Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins

International audienceStudies of the impact of climate change on water resources usually follow a top to bottom approach: a scenario of emissions is used to run a GCM simulation, which is downscaled (RCM and/or stastistical methods) and bias-corrected. Then, this data is used to force a hydrological model. Seldom, impact studies take into account all relevant uncertainties. In fact, many published studies only use one climate model and one downscaling technique. In this study, the outputs of an atmosphere-ocean regional climate model are downscaled and bias-corrected using three different techniques: a statistical method based on weather regimes, a quantile-mapping method and the method of the anomaly. The resulting data are used to force a distributed hydrological model to simulate the French Mediterranean basins. These are characterized by water scarcity and an increasing human pressure, which cause a demand in assessments on the impact of climate change hydrological systems. The purpose of the study is mainly methodological: the evaluation of the uncertainty related to the downscaling and bias-correction step. The periods chosen to compare the changes are the end of the 20th century (1970-2000) and the middle of the 21st century (2035-2065). The study shows that the three methods produce similar anomalies of the mean annual precipitation, but there are important differences, mainly in terms of spatial patterns. The study also shows that there are important differences in the anomalies of temperature. These uncertainties are amplified by the hydrological model. In some basins, the simulations do not agree in the sign of the anomalies and, in many others, the differences in amplitude of the anomaly are very important. Therefore, the uncertainty related to the downscaling and bias-correction of the climate simulation must be taken into account in order to better estimate the impact of climate change, with its uncertainty, on a specific basin. The study also shows that according to the RCM simulation used and to the periods studied, there might be significant increases of winter precipitation on the Cévennes region of the Massif Central, which is already affected by flash floods, and significant decreases of summer precipitation in most of the region. This will cause a decrease in the average discharge in the middle of the 21st in most of the gauging stations studied, specially in summer. Winter and, maybe spring, in some areas, are the exception, as discharge may increase in some basins

An updated assessment of past and future warming over France based on a regional observational constraint

Building on CMIP6 climate simulations, updated global and regional observations, and recently introduced statistical methods, we provide an updated assessment of past and future warming over France. Following the IPCC AR6 and recent global-scale studies, we combine model results with observations to constrain climate change at the regional scale. Over mainland France, the forced warming in 2020 with respect to 1900–1930 is assessed to be 1.66 [1.41 to 1.90] ∘C, i.e., in the upper range of the CMIP6 estimates, and is almost entirely human-induced. A refined view of the seasonality of this past warming is provided through updated daily climate normals. Projected warming in response to an intermediate emission scenario is assessed to be 3.8 ∘C (2.9 to 4.8 ∘C) in 2100 and rises up to 6.7 [5.2 to 8.2] ∘C in a very high emission scenario, i.e., substantially higher than in previous ensembles of global and regional simulations. Winter warming and summer warming are expected to be about 15 % lower than and 30 % higher than the annual mean warming, respectively, for all scenarios and time periods. This work highlights the importance of combining various lines of evidence, including model and observed data, to deliver the most reliable climate information. This refined regional assessment can feed adaptation planning for a range of activities and provides additional rationale for urgent climate action. Code is made available to facilitate replication over other areas or political entities.</p

Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach

Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost. We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest

Assessing observational constraints on future European climate in an out-of-sample framework

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in the European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections by combining the constrained projections, which are found to be competitive with the best-performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods

Assessing observational constraints on future European climate in an out-of-sample framework

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections, by combining the constrained projections, which are found to be competitive with the best performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods

Quantifying human contributions to past and future ocean warming and thermosteric sea level rise

More than 90% of the Earth’s energy imbalance is stored by the ocean. While previous studies have shown that changes in the ocean warming are detectable and distinct from internal variability of the climate system, an estimate of separate contributions by natural and individual anthropogenic forcings (such as greenhouse gases and aerosols) remains outstanding. Here we investigate anthropogenic and greenhouse-gas contributions to past ocean warming, and estimate their contributions to future sea level rise by the year 2100. By applying detection and attribution framework (regularized optimal fingerprinting), we show that ocean warming in the historical period is detectable and attributable to contributions from the aggregate anthropogenic forcing as well as greenhouse gas forcing alone. We also discuss the role of natural forcing on the ocean volume-averaged temperature and examine the impact of volcanic activity from the three main volcanoes occurring in the historical period 1955–2012. Our results suggest that estimated anthropogenic and greenhouse-gas contributions to ocean warming are consistent with observations, and observationally-constrained future thermosteric sea level rise projections support the central and lower part of the multi-model mean projection range distribution

Statistical methods in the detection and attribution of long-term climate changes

Detection and attribution of climate change has been a growing activity since the 90's when the question of a possible human influence on the observed climate arose. I will first briefly introduce this theme together with the standard definitions of detection and attribution. Second, I will review the statistical models that have been used over the last 20 years to deal with these questions. Those models were predominantly linear regression models where the observations are regressed onto expected response patterns to different external forcings. Several levels of complexity have been proposed, from usual linear regression models to sophisticated error-in-variable models where observational and climate modelling uncertainties are accounted for. A recent, simple alternative proposes to avoid using linear regression based on similar assumption regarding uncertainties. Third, I will discuss a few statistical issues common to those models. A first issue involves the estimation of internal variability and its covariance matrix, which is required to carry out optimal inference. A second issue involves the estimation of climate modelling uncertainty, and the underlying assumptions. A third issue involves the data preprocessing and the dimension reduction that is needed to use those models.Non UBCUnreviewedAuthor affiliation: Météo France - CNRSFacult

Modélisation statistique des changements climatiques, détection, et attribution

International audienceDans le cadre des études de détection et d'attribution, différents outils statistiques sont utilisés afin d'étudier les changements climatiques en cours. La détection d'un changement, tout d'abord, consiste à montrer qu'un phénomène est effectivement un changement ; on montre, statistiquement, que ce phénomène est incohérent avec la seule variabilité interne du système climatique, considérée comme aléatoire. L'attribution d'un changement à une ou plusieurs causes consiste à établir un lien de causalité entre différents facteurs explicatifs physiquement plausibles et le changement étudié. Nous présentons ici un bref descriptif des modèles et de la démarche statistiques mis en oeuvre dans ce cadre, qui accordent une place importante aux tests d'hypothèses. Nous introduisons ensuite quelques unes des problématiques statistiques pouvant être rencontrées pour mener à bien ces études

Is future climate predictable with statistics?

The purpose of this note is to briefly introduce the statistical models and methods used in climate sciences to estimate, from observations, the sensitivity of the Earth's climate to Greenhouse Gases. First the context of climatology is described with an explanation of how statistics can interact with the use of climate models. A description of the main models used, which are original variants of Error-in-Variables models, follows. Then a few issues for which methodological progresses would be helpful are mentioned. This includes the inference of large covariance matrices and uncertainty quantification