Closing the Global Water Cycle: Recent Results from Analyzing Long-term Climate Data Records

Abstract:

Comprehensive documentation of the terrestrial water and energy cycles at the global scale, and their evolution over time, is fundamental to understanding Earth’s climate system and assessing the impacts due to climate change. Such documentation is also needed to characterize the memories, pathways and feedbacks between key water, energy and biogeochemical cycles, and to resolve overarching scientific goals. The WCRP Global Energy and Water Exchanges (GEWEX) project has a long-term scientific goal to obtain a quantitative description of weather-scale variations in the global energy and water cycles over a period of at least 20 years, which will provide the needed scientific basis for understanding climate variability and change.

Presented will be the development of a multi-decadal (1982-2009) Climate Data Records (CDR) for the terrestrial water and energy budget variables using multiple remote sensing estimates, merged with in-situ and model estimates. The data sets include in-situ, re-analysis and satellite precipitation, satellite-based terrestrial evapotranspiration developed as part of the GEWEX/GDAP LandFlux initiative, land surface model (LSM/VIC) estimates of runoff and discharge into the oceans, LSM/VIC and GRACE-based total terrestrial water storage, and satellite-based surface net radiation and turbulent heat fluxes from 1984-2007.

This data set is the first multi-decadal terrestrial water cycle CDR that offers self-consistent estimates based on satellite, in-situ and reanalysis estimates. These data sets have been used to assess the variability and trends in the terrestrial water and energy budgets for ~1600 small basins, 32 large global basins and at continental scales. When combined with the ocean precipitation and evaporation estimates from GDAP SeaFlux initiative, global water cycle can be closed to within less than 2.5%. At regional scales, the data set is significantly more consistent than estimates from reanalysis.