Holger Pohlmann

Holger Pohlmann is a Research Scientist at the Max Planck Institute for Meteorology in Hamburg, Germany, affiliated with the Department Climate Variability and the Earth System Modelling and Predictions group. His work focuses on advancing seasonal and decadal climate prediction systems using state-of-the-art Earth system models such as ICON-ESM and MPI-ESM.

Research Interests:
His research centers on improving climate predictability through enhanced model initialization, data assimilation, and the analysis of external forcings (e.g., greenhouse gases, aerosols, solar variability). He investigates the skill of prediction systems by conducting retrospective analyses and large ensemble simulations, particularly within EU-funded projects like ASPECT and national initiatives like MiKlip.

Publication Trends:
His recent publications (2020–2025) reflect a strong focus on Earth system model development (ICON-ESM), initialization techniques, decadal prediction skill, and the impacts of solar and volcanic forcing. The work spans climate dynamics, model intercomparison, and operational climate services, with frequent contributions to high-impact journals and international climate assessments.

Scientific Projects & Affiliations:
• Current: ASPECT, Coming Decade, EPESC
• Former: MiKlip, SPECS, COMBINE, THOR, ENSEMBLES
• Scientific Networks: GC NTCP, DCVP, QBOi
• Guest Scientist at the University of Hamburg (since 2019)

Advising and Funding:
While no formal students are listed, Pohlmann collaborates extensively with early-career researchers and contributes to large collaborative projects funded by the EU, BMBF, and other agencies. His work is supported by substantial research grants enabling high-performance computing and model development.

Laboratories and Teams:
He is a key member of the Earth System Modelling and Predictions group at MPI-M, contributing to the development of the ICON modeling framework and participating in international modeling consortia. His team employs advanced data assimilation techniques, including the Ensemble Kalman Filter, to initialize climate predictions.