Quantifying and comparing internal variability, shift, and emergence of trends in Arctic Ocean properties and transports in CMIP6 models

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Although there have been improvements in modeling Arctic processes, individual computer models still disagree on projected changes in the Arctic Ocean, where trends in temperature, salinity, stratification, and volume transports are highly model dependent. Furthermore, it has been shown that the variability in the interior layers (internal variability) of the Arctic plays a central role in its processes. However, to date, there is no comprehensive comparison of this variability in the Arctic Ocean for the most recent Coupled Model Intercomparison Project (CMIP). This project will examine Arctic Ocean variability in CMIP6 models. The results will serve as a baseline for future studies of Arctic Ocean internal variability. Key results could also contribute to determining when and where future observational campaigns will be most beneficial for detecting and monitoring Arctic Ocean change. The proposed work aims to better understand present and future Arctic Ocean internal variability by comparing CMIP6 models with multiple ensemble members. The project will answer the following questions: How does internal variability in Arctic-wide upper ocean temperature, salinity, and stratification, as well as volume transports through the Arctic gateways compare across CMIP6 models? Do trends in these same variables shift and emerge during the 21st century in CMIP6 models, and if so, do the models agree on the timing? Are the timing of shift and emergence dependent on the future forcing scenario? A combination of observations, models, and an ocean state estimate (ECCO) will be used. Major outcomes will include an improved understanding of Arctic Ocean internal variability, the first assessment of this variability across the most recent suite of state-of-the-art climate models participating in CMIP6, and demonstration of the utility of the synthetic ensemble technique for Arctic Ocean variables in models, observations, and ECCO. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.