PCMDI

CAPT

Cloud Feedbacks

CMIP5

CMIP3

Other MIPs

Software

Publications

Calendar


Site Map

UCRL-WEB-152471

Privacy & Legal Notice

Thanks to Our Sponsors:

PCMDI > WCRP CMIP3 Model Output > Diagnostic Subprojects Printer Friendly Version
 
<< Back to WCRP CMIP3 Subproject Publications

  • Roesch, A., 2006: Evaluation of surface albedo and snow cover in AR4 coupled climate models. J. Geophys. Res., 111, D15111, doi:10.1029/2005JD006473.


Surface albedo (ALB), snow cover fraction (SCF) and
snow water equivalent (SWE) of state-of-the-art coupled climate
models are compared and validated against ground-based and
remote-sensed climatologies.

Most IPCC AR4 climate models predict excessive snow mass in spring
and suffer from a delayed spring snow melt while the onset
of the snow accumulation is generally well captured. This positive SWE bias
is mainly caused by too heavy snowfall during the winter and spring season.

Seasonal cycles of snow cover area (SCA) at continental scales are captured reasonably
well by most participating models. Two models clearly overestimate
SCA over both Eurasia and North America.
Year-to-year variations are reasonably well captured over both Eurasia and North
America in winter and spring. The most pronounced underestimation
in the interannual SCA variability is generally simulated during snow melt.

The pronounced negative SCA trend that has been observed from 1979-2000 is
only partly reproduced in the AR4 model simulations. Furthermore, the computed
trends show a large spread among the models. Results from
time slice simulations with the ECHAM5 climate model suggest that accurate
sea surface temperatures are vital for correctly predicting SCA trends.

Simulated global mean annual surface albedos are slightly above the remote-sensed
surface albedo estimates. The participating AR4 models generally reproduce the
seasonal cycle of the surface albedo with sufficient accuracy while systematic
albedo biases are predicted over both snow-free and snow-covered areas,
with the latter being distinctly more pronounced. The study shows that
the surface albedo over snow-covered forests is probably too high
in various state-of-the-art global climate models.

The analysis demonstrates that positive biases in SCA are
not necessarily related to positive albedo biases. Furthermore, an overestimation
of area-averaged SWEs is not necessarily related to positive SCA anomalies
since the relationship between SWE and SCF is highly nonlinear.


Last Updated: 2007-02-26

<< Back to WCRP CMIP3 Subproject Publications
 
For questions or comments regarding this website, please contact the Webmaster.
 
Lawrence Livermore National Laboratory  |  Physical & Life Sciences Directorate