Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM

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.

Future precipitation projection in Bangladesh using SimCLIM climate model: A multi‐model ensemble approach

“…SimCLIM is used in this study where rainfall and temperature were downscaled and projected using the pattern‐scaling method. Many studies have been conducted for climate downscaling using the pattern‐scaling approach (Fealy, 2013; Christensen et al ., 2019; Matte et al ., 2019; Zheng et al ., 2020; Wang et al ., 2021). Some early studies evaluated the method's performance compared to other downscaling methods (Mitchell, 2003; Shiogama et al ., 2010; Tebaldi and Arblaster, 2014).…”

Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data

“…Temperature is an important factor in determining the effects of climate change (CC) on the globe or on the region of interest (Almazroui et al, 2020b). Since 1850, the earth's temperature has risen significantly (Ozturk et al, 2018;Wang et al, 2021). This has altered the frequency, severity, duration, timing, and spatial variability of temperature extremes (Islam A. R. M. T. et al, 2021;Pérez et al, 2021;Mallick et al, 2022a), and significantly impacted ecological instability, public health, agricultural production, water supplies, and socioeconomic progress (Allen et al, 2010;Mallick et al, 2022b).…”

“…This has altered the frequency, severity, duration, timing, and spatial variability of temperature extremes (Islam A. R. M. T. et al, 2021;Pérez et al, 2021;Mallick et al, 2022a), and significantly impacted ecological instability, public health, agricultural production, water supplies, and socioeconomic progress (Allen et al, 2010;Mallick et al, 2022b). Even if all countries reduce greenhouse gas (GHG) emissions committed in the Paris Agreement, the earth's average temperature will increase by 2.6 °C-4.8 °C in 2100 (Wang et al, 2021). Hence, it is of absolute necessity to examine future temperature projections to reduce global warming consequences and for strategic planning of CC adaptations.…”

“…GCMs have been extensively used for climate modeling (IPCC, 2014). However, they still cannot fully describe many regional climate dynamics because of their coarser resolution (Ali et al, 2021;Wang et al, 2021). Downscaling strategies, which can retrieve higher resolution data from coarse resolution datasets, are crucial for addressing this difficulty for regional or local scale climate modeling (Kamruzzaman et al, 2019b;Kamruzzaman et al, 2021a;Kamruzzaman et al, 2021b;Das et al, 2022a;Das et al, 2022b).…”

“…Moreover, an individual GCM cannot model all atmospheric processes and identify every climatic variation at a smaller scale with greater accuracy (Almazroui et al, 2020b;Ali et al, 2021). Therefore, multi-model ensembles (MME) of GCMs are usually suggested for climate modeling to address uncertainty and improve projection performance (Xu and Xu, 2012;Wang et al, 2021). A large number of studies have projected temperature based on MME of CMIP5 models at various scales (Almazroui et al, 2016;2020a;Pattnayak et al, 2017;Amin et al, 2018a;Kumar and Sarthi, 2019;Ali et al, 2021).…”