UCLA

The ability of global climate models (GCMs) to simulate climatological precipitation and other features of the hydrological cycle accurately is acceptable by some metrics, especially at large scales. Regionally, however, there can be substantial discrepancy in a multi-model ensemble, both in the annual or seasonal historical precipitation climatology as well as in end-of-century changes. Characterizing this intermodel spread and identifying leading uncertainty patterns and underlying physical pathways is important in constraining climatological biases and projections of future change. This dissertation looks at three aspects of precipitation uncertainty in ensembles.

First, El Nino-Southern Oscillation (ENSO) teleconnections are analyzed in an atmosphere-only ensemble to gauge the ability of atmospheric components of GCMs to reproduce ENSO precipitation teleconnections. This serves as a test for how well models simulate the atmospheric response to sea surface temperature forcing in the immediate ENSO vicinity, as well as how accurately they reproduce the large-scale tropical-to-midlatitude dynamics leading to teleconnected precipitation. While individual models have difficulty in simulating the exact spatial pattern of teleconnections, they demonstrate skill in regional amplitude measures and sign agreement of the precipitation teleconnections at the grid point level, which lends value to the use of such measures in global warming projections.

Next, objective spatial analysis techniques are applied to a fully-coupled GCM ensemble in order to visualize patterns of uncertainty in end-of-century precipitation changes and in the historical climatology. Global patterns are considered first, with the tropics exerting a clear dominance in intermodel spread, mainly within zones of deep convection or along convective margins. Regional domains are considered second, with a focus on the wintertime midlatitude Pacific storm track. A key region of end-of-century precipitation change uncertainty is identified at the terminus of the storm track, and large-scale circulation processes related to model differences in upper-level jet increases are found to play a role. These results help pinpoint a source of intermodel spread in projected precipitation changes along the North American west coast, especially for the Southern California region.

Last, an existing perturbed physics ensemble is examined in order to understand the parameter sensitivity of climatological precipitation and other fields. This ensemble consists of integrations in which four parameters in the deep convection scheme were systematically varied. Models of parameter dependence are constructed for precipitation, and this process--termed metamodeling--is a computationally cheap alternative to brute-force sampling of parameter space in the GCM. A quadratic metamodel performs generally well but fails to capture sensitive regions of high nonlinearity for certain parameter ranges. A second metamodel is constructed by combining an approach from the engineering literature with the spatial uncertainty patterns used above, and it proves adept at capturing sensitive regions where its quadratic counterpart fails. Finally, when more than one field is optimized simultaneously, it is often the case that a set of parameter values that optimizes one field can degrade performance in another. Concepts from multiobjective optimization are used to quantify these tradeoffs.