Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4581-2023
© 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-16-4581-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
11 Aug 2023
Model experiment description paper |
| 11 Aug 2023
| 11 Aug 2023
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
Laura Muntjewerf
CORRESPONDING AUTHOR
Department of R&D Weather and Climate Models, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Richard Bintanja
Department of R&D Weather and Climate Models, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Groningen, the Netherlands
Thomas Reerink
Department of R&D Weather and Climate Models, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Karin van der Wiel
Department of R&D Weather and Climate Models, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Related authors
Michele Petrini, Meike D. W. Scherrenberg, Laura Muntjewerf, Miren Vizcaino, Raymond Sellevold, Gunter R. Leguy, William H. Lipscomb, and Heiko Goelzer
The Cryosphere, 19, 63–81, https://doi.org/10.5194/tc-19-63-2025, https://doi.org/10.5194/tc-19-63-2025, 2025
Short summary
Short summary
Anthropogenic warming is causing accelerated Greenland ice sheet melt. Here, we use a computer model to understand how prolonged warming and ice melt could threaten ice sheet stability. We find a threshold beyond which Greenland will lose more than 80 % of its ice over several thousand years, due to the interaction of surface and solid-Earth processes. Nearly complete Greenland ice sheet melt occurs when the ice margin disconnects from a region of high elevation in western Greenland.
Simone Tilmes, Douglas G. MacMartin, Jan T. M. Lenaerts, Leo van Kampenhout, Laura Muntjewerf, Lili Xia, Cheryl S. Harrison, Kristen M. Krumhardt, Michael J. Mills, Ben Kravitz, and Alan Robock
Earth Syst. Dynam., 11, 579–601, https://doi.org/10.5194/esd-11-579-2020, https://doi.org/10.5194/esd-11-579-2020, 2020
Short summary
Short summary
This paper introduces new geoengineering model experiments as part of a larger model intercomparison effort, using reflective particles to block some of the incoming solar radiation to reach surface temperature targets. Outcomes of these applications are contrasted based on a high greenhouse gas emission pathway and a pathway with strong mitigation and negative emissions after 2040. We compare quantities that matter for societal and ecosystem impacts between the different scenarios.
René M. van Westen, Karin van der Wiel, Swinda K. J. Falkena, and Frank Selten
EGUsphere, https://doi.org/10.5194/egusphere-2025-1440, https://doi.org/10.5194/egusphere-2025-1440, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) moderates the European climate. The AMOC is a tipping element and may collapse to a substantially weaker state under climate change. Such an event induces global and regional climate shifts. The European hydroclimate becomes drier under an AMOC collapse, this response is not considered in the 'standard' hydroclimate projections. Our results indicate a considerable influence of the AMOC on the European hydroclimate.
Michele Petrini, Meike D. W. Scherrenberg, Laura Muntjewerf, Miren Vizcaino, Raymond Sellevold, Gunter R. Leguy, William H. Lipscomb, and Heiko Goelzer
The Cryosphere, 19, 63–81, https://doi.org/10.5194/tc-19-63-2025, https://doi.org/10.5194/tc-19-63-2025, 2025
Short summary
Short summary
Anthropogenic warming is causing accelerated Greenland ice sheet melt. Here, we use a computer model to understand how prolonged warming and ice melt could threaten ice sheet stability. We find a threshold beyond which Greenland will lose more than 80 % of its ice over several thousand years, due to the interaction of surface and solid-Earth processes. Nearly complete Greenland ice sheet melt occurs when the ice margin disconnects from a region of high elevation in western Greenland.
Henrique M. D. Goulart, Irene Benito Lazaro, Linda van Garderen, Karin van der Wiel, Dewi Le Bars, Elco Koks, and Bart van den Hurk
Nat. Hazards Earth Syst. Sci., 24, 29–45, https://doi.org/10.5194/nhess-24-29-2024, https://doi.org/10.5194/nhess-24-29-2024, 2024
Short summary
Short summary
We explore how Hurricane Sandy (2012) could flood New York City under different scenarios, including climate change and internal variability. We find that sea level rise can quadruple coastal flood volumes, while changes in Sandy's landfall location can double flood volumes. Our results show the need for diverse scenarios that include climate change and internal variability and for integrating climate information into a modelling framework, offering insights for high-impact event assessments.
