Articles | Volume 13, issue 1
https://doi.org/10.5194/esd-13-81-2022
© Author(s) 2022. 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-13-81-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jan 2022
Research article |
| 19 Jan 2022
| 19 Jan 2022
The fractional energy balance equation for climate projections through 2100
Physics Department, McGill University, 3600 rue University, Montreal, Quebec, H3A 2T8, Canada
Physics Department, McGill University, 3600 rue University, Montreal, Quebec, H3A 2T8, Canada
Raphael Hébert
Alfred-Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany
Institute of Geosciences, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
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Mara Y. McPartland, Thomas Münch, Andrew M. Dolman, Raphaël Hébert, and Thomas Laepple
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-73, https://doi.org/10.5194/cp-2024-73, 2024
Preprint under review for CP
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Paleoclimate proxy records contain a combination of climate signals and non-climatic noise. This noise can affect year-to-year variations, or introduce uncertainty on medium and long timescales. Proxies contain different types, or "colors" of noise stemming from the diverse physical and biological processes that go into their creation. We show how non-climatic noise affects tree rings, corals and ice cores. We aim to improve representations of noise in paleoclimate research activities.
Nicolás Acuña Reyes, Elwin van't Wout, Shaun Lovejoy, and Fabrice Lambert
Clim. Past, 20, 1579–1594, https://doi.org/10.5194/cp-20-1579-2024, https://doi.org/10.5194/cp-20-1579-2024, 2024
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This study employs Haar fluctuations to analyse global atmospheric variability over the Last Glacial Cycle, revealing a latitudinal dependency in the transition from macroweather to climate regimes. Findings indicate faster synchronisation between poles and lower latitudes, supporting the pivotal role of poles as climate change drivers.
Laura Schild, Peter Ewald, Chenzhi Li, Raphaël Hébert, Thomas Laepple, and Ulrike Herzschuh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-486, https://doi.org/10.5194/essd-2023-486, 2024
Revised manuscript accepted for ESSD
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This study reconstructed past vegetation and forest cover from a global data set of pollen counts from sediment and peat cores. A model was applied to correct for differences in pollen production between different plants and modern remote-sensing forest cover was used to adjust the necessary correction factors and improve the reconstruction even further. Accurate data on past vegetation is invaluable for the investigation of vegetation-climate dynamics and the validation of vegetation models.
Shaun Lovejoy
Nonlin. Processes Geophys., 30, 311–374, https://doi.org/10.5194/npg-30-311-2023, https://doi.org/10.5194/npg-30-311-2023, 2023
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How big is a cloud?and
How long does the weather last?require scaling to answer. We review the advances in scaling that have occurred over the last 4 decades: (a) intermittency (multifractality) and (b) stratified and rotating scaling notions (generalized scale invariance). Although scaling theory and the data are now voluminous, atmospheric phenomena are too often viewed through an outdated scalebound lens, and turbulence remains confined to isotropic theories of little relevance.
Ulrike Herzschuh, Thomas Böhmer, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Chenzhi Li, Xianyong Cao, Odile Peyron, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Clim. Past, 19, 1481–1506, https://doi.org/10.5194/cp-19-1481-2023, https://doi.org/10.5194/cp-19-1481-2023, 2023
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A mismatch between model- and proxy-based Holocene climate change may partially originate from the poor spatial coverage of climate reconstructions. Here we investigate quantitative reconstructions of mean annual temperature and annual precipitation from 1908 pollen records in the Northern Hemisphere. Trends show strong latitudinal patterns and differ between (sub-)continents. Our work contributes to a better understanding of the global mean.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Shaun Lovejoy
Nonlin. Processes Geophys., 29, 93–121, https://doi.org/10.5194/npg-29-93-2022, https://doi.org/10.5194/npg-29-93-2022, 2022
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The difference between the energy that the Earth receives from the Sun and the energy it emits as black-body radiation is stored in a scaling hierarchy of structures in the ocean, soil and hydrosphere. The simplest scaling storage model leads to the fractional energy balance equation (FEBE). We examine the statistical properties of FEBE when it is driven by random fluctuations. In this paper, we explore the statistical properties of this mathematically simple yet neglected equation.
Raphaël Hébert, Kira Rehfeld, and Thomas Laepple
Nonlin. Processes Geophys., 28, 311–328, https://doi.org/10.5194/npg-28-311-2021, https://doi.org/10.5194/npg-28-311-2021, 2021
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Paleoclimate proxy data are essential for broadening our understanding of climate variability. There remain, however, challenges for traditional methods of variability analysis to be applied to such data, which are usually irregular. We perform a comparative analysis of different methods of scaling analysis, which provide variability estimates as a function of timescales, applied to irregular paleoclimate proxy data.
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
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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
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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.
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.
Shaun Lovejoy and Fabrice Lambert
Clim. Past, 15, 1999–2017, https://doi.org/10.5194/cp-15-1999-2019, https://doi.org/10.5194/cp-15-1999-2019, 2019
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We analyze the statistical properties of the eight past glacial–interglacial cycles as well as subsections of a generic glacial cycle using the high-resolution dust flux dataset from the Antarctic EPICA Dome C ice core. We show that the high southern latitude climate during glacial maxima, interglacial, and glacial inception is generally more stable but more drought-prone than during mid-glacial conditions.
Shaun Lovejoy and Fabrice Lambert
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-110, https://doi.org/10.5194/cp-2018-110, 2018
Manuscript not accepted for further review
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The Holocene has been strikingly long and stable when compared to earlier interglacials, and some have argued that the Holocene's exceptional stability permitted the development of agriculture and the spread of civilization. We characterize the past 800 000 years using a high resolution dust record from an Antarctic ice core. We find that although the Holocene is particularly stable when compared to other interglacials, it is not an outlier and other factors may have kickstarted civilization.
Shaun Lovejoy and Costas Varotsos
Earth Syst. Dynam., 7, 133–150, https://doi.org/10.5194/esd-7-133-2016, https://doi.org/10.5194/esd-7-133-2016, 2016
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We compare the statistical properties of solar, volcanic and combined forcings over the range from 1 to 1000 years to see over which scale ranges they additively combine, a prediction of linear response. The main findings are (a) that the variability in the Zebiac–Cane model and GCMs are too weak at centennial and longer scales; (b) for longer than ≈ 50 years, the forcings combine subadditively; and (c) at shorter scales, strong (intermittency, e.g. volcanic) forcings are nonlinear.
F. Landais, F. Schmidt, and S. Lovejoy
Nonlin. Processes Geophys., 22, 713–722, https://doi.org/10.5194/npg-22-713-2015, https://doi.org/10.5194/npg-22-713-2015, 2015
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In the present study, we investigate the scaling properties of the topography of Mars. Planetary topographic fields are well known to exhibit (mono)fractal behavior. Indeed, fractal formalism is efficient in reproducing the variability observed in topography. Our results suggest a multifractal behavior from the planetary scale down to 10 km. From 10 km to 300 m, the topography seems to be simple monofractal.
S. Lovejoy, L. del Rio Amador, and R. Hébert
Earth Syst. Dynam., 6, 637–658, https://doi.org/10.5194/esd-6-637-2015, https://doi.org/10.5194/esd-6-637-2015, 2015
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Numerical climate models forecast the weather well beyond the deterministic limit. In this “macroweather” regime, they are random number generators. Stochastic models can have more realistic noises and can be forced to converge to the real-world climate. Existing stochastic models do not exploit the very long atmospheric and oceanic memories. With skill up to decades, our new ScaLIng Macroweather Model (SLIMM) exploits this to make forecasts more accurate than GCMs.
C. A. Varotsos, S. Lovejoy, N. V. Sarlis, C. G. Tzanis, and M. N. Efstathiou
Atmos. Chem. Phys., 15, 7301–7306, https://doi.org/10.5194/acp-15-7301-2015, https://doi.org/10.5194/acp-15-7301-2015, 2015
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Varotsos et al. (Theor. Appl. Climatol., 114, 725–727, 2013) found that the solar ultraviolet (UV) wavelengths exhibit 1/f-type power-law correlations. In this study, we show that the residues of the spectral solar incident flux with respect to the Planck law over a wider range of wavelengths (i.e. UV-visible) have a scaling regime too.
