Enhancing sub-seasonal soil moisture forecasts through land initialization

Yanan Duan; Sanjiv Kumar; Montasir Maruf; Thomas M. Kavoo; Imtiaz Rangwala; Jadwiga H. Richter; Anne A. Glanville; Teagan King; Musa Esit; Brett Raczka; Kevin Raeder

doi:10.1038/s41612-025-00987-0

Enhancing sub-seasonal soil moisture forecasts through land initialization

Yanan Duan, Sanjiv Kumar, Montasir Maruf, Thomas M. Kavoo, Imtiaz Rangwala, Jadwiga H. Richter, Anne A. Glanville, Teagan King, Musa Esit, Brett Raczka, Kevin Raeder

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (SM) utilizing the Community Earth System Model version 2 Sub to Seasonal climate forecast experiments (CESM2-S2S). Using eight sensitivity experiments, we disentangle the individual impacts of these three components and their interactions on the forecast skill for the contiguous United States. The CESM2-S2S experiment, in which land states are initialized while atmosphere and ocean remain in their climatological states, contributes 91 ± 3% of the total sub-seasonal forecast skill across varying soil moisture conditions during summer and winter. Most SM predictability stems from the soil moisture memory effect. Additionally, land-atmosphere coupling contributes 50% of the land-driven soil moisture predictability. A comparative analysis of the CESM2-S2S SM forecast skills against two other climate models highlights the potential for enhancing soil moisture forecast accuracy by improving the representation of soil moisture-precipitation feedback.

Original language	English
Article number	100
Journal	npj Climate and Atmospheric Science
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41612-025-00987-0
State	Published - Dec 2025
Externally published	Yes

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title = "Enhancing sub-seasonal soil moisture forecasts through land initialization",

abstract = "We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (SM) utilizing the Community Earth System Model version 2 Sub to Seasonal climate forecast experiments (CESM2-S2S). Using eight sensitivity experiments, we disentangle the individual impacts of these three components and their interactions on the forecast skill for the contiguous United States. The CESM2-S2S experiment, in which land states are initialized while atmosphere and ocean remain in their climatological states, contributes 91 ± 3\% of the total sub-seasonal forecast skill across varying soil moisture conditions during summer and winter. Most SM predictability stems from the soil moisture memory effect. Additionally, land-atmosphere coupling contributes 50\% of the land-driven soil moisture predictability. A comparative analysis of the CESM2-S2S SM forecast skills against two other climate models highlights the potential for enhancing soil moisture forecast accuracy by improving the representation of soil moisture-precipitation feedback.",

author = "Yanan Duan and Sanjiv Kumar and Montasir Maruf and Kavoo, \{Thomas M.\} and Imtiaz Rangwala and Richter, \{Jadwiga H.\} and Glanville, \{Anne A.\} and Teagan King and Musa Esit and Brett Raczka and Kevin Raeder",

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doi = "10.1038/s41612-025-00987-0",

language = "English",

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T1 - Enhancing sub-seasonal soil moisture forecasts through land initialization

AU - Duan, Yanan

AU - Kumar, Sanjiv

AU - Maruf, Montasir

AU - Kavoo, Thomas M.

AU - Rangwala, Imtiaz

AU - Richter, Jadwiga H.

AU - Glanville, Anne A.

AU - King, Teagan

AU - Esit, Musa

AU - Raczka, Brett

AU - Raeder, Kevin

PY - 2025/12

Y1 - 2025/12

N2 - We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (SM) utilizing the Community Earth System Model version 2 Sub to Seasonal climate forecast experiments (CESM2-S2S). Using eight sensitivity experiments, we disentangle the individual impacts of these three components and their interactions on the forecast skill for the contiguous United States. The CESM2-S2S experiment, in which land states are initialized while atmosphere and ocean remain in their climatological states, contributes 91 ± 3% of the total sub-seasonal forecast skill across varying soil moisture conditions during summer and winter. Most SM predictability stems from the soil moisture memory effect. Additionally, land-atmosphere coupling contributes 50% of the land-driven soil moisture predictability. A comparative analysis of the CESM2-S2S SM forecast skills against two other climate models highlights the potential for enhancing soil moisture forecast accuracy by improving the representation of soil moisture-precipitation feedback.

AB - We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (SM) utilizing the Community Earth System Model version 2 Sub to Seasonal climate forecast experiments (CESM2-S2S). Using eight sensitivity experiments, we disentangle the individual impacts of these three components and their interactions on the forecast skill for the contiguous United States. The CESM2-S2S experiment, in which land states are initialized while atmosphere and ocean remain in their climatological states, contributes 91 ± 3% of the total sub-seasonal forecast skill across varying soil moisture conditions during summer and winter. Most SM predictability stems from the soil moisture memory effect. Additionally, land-atmosphere coupling contributes 50% of the land-driven soil moisture predictability. A comparative analysis of the CESM2-S2S SM forecast skills against two other climate models highlights the potential for enhancing soil moisture forecast accuracy by improving the representation of soil moisture-precipitation feedback.

UR - https://www.scopus.com/pages/publications/105000076171

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DO - 10.1038/s41612-025-00987-0

M3 - Article

AN - SCOPUS:105000076171

SN - 2397-3722

VL - 8

JO - npj Climate and Atmospheric Science

JF - npj Climate and Atmospheric Science

IS - 1

M1 - 100

ER -

Enhancing sub-seasonal soil moisture forecasts through land initialization

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