Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-173-2022
© 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-15-173-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
Yurii Shevchuk
MX Automotive GmbH, 13355 Berlin, Germany
Georgiy Kirillin
Department of Ecohydrology, Leibniz Institute of Freshwater Ecology
and Inland Fisheries (IGB), 12587 Berlin, Germany
Pedro M. M. Soares
Instituto Dom Luís (IDL), Faculdade de Ciências,
Universidade de Lisboa, Lisbon, 1749-016, Portugal
Rita M. Cardoso
Instituto Dom Luís (IDL), Faculdade de Ciências,
Universidade de Lisboa, Lisbon, 1749-016, Portugal
José P. Matos
Stucky SA, Rue du Lac 33, 1020 Renens, Switzerland
Ricardo M. Rebelo
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
António C. Rodrigues
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
Pedro S. Coelho
Faculdade de Ciências e Tecnologia, Universidade
Nova de Lisboa, Lisbon, 2825–516, Portugal
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Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202, https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD
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This study aims to assess the capabilities of the advanced CE-QUAL-W2 v4.5 sediment diagenesis model, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Our findings indicate that the model performs very well in simulating dissolved oxygen profiles, nutrient concentrations, and organic matter levels.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202, https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD
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This study aims to assess the capabilities of the advanced CE-QUAL-W2 v4.5 sediment diagenesis model, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Our findings indicate that the model performs very well in simulating dissolved oxygen profiles, nutrient concentrations, and organic matter levels.
Georgiy B. Kirillin, Tom Shatwell, and Alexander S. Izhitskiy
EGUsphere, https://doi.org/10.5194/egusphere-2025-113, https://doi.org/10.5194/egusphere-2025-113, 2025
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The restoration of the North Aral Sea caused remarkable shifts in its temperature, ice cover, and oxygen levels, which have not yet been fully understood so far. We demonstrate that the North Aral Sea has regained conditions similar to its state before drying out, but its seasonal mixing pattern remains unstable. Small changes in water levels or clarity could push the lake toward stable dimictic or polymictic conditions, highlighting its sensitivity to environmental and management factors.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Mengxiao Wang, Lijuan Wen, Zhaoguo Li, Matti Leppäranta, Victor Stepanenko, Yixin Zhao, Ruijia Niu, Liuyiyi Yang, and Georgiy Kirillin
The Cryosphere, 16, 3635–3648, https://doi.org/10.5194/tc-16-3635-2022, https://doi.org/10.5194/tc-16-3635-2022, 2022
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The under-ice water temperature of Ngoring Lake has been rising based on in situ observations. We obtained results showing that strong downward shortwave radiation is the main meteorological factor, and precipitation, wind speed, downward longwave radiation, air temperature, ice albedo, and ice extinction coefficient have an impact on the range and rate of lake temperature rise. Once the ice breaks, the lake body releases more energy than other lakes, whose water temperature remains horizontal.
Miguel Nogueira, Alexandra Hurduc, Sofia Ermida, Daniela C. A. Lima, Pedro M. M. Soares, Frederico Johannsen, and Emanuel Dutra
Geosci. Model Dev., 15, 5949–5965, https://doi.org/10.5194/gmd-15-5949-2022, https://doi.org/10.5194/gmd-15-5949-2022, 2022
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We evaluated the quality of the ERA5 reanalysis representation of the urban heat island (UHI) over the city of Paris and performed a set of offline runs using the SURFEX land surface model. They were compared with observations (satellite and in situ). The SURFEX-TEB runs showed the best performance in representing the UHI, reducing its bias significantly. We demonstrate the ability of the SURFEX-TEB framework to simulate urban climate, which is crucial for studying climate change in cities.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
João António Martins Careto, Pedro Miguel Matos Soares, Rita Margarida Cardoso, Sixto Herrera, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 2635–2652, https://doi.org/10.5194/gmd-15-2635-2022, https://doi.org/10.5194/gmd-15-2635-2022, 2022
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This work focuses on the added value of high-resolution models relative to their forcing simulations, with a recent observational gridded dataset as a reference, covering the entire Iberian Peninsula. The availability of such datasets with a spatial resolution close to that of regional climate models encouraged this study. For precipitation, most models reveal added value. The gains are even more evident for precipitation extremes, particularly at a more local scale.
