Atmospheric observations inform CO₂ flux responses to enviroclimatic drivers

Abstract

Understanding the response of the terrestrial biospheric carbon cycle to variability in enviroclimatic drivers is critical for predicting climate-carbon interactions. Here we apply an atmospheric-inversion-based framework to assess the relationships between the spatiotemporal patterns of net ecosystem CO 2 exchange (NEE) and those of enviroclimatic drivers. We show that those relationships can be directly observed at 1°× 1°3-hourly resolution from atmospheric CO 2 measurements for four of seven large biomes in North America, namely, (i) boreal forests and taiga; (ii) temperate coniferous forests; (iii) temperate grasslands, savannas, and shrublands; and (iv) temperate broadleaf and mixed forests. We find that shortwave radiation plays a dominant role during the growing season over all four biomes. Specific humidity and precipitation also play key roles and are associated with decreased CO 2 uptake (or increased release). The explanatory power of specific humidity is especially strong during transition seasons, while that of precipitation appears during both the growing and dormant seasons. We further find that the ability of four prototypical terrestrial biospheric models (TBMs) to represent the spatiotemporal variability of NEE improves as the influence of radiation becomes more dominant, implying that TBMs have a better skill in representing the impact of radiation relative to other drivers. Even so, we show that TBMs underestimate the strength of the relationship to radiation and do not fully capture its seasonality. Furthermore, the TBMs appear to misrepresent the relationship to precipitation and specific humidity at the examined scales, with relationships that are not consistent in terms of sign, seasonality, or significance relative to observations. More broadly, we demonstrate the feasibility of directly probing relationships between NEE and enviroclimatic drivers at scales with no direct measurements of NEE, opening the door to the study of emergent processes across scales and to the evaluation of their scaling within TBMs.

Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling

“…We use a form of the BIC that has been adapted for use within a geostatistical inverse modeling framework. This setup has previously been used to select either bottom-up models or environmental drivers of CO 2 and CH 4 fluxes (e.g., Mueller et al, 2010;Yadav et al, 2010;Gourdji et al, 2012;Miller et al, 2013Miller et al, , 2014Shiga et al, 2014;Fang et al, 2014;Fang and Michalak, 2015). The implementation here mirrors that of Fang et al (2014), Shiga et al (2014), and Fang and Michalak (2015):…”

“…We modify the model selection setup in Sect. 2.2 to focus on the spatial distribution of each estimate using a procedure developed by Fang et al (2014) and Fang and Michalak (2015). Instead of fixing the coefficients (β) to 1, we instead estimate the coefficients using real atmospheric CH 4 observations.…”

“…To this end, we use a version of the model selection framework that chooses wetland models based upon their spatial distribution (Fang et al, 2014;Fang and Michalak, 2015). WETCHIMP models that are chosen by the frame- work have a spatial distribution that is more consistent with atmospheric observations relative to those that are not selected.…”

“…To answer these questions, we utilize a model selection procedure based upon the Bayesian information criterion (BIC; Sect. 2.2, Shiga et al, 2014;Fang et al, 2014;Fang and Michalak, 2015). This procedure determines whether wetland fluxes from different regions and seasons are necessary to describe variability in synthetic atmospheric CH 4 observations.…”

Fundamentals of Data Assimilation

“…Fang et al, 2014;Fang and Michalak, 2015). The choice of prior is an expression of a choice of assumptions.…”