Submitted to: Biogeosciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/12/2011
Publication Date: 10/31/2011
Citation: Fay, P.A., Blair, J.M., Smith, M.D., Nippert, J.B., Carlisle, J.D., Knapp, A.K. 2011. Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences. 8:3053-3068. Interpretive Summary: Climate change scenarios and observed weather patterns indicate a continuing pattern of warming and increased precipitation variability, driven by ongoing increases in the concentrations of carbon dioxide and other ‘greenhouse gases’ in earth’s atmosphere. The impacts of warming and increased rainfall variability on grassland ecosystems remain unclear, in part because of a lack of long-term field experiments to allow evaluation of the effects on grassland productivity of warming and within season rainfall variability compared to the effects of among-year variability in rainfall. This knowledge is crucial to understanding the likely effects of climate change on livestock production. This paper reports findings from a 10 year study of the effect of rainfall and warming on tallgrass prairie. Among-year variability in rainfall was found to be the larger driver of variability in several measures of grassland productivity, including aboveground biomass, soil CO2 production, and leaf-level photosynthetic carbon uptake. Increased within-year variability in rainfall timing and amounts caused smaller, but still important effects on grassland productivity, reducing it compared to lower variability. Warming caused the least impact on grassland productivity, primarily affecting processes measured during cooler parts of the year. The results showed that each factor affected grassland productivity, and all three must be considered to accurately understand grassland capacity to support grazing under likely future climates.
Technical Abstract: Precipitation and temperature are primary drivers of many aspects of terrestrial ecosystem function, and vary in temporal hierarchies. Climate change scenarios predict increasing precipitation variability and temperature in the coming decades, and require long term experiments to evaluate the relative impacts of yearly variation in rainfall, more extreme within-year rainfall patterns, and warming on major ecosystem processes related to carbon cycling. Here we report a ten year study of the effects of among-year and within-year rainfall variability and year round warming on C4 grassland, using twelve 126 m2 rainfall exclusion shelters fitted with irrigation systems and infrared lamps. Among-year climatic variability resulted in 2-fold variation in growing season rainfall totals, and yielded ~50 – 200% variation in mid-season aboveground biomass, total aboveground net primary production (ANPP), leaf carbon assimilation (ACO2) and flowering culm production in dominant C4 grasses, and soil CO2 efflux (JCO2) despite only ~30 % variation in growing season mean soil water content (0 – 15 cm, '15). Among-year variation in soil moisture was thus amplified in most measures of ecosystem response. Regression analysis showed that mean '15 explained the greatest fraction (14 – 52 %) of the variation in these processes. Increased within season rainfall variability caused increased growing seasons soil moisture variation and smaller but still important effects on most ecosystem processes, compared to among year variability. Midseason biomass, ANPP, ACO2 in one or both C4 grasses, flowering culm production, and JCO2 decreased 8 - 40% in some or all years, suggesting reduced efficiency of ecosystem function. Warming treatments increased 5 cm soil temperature during spring, fall, and winter. Warming advanced canopy green up in spring, increased winter JCO2, while reducing summer JCO2 and forb biomass. These findings supported our hypothesis that rainfall variability predominates over warming as a driver of ecosystem processes critical to carbon cycling in this grassland. They also show that within year variability is tightly coupled to among year variability, with among year variability the dominant driver.