Articles | Volume 14, issue 2
https://doi.org/10.5194/esd-14-413-2023
© Author(s) 2023. 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-14-413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2023
Research article |
| 14 Apr 2023
| 14 Apr 2023
The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado at Boulder, Boulder, CO 80309, USA
Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder, Boulder, CO 80309, USA
Robert C. Jnglin Wills
Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307, USA
Pedro DiNezio
Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder, Boulder, CO 80309, USA
Jeremy Klavans
Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder, Boulder, CO 80309, USA
Sebastian Milinski
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307, USA
Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO 80307, USA
Sara C. Sanchez
Department of Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder, Boulder, CO 80309, USA
Samantha Stevenson
Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
Malte F. Stuecker
Department of Oceanography and International Pacific Research Center (IPRC), School of Ocean and Earth Science and Technology (SOEST), University of Hawai`i at Mānoa, Honolulu, HI 96822, USA
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80307, USA
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Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.
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El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
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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.
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Revised manuscript accepted for ESD
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Jakob Simon Dörr, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills
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Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher
Ocean Sci., 19, 499–515, https://doi.org/10.5194/os-19-499-2023, https://doi.org/10.5194/os-19-499-2023, 2023
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Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.
Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski
Earth Syst. Dynam., 13, 1289–1304, https://doi.org/10.5194/esd-13-1289-2022, https://doi.org/10.5194/esd-13-1289-2022, 2022
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El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
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Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
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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.
Nicola Maher, Sebastian Milinski, and Ralf Ludwig
Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, https://doi.org/10.5194/esd-12-401-2021, 2021
Kyung-Sook Yun, Axel Timmermann, and Malte F. Stuecker
Earth Syst. Dynam., 12, 121–132, https://doi.org/10.5194/esd-12-121-2021, https://doi.org/10.5194/esd-12-121-2021, 2021
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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.
Josephine R. Brown, Chris M. Brierley, Soon-Il An, Maria-Vittoria Guarino, Samantha Stevenson, Charles J. R. Williams, Qiong Zhang, Anni Zhao, Ayako Abe-Ouchi, Pascale Braconnot, Esther C. Brady, Deepak Chandan, Roberta D'Agostino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, Ryouta O'ishi, Bette L. Otto-Bliesner, W. Richard Peltier, Xiaoxu Shi, Louise Sime, Evgeny M. Volodin, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 16, 1777–1805, https://doi.org/10.5194/cp-16-1777-2020, https://doi.org/10.5194/cp-16-1777-2020, 2020
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El Niño–Southern Oscillation (ENSO) is the largest source of year-to-year variability in the current climate, but the response of ENSO to past or future changes in climate is uncertain. This study compares the strength and spatial pattern of ENSO in a set of climate model simulations in order to explore how ENSO changes in different climates, including past cold glacial climates and past climates with different seasonal cycles, as well as gradual and abrupt future warming cases.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
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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.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
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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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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.
