In this study, we explore observational, experimental, methodological, and practical aspects of the flux quantification of greenhouse gases from local point sources by using in situ airborne observations, and suggest a series of conceptual changes to improve flux estimates. We address the major sources of uncertainty reported in previous studies by modifying (1) the shape of the typical flight path, (2) the modeling of covariance and anisotropy, and (3) the type of interpolation tools used. We show that a cylindrical flight profile offers considerable advantages compared to traditional profiles collected as curtains, although this new approach brings with it the need for a more comprehensive subsequent analysis. The proposed flight pattern design does not require prior knowledge of wind direction and allows for the derivation of an ad hoc empirical correction factor to partially alleviate errors resulting from interpolation and measurement inaccuracies. The modified approach is applied to a use-case for quantifying CH4 emission from an oil field south of San Ardo, CA, and compared to a bottom-up CH4 emission estimate.

Climate variability associated with the El Nino-Southern Oscillation (ENSO) and its consequent impacts on land carbon sink interannual variability have been used as a basis for investigating carbon cycle responses to climate variability more broadly, and to inform the sensitivity of the tropical carbon budget to climate change. Past studies have presented opposing views about whether temperature or precipitation is the primary factor driving the response of the land carbon sink to ENSO. Here, we show that the dominant driver varies with ENSO phase. Whereas tropical temperature explains sink dynamics following El Nino conditions (rTG, P = 0.59, p < 0.01), the post La Nina sink is driven largely by tropical precipitation (rPG, T = -0.46, p = 0.04). This finding points to an ENSO-phase-dependent interplay between water availability and temperature in controlling the carbon uptake response to climate variations in tropical ecosystems. We further find that none of a suite of ten contemporary terrestrial biosphere models captures these ENSO-phase- dependent responses, highlighting a key uncertainty in modeling climate impacts on the future of the global land carbon sink.

Isoprene plays a critical role in air quality and climate. Photosynthesis (gross primary productivity, GPP) and formaldehyde (HCHO) are both related to isoprene emission at large spatiotemporal scales, but neither is a perfect proxy. We apply multiple satellite products and site-level measurements to examine the impact of water deficit on the three interlinked variables at the Missouri Ozarks site during a 20-day mild dryness stress in summer 2011 and a 3-month severe drought in summer 2012. Isoprene emission shows opposite responses to the short- and long-term droughts, while GPP was substantially reduced in both cases. In 2012, both remote-sensed solar-induced fluorescence (SIF) and satellite HCHO column qualitatively capture reductions in flux-derived GPP and isoprene emission, respectively, on weekly to monthly time scales, but with muted responses. For instance, as flux-derived GPP approaches zero in late summer 2012, SIF drops by 29-33% (July) and 19-27% (August) relative to year 2011. A possible explanation is that electron transport and photosystem activity are maintained to a certain extent under the drought stress. Similarly, flux tower isoprene emissions in July 2012 are 54% lower than July 2011, while the relative reductions in July for 3 independent satellite-derived HCHO data products are 27%, 12% and 6%, respectively. We attribute the muted HCHO response to a photochemical feedback whereby reduced isoprene emission increases the oxidation capacity available to generate HCHO from other volatile organic compound sources. Satellite SIF offers a potential alternative indirect method to monitor isoprene variability at large spatiotemporal scales from space, although further research is needed under different environmental conditions and regions. Our analysis indicates that fairly moderate reductions in satellite SIF and HCHO column may imply severe drought conditions at the surface. (C) 2017 Elsevier Ltd. All rights reserved.

