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:

We assess near-surface temperature and sea surface temperature trends in 8 seasonal forecast systems in the Copernicus Climate Change Service archive, over the common hindcast period (1993–2016). All but one of the systems show a faster warming of the global-mean, relative to observations in both boreal summer and winter seasons. On average, systems warm at 0.21K/decade and 0.22K/decade for winter and summer, respectively, compared to 0.17K/decade and 0.19K/decade for ERA5. In summer, forecast systems tend to show an excessive warming of the tropical Pacific, tropical Atlantic and southern mid-latitudes, which contributes to the difference in global warming rates compared to observations. In contrast, greater warming in the northern mid-latitudes contributes most to trend differences for winter. The faster warming of models over this period has important implications for seasonal forecasts of future global and regional temperature and suggests further work is required to understand this bias.

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.