Staff Research

CCR Staff Photo

Front row:  Julie Caron, Kirsten Mayer, Teagan King, Mari Tye, Katie Dagon;  Back row:  Steve Yeager, Nan Rosenbloom, Christine Shields, Hui Li, Yaga Richter, Aixue Hu, Gerald Meehl; Not shown:  Gary Strand

The CCR Section has a Climate Change and Prediction (CCP) group which focuses on climate variability and change which is primarily funded by the DOE's Biological and Environmental Research program. This is DOE's contribution to the U.S. Global Change Research Program that integrates federal research on global change. CCP has concentrated on using initialized climate models to predict near-term climate (decadal climate prediction), analyzing model simulations and observations to study climate variability and change in the 20th and early 21st centuries, and running and analyzing climate projections with a variety of possible future emission scenarios to relate earth system processes to possible future changes in the climate system. 

Climate Change Research Section science is divided into four 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, and 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.