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An anomalous signal-to-noise ratio (also called the signal-to-noise paradox) present in climate models has been widely reported, affecting predictions and projections from seasonal to centennial timescales and encompassing prediction skill from internal processes and external climate forcing. An anomalous signal-to-noise ratio describes a situation where the mean of a forecast ensemble correlates better with the corresponding verification than with its individual ensemble members. This situation has severe implications for climate science, meaning that large ensembles might be required to extract prediction signals. Although a number of possible physical mechanisms for this paradox have been proposed, none has been universally accepted. From a statistical point of view, an anomalous signal-to-noise ratio indicates that forecast ensemble members are not statistically interchangeable with the verification, and an apparent paradox arises only if such an interchangeability is assumed. It will be demonstrated in this study that an anomalous signal-to-noise ratio is a consequence of the relative magnitudes of the variance of the observations, the ensemble mean, and the error of the ensemble mean. By analysing the geometric triangle formed by these three quantities, and given that for typical seasonal forecasting systems both the correlation and the forecast signal are relatively small, it is concluded that an anomalous signal-to-noise ratio should, in fact, be expected in such circumstances.

The Arctic has warmed substantially over the last decades and will continue to do so owing to global warming in conjunction with polar amplification. The changing mean state poses many challenges to the construction, evaluation and calibration of subseasonal-to-seasonal forecasting systems, because it puts into question the representativeness of the system's retrospective forecasts (reforecasts). Furthermore, any inconsistencies with observed trends degrade the forecast skill and point to deficiencies in either the physical modelling or the initialization methods. Here, we assess the consistency of boreal winter trends of surface air temperature (SAT) in the Eurasian Arctic between the ERA5 reanalysis and ECMWF sub-seasonal reforecasts initialised from ERA5, for the 35-year period 1986-2021. We present methods to quantify robustness and importance of the observed trends, and to quantify the consistency of reforecast trends with these observed trends. We find that, in large parts of the marine Arctic, the reforecasts clearly underestimate the reanalsyis warming trend of about 1 K per decade at lead times beyond two weeks. For longer lead times, the reforecast trend is less than half of the reanalysis trend, with very high statistical significance. We present a series of numerical experiments to investigate potential reasons for the trend underestimation. These concern the sea-ice thermodynamic coupling to the atmosphere, impact of sea surface temperatures, and possible remote atmospheric influences from the North Atlantic and the Tropics. The outcome of these experiments provides guidance for future improvements in the physical forecast model and data assimilation methods needed to faithfully represent and predict Arctic climate variability and change.

The Indian Ocean Dipole (IOD) is a major source of seasonal climate variability. This dipole has strong impacts on the Indian Ocean region and through teleconnections can influence the seasonal climate of remote regions. The strong IOD of 2019/2020 influenced the state and predictability of the Northern Hemisphere extratropics. Thus, a good understanding of the mechanism of this teleconnection is desirable. In this contribution we investigate the special teleconnection of the winter 2019/2020.In model experiments with the OpenIFS from ECMWF we show that the NAO in the winter 2019/2020 is influenced by the IOD. We use hindcast ensembles of the DJF season 2019/2020 to analyse this teleconnection. In model experiments, the Sea Surface Temperature (SST) boundary conditions are perturbed. With these experiments we identify the relative importance of individual ocean regions to the state of the NAO in the winter of 2019/2020. We contrast the experiments to the operational ECMWF System5 forecast and ERA5. Experiments with the 2019/2020 SSTs in the Indian Ocean (with other boundary conditions set to climatology) reproduce many of the observed atmospheric 2019/2020 features. In contrast, experiments with SSTs in the Pacific show very different patterns. We identify eddy-mean-flow interactions as a mechanism that connects and transports information from the Indian Ocean to the North Atlantic. With Hoskins E-Vectors we show that anomalous eddy activity during IOD events impacts the position and strength of the Northern Hemisphere extratropical jet. This interaction provides a teleconnection mechanism in addition to the Rossby-wavetrain discussed in other studies., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Heatwaves constitute a major threat to human health and ecosystems. Projected increases in heatwave frequency and severity thus lead to the need for prediction to enhance preparedness and minimize adverse impacts. In this Review, we document current capabilities for heatwave prediction at daily to decadal timescales and outline projected changes under anthropogenic warming. Various local and remote drivers and feedbacks influence heatwave development. On daily timescales, extratropical atmospheric blocking and global land–atmosphere coupling are most pertinent, and on subseasonal to seasonal timescales, soil moisture and ocean surface anomalies contribute. Knowledge of these drivers allows heatwaves to be skilfully predicted at daily to weekly lead times. Predictions are challenging beyond timescales of a few weeks, but tendencies for above-average temperatures can be estimated. Further into the future, heatwaves are anticipated to become more frequent, persistent and intense in nearly all inhabited regions, with trends amplified by soil drying in some areas, especially the mid-latitudes. There is also an increased occurrence of humid heatwaves, especially in southern Asia. A better understanding of the relevant drivers and their model representation, including atmospheric dynamics, atmospheric and soil moisture, and surface cover should be prioritized to improve heatwave prediction and projection. ISSN:2662-138X