Raúl R. Cordero, Sarah Feron, Alessandro Damiani, Pedro J. Llanillo, Jorge Carrasco, Alia L. Khan, Richard Bintanja, Zutao Ouyang, and Gino Casassa
The Cryosphere, 17, 4995–5006, https://doi.org/10.5194/tc-17-4995-2023, https://doi.org/10.5194/tc-17-4995-2023, 2023
Short summary
Short summary
We investigate the response of Antarctic sea ice to year-to-year changes in the tropospheric–stratospheric dynamics. Our findings suggest that, by affecting the tropospheric westerlies, the strength of the stratospheric polar vortex has played a major role in recent record-breaking anomalies in Antarctic sea ice.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Fei Luo, Frank Selten, Kathrin Wehrli, Kai Kornhuber, Philippe Le Sager, Wilhelm May, Thomas Reerink, Sonia I. Seneviratne, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, and Dim Coumou
Weather Clim. Dynam., 3, 905–935, https://doi.org/10.5194/wcd-3-905-2022, https://doi.org/10.5194/wcd-3-905-2022, 2022
Short summary
Short summary
Recent studies have identified the weather systems in observational data, where wave patterns with high-magnitude values that circle around the whole globe in either wavenumber 5 or wavenumber 7 are responsible for the extreme events. In conclusion, we find that the climate models are able to reproduce the large-scale atmospheric circulation patterns as well as their associated surface variables such as temperature, precipitation, and sea level pressure.
Constantijn J. Berends, Heiko Goelzer, Thomas J. Reerink, Lennert B. Stap, and Roderik S. W. van de Wal
Geosci. Model Dev., 15, 5667–5688, https://doi.org/10.5194/gmd-15-5667-2022, https://doi.org/10.5194/gmd-15-5667-2022, 2022
Short summary
Short summary
The rate at which marine ice sheets such as the West Antarctic ice sheet will retreat in a warming climate and ocean is still uncertain. Numerical ice-sheet models, which solve the physical equations that describe the way glaciers and ice sheets deform and flow, have been substantially improved in recent years. Here we present the results of several years of work on IMAU-ICE, an ice-sheet model of intermediate complexity, which can be used to study ice sheets of both the past and the future.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
Short summary
Short summary
The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler
Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, https://doi.org/10.5194/bg-19-1979-2022, 2022
Short summary
Short summary
Droughts and heatwaves are expected to occur more often in the future, but their effects on land vegetation and the carbon cycle are poorly understood. We use six climate scenarios with differing extreme occurrences and a vegetation model to analyse these effects. Tree coverage and associated plant productivity increase under a climate with no extremes. Frequent co-occurring droughts and heatwaves decrease plant productivity more than the combined effects of single droughts or heatwaves.
Henrique M. D. Goulart, Karin van der Wiel, Christian Folberth, Juraj Balkovic, and Bart van den Hurk
Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, https://doi.org/10.5194/esd-12-1503-2021, 2021
Short summary
Short summary
Agriculture is sensitive to weather conditions and to climate change. We identify the weather conditions linked to soybean failures and explore changes related to climate change. Additionally, we build future versions of a historical extreme season under future climate scenarios. Results show that soybean failures are likely to increase with climate change. Future events with similar physical conditions to the extreme season are not expected to increase, but events with similar impacts are.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
Short summary
Short summary
This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Gijs van Kempen, Karin van der Wiel, and Lieke Anna Melsen
Nat. Hazards Earth Syst. Sci., 21, 961–976, https://doi.org/10.5194/nhess-21-961-2021, https://doi.org/10.5194/nhess-21-961-2021, 2021
Short summary
Short summary
In this study, we combine climate model results with a hydrological model to investigate uncertainties in flood and drought risk. With the climate model, 2000 years of
current climatewas created. The hydrological model consisted of several building blocks that we could adapt. In this way, we could investigate the effect of these hydrological building blocks on high- and low-flow risk in four different climate zones with return periods of up to 500 years.
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
Short summary
Short summary
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.
Sarah F. Kew, Sjoukje Y. Philip, Mathias Hauser, Mike Hobbins, Niko Wanders, Geert Jan van Oldenborgh, Karin van der Wiel, Ted I. E. Veldkamp, Joyce Kimutai, Chris Funk, and Friederike E. L. Otto
Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, https://doi.org/10.5194/esd-12-17-2021, 2021
Short summary
Short summary
Motivated by the possible influence of rising temperatures, this study synthesises results from observations and climate models to explore trends (1900–2018) in eastern African (EA) drought measures. However, no discernible trends are found in annual soil moisture or precipitation. Positive trends in potential evaporation indicate that for irrigated regions more water is now required to counteract increased evaporation. Precipitation deficit is, however, the most useful indicator of EA drought.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Simone Tilmes, Douglas G. MacMartin, Jan T. M. Lenaerts, Leo van Kampenhout, Laura Muntjewerf, Lili Xia, Cheryl S. Harrison, Kristen M. Krumhardt, Michael J. Mills, Ben Kravitz, and Alan Robock
Earth Syst. Dynam., 11, 579–601, https://doi.org/10.5194/esd-11-579-2020, https://doi.org/10.5194/esd-11-579-2020, 2020
Short summary
Short summary
This paper introduces new geoengineering model experiments as part of a larger model intercomparison effort, using reflective particles to block some of the incoming solar radiation to reach surface temperature targets. Outcomes of these applications are contrasted based on a high greenhouse gas emission pathway and a pathway with strong mitigation and negative emissions after 2040. We compare quantities that matter for societal and ecosystem impacts between the different scenarios.
