Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-4817-2018
© Author(s) 2018. 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-11-4817-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
04 Dec 2018
Model evaluation paper |
| 04 Dec 2018
| 04 Dec 2018
Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations
Michael A. Brunke
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, The University of
Arizona, Tucson, AZ 85719, USA
John J. Cassano
Cooperative Institute for Research in Environmental Sciences and
Department of Atmospheric and Oceanic Sciences, University of Colorado,
Boulder, CO 80309, USA
Nicholas Dawson
Idaho Power, Boise, ID 83702, USA
Alice K. DuVivier
National Center for Atmospheric Research, Boulder, CO 80305, USA
William J. Gutowski Jr.
Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA 50011, USA
Joseph Hamman
National Center for Atmospheric Research, Boulder, CO 80305, USA
Wieslaw Maslowski
Department of Oceanography, Naval Postgraduate School, Monterey, CA
93943, USA
Bart Nijssen
Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA 98195, USA
J. E. Jack Reeves Eyre
Department of Hydrology and Atmospheric Sciences, The University of
Arizona, Tucson, AZ 85719, USA
José C. Renteria
U.S. Department of Defense, High Performance Computing Modernization
Program, Lorton, VA 22079, USA
Andrew Roberts
Department of Oceanography, Naval Postgraduate School, Monterey, CA
93943, USA
Xubin Zeng
Department of Hydrology and Atmospheric Sciences, The University of
Arizona, Tucson, AZ 85719, USA
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Milena Veneziani, Wieslaw Maslowski, Younjoo J. Lee, Gennaro D'Angelo, Robert Osinski, Mark R. Petersen, Wilbert Weijer, Anthony P. Craig, John D. Wolfe, Darin Comeau, and Adrian K. Turner
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John J. Cassano, Melissa A. Nigro, Mark W. Seefeldt, Marwan Katurji, Kelly Guinn, Guy Williams, and Alice DuVivier
Earth Syst. Sci. Data, 13, 969–982, https://doi.org/10.5194/essd-13-969-2021, https://doi.org/10.5194/essd-13-969-2021, 2021
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Laura E. Queen, Philip W. Mote, David E. Rupp, Oriana Chegwidden, and Bart Nijssen
Hydrol. Earth Syst. Sci., 25, 257–272, https://doi.org/10.5194/hess-25-257-2021, https://doi.org/10.5194/hess-25-257-2021, 2021
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Using a large ensemble of simulated flows throughout the northwestern USA, we compare daily flood statistics in the past (1950–1999) and future (2050–1999) periods and find that nearly all locations will experience an increase in flood magnitudes. The flood season expands significantly in many currently snow-dominant rivers, moving from only spring to both winter and spring. These results, properly extended, may help inform flood risk management and negotiations of the Columbia River Treaty.
Bram Droppers, Wietse H. P. Franssen, Michelle T. H. van Vliet, Bart Nijssen, and Fulco Ludwig
Geosci. Model Dev., 13, 5029–5052, https://doi.org/10.5194/gmd-13-5029-2020, https://doi.org/10.5194/gmd-13-5029-2020, 2020
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Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Simon P. Alexander, John J. Cassano, Sally Garrett, Jamie Halla, Sean Hartery, Mike J. Harvey, Simon Parsons, Graeme Plank, Vidya Varma, and Jonny Williams
Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, https://doi.org/10.5194/acp-20-6607-2020, 2020
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We evaluate clouds over the Southern Ocean in the climate model HadGEM3 and reanalysis MERRA-2 using ship-based ceilometer and radiosonde observations. We find the models underestimate cloud cover by 18–25 %, with clouds below 2 km dominant in reality but lacking in the models. We find a strong link between clouds, atmospheric stability and sea surface temperature in observations but not in the models, implying that sub-grid processes do not generate enough cloud in response to these conditions.
David Dziubanski, Kristie J. Franz, and William Gutowski
Hydrol. Earth Syst. Sci., 24, 2873–2894, https://doi.org/10.5194/hess-24-2873-2020, https://doi.org/10.5194/hess-24-2873-2020, 2020
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We describe a socio-hydrologic model that couples an agent-based model (ABM) of human decision-making with a hydrologic model. We establish this model for a typical agricultural watershed in Iowa, USA, and simulate the evolution of large discharge events over a 47-year period under changing land use. Using this modeling approach, relationships between seemingly unrelated variables such as crop markets or crop yields and local peak flow trends are quantified.
Hossein Dadashazar, Ewan Crosbie, Mohammad S. Majdi, Milad Panahi, Mohammad A. Moghaddam, Ali Behrangi, Michael Brunke, Xubin Zeng, Haflidi H. Jonsson, and Armin Sorooshian
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Clearings in the marine-boundary-layer (MBL) cloud deck of the Pacific Ocean were studied. Remote sensing, reanalysis, and airborne data were used along with machine-learning modeling to characterize the spatiotemporal nature of clearings and factors governing their growth. The most significant implications of our results are linked to modeling of fog and MBL clouds, with implications for societal and environmental issues like climate, military operations, transportation, and coastal ecology.
