As one of the climate change hot spots, central Asia witnessed a significant warming in the past and is very likely to experience more heatwaves and drought events in the future. Unfortunately, scientists have difficulty studying the potential impacts of future climate changes on many sectors in central Asia, in particular, ecological and hydrological systems, due to lack of high-resolution climate projection datasets.

To tackle this problem, the research group of Prof. FENG Jinming with the Institute of Atmospheric Physics at the Chinese Academy of Sciences produced a 9KM resolution climate projection dataset in central Asia based on dynamically downscaling results of multiple bias-corrected global climate models, which is called HCPD-CA for short. This dataset has two periods (1986-2005 and 2031-2050), utilizes the emission scenario RCP4.5, and includes four geostatic variables and ten meteorological elements, which can be used to drive most of the ecological and hydrological models. The related research article is published in Earth System Science Data.

In the article, the HCPD-CA dataset is evaluated at various time scales. The results show that the dataset has high accuracy in describing the historical climatology in central Asia. In addition, projected changes (2031-2050 vs. 1986-2005) in the ten meteorology elements are assessed. It is found that surface air temperature, downwelling shortwave and longwave radiation are expected to significantly increase, with minor changes in other elements.

This dataset has three advantages compared to the previous ones. First, the horizontal resolution of the dataset increases from ≥30KM to 9KM, which largely improves its accuracy, especially in the mountainous regions. Second, multiple (instead of one) global climate models are used to drive the regional climate model, which can reduce the uncertainties in the downscaled results brought by the driving data. Third, the climatology of the driving data is bias-corrected with the reanalysis data, which largely reduced the biases in the regional climate simulations.

Central Asia is highly agrarian, with 60% of the population living in rural areas and agriculture accounting for over 45% of total employment and nearly 25% of the Gross Domestic Production. To understand the potential impacts of the projected climate changes on the local agricultures in central Asia, the research group also calculated six agroclimatic indicators and analyzed projected changes (2031-2050 vs. 1986-2005) in these indicators. The related research article is published in Advances in Atmospheric Sciences.

"Agroclimatic indicators are proxies for the effect of weather and climate on specific agricultural activities and both practical and understandable to farmers and policy makers." The corresponding author Prof. FENG Jinming said.

The results show that the growing season length (GSL), summer days (SD), warm spell duration index (WSDI) and tropical days (TD) are projected to significantly increase and meanwhile the frost days (FD) are projected to significantly decrease. Projected changes in biologically effective degree days (BEDD) are spatially heterogeneous, with an increase in northern CA and the mountainous areas and a decrease in other areas.

Fig. 1 Projected changes (2031-2050 vs. 1986-2005) in the six agroclimatic indicators in central Asia (Image by QIU Yuan)

"Five of these six indicators relate to absolute temperature thresholds and are sensitive to the systematic biases in the dynamically downscaled results. Therefore, we first applied the quantile mapping method to correct the downscaled results. We found that the bias-correction method largely reduced the biases in the indicators, which makes the projection more reasonable." The lead author Dr. QIU Yuan said.

 
The HCPD-CA dataset and the high-resolution projection dataset of agroclimatic indicators over Central Asia are archived at the National Tibetan Plateau Data Center and open to the public. These studies are supported by the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDA20020201), the General Project of the National Natural Science Foundation of China (grant no. 41875134), and the TianHe Qingsuo Project - special fund project in the field of climate, meteorology, and ocean.

References:
Qiu, Y., Feng, J., Yan, Z., and Wang, J. (2022). HCPD-CA: high-resolution climate projection dataset in central Asia. Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022.
Qiu, Y., Feng, J.M., Yan, Z.W., & Wang, J. (2022). High-resolution projection dataset of agroclimatic indicators over Central Asia. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-022-2008-3.