Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3123-2023
© 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/gmd-16-3123-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| 
02 Jun 2023
Review and perspective paper |
| 02 Jun 2023
| 02 Jun 2023
Differentiable programming for Earth system modeling
Maximilian Gelbrecht
CORRESPONDING AUTHOR
Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Alistair White
Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Sebastian Bathiany
Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Niklas Boers
Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, UK
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Nils Bochow, Anna Poltronieri, and Niklas Boers
The Cryosphere, 18, 5825–5863, https://doi.org/10.5194/tc-18-5825-2024, https://doi.org/10.5194/tc-18-5825-2024, 2024
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Using the latest climate models, we update the understanding of how the Greenland ice sheet responds to climate changes. We found that precipitation and temperature changes in Greenland vary across different regions. Our findings suggest that using uniform estimates for temperature and precipitation for modelling the response of the ice sheet can overestimate ice loss in Greenland. Therefore, this study highlights the need for spatially resolved data in predicting the ice sheet's future.
This article is included in the Encyclopedia of Geosciences
Takahito Mitsui, Peter Ditlevsen, Niklas Boers, and Michel Crucifix
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-39, https://doi.org/10.5194/esd-2024-39, 2024
Preprint under review for ESD
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The late Pleistocene glacial cycles are dominated by a 100-kyr periodicity, rather than other major astronomical periods like 19, 23, 41, or 400 kyr. Various models propose distinct mechanisms to explain this, but their diversity may obscure the key factor behind the 100-kyr periodicity. We propose a time-scale matching hypothesis, suggesting that the ice-sheet climate system responds to astronomical forcing at ~100 kyr because its intrinsic timescale is closer to 100 kyr than to other periods.
This article is included in the Encyclopedia of Geosciences
Clara Hummel, Niklas Boers, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-3567, https://doi.org/10.5194/egusphere-2024-3567, 2024
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We revisit early warning signals (EWS) for past abrupt climate changes known as Dansgaard-Oeschger (DO) events. Using advanced statistical methods, we find fewer significant EWS than previously reported. While some signals appear consistent across Greenland ice core records, they are not enough to identify the still unknown physical mechanisms behind DO events. This study highlights the complexity of predicting climate changes and urges caution in interpreting (paleo-)climate data.
This article is included in the Encyclopedia of Geosciences
Vasilis Dakos, Chris A. Boulton, Joshua E. Buxton, Jesse F. Abrams, Beatriz Arellano-Nava, David I. Armstrong McKay, Sebastian Bathiany, Lana Blaschke, Niklas Boers, Daniel Dylewsky, Carlos López-Martínez, Isobel Parry, Paul Ritchie, Bregje van der Bolt, Larissa van der Laan, Els Weinans, and Sonia Kéfi
Earth Syst. Dynam., 15, 1117–1135, https://doi.org/10.5194/esd-15-1117-2024, https://doi.org/10.5194/esd-15-1117-2024, 2024
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Tipping points are abrupt, rapid, and sometimes irreversible changes, and numerous approaches have been proposed to detect them in advance. Such approaches have been termed early warning signals and represent a set of methods for identifying changes in the underlying behaviour of a system across time or space that might indicate an approaching tipping point. Here, we review the literature to explore where, how, and which early warnings have been used in real-world case studies so far.
This article is included in the Encyclopedia of Geosciences
Maya Ben-Yami, Lana Blaschke, Sebastian Bathiany, and Niklas Boers
EGUsphere, https://doi.org/10.5194/egusphere-2024-1106, https://doi.org/10.5194/egusphere-2024-1106, 2024
Preprint archived
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Recent work has used observations to find statistical signs that the Atlantic Meridional Overturning Circulation (AMOC) may be approaching a collapse. We find that in complex climate models in which the AMOC does not collapse before 2100, the statistical signs that are present in the observations are not found in the 1850–2014 equivalent model time series. This indicates that the observed statistical signs are not prone to false positives.
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Takahito Mitsui and Niklas Boers
Clim. Past, 20, 683–699, https://doi.org/10.5194/cp-20-683-2024, https://doi.org/10.5194/cp-20-683-2024, 2024
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In general, the variance and short-lag autocorrelations of the fluctuations increase in a system approaching a critical transition. Using these indicators, we identify statistical precursor signals for the Dansgaard–Oeschger cooling events recorded in two climatic proxies of three Greenland ice core records. We then provide a dynamical systems theory that bridges the gap between observing statistical precursor signals and the physical precursor signs empirically known in paleoclimate research.