Alice K. DuVivier, Patricia DeRepentigny, Marika M. Holland, Melinda Webster, Jennifer E. Kay, and Donald Perovich
The Cryosphere, 14, 1259–1271, https://doi.org/10.5194/tc-14-1259-2020, https://doi.org/10.5194/tc-14-1259-2020, 2020
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In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system changes. We analyze climate model data from a group of experiments and follow virtual sea ice floes throughout a year. The modeled sea ice conditions along possible tracks are highly variable. Observations that sample a wide range of sea ice conditions and represent the variety and diversity in possible conditions are necessary for improving climate model parameterizations over all types of sea ice.
Yixin Mao, Wade T. Crow, and Bart Nijssen
Hydrol. Earth Syst. Sci., 24, 615–631, https://doi.org/10.5194/hess-24-615-2020, https://doi.org/10.5194/hess-24-615-2020, 2020
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The new generation of satellite soil moisture observations are used to correct the streamflow in a regional-scale river basin simulated by a mathematical model. The correction is done via both the direct updating of soil moisture and correction of rainfall input. Results show some streamflow improvement, but the magnitude is small. A larger improvement will need future generations of even higher-quality satellite soil moisture data and better process representation in the mathematical model.
John R. Yearsley, Ning Sun, Marisa Baptiste, and Bart Nijssen
Hydrol. Earth Syst. Sci., 23, 4491–4508, https://doi.org/10.5194/hess-23-4491-2019, https://doi.org/10.5194/hess-23-4491-2019, 2019
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This study investigates the impact of dam-induced hydrologic alterations and modification of riparian buffers on stream temperatures and thermal habitat for aquatic species. We enhanced and applied a model system (DHSVM-RBM) that couples a distributed hydrologic model, DHSVM, with the distributed stream temperature model, RBM, in the Farmington River basin in the Connecticut River system, which includes varying types of watershed development (e.g., deforestation and reservoirs).
Andrew R. Bennett, Joseph J. Hamman, and Bart Nijssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-179, https://doi.org/10.5194/gmd-2019-179, 2019
Preprint withdrawn
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MetSim is a software package for simulating meteorologic processes, and aims to be applied in the environmental and Earth sciences. It can simulate processes such as solar and thermal radiation, specific humidity, and vapor pressure across large spatial areas in an efficient manner. This paper describes the software and analyzes it's ability to be used in large simulations. We describe how MetSim can be used and provide details on the various options that are available.
Joseph J. Hamman, Bart Nijssen, Theodore J. Bohn, Diana R. Gergel, and Yixin Mao
Geosci. Model Dev., 11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, https://doi.org/10.5194/gmd-11-3481-2018, 2018
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Variable Infiltration Capacity (VIC) is a widely used hydrologic model. This paper documents the development of VIC version 5, which includes a reconfiguration of the model source code to support a wider range of modeling applications. It also represents a significant step forward for the VIC user community in terms of support for a range of modeling applications, reproducibility, and scientific robustness.
Abraham Endalamaw, W. Robert Bolton, Jessica M. Young-Robertson, Don Morton, Larry Hinzman, and Bart Nijssen
Hydrol. Earth Syst. Sci., 21, 4663–4680, https://doi.org/10.5194/hess-21-4663-2017, https://doi.org/10.5194/hess-21-4663-2017, 2017
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This study applies plot-scale and hill-slope knowledge to a process-based mesoscale model to improve the skill of distributed hydrological models to simulate the spatially and basin-integrated hydrological processes of complex ecosystems in the sub-arctic boreal forest. We developed a sub-grid parameterization method to parameterize the surface heterogeneity of interior Alaskan discontinuous permafrost watersheds.
Pablo A. Mendoza, Andrew W. Wood, Elizabeth Clark, Eric Rothwell, Martyn P. Clark, Bart Nijssen, Levi D. Brekke, and Jeffrey R. Arnold
Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, https://doi.org/10.5194/hess-21-3915-2017, 2017
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Water supply forecasts are critical to support water resources operations and planning. The skill of such forecasts depends on our knowledge of (i) future meteorological conditions and (ii) the amount of water stored in a basin. We address this problem by testing several approaches that make use of these sources of predictability, either separately or in a combined fashion. The main goal is to understand the marginal benefits of both information and methodological complexity in forecast skill.
J. E. Jack Reeves Eyre and Xubin Zeng
The Cryosphere, 11, 1591–1605, https://doi.org/10.5194/tc-11-1591-2017, https://doi.org/10.5194/tc-11-1591-2017, 2017
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We have used extensive air temperature measurements (~ 1400 station-years) to assess more than 10 gridded datasets over the Greenland ice sheet. We recommend the best datasets for estimating past melting of the ice sheet and show that choice of dataset is important for evaluating 31 earth system models. Most, but not all, of the datasets show similar history of temperature changes over the 20th century, and the earth system models generally capture long-term warming but not decadal variations.
