Articles | Volume 17, issue 15
https://doi.org/10.5194/gmd-17-5913-2024
© Author(s) 2024. 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-17-5913-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Aug 2024
Development and technical paper |
| 08 Aug 2024
| 08 Aug 2024
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
Christiane Jablonowski
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
Hunter Y. Brown
Sandia National Laboratories, Albuquerque, NM, USA
Benjamin R. Hillman
Sandia National Laboratories, Albuquerque, NM, USA
Diana L. Bull
Sandia National Laboratories, Albuquerque, NM, USA
Joseph L. Hart
Sandia National Laboratories, Albuquerque, NM, USA
Related authors
No articles found.
Alexander Lojko, Andrew C. Winters, Annika Oertel, Christiane Jablonowski, and Ashley E. Payne
Weather Clim. Dynam., 6, 387–411, https://doi.org/10.5194/wcd-6-387-2025, https://doi.org/10.5194/wcd-6-387-2025, 2025
Short summary
Short summary
Convective storms can produce intense anticyclonically rotating vortices (~10 km) defined by negative potential vorticity (NPV), which can elongate to larger scales (~1000 km). Our composite analysis shows that elongated NPV frequently occurs along the western North Atlantic tropopause, where we observed it enhancing jet stream kinematics. Elongated NPV may impinge on aviation turbulence and weather forecasting despite its small-scale origin.
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.
Ram Singh, Kostas Tsigaridis, Diana Bull, Laura P. Swiler, Benjamin M. Wagman, and Kate Marvel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2280, https://doi.org/10.5194/egusphere-2024-2280, 2024
Short summary
Short summary
Analysis of post-eruption climate conditions using the impact metrics is crucial for understanding the hydroclimatic responses. We used NASA’s Earth system model to perform the experiments and utilize the moisture-based impact metrics and hydrological variables to investigate the effect of volcanically induced conditions that govern plant productivity. This study demonstrates the Mt. Pinatubo’s impact on drivers of plant productivity and regional and seasonal dependence of different drivers.
Allen Hu, Xiaohong Liu, Ziming Ke, Benjamin Wagman, Hunter Brown, Zheng Lu, Diana Bull, and Kara Peterson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2227, https://doi.org/10.5194/egusphere-2024-2227, 2024
Short summary
Short summary
Volcanic eruptions have a major effect on temperature throughout the atmosphere and can be studied as a proxy for geo-engineering. The aerosol module in the Energy Exascale Earth System Model (E3SM) was originally intended for simulation of tropospheric aerosols and has problems handling stratospheric sulfate aerosols due to volcanic eruptions. We have made alterations to the aerosol module to overcome these problems, with simulation results more closely reproducing observations.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Owen K. Hughes and Christiane Jablonowski
Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, https://doi.org/10.5194/gmd-16-6805-2023, 2023
Short summary
Short summary
Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments, with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model design and the impact of mountains on the flow.
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.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
Short summary
Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Yang Shi, Xiaohong Liu, Mingxuan Wu, Xi Zhao, Ziming Ke, and Hunter Brown
Atmos. Chem. Phys., 22, 2909–2935, https://doi.org/10.5194/acp-22-2909-2022, https://doi.org/10.5194/acp-22-2909-2022, 2022
Short summary
Short summary
We perform a modeling study to evaluate the contribution to Arctic dust loading and ice-nucleating particle (INP) population from high-latitude local and low-latitude dust. High-latitude dust has a large contribution in the lower troposphere, while low-latitude dust dominates the upper troposphere. The high-latitude dust INPs result in a net cooling effect on the Arctic surface by glaciating mixed-phase clouds. Our results highlight the contribution of high-latitude dust to the Arctic climate.
Thomas Toniazzo, Mats Bentsen, Cheryl Craig, Brian E. Eaton, Jim Edwards, Steve Goldhaber, Christiane Jablonowski, and Peter H. Lauritzen
Geosci. Model Dev., 13, 685–705, https://doi.org/10.5194/gmd-13-685-2020, https://doi.org/10.5194/gmd-13-685-2020, 2020
Short summary
Short summary
We show that ensuring global conservation of the angular (rotational) momentum (AM) of the atmosphere along the Earth's axis of rotation, which is a property of the governing equations, has important and beneficial consequences for the quality of the numerical simulation of the general circulation of the atmosphere. We discuss the causes of non-conservation in the FV dynamical core of the Community Atmosphere Model (CAM), propose remedies, and show their impact in correcting systematic biases.
