The lifetime of excess atmospheric carbon dioxide

We explore the effects of a changing terrestrial biosphere on the atmospheric residence time of CO2 using three simple ocean carbon cycle models and a model of global terrestrial carbon cycling. We find differences in model behavior associated with the assumption of an active terrestrial biosphere (forest regrowth) and significant differences if we assume a donor-dependent flux from the atmosphere to the terrestrial component (e.g., a hypothetical terrestrial fertilization flux). To avoid numerical difficulties associated with treating the atmospheric CO2 decay (relaxation) curve as being well approximated by a weighted sum of exponential functions, we define the single half-life as the time it takes for a model atmosphere to relax from its present-day value half way to its equilibrium pCO2 value. This scenario-based approach also avoids the use of unit pulse (Dirac Delta) functions which can prove troublesome or unrealistic in the context of a terrestrial fertilization assumption. We also discuss some of the numerical problems associated with a conventional lifetime calculation which is based on an exponential model. We connect our analysis of the residence time of CO2 and the concept of single half-life to the residence time calculations which are based on using weighted sums of exponentials. We note that the single half-life concept focuses upon the early decline of CO2under a cutoff/decay scenario. If one assumes a terrestrial biosphere with a fertilization flux, then our best estimate is that the single half-life for excess CO2 lies within the range of 19 to 49 years, with a reasonable average being 31 years. If we assume only regrowth, then the average value for the single half-life for excess CO2 increases to 72 years, and if we remove the terrestrial component completely, then it increases further to 92 years

Continental scale variability in ecosystem processes: Models, data, and the role of disturbance

Management of ecosystems at large regional or continental scales and determination of the vulnerability of ecosystems to large-scale changes in climate or atmospheric chemistry require understanding how ecosystem processes are governed at large spatial scales. A collaborative project, the Vegetation and Ecosystem Modeling and Analysis Project (VEMAP), addressed modeling of multiple resource limitation at the scale of the conterminous United States, and the responses of ecosystems to environmental change. In this paper, we evaluate the model-generated patterns of spatial variability within and between ecosystems using Century, TEM, and Biome-BGC, and the relationships between modeled water balance, nutrients, and carbon dynamics. We present evaluations of models against mapped and site-specific data. In this analysis, we compare model-generated patterns of variability in net primary productivity (NPP) and soil organic carbon (SOC) to, respectively, a satellite proxy and mapped SOC from the VEMAP soils database (derived from USDA-NRCS [Natural Resources Conservation Service] information) and also compare modeled results to site-specific data from forests and grasslands. The VEMAP models simulated spatial variability in ecosystem processes in substantially different ways, reflecting the models’ differing implementations of multiple resource limitation of NPP. The models had substantially higher correlations across vegetation types compared to within vegetation types. All three models showed correlation among water use, nitrogen availability, and primary production, indicating that water and nutrient limitations of NPP were equilibrated with each other at steady state. This model result may explain a number of seemingly contradictory observations and provides a series of testable predictions. The VEMAP ecosystem models were implicitly or explicitly sensitive to disturbance in their simulation of NPP and carbon storage. Knowledge of the effects of disturbance (human and natural) and spatial data describing disturbance regimes are needed for spatial modeling of ecosystems. Improved consideration of disturbance is a key ‘‘next step’’ for spatial ecosystem models

