The mark of vegetation change on Earth's surface energy balance

Abstract

Changing vegetation cover alters the radiative and non-radiative properties of the surface. The result of competing biophysical processes on Earth's surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and background climate. Here we provide the first data-driven assessment of the potential effect on the full surface energy balance of multiple vegetation transitions at global scale. For this purpose we developed a novel methodology that is optimized to disentangle the effect of mixed vegetation cover on the surface climate. We show that perturbations in the surface energy balance generated by vegetation change from 2000 to 2015 have led to an average increase of 0.23 ± 0.03 °C in local surface temperature where those vegetation changes occurred. Vegetation transitions behind this warming effect mainly relate to agricultural expansion in the tropics, where surface brightening and consequent reduction of net radiation does not counter-balance the increase in temperature associated with reduction in transpiration. This assessment will help the evaluation of land-based climate change mitigation plans.

Fig. 1

Potential changes to surface temperatures caused by deforestation. Panels describe the expected average annual change of a day-time and b night-time clear sky land surface temperature (LST), of c mean LST (defined as the average between a and b) and of d LST diurnal amplitude (defined as the difference between a and b)

Fig. 2

Potential changes to the local energy balance caused by deforestation. Expected average annual changes are provided for a shortwave reflected radiation (SW), b longwave emitted radiation (LW), c latent heat flux (LE) and d the combination of sensible and ground heat fluxes (H+G). Deforestation is considered here to be a conversion of forests to either crops or grasses

Fig. 3

Global summary of the mean annual potential change in surface energy balance and temperature for various transitions in vegetation type as derived from satellite observations. The transitions shown involve the following vegetation classes: evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), evergreen needleleaf forests (ENF), savannas (SAV), shrublands (SHR), grasslands (GRA), croplands (CRO) and wetlands (WET). Because transitions are symmetric, reverse transitions can be derived by inverting the sign. The inset shows a more generic transition from forests to either crops or grasses corresponding to the maps shown in Figs. 1 and 2. For each transitions, the mean change is provided for the shortwave reflected radiative flux (SW), longwave emitted radiative flux (LW), latent heat flux (LE) and the combination of sensible and ground heat fluxes (H+G). The number above the bars represents the mean surface temperature change observed for that transition ± two times the standard error around the mean, as do the confidence intervals represented on the bar charts of the flux values

Fig. 4

Effect of actual changes in vegetation cover from 2000 to 2015 on the surface energy balance. Each panel illustrates how this energy change in exoJoules (EJ) varies across climatic gradients of mean annual temperature and annually cumulated precipitation for the various components of the surface energy balance: a shortwave reflected radiation, b longwave emitted radiation, c latent heat, and d the combination of sensible and ground heat fluxes. Each panel also provides the corresponding total net effect. The climate axes are calculated based on CRU data v4.00 at 0.5° × 0.5° resolution

Fig. 5

Cumulated changes in energy for each component of the surface energy balance resulting from recent major vegetation transitions. Transitions are sorted according to decreasing absolute change in the surface energy balance. The changed area per transition, calculated based on the ESA CCI land cover maps of 2015 and 2000, are reported in megahectares on the right of the bars. The transitions shown involve the following vegetation classes: evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), savannas (SAV), shrublands (SHR) and croplands (CRO)

Fig. 6

Changes in surface temperature resulting from major vegetation transitions between 2000 and 2015. The bars represent the respective contribution of each component of the surface energy balance to the change in surface temperature (Ts). a Illustrates changes at global scale or divided by broad latitudinal bands, while b shows changes for a selection of transitions types involving the following vegetation classes: evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), evergreen needleleaf forests (ENF), savannas (SAV), shrublands (SHR) and croplands (CRO). Each bar represents the mean of all changed pixels weighted by the actual change within each 1° pixel. The errorbars represent ± two times the weighted standard error around the weighted mean

Fig. 7

Illustration of the methodology for retrieving the local biophysical signal associated to potential vegetation change. The input consists of vegetation cover fraction maps for different classes, in this case: forests (a), crops/grasses (b) and other (c); and the spatial variation of the target biophysical variable, here day-time land surface temperature (LST) (d). e Ternary diagram showing the values of the 25 pixels in the red boxes of a–c respectively plotted in the three axes. The colour of the points represents the corresponding values of the biophysical variable. The regression in the compositional space allows the estimation of LST at the vertices of the triangle: vertex A representing a full forest cover and vertex B representing 100% cover of crops/grasses. The difference between these values is the estimated change in LST following a total change from forests to crops/grasses for the pixel in the centre of the 5 by 5 window. f The estimated change in LST for the transition from forests to crops/grasses for the entire region. Grey areas are masked out because of the topographical filtering or lack of co-occurrence of either forests or crops/grasses. The size of the pixels is 0.05° or ~5.5 km at the equator