Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios
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Global warming has considerably challenged the natural environment and livelihood conditions. Understanding potential future changes in critical climatic variables, such as temperature and precipitation, is important for regional agricultural and water resource management. This study proposes a new approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Intercomparison Project Phase 5 (CMIP5) emission scenarios and aims to test its applicability in cold areas and to evaluate the response of temperature and precipitation, in amount and form, under future warmer climate trends. Three stations in northeastern China are set as case sites, and 50 years of daily weather observations are used for model calibration and validation. Future synthetic time-series of daily precipitation and daily maximum and minimum temperatures is generated by the calibrated LARS-WG based on three Representative Concentration Pathway (RCP) scenarios with various radiative forcing levels of 14 general circulation models (GCMs) outputs for the periods 2041–2060 (2050s) and 2061–2080 (2070s). The results show that the CMIP5 scenarios can be successfully used in a LARS-WG model and that the model performs well in cold weather conditions to repeat the current status of the case sites; the model is able to provide downscaling analysis for future daily weather generation via updating calibrated model parameters based on various GCM outputs. A generally warming and wetting conversion would last into the future for the study sites, but there is great inconsistency among different GCMs. An ensemble approach is adopted with mean values of multi-GCMs to avoid the uncertainty associated with using a single GCM, based on which the changes in the form of precipitation are further estimated. As a result of the decrease in freezing conditions, although annual precipitation will continue to increase in the future, there will be relatively less annual snowfall, which will be primarily focused in deep winter. Such changes in snow cover conditions will potentially disturb the original rules of local overwintering agriculture. In addition, more intense and earlier snowmelt discharge and more rainfall in summer will latently impact the watershed hydrologic process. The influences of climate change are significant, and related projects for agricultural and water resource management should be of great concern in local decision-making.
The authors acknowledge the developer of the LARS-WG model for access to software and license agreement. The data used for model application were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn), and the China Meteorological Data Service Center (CMDC) (http://data.cma.cn).
The study is financially supported by the innovation team training plan of the Tianjin Education Committee (TD13-5073), the National Natural Science Foundation of China (No. 41372373), the Opening Fund of Tianjin Key Laboratory of Water Resources and Environment (117-YF11700102), and the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (2018KJ160).
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