Climate Change Research Section

Who We Are

Climate Change Research Section scientists use Earth system models such as NCAR’s CESM and DOE’s E3SM, as well as CMIP multi-model data sets, a hierarchy of models, observations, and machine learning (ML), to gain a predictive and process-level understanding of present and future modes of variability in the climate system. 

Our Mission

The mission of the Climate Change Research Section is to achieve a comprehensive understanding of current and future modes of variability, including prediction, mechanisms of interaction, related tipping points, and characterization of regional and global Earth system impacts.

Research Objectives (ROs):  

Research Objective 1 (RO1) provides research themes that tie together the other three ROs in that RO1 uses state of the art modeling tools and machine learning (ML) methods to quantify the limits of predictability for different modes of variability involving the processes and mechanisms that contribute to the predictability of those modes on different timescales. Understanding the limits of predictability requires knowledge of processes and mechanisms that produce such modes of variability, and thus RO1 is the starting point for research in the other three ROs. All ultimately tie together to provide a comprehensive research plan to advance our fundamental knowledge of modes of variability and change in the Earth system.  

Research Objective 2 (RO2) targets interactions of modes of variability and the fundamental processes that underpin them using a hierarchy of models to do so.  These connections are important to understand predictability in RO1, how modes of variability are represented in models, how they react to changes in external forcing in RO3, and how modes of variability relate to weather and climate extremes in RO4.  In RO2, we study, for example, interactions of MJO and ENSO, QBO and MJO, and IPO, ENSO and SAM.   

Research Objective 3 (RO3) follows research in RO1 related to limits of predictability of modes of variability, to RO2 where key processes involved with understanding and predicting modes of variability and their interactions are explored, RO3 then goes on to specifically evaluate and understand the model representation of modes of variability, their responses to external forcing, and connections to tipping points. 

Research Objective 4 (RO4) builds on the interconnected research in the first three ROs and addresses predictability and the processes and mechanisms of modes of variability related to high impact events and modes of variability.  RO4 uses high resolution model simulations and ML techniques to better understand feedbacks and how extreme weather events interact with modes of variability at regional scales.