João António Martins Careto, Pedro Miguel Matos Soares, Rita Margarida Cardoso, Sixto Herrera, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 2653–2671, https://doi.org/10.5194/gmd-15-2653-2022, https://doi.org/10.5194/gmd-15-2653-2022, 2022
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This work focuses on the added value of high-resolution models relative to their forcing simulations, with an observational gridded dataset as a reference covering the Iberian Peninsula. The availability of such datasets with a spatial resolution close to that of regional models encouraged this study. For the max and min temperature, although most models reveal added value, some display losses. At more local scales, coastal sites display important gains, contrasting with the interior.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Georgiy Kirillin, Ilya Aslamov, Vladimir Kozlov, Roman Zdorovennov, and Nikolai Granin
Hydrol. Earth Syst. Sci., 24, 1691–1708, https://doi.org/10.5194/hess-24-1691-2020, https://doi.org/10.5194/hess-24-1691-2020, 2020
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We found that heat transported from Lake Baikal to its ice cover is up to 10 times higher than traditionally assumed and strongly affects the ice melting. The heat is transported by under-ice currents on the background of a strong temperature gradient between the ice base and warmer waters beneath. To parameterize this newly quantified transport mechanism, an original boundary layer model was developed. The results are crucial for understanding seasonal ice dynamics on lakes and marginal seas.
Edouard L. Davin, Diana Rechid, Marcus Breil, Rita M. Cardoso, Erika Coppola, Peter Hoffmann, Lisa L. Jach, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Kai Radtke, Mario Raffa, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Tölle, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 11, 183–200, https://doi.org/10.5194/esd-11-183-2020, https://doi.org/10.5194/esd-11-183-2020, 2020
Sixto Herrera, Rita Margarida Cardoso, Pedro Matos Soares, Fátima Espírito-Santo, Pedro Viterbo, and José Manuel Gutiérrez
Earth Syst. Sci. Data, 11, 1947–1956, https://doi.org/10.5194/essd-11-1947-2019, https://doi.org/10.5194/essd-11-1947-2019, 2019
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A new observational dataset of daily precipitation and temperatures for the Iberian Peninsula and the Balearic Islands has been developed and made publicly available for the community. In this work the capabilities of the new dataset to reproduce the mean and extreme regimes of precipitation and temperature are assessed and compared with the E-OBS dataset (including the ensemble version for observational uncertainty assessment).
Inês Gomes Marques, João Nascimento, Rita M. Cardoso, Filipe Miguéns, Maria Teresa Condesso de Melo, Pedro M. M. Soares, Célia M. Gouveia, and Cathy Kurz Besson
Hydrol. Earth Syst. Sci., 23, 3525–3552, https://doi.org/10.5194/hess-23-3525-2019, https://doi.org/10.5194/hess-23-3525-2019, 2019
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Mediterranean cork woodlands are very particular agroforestry systems present in a confined area of the Mediterranean Basin. They are of great importance due to their high socioeconomic value; however, a decrease in water availability has put this system in danger. In this paper we build a model that explains this system's tree-species distribution in southern Portugal from environmental variables. This could help predict their future distribution under changing climatic conditions.
Dongsheng Su, Xiuqing Hu, Lijuan Wen, Shihua Lyu, Xiaoqing Gao, Lin Zhao, Zhaoguo Li, Juan Du, and Georgiy Kirillin
Hydrol. Earth Syst. Sci., 23, 2093–2109, https://doi.org/10.5194/hess-23-2093-2019, https://doi.org/10.5194/hess-23-2093-2019, 2019
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In this study, freshwater lake model simulation results, verified by satellite and buoy observation data, were used to quantify recent climate change effects on the thermal regime of the largest lake in China. Results indicate that the FLake model can reproduce the lake thermal pattern nicely. The lake surface is warming, while the lake bottom has no significant trend. Climate change also caused an earlier ice-off and later ice-on, leading to an obvious change in the energy balance of the lake.
Tom Shatwell, Wim Thiery, and Georgiy Kirillin
Hydrol. Earth Syst. Sci., 23, 1533–1551, https://doi.org/10.5194/hess-23-1533-2019, https://doi.org/10.5194/hess-23-1533-2019, 2019
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We used models to project future temperature and mixing in temperate lakes. Lakes will probably warm faster in winter than in summer, making ice less frequent and altering mixing. We found that the layers that form seasonally in lakes (ice, stratification) and water clarity affect how lakes accumulate heat. Seasonal changes in climate were thus important. This helps us better understand how different lake types respond to warming and which physical changes to expect in the future.
Georgiy Kirillin, Ilya Aslamov, Matti Leppäranta, and Elisa Lindgren
Hydrol. Earth Syst. Sci., 22, 6493–6504, https://doi.org/10.5194/hess-22-6493-2018, https://doi.org/10.5194/hess-22-6493-2018, 2018
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We have discovered transient appearances of strong turbulent mixing beneath the ice of an Arctic lake. Such mixing events increase heating of the ice base up to an order of magnitude and can significantly accelerate ice melting. The source of mixing was identified as oscillations of the entire lake water body triggered by strong winds over the lake surface. This previously unknown mechanism of ice melt may help understand the link between the climate conditions and the seasonal ice formation.
