Bhatla et al. (2026) Fidelity of nested RegCM in representing Indian monsoon droughts through resolution-dependent simulations

Identification

• Journal: Modeling Earth Systems and Environment
• Year: 2026
• Date: 2026-03-31
• Authors: R. Bhatla, Aashna Verma, R. K. Mall, Hari S. Patel
• DOI: 10.1007/s40808-026-02768-x

Research Groups

• Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, India
• Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India

Short Summary

This study evaluates the RegCM4.7 regional climate model's performance in simulating two major drought years (2009 and 2015) over India's Core Monsoon Region, comparing non-nested (25 km) and nested (9 km) configurations. The nested high-resolution setup significantly improves the representation of spatial rainfall variability, bias reduction, and mesoscale drought characteristics, highlighting the added value of increased spatial resolution.

Objective

• To evaluate the performance of the RegCM4.7 regional climate model in simulating two major drought years (2009 and 2015) over India's Core Monsoon Region (CMR).
• To compare the fidelity of nested (9 km) versus non-nested (25 km) model configurations in representing drought features and their underlying atmospheric dynamics.
• To analyze spatial and temporal drought characteristics using the Rainfall Anomaly Index (RAI) and other hydro-meteorological metrics.
• To understand the influence of model configuration and land surface initialization on drought representation and provide insights for enhancing regional climate modeling efforts.

Study Configuration

• Spatial Scale:
  • Horizontal resolutions: 25 km (non-nested) and 9 km (nested).
  • Vertical resolution: 18 sigma levels from the surface to 5000 Pa.
  • Model domain: South Asia CORDEX (SA-CORDEX), with a focus on India's Core Monsoon Region (CMR) bounded by 73°–84°E and 18°–28°N.
• Temporal Scale:
  • Study period: Monsoon months (June–September) for drought years 2009 and 2015.
  • Spin-up time: 1 month (May).

Methodology and Data

• Models used:
  • Regional Climate Model (RegCM4.7) developed by the International Centre for Theoretical Physics (ICTP).
  • Community Land Model (CLM4.5) for land–atmosphere coupling.
  • Holtslag Planetary Boundary Layer (PBL) scheme.
  • EL_TO (Emmanuel over land and Tiedtke over ocean) cumulus convection scheme.
  • Nested 9 km simulation used the non-hydrostatic (NH) dynamical core adapted from the mesoscale model MM5.
• Data sources:
  • Daily gridded precipitation data (25 km horizontal resolution) from the India Meteorological Department (IMD) for observations.
  • ERA-Interim reanalysis (EIN75) for initial and lateral boundary conditions.
  • Quantitative metrics: Rainfall Anomaly Index (RAI), Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI).
  • Taylor diagram statistics for assessing correlation, standard deviation, and centered root mean square difference.
  • Diagnostic analyses: Wind vector anomalies at 850 hPa, specific humidity at 700 hPa, and outgoing longwave radiation (OLR) anomalies.

Main Results

• Non-nested RegCM4.7 simulations reasonably captured the broad-scale spatial patterns of rainfall deficits and warming trends (temperature anomalies in degrees Celsius, equivalent to Kelvin for differences) during the 2009 and 2015 droughts. However, they exhibited regional biases, including underestimation of rainfall in central India and overestimation in orographic regions like the Western Ghats.
• Skill metrics (POD, FAR, CSI) indicated moderate model skill in detecting drought-prone areas, but also revealed tendencies for false alarms and underestimation of peak rainfall events.
• Diagnostic analyses confirmed that RegCM4.7 captured key drought characteristics: weakened southwesterly monsoonal circulation, reduced moisture transport, and suppressed convection (indicated by persistently positive OLR anomalies).
• The nested 9 km simulation demonstrated significant improvements in representing fine-scale spatial variability, particularly in precipitation, surface temperature, and atmospheric circulation. It captured sharper temperature gradients, more localized warming zones, and reproduced observed spatial distribution and magnitude of rainfall more realistically.
• Taylor diagram analysis showed that the nested simulation achieved higher correlation coefficients (e.g., >0.8 for 2015 vs. ~0.6 for non-nested) and reduced standard deviation biases (e.g., ~0.028 m/day for nested vs. ~0.040 m/day for non-nested in 2009), indicating superior skill in reproducing spatial and temporal rainfall patterns.

Contributions

• This study provides a comprehensive evaluation of RegCM4.7's fidelity in representing drought-specific processes (e.g., suppressed convection, weakened moisture transport, land-atmosphere feedbacks) over India's Core Monsoon Region, specifically comparing nested versus non-nested model configurations.
• It quantifies the "added value" of high-resolution (9 km nested) regional climate modeling for drought diagnostics, demonstrating significant improvements in spatial rainfall variability, bias reduction, and mesoscale representation.
• The research offers process-based diagnostics (wind, specific humidity, OLR) to link model performance with a physical understanding of drought mechanisms, bridging the gap between performance evaluation and physical insight.
• The findings underscore the critical importance of model configuration and spatial resolution for enhancing operational drought forecasting, seasonal climate assessments, and long-term impact studies in monsoon-sensitive regions.

Funding

No specific grant was received for this research. However, the work was part of an R&D initiative supported by the Science and Engineering Research Board (SERB), Government of India.

Citation

@article{Bhatla2026Fidelity,
  author = {Bhatla, R. and Verma, Aashna and Mall, R. K. and Patel, Hari S.},
  title = {Fidelity of nested RegCM in representing Indian monsoon droughts through resolution-dependent simulations},
  journal = {Modeling Earth Systems and Environment},
  year = {2026},
  doi = {10.1007/s40808-026-02768-x},
  url = {https://doi.org/10.1007/s40808-026-02768-x}
}