Hélène Seroussi, Sophie Nowicki, Erika Simon, Ayako Abe-Ouchi, Torsten Albrecht, Julien Brondex, Stephen Cornford, Christophe Dumas, Fabien Gillet-Chaulet, Heiko Goelzer, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Thomas Kleiner, Eric Larour, Gunter Leguy, William H. Lipscomb, Daniel Lowry, Matthias Mengel, Mathieu Morlighem, Frank Pattyn, Anthony J. Payne, David Pollard, Stephen F. Price, Aurélien Quiquet, Thomas J. Reerink, Ronja Reese, Christian B. Rodehacke, Nicole-Jeanne Schlegel, Andrew Shepherd, Sainan Sun, Johannes Sutter, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, and Tong Zhang
The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, https://doi.org/10.5194/tc-13-1441-2019, 2019
Short summary
Short summary
We compare a wide range of Antarctic ice sheet simulations with varying initialization techniques and model parameters to understand the role they play on the projected evolution of this ice sheet under simple scenarios. Results are improved compared to previous assessments and show that continued improvements in the representation of the floating ice around Antarctica are critical to reduce the uncertainty in the future ice sheet contribution to sea level rise.
Sjoukje Philip, Sarah Sparrow, Sarah F. Kew, Karin van der Wiel, Niko Wanders, Roop Singh, Ahmadul Hassan, Khaled Mohammed, Hammad Javid, Karsten Haustein, Friederike E. L. Otto, Feyera Hirpa, Ruksana H. Rimi, A. K. M. Saiful Islam, David C. H. Wallom, and Geert Jan van Oldenborgh
Hydrol. Earth Syst. Sci., 23, 1409–1429, https://doi.org/10.5194/hess-23-1409-2019, https://doi.org/10.5194/hess-23-1409-2019, 2019
Short summary
Short summary
In August 2017 Bangladesh faced one of its worst river flooding events in recent history. For the large Brahmaputra basin, using precipitation alone as a proxy for flooding might not be appropriate. In this paper we explicitly test this assumption by performing an attribution of both precipitation and discharge as a flooding-related measure to climate change. We find the change in risk to be of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach.
Sarah L. Bradley, Thomas J. Reerink, Roderik S. W. van de Wal, and Michiel M. Helsen
Clim. Past, 14, 619–635, https://doi.org/10.5194/cp-14-619-2018, https://doi.org/10.5194/cp-14-619-2018, 2018
Renske C. de Winter, Thomas J. Reerink, Aimée B. A. Slangen, Hylke de Vries, Tamsin Edwards, and Roderik S. W. van de Wal
Nat. Hazards Earth Syst. Sci., 17, 2125–2141, https://doi.org/10.5194/nhess-17-2125-2017, https://doi.org/10.5194/nhess-17-2125-2017, 2017
Short summary
Short summary
This paper provides a full range of possible future sea levels on a regional scale, since it includes extreme, but possible, contributions to sea level change from dynamical mass loss from the Greenland and Antarctica ice sheets. In contrast to the symmetric distribution used in the IPCC report, it is found that an asymmetric distribution toward high sea level change values locally can increase the mean sea level by 1.8 m this century.
Lennert B. Stap, Roderik S. W. van de Wal, Bas de Boer, Richard Bintanja, and Lucas J. Lourens
Clim. Past, 13, 1243–1257, https://doi.org/10.5194/cp-13-1243-2017, https://doi.org/10.5194/cp-13-1243-2017, 2017
Short summary
Short summary
We show the results of transient simulations with a coupled climate–ice sheet model over the past 38 million years. The CO2 forcing of the model is inversely obtained from a benthic δ18O stack. These simulations enable us to study the influence of ice sheet variability on climate change on long timescales. We find that ice sheet–climate interaction strongly enhances Earth system sensitivity and polar amplification.
