HR: 1340h
AN: H13D-0464    [Abstracts]
TI: Temporal variations in global simulated soil moisture
AU: Wood, E F
EM: efwood@princeton.edu
AF: Princeton University, Dept. Civil and Environmental Engineering, Princeton, NJ 08544 United States
AU: * Sheffield, J
EM: justin@princeton.edu
AF: Princeton University, Dept. Civil and Environmental Engineering, Princeton, NJ 08544 United States
AB: Soil moisture is a central term of the land surface water budget and is a key element in land-atmosphere coupling. It provides insight into drought occurrence and is an indicator of agricultural vigor and water resources potential. Soil moisture also provides opportunities for seasonal prediction of local and remote climate. Determining the temporal variation of soil moisture and how this varies globally will improve our understanding of these issues. In this study we analyse the temporal variability in global soil moisture fields, as derived from long-term simulations of the land surface water budget, and its potential predictability through climate teleconnections. The simulations use a hybrid meteorological forcing dataset, comprising high temporal resolution data from reanalysis combined with high spatial resolution data from monthly observation datasets, to drive the Variable Infiltration Capacity (VIC) hydrologic model. The resultant water and energy fluxes and states are available globally at 1 degree resolution for 1950-2000. We investigate the variability of the soil moisture fields over various time scales from daily to seasonal. From this we are able to make estimates of predictability of soil moisture at seasonal time scales. Potential predictability can be defined by the ratio of the magnitude of the seasonal variation of a variable to that of random variation due to the weather, which is basically a signal to noise problem. Statistical techniques are employed to quantify potential predictability for soil moisture and other water cycle components globally. The source of seasonal time scale signals in soil moisture may be from large-scale climate anomalies. We explore teleconnections with various climate indices such as ESNO and NAO and their role in seasonal prediction.
DE: 3322 Land/atmosphere interactions
DE: 3309 Climatology (1620)
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting