Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022.

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.

Publisher Correction: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors.

'In the original HTML version of this Article, ref.12 was incorrectly cited in the first sentence of the first paragraph of the Introduction. The correct citation is ref. 2. This has now been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.'

Critical Southern Ocean climate model biases traced to atmospheric model cloud errors.

The Southern Ocean is a pivotal component of the global climate system yet it is poorly represented in climate models, with significant biases in upper-ocean temperatures, clouds and winds. Combining Atmospheric and Coupled Model Inter-comparison Project (AMIP5/CMIP5) simulations, with observations and equilibrium heat budget theory, we show that across the CMIP5 ensemble variations in sea surface temperature biases in the 40-60°S Southern Ocean are primarily caused by AMIP5 atmospheric model net surface flux bias variations, linked to cloud-related short-wave errors. Equilibration of the biases involves local coupled sea surface temperature bias feedbacks onto the surface heat flux components. In combination with wind feedbacks, these biases adversely modify upper-ocean thermal structure. Most AMIP5 atmospheric models that exhibit small net heat flux biases appear to achieve this through compensating errors. We demonstrate that targeted developments to cloud-related parameterisations provide a route to better represent the Southern Ocean in climate models and projections.

No Robust Evidence of Future Changes in Major Stratospheric Sudden Warmings: A Multi-model Assessment from CCMI.

Major stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Due to their relevance for the troposphere-stratosphere system, several previous studies have focused on their potential response to anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to a decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs, and the impact of large climatological biases in single-model studies. Here we revisit the question of future SSWs changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry Climate Model Initiative. From analyzing future integrations we find no statistically significant change in the frequency of SSWs over the 21st century, irrespective of the metric used for the identification of SSWs. Changes in other SSWs characteristics, such as their duration and the tropospheric forcing, are also assessed: again, we find no evidence of future changes over the 21st century.

Predictability of European 2016/2017.

Winter 2016/2017 was one of the driest on record for central Europe and the United Kingdom. This was the result of blocked atmospheric circulation with high pressure centred over North-West Europe dominating the winter mean circulation pattern. Using large ensembles of simulated winters, we find that the observed winter 2016/2017 circulation was very similar in pattern and strength to the circulation associated with the top 10% of driest Central European winters. Here, we explore whether seasonal forecasts were able to predict this circulation pattern. Despite the fact that the observed circulation anomaly did not project on to the North Atlantic Oscillation (NAO), we find that forecasts starting in November did predict a high-pressure anomaly over North-Western Europe. We use two independent data sets, and methods, to probe the drivers of this circulation pattern. We find evidence for a Rossby Wave propagating out of the tropical Atlantic where there were anomalous local rainfall anomalies. This case study is another example of real-time seasonal forecast skill for Europe and provides evidence for predictability beyond the NAO pattern.

Deriving Global OH Abundance and Atmospheric Lifetimes for Long-Lived Gases: A Search for CH3CCl3 Alternatives.

An accurate estimate of global hydroxyl radical (OH) abundance is important for projections of air quality, climate, and stratospheric ozone recovery. As the atmospheric mixing ratios of methyl chloroform (CH3CCl3) (MCF), the commonly used OH reference gas, approaches zero, it is important to find alternative approaches to infer atmospheric OH abundance and variability. The lack of global bottom-up emission inventories is the primary obstacle in choosing a MCF alternative. We illustrate that global emissions of long-lived trace gases can be inferred from their observed mixing ratio differences between the Northern Hemisphere (NH) and Southern Hemisphere (SH), given realistic estimates of their NH-SH exchange time, the emission partitioning between the two hemispheres, and the NH versus SH OH abundance ratio. Using the observed long-term trend and emissions derived from the measured hemispheric gradient, the combination of HFC-32 (CH2F2), HFC-134a (CH2FCF3, HFC-152a (CH3CHF2), and HCFC-22 (CHClF2), instead of a single gas, will be useful as a MCF alternative to infer global and hemispheric OH abundance and trace gas lifetimes. The primary assumption on which this multispecies approach relies is that the OH lifetimes can be estimated by scaling the thermal reaction rates of a reference gas at 272 K on global and hemispheric scales. Thus, the derived hemispheric and global OH estimates are forced to reconcile the observed trends and gradient for all four compounds simultaneously. However, currently, observations of these gases from the surface networks do not provide more accurate OH abundance estimate than that from MCF.