Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4425-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-4425-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
09 Jun 2022
Model experiment description paper |
| 09 Jun 2022
| 09 Jun 2022
Climate projections over the Great Lakes Region: using two-way coupling of a regional climate model with a 3-D lake model
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Xinyu Ye
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Jeremy S. Pal
Department of Civil Engineering and Environmental Science, Loyola Marymount University, Los Angeles, CA, USA
Risk Assessment and Adaptation Strategies Division, Euro-Mediterranean Center on Climate Change and Ca' Foscari University, Venice, Italy
Philip Y. Chu
NOAA/Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA
Miraj B. Kayastha
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Chenfu Huang
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Related authors
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. Discuss., https://doi.org/10.5194/gmd-2024-146, https://doi.org/10.5194/gmd-2024-146, 2024
Preprint under review for GMD
Short summary
Short summary
This study introduces a new lake-ice-atmosphere coupled model that significantly improves winter climate simulation for the Great Lakes compared to traditional one-dimensional (1D) lake models. It better simulates both lake conditions and over-lake atmospheric conditions. More importantly, the study highlights three critical 3D lake processes—ice movement, heat transport, and turbulent mixing—as essential for accurately simulating lake-atmosphere interactions and the Great Lakes’ winter climate.
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 Sacks, Ethan Coon, and Robert Hetland
EGUsphere, https://doi.org/10.5194/egusphere-2024-1555, https://doi.org/10.5194/egusphere-2024-1555, 2024
Short summary
Short summary
We integrate 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 developments in LSMs to WRF-ELM.
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. Discuss., https://doi.org/10.5194/gmd-2024-146, https://doi.org/10.5194/gmd-2024-146, 2024
Preprint under review for GMD
Short summary
Short summary
This study introduces a new lake-ice-atmosphere coupled model that significantly improves winter climate simulation for the Great Lakes compared to traditional one-dimensional (1D) lake models. It better simulates both lake conditions and over-lake atmospheric conditions. More importantly, the study highlights three critical 3D lake processes—ice movement, heat transport, and turbulent mixing—as essential for accurately simulating lake-atmosphere interactions and the Great Lakes’ winter climate.
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 Sacks, Ethan Coon, and Robert Hetland
EGUsphere, https://doi.org/10.5194/egusphere-2024-1555, https://doi.org/10.5194/egusphere-2024-1555, 2024
Short summary
Short summary
We integrate 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 developments in LSMs to WRF-ELM.
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, and Sean G. T. Kelley
Geosci. Model Dev., 15, 6659–6676, https://doi.org/10.5194/gmd-15-6659-2022, https://doi.org/10.5194/gmd-15-6659-2022, 2022
Short summary
Short summary
Application of 1-D lake models coupled within earth-system prediction models will improve accuracy but requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within a weather prediction model to constrain lake temperature evolution. We compared these lake temperature values with other estimates and found much reduced errors (down to 1-2 K). The lake cycling initialization is now applied to two operational US NOAA weather models.
Theo Baracchini, Philip Y. Chu, Jonas Šukys, Gian Lieberherr, Stefan Wunderle, Alfred Wüest, and Damien Bouffard
Geosci. Model Dev., 13, 1267–1284, https://doi.org/10.5194/gmd-13-1267-2020, https://doi.org/10.5194/gmd-13-1267-2020, 2020
Short summary
Short summary
Lake physical processes occur at a wide range of spatiotemporal scales. 3D hydrodynamic lake models are the only information source capable of solving those scales; however, they still need observations to be calibrated and to constrain their uncertainties. The optimal combination of a 3D hydrodynamic model, in situ measurements, and remote sensing observations is achieved through data assimilation. Here we present a complete data assimilation experiment for lakes using open-source tools.
Related subject area
Climate and Earth system modeling
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
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Architectural Insights and Training Methodology Optimization of Pangu-Weather
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
Short-term effects of hurricanes on nitrate-nitrogen runoff loading: a case study of Hurricane Ida using E3SM land model (v2.1)
CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
GOSI9: UK Global Ocean and Sea Ice configurations
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
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.
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.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
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.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
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.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
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.
Deifilia Aurora To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
EGUsphere, https://doi.org/10.5194/egusphere-2024-1714, https://doi.org/10.5194/egusphere-2024-1714, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers three-dimensional 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 accessibility of training and working with the model.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Maria Rosa 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. Discuss., https://doi.org/10.5194/gmd-2024-73, https://doi.org/10.5194/gmd-2024-73, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Observational data and modelling capabilities are expanding in recent years, but there are still barriers preventing these two data sources to be used in synergy. Proper comparison requires generating, storing and handling a large amount of data. This manuscript 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.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1456, https://doi.org/10.5194/egusphere-2024-1456, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant covariances during convective and frontal precipitation events. Common statistical downscaling techniques preserve expected covariances during convective precipitation. However, they dampen future intensification of frontal precipitation captured in global climate models and dynamical downscaling. This suggests statistical downscaling may not fully resolve non-stationary hydrologic processes as compared to dynamical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-70, https://doi.org/10.5194/gmd-2024-70, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Hurricanes may worsen the 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 LMRB during Hurricane Ida in 2021, but less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni G. Seijo-Ellis, Donata Giglio, Gustavo M. Marques, and Frank O. Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1378, https://doi.org/10.5194/egusphere-2024-1378, 2024
Short summary
Short summary
A CESM/MOM6 regional configuration of the Caribbean Sea was developed as a response to the rising need of high-resolution models for climate impact studies. The configuration is validated for the period of 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 across the multiple passages in the Caribbean Sea agree with observations.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Catherine Guiavarc'h, Dave Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene T. Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
EGUsphere, https://doi.org/10.5194/egusphere-2024-805, https://doi.org/10.5194/egusphere-2024-805, 2024
Short summary
Short summary
GOSI9 is the new UK’s hierarchy of global ocean and sea ice models. Developed as part of a collaboration between several UK research institutes it will be used for various applications such as weather forecast and climate prediction. The models, based on NEMO, are available at three resolutions 1°, ¼° and 1/12°. GOSI9 improves upon previous version by reducing global temperature and salinity biases and enhancing the representation of the Arctic sea ice and of the Antarctic Circumpolar Current.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Cited articles
Anderson, E. J., Fujisaki-Manome, A., Kessler, J., Lang, G. A., Chu, P. Y.,
Kelley, J. G., Chen, Y., and Wang, J.: Ice forecasting in the next-generation
Great Lakes operational forecast system (GLOFS), J. Mar. Sci.
Eng., 6, 123, https://doi.org/10.3390/jmse6040123, 2018. a, b, c
Austin, J. and Colman, S.: A century of temperature variability in Lake
Superior, Limnol. Oceanogr., 53, 2724–2730, 2008. a
Austin, J. A. and Colman, S. M.: Lake Superior summer water temperatures are
increasing more rapidly than regional air temperatures: A positive ice-albedo
feedback, Geophys. Res. Lett., 34, L06604, https://doi.org/10.1029/2006GL029021, 2007. a
Basile, S. J., Rauscher, S. A., and Steiner, A. L.: Projected precipitation
changes within the Great Lakes and Western Lake Erie Basin: a multi-model
analysis of intensity and seasonality, Int. J. Climatol.,
37, 4864–4879, 2017. a
Briley, L. J., Rood, R. B., and Notaro, M.: Large lakes in climate models: A
Great Lakes case study on the usability of CMIP5, J. Great Lakes
Res., 47, 405–418, 2021. a
Byun, K., Chiu, C.-M., and Hamlet, A. F.: Effects of 21st century climate
change on seasonal flow regimes and hydrologic extremes over the Midwest and
Great Lakes region of the US, Sci. Total Environ., 650,
1261–1277, 2019. a
Chen, C., Liu, H., and Beardsley, R. C.: An unstructured grid, finite-volume,
three-dimensional, primitive equations ocean model: application to coastal
ocean and estuaries, J. Atmos. Ocean. Tech., 20,
159–186, 2003. a
Chen, C., Beardsley, R.C., Cowles, G., Qi, J., Lai, Z., Gao, G., David Stuebe, D., Liu, H., Xu, Q., Xue. P., Ge, J., Hu, S., Ji, R., Tian, R., Huang, H., Wu, L., Lin, H., Sun, Y., and Zhao L.: An unstructured grid, finite-volume community ocean model, FVCOM user manual, 4th Edn., SMAST/UMASSD Technical Report-13-0701, 404 pp., 2013. a
Collingsworth, P. D., Bunnell, D. B., Murray, M. W., Kao, Y.-C., Feiner, Z. S.,
Claramunt, R. M., Lofgren, B. M., Höök, T. O., and Ludsin, S. A.:
Climate change as a long-term stressor for the fisheries of the Laurentian
Great Lakes of North America, Rev. Fish Biol. Fish., 27,
363–391, 2017. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
Daloğlu, I., Cho, K. H., and Scavia, D.: Evaluating causes of trends in
long-term dissolved reactive phosphorus loads to Lake Erie, Environ.