Michiel M. Helsen, Roderik S. W. van de Wal, Thomas J. Reerink, Richard Bintanja, Marianne S. Madsen, Shuting Yang, Qiang Li, and Qiong Zhang
The Cryosphere, 11, 1949–1965, https://doi.org/10.5194/tc-11-1949-2017, https://doi.org/10.5194/tc-11-1949-2017, 2017
Short summary
Short summary
Ice sheets reflect most incoming solar radiation back into space due to their high reflectivity (albedo). The albedo of ice sheets changes as a function of, for example, liquid water content and ageing of snow. In this study we have improved the description of albedo over the Greenland ice sheet in a global climate model. This is an important step, which also improves estimates of the annual ice mass gain or loss over the ice sheet using this global climate model.
Karin van der Wiel, Sarah B. Kapnick, Geert Jan van Oldenborgh, Kirien Whan, Sjoukje Philip, Gabriel A. Vecchi, Roop K. Singh, Julie Arrighi, and Heidi Cullen
Hydrol. Earth Syst. Sci., 21, 897–921, https://doi.org/10.5194/hess-21-897-2017, https://doi.org/10.5194/hess-21-897-2017, 2017
Short summary
Short summary
During August 2016, heavy precipitation led to devastating floods in south Louisiana, USA. Here, we analyze the climatological statistics of the precipitation event, as defined by its 3-day total over 12–14 August. Using observational data and high-resolution global coupled model experiments, we find for a comparable event on the central US Gulf Coast an average return period of about 30 years and the odds being increased by at least 1.4 since 1900 due to anthropogenic climate change.
Thomas J. Reerink, Willem Jan van de Berg, and Roderik S. W. van de Wal
Geosci. Model Dev., 9, 4111–4132, https://doi.org/10.5194/gmd-9-4111-2016, https://doi.org/10.5194/gmd-9-4111-2016, 2016
Short summary
Short summary
Ice sheets are part of the climate system and interact with the atmosphere and the ocean. OBLIMAP is a powerful tool to map climate fields between GCMs and ISMs (ice sheet models), which run on grids that differ in curvature, resolution and extent. OBLIMAP uses optimal aligned oblique projections, which minimize area distortions. OBLIMAP 2.0 allows for high-frequency embedded coupling and masked mapping. A fast search strategy realizes a huge performance gain and enables high-resolution mapping.
L. B. Stap, R. S. W. van de Wal, B. de Boer, R. Bintanja, and L. J. Lourens
Clim. Past, 10, 2135–2152, https://doi.org/10.5194/cp-10-2135-2014, https://doi.org/10.5194/cp-10-2135-2014, 2014
B. W. Goodfellow, A. P. Stroeven, D. Fabel, O. Fredin, M.-H. Derron, R. Bintanja, and M. W. Caffee
Earth Surf. Dynam., 2, 383–401, https://doi.org/10.5194/esurf-2-383-2014, https://doi.org/10.5194/esurf-2-383-2014, 2014
R. S. W. van de Wal, B. de Boer, L. J. Lourens, P. Köhler, and R. Bintanja
Clim. Past, 7, 1459–1469, https://doi.org/10.5194/cp-7-1459-2011, https://doi.org/10.5194/cp-7-1459-2011, 2011
Related subject area
Climate and Earth system modeling
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
FACA v1 – Fully Automated Co-Alignment of UAV Point Clouds
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 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.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here 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. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new 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. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. 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.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is 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.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
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
Short summary
Short summary
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.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
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
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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).
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
Short summary
Short summary
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.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Short summary
Short summary
To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Nick Schüßler, Jewgenij Torizin, Claudia Gunkel, Karsten Schütze, Lars Tiepolt, Dirk Kuhn, Michael Fuchs, and Steffen Prüfer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-209, https://doi.org/10.5194/gmd-2024-209, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
FACA – Fully Automated Co-Alignment – is a tool designed to generate co-aligned point clouds. We aim to accelerate the application of the co-alignment method and achieve fast results with evolving temporal data and minimal site-specific preparation. FACA offers multiple ways to interact with the workflow, enabling new users to quickly generate initial results through the custom interface, as well as integration into larger automated workflows via the command line. Test datasets are provided.
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
Short summary
Short summary
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.
