Summary
To analyse the broad‐scale behaviour of 15 global models of the terrestrial biosphere, we evaluated the sensitivity of simulated net primary productivity (NPP) to spatial and seasonal ...variations in precipitation, temperature and solar radiation, and to the Normalized Difference Vegetation Index (NDVI). For annual NPP estimates, the models’ sensitivities to climate were the most similar in regions where NPP was not limited by precipitation. The largest differences in sensitivities occurred in regions where NPP was limited by both temperature and precipitation. Water use efficiencies within the models were relatively constant across latitudes so that higher correlations occurred between the latitudinal distribution of NPP and precipitation than with the other climate variables. The sensitivities of NPP estimates to solar radiation varied considerably with latitude. The largest differences in temperature sensitivity among NPP estimates occurred in the northern latitudes (50°N–70°N), i.e. the zone with the shortest active growing seasons. The sensitivity of NPP estimates to climate also varied seasonally. At the beginning and end of the active growing season in the boreal zone, monthly NPP estimates of all models were the most sensitive to temperature. In the tropics, sensitivities to climate varied widely among and within models. Seasonal changes in water balance and the structure of the vegetation canopy, as reflected by seasonal changes in NDVI, modified the sensitivity of NPP to climate in both boreal and tropical zones. Because these are both highly productive regions sensitive to climate change, continued investigations and better validation of models are necessary before we can fully understand and predict changes in ecosystem structure and function under various climatic conditions.
An atmospheric transport model and observations of atmospheric CO2 are used to evaluate the performance of four Terrestrial Carbon Models (TCMs) in simulating the seasonal dynamics and interannual ...variability of atmospheric CO2 between 1980 and 1991. The TCMs were forced with time varying atmospheric CO2 concentrations, climate, and land use to simulate the net exchange of carbon between the terrestrial biosphere and the atmosphere. The monthly surface CO2 fluxes from the TCMs were used to drive the Model of Atmospheric Transport and Chemistry and the simulated seasonal cycles and concentration anomalies are compared with observations from several stations in the CMDL network. The TCMs underestimate the amplitude of the seasonal cycle and tend to simulate too early an uptake of CO2 during the spring by approximately one to two months. The model fluxes show an increase in amplitude as a result of land‐use change, but that pattern is not so evident in the simulated atmospheric amplitudes, and the different models suggest different causes for the amplitude increase (i.e., CO2 fertilization, climate variability or land use change). The comparison of the modeled concentration anomalies with the observed anomalies indicates that either the TCMs underestimate interannual variability in the exchange of CO2 between the terrestrial biosphere and the atmosphere, or that either the variability in the ocean fluxes or the atmospheric transport may be key factors in the atmospheric interannual variability.
Results of an intercomparison among terrestrial biogeochemical models (TBMs) are reported, in which one diagnostic and five prognostic models have been run with the same long‐term climate forcing. ...Monthly fields of net ecosystem production (NEP), which is the difference between net primary production (NPP) and heterotrophic respiration RH, at 0.5° resolution have been generated for the terrestrial biosphere. The monthly estimates of NEP in conjunction with seasonal CO2 flux fields generated by the seasonal Hamburg Model of the Oceanic Carbon Cycle (HAMOCC3) and fossil fuel source fields were subsequently coupled to the three‐dimensional atmospheric tracer transport model TM2 forced by observed winds. The resulting simulated seasonal signal of the atmospheric CO2 concentration extracted at the grid cells corresponding to the locations of 27 background monitoring stations of the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory network is compared with measurements from these sites. The Simple Diagnostic Biosphere Model (SDBM1), which is tuned to the atmospheric CO2 concentration at five monitoring stations in the northern hemisphere, successfully reproduced the seasonal signal of CO2 at the other monitoring stations. The SDBM1 simulations confirm that the north‐south gradient in the amplitude of the atmospheric CO2 signal results from the greater northern hemisphere land area and the more pronounced seasonality of radiation and temperature in higher latitudes. In southern latitudes, ocean‐atmosphere gas exchange plays an important role in determining the seasonal signal of CO2. Most of the five prognostic models (i.e., models driven by climatic inputs) included in the intercomparison predict in the northern hemisphere a reasonably accurate seasonal cycle in terms of amplitude and, to some extent, also with respect to phase. In the tropics, however, the prognostic models generally tend to overpredict the net seasonal exchanges and stronger seasonal cycles than indicated by the diagnostic model and by observations. The differences from the observed seasonal signal of CO2 may be caused by shortcomings in the phenology algorithms of the prognostic models or by not properly considering the effects of land use and vegetation fires on CO2 fluxes between the atmosphere and terrestrial biosphere.
