The TransCom 3 experiment was begun to explore the estimation of carbon sources and sinks via the inversion of simulated tracer transport. We build upon previous TransCom work by presenting the ...seasonal inverse results which provide estimates of carbon flux for 11 land and 11 ocean regions using 12 atmospheric transport models. The monthly fluxes represent the mean seasonal cycle for the 1992 to 1996 time period. The spread among the model results is larger than the average of their estimated flux uncertainty in the northern extratropics and vice versa in the tropical regions. In the northern land regions, the model spread is largest during the growing season. Compared to a seasonally balanced biosphere prior flux generated by the CASA model, we find significant changes to the carbon exchange in the European region with greater growing season net uptake which persists into the fall months. Both Boreal North America and Boreal Asia show lessened net uptake at the onset of the growing season with Boreal Asia also exhibiting greater peak growing season net uptake. Temperate Asia shows a dramatic springward shift in the peak timing of growing season net uptake relative to the neutral CASA flux while Temperate North America exhibits a broad flattening of the seasonal cycle. In most of the ocean regions, the inverse fluxes exhibit much greater seasonality than that implied by the ΔpCO2 derived fluxes though this may be due, in part, to misallocation of adjacent land flux. In the Southern Ocean, the austral spring and fall exhibits much less carbon uptake than implied by ΔpCO2 derived fluxes. Sensitivity testing indicates that the inverse estimates are not overly influenced by the prior flux choices. Considerable agreement exists between the model mean, annual mean results of this study and that of the previously published TransCom annual mean inversion. The differences that do exist are in poorly constrained regions and tend to exhibit compensatory fluxes in order to match the global mass constraint. The differences between the estimated fluxes and the prior model over the northern land regions could be due to the prior model respiration response to temperature. Significant phase differences, such as that in the Temperate Asia region, may be due to the limited observations for that region. Finally, differences in the boreal land regions between the prior model and the estimated fluxes may be a reflection of the timing of spring thaw and an imbalance in respiration versus photosynthesis.
This paper explores the use of Monte Carlo carbon cycle data assimilation within the generalized likelihood uncertainty estimation (GLUE) framework to evaluate the sensitivities of a well-known ...complex land surface model (SiB v2.5) to its parameterization and the predictive uncertainty of simulated fluxes on a monthly basis, and for an entire year, at the WLEF tall-tower site in Park Falls, Wisconsin. An analysis is described wherein randomly generated parameter sets were ranked based on their capacity to simulate fluxes of latent (LE) and sensible heat (H) and the net ecosystem exchange of carbon (NEE) for each month of the year 1997 and for the entire year. Two criteria were used to evaluate the success of the simulations; the first evaluated the ability of SiB2.5 to simulate LE and H, the second included NEE as an additional constraint. The best-performing parameter sets for each criterion were used to assess model sensitivity to parameters, to calculate uncertainty bounds for predicted LE, H and NEE and to assess the information content of eddy covariance data on the analyzed time scales. Patterns in the sensitivity of the model to its parameterization and the uncertainty of the predictions were related to the physiological and phenological characteristics of the ecosystem, model structure and the relationship between deterministic models and comparatively stochastic measurements. The results show that model sensitivity varies through time for a larger set of parameters than those typically considered time varying in LSMs, thus optimization of model parameters on tower flux data should allow for variability at sub-annual time scales in order to capture the most information and best simulate fluxes. Further, constraining predictions annually versus monthly showed that some quantities (e.g. nighttime NEE) were on average better constrained annually, whereas other quantities that show more variability with vegetation phenology and structure (e.g. daytime NEE and LE) were better constrained monthly. The addition of the net ecosystem exchange of carbon in the data assimilation scheme improved model results by (a) constraining model parameterization (optimal parameter values), particularly during times of the year when the land surface was rapidly changing (spring and fall), and increasing the number of influential parameters, and (b) decreasing the uncertainty in NEE simulations (but not appreciably reducing uncertainty in LE and H simulations). We also found that there was an irreducible level of mismatch between the simulated and observed fluxes that could not be overcome through optimization due to variability in the observations and/or structural problems with the model. The uncertainty estimates can be used to characterize uncertainty in the simulations at multiple time scales.