Peter O. Zavialov, Alexander S. Izhitskiy, Georgiy B. Kirillin, Valentina M. Khan, Boris V. Konovalov, Peter N. Makkaveev, Vadim V. Pelevin, Nikolay A. Rimskiy-Korsakov, Salmor A. Alymkulov, and Kubanychbek M. Zhumaliev
Hydrol. Earth Syst. Sci., 22, 6279–6295, https://doi.org/10.5194/hess-22-6279-2018, https://doi.org/10.5194/hess-22-6279-2018, 2018
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This paper reports the results of field surveys conducted in Lake Issyk-Kul in 2015–2017 and compares the present-day data with the available historical records. Our data do not confirm the reports of progressive warming of the deep Issyk-Kul waters as suggested in some previous publications. However, they do indicate a positive trend of salinity in the lake’s interior over the last 3 decades. An important newly found feature is a persistent salinity maximum at depths of 70–120 m.
Georgiy Kirillin, Lijuan Wen, and Tom Shatwell
Hydrol. Earth Syst. Sci., 21, 1895–1909, https://doi.org/10.5194/hess-21-1895-2017, https://doi.org/10.5194/hess-21-1895-2017, 2017
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We report a first description of the seasonal temperature, mixing, and ice regime in the two largest freshwater lakes of the Tibetan Plateau. We perform a validation of lake model FLake for the parameterization of the Tibetan lake system in regional climate models and present evidence of the absent warming trend in the Tibetan lakes despite significant atmospheric warming. The reason for this unexpected behavior is the significant decrease in solar radiation at the surface.
J. Boike, C. Georgi, G. Kirilin, S. Muster, K. Abramova, I. Fedorova, A. Chetverova, M. Grigoriev, N. Bornemann, and M. Langer
Biogeosciences, 12, 5941–5965, https://doi.org/10.5194/bg-12-5941-2015, https://doi.org/10.5194/bg-12-5941-2015, 2015
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We show that lakes in northern Siberia are very efficient with respect to energy absorption and mixing using measurements as well as numerical modeling. We show that (i) the lakes receive substantial energy for warming from net short-wave radiation; (ii) convective mixing occurs beneath the ice cover, follow beneath the ice cover, following ice break-up, summer, and fall (iii) modeling suggests that the annual mean net heat flux across the bottom sediment boundary is approximately zero.
G. Kirillin, M. S. Lorang, T. C. Lippmann, C. C. Gotschalk, and S. Schimmelpfennig
Hydrol. Earth Syst. Sci., 19, 2605–2615, https://doi.org/10.5194/hess-19-2605-2015, https://doi.org/10.5194/hess-19-2605-2015, 2015
E. Katragkou, M. García-Díez, R. Vautard, S. Sobolowski, P. Zanis, G. Alexandri, R. M. Cardoso, A. Colette, J. Fernandez, A. Gobiet, K. Goergen, T. Karacostas, S. Knist, S. Mayer, P. M. M. Soares, I. Pytharoulis, I. Tegoulias, A. Tsikerdekis, and D. Jacob
Geosci. Model Dev., 8, 603–618, https://doi.org/10.5194/gmd-8-603-2015, https://doi.org/10.5194/gmd-8-603-2015, 2015
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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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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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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.
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.
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.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. 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.
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.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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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
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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
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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.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Revised manuscript accepted for GMD
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Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
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
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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.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2183, https://doi.org/10.5194/egusphere-2024-2183, 2024
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PaleoSTeHM v1.0-rc is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, 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.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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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 climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
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
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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.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C.,
Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I.
J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser,
L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D.
G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar,
K., Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals,
O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.:
TensorFlow: Large-scale machine learning on heterogeneous distributed
systems, Proceedings of the 12th USENIX conference on Operating Systems
Design and Implementation, Savannah, GA, USA, 2–4 November 2016, 265–283, ISBN 978-1-931971-33-1, 2016.
Almeida, M.: Models source code: CE-QUAL-W2 v3.6, FLake (windows version 1.0), Hostetler and ANN (momentum alg.) – Modeling reservoir surface
temperatures for regional and global climate models (Version 1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4803480, 2021a.
Almeida, M.: Model input files (hydrometric, water quality and
meteorological data sets): CE-QUAL-W2 v3.6, FLake (windows version),
Hostetler and ANN (momentum alg.) – Modeling reservoir surface temperatures
for regional and global climate models (Version 1.0), Zenodo [data set],
https://doi.org/10.5281/zenodo.4756312, 2021b.
Almeida, M. C., Coelho, P. S., Rodrigues, A. C., Diogo, P. A.,
Maurício, R., Cardoso, R. M., and Soares, P. M. M.: Thermal
stratification of Portuguese reservoirs: Potential impact of extreme climate
scenarios, J. Water Clim. Change, 6, 544–560,
https://doi.org/10.2166/wcc.2015.071, 2015.
