The Coupled Model Intercomparison Project Phase 6 (CMIP6, https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) scenarios provide the state-of-the- art climate projections that will be used to assess climate change in the upcoming IPCC reports. At the SMHI we plan to downscale the global CMIP6 scenarios for the North Sea and Baltic Sea region with a coupled regional climate model (RCM). These regionalized scenarios will be in itself a source for analysis of climate change in our region, both for changes in the mean climate as well as changes in frequency and intensity of extremes. The atmospheric surface variables of the RCM will be used to produce scenarios with a coupled physical ecosystem model of the North Sea and the Baltic Sea.
In the past we have used the coupled RCM RCA4-NEMO to downscale CMIP5 scenarios (Wang et al., 2015; Dieterich et al., 2019a). To keep pace with the state-of-the-art the CMIP6 scenarios will be downscaled with a new coupled RCM. The atmosphere component will be the same as the one used by the Rossby Centre (HARMONIE-Aladin, HCLIM cycle 43) in a resolution of 0.11 degrees in the EURO-CORDEX domain. The ice-ocean component will cover the same region and same horizontal resolution of two nautical miles as in the previous model. The code is derived from the current version NEMO 4.0.1. This model development is part of the havsmiljöscenarier project in the ocean group FoUo at the SMHI.
The new generation of high resolution climate models, known as convection permitting regional climate models (CPRCMs), reproduce precipitation extremes much more realistically compared to the regional and global climate models used until now. They also show larger increase in extreme precipitation for future climate compared to lower resolution models. Additionally, due to their high resolution, they have significant impact on climate projections of urban temperature, climate over mountainous regions and wind over complex coastlines. EDUCAS uses such models to systematically investigate the climate change information on local scales over Sweden, which is relevant to a wide range of stakeholders, from interdisciplinary researchers to municipalities.
Uncertainty quantification as part of climate information is essential to establish best possible climate adaptation strategies for decision makers. Uncertainty estimates are obtained from large ensembles of climate simulations, which are typically not feasible for convection permitting models due to their high computational costs. We make use of the first-ever international ensembles of convection permitting simulations to systematically investigate the dependence of uncertainty range on ensemble size, and its transferability to different regions. We will incorporate a clustering approach and high user engagement to develop an optimized approach for reducing ensembles with minimized loss of uncertainty information.