In any data assimilation framework, the background error covariance statistics play the critical role of filtering the observed information and determining the quality of the analysis. For atmospheric CO2 data assimilation, however, the background errors cannot be prescribed via traditional forecast or ensemble-based techniques as these fail to account for the uncertainties in the carbon emissions and uptake, or for the errors associated with the CO2 transport model. We propose an approach where the differences between two modeled CO2 concentration fields, based on different but plausible CO2 flux distributions and atmospheric transport models, are used as a proxy for the statistics of the background errors. The resulting error statistics: (1) vary regionally and seasonally to better capture the uncertainty in the background CO2 field, and (2) have a positive impact on the analysis estimates by allowing observations to adjust predictions over large areas. A state-of-the-art four-dimensional variational (4D-VAR) system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to illustrate the impact of the proposed approach for characterizing background error statistics on atmospheric CO2 concentration estimates. Observations from the Greenhouse gases Observing SATellite IBUKI (GOSAT) are assimilated into the ECMWF 4D-VAR system along with meteorological variables, using both the new error statistics and those based on a traditional forecast-based technique. Evaluation of the four-dimensional CO2 fields against independent CO2 observations confirms that the performance of the data assimilation system improves substantially in the summer, when significant variability and uncertainty in the fluxes are present.

Relieving phosphorus loading is a key management tool for controlling Lake Erie eutrophication. During the 1960s and 1970s, increased phosphorus inputs degraded water quality and reduced central basin hypolimnetic oxygen levels which, in turn, eliminated thermal habitat vital to cold-water organisms and contributed to the extirpation of important benthic macroinvertebrate prey species for fishes. In response to load reductions initiated in 1972, Lake Erie responded quickly with reduced water-column phosphorus concentrations, phytoplankton biomass, and bottom-water hypoxia (dissolved oxygen <2 mg/l). Since the mid-1990s, cyanobacteria blooms increased and extensive hypoxia and benthic algae returned. We synthesize recent research leading to guidance for addressing this re-eutrophication, with particular emphasis on central basin hypoxia. We document recent trends in key eutrophication-related properties, assess their likely ecological impacts, and develop load response curves to guide revised hypoxia-based loading targets called for in the 2012 Great Lakes Water Quality Agreement. Reducing central basin hypoxic area to levels observed in the early 1990s (ca. 2000 km(2)) requires cutting total phosphorus loads by 46% from the 2003-2011 average or reducing dissolved reactive phosphorus loads by 78% from the 2005-2011 average. Reductions to these levels are also protective of fish habitat We provide potential approaches for achieving those new loading targets, and suggest that recent load reduction recommendations focused on western basin cyanobacteria blooms may not be sufficient to reduce central basin hypoxia to 2000 km(2). (C) 2014 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Understanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state-of-the-art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global-scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

We use geostatistical universal kriging and conditional realizations to provide the first quantitative estimates, with robust estimates of uncertainties, of the seasonal and interannual variability in hypoxic volume in Chesapeake Bay, covering early April to late October for 1985 to 2010, and explore factors controlling that variability. Results show that the time when the hypoxic volume reaches its maximum has moved from late to early July over the examined period, but that there is no trend in the seasonal-maximum hypoxic volume itself. No significant trend was found in the timing of onset of hypoxia, but the end of the hypoxic period has moved from October to September. Including nutrient loading from the Rappahannock River in addition to the Susquehanna and Potomac Rivers is found to be beneficial for explaining the interannual variability of hypoxia. Overall, January to May total nitrogen loads from these three rivers, April to August southwesterly and northeasterly winds, and April and May precipitation explain >85% of the seasonally averaged interannual variability in hypoxic volumes. Southwesterly winds affect hypoxia by increasing vertical stratification, while precipitation likely acts as a surrogate for nonpoint sources of nitrogen downstream from monitoring stations. The relative contribution of nutrient loading to the overall interannual variability suggests that 28-35% reductions in monitored nutrient loads may not be sufficient to achieve a corresponding reduction in hypoxic conditions as had been suggested in previous studies, at least in the short term.

The characterization of fossil-fuel CO2 (ffCO(2)) emissions is paramount to carbon cycle studies, but the use of atmospheric inverse modeling approaches for this purpose has been limited by the highly heterogeneous and non-Gaussian spatiotemporal variability of emissions. Here we explore the feasibility of capturing this variability using a low-dimensional parameterization that can be implemented within the context of atmospheric CO2 inverse problems aimed at constraining regional-scale emissions. We construct a multiresolution (i. e., wavelet-based) spatial parameterization for ffCO(2) emissions using the Vulcan inventory, and examine whether such a parameterization can capture a realistic representation of the expected spatial variability of actual emissions. We then explore whether sub-selecting wavelets using two easily available proxies of human activity (images of lights at night and maps of built-up areas) yields a low-dimensional alternative. We finally implement this low-dimensional parameterization within an idealized inversion, where a sparse reconstruction algorithm, an extension of stagewise orthogonal matching pursuit (StOMP), is used to identify the wavelet coefficients. We find that (i) the spatial variability of fossil-fuel emission can indeed be represented using a low-dimensional wavelet-based parameterization, (ii) that images of lights at night can be used as a proxy for sub-selecting wavelets for such analysis, and (iii) that implementing this parameterization within the described inversion framework makes it possible to quantify fossil-fuel emissions at regional scales if fossil-fuel-only CO2 observations are available.

Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model-model and model-observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5 degrees x 0.5 degrees resolution) and regional (North American: 0.25 degrees x 0.25 degrees resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as predictive tools for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science communities. In this study, we present and assess an approach to evaluating the spatiotemporal patterns, rather than aggregated magnitudes, of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 measurements. The approach is based on statistical model selection implemented within a high-resolution atmospheric inverse model. Using synthetic data experiments, we find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of North America, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns as well as to differentiate between multiple competing TBMs. Experiments using real atmospheric observations and four prototypical TBMs further confirm the applicability of the method, and demonstrate that the performance of TBMs in simulating the spatiotemporal patterns of NEE varies substantially across seasons, with best performance during the growing season and more limited skill during transition seasons. This result is consistent with previous work showing that the ability of TBMs to model flux magnitudes is also seasonally-dependent. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome-scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.

Bias correction of meteorological variables from climate model simulations is a routine strategy for circumventing known limitations of state-of-the-art general circulation models. Although the assessment of climate change impacts often depends on the joint variability of multiple variables, commonly used bias correction methodologies treat each variable independently and do not consider the relationship among variables. Independent bias correction can therefore produce non-physical corrections and may fail to capture important multivariate relationships. Here, we introduce a joint bias correction methodology (JBC) and apply it to precipitation (P) and temperature (T) fields from the fifth phase of the Climate Model Intercomparison Project (CMIP5) model ensemble. This approach is based on a general bivariate distribution of P-T and can be seen as a multivariate extension of the commonly used univariate quantile mapping method. It proceeds by correcting either P or T first and then correcting the other variable conditional upon the first one, both following the concept of the univariate quantile mapping. JBC is shown to not only reduce biases in the mean and variance of P and T similarly to univariate quantile mapping, but also to correct model-simulated biases in P-T correlation fields. JBC, using methods such as the one presented here, thus represents an important step in impacts-based research as it explicitly accounts for inter-variable relationships as part of the bias correction procedure, thereby improving not only the individual distributions of P and T, but critically, their joint distribution.

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.