Climate Change and Global Warming

Biography

Biography: Yi Zhang

Abstract

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. A clear understanding of the influence degree of each source of uncertainty in ensemble simulations is important for determining their relative importance under limited conditions when assessing the impact of climate change on crop production with uncertainties. Statistical model, in which historical data on crop yields and weather are used to establish relatively simple regression equations, effectively avoid the extensive input parameter uncertainty in process-based model. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and 3 emission scenarios representing the climate projections uncertainty and 2 crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize (Zea mays L.) yield at 3 locations (Benxi, Changling and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-model’s ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5% in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.