, Volume 32, Issue 2-3, pp 287-299
Date: 01 Nov 2008

Greening the terrestrial biosphere: simulated feedbacks on atmospheric heat and energy circulation

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Abstract

Much research focuses on how the terrestrial biosphere influences climate through changes in surface albedo (reflectivity), stomatal conductance and leaf area index (LAI). By using a fully-coupled GCM (HadCM3LC), our research objective was to induce an increase in the growth of global vegetation to isolate the effect of increased LAI on atmospheric exchange of heat and moisture. Our Control simulation had a mean global net primary production (NPP) of 56.3 GtCyr−1 which is half that of our scenario value of 115.1 GtCyr−1. LAI and latent energy (Q E) were simulated to increase globally, except in areas around Antarctica. A highly productive biosphere promotes mid-latitude mean surface cooling of ~2.5°C in the summer, and surface warming of ~1.0°C in the winter. The former response is primarily the result of reduced Bowen ratio (i.e. increased production of Q E) in combination with small increases in planetary albedo. Response in winter temperature is likely due to decreased planetary albedo that in turn permits a greater amount of solar radiation to reach the Earth’s surface. Energy balance calculations show that between 75° and 90°N latitude, an additional 2.4 Wm−2 of surface heat must be advected into the region to maintain energy balance, and ultimately causes high northern latitudes to warm by up to 3°C. We postulate that large increases in Q E promoted by increased growth of terrestrial vegetation could contribute to greater surface-to-atmosphere exchange and convection. Our high growth simulation shows that convective rainfall substantially increases across three latitudinal bands relative to Control; in the tropics, across the monsoonal belt, and in mid-latitude temperate regions. Our theoretical research has implications for applied climatology; in the modeling of past “hot-house” climates, in explaining the greening of northern latitudes in modern-day times, and for predicting future changes in surface temperature with continued increases in atmospheric CO2.