The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau (TP) for the period 1979–2005. The
results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures
(Tas) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual
mean skin temperatures (Ts) over the TP. The cold biases are larger in Tas than in Ts, and are larger over the western
TP. By decomposing the Ts bias using the surface energy budget equation, we investigate the contributions to the cold
surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative
forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux,
and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias.
Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anticorrelated,
and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface
turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative
clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the
area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over
the TP in the models.

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• Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau