Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

Abstract

Abstract. Global terrestrial atmosphere-ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

The Influence of Near-field Fluxes on Seasonal Carbon Dioxide Enhancements: Results From the Indianapolis Flux Experiment (INFLUX)

“…Non-paved portions of the city were defined as deciduous broadleaf forest. Distributions for four land cover types (corn, soy, grassland/pasture, and forest) were derived from the United States Department of Agriculture National Agriculture Statistics Service (NASS; https://nassgeodata.gmu.edu/CropScape/, Han et al 2014), and the VPRM parameters were optimized for these land cover types (Hilton et al 2014) to produce hourly carbon fluxes at 1-km resolution as the weighted average of carbon fluxes from each type. Note that Tower 14 is outside the domain of the VPRM results and is thus not included in the model-data mismatch analysis.…”

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

“…Smaller sharpness values indicate better prediction precision. Reliability is measured using predictive coverage (e.g., Hoeting et al, 1999), which is the percentage of data contained in the prediction interval. Larger predictive coverage values are preferred.…”

Mitigation of Greenhouse Gases Emissions by Management of Terrestrial Ecosystem

“…Table 2 CO2 emissions caused by changes in land use, mainly including deforestation and drying of marshes [8] Years 1960-1969 1970-1979 1980-1989 1990-1999 As it turns out, the natural fluxes of CO 2 are much bigger. Terrestrial systems absorb 123 ±8 Gt C/year, converting it to biomass [10,11] and emit 119 ±1 Gt C/year (Table 3). Table 3 Net CO2 absorption by terrestrial ecosystems [8] Years 1960-1969 1970-1979 1980-1989 1990-1999 The observed increase of CO 2 absorption by terrestrial ecosystems is caused by the fertilising effect of rising atmospheric concentration of CO 2 on plant growth, as well as the fertilising effect of nitrogen compounds, mostly nitrogen oxides emitted from industrial plants.…”