Tim Woollings

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:

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.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>The winter North Atlantic Oscillation (NAO) is the dominant pattern of atmospheric circulation variability over the North Atlantic region. It influences climate and weather such as surface air temperatures (SAT) downstream over Eurasia through establishing a large-scale teleconnection, but past studies on the NAO’s downstream teleconnection have been largely limited to observational data, and further evidence of downstream impacts and associated mechanisms from comprehensive climate modelling is desirable. This study quantifies and analyzes this teleconnection on an interannual timescale by using both ERA5 reanalysis, and five large ensembles from four climate simulation models. A particular focus is placed on dynamical pathways, as well as variability among ensemble members that modulates the teleconnection strength. Results suggest that NAO signals are propagated downstream by Rossby waves, efficiently transmitted through waveguides along both the polar and subtropical jet streams to Eastern Eurasia; while heat can be advected weakly from upstream, advection plays a rather local effect inducing temperature anomalies from the Pacific Ocean onshore. Multiple linear regression shows that internal climate variability significantly modulates the teleconnection: a more locally dominant NAO pattern, and narrower waveguides could strengthen the teleconnection. These two factors combine to explain up to 70% of variance in the teleconnection strength, with each contributing almost equally. Reanalysis data marginally agree with the regression model (1.9 standardized residuals higher in strength), suggesting potential model biases in jets and the NAO variability. Monitoring these modulating factors would be crucial to understanding downstream climate predictability and improving climate prediction models linked to the NAO.</jats:p>