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.
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
Balmaseda, M. A., Davey, M. K., and Anderson, D. L. T.: Decadal and Seasonal
Dependence of ENSO Prediction Skill, J. Climate, 8, 2705–2715,
https://doi.org/10.1175/1520-0442(1995)008<2705:DASDOE>2.0.CO;2, 1995. a
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO
representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42,
1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014. a
Beobide-Arsuaga, G., Bayr, T., Reintges, A., and Latif, M.: Uncertainty of
ENSO-amplitude projections in CMIP5 and CMIP6 models, Clim. Dynam., 56, 3875–3888,
https://doi.org/10.1007/s00382-021-05673-4, 2021. a, b, c, d
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C.,
Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur,
G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G.,
Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L.,
Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y.,
Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A.,
Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J.,
Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the
IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sys.,
12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Brunner, L., Hauser, M., Lorenz, R., and Beyerle, U.: The ETH Zurich CMIP6 next generation archive: technical documentation, Zenodo [data set], https://doi.org/10.5281/zenodo.3734128, 2020. a
Burger, F. A., Terhaar, J., and Frölicher, T. L.: Compound marine heatwaves
and ocean acidity extremes, Nat. Commun., 13, 4722,
https://doi.org/10.1038/s41467-022-32120-7, 2022. a
Cai, W., Lengaigne, M., Borlace, S., Collins, M., Cowan, T., McPhaden, M. J.,
Timmermann, A., Power, S., Brown, J., Menkes, C., Ngari, A., Vincent, E. M.,
and Widlansky, M. J.: More extreme swings of the South Pacific convergence
zone due to greenhouse warming, Nature, 488, 365–369,
https://doi.org/10.1038/nature11358, 2012. a
Cai, W., Borlace, S., Lengaigne, M., van Rensch, P., Collins, M., Vecchi, G.,
Timmermann, A., Santoso, A., McPhaden, M. J., Wu, L., England, M. H., Wang,
G., Guilyardi, E., and Jin, F.-F.: Increasing frequency of extreme El Niño
events due to greenhouse warming, Nat. Clim. Change, 4, 111–116,
https://doi.org/10.1038/nclimate2100, 2014. a
Cai, W., Wang, G., Dewitte, B., Wu, L., Santoso, A., Takahashi, K., Yang, Y.,
Carréric, A., and McPhaden, M. J.: Increased variability of eastern Pacific
El Niño under greenhouse warming, Nature, 564, 201–206,
https://doi.org/10.1038/s41586-018-0776-9, 2018. a, b
Cai, W., Santoso, A., Collins, M., Dewitte, B., Karamperidou, C., Kug, J.-S.,
Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Taschetto, A. S.,
Timmermann, A., Wu, L., Yeh, S.-W., Wang, G., Ng, B., Jia, F., Yang, Y.,
Ying, J., Zheng, X.-T., Bayr, T., Brown, J. R., Capotondi, A., Cobb, K. M.,
Gan, B., Geng, T., Ham, Y.-G., Jin, F.-F., Jo, H.-S., Li, X., Lin, X.,
McGregor, S., Park, J.-H., Stein, K., Yang, K., Zhang, L., and Zhong, W.:
Changing El Niño-Southern Oscillation in a warming climate, Nat. Rev. Earth Environ., 2, 628–644, https://doi.org/10.1038/s43017-021-00199-z, 2021. a, b, c, d, e
Cai, W., Ng, B., Wang, G., Santoso, A., Wu, L., and Yang, K.: Increased ENSO
sea surface temperature variability under four IPCC emission scenarios,
Nat. Clim. Change, 12, 228–231, https://doi.org/10.1038/s41558-022-01282-z, 2022. a, b, c, d
Callahan, C. W., Chen, C., Rugenstein, M., Bloch-Johnson, J., Yang, S., and
Moyer, E. J.: Robust decrease in El Niño/Southern Oscillation amplitude
under long-term warming, Nat. Clim. Change, 11, 752–757,
https://doi.org/10.1038/s41558-021-01099-2, 2021. a, b, c
Capotondi, A. and Sardeshmukh, P. D.: Is El Niño really changing?,
Geophys. Res. Lett., 44, 8548–8556,
https://doi.org/10.1002/2017GL074515, 2017. a, b, c
Capotondi, A., Ham, Y., Wittenberg, A., and Kug, J.: Climate model biases and
El Niño Southern Oscillation (ENSO) simulation,
US CLIVAR Variations 13, 1, 21–25, 2015. a
Chen, H.-C. and Jin, F.-F.: Dynamics of ENSO Phase–Locking and Its Biases in
Climate Models, Geophys. Res. Lett., 49, e2021GL097603,
https://doi.org/10.1029/2021GL097603, 2022. a, b
Choi, K.-Y., Vecchi, G. A., and Wittenberg, A. T.: ENSO Transition, Duration,
and Amplitude Asymmetries: Role of the Nonlinear Wind Stress Coupling in a
Conceptual Model, J. Climate, 26, 9462–9476,
https://doi.org/10.1175/JCLI-D-13-00045.1, 2013. a, b
Chung, C. T. Y., Power, S. B., Sullivan, A., and Delage, F.: The role of the
South Pacific in modulating Tropical Pacific variability, Sci. Rep., 9, 18311, https://doi.org/10.1038/s41598-019-52805-2, 2019. a
