Abstract
Facing the western Pacific Ocean and backed by the Eurasian continent, the coastal area of China (hereafter as CAC) is sensitive and vulnerable to climate change due to the compound effects of land-ocean-atmosphere, and thus is prone to suffer huge climate-related disaster losses because of its large population density and fast developed economy in the context of global warming. Here in this study the near- (2040), mid- (2070), and long-future (2100) mean, minimum, and maximum temperature (Tmean, Tmin, and Tmax) projections based on the statistic downscaling climate prediction model (SimCLIM) integrated with 44 General Circulation Models (GCMs) of CMIP5 under three representative concentration pathway (RCP4.5, RCP6.0, and RCP8.5) scenarios are evaluated over CAC and its sub-regions. Multi-model ensemble of the selected GCMs demonstrated that there was a dominating and consistent warming trend of Tmean, Tmin, and Tmax in the Chinese coastal area in the future. Under RCP4.5, RCP6.0, and RCP8.5 scenarios, the annual temperature increase was respectively projected to be in the range of 0.8–1.2°C for 2040, 1.5–2.7°C for 2070, and 1.6–4.4°C for 2100 over the entire CAC. Moreover, a spatial differentiation of temperature changes both on the sub-regional and meteorological station scales was also revealed, generally showing an increment with “high south and low north” for annual average Tmean but “high north and low south” for Tmin and Tmax. An obvious lower increase of Tmean in the hotter months like July and August in the south and a significant sharper increment of Tmin and Tmax in the colder months such as January, February, and December in the north were expected in the future. Results derived from this study are anticipated to provide insights into future temperature changes and also assist in the development of target climate change mitigation and adaptation measures in the coastal area of China.
Highlights
The earth has experienced significant temperature rises both regionally and globally since 1850 (Li et al, 2015; Ozturk et al, 2018)
As forced by Representative Concentration Pathways (RCPs) greenhouse gas (GHG) emission scenarios, General Circulation Models (GCMs) participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the IPCC Fifth Assessment Report (AR5) have provided great opportunities to evaluate the projections of spatiotemporal performances of climatic change in the 21st century (Zhang et al, 2017; Li et al, 2019), and they have been widely applied to detect the variation and attribution of climate change as well as to formulate response measures against global warming (Azmat et al, 2018; Guo et al, 2020)
For the years of 2040, 2070, and 2100, a prevalent increase in Tmean, Tmin, and Tmax in the coastal area of China is projected by SimCLIM software integrated with 44 GCMs and IPCC AR5 GHG emissions scenarios, which is highly consistent with the previous studies focused on temperature prediction either at global scale (Wang et al, 2017b) or at regional scale such as research areas in North American (Zhang et al, 2020), European (D’Oria et al, 2017; Coppola et al, 2021), African (Ozturk et al, 2018), Asian (Salman et al, 2018; Almazroui et al, 2020; Ullah et al, 2020), and so on, where all will find an ascending trend in temperature in the future
Summary
The earth has experienced significant temperature rises both regionally and globally since 1850 (Li et al, 2015; Ozturk et al, 2018). What’s notable is that the nationally determined contributions pledged in the Paris Agreement are completely achieved, the global mean surface temperature is projected to rise by 2.6°C–4.8°C under the high emissions scenarios in 2100 (Onozuka et al, 2019; Feng and Chao, 2020), which is expected to pose a much more serious threat to the natural environment and human society. It is of extreme urgency and great importance to investigate the future projection of temperature for preventing global warming impacts and is highly beneficial to climate mitigation and adaptation. For the purpose of reducing the uncertainty and improving estimation accuracy, multi-model ensembles of GCMs are commonly recommended for climate change simulation and projection (Xu and Xu, 2012; Chhin and Yoden, 2018)
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