Sarah Ineson's research while affiliated with Met Office and other places

Global mean near surface temperature change is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office issues a global mean temperature forecast for the coming year. Following on from the new record in 2023, we predict that 2024 will likely (76% chance) be a new record year with a 1‐in‐3 chance of exceeding 1.5°C above pre‐industrial. Whilst a one‐year temporary exceedance of 1.5°C would not constitute a breach of the Paris Agreement target, our forecast highlights how close we are now to this. Our 2024 forecast is primarily driven by the strong warming trend of +0.2°C/decade (1981–2023) and secondly by the lagged warming effect of a strong tropical Pacific El Niño event. We highlight that 2023 itself was significantly warmer than the Met Office DePreSys3 forecast, with much of this additional observed warming coming from the southern hemisphere, the cause of which requires further understanding.

The Northern Hemisphere winter of 2021/2022 exhibited a positive North Atlantic Oscillation (NAO) which led to largely mild and wet conditions for Northern Europe. A moderate La Niña in the tropical Pacific and a stronger than average stratospheric polar vortex together explained the observed anomalies over the winter. Winter 2021/2022 was well predicted in general by seasonal forecast systems. The ensemble mean indicated a positive winter NAO and the forecast spread of forecasts from the Met Office GloSea6 seasonal prediction system spanned the observed mean sea level pressure anomaly for the whole winter and the individual months. However, December showed the largest departure from the mean of the forecast which is consistent with evidence from previous work that early winter ENSO teleconnections are too weak in model predictions. Nevertheless, around one in four members captured the negative NAO pattern in December. The strong pressure gradient and positive NAO predicted for the latter part of the winter allowed successful warning of the possibility of above average storminess and strong winds which occurred in February 2022. This is potentially useful information for the energy sector who increasingly rely on wind power and the insurance industry for warning of storm damage.

Using a large ensemble of initialised retrospective forecasts (hindcasts) from a seasonal prediction system, we explore various statistics relating to sudden stratospheric warmings (SSWs). Observations show that SSWs occur at a similar frequency during both El Niño and La Niña northern hemisphere winters. This is contrary to expectation, as the stronger stratospheric polar vortex associated with La Niña years might be expected to result in fewer of these extreme breakdowns. Here we show that this similar frequency may have occurred by chance due to the limited sample of years in the observational record. We also show that in these hindcasts, winters with two SSWs, a rare event in the observational record, on average have an increased surface impact. Multiple SSW events occur at a lower rate than expected if events were independent but somewhat surprisingly, our analysis also indicates a risk, albeit small, of winters with three or more SSWs, as yet an unseen event.

The Summer North Atlantic Oscillation is the primary mode of atmospheric variability in the North Atlantic region and has a significant influence on regional European, North American and Asian summer climate. However, current dynamical seasonal prediction systems show no significant Summer North Atlantic Oscillation prediction skill, leaving society ill-prepared for extreme summers. Here we show an unexpected role for the stratosphere in driving the Summer North Atlantic Oscillation in both observations and climate prediction systems. The anomalous strength of the lower stratosphere polar vortex in late spring is found to propagate downwards and influence the Summer North Atlantic Oscillation. Windows of opportunity are identified for useful levels of Summer North Atlantic Oscillation prediction skill, both in the 50% of years when the late spring polar vortex is anomalously strong/weak and possibly earlier if a sudden stratospheric warming event occurs in late winter. However, we show that model dynamical signals are spuriously weak, requiring large ensembles to obtain robust signals and we identify a summer ‘signal-to-noise paradox’ as found in winter atmospheric circulation Our results open possibilities for a range of new summer climate services, including for agriculture, water management and health sectors.

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.

