Tim Woollings

Abstract:

Plain Language Summary: Midlatitude storms transport water vapor poleward and upward. When ascending, the air cools, causing the vapor to condense, releasing latent heat. The latent heating boosts the ascent in which it occurs and amplifies the storms originally responsible for the heating. This circular chain of events couples latent heating and storms in a nonlinear relationship we call the latent heating feedback. We simulate an atmosphere where latent heating is static and not a consequence of warm, moist air ascending. Comparing this to an atmosphere with realistic latent heating, we show that realistic latent heating leads to more intense storms traveling further poleward, especially west of North America and Europe. Simultaneously, the longitudinally averaged jet streams and storms respond by retracting toward the equator, leaving reduced westerlies and a double jet tendency over North America and Europe. Previous works tend to focus on the effect of latent heating on the average atmospheric state. Our work shows that this effect is only part of the story and that the latent heating effect on storms directly causes regional differences that climate models struggle with.

Abstract:

Plain Language Summary: The Pacific Northwest (PNW) experienced a record‐breaking heatwave during the summer of 2021, resulting in significant adverse effects on both human society and ecosystems. A heavy rainfall band was observed stretching from south China to south of Japan 1 week prior to the heatwave, fueling the debate over whether the monsoon activity contributed to this event. Our study found that while the monsoon activity typically has a cooling effect on the PNW's climate, in this particular year, it had a warming effect and thus contributed to this specific heatwave. This unusual warming effect was driven by a stronger and more northward‐shifted Pacific jet stream, which altered the extratropical response to the monsoon, resulting in an anticyclonic pattern over the PNW instead of the typical cyclonic response seen under average climatic conditions. Therefore, it is important to distinguish between the general question of whether monsoon can influence such events on average, and the specific question of whether it did in any specific case. We argue that when discussing the influence of large‐scale climate drivers on extremes, it is crucial to clearly state whether the focus is on the general potential for influence or on the specific role in a particular event.

Abstract:

Evidence has emerged of a discrepancy in tropical Pacific sea surface temperature (SST) trends over the satellite era, where most coupled climate models struggle to simulate the observed La Niña-like SST trends. Here we highlight wider implications of the tropical Pacific SST trend discrepancy for global circulation trends during boreal winter, using two complementary methods to constrain coupled model SST trends: conditioning near-term climate prediction (hindcast) simulations, and pacemaking coupled climate simulations. The robust circulation trend response to constraining the tropical Pacific SST trend resembles the interannual La Niña response. Constraining tropical Pacific SST robustly reduces tropical tropospheric warming, improving agreement with reanalyses, and moderately shifts the zonal-mean jets poleward. It also improves surface air temperature and precipitation trends in ENSO-sensitive regions, such as the Americas, South Asia, and southern Africa. Our results underline the importance of tropical Pacific SST for achieving confidence in multidecadal model projections.

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.