Articles | Volume 11, issue 2
https://doi.org/10.5194/esd-11-329-2020
© Author(s) 2020. 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-11-329-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
08 Apr 2020
Research article | Highlight paper |
| 08 Apr 2020
| 08 Apr 2020
Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling
Eirik Myrvoll-Nilsen
CORRESPONDING AUTHOR
Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Sigrunn Holbek Sørbye
Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Hege-Beate Fredriksen
Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Håvard Rue
CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Martin Rypdal
Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
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Eirik Myrvoll-Nilsen, Luc Hallali, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-436, https://doi.org/10.5194/egusphere-2024-436, 2024
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Before a climate component reaches a tipping point, there may be observable changes in its statistical properties. These are known as early warning signals and include increased fluctuation and correlation times. We present a Bayesian approach to detect these signals, using a model where the correlation parameter depends linearly on time for which the slope can be estimated directly from the data. The model is then applied to Dansgaard-Oeschger events using Greenland Ice core data.
Eirik Myrvoll-Nilsen, Keno Riechers, Martin Wibe Rypdal, and Niklas Boers
Clim. Past, 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, https://doi.org/10.5194/cp-18-1275-2022, 2022
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In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Clara Hummel, Niklas Boers, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-3567, https://doi.org/10.5194/egusphere-2024-3567, 2024
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We revisit early warning signals (EWS) for past abrupt climate changes known as Dansgaard-Oeschger (DO) events. Using advanced statistical methods, we find fewer significant EWS than previously reported. While some signals appear consistent across Greenland ice core records, they are not enough to identify the still unknown physical mechanisms behind DO events. This study highlights the complexity of predicting climate changes and urges caution in interpreting (paleo-)climate data.
Eirik Myrvoll-Nilsen, Luc Hallali, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-436, https://doi.org/10.5194/egusphere-2024-436, 2024
Short summary
Short summary
Before a climate component reaches a tipping point, there may be observable changes in its statistical properties. These are known as early warning signals and include increased fluctuation and correlation times. We present a Bayesian approach to detect these signals, using a model where the correlation parameter depends linearly on time for which the slope can be estimated directly from the data. The model is then applied to Dansgaard-Oeschger events using Greenland Ice core data.
Eirik Myrvoll-Nilsen, Keno Riechers, Martin Wibe Rypdal, and Niklas Boers
Clim. Past, 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, https://doi.org/10.5194/cp-18-1275-2022, 2022
Short summary
Short summary
In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Tine Nilsen, Johannes P. Werner, Dmitry V. Divine, and Martin Rypdal
Clim. Past, 14, 947–967, https://doi.org/10.5194/cp-14-947-2018, https://doi.org/10.5194/cp-14-947-2018, 2018
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The BARCAST climate field reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
Kristoffer Rypdal and Martin Rypdal
Earth Syst. Dynam., 7, 597–609, https://doi.org/10.5194/esd-7-597-2016, https://doi.org/10.5194/esd-7-597-2016, 2016
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This comment on the paper by Lovejoy and Varotsos demonstrates that their methods for establishing nonlinearity in the global temperature response in climate models are flawed. One of their methods is based on an invalid approximation, which when corrected does not falsify the hypothesis that the response is linear. This conclusion is enforced when internal variability in the models are accounted for. The other results in their paper are also shown to be reproduced by linear-response models.
Tine Nilsen, Kristoffer Rypdal, and Hege-Beate Fredriksen
Earth Syst. Dynam., 7, 419–439, https://doi.org/10.5194/esd-7-419-2016, https://doi.org/10.5194/esd-7-419-2016, 2016
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In this article it is discussed how temperature variability on centennial timescales and longer can be described in a simplistic way. By analysing the scaling in late Holocene temperature reconstructions and longer temperature records from Greenland and Antarctic ice cores, we find that the choice of model depends heavily on the data material and timescale one chooses to emphasize. Ignoring data beyond the Holocene seems plausible when predicting temperature, but not for other purposes.
Martin Rypdal and Kristoffer Rypdal
Earth Syst. Dynam., 7, 281–293, https://doi.org/10.5194/esd-7-281-2016, https://doi.org/10.5194/esd-7-281-2016, 2016
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We analyse scaling in temperature signals for the late quaternary climate, and focus on the effects of regime shifting events such as the Dansgaard-Oeschger cycles and the shifts between glacial and interglacial conditions. When these events are omitted from a scaling description the climate noise is consistent with a 1/f law on timescales from months to 105 years. If the events are included in the description, we obtain a model that is inherently non-stationary.
