How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data

Citation:
Rodenbeck, C, Zaehle S, Keeling R, Heimann M.  2018.  How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data Biogeosciences. 15:2481-2498.

Date Published:

2018/04

Keywords:

cycle, dioxide, Environmental Sciences & Ecology, geology, growth, increase, land, northern ecosystems, rain-forest, sampling-network, tropical temperature, variability

Abstract:

The response of the terrestrial net ecosystem exchange (NEE) of CO2 to climate variations and trends may crucially determine the future climate trajectory. Here we directly quantify this response on inter-annual timescales by building a linear regression of inter-annual NEE anomalies against observed air temperature anomalies into an atmospheric inverse calculation based on long-term atmospheric CO2 observations. This allows us to estimate the sensitivity of NEE to inter-annual variations in temperature (seen as a climate proxy) resolved in space and with season. As this sensitivity comprises both direct temperature effects and the effects of other climate variables co-varying with temperature, we interpret it as "inter-annual climate sensitivity". We find distinct seasonal patterns of this sensitivity in the northern extratropics that are consistent with the expected seasonal responses of photosynthesis, respiration, and fire. Within uncertainties, these sensitivity patterns are consistent with independent inferences from eddy covariance data. On large spatial scales, northern extratropical and tropical interannual NEE variations inferred from the NEE-T regression are very similar to the estimates of an atmospheric inversion with explicit inter-annual degrees of freedom. The results of this study offer a way to benchmark ecosystem process models in more detail than existing effective global climate sensitivities. The results can also be used to gap-fill or extrapolate observational records or to separate inter-annual variations from longer-term trends.

Notes:

n/a

Website

DOI:

10.5194/bg-15-2481-2018