William J. Gutowski Jr., Filippo Giorgi, Bertrand Timbal, Anne Frigon, Daniela Jacob, Hyun-Suk Kang, Krishnan Raghavan, Boram Lee, Christopher Lennard, Grigory Nikulin, Eleanor O'Rourke, Michel Rixen, Silvina Solman, Tannecia Stephenson, and Fredolin Tangang
Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, https://doi.org/10.5194/gmd-9-4087-2016, 2016
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The Coordinated Regional Downscaling Experiment (CORDEX) is a diagnostic MIP in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global output to produce downscaled projected changes in regional climates, and assess sources of uncertainties in the projections.
Naoki Mizukami, Martyn P. Clark, Kevin Sampson, Bart Nijssen, Yixin Mao, Hilary McMillan, Roland J. Viger, Steve L. Markstrom, Lauren E. Hay, Ross Woods, Jeffrey R. Arnold, and Levi D. Brekke
Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016, https://doi.org/10.5194/gmd-9-2223-2016, 2016
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mizuRoute version 1 is a stand-alone runoff routing tool that post-processes runoff outputs from any distributed hydrologic models to produce streamflow estimates in large-scale river network. mizuRoute is flexible to river network representation and includes two different river routing schemes. This paper demonstrates mizuRoute's capability of multi-decadal streamflow estimations in the river networks over the entire contiguous Unites States, which contains over 54 000 river segments.
John J. Cassano, Mark W. Seefeldt, Scott Palo, Shelley L. Knuth, Alice C. Bradley, Paul D. Herrman, Peter A. Kernebone, and Nick J. Logan
Earth Syst. Sci. Data, 8, 115–126, https://doi.org/10.5194/essd-8-115-2016, https://doi.org/10.5194/essd-8-115-2016, 2016
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In September 2012 five Aerosonde unmanned aircraft were used to observe the atmosphere and ocean over the Terra Nova Bay polynya, Antarctica to explore the details of interactions between the ocean, sea ice, and atmosphere. A total of 14 flights and nearly 168 flight hours were completed as part of this project. A data set containing the atmospheric and surface data as well as operational aircraft data have been submitted to the United States Antarctic Program Data Coordination Center.
R. Rosolem, T. Hoar, A. Arellano, J. L. Anderson, W. J. Shuttleworth, X. Zeng, and T. E. Franz
Hydrol. Earth Syst. Sci., 18, 4363–4379, https://doi.org/10.5194/hess-18-4363-2014, https://doi.org/10.5194/hess-18-4363-2014, 2014
A. I. Gevaert, A. J. Teuling, R. Uijlenhoet, S. B. DeLong, T. E. Huxman, L. A. Pangle, D. D. Breshears, J. Chorover, J. D. Pelletier, S. R. Saleska, X. Zeng, and P. A. Troch
Hydrol. Earth Syst. Sci., 18, 3681–3692, https://doi.org/10.5194/hess-18-3681-2014, https://doi.org/10.5194/hess-18-3681-2014, 2014
P. Shao, X. Zeng, and X. Zeng
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esdd-5-991-2014, https://doi.org/10.5194/esdd-5-991-2014, 2014
Revised manuscript not accepted
S. L. Knuth, J. J. Cassano, J. A. Maslanik, P. D. Herrmann, P. A. Kernebone, R. I. Crocker, and N. J. Logan
Earth Syst. Sci. Data, 5, 57–69, https://doi.org/10.5194/essd-5-57-2013, https://doi.org/10.5194/essd-5-57-2013, 2013
Related subject area
Climate and Earth system modeling
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
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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
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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
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The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
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ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
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Climate model downscaling in central Asia: a dynamical and a neural network approach
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Software sustainability of global impact models
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ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
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Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
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
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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
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
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
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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
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version 2 Global Precipitation Climatology
Project (GPCP) monthly precipitation analysis (1979–present), J.
Hydrometeorol., 4, 1147–1167, 2003.
Adler, R. F., Gu, G., and Huffman, G. J.: Estimating climatological bias
errors for the Global Precipitation Climatology Project (GPCP), J. Appl. Meteorol. Clim., 51, 84–99,
https://doi.org/10.1175/JAMC-D-11-052.1, 2012.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W.,
Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala,
T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the temporal and
spatial variability of ecosystem-scale carbon dioxide, water vapor, and
energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.
Barlage, M., Zeng, X., Wei, H., and Mitchell, K. E.: A global 0.05∘
maximum albedo dataset of snow-covered land based on MODIS observations,
Geophys. Res. Lett., 32, L17405, https://doi.org/10.1029/2005GL022881, 2005.
Behrangi, A., Christensen, M., Richardson, M., Lebsock, M., Stephens, G.,
Huffman, G. J., Bolvin, D., Adler, R. F., Gardner, A., Lambrigtsen, B., and
Fetzer, E.: Status of high-latitude precipitation estimates from observations
and reanalyses, J. Geophys. Res., 121, 4468–4486,
https://doi.org/10.1002/2015JD024546, 2016.
