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    In: International Journal of Climatology, Wiley, Vol. 43, No. 2 ( 2023-02), p. 804-817
    Abstract: Knowledge of the responses and feedbacks of vegetation dynamics to interannual climate anomalies in the semiarid area of north China is lacking. This study addresses this issue through climate modelling for 1991–2006 with the Weather Research and Forecast (WRF) model, into which a locally empirical vegetation dynamic (VED) scheme was implemented. The VED scheme was built on the basis of regression models of the leaf area index (LAI), green vegetation fraction (GVF), and surface albedo (ALB) with ambient temperature and humidity as independent variables. Using the update WRF model, the climate simulations were carried out with turning on and off VED, respectively. By comparing the simulations outputs with each other, we find that the interannual variability of precipitation from simulations with turning on VED is larger than that from simulations with turning off VED. The larger interannual variability of precipitation could be explained by VED feedback. Vegetation growth would be more vigorous, as a response, under the more precipitation, because which brings more available water. In turn, vigorous vegetation leads to more evapotranspiration and, hence, precipitable water; as a result, there would be more precipitation. It is reversed for the years with less precipitation. Such interactions between vegetation and precipitation could be summarized as positive feedback loops. This study suggests that the interannual precipitation variability can be enlarged by VED through positive evapotranspiration–precipitation feedback. It is therefore necessary to consider the vegetation dynamics for future downscaled regional climate predictions to pursue more reliable interannual climate variability.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1491204-1
    detail.hit.zdb_id: 1000947-4
    SSG: 14
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