Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models

Abstract

With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon‐LAnd Model Intercomparison Project (C‐LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) – Carnegie‐Ames‐Stanford Approach′ (CASA′) and carbon–nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1–3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4 Pg C yr−1 for CASA′ vs. 1.2 Pg C yr−1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988–2004 based on comparison with atmospheric inversion results from TRANSCOM (r=0.66 for CASA′ and r=0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C‐LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community‐wide platform for model‐data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C‐LAMP are publicly available on the Earth System Grid.

Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling

“…Although an earlier version of CLM-CN had an attenuated seasonal cycle of atmospheric CO 2 across the northern hemisphere (19), introduction of a photoperiod correction on V cmax reduced bias and mean absolute error for seasonal cycle amplitude by 49% and 34%, respectively (mean of 60 stations globally) (Fig. 4 and Table S1).…”

“…As an evaluation of introducing a day-length correction on a broadly integrative global-scale metric, we gauged model performance with and without a simple photoperiod control for 1988-2004 to assess the amplitude and phase of seasonal cycles of atmospheric CO 2 concentration, using the C-LAMP protocol and metrics (19), adding observations from the southern hemisphere. Although an earlier version of CLM-CN had an attenuated seasonal cycle of atmospheric CO 2 across the northern hemisphere (19), introduction of a photoperiod correction on V cmax reduced bias and mean absolute error for seasonal cycle amplitude by 49% and 34%, respectively (mean of 60 stations globally) (Fig.…”

Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis

“…As a starting point, Stö ckli et al (2008) and Randerson et al (2009) have emphasized the importance of developing better data sets with which to test and evaluate model predictions. Comparative analyses of different phenological models have typically used data from only a single site (e.g., Richardson & O'Keefe, 2009), which hinders the development and parameterization of generalized models.…”

“…They concluded that model bias toward under-predicting temperate and boreal forest uptake of CO 2 could be attributed to a 1-3 month delay in predicting the timing of maximum LAI in these ecosystems, compared to estimates derived from MODIS data. Randerson et al (2009) also noted that the two models they evaluated tended to predict a longer growing season than was actually observed in temperate ecosystems, with photosynthetic uptake occurring too early in the spring and too late in the autumn, compared with ground observations. Errors in LAI would likely propagate to errors in partitioning the available energy to latent and sensible heat fluxes, and errors in the timing of photosynthetic uptake would also affect the seasonality of modeled atmospheric CO 2 concentrations, emphasizing the importance of accurate representation of phenologically mediated processes.…”

“…Randerson et al (2009) included phenological metrics as part of a systematic framework, the Carbon-LAnd Model intercomparison Project (C-LAMP), to assess the biogeochemical component of coupled climate-carbon models. They concluded that model bias toward under-predicting temperate and boreal forest uptake of CO 2 could be attributed to a 1-3 month delay in predicting the timing of maximum LAI in these ecosystems, compared to estimates derived from MODIS data.…”