Sci. Technol., 46, 10660–10666, 2012. a
Delaney, F. and Milner, G.: The State of Climate Modeling in the Great Lakes
Basin – A Synthesis in Support of a Workshop held on June 27, 2019 in Ann
Arbor, MI, https://climateconnections.ca/app/uploads/2020/05/The-State-of-Climate-Modeling-in-the-Great-Lakes-Basin_Sept132019.pdf (last access: 31 May 2022), 2019. a
d'Orgeville, M., Peltier, W. R., Erler, A. R., and Gula, J.: Climate change
impacts on Great Lakes Basin precipitation extremes, J. Geophys.
Res.-Atmos., 119, 10–799, 2014. a
Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., Zahn, M., Feser, F.,
Rockel, B., Storch, H. v., Winterfeldt, J., and Zahn, M.: Regional climate
models add value to global model data: a review and selected examples, B. Am.
Meteorol. Soc., 92, 1181–1192, 2011. a
Fujisaki, A., Wang, J., Hu, H., Schwab, D. J., Hawley, N., and Rao, Y. R.: A
modeling study of ice–water processes for Lake Erie applying coupled
ice-circulation models, J. Great Lakes Res., 38, 585–599, 2012. a
Fujisaki, A., Wang, J., Bai, X., Leshkevich, G., and Lofgren, B.:
Model-simulated interannual variability of Lake Erie ice cover, circulation,
and thermal structure in response to atmospheric forcing, 2003–2012, J.
Geophys. Res.-Oceans, 118, 4286–4304, 2013. a
Giorgi, F.: Thirty years of regional climate modeling: where are we and where
are we going next?, J. Geophys. Res.-Atmos., 124,
5696–5723, 2019. a
Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M. B., Bi, X., Elguindi, N., Diro, G. T., Nair, V., Giuliani, G., Turuncoglu, U. U., Cozzini, S., Güttler, I., O'Brien, T. A., Tawfik, A. B., Shalaby, A., Zakey, A. S., Steiner, A. L., Stordal, F., Sloan, L. C., and Brankovic, C.: RegCM4: model description and
preliminary tests over multiple CORDEX domains, Clim. Res., 52, 7–29,
2012. a
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3. 10
Dataset, Int. J. Climatol., 34, 623–642, 2014. a
Hayhoe, K., VanDorn, J., Croley II, T., Schlegal, N., and Wuebbles, D.:
Regional climate change projections for Chicago and the US Great Lakes,
J. Great Lakes Res., 36, 7–21, 2010. a
Hostetler, S. W., Bates, G. T., and Giorgi, F.: Interactive coupling of a lake
thermal model with a regional climate model, J. Geophys. Res.-Atmos., 98, 5045–5057, 1993. a
Huang, C.: Model Code for manuscript “Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model”, Zenodo [code], https://doi.org/10.5281/zenodo.6534139, 2022a. a
Huang, C.: Validation Data used for manuscript “Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model”, Zenodo [data set], https://doi.org/10.5281/zenodo.6540504, 2022b. a
Huang, C., Kuczynski, A., Auer, M. T., O’Donnell, D. M., and Xue, P.:
Management transition to the Great Lakes nearshore: Insights from
hydrodynamic modeling, J. Mar. Sci. Eng., 7, 129, https://doi.org/10.3390/jmse7050129,
2019. a
Huang, C., Zhu, L., Ma, G., Meadows, G. A., and Xue, P.: Wave Climate
Associated With Changing Water Level and Ice Cover in Lake Michigan,
Front. Marine Sci., 8, 746916, https://doi.org/10.3389/fmars.2021.746916, 2021b. a, b
Ibrahim, H. D., Xue, P., and Eltahir, E. A.: Multiple salinity equilibria and
resilience of Persian/Arabian Gulf basin salinity to brine discharge,
Front. Marine Sci., 7, 573, https://doi.org/10.3389/fmars.2020.00573, 2020. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, editedby: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, in press, 2021. a
Jones, M. L., Shuter, B. J., Zhao, Y., and Stockwell, J. D.: Forecasting
effects of climate change on Great Lakes fisheries: models that link habitat
supply to population dynamics can help, Can. J. Fish.
