Abstract
Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. However, the response of ecosystems to precipitation variability is difficult to constrain due to myriad physiological and abiotic variables that limit terrestrial productivity. Based on a combination of satellite imagery and a global network of daily precipitation data, we present here a statistical framework to isolate the impact of precipitation variability on the gross primary productivity of five biomes that collectively account for 50% of global land area. The productivity of mesic grasslands and forests decreases by ~28% and ~7% (respectively) in response to more irregular rain within the year, while the sensitivity is halved in response to higher year-to-year variability. Xeric grasslands are similarly impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. Conversely, the productivity of boreal forests increases under higher variability on both timescales. We conclude that projected changes in precipitation variability will have a measurable global impact on the terrestrial carbon sink.
Highlights
Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks
Climate models predict that precipitation variability is expected to be more impacted by climate change[2,3,4], with evidence of these changes emerging from observations[5,6,7,8]
To generate a more holistic estimate of the effects of rain frequency on productivity, we have developed here a variant of the third approach that establishes a global constraint on how productivity is affected by rainfall variance by comparing the productivity between ecosystems with similar mean climate states but different rain variability
Summary
Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. Xeric grasslands are impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. To generate a more holistic estimate of the effects of rain frequency on productivity, we have developed here a variant of the third approach that establishes a global constraint on how productivity is affected by rainfall variance by comparing the productivity between ecosystems with similar mean climate states but different rain variability. One of the challenges in generalizing the response of a biome to rainfall variance emerges from the diversity of ecophysiological characteristics (canopy height, rooting depth, and stomatal response to soil moisture), soil types (nutrient pool, porosity, and conductivity) and mean climate state that modulate the sensitivity of ecosystems to precipitation patterns. Far, existing literature has not established a set of agreed-upon rainfall variability parameters (RVP) that are globally relevant for ecosystem productivity and can be aggregated and scaled spatially and temporally
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