L. Østvand, T. Nilsen, K. Rypdal, D. Divine, and M. Rypdal
Earth Syst. Dynam., 5, 295–308, https://doi.org/10.5194/esd-5-295-2014, https://doi.org/10.5194/esd-5-295-2014, 2014
L. Østvand, K. Rypdal, and M. Rypdal
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esdd-5-327-2014, https://doi.org/10.5194/esdd-5-327-2014, 2014
Revised manuscript not accepted
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Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022, https://doi.org/10.5194/esd-13-1157-2022, 2022
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Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
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Amber Boot, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 1041–1058, https://doi.org/10.5194/esd-13-1041-2022, https://doi.org/10.5194/esd-13-1041-2022, 2022
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Atmospheric pCO2 of the past shows large variability on different timescales. We focus on the effect of the strength of Atlantic Meridional Overturning Circulation (AMOC) on this variability and on the AMOC–pCO2 relationship. We find that climatic boundary conditions and the representation of biology in our model are most important for this relationship. Under certain conditions, we find internal oscillations, which can be relevant for atmospheric pCO2 variability during glacial cycles.
Aloïs Tilloy, Bruce D. Malamud, and Amélie Joly-Laugel
Earth Syst. Dynam., 13, 993–1020, https://doi.org/10.5194/esd-13-993-2022, https://doi.org/10.5194/esd-13-993-2022, 2022
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Shruti Nath, Quentin Lejeune, Lea Beusch, Sonia I. Seneviratne, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, https://doi.org/10.5194/esd-13-851-2022, 2022
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Linh N. Luu, Robert Vautard, Pascal Yiou, and Jean-Michel Soubeyroux
Earth Syst. Dynam., 13, 687–702, https://doi.org/10.5194/esd-13-687-2022, https://doi.org/10.5194/esd-13-687-2022, 2022
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Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
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Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
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Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
Roman Procyk, Shaun Lovejoy, and Raphael Hébert
Earth Syst. Dynam., 13, 81–107, https://doi.org/10.5194/esd-13-81-2022, https://doi.org/10.5194/esd-13-81-2022, 2022
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This paper presents a new class of energy balance model that accounts for the long memory within the Earth's energy storage. The model is calibrated on instrumental temperature records and the historical energy budget of the Earth using an error model predicted by the model itself. Our equilibrium climate sensitivity and future temperature projection estimates are consistent with those estimated by complex climate models.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Kathrin Naegeli, and Stefan Wunderle
Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, https://doi.org/10.5194/esd-12-1061-2021, 2021
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Climate change over High Mountain Asia is investigated with CMIP6 climate models. A general cold bias is found in this area, often related to a snow cover overestimation in the models. Ensemble experiments generally encompass the past observed trends, suggesting that even biased models can reproduce the trends. Depending on the future scenario, a warming from 1.9 to 6.5 °C, associated with a snow cover decrease and precipitation increase, is expected at the end of the 21st century.
Benjamin Ward, Francesco S. R. Pausata, and Nicola Maher
Earth Syst. Dynam., 12, 975–996, https://doi.org/10.5194/esd-12-975-2021, https://doi.org/10.5194/esd-12-975-2021, 2021
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Using the largest ensemble of a climate model currently available, the Max Planck Institute Grand Ensemble (MPI-GE), we investigated the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. By selecting three eruptions with different aerosol distributions, we found that the shift of the Intertropical Convergence Zone (ITCZ) is the main driver of the ENSO response, while other mechanisms commonly invoked seem less important in our model.
Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
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Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Halima Usman, Thomas A. M. Pugh, Anders Ahlström, and Sofia Baig
Earth Syst. Dynam., 12, 857–870, https://doi.org/10.5194/esd-12-857-2021, https://doi.org/10.5194/esd-12-857-2021, 2021
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The study assesses the impacts of climate change on forest productivity in the Hindu Kush Himalayan region. LPJ-GUESS was simulated from 1851 to 2100. In first approach, the model was compared with observational estimates. The comparison showed a moderate agreement. In the second approach, the model was assessed for the temporal and spatial trends of net biome productivity and its components along with carbon pool. Increases in both variables were predicted in 2100.
Kerstin Hartung, Ana Bastos, Louise Chini, Raphael Ganzenmüller, Felix Havermann, George C. Hurtt, Tammas Loughran, Julia E. M. S. Nabel, Tobias Nützel, Wolfgang A. Obermeier, and Julia Pongratz
Earth Syst. Dynam., 12, 763–782, https://doi.org/10.5194/esd-12-763-2021, https://doi.org/10.5194/esd-12-763-2021, 2021
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In this study, we model the relative importance of several contributors to the land-use and land-cover change (LULCC) flux based on a LULCC dataset including uncertainty estimates. The uncertainty of LULCC is as relevant as applying wood harvest and gross transitions for the cumulative LULCC flux over the industrial period. However, LULCC uncertainty matters less than the other two factors for the LULCC flux in 2014; historical LULCC uncertainty is negligible for estimates of future scenarios.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, and Andrea Alessandri
Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, https://doi.org/10.5194/esd-12-725-2021, 2021
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The roots of vegetation largely control the Earth's water cycle by transporting water from the subsurface to the atmosphere but are not adequately represented in land surface models, causing uncertainties in modeled water fluxes. We replaced the root parameters in an existing model with more realistic ones that account for a climate control on root development and found improved timing of modeled river discharge. Further extension of our approach could improve modeled water fluxes globally.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, and Joel Finnis
Earth Syst. Dynam., 12, 581–600, https://doi.org/10.5194/esd-12-581-2021, https://doi.org/10.5194/esd-12-581-2021, 2021
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The current radiative imbalance at the top of the atmosphere is increasing the heat stored in the oceans, atmosphere, continental subsurface and cryosphere, with consequences for societies and ecosystems (e.g. sea level rise). We performed the first assessment of the ability of global climate models to represent such heat storage in the climate subsystems. Models are able to reproduce the observed atmosphere heat content, with biases in the simulation of heat content in the rest of components.
Francesco Piccioni, Céline Casenave, Bruno Jacques Lemaire, Patrick Le Moigne, Philippe Dubois, and Brigitte Vinçon-Leite
Earth Syst. Dynam., 12, 439–456, https://doi.org/10.5194/esd-12-439-2021, https://doi.org/10.5194/esd-12-439-2021, 2021
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Small lakes are ecosystems highly impacted by climate change. Here, the thermal regime of a small, shallow lake over the past six decades was reconstructed via 3D modelling. Significant changes were found: strong water warming in spring and summer (0.7 °C/decade) as well as increased stratification and thermal energy for cyanobacteria growth, especially in spring. The strong spatial patterns detected for stratification might create local conditions particularly favourable to cyanobacteria bloom.
Pablo Ortega, Jon I. Robson, Matthew Menary, Rowan T. Sutton, Adam Blaker, Agathe Germe, Jöel J.-M. Hirschi, Bablu Sinha, Leon Hermanson, and Stephen Yeager
Earth Syst. Dynam., 12, 419–438, https://doi.org/10.5194/esd-12-419-2021, https://doi.org/10.5194/esd-12-419-2021, 2021
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Deep Labrador Sea densities are receiving increasing attention because of their link to many of the processes that govern decadal climate oscillations in the North Atlantic and their potential use as a precursor of those changes. This article explores those links and how they are represented in global climate models, documenting the main differences across models. Models are finally compared with observational products to identify the ones that reproduce the links more realistically.
Calum Brown, Ian Holman, and Mark Rounsevell
Earth Syst. Dynam., 12, 211–231, https://doi.org/10.5194/esd-12-211-2021, https://doi.org/10.5194/esd-12-211-2021, 2021
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The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph A. Sauter, Elisabeth Tschumi, Karin van der Wiel, Tianyi Zhang, and Jakob Zscheischler
Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, https://doi.org/10.5194/esd-12-151-2021, 2021
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We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
Peter Pfleiderer, Aglaé Jézéquel, Juliette Legrand, Natacha Legrix, Iason Markantonis, Edoardo Vignotto, and Pascal Yiou
Earth Syst. Dynam., 12, 103–120, https://doi.org/10.5194/esd-12-103-2021, https://doi.org/10.5194/esd-12-103-2021, 2021
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In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely due to an extremely warm December 2015 and abnormally high precipitation during the following spring season. Using stochastic weather generators we investigate how severe the metrological conditions leading to the crop loss could be in current climate conditions. We find that December temperatures were close to the plausible maximum but that considerably wetter springs would be possible.
Jelle van den Berk, Sybren Drijfhout, and Wilco Hazeleger
Earth Syst. Dynam., 12, 69–81, https://doi.org/10.5194/esd-12-69-2021, https://doi.org/10.5194/esd-12-69-2021, 2021
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A collapse of the Atlantic Meridional Overturning Circulation can be described by six parameters and Langevin dynamics. These parameters can be determined from collapses seen in climate models of intermediate complexity. With this parameterisation, it might be possible to estimate how much fresh water is needed to observe a collapse in more complicated models and reality.
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
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Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
Christian B. Rodehacke, Madlene Pfeiffer, Tido Semmler, Özgür Gurses, and Thomas Kleiner
Earth Syst. Dynam., 11, 1153–1194, https://doi.org/10.5194/esd-11-1153-2020, https://doi.org/10.5194/esd-11-1153-2020, 2020
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In the warmer future, Antarctica's ice sheet will lose more ice due to enhanced iceberg calving and a warming ocean that melts more floating ice from below. However, the hydrological cycle is also stronger in a warmer world. Hence, more snowfall will precipitate on Antarctica and may balance the amplified ice loss. We have used future climate scenarios from various global climate models to perform numerous ice sheet simulations to show that precipitation may counteract mass loss.
Renate Anna Irma Wilcke, Erik Kjellström, Changgui Lin, Daniela Matei, Anders Moberg, and Evangelos Tyrlis
Earth Syst. Dynam., 11, 1107–1121, https://doi.org/10.5194/esd-11-1107-2020, https://doi.org/10.5194/esd-11-1107-2020, 2020
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Two long-lasting high-pressure systems in summer 2018 led to heat waves over Scandinavia and an extended summer period with devastating impacts on both agriculture and human life. Using five climate model ensembles, the unique 263-year Stockholm temperature time series and a composite 150-year time series for the whole of Sweden, we found that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed in 2018, occurring in Sweden.
Jeemijn Scheen and Thomas F. Stocker
Earth Syst. Dynam., 11, 925–951, https://doi.org/10.5194/esd-11-925-2020, https://doi.org/10.5194/esd-11-925-2020, 2020
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Variability of sea surface temperatures (SST) in 1200–2000 CE is quite well-known, but the history of deep ocean temperatures is not. Forcing an ocean model with these SSTs, we simulate temperatures in the ocean interior. The circulation changes alter the amplitude and timing of deep ocean temperature fluctuations below 2 km depth, e.g. delaying the atmospheric signal by ~ 200 years in the deep Atlantic. Thus ocean circulation changes are shown to be as important as SST changes at these depths.
Sebastian Milinski, Nicola Maher, and Dirk Olonscheck
Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, https://doi.org/10.5194/esd-11-885-2020, 2020
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Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system, but there is no established method to determine the required ensemble size for a given problem. We propose a new framework that can be used to estimate the required ensemble size from a model's control run or an existing large ensemble.
Yu Huang, Lichao Yang, and Zuntao Fu
Earth Syst. Dynam., 11, 835–853, https://doi.org/10.5194/esd-11-835-2020, https://doi.org/10.5194/esd-11-835-2020, 2020
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We investigate the applicability of machine learning (ML) on time series reconstruction and find that the dynamical coupling relation and nonlinear causality are crucial for the application of ML. Our results could provide insights into causality and ML approaches for paleoclimate reconstruction, parameterization schemes, and prediction in climate studies.
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
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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.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, and Bjorn Stevens
Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, https://doi.org/10.5194/esd-11-709-2020, 2020
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In this paper we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
Andrea Böhnisch, Ralf Ludwig, and Martin Leduc
Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020, https://doi.org/10.5194/esd-11-617-2020, 2020
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North Atlantic air pressure variations influencing European climate variables are simulated in coarse-resolution global climate models (GCMs). As single-model runs do not sufficiently describe variations of their patterns, several model runs with slightly diverging initial conditions are analyzed. The study shows that GCM and regional climate model (RCM) patterns vary in a similar range over the same domain, while RCMs add consistent fine-scale information due to their higher spatial resolution.