J. Pinel and S. Lovejoy
Atmos. Chem. Phys., 14, 3195–3210, https://doi.org/10.5194/acp-14-3195-2014, https://doi.org/10.5194/acp-14-3195-2014, 2014
G. A. Schmidt, J. D. Annan, P. J. Bartlein, B. I. Cook, E. Guilyardi, J. C. Hargreaves, S. P. Harrison, M. Kageyama, A. N. LeGrande, B. Konecky, S. Lovejoy, M. E. Mann, V. Masson-Delmotte, C. Risi, D. Thompson, A. Timmermann, L.-B. Tremblay, and P. Yiou
Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, https://doi.org/10.5194/cp-10-221-2014, 2014
S. Lovejoy, D. Schertzer, and D. Varon
Earth Syst. Dynam., 4, 439–454, https://doi.org/10.5194/esd-4-439-2013, https://doi.org/10.5194/esd-4-439-2013, 2013
A. Gires, I. Tchiguirinskaia, D. Schertzer, and S. Lovejoy
Nonlin. Processes Geophys., 20, 343–356, https://doi.org/10.5194/npg-20-343-2013, https://doi.org/10.5194/npg-20-343-2013, 2013
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The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
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Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System
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Potential for bias in effective climate sensitivity from state-dependent energetic imbalance
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Estimating the lateral transfer of organic carbon through the European river network using a land surface model
Effect of the Atlantic Meridional Overturning Circulation on atmospheric pCO2 variations
A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019)
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Keno Riechers, Leonardo Rydin Gorjão, Forough Hassanibesheli, Pedro G. Lind, Dirk Witthaut, and Niklas Boers
Earth Syst. Dynam., 14, 593–607, https://doi.org/10.5194/esd-14-593-2023, https://doi.org/10.5194/esd-14-593-2023, 2023
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Paleoclimate proxy records show that the North Atlantic climate repeatedly transitioned between two regimes during the last glacial interval. This study investigates a bivariate proxy record from a Greenland ice core which reflects past Greenland temperatures and large-scale atmospheric conditions. We reconstruct the underlying deterministic drift by estimating first-order Kramers–Moyal coefficients and identify two separate stable states in agreement with the aforementioned climatic regimes.
Manoj Joshi, Robert A. Hall, David P. Stevens, and Ed Hawkins
Earth Syst. Dynam., 14, 443–455, https://doi.org/10.5194/esd-14-443-2023, https://doi.org/10.5194/esd-14-443-2023, 2023
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The 18.6-year lunar nodal cycle arises from variations in the angle of the Moon's orbital plane and affects ocean tides. In this work we use a climate model to examine the effect of this cycle on the ocean, surface, and atmosphere. The timing of anomalies is consistent with the so-called slowdown in global warming and has implications for when global temperatures will exceed 1.5 ℃ above pre-industrial levels. Regional anomalies have implications for seasonal climate areas such as Europe.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
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Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
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This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
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In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
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Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Han Qiu, Dalei Hao, Yelu Zeng, Xuesong Zhang, and Min Chen
Earth Syst. Dynam., 14, 1–16, https://doi.org/10.5194/esd-14-1-2023, https://doi.org/10.5194/esd-14-1-2023, 2023
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The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
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Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di
Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, https://doi.org/10.5194/esd-13-1625-2022, 2022
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We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two workarounds for this – an expensive physical model and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate and has limited resolution and a different seasonality from the physical model.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
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We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
Changgui Lin, Erik Kjellström, Renate Anna Irma Wilcke, and Deliang Chen
Earth Syst. Dynam., 13, 1197–1214, https://doi.org/10.5194/esd-13-1197-2022, https://doi.org/10.5194/esd-13-1197-2022, 2022
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This study endorses RCMs' added value on the driving GCMs in representing observed heat wave magnitudes. The future increase of heat wave magnitudes projected by GCMs is attenuated when downscaled by RCMs. Within the downscaling, uncertainties can be attributed almost equally to choice of RCMs and to the driving data associated with different GCMs. Uncertainties of GCMs in simulating heat wave magnitudes are transformed by RCMs in a complex manner rather than simply inherited.
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
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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The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on both tropical and extratropical weather extremes. In this study, we use machine learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
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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We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
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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Compound hazards occur when two different natural hazards impact the same time period and spatial area. This article presents a methodology for the spatiotemporal identification of compound hazards (SI–CH). The methodology is applied to compound precipitation and wind extremes in Great Britain for the period 1979–2019. The study finds that the SI–CH approach can accurately identify single and compound hazard events and represent their spatial and temporal properties.
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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Uncertainty within climate model projections on inter-annual timescales is largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
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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This study downscales climate information from EURO-CORDEX (approx. 12 km) output to a higher horizontal resolution (approx. 3 km) for the south of France. We also propose a matrix of different indices to evaluate the high-resolution precipitation output. We find that a higher resolution reproduces more realistic extreme precipitation events at both daily and sub-daily timescales. Our results and approach are promising to apply to other Mediterranean regions and climate impact studies.
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.
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.