Stefan Rahimi, Xiaohong Liu, Chenglai Wu, William K. Lau, Hunter Brown, Mingxuan Wu, and Yun Qian
Atmos. Chem. Phys., 19, 12025–12049, https://doi.org/10.5194/acp-19-12025-2019, https://doi.org/10.5194/acp-19-12025-2019, 2019
Short summary
Short summary
Light-absorbing particles impact the Earth system in a variety of ways. They can warm the atmosphere by their very presence, or they can warm the atmosphere after they deposit on snow, warm it, and warm the overlying atmosphere. This paper focuses on these two processes as they pertain to black carbon and dust's impacts on the South Asian monsoon. It will be shown that these two aerosols have a significant effect on the monsoon.
Colin M. Zarzycki, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Paul A. Ullrich, David M. Hall, Mark A. Taylor, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Xi Chen, Lucas Harris, Marco Giorgetta, Daniel Reinert, Christian Kühnlein, Robert Walko, Vivian Lee, Abdessamad Qaddouri, Monique Tanguay, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Sang-Hun Park, Joseph B. Klemp, and William C. Skamarock
Geosci. Model Dev., 12, 879–892, https://doi.org/10.5194/gmd-12-879-2019, https://doi.org/10.5194/gmd-12-879-2019, 2019
Short summary
Short summary
We summarize the results of the Dynamical Core Model Intercomparison Project's idealized supercell test case. Supercells are storm-scale weather phenomena that are a key target for next-generation, non-hydrostatic weather prediction models. We show that the dynamical cores of most global numerical models converge between approximately 1 and 0.5 km grid spacing for this test, although differences in final solution exist, particularly due to differing grid discretizations and numerical diffusion.
Hunter Brown, Xiaohong Liu, Yan Feng, Yiquan Jiang, Mingxuan Wu, Zheng Lu, Chenglai Wu, Shane Murphy, and Rudra Pokhrel
Atmos. Chem. Phys., 18, 17745–17768, https://doi.org/10.5194/acp-18-17745-2018, https://doi.org/10.5194/acp-18-17745-2018, 2018
Short summary
Short summary
In climate models, organic carbon (OC) in wildfire smoke has been treated as an atmospheric cooling component by reflecting sunlight back to space. This study incorporates the observationally identified absorbing brown carbon component of OC into the Community Earth System Model, improving the agreement between the model and observations and effectively increasing absorption of solar radiation. This change contributes to altered atmospheric dynamics and changes in cloud cover in the model.
Paul A. Ullrich, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Joseph Klemp, Sang-Hun Park, William Skamarock, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Robert Walko, Alex Reinecke, and Kevin Viner
Geosci. Model Dev., 10, 4477–4509, https://doi.org/10.5194/gmd-10-4477-2017, https://doi.org/10.5194/gmd-10-4477-2017, 2017
Short summary
Short summary
Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school.
Diana R. Thatcher and Christiane Jablonowski
Geosci. Model Dev., 9, 1263–1292, https://doi.org/10.5194/gmd-9-1263-2016, https://doi.org/10.5194/gmd-9-1263-2016, 2016
Short summary
Short summary
A moist idealized test case of intermediate complexity for atmospheric general circulation models is presented. The test case is based on the Held-Suarez dry test case and includes a simplified moist physics parameterization. The results are compared to aquaplanet simulations both with and without deep convection parameterizations. The results demonstrate how the moist idealized test can be used to intercompare dynamical cores and diagnose issues from the physics-dynamics coupling mechanism.
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
J. Kent, C. Jablonowski, J. P. Whitehead, and R. B. Rood
Geosci. Model Dev., 5, 1517–1530, https://doi.org/10.5194/gmd-5-1517-2012, https://doi.org/10.5194/gmd-5-1517-2012, 2012
Related subject area
Climate and Earth system modeling
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
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)
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
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
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
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.
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.
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.
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.