We investigated the role of biomass burning in simulating the seasonal signal in both prognostic and diagnostic analyses. The prognostic analysis involved the High‐Resolution Biosphere Model, a ...prognostic terrestrial biosphere model, and the coupled vegetation fire module, which together produce a prognostic data set of biomass burning. The diagnostic analysis involved the Simple Diagnostic Biosphere Model (SDBM) and the Hao and Liu 1994 diagnostic data set of biomass burning, which have been scaled to global 2 and 4 Pg C yr−1, respectively. The monthly carbon exchange fields between the atmosphere and the biosphere with a spatial resolution of 0.5° × 0.5°, the seasonal atmosphere‐ocean exchange fields, and the emissions from fossil fuels have been coupled to the three‐dimensional atmospheric transport model TM2. We have chosen eight monitoring stations of the National Oceanic and Atmospheric Administration network to compare the predicted seasonal atmospheric CO2 signals with those deduced from atmosphere‐biosphere carbon exchange fluxes without any contribution from biomass burning. The prognostic analysis and the diagnostic analysis with global burning emissions of 4 Pg C yr−1 agree with respect to the change in the amplitude of the seasonal CO2 concentration introduced through biomass burning. We find that the seasonal CO2 signal at stations in higher northern latitudes (north of 30°N) is marginally influenced by biomass burning. For stations in tropical regions an increase in the CO2 amplitude of more than 1 ppmv (up to 50% with respect to the observed trough to peak amplitude) has been calculated. Biomass burning at stations farther south accounts for an increase in the CO2 amplitude of up to 59% (0.6 ppmv). A change in the phase of the seasonal CO2 signal at tropical and southern stations has been shown to be strongly influenced by the onset of biomass burning in southern tropical Africa and America. Comparing simulated and observed seasonal CO2 signals, we find higher discrepancies at southern tropical stations if biomass burning emissions are included. This is caused by the additional increase in the amplitude in the prognostic analysis and a phase shift in a diagnostic analysis. In contrast, at the northern tropical stations biomass burning tends to improve the estimates of the seasonal CO2 signal in the prognostic analysis because of strengthening of the amplitude. Since the SDBM predicts the seasonal CO2 signal reasonably well for the northern hemisphere tropical stations, no general improvement of the fit occurs if biomass burning emissions are considered.
Strategies are developed to analyze and represent spatially resolved biosphere models for carbon sequestration in response to changes in atmospheric CO2 and climate by reduced‐form, substitute ...models. We explore the High‐Resolution Terrestrial Biosphere Model as implemented in the Community Terrestrial Biosphere Model (HRBM/CTBM), the Frankfurt Biosphere Model (FBM), and the box‐type biosphere of the Bern model. Storage by CO2 fertilization is described by combining analytical representations of (1) net primary productivity (NPP) as a function of atmospheric CO2 and (2) a decay impulse response function to characterize the timescales of biospheric carbon turnover. Storage in response to global warming is investigated for the HRBM/CTBM. The relation between the evolution of radiative forcing and climate change is expressed by a combination of impulse response functions and empirical orthogonal functions extracted from results of the European Center/Hamburg (ECHAM3) coupled atmosphere‐ocean general circulation model. A box‐type, differential‐analogue substitute model is developed to represent global carbon storage of the HRBM/CTBM in response to regional changes in Temperature, Precepitation and cloud cover. The substitute models represent the spatially resolved models accurately and cost‐efficiently for carbon sequestration in response to changes in CO2 or in CO2 and climate and for simulations of the global isotopic signals. Deviations in carbon uptake simulated by the spatially resolved models and their substitutes are less than a few percent.
