Abstract
Simulation of surface air temperature over China from a set of regional climate model (RCM) climate change experiments are analyzed with the focus on bias and change signal of the RCM and driving general circulation models (GCMs). The set consists of 4 simulations by the RCM of RegCM4 driven by 4 different GCMs for the period of 1979–2099 under the mid-range RCP4.5 (representative concentration pathway) scenario. Results show that for present day conditions, the RCM provides with more spatial details of the distribution and in general reduces the biases of GCM, in particular in DJF (December–January–February) and over areas with complex topography. Bias patterns show some correlation between the RCM and driving GCM in DJF but not in JJA (June–July–August). In JJA, the biases in RCM simulations show similar pattern and low sensitivity to the driving GCM, which can be attributed to the large effect of internal model physics in the season. For change signals, dominant forcings from the driving GCM are evident in the RCM simulations as shown by the magnitude, large scale spatial distribution, as well as interannual variation of the changes. The added value of RCM projection is characterized by the finer spatial detail in sub-regional (river basins) and local scale. In DJF, profound warming over the Tibetan Plateau is simulated by RCM but not GCMs. In general no clear relationships are found between the model bias and change signal, either for the driving GCMs or nested RCM.
Highlights
At present, general circulation models (GCMs) are the most commonly used tool in climate change simulations and projections
We focus our analysis on temperature, aim at answering the above questions of how the driving GCM affect the regional climate model (RCM) simulations, and what are the agreements and disagreements of them over China and its sub-regions
We report analysis on temperature simulations from a set of 4 RCM 21st century climate change experiments over China
Summary
General circulation models (GCMs) are the most commonly used tool in climate change simulations and projections. Eum et al (2015) investigated the relationship between climate change signals and biases for RCM/AOGCM simulations to assess the dependence of future projections on regional model biases for extreme temperature over southern Québec (Canada) They found that changes of extreme temperatures could be significantly affected by a large-scale forcing from boundary conditions, rather than systematic biases in the RCMs. Three RCMs were applied over south-east Australia by Olson et al (2016). Sørland et al (2018) investigated the bias patterns and climate change signals from a set of RCM simulations under the EUROCORDEX (the International COordinated Regional climate Downscaling Experiment, Giorgi et al 2009) framework (Jacob et al 2014) They found that the two RCMs systematically reduced the biases and modified climate change signals of the driving GCMs, most noticeably by lowering the warming of the driving GCMs. Of the limited studies been conducted over East Asia, Gao et al (2012) compared temperature changes simulated by a RCM and 2 driving GCMs during the monsoon season of May–September. The river basins agree roughly with the major climatic zones in China
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