Preprints
https://doi.org/10.5194/gmd-2023-217
https://doi.org/10.5194/gmd-2023-217
Submitted as: development and technical paper
 | 
04 Jan 2024
Submitted as: development and technical paper |  | 04 Jan 2024
Status: this preprint is currently under review for the journal GMD.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside

Abstract. This study emphasises the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at midlatitudes. To address this we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. It also better captures extreme (high and low) temperature events in parts of Europe, the United States, and Asia, at mid and high latitudes. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study provides an technique for improved land initialisation, utilising the same model employed in climate predictions.

Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2023-217', Juan Antonio Añel, 26 Jan 2024
    • AC1: 'Reply on CEC1', Akhilesh S. Nair, 27 Jan 2024
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 28 Jan 2024
        • AC2: 'Reply on CEC2', Akhilesh S. Nair, 30 Jan 2024
          • CC1: 'Reply on AC2', Francois Counillon, 30 Jan 2024
            • CEC3: 'Reply on CC1', Juan Antonio Añel, 01 Feb 2024
              • AC3: 'Reply on CEC3', Akhilesh S. Nair, 08 Feb 2024
  • EC1: 'Comment on gmd-2023-217', Patricia Lawston-Parker, 20 Feb 2024
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside

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Short summary
This study demonstrates the importance of soil moisture (SM) in subseasonal-to-seasonal predictions. To addess this, we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land data assimilation system developed for the NorCPM. NorCPM-Land reduces error in SM by 10.5 % by assimilating satellite SM products. Enhanced land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation.