Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3133-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
12 Apr 2022
Model evaluation paper |
| 12 Apr 2022
| 12 Apr 2022
An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model
Los Alamos National Laboratory, Los Alamos, NM, USA
Wieslaw Maslowski
Naval Postgraduate School, Monterey, CA, USA
Younjoo J. Lee
Naval Postgraduate School, Monterey, CA, USA
Gennaro D'Angelo
Los Alamos National Laboratory, Los Alamos, NM, USA
Robert Osinski
Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
Mark R. Petersen
Los Alamos National Laboratory, Los Alamos, NM, USA
Wilbert Weijer
Los Alamos National Laboratory, Los Alamos, NM, USA
Anthony P. Craig
independent researcher
John D. Wolfe
Los Alamos National Laboratory, Los Alamos, NM, USA
Darin Comeau
Los Alamos National Laboratory, Los Alamos, NM, USA
Adrian K. Turner
Los Alamos National Laboratory, Los Alamos, NM, USA
Related authors
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
Short summary
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
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.
Irena Vaňková, Xylar Asay-Davis, Carolyn Branecky Begeman, Darin Comeau, Alexander Hager, Matthew Hoffman, Stephen F. Price, and Jonathan Wolfe
The Cryosphere, 19, 507–523, https://doi.org/10.5194/tc-19-507-2025, https://doi.org/10.5194/tc-19-507-2025, 2025
Short summary
Short summary
We study the effect of subglacial discharge on basal melting for Antarctic ice shelves. We find that the results from previous studies of vertical ice fronts and two-dimensional ice tongues do not translate to the rotating ice-shelf framework. The melt rate dependence on discharge is stronger in the rotating framework. Further, there is a substantial melt-rate sensitivity to the location of the discharge along the grounding line relative to the directionality of the Coriolis force.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
Short summary
Short summary
Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Matthew J. Hoffman, Carolyn Branecky Begeman, Xylar S. Asay-Davis, Darin Comeau, Alice Barthel, Stephen F. Price, and Jonathan D. Wolfe
The Cryosphere, 18, 2917–2937, https://doi.org/10.5194/tc-18-2917-2024, https://doi.org/10.5194/tc-18-2917-2024, 2024
Short summary
Short summary
The Filchner–Ronne Ice Shelf in Antarctica is susceptible to the intrusion of deep, warm ocean water that could increase the melting at the ice-shelf base by a factor of 10. We show that representing this potential melt regime switch in a low-resolution climate model requires careful treatment of iceberg melting and ocean mixing. We also demonstrate a possible ice-shelf melt domino effect where increased melting of nearby ice shelves can lead to the melt regime switch at Filchner–Ronne.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
Short summary
Short summary
We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
The Cryosphere, 17, 2681–2700, https://doi.org/10.5194/tc-17-2681-2023, https://doi.org/10.5194/tc-17-2681-2023, 2023
Short summary
Short summary
We find that E3SM-HR reproduces the main features of the Antarctic coastal polynyas. Despite the high amount of coastal sea ice production, the densest water masses are formed in the open ocean. Biases related to the lack of dense water formation are associated with overly strong atmospheric polar easterlies. Our results indicate that the large-scale polar atmospheric circulation must be accurately simulated in models to properly reproduce Antarctic dense water formation.
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
Short summary
Short summary
FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
Short summary
Short summary
Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Short summary
During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Adrian K. Turner, William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Darren Engwirda, Todd D. Ringler, and Jonathan D. Wolfe
Geosci. Model Dev., 15, 3721–3751, https://doi.org/10.5194/gmd-15-3721-2022, https://doi.org/10.5194/gmd-15-3721-2022, 2022
Short summary
Short summary
We present the dynamical core of the MPAS-Seaice model, which uses a mesh consisting of a Voronoi tessellation with polygonal cells. Such a mesh allows variable mesh resolution in different parts of the domain and the focusing of computational resources in regions of interest. We describe the velocity solver and tracer transport schemes used and examine errors generated by the model in both idealized and realistic test cases and examine the computational efficiency of the model.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Adrian K. Turner, Kara J. Peterson, and Dan Bolintineanu
Geosci. Model Dev., 15, 1953–1970, https://doi.org/10.5194/gmd-15-1953-2022, https://doi.org/10.5194/gmd-15-1953-2022, 2022
Short summary
Short summary
We developed a technique to remap sea ice tracer quantities between circular discrete element distributions. This is needed for a global discrete element method sea ice model being developed jointly by Los Alamos National Laboratory and Sandia National Laboratories that has the potential to better utilize newer supercomputers with graphics processing units and better represent sea ice dynamics. This new remapping technique ameliorates the effect of element distortion created by sea ice ridging.
Jaclyn Clement Kinney, Karen M. Assmann, Wieslaw Maslowski, Göran Björk, Martin Jakobsson, Sara Jutterström, Younjoo J. Lee, Robert Osinski, Igor Semiletov, Adam Ulfsbo, Irene Wåhlström, and Leif G. Anderson
Ocean Sci., 18, 29–49, https://doi.org/10.5194/os-18-29-2022, https://doi.org/10.5194/os-18-29-2022, 2022
Short summary
Short summary
We use data crossing Herald Canyon in the Chukchi Sea collected in 2008 and 2014 together with numerical modelling to investigate the circulation in the western Chukchi Sea. A large fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. To assess the differences between years, we use numerical modelling results, which show that high-frequency variability dominates the flow in Herald Canyon.
Michael A. Brunke, John J. Cassano, Nicholas Dawson, Alice K. DuVivier, William J. Gutowski Jr., Joseph Hamman, Wieslaw Maslowski, Bart Nijssen, J. E. Jack Reeves Eyre, José C. Renteria, Andrew Roberts, and Xubin Zeng
Geosci. Model Dev., 11, 4817–4841, https://doi.org/10.5194/gmd-11-4817-2018, https://doi.org/10.5194/gmd-11-4817-2018, 2018
Short summary
Short summary
The Regional Arctic System Model version 1 (RASM1) was recently developed for high-resolution simulation of the coupled atmosphere–ocean–sea ice–land system in the Arctic. Its simulation of the atmosphere–land–ocean–sea ice interface is evaluated by using the spread in recent reanalyses and a global Earth system model as baselines. Such comparisons reveal that RASM1 simulates precipitation well and improves the simulation of surface fluxes over sea ice.
Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
Short summary
Short summary
The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Anthony Craig, Sophie Valcke, and Laure Coquart
Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, https://doi.org/10.5194/gmd-10-3297-2017, 2017
Short summary
Short summary
The OASIS software package provides capabilities that allow different models to be coupled together to carry out new scientific investigation. This is particularly useful in climate model simulations where atmosphere, ocean, sea ice, hydrology, land, ocean wave, chemistry, and other types of Earth system models are often coupled together. The OASIS software package is used by several groups around the world, and this paper describes features of the latest implementation.
Related subject area
Climate and Earth system modeling
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
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
OpenBench: a land models evaluation system
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
Evaluation of Ozone and its Precursors using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) during the Michigan-Ontario Ozone Source Experiment (MOOSE)
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)
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Short summary
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Short summary
This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Short summary
This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
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.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
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.
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
Short summary
Short summary
The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
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.
Cited articles
Adcroft, A. and Campin, J.-M.: Rescaled height coordinates for accurate
representation of free-surface flows in ocean circulation models, Ocean Model., 7, 269–284, https://doi.org/10.1016/j.ocemod.2003.09.003, 2004. a
Beszczynska-Möller, A., Woodgate, R. A., Lee, C. M., Melling, H., and
Karcher, M.: A Synthesis of Exchanges Through the Main Oceanic Gateways to
the Arctic Ocean, Oceanography, 24, 82–99, https://doi.org/10.5670/oceanog.2011.59, 2011. a
Beszczynska-Möller, A., Fahrbach, E., Schauer, U., and Hansen, E.:
Variability in Atlantic water temperature and transport at the entrance to
the Arctic Ocean, 1997–2010, ICES J. Marine Sci., 69, 852–863,
https://doi.org/10.1093/icesjms/fss056, 2012. a
Brunke, M. A., Cassano, J. J., Dawson, N., DuVivier, A. K., Gutowski Jr., W. J., Hamman, J., Maslowski, W., Nijssen, B., Reeves Eyre, J. E. J., Renteria, J. C., Roberts, A., and Zeng, X.: Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations, Geosci. Model Dev., 11, 4817–4841, https://doi.org/10.5194/gmd-11-4817-2018, 2018. a
Bryan, F. O., Gent, P. R., and Tomas, R.: Can Southern Ocean Eddy Effects Be
Parameterized in Climate Models?, J. Climate, 27, 411–425,
https://doi.org/10.1175/JCLI-D-12-00759.1, 2014. a
Bunzel, F., Notz, D., and Pedersen, L. T.: Retrievals of Arctic Sea-Ice
Volume and Its Trend Significantly Affected by Interannual Snow Variability,
Geophys. Res. Lett., 45, 11751–11759, https://doi.org/10.1029/2018GL078867,
2018. a
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.-C.,
Lin, W., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E.,
Roberts, A. F., Taylor, M. A., Veneziani, M., Wang, H., Wolfe, J. D.,
Balaguru, K., Cameron-Smith, P., Dong, L., Klein, S. A., Leung, L. R., Li,
H.-Y., Li, Q., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P. A.,
Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM Coupled
Model Version 1: Description and Results at High Resolution, J.