György Károlyi, Rudolf Dániel Prokaj, István Scheuring, and Tamás Tél
Earth Syst. Dynam., 11, 603–615, https://doi.org/10.5194/esd-11-603-2020, https://doi.org/10.5194/esd-11-603-2020, 2020
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We construct a conceptual model to understand the interplay between the atmosphere and the ocean biosphere in a climate change framework, including couplings between extraction of carbon dioxide by phytoplankton and climate change, temperature and carrying capacity of phytoplankton, and wind energy and phytoplankton production. We find that sufficiently strong mixing can result in decaying global phytoplankton content.
Kira Rehfeld, Raphaël Hébert, Juan M. Lora, Marcus Lofverstrom, and Chris M. Brierley
Earth Syst. Dynam., 11, 447–468, https://doi.org/10.5194/esd-11-447-2020, https://doi.org/10.5194/esd-11-447-2020, 2020
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Under continued anthropogenic greenhouse gas emissions, it is likely that global mean surface temperature will continue to increase. Little is known about changes in climate variability. We analyze surface climate variability and compare it to mean change in colder- and warmer-than-present climate model simulations. In most locations, but not on subtropical land, simulated temperature variability up to decadal timescales decreases with mean temperature, and precipitation variability increases.
Lea Beusch, Lukas Gudmundsson, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, https://doi.org/10.5194/esd-11-139-2020, 2020
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Earth system models (ESMs) are invaluable to study the climate system but expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of yearly land temperature field time series. Thereby, 40 ESMs are considered, and for each ESM, a single simulation is required to train the tool. The resulting ESM-specific realizations closely resemble ESM simulations not employed during training at point to regional scales.
Yu Sun and Riccardo E. M. Riva
Earth Syst. Dynam., 11, 129–137, https://doi.org/10.5194/esd-11-129-2020, https://doi.org/10.5194/esd-11-129-2020, 2020
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The solid Earth is still deforming because of the effect of past ice sheets through glacial isostatic adjustment (GIA). Satellite gravity observations by the Gravity Recovery and Climate Experiment (GRACE) mission are sensitive to those signals but are superimposed on the redistribution effect of water masses by the hydrological cycle. We propose a method separating the two signals, providing new constraints for forward GIA models and estimating the global water cycle's patterns and magnitude.
Mareike Schuster, Jens Grieger, Andy Richling, Thomas Schartner, Sebastian Illing, Christopher Kadow, Wolfgang A. Müller, Holger Pohlmann, Stephan Pfahl, and Uwe Ulbrich
Earth Syst. Dynam., 10, 901–917, https://doi.org/10.5194/esd-10-901-2019, https://doi.org/10.5194/esd-10-901-2019, 2019
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Decadal climate predictions are valuable to society as they allow us to estimate climate conditions several years in advance. We analyze the latest version of the German MiKlip prediction system (https://www.fona-miklip.de) and assess the effect of the model resolution on the skill of the system. The increase in the resolution of the system reduces the bias and significantly improves the forecast skill for North Atlantic extratropical winter dynamics for lead times of two to five winters.
Calum Brown, Bumsuk Seo, and Mark Rounsevell
Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, https://doi.org/10.5194/esd-10-809-2019, 2019
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Concerns are growing that human activity will lead to social and environmental breakdown, but it is hard to anticipate when and where such breakdowns might occur. We developed a new model of land management decisions in Europe to explore possible future changes and found that decision-making that takes into account social and environmental conditions can produce unexpected outcomes that include societal breakdown in challenging conditions.
Francine Schevenhoven, Frank Selten, Alberto Carrassi, and Noel Keenlyside
Earth Syst. Dynam., 10, 789–807, https://doi.org/10.5194/esd-10-789-2019, https://doi.org/10.5194/esd-10-789-2019, 2019
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Weather and climate predictions potentially improve by dynamically combining different models into a
supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
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Short summary
This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record and historical model runs in the CMIP5 ensemble to provide estimates of the transient climate response and temperature projections under the Representative Concentration Pathways.
This paper presents efficient Bayesian methods for linear response models of global mean surface...
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