Betts, A. K., Ball, J. H., Barr, A. G., Black, T. A., McCaughey, J. H., and
Viterbo, P.: Assessing land-surface-atmosphere coupling in the ERA-40
reanalysis with boreal forest data, Agr. Forest Meteorol., 140, 365–382,
https://doi.org/10.1016/j.agrformet.2006.08.009, 2006.
Berg, P., Döscher, R., and Koenigk, T.: Impacts of using spectral nudging
on regional climate model RCA4 simulations of the Arctic, Geosci. Model Dev.,
6, 849–859, https://doi.org/10.5194/gmd-6-849-2013, 2013.
Berg, P., Döscher, R., and Koenigk, T.: On the effects of constraining
atmospheric circulation in a coupled atmosphere-ocean Arctic regional climate
model, Clim. Dynam., 46, 3499–3515, https://doi.org/10.1007/s00382-015-2783-y, 2016.
Bromwich, D. H., Cassano, J. J., Klein, T., Heinemann, G., Hines, K. M.,
Steffen, K., and Box, J. E.: Mesoscale modeling of katabatic winds over
Greenland with the Polar MM5, Mon. Weather Rev., 129, 2290–2309, 2001.
Bromwich, D. H., Hines, K. M., and Bai, L.-S.: Development and testing of
Polar Weather Research and Forecasting model: 2. Arctic Ocean, J. Geophys.
Res., 114, D08122, https://doi.org/10.1029/2008JD010300, 2009.
Bromwich, D. H., Wilson, A. B., Bai, L.-S., Moore, G. W. K., and Bauer, P.:
A comparison of the regional Arctic System Reanalysis and the global
ERA-Interim Reanalysis for the Arctic, Q. J. Roy. Meteor. Soc., 142,
644–658, https://doi.org/10.1002/qj.2527, 2016.
Broxton, P., Zeng, X., and Dawson, N.: Why do global reanalyses and land
data assimilation products underestimate snow water equivalent?, J.
Hydrometeorol., 17, 2743–2761, https://doi.org/10.1175/JHM-D-16-0056.1, 2016.
Brunke, M. A., Fairall, C. W., Zeng, X., Eymard, L., and Curry, J. A.: Which
bulk aerodynamic flux algorithms are least problematic in computing ocean
surface turbulent fluxes?, J. Climate, 16, 619–635, 2003.
Brunke, M. A., Zhou, M., Zeng, X., and Andreas, E. L: An intercomparison of
bulk aerodynamic algorithms used over sea ice with data from the Surface Heat
Budget for the Arctic Ocean (SHEBA) experiment, J. Geophys. Res., 111,
C09001, https://doi.org/10.1029/2005JC002907, 2006.
Cassano, J. J., Box, J. E., Bromwich, D. H., Li, L., and Steffen, K.:
Evaluation of Polar MM5 simulations of Greenland's atmospheric circulation,
J. Geophys. Res., 106, 33867–33889, 2001.
Cassano, J. J., Higgins, M. E., and Seefeldt, M. W.: Performance of the
Weather Research and Forecasting Model for month-long pan-Arctic simulations,
Mon. Weather Rev., 139, 3469–3488, 2011.
Cassano, J. J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke,
M., Craig, A., Fisel, B., Gutowski, W., Hamman, J., Higgins, M., Maslowski,
W., Nijssen, B., Osinski, R., and Zeng, X.: Development of the Regional
Arctic System Model (RASM): Near surface atmospheric climate sensitivity, J.
Climate, 30, 5729–5753, https://doi.org/10.1175/JCLI-D-15-0775.1, 2017.
Comiso, J. C. and Hall, D. K.: Climate trends in the Arctic as observed from
space, Wires Clim. Change, 5, 389–409, 2014.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007GL031972, 2008.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for
Earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform.
C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
Dawson, N., Broxton, P., Zeng, X., Leuthold, M., Barlage, M., and Holbrook,
P.: Evaluation of Snow Initializations for NCEP Global and Regional
Forecasting Models, J. Hydrometeorol., 17, 1885–1901,
https://doi.org/10.1175/JHM-D-15-0227.1, 2016.
Decker, M., Brunke, M. A., Wang, Z., Sakaguchi, K., Zeng, X., and
Bosilovich, M. G.: Evaluation of the reanalysis products from GSFC, NCEP, and
ECMWF using flux tower observations, J. Climate, 25, 1916–1944,
https://doi.org/10.1175/JCLI-D-11-00004.1, 2012.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the
data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
DeRepentigny, P., Tremblay, L. B., Newton, R., and Pfirman, S.: Patterns of
sea ice retreat in the transition to a seasonally ice-free Arctic, J.
Climate, 29, 6993–7008, https://doi.org/10.1175/JCLI-D-15-0733.1, 2016.
Dethloff, K., Rinke, A., Lehmann, R., Christensen, J. H., Botzet, M., and
Machenhauser, B.: A regional climate model of the Arctic atmosphere, J.