This article is included in the Encyclopedia of Geosciences
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
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This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
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Takahito Mitsui, Matteo Willeit, and Niklas Boers
Earth Syst. Dynam., 14, 1277–1294, https://doi.org/10.5194/esd-14-1277-2023, https://doi.org/10.5194/esd-14-1277-2023, 2023
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The glacial–interglacial cycles of the Quaternary exhibit 41 kyr periodicity before the Mid-Pleistocene Transition (MPT) around 1.2–0.8 Myr ago and ~100 kyr periodicity after that. The mechanism generating these periodicities remains elusive. Through an analysis of an Earth system model of intermediate complexity, CLIMBER-2, we show that the dominant periodicities of glacial cycles can be explained from the viewpoint of synchronization theory.
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Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
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Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
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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.
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Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
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Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
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Eirik Myrvoll-Nilsen, Keno Riechers, Martin Wibe Rypdal, and Niklas Boers
Clim. Past, 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, https://doi.org/10.5194/cp-18-1275-2022, 2022
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In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
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Keno Riechers, Takahito Mitsui, Niklas Boers, and Michael Ghil
Clim. Past, 18, 863–893, https://doi.org/10.5194/cp-18-863-2022, https://doi.org/10.5194/cp-18-863-2022, 2022
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Building upon Milancovic's theory of orbital forcing, this paper reviews the interplay between intrinsic variability and external forcing in the emergence of glacial interglacial cycles. It provides the reader with historical background information and with basic theoretical concepts used in recent paleoclimate research. Moreover, it presents new results which confirm the reduced stability of glacial-cycle dynamics after the mid-Pleistocene transition.
This article is included in the Encyclopedia of Geosciences
Keno Riechers and Niklas Boers
Clim. Past, 17, 1751–1775, https://doi.org/10.5194/cp-17-1751-2021, https://doi.org/10.5194/cp-17-1751-2021, 2021
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Greenland ice core data show that the last glacial cycle was punctuated by a series of abrupt climate shifts comprising significant warming over Greenland, retreat of North Atlantic sea ice, and atmospheric reorganization. Statistical analysis of multi-proxy records reveals no systematic lead or lag between the transitions of proxies that represent different climatic subsystems, and hence no evidence for a potential trigger of these so-called Dansgaard–Oeschger events can be found.
This article is included in the Encyclopedia of Geosciences
Denis-Didier Rousseau, Pierre Antoine, Niklas Boers, France Lagroix, Michael Ghil, Johanna Lomax, Markus Fuchs, Maxime Debret, Christine Hatté, Olivier Moine, Caroline Gauthier, Diana Jordanova, and Neli Jordanova
Clim. Past, 16, 713–727, https://doi.org/10.5194/cp-16-713-2020, https://doi.org/10.5194/cp-16-713-2020, 2020
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New investigations of European loess records from MIS 6 reveal the occurrence of paleosols and horizon showing slight pedogenesis similar to those from the last climatic cycle. These units are correlated with interstadials described in various marine, continental, and ice Northern Hemisphere records. Therefore, these MIS 6 interstadials can confidently be interpreted as DO-like events of the penultimate climate cycle.
This article is included in the Encyclopedia of Geosciences
Niklas Boers, Mickael D. Chekroun, Honghu Liu, Dmitri Kondrashov, Denis-Didier Rousseau, Anders Svensson, Matthias Bigler, and Michael Ghil
Earth Syst. Dynam., 8, 1171–1190, https://doi.org/10.5194/esd-8-1171-2017, https://doi.org/10.5194/esd-8-1171-2017, 2017
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We use a Bayesian approach for inferring inverse, stochastic–dynamic models from northern Greenland (NGRIP) oxygen and dust records of subdecadal resolution for the interval 59 to 22 ka b2k. Our model reproduces the statistical and dynamical characteristics of the records, including the Dansgaard–Oeschger variability, with no need for external forcing. The crucial ingredients are cubic drift terms, nonlinear coupling terms between the oxygen and dust time series, and non-Markovian contributions.
This article is included in the Encyclopedia of Geosciences
Denis-Didier Rousseau, Anders Svensson, Matthias Bigler, Adriana Sima, Jorgen Peder Steffensen, and Niklas Boers
Clim. Past, 13, 1181–1197, https://doi.org/10.5194/cp-13-1181-2017, https://doi.org/10.5194/cp-13-1181-2017, 2017
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We show that the analysis of δ18O and dust in the Greenland ice cores, and a critical study of their source variations, reconciles these records with those observed on the Eurasian continent. We demonstrate the link between European and Chinese loess sequences, dust records in Greenland, and variations in the North Atlantic sea ice extent. The sources of the emitted and transported dust material are variable and relate to different environments.
