Abstract
Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson’s correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid- to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.
Highlights
Over the past 30 years atmospheric CO2 levels have been rising as a result of fossil fuel emissions.Superimposed on the atmospheric CO2 growing trend are large interannual excursions, and evidence strongly suggests that this interannual variability in the atmospheric CO2 growth rate is driven largely by the terrestrial biosphere [1,2]
Global carbon cycle models are challenged to reproduce this CO2 growth rate variability, and explanations largely involve independent responses of terrestrial net primary production (NPP), heterotrophic respiration (RH), and fire emissions to climate anomalies such as those associated with ENSO events [3,4,5,6]
Precise modeling of NPP is key in terrestrial carbon cycle models, because NPP represents the net uptake of carbon from the atmosphere by vegetation and affects other carbon fluxes by providing the substrate for RH and fuel for combustion by fire
Summary
Over the past 30 years atmospheric CO2 levels have been rising as a result of fossil fuel emissions.Superimposed on the atmospheric CO2 growing trend are large interannual excursions (nearly 100% of the trend), and evidence strongly suggests that this interannual variability in the atmospheric CO2 growth rate is driven largely by the terrestrial biosphere [1,2]. Global carbon cycle models are challenged to reproduce this CO2 growth rate variability, and explanations largely involve independent responses of terrestrial net primary production (NPP), heterotrophic respiration (RH), and fire emissions to climate anomalies such as those associated with ENSO events [3,4,5,6]. Understanding the potential for the terrestrial biosphere to mitigate or perhaps aggravate CO2 accumulation in the atmosphere may be as important for predicting future climate change as projections of future fossil fuel emissions. Improved forecasts of future trends in atmospheric CO2 will be possible, through the development of models that can realistically represent the processes controlling current and past terrestrial carbon fluxes [7]. Terrestrial NPP has been showed to play a central role in determining the local and global CO2 content of the atmosphere at temporal scales spanning hours [8] to epochs [9]
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