Articles | Volume 16, issue 12
https://doi.org/10.5194/tc-16-4985-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-4985-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2022
Research article |
| 16 Dec 2022
| 16 Dec 2022
Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, China
Jie Chen
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, China
NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
Yvan J. Orsolini
NILU – Norwegian Institute for Air Research, Kjeller, Norway
Yiheng Xiang
Institute of Heavy Rain, China Meteorological Administration (CMA),
Wuhan, China
Retish Senan
European Centre for Medium-Range Weather Forecasts (ECWMF), Reading, UK
Patricia de Rosnay
European Centre for Medium-Range Weather Forecasts (ECWMF), Reading, UK
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Ekaterina Vorobeva, Yvan Orsolini, Patricia de Rosnay, Jonathan Day, Retish Senan, Damien Decremer, and Frederic Vitart
The Cryosphere, 20, 4605–4618, https://doi.org/10.5194/tc-20-4605-2026, https://doi.org/10.5194/tc-20-4605-2026, 2026
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How reliable are probabilistic seasonal snow forecasts in winter? Although they are routinely issued by operational prediction centres, their reliability has never been evaluated. We close this gap by analyzing 30 years of seasonal snow re-forecasts and evaluating them against two snow datasets. Our results provide comprehensive assessment of seasonal snow forecast reliability and offer new insights into their performance in different parts of the Northern Hemisphere.
Nina Raoult, Ewan Pinnington, Mario Santa Cruz, Florian Pinault, Baudouin Raoult, Natalie Zelenka, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Matthew Chantry, Patricia de Rosnay, Peter Dueben, and Christoph Rüdiger
EGUsphere, https://doi.org/10.5194/egusphere-2026-3620, https://doi.org/10.5194/egusphere-2026-3620, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The land surface shapes our weather and climate by controlling how heat and water move between the ground and the air, so the models representing it matter enormously. But they are costly to run and carry built-in errors. We built a faster artificial-intelligence version of one, then used real measurements from sites worldwide to correct some of these errors. The result stays reliable over years and better captures these heat and water exchanges—a quicker, more accurate tool for forecasting.
Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio, Simon Lang, Zied Ben Bouallègue, Jesper Dramsch, Mihai Alexe, Mario Santa Cruz, Sara Hahner, Harrison Cook, Helen Theissen, Mariana Clare, Cathal O'Brien, Jan Polster, Linus Magnusson, Gert Mertes, Florian Pinault, Baudouin Raoult, Patricia de Rosnay, Richard Forbes, and Matthew Chantry
Geosci. Model Dev., 19, 4703–4724, https://doi.org/10.5194/gmd-19-4703-2026, https://doi.org/10.5194/gmd-19-4703-2026, 2026
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We present the latest release of the Artificial Intelligence Forecasting System, AIFS 1.1.0, which shows improved headline forecasting skill through an expanded dataset and enhanced training schedule. The model also incorporates hard physical constraints that facilitate training and improve rainfall prediction. Finally, we extend the set of forecasted variables to include soil conditions and energy-related fields, strengthening the operational value of AIFS.
Pierre Vanderbecken, Jasmin Vural, Oscar Rojas-Munoz, Sébastien Garrigues, Bertrand Bonan, Cédric Bacour, Uwe Rascher, Bastian Siegmann, Patricia de Rosnay, and Jean-Christophe Calvet
EGUsphere, https://doi.org/10.5194/egusphere-2026-941, https://doi.org/10.5194/egusphere-2026-941, 2026
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New satellite observations are used to improve our understanding of regional-scale vegetation. Innovative artificial intelligence methods are employed to combine the available information. The effectiveness of this approach is evaluated using independent observations. The results show that agricultural practices such as irrigation can be modelled more accurately.
Ekaterina Vorobeva and Yvan Orsolini
Weather Clim. Dynam., 6, 1015–1026, https://doi.org/10.5194/wcd-6-1015-2025, https://doi.org/10.5194/wcd-6-1015-2025, 2025
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The factors behind why some polar stratospheric wind disturbances persist while others remain short are unclear. This study examines the impact of tropospheric high-pressure systems on the duration of three recent polar stratospheric wind disturbances in the winter of 2023/24. Two were short-lived, lasting under 7 d. The findings in this study show that two short disturbances could not develop into long-lasting events due to developing high-pressure systems over the planetary wave troughs.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Xiaoling Chen, Jianzhong Lu, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, Harald Kunstmann, and Zhengkang Zuo
Hydrol. Earth Syst. Sci., 29, 4109–4132, https://doi.org/10.5194/hess-29-4109-2025, https://doi.org/10.5194/hess-29-4109-2025, 2025
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To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Jiaoyang Wang, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Hua Chen, Jie Chen, Jiabo Yin, and Yuling Zhang
Hydrol. Earth Syst. Sci., 29, 3315–3339, https://doi.org/10.5194/hess-29-3315-2025, https://doi.org/10.5194/hess-29-3315-2025, 2025
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The unclear feedback loops of water supply–hydropower generation–environmental conservation (SHE) nexuses with inter-basin water diversion projects (IWDPs) increase the uncertainty in the rational scheduling of water resources for water receiving and water donation areas. To address the different impacts of IWDPs on dynamic SHE nexuses and explore synergies, a framework is proposed to identify these effects across the different temporal and spatial scales in a reservoir group.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Ruikang Zhang, Dedi Liu, Lihua Xiong, Jie Chen, Hua Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 28, 5229–5247, https://doi.org/10.5194/hess-28-5229-2024, https://doi.org/10.5194/hess-28-5229-2024, 2024
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Flash flood warnings cannot be effective without people’s responses to them. We propose a method to determine the threshold of issuing warnings based on a people’s response process simulation. The results show that adjusting the warning threshold according to people’s tolerance levels to the failed warnings can improve warning effectiveness, but the prerequisite is to increase forecasting accuracy and decrease forecasting variance.
