Sanderson et al. (2026) METEORv1.6: Spatial climate variability and integrated impact emulation

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-09-30
• Authors: Benjamin Mark Sanderson, Marit Sandstad, Maura Dewey, Norman Julius Steinert, Johannes Larsen Fjeldså
• DOI: 10.5194/gmd-19-9263-2026

Research Groups

• CICERO Center for International Climate Research, Oslo 0349, Norway
• Section for Meteorology and Oceanography, University of Oslo, Oslo 0349, Norway

Short Summary

METEORv1.6 extends the established METEOR spatial multi-timescale, multi-forcer climate emulation framework with two major capabilities: (1) a monthly climate variability model that generates realistic sub-annual climate sequences with seasonal cycles and inter-annual variability, enabling the generation of ensemble projections and (2) a modular impact assessment framework that translates climate projections into impact metrics.

Objective

• Develop a spatial multi-timescale, multi-forcer climate emulation framework capable of generating realistic sub-annual climate sequences and translating climate projections into sectoral impact metrics.

Study Configuration

• Spatial Scale: Global
• Temporal Scale: Monthly resolution

Methodology and Data

• Models used:
  • METEOR-CORE (core pattern scaling framework)
  • METEOR-NOISE (monthly variability model)
  • PCA-VARX (Principal Component Analysis and Vector Autoregressive with eXogenous variables)
• Data sources: CMIP6 data

Main Results

• The monthly climate variability model (METEOR-NOISE) generates realistic sub-annual climate sequences with seasonal cycles and inter-annual variability.
• The modular impact assessment framework translates climate projections into sectoral impact metrics, enabling uncertainty quantification for climate risk assessment.

Contributions

• METEORv1.6 preserves the multi-timescale and forcing-dependent capabilities of the original METEOR framework while extending it to monthly resolution and adding integrated impact assessment capabilities.
• The framework enables rapid generation of probabilistic climate risk assessments at spatial and temporal resolutions required by sectoral applications.

Funding

• This research was funded by [insert funding projects, programs, and reference codes].

Citation

@article{Sanderson2026METEORv16,
  author = {Sanderson, Benjamin Mark and Sandstad, Marit and Dewey, Maura and Steinert, Norman Julius and Fjeldså, Johannes Larsen},
  title = {METEORv1.6: Spatial climate variability and integrated impact emulation},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-9263-2026},
  url = {https://doi.org/10.5194/gmd-19-9263-2026}
}