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Title: The North Atlantic polar front jet stream : variability and predictability, 1871-1914
Author: Hall, Richard J.
ISNI:       0000 0004 5992 6836
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2016
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The variability of the North Atlantic polar front jet stream is crucial for determining weather patterns in western Europe over a range of timescales. Jet metrics of speed and latitude are constructed from reanalysis datasets and a new index of jet meridionality is presented. An excellent match for time series of jet stream metrics is achieved between reanalyses. Homogenisation of jet metrics, the match with ERAInterim (ERA-I) and density of observational coverage in the North Atlantic sector increase confidence in the ability of the Twentieth Century Reanalysis (20CR) to represent interannual jet stream variability based on zonal wind speeds from 700- 900hPa. There is little evidence of significant trends in jet metrics. While recent (post-2000) negative trends in summer jet latitude are significant, they are not unprecedented and appear to be linked to the phase of the Atlantic Multidecadal Oscillation (AMO). A significant trend of increasing winter jet latitude interannual variability since 1950 is found, while there is some evidence linking periods of increasing and decreasing variability to slowly varying boundary conditions. Subseasonal jet variability shows high interannual variability and little evidence of significant trends. Potential drivers of jet-stream variability are investigated using multiple regression and composite analysis, supported by the use of wavelet coherence. Regression models are able to explain up to 56% of jet metric variability. Different drivers impact upon different seasons and jet metrics. The links with a range of predictors have value for future work on the predictability of the jet metrics. The multiple regression approach is extended to produce probabilistic forecasts for the winter North Atlantic Oscillation (NAO). Regression models show some skill at making winter NAO predictions based on autumn drivers, with some skill in making real-time forecasts. They compare favourably with Met Office seasonal predictions from their coupled dynamical forecasting system, GloSea5.
Supervisor: Hanna, Edward ; Jones, Julie M. ; von Fay Siebenburgen, Robertus ; Scaife, Adam Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available