The ability to monitor fossil fuel carbon dioxide (FFCO2) emissions from subcontinental regions using atmospheric CO2 observations remains an important but unrealized goal. Here we explore a necessary but not sufficient component of this goal, namely, the basic question of the detectability of FFCO2 emissions from subcontinental regions. Detectability is evaluated by examining the degree to which FFCO2 emissions patterns from specific regions are needed to explain the variability observed in high-frequency atmospheric CO2 observations. Analyses using a CO2 monitoring network of 35 continuous measurement towers over North America show that FFCO2 emissions are difficult to detect during nonwinter months. We find that the compounding effects of the seasonality of atmospheric transport patterns and the biospheric CO2 flux signal dramatically hamper the detectability of FFCO2 emissions. Results from several synthetic data case studies highlight the need for advancements in data coverage and transport model accuracy if the goal of atmospheric measurement-based FFCO2 emissions detection and estimation is to be achieved beyond urban scales.

Understanding the response of the terrestrial biospheric carbon cycle to variability in enviroclimatic drivers is critical for predicting climate-carbon interactions. Here we apply an atmospheric-inversion-based framework to assess the relationships between the spatiotemporal patterns of net ecosystem CO2 exchange (NEE) and those of enviroclimatic drivers. We show that those relationships can be directly observed at 1 degrees x1 degrees 3-hourly resolution from atmospheric CO2 measurements for four of seven large biomes in North America, namely, (i) boreal forests and taiga; (ii) temperate coniferous forests; (iii) temperate grasslands, savannas, and shrublands; and (iv) temperate broadleaf and mixed forests. We find that shortwave radiation plays a dominant role during the growing season over all four biomes. Specific humidity and precipitation also play key roles and are associated with decreased CO2 uptake (or increased release). The explanatory power of specific humidity is especially strong during transition seasons, while that of precipitation appears during both the growing and dormant seasons. We further find that the ability of four prototypical terrestrial biospheric models (TBMs) to represent the spatiotemporal variability of NEE improves as the influence of radiation becomes more dominant, implying that TBMs have a better skill in representing the impact of radiation relative to other drivers. Even so, we show that TBMs underestimate the strength of the relationship to radiation and do not fully capture its seasonality. Furthermore, the TBMs appear to misrepresent the relationship to precipitation and specific humidity at the examined scales, with relationships that are not consistent in terms of sign, seasonality, or significance relative to observations. More broadly, we demonstrate the feasibility of directly probing relationships between NEE and enviroclimatic drivers at scales with no direct measurements of NEE, opening the door to the study of emergent processes across scales and to the evaluation of their scaling within TBMs.

Hypoxia has been observed in the central basin of Lake Erie for decades. To understand the impact of various controlling factors, we analyze a record of hypoxic extents for Lake Erie for 1985-2012 and develop a parsimonious model of their interannual variability. We find that the 2012 North American drought and accompanying low tributary discharge was associated with a record-breaking hypoxic event in Lake Erie, whereas a record-setting harmful algal bloom in 2011 was likely associated with only mild hypoxia. River discharge and the timing of nutrient input therefore impact western basin bloom growth and central basin oxygen demand in distinct ways that merit further investigation. Overall, April to June tributary discharge, May to July soluble reactive phosphorus loading, July wind stress, and June northwesterly wind duration explain 82% of the interannual variability of hypoxia, and discharge alone explains 39%, indicating that meteorological factors need to be considered in the development of nutrient management strategies, especially as both extreme precipitation events and droughts become more frequent under a changing climate.

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO(2)) emissions in the lower 48 states of the USA.

Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space-based lidar such as the active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely, the thawing of permafrost in the northern high latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source/sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise-free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the El Nino-Southern Oscillation climatic mode are primarily detectable as a zonal increase.

Multimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10-member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present-day carbon cycling) versus naive (one model-one vote) integration. MsTMIP optimal and naive mean land sink strength estimates (-1.16 versus -1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill-based integration does not materially change MME values. This suggests that carbon metabolism has predictability limits and/or that all models and references are misspecified. Resolving this issue requires addressing specific uncertainty types (initial conditions, structure, and references) and a change in model development paradigms currently dominant in the TBM community.

During 2014 Water Resources Research benefited from the voluntary effort of 2103 reviewers. Their constructive and professional effort was instrumental for publishing high-quality contributions thereby supporting the development of our knowledge of water resources. The contribution of the reviewers is instrumental to science for reaching the target of benefiting humanity. Editors and Associate Editors of Water Resources Research are grateful to the reviewers for their talented, unselfish, and continuous support to the journal.

Water issues are rarely simple. At the global scale, water is at the focus of a powerful multifaceted challenge. Demands for both consumptive and nonconsumptive uses are growing, while climate change is at the same time decreasing availability in some places and increasing risks of heavy precipitation in many others. Through diverse mechanisms that interact with natural processes, human activities impact not only the quantity of water available but also its quality. Here we explore the multiway interactions among water, climate, energy, and food through a number of case studies illustrating the interconnected web of competing drivers, demands, and trade-offs that frame humanity's decisions about water use. The net result of this complex mix of drivers and processes is that water issues need to be addressed with a systems perspective. While a systems framing can be daunting, integrated approaches are fundamental to identifying and evaluating options for sustainable solutions.

Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land-atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process-based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century-long (1901-2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project and two observation-based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111Pg C (1Pg=10(15)g) with a median value of 1158Pg C in 2010. The models estimate a broad range of Rh from 35 to 69PgCyr(-1) with a median value of 51PgCyr(-1) during 2001-2010. The largest uncertainty in SOC stocks exists in the 40-65 degrees N latitude whereas the largest cross-model divergence in Rh are in the tropics. The modeled SOC change during 1901-2010 ranges from -70Pg C to 86Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble-estimated mean residence time of SOC shows a reduction of 3.4years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stockseven though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.