Global Net Primary Productivity (NPP) of ecosystems on land and in the oceans is a crucial component of biogeochemical model development within the IGBP. IGBP/GAIM, in conjunction with GCTE and IGBP-DIS has been investigating the performance of terrestrial ecosystem models which treat global NPP. This began with two model intercomparison workshops in 1994 and 1995 at the Potsdam Institute for Climate Impact Research (PIK). The purpose of the workshops was to support a series of model intercomparisons by the various designing teams around the globe that are currently modeling the terrestrial biosphere at large scales with a focus on NPP. The terrestrial carbon cycle models and models that treat energy and water fluxes must address in a fundamental manner either gross primary productivity (GPP) and/or net primary productivity (NPP).
Model intercomparison is necessary for model improvement as part of an iterative process. Primarily, the model output is evaluated by comparison with observations; also, strengths and weaknesses are identified. Next, models are reformulated or parameters are adjusted with the aim of model improvement. Finally, models are run again and the output is re-evaluated. This iterative process is time-consuming and sometimes inefficient. Coordinated model intercomparison can accelerate model improvement because different models perform with varying degrees of success, and the identification of the causes of model strengths and weaknesses is facilitated.
There were significant differences in the calculation of NPP within the global terrestrial models compared in 1994, and Potsdam '95 was held in order to compare model parameters and outputs.. Based on the results of model comparisons from the workshops, modeling teams returned to their codes to explore details of model performance. This has resulted in new insights regarding the terrestrial carbon cycle and the function of terrestrial ecosystems. On the basis of these new findings, several papers have been organized into a special issue of "Global Change Biology" featuring Net Primary Productivity. This portion of the 93-97 GAIM report summarizes the results of the NPP model intercomparison activity in an attempt to set the stage for future refinement of terrestrial ecosystem models in the context of IGBP.
Geographically referenced GPP and NPP and their corresponding seasonal fluctuations are vital to enhance our understanding of both the functioning of living ecosystems and their subsequent feedbacks to the environment. Biospheric flux models all (either explicitly or implicitly) relate geographically specific and comprehensive estimates of temperature, water availability and photosynthetically active radiation (PAR), as well as their seasonal changes, to some or all of the basic processes of photosynthesis, growth and maintenance respiration, water and nitrogen fluxes, allocation of photosynthates in the plant and the production and decomposition of litter. Productivity is also a keystone variable for the sustainability of human use of the biosphere through agriculture and forestry. Since agricultural and forestry production provide the principal food and fuel resources for the world, monitoring and modeling of biospheric primary production are important in order to support global economic and political policy making.
Sufficient data have only recently become available to properly characterize the productivity of the terrestrial biosphere. NPP is a measurable and quantifiable characteristic of the biosphere and is a fundamental ecosystem variable modeled at the global scale. From a theoretical viewpoint, NPP incorporates most of the annual carbon flux from the atmosphere to the biosphere and is considered to be a primary driver of seasonal fluctuations in atmospheric CO2 concentrations [Ciais et al., 1995; Keeling et al., 1996]. Practical considerations for estimating NPP exist in its utility to measure crop yield, forest production and other economically and socially significant products of vegetation growth.
Among the models participating in the NPP intercomparison, two major groups can be distinguished based on the way they use various data sources. One group is essentially driven by observations made by space-borne sensors, most particularly the National Oceanic/Atmospheric Administration's-Advanced Very High Resolution Radiometer (NOAA-AVHRR). This sensor now provides a relatively long time series and full global coverage with high temporal resolution. These models provide a steadily improved picture of the NPP of the world's actual vegetation. In contrast, other models use data on soils and/or climate alone to derive estimates of the biological activity in the world's potential vegetation (Fig. 3.1 )
Figure 3.1: Potential natural vegetation map (modified from Melillo et al. 1993) used for biome-scale comparisons of net primary production among models. Modifications from Melillo et al. (1993) include the aggregation of Arid Shrubland; Short Grassland; and Tall Grassland into Grassland; and Xeromorphic Woodland and Mediterranean Shrubland and Xeromorphic Woodlands.
Early results that emerged from the first NPP model intercomparison workshop indicated that variable data input sources and their inherent uncertainties resulted in broad differences in modeled output. This was true for both ground-based observations such as climatic data and for remote sensing data such as the AVHRR-derived Normalized Difference Vegetation Index (NDVI). Consequently, many of these data were standardized for the second workshop. In this exercise, there was a standard data base for temperature, precipitation, solar irradiance, soil texture, a weather generator, AVHRR, and NDVI. Differences still existed in vegetation cover, leaf area index (LAI), and the parameterization of the models for processes such as decomposition, nutrient cycling, and evapotranspiration. Biome definition and distribution was not successfully standardized throughout the models used in the intercomparison because of differences in approaches, which may also account for a significant portion of the differences in resulting modeled NPP. It was necessary to use as many standardized input data sets as possible in order to make relevant comparisons between modeled NPP formulations.
A fundamental problem of such comparisons was that the target variable, net biospheric carbon flux, could not be measured at the appropriate spatial scale for any significant part of the globe. Direct validation of any of the models was therefore impractical, although several indirect validation methods exist. Another limitation concerns the quality of the existing observation data sets. For climate, a range of efforts have been made to improve available data sets for the application of biospheric models (including a May, 1996 GCTE/GAIM workshop on that topic). However, climate is only one significant variable. Another one (for potential vegetation) includes soils; for which several activities are currently underway. Two crucial gaps in data exist in the area of historical changes in global land-use, which is clearly a significant element in the world's carbon balance, and in the compilation of point-based observations of biospheric carbon fluxes.
A comprehensive data strategy in support of atmosphere-biosphere interaction models is still lacking. The NPP model intercomparison effort has made it clear that existing data must be chosen and used in a standardized way if similar models are to be compared, and ultimately, if complementary models are to be coupled. It has also clarified data gaps which can now be filled before models can reliably simulate the role of terrestrial ecosystems in the global carbon cycle. However, it is not necessary for model development to wait until all gaps in the global observing systems are closed. Rather, the IGBP can take the lead in coordinating existing and future data sources in a way that will optimize their utility throughout the global change research community. This is being planned in the context of the GPPDI data initiative, which will in turn support the Ecosystem Model-Data Intercomparison (EMDI).
The productivity of the terrestrial biosphere is primarily controlled by the amount of incident radiation and the climatic conditions, i.e. by the meteorological conditions under which plants are able to carry out photosynthesis and then allocate photosynthates to various components. Precipitation and temperature are the two major climatic conditions that govern the absorption of photosynthetically active radiation (PAR) and its conversion into dry matter, i.e. the net primary productivity (NPP) of the biosphere. The pioneer NPP model, MIAMI, (Lieth 1975) used an empirical regression to relate annual NPP to the annual average (bio)temperature and precipitation without accounting for radiation. Because of its simplicity and its empirical basis, this model is still used as a baseline for the evaluation and development of more sophisticated mechanistic models.
A large range of more complex models have been developed to account for the ecophysiological and biophysical processes which determine the spatio-temporal features of NPP with a goal of providing prognostic capabilities. Mechanistic relationships were used to describe the fluxes of CO2 (all models), water (most of the models), and nutrients (few models) between the different compartments of the vegetation, the soil and the atmosphere. The major processes are photosynthesis, growth and maintenance respiration, evapotranspiration, uptake and release of nitrogen, allocation of photosynthates to the various parts of the plant, litter production and decomposition, and phenological development. Some models focus on detailed mechanistic relationships for some processes (for example, stomatal conductance for water and CO2 fluxes and net nitrogen mineralization for the nitrogen cycle) and rely on simple empirical relationships or satellite observations to derive or constrain other important features (for example, canopy characteristics, phenology). The seventeen models analyzed in the intercomparison are listed in Table 3.1, with their key references. The major features of the models are described below.
