Search Results

You are looking at 1 - 10 of 12 items for :

  • The Global Energy and Water Cycle Experiment (GEWEX) x
  • All content x
Clear All
Guoxiong Wu, Yimin Liu, Qiong Zhang, Anmin Duan, Tongmei Wang, Rijin Wan, Xin Liu, Weiping Li, Zaizhi Wang, and Xiaoyun Liang

) reanalysis datasets provided other important data sources for the relevant study. In addition, the progress of the development of the Global Ocean–Atmosphere–Land System climate model (GOALS) ( Wu et al. 1997b ; Zhang et al. 2000 ) at the National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) made numerical experiments available. Great efforts have been made to understand the mechanism concerning how the TP forcing, either mechanical or thermodynamic

Full access
Kevin E. Trenberth, Lesley Smith, Taotao Qian, Aiguo Dai, and John Fasullo

forcings of the hydrological cycle, such as solar radiation ( Qian et al. 2006 ). It is well established that latent heating in the atmosphere dominates the structural patterns of total diabatic heating ( Trenberth and Stepaniak 2003a , b ) and thus there is a close relationship between the water and energy cycles in the atmosphere. Water vapor is the dominant greenhouse gas ( Kiehl and Trenberth 1997 ) and is responsible for the dominant feedback in the climate system ( Karl and Trenberth 2003

Full access
Xubin Zeng and Aihui Wang

= 0 and 0.1 in Fig. 2 , and is not realistic, because modeling results using L t = 0.049 versus 0.051 (which are essentially the same in practice) with the same underlying soil should be nearly the same. Using similar atmospheric forcing data as those in Fig. 2 of Zeng et al. (2005) , Fig. 2 shows that, with the implementation of (1) – (3) , the variation of T g for L t = 0 to 1 becomes much smoother in CLM3. In particular, the T g difference between L t = 0.049 and 0.0.51 becomes

Full access
J. Li, X. Gao, and S. Sorooshian

( Chen and Dudhia 2001 ). National Centers for Environmental Prediction (NCEP) reanalysis data (at a 2.5° resolution) were used as the initial and boundary forcing data. Reynolds 1° × 1° sea surface temperature (SST) data were used as the oceanic surface boundary forcing. The Reynolds SST is the only dataset archived in the study’s time periods, although a preliminary study by our group showed that rainfall distribution and amount may be influenced by using different types of SST datasets in MM5 ( Li

Full access
Song Yang, S-H. Yoo, R. Yang, K. E. Mitchell, H. van den Dool, and R. W. Higgins

1. Introduction It is widely recognized that, in addition to sea surface temperature (SST), soil moisture provides a strong forcing for governing atmospheric processes on various time scales (see reviews in Betts et al. 1996 ; Dirmeyer et al. 1999 ; Yang and Lau 2006 ; Koster et al. 2006 ). In the midlatitude continents, it may be the most important boundary condition during warm seasons (e.g., Koster and Suarez 1995 ; Lau and Bua 1998 ; Koster et al. 2000 ), especially in relatively dry

Full access
Ana M. B. Nunes and John O. Roads

forcing applied to the soil moisture prognostic equation. Control and PA use the same land surface model, and therefore have comparable surface water values. Figure 6 is similar to Fig. 5 except that it shows the difference between 1993 and 1988 regional seasonal cycles. The climatology influence was removed by making this difference. Basically, Fig. 6 shows the interannual differences. Note that PA precipitation difference agrees quite well over the entire United States with NARR and OBS ( Figs

Full access
Richard G. Lawford, John Roads, Dennis P. Lettenmaier, and Phillip Arkin

precipitation, radiation, and clouds. 2. Precipitation: The primary forcing for land surface hydrology a. Precipitation measurement and analysis Precipitation is arguably the most widely measured water cycle variable, but it is still poorly predicted. GEWEX has a strong interest in precipitation products for a number of reasons. Precipitation is needed to force hydrologic models and to initialize weather and climate models. The assimilation of precipitation into weather prediction and climate models can

Full access
Jinwon Kim and Hyun-Suk Kang

. 2006 ). b. Simulation and case selection A winter season simulation has been performed over the 4-month period December 1997–March 1998 using large-scale atmospheric and sea surface temperature (SST) forcing data from National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis version 2 ( Kanamitsu et al. 2002 ). To investigate orographic blocking by the Sierra Nevada and its impact on low-level winds, moisture transport, and precipitation over the mountain range, we

Full access
Xi Chen, Yongqin David Chen, and Zhicai Zhang

step). 3) Groundwater recharge and loss Water flux that crosses the interface between saturated and unsaturated zones is either groundwater recharge from soil moisture driven by gravity or groundwater loss in soil layers driven by capillary force. The water flux W e can be estimated by the following equation: where D ((∂ θ /∂ z )) 4 = D ( θ 4 − θ s / Z g ), and Z g is the distance between the groundwater table and the midpoint of the soil layer located immediately above the

Full access
Xia Zhang, Shu Fen Sun, and Yongkang Xue

conductivity (m s −1 ), T is soil temperature (°C), ψ is soil matric potential (m), D TV is thermal vapor diffusivity (kg m −2 s −1 ) due to temperature gradient, and D ψ V is the vapor diffusivity (kg m −1 °C −1 s −1 ) due to soil matric potential gradient. The driving force of the liquid water movement in frozen soil is not simply expressed by volumetric water content gradient because matric potential also is affected by ice content. Therefore, we selected the mixed form of Richards’ equation

Full access