Extreme weather attribution, quantifying the role of human influence in specific weather events, is of interest to scientists, adaptation planners and the general public. However, the devastating 2021 Pacific Northwest heatwave challenged conventional statistical approaches to attribution due to the absence of similar events in the historical record, and model-based approaches due to poor representation of key causal processes in current climate models. Here we use state-of-the-art operational medium-range and seasonal weather prediction systems, applied for the first time to this kind of climate question and unequivocally able to simulate the detailed physics of the heatwave in question, to show that human influence on the climate made this event at least 8 [2--30] times more likely to occur. Quantifying the absolute probability of such an unprecedented event is more challenging, but the length of the observational record suggests at least a multi-decade return-time in the current climate, with the likelihood doubling every 17 [10--50] years at the current rate of global warming. Our forecast-based approach synthesises the storyline approach, which examines human influence on the physical drivers of an event in a deterministic manner, and the probabilistic approach, which assesses how the frequency of a class of events has been affected by human influence. If developed as a routine service in a number of forecasting centres, it could provide reliable estimates of the changing probabilities of all extreme events that can be represented in forecast models, which is critical to supporting effective adaptation planning.

The European Climate Prediction system (EUCP) project aimed to lay the foundation for a future regional climate prediction system for Europe. An important element of this is the role of narrative or storylines approaches in data production and scientific investigation, as well as a user product. We will present the results of our investigations which sought to understand the potential advantages and challenges in developing physically based climate storylines as part of a climate service by addressing the questionsWhat are climate storylines and where are they useful? How could storylines bring together various outputs of EUCP state-of-the-art climate science attempting to reduce uncertainty and complexity in climate projections, and seamlessly combine them with decadal predictions? What are the challenges of producing them as a service? The body of EUCP work included two case studies of co-producing storylines as a user product, revealing the potential usefulness for applications. These included the heritage and water supply management sectors which are at different stages of adaptation management. We also reflect on the potential of event-based future storylines: one using a convection-permitting model to provide attribution statements for the Copenhagen flooding event in July 2011 and another using large ensembles to construct storylines of the European summer 2018 drought under different pseudo-global warming. Novel scientific studies were also performed which could form the scientific building blocks of climate storylines including an algorithmic clustering approach and approaches to producing more realistic climate variability. Finally, we will present a tool for performing multiple lines of evidence assessments that could aid storylines development.

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901-2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long-range skill at the beginning and at the end of the twentieth century, and an extended multi-decadal epoch of reduced skill during the 1930s-1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi-decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non-monotonic skill modulations.

There is an increasing demand from society and policy makers for reliable, robust and actionable climate information for the upcoming 40 years. However, providing such seamless information poses a challenge to the scientific community. Traditionally, the scientific community developed targeted forecasts for specific time scales, e.g. short-term, seasonal or decadal predictions. These model integrations have thus a limited forecast period and do not provide seamless information on time scales up to 40 years.This work discusses two alternative approaches to combine information from initialized decadal predictions (providing information up to ten years) with uninitialized climate projections (available until 2100 and beyond). The first is a novel framework, which is designed to implicitly make use of the (added) values from initialization by constraining uninitialized climate projections using decadal predictions. This approach is applied to near-surface temperatures over the North Atlantic Subpolar gyre region from CMIP5 models. Results suggest that such a constraining approach is able to provide more skillful, seamless climate information beyond decadal time-scales compared to using unconstrained climate projections. The second approach is based on the simple temporal concatenation of decadal predictions and climate projections. It is shown that this can introduce inconsistencies, which may impact the usability for potential end users. Two different methods to overcome these issues are discussed: the application of a simple calibration method and a weighting scheme based on model performance. Results for the calibration method are in general promising, whereas the impact of the model weighting scheme is smaller. The latter is mainly associated with the small size of the decadal prediction ensemble, which hinders the usual application of the weighting scheme as done in previous studies based on much larger ensembles.