Eirik Myrvoll-Nilsen, Sigrunn Holbek Sørbye, Hege-Beate Fredriksen, Håvard Rue, and Martin Rypdal
Earth Syst. Dynam., 11, 329–345, https://doi.org/10.5194/esd-11-329-2020, https://doi.org/10.5194/esd-11-329-2020, 2020
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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.
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.
Cited articles
Annan, J. D. and Hargreaves, J. C.: Reliability of the CMIP3 ensemble,
Geophys. Res. Lett., 37, L02703, https://doi.org/10.1029/2009gl041994, 2010. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris,
D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne,
J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M.,
Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G.,
Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y.,
Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker,
D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate
Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b
Bretherton, S.: A National Strategy for Advancing Climate Modeling, The
National Academies Press, Washington, DC, https://doi.org/10.17226/13430, 2012. a
Chan, D. and Huybers, P.: Correcting Observational Biases in Sea-Surface
Temperature Observations Removes Anomalous Warmth during World War II,
J. Climate, 34, 4585–4602, https://doi.org/10.1175/JCLI-D-20-0907.1, 2021. a
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T.,
Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G.,
Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Long-term climate
change: Projections, commitments and irreversibility, Cambridge University Press, Cambridge, UK, 1029–1136, https://doi.org/10.1017/CBO9781107415324.024, 2013. a
Cowtan, K. and Way, R.: Update to `Coverage bias in the HadCRUT4 temperature
series and its impact on recent temperature trends'. Temperature
reconstruction by domain: version 2.0 temperature series, Quarterly Journal of the Royal Meteorological Society, Q. J. Roy. Meteorol. Soc., 140,
1935–1944, https://doi.org/10.1002/qj.2297, 2014a. a
Cowtan, K. and Way, R. G.: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends, Q. J. Roy. Meteorol. Soc. 140, 1935–1944, https://doi.org/10.1002/qj.2297, 2014b. a
Cowtan, K., Hausfather, Z., Hawkins, E., Jacobs, P., Mann, M. E., Miller, S. K., Steinman, B. A., Stolpe, M. B., and Way, R. G.: Robust comparison of climate models with observations using blended land air and ocean sea surface
temperatures, Geophys. Res. Lett., 42, 6526–6534, 2015. a
Crowley, T. J., Zielinski, G., Vinther, B., Udisti, R., Kreutz, K., Cole-Dai,
J., and Castellano, E.: Volcanism and the little ice age, PAGES News, 16,
22–23, 2008. a
de Lima, M. I. P. and Lovejoy, S.: Macroweather precipitation variability up to global and centennial scales, Water Resour. Res., 51, 9490–9513,
https://doi.org/10.1002/2015WR017455, 2015. a
Del Rio Amador, L. and Lovejoy, S.: Long-Range Forecasting as a Past Value
Problem: Untangling Correlations and Causality With Scaling, Geophy. Res. Lett., 48, e2020GL092147, https://doi.org/10.1029/2020GL092147, 2021a. a, b, c
Del Rio Amador, L. and Lovejoy, S.: Using regional scaling for temperature
forecasts with the Stochastic Seasonal to Interannual Prediction System (StocSIPS), Clim. Dynam., 57, 727–756, https://doi.org/10.1007/s00382-021-05737-5, 2021b. a, b
ESGF – Earth System Grid Federation: ESGF Node at DOE/LLNL, Earth System Grid Federation (ESGF), available at: https://esgf-node.llnl.gov/projects/esgf-llnl/, last access: 20 December 2019. a
Flynn, C. M. and Mauritsen, T.: On the climate sensitivity and historical warming evolution in recent coupled model ensembles, Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, 2020. a, b, c, d
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. a
Forest, C. E., Stone, P. H., and Sokolov, A. P.: Estimated PDFs of climate
system properties including natural and anthropogenic forcings, Geophys. Res. Lett., 33, L01705, https://doi.org/10.1029/2005GL023977, 2006. a
Forster, P. M., Maycock, A. C., McKenna, C. M., and Smith, C. J.: Latest
