Understanding Carbon Cycling of Terrestrial Ecosystems as a Fuzzy System

We outline a methodology of full and verified carbon account of terrestrial ecosystems (FCA) that supposes unbiased assessment of relevant proxy values (here: Net Ecosystem Carbon Budget) and reliable estimation of uncertainties. The FCA is a fuzzy (underspecified) system, of which membership function is inherently stochastic. Thus, any individually used method of FCA is not able to estimate structural uncertainties, that is why usually reported “within method” uncertainties are inevitably partial. Attempting at estimation of “full uncertainties” of the studied system we combine the major methods of terrestrial ecosystems carbon account (landscape-ecosystem method, LEA; process-based models; eddy covariance; and inverse modeling). Assessment of the uncertainties of FCA is provided within each method. Landscape-ecosystem approach (LEA) presents the empirical basis of the FCA in form of an Integrated Land Information System; serves for strict systems designing the account; contains all relevant empirical and semi-empirical data and models. By-pixel parametrization of land cover is provided by utilizing multi-sensor remote sensing data within Geo-Wiki platform and other relevant information based on special optimization algorithms. Major carbon fluxes within the LEA (NPP, HR, disturbances, etc.) are estimated based on fusion of empirical data with process-based elements by sets of regionally distributed models. “Within method” results and uncertainties of the methods examined are harmonized and mutually constrained based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2010; uncertainties of the FCA for individual years were estimated in limits of ±25%, CI 0.9. We discussed strengths and weaknesses of the approach; system requirements to different methods of the FCA, information and research needs; unresolved problems of cognition of fuzzy system; and obtained and potential levels of uncertainties

Carbon Budget and its Dynamics over Northern Eurasia Forest Ecosystems

The presentation contains an overview of recent findings and results of assessment of carbon cycling of forest ecosystems of Northern Eurasia. From a methodological point of view, there is a clear tendency in understanding a need of a Full and Verified Carbon Account (FCA), i.e. in reliable assessment of uncertainties for all modules and all stages of FCA. FCA is considered as a fuzzy (underspecified) system that supposes a system integration of major methods of carbon cycling study (land-ecosystem approach, LEA; process-based models; eddy covariance; and inverse modelling). Landscape-ecosystem approach 1) serves for accumulation of all relevant knowledge of landscape and ecosystems; 2) for strict systems designing the account, 3) contains all relevant spatially distributed empirical and semi-empirical data and models, and 4) is presented in form of an Integrated Land Information System (ILIS). The ILIS includes a hybrid land cover in a spatially and temporarily explicit way and corresponding attributive databases. The forest mask is provided by utilizing multi-sensor remote sensing data, geographically weighed regression and validation within GEO-wiki platform. By-pixel parametrization of forest cover is based on a special optimization algorithms using all available knowledge and information sources (data of forest inventory and different surveys, observations in situ, official statistics of forest management etc.). Major carbon fluxes within the LEA (NPP, HR, disturbances etc.) are estimated based on fusion of empirical data and aggregations with process-based elements by sets of regionally distributed models. Uncertainties within LEA are assessed for each module and at each step of the account. Within method results of LEA and corresponding uncertainties are harmonized and mutually constrained with independent outputs received by other methods based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2012. It has been shown that the Net Ecosystem Carbon Budget (NECB) of Russian forests for this period was in range of 0.5-0.7 Pg C yr-1 with a slight negative trend during the period due to acceleration of disturbance regimes and negative impacts of weather extremes (heat waves etc.). Uncertainties of the FCA for individual years were estimated at about 25% (CI 0.9). It has been shown that some models (e.g. majority of DGVMs) do not describe some processes on permafrost satisfactory while results of applications of ensembles of inverse models on average are closed to empirical assessments. A most important conclusion from this experience is that future improvements of knowledge of carbon cycling of Northern Eurasia forests requires development of an integrated observing system as a unified information background, as well as systems methodological improvements of all methods of cognition of carbon cycling

