Dirmeyer et al. (2025) Land surface processes relevant to subseasonal-to-seasonal prediction

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-14
• Authors: Paul A. Dirmeyer, Michael Ek
• DOI: 10.1016/b978-0-443-31538-1.00008-7

Research Groups

• Department of Atmospheric, Oceanic, and Earth Science, and Center for Ocean-Land-Atmosphere Studies, George Mason University, Fairfax, VA, United States
• Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, United States

Short Summary

This chapter provides a comprehensive background on the theories and physical processes linking land-surface states, particularly soil moisture and snowpack, to subseasonal-to-seasonal (S2S) weather and climate predictability, highlighting their impact between 1 week and 2 months after forecast initialization.

Objective

• Provide theoretical background on the relevance of land-surface states to weather and climate.
• Describe the evolution of land-surface models (LSMs) for operational prediction.
• Detail basic physical processes in land-atmosphere interactions and key land-surface states for S2S prediction.
• Explain atmospheric boundary layer responses to land-surface variations.
• Outline necessary conditions for S2S predictability and prediction skill derived from the land surface.
• Introduce the concept of soil moisture regimes with differing land-atmosphere interaction properties.

Study Configuration

• Spatial Scale: Conceptual, addressing global land-atmosphere interactions relevant to subseasonal-to-seasonal (S2S) prediction.
• Temporal Scale: Subseasonal-to-seasonal (S2S), with peak impact between 1 week and 2 months, also considering diurnal and seasonal scales.

Methodology and Data

• Models used: Reviews the role and evolution of land-surface models (LSMs) in operational prediction.
• Data sources: Not applicable; the chapter is a review of concepts and theories, not an empirical study using specific data. It discusses the importance of assimilation and prediction of soil states and vegetation growth.

Main Results

• Slowly varying land-surface aspects, including soil moisture and snowpack, are fundamental to a large portion of S2S predictability.
• The land surface's peak impact on S2S prediction typically occurs between 1 week and 2 months post-initialization.
• Improved representation of biophysical and hydrological processes, assimilation of soil states, and seasonal vegetation growth are critical for advancing S2S prediction.
• Three specific "ingredients" are necessary for the land surface to contribute to subseasonal prediction.

Contributions

• Synthesizes the theoretical underpinnings and historical evolution of land-surface modeling for S2S prediction.
• Provides a structured overview of key land-atmosphere interaction processes and states relevant to S2S predictability.
• Highlights the critical role of soil moisture and snowpack as sources of S2S predictability and outlines conditions for skill derivation.
• Integrates recommendations from authoritative reports (e.g., National Academies) on advancing Earth system prediction through land-surface understanding.

Funding

Not specified in the provided text.

Citation

@article{Dirmeyer2025Land,
  author = {Dirmeyer, Paul A. and Ek, Michael},
  title = {Land surface processes relevant to subseasonal-to-seasonal prediction},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-31538-1.00008-7},
  url = {https://doi.org/10.1016/b978-0-443-31538-1.00008-7}
}