Cited articles
Aven, T. and Renn, O.: An Evaluation of the Treatment of Risk and
Uncertainties in the IPCC Reports on Climate Change, Risk Analysis, 35, 701–712,
https://doi.org/10.1111/risa.12298, 2015. a
Balsamo, G., Viterbo, P., Beijaars, A., van den Hurk, B., Hirschi, M., Betts,
A. K., and Scipal, K.: A revised hydrology for the ECMWF model: Verification
from field site to terrestrial water storage and impact in the integrated
forecast system, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009. a
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P.,
Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers,
D., Soci, C., Villaume, S., Bidlot, J. R., Haimberger, L., Woollen, J.,
Buontempo, C., and Thépaut, J. N.: The ERA5 global reanalysis:
Preliminary extension to 1950, Q. J. Roy. Meteor.
Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021. a
Ben-Ari, T., Boé, J., Ciais, P., Lecerf, R., van der Velde, M., and
Makowski, D.: Causes and implications of the unforeseen 2016 extreme yield
loss in the breadbasket of France, Nat. Commun., 9, 1627,
https://doi.org/10.1038/s41467-018-04087-x, 2018. a, b
Bevacqua, E., De Michele, C., Manning, C., Couasnon, A., Ribeiro, A. F., Ramos,
A. M., Vignotto, E., Bastos, A., Blesić, S., Durante, F., Hillier, J.,
Oliveira, S. C., Pinto, J. G., Ragno, E., Rivoire, P., Saunders, K., van der
Wiel, K., Wu, W., Zhang, T., and Zscheischler, J.: Guidelines for Studying
Diverse Types of Compound Weather and Climate Events, Earth's Future, 9, e2021EF002340,
https://doi.org/10.1029/2021EF002340, 2021. a
Bintanja, R., van der Wiel, K., van der Linden, E. C., Reusen, J., Bogerd, L.,
Krikken, F., and Selten, F. M.: Strong future increases in Arctic
precipitation variability linked to poleward moisture transport, Science
Advances, 6, eaax6869, https://doi.org/10.1126/sciadv.aax6869, 2020. a
Blackport, R., Screen, J. A., van der Wiel, K., and Bintanja, R.: Minimal
influence of reduced Arctic sea ice on coincident cold winters in
mid-latitudes, Nat. Clim. Change, 9, 697–704, https://doi.org/10.1038/s41558-019-0551-4,
2019. a
Bonekamp, P. N., Wanders, N., van der Wiel, K., Lutz, A. F., and Immerzeel,
W. W.: Using large ensemble modelling to derive future changes in mountain
specific climate indicators in a 2 and 3 ∘C warmer world in High
Mountain Asia, Int. J. Climatol., 41, E964–E979,
https://doi.org/10.1002/joc.6742, 2021. a
Boulaguiem, Y., Zscheischler, J., Vignotto, E., van der Wiel, K., and Engelke,
S.: Modeling and simulating spatial extremes by combining extreme value
theory with generative adversarial networks, Environmental Data Science, 1,
e5, https://doi.org/10.1017/eds.2022.4, 2022. a
Brown, P. T., Ming, Y., Li, W., and Hill, S. A.: Change in the magnitude and
mechanisms of global temperature variability with warming, Nat. Clim.
Change, 7, 743–748, https://doi.org/10.1038/nclimate3381, 2017. a
Champagne, O., Arain, M. A., Leduc, M., Coulibaly, P., and McKenzie, S.: Future shift in winter streamflow modulated by the internal variability of climate in southern Ontario, Hydrol. Earth Syst. Sci., 24, 3077–3096, https://doi.org/10.5194/hess-24-3077-2020, 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,
2020a. a, b, c
Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D.,
Lehner, F., Pendergrass, A. G., Dinezio, P., and Stevenson, S.: Isolating
the evolving contributions of anthropogenic aerosols and greenhouse gases: A
new CESM1 large ensemble community resource, J. Climate, 33, 7835–7858,
https://doi.org/10.1175/JCLI-D-20-0123.1, 2020b. a
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, b, c, d, e, f, g
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M., Beljaars, A., Schar, C.,
and Elder, K.: An improved snow scheme for the ECMWF land surface model:
Description and offline validation, J. Hydrometeorol., 11, 899–916,
https://doi.org/10.1175/2010JHM1249.1, 2010. a
Goulart, H. M. D., van der Wiel, K., Folberth, C., Balkovic, J., and van den Hurk, B.: Storylines of weather-induced crop failure events under climate change, Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, 2021. a
Haarsma, R., Acosta, M., Bakhshi, R., Bretonnière, P.-A., Caron, L.-P., Castrillo, M., Corti, S., Davini, P., Exarchou, E., Fabiano, F., Fladrich, U., Fuentes Franco, R., García-Serrano, J., von Hardenberg, J., Koenigk, T., Levine, X., Meccia, V. L., van Noije, T., van den Oord, G., Palmeiro, F. M., Rodrigo, M., Ruprich-Robert, Y., Le Sager, P., Tourigny, E., Wang, S., van Weele, M., and Wyser, K.: HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR – description, model computational performance and basic validation, Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, 2020. a
Hawkins, E., Smith, R. S., Gregory, J. M., and Stainforth, D. A.: Irreducible
uncertainty in near-term climate projections, Clim. Dynam., 46, 3807–3819,
https://doi.org/10.1007/s00382-015-2806-8, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Jerez, S., Thais, F., Tobin, I., Wild, M., Colette, A., Yiou, P., and Vautard,
R.: The CLIMIX model: A tool to create and evaluate spatially-resolved
scenarios of photovoltaic and wind power development, Renew. Sust. Energ. Rev., 42, 1–15,
https://doi.org/10.1016/j.rser.2014.09.041, 2015. 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
Kelder, T., Wanders, N., van der Wiel, K., Marjoribanks, T. I., Slater, L. J.,
Wilby, R. L., and Prudhomme, C.: Interpreting extreme climate impacts from
large ensemble simulations – Are they unseen or unrealistic?, Environ.
