Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets.

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.

Limited evidence for CO2 -related growth enhancement in northern Rocky Mountain lodgepole pine populations across climate gradients.

Forests sequester large amounts of carbon annually and are integral in buffering against effects of global change. Increasing atmospheric CO2 may enhance photosynthesis and/or decrease stomatal conductance (gs ) thereby enhancing intrinsic water-use efficiency (iWUE), having potential indirect and direct benefits to tree growth. While increasing iWUE has been observed in most trees globally, enhanced growth is not ubiquitous, possibly due to concurrent climatic constraints on growth. To investigate our incomplete understanding of interactions between climate and CO2 and their impacts on tree physiology and growth, we used an environmental gradient approach. We combined dendrochronology with carbon isotope analysis (δ13 C) to assess the covariation of basal area increment (BAI) and iWUE over time in lodgepole pine. Trees were sampled at 18 sites spanning two climatically distinct elevation transects on the lee and windward sides of the Continental Divide, encompassing the majority of lodgepole pine's northern Rocky Mountain elevational range. We analyzed BAI and iWUE from 1950 to 2015, and explored correlations with monthly climate variables. As expected, iWUE increased at all sites. However, concurrent growth trends depended on site climatic water deficit (CWD). Significant growth increases occurred only at the driest sites, where increases in iWUE were strongest, while growth decreases were greatest at sites where CWD has been historically lowest. Late summer drought of the previous year negatively affected growth across sites. These results suggest that increasing iWUE, if strong enough, may indirectly benefit growth at drier sites by effectively extending the growing season via reductions in gs . Strong growth decreases at high elevation windward sites may reflect increasing water stress as a result of decreasing snowpack, which was not offset by greater iWUE. Our results imply that increasing iWUE driven by decreasing gs may benefit tree growth in limited scenarios, having implications for future carbon uptake potential of semiarid ecosystems.

The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale.

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-1 , but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.