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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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Doug Richardson, Amanda S. Black, Didier P. Monselesan, Thomas S. Moore II, James S. Risbey, Andrew Schepen, Dougal T. Squire, and Carly R. Tozer

-matching archetype by minimizing the sum of squared differences (SSD) between the field and each archetype definition. For the observed time series, the fields are from the JRA55 reanalysis, on the same grid and over the same period as for the archetype generation. For the hindcast dataset, we use the ECMWF ensemble prediction system from the S2S prediction project (ECMWF-S2S; Vitart et al. 2017 ). This is a coupled atmosphere–ocean–sea ice model with a lead time of 46 days. The horizontal atmospheric

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Erin Dougherty and Kristen L. Rasmussen

− CMIP 5 1976 − 2005 . The perturbed fields in the PGW simulations include horizontal wind, geopotential, temperature, specific humidity, sea surface temperature, soil temperature, sea level pressure, and sea ice. Over the CONUS, this PGW approach results in a +3°–6°C warming and an increase in water vapor mixing ratio of ~20%–40%, consistent with the Clausius–Clapeyron theory ( Trenberth et al. 2003 ; Liu et al. 2017 ). This PGW approach has been similarly utilized in future regional climate change

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Junxia Li, Xueping Bai, Yuting Jin, Fangbo Song, Zhenju Chen, Lixin Cai, Fenghua Zou, Mengzhu Jiang, Ruixin Yun, and Zhaoyang Lv

as well as the whole basin. The models’ performance was examined by the split-sample calibration–verification method. The drought–pluvial years and periods were defined, and the dominant cycles of the reconstructed runoff were identified. Correlation analysis of the reconstructed/measured runoffs with sea surface temperature (SST) was undertaken to investigate the relationship between runoff and the El Niño–Southern Oscillation (ENSO). The running variance of reconstruction was calculated to

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Jian Zhang, Lin Tang, Stephen Cocks, Pengfei Zhang, Alexander Ryzhkov, Kenneth Howard, Carrie Langston, and Brian Kaney

QPE (“Q3DP” in short for the third generation of MRMS radar QPE) calculates R based on a combination of A , K DP , and Z . While A has the aforementioned advantages for rain rate estimation, it is not applicable in radar observations that contain ice. Therefore, Q3DP applies R ( A ) relationship in areas where radar is observing pure rain and R ( K DP ) in areas with potential presence of hail. The area of pure rain was defined as where the radar observations were below the ML bottom and Z

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Dimitrios Stampoulis, Emmanouil N. Anagnostou, and Efthymios I. Nikolopoulos

. 2008 ). The elevation gradient is relatively weaker than the two northern Italy study areas (from sea level to 1700 m over roughly 70 km). The main Cevennes river basins are Virdourle, Ardeche, Ceze, and Gard and are characterized by a typical Mediterranean hydrological regime with very low levels of water in the summers and floods occurring mainly during fall ( Younis et al. 2008 ). The four storm events selected for this study occurred in the Gard basin. The first event was on 29 September 2007

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M. Belke-Brea, F. Domine, S. Boudreau, G. Picard, M. Barrere, L. Arnaud, and M. Paradis

surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 Nm . Cryosphere , 3 , 167 – 182 , . 10.5194/tc-3-167-2009 Jacobson , M.-Z. , 2004 : Climate response of fossil fuel and biofuel soot, accounting for soot’s feedback to snow and sea ice albedo and emissivity . J. Geophys. Res. , 109 , D21201 , . 10.1029/2004JD004945 Jahn , A. , M. Claussen , A. Ganopolski , and V

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

atmospheric conductance ( Campbell and Norman 1998 ). Transpiration rates are calculated independently for each PFT by taking into account plant physiological parameters such as leaf area index and stomatal resistance. Total runoff in Agro-IBIS is calculated as the sum of surface runoff and drainage. When rainfall occurs, precipitation (including melting snow) is apportioned between surface runoff and puddle water/ice. Some portions of the puddle liquid go to evaporation, while some are transferred into

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Sha Lu, Marie-claire ten Veldhuis, and Nick van de Giesen

north and south, and coastal plains. Northeast Tanzania is mountainous and includes Mount Meru, Mount Kilimanjaro (the highest point in Africa, 5950 m above mean sea level), and the Usambara and Pare mountain ranges. West of those mountains lies the Gregory Rift, which is the eastern arm of the Great Rift Valley. The center of Tanzania is a large plateau, which is part of the East African Plateau. Most of Tanzania, except the eastern coastline lies above 200 m above mean sea level (MSL) as shown in

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M. Alves, D. F. Nadeau, B. Music, F. Anctil, and A. Parajuli

information at , accessed December 2019). The LSM divides the relatively thin layer of interface between the surface and atmosphere into fractions (tiles), with up to six fractions over land (bare ground, low and high canopy, shaded and exposed snow, and intercepted water) and up to two fractions over sea and freshwater, with separate energy and water budget calculation ( Balsamo et al. 2009 ). It is important to highlight that the

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