A major problem in climate science is providing reliable regional climate projections. Existing model ensembles are imperfect so a number of different methods for constraining future climate projections using the observational record have been developed, with the aim of improving the reliability of the projections. Whilst these constrained projections displayed some overlap and common characteristics, there were also some distinct differences. However, how these different projections should be interpreted is not clear. For example, are some of the methods more accurate and reliable than others?To address this question, we performed a novel out-of-sample “perfect model” experiment to test the different constraining methods. In this experiment we took advantage of newly available CMIP6 archive of coupled climate simulations to act as (anonymised) pseudo-observations over only the observational period, to test the methods which were all previously used CMIP5-era model data; for this reason, we refer to this as an out-of-sample test. Five different groups used these pseudo-observations to constrain their CMIP5-era model projections. Detailed results from these projections, verified against the target pseudo-observations, will be presented. The constraining methods all improve the projections for regional summer temperatures but for precipitation and in the winter season the improvements are less clear. There are substantial differences across the constraining methods and reasons for these and the utility of these different methods will be discussed.

Our study shows the relevance of the interactions between atmospheric chemistry and physics to achieve better weather predictions. We provide evidence of the role that stratospheric ozone plays to improve weather forecasts on several timescales, and highlight the need for seamless models to include realistic prognostic ozone interactive with radiation.Representing realistic feedbacks between ozone, radiation, temperature and dynamics is essential to correctly simulate the behaviour of the stratosphere and its links with tropospheric weather and climate. We have implemented an alternative stratospheric ozone model (Monge-Sanz et al., 2011) in the ECMWF system interactively with radiation, and we have assessed its performance and feedbacks with meteorological fields for different timescales, from medium-range to seasonal. Here we will discuss results from the experiments and analyses conducted in our study (Monge-Sanz et al., 2021), showing the feasibility of this ozone model for a seamless numerical weather prediction approach.We will show that the stratospheric ozone distribution provided by this new prognostic ozone model compares very well with observations even for unusual meteorological conditions. On assessing impacts on meteorological variables, the new ozone model improves the representation of the stratosphere, clearly reducing temperature biases in this region. We will also show the benefits it brings to tropospheric meteorological fields, highlighting the potential of this new ozone description to exploit stratospheric sources of predictability and improve weather predictions over Europe on a range of time scales.Therefore, our results show the value of this prognostic stratospheric ozone model for seamless Earth System Models, as well as for global systems where atmospheric composition is coupled to weather forecasts, like the systems being run within the Copernicus Atmosphere Monitoring Service (CAMS). We will also discuss challenges and possible strategies for the inclusion of chemistry-dynamics feedbacks in seamless Earth System Models. References:Monge-Sanz BM, Chipperfield MP, Cariolle D, Feng W. Results from a new linear O3 scheme with embedded heterogeneous chemistry compared with the parent full-chemistry 3-D CTM. Atmos. Chem. Phys. 11, 1227-1242, 2011.Monge-Sanz, B. M., Bozzo, A., Byrne, N., Chipperfield, M. P., Diamantakis, M., Flemming, J., Gray, L. J., Hogan, R. J., Jones, L., Magnusson, L., Polichtchouk, I., Shepherd, T. G., Wedi, N., and Weisheimer, A.: A stratospheric prognostic ozone for seamless Earth System Models: performance, impacts and future, Atmos. Chem. Phys., accepted, https://doi.org/10.5194/acp-2020-1261, 2021.

