Windows of opportunity in subseasonal weather regime forecasting: A statistical-dynamical approach

TL;DR

This paper explores how atmospheric phenomena like MJO and SPV influence subseasonal weather patterns, and develops neural network models that improve three-week ahead weather regime forecasts, especially for European blocking events.

Contribution

It introduces a statistical-dynamical approach combining atmospheric state data with neural networks to enhance extended-range weather regime predictions.

Findings

Increased Greenland blocking activity following specific MJO and SPV phases.

Neural network models improve forecast accuracy by 2.9% over ECMWF.

Climatological forecasts based on MJO/SPV relationships show clear signals.

Abstract

MJO and SPV are prominent sources of subseasonal predictability in the Extratropics. With relevance for European weather it has been shown that the joint interaction of MJO and the SPV can modulate the preferred phase of the NAO and the occurrence of weather regimes. However, improving extended-range NWP at three-week lead times remain under-explored. This study investigates how MJO and SPV phases affect Greenland Blocking (GL) activity and integrates atmospheric state information into a neural network to enhance week-three weather regime activity forecasts. We define a weather regime activity metric using ECMWF reanalysis and reforecasts. In reanalyses we find increased GL activity following MJO phases 7,8 and 1, as well as weak SPV phases, indicating climatological windows of opportunity in line with previous studies. However, ECMWF forecast skill improves only in MJO phases 8 and 1 and weak SPV phases, identifying somewhat different model windows of opportunities. Next we explore using these findings in post-processing tools. Climatological forecasts based on MJO/SPV-NAO relationships provide a purely statistical approach to extended-range GL activity forecasting, independent of NWP models. Notably, MJO conditioned climatological forecasts show clear signals when evaluated against observed GL activity. Statistical-dynamical models, using neural networks that combine historical atmospheric state data with NWP-derived weather regime metrics improve weather regime activity forecasts across all regimes considered, achieving an absolute accuracy increase of 2.9% in forecasting the dominant weather regime compared to ECMWF. This is particularly beneficial to Blocking in the European domain, where NWP models often underperform. Atmospheric conditioned and neural network forecasts serve as valuable decision-support tools alongside NWP models, enhancing the reliability of S2S predictions.