Current state of forest mapping with Landsat data in Siberia

We review a current state of a forest type mapping with Landsat data in Siberia. Target algorithm should be based on dynamic vegetation approach to be applicable to the analysis of the forest type distribution for Siberia, aiming at capability of mapping Siberian forest landscapes for applications such as predicting response of forest composition to climate change. We present data for several areas in West Siberian middle taiga, Central Siberia and East Siberia near Yakutsk. Analysis of the field survey, forest inventory data was made to produce forest type classification accounting for several stages for forest succession and variations in habitats and landforms. Supervised classification was applied to the areas were the ground truth and inventory data are available, including several limited area maps and vegetation survey transects. In Laryegan basin in West Siberia the upland forest areas are dominated by mix of Scots pine on sandy soils and Siberian pine with presence of fir and spruce on the others. Abundance of Scots pine decreases to the west due to change in soils. Those types are separable using Landsat spectral data. In the permafrost area around Yakutsk the most widespread succession type is birch to larch. Three stages of the birch to larch succession are detectable from Landsat image. When Landsat data is used in both West and East Siberia, distinction between deciduous broad-leaved species (birch, aspen, and willow) is generally difficult. Similar problem exist for distinguishing between dark coniferous species (Siberian pine, fir and spruce). Image classification can be improved by applying landform type analysis, such as separation into floodplain, terrace, sloping hills. Additional layers of information can be a promising way to complement Landsat data

Mass-conserving tracer transport modelling on a reduced latitude-longitude grid with NIES-TM

The need to perform long-term simulations with reasonable accuracy has led to the development of mass-conservative and efficient numerical methods for solving the transport equation in forward and inverse models. We designed and implemented a flux-form (Eulerian) tracer transport algorithm in the National Institute for Environmental Studies Transport Model (NIES TM), which is used for simulating diurnal and synoptic-scale variations of tropospheric long-lived constituents, as well as their seasonal and inter-annual variability. Implementation of the flux-form method requires the mass conservative wind fields. However, the model is off-line and is driven by datasets from a global atmospheric model or data assimilation system, in which vertically integrated mass changes are not in balance with the surface pressure tendency and mass conservation is not achieved. To rectify the mass-imbalance, a flux-correction method is employed. To avoid a singularity near the poles, caused by the small grid size arising from the meridional convergence problem, the proposed model uses a reduced latitude–longitude grid scheme, in which the grid size is doubled several times approaching the poles. This approach overcomes the Courant condition in the Polar Regions, maintains a reasonably high integration time-step, and ensures adequate model performance during simulations. To assess the model performance, we performed global transport simulations for SF<sub>6</sub>, <sup>222</sup>Rn, and CO<sub>2</sub>. The results were compared with observations available from the World Data Centre for Greenhouse Gases, GLOBALVIEW, and the Hateruma monitoring station, Japan. Overall, the results show that the proposed flux-form version of NIES TM can produce tropospheric tracer transport more realistically than previously possible. The reasons for this improvement are discussed

On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?

