Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño–Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year−1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.
51Understanding tropical rainforest carbon exchange and its response to heat and 52 drought is critical for quantifying the effects of climate change on tropical ecosystems, 53including global climate-carbon feedbacks. Of particular importance for the global 54 carbon budget is net biome exchange of CO2 with the atmosphere (NBE), which 55 represents non-fire carbon fluxes into and out of biomass and soils. Sub-annual and sub-56Basin Amazon NBE estimates have relied heavily on process-based biosphere models, 57 despite lack of model agreement with plot-scale observations. We present a new analysis 58 of airborne measurements that reveals monthly, regional-scale (~1 -8 x 10 6 km 2 ) NBE 59variations. We develop a regional atmospheric CO2 inversion that provides the first 60 analysis of geographic and temporal variability in Amazon biosphere-atmosphere carbon 61 exchange and that is minimally influenced by biosphere model-based first guesses of 62 seasonal and annual-mean fluxes. We find little evidence for a clear seasonal cycle in 63Amazon NBE but do find NBE sensitivity to aberrations from long-term mean climate. In 64 particular, we observe increased NBE (more carbon emitted to the atmosphere) 65 associated with heat and drought in 2010, and correlations between wet season NBE and 66 precipitation (negative correlation) and temperature (positive correlation). In the eastern 67 Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 68 2010 heat and drought. We also identify regional differences in post-drought NBE that 69 appear related to long-term water availability. We examine satellite proxies and find 70 evidence for higher gross primary productivity (GPP) during a pulse of increased carbon 71
Abstract. Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as predictive tools for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science communities. In this study, we present and assess an approach to evaluating the spatiotemporal patterns, rather than aggregated magnitudes, of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 measurements. The approach is based on statistical model selection implemented within a high-resolution atmospheric inverse model. Using synthetic data experiments, we find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of North America, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns as well as to differentiate between multiple competing TBMs. Experiments using real atmospheric observations and four prototypical TBMs further confirm the applicability of the method, and demonstrate that the performance of TBMs in simulating the spatiotemporal patterns of NEE varies substantially across seasons, with best performance during the growing season and more limited skill during transition seasons. This result is consistent with previous work showing that the ability of TBMs to model flux magnitudes is also seasonally-dependent. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome-scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.
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