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.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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 key 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 irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
Short summary
Short summary
The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Cited articles
Abalos, M., Calvo, N., Benito-Barca, S., Garny, H., Hardiman, S. C., Lin, P., Andrews, M. B., Butchart, N., Garcia, R., Orbe, C., Saint-Martin, D., Watanabe, S., and Yoshida, K.: The Brewer–Dobson circulation in CMIP6, Atmos. Chem. Phys., 21, 13571–13591, https://doi.org/10.5194/acp-21-13571-2021, 2021. a
Andrews, D. G., Holton, J. R., and Leovy, C. B.: Middle Atmosphere Dynamics, Academic Press, ISBN 978-0-12-058576-2, 1987. a
Barnes, J. E. and Hofmann, D. J.: Lidar measurements of stratospheric aerosol over Mauna Loa Observatory, Geophys. Rese. Lett., 24, 1923–1926, https://doi.org/10.1029/97GL01943, 1997. a, b, c
Bekki, S.: Oxidation of volcanic SO2: A sink for stratospheric OH and H2O, Geophys. Rese. Lett., 22, 913–916, https://doi.org/10.1029/95GL00534, 1995. a, b, c, d
Bekki, S. and Pyle, J. A.: A two-dimensional modeling study of the volcanic eruption of Mount Pinatubo, J. Geophys. Res.-Atmos., 99, 18861–18869, https://doi.org/10.1029/94JD00667, 1994. a
Bluth, G., Rose, W., Sprod, I., and Krueger, A.: Stratospheric Loading of Sulfur from Explosive Volcanic Eruptions, J. Geol., 105, 671–684, https://doi.org/10.1086/515972, 1997. a, b, c
Bradley, A. M., Bosler, P. A., and Guba, O.: Islet: interpolation semi-Lagrangian element-based transport, Geosci. Model Dev., 15, 6285–6310, https://doi.org/10.5194/gmd-15-6285-2022, 2022. a
Brown, H. Y., Wagman, B., Bull, D., Peterson, K., Hillman, B., Liu, X., Ke, Z., and Lin, L.: Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using the Mount Pinatubo eruption, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-3041, 2024. a, b, c, d, e, f
Burger, M., Wentz, J., and Horton, R.: The Law and Science of Climate Change Attribution, Columbia J. Environ. Law, 45, 184 pp., https://doi.org/10.7916/cjel.v45i1.4730, 2020. a
Butchart, N.: The Brewer-Dobson circulation, Rev. Geophys., 52, 157–184, https://doi.org/10.1002/2013RG000448, 2014. a, b
Butler, A. H., Thompson, D. W. J., and Heikes, R.: The Steady-State Atmospheric Circulation Response to Climate Change – like Thermal Forcings in a Simple General Circulation Model, J. Clim., 23, 3474–3496, https://doi.org/10.1175/2010JCLI3228.1, 2010. a
Clyne, M., Lamarque, J.-F., Mills, M. J., Khodri, M., Ball, W., Bekki, S., Dhomse, S. S., Lebas, N., Mann, G., Marshall, L., Niemeier, U., Poulain, V., Robock, A., Rozanov, E., Schmidt, A., Stenke, A., Sukhodolov, T., Timmreck, C., Toohey, M., Tummon, F., Zanchettin, D., Zhu, Y., and Toon, O. B.: Model physics and chemistry causing intermodel disagreement within the VolMIP-Tambora Interactive Stratospheric Aerosol ensemble, Atmos. Chem. Phys., 21, 3317–3343, https://doi.org/10.5194/acp-21-3317-2021, 2021. a
Cook, K. H.: Role of Continents in Driving the Hadley Cells, J. Atmos. Sci., 60, 957–976, https://doi.org/10.1175/1520-0469(2003)060<0957:ROCIDT>2.0.CO;2, 2003. a, b
Crutzen, P. J.: Albedo Enhancement by Stratospheric Sulfur Injections: A Contribution to Resolve a Policy Dilemma?, Climatic Change, 77, 211–220, https://doi.org/10.1007/s10584-006-9101-y, 2006. a
DallaSanta, K., Gerber, E. P., and Toohey, M.: The Circulation Response to Volcanic Eruptions: The Key Roles of Stratospheric Warming and Eddy Interactions, J. Clim., 32, 1101–1120, https://doi.org/10.1175/JCLI-D-18-0099.1, 2019. a, b
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto‐Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., Kampenhout, L. v., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox‐Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Dutton, E. G. and Christy, J. R.: Solar radiative forcing at selected locations and evidence for global lower tropospheric cooling following the eruptions of El Chichón and Pinatubo, Geophys. Rese. Lett., 19, 2313–2316, https://doi.org/10.1029/92GL02495, 1992. a, b, c, d
Elson, P., Andrade, E. S. d., Lucas, G., May, R., Hattersley, R., Campbell, E., Dawson, A., Little, B., Raynaud, S., scmc72, Snow, A. D., Comer, R., Donkers, K., Blay, B., Killick, P., Wilson, N., Peglar, P., lgolston, lbdreyer, Andrew, Szymaniak, J., Berchet, A., Bosley, C., Davis, L., Filipe, Krasting, J., Bradbury, M., Kirkham, D., Worsley, S., and Havlin, C.: SciTools/cartopy: v0.22.0, Zenodo, https://doi.org/10.5281/zenodo.8216315, 2023. a
Eluszkiewicz, J., Crisp, D., Zurek, R., Elson, L., Fishbein, E., Froidevaux, L., Waters, J., Grainger, R. G., Lambert, A., Harwood, R., and Peckham, G.: Residual Circulation in the Stratosphere and Lower Mesosphere as Diagnosed from Microwave Limb Sounder Data, J. Atmos. Sci., 53, 217–240, https://doi.org/10.1175/1520-0469(1996)053<0217:RCITSA>2.0.CO;2, 1996. a
Eyring, V., Lamarque, J.-F., Hess, P., Arfeuille, F., Bowman, K., Duncan, B., Fiore, A., Gettelman, A., Giorgetta, M. A., Granier, C., Hegglin, M., Kinnison, D., Kunze, M., Langematz, U., Luo, B., Martin, R., Matthes, K., Newman, P. A., Peter, T., Robock, A., Ryerson, T., Saiz-Lopez, A., Salawitch, R., Schultz, M., Shepherd, T. G., Shindell, D., Staehelin, J., Thomason, L., Tilmes, S., Vernier, J.-P., Waugh, D. W., and Young, P. J.: Overview of IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) Community Simulations in Support of Upcoming Ozone and Climate Assessments, SPARC Newsletter, 40, 48–66, 2013. a, b
Fisher, B. L., Krotkov, N. A., Bhartia, P. K., Li, C., Carn, S. A., Hughes, E., and Leonard, P. J. T.: A new discrete wavelength backscattered ultraviolet algorithm for consistent volcanic SO2 retrievals from multiple satellite missions, Atmos. Meas. Tech., 12, 5137–5153, https://doi.org/10.5194/amt-12-5137-2019, 2019. a, b, c
Fleming, E. L., Chandra, S., Barnett, J. J., and Corney, M.: Zonal mean temperature, pressure, zonal wind and geopotential height as functions of latitude, Adv. Space Res., 10, 11–59, https://doi.org/10.1016/0273-1177(90)90386-E, 1990. a, b
Frazer, M. E. and Ming, Y.: Understanding Controlling Factors of Extratropical Humidity and Clouds with an Idealized General Circulation Model, J. Clim., 35, 5321–5337, https://doi.org/10.1175/JCLI-D-21-0580.1, 2022. a
Frierson, D. M. W., Held, I. M., and Zurita-Gotor, P.: A Gray-Radiation Aquaplanet Moist GCM, Part I: Static Stability and Eddy Scale, J. Atmos. Sci., 63, 2548–2566, https://doi.org/10.1175/JAS3753.1, 2006. a
Fujiwara, M., Manney, G. L., Gray, L. J., and Wright, J. S. E.: SPARC Reanalysis Intercomparison Project (S-RIP) Final Report, SPARC Report No. 10, WCRP-6/2021, https://doi.org/10.17874/800dee57d13, 2022. a, b, c
Fulton, D. J. and Hegerl, G. C.: Testing Methods of Pattern Extraction for Climate Data Using Synthetic Modes, J. Clim., 34, 7645–7660, https://doi.org/10.1175/JCLI-D-20-0871.1, 2021. a
Fyfe, J. C., von Salzen, K., Cole, J. N. S., Gillett, N. P., and Vernier, J.-P.: Surface response to stratospheric aerosol changes in a coupled atmosphere–ocean model, Geophys. Rese. Lett., 40, 584–588, https://doi.org/10.1002/grl.50156, 2013. a
Gao, C., Robock, A., and Ammann, C.: Volcanic forcing of climate over the past 1500 years: An improved ice core-based index for climate models, J. Geophys. Res.-Atmos., 113, D23111, https://doi.org/10.1029/2008JD010239, 2008. a
Gerber, E. P. and Manzini, E.: The Dynamics and Variability Model Intercomparison Project (DynVarMIP) for CMIP6: assessing the stratosphere–troposphere system, Geosci. Model Dev., 9, 3413–3425, https://doi.org/10.5194/gmd-9-3413-2016, 2016. a, b