This article is included in the Encyclopedia of Geosciences
Niklas Boers, Bedartha Goswami, and Michael Ghil
Clim. Past, 13, 1169–1180, https://doi.org/10.5194/cp-13-1169-2017, https://doi.org/10.5194/cp-13-1169-2017, 2017
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We introduce a Bayesian framework to represent layer-counted proxy records as probability distributions on error-free time axes, accounting for both proxy and dating errors. Our method is applied to NGRIP δ18O data, revealing that the cumulative dating errors lead to substantial uncertainties for the older parts of the record. Applying our method to the widely used radiocarbon comparison curve derived from varved sediments of Lake Suigetsu provides the complete uncertainties of this curve.
This article is included in the Encyclopedia of Geosciences
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, and Miguel D. Mahecha
Earth Syst. Dynam., 8, 677–696, https://doi.org/10.5194/esd-8-677-2017, https://doi.org/10.5194/esd-8-677-2017, 2017
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Anomalies and extremes are often detected using univariate peak-over-threshold approaches in the geoscience community. The Earth system is highly multivariate. We compare eight multivariate anomaly detection algorithms and combinations of data preprocessing. We identify three anomaly detection algorithms that outperform univariate extreme event detection approaches. The workflows have the potential to reveal novelties in data. Remarks on their application to real Earth observations are provided.
This article is included in the Encyclopedia of Geosciences
Helge F. Goessling and Sebastian Bathiany
Earth Syst. Dynam., 7, 697–715, https://doi.org/10.5194/esd-7-697-2016, https://doi.org/10.5194/esd-7-697-2016, 2016
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Carbon dioxide, while warming the Earth's surface, cools the atmosphere beyond about 15 km (the middle atmosphere). This cooling is considered a fingerprint of anthropogenic global warming, yet the physical reason behind it remains prone to misconceptions. Here we use a simple radiation model to illustrate the physical essence of stratospheric cooling, and a complex climate model to quantify how strongly different mechanisms contribute.
This article is included in the Encyclopedia of Geosciences
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, https://doi.org/10.5194/tc-10-1631-2016, 2016
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We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
This article is included in the Encyclopedia of Geosciences
Mark S. Williamson, Sebastian Bathiany, and Timothy M. Lenton
Earth Syst. Dynam., 7, 313–326, https://doi.org/10.5194/esd-7-313-2016, https://doi.org/10.5194/esd-7-313-2016, 2016
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We find early warnings of abrupt changes in complex dynamical systems such as the climate where the usual early warning indicators do not work. In particular, these are systems that are periodically forced, for example by the annual cycle of solar insolation. We show these indicators are good theoretically in a general setting then apply them to a specific system, that of the Arctic sea ice, which has been conjectured to be close to such a tipping point. We do not find evidence of it.
This article is included in the Encyclopedia of Geosciences
S. Bathiany, M. Claussen, and K. Fraedrich
Earth Syst. Dynam., 4, 63–78, https://doi.org/10.5194/esd-4-63-2013, https://doi.org/10.5194/esd-4-63-2013, 2013
S. Bathiany, M. Claussen, and K. Fraedrich
Earth Syst. Dynam., 4, 79–93, https://doi.org/10.5194/esd-4-79-2013, https://doi.org/10.5194/esd-4-79-2013, 2013
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This article is included in the Encyclopedia of Geosciences
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This article is included in the Encyclopedia of Geosciences
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This article is included in the Encyclopedia of Geosciences
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
This article is included in the Encyclopedia of Geosciences
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Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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This article is included in the Encyclopedia of Geosciences
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
This article is included in the Encyclopedia of Geosciences
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
This article is included in the Encyclopedia of Geosciences
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
This article is included in the Encyclopedia of Geosciences
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
This article is included in the Encyclopedia of Geosciences
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
This article is included in the Encyclopedia of Geosciences
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
This article is included in the Encyclopedia of Geosciences
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
This article is included in the Encyclopedia of Geosciences
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
This article is included in the Encyclopedia of Geosciences
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
This article is included in the Encyclopedia of Geosciences
