Malte is Science Director at Climate Resource. A Professor of Climate Science at the University of Melbourne, he was a Lead Author in the IPCC Sixth Assessment Report (AR6) Working Group I and a member of the Core Writing Team for the AR6 Synthesis Report. His work focuses on probabilistic climate projections, remaining carbon budgets and scenario assessment. Malte has been central to the creation and evaluation of greenhouse‑gas concentration pathways (e.g., the RCP and SSP series) and to the MAGICC climate model emulator for many years.
Before co‑founding Climate Resource in 2020, Malte founded and directed the University of Melbourne’s Climate & Energy College, served as Co‑Director of the Energy Transition Hub - which came to a sudden end at the beginning of COVID. That gave the impetus to found Climate Resource. Earlier in his career, he advised the German Environment Ministry as part of Germany’s UNFCCC negotiation team for over a decade. He was a senior researcher and PRIMAP group lead at the Potsdam Institute for Climate Impact Research (PIK) and a post‑doc at NCAR (Boulder). He holds a PhD (ETH Zürich) and an MSc (Environmental Change & Management, University of Oxford), is an Australian Research Council Future Fellow, a highly cited researcher, and has been ranked on Reuters’ global “Hot List” of influential climate scientists.
As Science Director, Malte' contributes across Climate Resource's work areas, and plays a leading role in:
- NDC Quantification, and the development of our methodology for:
- Constructing emissions projections based on countries' greenhouse gas (GHG) emissions reduction commitments.
- Translating country emissions projections into global emissions and global mean temperature rise using MAGICC.
- Disaggregating country-level whole-of-economy emissions targets into a range of possible sectoral and gas-specific pathways for major emitters.
- Regional Climate Impacts and the development of climate impact data at the appropriate resolution for relevant warming scenarios and metrics. Malte is extending this methodology, working with collaborators to develop a machine learning model that generates super-ensembles and custom scenarios, covering AR6 projection ranges, custom scenarios (such as the International Energy Agency (IEA), and NGFS (Network for Greening the Financial System)), and user-specified scenarios. These methods will better quantify spatially and temporally correlated climate impacts.