Clement, A. C., Seager, R., Cane, M. A., and Zebiak, S. E.: An Ocean Dynamical
Thermostat, J. Climate, 9, 2190–2196,
https://doi.org/10.1175/1520-0442(1996)009<2190:AODT>2.0.CO;2, 1996. a
Coats, S. and Karnauskas, K. B.: Are Simulated and Observed Twentieth Century
Tropical Pacific Sea Surface Temperature Trends Significant Relative to
Internal Variability?, Geophys. Res. Lett., 44, 9928–9937,
https://doi.org/10.1002/2017GL074622, 2017. a
Cobb, K. M., Westphal, N., Sayani, H. R., Watson, J. T., Lorenzo, E. D., Cheng,
H., Edwards, R. L., and Charles, C. D.: Highly Variable El Nino Southern
Oscillation Throughout the Holocene, Science, 339, 67–70,
https://doi.org/10.1126/science.1228246, 2013. a
Collins, M., An, S.-I., Cai, W., Ganachaud, A., Guilyardi, E., Jin, F.-F.,
Jochum, M., Lengaigne, M., Power, S., Timmermann, A., Vecchi, G., and
Wittenberg, A.: The impact of global warming on the tropical Pacific Ocean
and El Niño, Nat. Geosci., 3, 391–397, https://doi.org/10.1038/ngeo868, 2010. a
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J.-H., Dunne,
K. A., Ginoux, P., Gudgel, R., Hallberg, R. W., Harris, L., Harrison, M. J.,
Johnson, N., Kapnick, S. B., Lin, S.-J., Lu, F., Malyshev, S., Milly, P. C.,
Murakami, H., Naik, V., Pascale, S., Paynter, D., Rosati, A., Schwarzkopf,
M., Shevliakova, E., Underwood, S., Wittenberg, A. T., Xiang, B., Yang, X.,
Zeng, F., Zhang, H., Zhang, L., and Zhao, M.: SPEAR: The Next Generation
GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection,
J. Adv. Model. Earth Sys., 12, e2019MS001895,
https://doi.org/10.1029/2019MS001895, 2020. a
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E.,
Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S.,
Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from
Earth system model initial-condition large ensembles and future prospects,
Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a
DiNezio, P. N., Kirtman, B. P., Clement, A. C., Lee, S.-K., Vecchi, G. A., and
Wittenberg, A.: Mean Climate Controls on the Simulated Response of ENSO to
Increasing Greenhouse Gases, J. Climate, 25, 7399–7420,
https://doi.org/10.1175/JCLI-D-11-00494.1, 2012. a, b
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
England, M. H., McGregor, S., Spence, P., Meehl, G. A., Timmermann, A., Cai,
W., Gupta, A. S., McPhaden, M. J., Purich, A., and Santoso, A.: Recent
intensification of wind-driven circulation in the Pacific and the ongoing
warming hiatus, Nat. Clim. Change, 4, 222–227,
https://doi.org/10.1038/nclimate2106, 2014. a
ESGF (The Earth System Grid Federation): An open infrastructure for access to distributed geospatial data, Future Generation Computer Systems, 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014 (data available at https://esgf-node.llnl.gov/projects/cmip6/, last access: 13 April 2023). a
Fasullo, J. T. and Richter, J. H.: Scenario and Model Dependence of Strategic Solar Climate Intervention in CESM, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-779, 2022. a
Fasullo, J. T., Otto-Bliesner, B. L., and Stevenson, S.: ENSO's Changing
Influence on Temperature, Precipitation, and Wildfire in a Warming Climate,
Geophys. Res. Lett., 45, 9216–9225, https://doi.org/10.1029/2018GL079022, 2018. a
Fasullo, J. T., Phillips, A. S., and Deser, C.: Evaluation of Leading Modes of
Climate Variability in the CMIP Archives, J. Climate, 33, 5527–5545, https://doi.org/10.1175/JCLI-D-19-1024.1, 2020. a
Fedorov, A. V. and Philander, S. G.: Is El Nino Changing?, Science, 288,
1997–2002, https://doi.org/10.1126/science.288.5473.1997, 2000. a
Gan, R., Liu, Q., Huang, G., Hu, K., and Li, X.: Greenhouse warming and
internal variability increase extreme and central Pacific El Niño frequency
since 1980, Nat. Commun., 14, 394, https://doi.org/10.1038/s41467-023-36053-7,
2023. a, b
GFDL SPEAR Large Ensembles: https://www.gfdl.noaa.gov/spear_large_ensembles/ [data set], last access: 6 April 2023. a
Grothe, P. R., Cobb, K. M., Liguori, G., Di Lorenzo, E., Capotondi, A., Lu, Y.,
Cheng, H., Edwards, R. L., Southon, J. R., Santos, G. M., Deocampo, D. M.,
Lynch-Stieglitz, J., Chen, T., Sayani, H. R., Thompson, D. M., Conroy, J. L.,
Moore, A. L., Townsend, K., Hagos, M., O'Connor, G., and Toth, L. T.:
Enhanced El Niño–Southern Oscillation Variability in Recent Decades,
Geophys. Res. Lett., 47, e2019GL083906,
https://doi.org/10.1029/2019GL083906, 2020. a
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hayashi, M., Jin, F.-F., and Stuecker, M. F.: Dynamics for El Niño-La Niña
asymmetry constrain equatorial-Pacific warming pattern, Nat.