For the Potsdam'95 intercomparison, modeling groups were provided a standard set of climate, soil texture and NDVI data fields. Spatial resolution of the data was a 0.5° by 0.5° Cartesian grid, and the temporal resolution was one month. Data that were used by a small number of models and thus not part of the standardized input data set include wind speed, humidity, spatial distribution and classification of vegetation, biomass and daily minimum and maximum temperature. Parameterizations based on vegetation and soils were also model-dependent.
NPP was the output variable chosen for a focused evaluation as it was the only variable that was supplied by every model. Noticeable differences occurred for other variables such as gross primary production (GPP), leaf area index (LAI), and simulated, heterotrophic respiration (RH). At the global scale, input datasets appropriate to initialize models for photosynthetic and respiration processes, soil characteristics, texture and rooting depth, and the availability of nutrients from soils and from litter decomposition may be very limited. The extent to which each process or condition is inferred, modeled or prescribed from literature values differs between models, as does the temporal resolution at which the models operate.
Table 3.1: List of Participating models, modeling teams and references, listed in alphabetical order.
Two basic approaches were used to calculate NPP. Some models (e.g., CASA, CENTURY and HRBM) related NPP directly to vegetation characteristics and environmental variables or indicators such as temperature, precipitation, available soil nitrogen or other fertility factors. Other models estimated NPP as the difference between two processes which were modeled independently: gross primary production (GPP: the total uptake of carbon from the atmosphere by plants), and autotrophic respiration (RA: the release of carbon to the atmosphere by plant respiration). In the latter case, the environmental and structural variables influenced GPP and RA instead of NPP. Among the seventeen models in this intercomparison, three major groups of models were identified (Table 3.2) based on whether the models used a prescribed seasonal behavior of the radiative activity of the canopy and/or a prescribed vegetation distribution. The three categories used three generic approaches to assess the effect of climate on NPP:
Table 3.2: Categories of participating NPP models, defined on the basis of required input and typical output variables.
Being climate-driven, all NPP models had data requirements for temperature and moisture availability. With the exception of GLO-PEM (spatial resolution 8 km x 8 km) and SIB2 (4.0o x 5.0o), the models already used climate data sets that were gridded at 0.5o longitude/latitude resolution (approx. 55 km at the equator, but shorter in longitudinal direction towards the poles) -- Simulations were performed at 0.5o resolution by all models with the exception of GLO-PEM and SIB2 to allow direct comparisons at the workshop. Again, apart from GLO-PEM and SIB2, the models utilized monthly long term means of climate. Those running at daily time steps usually generated quasi-daily climate data from monthly averages internally. A few models interpolated quasi-hourly climate data for estimating GPP whereas others (e.g., CARAIB, HYBRID) applied weather generators to simulate daily variability. The models operating on 0.5o spatial grids used the same land sea mask included all land areas except Antarctica, resulting in a global grid containing 62,483 cells. For this land mask, the CLIMATE data base (long term mean 1931-1960, version 2.1, [Cramer et al., in prep; Leemans and Cramer, 1991] was provided to the participants for monthly mean temperatures and precipitation values.
Solar radiation was required by most models, but the formulations for estimating it differed among the models. Those models that estimate radiation from location (latitude) and cloudiness used a gridded estimate of the number of sunshine hour percentages (as a proxy for cloudiness) from the same CLIMATE data base [Otto et al., manuscript]. SIB2 was intimately linked with the Colorado State University general circulation model (CSU-GCM), which, relative to the other models, contained a coarser spatial resolution (4o latitude x 5o longitude), and a higher temporal resolution (hourly or less). SIB2 NPP predictions were from these spatial and temporal resolutions.
With the same objective of simplifying the model comparisons, it was suggested that those models that required satellite data for the quantitative prescription of seasonal changes in FPAR use the same data product from the global records of the Advanced Very High Resolution Radiometer (AVHRR): the FASIR dataset for 1987 of the ISLSCP CD-ROM database [Meeson et al., 1995; Sellers et al., 1994]. This dataset consists of monthly NDVI at a spatial resolution of 1o with improved processing compared to the earlier vegetation indices widely used before. The seasonal FPAR required by the satellite-based models was derived from this FASIR-NDVI. GLO-PEM, however, was designed to use satellite data at a higher temporal and spatial resolution (8 km, AVHRR Pathfinder) which also integrates other information (e.g. surface temperature, humidity) that are not available from the FASIR data set. Therefore, the results of GLO-PEM analyzed at the Potsdam'95 workshop were neither the optimal nor did they correspond to the published values. The results of TURC were also non-optimal and different from published values, because it was not possible to adjust some relationships for the use of the FASIR data in time for the workshop. Only CASA and SIB2 were designed for the FASIR dataset. In addition, the results available from SDBM are those from the Potsdam'94 workshop, which used another satellite data set. Therefore the outputs of SDBM have generally not been considered in the intercomparison results presented here, apart from in the section covering light interception/light use efficiency and seasonality. Future intercomparisons need to be aware of the fact that standardization of data sets may also create these types of artifacts.
Among the data sets that could not be standardized for the intercomparison were average humidity and wind speed, which were required by some models. Vegetation distribution was an input to several models, and the selected maps (or models) affected results both at the levels of model calibration and application. This feature is central to most models -- a strict standardization could have removed features that were critical for the individual model design. Furthermore, the models that estimated both fluxes and vegetation structure (BIOME3, DOLY, HYBRID) did not predict identical vegetation distributions. The primary source of soils data came from the FAO Soil Map of the World (FAO/UNESCO 1974), but the interpretations of FAO categories in terms of soil factors were not standardized across models. Additionally, some models used the Zobler soil texture (1986) translation to field capacity and wilting point. Pan et al. (1996) have recently shown the uncertainty of biosphere models to such different soil data sets.
The input requirements for all models in more detail are presented in Table 3.3, below. Only input driving variables used to extrapolate simulations across the globe are listed, which suggests that additional variables required at an early stage in the calibration procedure are not presented. For example, FBM and PLAI used soil types [Fung et al., 1987] to compute heterotrophic respiration. The calibration procedure of these models requires that annual NPP equals annual RH -- indicating that NPP cannot be estimated without a soils dataset. Another example is the NDVI time-series data [Gallo, 1992] used by SILVAN to calibrate the phenological sub-model applied for temperate deciduous forests.
Table 3.3: Input variable as they are required by the difference models - same order as in Table 3.2. (deta for calibration are not included)
The models estimated global net primary production of the terrestrial biosphere between 39.9 and 80.5 PgC yr-1with a mean of 54.9 PgC yr-1. Table 3.5 shows the estimated global NPP values for each model in decreasing order. Although considerable effort was made to reduce the variability among modeled NPP estimates through the use of common data sets, these global NPP estimates were still based on different land areas used by the models. The area of the terrestrial biosphere varies from 105.6 to 128.7 106 km2, which is around 18 percent of the 128.7 106 km2 represented by the 56,785 grid cells of the common input data sets. This variation is partly the result of (1) missing data -- some models did not estimate NPP for some vegetation classes (e.g., FBM did not estimate NPP for desert regions; TEM did not estimate NPP for wetland or floodplain vegetation types); (2) the use of additional data sets that did not exactly match the grid cells used in the common data sets; and (3) for SIB2, the coarse spatial resolution used to calculate NPP. These differences did not appear to have a major influence on global NPP estimates because they were partially compensated by other factors, or because missing regions were those with relatively low productivity.