Geophys. Res., 101, 23401–23422, 1996.
Dorn, W., Dethloff, K., Rinke, A., Frickenhaus, S., Gerdes, R., Karcher, M.,
and Kauker, F.: Sensitivities and uncertainties in a coupled regional
atmosphere-ocean-ice model with respect to the simulation of Arctic sea ice,
J. Geophys. Res., 112, D10118, https://doi.org/10.1029/2006JD007814, 2007.
Döscher, R., Willén, U., Jones, C., Rutgersson, A., Markus Meier, H.
E., Hansson, U., and Graham, L. P.: The development of the regional coupled
ocean-atmosphere model RCAO, Boreal Environ. Res., 7, 183–192, 2002.
D
öscher, R., Wyser, K., Markus Meier, H. E., Qian, M., and Redler, R.:
Quantifying Arctic contributions to climate predictability in a regional
coupled ocean-ice-atmosphere model, Clim. Dynam., 34, 1157–1167,
https://doi.org/10.1007/s00382-009-0567-y, 2010.
Du, J., Wang, K., Wang, J., Jiang, S., and Zhou, C.: Diurnal cycle of surface
air temperature within China in current reanalyses: Evaluation and
diagnostics, J. Climate, 31, 4585–4603, https://doi.org/10.1175/JCLI-D-0773.1, 2018.
Dukowicz, J. K. and Smith, R. D.: Implicit free-surface method for the
Bryan-Cox-Semtner ocean model, J. Geophys. Res., 99, 7991–8014,
https://doi.org/10.1029/93JC03455, 1994.
DuVivier, A. K. and Cassano, J. J.: Exploration of turbulent heat fluxes
and wind stress curl in WRF and ERA-Interim during wintertime mesoscale wind
events around southeastern Greenland, J. Geophys. Res., 120, 3593–3609,
https://doi.org/10.1002/2014JD022991, 2015.
Estilow, T. W., Young, A. H., and Robinson, D. A.: A long-term Northern
Hemisphere snow cover extent data record for climate studies and monitoring,
Earth Syst. Sci. Data, 7, 137–142, https://doi.org/10.5194/essd-7-137-2015,
2015.
European Centre for Medium-Range Weather Forecasts: ERA-Interim Project,
Monthly Means, Research Data Archive at the National Center for Atmospheric
Research, https://doi.org/10.5065/D68050NT, 2012.
Gelaro, R., McCarty, W., Suarez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da
Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, 2017.
Glisan, J. M., Gutowski, W. J., Cassano, J. J., and Higgins, M. E.: Effects
of spectral nudging in WRF on Arctic temperature and precipitation
simulations, J. Climate, 26, 3985–3999, https://doi.org/10.1175/JCLI-D-12-00318.1,
2012.
Grell, G. A. and Dévényi, D.: A generalized approach to
parameterizing convection combining ensemble and data assimilation
techniques, Geophys. Res. Lett., 29, 1693, https://doi.org/10.1029/2002GL015311, 2002.
Gupta, S. K., Whitlock, C. H., Ritchey, N. A., and Wilber, A. C.: An
algorithm for longwave surface radiation budget for total skies (Subsystem
4.6.3), Clouds and Earth's Radiant Energy System (CERES) ATBD, 21 pp., 1997.
Hamman, J., Nijssen, B., Brunke, M., Cassano, J., Craig, A., DuVivier, A.,
Hughes, M., Lettenmaier, D. P., Maslowski, W., Osinski, R., Roberts, A., and
Zeng, X.: Land surface climate in the Regional Arctic System Model, J.
Climate, 29, 6543–6562, https://doi.org/10.1175/JCLI-D-15-0415.1, 2016.
Hamman, J., Nijssen, B., Roberts, A., Craig, A., Maslowski, W., and Osinski,
R.: The Coastal Streamflow Flux in the Regional Arctic System Model, J.
Geophys. Res., 122, 1683–1701, https://doi.org/10.1002/2016JC012323, 2017.
Hartmann, D. L.: Global Physical Climatology, Academic Press, San Diego, Calif., 1994.
Hines, K. M. and Bromwich, D. H.: Development and testing of Polar WRF. Part
I: Greenland Ice Sheet meteorology, Mon. Weather Rev., 136, 1971–1989,
https://doi.org/10.1175/2007MWR2112.1, 2008.
Holland, M. M. and Bitz, C. M.: Polar amplification of climate change in
coupled models, Clim. Dynam., 21, 221–232, 2003.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2314, https://doi.org/10.1175/MWR3199.1, 2006.
Hunke, E. C., Hebert, D. A., and Lecomte, O.: Level-ice melt ponds in the
Los Alamos sea ice model, CICE, Ocean Model., 71, 26–42,
https://doi.org/10.1016/j.ocemod.2012.11.008, 2013.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott,
S.: CICE?: the Los Alamos Sea Ice Model Documentation and Software User's
Manual Version 5.1, Los Alamos National Lab., Los Alamos, N.M., LA-CC-06-012,
2015.
