Preprints
https://doi.org/10.5194/egusphere-2025-1112
https://doi.org/10.5194/egusphere-2025-1112
27 Mar 2025
 | 27 Mar 2025

Bias Correcting Regional Scale Earth Systems Model Projections: Novel Approach using Empirical Mode Decomposition

Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi

Abstract. Bias correction is a crucial step in using Earth systems model outputs for assessments, as it adjusts systematic errors by comparing the model to observations. However, standard methods – ranging from mean-based linear scaling to distribution-based quantile mapping typically treat bias correction as a single-scale process, overlooking the fact that biases can manifest differently across daily, seasonal, and annual timescales. In this study, we propose a novel, timescale-aware bias-correction approach built on Empirical Mode Decomposition (EMD). By decomposing the meteorological signal into multiple oscillatory components and aggregating them to represent distinct timescales, we apply targeted corrections to each component, thereby preserving both short- and long-term structure in the data. Experimental validations demonstrate that this finer-grained method substantially improves upon existing bias-correction techniques such as quantile mapping. As a result, the proposed approach offers a more robust path to accurate and reliable Earth systems projections, strengthening their utility for resilience and adaptation planning.

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Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2025-1112 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Apr 2025
    • AC1: 'Reply on CEC1', Arkaprabha Ganguli, 19 Apr 2025
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 19 Apr 2025
        • AC2: 'Reply on CEC2', Arkaprabha Ganguli, 24 Apr 2025
          • CEC3: 'Reply on AC2', Juan Antonio Añel, 24 Apr 2025
            • AC3: 'Reply on CEC3', Arkaprabha Ganguli, 25 Apr 2025
              • CEC4: 'Reply on AC3 - No compliance with the policy of the journal', Juan Antonio Añel, 09 May 2025
  • RC1: 'Comment on egusphere-2025-1112', Anonymous Referee #1, 12 Jun 2025
    • AC4: 'Reply on RC1', Arkaprabha Ganguli, 28 Jun 2025
  • RC2: 'Comment on egusphere-2025-1112', Anonymous Referee #2, 23 Jun 2025
    • AC5: 'Reply on RC2', Arkaprabha Ganguli, 09 Jul 2025
  • AC6: 'Comment on egusphere-2025-1112', Arkaprabha Ganguli, 11 Jul 2025
Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi
Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi

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Short summary
This study introduces a timescale-aware bias-correction framework to enhance Earth system model assessments, vital for the geoscience community. By decomposing model outputs into oscillatory components, we preserve critical information across various timescales, ensuring more reliable projections. This improved reliability supports strategic decisions in sectors such as agriculture, water resources, and disaster preparedness.
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