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.

A mechanistic model was developed to predict midsummer bottom-water dissolved oxygen (BWDO) concentration and hypoxic area on the Louisiana shelf of the northern Gulf of Mexico, USA (1985-2011). Because of its parsimonious formulation, the model possesses many of the benefits of simpler, more empirical models, in that it is computationally efficient and can rigorously account for uncertainty through Bayesian inference. At the same time, the model incorporates important biophysical processes such that its parameterization can be informed by field-measured biological and physical rates. The model is used to explore how freshwater flow, nutrient load, benthic oxygen demand, and wind velocity affect hypoxia on the western and eastern sections of the shelf, delineated by the Atchafalaya River outfall. The model explains over 70% of the variability in BWDO on both shelf sections, and outperforms linear regression models developed from the same input variables. Model results suggest that physical factors (i.e., wind and flow) control a larger portion of the year-to-year variability in hypoxia than previously thought, especially on the western shelf, though seasonal nutrient loads remain an important driver of hypoxia, as well. Unlike several previous Gulf hypoxia modeling studies, results do not indicate a temporal shift in the system's propensity for hypoxia formation (i.e., no regime change). Results do indicate that benthic oxygen demand is a substantial BWDO sink, and a better understanding of the long-term dynamics of this sink is required to better predict how the size of the hypoxic zone will respond to proposed reductions in nutrient loading.

Harmful algal blooms (HABs) are becoming increasingly common in freshwater ecosystems globally, raising complex questions about the factors that influence their initiation and growth. These questions have increasingly been answered through mechanistic and stochastic modeling efforts that rely on historical information about HABs in a given system for development, validation, and calibration. Therefore, understanding processes that control HABs is predicated on the ability to answer much more basic questions about what has actually occurred in a given system, namely questions of HAB occurrence, extent, intensity, and timing. Here we explore the state of the science in answering these basic questions; we use Lake Erie as a case study, where nearly two decades after the resurgence of HABs, a summer 2014 event caused a mandatory three day tap water ban for Toledo, Ohio. We find that, even for well-studied systems, unambiguous answers to basic questions about HAB occurrence are lacking, raising concerns about their use as a basis for addressing mechanistic questions about controlling factors. This ambiguity is found to be caused by differences in the methods used to track HABs, the specific harm being considered, the linkage to that harm (direct or indirect), the threshold defining harm, and spatiotemporal variability in sampling. Further work is therefore needed to integrate heterogeneous types of observations in order to better leverage existing and future monitoring programs, and to guide modeling efforts toward deeper understanding of HAB causes and consequences. (C) 2015 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. This is an open access article under the CC BY-NC-ND.

Global gridded maps (a. k. a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty.

We examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982 to 2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increasing trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO2 ranked second in these models after the predominant climatic influences, and yielded decreasing trends in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increasing nitrogen deposition slightly amplified global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.