Abstract. An ensemble simulation of five regional climate models (RCMs) from the
coordinated regional downscaling experiment in East Asia is evaluated and
used to project future regional climate change in China. The influences of
model uncertainty and internal variability on projections are also
identified. The RCMs simulate the historical (1980–2005) climate and future
(2006–2049) climate projections under the Representative Concentration
Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China,
including northeastern China, northern China, southern China, northwestern
China, and the Tibetan Plateau, are highlighted in this study. Results show
that (1) RCMs can capture the climatology, annual cycle, and interannual
variability of temperature and precipitation and that a multi-model ensemble
(MME) outperforms that of an individual RCM. The added values for RCMs are
confirmed by comparing the performance of RCMs and global climate models
(GCMs) in reproducing annual and seasonal mean precipitation and temperature
during the historical period. (2) For future (2030–2049) climate, the MME
indicates consistent warming trends at around 1 ∘C in the entire
domain and projects pronounced warming in northern and western China. The
annual precipitation is likely to increase in most of the simulation region,
except for the Tibetan Plateau. (3) Generally, the future projected change in
annual and seasonal mean temperature by RCMs is nearly consistent with the
results from the driving GCM. However, changes in annual and seasonal mean
precipitation exhibit significant inter-RCM differences and possess a larger
magnitude and variability than the driving GCM. Even opposite signals for
projected changes in average precipitation between the MME and the driving
GCM are shown over southern China, northeastern China, and the Tibetan
Plateau. (4) The uncertainty in projected mean temperature mainly arises from
the internal variability over northern and southern China and the model
uncertainty over the other three subregions. For the projected mean
precipitation, the dominant uncertainty source is the internal variability
over most regions, except for the Tibetan Plateau, where the model
uncertainty reaches up to 60 %. Moreover, the model uncertainty increases
with prediction lead time across all subregions.
Improving the Resolution of Climate Models to Address Regional Climate Change: Simulating the Spatial-Temporal Patterns of Anthropogenic Climate Change: A Workshop in the Bridging Disciplines, Bridging Scale Series; Santa Fe, New Mexico, 20–22 July 2009