Adv. Model. Earth Sy., 11, 4095–4146,
https://doi.org/10.1029/2019MS001870, 2019. a, b
Cassano, J. J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke,
M., Craig, A., Fisel, B., Gutowski, W., Hamman, J., Higgins, M., Maslowski,
W., Nijssen, B., Osinski, R., and Zeng, X.: Development of the Regional
Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity,
J. Climate, 30, 5729–5753, https://doi.org/10.1175/JCLI-D-15-0775.1, 2017. a
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C., and Zwally,
H. J.: Deriving long-term time series of sea ice cover from satellite passive
microwave multisensor data sets, J. Geophys. Res.-Oceans,
104, 15803–15814, https://doi.org/10.1029/1999JC900081, 1999. a
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii,
Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda, T.,
Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S., Ferry,
N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T., Masina,
S., Tang, Y., Tsujino, H., and Wang, X.: Intercomparison of the Arctic sea
ice cover in global ocean–sea ice reanalyses from the ORA-IP project,
Clim. Dynam., 49, 1107–1136, https://doi.org/10.1007/s00382-016-2985-y, 2017. a
Clement Kinney, J., Maslowski, W., Aksenov, Y., de Cuevas, B., Jakacki, J.,
Nguyen, A., Osinski, R., Steele, M., Woodgate, R. A., and Zhang, J.: On the
Flow Through Bering Strait: A Synthesis of Model Results and Observations,
167–198, Springer Netherlands, Dordrecht,
https://doi.org/10.1007/978-94-017-8863-2_7, 2014. a
Cunningham, S. A., Kanzow, T., Rayner, D., Baringer, M. O., Johns, W. E.,
Marotzke, J., Longworth, H. R., Grant, E. M., Hirschi, J. J.-M., Beal, L. M.,
Meinen, C. S., and Bryden, H. L.: Temporal Variability of the Atlantic
Meridional Overturning Circulation at 26.5∘ N, Science, 317,
935–938, https://doi.org/10.1126/science.1141304, 2007. a
Cuny, J., Rhines, P. B., and Kwok, R.: Davis Strait volume, freshwater and
heat fluxes, Deep-Sea Res. Pt. I, 52,
519–542, https://doi.org/10.1016/j.dsr.2004.10.006, 2005. a, b
Curry, B., Lee, C. M., Petrie, B., Moritz, R. E., and Kwok, R.: Multiyear
Volume, Liquid Freshwater, and Sea Ice Transports through Davis Strait,
2004–10, J. Phys. Oceanogr., 44, 1244–1266,
https://doi.org/10.1175/JPO-D-13-0177.1, 2014. a, b, c, d
Dai, A., Luo, D., Song, M., and Liu, J.: Arctic amplification is caused by
sea-ice loss under increasing CO2, Nat. Commun., 10, 121,
https://doi.org/10.1038/s41467-018-07954-9, 2019. a
Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D.,
Biastoch, A., Böning, C., Bozec, A., Canuto, V. M., Cassou, C.,
Chassignet, E., Coward, A. C., Danilov, S., Diansky, N., Drange, H., Farneti,
R., Fernandez, E., Fogli, P. G., Forget, G., Fujii, Y., Griffies, S. M.,
Gusev, A., Heimbach, P., Howard, A., Jung, T., Kelley, M., Large, W. G.,
Leboissetier, A., Lu, J., Madec, G., Marsland, S. J., Masina, S., Navarra,
A., George Nurser, A., Pirani, A., Salas y Mélia, D., Samuels, B. L.,
Scheinert, M., Sidorenko, D., Treguier, A.-M., Tsujino, H., Uotila, P.,
Valcke, S., Voldoire, A., and Wang, Q.: North Atlantic simulations in
Coordinated Ocean-ice Reference Experiments phase II (CORE-II).
