Articles | Volume 15, issue 3
https://doi.org/10.5194/esd-15-735-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-15-735-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jun 2024
Research article |
| 13 Jun 2024
| 13 Jun 2024
Distribution-based pooling for combination and multi-model bias correction of climate simulations
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay, Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Denis Allard
Biostatistics and Spatial Processes (BioSP), INRAE, 84914 Avignon, France
Grégoire Mariéthoz
CORRESPONDING AUTHOR
University of Lausanne, Expertise Center for Climate Extremes (ECCE), Institute of Earth Surface Dynamics (IDYST), UNIL-Mouline, Geopolis, 1015 Lausanne, Switzerland
Soulivanh Thao
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay, Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Lucas Schmutz
University of Lausanne, Expertise Center for Climate Extremes (ECCE), Institute of Earth Surface Dynamics (IDYST), UNIL-Mouline, Geopolis, 1015 Lausanne, Switzerland
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Short summary
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Short summary
Short summary
This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
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Short summary
Short summary
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Cedric Gacial Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, and Cyrille Flamant
Nat. Hazards Earth Syst. Sci., 23, 1313–1333, https://doi.org/10.5194/nhess-23-1313-2023, https://doi.org/10.5194/nhess-23-1313-2023, 2023
Short summary
Short summary
Heat waves (HWs) are climatic hazards that affect the planet. We assess here uncertainties encountered in the process of HW detection and analyse their recent trends in West Africa using reanalysis data. Three types of uncertainty have been investigated. We identified 6 years with higher frequency of HWs, possibly due to higher sea surface temperatures in the equatorial Atlantic. We noticed an increase in HW characteristics during the last decade, which could be a consequence of climate change.
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
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Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Clim. Past, 18, 2289–2301, https://doi.org/10.5194/cp-18-2289-2022, https://doi.org/10.5194/cp-18-2289-2022, 2022
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This paper presents a compilation of high-resolution (11 cm) water isotopic records, including published and new measurements, for the last 800 000 years from the EPICA Dome C ice core, Antarctica. Using this new combined water isotopes (δ18O and δD) dataset, we study the variability and possible influence of diffusion at the multi-decadal to multi-centennial scale. We observe a stronger variability at the onset of the interglacial interval corresponding to a warm period.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, https://doi.org/10.5194/gmd-15-4941-2022, 2022
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We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
Miriam D'Errico, Flavio Pons, Pascal Yiou, Soulivanh Tao, Cesare Nardini, Frank Lunkeit, and Davide Faranda
Earth Syst. Dynam., 13, 961–992, https://doi.org/10.5194/esd-13-961-2022, https://doi.org/10.5194/esd-13-961-2022, 2022
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Climate change is already affecting weather extremes. In a warming climate, we will expect the cold spells to decrease in frequency and intensity. Our analysis shows that the frequency of circulation patterns leading to snowy cold-spell events over Italy will not decrease under business-as-usual emission scenarios, although the associated events may not lead to cold conditions in the warmer scenarios.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Yoann Robin and Mathieu Vrac
Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, https://doi.org/10.5194/esd-12-1253-2021, 2021
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We propose a new multivariate downscaling and bias correction approach called
time-shifted multivariate bias correction, which aims to correct temporal dependencies in addition to inter-variable and spatial ones. Our method is evaluated in a
perfect model experimentcontext where simulations are used as pseudo-observations. The results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
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This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, https://doi.org/10.5194/os-17-1011-2021, 2021
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In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
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Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Moctar Dembélé, Bettina Schaefli, Nick van de Giesen, and Grégoire Mariéthoz
Hydrol. Earth Syst. Sci., 24, 5379–5406, https://doi.org/10.5194/hess-24-5379-2020, https://doi.org/10.5194/hess-24-5379-2020, 2020
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This study evaluates 102 combinations of rainfall and temperature datasets from satellite and reanalysis sources as input to a fully distributed hydrological model. The model is recalibrated for each input dataset, and the outputs are evaluated with streamflow, evaporation, soil moisture and terrestrial water storage data. Results show that no single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
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We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Jason Bula, Marc-Henri Derron, and Gregoire Mariethoz
Geosci. Instrum. Method. Data Syst., 9, 385–396, https://doi.org/10.5194/gi-9-385-2020, https://doi.org/10.5194/gi-9-385-2020, 2020
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We developed a method to acquire dense point clouds with a low-cost Velodyne Puck lidar system, without using expensive Global Navigation Satellite System (GNSS) positioning or IMU. We mounted the lidar on a motor to continuously change the scan direction, leading to a significant increase in the point cloud density. The system was compared with a more expensive system based on IMU registration and a SLAM algorithm. The alignment between acquisitions with those two systems is within 2 m.
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Short summary
We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
We aim to combine multiple global climate models (GCMs) to enhance the robustness of future...
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