The TURC simulations for Potsdam'95 were made with the FASIR data set without appropriate re-calibration, which resulted in an overestimation of the NPP fluxes compared to other published results: 62.3 Pg C yr-1 for the reference computation and 71.3 Pg C yr-1 when the FASIR data set was used with re-calibration [Ruimy et al., 1996]. The use of FASIR without re-calibration also unrealistically lengthened the growing season. A pre-industrial atmospheric [CO2] of 280 ppmv may be the cause for low global NPP estimates from HYBRID (Andrew Friend, pers. comm.), whereas the other models considered atmospheric [CO2] concentrations between 340 and 360 ppmv. If TURC and HYBRID are put aside, then the range of global NPP variability is reduced from 100% to 50%, a relatively good agreement for the estimation of this poorly understood variable. The explicit calibration procedures used by some models were likely partly responsible for this, and results presented below suggest that the models may indeed be unconsciously calibrated such that annual global NPP predictions are within 'commonly admitted values'.
The overall ranking of global NPP cannot be explained by any of the specific model features, and certainly not by basic model-categories (Table 3.2). Satellite-driven models estimated higher values of global NPP (GLO-PEM, TURC), but they also estimated low values (CASA, SIB2). The three models which did not estimate GPP (CASA, CENTURY, HRBM) displayed intermediate to low NPP values. It is noteworthy, however, that four of the six models which applied nutrient constraints on NPP estimated lower global NPP than models that did not incorporate nutrient limitations in their model formulations. In addition, four of the six models which included a vapor pressure deficit calculation to determine NPP predicted higher global NPP values than those using other methodologies to estimate the influence of moisture on NPP. These results suggest that NPP estimates may be sensitive to the modeling strategy used to simulate the water balance and the effect of water stress on NPP. One of the models using vapor pressure deficit that predicted a lower global NPP (DOLY) also implemented nutrient constraints on NPP.
Global annual NPP, averaged across all models is shown in Figure 3.2. The highest productivity (>1200 g C m-2 y-1) was found in tropical biomes (Amazonia, Central Africa, Southeast Asia), where both temperature and precipitation requirements were fully satisfied for photosynthesis. Temperate regions had an intermediate NPP (500 - 700 g C m-2 y-1), and the lowest NPP (< 200 g C m-2 y-1) was found in cold or arid regions, where either temperature or precipitation were limiting. The intertropical band in Africa contained the highest spatial variability, from the most productive biomes (tropical evergreen forest) near the equator to the least productive ones (arid shrublands) around latitudes 20 - 25oN.
Figure 3.2: Annual net primary production (g C m-2yr-1) estimated as the average of all model estimates.
The variation in NPP estimates among the models reflected the distribution of NPP. The map of the standard deviation of the individual model estimates from the combined model's mean (Figure 3.3a ) suggests that the standard deviation was larger where NPP was high and smaller where NPP was low. The highest standard deviation occurred at the borders of areas with high NPP values. This may partially be an artifact due to the different vegetation data sets which were used as input. In contrast, the highest coefficient of variance was below 15%. In the areas of tropical forests and boreal forests, the coefficient of variation could be less than 5%. However, the coefficient of variance (Figure 3.3b ) was less than 15% for most areas -- it was therefore possible to consider the broad features of this figure as a comprehensive representation of NPP fluxes estimated similarly by different models.
Figure 3.3: Spatial distribution of the variability in NPP estimates among the models as represented by: (a) the standard deviation of model NPP estimated in a grid cell; and (b) the coefficient of variance of model NPP estimated in a grid cell. The coefficient of variance is determined by dividing the standard deviation by the mean of the model NPP estimates within a grid cell.
Average global NPP estimates of all the models varied seasonally and among models (Fig 3.4). Because most of the terrestrial biosphere is located in the northern hemisphere (74% of the global land area, excluding Antarctica), the monthly global NPP estimates of all models were low during the northern hemisphere's winter and high during its summer, but the seasonal pattern and magnitude varied among the models. Most models predicted the lowest NPP in February (CARAIB, CASA, TURC in January, HYBRID in December). The lowest monthly global NPP ranged from ?1.6 Pg C mo-1 (HYBRID) to 4.8 Pg C mo-1 (TURC), the extremes being the same, partly for the same reasons as for the global values. A negative NPP indicates that plant respiration is greater than the uptake of carbon by plants during a month when vegetation is stressed by drought conditions or low temperatures. All models estimated that the highest monthly global NPP occurs during the northern summer, apart from CENTURY which estimated the peak to occur in September due to a particular artifact (W. Parton, pers. comm.). The highest monthly global NPP ranged from 5.6 Pg C mo-1 (HRBM) to 9.1 Pg C mo-1 (KGBM and TURC).
Figure 3.4: Comparison of seasonal variations in global net primary production among models.
Areally-weighted latitudinal distributions of annual and seasonal NPP estimates for fourteen key biomes were compared among the models. A trimodal distribution of annual NPP across latitudinal zones emerged with the highest areal NPP occurring near the equator (5o S to 5o N); a second, smaller peak between 35o and 45o S; and a third, smaller peak between 50o and 60o N (Fig. 3.5).
Figure 3.5: Comparison of the latitudinal distribution of the median (solod line), and 10th and 90th percentiles (dotted lines) of area-weighted mean annual net primary productivity estimated by fifteen models within a 0.5o latitudinal band.
There was considerable variability in the magnitude of annual NPP among all the models, particularly around the three zonal peaks, however, none of the models produced consistently higher or lower NPP across the entire latitudinal gradient. The latitudinal pattern of simulated annual NPP reflected the relative distribution and productivity of vegetation types, or biomes with NPP generally increasing from dry and cold biomes (desert, tundra) to warm, moist biomes (temperate and tropical forests) (Fig. 3.6. Mean annual NPP in tropical savannas was the most variable among the models, with SIB2 ranking them the second most productive. In contrast, GLO-PEM ranked tropical savannas as the tenth most productive biome of the fourteen biomes identified in the comparison of spatial variability for annual NPP.
Figure 3.6: Box plots comparing the variability in model estimates among biomes for mean net primary productivity. Biomes included arid shrublands/deserts (DES), tundra (TUN), boreal woodlands (BW), temperate savannas (TMS), boreal forests (BF), grasslands (GRS), xeromorphic woodlands (XFW), temperate coniferous forests (TMC), tropical savannas (TRS), temperate deciduous forests (TMD), temperate mixed forests (TMM), tropical deciduous forests (TRD), temperate broad-leaved evergreen forests (TMB), and tropical evergreen forests (TRE). Biomes are arranged in ascending order of the mean biome NPP estimated from the combined model results. Bars within the boxes represent median values. The bottom and top of the box represents the 25th and 75th percentile, respectively. The bars outside the box represent the 10th and 90th percentiles. Open circles represent outliers.
Several approaches were used by the models to account for spatial and temporal variations in NPP. Each approach was based on simplifying assumptions about how ecosystems were structured and how vegetation may respond to changes in various environmental factors. As each approach was imperfect, NPP estimates were biased by the formulations and/or parameter values used by the models to develop them. For example, the formulations used in the models that calculate NPP directly (HRBM, CASA, CENTURY) never estimated a negative NPP. In contrast, the models that calculated NPP as the difference between gross primary productivity (GPP) and autotrophic respiration (RA) could calculate a negative NPP in months when RA was larger than GPP.
As the FPAR formulations in CASA and TURC depended on the input of seasonal FASIR-NDVI data, the relatively constant monthly NPP estimated by these models in the tropics was partly explained by the weak seasonality of the remotely sensed data set. There was a weak seasonality of the NDVI data in the tropics suggesting that seasonal drought conditions may not be as pronounced as indicated by most of the NPP models. This may be partly explained by Nepstad et al. (1994) who observed that tropical trees can have roots down to soil depths greater than 8 meters such that evergreen forests may be able to maintain evapotranspiration during five-month dry periods by accessing deep soil water. Most of the NPP models in this intercomparison had rooting zones for tropical forests that ranged from 1 to 3 m. Therefore, the simulated vegetation would never have access to deep water resulting in decreased NPP and a more pronounced seasonality relative to those models driven remotely sensed phenology. The use of NDVI by the CASA and TURC models may have implicitly accounted for the effects of deep rooting on phenology, but other models that used NDVI data (e.g. GLO-PEM, SIB2) still simulated a pronounced seasonality in the tropics, suggesting some other phenological of ecophysiological mechanism was driving NPP cycles in these models.