A diagnostic study of temperature controls on global terrestrial carbon exchange

The observed interannual variability of atmospheric CO2 reflects short-term variability in sources and sinks of CO2 . Analyses using 13CO2 and O2 suggest that much of the observed interannual variability is due to changes in terrestrial CO2 exchange. First principles, empirical correlations and process models suggest a link between climate variation and net ecosystem exchange, but the scaling of ecological process studies to the globe is notoriously difficult. We sought to identify a component of global CO2 exchange that varied coherently with land temperature anomalies using an inverse modeling approach. We developed a family of simplified spatially aggregated ecosystem models (designated K-model versions) consisting of five compartments: atmospheric CO2 , live vegetation, litter, and two soil pools that differ in turnover times. The pools represent cumulative differences from mean C storage due to temperature variability and can thus have positive or negative values. Uptake and respiration of CO2 are assumed to be linearly dependent on temperature. One model version includes a simple representation of the nitrogen cycle in which changes in the litter and soil carbon pools result in stoichiometric release of plant-available nitrogen, the other omits the nitrogen feedback. The model parameters were estimated by inversion of the model against global temperature and CO2 anomaly data using the variational method. We found that the temperature sensitivity of carbon uptake (NPP) was less than that of respiration in all model versions. Analyses of model and data also showed that temperature anomalies trigger ecosystem changes on multiple, lagged time-scales. Other recent studies have suggested a more active land biosphere at Northern latitudes in response to warming and longer growing seasons. Our results indicate that warming should increase NPP, consistent with this theory, but that respiration should increase more than NPP, leading to decreased or negative NEP. A warming trend could, therefore increase NEP if the indirect feedbacks through nutrients were larger than the direct effects of temperature on NPP and respiration, a conjecture which can be tested experimentally. The fraction of the growth rate not predicted by the K-model represents model and data errors, and variability in anthropogenic release, ocean uptake, and other processes not explicitly represented in the model. These large positive and negative residuals from the K-model may be associated with the Southern Oscillation Index

Equilibration of the terrestrial water, nitrogen, and carbon cycles

Recent advances in biologically based ecosystem models of the coupled terrestrial, hydrological, carbon, and nutrient cycles have provided new perspectives on the terrestrial biosphere’s behavior globally, over a range of time scales. We used the terrestrial ecosystem model Century to examine relationships between carbon, nitrogen, and water dynamics. The model, run to a quasi-steady-state, shows strong correlations between carbon, water, and nitrogen fluxes that lead to equilibration of wateryenergy and nitrogen limitation of net primary productivity. This occurs because as the water flux increases, the potentials for carbon uptake (photosynthesis), and inputs and losses of nitrogen, all increase. As the flux of carbon increases, the amount of nitrogen that can be captured into organic matter and then recycled also increases. Because most plant-available nitrogen is derived from internal recycling, this latter process is critical to sustaining high productivity in environments where water and energy are plentiful. At steady-state, wateryenergy and nitrogen limitation ‘‘equilibrate,’’ but because the water, carbon, and nitrogen cycles have different response times, inclusion of nitrogen cycling into ecosystem models adds behavior at longer time scales than in purely biophysical models. The tight correlations among nitrogen fluxes with evapotranspiration implies that either climate change or changes to nitrogen inputs (from fertilization or air pollution) will have large and long-lived effects on both productivity and nitrogen losses through hydrological and trace gas pathways. Comprehensive analyses of the role of ecosystems in the carbon cycle must consider mechanisms that arise from the interaction of the hydrological, carbon, and nutrient cycles in ecosystems

Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology in the eastern U.S.

We performed a diagnostic analysis of AVHRR-NDVI and gridded, temperature data for the deciduous forests of the eastern U.S., calibrating temperature accumulation model with satellite data for 1982–1993. The model predicts interannual variability in onset date based upon year-to-year changes in springtime temperature. RMS error over the period ranges from 6.9 days in the northern portion of the domain to 10.7 days in the south. The analysis revealed a relationship between temperature accumulation and satellite derived onset date (rank correlation = 0.31–0.62). The required temperature accumulation threshold can be expressed as a function of mean temperature (R2 of 0.90) to facilitate predictive analysis of phenological onset, or when remote sensing data are unavailable

Nitrogen deposition onto the United States and Western Europe: A synthesis of observations and models.

