Abstract
As a typical arid and semi-arid area, central Asia (CA) has scarce water resources and fragile ecosystems that are particularly sensitive and vulnerable to climate change. In this study, dynamic downscaling was conducted to produce a regional dataset that incorporated the time period 1986–2100 for the CA. The results show that dynamic downscaling significantly improves the simulation for the mean and extreme climate over the CA, compared to the driving CCSM4 model. We show that significant warming will occur over CA with 2.0 °C and 5.0 °C increasing under the RCP4.5 and RCP8.5 scenarios, respectively by the end of twenty-first century. The daily maximum temperature, the daily minimum temperature and the annual total number of days with a minimum temperature greater than 25 °C will also increase significantly. The annual total number of days with a minimum temperature less than 0 °C will decrease significantly. Long-term trends in the projected winter precipitation under different emission scenarios exhibit robust and increasing changes during the twenty-first century, especially under the RCP8.5 scenario with an increasing about 0.1 mm/day. Significant differences are shown in the projection of precipitation-related indices over CA under different emission scenarios, and the impact of emissions is apparent for the number of days with ≥ 10 mm of precipitation, the density of precipitation on days with ≥ 1 mm of precipitation, and particularly for the maximum consecutive number of dry days that will increase significantly under the RCP8.5 scenario. Therefore, reduced greenhouse gases emissions have implications for mitigating extreme drought events over the CA in the future.
Highlights
Central Asia (CA) is located within continental Eurasia and a long distance from the sea
In the spatial distribution of JJA temperatures simulated by the WRFE2 and WRFC2 experiments, warm biases are centered in central CA and Xinjiang, and cold biases are centered in the Tibetan Plateau and Turkmenistan
The projection by the dynamic downscaling experiments show that the maximum increase in summer days (SU) will be 25 days under the RCP4.5 scenario and 50 days under the RCP8.5 scenario, and the areas where large increases in SU occur are much smaller in the WRFR1 and WRFR2 results than the CCSM4 results (Fig. 12i, m, q, u)
Summary
Central Asia (CA) is located within continental Eurasia and a long distance from the sea. Compared to GCMs, regional climate models (RCMs) were essentially developed with the aim of downscaling climate fields produced by coarse resolution GCMs, thereby providing information at fineer, sub-GCM grid scales which is more suitable for studies of regional applications in vulnerability, impacts and adaptation (VIA) assessments. They can better resolve detailed regional atmospheric and terrestrial processes. The weather research and forecasting (WRF) model (Skamarock et al 2005) was used to conduct dynamic downscaling of reanalysis datasets and GCM outputs with different LSM parameterizations to produce high- resolution climate data for the CA.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
