Date of Award

Spring 1-1-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Jennifer E. Kay

Second Advisor

Jeffrey B. Weiss

Third Advisor

Katja Friedrich

Fourth Advisor

Will Kleiber

Fifth Advisor

Andrew Gettelman

Abstract

Unforced internal variability abounds in the climate system and often confounds the identification of climate change due to external forcings. Given that greenhouse gas concentrations are projected to increase for the foreseeable future, separating forced climate change from internal variability is a key concern with important implications. Here, we leverage a 40-member ensemble, the Community Earth System Model Large Ensemble (CESM-LE) to investigate the influence of internal variability on the detection of forced changes in two climate phenomena. First, using cyclone identification and compositing techniques within the CESM-LE, we investigate precipitation changes in extratropical cyclones under greenhouse gas forcing and the effect of internal variability on the detection of these changes. We find that the ensemble projects increased cyclone precipitation under twenty-first century business-as-usual greenhouse gas forcing and this response exceeds internal variability in both near- and far- futures. Further, we find that these changes are almost entirely driven by increases in cyclone moisture. Next, we explore the role of internal variability in projections of the annual cycle of surface temperature over Northern Hemisphere land. Internal variability strongly confounds forced changes in the annual cycle over many regions of the Northern Hemisphere. Changes over Europe, North Africa and Siberia, however, are large and easily detectable and further, are remarkably robust across model ensembles from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Using a simple energy balance model, we find that changes in the annual cycle over the three regions are mostly driven by changes in surface heat fluxes.

The thesis also presents a novel ensemble-based framework for diagnosing forced changes in regional climate variability. Changes in climate variability are commonly assessed in terms of changes in the variances of climate variables. The covariance response has received much less attention, despite the existence of large-scale modes of variability that induce covariations in climate variables over a wide range of spatial scales. Addressing this, the framework facilitiates a unified assessment of forced changes in the regional variances and covariances of climate variables.

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