Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature

Optimising predictor domains for spatially coherent precipitation downscaling

Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east–west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east–west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications

Evolution du cycle hydrologique continental en France au cours des prochaines décennies

The assessment of the impact of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess impacts of climate change on the hydrological cycle over France and associated uncertainties. The contribution of each sources of uncertainty is not addressed, mainly that associated with greenhouse gases emission scenario, climate models and internal variability. In the context of impacts of climate change on the hydrological cycle over France, it is possible to ask what is the contribution of each sources of uncertainty to the total uncertainty associated with mean changes. Is it possible to reduce, and if so how, the contribution of one source or another ? We propose in this work an approach to assess the transferability in the future climate of a statistical method to downscale climate simulations. The transferability assumption is one the main sources of uncertainty in statistical downscaling method. The assessment suggested here relies on the use of regional climate models, in a perfect model framework, and shows that some predictors are useful to ensure the transferability of the downscaling method in the future climate. This framework, proposed for a statistical downscaling method, is also applicable to bias correction methods in regional climate models. Recent atmospheric reanalyses of the 20th century are downscaled with the method developed in this work, associated with observations of temperature and precipitation. The hydrological cycle over France is characterized with these reconstructions. We show that the multi-decadal variability of observed streamflows during the 20th century is generalized to the whole country and is partly due to atmospheric variability. This multi-decadal variability of streamflows is generally weaker in hydrological simulations done with historical simulations from climate models. The climate projections have been downscaled with the method developed in this work. The temperature on the country, on average over climate models, could increased by 3,5°C in winter and 6,5°C in summer in the course of this century. Precipitations will decrease all over the country in summer, nearly by half on southern part of France for the most severe scenario. In winter, precipitations will increase in the northern part of the country and will decrease slightly in the southern part. In the next few decades, the decrease in precipitation is important in summer, and changes are less pronounced for other seasons. Results of hydrological projections done with one hydrological model and an ensemble of climate models are presented for the coming decades and for the end of the century. On the Seine river, results slightly differ in winter from those presented in previous studies. Here, precipitations and streamflow increase in winter and decrease in summer on that river basin. Elsewhere in France, results are consistent with previous studies, namely an increase in evapotranspiration, a decrease in streamflow and much drier soil. The uncertainty due to both climate models and internal variability on relative changes in streamflows always increase during the 21st century, to over 20% in winter for the most severe scenario. In the coming decades, the uncertainty due to internal variability only on streamflow changes is as strong as the uncertainty due to both climate models and internal variability. In the coming decades, annual streamflow changes of the Loire, Garonne and Rhône rivers are stronger than the maximum changes observed during the 20th century.L'étude des impacts du changement climatique demande souvent de mettre en place de longues chaînes de modélisation. Du modèle qui servira à estimer les concentrations futures en gaz à effet de serre jusqu'au modèle d'impact. Tout au long de cette chaîne de modélisation, les sources d'incertitudes s'accumulent et compliquent l'exploitation des résultats pour l'élaboration de stratégies d'adaptation. Il est proposé ici d'évaluer les impacts du changement climatique sur le cycle hydrologique en France ainsi que les incertitudes qui y sont associées. La contribution de chacune des sources d'incertitudes n'est pas abordée, principalement celle associée aux scénarios d'émission de gaz à effet de serre, aux modèles climatiques et à la variabilité interne. Nous proposons dans ce travail une approche pour évaluer la transférabilité dans un climat futur de la méthode statistique de régionalisation des simulations climatiques. La vérification de l'hypothèse de transférabilité effectuée est l'une des principales sources d'incertitudes des méthodes statistiques de régionalisation. L'évaluation proposée ici s'appuie sur l'utilisation de modèles régionaux, dans un cadre dit de modèle parfait, et permet de montrer que l'utilisation de certain prédicteurs s'avèrent utile à assurer la transférabilité de la méthode de régionalisation dans un climat futur. Cette approche proposée pour une méthode de désagrégation statistique est également applicable à des méthodes de correction des biais des modèles régionaux. Les récentes réanalyses atmosphériques sur l'ensemble du XXème siècle, régionalisées avec la méthode développée dans ce travail, et associées aux observations de température et précipitations permettent de caractériser le cycle hydrologique en France. Elles permettent notamment de montrer que la variabilité multi-décennale des débits observés pendant le XXème siècle est généralisée à l'ensemble du pays et est liée à la variabilité des conditions atmosphériques. Cette variabilité multi-décennale des débits est généralement plus faible dans les simulations hydrologiques réalisées avec les simulations historiques des modèles climatiques. Les projections climatiques ont été régionalisées avec la méthode développée dans ce travail. La température sur l'ensemble du pays, en moyenne sur les modèles climatiques, augmente jusqu'à 3,5°C en hiver et 6,5°C en été d'ici la fin du siècle. Les précipitations vont diminuer sur l'ensemble du pays en été, de presque moitié sur le sud du pays pour le scénario le plus sévère. En hiver, elles augmentent sur la moitié nord du pays et diminuent légèrement sur la partie sud. Dès les prochaines décennies, la diminution des précipitations est importante en été, l'évolution est moins marquée pour les autres saisons. Enfin, les résultats des projections hydrologiques réalisées avec un modèle hydrologique et un ensemble de modèles climatiques sont présentés pour les prochaines décennies et également pour la fin du XXIème siècle. Sur la Seine, les résultats sont différents en hiver de ceux présentés dans de précédentes études. Ici, les précipitations et les débits augmentent en hiver et diminuent en été sur ce bassin versant. Ailleurs en France, les résultats convergent avec les études précédentes, à savoir une augmentation de l'évapotranspiration, une diminution généralisée des débits et un assèchement des sols. L'incertitude due aux modèles climatiques et à la variabilité interne sur les changements relatifs de débits augmente systématiquement pendant le XXIème siècle, jusqu'à atteindre plus de 20% en hiver pour le scénario le plus sévère. Dans les prochaines décennies, l'incertitude due uniquement à la variabilité interne sur les changements de débits est aussi forte que l'incertitude due aux modèles climatiques et à la variabilité interne. Dès les prochaines décennies, les changements de débits annuels sont plus forts sur la Loire, la Garonne et le Rhône que les changements maximaux observés pendant le XXème siècle