Climate dynamics

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.

Abstract:

To aid the search for atmospheres on rocky exoplanets, we should know what to look for. An unofficial paradigm is to anticipate CO2 present in these atmospheres, through analogy to the solar system and through theoretical modelling. This CO2 would be outgassed from molten silicate rock produced in the planet’s mostly-solid interior—an ongoing self-cooling mechanism that should proceed, in general, so long as the planet has sufficient internal heat to lose.Outgassing of CO2 requires relatively oxidising conditions. Previous work has noted the importance of how oxidising the planet interior is (the oxygen fugacity), which depends strongly on its rock composition. Current models presume that redox reactions between iron species control oxygen fugacity. However, iron alone need not be the sole dictator of how oxidising a planet is. Indeed, carbon itself is a powerful redox element, with great potential to feed back upon the mantle redox state as it melts. Whilst Earth is carbon-poor, even a slightly-higher volatile endowment could trigger carbon-powered geochemistry.We offer a new framework for how carbon is transported from solid planetary interior to atmosphere. The model incorporates realistic carbon geochemistry constrained by recent experiments on CO2 solubility in molten silicate, as well as redox couplings between carbon and iron that have never before been applied to exoplanets. We also incorporate a coupled 1D energy- and mass-balance model to provide first-order predictions of the rate of volcanism.We show that carbon-iron redox coupling maintains interior oxygen fugacity in a narrow range: more reducing than Earth magma, but not reducing enough to destabilise CO2 gas. We predict that most secondary atmospheres, if present, should contain CO2, although the total pressure could be low. An atmospheric non-detection may indicate a planet either born astonishingly dry, or having shut off its internal heat engine.

Abstract:

Survive or not survive, that is the question of the 500-hour JWST Rocky Worlds DDT Program. Whether a terrestrial planets’ atmosphere can suffer under the intense XUV of its host, or if it completely escapes, these are the questions we explore. Zahnle & Catling (2017) defined the Cosmic Shoreline, but recent observations from JWST reveal airless worlds around M-stars, calling for a refinement of this “receding” shoreline (Pass et al. 2025). M-stars spend a longer time in pre-main sequence, subjecting their orbiting worlds to some higher intensity XUV activity. This complicates our present understanding of this shoreline. Investigating chemical effects of planet-star interactions could be the key to a more complete picture of this shoreline.  We investigate the interplay between photochemistry, mixing, and escape of carbon dioxide atmospheres under intense and mild XUV fluxes as follow on work to both Johnstone et al. (2018) and Nakayama et al. (2022). We expand on this work by adopting thermal structure models from Nakayama et al. (2022) and apply them to identify key chemical pathways for escape. We create a reduced C-O chemical network including neutral and ionic species to identify these pathways. As photochemistry simulations take into account many reactions, these 1D calculations are too computationally expensive to be done in 3D. Although rudimentary at best, the mixing parameter– eddy diffusion term, K_zz, comprises the dynamical element of 1D photochemical simulations. Here, we consider the mixing of photochemical products in competition with escape to explore the chemical pathways of retention and loss. We compare the photochemical model results for active and inactive cases for the Trappist-1 system planets. Then, using the resulting composition-dependent heating and cooling rates for Trappist-1 planets, we assess their propensity for efficient atomic line cooling versus escape. We follow the work of Chatterjee & Pierrehumbert (2024) in this assessment.  Finally, using our pathway analysis, we find an analytical formula for calculating an energy-limited escape boundary for these planets based on composition.  It is important here to note the limitations of 1D work. First, there exists an exchange of rigor between modelling chemistry and dynamics. Insights from this work are ripe for implementation into 3D GCMs, especially in response to incorporating UV-driven processes for thermospheric modelling mentioned in Ding and Wordsworth (2019). Second, interaction with the interior is important in the early phase of planetary formation, i.e., the magma ocean phase. Due to exchange between atmosphere and magma early in the planet’s formation, incorporation with an interior-atmosphere model would better constrain higher pressure chemical abundances. Although this work focuses on the upper atmosphere, extrapolation to the surface environment is a key goal for understanding a planet.  Considering planet-star interaction is imperative for the selection of targets for observation. However, it is also important when considering anomalous detections of atmospheres around planets predicted to not have an atmosphere. This could be a first step in determining an atmosphere as non-primary and/or distinguishing between an airless planet and one with high altitude haze.