To analyze simulated phenology, two main categories of global NPP models were considered: 1) models driven by satellite data to estimate the temporal variations of the fraction of absorbed photosynthetically active radiation (FPAR); and 2) models that simulated the temporal behavior of the canopy (i.e., changes in LAI) using climate and soils data alone. Thus, the importance of the radiative activity of the canopy was explicitly recognized (either derived from satellite observations or simulated) for carbon assimilation. The seasonal absorption of the photosynthetically active radiation, as well as the relationships between absorbed radiation and photosynthesis were examined to explore the differences among predicted NPP. By converting LAI to FPAR, the importance of the description of leaf phenology for the estimation of the seasonal NPP fluxes could be evaluated.
To determine whether differences in the seasonality of NPP among models were explained by either the canopy radiative behavior forcing the seasonal radiation, or by differences in the seasonal conversion of the absorbed PAR (APAR) into carbon, also referred to here as light-use efficiency (LUE), the environmental constraints affecting the relationship of FPAR and LUE to NPP, were examined over regions with a strong gradient of the major driving climatic variables responsible for the seasonal cycle. Summergreen phenology was analyzed along a temperature/radiation gradient in North America, and raingreen phenology was analyzed along a precipitation gradient in Africa (Fig. 3.7). Because many models used a prescribed vegetation distribution (or simulated the vegetation distribution together with the NPP), it was useful to look at the changes in simulated seasonal NPP behavior at ecotones, for example, the transition from evergreen to deciduous forests.
Figure 3.7: Annual NPP over eastern North America (top) and central Africa (bottom) estimated by 11 NPP models. The position of the transect and the location of the individual grid cells are marked in each case. The PEMs are grouped on the first row. The canopy models simulating both phenological development and biogeochemical fluxes according to some vegetation map are grouped on the second row. KGBM and models of vegetation structure and function are grouped on the third row.
To simulate phenology, canopy models generally used one of two different strategies: (1) a separate module estimated the timing of crucial phenological events such as leaf on/off dates, without considering the current NPP values, or (2) phenological stages were directly determined from the current carbon balance, i.e. NPP. To examine whether the differences in annual estimates of global NPP were related to general differences in the growing season as it was estimated by the models, seasonal estimates of global NPP were compared among the models across environmental gradients for eastern North America and Africa. Most of the models predicted increased NPP southward with annual temperature and radiation increases in America or with increased annual precipitation in Africa. However, annual NPP sometimes differed up to 200% between models. All models except KGBM predicted similar NPP values for the non-vegetated Sahara and the tundra, but differed by over 100% in the more productive areas.
The NDVI-derived FPAR from the PEMs was strongly correlated to monthly FASIR-FPAR (Table 3.4) for global and the four biome-types analyzed, whereas correlations were weaker from canopy models. Generally, the simulated seasonal FPAR agreed well with the FASIR-FPAR for the temperate deciduous forests, and the highest correlations occurred with SILVAN, which calibrated its phenology submodel for temperate and boreal biomes using satellite observations. These relatively high correlations were obtained despite the fact that a large part of the natural area for deciduous forests was under cultivation, where the timing of harvest removal strongly influences the satellite FPAR. In North America, however, crops are primarily spring/summergreen, roughly in phase with temperate deciduous forests. There were fairly weak correlations between simulated and satellite-derived FPAR for savannas, and boreal forests.
Table 3.4: Monthly grid cell level correlations (P < 0.1) between the modeled FPAR and the satellite FASIR-FPAR for the globe and four major ecosystems. The first three models are PEMs, the remainder are canopy models. GLO-PEM is not considered because this model used the FASIR-FPAR directly.
The poorest correlations were from tropical evergreen biomes where seasonal changes in the canopy cover were limited. While both simulated FPAR and FASIR-FPAR were relatively constant throughout the year, a positive correlation simply indicated that the spatial variability of FPAR values were in agreement, however, at times, their absolute values still strongly disagreed. These results suggest that the models better represented the seasonal effects of the variability in solar radiation and temperature on canopy development for summergreen deciduous forests relative to the effects of the seasonal precipitation on canopy development in the raingreen savanna.
Models that assumed homogeneous vegetation characteristics within biomes usually considered only one set of parameters for each biome. This parameterisation included the decision to assign a vegetation type as evergreen, deciduous or mixed in its canopy seasonality. As a consequence the models generally exhibited sudden changes of various simulated at ecotones where the value of some parameters might suddenly change. This was particularly true for the canopy models but also for all the PEMs that used the FASIR data due to their vegetation-map related processing. Along the two transects, differences in the vegetation maps and associated parameters influenced seasonal LAI, FPAR and NPP among the models at least as much as the differences in model assumptions about ecophysiology. Often, these differences in seasonal NPP across ecotones were manifested as banding patterns (e.g., FBM, PLAI, SILVAN and BIOME3) or small scale spatial variability (e.g., GLO-PEM, SDBM, CASA, TURC, CARAIB, KGBM, HYBRID) for annual NPP. Differences in the spatial pattern of vegetation structure among the models may have been caused by: (1) the better spatial resolution of vegetation characteristics by NDVI data (e.g., SDBM, CASA, TURC, GLO-PEM, KGBM); (2) the representation of ecosystems as a mosaic of plant functional types (e.g., CARAIB) rather than a dominant vegetation type; and (3) stochastic initialization of the biogeography component of dynamic vegetation models (e.g., the gap model in HYBRID).
This analysis suggests that APAR and LUE may not be completely independent. In evergreen ecosystems where high LAI generated high APAR, maintenance respiration costs during months with unfavorable conditions may have been influenced low LUE. This relationship was more obvious in the calibrated canopy models than in the uncalibrated canopy models (BIOME3, HYBRID, KGBM). It appears as if the calibrated models used parameterisations that enforce the negative link between APAR and LUE. A negative correlation between global APAR and global LUE among the different models is noted below.
Correctly calibrated models are required to give stable NPP estimates and to aid in the evaluation of uncalibrated models, however, the accuracy and representativeness of the field data found in the ecological literature is still a matter of debate (Kohlmaier et al. 1997). It should also be noted that field measurements of LAI are likely biased toward high values which are be more representative for well developed sites chosen by ecologists rather than the average condition of vegetation contained in an average 0.5 degree grid cell. Field measurements of NPP usually occur over a temporal resolution of a few months to a year. Therefore, other sources of information must be used to infer the validity of seasonal NPP estimates. In deciduous ecosystems, the positive correlations between simulated seasonal FPAR and FASIR-FPAR from satellite data provide some confidence in the seasonal behavior of some models, but this does not per se constitute a validation of seasonal NPP. Models with similar seasonal FPAR can simulate very different seasonal NPP, and vice-versa, due to different assumptions and parameterisations which determine LUE. Although satellite data may not be useful for evaluating NPP directly, this analysis suggests that surrogate measures of vegetation structure (i.e. FPAR) are useful for evaluating seasonal changes in NPP. However, there was some variability in the phenology portrayed by the FPAR derived from different NDVI data sets and the algorithms used for the FPAR computation. Consequently, a single satellite data set cannot be considered as a sufficiently precise evaluation tool. Furthermore, the one month's resolution of the satellite data set used here is not sufficient to determine spring growth accurately and to test the simulated timing of budburst, in which a shift of 15 days may have considerable effect on estimates of annual production.
