Aims and Objectives Science Topics Organising Committee Important Dates Previous Editions

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When

15th-19th February 2027

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Where

IITM, Pune, India

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Aims and Objectives

The Working Group on Numerical Experimentation (WGNE) will organise the 7th hybrid Workshop on Systematic Errors in Weather and Earth System  Models, to be hosted by the Indian Institute of Tropical Meteorology (IITM) in Pune, India on 15th-19th February 2027.

Earth System Models (ESMs) are used to predict weather and climate from days to centuries ahead. Advances in computing and techniques such as machine learning have enabled more complex models and larger ensembles, supporting increasingly seamless weather–climate prediction systems. While these developments have improved forecast accuracy and relevance, significant challenges remain, including individual model errors as well as complex interactions within ESMs that generate systematic errors which are difficult to diagnose and reduce. Observational data are often insufficient to fully constrain models. Although major biases have been reduced, persistent and emerging errors highlight the need for continued model improvement.

The workshop aims to bring together a wide range of experts on simulating the Earth System including atmosphere, ocean, waves, land-surface, atmospheric composition, and associated disciplines to advance the understanding of systematic simulation errors at all timescales. A summary paper will document key findings and identify priority biases in ESMs.

Sponsors:

Logo of the World Weather Research Programme (WWRP)Logo of the World Meteorological Organization for the 75th anniversary

Science Topics

We are interested in abstract submissions on systematic errors in physics based or machine learning based models, of all components of the Earth System including coupled and individual component models. In broad, submissions can be made under one of these 6 topics. These topics invite contributions that help to increase understanding of the nature and cause of systematic errors in ESMs.

Diagnosing and Attributing Systematic Errors

  • Process-based and statistical diagnostics across scales

  • Structural vs parametric errors

  • Observational constraints and emergent relationships

  • Explainability and AI/ML-assisted attribution

  • Intercomparison between AI and physics based model outputs

  • Bias correction

  • Conditional and flow dependent systematic errors

Scale Interactions and Resolution Transitions

  • Resolution-dependent biases (from parameterized to resolved processes)

  • Multiscale error propagation and scale-aware modeling

  • New challenges and biases at km-scale resolution

  • Scale consistent evaluation and benchmarking

  • Use of model hierarchies, including single column models and constrained ESM components

Deficiencies in physical parameterisation 

  • Cloud microphysics and process-level biases

  • Cloud–radiation interactions

  • Aerosol-cloud-radiation interactions and feedbacks

  • Convective processes, organization, and extremes

  • Precipitation, diurnal cycle, and orographic effects

  • Ocean, sea-ice and wave model parameterisations

  • Land surface parameterisations

  • Physics-dynamics and physics-physics cross-component coupling

Coupled Earth System Feedbacks

  • Atmosphere–ocean–land–cryosphere interactions

  • Air–sea fluxes and surface exchanges

  • Boundary-layer, land-surface, and sea-ice processes

  • Coupling-induced biases across components

  • Coupled aerosol-chemistry-radiation processes

Impacts on Circulation, Variability, and Predictability

  • Large-scale circulation and modes of variability (e.g. monsoons, MJO, ENSO)

  • Stratosphere–troposphere coupling and composition effects

  • Biases in variability, extremes, and forecast skill

  • Pathways for bias mitigation and improved predictability

  • Representation of meteorological variability and its impact on atmospheric composition biases

Uncertainty and Ensembles

  • Characterization and attribution of systematic errors using ensembles, including stochastic parameterizations, spread–error relationships, multi-model approaches, and process-level attribution frameworks

  • Data assimilation and initialization biases

  • Systematic and random biases in reanalysis

  • Uncertainty estimation in ML-based data products

  • Uncertainty in Earth system model outputs

  • Strategies for spin-up of ESMs

Organising Committee

Ariane Frassoni, INPE/CPTEC (WGNE, co-chair)

Tim Graham, Met Office (WGNE, co-chair)

Ankur Srivastava, IITM (WGNE)

Fanny Adloff, DKRZ (ESMO IPO)

Bimochan Niraula, DKRZ (ESMO IPO)

Sara Pasqualetto, DKRZ (ESMO IPO)

Programme Committee

Romain Roehrig, CNRM / Meteo-France (WGNE)

Mohau Mateyisi, CSIR S.A (WGNE)

Fanglin Yang, NCEP (WGNE)

Indira Rani, NCMRWF / MoES India (ESMO SSG)

Rajesh Kumar, NEA Singapore (WGCM)

Debbie Hudson, BoM Au (WGSIP)

Chellappan Gnanaseelan, IITM (DCPP)

Kunal Chakraborty, INCOIS (WGORC)

Zied Ben Bouallegue, ECMWF (JWGFVR)

Andrew Gettelman, University of Colorado (Digital Earth)

Volker Vulfmeyer, Uni Hohenheim (GLASS)

Claudia Stubenrauch, LMD, CNRS (GASS)

Yuhei Takaya, JMA (WIPPS & S2SP)

Takemasa Miyoshi, R-CCS (DAOS)

Maria Laura Bettolli, CONICET (JAG-AI)

Daehyun Kim, SNU (MJO TF)

Chunxue Yang, CNR (CLIVAR)

Angeline Pendergrass, Cornell (JWGFVR)

Important Dates

The call for abstracts is coming out soon! 

  • May 2026: Workshop Announcement 

  • Early June 2026: Abstract submission open

  • End July 2026: Abstract submission closed

  • September 2026: Notifications about presentations +  Registrations opening

  • Mid/End October 2026: Registration closed + preliminary programme

  • 15-19 Feb 2027: Systematic Errors Workshop takes place!

Previous Editions

Keywords
WGNE