Part I: Mean states, Ocean Model., 73, 76–107,
https://doi.org/10.1016/j.ocemod.2013.10.005, 2014. a
de Steur, L.: Fram Strait freshwater transport and gridded monthly mean
velocity and salinity 1997–2015, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2018.9e01a801, 2018. a
de Steur, L., Hansen, E., Gerdes, R., Karcher, M., Fahrbach, E., and Holfort,
J.: Freshwater fluxes in the East Greenland Current: A decade of
observations, Geophys. Res. Lett., 36, L23611,
https://doi.org/10.1029/2009GL041278, 2009. a
Gent, P. R. and McWilliams, J. C.: Isopycnal Mixing in Ocean
Circulation Models, J. Phys. Oceanogr., 20, 150–155,
https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke,
M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar,
J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hunke, E. C.,
Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein,
S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma,
P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy,
R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre,
J. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L.,
Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A.,
Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S.,
Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon,
J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang,
Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM coupled model version
1: Overview and evaluation at standard resolution, J. Adv.
Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a, b, c, d, e
Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G.,
Chassignet, E. P., England, M. H., Gerdes, R., Haak, H., Hallberg, R. W.,
Hazeleger, W., Jungclaus, J., Large, W. G., Madec, G., Pirani, A., Samuels,
B. L., Scheinert, M., Gupta, A. S., Severijns, C. A., Simmons, H. L.,
Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice
Reference Experiments (COREs), Ocean Model., 26, 1–46,
https://doi.org/10.1016/j.ocemod.2008.08.007, 2009. a, b
Haine, T. W., Curry, B., Gerdes, R., Hansen, E., Karcher, M., Lee, C., Rudels,
B., Spreen, G., de Steur, L., Stewart, K. D., and Woodgate, R.: Arctic
freshwater export: Status, mechanisms, and prospects, Global Planet.
Change, 125, 13–35, https://doi.org/10.1016/j.gloplacha.2014.11.013, 2015. a
Häkkinen, S.: An Arctic source for the great salinity anomaly: A simulation
of the Arctic ice-ocean system for 1955–1975, J. Geophys.
Res.-Oceans, 98, 16397–16410, https://doi.org/10.1029/93JC01504, 1993. a
Hamman, J., Nijssen, B., Brunke, M., Cassano, J., Craig, A., DuVivier, A.,
Hughes, M., Lettenmaier, D. P., Maslowski, W., Osinski, R., Roberts, A., and
Zeng, X.: Land surface climate in the Regional Arctic System Model, J. Climate, 29, 6543–6562, https://doi.org/10.1175/JCLI-D-15-0415.1, 2016. a
Ilicak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., Bentsen,
M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet, E.,
Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E., Fogli,
P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T., Large,
W. G., Lee, C., Lique, C., Lu, J., Masina, S., George Nurser, A., Roth, C.,
Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H., Valcke, S.,
Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the Arctic
Ocean in a suite of interannual CORE-II simulations. Part III:
Hydrography and fluxes, Ocean Model., 100, 141–161,
https://doi.org/10.1016/j.ocemod.2016.02.004, 2016. a, b
jedwards4b, Foucar, J., Mametjanov, A., Jacob, R., Taylor, M., singhbalwinder, Sacks, B., mvertens, Wolfe, J., jayeshkrishna, Paul, K., noel, onguba, fischer-ncar, Hartnett, E., Deakin, M., Jacobsen, D., Shollenberger, J., susburrows, Wilke, A., Bertini, A., jqyin, Norman, M., Thayer-Calder, K., Petersen, M., Hillman, B. R., Sarich, J., Hoffman, M., Salinger, A., Sreepathi, S.: milenaveneziani/E3SM: e3sm-arctic-osi-2019 (e3sm-arctic-2019), Zenodo [code], https://doi.org/10.5281/zenodo.5548434, 2020. a