The assumption that water availability is the primary limiting factor of NPP was tested for the following models: BIOME-BGC, BIOME3, CARAIB, CASA, CENTURY, GLO-PEM, FBM, HRBM, KGBM, PLAI, SDBM, SILVAN, TEM and TURC. Different approaches have been evaluated and used in these models for introducing water budget limitations on NPP. Three methods to restrict NPP by water availability in global NPP models were distinguished. The first included a direct physiological control on evapotranspiration through canopy conductance. Secondly, climatological supply/demand constraints on ecosystem productivity were calculated. Finally, a water limitation was inferred from satellite data alone. In addition, a water balance coefficient (WBC) was calculated as the difference between mean annual precipitation and potential evapotranspiration and compared to NPP for each grid cell in each model. The NPP versus WBC correlation plots exhibited comparable patterns among the models using the same methods for water balance limitations on NPP. While correlation plots revealed similar boundary lines for most global models, there was considerable variability in these distributions related to numerous environmental controls on NPP. The models with physiological control on evapotranspiration had the highest variability in those distributions because they were able to capture effects of more environmental controls on NPP. To compare the estimated annual NPP in relation to water availability among global models, a water balance coefficient (WBC) reflecting water availability was defined as the difference between precipitation (Precip) and potential evapotranspiration (PET):
| WBC = Precip - PET | Equation (3.1) |
The potential evapotranspiration was computed as a function of temperature and radiation from Priestley and Taylor (1972):
| l PET = a [s/(s+g)](R+G) | Equation (3.2) |
where l PET is the latent heat flux density, a is Priestley-Taylor parameter, R is the net radiation above the surface, G is the soil heat flux, s is the slope of the saturation vapor pressure-temperature curve at the dry bulb temperature, g is the psychrometer constant. In this analysis, R was calculated as the proportion of net solar radiation and G was a function of temperature. Water balance coefficients calculated by the method described above had the advantage of being independent of any model results and dependent only on input climate data. The global annual water balance coefficient was calculated at each grid cell (0.5o x 0.5olongitude/latitude) as a simple scalar that could be used with all models, regardless of their individual hydrologic computations (Fig. 3.7b).
Figure 3.7b: Water balance coefficient computed as the difference between annual precipitation and potential evapotranspiration (Eq. 3.1). This coefficient was calculated at each 0.5o x 0.5o longitude/latitude grid cell. Potential evapotranspiration computed by Priestley-Taylor method (Eq. 3.2) using the Cramer and Leemans (1991) global climate databases.
Assuming that water availability was the primary controlling factor of global NPP patterns, NPP limitation ranges by the WBC were scrutinized. In dry regions, gradually increasing water availability facilitates the regular increment of maximum potential vegetation productivity. If an ecosystem receives sufficient water available for vegetation, then moisture is not a limiting factor and the maximum NPP saturates. At high WBC, NPP below maximum reflects control by other climatic variables. For example, at high latitudes, low annual sunlight inhibits NPP and in cold climates low temperatures restrict photosynthesis and NPP. In addition, low nutrients may limit optimum NPP in some areas.
A comparison of NPP estimated by the various models to WBC for all the grid cells of the globe (Figs. 3.8, 3.9, 3.10) indicated low correlation (R2 = 0.05-0.3) between these two variables. However, a closer examination of these correlation plots revealed some general characteristics of the relationship between WBC and NPP for all models. As the WBC became increasingly more negative, the upper boundary of NPP estimates decreased in all models. The decrease of the upper boundary indicated that water was the ultimate limiting factor of NPP in these grid cells. However, the wide distribution of NPP estimates between zero and the upper boundary in this part of the correlation plots indicated that secondary factors simultaneously limit NPP at these grid cells. These secondary factors probably included differences in temperature, solar radiation, and/or nutrient constraints among models. Grid cells with a negative WBC covered a large proportion of the globe (Fig. 3.9). Spatial variations in the density of NPP estimates in this part of the correlation plots suggested that the interaction among the various environmental controls was different among the models. Around WBC equal to zero, there was a large density spot of grid cells with low NPP estimates (Figs. 3.8, 3.9, 3.10). These estimates represented the productivity of tundra and boreal forests where the difference between annual precipitation and evapotranspiration demand was subtle together with NPP primarily limited by low temperatures (Fig. 3.8). As the WBC became more positive, NPP estimates from many models appeared to reach a maximum of 1500 to 3000 g C m-2 y-1. These estimates represented the NPP of moist tropical regions. For BIOME-3, CARAIB, FBM, KGBM, PLAI, SILVAN and TEM, the high density of NPP estimates at this maximum indicated that the models assumed optimum environmental conditions for NPP for most grid cells within this region. The other models had more variability in NPP estimates for the moist tropics indicating the importance of secondary factors such as nutrient constraints or land-use in these regions. The maximum NPP predicted by the models varied over a WBC range from -1500 to 800 mm y-1, or, about one-fourth of the entire range of the WBC estimates. Half of the models (i.e., BIOME-3, CARAIB, CASA, FBM, GLO-PEM, SILVAN and TURC) estimated the maximum NPP to be in regions with a negative water balance. In contrast, BIOME-BGC, CENTURY, HRBM, KGBM, SDBM, TEM and PLAI predicted the maximum NPP to occur where the water balance was positive.
Figure 3.8: Relationship between estimated NPP and water balance coefficient for models with physiological control on evapotranspiration through stomatal control. Each data point represents one 0.5o x 0.5o longitude/latitude grid-cell.
Figure 3.9: a, b. Relationships between estimated NPP and water balance coefficient for models with climatic supply/demand control on ecosystem productivity. Each data point represents one 0.5o x 0.5o longitude/latitude grid-cell.
Figure 3.10: Relationship between estimated NPP and water balance coefficient for models with water availability limitation inferred through satellite data. Each data point represents one 0.5o x 0.5o longitude/latitude grid-cell.
A number of reasons can account for the differences in the model results discussed above. As the various models used different functions to calculate PET from the approach used to compute the water balance coefficient, the variations in the relationship of maximum NPP to WBC may be a result of these differences. It is also acknowledged that the WBC calculated on an annual basis does not account for seasonality of precipitation or the interaction of seasonality of precipitation and PET. Overall, it is important to mention that a WBC does not provide an absolute measure for water balance, but it supplies a general scale of the potential water increment within an ecosystem.
It appears that the methods used to estimate water budget limitation on ecosystem productivity had a significant effect on the model outputs. Models with physiological controls over evapotranspiration and NPP consequently were characterized by even distributions of NPP versus the water balance coefficient (Fig. 3.8). This implies that a variety of additional environmental factors may be controlling ecosystem productivity. In addition, these models predicted a smooth increase in the range of NPP with an increasing WBC and the slope of the edge of NPP versus WBC was steeper (except BIOME-3) suggesting that the deficit of water available to plants set the upper limit on ecosystem productivity.
From the standpoint of water availability, while water balance may be the most influential control on global NPP, regional and biome specific NPP variability must be described with more than water balance alone. Improved biospheric models should account for multiple environmental factors controlling global NPP in a non-linear fashion for improved predictions.
