Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observations.

Forest disturbances alter land biophysics. Their impacts on local climate and land surface temperature (LST) cannot be directly measured by comparing pre- and post-disturbance observations of the same site over time (e.g., due to confounding such as background climate fluctuations); a common remedy is to compare spatially-adjacent undisturbed sites instead. This space-for-time substitution ignores the inherent biases in vegetation between two paired sites, interannual variations, and temporal dynamics of forest recovery. Besides, there is a lack of observation-based analyses at fine spatial resolutions capable of capturing spatial heterogeneity of small-scale forest disturbances. To address these limitations, here we report new satellite analyses on local climate impacts of forest loss at 30 m resolution. Our analyses combined multiple long-term satellite products (e.g., albedo and evapotranspiration [ET]) at 700 sites across major climate zones in the conterminous United States, using time-series trend and changepoint detection methods. Our method helped isolate the biophysical changes attributed to disturbances from those attributed to climate backgrounds and natural growth. On average, forest loss increased surface albedo, decreased ET, and reduced leaf area index (LAI). Net annual warming-an increase in LST-was observed after forest loss in the arid/semiarid, northern, tropical, and temperate regions, dominated by the warming from decreased ET and attenuated by the cooling from increased albedo. The magnitude of post-disturbance warming was related to precipitation; climate zones with greater precipitation showed stronger and longer warming. Reduction in leaf or LAI was larger in evergreen than deciduous forests, but the recovery in LAI did not always synchronize with those of albedo and ET. Overall, this study presents new evidence of biophysical effects of forest loss on LST at finer spatial resolutions; our time-series method can be further leveraged to derive local policy-relevant ecosystem climate regulation metrics or support model-based climate-biosphere studies.

Environmental tipping points significantly affect the cost-benefit assessment of climate policies.

Most current cost-benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost-benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost-benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost-benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.