The considerable interannual variability (IAV) (5 PgC yr1) observed in atmospheric CO2 is dominated by variability in terrestrial productivity. Among terrestrial ecosystems, grassland productivity IAV is greatest. Relationships between grassland productivity IAV and climate drivers are poorly explained by traditional multiple-regression approaches. We propose a novel method, the perfect-deficit approach, to identify climate drivers of grassland IAV from observational data. The maximum daily value of each ecological or meteorological variable for each day of the year, over the period of record, defines the perfect annual curve. Deficits of these variables can be identified by comparing daily observational data for a given year against the perfect curve. Links between large deficits of ecosystem activity and extreme climate events are readily identified. We applied this approach to five grassland sites with 26 site-years of observational data. Large deficits of canopy photosynthetic capacity and evapotranspiration derived from eddy-covariance measurements, and leaf area index derived from satellite data occur together and are driven by a local-dryness index during the growing season. This new method shows great promise in using observational evidence to demonstrate how extreme climate events alter yearly dynamics of ecosystem potential productivity and exchanges with atmosphere, and shine a new light on climate-carbon feedback mechanisms.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Public Health, Environmental and Occupational Health