Christopher Potter

Terrestrial ecosystem production: A process model based on global satellite and surface data

This paper presents a modeling approach aimed at seasonal resolution of global climatic and edaphic controls on patterns of terrestrial ecosystem production and soil microbial respiration. We use satellite imagery (Advanced Very High Resolution Radiometer and International Satellite Cloud Climatology Project solar radiation), along with historical climate (monthly temperature and precipitation) and soil attributes (texture, C and N contents) from global (1°) data sets as model inputs. The Carnegie-Ames-Stanford approach (CASA) Biosphere model runs on a monthly time interval to simulate seasonal patterns in net plant carbon fixation, biomass and nutrient allocation, litterfall, soil nitrogen mineralization, and microbial CO2 production. The model estimate of global terrestrial net primary production is 48 Pg C yr−1 with a maximum light use efficiency of 0.39 g C MJ−1PAR. Over 70% of terrestrial net production takes place between 30°N and 30°S latitude. Steady state pools of standing litter represent global storage of around 174 Pg C (94 and 80 Pg C in nonwoody and woody pools, respectively), whereas the pool of soil C in the top 0.3 m that is turning over on decadal time scales comprises 300 Pg C. Seasonal variations in atmospheric CO2 concentrations from three stations in the Geophysical Monitoring for Climate Change Flask Sampling Network correlate significantly with estimated net ecosystem production values averaged over 50°–80° N, 10°–30° N, and 0°–10° N.

Large-scale impoverishment of Amazonian forests by logging and fire

Amazonian deforestation rates are used to determine human effects on the global carbon cycle and to measure Brazils progress in curbing forest impoverishment,,. But this widely used measure of tropical land use tells only part of the story. Here we present field surveys of wood mills and forest burning across Brazilian Amazonia which show that logging crews severely damage 10,000 to 15,000 km2 yr−1 of forest that are not included in deforestation mapping programmes. Moreover, we find that surface fires burn additional large areas of standing forest, the destruction of which is normally not documented. Forest impoverishment due to such fires may increase dramatically when severe droughts provoke forest leaf-shedding and greater flammability; our regional water-balance model indicates that an estimated 270,000 km2 of forest became vulnerable to fire in the 1998 dry season. Overall, we find that present estimates of annual deforestation for Brazilian Amazonia capture less than half of the forest area that is impoverished each year, and even less during years of severe drought. Both logging and fire increase forest vulnerability to future burning, and release forest carbon stocks to the atmosphere, potentially doubling net carbon emissions from regional land-use during severe El Niño episodes. If this forest impoverishment is to be controlled, then logging activities need to be restricted or replaced with low-impact timber harvest techniques, and more effective strategies to prevent accidental forest fires need to be implemented.

Global patterns of carbon dioxide emissions from soils

Semimechanistic, empirically based statistical models were used to predict the spatial (0.5-degree grid cells) and temporal (monthly and annual) patterns of global carbon emissions form terrestrial soils. Emissions include the respiration of both soil organisms and plant roots. Geographically referenced data of mean monthly air temperature and precipitation, soil organic carbon and nitrogen content, soil type and natural vegetation type were used in the model development. It was found that, at the global scale, the rates of soil CO{sub 2} efflux correlate significantly with temperature and precipitation, have a pronounced seasonal pattern in most locations, and contribute to observed wintertime increases in atmospheric CO{sub 2}.

Simulating the Impacts of Disturbances on Forest Carbon Cycling in North America: Processes, Data, Models, and Challenges

