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In Global change biology

Stability of the soil carbon (C) pool under decadal scale variability in temperature and precipitation is an important source of uncertainty in our understanding of land-atmosphere climate feedbacks. This depends on how two opposing C-fluxes - influx from net primary production (NPP) and efflux from heterotrophic soil respiration (Rh ) - respond to covariation in temperature and precipitation. There is scant evidence to judge whether field experiments which manipulate both temperature and precipitation align with Earth System Models, or not. As a result, even though the world is generally greening it remains uncertain whether the resultant gains in NPP can offset climate change impacts on Rh , where, and by how much. Here, we use decadal-scale global time-series datasets on NPP, Rh , temperature, and precipitation to estimate the two opposing C-fluxes and address whether one can outpace the other. We implement machine-learning tools on recent (2001-2019) and near-future climate scenarios (2020-2040) to assess the response of both C-fluxes to temperature and precipitation variation. We find that changes in C-influx may not compensate for C-efflux, particularly in wetter-and-warmer conditions. Soil-C loss can occur in both tropics and at high-latitudes since C-influx from NPP can fall behind C-efflux from Rh . Precipitation emerges as the key determinant of soil-C vulnerability in a warmer world, implying that hotspots for soil-C loss/gain can shift rapidly and highlighting that soil-C is vulnerable to climate change despite widespread greening of the world. Direction of covariation between change in temperature and precipitation, rather than their magnitude, can help conceptualize highly variable patterns in C-fluxes to guide soil-C stewardship.

Naidu Dilip Gt, Bagchi Sumanta


Carbon sequestration, Machine-learning, Microbial decomposition, Primary Production, Soil organic matter