Prediction and predictability of North American seasonal climate variability

of a dissertation at the University of Miami. Dissertation supervised by Professor Ben P. Kirtman No. of pages in text. (160) Climate prediction on short time-scales such as months to seasons is of broad and current interest in the scientific research community. Monthly and seasonal climate prediction of variables such as precipitation, temperature, and sea surface temperature (SST) has implications for users in the agricultural and water management domains, among others. It is thus important to further understand the complexities of prediction of these variables using the most recent practices in climate prediction. The overarching goal of this dissertation is to determine the important contributions to seasonal prediction skill, predictability, and variability over North America using current climate prediction models and approaches. This dissertation aims to study a variety of approaches to seasonal climate prediction of variables over North America, including both climate prediction systems and methods of analysis. We utilize the North American Multi-Model Ensemble (NMME) System for Intra-Seasonal to Inter-Annual Prediction (ISI) to study seasonal climate prediction skill of North American and in particular for southeast US precipitation. We find that NMME results are often equal to or better than individual model results in terms of skill, as expected, making it a reasonable choice for southeast US seasonal climate predictions. However, climate models, including those involved in NMME, typically overestimate eastern Pacific warming during central Pacific El Niño events, which can affect regions that are influenced by teleconnections, such as the southeast US. Community Climate System Model version 4.0 (CCSM4) hindacasts and forecasts are included in NMME, and we preform a series of experiments that examine contributions to skill from certain drivers of North American climate prediction. The drivers we focus on are sea surface temperatures (SSTs) and their accuracy, land and atmosphere initialization, and ocean-atmosphere coupling. We compare measures of prediction skill of precipitation and 2-meter temperature to perfect model predictability estimates, which can offer guidance on seasons or regions in which forecasts could be improved. We find that errors in SSTs, including those in the tropical Pacific but not necessarily isolated to, lead to deficiencies in hindcast skill over North America. Predictability estimates suggest that if SST forecasts could be improved, winter forecast skill of some North American regions could increase to at least the 4 seasonal lead. In an experiment where we isolate the influence of land and atmosphere initialization from SST influence in CCSM4, we find that their impact on spring prediction skill occurs primarily during the first month of the predictions, yet does not match estimates of model predictability. This suggests that if land and atmosphere initialization errors were decreased, there is potential for prediction skill to increase, however this result occurs only for very short leads of 1 month. Ocean-atmosphere coupling in fully coupled winter climate predictions versus prescribed SST predictions that lack ocean-atmosphere coupling do not show any significant difference in skill, predictability, or variability of precipitation and 2-meter temperature. Overall, these findings suggest that monthly and seasonal climate prediction skill and predictability is complex over North America. While some regions demonstrate skillful predictions, there is nevertheless room for improvement should model initialization or SST forecasts improve. SST errors are a leading source of inconsistencies in winter predictions. Land and atmosphere initialization are moderately important, and significantly enhance predictability and add to prediction skill versus no initialization, but only at very short leads. Finally, inconsistencies in air-sea heat fluxes in CCSM4 fully coupled versus prescribed SST hindcasts do not lead to significant differences in model skill. The results of this dissertation act not only to increase understanding of climate model predictions in NMME and CCSM4, but also to highlight areas of focus for model improvements.