Johannessen, O. M., Bengtsson, L., Miles, M. W., Kuzmina, S. I., Semenov, V.
A., Alekseev, G. V., Nagurnyi, A. P., Zakharov, V. F., Bobylev, L. P.,
Pettersson, L. H., Hasselmann, K., and Cattle, H. P.: Arctic climate change:
observed and modelled temperature and sea-ice variability, Tellus, 56A,
328–341, 2004.
Jousse, A., Hall, A., Sun, F., and Teixeira, J.: Causes of WRF surface
energy fluxes biases in a stratocumulus region, Clim. Dynam., 46, 571–584,
https://doi.org/10.1007/s00382-015-2599-9, 2016.
Kain, J. S.: The Kain-Fritsch convective parameterization: an update,
J. Appl. Meteorol., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.C0;2, 2004.
Kato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A.,
Caldwell, T. E., Yu, L., and Weller, R. A.: Surface irradiances consistent
with CERES-derived top-of-atmosphere shortwave and longwave irradiances, J.
Climate, 26, 2719–2740, https://doi.org/10.1175/JCLI-D-12-00436.1, 2013.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P.: The Community Earth System Model (CESM) Large Ensemble Project:
A community resource for studying climate change in the presence of internal
climate variability, B. Am. Meterol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015.
Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E., and Saunby, M.:
Reassessing biases and other uncertainties in sea surface temperature
observations measured in situ since 1850: 1. Measurement and sampling
uncertainties, J. Geophys. Res., 116, D14103, https://doi.org/10.1029/2010JD015218,
2011a.
Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E., and Saunby, M.:
Reassessing biases and other uncertainties in sea surface temperature
observations measured in situ since 1850: 2. Biases and homogenization, J.
Geophys. Res., 116, D14104, https://doi.org/10.1029/2010JD015220, 2011b.
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air-sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009.
Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J., and Slater, A.
G.: Permafrost thaw and resulting soil moisture changes regulate projected
high-latitude CO2 and CH4 emissions, Environ. Res.
Lett., 10, 094011, https://doi.org/10.1088/1748-9326/10/9/094011, 2015.
Li, Z. and Kratz, D. P.: Estimate of shortwave surface radiation budget
from CERES (Subsystem 4.6.1), Clouds and Earth's Radiant Energy System
(CERES) ATBD, 18 pp., 1997.
Li, Z., Leighton, H. G., Masuda, K., and Takashima, T.: Estimation of SW
flux absorbed at the surface from TOA reflected flux, J. Climate, 6,
317–330, 1993.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture
parameterization of the VIC-2L model: Evaluation and modification, Global
Planet. Change, 13, 195–206, 1996.
Lindsay, R., Wensnahan, M., Schweiger, A., and Zhang, J.: Evaluation of
seven different atmospheric reanalysis products in the Arctic, J. Climate,
27, 2588–2606, https://doi.org/10.1175/JCLI-D-13-00014.s1, 2014.
Lynch, A. H. and Cullather, R. I.: An investigation of boundary-forcing
sensitivities in a regional climate model, J. Geophys. Res., 105,
26603–26617, 2000.
Lynch, A. H., Chapman, W. L., Walsh, J. E., and Weller, G.: Development of a
regional climate model of the western Arctic, J. Climate, 8, 1555–1570,
1995.
Lynch, A. H., McGinnis, D. L., and Bailey, D. A.: Snow-albedo feedback and
the spring transition in a regional climate system model: Influence of land
surface model, J. Geophys. Res., 103, 29037–29049, 1998.
Lynch, A. H., Maslanik, J. A., and Wu, W.: Mechanisms in the development of
anomalous sea ice extent in the western Arctic: A case study, J. Geophys.
Res., 106, 28097–28105, 2001.
Maslowski, W., Kinney, J. C., Higgins, M., and Roberts, A.: The future of
Arctic sea ice, Annu. Rev. Earth Pl. Sc., 40, 625–654, 2012.
Maykut, G. A.: Energy exchange over young sea ice in the central Arctic,
J. Geophys. Res., 23, 3646–3658, 1978.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, 1971.
Meier, W., Peng, G., Scott, D. J., and Savoie, M. H.: Verification of a new
NOAA/NSIDC passive microwave sea-ice concentration climate record, Polar
Res., 33, 21004, https://doi.org/10.3402/polar.v33.21004, 2014.
Meier, W., Fetterer, F., Savoie, M., Mallory, S., Duerr, R., and Stroeve, J.:
NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration,
Version 3, National Snow and Ice Data Center, https://doi.org/10.7265/N59P2ZTG, 2017.
Militzer, J. M., Michaelis, M. C., Semmer, S. R., Norris, K. S., Horst, T.
W., Oncley, S. P., Delany, A. C., and Brock, F. V.: Development of the
prototype PAM III/Flux-PAM surface meteorological station, paper presented
at 9th Symposium on Meteorological Observations and Instrumentation, American
Meteorological Society, Charlotte, N.C., 1995.