Bates, G. T., Giorgi, F., and Hostetler, S. W.: Towards the simulation of the
effects of the Great Lakes on climate, Mon. Weather Rev., 121, 1373–1387,
https://doi.org/10.1175/1520-0493(1993)121<1373:TTSOTE>2.0.CO;2, 1993.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956,
2015.
Bennington, V., Notaro, M., and Holman, K. D.: Improving climate sensitivity
of deep lakes within a regional climate model and its impact on simulated
climate, J. Climate, 27, 2886–2911, https://doi.org/10.1175/JCLI-D-13-00110.1,
2014.
Burchard, H., Bolding, K., and Villarreal, M. R.: GOTM: A General Ocean Turbulence Model: theory, implementation and test cases, Tech. Rep. EUR 18745 EN, European Commission, Joint Research Center, Space Applications Institute, 1999.
Calamita, E., Sebastiano Piccolroaz, S., Majone, B., and Toffolon, M.: On the
role of local depth and latitude on surface warming heterogeneity in the
Laurentian Great Lakes, Inland Waters, 11, 208–222, https://doi.org/10.1080/20442041.2021.1873698, 2021.
Cardoso, R. M., Soares, P. M. M., Miranda, P. M. A., and Belo-Pereira, M.:
WRF High resolution simulation of Iberian mean and extreme precipitation
climate, Int. J. Climatol., 33, 2591–2608,
https://doi.org/10.1002/joc.3616, 2013.
Carr, M. K., Sadeghian, A., Lindenschmidt, Karl-Erich, Rinke, K., and
Morales-Marin, L.: Impacts of Varying Dam Outflow Elevations on Water
Temperature, Dissolved Oxygen, and Nutrient Distributions in a Large Prairie
Reservoir, Environ. Eng. Sci., 37, 78–79, https://doi.org/10.1089/ees.2019.0146,
2020.
Chenard, J. F. and Caissie, D.: Stream temperature modelling using
artificial neural networks: application on Catamaran Brook, New Brunswick,
Canada, Hydrol. Process., 22, 3361–3372, https://doi.org/10.1002/hyp.6928, 2008.
Cole, T. M. and Wells, S. A.: CE-QUAL-W2: A Two- Dimensional, Laterally
Averaged, Hydrodynamic and Water Quality Model, Version 3.6, User manual,
Report of Department of Civil and Environmental Engineering, Portland State
University, Portland, OR, 797, available at: http://www.ce.pdx.edu/w2/ (last access: 5 January 2022), 2008.
Deng, B., Liu, S., Xiao, W., Wang, W., Jin, J., and Lee, X.: Evaluation of
the CLM4 Lake Model at a Large and Shallow Freshwater Lake, J.
Hydrometeorol., 14, 636–649, https://doi.org/10.1175/JHM-D-12-067.1, 2013.
Diogo, P. A., Fonseca, M., Coelho, P., Mateus, N., Almeida, M., and
Rodrigues, A.: Reservoir phosphorous sources evaluation and water quality
modeling in a transboundary watershed, Desalination, 226, 200–214,
https://doi.org/10.1016/j.desal.2007.01.242, 2008.
Doubek, J. P. and Carey, C. C.: Catchment, morphometric, and water quality
characteristics differ between reservoirs and naturally formed lakes on a
latitudinal gradient in the conterminous United States, Inland Waters, 7,
171–180, https://doi.org/10.1080/20442041.2017.1293317, 2017.
Dutra, E., Stepanenko, V. M., Balsamo, G., Viterbo, P., Miranda, P.,
Mironov, D., and Schär, C.: An offline study of the impact of lakes in
the performance of the ECMWF surface scheme boreal, Environ. Res., 15,
100–112, 2010.
Edinger, J. E., Duttweiler, D. W., and Geyer, J. C.: The response of water
temperature to meteorological conditions, Water Resour. Res., 4, 1137–1143,
https://doi.org/10.1029/WR004i005p01137, 1968.
Fang, X. and Stefan, H. G.: Long-term lake water temperature and ice cover
simulations/measurements, Cold Reg. Sci. Technol., 24, 289–304,
https://doi.org/10.1016/0165-232X(95)00019-8, 1996.
Flato, G., J., Marotzke, B., Abiodun, P., Braconnot, S. C., Chou, W.,
Collins, P., Cox, F., Driouech, S., Emori, V., Eyring, C., Forest, P.,
Gleckler, E., Guilyardi, C., Jakob, V., Kattsov, C., Reason, and Rummukainen,
M.: Evaluation of Climate Models, in: 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, 578–635, ISBN 978-1-107-05799-1, 2013.
Forster, P.: Half a century of robust climate models, Nature, 545, 296–297,
https://doi.org/10.1038/545296a, 2017.
Friedrich, K., Grossman, R. L., Huntington, J., Blanken, P. D., Lenters, J.,
Holman, K. D., Gochis, D., Livneh, B., Prairie, J., Skeie, E., Healey, N. C,
Dahm, K., Pearson, C., Finnessey, T., Hook, S. J., and Kowalski T.:
Reservoir Evaporation in the Western United States: Current Science,
Challenges, and Future Needs, B. Am. Meteorol.