To analyze the broad-scale behavior of fifteen global biosphere models, the sensitivity of simulated net primary productivity (NPP) to precipitation, temperature, solar radiation, and to the Normalized Difference Vegetation Index (NDVI), was evaluated spatially and temporally. Annual NPP estimates for 41,344 grid cells were averaged across all models and compared to annual precipitation and mean annual temperature of associated grid cells as well as the corresponding coefficient of variation of the average NPP estimates. To relate the latitudinal distribution of NPP to climate and NDVI, the area-weighted mean annual precipitation, temperature, solar radiation and NDVI (1987 FASIR-NDVI, Sellers et al. 1994) was calculated across all longitudes in a 0.5o latitudinal band. To examine seasonal changes in sensitivity of NPP to climate and the NDVI, the total annual grid-cell NPP was divided by each annual climate variable or NDVI value, then averaged the ratios across each 0.5o latitude band between 60o S and 85o N. Annual values of NDVI are equivalent to the annual integral of NDVI (iNDVI) derived by Schimel et al. (1997) as a proxy for annual NPP. The relative importance of climate to simulated NPP was assessed using Spearman Rank correlations (r) between the ratios and individual climate variables across latitudes.
The highest annual NPP estimates for all models occurred in areas with warm (average annual temperature 16o - 27o C) and wet (annual precipitation > 1500 mm) climates and declined as precipitation and temperature decreased (Fig. 3.11a). The smallest relative variability between model estimates occurred in areas of highest NPP (Fig. 3.11b), indicating that the best correspondence between model estimates occurred where productivity was least limited by climate, such as in the humid tropics. Although the relative variability was lower in these areas, the absolute variability in NPP among models was not small because NPP estimates were large. Within bands of equal temperature, if temperature was above 10o C, variability between models decreased with increasing precipitation, suggesting that differences in models became less important as water limitations decreased (Fig. 3.11b). In areas having average temperature below 0o C, variability was generally higher, suggesting multiple limitations on NPP or very dissimilar effects of low temperature limitation that produced different behavior among models.
Figure 3.11: (a) Annual mean net primary production (NPP) for fifteen models in relation to temperature and precipitation; (b) Normalized coefficient of variation for the data shown in panel (a). Data points in both panels represent the mean annual NPP for all models and all grid-cells within a particular temperature-precipitation combination, re-sampled into bins with a temperature interval of 1oC and a precipitation interval of 50 mm yr-1.
Water-use efficiencies within the models were relatively constant across latitudes (Fig 3.12e), resulting in higher correlations between the latitudinal distribution of NPP and precipitation than with the other climate variables (r = 0.54 - 0.85). The greatest variability among models occurred outside of the wet tropics, indicating that differences in moisture sensitivity became important as precipitation limitation on NPP increased (Fig 15e). Differences among models for temperature sensitivity were greatest in the northern latitudes (50o N - 70o N), i.e. the zone with the shortest active growing seasons (Fig. 12f). Not surprisingly, NPP estimates were strongly correlated with NDVI (r = 0.51 - 0.93), although the magnitude of NPP estimates at any particular NDVI value varied considerably between models. The strong correlations between the latitudinal distributions of NPP and NDVI were also associated with relatively consistent NPP:NDVI ratios across latitudes (Fig. 19h). However, the very high variability in NPP:NDVI around 45o S was associated with higher precipitation coupled with lower NDVI and different modeling strategies, such that CASA, GLO-PEM and TURC estimated much smaller NPP:NDVI ratios compared to other models.
Figure 3.12: Left panels: Latitudinal distribution of mean values over 0.5o latitudinal bands of a) annual precipitation, b) temperature, c) solar radiation and d) NDVI. Because some land areas were not included in the analysis and data represent only common grid cells, values are somewhat different from those based on all land area. In latitudes dominated by deserts, between 15o N - 30o N, precipitation and NDVI as shown are higher, solar radiation is somewhat lower, while there was no change in temperature than if desert areas had been included. Right panels: Latitudinal distributions of the ratio of annual net primary production to e) total annual precipitation [NPP:P], f) average annual temperature [NPP:T], g) annual solar radiation [NPP:Rs], and h) average annual NDVI [NPP:NDVI]. Solid lines represent the median ratios for fifteen models within a 0.5o latitudinal band, and dotted lines represent ± 90% confidence intervals of the mean ratios for the models.
Differences in the latitudinal distribution of model sensitivities to climate (Fig. 3.13) were more conspicuous for monthly than for annual values. The observed uniformity in the latitudinal distribution of annual NPP:Precipitation ratios (Fig. 3.13e) did not reflect the large seasonal differences in water-use among and within models, demonstrating that similarity in the annual NPP estimates among models does not imply agreement in their distributions of seasonal NPP. For example, the higher sensitivities in the annual NPP:Temperature ratios at northern latitudes above 45o N (Fig. 3.13f) were detectable as larger monthly ratios during those months when NPP was positive. In contrast, the relatively low and stable annual NPP:Temperature ratios between 30o S and 30o N (Fig. 3.13f) corresponded with a wide range of NPP responses to temperature, from the consistent monthly ratios in TURC to the extremely large seasonal fluctuations between positive and negative NPP in PLAI and HYBRID (Fig. 3.13b). Similarly, peaks in annual NPP:Radiation ratios were due to either somewhat higher monthly ratios that changed very little seasonally (between 5o S - 5o N in the tropics) or to larger monthly NPP:Radiation ratios during those months when NPP was positive (between 50o N - 60o N in the north, except for TURC) (Fig. 3.13c).
Figure 3.13: Latitudinal distributions of monthly ratios of simulated net primary productivity to monthly values of a) precipitation [NPP:P]; b) temperature in degrees above 0o C [NPP:T]; c) solar radiation [NPP:R2]; and d) NDVI [NPP:NDVI]. Models are shown in descending order by their global NPP estimates. Numbers above the scale-bars represent the values of the climate variable or NDVI plotted on the top panels, and numbers below the scale-bars represent the ratios for the six models plotted on the lower panels.
Seasonal analyses in the boreal zone revealed that model sensitivities to cold temperatures strongly influenced the timing of phenological onset and offset of the active growing season (Fig. 3.14). However, different values of NPP were estimated in spring compared to fall for equivalent inputs of a climate variable (i.e. the sensitivity changed), forming an elliptical pattern. Such a pattern is indicative of feedbacks operating within the models that influence the response of NPP to environmental conditions. The different sizes of the ellipsoids indicate that the strength of the feedbacks vary among the models. This influence was also seen as differences in the seasonal pattern of NPP compared to the NDVI, being most pronounced in the beginning and end of the active growing season and in the months of peak productivity. Phenology, in the sense of the seasonally varying display of foliage, may be primarily responsible for the changing relationship between NDVI and solar radiation (Fig. 3.14). The lower NDVI values during the spring were associated with a dormant or developing canopy whereas the higher NDVI values during late summer/early fall occurred with a fully-developed vegetation canopy. Higher NDVI values from June to August were consistent with increased precipitation and solar radiation, warmer temperatures and a full canopy. This same pattern was reflected, to varying degrees, in the NPP estimates of the models, but the hysteresis between NDVI and NPP in TEM, HRBM and HYBRID indicated that these models did not produce the same canopy dynamics in boreal ecosystems as indicated by the NDVI.
Figure 3.14: Simulated monthly net primary productivity in the boreal zone compared to monthly climate (a?f) and the NDVI (g) averaged over all grid-cells (n = 8266) between 50o N ? 60o N. Symbols represent (d) December, (1) January, (2) February; (3) March, (4) April, (5) May; (6) June, (7) July, (8) August, (9) September, (o) October, and (n) November.
Water balance was likely responsible for the changing sensitivities of monthly NPP to precipitation and the changing relationship between monthly NDVI and precipitation. During the late spring, the availability of moisture for NPP was very high as melting snow adds water to soils and groundwater so that plants do not have to depend as much on monthly precipitation during this period to support productivity. In contrast, evapotranspiration during the summer depletes soil moisture so that less moisture is generally available for NPP during late summer/early fall and the plants are more dependent upon monthly precipitation. Differences in the elliptical patterns describing the relationship between monthly precipitation and NPP among the models reflect differences in the water balance algorithms used by the models.