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
This article is included in the Encyclopedia of Geosciences
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
This article is included in the Encyclopedia of Geosciences
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
This article is included in the Encyclopedia of Geosciences
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
This article is included in the Encyclopedia of Geosciences
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
This article is included in the Encyclopedia of Geosciences
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
This article is included in the Encyclopedia of Geosciences
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
This article is included in the Encyclopedia of Geosciences
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
This article is included in the Encyclopedia of Geosciences
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
This article is included in the Encyclopedia of Geosciences
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
This article is included in the Encyclopedia of Geosciences
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
This article is included in the Encyclopedia of Geosciences
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
This article is included in the Encyclopedia of Geosciences
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
This article is included in the Encyclopedia of Geosciences
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
This article is included in the Encyclopedia of Geosciences
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
This article is included in the Encyclopedia of Geosciences
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
This article is included in the Encyclopedia of Geosciences
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
This article is included in the Encyclopedia of Geosciences
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
This article is included in the Encyclopedia of Geosciences
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
This article is included in the Encyclopedia of Geosciences
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
This article is included in the Encyclopedia of Geosciences
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
This article is included in the Encyclopedia of Geosciences
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
This article is included in the Encyclopedia of Geosciences
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
This article is included in the Encyclopedia of Geosciences
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
This article is included in the Encyclopedia of Geosciences
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
This article is included in the Encyclopedia of Geosciences
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
This article is included in the Encyclopedia of Geosciences
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
This article is included in the Encyclopedia of Geosciences
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
This article is included in the Encyclopedia of Geosciences
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
Short summary
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
This article is included in the Encyclopedia of Geosciences
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
This article is included in the Encyclopedia of Geosciences
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
This article is included in the Encyclopedia of Geosciences
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
This article is included in the Encyclopedia of Geosciences
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Revised manuscript accepted for GMD
Short summary
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Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
This article is included in the Encyclopedia of Geosciences
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
This article is included in the Encyclopedia of Geosciences
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2183, https://doi.org/10.5194/egusphere-2024-2183, 2024
Short summary
Short summary
PaleoSTeHM v1.0-rc is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
This article is included in the Encyclopedia of Geosciences
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
Short summary
Short summary
Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
This article is included in the Encyclopedia of Geosciences
Cited articles
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J.,
Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J.,
Storelvmo, T., Thorne, P., Trewin, B., Achuta Rao, K., Adhikary, B., Allan,
R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J., Cassou,
C., Cherchi, A., Collins, W., Collins, W., Connors, S., Corti, S., Cruz, F.,
Dentener, F., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes,
F., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E.,
Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J., Gillett, N.,
Goldfarb, L., Gorodetskaya, I., Gutierrez, J., Hamdi, R., Hawkins, E.,
Hewitt, H., Hope, P., Islam, A., Jones, C., Kaufman, D., Kopp, R., Kosaka,
Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock,
T., Meinshausen, M., Min, S.-K., Monteiro, P., Ngo-Duc, T., Otto, F., Pinto,