Commun., 11, 4230, https://doi.org/10.1038/s41467-020-17983-y, 2020. a, b, c, d
Heede, U. K. and Fedorov, A. V.: Eastern equatorial Pacific warming delayed by
aerosols and thermostat response to CO2 increase, Nat. Clim. Change, 11,
696–703, https://doi.org/10.1038/s41558-021-01101-x, 2021. a, b
Heede, U. K., Fedorov, A. V., and Burls, N. J.: Time Scales and Mechanisms for
the Tropical Pacific Response to Global Warming: A Tug of War between the
Ocean Thermostat and Weaker Walker, J. Climate, 33, 6101–6118,
https://doi.org/10.1175/JCLI-D-19-0690.1, 2020. a
Jeffrey, S., Rotstayn, L., Collier, M., Dravitzki, S., Hamalainen, C.,
Moeseneder, C., Wong, K., and Syktus, J.: Australia's CMIP5 submission using
the CSIRO-Mk3.6 model, Aust. Meteorol. Ocean.,
63, 1–13, 2012. a
Jin, F.-F. and Neelin, J. D.: Modes of Interannual Tropical Ocean–Atmosphere
Interaction—a Unified View. Part I: Numerical Results, J. Atmos. Sci., 50, 3477–3503,
https://doi.org/10.1175/1520-0469(1993)050<3477:MOITOI>2.0.CO;2, 1993. a
Jin, F.-F., Kim, S. T., and Bejarano, L.: A coupled-stability index for ENSO,
Geophys. Res. Lett., 33, L23708, https://doi.org/10.1029/2006GL027221,
2006. a, b
Kang, S. M., Xie, S.-P., Shin, Y., Kim, H., Hwang, Y.-T., Stuecker, M. F.,
Xiang, B., and Hawcroft, M.: Walker circulation response to extratropical
radiative forcing, Sci. Adv., 6, eabd3021,
https://doi.org/10.1126/sciadv.abd3021, 2020. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a
Kim, S. T., Cai, W., Jin, F.-F., Santoso, A., Wu, L., Guilyardi, E., and An,
S.-I.: Response of El Niño sea surface temperature variability to
greenhouse warming, Nat. Clim. Change, 4, 786–790,
https://doi.org/10.1038/nclimate2326, 2014. a
Kirchmeier-Young, M., Zwiers, F., and Gillett, N.: Attribution of Extreme
Events in Arctic Sea Ice Extent, J. Climate, 30, 553–571,
https://doi.org/10.1175/JCLI-D-16-0412.1, 2017. a
Kociuba, G. and Power, S. B.: Inability of CMIP5 Models to Simulate Recent
Strengthening of the Walker Circulation: Implications for Projections,
J. Climate, 28, 20–35, https://doi.org/10.1175/JCLI-D-13-00752.1, 2015. a, b, c
Kohyama, T. and Hartmann, D. L.: Nonlinear ENSO Warming Suppression (NEWS),
J. Climate, 30, 4227–4251, https://doi.org/10.1175/JCLI-D-16-0541.1, 2017. a, b
Kohyama, T., Hartmann, D. L., and Battisti, D. S.: La Niña–like Mean-State
Response to Global Warming and Potential Oceanic Roles, J. Climate,
30, 4207–4225, https://doi.org/10.1175/JCLI-D-16-0441.1, 2017. a, b
Kosaka, Y. and Xie, S.-P.: Recent global-warming hiatus tied to equatorial
Pacific surface cooling, Nature, 501, 403–407, https://doi.org/10.1038/nature12534,
2013. a
Lee, J., Planton, Y. Y., Gleckler, P. J., Sperber, K. R., Guilyardi, E.,
