., Frame,
D. J., Ingram, W. J., Piani, C., Stainforth, D. A., Stone, D. A., and Allen,
M. R.: Constraints on Model Response to Greenhouse Gas Forcing and the Role
of Subgrid-Scale Processes, J. Climate, 21, 2384–2400,
https://doi.org/10.1175/2008jcli1869.1, 2008.
Sanderson, B. M., Shell, K. M., and Ingram, W.: Climate feedbacks determined
using radiative kernels in a multi-thousand member ensemble of AOGCMs,
Clim. Dynam., 35, 1219–1236, https://doi.org/10.1007/s00382-009-0661-1, 2010.
Sanderson, B. M., Knutti, R., and Caldwell, P.: A Representative Democracy to
Reduce Interdependency in a Multimodel Ensemble, J. Climate, 28,
5171–5194, https://doi.org/10.1175/jcli-d-14-00362.1, 2015.
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.
Sandu, I., Beljaars, A., Bechtold, P., Mauritsen, T., and Balsamo, G.: Why is
it so difficult to represent stably stratified conditions in numerical
weather prediction (NWP) models?: STABLE CONDITIONS IN NWP MODELS, J. Adv.
Model. Earth Sy., 5, 117–133, https://doi.org/10.1002/jame.20013, 2013.
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020.
Schmidt, G. A., Annan, J. D., Bartlein, P. J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., Harrison, S. P., Kageyama, M., LeGrande, A. N., Konecky, B., Lovejoy, S., Mann, M. E., Masson-Delmotte, V., Risi, C., Thompson, D., Timmermann, A., Tremblay, L.-B., and Yiou, P.: Using palaeo-climate comparisons to constrain future projections in CMIP5, Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, 2014.
Schmidt, G. A., Bader, D., Donner, L. J., Elsaesser, G. S., Golaz, J.-C., Hannay, C., Molod, A., Neale, R. B., and Saha, S.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, 2017.
Schurer, A., Hegerl, G., Ribes, A., Polson, D., Morice, C., and Tett, S.:
Estimating the Transient Climate Response from Observed Warming, J.
Climate, 31, 8645–8663, https://doi.org/10.1175/jcli-d-17-0717.1, 2018.
Sexton, D. M. H. and Murphy, J. M.: Multivariate probabilistic projections
using imperfect climate models. Part II: robustness of methodological
choices and consequences for climate sensitivity, Clim. Dynam.,
38, 2543–2558, https://doi.org/10.1007/s00382-011-1209-8, 2012.
Shao, P., Zeng, X., Moore, D. J. P., and Zeng, X.: Soil microbial respiration
from observations and Earth System Models, Environ. Res. Lett.,
8, 034034, https://doi.org/10.1088/1748-9326/8/3/034034, 2013.
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505,
37–42, https://doi.org/10.1038/nature12829, 2014.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J.
R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678,
https://doi.org/10.1029/2019RG000678, 2020.
Shi, Z., Crowell, S., Luo, Y., and Moore, B., 3rd: Model structures amplify
uncertainty in predicted soil carbon responses to climate change, Nat.
Commun., 9, 2171, https://doi.org/10.1038/s41467-018-04526-9, 2018.
Shiogama, H., Watanabe, M., Yoshimori, M., Yokohata, T., Ogura, T., Annan,
J. D., Hargreaves, J. C., Abe, M., Kamae, Y., O'ishi, R., Nobui, R., Emori,
S., Nozawa, T., Abe-Ouchi, A., and Kimoto, M.: Perturbed physics ensemble
using the MIROC5 coupled atmosphere–ocean GCM without flux corrections:
experimental design and results, Clim. Dynam., 39, 3041–3056,
https://doi.org/10.1007/s00382-012-1441-x, 2012.
Siler, N., Po-Chedley, S., and Bretherton, C. S.: Variability in modeled
cloud feedback tied to differences in the climatological spatial pattern of
clouds, Clim. Dynam., 50, 1209–1220, https://doi.org/10.1007/s00382-017-3673-2,
2018.
