Extreme Associated Functions: Optimally Linking Local Extremes to Large-scale Atmospheric Circulation Structures

TL;DR

This paper introduces a new statistical method to link local weather extremes with large-scale atmospheric patterns, demonstrated on temperature data over the Netherlands and geopotential height patterns over the Euroatlantic region.

Contribution

The paper presents a novel optimal statistical approach to connect local temperature extremes with specific large-scale atmospheric circulation patterns.

Findings

Identified two key atmospheric patterns linked to temperature extremes.

The patterns are robust across different data subsamples.

The method can evaluate climate model performance in simulating extremes.

Abstract

We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July-August daily mean temperature at 2m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performance of climate models in simulating local weather extremes.