Gerber, E. P. and Polvani, L. M.: Stratosphere–Troposphere Coupling in a Relatively Simple AGCM: The Importance of Stratospheric Variability, J. Clim., 22, 1920–1933, https://doi.org/10.1175/2008JCLI2548.1, 2009. a
Gerber, E. P., Voronin, S., and Polvani, L. M.: Testing the Annular Mode Autocorrelation Time Scale in Simple Atmospheric General Circulation Models, Mon. Weather Rev., 136, 1523–1536, https://doi.org/10.1175/2007MWR2211.1, 2008. a, b, c
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a, b
Guo, S., Bluth, G. J. S., Rose, W. I., Watson, I. M., and Prata, A. J.: Re-evaluation of SO2 release of the 15 June 1991 Pinatubo eruption using ultraviolet and infrared satellite sensors, Geochem. Geophy. Geosy., 5, Q04001, https://doi.org/10.1029/2003GC000654, 2004a. a, b, c
Guo, S., Rose, W. I., Bluth, G. J. S., and Watson, I. M.: Particles in the great Pinatubo volcanic cloud of June 1991: The role of ice, Geochem. Geophy. Geosy., 5, Q05003, https://doi.org/10.1029/2003GC000655, 2004b. a, b, c
Gupta, A., Gerber, E. P., and Lauritzen, P. H.: Numerical impacts on tracer transport: A proposed intercomparison test of Atmospheric General Circulation Models, Q. J. Roy. Meteor. Soc., 146, 3937–3964, https://doi.org/10.1002/qj.3881, 2020. a
Hannah, W. M., Bradley, A. M., Guba, O., Tang, Q., Golaz, J.-C., and Wolfe, J.: Separating Physics and Dynamics Grids for Improved Computational Efficiency in Spectral Element Earth System Models, J. Adv. Model. Earth Sy., 13, e2020MS002419, https://doi.org/10.1029/2020MS002419, 2021. a
Hansen, J., Lacis, A., Ruedy, R., and Sato, M.: Potential climate impact of Mount Pinatubo eruption, Geophys. Rese. Lett., 19, 215–218, https://doi.org/10.1029/91GL02788, 1992. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Harris, L., Chen, X., Putman, W., Zhou, L., and Chen, J.-H.: A Scientific Description of the GFDL Finite-Volume Cubed-Sphere Dynamical Core, Technical Memorandum GFDL2021001, Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, https://github.com/NOAA-GFDL/GFDL_atmos_cubed_sphere/tree/master/docs (last access: 2 February 2024), 2021. a
Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M., Kovats, S., Parmesan, C., Pierce, D., and Stott, P.: Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change, in: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change, IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, 2010. a
Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X. T.: The Pathfinder Atmospheres–Extended AVHRR Climate Dataset, Bull. Am. Meteorol. Soc., 95, 909–922, https://doi.org/10.1175/BAMS-D-12-00246.1, 2014. a
Herrington, A. R., Lauritzen, P. H., Reed, K. A., Goldhaber, S., and Eaton, B. E.: Exploring a Lower-Resolution Physics Grid in CAM-SE-CSLAM, J. Adv. Model. Earth Sy., 11, 1894–1916, https://doi.org/10.1029/2019MS001684, 2019. a
Hollowed, J., Jablonowski, C., Brown, H., Bull, D., Hillman, B., and Hart, J.: Code Supplement for “Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model”, Zenodo [code], https://doi.org/10.5281/zenodo.10524801, 2024. a
Holton, J. R. and Hakim, G. J.: The General Circulation, Chap. 10, in: An Introduction to Dynamic Meteorology (Fifth Edition), edited by: Holton, J. R. and Hakim, G. J., 325–375, Academic Press, Boston, ISBN 978-0-12-384866-6, https://www.sciencedirect.com/science/article/pii/B9780123848666000106 (last access: 3 February 2024), 2013. a
Hoskins, B. J., Yang, G.-Y., and Fonseca, R. M.: The detailed dynamics of the June–August Hadley Cell, Q. J. Roy. Meteor. Soc., 146, 557–575, https://doi.org/10.1002/qj.3702, 2020. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Hughes, O. K. and Jablonowski, C.: A mountain-induced moist baroclinic wave test case for the dynamical cores of atmospheric general circulation models, Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, 2023. a