Summary
Seventeen global models of terrestrial biogeochemistry were compared with respect to annual and seasonal fluxes of net primary productivity (NPP) for the land biosphere. The comparison, ...sponsored by IGBP‐GAIM/DIS/GCTE, used standardized input variables wherever possible and was carried out through two international workshops and over the Internet. The models differed widely in complexity and original purpose, but could be grouped in three major categories: satellite‐based models that use data from the NOAA/AVHRR sensor as their major input stream (CASA, GLO‐PEM, SDBM, SIB2 and TURC), models that simulate carbon fluxes using a prescribed vegetation structure (BIOME‐BGC, CARAIB 2.1, CENTURY 4.0, FBM 2.2, HRBM 3.0, KGBM, PLAI 0.2, SILVAN 2.2 and TEM 4.0), and models that simulate both vegetation structure and carbon fluxes (BIOME3, DOLY and HYBRID 3.0). The simulations resulted in a range of total NPP values (44.4–66.3 Pg C year–1), after removal of two outliers (which produced extreme results as artefacts due to the comparison). The broad global pattern of NPP and the relationship of annual NPP to the major climatic variables coincided in most areas. Differences could not be attributed to the fundamental modelling strategies, with the exception that nutrient constraints generally produced lower NPP. Regional and global NPP were sensitive to the simulation method for the water balance. Seasonal variation among models was high, both globally and locally, providing several indications for specific deficiencies in some models.
ABSTRACT
We compared the simulated responses of net primary production, heterotrophic respiration, net ecosystem production and carbon storage in natural terrestrial ecosystems to historical (1765 to ...1990) and projected (1990–2300) changes of atmospheric CO2 concentration of four terrestrial biosphere models: the Bern model, the Frankfurt Biosphere Model (FBM), the High‐Resolution Biosphere Model (HRBM) and the Terrestrial EcosystemModel (TEM). The results of the model intercomparison suggest that CO2 fertilization of natural terrestrial vegetation has the potential to account for a large fraction of the so‐called ‘‘missing carbon sink’′ of 2.0 Pg C in 1990. Estimates of this potential are reduced when the models incorporate the concept that CO2 fertilization can be limited by nutrient availability. Although the model estimates differ on the potential size (126 to 461 Pg C) of the future terrestrial sink caused by CO2 fertilization, the results of the four models suggest that natural terrestrial ecosystems will have a limited capacity to act as a sink of atmospheric CO2 in the future as a result of physiological constraints and nutrient constraints on NPP. All the spatially explicit models estimate a carbon sink in both tropical and northern temperate regions, but the strength of these sinks varies over time. Differences in the simulated response of terrestrial ecosystems to CO2 fertilization among the models in this intercomparison study reflect the fact that the models have highlighted different aspects of the effect of CO2 fertilization on carbon dynamics of natural terrestrial ecosystems including feedback mechanisms. As interactions with nitrogen fertilization, climate change and forest regrowth may play an important role in simulating the response of terrestrial ecosystems to CO2 fertilization, these factors should be included in future analyses. Improvements in spatially explicit data sets, whole‐ecosystem experiments and the availability of net carbon exchange measurements across the globe will also help to improve future evaluations of the role of CO2 fertilization on terrestrial carbon storage.
ABSTRACT
Assessing the global budget for atmospheric CO2 through analyses of observed atmospheric and oceanic data sets, some approaches rely partially upon estimates on the magnitude of the isotopic ...disequilibrium between the terrestrial biosphere and the perturbed atmosphere. To test if these estimates agree with the results of grid‐based global carbon cycle models, we developed a module that considers the carbon cycle of the stable isotope 13C, besides 12C, and coupled it with the high‐resolution biosphere model. The module describes the fluxes of carbon in the system compartments phytomass, litter, soil, and atmosphere, separately for 13C and 12C. It enables us to predict the changes of the isotope ratios in historical times. Based on a transient model run since preindustrial times forced by observed atmospheric CO2 concentrations and its 13C/12C ratios we find a global mean terrestrial biosphere 13C disequilibrium of 0.41‰ (18 Pg C‰ yr−1) for the period 1970‐1987. This result is consistent with the estimates used in global carbon budget analyses.