Kurtz, N. and Harbeck, J.: CryoSat-2 Level-4 Sea Ice Elevation, Freeboard, and
Thickness, Version 1, Boulder, Colorado USA. NASA National Snow and Ice Data
Center Distributed Active Archive Center, https://doi.org/10.5067/96JO0KIFDAS8 (last access: May 2019), 2017. a
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A
review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994. a
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M.,
Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and
Smolyar, I.: World Ocean Atlas 2018, Volume 1: Temperature, Tech. rep.,
NOAA Atlas NESDIS, Silver Spring, MD, edited by: Mishonov, A., 2018. a
Maslowski, W., Clement Kinney, J., Higgins, M., and Roberts, A.: The Future of
Arctic Sea Ice, Annual Review of Earth Planet. Sci., 40,
625–654, https://doi.org/10.1146/annurev-earth-042711-105345, 2012. a
McCarthy, G. D., Brown, P. J., Flagg, C. N., Goni, G., Houpert, L., Hughes,
C. W., Hummels, R., Inall, M., Jochumsen, K., Larsen, K. M. H., Lherminier,
P., Meinen, C. S., Moat, B. I., Rayner, D., Rhein, M., Roessler, A., Schmid,
C., and Smeed, D. A.: Sustainable Observations of the AMOC: Methodology
and Technology, Rev. Geophys., 58, e2019RG000654,
https://doi.org/10.1029/2019RG000654, 2020. a, b
Münchow, A.: Volume and Freshwater Flux Observations from Nares Strait to
the West of Greenland at Daily Time Scales from 2003 to 2009, J.
Phys. Oceanogr., 46, 141–157, https://doi.org/10.1175/JPO-D-15-0093.1, 2016. a, b, c, d
Orsi, A. H., Smethie Jr., W. M., and Bullister, J. L.: On the total input of
Antarctic waters to the deep ocean: A preliminary estimate from
chlorofluorocarbon measurements, J. Geophys. Res.-Oceans,
107, 3122, https://doi.org/10.1029/2001JC000976, 2002. a
Perovich, D., Meier, W., Tschudi, M., Farrell, S., Hendricks, S., Gerland, S.,
Kaleschke, L., Ricker, R., Tian-Kunze, X., Webster, M., and Wood, K.: Sea
Ice, Tech. rep., Arctic Report Card 2019,
http://www.arctic.noaa.gov/Report-Card (last access: 7 April 2022), 2019. a
Petersen, M. R., Jacobsen, D. W., Ringler, T. D., Hecht, M. W., and Maltrud,
M. E.: Evaluation of the arbitrary Lagrangian–Eulerian vertical
coordinate method in the MPAS-Ocean model, Ocean Model., 86, 93–113,
https://doi.org/10.1016/j.ocemod.2014.12.004, 2015. a
Petersen, M. R., Asay-Davis, X. S., Berres, A. S., Chen, Q., Feige, N.,
Hoffman, M. J., Jacobsen, D. W., Jones, P. W., Maltrud, M. E., Price, S. F.,
Ringler, T. D., Streletz, G. J., Turner, A. K., Van Roekel, L. P., Veneziani,
M., Wolfe, J. D., Wolfram, P. J., and Woodring, J. L.: An Evaluation of the
Ocean and Sea Ice Climate of E3SM Using MPAS and Interannual CORE-II
Forcing, J. Adv. Model. Earth Sy., 11, 1438–1458,
https://doi.org/10.1029/2018MS001373, 2019. a, b, c, d, e
Polyakov, I. V., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T. M.,
Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T.,
Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin,
A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian
Basin of the Arctic Ocean, Science, 356, 285–291,
https://doi.org/10.1126/science.aai8204, 2017. a, b
Prinsenberg, S. J. and Hamilton, J.: Monitoring the volume, freshwater and heat
fluxes passing through Lancaster sound in the Canadian Arctic
Archipelago, Atmosphere-Ocean, 43, 1–22, https://doi.org/10.3137/ao.430101, 2005. a, b, c, d
Proshutinsky, A., Krishfield, R., Timmermans, M.-L., Toole, J., Carmack, E.,
McLaughlin, F., Williams, W. J., Zimmermann, S., Itoh, M., and Shimada, K.:
Beaufort Gyre freshwater reservoir: State and variability from
observations, J. Geophys. Res.-Oceans, 114, C00A10,
https://doi.org/10.1029/2008JC005104, 2009. a
Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M.-L., Williams,
W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D.,
Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T.,
Nishino, S., Itoh, M., Kang, S.-H., Cho, K.-H., Tateyama, K., and Zhao, J.:
Analysis of the Beaufort Gyre Freshwater Content in 2003–2018, J. Geophys. Res.-Oceans, 124, 9658–9689, https://doi.org/10.1029/2019JC015281, 2019. a, b
Rabe, B., Karcher, M., Kauker, F., Schauer, U., Toole, J. M., Krishfield,
R. A., Pisarev, S., Kikuchi, T., and Su, J.: Arctic Ocean basin liquid
freshwater storage trend 1992–2012, Geophys. Res. Lett., 41,
961–968, https://doi.org/10.1002/2013GL058121, 2014. a
Ringler, T., Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., and
Maltrud, M.: A multi-resolution approach to global ocean modeling, Ocean Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010, 2013. a
Roberts, A., Craig, A., Maslowski, W., Osinski, R., Duvivier, A., Hughes, M.,
Nijssen, B., Cassano, J., and Brunke, M.: Simulating transient ice-ocean
Ekman transport in the Regional Arctic System Model and Community Earth
System Model, Ann. Glaciol., 56, 211–228,
https://doi.org/10.3189/2015AoG69A760, 2015. a
Roemmich, D. and Gilson, J.: The 2004–2008 mean and annual cycle of
temperature, salinity, and steric height in the global ocean from the Argo
Program, Prog. Oceanogr., 82, 81–100,
https://doi.org/10.1016/j.pocean.2009.03.004, 2009. a
Rudels, B.: Arctic Ocean circulation and variability – advection and external forcing encounter constraints and local processes, Ocean Sci., 8, 261–286, https://doi.org/10.5194/os-8-261-2012, 2012. a
Schauer, U. and Beszczynska-Möller, A.: Problems with estimation and interpretation of oceanic heat transport – conceptual remarks for the case of Fram Strait in the Arctic Ocean, Ocean Sci., 5, 487–494, https://doi.org/10.5194/os-5-487-2009, 2009. a, b
Schauer, U., Beszczynska-Möller, A., Walczowski, W., Fahrbach, E.,
Piechura, J., and Hansen, E.: Variation of Measured Heat Flow Through the
Fram Strait Between 1997 and 2006, 65–85, Springer Netherlands,
Dordrecht, https://doi.org/10.1007/978-1-4020-6774-7_4, 2008. a, b, c, d
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Serreze, M. C., Barrett, A. P., Slater, A. G., Woodgate, R. A., Aagaard, K.,
Lammers, R. B., Steele, M., Moritz, R., Meredith, M., and Lee, C. M.: The
large-scale freshwater cycle of the Arctic, J. Geophys.
Res.-Oceans, 111, C11010, https://doi.org/10.1029/2005JC003424, 2006. a, b
SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47,
e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Smedsrud, L. H., Ingvaldsen, R., Nilsen, J. E. Ø., and Skagseth, Ø.: Heat in the Barents Sea: transport, storage, and surface fluxes, Ocean Sci., 6, 219–234, https://doi.org/10.5194/os-6-219-2010, 2010. a, b
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li,
C., Lien, V. S., Olsen, A., Omar, A. M., Otterȧ, O. H., Risebrobakken,
B., Sandø, A. B., Semenov, V. A., and Sorokina, S. A.: The role of the
Barents Sea in the Arctic Climate System, Rev. Geophys.,
51, 415–449, https://doi.org/10.1002/rog.20017, 2013. a, b, c
Steele, M., Morley, R., and Ermold, W.: PHC: A Global Ocean Hydrography with
a High-Quality Arctic Ocean, J. Climate, 14, 2079–2087,
https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2, 2001. a
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676,
2012. a
Tang, C. C. L., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., and Dunlap,
E.: The circulation, water masses and sea-ice of Baffin Bay, Prog. Oceanogr., 63, 183–228, https://doi.org/10.1016/j.pocean.2004.09.005, 2004. a
Tilling, R. L., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice
thickness and volume using CryoSat-2 radar altimeter data, Adv. Space
Res., 62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018. a
Timmermans, M.-L., Toole, J., and Krishfield, R.: Warming of the interior
Arctic Ocean linked to sea ice losses at the basin margins, Sci.