Jablonowski, C. and Williamson, D.: The Pros and Cons of Diffusion, Filters and Fixers in Atmospheric General Circulation Models, in: Numerical Techniques for Global Atmospheric Models, edited by: Lauritzen, P. H., Jablonowski, C., Taylor, M. A., and Nair, R. D., Vol. 80 of Lecture Notes in Computational Science and Engineering, 381–493, Springer, ISBN 978-3-642-11639-1, 2011. a
Karpechko, A. Y., Gillett, N. P., Dall'Amico, M., and Gray, L. J.: Southern Hemisphere atmospheric circulation response to the El Chichón and Pinatubo eruptions in coupled climate models, Q. J. Roy. Meteor. Soc., 136, 1813–1822, https://doi.org/10.1002/qj.683, 2010. a, b
Kinne, S., Toon, O. B., and Prather, M. J.: Buffering of stratospheric circulation by changing amounts of tropical ozone a Pinatubo Case Study, Geophys. Rese. Lett., 19, 1927–1930, https://doi.org/10.1029/92GL01937, 1992. a
Kovilakam, M., Thomason, L. W., Ernest, N., Rieger, L., Bourassa, A., and Millán, L.: The Global Space-based Stratospheric Aerosol Climatology (version 2.0): 1979–2018, Earth Syst. Sci. Data, 12, 2607–2634, https://doi.org/10.5194/essd-12-2607-2020, 2020. a
Kremser, S., Thomason, L. W., von Hobe, M., Hermann, M., Deshler, T., Timmreck, C., Toohey, M., Stenke, A., Schwarz, J. P., Weigel, R., Fueglistaler, S., Prata, F. J., Vernier, J.-P., Schlager, H., Barnes, J. E., Antuña-Marrero, J.-C., Fairlie, D., Palm, M., Mahieu, E., Notholt, J., Rex, M., Bingen, C., Vanhellemont, F., Bourassa, A., Plane, J. M. C., Klocke, D., Carn, S. A., Clarisse, L., Trickl, T., Neely, R., James, A. D., Rieger, L., Wilson, J. C., and Meland, B.: Stratospheric aerosol – Observations, processes, and impact on climate, Rev. Geophys., 54, 278–335, https://doi.org/10.1002/2015RG000511, 2016. a
Labitzke, K. and McCormick, M. P.: Stratospheric temperature increases due to Pinatubo aerosols, Geophys. Rese. Lett., 19, 207–210, https://doi.org/10.1029/91GL02940, 1992. a
Lauritzen, P. H., Conley, A. J., Lamarque, J.-F., Vitt, F., and Taylor, M. A.: The terminator “toy” chemistry test: a simple tool to assess errors in transport schemes, Geosci. Model Dev., 8, 1299–1313, https://doi.org/10.5194/gmd-8-1299-2015, 2015. a
MacDonald, C. G. and Ming, Y.: Tropical Intraseasonal Variability Response to Zonally Asymmetric Forcing in an Idealized Moist GCM, J. Clim., 35, 8079–8101, https://doi.org/10.1175/JCLI-D-22-0344.1, 2022. a
Mamalakis, A., Ebert-Uphoff, I., and Barnes, E. A.: Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset, Environ. Data Sci., 1, e8, https://doi.org/10.1017/eds.2022.7, 2022. a
Marshall, L. R., Maters, E. C., Schmidt, A., Timmreck, C., Robock, A., and Toohey, M.: Volcanic effects on climate: recent advances and future avenues, B. Volcanol., 84, 54, https://doi.org/10.1007/s00445-022-01559-3, 2022. a
Matthes, K., Marsh, R., Garcia, R., Kinnison, E., Sassi, F., and Walters, S.: Role of the QBO in modulating the influence of the 11 year solar cycle on the atmosphere using constant forcings, J. Geophys. Res., 115, D18110, https://doi.org/10.1029/2009JD013020, 2010. a
May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T.: MetPy: A Meteorological Python Library for Data Analysis and Visualization, Bull. Am. Meteorol. Soc., 103, E2273–E2284, https://doi.org/10.1175/BAMS-D-21-0125.1, 2022. a
McClernon, K., Goode, K., and Ries, D.: A comparison of model validation approaches for echo state networks using climate model replicates, Spat. Stat.-Neth., 59, 100813, https://doi.org/10.1016/j.spasta.2024.100813, 2024. a
McCormick, M. P., Thomason, L. W., and Trepte, C. R.: Atmospheric effects of the Mt Pinatubo eruption, Nature, 373, 399–404, https://doi.org/10.1038/373399a0, 1995. a, b, c
McCusker, K., Battisti, D., and Bitz, C.: The Climate Response to Stratospheric Sulfate Injections and Implications for Addressing Climate Emergencies, J. Clim., 25, 3096–3116, https://doi.org/10.1175/JCLI-D-11-00183.1, 2012. a
