Benoit Hingray

The socioeconomic challenges associated with the effects of global warming are such that there is a clearly expressed need for tailored climate information to support the implementation of mitigation and/or adaptation strategies, as articulated by economic sectors (e.g., agriculture, energy, tourism, land and maritime infrastructure, etc.) and by territories/regions that fully understand their vulnerabilities. In response to these demands, numerous national, European, and international research projects have enabled the funding of “climate services.”Several national and European operational actors are developing and making “climate services” available via platforms that are often freely accessible; finally, consulting firms are emerging on the market whose commercial activity focuses on developing “climate services” tailored to clients’ specific needs.The range of “climate services” is therefore diverse today, both in terms of the information provided (“simple” climate data, indicators, decision-support tools) and in terms of how they are developed. Given this diversity, the challenges lie in documenting and understanding the current landscape of climate services, identifying needs, and equipping ourselves with the means to characterize the success of climate services, to evaluate existing offerings, and to guide the development of new projects. The aim of this presentation has four objectives:i) To document the current landscape of climate services in France and worldwide (as identified by our TRACCS community), presenting them by use and target audience;ii) Identify unmet needs regarding climate services;iii) Identify a set of success criteria for climate services to evaluate them;iv) Propose best practices for meeting these success criteria. This work is based on a combination of collaborative research and the collection of statements from stakeholders in “climate services”. This study has received funding from Agence Nationale de la Recherche - France 2030 as part of the PEPR TRACCS programme under grant number ANR-22-EXTR-0002 and ANR-22-EXTR-0004.

A large transient multi-scenario and multi-model ensemble of future streamflow and groundwater projections in France developed in a national project named Explore2 was recently made available. The main objective of Explore2 is to provide rich and spatially-consistent information for the future evolution of hydrological (surface and groundwater) resources and extremes in France to support adaptation strategies. The Explore2 dataset was obtained using a nested multi-scenario multi-model approach to estimate future uncertainty and to assess local climate at the catchment scale: three greenhouse gas (GHG) emission scenarios, a set of 17 combinations of Global Climate Models and Regional Climate Models (GCM/RCM), and two bias correction methods provide the meteorological forcing for nine surface hydrology models and four groundwater hydrology models (one to simulate groundwater recharge and three to simulate groundwater level). In this paper, we present the methodology underlying the dataset, the evaluation of the hydrological models against daily observations of streamflow and groundwater level, and the key messages on the impact of climate change on both mean river flows and groundwater recharge. This large set of hydrological projections shows a high model agreement on the decrease in seasonal flows in the South of France under the RCP8.5 high-emission scenario, confirming its hotspot status. The surface hydrological models agree on the decrease in summer flows across France under the RCP8.5 scenario, with the exception of northern part France. This area may indeed benefit from more active winter recharge that may counterbalance decrease in summer precipitation and increase in evapotranspiration. In addition to northern France, annual groundwater recharge is projected to increase slightly in the north-east while remaining unchanged elsewhere by the end of the century, according to the RCP8.5 scenario. In the mountainous areas, winter flows will increase as a result of higher air temperature and the high degree of agreement between the models holds regardless of the RCP considered. Unsurprisingly, the higher the GHG emission scenario, the higher the median changes. Most of these changes are organised in France along a north-south gradient, regardless of the RCP considered.

