Figure 1: (a) Annual temperature time-series over Central Asia for the Berkeley-Earth data set (black line), historical models’ ensemble mean (red line), hist-nat models’ ensemble mean (green line) and cumulative global annual mean CO2 concentration (ppm); (b) Normalized probability density functions of daily temperature anomalies (1995–2014 w.r.t. 1961–1980) for hist (solid red) and hist-nat (solid green), (c) ratio of daily temperature anomalies’ PDF (i.e., P(tas_hist)/P(tas_hist−nat). Shadings in (a) show the ensembles’ spread.
The study investigates the contribution of anthropogenic forcing to extreme temperature and precipitation events in Central Asia during the last six decades. We bias-adjust and statistically downscale two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ensemble outputs, with natural (driven only by solar and volcanic forcing) and natural plus anthropogenic forcing (driven by all-forcings), to a spatial resolution of 0.25° x 0.25°. Each ensemble contains six models from ISIMIP, based on the Coupled Model Inter-comparison Project phase 6 (CMIP6). The presented downscaling methodology is necessary to create a reliable climate state for regional climate impact studies.
The analysis shows a higher risk of extreme heat events over large parts of Central Asia due to anthropogenic influence (Fig. 1). Furthermore, a higher likelihood of extreme precipitation over Central Asia, especially over Kyrgyzstan and Tajikistan, can be attributed to anthropogenic forcing. Given that these regions show a high risk of rainfall-triggered landslides and floods during historical times, the study reports that human-induced climate warming can contribute to extreme precipitation events over vulnerable areas of Central Asia.
The added value of this study lies in its use of high-resolution data and downscaling methodology to investigate the contribution of anthropogenic forcing to extreme temperature and precipitation events in Central Asia. In addition, the high-resolution data set can be used in impact studies focusing on attributing extreme events in Central Asia and is freely available to the scientific community.
One potential caveat of this study is that it relies on model outputs from ISIMIP and CMIP6, which may have limitations and uncertainties. In addition, the number of models and members used is limited due to computational constraints. However, we have taken steps to bias-adjust and downscale the model outputs to improve their reliability for regional climate impact studies.
This study is unique in its focus on the contribution of anthropogenic forcing to extreme temperature and precipitation events in Central Asia using high-resolution data and downscaling methodology. Furthermore, the results can inform mitigation and adaptation policies in the region.
The paper can be accessed here.