Aqua. Sci., 63, 457–468, 2006. a
Lynch, A. J., Myers, B. J., Chu, C., Eby, L. A., Falke, J. A., Kovach, R. P.,
Krabbenhoft, T. J., Kwak, T. J., Lyons, J., Paukert, C. P., and Whitney, J. E.: Climate
change effects on North American inland fish populations and assemblages,
Fisheries, 41, 346–361, 2016. a
MacKay, M. and Seglenieks, F.: On the simulation of Laurentian Great Lakes
water levels under projections of global climate change, Climatic Change,
117, 55–67, 2013. a
Mailhot, E., Music, B., Nadeau, D. F., Frigon, A., and Turcotte, R.: Assessment
of the Laurentian Great Lake'’ hydrological conditions in a changing
climate, Climatic Change, 157, 243–259, 2019. a
McCormick, M. J. and Fahnenstiel, G. L.: Recent climatic trends in nearshore
water temperatures in the St. Lawrence Great Lakes, Limnol.
Oceanogr., 44, 530–540, 1999. a
Melillo, J. M., Richmond, T., and Yohe, G. (Eds.): Climate Change Impacts in the United States: The Third National Climate Assessment, U.S. Global Change Research Program, 841 pp., https://doi.org/10.7930/J0Z31WJ2,
2014. a
Miao, C., Duan, Q., Sun, Q., Huang, Y., Kong, D., Yang, T., Ye, A., Di, Z., and
Gong, W.: Assessment of CMIP5 climate models and projected temperature
changes over Northern Eurasia, Environ. Res. Lett., 9, 055007, https://doi.org/10.1088/1748-9326/9/5/055007,
2014. a
Music, B., Frigon, A., Lofgren, B., Turcotte, R., and Cyr, J.-F.: Present and
future Laurentian Great Lakes hydroclimatic conditions as simulated by
regional climate models with an emphasis on Lake Michigan-Huron, Climatic
Change, 130, 603–618, 2015. a
Notaro, M., Zhong, Y., Xue, P., Peters-Lidard, C., Cruz, C., Kemp, E.,
Kristovich, D., Kulie, M., Wang, J., Huang, C., and Vavrus, S. J.: Cold Season
Performance of the NU-WRF Regional Climate Model in the Great Lakes Region,
J. Hydrometeorol., 22, 2423–2454, 2021. a
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis,
S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A.,
Fisher, R., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L.,
Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and
Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model
(CLM), Tech. rep., https://doi.org/10.5065/D6RR1W7M, 2013. a
Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter,
J., Levis, S., Swenson, S. C., Thornton, E., Feddema, J., Heald, C. L.,
Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.: Technical
description of version 4.0 of the Community Land Model (CLM), https://doi.org/10.5065/D6FB50WZ, 2010. a
Poesch, M. S., Chavarie, L., Chu, C., Pandit, S. N., and Tonn, W.: Climate
change impacts on freshwater fishes: a Canadian perspective, Fisheries, 41,
385–391, 2016. a
Pryor, S. C., Scavia, D., Downer, C., Gaden, M., Iverson, L., Nordstrom, R.,
Patz, J., and Robertson, G. P.: chap. 18: Midwest. Climate change impacts in the United
States: The third national climate assessment, in: National Climate Assessment Report, edited by: Melillo, J. M., Richmond, T. C.,
and Yohe, G. W., Washington, DC: US Global
Change Research Program, 418–440, https://doi.org/10.7930/J0J1012N, 2014. a
Rau, E., Vaccaro, L., Riseng, C., and Read, J.: The dynamic great lakes economy
employment trends from 2009 to 2018, https://www.michiganseagrant.org/wp-content/uploads/2020/10/MICHU-20-203Great-Lakes-Jobs-Report.pdf (last access: 31 May 2022), 2020. a
Scavia, D., Allan, J. D., Arend, K. K., Bartell, S., Beletsky, D., Bosch,
N. S., Brandt, S. B., Briland, R. D., Daloğlu, I., DePinto, J. V.,
Dolan, D. M., Evans, M. A., Farmer, T. M., Goto, D., Han, H., Höök, T. O., Knight, R., Ludsin, S. A., Mason, D., Michalak, A. M., Richards, R. P., Roberts, J. J., Rucinski, D. K., Rutherford, E., Schwab, D. J., Sesterhenn, T. M., Zhang, H., and Zho, Y.: Assessing and addressing the re-eutrophication of Lake Erie: Central
basin hypoxia, J. Great Lakes Res., 40, 226–246, 2014. a
Schwalm, C. R., Glendon, S., and Duffy, P. B.: RCP8. 5 tracks cumulative CO2
emissions, P. Natl. Acad. Sci. USA, 117,
19656–19657, 2020. a
Sharma, S., Jackson, D. A., Minns, C. K., and Shuter, B. J.: Will northern fish
populations be in hot water because of climate change?, Glob. Change
Biol., 13, 2052–2064, 2007. a
Shi, Q. and Xue, P.: Impact of lake surface temperature variations on lake
effect snow over the Great Lakes region, J. Geophys. Res.-Atmos., 124, 12553–12567, 2019. a
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for climate
simulations: Model structure, evaluation, and sensitivity analyses in CESM1,
J. Adv. Model. Earth Sy., 4, M02001, https://doi.org/10.1029/2011MS000072, 2012. a
Sun, L., Liang, X.-Z., and Xia, M.: Developing the Coupled CWRF-FVCOM Modeling
System to Understand and Predict Atmosphere-Watershed Interactions Over the
Great Lakes Region, J. Adv. Model. Earth Sy., 12,
e2020MS002319, https://doi.org/10.1029/2020MS002319, 2020. a
Wang, G., Yu, M., Pal, J. S., Mei, R., Bonan, G. B., Levis, S., and Thornton,
P. E.: On the development of a coupled regional climate–vegetation model
RCM–CLM–CN–DV and its validation in Tropical Africa, Clim. Dynam., 46,
515–539, 2016. a
Wang, J., Bai, X., Hu, H., Clites, A., Colton, M., and Lofgren, B.: Temporal
and spatial variability of Great Lakes ice cover, 1973–2010, J.