As solar photovoltaic (PV) systems are deployed globally to decarbonize energy production systems, atmospheric dust has emerged as a critical challenge due to its potential to drastically reduce production efficiency in many regions. Dust particles both attenuate incoming solar radiation and accumulate on photovoltaic module surfaces thereby reducing light transmission and power output. Soiling losses (defined as power production losses due to dust accumulation on PV panels) vary at daily, monthly and interannual timescales, as dust accumulation and removal processes depend on time-varying factors such as particulate matter concentration, wind, relative humidity, precipitation and cleaning operations. Capturing these dynamics thus requires assessments spanning several years.Numerous studies have examined dust impacts on solar power generation, most relying on observations from solar farms or experimental sites. However, such observations remain scarce and often cover short time periods, particularly in data-scarce regions thus preventing comprehensive dust impact assessments. Dust simulation models offer an alternative approach: they enable the reconstruction of dust accumulation dynamics and their impacts on power production from meteorological data over extended periods.This simulation approach was applied by Isaacs et al. (2023) for West Africa with atmospheric reanalysis (MERRA-2) and satellite-derived data. However, the extent to which input data and modelling choices may influence the conclusions of simulated estimates remains unclear. Reanalysis products are subject to substantial uncertainties and errors, especially in regions where ground-based observations used for their development are scarce. Dust models also typically rely on simplified process representations and poorly constrained parametrizations.In this study, we introduce PVWAT, a simple dust simulation model developed for dust impact assessment as part of the ANR-funded NETWAT project, which examines water-energy nexus challenges in West Africa. Linking different sub-models from literature, it uses meteorological inputs from on-site observations or atmospheric reanalysis to simulate time series of dust deposition fluxes, deposited dust amounts and the resulting soiling losses.We then use PVWAT to demonstrate how simulated dust impacts depend on input data and modeling choices. For this, we consider West Africa, a hot spot for dust-related PV production losses. The region's high solar potential and unmet energy demand are expected to drive large PV expansion in the coming years (10+ GW of solar capacity by 2030; IRENA, 2023) but the region borders the Sahara and Bodélé depression, the world's most prolific dust source. Our analysis considers three sites along a north-south transect, representing contrasting dust conditions, climates (arid to humid), and land covers (savanna to tropical forest), in order to draw recommendations for diverse solar production contexts.Through systematic sensitivity analysis, we perturb model parameters up to 8× and meteorological variables up to 2× to quantify their effects on long-term soiling ratios. This reveals the dominant sources of uncertainty and assesses how the model responds to parametric versus variable perturbations across contrasting sites.ReferencesInternational Renewable Energy Agency. (2023). Scaling up renewable energy investments in West Africa. https://www.irena.orgIsaacs et al., 2023. Dust soiling effects on decentralized solar in West Africa. Applied Energy, 340, 120993. https://doi.org/10.1016/j.apenergy.2023.120993

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1,735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

A large transient multi-scenario and multi-model ensemble of future streamflow and groundwater projections in France developed in a national project named Explore2 was recently made available. The main objective of Explore2 is to provide rich and spatially-consistent information for the future evolution of hydrological (surface and groundwater) resources and extremes in France to support adaptation strategies. The Explore2 dataset was obtained using a nested multi-scenario multi-model approach to estimate future uncertainty and to assess local climate at the catchment scale: three greenhouse gas (GHG) emission scenarios, a set of 17 combinations of Global Climate Models and Regional Climate Models (GCM-RCM), and two bias correction methods provide the meteorological forcing for nine surface hydrology models and four groundwater hydrology models (one to simulate groundwater recharge and three to simulate groundwater level). In this paper, we present the methodology underlying the dataset, the evaluation of the hydrological models against daily streamflow and groundwater level observations, the assessment of the future streamflow and recharge projections, the data availability and the ways of accessing the data and understanding the results (mainly through visualisation tools). This large set of hydrological projections shows a high model agreement on the decrease in seasonal flows in the South of France under the RCP8.5 high-emission scenario, confirming its hotspot status. The surface HMs agree on the decrease in summer flows across France under the RCP8.5 scenario, with the exception of northern part France. This area may indeed benefit from more active winter recharge that may counterbalance decrease in summer precipitation and increase in evapotranspiration. In the mountainous areas, winter flows will increase as a result of higher air temperature and the high degree of agreement between the models holds regardless of the RCP considered. Unsurprisingly, the higher the GHG emission scenario, the higher the median changes. Most of these changes are organised in France along a north-south gradient, regardless of the RCP considered.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1,735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1,735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability. Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.