Here we explore the use of “counterfactual” weather forecasts, using perturbed initial condition runs of a state-of-the-art high-resolution coupled ocean-atmosphere-sea-ice ensemble NWP system, for the attribution of extreme weather events to anthropogenic climate change. We use the “record-shattering” heatwave experienced by Western North America during summer 2021 as a case study - though our forecast-based approach is applicable to other events.Since we cannot make direct observations of a world without human influence on climate, all approaches to extreme event attribution involve some kind of modelling, either statistical or numerical. Both approaches struggle with the most extreme weather events, which are poorly represented in both observational records and the climate models normally used for attribution studies. Recognising the compromises involved, researchers have traditionally relied on comparing results from several different approaches to assess the robustness of conclusions. We argue that a better approach would be to use initialised numerical models that have demonstrated their ability to simulate the event in question through a successful forecast.This work represents a continuation of a previous EGU talk and published study (https://meetingorganizer.copernicus.org/EGU21/EGU21-5731.html & https://doi.org/10.1073/pnas.2112087118), in which we used demonstrably successful weather forecasts to estimate the direct impact of increased CO2 concentrations (one component, but not the entirety, of human influence) on the 2019 European winter heatwave. In the previous and current work we use the operational ECMWF ensemble prediction system. This state-of-the-art weather forecast system is run at a much higher resolution (Tco639 / 18km) than most climate model simulations - important as even small reductions in resolution often change the representation of extreme events in numerical models. Using a reliable forecast ensemble allows us to quantify the associated uncertainties in our attribution analyses.We have built on this work with the aim of providing a more complete estimate of the human influence on an isolated extreme event. In addition to the reduction of CO2 concentrations back to pre-industrial levels, we now also remove an estimate of the human influence on 3D ocean temperatures since the pre-industrial period from the initial state of the forecast model. These changes allow the model to provide a “counterfactual” picture of what an extreme event might have looked like if it had occurred before human influence on the climate.Using this perturbed initial condition approach, we produce counterfactual forecasts of the Pacific Northwest heatwave at the end of June 2021. This event broke records throughout Western North America, including a new Canadian high temperature record of 49.6°C, shattering the previous record by almost 5°C. The heatwave was driven by a combination of meteorological factors, including an omega block and water vapour transport at the synoptic scale, and high solar irradiation and subsidence at the meso-scale (research into the drivers is ongoing). Crucially, the event was well-predicted by weather forecast models over a week in advance.We estimate the human contribution to this exceptional heatwave by comparing our counterfactual forecasts to the operational forecasts that successfully predicted the event.

We have implemented a new stratospheric ozone model in the European Centre for Medium-Range Weather Forecasts (ECMWF) system and tested its performance for different timescales to assess the impact of stratospheric ozone on meteorological fields. We have used the new ozone model to provide prognostic ozone in medium-range and long-range (seasonal) experiments, showing the feasibility of this ozone scheme for a seamless numerical weather prediction (NWP) modelling approach. We find that the stratospheric ozone distribution provided by the new scheme in ECMWF forecast experiments is in very good agreement with observations, even for unusual meteorological conditions such as Arctic stratospheric sudden warmings (SSWs) and Antarctic polar vortex events like the vortex split of year 2002. To assess the impact it has on meteorological variables, we have performed experiments in which the prognostic ozone is interactive with radiation. The new scheme provides a realistic ozone field able to improve the description of the stratosphere in the ECMWF system, as we find clear reductions of biases in the stratospheric forecast temperature. The seasonality of the Southern Hemisphere polar vortex is also significantly improved when using the new ozone model. In medium-range simulations we also find improvements in high-latitude tropospheric winds during the SSW event considered in this study. In long-range simulations, the use of the new ozone model leads to an increase in the correlation of the winter North Atlantic Oscillation (NAO) index with respect to ERA-Interim and an increase in the signal-to-noise ratio over the North Atlantic sector. In our study we show that by improving the description of the stratospheric ozone in the ECMWF system, the stratosphere–troposphere coupling improves. This highlights the potential benefits of this new ozone model to exploit stratospheric sources of predictability and improve weather predictions over Europe on a range of timescales.

The East Asian summer jet (EASJ) is an important component of the East Asian summer monsoon system and its variability is correlated with precipitation and surface temperature variations over this region. Whilst many studies have considered the interannual variability of the EASJ, less is known about variations on a decadal time-scale. This study investigates the relationship between decadal EASJ variability and sea surface temperatures (SSTs) and thus the potential predictability that SSTs may provide. Given the relatively short observational record, we make use of the long pre-industrial control simulations in the Coupled Model Intercomparison Project phase 6 (CMIP6) in addition to a large ensemble of atmosphere-only experiments, forced with random SST patterns. We then create an SST-based reconstruction of the dominant modes of EASJ variability in the CMIP6 models, finding a median EASJ–reconstruction correlation for the dominant mode of 0.43. Much of the skill in the reconstruction arises from variations in Pacific SSTs, however the tropical Atlantic also makes a significant contribution. These findings suggest the potential for multi-year predictions of the EASJ, provided that skilful SST forecasts are available.