For more than 20 years, research environments in Bergen and Oslo have collaborated to develop the Norwegian Earth System Model “NorESM”. This is an internationally recognized climate model that has provided data for the last three main reports to the UN’s climate panel – in 2007, 2013 and in 2021. With “NorESM” we produce long-term forecasts for 50-100 years into the future. The model can also be used to increase understanding of climate change and for research into remote connections and variations in climate over large distances. The model takes into account various factors that affect the climate, such as the greenhouse gases carbon dioxide and methane, and particles that originate from fossil fuels.
The model is therefore very useful when simulating how the climate will be several decades from now. The Norwegian climate prediction model “NorCPM” is based on “NorESM”. This model is used to study climate development for months to years into the future. The model is central to a number of projects such as for example “Climate Futures”, a center for research-based innovation – SFI. Data assimilation is used to include observations from the real world in the “NorCPM” model’s calculations, which then deliver predictions of climate development.
The climate warning model can also be used to create reanalyses of the past climate. A reanalysis gives the most complete picture of past climate conditions, where you will often have little and uncertain data available. Climate models are important tools for understanding the causes of climate change, and for calculating future developments given different scenarios for greenhouse gas and particle emissions. The models are important as a basis for designing climate policy, which ranges from global measures such as the Paris Agreement, to national and local measures for emission reductions and climate adaptation.
Our researchers use data from “NorESM” and “NorCPM” in their research. In this way, they contribute to the development of the model tools by making comparisons between measurements, improving process descriptions, and developing more efficient methods for data assimilation.