I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A., Ruiz, L., Sallée,
J.-B., Samset, B., Sathyendranath, S., Seneviratne, S., Sörensson, A.,
Szopa, S., Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R., von
Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA,
33–144, https://doi.org/10.1017/9781009157896.002, 2021. a
Berger, M., Aftosmis, M., and Muman, S.: Analysis of Slope Limiters on
Irregular Grids, 43rd AIAA Aerospace Sciences Meeting and Exhibit
10–13 January 2005, https://doi.org/10.2514/6.2005-490, 2005. a
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving Conservation
of Energy in Neural Network Emulators for Climate Modeling, ArXiv,
https://doi.org/10.48550/ARXIV.1906.06622, 2019. a
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.:
Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems,
Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a
Bezgin, D. A., Buhendwa, A. B., and Adams, N. A.: JAX-Fluids: A
fully-differentiable high-order computational fluid dynamics solver for
compressible two-phase flows, Comput. Phys. Commun., 282, 108527,
https://doi.org/10.1016/j.cpc.2022.108527, 2023. a
Blondel, M., Berthet, Q., Cuturi, M., Frostig, R., Hoyer, S., Llinares-López,
F., Pedregosa, F., and Vert, J.-P.: Efficient and Modular Implicit
Differentiation, ArXiv, https://doi.org/10.48550/ARXIV.2105.15183, 2021. a
Blonigan, P. J., Fernandez, P., Murman, S. M., Wang, Q., Rigas, G., and Magri,
L.: Toward a chaotic adjoint for LES, ArXiv, https://doi.org/10.48550/ARXIV.1702.06809, 2017. a
Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference
and Subgrid Parameterization, J. Adv. Model. Earth Sy.,
11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin,
D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and
Zhang, Q.: JAX: composable transformations of Python+NumPy programs, GitHub [code],
http://github.com/google/jax (last access: 30 May 2023), 2018. a, b, c, d
Campagne, J.-E., Lanusse, F., Zuntz, J., Boucaud, A., Casas, S., Karamanis, M.,
Kirkby, D., Lanzieri, D., Li, Y., and Peel, A.: JAX-COSMO: An End-to-End
Differentiable and GPU Accelerated Cosmology Library, 6, Cosmology and Nongalactic Astrophysics, https://doi.org/10.21105/astro.2302.05163, 2023. a
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D.: Neural
Ordinary Differential Equations, ArXiv, https://doi.org/10.48550/ARXIV.1806.07366, 2018. a
Chizat, L., Oyallon, E., and Bach, F.: On Lazy Training in Differentiable
Programming, in: Advances in Neural Information Processing Systems, edited by:
Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., Curran Associates, vol. 32,
Inc.,
https://proceedings.neurips.cc/paper/2019/file/ae614c557843b1df326cb29c57225459-Paper.pdf (last access: 30 May 2023),
2019. a
Dauvergne, B. and Hascoët, L.: The Data-Flow Equations of Checkpointing in
Reverse Automatic Differentiation, in: Computational Science – ICCS 2006,
edited by: Alexandrov, V. N., van Albada, G. D., Sloot, P. M. A., and
Dongarra, J., 566–573, Springer Berlin Heidelberg, Berlin, Heidelberg,
2006. a
de Bézenac, E., Pajot, A., and Gallinari, P.: Deep learning for physical
processes: incorporating prior scientific knowledge, J. Statist.
Mech. Theory and Experiment, 2019, 124009,
https://doi.org/10.1088/1742-5468/ab3195, 2019. a
Duane, S., Kennedy, A., Pendleton, B. J., and Roweth, D.: Hybrid Monte Carlo,
Phys. Lett. B, 195, 216–222,
https://doi.org/10.1016/0370-2693(87)91197-X, 1987. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Farrell, P. E., Ham, D. A., Funke, S. W., and Rognes, M. E.: Automated
Derivation of the Adjoint of High-Level Transient Finite Element Programs,
SIAM J. Sci. Comput., 35, C369–C393,
https://doi.org/10.1137/120873558, 2013. a, b, c, d
Ge, H., Xu, K., and Ghahramani, Z.: Turing: A Language for Flexible
Probabilistic Inference, in: Proceedings of the Twenty-First International
Conference on Artificial Intelligence and Statistics, edited by: Storkey, A.
and Perez-Cruz, F., Proc. Mach. Learn.
Res., 84, 1682–1690,
https://proceedings.mlr.press/v84/ge18b.html (last access: 30 May 2023), 2018. a
Gelbrecht, M., Boers, N., and Kurths, J.: Neural partial differential equations
for chaotic systems, New J. Phys., 23, 043005,
https://doi.org/10.1088/1367-2630/abeb90, 2021. a
Giering, R. and Kaminski, T.: Recipes for Adjoint Code Construction, ACM Trans.
Math. Softw., 24, 437–474, https://doi.org/10.1145/293686.293695, 1998. a, b
Griewank, A. and Walther, A.: Algorithm 799: Revolve: An Implementation of
Checkpointing for the Reverse or Adjoint Mode of Computational
Differentiation, ACM Trans. Math. Softw., 26, 19–45,
https://doi.org/10.1145/347837.347846, 2000. a
Guillaumin, A. P. and Zanna, L.: Stochastic-Deep Learning Parameterization of
Ocean Momentum Forcing, J. Adv. Model. Earth Sy., 13,
e2021MS002534, https://doi.org/10.1029/2021MS002534, 2021. a
Gutiérrez, M. S. and Lucarini, V.: Response and Sensitivity Using Markov
Chains, J. Stat. Phys., 179, 1572–1593,
https://doi.org/10.1007/s10955-020-02504-4, 2020. a
Häfner, D., Jacobsen, R. L., Eden, C., Kristensen, M. R. B., Jochum, M., Nuterman, R., and Vinter, B.: Veros v0.1 – a fast and versatile ocean simulator in pure Python, Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, 2018. a
Häfner, D., Nuterman, R., and Jochum, M.: Fast, Cheap, and
Turbulent—Global Ocean Modeling With GPU Acceleration in Python, J.
Adv. Model. Earth Sy., 13, e2021MS002717,
https://doi.org/10.1029/2021MS002717, 2021. a, b
Hascoët, L. and Pascual, V.: The Tapenade Automatic Differentiation tool:
Principles, Model, and Specification, ACM T. Math.
Softw., 39, 20:1–20:43,
https://doi.org/10.1145/2450153.2450158, 2013. a, b
Hatfield, S., Chantry, M., Dueben, P., Lopez, P., Geer, A., and Palmer, T.:
Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural
Networks, J. Adv. Model. Earth Sy., 13, e2021MS002521,
https://doi.org/10.1029/2021MS002521, 2021. a
Holl, P., Thuerey, N., and Koltun, V.: Learning to Control PDEs with Differentiable Physics, International Conference on Learning Representations, https://openreview.net/forum?id=HyeSin4FPB (last access: 31 May 2023), 2020. a
Hopcroft, P. O. and Valdes, P. J.: Paleoclimate-conditioning reveals a North
Africa land–atmosphere tipping point, P. Natl.
Acad. Sci. USA, 118, e2108783118, https://doi.org/10.1073/pnas.2108783118, 2021. a
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q.,
Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L.,
Watanabe, M., and Williamson, D.: The Art and Science of Climate Model
Tuning, B. Am. Meteorol. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. 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. Int.,
3, 667–674, https://doi.org/10.1038/s42256-021-00374-3, 2021. a, b
Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., and Aschwanden, A.:
Deep learning speeds up ice flow modelling by several orders of magnitude,
J. Glaciol., 68, 651–664, https://doi.org/10.1017/jog.2021.120, 2022. a
Kalmikov, A. G. and Heimbach, P.: A Hessian-Based Method for Uncertainty
Quantification in Global Ocean State Estimation, SIAM J. Sci.
Comput., 36, S267–S295, https://doi.org/10.1137/130925311, 2014. a
Kaminski, T., Knorr, W., Schürmann, G., Scholze, M., Rayner, P. J., Zaehle,
S., Blessing, S., Dorigo, W., Gayler, V., Giering, R., Gobron, N., Grant,
J. P., Heimann, M., Hooker-Stroud, A., Houweling, S., Kato, T., Kattge, J.,
Kelley, D., Kemp, S., Koffi, E. N., Köstler, C., Mathieu, P.-P., Pinty,
B., Reick, C. H., Rödenbeck, C., Schnur, R., Scipal, K., Sebald, C.,
Stacke, T., van Scheltinga, A. T., Vossbeck, M., Widmann, H., and Ziehn, T.:
The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and
challenges, J. Geophys. Res.-Biogeo., 118, 1414–1426,
https://doi.org/10.1002/jgrg.20118, 2013. a
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models,
J. Ro. Stat. Soc. B,
63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a
Kim, S., Ji, W., Deng, S., Ma, Y., and Rackauckas, C.: Stiff neural ordinary
differential equations, Chaos, 31, 093122, https://doi.org/10.1063/5.0060697, 2021. a
Klöwer, M., Hatfield, S., Croci, M., Düben, P. D., and Palmer, T. N.:
Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX
by squeezing ShallowWaters.jl into Float16, J. Adv. Model.
Earth Sy., 14, e2021MS002684, https://doi.org/10.1029/2021MS002684, 2022. a
Logg, A., Mardal, K.-A., and Wells, G. (Eds.): Automated Solution of Differential Equations by the Finite Element Method, vol. 84, Springer
Science & Business Media, https://doi.org/10.1007/978-3-642-23099-8, 2012. a, b
Loose, N. and Heimbach, P.: Leveraging Uncertainty Quantification to Design
Ocean Climate Observing Systems, J. Adv. Model. Earth Sy., 13, e2020MS002386, https://doi.org/10.1029/2020MS002386, 2021. a, b
Lucarini, V., Ragone, F., and Lunkeit, F.: Predicting Climate Change Using
Response Theory: Global Averages and Spatial Patterns, J. Stat.
Phys., 166, 1036–1064, https://doi.org/10.1007/s10955-016-1506-z, 2017. a
Lyu, G., Köhl, A., Matei, I., and Stammer, D.: Adjoint-Based Climate Model
Tuning: Application to the Planet Simulator, J. Adv. Model.
