Abstract Climate‐change assessments project increasing precipitation variability through increased frequency of extreme events. However, the effects of interannual precipitation variance per se on ecosystem functioning have been largely understudied. Here, we report on the effects of interannual precipitation variability on the primary production of global drylands, which include deserts, steppes, shrublands, grasslands, and prairies and cover about 40% of the terrestrial earth surface. We used a global database that has 43 datasets, which are uniformly distributed in parameter space and each has at least 10 years of data. We found (a) that at the global scale, precipitation variability has a negative effect on aboveground net primary production. (b) Expected increases in interannual precipitation variability for the year 2,100 may result in a decrease of up to 12% of the global terrestrial carbon sink. (c) The effect of precipitation interannual variability on dryland productivity changes from positive to negative along a precipitation gradient. Arid sites with mean precipitation under 300 mm/year responded positively to increases in precipitation variability, whereas sites with mean precipitation over 300 mm/year responded negatively. We propose three complementary mechanisms to explain this result: (a) concave‐up and concave‐down precipitation–production relationships in arid vs. humid systems, (b) shift in the distribution of water in the soil profile, and (c) altered frequency of positive and negative legacies. Our results demonstrated that enhanced precipitation variability will have direct impacts on global drylands that can potentially affect the future terrestrial carbon sink. 
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                            Dryland sensitivity to climate change and variability using nonlinear dynamics
                        
                    
    
            Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system. 
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                            - PAR ID:
- 10501423
- Publisher / Repository:
- National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 120
- Issue:
- 35
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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