Wittenberg, A. T., McPhaden, M. J., and Pallotta, G.: Robust Evaluation of
ENSO in Climate Models: How Many Ensemble Members Are Needed?, Geophys. Res. Lett., 48, e2021GL095041,
https://doi.org/10.1029/2021GL095041, 2021. a, b, c, d
CESM2 Large Ensemble Community Project (LENS2), https://www.cesm.ucar.edu/community-projects/lens2 [data set], last access: 6 April 2023. a
Li, J., Xie, S.-P., Cook, E. R., Morales, M. S., Christie, D. A., Johnson,
N. C., Chen, F., D’Arrigo, R., Fowler, A. M., Gou, X., and Fang, K.: El
Niño modulations over the past seven centuries, Nat. Clim. Change, 3,
822–826, https://doi.org/10.1038/nclimate1936, 2013. a
Lian, T., Chen, D., Ying, J., Huang, P., and Tang, Y.: Tropical Pacific trends
under global warming: El Niño-like or La Niña-like?, Nat. Sci.
Rev., 5, 810–812, https://doi.org/10.1093/nsr/nwy134, 2018. a
Maher, N., Matei, D., Milinski, S., and Marotzke, J.: ENSO Change in Climate
Projections: Forced Response or Internal Variability?, Geophys. Res. Lett., 45, 11390–11398, https://doi.org/10.1029/2018GL079764, 2018. a, b, c, d
Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M.,
Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li,
H., Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina,
T., Olonscheck, D., Raddatz, T., Stevens, B., and Marotzke, J.: The Max
Planck Institute Grand Ensemble: Enabling the Exploration of Climate System
Variability, J. Adv. Model. Earth Sys., 11, 2050–2069,
https://doi.org/10.1029/2019MS001639, 2019. a
Max-Planck-Institut für Meteorologie: MPI Grand Ensemble, https://esgf-data.dkrz.de/projects/mpi-ge/ [data set], last access: 6 April 2023. a
McGregor, S., Timmermann, A., Schneider, N., Stuecker, M. F., and England,
M. H.: The Effect of the South Pacific Convergence Zone on the Termination of
El Niño Events and the Meridional Asymmetry of ENSO, J. Climate, 25,
5566–5586, https://doi.org/10.1175/JCLI-D-11-00332.1, 2012. a
McGregor, S., Timmermann, A., Stuecker, M. F., England, M. H., Merrifield, M.,
Jin, F.-F., and Chikamoto, Y.: Recent Walker circulation strengthening and
Pacific cooling amplified by Atlantic warming, Nat. Clim. Change, 4,
888–892, https://doi.org/10.1038/nclimate2330, 2014. a, b
McGregor, S., Stuecker, M. F., Kajtar, J. B., England, M. H., and Collins, M.:
Model tropical Atlantic biases underpin diminished Pacific decadal
variability, Nat. Clim. Change, 8, 493–498,
https://doi.org/10.1038/s41558-018-0163-4, 2018. a
McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an integrating
concept in earth science., Science, 314, 1740–1745, 2006. a
McPhaden, M. J., Lee, T., and McClurg, D.: El Niño and its relationship to
changing background conditions in the tropical Pacific Ocean, Geophys.