Earth Sy., 10, 207–222, https://doi.org/10.1002/2017MS001194,
2018. a, b, c
Marotzke, J., Giering, R., Zhang, K. Q., Stammer, D., Hill, C., and Lee, T.:
Construction of the adjoint MIT ocean general circulation model and
application to Atlantic heat transport sensitivity, J. Geophys. Res.-Oceans, 104, 29529–29547,
https://doi.org/10.1029/1999JC900236, 1999. a
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M.,
Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D.,
Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a global
model, J. Adv. Model. Earth Sy., 4,
https://doi.org/10.1029/2012MS000154, 2012. a, b, c, d, e
Metz, L., Freeman, C. D., Schoenholz, S. S., and Kachman, T.: Gradients are Not
All You Need, ArXiv, https://doi.org/10.48550/ARXIV.2111.05803, 2021. a, b
Michalak, K. and Ollivier-Gooch, C.: Differentiability of slope limiters on
unstructured grids, in: Proceedings of fourteenth annual conference of the
computational fluid dynamics society of Canada, https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Differentiability+of+Slope+Limiters+on+Unstructured+Grids&btnG= (last access: 31 May 2023), 2006. a
Mitusch, S. K., Funke, S. W., and Dokken, J. S.: dolfin-adjoint 2018.1:
automated adjoints for FEniCS and Firedrake, J. Open Source Softw.,
4, 1292, https://doi.org/10.21105/joss.01292, 2019. a, b, c, d
Moses, W. and Churavy, V.: Instead of Rewriting Foreign Code for Machine
Learning, Automatically Synthesize Fast Gradients, in: Advances in Neural
Information Processing Systems, edited by: Larochelle, H., Ranzato, M.,
Hadsell, R., Balcan, M. F., and Lin, H., 33, 12472–12485,
Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf (last access: 30 May 2023),
2020. a, b, c, d, e
Ni, A. and Wang, Q.: Sensitivity analysis on chaotic dynamical systems by
Non-Intrusive Least Squares Shadowing (NILSS), J. Comput.
Phys., 347, 56–77, https://doi.org/10.1016/j.jcp.2017.06.033, 2017. a
OpenAI: ChatGPT: Optimizing Language Models for Dialogue,
https://openai.com/blog/chatgpt/ (last access: 30 May 2023), 2022. a
Palmer, T. and Stevens, B.: The scientific challenge of understanding and
estimating climate change, P. Natl. Acad. Sci. USA,
116, 24390–24395, https://doi.org/10.1073/pnas.1906691116, 2019. a
Petra, N., Martin, J., Stadler, G., and Ghattas, O.: A Computational Framework
for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic
Newton MCMC with Application to Ice Sheet Flow Inverse Problems, SIAM J. Sci. Comput., 36, A1525–A1555, https://doi.org/10.1137/130934805, 2014. a
Rabier, F., Thépaut, J.-N., and Courtier, P.: Extended assimilation and
forecast experiments with a four-dimensional variational assimilation system,
Q. J. Roy. Meteor. Soc., 124, 1861–1887,
https://doi.org/10.1002/qj.49712455005, 1998. a
Rackauckas, C., Ma, Y., Martensen, J., Warner, C., Zubov, K., Supekar, R.,
Skinner, D., Ramadhan, A., and Edelman, A.: Universal Differential Equations
for Scientific Machine Learning, ArXiv, https://doi.org/10.48550/ARXIV.2001.04385, 2020. a, b, c
Raissi, M., Perdikaris, P., and Karniadakis, G.: Physics-informed neural
networks: A deep learning framework for solving forward and inverse problems
involving nonlinear partial differential equations, J. Comput.
Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045,
2019. a
Rasp, S.: Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0), Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, 2020. a, b
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci. USA,
115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a, b
Rathgeber, F., Ham, D. A., Mitchell, L., Lange, M., Luporini, F., Mcrae, A.