Soc., 99, 167–187, https://doi.org/10.1175/BAMS-D-15-00224.1, 2018.
Goudsmit, G. H., Burchard, H., Peeters, F., and Wüest, A.: Application
of k–turbulence models to enclosed basins: The role of internal seiches, J.
Geophys. Res., 107, 3230, https://doi.org/10.1029/2001JC000954, 2002.
Gu, H., Jin, J., Wu, Y., Ek, M. B., and Subin, Z. M.: Calibration and
validation of lake surface temperature simulations with the coupled WRF-lake
model, Climatic Change, 129, 471–483,
https://doi.org/10.1007/s10584-013-0978-y, 2015.
Gula, J. and Peltier, W. R.: Dynamical Downscaling over the Great Lakes
Basin of North America Using the WRF Regional Climate Model: The Impact of
the Great Lakes System on Regional Greenhouse Warming, J.
Climate, 25, 7723–7742, https://doi.org/10.1175/JCLI-D-11-00388.1, 2012.
Guo, M., Zhuang, Q., Yao, H., Golub, M., Leung, L. R., Pierson, D., and Tan, Z.: Validation and Sensitivity Analysis of a 1-D Lake Model Across Global Lakes, J. Geophys. Res.-Atmos., 126, e2020JD033417, https://doi.org/10.1029/2020JD033417, 2021.
Guseva, S., Bleninger, T., Jöhnk, K., Polli, B. A., Tan, Z., Thiery, W., Zhuang, Q., Rusak, J. A., Yao, H., Lorke, A., and Stepanenko, V.: Multimodel simulation of vertical gas transfer in a temperate lake, Hydrol. Earth Syst. Sci., 24, 697–715, https://doi.org/10.5194/hess-24-697-2020, 2020.
Hayes, N. M., Deemer, B. M., Corman, J. R., Razavi, N. R., and Strock, K.
E.: Key differences between lakes and reservoirs modify climate signals: A
case for a new conceptual model, Limnol. Oceanogr. Lett., 2, 47–62, https://doi.org/10.1002/lol2.10036, 2017.
Hebert, C., Caissie, D., Satish, M., and El-Jabi, N.: Modeling of hourly
river water temperature using artificial neural networks, Water Qual. Res.
J. Can., 49, 144–162, https://doi.org/10.2166/wqrjc.2014.007, 2014.
Henderson-Sellers, B.: New formulation of eddy diffusion thermocline models,
Appl. Math. Model., 9, 441–446,
https://doi.org/10.1016/0307-904X(85)90110-6, 1985.
Hipsey, M. R., Bruce, L. C., Boon, C., Busch, B., Carey, C. C., Hamilton, D. P., Hanson, P. C., Read, J. S., de Sousa, E., Weber, M., and Winslow, L. A.: A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON), Geosci. Model Dev., 12, 473–523, https://doi.org/10.5194/gmd-12-473-2019, 2019.
Hostetler, S. and Bartlein, P. J.: Simulation of lake evaporation with
application to modeling lake-level variations at Harney-Malheur Lake,
Oregon, Water Resour. Res., 26, 2603–2612,
https://doi.org/10.1029/WR026i010p02603, 1990.
Hostetler, S. W. and Giorgi, F.: Effects of a 2×3 CO2
climate on two large lake systems: Pyramid Lake, Nevada, and Yellowstone
Lake, Wyoming, Global Planet. Change, 10, 43–54,
https://doi.org/10.1016/0921-8181(94)00019-A, 1995.
Huang, A., Lazhu, Wang, J., Dai, Y., Yang, K., Wei, N., Wen, L., Wu, Y., Zhu, X., Zhang, X., and Cai, S.: Evaluating and improving the performance of three 1‐D Lake models in a large deep Lake of the central Tibetan Plateau, J. Geophys. Res., 124, 3143–3167. https://doi.org/10.1029/2018JD029610, 2019.
Irambona, C., Music, B., Nadeau, D. F., Mahdi, T. F., and Strachan, I. B.:
Impacts of boreal hydroelectric reservoirs on seasonal climate and
precipitation recycling as simulated by the CRCM5: a case study of the La
Grande River watershed, Canada, Theor. Appl. Climatol., 131, 1529–1544,
https://doi.org/10.1007/s00704-016-2010-8, 2018.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda,
M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva,
A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin
E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner,
M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G. , Gutiérrez, C., Gutiérrez J. M., Güttler, I.,
Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G.,
Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de
Noblet-Ducoudré, N., Panitz, H. J. , Pfeifer, S., Piazza, M., Pichelli, E.,
Pietikäinen, J. P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B.,
Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M. , Somot, S., Srnec, L.,
Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, and
K., Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change., 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020..
