Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2409.16988

European summer weather regimes 1990-2019: Automatic classification and representation in a small global climate model ensemble

Sibille Wehrmann·Carolyne Pickler·Marlene Schramm·Thomas Mölg
View original source ↗Read source document ↗Export metadata ↓

Abstract

In Central Europe, the occurrence of different weather regimes (WRs) plays a major role in spatiotemporal temperature and precipitation patterns. In the context of increasingly extreme summers, this study focuses on European summer WRs (June-August, JJA) over the last three decades (1990-2019), and aims to examine the changing characteristics of these WRs and their potential implications. In addition, based on ERA5 reanalysis data, the WR representation from a carefully preselected, small ensemble of global general circulation models (GCMs) is analyzed. A methodological refinement concerns the combination of Self-Organizing Maps (SOM) with a novel GCM selection technique, which enhances the robustness of the simulated large-scale circulation patterns. WRs are defined using daily sea level pressure (SLP) and wind in the upper troposphere. Results reveal that the SOM captures predominant European summer synoptic patterns, and the salient result is a positive trend in 2 m air temperature across nearly all WRs. The selected GCMs - MPI-ESM1-2-LR r29i1p1f1, CanESM5 r1i1p1f1 and MRI-ESM2-0 r5i1p1f1 - identify WRs correctly and ERA5-based results are always within the range of this small GCM ensemble. Its members clearly show skill in representing large-scale WRs accurately and, thus, serve as valuable tools for studying synoptic weather patterns during summer in Central Europe. Therefore, we can recommend these GCMs for studies on WR-related climate projections of future summer conditions, particularly for those interested in climate impacts in Central Europe from a synoptic-scale perspective.

Explore related subjects

physics.ao-phphysics.geo-ph

Keep this discovery

BibTeXRIS

Sibille Wehrmann, Carolyne Pickler, Marlene Schramm, Thomas Mölg. 2024-09-25. European summer weather regimes 1990-2019: Automatic classification and representation in a small global climate model ensemble. https://arxiv.org/abs/2409.16988

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