In the tropics, sensitivities to climate varied widely among and within models. The seasonal pattern of NDVI in the tropics corresponded more favorably to monthly precipitation inputs than did simulated NPP, suggesting that the NDVI signal may be more strongly influenced by soil physical properties such as water-holding capacity relative to simulated soil processes. The changing relationship between NDVI and solar radiation was probably also influenced by phenology, but here, phenology was related to changes in water balance rather than changes in temperature and solar radiation. As very little moisture is stored as snow in the tropics, the availability of soil moisture for NPP was more dependent on the timing of precipitation. Correspondence between NPP and NDVI was generally poor among models, because the pattern of NDVI more strongly reflected monthly inputs of precipitation than did the models. The lag between precipitation and the canopy response shown by the slight elliptical pattern in NDVI compared to precipitation suggests a buffering against water stress by soil moisture storage that was less clear in the models. The distribution of NPP among the models reflected differences in assumptions about the ability of tropical ecosystems to store water. Overall, models estimated higher NPP at larger values of NDVI (and precipitation) than at smaller values, suggesting that month-to-month changes in water availability that were inferred from elevated NDVI values were too slight to be simulated by these models at this spatial and temporal scale.
Differences in model sensitivity to one or more climate variables may not be distinguishable when climate is not limiting or when values are averaged over the year. Despite substantial seasonal differences, models may nevertheless generate comparable annual NPP values over large regions, such as across latitudinal bands, or for the globe, as has been observed for the climatologically (see above) and biophysically (see below) averaged conditions of this intercomparison. Similarities between model estimates may disappear when climate varies from the average conditions, either as a change in annual averages or as greater amplitude in monthly values.
Fifteen out of seventeen global models in this intercomparison used solar radiation as a driver of plant production. Twelve global models were compared which: 1) use solar radiation as an input, and 2) either derive directly the fraction of light absorbed by the canopy, or provide the basis to calculate it.
For each model, the fraction of light absorbed by plant canopies was estimated by converting incident global radiation provided by the standard data into photosynthetically active radiation (PAR), using a constant ratio of 0.48 MJ(PAR) MJ-1(global radiation) (McCree, 1972). In fact, the models may have used a different ratio of PAR to global radiation, or even time- or space-varying ratios. BIOME3, CARAIB, FBM, HYBRID, KGBM, PLAI and SILVAN did not calculate explicitly light absorption at the canopy level, but computed a leaf area index (LAI). For these models, the total fraction of incident PAR absorbed by the canopy was estimated using the Beer-Lambert law, similar to the scheme used in most CPMs in this study to integrate leaf photosynthesis to the canopy. All models provided annual and monthly NPP output. For the eleven models above, plus SIB2 which supplied annual APAR, NPP was decomposed a posteriori into absorbed PAR, and conversion of absorbed PAR into dry matter as follows:
| NPP = (NPP / APAR) * APAR = LUE * APAR | (Equation 3.3) |
The relationships between NPP and its components, APAR and LUE, were examined at three spatial levels: 1) grid cell, 2) zonal; and 3) global. For the grid cell level analysis, the values of NPP versus APAR and LUE were plotted for each model over all grid cells of the common area, providing insight into intra-model relationships, or the relationships between NPP and its components within models. At the global scale, global values of NPP versus APAR and LUE were plotted for the twelve models in this study, enabling an analysis of NPP and its components among models. Linear regressions were performed for global and grid cell level plots.
Correlations between annual NPP and annual APAR at the grid cell level were generally high (R2 = 0.26-0.98; mean R2 = 0.70), whereas correlations between annual NPP and LUE were weaker (R2 = 0.06 - 0.71; mean R2 = 0.24) ), and the intercepts usually departed from zero, suggesting that variable NPP estimates within models primarily depended on APAR, and effects of LUE were second order effects.
The high correlations between NPP and APAR and the near-zero intercept for most models, PEMs and CPMs alike, suggest that these models can be, at first order, approximated by a PEM structure, with a constant, model-specific light use efficiency (the slopes of the regression range from 0.39 to 0.52 for the seven highest correlations). Thus, at first glance, the distinction between PEMs and CPMs may not be very important. Similar behavior between CPMs and PEMs may have emerged for several reasons: 1) While the first generation of PEMs may have been less mechanistic than CPMs, refinements introduced to the current generation of PEMs (i.e. stress factors reducing LUE), indicate a developmental convergence with CPMs. 2) The use of a linear light-response curve (in PEMs) versus a saturating curve (in CPMs) may not produce significant differences at the temporal and spatial scales considered as integration in space and time tends to linearize light response curves (Ruimy et al. 1995). 3) Some CPMs used simplifications (e.g. “optimized Farquhar models” in BIOME3 and SILVAN) that reduced GPP to the product of a light-response curve by other factors, much like PEMs. 4) Although photosynthesis and autotrophic respiration are sensitive to different environmental variables (McCree 1974), at first order, simulated respiration was proportional to GPP.
In contrast to grid cell level relationships, differences among the models in global NPP estimates did not correspond to differences in global APAR estimates. The models were also clearly segregated (P < 0.01) from NDVI-derived APAR (CASA, GLO-PEM, SDBM, and TURC) from those with modeled APAR (BIOME3, CARAIB, FBM, HYBRID, KGBM, PLAI and SILVAN) where the mean and standard deviation for NDVI-derived modeled APAR were 94 x 1018; 6.9 J yr-1 and 120 x 1018 ; 13.5 J yr-1, respectively. SIB2 was not included this analysis because incident PAR, being simulated within a coupled General Circulation Model, differed from the data used by the other models. There was a weak, negative correlation (r = -0.40) between simulated PAR absorption and NPP and strong, positive correlations with LUE (r = 0.85) (Fig. 3.15b), suggesting that differences in global NPP among models may have been due to differences in LUE formulations.
Figure 3.15: Global level regressions. (a) Global NPP (Pg C yr-1 ) against mean global APAR (1018 J yr-1); (b) Global NPP (Pg C yr-1) against mean LUE (g C MJ-1); (c) Global LUE (g C MJ-1) against mean global APAR (1018J yr-1). Linear correlation coefficients (r) are indicated. The models are: BIOME3 (b), CARAIB (cr), CASA (cs), FBM (f), GLO-PEM (g), HYBRID (h), KGBM (k), PLAI (p), SDBM (sd), SIB2 (sb), SILVAN (sl), and TURC (t).
The strong negative correlation between LUE and APAR (Fig 3.15c) suggested that estimates of global NPP among models were relatively constant (see Eq. 3.3), either by explicit or unconscious adjustment of parameters within models with the consequence that global results were within a commonly accepted range. The fact that these models could be calibrated puts a damper on the confidence that we may have in global models of NPP, confidence resulting from the fact that global estimates have changed little since Lieth (1975) estimated global NPP to be around 60 Pg C yr-1. On the other hand, many models were conceived as tools for formalizing the complex relationships between NPP and forcing factors, rather than estimating the absolute value of NPP. Some models explicitly calibrated certain parameters with values of either global NPP, or NPP per biome. For instance, CASA calibrated a globally uniform value of optimum LUE in the absence of any environmental stresses, using NPP data for about 20 sites. Historically, only a limited number of compilations of NPP exist per biome, for instance the Whittaker and Likens (1975) estimates were used to calibrate and evaluate current models.
NPP and APAR shared the same double-peak pattern (Figures 3.16a and 3.16b), while latitudinal variations of LUE were smaller (Figure 3.16c), and the same order of magnitude for inter-model variability. Thus, the analysis of zonally averaged factors was another indication that variations of NPP within models were determined by APAR on a first order, while variations of NPP among models were determined by LUE. GLO-PEM was an exception, as it presented obvious maxima for mid- to high-latitudes, and minima for the seasonal tropics, much like the latitudinal variations of NPP and APAR. Only in GLO?PEM was LUE a significant factor in explaining spatial variations of NPP.
