Lagged effects dominate the inter-annual variability of the 2010–2015 tropical carbon balance

Abstract

Inter-annual variations in the tropical land carbon (C) balance are a dominant component of the global atmospheric CO2 growth rate. Currently, the lack of quantitative knowledge on processes controlling net tropical ecosystems C balance on inter-annual timescales inhibits accurate understanding and projections of land-atmosphere C exchanges. In particular, uncertainty on the 20 relative contribution of ecosystem C fluxes attributable to concurrent meteorological forcing anomalies (concurrent effects) and those attributable to the continuing influence of past phenomena (lagged effects) stifles efforts to explicitly understand the integrated sensitivity of tropical ecosystem to climatic variability. Here we present a conceptual framework-applicable in principle to any meteorology-forced land biosphere model-to explicitly quantify net biospheric exchange (NBE) as the sum of anomaly-induced concurrent changes and climatology-induced lagged changes to terrestrial ecosystem C states (NBE = 25 NBE CON + NBE LAG ). We apply this framework to an observation-constrained analysis of the 2010-2015 tropical C balance:we use a data-model integration approach (CARDAMOM) to merge satellite-retrieved land-surface C observations (leaf area, biomass, solar-induced fluorescence), soil C inventory data and satellite-based atmospheric inversion estimates of CO2 and CO fluxes to produce a data-constrained analysis of the 2010-2015 tropical C cycle. We find that the inter-annual variability of lagged effects explains the majority of NBE inter-annual variability (IAV) throughout 2010-2015 across the tropics (NBE LAG 30 IAV = 112% of NBE IAV, r = 0.87) relative to concurrent effects (NBE CON IAV = 54% of total NBE IAV, r = 0.03) and the dominance of NBE LAG IAV persists across both wet and dry tropical ecosystems. The magnitude of lagged effect variations on NBE across the tropics is largely attributable to lagged effects on net primary productivity (NPP; NPP LAG IAV 88% of NBE LAG IAV, r = -0.99, p-value<0.05), which emerge due to the dependence of NPP on inter-annual variations in canopy C mass and

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model-data fusion framework

“…While further experiments are needed, these results demonstrate the value of (1) site-level data assimilation for local scale prediction of GPP magnitude and variability, (2) global data assimilation for reducing magnitude biases, and (3) process formulation for accounting for sensitivity to temperature limitation and water stress. Overall, these results are encouraging for model-data fusion systems which have developed the capacity to bring together temporally and spatially resolved functional and structural vegetation components such as LAI, SIF, soil organic matter, and above-and below-ground biomass (e.g., Bacour et al, 2019;Smith et al, 2020;Bloom et al, 2020).…”

“…ACM GPP estimates are contingent on plant structural and biochemical variables (including LAI, foliar nitrogen and nitrogen-use efficiency) and meteorological forcings (total daily irradiance, maximum and minimum daily temperature, day length, atmospheric CO2 concentration). In DALEC2, water limitation on ACM is prescribed as a linear response to soil water deficit (Bloom et al, 2020). For more details on the model-data fusion methodology and CARD ensembles, we refer the reader to Appendix A.…”

“…For more details on the model-data fusion methodology and CARD ensembles, we refer the reader to Appendix A. For a comprehensive overview of DALEC2 model, we refer the reader to Bloom et al, (2020) and references therein.…”

“…To answer this question, we combine a mechanistic ecosystem C model (Data Assimilation Linked Ecosystem Carbon, DALEC2; Williams et al, 2005;Bloom et al, 2016) with the CARbon DAta-MOdel fraMework (CARDAMOM; Bloom and Williams, 2015;Bloom et al, 2020) driven by observed meteorological forcing and constrained against eddy covariance fluxes at US-NR1, to investigate the temperature sensitivity of this subalpine evergreen forest at seasonal and interannual timescales. We introduce a new cold temperature limitation function, trained on observed temperature, for more realistic simulation of spring GPP onset.…”

Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century

“…In Section 5, we discuss how integrating and assimilating satellite observations into terrestrial biosphere models may better constrain these processes to ensure consistency in the inferred feedback mechanisms. This section also provides examples for combining observations with a new class of models that can assimilate these data for quantifying carbon/water interactions (e.g., Bloom et al., 2020; T. Schneider et al., 2017). Finally, we make recommendations on new observations, joint satellite/aircraft/ground field campaigns and model/assimilation development in Section 6.…”