• Mechanisms of climate variability and predictability

Disentangle the anthropogenic climate change signal from the natural climate variability and elucidate the mechanisms of variability from sub-seasonal to multi-decadal time scales, for both developing understanding and aiding prediction.

• Understand and reduce errors in climate projections/predictions

Identify climate model errors, diagnose their origins, and assess their impacts in order to improve our simulations and build confidence in our climate simulations.

• Identify the role of teleconnections between high and low latitudes

Explore the impacts of Arctic sea ice decline on climate variability and mid-latitude extreme weather events, and the converse influence of lower latitudes on the Arctic in order to broaden the usefulness and applicability of climate predictions.

• Innovation and development of Climate services

Support the development of climate services in Norway through global climate reconstruction and prediction; advanced statistical analysis of extreme events; urban climate and air quality assessment, with the aim to deliver information of interest to stakeholders and society.

Tools and Software

We actively develop and maintain:    

• The Norwegian Climate Prediction Model (NorCPM) that combines the Norwegian Earth System model with the ensemble Kalman Filter data assimilation method.
• Fine scale climate assessment (FineClim) based on PALM, ECMWF boundary conditions, and measurements. 
• The Norwegian Earth System Model (NorESM): responsible for diagnostics, CMIP processing and tracer advection
• The KrigR statistical downscaling tool (KrigR): an R package for acquiring and statistically downscaling ERA5(-Land) data
• Atmospheric river tracking algorithm (Atmos River): A python script for detecting and tracking atmospheric rivers 
• Common Basis Function analysis tool (CBF): A python script for applying the common basis function approach for diagnosing model skill in reproducing observed modes of variability