climate models confirm need for urgent mitigation, Nat. Clim. Change, 10,
7–10, https://doi.org/10.1038/s41558-019-0660-0, 2020. a
Ghil, M. and Lucarini, V.: The physics of climate variability and climate
change, Rev. Mod. Phys., 92, 035002, https://doi.org/10.1103/RevModPhys.92.035002, 2020. a
Gregory, J. M. and Andrews, T.: Variation in climate sensitivity and feedback
parameters during the historical period, Geophys. Res. Lett., 43, 3911–3920, 2016. a
Harries, J. E. and Belotti, C.: On the Variability of the Global Net Radiative Energy Balance of the Nonequilibrium Earth, J. Climate, 23, 1277–1290, https://doi.org/10.1175/2009JCLI2797.1, 2010. a
Harvey, L. D. and Kaufmann, R. K.: Simultaneously constraining climate
sensitivity and aerosol radiative forcing, J. Climate, 15, 2837–2861, 2002. a
Hasselmann, K.: Stochastic climate models Part I. Theory, Tellus, 28, 473–485, https://doi.org/10.3402/tellusa.v28i6.11316, 1976. a, b
Hébert, R. and Lovejoy, S.: Interactive comment on “Global warming
projections derived from an observation-based minimal model” by K. Rypdal,
Earth Syst. Dynam., 7, 51–70, https://doi.org/10.5194/esd-7-51-2016, 2015. a
Hébert, R. and Lovejoy, S.: Regional Climate Sensitivity- and Historical-Based Projections to 2100, Geophys. Res. Lett., 45, 4248–4254, https://doi.org/10.1002/2017GL076649, 2018. a
Held, I., Winton, M., Takahashi, K., Delworth, T., Zeng, F., and Vallis, G. K.: Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing, J. Climate, 23, 2418–2427,
https://doi.org/10.1175/2009JCLI3466.1, 2010. a, b
Huang, B., Menne, M. J., Boyer, T., Freeman, E., Gleason, B. E., Lawrimore, J. H., Liu, C., Rennie, J. J., Schreck, C. J., Sun, F., Vose, R., Williams, C. N., Yin, X., and Zhang, H.-M.: Uncertainty Estimates for Sea Surface Temperature and Land Surface Air Temperature in NOAAGlobalTemp Version 5, J. Climate, 33, 1351–1379, https://doi.org/10.1175/JCLI-D-19-0395.1, 2020. a
Imkeller, P. and Von Storch, J.-S.: Stochastic climate models, in: vol. 49,
Birkhäuser, Birkhäuser, Basel, 2001. a
IPCC: Climate Change 2013: The Physical Science Basis, in: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/CBO9781107415324, 2013. a, b, c, d
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J.,
and Saynisch-Wagner, J.: Towards neural Earth system modelling by integrating
artificial intelligence in Earth system science, Nat. Mach. Intell., 3, 667–674, 2021. a
Iseri, Y., Yoshikawa, S., Kiguchi, M., Tawatari, R., Kanae, S., and Oki, T.:
Towards the incorporation of tipping elements in global climate risk
management: probability and potential impacts of passing a threshold, Sustainabil. Sci., 13, 315–328, https://doi.org/10.1007/s11625-018-0536-7, 2018. a
Johansson, D. J., O'Neill, B. C., Tebaldi, C., and Häggström, O.:
Equilibrium climate sensitivity in light of observations over the warming
hiatus, Nat. Clim. Change, 5, 449–453, 2015. a
Kaufmann, R. K., Kauppi, H., Mann, M. L., and Stock, J. H.: Reconciling
anthropogenic climate change with observed temperature 1998–2008, P. Natl. Acad. Sci. USA, 108, 11790–11793, https://doi.org/10.1073/pnas.1102467108, 2011. a
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.: Challenges
in Combining Projections from Multiple Climate Models, J. Climate, 23, 2739–2758, https://doi.org/10.1175/2009JCLI3361.1, 2010. a
Lenssen, N., Schmidt, G., Hansen, J., Menne, M., Persin, A., Ruedy, R., and
Zyss, D.: Improvements in the GISTEMP uncertainty model, J. Geophys. Res.-Atmos., 124, 6307–6326, https://doi.org/10.1029/2018JD029522, 2019. a
Lewis, N. and Curry, J. A.: The implications for climate sensitivity of AR5
forcing and heat uptake estimates, Clim. Dynam., 45, 1009–1023,
https://doi.org/10.1007/s00382-014-2342-y, 2015. a, b
Lewis, N. and Curry, J.: The Impact of Recent Forcing and Ocean Heat Uptake
Data on Estimates of Climate Sensitivity, J. Climate, 31, 6051–6071,
https://doi.org/10.1175/JCLI-D-17-0667.1, 2018. a
Lovejoy, S.: What Is Climate?, Eos Trans. Am. Geophys. Union, 94, 1–2, https://doi.org/10.1002/2013EO010001, 2013. a, b