Karagounis, I., Trösch, J., and Zamboni, F.: A coupled
physical-biochemical lake model for forecasting water quality, Aquat. Sci.,
55, 87–102, https://doi.org/10.1007/BF00877438, 1993.
Kirillin, G.: Modeling the impact of global warming on water temperature and
seasonal mixing regimes in small temperate lakes, Boreal Environ. Res., 15,
279–293, 2010.
Kitaigorodskii, S. A. and Miropolsky, Y.: On the theory of the open ocean
active layer, Atmos. Ocean. Phys., 6, 178–188, 1970.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011,
2012.
Kraus, E. B. and Turner, J. S.: A one-dimensional model of the seasonal thermocline II. The general theory and its consequences, Tellus, 19, 98–106, https://doi.org/10.1111/j.2153-3490.1967.tb01462.x, 1967.
Le Moigne, P., Colin, J., and Decharme B.: Impact of lake surface temperatures simulated by the FLake scheme in the CNRM-CM5 climate model, Tellus A, 68, 31274, https://doi.org/10.3402/tellusa.v68.31274, 2016.
Ljungemyr, P., Gustafsson, N., and Omstedt, A.: Parameterization of lake
thermodynamics in a high-resolution weather forecasting model, Tellus A, 48,
608–621, https://doi.org/10.3402/tellusa.v48i5.12155, 1996.
Lofgren B.: Land Surface Roughness Effects on Lake Effect Precipitation, J. Great Lakes Res., 32, 839–851,
https://doi.org/10.3394/0380-1330(2006)32[839:LSREOL]2.0.CO;2, 2006.
Long, Z., Perrie, W., Gyakum, J., Caya, D., and Laprise, R.: Northern Lake
Impacts on Local Seasonal Climate, J. Hydrometeorol., 8, 881–896,
https://doi.org/10.1175/JHM591.1, 2007.
MacKay, M. D.: Incorporating wind sheltering and sediment heat flux into 1-D models of small boreal lakes: a case study with the Canadian Small Lake Model V2.0, Geosci. Model Dev., 12, 3045–3054, https://doi.org/10.5194/gmd-12-3045-2019, 2019.
Magee, M. R. and Wu, C. H.: Response of water temperatures and stratification to changing climate in three lakes with different morphometry, Hydrol. Earth Syst. Sci., 21, 6253–6274, https://doi.org/10.5194/hess-21-6253-2017, 2017.
Martinov, A., Sushama L., and Laprise, R.: Simulation of temperate freezing
lakes by one-dimensional lake models: performance assessment for interactive
coupling with regional climate models, Boreal Environ. Res., 15, 143–164,
2010.
Mironov, D., Heise, E., Kourzeneva, E., Ritter, B., Schneider, N., and
Terzhevik, A.: Implementation of the lake parameterisation scheme FLake into
the numerical weather prediction model COSMO, Boreal. Environ. Res. 15,
218–230, 2010.
Niziol, T. A., Snyder, W. R., and Waldstreicher, J. S.: Winter Weather
Forecasting throughout the Eastern United States. Part IV: Lake Effect Snow,
Weather
Forecast., 10, 61–77, https://doi.org/10.1175/1520-0434(1995)010<0061:WWFTTE>2.0.CO;2, 1995.
Nogueira, M., Soares, P. M. M., Tomé, R., and Cardoso, R. M.:
High-resolution multi-model projections of onshore wind resources over
Portugal under a changing climate, Theor. Appl. Climatol., 136, 347–362,
https://doi.org/10.1007/s00704-018-2495-4, 2019.
Nordbo, A., Launiainen, S., Mammarella, I., Leppäranta, M., Huotari, J., Ojala, A., and Vesala, T.: Long-term energy flux measurements and energy balance over a small boreal lake using eddy covariance technique, J. Geophys Res, 116, D02119, https://doi.org/10.1029/2010JD014542, 2011.
Notaro, M., Zarrin, A., Vavrus, S., and Bennington, V.: Simulation of heavy
lake-effect snowstorms across the Great Lakes basin by RegCM4: Synoptic
climatology and variability, Mon. Weather Rev., 141, 1990–2014,
https://doi.org/10.1175/MWR-D-11-00369.1, 2013.
OECD (Eds.): Eutrophication of waters – Monitoring Assessment and control, Organization for the Economic Cooperation and Development, OECD, Paris, 154, ISBN 92-64-22298-7, 1982.
Oleson, K. W., Dai, Y., Bonan, G. B., Bosilovich, M., Dickinson, R.,
Dirmeyer P., Hoffman F., Houser, P., Levis, S., Niu, G., Thornton, P.,
Vertenstein M., Yang, Z., and Zeng, X.: Technical Description of the
Community Land Model (CLM), NCAR Technical Note NCAR TN-461+STR, National
Center for Atmospheric Research, Boulder, Colorado, 174, https://doi.org/10.5065/D6N877R0, 2004.
