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Lu Yi, Bin Yong, Junxu Chen, Ziyan Zheng, and Ling Li

1. Introduction The coupled land–atmosphere model based on the regional climate model and hydrological model is an important tool to extend the forecast period of local flood ( Bosilovich and Sun 1999 ; Wu and Zhang 2013 ). In a coupled land–atmosphere model, the regional climate model can provide a hydrological model with continuous spatiotemporal variation fields of hydrological variables such as precipitation, evaporation, temperature, and radiation. The hydrological model has more refined

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

1984 to 2017. SnowModel is a spatially distributed physically based snow evolution modeling system designed for application in a wide range of environments where snow occurs ( Liston and Elder 2006b ). SnowModel includes the following submodels: MicroMet ( Liston and Elder 2006a ), a high-resolution meteorological distribution model; EnBal ( Liston 1995 ), which computes surface energy exchanges between the snow and atmosphere; SnowPack ( Liston and Hall 1995 ), which simulates the seasonal

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Hector Macian-Sorribes, Ilias Pechlivanidis, Louise Crochemore, and Manuel Pulido-Velazquez

hydrological features as the Magro and Albaida subbasins. Skillful hydrological forecasts are particularly important in these headwater subbasins during the refill season (from October to April) to anticipate the state of the main Jucar reservoirs for the upcoming irrigation season (from May to September). The Mancha and Middle subbasins refer to the middle Jucar and Cabriel watercourses, from Alarcon and Contreras to Tous. The Mancha subbasin mainly corresponds to the drainage of the Mancha Oriental

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Ji-Qin Zhong, Bing Lu, Wei Wang, Cheng-Cheng Huang, and Yang Yang

in the interaction between the land surface and atmosphere, affecting the near-surface air temperature due to snow’s insulating properties and the latent heat needed for snowmelt ( Thomas 2008 ; Tomasi et al. 2017 ). It is clear that the accurate representation of snow cover in NWP models is vital for the calculation of surface fluxes over snow-covered surfaces and subsequent forecasts of atmospheric variables. It might be a cause of the systematic bias in the near surface forecasts in the

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David A. Lavers, Shaun Harrigan, and Christel Prudhomme

explored. In an Earth system model, skillful precipitation forecasts are also important for soil moisture, evaporation, temperature, and land–atmosphere feedbacks, all of which can influence Earth system predictability. This means it is important to identify and investigate any issues with NWP precipitation to allow for a broad range of future NWP improvements to be realized. The aim of this paper is therefore to evaluate precipitation forecasts from the ECMWF IFS to elucidate precipitation biases

Open access
Alyssa M. Stansfield, Kevin A. Reed, Colin M. Zarzycki, Paul A. Ullrich, and Daniel R. Chavas

only weakly dependent on intensity ( Merrill 1984 ; Chavas and Emanuel 2010 ; Lee et al. 2010 ; Chan and Chan 2012 , 2015 ; Chavas et al. 2015 , 2016 ). Limited work has been done to compare TC outer sizes in climate models to those derived from observations and reanalysis. The representation of TC structure in high-resolution global climate models (GCMs), such as the Community Atmosphere Model (CAM), is comparable to observations, including spatial mean composites and radial profiles of

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Eli J. Dennis and Ernesto Hugo Berbery

1. Introduction It has long been understood that the land surface is a critical component of the climate system and that soil moisture is a key factor for determining land surface–atmosphere interactions and coupling ( Sellers et al. 1996 ; Koster et al. 2004 ; Seneviratne et al. 2010 ). The strength of the coupling between soil moisture and other variables depends on the time scale, ranging from daily-to-weekly time scales ( Santanello et al. 2011 ; Tawfik and Dirmeyer 2014 ) to monthly

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Lingjing Zhu, Jiming Jin, and Yimin Liu

a total area of about 43 000 km 2 ) ( Zhang et al. 2014 ), the lakes in the Tibetan Plateau play an important role in land–atmosphere systems ( Li et al. 2009 ; Rüthrich et al. 2015 ; Singh and Nakamura 2009 ). The strong surface heterogeneity of the TP, due partly to different sizes of lakes, affects large-scale circulations and further downstream climate ( Bao et al. 2010 ; Chow et al. 2008 ; Duan et al. 2012 ; Liu et al. 2012 ; Wu et al. 2012 ; Ye and Wu 1998 ). With the significant

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Hong Wang and Fubao Sun

). Fig . 3. Results of the 30-yr mean precipitation within the 90% CL during the period (a) 1959–88 and (b) 1989–2018. Fig . 4. The grid cells of the 30-yr mean precipitation beyond the 90% CL over the periods 1959–88 and 1989–2018. Autocorrelation of precipitation from lags 1–20 with 90% CL (dashed). Annual precipitation from 1959 to 2018 (vertical step). Averages (dotted line) over 30-yr time periods with 90% CL (dashed). The analysis of other seven grid cells in the middle are shown in Fig. S3

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Kshitij Parajuli, Scott B. Jones, David G. Tarboton, Lawrence E. Hipps, Lin Zhao, Morteza Sadeghi, Mark L. Rockhold, Alfonso Torres-Rua, and Gerald N. Flerchinger

1. Introduction Land surface models (LSMs) have been used widely in studying interactions within the soil, vegetation and atmosphere continuum, in addition to predicting water and energy fluxes. Improved understanding of land–atmosphere interactions potentially enhances the ability of weather and climate models to predict future conditions ( Barlage et al. 2015 ; Chen and Dudhia 2001 ; Gao et al. 2015 ; Kumar et al. 2014 ; Sadeghi et al. 2019 ). Detailed land–atmosphere processes and

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