Atmospheric mixing ratios of CO2 are strongly seasonal in the Arctic due to mid‐latitude transport. Here we analyze the seasonal influence of moist synoptic storms by diagnosing CO2 transport from a ...global model on moist isentropes (to represent parcel trajectories through stormtracks) and parsing transport into eddy and mean components. During winter when northern plants respire, warm moist air, high in CO2, is swept poleward into the polar vortex, while cold dry air, low in CO2, that had been transported into the polar vortex earlier in the year is swept equatorward. Eddies reduce seasonality in mid‐latitudes by ∼50% of NEE (∼100% of fossil fuel) while amplifying seasonality at high latitudes. Transport along stormtracks is correlated with rising, moist, cloudy air, which systematically hides this CO2 transport from satellites. We recommend that (1) regional inversions carefully account for meridional transport and (2) inversion models represent moist and frontal processes with high fidelity.
Key Points
Transport by baroclinic waves amplifies seasonality at northern polar latitudes
Moist processes embedded in stormtracks enhance meridional transport
Moist processes are unobservable by satellites and poorly resolved in models
Serious fungal diseases in the Republic of Uzbekistan Tilavberdiev, S. A.; Denning, D. W.; Klimko, N. N.
European journal of clinical microbiology & infectious diseases,
06/2017, Volume:
36, Issue:
6
Journal Article
Peer reviewed
Open access
We have undertaken the first and preliminary estimation of severe and chronic mycotic diseases in the Republic of Uzbekistan, using a model proposed by LIFE (Leading International Fungal Education). ...Calculation was carried out based on data from 2014. Published results describing mycoses in Uzbekistan were identified. In the absence of published or official data, information about the frequency of mycoses from scientific literature elsewhere in groups at risk of development of fungal infections were taken into account. We also utilized methodology used in analogous estimations of mycoses in the Russian Federation. We estimate that of the 30.8 million population, 536,978 people (1.8% of the population) were affected by severe and chronic mycotic diseases. In 2014, there were 12,351 cases of acute invasive fungal diseases and 524,627 cases of chronic fungal diseases, including 1,941 cases of chronic pulmonary aspergillosis. The most frequent problems were recurrent vulvovaginal candidiasis (513,600 cases), trichophytosis of the scalp (6,414), and relapsed oral candidiasis (4,950). Results of the investigation indicate a significant prevalence of mycoses in the Republic of Uzbekistan.
Surface ecophysiology at five sites in tropical South America across vegetation and moisture gradients is investigated. From the moist northwest (Manaus) to the relatively dry southeast (Pé de ...Gigante, state of São Paulo) simulated seasonal cycles of latent and sensible heat, and carbon flux produced with the Simple Biosphere Model (SiB3) are confronted with observational data. In the northwest, abundant moisture is available, suggesting that the ecosystem is light-limited. In these wettest regions, Bowen ratio is consistently low, with little or no annual cycle. Carbon flux shows little or no annual cycle as well; efflux and uptake are determined by high-frequency variability in light and moisture availability. Moving downgradient in annual precipitation amount, dry season length is more clearly defined. In these regions, a dry season sink of carbon is observed and simulated. This sink is the result of the combination of increased photosynthetic production due to higher light levels, and decreased respiratory efflux due to soil drying. The differential response time of photosynthetic and respiratory processes produce observed annual cycles of net carbon flux. In drier regions, moisture and carbon fluxes are in-phase; there is carbon uptake during seasonal rains and efflux during the dry season. At the driest site, there is also a large annual cycle in latent and sensible heat flux.
Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a ...high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from the RAMS and the Parameterized Chemical Transport Model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO2. RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America. We perform the inversion with two independently derived boundary inflow conditions and calculate jackknife-based statistics to test the robustness of the model results. We then compare final results to estimates obtained from the CarbonTracker inversion system and at the Southern Great Plains flux site. Results are promising, showing the ability to correct carbon fluxes from the biosphere models over annual and seasonal time scales, as well as over the different GPP and ER components. Additionally, the correlation of an estimated sink of carbon in the South Central United States with regional anomalously high precipitation in an area of managed agricultural and forest lands provides interesting hypotheses for future work.
THE COMMON LAND MODEL Dai, Yongjiu; Zeng, Xubin; Dickinson, Robert E. ...
Bulletin of the American Meteorological Society,
08/2003, Volume:
84, Issue:
8
Journal Article
Peer reviewed
Open access
The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further ...development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photosynthesis–conductance model that describes the simultaneous transfer of CO₂ and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models the Biosphere–Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94).
Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem ...exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO₂ seasonal uptake over agricultural regions.
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