Climate, 25, 1318–1329, 2012. a
Wang, S., Sun, X., and Lall, U.: A hierarchical Bayesian regression model for
predicting summer residential electricity demand across the USA, Energy, 140,
601–611, 2017. a
Woolway, R. I. and Merchant, C. J.: Worldwide alteration of lake mixing regimes
in response to climate change, Nat. Geosci., 12, 271–276, 2019. a
Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A., Golub, M.,
Mercado-Bettín, D., Perroud, M., Stepanenko, V., Tan, Z., Grant, L.,
Ladwig, R., Mesman, J., Moore, T. N., Shatwell, T., Vanderkelen, I., Austin, J. A., DeGasperi, C. L., Dokulil, M., La Fuente, S., Mackay, E. B., Schladow, S. G., Watanabe, S., Marcé, R., Pierson, D. C., Thiery, W., and Jennings, E.: Phenological shifts in lake stratification under climate change,
Nat. Commun., 12, 1–11, 2021. a, b
Wuebbles, D., Cardinale, B., Cherkauer, K., Davidson-Arnott, R., Hellmann, J.,
Infante, D., and Ballinger, A.: An assessment of the impacts of climate
change on the Great Lakes, Environmental Law & Policy Center, https://elpc.org/wp-content/uploads/2020/04/2019-ELPCPublication-Great-Lakes-Climate-Change-Report.pdf (last access: 31 May 2022), 2019.
a, b, c, d
Xue, P., Eltahir, E. A., Malanotte-Rizzoli, P., and Wei, J.: Local feedback
mechanisms of the shallow water region around the M aritime C ontinent,
J. Geophys. Res.-Oceans, 119, 6933–6951, 2014. a
Xue, P., Malanotte-Rizzoli, P., Wei, J., and Eltahir, E. A.: Coupled
ocean-atmosphere modeling over the Maritime Continent: A review, J.
Geophys. Res.-Oceans, 125, e2019JC014978, https://doi.org/10.1029/2019JC014978, 2020. a
Ye, X., Anderson, E. J., Chu, P. Y., Huang, C., and Xue, P.: Impact of water
mixing and ice formation on the warming of Lake Superior: A model-guided
mechanism study, Limnol. Oceanogr., 64, 558–574, 2019. a
Ye, X., Chu, P. Y., Anderson, E. J., Huang, C., Lang, G. A., and Xue, P.:
Improved thermal structure simulation and optimized sampling strategy for
Lake Erie using a data assimilative model, J. Great Lakes Res.,
46, 144–158, 2020. a
Zhang, L., Zhao, Y., Hein-Griggs, D., and Ciborowski, J. J.: Projected monthly
temperature changes of the Great Lakes Basin, Environ. Res., 167,
453–467, 2018. a
Zhong, Y., Notaro, M., Vavrus, S. J., and Foster, M. J.: Recent accelerated
warming of the Laurentian Great Lakes: Physical drivers, Limnol.
Oceanogr., 61, 1762–1786, 2016. a
Short summary
The Great Lakes are the world's largest freshwater system. They are a key element in regional climate influencing local weather patterns and climate processes. Many of these complex processes are regulated by interactions of the atmosphere, lake, ice, and surrounding land areas. This study presents a Great Lakes climate change projection that employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes.
The Great Lakes are the world's largest freshwater system. They are a key element in regional...
