Temperature Response to Changes in Vegetation Fraction Cover in a Regional Climate Model

Abstract

Vegetation plays a key role in partitioning energy at the surface. Meteorological and Climate Models, both global and regional, implement vegetation using two parameters, the vegetation fraction and the leaf area index, obtained from satellite data. In most cases, models use average values for a given period. However, the vegetation is subject to strong inter-annual variability. In this work, the sensitivity of the near surface air temperature to changes in the vegetation is analyzed using a regional climate model (RCM) over the Iberian Peninsula. The experiments have been designed in a way that facilitates the physical interpretation of the results. Results show that the temperature sensitivity to vegetation depends on the time of year and the time of day. Minimum temperatures are always lower when vegetation is increased; this is due to the lower availability of heat in the ground due to the reduction of thermal conductivity. Regarding maximum temperatures, the role of increasing vegetation depends on the available moisture in the soil. In the case of hydric stress, the maximum temperatures increase, and otherwise decrease. In general, increasing vegetation will lead to a higher daily temperature range, since the decrease in minimum temperature is always greater than the decrease for maximum temperature. These results show the importance of having a good estimate of the vegetation parameters as well as the implications that vegetation changes due to natural or anthropogenic causes might have in regional climate for present and climate change projections.

Vegetation coverage change and its influencing factors in the Yellow River Delta

“…Mingsheng Jin et al analyzed the driving effects of different influencing factors on vegetation based on RS vegetation coverage change and driving forces [5]. JiménezGutiérrez et al analyzed the sensitivity of surface temperature to FVC changes by using the regional climate model of the Libyan peninsula [6]. Bing Lu et al studied the influence of vegetation index on weather forecast by using NDVI synthetic data [7].…”