We are interested in climate change in the Arctic region, including Arctic amplification, changes in sea ice cover, and Arctic greening, as well as linkages with mid-latitude weather and climate variability.
We study the atmospheric circulation and synoptic-scale teleconnections to understand both the atmosphere’s natural internal variability, as well as how climate change has affected and will affect the circulation patterns.
Atmospheric rivers are long and narrow bands of strong water vapor transport in the atmosphere. We study atmospheric rivers with a focus on their occurrence and impact on climate in the northern high-latitudes and Arctic region.
Climate change has significantly altered the carbon cycle, but major knowledge gaps remain, especially regarding the land sink. Our research aims to fill these gaps by combining different types of observations and models.
We study climate variability and change on interannual to decadal timescales using observations and models. Our main focus is the atmosphere and its connections with the land, ocean, sea ice, ecosystems, and human systems.
Data assimilation is a class of methods that seek to optimally combine information from observations and models. We use and develop data assimilation methods, for example to estimate surface carbon fluxes.
We develop methods to estimate greenhouse gas emissions based on atmospheric measurements and inverse modeling. One of our focuses is to use next-generation satellite measurements to track national-scale emissions.
We use machine learning to combine information from different data sources and to unveil patterns in the climate system. Examples of applications include classification of weather patterns and interpretation of satellite images.