Adv., 4, eaat6773, https://doi.org/10.1126/sciadv.aat6773, 2018. a
Toole, J. M., Krishfield, R. A., Timmermans, M.-L., and Proshutinsky, A.: The
Ice-Tethered Profiler: Argo of the Arctic, Oceanography, 24, 126–135,
https://doi.org/10.5670/oceanog.2011.64, 2011. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G.,
Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Dias, F. B., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Sommer, J. L., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.:
JRA-55 based surface dataset for driving ocean–sea-ice models
(JRA55-do), Ocean Model., 130, 79–139,
https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., Arsouze, T., Bentsen, M., Bernardello, R., Böning, C. W., Bozec, A., Chassignet, E. P., Danilov, S., Dussin, R., Exarchou, E., Fogli, P. G., Fox-Kemper, B., Guo, C., Ilicak, M., Iovino, D., Kim, W. M., Koldunov, N., Lapin, V., Li, Y., Lin, P., Lindsay, K., Liu, H., Long, M. C., Komuro, Y., Marsland, S. J., Masina, S., Nummelin, A., Rieck, J. K., Ruprich-Robert, Y., Scheinert, M., Sicardi, V., Sidorenko, D., Suzuki, T., Tatebe, H., Wang, Q., Yeager, S. G., and Yu, Z.: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2), Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, 2020. a
Turner, A. K., Lipscomb, W. H., Hunke, E. C., Jacobsen, D. W., Jeffery, N., Engwirda, D., Ringler, T. D., and Wolfe, J. D.: MPAS-Seaice (v1.0.0): Sea-ice dynamics on unstructured Voronoi meshes, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-355, in review, 2021. a, b
Veneziani, M.: Model data in support of the E3SMv1-Arctic-OSI GMD manuscript (2021) (Version v1), Zenodo [data set], https://doi.org/10.5281/zenodo.5548528, 2021. a
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet,
E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E.,
Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T.,
Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. G., Rabe,
B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H.,
Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the
Arctic Ocean in a suite of interannual CORE-II simulations. Part
II: Liquid freshwater, Ocean Model., 99, 86–109,
https://doi.org/10.1016/j.ocemod.2015.12.009, 2016a. a
Wang, Q., Ilicak, M., Gerdes, R., Drange, H., Aksenov, Y., Bailey, D. A.,
Bentsen, M., Biastoch, A., Bozec, A., Böning, C., Cassou, C., Chassignet,
E., Coward, A. C., Curry, B., Danabasoglu, G., Danilov, S., Fernandez, E.,
Fogli, P. G., Fujii, Y., Griffies, S. M., Iovino, D., Jahn, A., Jung, T.,
Large, W. G., Lee, C., Lique, C., Lu, J., Masina, S., Nurser, A. G., Rabe,
B., Roth, C., Salas y Mélia, D., Samuels, B. L., Spence, P., Tsujino, H.,
Valcke, S., Voldoire, A., Wang, X., and Yeager, S. G.: An assessment of the
Arctic Ocean in a suite of interannual CORE-II simulations. Part
I: Sea ice and solid freshwater, Ocean Model., 99, 110–132,
https://doi.org/10.1016/j.ocemod.2015.12.008, 2016b. a
Wang, Q., Wekerle, C., Danilov, S., Wang, X., and Jung, T.: A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4, Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, 2018. a
Watts, M., Maslowski, W., Lee, Y. J., Kinney, J. C., and Osinski, R.: A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations, J. Climate, https://doi.org/10.1175/JCLI-D-20-0491.1, 2022. a
Weijer, W., Cheng, W., Garuba, O. A., Hu, A., and Nadiga, B. T.: CMIP6 models
predict significant 21st Century secline of the Atlantic Meridional
Overturning Circulation, Geophys. Res. Lett., 47, e2019GL086075,
https://doi.org/10.1029/2019GL086075, 2020. a
Wekerle, C., Wang, Q., Danilov, S., Jung, T., and Schröter, J.: The Canadian
Arctic Archipelago throughflow in a multiresolution global model: Model
assessment and the driving mechanism of interannual variability, J.
