Analyzing the present and future Pacific-North American teleconnection using global and regional climate models Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/h128ng95m

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  • In this thesis I present the results of a comprehensive assessment of the Pacific-North American (PNA) teleconnection pattern in general circulation models (GCMs) and a regional climate model (RCM). The PNA teleconnection pattern is a quasi-stationary wave field over the North Pacific and North America that has long been recognized as a robust feature of Northern Hemisphere atmospheric circulation, and directly affects the interannual variability of North American temperature and precipitation. The teleconnection is evaluated under present (1950-2000) and future (2050-2100) climate in a coupled GCM (MPI/ECHAM5) and a high-resolution regional climate model (RegCM3). I further assess the PNA in 27 atmosphere-ocean GCMs and earth system models (ESMs) from the ongoing fifth phase of the Coupled Model Intercomparison Project (CMIP5). The National Centers for Environmental Prediction and Atmospheric Research (NCEP/NCAR) Reanalysis serves a quasi-observational baseline against which the models are evaluated. For each analysis, changes in the spatial and temporal patterns of the PNA spatial are assessed for both the present and future climates, and these changes are then related to changes in climate and surface hydrology in North America. Coupling the NCEP and ECHAM5 GCMs with RegCM3 is very successful in that the PNA is resolved in both models with little loss of information between the GCMs and RegCM3, thereby allowing an assessment of high-resolution climate with an inherent skill comparable to that of the global models. The value of the PNA index is generally independent of the method used to calculate it: three- and four-point modified linear pointwise calculations for both the RegCM3 and ECHAM5 model simulations produce very similar indices compared with each other, and compared with those extracted from a rotated principle component analysis (RPCA) which is also used to determine the PNA spatial pattern. The spatial pattern of the PNA teleconnection emerges as a leading mode of variability from the RPCA, although the strength of the teleconnections are consistently weaker than NCEP as defined by four main "centers of action". This discrepancy translates into the strength of the controls of the PNA on surface climate. Maps of the correlations between the GCM PNA indices and RCM surface climate variables are compared to the results from the NCEP/NCAR Reanalysis. I find that correlation patterns with temperature and precipitation are directly related to the positioning of the Aleutian low and Canadian high, the two main drivers of upper-atmospheric circulation in the PNA sector. The CMIP5 models vary significantly in their ability to simulate the quasi-observed features of the PNA teleconnections. The behavior of the models relative to NCEP is more definite than the trends within the models. Most models are unable to resolve the temporal variability of NCEP; however, on the other hand most of the models are able to capture the PNA as a low-frequency quasi-oscillation. Many of the models are unable to simulate the barotropic instability that initiates wave energy propagation through the 500-hPa geopotential height field, thereby leading to phase-locking and thus the positive and negative modes of PNA are indistinguishable. The behavior and the spatial patterns of the PNA throughout the 21st century are consistent with other projections of future climate change in that most models exhibit a lengthening of the eddy length scale and a poleward shift of the mid-latitude jet stream associated with polar amplification of greenhouse-gas driven global warming. Finally, my analyses underscore the robustness of multi-model means, suggesting that the cumulative results of multiple climate models outperform the results from individual models because ensemble means effectively cancel discrepancies and hereby expose only the most robust common features of the model runs. While ensembles provide better representation of the average climate, they potentially mask climate dynamics associated with inter-annual and longer time scales. Relying on ensemble means to limit model spread and uncertainties remains a necessity in using models to project future climate.
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