This is the final version. Available on open access from European Geosciences Union via the DOI in this recordData availability. CarbonTracker CT2016 results were provided by NOAA ESRL, Boulder, Colorado, USA, from the website at https://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ (National Oceanic and Atmospheric Administration (NOAA) Earth System Laboratory (ESRL), 2019a). CASA GFED 4.1 and CASA CMS NEE fluxes were also downloaded from the CT2016 website. The GOSAT L4 product and VISIT NEE were downloaded from the GOSAT Data Archive Service (https://data2.gosat.nies.go.jp; NIES, 2019). The Dai Global Palmer Drought Severity Index was downloaded from the Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (https://doi.org/10.5065/D6QF8R93; Dai, 2017). NASA GOME-2 SIF products were obtained from the Aura Validation Data Center (https://avdc.gsfc.nasa.gov/; Aura Validation Data Center, 2019). FLUXCOM products were obtained from the data portal of the Max Planck Institute for Biochemistry (https://www.bgc-jena.mpg.de/geodb/projects/Home.php.; Max Plank Institue for Biogeochemistry, 2019). MERRA-2 products were downloaded from MDISC (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/; Global Modeling and Assimilation Office, 2019), managed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The GEOS-Chem forward and adjoint models are freely available to the public. Instructions for downloading and running the models can be found at http://wiki.seas.harvard.edu/geos-chem (Atmospheric Chemistry Modeling Group at Harvard University , 2019). ACOS GOSAT lite files were obtained from the CO2 Virtual Science Data Environment (https://co2.jpl.nasa.gov/; Jet Propulsion Laboratory, California Institute of Technology, 2019). The SST anomalies were downloaded from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) website (https://www.esrl.noaa.gov; National Oceanic and Atmospheric Administration (NOAA) Earth System Laboratory (ESRL), 2019b).Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem adjoint model to assimilate GOSAT observations. Monthly NEE anomalies are compared to "proxies", variables that are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Statistically significant correlations (P<0.05) are obtained between posterior NEE anomalies and anomalies in soil temperature and FLUXCOM NEE on continental and larger scales in the tropics, as well as in the northern extratropics on subcontinental scales during the summer (R2≥0.49), suggesting that GOSAT measurements provide a constraint on NEE interannual variability (IAV) on these spatial scales. Furthermore, we show that GOSAT flux inversions are generally better correlated with the environmental proxies and FLUXCOM NEE than NEE anomalies produced by a set of terrestrial biosphere models (TBMs), suggesting that GOSAT flux inversions could be used to evaluate TBM NEE fluxes.Environment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Space Agenc

Orthopoxvirus Infections: Epidemiology, Clinical Picture, and Diagnostics (Scientific Review)

Lack of immunity among the population against pathogenic orthopoxviruses and an increased number of these infections human cases testify to the need of development of the rapid high-sensitive methods for species-specific orthopoxvirus diagnostics. The review presents current epidemiological situation on human orthopoxvirus infections. Addressed are clinical aspects of the disease, caused by small pox virus (SPV), Monkeypox virus, cowpox virus, and vaccinia virus. Represented is a historical survey of the conventional methods for diagnostics of these particular viruses. Reconsidered are the benefits of researches into the sphere of state-of-the-art molecular-diagnostic techniques taking into view both genus-specific and species-specific detection of agents, causing orthopoxvirus infections in humans. Demonstrated is the urgency of new-generation typing in view of occurrence of a novel SPV-like virus emerged as a result of natural evolution of existing zoonotic orthopoxviruses or SPV application as a biological terroristic agent

A Decadal Inversion of CO2 Using the Global Eulerian-Lagrangian Coupled Atmospheric Model (GELCA): Sensitivity to the Ground-Based Observation Network

We present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GELCA, our system assimilates non-smoothed CO2 data from observational data products such as the Observation Package (ObsPack) data products as constraints on surface fluxes. We conducted sensitivity tests to examine the impact of the site selections and the prior uncertainty settings of observation on the inversion results. For these sensitivity tests, we made five different sitedata selections from the ObsPack product. In all cases, the time series of the global net CO2 flux to the atmosphere stayed close to values calculated from the growth rate of the observed global mean atmospheric CO2 mixing ratio. At regional scales, estimated seasonal CO2 fluxes were altered, depending on the CO2 data selected for assimilation. Uncertainty reductions (URs) were determined at the regional scale and compared among cases. As measures of the model-data mismatch, we used the model-data bias, root-mean-square error, and the linear correlation. For most observation sites, the model-data mismatch was reasonably small. Regarding regional flux estimates, tropical Asia was one of the regions that showed a significant impact from the observation network settings. We found that the surface fluxes in tropical Asia were the most sensitive to the use of aircraft measurements over the Pacific, and the seasonal cycle agreed better with the results of bottom-up studies when the aircraft measurements were assimilated. These results confirm the importance of these aircraft observations, especially for constraining surface fluxes in the tropics