Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

Long-term records of phytoplankton blooms in freshwater lakes are necessary both for understanding basin scale changes to watersheds and for providing a key constraint for assessing processes driving blooms. However, due to the inherent constraints of in situ sampling and the short time period covered by modem space borne sensors, few long-term records exist. Historical data from sensors such as Landsat offer strong potential for creating new records of past blooms. Here, we use a novel evaluation procedure based on multiple metrics to assess algorithm suitability and robustness for generating long-term bloom records using Landsat 5 imagery. Evaluation metrics are based on bloom presence, spatial distribution, magnitude and timing, using both in situ Microcystis biovolume and remotely-sensed Cyanobacterial Index (CI) data from MERIS for 2002-2011. Applying this procedure for a test case focusing on Lake Erie's western basin, an algorithm based on a near infrared threshold with simple atmospheric correction through subtraction of the shortwave infrared band, combined with an additional "greenness" filter based on a hue threshold, performs best. Implementing this algorithm for 1984-2001 reveals the long-term trends in peak bloom magnitude prior to the start of the MERIS and MODIS record (2002-2015), and more than doubles the period of record that can be used to understand bloom occurrence and growth for this system. More broadly, we demonstrate that Landsat observations can be used to identify macro-scale features of blooms. For Lake Erie, the performance of the final Landsat algorithm is comparable to that of the MERIS CI algorithm, despite Landsat's broad spectral bands and long revisit time. (C) 2017 The Authors. Published by Elsevier Inc.

Eutrophication, or excessive nutrient enrichment, threatens water resources across the globe. We show that climate change-induced precipitation changes alone will substantially increase (19 +/- 14%) riverine total nitrogen loading within the continental United States by the end of the century for the "business-as-usual" scenario. The impacts, driven by projected increases in both total and extreme precipitation, will be especially strong for the Northeast and the corn belt of the United States. Offsetting this increase would require a 33 +/- 24% reduction in nitrogen inputs, representing a massive management challenge. Globally, changes in precipitation are especially likely to also exacerbate eutrophication in India, China, and Southeast Asia. It is therefore imperative that water quality management strategies account for the impact of projected future changes in precipitation on nitrogen loading.

Harmful algal blooms in Lake Erie have been increasing in severity over the past two decades, prompting new phosphorus loading target recommendations. We explore long-term drivers of phytoplankton blooms by leveraging new estimates of historical bloom extent from Landsat 5 covering 1984-2001 together with existing data covering 2002-2015. We find that a linear combination of springtime and long-term cumulative dissolved reactive phosphorus (DRP) loading explains a high proportion of interannual variability in maximum summertime bloom extent for 1984-2015 (R-2 = 0.75). This finding suggests that the impacts of internal loading are potentially greater than previously understood, and that the hypothesized recent increased susceptibility to blooms may be attributable to high decadal-scale cumulative loading. Based on this combined loading model, achieving mild bloom conditions in Lake Erie (defined in recent studies as bloom areas below 600 km(2) nine years out of ten) would require DRP loads to be reduced by 58% relative to the 2001-2015 average (equivalent to annual DRP loading of 240 MT and April to July DRP loading of 78 MT). Reaping the full benefits of load reductions may therefore take up to a decade due to the effects of historical loading. (C) 2017 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

On behalf of the journal, the American Geophysical Union, and the scientific community, the editors would like to sincerely thank those who reviewed manuscripts for Water Resources Research in 2016. Their time spent reading and commenting on manuscripts not only improves the manuscripts themselves but also increases the scientific rigor of future research in the field. Many of those listed below went above and beyond and reviewed three or more manuscripts for our journal, and those are indicated in italics. Together, they contributed 3674 individual reviews of manuscripts submitted to Water Resources Research for consideration, of which 562 were eventually published. Thank you again. We look forward to a 2017 of exciting advances in the field and communicating those advances to our community and to the broader public.

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.