RESUMEN
In the Arctic and Boreal region (ABR) where warming is especially pronounced, the increase of gross primary production (GPP) has been suggested as an important driver for the increase of the atmospheric CO2 seasonal cycle amplitude (SCA). However, the role of GPP relative to changes in ecosystem respiration (ER) remains unclear, largely due to our inability to quantify these gross fluxes on regional scales. Here, we use atmospheric carbonyl sulfide (COS) measurements to provide observation-based estimates of GPP over the North American ABR. Our annual GPP estimate is 3.6 (2.4 to 5.5) PgC · y-1 between 2009 and 2013, the uncertainty of which is smaller than the range of GPP estimated from terrestrial ecosystem models (1.5 to 9.8 PgC · y-1). Our COS-derived monthly GPP shows significant correlations in space and time with satellite-based GPP proxies, solar-induced chlorophyll fluorescence, and near-infrared reflectance of vegetation. Furthermore, the derived monthly GPP displays two different linear relationships with soil temperature in spring versus autumn, whereas the relationship between monthly ER and soil temperature is best described by a single quadratic relationship throughout the year. In spring to midsummer, when GPP is most strongly correlated with soil temperature, our results suggest the warming-induced increases of GPP likely exceeded the increases of ER over the past four decades. In autumn, however, increases of ER were likely greater than GPP due to light limitations on GPP, thereby enhancing autumn net carbon emissions. Both effects have likely contributed to the atmospheric CO2 SCA amplification observed in the ABR.
RESUMEN
We show that transport differences between two commonly used global chemical transport models, GEOS-Chem and TM5, lead to systematic space-time differences in modeled distributions of carbon dioxide and sulfur hexafluoride. The distribution of differences suggests inconsistencies between the transport simulated by the models, most likely due to the representation of vertical motion. We further demonstrate that these transport differences result in systematic differences in surface CO2 flux estimated by a collection of global atmospheric inverse models using TM5 and GEOS-Chem and constrained by in situ and satellite observations. While the impact on inferred surface fluxes is most easily illustrated in the magnitude of the seasonal cycle of surface CO2 exchange, it is the annual carbon budgets that are particularly relevant for carbon cycle science and policy. We show that inverse model flux estimates for large zonal bands can have systematic biases of up to 1.7 PgC/year due to large-scale transport uncertainty. These uncertainties will propagate directly into analysis of the annual meridional CO2 flux gradient between the tropics and northern midlatitudes, a key metric for understanding the location, and more importantly the processes, responsible for the annual global carbon sink. The research suggests that variability among transport models remains the largest source of uncertainty across global flux inversion systems and highlights the importance both of using model ensembles and of using independent constraints to evaluate simulated transport.