An improved land surface parameter dataset for global and regional climate 
models

Hagemann, Stefan

doi:10.17617/2.2344576

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_2344576_7

DetailsSummary

Released

Paper

An improved land surface parameter dataset for global and regional climate models

MPS-Authors

/persons/resource/persons37170

Hagemann, Stefan
Terrestrial Hydrology, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

max_scirep_336.pdf
(Publisher version), 3MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Hagemann, S. (2002). An improved land surface parameter dataset for global and regional climate models. Report / Max-Planck-Institut für Meteorologie, 336.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002B-539B-6

Abstract

In 1999, Hagemann et al. derived a global dataset of land surface parameters (LSP) from a global distribution of major ecosystem types that was made available by the U.S. Geological Survey. These parameters are: background surface albedo, surface roughness length due to vegetation, fractional vegetation cover and leaf area index for the growing and dormancy season, forest ratio, plant-available and total soil water holding capacity. The LSP dataset is provided for the use in global and regional climate modelling and it was successfully implemented in the regional climate models HIRHAM and REMO as well as in the global ECHAM model. As the U.S. Geological Survey has recently made an updated version of their ecosystem dataset available, these changes were incorporated in the LSP dataset. During this implementation, several improvements were made to the LSP dataset. Over Africa, the background surface albedo of bare soil was corrected with METEOSAT albedo data. In addition, the seasonal variation of vegetation characteristics was considered and monthly mean fields of vegetation ratio, leaf area index and background albedo were developed and implemented.