Thompson, LuAnneCohen, Jacob T.2025-10-022025-10-022025-10-022025Cohen_washington_0250E_28798.pdfhttps://hdl.handle.net/1773/54080Thesis (Ph.D.)--University of Washington, 2025Ocean dynamics drive the memory of the climate system. Long-term predictions, therefore, rely on an understanding of the ocean processes that control climate variability, and the usefulness of these predictions requires an understanding of current models' predictive skill. Across three chapters, we study the influence of upper ocean dynamics influence on sea surface temperature variability, predictions of spatially coherent marine heatwave events, and predictions of seasonal-to-decadal ocean heat transport and sea ice. We first study the turbulent surface heat flux (Q) response to sea surface temperature (SST) anomalies and to mixed-layer heat content (HC) anomalies to understand where and when ocean processes control SST variability and air-sea interaction. From observational data of SST, HC, and Q, we use lagged covariances to define the feedback of SST to Q and the feedback of HC to Q. The feedback sensitivity, defined as the difference between SST-Q feedback and HC-Q feedback, illustrates the relative importance of ocean processes to atmospheric processes in controlling SST and Q variability. The regional and seasonal patterns of the sensitivity feedback demonstrate the varying pathways by which the ocean influences the surface heat flux feedback. Determining these patterns of ocean-dominated variability improves predictive understanding of the climate. We then evaluate how well climate models detect and predict spatially connected extreme SST events known as marine heatwaves (MHWs). Here, we evaluate a method of detecting and predicting spatially connected MHW objects. We apply object-based forecast verification to the CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) experiment, a set of initialized hindcasts with 20-member ensembles of 24-month simulations initialized quarterly from 1970–2019. We demonstrate that SMYLE predicts MHWs that occur near observed MHWs with high skill at long lead times, but with errors in location, area, and intensity that grow with lead time. SMYLE exhibits improved skill in predicting the intensity of MHWs in December and January, and worse skill from August to October. This work illustrates the capacity to forecast connected MHW objects and to quantify the uncertainty in those forecasts with potential applications for future community use. The final chapter examines the prediction skill of Arctic sea ice and ocean heat transport (OHT) through the Pacific and Atlantic regions. Using the SMYLE dataset and a decadal prediction hindcast dataset (DP), we interrogate how the prediction skill of sea ice is related to the prediction skill of OHT across regions and timescales. We examine the co-occurrence of high OHT and sea ice prediction skill and evaluate the skill of DP in predicting the short-term tendency of the Arctic climate. We find high seasonal-to-decadal prediction skill and a strong relationship between the predictability of OHT and sea ice. This chapter demonstrates how ocean variability influences Arctic sea ice predictability. Together, the chapters in this dissertation contribute to our understanding of the ocean's role in climate variability and predictability.application/pdfen-USCC BYair-sea interactionclimate predictionmarine heatwavesea icePhysical oceanographyOceanographyThesis