Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison

© 2016 John Wiley & Sons Ltd To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments

Determining biophysical, carbon and climate feedbacks of tropical deforestation: Do the pathways matter in both science and policy?

Research on climate and land surface coupling has made considerable progress in recent years, and this study contributes to our understanding of key feedbacks and to discussion in the science-policy nexus by exploring the question, Do the deforestation pathways matter? Deforestation perturbs both biophysical and carbon feedbacks on climate. However, biophysical feedbacks operate at temporally-immediate and spatially-focused scales, and thus may be sensitive to the rate of deforestation rather than just to the total forest cover loss. Transient shocks or non-linearities may exist but are not detected in equilibrium deforestation simulations. Described here is a method for implementing annual tree-to-grass conversion in the tropics using a fully-coupled global climate model (GCM) with interactive vegetation for the purpose of testing biophysical, carbon and climate response to deforestation pathways . Deforestation pathways are characterized here as the rates of tree loss paired with a preservation target (10% per tropical tree type). Code modifications are tested, and methods of analysis are initially explored using Community Climate System Model 3.0 (CCSM3) with the Dynamic Global Vegetation Model (DGVM) simulating a 10% vs. 1% rate. A newer version, CCSM4 with prognostic carbon and nitrogen cycles and dynamic vegetation (CNDV), which corrects crucial biases in the previous model, is used to simulate a range of deforestation rates—5%, 2%, 1% and 0.5%—again paired with a 10% preservation target. Sensitivity analyses are conducted on two levels—firstly, trends in biophysical, carbon and climate variables during the period of active deforestation are quantified; secondly, these trends are compared across deforestation pathways to determine the effect of the rate. These analyses are applied over tropical land, the Amazon Basin, Central Africa, and Southeast Asia. On a local to regional scale, the biophysical impacts of tree cover loss on climate were found to be comparable (as much as 55% in magnitude) to the combined biophysics and carbon impacts. The results further show that the biophysical, total ecosystem carbon and climate sensitivities to transient deforestation can be approximated as linear, and vary more across regions than across rates. Post-deforestation climate means are also similar across the different pathways. Only when the trends and means are averaged over Southeast Asia and over global land do the rates consistently produce statistically different results for the climate variables (as determined by an analysis of variance). However, there are no clear trends or patterns that are consistent across rates and regions, and in the post-deforestation period, the means are still within each other\u27s standard deviation. These suggest that the earth system response in CCSM4-CNDV to this particular range of rates may be less dependent on the rate than originally hypothesized. It is unclear whether a slow but prolonged deforestation is any better or worse than one that is rapid but short given the same preservation target. However, the deforestation rate is still an important consideration in that it hastens or delays significant warming on the order of decades, which matters from a mitigation, adaptation and development planning perspective. Implications on climate policy, particularly on reducing emissions from deforestation and forest degradation in developing countries (REDD), are discussed. The linearity of the earth system response allows for flexibility in project design and supports proposals to streamline monitoring methodologies by assigning fixed amounts of carbon per unit forest area. The results also imply that the use of projected rather than historical baselines would be more appropriate. However, the significance of biophysical impacts on local temperature and precipitation also suggests developing countries (especially in the Amazon Basin) have climate benefits to gain beyond the income from REDD+ carbon credits, and that carbon-based metrics are inadequate for the forestry sector, as well as for land cover/land use change in general. The modeling methodology shows it is possible to develop a direct climate sensitivity metric normalized per unit forest area, although it is unclear how this can be operationalized alongside the current carbon metric of fossil fuel-based sectors. Thus, composite indices of carbon credits which are enhanced or discounted by net biophysical impacts remain a viable alternative to be explored