Lovejoy, S.: A voyage through scales, a missing quadrillion and why the climate is not what you expect, Clim. Dynam., 44, 3187–3210,
https://doi.org/10.1007/s00382-014-2324-0, 2015b. a, b, c
Lovejoy, S. and Schertzer, D.: The Weather and Climate: Emergent Laws and
Multifractal Cascades, Cambridge University Press, Cambridge,
https://doi.org/10.1017/CBO9781139093811, 2013. a, b, c, d
Lovejoy, S. and Varotsos, C.: Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forcings, Earth Syst. Dynam., 7, 133–150, https://doi.org/10.5194/esd-7-133-2016, 2016. a, b
Lovejoy, S., del Rio Amador, L., and Hébert, R.: The ScaLIng Macroweather Model (SLIMM): using scaling to forecast global-scale macroweather from months to decades, Earth Syst. Dynam., 6, 637–658, https://doi.org/10.5194/esd-6-637-2015, 2015. a, b, c, d
Lovejoy, S., Del Rio Amador, L., and Hébert, R.: Harnessing Butterflies:
Theory and Practice of the Stochastic Seasonal to Interannual Prediction
System (StocSIPS), in: Advances in Nonlinear Geosciences, edited by: Tsonis, A., Springer, Cham, 305–355, https://doi.org/10.1007/978-3-319-58895-7_17, 2017. a
Medhaug, I., Stolpe, M., Fischer, E., and Knutti, R.: Reconciling controversies about the `global warming hiatus', Nature, 545, 41–47,
https://doi.org/10.1038/nature22315, 2017. a
Meehl, G., Arblaster, J., Fasullo, J., Hu, A., and Trenberth, K.: Model-based
evidence of deep-ocean heat uptake during surface-temperature hiatus periods,
Nat. Clim. Change, 1, 360–364, https://doi.org/10.1038/nclimate1229, 2011. a
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled
atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 –
Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011a. a
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, S. J., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, J. F., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011b. a, b, c
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E.,
Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G.,
Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N.,
Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M.,
Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605,
https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Millar, R. J., Otto, A., Forster, P. M., Lowe, J. A., Ingram, W. J., and Allen, M. R.: Model structure in observational constraints on transient climate response, Climatic Change, 131, 199–211, 2015. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos., 117, D08101, https://doi.org/10.1029/2011JD017187, 2012. a
Murphy, D., Solomon, S., Portmann, R., Rosenlof, K., Forster, P., and Wong, T.: An observationally based energy balance for the Earth since 1950, J. Geophys. Res.-Atmos., 114, D17107, 2009. a
Myhre, G., Highwood, E. J., Shine, K. P., and Stordal, F.: New estimates of
radiative forcing due to well mixed greenhouse gases, Geophys. Res. Lett., 25, 2715–2718, 1998. a
Myhre, G., Samset, B. H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T. K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R. C., Feichter, J., Ghan, S. J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkevåg, A.,
Lamarque, J.-F., Lin, G., Liu, X., Lund, M. T., Luo, G., Ma, X., van Noije,
T., Penner, J. E., Rasch, P. J., Ruiz, A., Seland, Ø., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H.,
Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., and Zhou, C.: Radiative forcing of
the direct aerosol effect from AeroCom Phase II simulations, Atmos. Chem. Phys., 13, 1853–1877, https://doi.org/10.5194/acp-13-1853-2013, 2013. a
Myrvoll-Nilsen, E., Sørbye, S. H., Fredriksen, H.-B., Rue, H., and Rypdal,
M.: Statistical estimation of global surface temperature response to forcing
under the assumption of temporal scaling, Earth Syst. Dynam., 11, 329–345, https://doi.org/10.5194/esd-11-329-2020, 2020. a
Nazarenko, L., Rind, D., Tsigaridis, K., Del Genio, A. D., Kelley, M., and
Tausnev, N.: Interactive nature of climate change and aerosol forcing, J. Geophys. Res.-Atmos., 122, 3457–3480, https://doi.org/10.1002/2016JD025809, 2017. a