Mills, M. J., Schmidt, A., Easter, R., Solomon, S., Kinnison, D. E., Ghan, S. J., Neely, R. R., Marsh, D. R., Conley, A., Bardeen, C. G., and Gettelman, A.: Global volcanic aerosol properties derived from emissions, 1990–2014, using CESM1(WACCM), J. Geophys. Re.-Atmos., 121, 2332–2348, https://doi.org/10.1002/2015JD024290, 2016. a, b, c, d, e
Mills, M. J., Richter, J. H., Tilmes, S., Kravitz, B., MacMartin, D. G., Glanville, A. A., Tribbia, J. J., Lamarque, J.-F., Vitt, F., Schmidt, A., Gettelman, A., Hannay, C., Bacmeister, J. T., and Kinnison, D. E.: Radiative and Chemical Response to Interactive Stratospheric Sulfate Aerosols in Fully Coupled CESM1(WACCM), J. Geophys. Res.-Atmos., 122, 13061–13078, https://doi.org/10.1002/2017JD027006, 2017. a
Ming, Y. and Held, I. M.: Modeling Water Vapor and Clouds as Passive Tracers in an Idealized GCM, J. Clim., 31, 775–786, https://doi.org/10.1175/JCLI-D-16-0812.1, 2018. a
National Academies of Sciences and Medicine, E.: Reflecting Sunlight: Recommendations for Solar Geoengineering Research and Research Governance, The National Academies Press, Washington, DC, ISBN 978-0-309-67605-2, https://nap.nationalacademies.org/catalog/25762/ (last access: 17 January 2024), 2021. a
Office of Science and Technology Policy (OSTP): Congressionally-Mandated Report on Solar Radiation Modification – OSTP, https://www.whitehouse.gov/ostp/news-updates/ (last access: 17 January 2024), 2023. a
Oort, A. H. and Yienger, J. J.: Observed Interannual Variability in the Hadley Circulation and Its Connection to ENSO, J. Clim., 9, 2751–2767, https://www.jstor.org/stable/26201418 (last access: 3 December 2023), 1996. a
Petty, G. W.: A first course in atmospheric radiation, Madison, Wis: Sundog Pub, 2nd Edn., ISBN 978-0-9729033-1-8, 2006. a
Pikovnik, M., Zaplotnik, Ž., Boljka, L., and Žagar, N.: Metrics of the Hadley circulation strength and associated circulation trends, Weather Clim. Dynam., 3, 625–644, https://doi.org/10.5194/wcd-3-625-2022, 2022. a
Polvani, L. M. and Kushner, P. J.: Tropospheric response to stratospheric perturbations in a relatively simple general circulation model, Geophys. Rese. Lett., 29, 18-1–18-4, https://doi.org/10.1029/2001GL014284, 2002. a, b
Ramachandran, S., Ramaswamy, V., Stenchikov, G. L., and Robock, A.: Radiative impact of the Mount Pinatubo volcanic eruption: Lower stratospheric response, J. Geophys. Res.-Atmos., 105, 24409–24429, https://doi.org/10.1029/2000JD900355, 2000. a, b, c
Reed, K. A. and Jablonowski, C.: Idealized tropical cyclone simulations of intermediate complexity: A test case for AGCMs, J. Adv. Model. Earth Sy., 4, M04001, https://doi.org/10.1029/2011MS000099, 2012. a
Rieger, L. A., Cole, J. N. S., Fyfe, J. C., Po-Chedley, S., Cameron-Smith, P. J., Durack, P. J., Gillett, N. P., and Tang, Q.: Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experiment, Geosci. Model Dev., 13, 4831–4843, https://doi.org/10.5194/gmd-13-4831-2020, 2020. a
Robock, A.: Volcanic Eruptions and Climate, Rev. Geophys., 38, 191–219, https://doi.org/10.1029/1998RG000054, 2000. a
Robock, A.: The Climatic Aftermath, Science, 295, 1242–1244, https://doi.org/10.1126/science.1069903, 2002. a
Rosen, J. M.: The Boiling Point of Stratospheric Aerosols, J. Appl. Meteorol. Clim., 10, 1044–1046, https://doi.org/10.1175/1520-0450(1971)010<1044:TBPOSA>2.0.CO;2, 1971. a
Sato, M., Hansen, J. E., McCormick, M. P., and Pollack, J. B.: Stratospheric aerosol optical depths, 1850–1990, J. Geophys. Res.-Atmos., 98, 22987–22994, https://doi.org/10.1029/93JD02553, 1993. a
Schneider, T.: The General Circulation of the Atmosphere, Annu. Rev. Earth Pl. Sc., 34, 655–688, https://doi.org/10.1146/annurev.earth.34.031405.125144, 2006. a