Warming temperatures in the Northern Hemisphere have enhanced terrestrial productivity. Despite the warming trend, North America has experienced more frequent and more intense cold weather events during winters and springs. These events have been linked to anomalous Arctic warming since 1990, and may affect terrestrial processes. Here we analyse multiple observation data sets and numerical model simulations to evaluate links between Arctic temperatures and primary productivity in North America. We find that positive springtime temperature anomalies in the Arctic have led to negative anomalies in gross primary productivity over most of North America during the last three decades, which amount to a net productivity decline of 0.31 PgC yr(-1) across the continent. This decline is mainly explained by two factors: severe cold conditions in northern North America and lower precipitation in the South Central United States. In addition, United States crop-yield data reveal that during years experiencing anomalous warming in the Arctic, yields declined by approximately 1 to 4% on average, with individual states experiencing declines of up to 20%. We conclude that the strengthening of Arctic warming anomalies in the past decades has remotely reduced productivity over North America.

Water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO2 explained a clear majority of total variation (6632%: meanone standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO2 effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO2, but this direct CO2 effect was offset by 204% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO2 was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO2 concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration.

NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO2 fluxes and constrain monthly CO2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during Northern Hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16% of all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

Recent studies have shown the promise of remotely sensed solar-induced chlorophyll fluorescence (SIF) in informing terrestrial carbon exchange, but analyses have been limited to either plot level (similar to 1 km(2)) or hemispheric/global (similar to 10(8) km(2)) scales due to the lack of a direct measure of carbon exchange at intermediate scales. Here we use a network of atmospheric CO2 observations over North America to explore the value of SIF for informing net ecosystem exchange (NEE) at regional scales. We find that SIF explains space-time NEE patterns at regional (similar to 100 km(2)) scales better than a variety of other vegetation and climate indicators. We further show that incorporating SIF into an atmospheric inversion leads to a spatial redistribution of NEE estimates over North America, with more uptake attributed to agricultural regions and less to needleleaf forests. Our results highlight the synergy of ground-based and spaceborne carbon cycle observations.

Drought, a recurring phenomenon with major impacts on both human and natural systems(1-3), is the most widespread climatic extreme that negatively affects the land carbon sink(2,4). Although twentieth-century trends in drought regimes are ambiguous(5-7), across many regions more frequent and severe droughts are expected in the twenty-first century(3,7-9). Recovery time-how long an ecosystem requires to revert to its pre-drought functional state-is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth's climate system(10)) and second, that drought impacts(11) (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

Wet bulb globe temperature (WBGT) accounts for the effect of environmental temperature and humidity on thermal comfort, and can be directly related to the ability of the human body to dissipate excess metabolic heat and thus avoid heat stress. Using WBGT as a measure of environmental conditions conducive to heat stress, we show that anthropogenic influence has very substantially increased the likelihood of extreme high summer mean WBGT in northern hemispheric land areas relative to the climate that would have prevailed in the absence of anthropogenic forcing. We estimate that the likelihood of summer mean WGBT exceeding the observed historical record value has increased by a factor of at least 70 at regional scales due to anthropogenic influence on the climate. We further estimate that, in most northern hemispheric regions, these changes in the likelihood of extreme summer mean WBGT are roughly an order of magnitude larger than the corresponding changes in the likelihood of extreme hot summers as simply measured by surface air temperature. Projections of future summer mean WBGT under the RCP8.5 emissions scenario that are constrained by observations indicate that by 2030s at least 50% of the summers will have mean WBGT higher than the observed historical record value in all the analyzed regions, and that this frequency of occurrence will increase to 95% by mid-century.

Sophisticated methods for uncertainty quantification have been proposed for overcoming the pitfalls of simple statistical inference in hydrology. The implementation of such methods is conceptually and computationally challenging, however, especially for large-scale models. Here, we explore whether there are circumstances in which simple approaches, such as least squares, produce comparably accurate and reliable predictions. We do so using three case studies, with two involving a small sewer catchment with limited calibration data, and one an agricultural river basin with rich calibration data. We also review additional published case studies. We find that least squares performs similarly to more sophisticated approaches such as a Bayesian autoregressive error model in terms of both accuracy and reliability if calibration periods are long or if the input data and the model have minimal bias. Overall, we find that, when mindfully applied, simple statistical methods such as least squares can still be useful for uncertainty quantification. (c) 2018 Elsevier Ltd. All rights reserved.