Maruf et al. (2026) Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling

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Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-03-27
• Authors: Montasir Maruf, Sanjiv Kumar
• DOI: 10.1175/jhm-d-25-0062.1

Research Groups

Not specified in the provided abstract.

Short Summary

This study investigates the sensitivity of soil moisture decorrelation timescales to meteorological forcing autocorrelation and land-atmosphere coupling using the Community Land Model version 5 (CLM5). It finds that precipitation autocorrelation and soil moisture-precipitation feedback significantly enhance decorrelation timescales, with randomized forcing substantially reducing them, highlighting the influence of climate variability.

Objective

• To investigate the sensitivity of soil moisture decorrelation timescales (soil moisture memory) to the autocorrelation structure in precipitation data and land-atmosphere coupling.

Study Configuration

• Spatial Scale: Global, with specific focus on tropical and subtropical regions.
• Temporal Scale: Analysis of soil moisture decorrelation timescales (typically days to weeks), derived from simulations forced by multi-year meteorological datasets.

Methodology and Data

• Models used: Community Land Model version 5 (CLM5), CESM2 Large Ensemble (CESM2-LE).
• Data sources: Climate Forecast System Reanalysis (CFSR), Global Soil Wetness Project phase 3 (GSWP3), randomized meteorological forcing.

Main Results

• CFSR-forced CLM5 simulations yield soil moisture decorrelation timescales that are, on average, twice as high as those from GSWP3-forced simulations, particularly in tropical and subtropical regions.
• This difference is attributed to significant precipitation autocorrelation and enhanced soil moisture–precipitation feedback in the CFSR dataset.
• 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%).
• The drydown timescale metric shows minimal differences between CFSR- and GSWP3-forced simulations, indicating metric dependency.
• A fully coupled model (CESM2-LE) underestimates 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.

Contributions

• Quantifies the sensitivity of soil moisture decorrelation timescales to the autocorrelation structure of meteorological forcing and land-atmosphere coupling.
• Demonstrates the significant role of precipitation autocorrelation and soil moisture–precipitation feedback in determining soil moisture memory.
• Highlights the dependency of soil moisture memory findings on the chosen memory metric (decorrelation timescale vs. drydown timescale).
• Provides insights into the drivers of soil moisture reemergence, linking it to climate variability rather than intrinsic land surface processes.

Funding

Not specified in the provided abstract.

Citation

@article{Maruf2026Soil,
  author = {Maruf, Montasir and Kumar, Sanjiv},
  title = {Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-25-0062.1},
  url = {https://doi.org/10.1175/jhm-d-25-0062.1}
}