Res. Lett., 17, 044052, https://doi.org/10.1088/1748-9326/ac5cf4, 2022. a
Kew, S. F., Philip, S. Y., Hauser, M., Hobbins, M., Wanders, N., van Oldenborgh, G. J., van der Wiel, K., Veldkamp, T. I. E., Kimutai, J., Funk, C., and Otto, F. E. L.: Impact of precipitation and increasing temperatures on drought trends in eastern Africa, Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, 2021. a
Kirchmeier-Young, M. C., Zwiers, F. W., and Gillett, N. P.: 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
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation
CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199,
https://doi.org/10.1002/grl.50256, 2013. a
Kornhuber, K., Osprey, S., Coumou, D., Petri, S., Petoukhov, V., Rahmstorf, S.,
and Gray, L.: Extreme weather events in early summer 2018 connected by a
recurrent hemispheric wave-7 pattern, Environ. Res. Lett., 14, 054002,
https://doi.org/10.1088/1748-9326/ab13bf, 2019. a
Leduc, M., Mailhot, A., Frigon, A., Martel, J. L., Ludwig, R., Brietzke, G. B.,
Giguère, M., Brissette, F., Turcotte, R., Braun, M., and Scinocca, J.:
The ClimEx project: A 50-member ensemble of climate change projections at
12-km resolution over Europe and northeastern North America with the Canadian
Regional Climate Model (CRCM5), J. Appl. Meteorol.
Clim., 58, 663–693, https://doi.org/10.1175/JAMC-D-18-0021.1, 2019. a
Lenderink, G., van den Hurk, B. J., Klein Tank, A. M., van Oldenborgh, G. J.,
van Meijgaard, E., de Vries, H., and Beersma, J. J.: Preparing local climate
change scenarios for the Netherlands using resampling of climate model
output, Environ. Res. Lett., 9, 115008,
https://doi.org/10.1088/1748-9326/9/11/115008, 2014. a
Lloyd, E. A. and Shepherd, T. G.: Climate change attribution and legal
contexts: evidence and the role of storylines, Climatic Change, 167, 28,
https://doi.org/10.1007/s10584-021-03177-y, 2021. a
Lock, S. J., Lang, S. T., Leutbecher, M., Hogan, R. J., and Vitart, F.:
Treatment of model uncertainty from radiation by the Stochastically
Perturbed Parametrization Tendencies (SPPT) scheme and associated revisions
in the ECMWF ensembles, Q. J. Roy. Meteor.
Soc., 145, 75–89, https://doi.org/10.1002/qj.3570, 2019. a
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 Sy., 11, 2050–2069,
https://doi.org/10.1029/2019MS001639, 2019. a
Maher, N., Milinski, S., and Ludwig, R.: Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble, Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, 2021. a
Marotzke, J.: Quantifying the irreducible uncertainty in near-term climate
projections, WIRES Clim. Change, 10, e563,
https://doi.org/10.1002/wcc.563, 2019. a
Massey, N., Jones, R., Otto, F. E., Aina, T., Wilson, S., Murphy, J. M.,
Hassell, D., Yamazaki, Y. H., and Allen, M. R.: weather@home – development and
validation of a very large ensemble modelling system for probabilistic event
attribution, Q. J. Roy. Meteor. Soc., 141, 1528–1545,
https://doi.org/10.1002/qj.2455, 2015. a
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J. F., Stouffer,
R. J., Taylor, K. E., and Schlund, M.: Context for interpreting equilibrium
climate sensitivity and transient climate response from the CMIP6 Earth
system models, Science Advances, 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020. a
Milinski, S., Maher, N., and Olonscheck, D.: How large does a large ensemble need to be?, Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, 2020. a
Muntjewerf, L., Bintanja, R., Reerink, T., and Van der Wiel, K.: KNMI-LENTIS
large ensemble time slice dataset description, Zenodo [data set], https://doi.org/10.5281/zenodo.7573137,
2023a. a
Muntjewerf, L., Bintanja, R., Reerink, T., and Van der Wiel, K.: KNMI-LENTIS
production scripts, Zenodo [code], https://doi.org/10.5281/zenodo.7594694, 2023b. a
Nanditha, J. S., van der Wiel, K., Bhatia, U., Stone, D., Selton, F., and
Mishra, V.: A seven-fold rise in the probability of exceeding the observed
hottest summer in India in a 2 ∘C warmer world, Environ. Res.