Angular momentum is fundamental to the structure and variability of the atmosphere and therefore has an important influence on regional weather and climate. Total atmospheric angular momentum is also directly related to the rotation rate of the Earth and, hence, the length of day. However, the long-range predictability of fluctuations in the length of the day and atmospheric angular momentum is unknown. Here we show that fluctuations in atmospheric angular momentum and the length of day are predictable out to more than a year ahead and that this provides an atmospheric source of long-range predictability for surface climate. Using ensemble forecasts from a dynamical climate model, we demonstrate long-range predictability of signals in the atmospheric angular momentum field that propagate slowly and coherently polewards due to wave–mean flow interaction within the atmosphere. These predictable signals are also shown to precede changes in extratropical climate via the North Atlantic Oscillation and the extratropical jet stream. These results extend the lead time for length-of-day predictions, provide a source of long-range predictability from within the atmosphere and provide a link between geodesy and climate prediction.

Abstract Boreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over‐predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.

Long climate simulations with the Met Office Hadley Centre General Circulation Model show weak El Niño-Southern Oscillation (ENSO) amplitude asymmetry between El Niño and La Niña phases compared with observations. This lack of asymmetry is explored through the framework of a perturbed parameter experiment. Two key hypotheses for the lack of asymmetry are tested. First, the possibility that westerly wind burst activity is biased is explored. It is found that the observed difference in wind burst activity during El Niño and La Niña tends to be underestimated by the model. Secondly, the warming due to subsurface non-linear advection is examined. While the model exhibits non-linear dynamic warming during both La Niña and El Niño, and thus a contribution to ENSO asymmetry, it is shown to be consistently underestimated in comparison with ocean reanalyses. The non-linear zonal advection term contributes most to the deficiency and the simulation of the anomalous zonal currents may be playing a key role in its underestimation. Compared with the ocean reanalyses, the anomalous zonal currents associated with ENSO are too weak in the vicinity of the equatorial undercurrent and the surface wind driven zonal currents extend too deep.

The phase-locking behavior of El Niño-Southern Oscillation (ENSO) in models from Coupled Model Intercomparison Project (CMIP) phase 5 to phase 6 is assessed in terms of the locking-month of ENSO peak and the sharpness of locking tendency. Overall, a robust improvement exists in CMIP6. Compared to CMIP5, more CMIP6 models truly reproduce the locking-month in November-January. Meanwhile, the sharpness of phase-locking in CMIP6 models also improves, though most of them are still far from the observations. The locking-month is verified to be highly corresponding to the phase of seasonal modulation of ENSO’s instabilities. The sharpness is mainly controlled by the intensity of this modulation and noise. Compared to CMIP5, CMIP6 models generally simulate these affecting factors better. Besides, models displaying an exaggerated semi-annual variation of ENSO’s instabilities simulate the ENSO phase-locking relative-poorly, and these models show no reduction from CMIP5 to CMIP6.

The European winters of 2017–18 and 2018–19 were not climatically extreme, but both winters had a major sudden stratospheric warming (SSW). In February 2018, an SSW led to an intense cold outbreak across Europe and further spells of cold weather in March. The SSW of January 2019, although well predicted and expected to increase the chance of a cold end to winter, apparently produced little impact. In this study, we examine the performance of the Met Office seasonal prediction system in these winters, and the influences that led to these outcomes. To achieve this latter objective, sets of numerical experiments are performed in which the tropical troposphere and the extratropical stratosphere are relaxed towards their observed state, allowing the influence of each on the North Atlantic‐European atmospheric circulation to be identified. Using these experiments, we show that the SSWs had similar impacts in each case, creating a signal of easterly surface wind anomalies in the weeks following the event. In contrast, tropical influences were opposite in the two winters, acting to strengthen the easterly signal at the end of February 2018 and opposing it in January 2019. The different apparent responses to the two events therefore came about largely through tropical tropospheric variability. Furthermore, we highlight the importance of a very strong cycle of the Madden‐Julian Oscillation (MJO) in late January and early February 2018 as an important driver for the February 2018 SSW. MJO teleconnections appear to have been critical in creating the large mid‐latitude wave 2 amplitude that has been identified as the immediate cause of this event.