[1] Forest disturbances greatly alter the carbon cycle at various spatial and temporal scales. It is critical to understand disturbance regimes and their impacts to better quantify regional and global carbon dynamics. This review of the status and major challenges in representing the impacts of disturbances in modeling the carbon dynamics across North America revealed some major advances and challenges. First, significant advances have been made in representation, scaling, and characterization of disturbances that should be included in regional modeling efforts. Second, there is a need to develop effective and comprehensive process‐based procedures and algorithms to quantify the immediate and long‐term impacts of disturbances on ecosystem succession, soils, microclimate, and cycles of carbon, water, and nutrients. Third, our capability to simulate the occurrences and severity of disturbances is very limited. Fourth, scaling issues have rarely been addressed in continental scale model applications. It is not fully understood which finer scale processes and properties need to be scaled to coarser spatial and temporal scales. Fifth, there are inadequate databases on disturbances at the continental scale to support the quantification of their effects on the carbon balance in North America. Finally, procedures are needed to quantify the uncertainty of model inputs, model parameters, and model structures, and thus to estimate their impacts on overall model uncertainty. Working together, the scientific community interested in disturbance and its impacts can identify the most uncertain issues surrounding the role of disturbance in the North American carbon budget and develop working hypotheses to reduce the uncertainty.

Process modeling of controls on nitrogen trace gas emissions from soils worldwide

We report on an ecosystem modeling approach that integrates global satellite, climate, vegetation, and soil data sets to (1) examine conceptual controls on nitrogen trace gas (NO, N2O, and N2) emissions from soils and (2) identify weaknesses in our bases of knowledge and data for these fluxes. Nitrous and nitric oxide emissions from well-drained soils were estimated by using an expanded version of the Carnegie-Ames-Stanford (CASA) Biosphere model, a coupled ecosystem production and soil carbon-nitrogen model on a 1° global grid. We estimate monthly production of NO, N2O, and N2 based on predicted rates of gross N mineralization, together with an index of transient water-filled pore space in soils. Analyses of model performance along selected climate gradients support the hypothesis that low temperature restricts predicted N mineralization and trace gas emission rates in moist northern temperate and boreal forest ecosystems, whereas in tropical zones, seasonal patterns in N mineralization result in emission peaks for N2O that coincide with wetting and high soil moisture content. The model predicts the annual N2O:NO flux ratio at a mean value of 1.2 in wet tropical forests, decreasing to around 0.6 in the seasonally dry savannas. Global emission estimates at the soil surface are 6.1 Tg N and 9.7 Tg N yr−1 for N2O and NO, respectively. Tropical dry forests and savannas are identified by using this formulation as important source areas for nitrogen trace gas emissions. Because humans continue to alter these ecosystems extensively for agricultural uses, our results suggest that more study is needed in seasonally dry ecosystems of the tropics in order to understand the global impacts of land use change on soil sources for N2O and NO.

Interannual Variability in Terrestrial Net Primary Production: Exploration of Trends and Controls on Regional to Global Scales

ABSTRACT Climate and biophysical regulation of terrestrial plant production and interannual responses to anomalous events were investigated using the NASA Ames model version of CASA (Carnegie–Ames–Stanford Approach) in a transient simulation mode. This ecosystem model has been calibrated for simulations driven by satellite vegetation index data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. Relatively large net source fluxes of carbon were estimated from terrestrial vegetation about 6 months to 1 year following El Niño events of 1983 and 1987, whereas the years 1984 and 1988 showed a drop in net primary production (NPP) of 1–2 Pg (1015 g) C from their respective previous years. Zonal discrimination of model results implies that the northern hemisphere low latitudes could account for almost the entire 2 Pg C decrease in global terrestrial NPP predicted from 1983 to 1984. Model estimates further suggest that from 1985 to 1988, the northern middle-latitude zone (between 30° and 60°N) was the principal region driving progressive increases in NPP, mainly by an expanded growing season moving toward the zonal latitude extremes. Comparative regional analysis of model controls on NPP reveals that although Normalized Difference Vegetation Index “greenness” can alone account for 30%–90% of the variation in NPP interannual anomalies, temperature or radiation loading can have a fairly significant 1-year lag effect on annual NPP at middle- to high-latitude zones, whereas rainfall amount and temperature drying effects may carry over with at least a 2-year lag time to influence NPP in semiarid tropical zones.

