Collaborative Research: Understanding Transient Behavior of Climate Feedbacks and Its Role in Decadal Climate Variability and Prediction

Florida State University, Tallahassee FL

Investigators

Abstract

Representation of climate feedbacks in a coupled-model remains the largest source of uncertainty in its projection of future climate change driven by increasing greenhouse gases. The sign and magnitude of a feedback, however, depends critically on the dynamical and thermodynamical properties of a background climate. Due to internal variability of the climate system and fluctuations in external forcing, climate models inevitably excite low frequency variability and generate a wide range of background climates. The drift of feedbacks with these background climates, hence the transient behavior of climate feedbacks, presents a major challenge in our assessment of the risk of climate change acceleration and abrupt climate change. Delineating the evolving nature of feedback processes in a model is of particular importance for understanding the source of both the skill and bias in decadal climate prediction. However, the lack of a local measure of feedback strength has greatly limited our ability to make connections between actual model predictions (e.g., local temperature change) and transient behavior of climate feedbacks. This project will use a newly-developed technique, the coupled atmosphere-surface climate feedback-response analysis method (CFRAM) to tackle this problem. The study will take advantage of modern reanalysis products and the multi-model ensemble products from the decadal prediction experiments of the Coupled Model Intercomparison Project (CMIP5) to explore CFRAM's full potential in quantifying local and transient behavior of climate feedbacks and understanding the skill and bias of a model's ability to simulate decadal climate prediction. Specific research tasks that will be undertaken are (i) to quantify the relative contributions of various feedbacks to the annual cycle and interannual to decadal variations in surface air temperature (SAT) using reanalyses data; (ii)to evaluate the fidelity of CMIP5 models in representing the SAT annual cycle and decadal prediction of SAT; and (iii) to examine the attribution of models skills and biases in the ability to simulate decadal climate predictions and identify feedback processes that have major contributions to both systematic errors in the SAT annual cycle and forecast errors in decadal prediction of SAT.