Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high‐resolution regional climate models

Abstract

Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders.

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

“…These extreme conditions have the potential to increase flash floods and runoff (Roudier et al., 2014), reduce soil moisture (Diedhiou et al., 2018; Koné et al., 2020), and increase the risk of agricultural droughts (Mechiche‐Alami & Abdi, 2020). However, it has to be mentioned that the bias‐correction has the potential to alter the projected patterns of extreme precipitation and temperature; this is not limited to this study or region but has been shown for other works (e.g., Adeyeri et al., 2020; Guo et al., 2018; Yin et al., 2020). The projected declines in precipitation and increases in temperature in Western Africa contribute to the decline in surface and groundwater availability and accessibility, affect the agriculture and hydrology sectors.…”

High Mountains Becoming Wetter While Deserts Getting Drier in Xinjiang, China since the 1980s

“…For the whole of Northwest China, both annual precipitation and annual average temperature are projected to increase under different emissions scenarios. Among them, the highest value of precipitation increase appears in high-elevation mountainous areas, but the highest value of temperature increase appears in low-elevation oases and deserts [40,41]. According to the analysis results of the CMIP6 ensemble modeling, annual precipitation in most arid regions of Central Asia, especially in Tianshan Mountains Ecozone and Northern Central Asia, increases significantly with the increase of temperature.…”

A novel bias correction method for extreme rainfall events based on L‐moments

“…The bias in the data could be related to the mean, SD , temporal distribution, frequencies, and the nature of extreme occurrences (Leander and Buishand, 2007). Several BC methods are available that vary in applicability and limitations and are discussed extensively in the climate impact studies (Ehret et al ., 2012; Teutschbein and Seibert, 2012; Chen et al ., 2013; Rajczak et al ., 2016; Rohith and Jayakumar, 2017; Turco et al ., 2017; Yin et al ., 2020).…”