Lett., 15, 044028, https://doi.org/10.1088/1748-9326/ab7555, 2020. a
Ollinaho, P., Lock, S. J., Leutbecher, M., Bechtold, P., Beljaars, A., Bozzo,
A., Forbes, R. M., Haiden, T., Hogan, R. J., and Sandu, I.: Towards
process-level representation of model uncertainties: stochastically perturbed
parametrizations in the ECMWF ensemble, Q. J. Roy.
Meteor. Soc., 143, 408–422, https://doi.org/10.1002/qj.2931, 2017. a
Pascale, S., Kapnick, S. B., Delworth, T. L., Hidalgo, H. G., and Cooke, W. F.:
Natural variability vs forced signal in the 2015–2019 Central American
drought, Climatic Change, 168, 16, https://doi.org/10.1007/s10584-021-03228-4, 2021. a
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C., and Sanderson, B. M.:
Precipitation variability increases in a warmer climate, Scientific
Reports, 7, 17966, https://doi.org/10.1038/s41598-017-17966-y, 2017. a
Philip, S., Sparrow, S., Kew, S. F., van der Wiel, K., Wanders, N., Singh, R., Hassan, A., Mohammed, K., Javid, H., Haustein, K., Otto, F. E. L., Hirpa, F., Rimi, R. H., Islam, A. K. M. S., Wallom, D. C. H., and van Oldenborgh, G. J.: Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives, Hydrol. Earth Syst. Sci., 23, 1409–1429, https://doi.org/10.5194/hess-23-1409-2019, 2019. a
Philip, S. Y., Kew, S. F., van der Wiel, K., Wanders, N., van Oldenborgh,
G. J., and Philip, S. Y.: Regional differentiation in climate change induced
drought trends in the Netherlands, Environ. Res. Lett., 15, 094081,
https://doi.org/10.1088/1748-9326/ab97ca, 2020. a
Poschlod, B., Willkofer, F., and Ludwig, R.: Impact of climate change on the
hydrological regimes in Bavaria, Water (Switzerland), 12, 1599,
https://doi.org/10.3390/w12061599, 2020. a
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
Schaeffer, M., Selten, F. M., and Opsteegh, J. D.: Shifts of means are not a
proxy for changes in extreme winter temperatures in climate projections,
Clim. Dynam., 25, 51–63, https://doi.org/10.1007/s00382-004-0495-9, 2005. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West,
I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Senior, C. A.,
Sobel, A. H., Stainforth, D. A., Tett, S. F., Trenberth, K. E., van den Hurk,
B. J., Watkins, N. W., Wilby, R. L., and Zenghelis, D. A.: Storylines: an
alternative approach to representing uncertainty in physical aspects of
climate change, Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a
Sillmann, J., Shepherd, T. G., van den Hurk, B., Hazeleger, W., Martius, O.,
Slingo, J., and Zscheischler, J.: Event-Based Storylines to Address Climate
Risk, Earth's Future, 9, e2020EF001783, https://doi.org/10.1029/2020EF001783, 2021. a
Singh, H. K., Goldenson, N., Fyfe, J. C., and Polvani, L. M.: Uncertainty in
Preindustrial Global Ocean Initialization Can Yield Irreducible Uncertainty
in Southern Ocean Surface Climate, J. Climate, 36, 383–403,
https://doi.org/10.1175/JCLI-D-21-0176.1, 2023. a
Sperna Weiland, R., van der Wiel, K., Selten, F., and Coumou, D.: Intransitive
atmosphere dynamics leading to persistent hot-dry or cold-wet European
summers, J. Climate, 34, 6303–6317, https://doi.org/10.1175/JCLI-D-20-0943.1, 2021. a
Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: A
climate database with varying drought-heat signatures for climate impact
modelling, Geosci. Data J., 9, 154–166, https://doi.org/10.1002/gdj3.129, 2021. a
Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition, Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, 2022. a
van den Hurk, B. J. J., Viterbo, P., Beljaars, A., and Betts, A.: Offline
validation of the ERA40 surface scheme, 295, Technical memorandum, ECMWF, https://doi.org/10.21957/9aoaspz8, 2000. a
van der Wiel, K. and Bintanja, R.: Contribution of climatic changes in mean
and variability to monthly temperature and precipitation extremes,
Communications Earth & Environment, 2, 1, https://doi.org/10.1038/s43247-020-00077-4,
2021. a, b, c
van der Wiel, K., Bloomfield, H. C., Lee, R. W., Stoop, L. P., Blackport, R.,
Screen, J. A., and Selten, F. M.: The influence of weather regimes on
European renewable energy production and demand, Environ. Res.