Moritz, R. E., Bitz, C. M., and Steig, E. J.: Dynamics of recent climate
change in the Arctic, Science, 297, 1497–1502, 2002.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics
on the development of trailing stratiform precipitation in a simulated squall
line: Comparison of one- and two-moment schemes, Mon. Weather Rev., 137,
991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8,
2006.
New, M., Lister, D., Hulme, M., and Makin, I.: A high-resolution data set of
surface climate over global land areas, Clim. Res., 21, 1–25,
https://doi.org/10.3354/cr021001, 2002.
Osborn, T. J. and Jones, P. D.: The CRUTEM4 land-surface air temperature data
set: construction, previous versions and dissemination via Google Earth,
Earth Syst. Sci. Data, 6, 61–68, https://doi.org/10.5194/essd-6-61-2014,
2014.
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and
reproducible passive microwave sea ice concentration data record for climate
studies and monitoring, Earth Syst. Sci. Data, 5, 311–318,
https://doi.org/10.5194/essd-5-311-2013, 2013.
Persson, P. O. G., Fairall, C. W., Andreas, E. L, Guest, P. S., and
Perovich, D. K.: Measurements near the Atmospheric Surface Flux Group tower
at SHEBA: Near-surface conditions and surface energy budget, J. Geophys.
Res., 107, 8045, https://doi.org/10.1029/2000JC000705, 2002.
Porter, D. F., Cassano, J. J., and Serreze, M. C.: Analysis of the Arctic
atmospheric energy budget in WRF: A comparison with reanalyses and satellite
observations, J. Geophys. Res., 116, D22108, https://doi.org/10.1029/2011JD016622,
2011.
Reeves Eyre, J. E. J. and Zeng, X.: Evaluation of Greenland near surface air
temperature datasets, The Cryosphere, 11, 1591–1605,
https://doi.org/10.5194/tc-11-1591-2017, 2017.
Rinke, A., Gerdes, R., Dethloff, K., Kandlbinder, T., Karcher, M., Kauker,
F., Frickenhaus, S., Köberle, C., and Hiller, W.: A case sudy of the
anomalous Arctic sea ice conditions during 1990: Insights from coupled and
uncoupled regional climate model simulations, J. Geophys. Res., 108, 4275,
https://doi.org/10.1029/2002JD003146, 2003.
Roberts, A., Cassano, J., Döscher, Hinzman, L., Holland, M., Mitsudera,
H., Sumi, A., Walsh, J. E., Alessa, L., Alexeev, V., Arendt, A., Altaweel,
M., Bhatt, U., Cherry, J., Deal, C., Elliot, S., Follows, M., Hock, R.,
Kliskey, A., Lantuit, H., Lawrence, D., Maslowski, W., McGuire, A. D.,
Overduin, P. P., Overeem, I., Proshutinsky, A., Romanovsky, V., Sushama, L.,
and Truffer, M.: A science plan for regional Arctic system modeling: A report
by the Arctic research community for the National Science Foundation Office
of Polar Programs, International Arctic Res. Center, Fairbanks, AK,
International Arctic Research Center Technical Paper 10-0001,
https://doi.org/10.13140/2.1.1828.9441, 2010.
Roberts, A., Craig, A., Maslowski, W., Osinski, R., DuVivier, A., Hughes, M.,
Nijssen, B., and Brunke, M.: Simulating transient ice-ocean Ekman transport
in the Regional Arctic System Model and Community Earth System Model, Ann.
Glaciol., 56, 211–228, 2015.
Roberts, A. F., Cherry, J., Döscher, R., Elliott, S., and Sushama, L.:
Exploring the Potential for Arctic System Modeling, B. Am. Meteorol. Soc.,
92, 203–206, https://doi.org/10.1175/2010bams2959.1, 2011.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Aresenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. J., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M, Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen,Y., Han, Y., and Cucuruul, L.: The NCEP Climate Forecast System
reanalysis, B. Am. Meteorol. Soc., 91, 1015–1057, 2010.
Schuur, E. A. G., McGuire, A. D., Schadel, C., Grosse, G., Harden, J. W.,
Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., Natali,
S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M. R., Treat,
C. C., and Vonk, J. E.: Climate change and the permafrost carbon feedback,
Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015.
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in
recent Arctic temperature amplification, Nature, 464, 1334–1337,
https://doi.org/10.1038/nature09051, 2010.
Serreze, M. C. and Francis, J. A.: The Arctic amplification debate, Climatic
Change, 76, 241–264, https://doi.org/10.1007/s10584-005-9017-y, 2006.
Serreze, M. C., Barrett, A. P. and Lo, F.: Northern high-latitude
precipitation by atmospheric reanalyses and satellite retrievals, Mon.
Weather Rev., 133, 3407–3430, 2005.
