Title:	S-CMIP: Swedish climate modelling and contributions to international projects
DNr:	NAISS 2025/1-11
Project Type:	NAISS Large Compute
Principal Investigator:	Erik Kjellström <erik.kjellstrom@smhi.se>
Affiliation:	SMHI
Duration:	2025-07-01 – 2026-07-01
Classification:	10501 10508 10599
Homepage:	https://www.smhi.se/forskning/forskningsomraden/klimatforskning
Keywords:

Abstract

The warming of the Earth continues with unprecedented speed, exceeding in 2025 1.5 Celsius compared to pre-industrial value indicating the urgency to improve understanding of the risks and consequences related to the ongoing climate change. The goal of S-CMIP is to better describe and understand the Earth system, its response to changes in greenhouse gas forcing and land-use, its internal variability and the interactions between Earth system components. Scientific progress in these areas will enable actionable information on climate change in the fields of climate change adaptation (adjustment to a new climate) and mitigation (control of greenhouse gas emissions). International climate simulations to address the above questions are coordinated by the Coupled Model Intercomparison Project (CMIP). The 7th phase of CMIP, CMIP7, is now starting and a number of CMIP7 fast track simulations have been defined to respond to the needs of the 7th IPCC Assessment Report, AR7 (Dunne et al. 2024). S-CMIP will contribute with EC-Earth to the fast track of CMIP7 with a substantial number of simulations. S-CMIP will also carry out numerical climate model simulations connected to research projects funded by European and Swedish agencies. All projects are externally scientifically reviewed, are considered state of the art and are expected to generate publications by S-CMIP members. Our contributions to CMIP7, and to other international activities targeting IPCC AR7 such as the Paleo Model Intercomparison Project (PMIP) and the Tipping Point Model Intercomparison Project (TIPMIP) motivates higher requests for resources in this application compared to the current S-CMIP project. In addition to traditional dynamical models, machine learning (ML) methods become increasingly relevant in the development and interpretation of climate models and model simulations. We, thus, for the first time include ML-applications to support and complement traditional dynamical simulations and analysis. The focus will be mainly on regional downscaling, detection of extremes and generation of ensemble members. This will require in addition to our CPU-requests also separated access to GPU-resources.