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Statistical models for soil organic carbon (SOC) differ substantially regarding the degree to which climate variables, such as precipitation and air temperature, influence SOC. These discrepancies contribute to uncertainties regarding the potential feedback effects of climate change on SOC. Soil-climate variables, such as soil moisture and soil temperature, can strongly influence SOC gains and losses, but these variables have not been widely utilized in statistical SOC models. We hypothesized that SOC is more strongly related to soil-climate variables than to climate variables and that using soil-climate variables in statistical SOC models would improve SOC estimates. We tested these hypotheses using >10 years of daily climate and soil-climate data from 67 temperate grassland sites across Oklahoma, USA, along with soil physical properties, and corresponding SOC data at 5-, 25-, and 60-cm depths. Soil moisture was the single most influential predictor variable, as indicated by least absolute shrinkage and selection operator (LASSO) regression analysis. The effects of precipitation and air temperature were insignificant, apart from a weak negative effect of precipitation at 25 cm. Models including soil-climate variables provided more accurate SOC estimates than models excluding soil-climate variables and were statistically superior based on the Akaike information criterion. Future soil moisture conditions, not precipitation or air temperature, may be the key determinant of climate change-SOC feedback effects at these grassland sites and similar sites worldwide.

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