4.7 Article

Conserving Land-Atmosphere Synthesis Suite (CLASS)

期刊

JOURNAL OF CLIMATE
卷 33, 期 5, 页码 1821-1844

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-19-0036.1

关键词

Land surface; Energy budget; balance; Surface fluxes; Water budget; balance; Data processing; Optimization

资金

  1. Australian Research Council Centre of Excellence for Climate Extremes [CE170100023]
  2. NASA MEaSUREs Program
  3. Australian Government
  4. CDIAC
  5. ICOS Ecosystem Thematic Center
  6. OzFlux office
  7. ChinaFlux office
  8. AsiaFlux office
  9. U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program [DE-FG02-04ER63917, DE-FG02-04ER63911]
  10. CFCAS
  11. NSERC
  12. BIOCAP
  13. Environment Canada
  14. NRCan
  15. CarboEuropeIP
  16. FAO-GTOS-TCO
  17. iLEAPS
  18. Max Planck Institute for Biogeochemistry
  19. National Science Foundation
  20. University of Tuscia
  21. Universite Laval
  22. U.S. Department of Energy

向作者/读者索取更多资源

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models' ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land-Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available via https://doi.org/10.25914/5c872258dc183..

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