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Accomplishing the objective of the current climate policies will require establishing carbon budget and flux estimates in each region and county of the globe by comparing and reconciling multiple estimates including the observations and the results of top-down atmospheric carbon dioxide (CO2) inversions and bottom-up dynamic global vegetation models. With this in view, this study synthesizes the carbon source/sink due to net ecosystem productivity (NEP), land cover land use change (ELUC), fires and fossil burning (EFIRE) for the South Asia (SA), Southeast Asia (SEA) and South and Southeast Asia (SSEA = SA + SEA) and each country in these regions using the multiple top-down and bottom-up modeling results. The terrestrial net biome productivity (NBP = NEP - ELUC - EFIRE) calculated based on bottom-up models in combination with EFIRE based on GFED4s data show net carbon sinks of 217 147, 10 55, and 227 279 TgC yr−1 for SA, SEA, and SSEA. The top-down models estimated NBP net carbon sinks were 20 170, 4 90 and 24 180 TgC yr−1. In comparison, regional emissions from the combustion of fossil fuels were 495, 275, and 770 TgC yr−1, which are many times higher than the NBP sink estimates, suggesting that the contribution of the fossil fuel emissions to the carbon budget of SSEA results in a significant net carbon source during the 2000s. When considering both NBP and fossil fuel emissions for the individual countries within the regions, Bhutan and Laos were net carbon sinks and rest of the countries were net carbon source during the 2000s. The relative contributions of each of the fluxes (NBP, NEP, ELUC, and EFIRE, fossil fuel emissions) to a nation's net carbon flux varied greatly from country to country, suggesting a heterogeneous dominant carbon fluxes on the country-level throughout SSEA.

This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.

Recent studies using satellite observations show that operational wind farms in west-central Texas increase local nighttime land surface temperature (LST) by 0.31–0.70 °C, but no noticeable impact is detected during daytime, and that the diurnal and seasonal variations in the magnitude of this warming are likely determined by those in the magnitude of wind speed. This paper further explores these findings by using the data from a year-long field campaign and nearby radiosonde observations to investigate how thermodynamic profiles and surface–atmosphere exchange processes work in tandem with the presence of wind farms to affect the local climate. Combined with satellite data analyses, we find that wind farm impacts on LST are predominantly determined by the relative ratio of turbulence kinetic energy (TKE) induced by the wind turbines compared to the background TKE. This ratio explains not only the day–night contrast of the wind farm impact and the warming magnitude of nighttime LST over the wind farms, but also most of the seasonal variations in the nighttime LST changes. These results indicate that the diurnal and seasonal variations in the turbine-induced turbulence relative to the background TKE play an essential role in determining those in the magnitude of LST changes over the wind farms. In addition, atmospheric stability determines the sign and strength of the net downward heat transport as well as the magnitude of the background TKE. The study highlights the need for better understanding of atmospheric boundary layer and wind farm interactions, and for better parameterizations of sub-grid scale turbulent mixing in numerical weather prediction and climate models.