Predictability of weather and climate

Abstract:

Abstract We correct an error relating to the comparison of precipitation variability in coupled models versus models run with prescribed SSTs (“AMIP models”) and discuss what conclusions to draw from the corrected result.

Abstract:

Combining climate models with statistical learning allows an assessment of the relative contributions of different factors to trends in winter precipitation at mid-latitudes. Thermodynamic (non-circulation-related) effects are mostly consistent between models and observations, but whether circulation-related changes are forced or unforced remains unclear.

Abstract:

Understanding changes in precipitation is crucial for society and ecosystems. Studies have documented the respective contributions of anthropogenic forcing and internal variability to precipitation trends, yet discrepancies persist between observed and simulated patterns. In Northern Hemisphere winter, these mismatches are often attributed to unforced internal variability that dominates observed trends. However, growing evidence also indicates that climate models underestimate the total response of precipitation to human forcings. Here we show that the thermodynamic contribution is broadly reproduced by climate models, whereas the dynamic contribution can diverge more substantially. Our approach disentangles the anthropogenic forced thermodynamic and dynamic components from internal variability in winter precipitation trends (1950-2022) to investigate their contribution to the trend discrepancies. In the Mediterranean, the forced dynamic signal from model simulations explains only about 10% of the observed dynamic trend, making detection challenging. Under continued anthropogenic emissions, the projected circulation response intensifies and more closely resembles observed trend patterns. Although internal variability in the observed record may contribute to this similarity, the results indicate an uncertain yet potentially emerging role of dynamic response in shaping regional winter precipitation trends. A reliable representation of the forced large-scale circulation response in climate models remains key for increasing confidence in regional precipitation projections.

Abstract:

The North Atlantic sector has been identified as a region where the 11‐year solar cycle has small but potentially non‐negligible impacts on winter climate, but a debate persists about the robustness of such impacts. This work explores the signatures of the 11‐year solar cycle over the North Atlantic in the ERA5 and 20th Century Reanalysis datasets. The results confirm previous studies with a robust positive boreal winter response in mean‐sea‐level pressure (mslp) in the region of the Azores at lags of three years after solar maximum. The spatial evolution of the response is examined in detail by first decomposing the mslp time series into the dominant modes of North Atlantic winter mslp variability, including the North Atlantic Oscillation (NAO), the East Atlantic (EA) and the Scandinavian patterns, before performing a multilinear regression analysis. We find that the maximum 11‐year solar response in the December–January–February (DJF) average does not project directly onto the NAO. However, when the early/late‐winter responses are examined separately, a statistically significant NAO response is seen in late winter (January–February) at lag 0–1 years and a statistically significant NAO response is also seen at lag +3 years in early winter (November–December). These results are consistent with predicted responses from previously proposed top‐down influences from the stratosphere in late winter followed by the re‐emergence of a signal from underlying sea surface temperatures in early winter. However, the NAO response is not the primary contributor to the total DJF response at lag +3 years. A previously unidentified solar‐cycle response in the EA pattern is found in late winter at lag +3 years with larger amplitude than the NAO response. The evolution of the DJF mslp response over the Azores region can thus be understood as a summation of the NAO and EA patterns at lag +3 years.