North, G. R.: Theory of Energy-Balance Climate Models, J. Atmos. Sci., 32, 2033–2043, https://doi.org/10.1175/1520-0469(1975)032<2033:TOEBCM>2.0.CO;2, 1975. a
North, G. R. and Kim, K.-Y.: Energy Balance Climate Models, 1st Edn., Wiley, available at: https://www.perlego.com/book/992583/energy-balance-climate-models-pdf (last access: 17 December 2021), 2017. a
North, G. R., Cahalan, R. F., and Coakley Jr., J. A.: Energy balance climate
models, Rev. Geophys., 19, 91–121, 1981. a
Otto, A., Otto, F. E. L., Boucher, O., Church, J., Hegerl, G., Forster, P. M., Gillett, N. P., Gregory, J., Johnson, G. C., Knutti, R., Lewis, N., Lohmann, U., Marotzke, J., Myhre, G., Shindell, D., Stevens, B., and Allen, M. R.: Energy budget constraints on climate response, Nat. Geosci., 6, 415–416, https://doi.org/10.1038/ngeo1836, 2013. a
Padilla, L. E., Vallis, G. K., and Rowley, C. W.: Probabilistic Estimates of
Transient Climate Sensitivity Subject to Uncertainty in Forcing and Natural
Variability, J. Climate, 24, 5521–5537, https://doi.org/10.1175/2011JCLI3989.1, 2011. a, b
Penner, J., Andreae, M., Annegarn, H., Barrie, L., Feichter, J., Hegg, D.,
Achuthan, J., Leaitch, R., Murphy, D., Nganga, J., and Pitari, G.: Aerosols,
their Direct and Indirect Effects, Climate Change 2001: The Scientific Basis, in: Contribution of Working Group I to the Third Assessment Report of the
Intergovernmental Panel on Climate Change, World Meteorological Organization United Nations Environment Program, 289–348, 2001. a
Procyk, R.: The Fractional Energy Balance Equation: the Unification of
Externally Forced and Internal Variability, MS thesis, McGill University, Montreal, Canada, 2021. a
Proistosescu, C., Donohoe, A., Armour, K. C., Roe, G. H., Stuecker, M. F., and Bitz, C. M.: Radiative feedbacks from stochastic variability in surface
temperature and radiative imbalance, Geophys. Res. Lett., 45, 5082–5094, 2018. a
Richardson, L. F.: Atmospheric diffusion shown on a distance-neighbour graph,
P. Roy. Soc. Lond. A, 110, 709–737, https://doi.org/10.1098/rspa.1926.0043, 1926. a
Ring, M. J., Lindner, D., Cross, E. F., and Schlesinger, M. E.: Causes of the
Global Warming Observed since the 19th Century, Atmos. Clim. Sci., 02, 401–415, https://doi.org/10.4236/acs.2012.24035, 2012. a
Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature
Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020. a
Rypdal, K.: Global temperature response to radiative forcing: Solar cycle
versus volcanic eruptions, J. Geophys. Res.-Atmos., 117, D06115, https://doi.org/10.1029/2011JD017283, 2012. a
Rypdal, M. and Rypdal, K.: Long-Memory Effects in Linear Response Models of
Earth's Temperature and Implications for Future Global Warming, J. Climate, 27, 5240–5258, https://doi.org/10.1175/JCLI-D-13-00296.1, 2014. a
Sato, M., Hansen, J. E., McCormick, M. P., and Pollack, J. B.: Stratospheric aerosol optical depths, 1850–1990, J. Geophys. Res., 98, 22987–22994, 1993. a
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and
Nakajima, T.: Aerosol effects on cloud water amounts were successfully
simulated by a global cloud-system resolving model, Nat. Commun., 9, 985, https://doi.org/10.1038/s41467-018-03379-6, 2018. a
Schurer, A. P., Mann, M. E., Hawkins, E., Tett, S. F. B., and Hegerl, G. C.:
Importance of the pre-industrial baseline for likelihood of exceeding Paris
goals, Nat. Clim. Change, 7, 563–567, https://doi.org/10.1038/nclimate3345, 2017. a
Schwartz, S. E.: Uncertainty in climate sensitivity: causes, consequences,
challenges, Energ. Environ. Sci., 1, 430–453, 2008. a
Sherwood, S., Webb, M. J., Annan, J. D., Armour, K., 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. a
Skeie, R. B., Berntsen, T., Aldrin, M., Holden, M., and Myhre, G.: A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series, Earth Syst. Dynam., 5, 139–175, https://doi.org/10.5194/esd-5-139-2014, 2014. a
Smith, C. J.: Effective Radiative Forcing from Shared Socioeconomic Pathways (v0.3.1). Zenodo [data set], https://doi.org/10.5281/zenodo.3515339, 2019. a
Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., and Regayre, L. A.: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model, Geosci. Model Dev., 11, 2273–2297, https://doi.org/10.5194/gmd-11-2273-2018, 2018. a, b, c
Smith, D. M., Scaife, A. A., Hawkins, E., Bilbao, R., Boer, G. J., Caian, M.,
Caron, L.-P., Danabasoglu, G., Delworth, T., Doblas-Reyes, F. J., Doescher, R., Dunstone, N. J., Eade, R., Hermanson, L., Ishii, M., Kharin, V., Kimoto,
M., Koenigk, T., Kushnir, Y., Matei, D., Meehl, G. A., Menegoz, M., Merryfield, W. J., Mochizuki, T., Müller, W. A., Pohlmann, H., Power, S., Rixen, M., Sospedra-Alfonso, R., Tuma, M., Wyser, K., Yang, X., and Yeager,
S.: Predicted Chance That Global Warming Will Temporarily Exceed 1.5 ∘C, Geophys. Res. Lett., 45, 11895–11903, https://doi.org/10.1029/2018GL079362, 2018. a
Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J.: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006), J. Climate, 21, 2283–2296, 2008. a
Solomon, S.: Climate Change 2007 the physical science basis: contribution of
Working Group I to the Fourth Assessment Report of the IPCC, Cambridge
University Press, Cambridge, 2007. a
Solomon, S., Plattner, G.-K., and Friedlingstein, P.: Irreversible climate
change due to carbon dioxide emissions, P. Natl. Acad. Sci. USA, 106, 1704–1709, https://doi.org/10.1073/pnas.0812721106, 2009. a
Stainforth, D., Allen, M., Tredger, E., and Smith, L.: Confidence, uncertainty and decision-support relevance in climate predictions, Philos. T. Roy. Soc. A, 365, 2145–2161, https://doi.org/10.1098/rsta.2007.2074, 2007. a
Stevens, B.: Rethinking the Lower Bound on Aerosol Radiative Forcing, J. Climate, 28, 4794–4819, https://doi.org/10.1175/JCLI-D-14-00656.1, 2015. a, b, c, d
Stouffer, R. J.: Time scales of climate response, J. Climate, 17, 209–217, https://doi.org/10.1175/1520-0442(2004)017<0209:TSOCR>2.0.CO;2, 2004. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012. a
Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in
probabilistic climate projections, Philos. T. Roy. Soc. A, 365, 2053–2075,
https://doi.org/10.1098/rsta.2007.2076, 2007. a
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, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549, 2020. a, b
Tomassini, L., Reichert, P., Knutti, R., Stocker, T. F., and Borsuk, M. E.:
Robust Bayesian Uncertainty Analysis of Climate System Properties Using
Markov Chain Monte Carlo Methods, J. Climate, 20, 1239–1254,
https://doi.org/10.1175/JCLI4064.1, 2007. a
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009. a
Trenberth, K. E., Fasullo, J. T., and Balmaseda, M. A.: Earth’s energy imbalance, J. Climate, 27, 3129–3144, 2014. a
Wang, Y.-M., Lean, J., and Sheeley Jr., N.: Modeling the Sun's magnetic field
and irradiance since 1713, Astrophys. J., 625, 522, https://doi.org/10.1086/429689, 2005. a
Weisheimer, A. and Palmer, T. N.: On the reliability of seasonal climate
forecasts, J. Roy. Soc. Interface, 11, 20131162, https://doi.org/10.1098/rsif.2013.1162, 2014. a
Wolfram Research, Inc.: Mathematica, Version 12.2, Champaign, IL, available at: https://www.wolfram.com/mathematica (last access: 15 December 2021), 2020. a
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. a, b, c, d
Zhang, H., Huang, B., Lawrimore, J., Menne, M., and Smith, T. M.: Global
Surface Temperature Dataset (NOAAGlobalTemp), Version 4.0, [NOAA Global
Surface Temperature Data], https://doi.org/10.7289/V5FN144H, 2019. a
Zhou, C. and Penner, J. E.: Why do general circulation models overestimate the aerosol cloud lifetime effect? A case study comparing CAM5 and a CRM,
Atmos. Chem. Phys., 17, 21–29, https://doi.org/10.5194/acp-17-21-2017, 2017.
a
Ziegler, E. and Rehfeld, K.: TransEBM v. 1.0: description, tuning, and validation of a transient model of the Earth's energy balance in two dimensions, Geosci. Model Dev., 14, 2843–2866, https://doi.org/10.5194/gmd-14-2843-2021, 2021. a
Short summary
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.
This paper presents a new class of energy balance model that accounts for the long memory within...
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