Figure 3.16: Zonal profiles per 0.5o latitude zones. (a) NPP (Pg C yr-1 0.5o (lat)-1); (b) APAR (MJ yr-1 0.5o (lat)-1); (c) LUE (g C MJ-1). The models are: BIOME3 (solid green), CARAIB (solid light blue), CASA (solid purple), FBM (solid dark blue), GLO-PEM (solid red), HYBRID (dashed green), KGBM (dashed grey), PLAI (dashed dark blue), SDBM (dashed red), SIB2 (solid yellow), SILVAN (dashed light blue), and TURC (dashed purple).
The zonal profiles were a qualitative assessment of variable response (NPP and its drivers) over environmental gradients (such as water and temperature. The primary effect of limitations in available resources is to reduce APAR, while effects on LUE were secondary. Some features were more or less constant in the zonal profiles of LUE (Fig. 3.16c). In the dry tropics (15o - 25o N), all models exhibited a dip in LUE, reflecting a water shortage. In the equatorial zone (0o - -10o), all models, except TURC, predicted a peak in LUE, reflecting the abundance of environmental resources available for plant production (water, light, nitrogen). This can be explained for all models, except TURC, where simulated stress factors resulted in a reduction of LUE when water, and sometimes also nitrogen, were limiting. Important discrepancies among models were visible in Northern temperate and boreal zones (30o - 60o N). Mean values of LUE were the most variable in these latitudes, primarily because of the extremely high LUE for GLO-PEM. In addition, the temperature gradient which characterized this zone corresponded to either a strong increase (GLO-PEM), a slight increase (HYBRID, TURC, SDBM, PLAI), no significant trend (CARAIB, CASA, FBM, KGBM, SILVAN), or a decrease in LUE (BIOME3). Thus, models generally agreed on the effects of water stress on light use efficiency, but did not agree on the effects of temperature.
Spatial patterns of APAR were very similar among models. The main difference was in the overall value of APAR: the NDVI-derived APAR was significantly lower than modeled APAR. The following suggest different possible reasons behind the consistent global 28% discrepancy:
Underestimation of NDVI-derived FPAR: The NDVI product was pre-processed with atmospheric corrections, calibration, filtering, compositing, and reconstruction where cloud contamination is known to be particularly important (Sellers et al. 1994). The effect of most of the contamination was to decrease the satellite signal compared to what could be measured at the surface. Some additional cloud, atmospheric or instrumental contamination may remain after processing (Ouadrari et al. 1997). However, the various algorithms used to calibrate the FPAR/NDVI relationship assumed that maximum FPAR (in the range 0.9-0.98 depending on the model) corresponded to maximum NDVI, which should correct some of the remaining underestimation of the satellite signal.
Overestimation of modeled FPAR.: Some models might overestimate LAI because they did not include the entire range of possible constraints on LAI: there was no upper limit, for instance, on the LAI of KGBM provided there was enough water. In addition, some models calibrated some of their relationships such that maximum LAI agreed with the literature data, but these data generally come from more productive stands, which could be biased towards high values, and not representative of a 0.5o longitude/latitude grid cell area.
Potential versus actual land cover: Satellite-derived FPAR corresponded to actual land cover including natural, agricultural and urban areas, while models that computed LAI usually considered potential natural vegetation. Potential vegetation generally has higher FPAR than agricultural and urban areas. Even though crops develop a very dense canopy and the resulting APAR could be of the same order as natural vegetation, the active period was shorter for crops compared to grassland or deciduous forests. Results from CARAIB, however, which computed LAI but did incorporate land use, did not support this hypothesis. In regions with little or no land use, simulated APAR from CARAIB were generally closer to satellite-derived APAR than other models simulating LAI, but in regions which were strongly affected by land use, its simulated APAR remained higher than the satellite-derived APAR (not shown). Presently, it is not possible to determine which of these possible reasons was the primary cause of the discrepancies. An answer to this question will likely come from improved remote sensing and modeling techniques. In the last decade, NDVI derived from NOAA-AVHRR has been the only reliable source of satellite data available to monitor the activity of vegetation from space. New sensors (POLDER, VEGETATION, MODIS, etc.), having increased spatial resolution as well as spectral and directional properties, will provide improved land cover estimates and vegetation properties, including FPAR.
Differences in NPP among models were determined by differences in LUE. Light use efficiencies were supposedly not very variable (e.g., Monteith 1972, 1977), so they could be considered a “characteristic” of a vegetation type or a climatic zone. In addition, LUE was not a variable used explicitly in the CPMs. Thus, values of LUE derived from the literature could constitute an independent check of model behavior.
The comparison with literature data was limited here to the overall range of variation, not exact grid cell by grid cell values or even means per biomes. Indeed, many other factors, apart from model errors could generate discrepancies between simulated and measured light use efficiencies: 1) differences in the definition of biomes (see ecotones, above); 2) differences in the definition of light-use efficiencies (for instance, literature-derived LUE generally corresponded to above-ground LUE, while model values corresponded to total LUE); and 3) sampling biases in the literature data (because of the scarcity of data, the values corresponding to mean per biomes were generally not representative). More information will come from the development of improved techniques for measuring fluxes of CO2 over whole canopies. From these measurements, it will be generally possible to extract light use efficiencies for GPP that are more representative of entire ecosystems and their seasonal variations (Ruimy et al. 1995).
This first systematic comparison of terrestrial biogeochemical models was performed using the terrestrial carbon flux variable (NPP). Results have shown generally good agreement between the present generation of models over many broad features, despite the fact that the models had been developed for widely differing purposes and with widely differing resource bases for personnel and computing power. However, and perhaps more interestingly, both the differences in model behavior and the unresolved question of explicit or unconscious calibration to an assumed global NPP have highlighted potentially important shortcomings in our understanding of the total Earth system. These issues need to be resolved if the demands (e.g. Kyoto Protocol, 1988) for predictions or sensitivity assessments of the stability and sustainability of the terrestrial biosphere are to be credible. Improved physical models of the ocean and atmosphere alone cannot improve the predictability of the Earth's system carbon-exchange processes. Moreover, a recent assessment of the future of coupled systems [Melillo et al., 1996] indicated that atmospheric and oceanic simulations are likely to be highly sensitive to the dynamics of the terrestrial biosphere.
Recently, new technological and scientific developments have indicated that our ability to observe changes in biospheric activity may be greater than anticipated: Keeling et al. (1996) made the convincing point that, at least over the period since 1960, biospheric activity in northern latitudes may have changed strongly enough to produce an average extension of the growing period by seven days, and Myneni et al. (1997) used the AVHRR satellite record to confirm this result, while also indicating the regional uncertainties. The modeling teams aiming at simulating global carbon fluxes now face the challenge to explain or reject these and other hypotheses about the dynamics of the Earth's vegetation.
To meet this challenge, several activities are now underway -- some of them being spawned from the NPP workshops: Numerous 'minor findings' from the working group discussions have already lead to improved versions of the various models. In several cases, only the thorough scrutiny provided by colleagues during the comparison process could identify errors or minor inadequacies that have initiated model improvement. The importance of an improved data basis for several key features of model development and application has been illustrated and is now leading to accelerated activities in several key areas, such as climate [Cramer, 1996; Cramer et al., 1996], soils [Scholes, 1996], and land use [Turner et al., 1995]. A particular area where better data products are achievable and urgently needed is the wealth of site-based observations of biogeochemical fluxes. As a direct spin-off from the Potsdam comparisons, an international team, the Global Primary Production Data Initiative (GPPDI) is now developing a new collection of such observed variables, as well as improved methods to make this data useful at the broad scale required for global applications [Olson and Prince, 1996; Prince et al., 1995].