An atmospheric transport model and observations of atmospheric CO
2
are used to evaluate the performance of four Terrestrial Carbon Models (TCMs) in simulating the seasonal dynamics and interannual ...variability of atmospheric CO
2
between 1980 and 1991. The TCMs were forced with time varying atmospheric CO
2
concentrations, climate, and land use to simulate the net exchange of carbon between the terrestrial biosphere and the atmosphere. The monthly surface CO
2
fluxes from the TCMs were used to drive the Model of Atmospheric Transport and Chemistry and the simulated seasonal cycles and concentration anomalies are compared with observations from several stations in the CMDL network. The TCMs underestimate the amplitude of the seasonal cycle and tend to simulate too early an uptake of CO
2
during the spring by approximately one to two months. The model fluxes show an increase in amplitude as a result of land‐use change, but that pattern is not so evident in the simulated atmospheric amplitudes, and the different models suggest different causes for the amplitude increase (i.e., CO
2
fertilization, climate variability or land use change). The comparison of the modeled concentration anomalies with the observed anomalies indicates that either the TCMs underestimate interannual variability in the exchange of CO
2
between the terrestrial biosphere and the atmosphere, or that either the variability in the ocean fluxes or the atmospheric transport may be key factors in the atmospheric interannual variability.
We investigated the role of biomass burning in simulating the seasonal signal in both prognostic and diagnostic analyses. The prognostic analysis involved the High-Resolution Biosphere Model, a ...prognostic terrestrial biosphere model, and the coupled vegetation fire module, which together produce a prognostic data set of biomass burning. The diagnostic analysis involved the Simple Diagnostic Biosphere Model (SDBM) and the Hao and Liu 1994 diagnostic data set of biomass burning, which have been scaled to global 2 and 4 Pg C yr super(-1), respectively. The monthly carbon exchange fields between the atmosphere and the biosphere with a spatial resolution of 0.5 degree x 0.5 degree , the seasonal atmosphere-ocean exchange fields, and the emissions from fossil fuels have been coupled to the three-dimensional atmospheric transport model TM2. We have chosen eight monitoring stations of the National Oceanic and Atmospheric Administration network to compare the predicted seasonal atmospheric CO sub(2) signals with those deduced from atmosphere-biosphere carbon exchange fluxes without any contribution from biomass burning. The prognostic analysis and the diagnostic analysis with global burning emissions of 4 Pg C yr super(-1) agree with respect to the change in the amplitude of the seasonal CO sub(2) concentration introduced through biomass burning. We find that the seasonal CO sub(2) signal at stations in higher northern latitudes (north of 30 degree N) is marginally influenced by biomass burning. For stations in tropical regions an increase in the CO sub(2) amplitude of more than 1 ppmv (up to 50% with respect to the observed trough to peak amplitude) has been calculated. Biomass burning at stations farther south accounts for an increase in the CO sub(2) amplitude of up to 59% (0.6 ppmv). A change in the phase of the seasonal CO sub(2) signal at tropical and southern stations has been shown to be strongly influenced by the onset of biomass burning in southern tropical Africa and America. Comparing simulated and observed seasonal CO sub(2) signals, we find higher discrepancies at southern tropical stations if biomass burning emissions are included. This is caused by the additional increase in the amplitude in the prognostic analysis and a phase shift in a diagnostic analysis. In contrast, at the northern tropical stations biomass burning tends to improve the estimates of the seasonal CO sub(2) signal in the prognostic analysis because of strengthening of the amplitude. Since the SDBM predicts the seasonal CO sub(2) signal reasonably well for the northern hemisphere tropical stations, no general improvement of the fit occurs if biomass burning emissions are considered.
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