NPP Model Research Articles

Terrestrial net primary production estimates for 0.5° grid cells from field observations—a contribution to global biogeochemical modeling

AbstractNet Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. While easier than some other steps to measure, direct measurement of NPP is tedious and not practical for large areas and so models are generally used to study the carbon cycle at a global scale. Nevertheless these models require field measurements of NPP for parameterization, calibration and validation. Most NPP data are for relatively small field plots that cannot represent the 0.5° × 0.5° grid cells that are commonly used in global scale models. Furthermore, technical difficulties generally restrict NPP measurements to aboveground parts and sometimes do not even include all components of aboveground NPP. Thus direct inter‐comparison between field data obtained in different studies or comparison of these results with coarse resolution model outputs can be misleading. We summarize and present a series of methods that were used by original authors to estimate NPP and how and what we have done to prepare a consistent data set of NPP for 0.5 °grid cells for a range of biomes from these studies. The methods used for estimation of NPP include: (i) aggregation of fine‐scale (plot or stand‐level) vegetation inventory data to larger grid cells, (ii) mapping of grid cells and area weighting of field NPP observations in each mapped class, (iii) direct correlation of extensive data sets of ground measurements with remotely sensed spectral vegetation indices, (iv) local modeling of NPP using key independent variables, for which maps are available at the scale of the grid cell, and (v) regression analysis to link productivity with controlling environmental variables. For a few grid cells whose NPP were obtained for multiple years, temporal analysis was conducted. The grid cells are grouped to the biome level and are compared with existing compilations of field NPP and the results of the Miami potential NPP model. Mean NPP was similar to the well‐known compilation of Whittaker and Likens, except for temperate evergreen needle‐leaved forest, woodland, and shrubland. The grid cell datasets are a contribution to the International Geosphere‐Biosphere Programme (IGBP) Data and Information System (DIS) Global Primary Production Data Initiative (GPPDI). The full dataset currently contains 3654 cells (including replicate measurements) developed from 15 studies representing NPP in croplands, sparse vegetation, shrub lands, grasslands, and forests worldwide. An edited subset consists of 2335 cells in which outliers were removed and all replicate measurements were averaged for each unique geographical location. Most of the data incorporated into GPPDI were wholly or partly developed by participants in the GPPDI, in addition to the present authors. These studies are gathered together here to provide a consistent account of the grid cell component of GPPDI and an analysis of the entire data set. The datasets have been deposited in an IGBP‐DIS GPPDI database (http://daacl.esd.ornl.gov/npq/GPPDI/Combined_GPPDI_des.html).

Impulse response functions of terrestrial carbon cycle models: method and application

AbstractTo provide a common currency for model comparison, validation and manipulation, we suggest and describe the use of impulse response functions, a concept well‐developed in other fields, but only partially developed for use in terrestrial carbon cycle modelling. In this paper, we describe the derivation of impulse response functions, and then examine (i) the dynamics of a simple five‐box biosphere carbon model; (ii) the dynamics of the CASA biosphere model, a spatially explicit NPP and soil carbon biogeochemistry model; and (iii) various diagnostics of the two models, including the latitudinal distribution of mean age, mean residence time and turnover time. We also (i) deconvolve the past history of terrestrial NPP from an estimate of past carbon sequestration using a derived impulse response function to test the performance of impulse response functions during periods of historical climate change; (ii) convolve impulse response functions from both the simple five‐box model and the CASA model against a historical record of atmospheric δ13C to estimate the size of the terrestrial 13C isotopic disequilibrium; and (iii) convolve the same impulse response functions against a historical record of atmospheric 14C to estimate the 14C content and isotopic disequilibrium of the terrestrial biosphere at the 1° × 1° scale. Given their utility in model comparison, and the fact that they facilitate a number of numerical calculations that are difficult to perform with the complex carbon turnover models from which they are derived, we strongly urge the inclusion of impulse response functions as a diagnostic of the perturbation response of terrestrial carbon cycle models.

Contemporary variations of terrestrial net primary production: The use of satellite data in the light of an extremal principle

Combining ecological models and satellite data is likely to be the most feasible approach for resolving a current problem of carbon cycle modelling: evaluation of global NPP response to changes in climate and atmospheric CO 2. The main difficulties encountered in implementing this approach are as follows. First, the dimensionality (the number of the parameters required to specify) of the comprehensive models of NPP is greater than the dimensionality (the number of independent measurements) of the available satellite data. Second, remotely sensed (macroscale) variables and modelled (microscale) variables are not equivalent in an ecological sense. In this paper we propose two means of coping with the difficulties: to introduce some ecological extremal principles for lowering the dimensionality of the NPP models, and to use satellite data for evaluating biogeographical (e.g., vegetation period) rather than biophysical (e.g., leaf area index) parameters of the NPP models.