Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP

Hongju Chen,Qingshan He,Yongjian Ding,Jianping Yang,Qin Ji

doi:10.3390/w13243599

Abstract

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.

Highlights

Snow cover is an important component of the cryosphere and indicator of climate change [1] as its properties change rapidly in response to changes in heat and water on the earth’s surface [2,3,4]
The results showed that the modeled precipitation of GFDL-ESM2G was higher than that of the meteorological station observations over Sichuan, Qinghai, and Tibet during the snowfall period; the remainder of the major snowfall provinces displayed the opposite phenomenon (Figure 15)
Reference Station data, and the results showed that the iterative simulation capability of the model was stronger for both spatiotemporal sequences, with temporal and regional correlations (R2 ) of monthly snow cover duration equal to 0.94 and 0.97, and 0.88 and 0.91 for monthly cumulative snow depth, respectively

Summary

Introduction

Snow cover is an important component of the cryosphere and indicator of climate change [1] as its properties change rapidly in response to changes in heat and water on the earth’s surface [2,3,4]. The primary methods of acquiring snow parameter data include (1) on-site observations of meteorological stations [10];. Under changing global climate conditions, snow cover can serve as an important indicator. Some analyses have studied future changes in snow cover based on the experimental data of the Coupled Model Intercomparison Project (CMIP) organized by the World Climate Research

Methods

Results

Discussion

Conclusion