Predicting the patterns of change in spring onset and false springs in China during the twenty-first century
Spring onset has generally shifted earlier in China over the past several decades in response to the warming climate. However, future changes in spring onset and false springs, which will have profound effects on ecosystems, are still not well understood. Here, we used the extended form of the Spring Indices model (SI-x) to project changes in the first leaf and first bloom dates, and predicted false springs for the historical (1950–2005) and future (2006–2100) periods based on the downscaled daily maximum/minimum temperatures under two emission scenarios from 21 General Circulation Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5). On average, first leaf and first bloom in China were projected to occur 21 and 23 days earlier, respectively, by the end of the twenty-first century in the Representative Concentration Pathway (RCP) 8.5 scenario. Areas with greater earlier shifts in spring onset were in the warm temperate zone, as well as the north and middle subtropical zones of China. Early false spring risk increased rapidly in the warm temperate and north subtropical zones, while that declined in the cold temperate zone. Relative to early false spring risk, late false spring risk showed a common increase with smaller magnitude in the RCP 8.5 scenario but might cause greater damage to ecosystems because plants tend to become more vulnerable to the later occurrence of a freeze event. We conclude that future climate warming will continue to cause earlier occurrence of spring onset in general, but might counterintuitively increase plant damage risk in natural and agricultural systems of the warm temperate and subtropical China.
KeywordsPhenology Spring indices False spring China NASA NEX-GDDP dataset
We gratefully acknowledge the support for this work by the National Natural Science Foundation of China (grant No. 41371097). Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We thank Dr. Toby R. Ault who developed a Matlab toolbox for implementing the SI-x model, posted for online access (https://github.com/cornell-eas/SI-X). We are grateful to two anonymous reviewers whose comments helped improve the manuscript. We thank Dr. Anu Kramer for making helpful edits to our manuscript.
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