Predictability of weather and climate

Abstract:

<jats:p>Abstract. The Atmospheric Processes And their Role in Climate (APARC) Quasi-Biennial Oscillation initiative (QBOi) has conducted new experiments to explore the modulation of the QBO by El Niño–Southern Oscillation (ENSO). This paper provides an overview of the experimental design and investigates the modulation of the QBO by ENSO using nine climate models used in QBOi. A key finding is a consistent lengthening of the QBO period during La Niña compared to El Niño across all models, aligning with observational evidence. Although several models simulate QBO periods that deviate from the observed mean of approximately 28 months, the relative difference between La Niña and El Niño remains interpretable within each model. The simulated QBO periods during La Niña tend to be longer than those during El Niño, although, in most models, the differences are small compared to that observed. However, the magnitude of this lengthening shows large inter-model differences. By contrast, even the sign of the ENSO effect on QBO amplitude varies among models. Models employing variable parameterized gravity wave sources generally exhibit greater sensitivity of the QBO amplitude to the presence of ENSO than those models using fixed sources. The models capture key observed ENSO-related characteristics, including a weaker Walker circulation and increased equatorial precipitation during El Niño compared to La Niña, as well as a characteristic response in zonal mean zonal wind and temperature. All models also simulate stronger equatorial tropical upwelling in El Niño compared to La Niña up to ∼ 10 hPa, consistent with ERA5 reanalysis. These modulations influence the propagation and filtering of gravity waves. Notably, models with variable parameterized gravity wave sources show stronger wave forcing during El Niño, potentially explaining the shorter QBO period modulation in these models. Further investigation into the complex interplay between ENSO, gravity waves, and the QBO can contribute to improved model formulations. </jats:p>

Abstract:

Volcanic eruptions are among the strongest climate drivers, yet their regional impacts on the Middle East and North Africa (MENA) remain poorly constrained. Post-eruption amplified winter cooling in MENA is often attributed to a volcanically forced positive North Atlantic Oscillation (NAO), but the concurrent occurrence of El Niño–Southern Oscillation (ENSO) complicates attribution. Furthermore, summer climatic responses, including tropical warming and mid-latitude cooling, remain underexplored. Here, we present a pioneering research using a high-top coupled climate model (MIROC6) to assess volcanic-induced ENSO–NAO interactions and their regional climate impacts. We demonstrate that volcanic-induced NAO variability is the primary driver of post-eruption MENA winter cooling, with El Niño acting as a modulator rather than a fundamental trigger. We further reveal distinct summer climate anomalies, including suppressed precipitation over the Intertropical Convergence Zone (ITCZ). These findings underscore the importance of high-top models to resolve stratospheric influences on post-volcanic climate variability.

Abstract:

Seasonal forecasting systems have been operational for over two decades. Here we present a systematic analysis of the performance of operational seasonal forecasting models since their inception. We analyse seasonal forecasting systems from three major international operational centres that have produced and coordinated continuously on operational seasonal forecasts over the past 20 years. Due to the small sample size of available forecasts, it is difficult to draw meaningful conclusions using historical operational forecasts alone, therefore we focus primarily on available model hindcasts. Our analysis, which accounts for differences in ensemble size and period across the forecasting systems, demonstrates that there have been clear improvements in some regions through the different model eras. For both the boreal winter and summer hindcasts, there have been significant improvements in forecasting the tropical regions, which are concurrent with improvements in the skill of tropical sea‐surface temperature (SST) forecasts. These improvements in the Tropics are associated with increased predictability of temperature and precipitation across various continental regions on seasonal timescales. For the extratropics, the picture is more mixed, with strong improvements only evident during the boreal winter season over the North Pacific and North America. The sources of improvement over the winter extratropics are found to be strongly related to improvements in tropical SST skill and related improvements in the strength of the El Niño/Southern Oscillation (ENSO) teleconnection to the Pacific/North America pattern (PNA). Improvements of seasonal forecast skill over the rest of the extratropics, such as over Eurasia, are generally absent or patchy in individual models. The improvements that are found are most pronounced in the newest era models and are broadly associated with improvements in atmospheric model resolution. These improvements in skill are also evident in representative multi‐model ensembles that represent more closely how operational forecasts are used in practice.

Abstract:

Weather regimes and weather patterns, here jointly referred to as circulation types, are used to generate forecasts for a variety of applications, such as energy demand and flood risk. However, there are usually many different choices available for precisely which circulation types to use. Ideally, one would like to use circulation types that are both highly informative for the application and also highly predictable, but in practice, there is often a tradeoff between informativity and predictability. We present a simple, general framework for how to construct a circulation type forecast that optimally balances these factors by segueing between different choices of circulation types at different lead times based on information‐theoretic considerations. As an example, we apply this framework to the case of forecasting energy demand in Great British winters. We compare a set of 30 weather patterns produced by the UK Met Office with the much simpler two‐state framework consisting of a positive and negative North Atlantic Oscillation (NAO) regime and show how to optimally combine the two across a winter season.