Oswald, C. J. and Rouse, W. R.: Thermal Characteristics and Energy Balance of
Various-Size Canadian Shield Lakes in the Mackenzie River Basin, J.
Hydrometeorol., 5,
129–144, https://doi.org/10.1175/1525-7541(2004)005<0129:TCAEBO>2.0.CO;2, 2004.
Perroud, M., Goyette, S., Martynov, A., Beniston, M., and Annevillec, O.:
Simulation of multiannual thermal profiles in deep Lake Geneva: a comparison
of onedimensional lake models, Limnol. Oceanogr., 54, 1574–1594,
https://doi.org/10.4319/lo.2009.54.5.1574, 2009.
Philips, D. W.: Modification of surface air over Lake Ontario in Winter, Mon. Weather Rev., 100, 662–670, https://doi.org/10.1175/1520-0493(1972)100<0662:MOSAOL>2.3.CO;2, 1972.
Qian, N.: On the momentum term in gradient descent learning algorithms,
Neural Networks, 12, 145–151, https://doi.org/10.1016/S0893-6080(98)00116-6,
1999.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S.
K., Karpatne, A., Hansen, G., Hansin, P., Watkins W., Steinbach, M., and
Kumar, V.: Process-guided deep learning predictions of lake water
temperature, Water Resour. Res., 55, 9173–9190,
https://doi.org/10.1029/2019WR024922, 2019.
Rijo, N., Lima, D. C. A., Semedo, A., Miranda, P. M. A., Cardoso, R. M., and Soares, P. M. M.: Spatial and Temporal Variability of the Iberian Peninsula
Coastal Low-Level Jet, Int. J. Climatol., 38, 1605–1622,
https://doi.org/10.1002/joc.5303, 2018.
Rimmer, A., Gal, G., Opher, T., Lechinsky, Y., and Yacobi, Y. Z.: Mechanisms
of long-term variations in the thermal structure of a warm lake, Limnol.
Oceanogr., 56, 974–988, https://doi.org/10.4319/lo.2011.56.3.0974, 2011.
Rouse, W., Oswald, C., Binyamin, J., Blanken, P., Schertzer, W., and Spence,
C.: Interannual and Seasonal Variability of the Surface Energy Balance and
Temperature of Central Great Slave Lake, J. Hydrometeorol., 4, 720–730,
https://doi.org/10.1175/1525-7541(2003)004<0720:IASVOT>2.0.CO;2, 2003.
Samadianfard, S., Kazemi, H., Kisi, O., and Liu W.: Water temperature prediction in a subtropical subalpine lake using soft computing techniques, Earth Sci. Res. J., 20, 2, https://doi.org/10.15446/esrj.v20n2.43199, 2016.
Samuelsson, P., Kourzeneva, E., and Mironov, D.: The impact of lakes on the
European climate as simulated by a regional climate model, Boreal. Environ.
Res., 15, 113–129, 2010.
Schertzer, W. M.: Freshwater lakes, in: The Surface Climates of Canada,
edited by: Bailey, W. G., Oke, T. R., and Rouse, W. R., McGill-Queen's
University Press, Montreal and Kingston, London, 124–148, ISBN 9780773516724, 1997.
Sharma, S., Walker, S. C., and Jackson, D. A.: Empirical modelling of lake
water-temperature relationships: A comparison of approaches, Freshwater
Biol., 53, 897–911, https://doi.org/10.1111/j.1365-2427.2008.01943.x, 2008.
Shatwell, T., Thiery, W., and Kirillin, G.: Future projections of temperature and mixing regime of European temperate lakes, Hydrol. Earth Syst. Sci., 23, 1533–1551, https://doi.org/10.5194/hess-23-1533-2019, 2019.
Shevchuk, Y.: Python library, available at: http://neupy.com/pages/home.html (last access: 5 January 2022), 2015.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A description of the advanced research WRF, Version 3, Technical report (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008.
Soares, P. M. M., Cardoso, R. M., Medeiros, J., Miranda, P. M. A.,
Belo-Pereira, M., and Espirito-Santo, F.: WRF High resolution dynamical
downscaling of ERA-Interim for Portugal, Climate Dynamics, 39, 2497–2522,
https://doi.org/10.1007/s00382-012-1315-2#Bib1, 2012a.
Soares, P. M. M., Cardoso, R. M., Miranda, P. M. A., Viterbo, P., and Belo-Pereira, M.: Assessment of the ENSEMBLES regional climate models in the representation of precipitation variability and extremes over Portugal, J. Geophys. Res., 117, 7, https://doi.org/10.1029/2011JD016768, 2012b.