Geophys. Res.-Oceans, 118, 4525–4541, 2013. a
Wekerle, C., Wang, Q., Danilov, S., Schourup-Kristensen, V., von Appen, W.-J.,
and Jung, T.: Atlantic Water in the Nordic Seas: Locally eddy-permitting
ocean simulation in a global setup, J. Geophys. Res.-Oceans,
122, 914–940, https://doi.org/10.1002/2016JC012121, 2017a. a
Wekerle, C., Wang, Q., von Appen, W.-J., Danilov, S., Schourup-Kristensen, V.,
and Jung, T.: Eddy-Resolving Simulation of the Atlantic Water Circulation
in the Fram Strait With Focus on the Seasonal Cycle, J. Geophys.
Res.-Oceans, 122, 8385–8405, https://doi.org/10.1002/2017JC012974,
2017b. a
Woodgate, R. A.: Increases in the Pacific inflow to the Arctic from 1990 to
2015, and insights into seasonal trends and driving mechanisms from
year-round Bering Strait mooring data, Prog. Oceanogr., 160,
124–154, https://doi.org/10.1016/j.pocean.2017.12.007, 2018. a
Woodgate, R. A. and Aagaard, K.: Revising the Bering Strait freshwater flux
into the Arctic Ocean, Geophys. Res. Lett., 32, L02602,
https://doi.org/10.1029/2004GL021747, 2005. a, b, c, d
Woodgate, A. R. and Peralta-Ferriz, C.: Warming and Freshening of the Pacific
Inflow to the Arctic From 1990–2019 Implying Dramatic Shoaling in Pacific
Winter Water Ventilation of the Arctic Water Column, Geophys. Res.
Lett., 48, e2021GL092528, https://doi.org/10.1029/2021GL092528,
2021. a
Woodgate, R. A., Weingartner, T., and Lindsay, R.: The 2007 Bering Strait
oceanic heat flux and anomalous Arctic sea-ice retreat, Geophys. Res. Lett., 37, L01602, https://doi.org/10.1029/2009GL041621, 2010. a, b
Woodgate, R. A., Stafford, K. M., and Prahl, F. G.: A Synthesis of Year-Round
Interdisciplinary Mooring Measurements in the Bering Strait (1990–2014)
and the RUSALCA Years (2004–2011), Oceanography, 28, 46–67,
https://doi.org/10.5670/oceanog.2015.57, 2015. a
Yi, D. and Zwally, H. J.: Arctic Sea Ice Freeboard and Thickness, Version 1,
Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed
Active Archive Center, https://doi.org/10.5067/SXJVJ3A2XIZT (last access: April 2014),
2009, updated 15 April 2014. a
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a Thickness and
Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Mon.
Weather Rev., 131, 845–861,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2, 2003. a
Zhang, J., Weijer, W., Steele, M., Cheng, W., Verma, T., and Veneziani, M.:
Labrador Sea freshening linked to Beaufort Gyre freshwater release,
Nat. Commun., 12, 1229, https://doi.org/10.1038/s41467-021-21470-3, 2021.
a
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A.,
Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R.,
and Smolyar, I.: World Ocean Atlas 2018, Volume 2: Salinity, Tech. rep.,
NOAA Atlas NESDIS, Silver Spring, MD, edited by: Mishonov, A., 2018. a
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a
Short summary
We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to better resolve the Arctic ocean and sea-ice system and low spatial resolution elsewhere. The configuration satisfactorily represents many aspects of the Arctic system and its interactions with the sub-Arctic, while keeping computational costs at a fraction of those necessary for global high-resolution ESMs. E3SM-Arctic can thus be an efficient tool to study Arctic processes on climate-relevant timescales.
We present an Earth system model (ESM) simulation, E3SM-Arctic-OSI, with a refined grid to...
