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Manuel Punzet, Frank Voß, Anja Voß, Ellen Kynast, and Ilona Bärlund

Central America, western Africa, India, the Mekong basin, and a few stations in central Europe and the western part of North America. Analysis of maximum likelihood did not meet the criterion of normal distribution because of heteroskedasticity. However, the test on quasi maximum likelihood using heteroskedasticity consistent standard errors ( Hansen et al. 2006 ) showed high significance ( p = 0.0001). Intension of heteroskedasticity was weak but it reveals some uncertainty of model performance

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Richard Harding, Martin Best, Eleanor Blyth, Stefan Hagemann, Pavel Kabat, Lena M. Tallaksen, Tanya Warnaars, David Wiberg, Graham P. Weedon, Henny van Lanen, Fulco Ludwig, and Ingjerd Haddeland

over most of Africa, southern Europe, South and East Asia, eastern Australia, Central America, the central Pacific coasts of North America, and some parts of South America, which is broadly consistent with patterns of precipitation ( Dai 2010 ). In a study of comparatively undisturbed small catchments across Europe, Stahl et al. (2010) show a regionally coherent picture of decreasing annual streamflow in southern and eastern Europe over the period 1962–2004 and generally positive trends elsewhere

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D. Gerten, J. Heinke, H. Hoff, H. Biemans, M. Fader, and K. Waha

level projected by the Food and Agriculture Association (FAO) to be reached in developing countries by 2030 ( FAO 2003 ). We note that present diets differ from it in many countries—for example, exceeding 3700 kilocalories per capita per day (27% share of animal products) in North America and falling below 2500 kilocalories per capita per day (<8% share of animal products) in many African countries (see http://faostat.fao.org/site/609/default.aspx#ancor ). While the abovementioned threshold of 1300

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Christel Prudhomme, Simon Parry, Jamie Hannaford, Douglas B. Clark, Stefan Hagemann, and Frank Voss

1. Introduction and background There is growing evidence that the hydrological cycle is intensifying (e.g., Huntington 2006 ; Stott et al. 2010 ) as a result of anthropogenically forced climatic change. Generally speaking, at regional to continental scales, two contrasting approaches are used to examine the influence of climate change on the hydrological cycle: through analysis of historical data, to detect emerging trends (e.g., in Europe by Stahl et al. 2010 , in North America by Douglas

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Pete Falloon, Richard Betts, Andrew Wiltshire, Rutger Dankers, Camilla Mathison, Doug McNeall, Paul Bates, and Mark Trigg

cycle in HadGEM1 is stronger than that in HadCM3, with global mean precipitation being ~0.15 mm day −1 higher in HadGEM1. Compared to observations, HadGEM1 has too much annual precipitation over the Southern Ocean and high latitudes of the North Atlantic and North Pacific; over land, HadGEM1 is too wet over India and too dry over Southeast Asia, Indonesia, and the coast of western South America. These errors are broadly similar to HadCM3, although over land HadCM3 is too wet over Amazonia, too dry

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Kerstin Stahl, Lena M. Tallaksen, Lukas Gudmundsson, and Jens H. Christensen

). Note that this study follows the nonexceedance notation common in North America, whereas in Europe the reverse exceedance notation, with Q90 being low flows and Q10 being high flows, is commonly used. Figure 2 shows an example of observed and simulated anomalies for the river Skjern at Alergård in Denmark. Fig . 2. Time series of anomalies and derived anomaly indicator series for the river Skjern at Alergård in Denmark. c. Similarity indices Four indices were chosen for the comparison of

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G. P. Weedon, S. Gomes, P. Viterbo, W. J. Shuttleworth, E. Blyth, H. Österle, J. C. Adam, N. Bellouin, O. Boucher, and M. Best

sites allowed direct comparison of data from the mid-1990s to 2001 (consequently restricting the geographic availability of data principally to Europe and North America) and included a variety of latitudes and climatic regimes and a variety of land-cover types and elevations. Weedon et al. (2010) illustrate time series of several variables and also provide spatial comparisons of (a) the seasonal averages of the vapor pressure implied in WFD with data from CRU, (b) WFD downward longwave and

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Aristeidis G. Koutroulis, Aggeliki-Eleni K. Vrohidou, and Ioannis K. Tsanis

issue is how to describe and how to quantify them. Because of this, drought is often referred to as a “creeping phenomenon” ( Tannehill 1947 ). There can be significant variations of drought impacts between regions due to the different characteristics of the economy, society, and the environment. Hence, different types of droughts are identified ( American Meteorological Society 2004 ). Meteorological drought is defined usually by the departure of precipitation from the “normal” or average amount

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Ingjerd Haddeland, Douglas B. Clark, Wietse Franssen, Fulco Ludwig, Frank Voß, Nigel W. Arnell, Nathalie Bertrand, Martin Best, Sonja Folwell, Dieter Gerten, Sandra Gomes, Simon N. Gosling, Stefan Hagemann, Naota Hanasaki, Richard Harding, Jens Heinke, Pavel Kabat, Sujan Koirala, Taikan Oki, Jan Polcher, Tobias Stacke, Pedro Viterbo, Graham P. Weedon, and Pat Yeh

Laurentian Great Lakes in North America, which is a result of the models handling the presence of lakes very differently. The parameterizations of evapotranspiration and runoff vary substantially between the models (see Table 1 ), and the complicated interactions between the various processes make it infeasible to explain the causes of many simulation differences in detail, as noted in previous model intercomparisons (e.g., Koster and Milly 1997 ). b. Basin analyses Some general conclusions can be made

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Stefan Hagemann, Cui Chen, Jan O. Haerter, Jens Heinke, Dieter Gerten, and Claudio Piani

consistently have biases in temperature fluctuations (such as the Amazon, Greenland, western North America, and central Siberia) but in other regions there is little overlap between the different models. In a future climate as projected by the GCM simulations the globe may experience a general increase of temperatures. This increase would be interpreted by the bias correction as a fluctuation with respect to the control-period mean and hence an increase would be corrected subject to the slope given in the

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