Serreze, M. C., Holland, M. H., and Stroeve, J.: Perspectives on the
Arctic's shrinking sea-ice cover, Science, 315, 1533–1536, 2007.
Serreze, M. C., Barrett, A. P., Stroeve, J. C., Kindig, D. N., and Holland,
M. M.: The emergence of surface-based Arctic amplification, The Cryosphere,
3, 11–19, https://doi.org/10.5194/tc-3-11-2009, 2009.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/jcli3790.1, 2006.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
Advanced Research WRF version 3, National Center for Atmos. Res., Boulder,
Colo., NCAR Tech. Note NCAR/TN-457+STR, 113 pp., https://doi.org/10.5065/D68S4MVH,
2008.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S.,
Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S.,
Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) Reference
Manual Ocean Component of the Community Climate System Model (CCSM) and
Community Earth System Model (CESM), Los Alamos National Lab., Los Alamos,
N.M., Rep. LAUR-10-01853, 2010.
Smith, R. D., Dukowicz, J. K., and Malone, R. C.: Parallel ocean circulation
modeling, Physica D, 60, 38–61, https://doi.org/10.1016/0167-2789(92)90225-C, 1992.
Steffen, K. and Box, J.: Surface climatology of the Greenland ice sheet:
Greenland Climate Network 1995–1999, J. Geophys. Res., 106, 33951–33964,
2001.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., and Serreze, M.: Arctic
sea ice decline: Faster than forecast, Geophysical Research Letters, 34, L09501,
https://doi.org/10.1029/2007GL029703, 2007.
Stroeve, J., Serreze, M. C., Holland, M. M., Kay, J. E., Malanik, J., and
Barrett, A. P.: The Arctic's rapidly shrinking sea ice cover: a research
synthesis, Climatic Change, 110, 1105–1027, https://doi.org/10.1007/s10584-011-0101-1,
2012.
Sturm, M., Holmgren, J., and Perovich, D. K.: Spatial variation in the
winter heat flux at SHEBA: estimates from snow-ice interface temperatures,
Ann. Glaciol., 33, 213–220, 2001.
Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., and Jahn, A.: Influence
of internal variability on Arctic sea-ice trends, Nat. Clim. Change, 5,
86–89, 2015.
Tsamados, M., Feltham, D. L., and Wilchinsky, A. V.: Increased Arctic sea
ice volume after anomalously low melting in 2013, Nat. Geosci., 8,
643–646, https://doi.org/10.1038/ngeo2489, 2013.
Turner, A. K. and Hunke, E. C.: Impacts of a mushy-layer thermodynamic
approach in global sea-ice simulations using the CICE sea-ice model,
J. Geophys. Res.-Oceans, 120, 1253–1275, https://doi.org/10.1002/2014JC010358, 2015.
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. K., Moritz, R. E.,
Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R.,
Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A.,
Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe, M.
D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W. S.,
Stanton, T. P., and Grenfeld, T. C.: Surface Heat Budget of the Arctic Ocean,
B. Am. Meteorol. Soc., 83, 255–275, 2002.
Vavrus, S.: The impact of cloud feedbacks on Arctic climate under Greenhouse
forcing, J. Climate, 17, 603–615,
https://doi.org/10.1175/1520-0442(2004)017<0603:TIOCFO>2.0.CO;2, 2004.
Wang, A. and Zeng, X.: Development of global hourly 0.5∘ land
surface air temperature datasets, J. Climate, 26, 7676–7691,
https://doi.org/10.1175/JCLI-D-12-00682.1, 2013.
Wang, A. and Zeng, X.: Range of monthly mean hourly land surface air
temperature diurnal cycle over high northern latitudes, J. Geophys. Res.,
119, 5836–5844, https://doi.org/10.1002/2014JD021602, 2014.
Xie, P. P. and Arkin, P. A.: Global precipitation: A 17-year monthly
analysis based on gauge observations, satellite estimates, and numerical
model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
Zhang, X.: Sensitivity of arctic summer sea ice coverage to global warming
forcing: towards reducing uncertainty in arctic climate change projections,
Tellus, 62A, 220–227, 2010.
Zhang, X. and Walsh, J. E.: Toward a seasonally ice-covered Arctic Ocean:
scenarios from the IPCC AR4 model simulations, J. Climate, 19, 1730–1747,
2006.
Zhou, C. and Wang, K.: Evaluation of surface fluxes in ERA-Interim using flux
tower data, J. Climate, 29, 1573–1582, https://doi.org/10.1175/JCLI-D-15-0523.1,
2016.
Short summary
The Regional Arctic System Model version 1 (RASM1) was recently developed for high-resolution simulation of the coupled atmosphere–ocean–sea ice–land system in the Arctic. Its simulation of the atmosphere–land–ocean–sea ice interface is evaluated by using the spread in recent reanalyses and a global Earth system model as baselines. Such comparisons reveal that RASM1 simulates precipitation well and improves the simulation of surface fluxes over sea ice.
The Regional Arctic System Model version 1 (RASM1) was recently developed for high-resolution...