Res. Lett., 38, L15709, https://doi.org/10.1029/2011GL048275, 2011. a
Meehl, G. A. and Washington, W. M.: El Niño-like climate change in a model
with increased atmospheric CO2 concentrations, Nature, 382, 56–60,
https://doi.org/10.1038/382056a0, 1996. a
Multi-Model Large Ensemble Archive (MMLEA): https://www.cesm.ucar.edu/community-projects/mmlea [data set], last access: 6 April 2023. a
Ng, B., Cai, W., Cowan, T., and Bi, D.: Impacts of Low-Frequency Internal
Climate Variability and Greenhouse Warming on El Niño–Southern
Oscillation, J. Climate, 34, 2205–2218,
https://doi.org/10.1175/JCLI-D-20-0232.1, 2021. a, b
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter,
T. R., Mathur, R., and van Vuuren, D. P.: A new scenario framework for
climate change research: the concept of shared socioeconomic pathways,
Clim. Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014. a
Pausata, F. S. R., Zanchettin, D., Karamperidou, C., Caballero, R., and
Battisti, D. S.: ITCZ shift and extratropical teleconnections drive ENSO
response to volcanic eruptions, Sci. Adv., 6, eaaz5006,
https://doi.org/10.1126/sciadv.aaz5006, 2020. a
Planton, Y. Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J.,
Bayr, T., McGregor, S., McPhaden, M. J., Power, S., Roehrig, R., Vialard, J.,
and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO
Metrics Package, B. Am. Meteorol. Soc., 102, 193–217, https://doi.org/10.1175/BAMS-D-19-0337.1, 2021. a, b
Power, S., Delage, F., Chung, C., Kociuba, G., and Keay, K.: Robust
twenty-first-century projections of El Niño and related precipitation
variability, Nature, 502, 541–545, https://doi.org/10.1038/nature12580, 2013. a
Rodgers, K. B., Friederichs, P., and Latif, M.: Tropical Pacific Decadal
Variability and Its Relation to Decadal Modulations of ENSO, J. Climate, 17, 3761–3774,
https://doi.org/10.1175/1520-0442(2004)017<3761:TPDVAI>2.0.CO;2, 2004. a, b
Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E.-S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D. L., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, 2021. a, b, c
Seager, R., Cane, M., Henderson, N., Lee, D.-E., Abernathey, R., and Zhang, H.:
Strengthening tropical Pacific zonal sea surface temperature gradient
consistent with rising greenhouse gases, Nat. Clim. Change, 9, 517–522,
https://doi.org/10.1038/s41558-019-0505-x, 2019. a
Seager, R., Henderson, N., and Cane, M.: Persistent discrepancies between
observed and modeled trends in the tropical Pacific Ocean, J. Climate, 1–41, https://doi.org/10.1175/JCLI-D-21-0648.1, 2022. a, b, c
Stein, K., Timmermann, A., Schneider, N., Jin, F.-F., and Stuecker, M. F.:
ENSO Seasonal Synchronization Theory, J. Climate, 27, 5285–5310,
https://doi.org/10.1175/JCLI-D-13-00525.1, 2014. a
Stevenson, S., Fox-Kemper, B., Jochum, M., Rajagopalan, B., and Yeager, S. G.:
ENSO Model Validation Using Wavelet Probability Analysis, J. Climate, 23, 5540–5547, https://doi.org/10.1175/2010JCLI3609.1, 2010. a
Stevenson, S., Wittenberg, A. T., Fasullo, J., Coats, S., and Otto-Bliesner,
B.: Understanding Diverse Model Projections of Future Extreme El Niño,
J. Climate, 34, 449–464, https://doi.org/10.1175/JCLI-D-19-0969.1, 2021. a
Stevenson, S. L.: Significant changes to ENSO strength and impacts in the
twenty-first century: Results from CMIP5, Geophys. Res. Lett., 39, L17703,
https://doi.org/10.1029/2012GL052759, 2012. a, b
Stuecker, M. F., Timmermann, A., Jin, F.-F., McGregor, S., and Ren, H.-L.: A
combination mode of the annual cycle and the El Niño/Southern Oscillation,
Nat. Geosci., 6, 540–544, https://doi.org/10.1038/ngeo1826, 2013. a, b
Sun, L., Alexander, M., and Deser, C.: Evolution of the Global Coupled Climate
Response to Arctic Sea Ice Loss during 1990-2090 and Its Contribution to
Climate Change, J. Climate, 31, 7823–7843, 2018. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Taschetto, A. S., Ummenhofer, C. C., Stuecker, M. F., Dommenget, D., Ashok, K.,
Rodrigues, R. R., and Yeh, S.-W.: ENSO Atmospheric Teleconnections,
chap. 14, American Geophysical Union (AGU), 309–335
https://doi.org/10.1002/9781119548164.ch14, 2020. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Timmermann, A., An, S.-I., Kug, J.-S., Jin, F.-F., Cai, W., Capotondi, A.,
Cobb, K. M., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Stein, K.,
Wittenberg, A. T., Yun, K.-S., Bayr, T., Chen, H.-C., Chikamoto, Y., Dewitte,
B., Dommenget, D., Grothe, P., Guilyardi, E., Ham, Y.-G., Hayashi, M.,
Ineson, S., Kang, D., Kim, S., Kim, W., Lee, J.-Y., Li, T., Luo, J.-J.,
McGregor, S., Planton, Y., Power, S., Rashid, H., Ren, H.-L., Santoso, A.,
Takahashi, K., Todd, A., Wang, G., Wang, G., Xie, R., Yang, W.-H., Yeh,
S.-W., Yoon, J., Zeller, E., and Zhang, X.: El Niño-Southern Oscillation
complexity, Nature, 559, 535–545, https://doi.org/10.1038/s41586-018-0252-6, 2018.
