Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling.
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| Title: | Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling. |
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| Authors: | Maruf, Montasir1 (AUTHOR), Kumar, Sanjiv1 (AUTHOR) szk0139@auburn.edu |
| Source: | Journal of Hydrometeorology. May2026, Vol. 27 Issue 5, p1-22. 22p. |
| Subjects: | Precipitation variability, Land-atmosphere interactions, Atmospheric models, Timescale number, Soil moisture |
| Abstract: | This study investigates soil moisture decorrelation timescales, commonly referred to as soil moisture memory, by examining their sensitivity to meteorological forcing, specifically autocorrelation structure in precipitation data and land-atmosphere coupling using the Community Land Model version 5 (CLM5). We conduct CLM5 experiments using two widely used meteorological datasets: the Climate Forecast System Reanalysis (CFSR) and the Global Soil Wetness Project phase 3 (GSWP3), along with randomized meteorological forcing to isolate the role of climate variability and persistence on soil moisture decorrelation timescales. Results show that the CFSR-forced CLM5 simulation yields decorrelation timescales that are, on average, twice as high as those from the GSWP3-forced simulation, particularly in tropical and subtropical regions, due to significant precipitation autocorrelation and enhanced soil moistureprecipitation feedback in the CFSR case. Randomized meteorological forcing significantly reduces decorrelation timescales in CFSR-forced CLM5 simulations (by 50–70%) but only marginally in GSWP3-forced simulations (by 10–20%). Additional analysis using the drydown timescale metric reveals minimal differences between CFSR- and GSWP3-forced simulations, highlighting the dependency of findings on the memory metrics and the role of land surface hydrologic processes. Comparisons with CESM2 Large Ensemble (CESM2-LE) reveal that a fully coupled model underestimates the decorrelation timescales relative to the CFSR-forced CLM5 simulation and aligns more closely with the GSWP3-forced CLM5 simulation. Soil moisture reemergence, identified as a secondary autocorrelation peak, disappears under randomized forcing, indicating its dependence on climate variability rather than solely on land surface processes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study investigates soil moisture decorrelation timescales, commonly referred to as soil moisture memory, by examining their sensitivity to meteorological forcing, specifically autocorrelation structure in precipitation data and land-atmosphere coupling using the Community Land Model version 5 (CLM5). We conduct CLM5 experiments using two widely used meteorological datasets: the Climate Forecast System Reanalysis (CFSR) and the Global Soil Wetness Project phase 3 (GSWP3), along with randomized meteorological forcing to isolate the role of climate variability and persistence on soil moisture decorrelation timescales. Results show that the CFSR-forced CLM5 simulation yields decorrelation timescales that are, on average, twice as high as those from the GSWP3-forced simulation, particularly in tropical and subtropical regions, due to significant precipitation autocorrelation and enhanced soil moistureprecipitation feedback in the CFSR case. Randomized meteorological forcing significantly reduces decorrelation timescales in CFSR-forced CLM5 simulations (by 50–70%) but only marginally in GSWP3-forced simulations (by 10–20%). Additional analysis using the drydown timescale metric reveals minimal differences between CFSR- and GSWP3-forced simulations, highlighting the dependency of findings on the memory metrics and the role of land surface hydrologic processes. Comparisons with CESM2 Large Ensemble (CESM2-LE) reveal that a fully coupled model underestimates the decorrelation timescales relative to the CFSR-forced CLM5 simulation and aligns more closely with the GSWP3-forced CLM5 simulation. Soil moisture reemergence, identified as a secondary autocorrelation peak, disappears under randomized forcing, indicating its dependence on climate variability rather than solely on land surface processes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 1525755X |
| DOI: | 10.1175/JHM-D-25-0062.1 |
