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Start Over Topic [sdu.stu.me]sciences of the universe [physics]/earth sciences/meteorology
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1. Quantifying CMIP6 model uncertainties in extreme precipitation projections

Author

John, Amal, Douville, Hervé, Ribes, Aurélien, Yiou, Pascal, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Horizon 2020 Framework Programme, H2020, H2020 Marie Skłodowska-Curie Actions, MSCA: 813844, Center for Agriculture, Food and the Environment, University of Massachusetts Amherst, CAFE, Horizon 2020, This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844 . We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, [SDU]Sciences of the Universe [physics], [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, [SDE.MCG]Environmental Sciences/Global Changes, Geography, Planning and Development, Extremes, Uncertainty, Climate change, Precipitation, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, Management, Monitoring, Policy and Law
Abstract

International audience; Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (ΔGSAT) or in local surface temperature (ΔT) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius–Clapeyron (CC) rate of ≈7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of ≈7%/K over about 83% of the global land domain when scaled by (ΔGSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.

Published
2022

2. An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Author

Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, Vinciane Unger, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This research has been supported by the Agence Nationale de la Recherche (grant no. AAPG-2018-CE01-004.)., The instrumental data used in this study are part of the SOFOG-3D experiment. The SOFOG-3D field campaign was supported by METEO-FRANCE and ANR through grant AAPG-2018-CE01-004. Data are managed by the French National Centre for Atmospheric Data and Services (AERIS).The MWR network deployment was carried out thanks to support by IfU GmbH, the University of Cologne, the Met Office, Laboratoire d’Aérologie, MeteoSwiss, ONERA, and Radiometer Physics GmbH. MWR data have been made available and were quality-controlled and processed in the frame of CPEXLAB (Cloud and Precipitation Exploration LABoratory, http://www.cpex-lab.de/, last access: 8 September 2022), a competence centre within the Geoverbund ABC/J by acting support of Ulrich Löhnert, Rainer Haseneder-Lind and Arthur Kremer from the University of Cologne. This article is based upon work from COST action CA18235 PROBE, supported by COST (European Cooperation in Science and Technology, https://www.cost.eu/, 8 September 2022). Thibaut Montmerle and Yann Michel are thanked for their support on the use of the AROME EDA to compute background error covariance matrices. The authors also thank the two anonymous reviewers., Author contributions. AB and PM made developments to the 1DVar algorithm. AB performed the analysis documented in the paper, which was supervised by PM and OC. JD and SJ provided the radar data, which they also used to perform verification checks, and they also provided relevant assistance. FB provided the in situ data from the SOFOG-3D field campaign. YS provided the configuration to generate AROME forecasts. VU provided treatment for the microwave radiometer data., Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), SPACE - LATMOS, Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), and Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)

Subjects
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], Atmospheric Science, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology
Abstract

The 1D-Var retrieval framework described hereused the NWPSAF 1D package. This can be downloaded fromthe NWPSAF website (https://nwp-saf.eumetsat.int/site/software/1d-var/download/, last access: 22 August 2022, Pavelin and Collard, 2009). The radiative transfer model RTTOV-gb v1.0 is available to licensed users free of charge. RTTOV-gb may be obtainedby registering (https://www.nwpsaf.eu/site/register/, last access: 22August 2022) and then selecting RTTOV-gb in your software preferences. Developments and updates of the RTTOV-gb code willbe published on the MWRnet website (http://cetemps.aquila.infn.it/rttovgb/rttovgb_updates.html, last access: 8 September 2022). Foraccess to the radar simulator and modified 1D-Var code, please contact pauline.martinet@meteo.fr; International audience; A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm −3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.06 gm −3 , was found. This was reduced to 0.05 gm −3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the for- ward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational back- ground profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging re- sults for future improvement of the AROME model initial state during fog conditions.

Published
2022

3. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales

Author

Yoshihide Wada, Fang Zhao, Yasushi Honda, Thomas Hickler, Jörg Steinkamp, Nikolay Khabarov, Tobias Stacke, Lila Warszawski, David N. Bresch, Tobias Geiger, Wim Thiery, Kazuya Nishina, Jonas Jägermeyr, Sebastian Ostberg, Hannes Müller Schmied, Manolis Grillakis, Iliusi Vega, Kerry Emanuel, Akihiko Ito, Philippe Ciais, Sonia I. Seneviratne, Stefan Lange, Naota Hanasaki, Jinfeng Chang, Jan Volkholz, Aristeidis Koutroulis, Christian Folberth, Matthias Büchner, Christopher P. O. Reyer, Minoru Yoshikawa, Sven Willner, Christoph Müller, Katja Frieler, Hong Yang, Jacob Schewe, Simon N. Gosling, Alexandra Henrot, Dieter Gerten, Marie Dury, Veronika Huber, Chao Yue, Wenfeng Liu, Ted Veldkamp, Hydrology and Hydraulic Engineering, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Environmental Restoration and Conservation Agency, ERCA 821010 641816 SAW‐2016‐PIK‐1, 603864 Bundesministerium für Bildung und Forschung, BMBF: 01LA1829A, 01LS1201A2, 01LS1711F Ministry of Economy, Trade and Industry, METI Eidgenössische Technische Hochschule Zürich, ETH: Fel‐45 15‐1 Ministerio de Economía y Competitividad, MINECO: PCIN‐2017‐046, We thank the editor and two anonymous reviewers for constructive feedback. We thank three other anonymous reviewers for helpful comments on an earlier version of this paper submitted to a different journal. This research was supported in part by the German Federal Ministry of Education and Research (BMBF, grant numbers 01LS1201A2, 01LS1711F, and 01LA1829A) and the EU FP7 HELIX project (grant number 603864). Some authors acknowledge support from the Leibniz Competition projects SAW‐2013‐PIK‐5 (EXPACT) and SAW‐2016‐PIK‐1 (ENGAGE). Some authors acknowledge funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 821010 (CASCADES). N. H., K. N., and Y. H. acknowledge support from the ERTD Funds 2RF‐1802 and S‐14 of the Environmental Restoration and Conservation Agency of Japan. W. T. was supported by an ETH Zurich postdoctoral fellowship (Fel‐45 15‐1). V. H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO, grant number PCIN‐2017‐046). P. C. acknowledges support from the CLAND ANR Convergence Institute. S. L. acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 641816 (CRESCENDO). Open access funding enabled and organized by Projekt DEAL., Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Water and Climate Risk

Subjects
010504 meteorology & atmospheric sciences, HYDROLOGICAL MODELS, Population, 0207 environmental engineering, FLOOD RISK, Environmental Sciences & Ecology, 02 engineering and technology, Subtropics, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, 01 natural sciences, Population density, Latitude, Climate-related extreme events, Earth and Planetary Sciences (miscellaneous), SDG 13 - Climate Action, Meteorology & Atmospheric Sciences, BURNED AREA, GLOBAL CROP PRODUCTION, Geosciences, Multidisciplinary, 020701 environmental engineering, education, 0105 earth and related environmental sciences, General Environmental Science, Event (probability theory), education.field_of_study, Science & Technology, Land use, Global warming, VEGETATION MODEL ORCHIDEE, Geology, 15. Life on land, TERRESTRIAL CARBON BALANCE, 13. Climate action, Climatology, Physical Sciences, TROPICAL CYCLONE ACTIVITY, HURRICANE INTENSITY, Environmental science, Tropical cyclone, INTERANNUAL VARIABILITY, Life Sciences & Biomedicine, Environmental Sciences, INCORPORATING SPITFIRE
Abstract

Summarization: The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Presented on: Earth's Future

Published
2020