Qian Lin, Jie Chen, and Deliang Chen
EGUsphere, https://doi.org/10.5194/egusphere-2024-826, https://doi.org/10.5194/egusphere-2024-826, 2024
Preprint archived
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Glaciers of the Tibetan Plateau (TP) have experienced widespread retreat in recent decades, but impacts of glacier changes that have occurred on regional climate, including precipitation, is still unknown. Thus, this study addressed this knowledge gap, and found that glacier changes exert a more pronounced impact on summer extreme precipitation events than mean precipitation over the TP. This provides a certain theoretical reference for the further improvement of long-term glacier projection.
Leonie Bernet, Tove Svendby, Georg Hansen, Yvan Orsolini, Arne Dahlback, Florence Goutail, Andrea Pazmiño, Boyan Petkov, and Arve Kylling
Atmos. Chem. Phys., 23, 4165–4184, https://doi.org/10.5194/acp-23-4165-2023, https://doi.org/10.5194/acp-23-4165-2023, 2023
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After the severe destruction of the ozone layer, the amount of ozone in the stratosphere is expected to increase again. At northern high latitudes, however, such a recovery has not been detected yet. To assess ozone changes in that region, we analyse the amount of ozone above specific locations (total ozone) measured at three stations in Norway. We found that total ozone increases significantly at two Arctic stations, which may be an indication of ozone recovery at northern high latitudes.
Jinghua Xiong, Shenglian Guo, Abhishek, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci., 26, 6457–6476, https://doi.org/10.5194/hess-26-6457-2022, https://doi.org/10.5194/hess-26-6457-2022, 2022
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Although the "dry gets drier, and wet gets wetter (DDWW)" paradigm is prevalent in summarizing wetting and drying trends, we show that only 11.01 %–40.84 % of the global land confirms and 10.21 %–35.43 % contradicts the paradigm during 1985–2014 from a terrestrial water storage change perspective. Similar proportions that intensify with the increasing emission scenarios persist until the end of the 21st century. Findings benefit understanding of global hydrological responses to climate change.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Steve Delhaye, Thierry Fichefet, François Massonnet, David Docquier, Rym Msadek, Svenya Chripko, Christopher Roberts, Sarah Keeley, and Retish Senan
Weather Clim. Dynam., 3, 555–573, https://doi.org/10.5194/wcd-3-555-2022, https://doi.org/10.5194/wcd-3-555-2022, 2022
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It is unclear how the atmosphere will respond to a retreat of summer Arctic sea ice. Much attention has been paid so far to weather extremes at mid-latitude and in winter. Here we focus on the changes in extremes in surface air temperature and precipitation over the Arctic regions in summer during and following abrupt sea ice retreats. We find that Arctic sea ice loss clearly shifts the extremes in surface air temperature and precipitation over terrestrial regions surrounding the Arctic Ocean.
Jiacheng Chen, Jie Chen, Xunchang John Zhang, Peiyi Peng, and Camille Risi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-460, https://doi.org/10.5194/essd-2021-460, 2022
Manuscript not accepted for further review
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To make full use of the advantages of isotope observations and simulations, this study generates a new dataset by integrating multi-GCM data based on data fusion and bias correction methods. This dataset contains monthly δ18Op over mainland China for the 1870–2017 period with a spatial resolution of 50–60 km. The built isoscape shows similar spatial and temporal distribution characteristics to observations, which is reliable and useful to extend the time and space of observations in China.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
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Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
Jinghua Xiong, Shenglian Guo, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-645, https://doi.org/10.5194/hess-2021-645, 2022
Manuscript not accepted for further review
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Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.
Martin Wegmann, Yvan Orsolini, Antje Weisheimer, Bart van den Hurk, and Gerrit Lohmann
Weather Clim. Dynam., 2, 1245–1261, https://doi.org/10.5194/wcd-2-1245-2021, https://doi.org/10.5194/wcd-2-1245-2021, 2021
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Northern Hemisphere winter weather is influenced by the strength of westerly winds 30 km above the surface, the so-called polar vortex. Eurasian autumn snow cover is thought to modulate the polar vortex. So far, however, the modeled influence of snow on the polar vortex did not fit the observed influence. By analyzing a model experiment for the time span of 110 years, we could show that the causality of this impact is indeed sound and snow cover can weaken the polar vortex.
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Short summary
Snow assimilation over the Tibetan Plateau (TP) may influence seasonal forecasts over this region. To investigate the impacts of snow assimilation on the seasonal forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer in 2018. The results show that snow assimilation can improve seasonal forecasts over the TP through the interaction between land and atmosphere.
Snow assimilation over the Tibetan Plateau (TP) may influence seasonal forecasts over this...
