The importance of ocean internal variability for coupled climate modeling in the community earth system model

Internal Variability;Climate Modeling;Ocean Internal Variability;Ocean Modeling;Community Earth System Model;Model Ensemble

Hogan, Emily

关键词: Internal Variability;    Climate Modeling;    Ocean Internal Variability;    Ocean Modeling;    Community Earth System Model;    Model Ensemble;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101280/HOGAN-DISSERTATION-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Considerable efforts have been made in recent decades to diagnose how the climate of our planet is changing in response to anthropogenic greenhouse gas forcing. There are considerable risks associated with a warming climate, with possible hazards to life, property, economy, and the environment. These changes and the risks associated with them are inherently uncertain, and scientists use tools such as global coupled climate model ensembles to attempt to quantify these uncertainties. Quantifying different types of uncertainties involved in modeling the Earth’s climate system is of high importance as processes within the climate system are chaotic and challenging to predict.This dissertation contains a comprehensive quantification of climate uncertainty, focusing primarily on the uncertainty due to coupled atmosphere-ocean internal variability, utilizing a global coupled climate model ensemble (the Community Earth System Model; CESM). Here, this work poses key science questions related to quantifying internal variability in three different model variables, all of which are important in the context of a changing climate. Firstly, uncertainties surrounding decadal trends in depth-integrated, drift-removed global steric sea-level rise are evaluated. Results show that the effects of both internal variability and structural model differences contribute substantially to uncertainties in modeled steric sea-level trends for recent decades and the magnitude of these effects vary with depth. Uncertainties are amplified for regional assessments, highlighting the importance of both sources of variability when considering uncertainties surrounding modeled sea-level trends. Results can provide useful constraints on estimations of global and regional sea-level variability, in particular for areas with few observations such as the deep ocean and the Southern Hemisphere.Secondly, a statistical framework using a block-maxima approach is used to analyze the representation of warm temperature extremes in global climate model ensembles. Uncertainties due to structural model differences, grid resolution and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, but that there is overwhelming evidence suggesting variability in tail events is dependent on time and anthropogenic warming. These sources of variability can considerably influence the uncertainty of modeled extremes.Several idealized regional applications are highlighted for evaluating ensemble skill, based on quantile analysis and root mean square errors in the overall sample and the upper tail.Results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme temperatures.Lastly, this dissertation evaluates internal variability in ocean adjustment in the low-resolution CESM ensembles, by assessing ocean temperature. Uncertainty due to internal variability is used as a proxy to quantify the timescales on which different ocean depths and basins equilibrate in CESM. These results go beyond implications for CESM, reflecting timescales of internal variability in global coupled models. Results are discussed in the context of the global climate hiatus, of which internal variability is thought to be a predominant cause. Timescales for equilibration are longer in the deep ocean than the upper ocean, where ocean mixing enhances the speed of ensemble spread. The Atlantic equilibrates on shorter timescales relative to the Pacific, as North Atlantic Deep Water formation due to differential solar heating between high and low latitudes spurs the overturning circulation, whereas the Pacific has slower dynamics. Results have implications on the choice of climate model initialization method and imply that ensembles sampling the initial conditions of the atmosphere only may be appropriate for the evaluation of internal variability of the atmosphere and upper ocean, but not for the deep ocean. Additionally, the issue of accounting for deep ocean drift is of concern, when considering the production of large climate ensemble projects, such as the upcoming Coupled Model Intercomparison Project Phase 6 (CMIP6).