Scinocca et al. (2026) Runtime Bias Correction of Parameterized Lakes in Climate Models

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Advances in Modeling Earth Systems
• Year: 2026
• Date: 2026-10-31
• Authors: John Scinocca, Slava Kharin, Yanjun Jiao, Ayodeji Akingunola, Narayan Kumar Shrestha, Frank R. Seglenieks
• DOI: 10.1029/2025ms005676

Research Groups

Groups involved in regional climate modeling, particularly within the North-American CORDEX domain, and those developing or applying single-column thermodynamic lake models and offline hydrological models.

Short Summary

This paper introduces a novel runtime bias correction method for lake surface water temperature (LSWT) and lake ice in climate models. Applied within a regional climate model over the Great Lakes, this method significantly improves the climatological annual cycle, spatial distributions, and interannual variability of LSWT, ice, evaporation, and Net Basin Supply (NBS).

Objective

• To develop and apply a novel runtime bias correction procedure for lake surface water temperature (LSWT) and lake ice in climate models to reduce biases in their climatological annual cycle.
• To evaluate the impact of this bias correction on atmosphere-lake heat and evaporative fluxes, and on the components of Net Basin Supply (NBS) over the Great Lakes within a regional climate model.

Study Configuration

• Spatial Scale: Great Lakes (GLs) water system, North-American CORDEX domain.
• Temporal Scale: Climatological annual cycle, seasonal cycle, interannual variability, historical period.

Methodology and Data

• Models used:
  • Single-column thermodynamic lake model (with bias correction procedure).
  • Regional Climate Model (RCM) employing the North-American CORDEX domain.
  • Offline hydrological model.
• Data sources:
  • Temperature and precipitation fields derived from the Regional Climate Model (RCM) to drive the offline hydrological model.
  • Precipitation and evaporation over the lakes derived from the RCM for Net Basin Supply (NBS) calculations.

Main Results

• The novel bias correction procedure significantly improves the climatological seasonal cycle, spatial distributions, and interannual variability of Lake Surface Water Temperature (LSWT) and ice over the Great Lakes.
• These improvements lead to a reduction in significant biases in the seasonal cycle of evaporation over the Great Lakes.
• The lake bias correction generally improves each component of Net Basin Supply (NBS) for each of the Great Lakes during the historical period when compared to the uncorrected model.

Contributions

• Presents a novel and generalizable approach for runtime bias correction of lake surface water temperature and lake ice in climate models.
• Demonstrates the effectiveness of this bias correction in improving atmosphere-lake heat and evaporative fluxes within a regional climate model.
• Shows that improved lake modeling leads to more accurate predictions of Net Basin Supply (NBS), which is crucial for water resource management and flood mitigation in large lake systems.

Funding

Not specified in the abstract.

Citation

@article{Scinocca2026Runtime,
  author = {Scinocca, John and Kharin, Slava and Jiao, Yanjun and Akingunola, Ayodeji and Shrestha, Narayan Kumar and Seglenieks, Frank R.},
  title = {Runtime Bias Correction of Parameterized Lakes in Climate Models},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2026},
  doi = {10.1029/2025ms005676},
  url = {https://doi.org/10.1029/2025ms005676}
}