General CH4 oxidation model and comparisons of CH4 Oxidation in natural and managed systems

Fluxes of methane from field observations of native and cropped grassland soils in Colorado and Nebraska were used to model CH 4 oxidation as a function of soil water content, temperature, porosity, and field capacity (FC). A beta function is used to characterize the effect of soil water on the physical limitation of gas diffusivity when water is high and biological limitation when water is low. Optimum soil volumetric water content (W opt ) increases with PC. The site specific maximum CH 4 oxidation rate (CH 4max ) varies directly with soil gas diffusivity (D opt ) as a function of soil bulk density and FC. Although soil water content and physical properties are the primary controls on CH 4 uptake, the potential for soil temperature to affect CH 4 uptake rates increases as soils become less limited by gas diffusivity, Daily CH 4 oxidation rate is calculated as the product of CH 4max , the normalized (0-100%) beta function to account for water effects, a temperature multiplier, and an adjustment factor to account for the effects of agriculture on methane flux. The model developed with grassland soils also worked well in coniferous and tropical forest soils. However, soil gas diffusivity as a function of field capacity, and bulk density did not reliably predict maximum CH 4 oxidation rates in deciduous forest soils, so a submodel for these systems was developed assuming that CH 4max is a function of mineral soil bulk density. The overall model performed well with the data used for model development (r 2 = 0.76) and with independent data from grasslands, cultivated lands, and coniferous, deciduous, and tropical forests (r 2 = 0.73, mean error < 6%).

Mapping the land surface for global atmosphere-biosphere models: Toward continuous distributions of vegetation's functional properties

Global land surface characteristics are important boundary conditions for global models that describe exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere. Existing data sets of global land cover are based on classification schemes that characterize each grid cell as a discrete vegetation type. Consequently, parameter fields derived from these data sets are dependent on the particular scheme and the number of vegetation types it includes. The functional controls on exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere are now well enough understood that it is increasingly feasible to model these exchanges using a small number of vegetation characteristics that either are related to or closely related to the functional controls. Ideally, these characteristics would be mapped as continuous distributions to capture mixtures and gradients in vegetation within the cell size of the model. While such an approach makes it more difficult to build models from detailed observations at a small number of sites, it increases the potential for capturing functionally important variation within, as well as between, vegetation types. Globally, the vegetation characteristics that appear to be most important in controlling fluxes of water, energy, and carbon dioxide include (1) growth form (tree, shrub, herb), (2) seasonality of woody vegetation (deciduous, evergreen), (3) leaf type (broadleaf, coniferous), (4) photosynthetic pathway of nonwoody vegetation (C3, C4), (5) longevity (annual, perennial), and (6) type and intensity of disturbance (e.g., cultivation, fire history). Many of these characteristics can be obtained through remote sensing, though some require ground-based information. The minimum number and the identity of the required land surface characteristics almost certainly vary with the intended objective, but the philosophy of driving models with continuous distributions of a small number of land surface characteristics is likely to be applicable to a broad range of problems.

Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models: Intermodel comparisons and relationships to field measurements

Nine ecosystem process models were used to predict CO2 and water vapor exchanges by a 150-year-old black spruce forest in central Canada during 1994–1996 to evaluate and improve the models. Three models had hourly time steps, five had daily time steps, and one had monthly time steps. Model input included site ecosystem characteristics and meteorology. Model predictions were compared to eddy covariance (EC) measurements of whole-ecosystem CO2 exchange and evapotranspiration, to chamber measurements of nighttime moss-surface CO2 release, and to ground-based estimates of annual gross primary production, net primary production, net ecosystem production (NEP), plant respiration, and decomposition. Model-model differences were apparent for all variables. Model-measurement agreement was good in some cases but poor in others. Modeled annual NEP ranged from −11 g C m−2 (weak CO2 source) to 85 g C m−2 (moderate CO2 sink). The models generally predicted greater annual CO2 sink activity than measured by EC, a discrepancy consistent with the fact that model parameterizations represented the more productive fraction of the EC tower “footprint.” At hourly to monthly timescales, predictions bracketed EC measurements so median predictions were similar to measurements, but there were quantitatively important model-measurement discrepancies found for all models at subannual timescales. For these models and input data, hourly time steps (and greater complexity) compared to daily time steps tended to improve model-measurement agreement for daily scale CO2 exchange and evapotranspiration (as judged by root-mean-squared error). Model time step and complexity played only small roles in monthly to annual predictions.

Discovery of climate indices using clustering

To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earths oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitiations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.