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame,
D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., Piani,
C., Sexton, D., Smith, L. A., Spicer, R. A., Thorpe, A. J., and Allen, M. R.:
Uncertainty in predictions of the climate response to rising levels of
greenhouse gases, Nature, 433, 403–406, https://doi.org/10.1038/nature03301,
2005.
Stock, C. A.: Comparing apples to oranges: Perspectives on satellite-based
primary production estimates drawn from a global biogeochemical model, J.
Mar. Res., 77, 259–282, https://doi.org/10.1357/002224019828474296, 2019.
Su, H., Jiang, J. H., Zhai, C., Shen, T. J., David Neelin, J., Stephens, G.
L., and Yung, Y. L.: Weakening and strengthening structures in the Hadley
Circulation change under global warming and implications for cloud response
and climate sensitivity, J. Geophys. Res.-Atmos.,
119, 5787–5805, https://doi.org/10.1002/2014jd021642, 2014.
Svensson, G. and Lindvall, J.: Evaluation of Near-Surface Variables and the
Vertical Structure of the Boundary Layer in CMIP5 Models, J.
Climate, 28, 5233–5253, https://doi.org/10.1175/jcli-d-14-00596.1, 2015.
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., and Lasslop, G.: Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models, Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, 2019.
Terhaar, J., Kwiatkowski, L., and Bopp, L.: Emergent constraint on Arctic
Ocean acidification in the twenty-first century, Nature, 582,
379–383, https://doi.org/10.1038/s41586-020-2360-3, 2020.
Terhaar, J., Frölicher, T. L., and Joos, F.: Southern Ocean anthropogenic
carbon sink constrained by sea surface salinity, Sci. Adv., 7, eabd5964,
https://doi.org/10.1126/sciadv.abd5964, 2021.
Teskey, R., Wertin, T., Bauweraerts, I., Ameye, M., McGuire, M. A., and
Steppe, K.: Responses of tree species to heat waves and extreme heat events,
Plant Cell Environ., 38, 1699–1712, https://doi.org/10.1111/pce.12417, 2015.
Tett, S. F. B., Yamazaki, K., Mineter, M. J., Cartis, C., and Eizenberg, N.: Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3, Geosci. Model Dev., 10, 3567–3589, https://doi.org/10.5194/gmd-10-3567-2017, 2017.
Thackeray, C. W. and Hall, A.: An emergent constraint on future Arctic
sea-ice albedo feedback, Nat. Clim. Change, 9, 972–978,
https://doi.org/10.1038/s41558-019-0619-1, 2019.
Tian, B.: Spread of model climate sensitivity linked to double-Intertropical
Convergence Zone bias, Geophys. Res. Lett., 42, 4133–4141,
https://doi.org/10.1002/2015gl064119, 2015.
Tjiputra, J. F., Schwinger, J., Bentsen, M., Morée, A. L., Gao, S., Bethke, I., Heinze, C., Goris, N., Gupta, A., He, Y.-C., Olivié, D., Seland, Ø., and Schulz, M.: Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2), Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, 2020.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J.,
Lehner, F., and Knutti, R.: Past warming trend constrains future warming in
CMIP6 models, Science Advances, 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549,
2020.
Trenberth, K. E. and Fasullo, J. T.: Simulation of Present-Day and
Twenty-First-Century Energy Budgets of the Southern Oceans, J. Climate, 23,
440–454, https://doi.org/10.1175/2009JCLI3152.1, 2010.
Varney, R. M., Chadburn, S. E., Friedlingstein, P., Burke, E. J., Koven, C.
D., Hugelius, G., and Cox, P. M.: A spatial emergent constraint on the
sensitivity of soil carbon turnover to global warming, Nat. Commun., 11,
1–8, https://doi.org/10.1038/s41467-020-19208-8, 2020.
Volodin, E. M.: Relation between temperature sensitivity to doubled carbon
dioxide and the distribution of clouds in current climate models, Izv.
Atmos. Ocean. Phys., 44, 288–299, https://doi.org/10.1134/S0001433808030043, 2008.
Wang, J., Zeng, N., Liu, Y., and Bao, Q.: To what extent can interannual CO2
variability constrain carbon cycle sensitivity to climate change in CMIP5
Earth System Models?, Geophys. Res. Lett., 41, 3535–3544,
https://doi.org/10.1002/2014GL060004, 2014.