Schneider, T., Bischoff, T., and Haug, G. H.: Migrations and dynamics of the intertropical convergence zone, Nature, 513, 45–53, https://doi.org/10.1038/nature13636, 2014. a
Schurer, A., Hegerl, G., Mann, M., Tett, S., and Phipps, S.: Separating Forced from Chaotic Climate Variability over the Past Millennium, J. Clim., 26, 6954–6973, https://doi.org/10.1175/JCLI-D-12-00826.1, 2013. a
Sheng, J.-X., Weisenstein, D. K., Luo, B.-P., Rozanov, E., Arfeuille, F., and Peter, T.: A perturbed parameter model ensemble to investigate Mt. Pinatubo's 1991 initial sulfur mass emission, Atmos. Chem. Phys., 15, 11501–11512, https://doi.org/10.5194/acp-15-11501-2015, 2015. a
Sheshadri, A., Plumb, R. A., and Gerber, E. P.: Seasonal Variability of the Polar Stratospheric Vortex in an Idealized AGCM with Varying Tropospheric Wave Forcing, J. Atmos. Sci., 72, 2248–2266, https://doi.org/10.1175/JAS-D-14-0191.1, 2015. a
Taylor, M. A., Guba, O., Steyer, A., Ullrich, P. A., Hall, D. M., and Eldred, C.: An Energy Consistent Discretization of the Nonhydrostatic Equations in Primitive Variables, J. Adv. Model. Earth Sy., 12, e2019MS001783, https://doi.org/10.1029/2019MS001783, 2020. a
Thatcher, D. R. and Jablonowski, C.: A moist aquaplanet variant of the Held–Suarez test for atmospheric model dynamical cores, Geosci. Model Dev., 9, 1263–1292, https://doi.org/10.5194/gmd-9-1263-2016, 2016. a
Thomas, M. A., Giorgetta, M. A., Timmreck, C., Graf, H.-F., and Stenchikov, G.: Simulation of the climate impact of Mt. Pinatubo eruption using ECHAM5 – Part 2: Sensitivity to the phase of the QBO and ENSO, Atmos. Chem. Phys., 9, 3001–3009, https://doi.org/10.5194/acp-9-3001-2009, 2009. a
Tilmes, S., Richter, J. H., Mills, M. J., Kravitz, B., MacMartin, D. G., Vitt, F., Tribbia, J. J., and Lamarque, J.-F.: Sensitivity of Aerosol Distribution and Climate Response to Stratospheric SO2 Injection Locations, J. Geophys. Res.-Atmos., 122, 12591–12615, https://doi.org/10.1002/2017JD026888, 2017. a
Tilmes, S., Richter, J. H., Kravitz, B., MacMartin, D. G., Mills, M. J., Simpson, I. R., Glanville, A. S., Fasullo, J. T., Phillips, A. S., Lamarque, J.-F., Tribbia, J., Edwards, J., Mickelson, S., and Ghosh, S.: CESM1(WACCM) Stratospheric Aerosol Geoengineering Large Ensemble Project, Bull. Am. Meteorol. Soc., 99, 2361–2371, https://doi.org/10.1175/BAMS-D-17-0267.1, 2018. a
Timmreck, C.: Modeling the climatic effects of large explosive volcanic eruptions, WIREs Clim. Change, 3, 545–564, https://doi.org/10.1002/wcc.192, 2012. a
Yao, W. and Jablonowski, C.: The Impact of GCM Dynamical Cores on Idealized Sudden Stratospheric Warmings and Their QBO Interactions, J. Atmos. Sci., 73, 3397–3421, https://doi.org/10.1175/JAS-D-15-0242.1, 2016. a, b, c
Yue, G. K., Poole, L. R., Wang, P.-H., and Chiou, E. W.: Stratospheric aerosol acidity, density, and refractive index deduced from SAGE II and NMC temperature data, J. Geophys. Res.-Atmos., 99, 3727–3738, https://doi.org/10.1029/93JD02989, 1994. a, b
Zanchettin, D., Khodri, M., Timmreck, C., Toohey, M., Schmidt, A., Gerber, E. P., Hegerl, G., Robock, A., Pausata, F. S. R., Ball, W. T., Bauer, S. E., Bekki, S., Dhomse, S. S., LeGrande, A. N., Mann, G. W., Marshall, L., Mills, M., Marchand, M., Niemeier, U., Poulain, V., Rozanov, E., Rubino, A., Stenke, A., Tsigaridis, K., and Tummon, F.: The Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP): experimental design and forcing input data for CMIP6, Geosci. Model Dev., 9, 2701–2719, https://doi.org/10.5194/gmd-9-2701-2016, 2016. a
Zhao, T. X.-P., Chan, P. K., and Heidinger, A. K.: A global survey of the effect of cloud contamination on the aerosol optical thickness and its long-term trend derived from operational AVHRR satellite observations, J. Geophys. Res.-Atmos., 118, 2849–2857, https://doi.org/10.1002/jgrd.50278, 2013. a
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering...
