Abstract 221

Abstract ID: 221

Probabilistic seasonal forecasts using complex systems modelling, comparisons with dynamical models and linking North Atlantic atmospheric circulation and jet stream variability to UK and northwest Europe surface weather conditions

Lead Author: Ian Simpson
University of Lincoln, United Kingdom

Keywords: seasonal forecasting, NARMAX, jet stream, predictability, EOF

Abstract: Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability (the “signal to noise” problem) and to have lower skill in predicting summer variability than in winter. Here we construct nonlinear autoregressive moving average models with exogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from Empirical Orthogonal Function (EOF) analysis. Sets of predictors with known associations with these drivers are developed and fed into NARMAX. Predictions from NARMAX are compared with forecasts and hindcasts from the SEAS5, GloSea5 and NCEP models, highlighting areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer. Drivers of North Atlantic atmospheric circulation and jet stream variability are downscaled to examine their links with temperatures and precipitation in north-west Europe, allowing us to directly forecast seasonal temperature and precipitation anomalies in north-west Europe using NARMAX. High resolution downscaling is included for the UK to enable a more detailed case study of the relationships between the EOFs and the UK weather.

Prof. Edward Hanna (University of Lincoln)
Yiming Sun (University of Sheffield)
Hualiang Wei (University of Sheffield)