| Summary: | The atmosphere and ocean of the North Atlantic have undergone significant changes in the past century. To understand these changes, their mechanisms, and their regional implications requires a quantitative understanding of processes in the coupled ocean and atmosphere system. Central to this understanding is the role played by the dominant patterns of ocean and atmospheric variability which define coherent variations in physical characteristics over large areas. Four dominant subseasonal weather regimes are defined using Bayesian Gaussian mixture models. All correlation patterns of the Sea Level Pressure (SLP) anomalies with the membership probability timeseries for the weather regimes show similarities with the dipole structure typical for the North Atlantic Oscillation (NAO). The SLP patterns of two of the regimes represent the opposite phases NAO+ and NAO-. The two other weather regimes, the Atlantic Ridge (AR) and Scandinavian-Greenland dipole (SG), have dipole spatial structures with the northern and southern centres of action shifted with respect to the NAO pattern. These two patterns define blocking structures over Scandinavia and near the southern tip of Greenland, respectively. The storm tracks typical for the four regimes resemble the well known paths for positive/negative phases of NAO for the NAO+/NAO- weather regimes, and paths influenced by blocking off the south Greenland tip for AR and over Scandinavia for SG. The correlation patterns of momentum and heat fluxes to the ocean for the four regimes have tripole structures with positive (warm) downward heat flux anomalies over the Subpolar North Atlantic (SPNA) for the NAO- and the AR and negative heat flux anomalies over the SPNA for the NAO+. The downward heat flux anomalies associated with the SG are negative over the Labrador Sea and positive over the eastern SPNA. The long-term impact of the weather regimes on the regional climate is characterized by their distribution; i.e. the frequency of occurrence and persistence in time of each of them. Four typical distributions of the weather regimes are identified in this study which are associated with four dominant spatial interannual patterns representing the phases of two asymmetrical ``modes''. The first two patterns have the spatial structures of positive and negative phases of the North Atlantic Oscillation (NAO). The third and fourth patterns, here referred to as G+ and G-, define the opposite phases of a mode, that has a spatial structure defined by three centers found over Florida, south of Greenland and over Scandinavia. The NAO+ interannual patterns are associated with negative anomalies of the surface downward heat flux and ocean heat content over the SPNA. The NAO- and G+ are associated with positive anomalies of heat flux and ocean heat content. In the 1960s the dominant NAO- and G+ interannual patterns favored warmer than normal atmospheric and ocean temperatures over the SPNA. The winters in the late 1980s and early 1990s over the SPNA were colder than normal. This decadal shift in the atmospheric state between the 1970s and 1980s was associated with a change in the dominant interannual patterns towards NAO+~and~G- in the late 1980s and early 1990s. The recent warming of the SPNA since the mid-1990s was related to the dominance of the G+/G- interannual patterns in the distribution of interannual patterns probability membership. Our analysis suggests that this decadal variability was associated with a long-term shifts in atmospheric behavior over the SPNA that can be described by a change in the 1980s of the distribution of membership probabilities for interannual patterns. In the phase space of the interannual patterns, this transition is characterized with a shift from the NAO-/G+/G- subspace subspace in the 1950 and 1960s, towards NAO+/G+/G- since the mid 1980s. Based on this analysis we developed a a computationally efficient stochastic weather generator for analysis and prediction of the Subpolar North Atlantic amospheric decadal variability. The method is tested by the stochastic simulation of sea level pressure over the sub-polar North Atlantic. The weather generator includes a hidden Markov model, which propagates regional circulation patterns identified by a self organising map analysis, conditioned on the state of large-scale interannual patterns. The remaining residual effects are propagated by a regression model with added noise components. The regression step is performed by one of two methods, a linear model or artificial neural networks and the performance of these two methods is assessed and compared. Non UBC Unreviewed Author affiliation: Memorial University Researcher
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