a
Watanabe, M., Dufresne, J.-L., Kosaka, Y., Mauritsen, T., and Tatebe, H.:
Enhanced warming constrained by past trends in equatorial Pacific sea
surface temperature gradient, Nat. Clim. Change, 11, 33–37,
https://doi.org/10.1038/s41558-020-00933-3, 2021. a
Wengel, C., Lee, S.-S., Stuecker, M. F., Timmermann, A., Chu, J.-E., and
Schloesser, F.: Future high-resolution El Niño/Southern Oscillation
dynamics, Nat. Clim. Change, 11, 758–765,
https://doi.org/10.1038/s41558-021-01132-4, 2021. a, b
Wills, R. C. J., Battisti, D. S., Armour, K. C., Schneider, T., and Deser, C.:
Pattern Recognition Methods to Separate Forced Responses from Internal
Variability in Climate Model Ensembles and Observations, J. Climate,
33, 8693–8719, https://doi.org/10.1175/JCLI-D-19-0855.1, 2020. a
Wills, R. C. J., Dong, Y., Proistosecu, C., Armour, K. C., and Battisti, D. S.:
Systematic Climate Model Biases in the Large-Scale Patterns of Recent
Sea-Surface Temperature and Sea-Level Pressure Change, Geophys. Res. Lett., 49,
e2022GL100011, https://doi.org/10.1029/2022GL100011, 2022. a, b, c
Wittenberg, A. T.: Are historical records sufficient to constrain ENSO
simulations?, Geophys. Res. Lett., 36, L12702, https://doi.org/10.1029/2009GL038710,
2009. a
Wyman, D. A., Conroy, J. L., and Karamperidou, C.: The Tropical Pacific
ENSO–Mean State Relationship in Climate Models over the Last Millennium,
J. Climate, 33, 7539–7551, https://doi.org/10.1175/JCLI-D-19-0673.1, 2020. a, b
Wyser, K., Koenigk, T., Fladrich, U., Fuentes-Franco, R., Karami, M. P., and Kruschke, T.: The SMHI Large Ensemble (SMHI-LENS) with EC-Earth3.3.1, Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, 2021. a
Ying, J., Collins, M., Cai, W., Timmermann, A., Huang, P., Chen, D., and Stein,
K.: Emergence of climate change in the tropical Pacific, Nat. Clim.
Change, 12, 356–364, https://doi.org/10.1038/s41558-022-01301-z, 2022. a
Yun, K.-S., Lee, J.-Y., Timmermann, A., Stein, K., Stuecker, M. F., Fyfe,
J. C., and Chung, E.-S.: Increasing ENSO-rainfall variability due to changes
in future tropical temperature-rainfall relationship, Commun. Earth Environ., 2, 43, https://doi.org/10.1038/s43247-021-00108-8, 2021. a
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M.,
Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System
Model: ACCESS-ESM1.5, J. South. Hemisphere Earth Syst. Sci., 70, 193–214,
2020. a
Short summary
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.
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future...
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