Soares, P. M. M., Cardoso, R. M., Semedo, A., Chinita, M. J., and Ranjha, R.: Climatology of Iberia Coastal Low-Level Wind Jet: WRF High Resolution Results, Tellus A, 66, 1, https://doi.org/10.3402/tellusa.v66.22377, 2014.
Stepanenko, V. M. and Lykossov, V. N.: Numerical modeling of heat and
moisture transfer processes in a system lake–soil, Russ. Meteorol.
Hydrol., 3, 95–104, 2005.
Stepanenko, V. M., Goyette, S., Martynov, A., Perroud M., Fang, X., and
Mironov A.: First steps of a lake model intercomparison Project: LakeMIP,
Boreal Environ. Res., 15, 191–202, 2010.
Stepanenko, V. M., Martynov, A., Jöhnk, K. D., Subin, Z. M., Perroud, M., Fang, X., Beyrich, F., Mironov, D., and Goyette, S.: A one-dimensional model intercomparison study of thermal regime of a shallow, turbid midlatitude lake, Geosci. Model Dev., 6, 1337–1352, https://doi.org/10.5194/gmd-6-1337-2013, 2013.
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, 1, https://doi.org/10.1029/2011MS000072, 2012.
Svensson, U.: A mathematical model of the seasonal thermocline, Ph.D. thesis, Lund Institute of Technology, Lund, 1978.
Thackston, E. L. and Parker, F. L.: Effect of Geographical Location on
Cooling Pond Requirements and Performance, water Pollution Control Research
Series 16130 FDQ 03/71, Vanderbilt University, Dept. of Environmental and
Water Resources Engineering, Environmental Protection Agency (EPA),
Washington, D. C., 244, 1971.
Thiery, W., Martynov, A., Darchambeau, F., Descy, J.-P., Plisnier, P.-D., Sushama, L., and van Lipzig, N. P. M.: Understanding the performance of the FLake model over two African Great Lakes, Geosci. Model Dev., 7, 317–337, https://doi.org/10.5194/gmd-7-317-2014, 2014.
Thiery, W., Stepanenko, V. M., Fang, X., Jöhnk, K. D., Li, Z., Martynov, A., Perroud, M., Subin, Z. M., Darchambeau, F., Mironov, D., and Van Lipzig, N. P. M.: LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set 835 of one-dimensional lake models, Tellus A, 66, 1, https://doi.org/10.3402/tellusa.v66.21390, 2016.
Toffolon, M. and Piccolroaz, S.: A hybrid model for river water temperature
as a function of air temperature and discharge, Environ. Res.
Lett., 10, 114011, https://doi.org/10.1088/1748-9326/10/11/114011, 2015.
Wang, F., Ni, G., Riley, W. J., Tang, J., Zhu, D., and Sun, T.: Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir, Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, 2019.
Wang, Y., Ma, Q., Gao, Y., Hao, X., and Liu, S.: Simulation of the surface
energy flux and thermal stratification of lake Taihu with three 1-D models,
Water, 11, 1026, https://doi.org/10.3390/w11051026, 2019.
Wright, D. M., Posselt, D. J., and Steiner, A. L.: Sensitivity of Lake-Effect
Snowfall to Lake Ice Cover and Temperature in the Great Lakes Region, Mon.
Weather Rev., 141, 670–689, https://doi.org/10.1175/MWR-D-12-00038.1, 2013.
Wunderlich, W.: Heat and Mass Transfer between a Water Surface and the
Atmosphere, Rpt. No. 14, Rpt. Publication No. 0-6803, Water Resources
Research Laboratory, Tennessee Valley Authority, Division of Water Control
Planning, Engineering Laboratory, Norris, TN, 1972.
Xiao, C., Lofgren, B., Wang, J., and Chu, P.: Improving the lake scheme within a coupled WRF-Lake model in the Laurentian Great Lakes, J. Adv. Model. Earth Sy., 8, 4, https://doi.org/10.1002/2016MS000717, 2016.
Xue, P., Pal, J. S., Ye, X., Lenters, J. D., Huang, C., and Chu, P. Y.:
Improving the simulation of large lakes in regional climate modeling:
Two-way lake–atmosphere coupling with a 3D hydrodynamic model of the Great
Lakes, J. Climate, 30, 1605–1627, https://doi.org/10.1175/JCLI-D-16-0225.1
2017.
Zamboni, F., Barbieri, A., Polli, B., Salvadè, G., and Simona, M.: The
dynamic model SEEMOD applied to the southern basin of lake Lugano, Aquat.
Sci., 54, 367–380, https://doi.org/10.1007/BF00878148, 1992.
Short summary
In this study, we have evaluated the importance of the input of energy conveyed by river inflows into lakes and reservoirs when modeling surface water energy fluxes. Our results suggest that there is a strong correlation between water residence time and the surface water temperature prediction error and that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes.
In this study, we have evaluated the importance of the input of energy conveyed by river inflows...