Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.

No abstract

Soil temperature is a necessary component for estimating below-ground processes for continental and global carbon budgets; however, there are an insufficient number of climatic stations monitoring soil temperature. We used a n 11-day running average of daily mean air temperature to estimate daily mean soil temperature at a depth of 10 cm using linear regression. This model was tested using data from 6 climate reg~ons across the United States. Frequency analyses for 17 of 19 data sets showed that the number of days which were w~thin a k3.5OC range centered on the measured sol1 temperature v a r~e d from 77 to 96%. The values of R2 between observed and final predicted soil temperatures ranged from 0.85 to 0.96 with standard errors from 1.5 to 2.9"C for all 19 simulations. Changes of soil temperature under snow cover were smaller than those without snow cover. Soil temperature under vegetation cover was also simulated assuming the rate of soil warming under vegetation cover would be reduced with increasing leaf area index according to the Beer-Lambert Law. Annual soil respiration can be estimated from the predicted soil temperature with reasonable accuracy. Daily soil temperature may be predicted from daily air temperature once regional equations have been established, because weather stations in the United States can be generalized into a few regions and sites wthin each region may use the same equation.

Net 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 a...

The effects of silvicultural treatments (e.g., even-aged management, EAM, and unevenaged, UAM) on 4 microclimatic variables (air temperature, incoming solar radiation, humidity, and soil temperature) were examined in oak forests of southeastern Missouri Ozarks, USA. Nine mobile climatic stations were used to collect field data during the summers of 1995 (pre-harvest), and 1997 and 1998 (post-harvest). Spatial variation of air temperature at 2 m height increased 96 and 35% (2-year average) after harvest in UAM and EAM sites, respectively, as quantified by 95% confidence intervals (CI). UAM increased the variability of air temperature at the lower end of the daily range in the CI more than at the upper end, while EAM had a stronger effect on raising spatial variation at the upper end of the CI than at the lower end. Spatial variation of soil temperature within an 80 × 80 m grid increased significantly during daytime after harvest, especially at the surface, but did not change much during nighttime. EAM resulted in a larger increase of soil temperature variation than did UAM. Greater amplitudes of diurnal soil temperatures (especially at the surface) were observed at depths of 0, 5, and 10 cm and were more evident at the EAM site after harvest. The duration of variation in post-harvest soil surface temperature during daytime was about 3 times longer than pre-harvest at the EAM site. Spatial variation in radiation increased 56 and 128% in UAM and EAM sites after harvest, respectively. Except for radiation, significance levels of differences in means of microclimatic variables were reduced after harvest among the 3 Ecological Land Types (ELTs); the spatial variation of microclimate was smaller among ELTs within the same treatment than between treatments. Our results suggested that, usually, EAM affected the microclimate more than UAM did, especially in raising soil temperatures on northeast slopes (ELT 18 ).