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Anna-Maria Tilg, Flemming Vejen, Charlotte Bay Hasager, and Morten Nielsen

, the underlying N ( D ) is different and causes the seasonal variation of the mean 10-min values of N ( D ) and KE( D ). The causes for annual variation are numerous and may be related also for example to the current sea ice extent or teleconnections (e.g., North Atlantic Oscillation). The strong deviation of the winter values in 2013 from the seasonal mean might also be influenced by the high percentage of snow in that winter (see also Con3 in Table 3 ). The most unexpected result in this

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Muattar Saydi, Guoping Tang, Yan Qin, Hong Fang, and Xiaohua Chen

. 2013 ), the changes in location and intensity of the Siberia high ( Xu et al. 2010 ), and variation of evaporation in upwind lakes (e.g., the Caspian Sea and Mediterranean) ( Dai et al. 2007 ; Huang et al. 2013 ) likely have their contribution in the variations of winter snowfall in the arid Central Asia. Fig . 11. Trend and magnitude of change in precipitation during the (a) winter, (b) summer, (c) fall, and (d) spring across Xinjiang during the last ~6 decades. As shown in Fig. 12a , winter

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Dashan Wang, Xianwei Wang, Lin Liu, Dagang Wang, and Zhenzhong Zeng

Mountains) distributes in the upwind direction ( Fig. S4 ). The combination of large-scale forcing such as the plum rainfall, land–sea circulation and the regional topography are considered as the dominant factors affecting the precipitation clustering in this region ( Fu et al. 2019 ). Although there are clustered patterns of precipitation extremes, the urban signatures are not evident in the YRD ( Figs. 5d–f ). In the following session, we present two typical regions of Beijing and PRD, where urban

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Gonzalo Leonardini, François Anctil, Vincent Vionnet, Maria Abrahamowicz, Daniel F. Nadeau, and Vincent Fortin

platform (GEM-Hydro) to produce runoff simulations in the Lake Ontario basin ( Gaborit et al. 2017 ) and hindcasts of the major June 2013 flood in Alberta (Canada) ( Vionnet et al. 2020 ). More recently, point-scale evaluations showed comparable results relative to field observations and more complex models for Mediterranean and temperate climates in the United States ( Maheu et al. 2018 ). These evaluations also yielded accurate results for energy fluxes and soil moisture in arid, Mediterranean, and

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R. Rosolem, W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc

in the 8–13 μ m spectral region for different atmospheric conditions . Pure Appl. Geophys. , 116 , 1063 – 1076 . Tomasi, C. , 1984 : Vertical distribution features of atmospheric water vapor in the Mediterranean, Red Sea and Indian Ocean . J. Geophys. Res. , 89 , 2563 – 2566 . Tomasi, C. , and Paccagnella T. , 1988 : Vertical distribution features of atmospheric water vapour in the Po Valley area. Pure Appl. Geophys., 127, 93–115. Trenberth, K. E. , 1998 : Atmospheric moisture

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Yafang Zhong, Jason A. Otkin, Martha C. Anderson, and Christopher Hain

through use of lead–lag correlation analysis, as discussed in the next section. c. Lead–lag correlation analysis Lead–lag correlation analysis is widely used in the study of ocean–atmosphere coupling to help identify the driving mechanisms. For example, the largely symmetric correlations of monthly wind and sea surface temperature (SST) with respect to the lags indicate essentially two-way interactions in the tropics ( Lian et al. 2018 ); whereas in the extratropics, the much heavier loading at wind

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Hisham Eldardiry and Faisal Hossain

capital Khartoum and the Nile River then flows north through Sudan and Egypt to drain into the Mediterranean Sea. Fig. 1 . (a) The Nile River basin with the location of HAD and GERD dams. (b) Climatological HAD and GERD inflow (averaged over 37 years 1981–2017) as modeled by the satellite-based framework developed by Eldardiry and Hossain (2019) . The red and green lines represent the HAD target storage and demand used for deriving HAD optimal operation, respectively. (c) The annual GERD inflow

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Graham A. Sexstone, Colin A. Penn, Glen E. Liston, Kelly E. Gleason, C. David Moeser, and David W. Clow

. Itkin , J. King , I. Merkouriadi , and J. Haapala , 2018 : A distributed snow-evolution model for sea-ice applications (SnowModel) . J. Geophys. Res. Oceans , 123 , 3786 – 3810 , . 10.1002/2017JC013706 López-Moreno , J. I. , S. R. Fassnacht , S. Beguería , and J. B. P. Latron , 2011 : Variability of snow depth at the plot scale: Implications for mean depth estimation and sampling strategies . Cryosphere , 5 , 617 – 629 , https

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Yuning Shi, Kenneth J. Davis, Christopher J. Duffy, and Xuan Yu

1. Introduction The predictability of the atmosphere is limited by the chaotic nature of atmospheric turbulence to a time span of the order of one week or less ( Lorenz 1969 ; Smagorinsky 1969 ; Lorenz 1982 ). Earth's surface, however, has “memories” much longer than those of the atmosphere. Significant improvements in short-term climate forecasts as well as weather forecasts can be found by including the modeling of Earth's surface, for example, land surface processes and sea surface

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Bo Dong, John D. Lenters, Qi Hu, Christopher J. Kucharik, Tiejun Wang, Mehmet E. Soylu, and Phillip M. Mykleby

. Kucharik , 2003 : Evaluating the impacts of land management and climate variability on crop production and nitrate export across the Upper Mississippi Basin . Global Biogeochem. Cycles , 17 , 1085 , . 10.1029/2001GB001808 Elguindi , N. , S. Somot , M. Déqué , and W. Ludwig , 2011 : Climate change evolution of the hydrological balance of the Mediterranean, Black and Caspian Seas: Impact of climate model resolution . Climate Dyn. , 36 , 205 – 228

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