T. T., Bercea, G.-T., Markall, G. R., and Kelly, P. H. J.: Firedrake, ACM
T. Math. Softw., 43, 1–27, https://doi.org/10.1145/2998441,
2016. a, b
Rayner, P. J., Scholze, M., Knorr, W., Kaminski, T., Giering, R., and Widmann,
H.: Two decades of terrestrial carbon fluxes from a carbon cycle data
assimilation system (CCDAS), Global Biogeochem. Cycles, 19, GB2026,
https://doi.org/10.1029/2004GB002254, 2005. a
Ruelle, D.: General linear response formula in statistical mechanics, and the
fluctuation-dissipation theorem far from equilibrium, Phys. Lett. A, 245,
220–224, https://doi.org/10.1016/S0375-9601(98)00419-8, 1998. a
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling
2.0: A Blueprint for Models That Learn From Observations and Targeted
High-Resolution Simulations, Geophys. Res. Lett., 44,
12396–12417, https://doi.org/10.1002/2017GL076101, 2017. a
Schoenholz, S. and Cubuk, E. D.: JAX MD: A Framework for Differentiable
Physics, in: Advances in Neural Information Processing Systems, edited by:
Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., 33,
11428–11441, Curran Associates, Inc.,
https://proceedings.neurips.cc/paper_files/paper/2020/file/83d3d4b6c9579515e1679aca8cbc8033-Paper.pdf (last access: 30 May 2023),
2020. a
Souhar, O., Faure, J. B., and Paquier, A.: Automatic sensitivity analysis of a
finite volume model for two-dimensional shallow water flows, Environ.
Fluid Mech., 7, 303–315, https://doi.org/10.1007/s10652-007-9028-5, 2007. a
Stammer, D., Wunsch, C., Giering, R., Eckert, C., Heimbach, P., Marotzke, J.,
Adcroft, A., Hill, C. N., and Marshall, J.: Global ocean circulation during
1992–1997, estimated from ocean observations and a general circulation
model, J. Geophys. Res.-Oceans, 107, 1-1–1-27,
https://doi.org/10.1029/2001JC000888, 2002. a
Thacker, W. C.: The role of the Hessian matrix in fitting models to
measurements, J. Geophys. Res.-Oceans, 94, 6177–6196,
https://doi.org/10.1029/JC094iC05p06177, 1989.
a
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12,
5988, https://doi.org/10.1038/s41467-021-26107-z, 2021. a
Valdes, P.: Built for stability, Nat. Geosci., 4, 414–416,
https://doi.org/10.1038/ngeo1200, 2011. a
Vettoretti, G., Ditlevsen, P., Jochum, M., and Rasmussen, S. O.: Atmospheric
CO2 control of spontaneous millennial-scale ice age climate oscillations,
Nat. Geosci., 15, 300–306, https://doi.org/10.1038/s41561-022-00920-7, 2022. a
Villa, U., Petra, N., and Ghattas, O.: HIPPYlib: An Extensible Software
Framework for Large-Scale Inverse Problems Governed by PDEs: Part I:
Deterministic Inversion and Linearized Bayesian Inference, ACM Trans. Math.
Softw., 47, 1–34, https://doi.org/10.1145/3428447, 2021. a
Volodina, V. and Challenor, P.: The importance of uncertainty quantification in
model reproducibility, Philosophical Transactions of the Royal Society A:
Mathematical, Phys. Eng. Sci., 379, 20200071,
https://doi.org/10.1098/rsta.2020.0071, 2021. a
Wang, P., Jiang, J., Lin, P., Ding, M., Wei, J., Zhang, F., Zhao, L., Li, Y., Yu, Z., Zheng, W., Yu, Y., Chi, X., and Liu, H.: The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application , Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, 2021. a
Wang, Q., Hu, R., and Blonigan, P.: Least Squares Shadowing sensitivity
analysis of chaotic limit cycle oscillations, J. Comput.
Phys., 267, 210–224, https://doi.org/10.1016/j.jcp.2014.03.002, 2014. a, b
Williamson, D. B., Blaker, A. T., and Sinha, B.: Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geosci. Model Dev., 10, 1789–1816, https://doi.org/10.5194/gmd-10-1789-2017, 2017. a
Yuval, J., O'Gorman, P. A., and Hill, C. N.: Use of Neural Networks for Stable,
Accurate and Physically Consistent Parameterization of Subgrid Atmospheric
Processes With Good Performance at Reduced Precision, Geophys. Res. Lett., 48, e2020GL091363, https://doi.org/10.1029/2020GL091363, 2021. a
Zanna, L. and Bolton, T.: Deep Learning of Unresolved Turbulent Ocean Processes
in Climate Models, John Wiley & Sons, Ltd, chap. 20, 298–306,
https://doi.org/10.1002/9781119646181.ch20, 2021. a
Executive editor
This paper reviews the technique of differentiable programming in Earth System Modeling.
Short summary
Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Differential programming is a technique that enables the automatic computation of derivatives of...
