Tim Woollings

Abstract:

Abstract Initialised hindcasts inherit knowledge of the observed climate state, so studies of multidecadal trends in seasonal and decadal hindcast models have focused on the ensemble-mean when benchmarking against observed trends. However, this neglects the role of short-timescale variability in contributing to longer-term trends, and hence trend errors. Using a single-model coupled hindcast ensemble, we generate a distribution of 10,000 hindcast trends over 1981-2022 by randomly sampling a single ensemble member in each year. We find that the hindcast model supports a wide range of trends in various features of the large-scale climate, even when sampled at leads of just 1-3 months following initialisation. The spread in hindcast global surface temperature trends is equivalent to approximately a sixth of the total observed warming over the same period, driven by large seasonal variability of temperatures over land. The hindcasts also lend support for observed poleward jet shifts, but the magnitude of the shifts varies widely across the ensemble. Our results show that a fair comparison of hindcast trends to observations should consider the full range of model trends, not only the ensemble mean. More broadly, we argue that the hindcast trend distribution offers a largely untapped tool for studying multidecadal climate trends in a very large ensemble, through exploiting existing hindcast data.

Abstract:

Mesoscale Convective Systems (MCSs), with length scales of 100 to 1000 km or more, fall into the "grey zone" of global models with grid spacings of 10s of km. Their under-resolved nature leads to model deficiencies in representing MCS latent heating, whose vertical structure critically shapes large-scale circulations. To address this challenge, we use analysis increments—the corrections applied by Data Assimilation (DA) to the model's prior state—from a 10 km Met Office operational forecast model to inform the development of a stochastic parameterization for MCS latent heating. To focus on errors in MCS feedback rather than errors due to a missing MCS, we select analysis increments from 1037 MCS tracks that the model successfully captures at the start of the DA cycle.A Machine Learning–based Gaussian Mixture Model reveals that the vertical structure of temperature analysis increments is probabilistically linked to the atmospheric environment. Bottom-heavy heating increments tend to occur in low Total Column Water Vapor (TCWV) conditions, suggesting that the model underestimates low-level convective heating in relatively dry environments. In contrast, top-heavy heating increments are linked to a moist layer overturning structure—characterized by high TCWV and strong vertical wind shear—indicating model underestimation of upper-level condensate detrainment in such environments. This probabilistic relationship is implemented in the Met Office operational forecast model as part of the MCS: PRIME stochastic scheme, which corrects MCS-related uncertainties during model integration. By enhancing top-heavy heating, the scheme backscatters kinetic energy from the mesoscale to larger scales, improving predictions of Indian seasonal rainfall and the Madden–Julian Oscillation (MJO). Future work will assess its impact on forecast busts and its potential to extend predictability.

Abstract:

Abstract. Recent studies have highlighted the link between upper-level jet stream dynamics, especially the persistence of certain jet configurations, and extreme summer weather in Europe. The weaker and more variable nature of the jets in summer makes it difficult to apply the tools developed to study them in winter, at least not without modifications. Here, to further investigate the link between jets and persistent summer weather, we present two complementary approaches to characterize the jet dynamics in the North Atlantic sector and use them primarily on the Northern Hemisphere summer circulation. First, we apply the self-organizing map (SOM) clustering algorithm to create a 2D distance-preserving discrete feature space for the tropopause-level summer wind field over the North Atlantic. The dynamics of the tropopause-level summer wind can then be described by the time series of visited SOM clusters, in which a long stay in a given cluster relates to a persistent state and a transition between clusters that are far apart relates to a sudden considerable shift in the configuration of upper-level flow. Second, we adapt and apply a jet core detection and tracking algorithm to extract individual jets and classify them into the canonical categories of eddy-driven and subtropical jets (EDJs and STJs, respectively). Then, we compute a wide range of jet indices for each jet category for the entire year to provide easily interpretable scalar time series representing upper-tropospheric dynamics. This work will focus on the characterization of historical trends, seasonal cycles, and persistence properties of the jet stream dynamics, while ongoing and future work will use the tools presented here and apply them to the study of connections between jet dynamics and extreme weather. The SOM allows the identification of specific summer jet configurations, each one representative of a large number of days in historical time series, whose frequency or persistence had increased or decreased in the last few decades. Detecting and categorizing jets adds a layer of interpretability and precision to previously and newly defined jet properties, allowing for a finer characterization of their trends and seasonal signals. Detecting jets at pressure levels of maximum wind speed at each grid point instead of in the dynamical tropopause is more reliable in summer, and finding wind-direction-aligned subsets of 0 contours in a normal wind shear field is a fast and robust way to extract jet cores. Using the SOM, we isolate persistent circulation patterns and assess if they occur more or less frequently over time. Using properties of the jets, we confirm that the Northern Hemisphere summer subtropical jet is weakening, that both jets get wavier, and that these jets overlap less frequently over time. We find no significant trend in jet latitude or in jet persistence. Finally, both approaches agree on a rapid shift in the subtropical jet position between early and late June.

Abstract:

Tropical cloud feedbacks are an important source of uncertainty in estimates of climate sensitivity. The extent to which changes in atmospheric circulation contribute to these feedbacks remains an open question. Here, all-sky radiative flux observations and an atmospheric reanalysis are used to estimate tropical cloud feedbacks from multi-decadal trends (1985 – 2020) in cloud radiative effect and surface temperature. We decompose the observed feedbacks into dynamic and non-dynamic components to quantify the impact of circulation trends. Narrowing and strengthening of tropical ascent lead to substantial dynamic feedbacks on regional scales that are similar in magnitude to the non-dynamic feedbacks. However, as previously shown for high- and low-resolution climate models, large dynamic feedbacks in different circulation regimes are connected by the atmospheric mass budget and approximately cancel when averaged across the tropics due the quasi-linear relationship between cloud radiative effect and vertical velocity. This results in small dynamic contributions to the tropical-mean net, longwave and shortwave feedbacks. We suggest that this result will hold in future and thus that isolating the non-dynamic components associated with individual cloud types can provide important insights into the processes controlling the tropical-mean cloud feedback and its uncertainty. Additionally, we show that feedbacks estimated from multi-decadal trends differ from those estimated from inter-annual variability. We demonstrate that, for dynamic feedbacks, this is because changes are controlled by different mechanisms and this leads to a differing spatial distribution of temperature sensitivity. Finally we provide new estimates of the uncertain combined tropical anvil area and albedo feedback using both multi-decadal trends and inter-annual variability.

Abstract:

Many Mediterranean‐type climates (MCs) have experienced wintertime drying trends since 1979. Using a dynamical adjustment method, we separate the effects of circulation‐induced drying trends from other residual trends. Our analysis reveals that circulation trends are the leading cause of the observed drying in Central Chile and the US Southwest, and that models show the drying across Southern Hemisphere MCs is independent of trends in the Southern Annular Mode. All Mediterranean‐type climates have exhibited residual drying trends from both internal variability and externally forced thermodynamic processes. Large ensembles suggest internal variability contributes significantly to the observed drying. However, in many regions the observed drying lies outside the ensemble distribution, raising questions about model accuracy.