The documented acceleration of NH3 and NOx (NO + NO2) emissions over the last 150 years has accelerated N deposition, compromising air and water quality and altering the functioning of terrestrial and aquatic ecosystems worldwide. To construct continental-scale N budgets, we produced maps of N deposition fluxes from site-network observations for the United States and Western Europe. Increases in the rates of N cycling for these two regions of the world are large, and they have undergone profound modification of biospheric–atmospheric N exchanges, and ecosystem function. The maps are necessarily restricted to the network measured quantities and consist of statistically interpolated fields of aqueous NO3− and NH4+, gaseous HNO3 and NO2 (in Europe), and particulate NO3− and NH4+. There remain a number of gaps in the budgets, including organic N and NH3 deposition. The interpolated spatially continuous fields allow estimation of regionally integrated budget terms. Dry-deposition fluxes were the most problematic because of low station density and uncertainties associated with exchange mechanisms. We estimated dry N deposition fluxes by multiplying interpolated surface-air concentrations for each chemical species by model-calculated, spatially explicit deposition velocities. Deposition of the oxidized N species, by-products of fossil-fuel combustion, dominate the U.S. N deposition budget with 2.5 Tg of NOy-N out of a total of 3.7–4.5 Tg of N deposited annually onto the conterminous United States. Deposition of the reduced species, which are by-products of farming and animal husbandry, dominate the Western European N-deposition budget with a total of 4.3–6.3 Tg N deposited each year out of a total of 8.4–10.8 Tg N. Western Europe receives five times more N in precipitation than does the conterminous United States. Estimated N emissions exceed measured deposition in the United States by 5.3– 7.81 Tg N, suggesting significant N export or under-sampling of urban influence. In Europe, estimated emissions better balance measured deposition, with an imbalance of between −0.63 and 2.88 Tg N, suggesting that much of the N emitted in Europe is deposited there, with possible N import from the United States. The sampling network in Europe includes urban influences because of the greater population density of Western Europe. Our analysis of N deposition for both regions was limited by sampling density. The framework we present for quantification of patterns of N deposition provides a constraint on our understanding of continental biospheric–atmospheric N cycles. These spatially explicit wet and dry N fluxes also provide a tool for verifying regional and global models of atmospheric chemistry and transport, and they represent critical inputs into terrestrial models of biogeochemistry

Trends in wintertime climate in the northeastern United States: 1965–2005

Humans experience climate variability and climate change primarily through changes in weather at local and regional scales. One of the most effective means to track these changes is through detailed analysis of meteorological data. In this work, monthly and seasonal trends in recent winter climate of the northeastern United States (NE-US) are documented. Snow cover and snowfall are important components of the region\u27s hydrological systems, ecosystems, infrastructure, travel safety, and winter tourism and recreation. Temperature, snowfall, and snow depth data were collected from the merged United States Historical Climate Network (USHCN) and National Climatic Data Center Cooperative Network (COOP) data set for the months of December through March, 1965–2005. Monthly and seasonal time series of snow-covered days (snow depth \u3e2.54 cm) are constructed from daily snow depth data. Spatial coherence analysis is used to address data quality issues with daily snowfall and snow depth data, and to remove stations with nonclimatic influences from the regional analysis. Monthly and seasonal trends in mean, minimum, and maximum temperature, total snowfall, and snow-covered days are evaluated over the period 1965–2005, a period during which global temperature records and regional indicators exhibit a shift to warmer climate conditions. NE-US regional winter mean, minimum, and maximum temperatures are all increasing at a rate ranging from 0.42° to 0.46°C/decade with the greatest warming in all three variables occurring in the coldest months of winter (January and February). The regional average reduction in number of snow-covered days in winter (−8.9 d/decade) is also greatest during the months of January and February. Further analysis with additional regional climate modeling is required to better investigate the causal link between the increases in temperature and reduction in snow cover during the coldest winter months of January and February. In addition, regionally averaged winter snowfall has decreased by about 4.6 cm/decade, with the greatest decreases in snowfall occurring in December and February. These results have important implications for the impacts of regional climate change on the northeastern United States hydrology, natural ecosystems, and economy

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Near-surface remote sensing of spatial and temporal variation in canopy phenology

There is a need to document how plant phenology is responding to global change factors, particularly warming trends. “Near-surface” remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras (“webcams”) can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for “regions of interest” within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface–atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux

Storm intensity and old-growth forest disturbances in the Amazon region

We analyzed the pattern of large forest disturbances or blow-downs apparently caused by severe storms in a mostly unmanaged portion of the Brazilian Amazon using 27 Landsat images and daily precipitation estimates from NOAA satellite data. For each Landsat a spectral mixture analysis (SMA) was applied. Based on SMA, we detected and mapped 279 patches (from 5 ha to 2,223 ha) characteristic of blow-downs. A total of 21,931 ha of forest were disturbed. We found a strong correlation between occurrence of blow-downs and frequency of heavy rainfall (Spearman\u27s rank, r2 = 0.84, p \u3c 0.0003). The recurrence intervals of large disturbances were estimated to be 90,000 yr for the eastern Amazon and 27,000 yr for the western Amazon. This suggests that weather patterns affect the frequency of large forest disturbances that may produce different rates of forest turnover in the eastern and western Amazon basin