Watanabe, M., Kamae, Y., Shiogama, H., DeAngelis, A. M., and Suzuki, K.: Low
clouds link equilibrium climate sensitivity to hydrological sensitivity,
Nat. Clim. Change, 8, 901–906, https://doi.org/10.1038/s41558-018-0272-0, 2018.
Wei, N., Zhou, L., and Dai, Y.: Evaluation of simulated climatological
diurnal temperature range in CMIP5 models from the perspective of planetary
boundary layer turbulent mixing, Clim. Dynam., 49, 1–22,
https://doi.org/10.1007/s00382-016-3323-0, 2017.
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent
constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system
models, J. Geophys. Res.-Biogeo., 119, 794–807,
https://doi.org/10.1002/2013JG002591, 2014.
Wigley, T. M. L.: Effect of climate sensitivity on the response to volcanic
forcing, J. Geophys. Res., 110, D09107, https://doi.org/10.1029/2004jd005557,
2005.
Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D., Evans, B.,
Ferraro, R., Hansen, R., Lautenschlager, M., and Trenham, C.: A Global
Repository for Planet-Sized Experiments and Observations, B.
Am. Meteorol. Soc., 97, 803–816,
https://doi.org/10.1175/bams-d-15-00132.1, 2016.
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P.,
Jackson, L., and Yamazaki, K.: History matching for exploring and reducing
climate model parameter space using observations and a large perturbed
physics ensemble, Clim. Dynam., 41, 1703–1729,
https://doi.org/10.1007/s00382-013-1896-4, 2013.
Williamson, D. B. and Sansom, P. G.: How Are Emergent Constraints
Quantifying Uncertainty and What Do They Leave Behind?, B.
Am. Meteorol. Soc., 100, 2571–2588,
https://doi.org/10.1175/bams-d-19-0131.1, 2019.
Williamson, M. S., Cox, P. M., and Nijsse, F. J. M. M.: Theoretical
foundations of emergent constraints: relationships between climate
sensitivity and global temperature variability in conceptual models, Dyn.
Stat. Clim. Syst., 3, dzy006, https://doi.org/10.1093/climsys/dzy006, 2019.
Yan, X., Zhang, R., and Knutson, T. R.: Underestimated AMOC variability and
implications for AMV and predictability in CMIP models, Geophys. Res. Lett.,
45, 4319–4328, https://doi.org/10.1029/2018gl077378, 2018.
Yokohata, T., Webb, M. J., Collins, M., Williams, K. D., Yoshimori, M.,
Hargreaves, J. C., and Annan, J. D.: Structural Similarities and Differences
in Climate Responses to CO2 Increase between Two Perturbed Physics
Ensembles, J. Climate, 23, 1392–1410,
https://doi.org/10.1175/2009jcli2917.1, 2010.
Zaehle, S., Jones, C. D., Houlton, B., Lamarque, J.-F., and Robertson, E.:
Nitrogen Availability Reduces CMIP5 Projections of Twenty-First-Century Land
Carbon Uptake, J. Climate, 28, 2494–2511,
https://doi.org/10.1175/jcli-d-13-00776.1, 2015.
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate
Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019gl085782, 2020.
Zhai, C., Jiang, J. H., and Su, H.: Long-term cloud change imprinted in
seasonal cloud variation: More evidence of high climate sensitivity,
Geophys. Res. Lett., 42, 8729–8737, https://doi.org/10.1002/2015gl065911,
2015a.
Zhai, C., Jiang, J. H., and Su, H.: Long-term cloud change imprinted in
seasonal cloud variation: More evidence of high climate sensitivity: Cloud
Feedback and Seasonal Variation, Geophys. Res. Lett., 42, 8729–8737,
https://doi.org/10.1002/2015GL065911, 2015b.
Zhang, T., Zhang, M., Lin, W., Lin, Y., Xue, W., Yu, H., He, J., Xin, X., Ma, H.-Y., Xie, S., and Zheng, W.: Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method, Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, 2018.
Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by...
Altmetrics
Final-revised paper
Preprint