Lett., 14, 094010, https://doi.org/10.1088/1748-9326/ab38d3, 2019a. a
van der Wiel, K., Stoop, L. P., van Zuijlen, B. R., Blackport, R., van den
Broek, M. A., and Selten, F. M.: Meteorological conditions leading to
extreme low variable renewable energy production and extreme high energy
shortfall, Renew. Sust. Energ. Rev., 111, 261–275,
https://doi.org/10.1016/j.rser.2019.04.065, 2019b. a, b
van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F.: Added Value
of Large Ensemble Simulations for Assessing Extreme River Discharge in a
2 ∘C Warmer World, Geophys. Res. Lett., 46, 2093–2102,
https://doi.org/10.1029/2019GL081967, 2019c. a, b, c, d
van der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R., and Screen,
J. A.: Ensemble climate-impact modelling: extreme impacts from moderate
meteorological conditions, Environ. Res. Lett., 15, 034050,
https://doi.org/10.1088/1748-9326/ab7668, 2020. a, b
van der Wiel, K., Lenderink, G., and de Vries, H.: Physical storylines of
future European drought events like 2018 based on ensemble climate
modelling, Weather and Climate Extremes, 33, 100350,
https://doi.org/10.1016/j.wace.2021.100350, 2021.
a, b, c
van der Wiel, K., Batelaan, T. J., and Wanders, N.: Large increases of
multi-year droughts in north-western Europe in a warmer climate, Clim.
Dynam., Clim. Dynam., 60, 1781–1800, https://doi.org/10.1007/s00382-022-06373-3, 2022. a
van Kempen, G., van der Wiel, K., and Melsen, L. A.: The impact of hydrological model structure on the simulation of extreme runoff events, Nat. Hazards Earth Syst. Sci., 21, 961–976, https://doi.org/10.5194/nhess-21-961-2021, 2021. a, b
van Oldenborgh, G. J., van der Wiel, K., Kew, S., Philip, S., Otto, F.,
Vautard, R., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.:
Pathways and pitfalls in extreme event attribution, Climatic Change, 166, 13,
https://doi.org/10.1007/s10584-021-03071-7, 2021. a
Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., and Zscheischler, J.: Identifying meteorological drivers of extreme impacts: an application to simulated crop yields, Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, 2021. a
Watkins, N. W.: Bunched black (and grouped grey) swans: Dissipative and
non-dissipative models of correlated extreme fluctuations in complex
geosystems, Geophys. Res. Lett., 40, 402–410, https://doi.org/10.1002/grl.50103, 2013. a
Wood, R. R., Lehner, F., Pendergrass, A. G., and Schlunegger, S.: Changes in
precipitation variability across time scales in multiple global climate model
large ensembles, Environ. Res. Lett., 16, 084022,
https://doi.org/10.1088/1748-9326/ac10dd, 2021. a
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, b
Zhang, T., van der Wiel, K., Wei, T., Screen, J., Yue, X., Zheng, B., Selten,
F., Bintanja, R., Anderson, W., Blackport, R., Glomsrød, S., Liu, Y., Cui,
X., and Yang, X.: Increased wheat price spikes and larger economic
inequality with 2 ∘C global warming, One Earth, 5, 907–916, 2022. a
Zscheischler, J., Westra, S., van den Hurk, B. J., Seneviratne, S. I., Ward,
P. J., Pitman, A., Aghakouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and
Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477,
https://doi.org/10.1038/s41558-018-0156-3, 2018. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton,
R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha,
M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.:
A typology of compound weather and climate events, Nature Reviews Earth and Environment, 1, 333–347,
https://doi.org/10.1038/s43017-020-0060-z, 2020. a
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model...
