Assessing GCM Sensitivity to Soil Wetness Using GSWP Data

Abstract

A new data set of global high-resolution soil wetness for [1987][1988] has been prepared as part of the Global Soil Wetness Project (GSWP). To produce this data, the Simplified Simple Biosphere (SSiB) land surface process model (LSP) has been integrated oflline, driven by observed and assimilated meteorological data to produce a two-year global climatology of soil wetness at 1x1 resolution. GSWP data set has potentially higher quality data than those previously available. We are testing the impact of the GSWP data for climate simulations using the Center for Ocean-Land-Atmosphere Studies (COLA) general circulation model (GCM), coupled to the SSiB LSP.There are two principle questions which we will address with our preliminary GCM/LSP sensitivity experiments. First, does the inclusion of presumably more realistic GSWP soil wetness significantly improve the simulation and predictability of summer season climate? We use the 1987-1988 GSWP product as a specified boundary condition in seasonal simulations (June August), and compared to existing GCM/LSP integrations, where soil wetness is initialized from operational analyses and allowed to evolve freely in the coupled system. In both sets of integrations, identical observed sea surface temperatures are specified. Results show that the GSWP soil wetness is significantly different from that of the coupled model's own climatology, and produces a better simulation of precipitation anomaly patterns over monsoon regions and the summer hemisphere extratropics. However, there is little improvement in the systematic error of the coupled model. Improvements can be attributed to changes in surface fluxes induced by the different soil wetness.Second, does the interannual variability in a multi-year soil wetness data set contribute to interannual variability in climate simulations? A parallel set of GCM/LSP integrations have been produced using specified GSWP soil wetness from the "wrong" (other) year (i. e., 1988 soil wetness applied in 1987 integrations, and vice versa). The use of soil wetness data from the wrong year significantly degrades the simulation of precipitation anomaly patterns. This indicates that interannual variability in soil wetness is important to climate. However, differences in precipitation due to SST variability generally dominated those apparently caused by soil wetness variations.

New Snow-Physics to Complement SSiB

“…Based on the above findings, we conclude that the new snow model is more realistic than the old. Dirmeyer's (1999) results suggest that such an improvement would help to simulate more realistic climate with GCMs; we evaluate this in Part II. We also infer that a better prescription of the soil properties, as proposed by Xue et al (1997), may not be Fig.…”

“…Dirmeyer (1999) found that if the soil moisture is better initialized with GSWP data produced with COLA-SSiB, the COLA GCM has the ability to produce a more skillful climate forecast. Dirmeyer had reaffirmed the importance of prescribing a more accurate initial soil moisture field for the seasonal climate and rainfall prediction.…”

Relationship between soil moisture of near surface and multiple depths of the root zone under heterogeneous land uses and varying hydroclimatic conditions

“…They found a strong correlation between SM and surface temperature anomalies. Dirmeyer (1999Dirmeyer ( , 2000 also noted that accuracy of simulated precipitation anomaly was lowered due to incorrect SM inputs. Huang et al (1996) found that SM provides improved prediction, compared with precipitation, of temperature over large areas during the summer season.…”

The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System

“…The accuracy with which soil moisture is predicted by numerical models is of importance for weather and seasonal climate projections (Beljaars et al 1996;Findell and Eltahir 1997;Dirmeyer 1999;Koster et al 2006) because of its significant role in controlling the partition of rainfall into infiltration and runoff and the partition of surface radiation energy into sensible and latent heat exchange with the atmosphere (Entekhabi et al 1996;Western and Bl€ oschl 1999). The knowledge of soil water content is also important for vegetation dynamics and carbon cycle studies (Rodriguez-Iturbe and Porporato 2004;Cox et al 2000;Zeng et al 2008).…”