Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr , but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.

The biophysical feedbacks of forest fire on Earth’s surface radiative budget remain uncertain at the global scale. Using satellite observations, we show that fire-induced forest loss accounts for about 15% of global forest loss, mostly in northern high latitudes. Forest fire increases surface temperature by 0.15 K (0.12 to 0.19 K) one year following fire in burned area globally. In high-latitudes, the initial positive climate-fire feedback was mainly attributed to reduced evapotranspiration and sustained for approximately 5 years. Over longer-term (> 5 years), increases in albedo dominated the surface radiative budget resulting in a net cooling effect. In tropical regions, fire had a long-term weaker warming effect mainly due to reduced evaporative cooling. Globally, biophysical feedbacks of fire-induced surface warming one year after fire are equivalent to 62% of warming due to annual fire-related CO2 emissions. Our results suggest that changes in the severity and/or frequency of fire disturbance may have strong impacts on Earth’s surface radiative budget and climate, especially at high latitudes.

Abstract. Over the last 5 decades monitoring systems have been developed to detect changes in the accumulation of carbon (C) in the atmosphere and ocean; however, our ability to detect changes in the behavior of the global C cycle is still hindered by measurement and estimate errors. Here we present a rigorous and flexible framework for assessing the temporal and spatial components of estimate errors and their impact on uncertainty in net C uptake by the biosphere. We present a novel approach for incorporating temporally correlated random error into the error structure of emission estimates. Based on this approach, we conclude that the 2σ uncertainties of the atmospheric growth rate have decreased from 1.2 Pg C yr−1 in the 1960s to 0.3 Pg C yr−1 in the 2000s due to an expansion of the atmospheric observation network. The 2σ uncertainties in fossil fuel emissions have increased from 0.3 Pg C yr−1 in the 1960s to almost 1.0 Pg C yr−1 during the 2000s due to differences in national reporting errors and differences in energy inventories. Lastly, while land use emissions have remained fairly constant, their errors still remain high and thus their global C uptake uncertainty is not trivial. Currently, the absolute errors in fossil fuel emissions rival the total emissions from land use, highlighting the extent to which fossil fuels dominate the global C budget. Because errors in the atmospheric growth rate have decreased faster than errors in total emissions have increased, a ~20% reduction in the overall uncertainty of net C global uptake has occurred. Given all the major sources of error in the global C budget that we could identify, we are 93% confident that terrestrial C uptake has increased and 97% confident that ocean C uptake has increased over the last 5 decades. Thus, it is clear that arguably one of the most vital ecosystem services currently provided by the biosphere is the continued removal of approximately half of atmospheric CO2 emissions from the atmosphere, although there are certain environmental costs associated with this service, such as the acidification of ocean waters.

Wildfire is the most prevalent natural disturbance in boreal forests and impacts climate through biogeochemical (e.g., greenhouse gas emission from biomass burning) and biophysical (e.g., albedo [α], evapotranspiration [ET], and roughness) processes. We used satellite observations to investigate the immediate (i.e., 1 year after fire) biophysical effects of fire in Siberian boreal forests. We found that boreal forest fires have a net annual warming effect (0.0728 to 0.325 K) due to strong summer warming and weak winter cooling. Fires also increased the diurnal temperature range and seasonal amplitude. These effects are strongest in summer and significantly higher in evergreen than in deciduous coniferous forests. Decreases in ET contributed to warming effects in summer, and increases in α contributed to cooling in winter. Our results suggest that the increase in observed land surface temperature immediately following fires in boreal ecosystems is most likely due to reduced ET leading to a strong positive feedback on the surface radiative budget.

Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty.Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C·y −2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes.global carbon budget | carbon cycle | decadal variations | Bayesian fusion