To understand the potential impact of projected climate changes on the vulnerable agriculture in Central Asia (CA) in the future, six agroclimatic indicators are calculated based on the 9km-resolution dynamical downscaled results of three different global climate models and a high-resolution projection dataset of agroclimatic indicators over CA is produced. These indicators are growing season length (GSL, days), biologically effective degree days (BEDD, ℃), frost days (FD, days), summer days (SU, days), warm spell duration index (WSDI, days), and tropical nights (TR, days). The periods are 1986-2005 and 2031-2050. The spatial resolution is 0.1°. As all the indicators except WSDI are defined with absolute temperature thresholds and particularly sensitive to the systematics biases in the model data, the quantile mapping (QM) method is applied to correct the simulated temperature. Results show the QM method largely reduces the biases in all the indicators. GSL, SU, WSDI, and TR will significantly increase over CA and FD will decrease. However, changes in BEDD are spatially heterogeneous, with the increases in northern CA and the mountainous areas and decreases in the southern and middle part of the plain areas. This dataset can be applied for assessing the future risks in the local agriculture for climate changes and will be beneficial to adaption and mitigation actions for food security in this region.

File naming and required software

The names of the files follow the order: [experiment_name].[variable_name].[period].nc. For example, the file name, WRF_CCSM_COR.WSDI.2031-2050.nc, represents the WSDI of the future period calculated based on the model data from the experiment WRF_CCSM_COR.
The Climate Data Operators (CDO, https://code.mpimet.mpg.de/projects/cdo), Python modules (like netCDF4, Xarray, and Numpy), and NCAR Command Languages (NCL, https://www.ncl.ucar.edu/) are recommended to do operations on the netCDF files.

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Cite as:

Qiu, Y. (2022). High-resolution projection dataset of agroclimatic indicators over Central Asia (1986-2005 and 2031-2050). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Atmos.tpdc.271934. CSTR: 18406.11.Atmos.tpdc.271934. (Download the reference： RIS | Bibtex )

Related Literatures:

1. 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( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

The General Project of the National Natural Science Foundation of China (No:41875134)

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