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Geographically distributed predictions of future climate, obtained through climate models, are widely used in hydrology and many other disciplines, typically without assessing their reliability. Here we compare the output of various models to temperature and precipitation observations from eight stations with long (over 100 years) records from around the globe. The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported.Key words climate models; general circulation models; falsifiability; climate change; Hurst-Kolmogorov climate De la crédibilité des prévisions climatiquesRésumé Des prévisions distribuées dans l'espace du climat futur, obtenues à l'aide de modèles climatiques, sont largement utilisées en hydrologie et dans de nombreuses autres disciplines, en général sans évaluation de leur confiance. Nous comparons ici les sorties de plusieurs modèles aux observations de température et de précipitation de huit stations réparties sur la planète qui disposent de longues chroniques (plus de 100 ans). Les résultats montrent que les modèles ont de faibles performances, y compris à une échelle climatique (30 ans). Les projections locales des modélisations ne peuvent donc pas être crédibles, alors que l'argument courant selon lequel les modèles ont de meilleures performances à des échelles spatiales plus larges n'est pas vérifié.

A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55 (7), 1094-1110.Abstract We compare the output of various climate models to temperature and precipitation observations at 55 points around the globe. We also spatially aggregate model output and observations over the contiguous USA using data from 70 stations, and we perform comparison at several temporal scales, including a climatic (30-year) scale. Besides confirming the findings of a previous assessment study that model projections at point scale are poor, results show that the spatially integrated projections are also poor. Comparaison de sorties locales et agrégées de modèles climatiques avec des données observéesRésumé Nous comparons les résultats de plusieurs modèles climatiques avec les observations de température et de précipitation en 55 points du globe. De plus, nous agrégeons spatialement les sorties de modèles et les observations couvrant les Etats-Unis d'Amérique à partir des données de 70 stations, et nous procédons à une comparaison à plusieurs échelles temporelles, y compris à l'échelle climatique (30 ans). Les résultats sont non seulement cohérents avec ceux d'une évaluation antérieure pour conclure que les projections par modélisation à l'échelle ponctuelle sont pauvres, mais montrent aussi que les projections intégrées dans l'espace sont également pauvres.

We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out.

Abstract. Since 1990 extensive funds have been spent on research in climate change. Although Earth Sciences, including climatology and hydrology, have benefited significantly, progress has proved incommensurate with the effort and funds, perhaps because these disciplines were perceived as "tools" subservient to the needs of the climate change enterprise rather than autonomous sciences. At the same time, research was misleadingly focused more on the "symptom", i.e. the emission of greenhouse gases, than on the "illness", i.e. the unsustainability of fossil fuel-based energy production. Unless energy saving and use of renewable resources become the norm, there is a real risk of severe socioeconomic crisis in the not-too-distant future. A framework for drastic paradigm change is needed, in which water plays a central role, due to its unique link to all forms of renewable energy, from production (hydro and wave power) to storage (for time-varying wind and solar sources), to biofuel production (irrigation). The extended role of water should be considered in parallel to its other uses, domestic, agricultural and industrial. Hydrology, the science of water on Earth, must move towards this new paradigm by radically rethinking its fundamentals, which are unjustifiably trapped in the 19th-century myths of deterministic theories and the zeal to eliminate uncertainty. Guidance is offered by modern statistical and quantum physics, which reveal the intrinsic character of uncertainty/entropy in nature, thus advancing towards a new understanding and modelling of physical processes, which is central to the effective use of renewable energy and water resources.Correspondence to: D. Koutsoyiannis (dk@itia.ntua.gr) Only the small secrets need to be protected. The big ones are kept secret by public incredulity. (attributed to Marshall McLuhan) Climate and climate change impactsSince 1990, major funds of the order of billions of euro have been spent in Europe and worldwide on research into projected climate change, its impacts, and emerging vulnerabilities. Earth sciences including climatology and hydrology have played a central role in this scene and benefited significantly. Technological advances in satellite observations and supercomputing have also been beneficial to these scientific disciplines. On the other hand, scientific progress has been arguably incommensurate to the effort and funds spent, perhaps because these disciplines have been perceived as "tools" subservient to the needs of the climate change enterprise rather than autonomous sciences. Despite generous funds, the targets set have not been achieved. Uncertainties in projections of future climate change have not lessened substantially in past decades (Roe and Baker, 2007). The value added by the Intergovernmental Panel for Climate Change (IPCC) Fourth Assessment Report (AR4; IPCC, 2007) to that of the Third Assessment Report (TAR; IPCC, 2001) is, in effect, marginal. According to IPCC AR4, "A major advance of this assessment of climate change projections compared wit...