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Large explosive volcanic eruptions are a potential source of uncertainty in future climate projections as they cannot be predicted in advance, but eventually will occur, causing short-term climatic impacts on both local and global scale. Still, an open topic is the volcanic impact on tropical climate variability, in particular El Niño Southern Oscillation (ENSO) and tropical precipitation and the combined effect of both. Sufficient large ensembles simulations with the same model and radiative forcing scenario but varying initial conditions have become a great tool in recent years to disentangle forced and internal variability). Here we use the EVA-ENS ensemble (Azoulay et al., 2021) which consists of 100-member ensembles of the MPI-ESM-LR for idealized equatorial Pinatubo-like eruptions of different eruption strength and an additional 100-member ensemble without forcing. We are in particular interested if there is a linear volcanic signal on tropical precipitation dependent on the eruption strength and when did it emerge from tropical internal variability.Our results show that for Idealized tropical eruptions global and large hemispheric mean 2m temperature and precipitation anomalies seemed to be scalable with the sulfur emission strength above a certain threshold, except for tropical temperatures for an emission strength > 40 Tg sulfur (S). 10 Tg S emission, the upper estimate of the 1991 Pinatubo eruption, seems to be a threshold where the signal is discernible from internal variability. We also find that seasonal and ensemble mean pattern correlation of 2m temperature and precipitation anomalies are highly correlated in particular for larger emission strengths in the tropics and strongly modulated by ENSO. There is an increasing tendency for a warm ENSO increases with eruption strength. Emergence of the volcanic signal appears for smaller eruption strength when looking to ENSO composites. Emergence of cooling appears on a hemispheric scale, while precipitation response is more localized and mainly confined to the tropics and subtropics. References:Azoulay, A., Schmidt, H., and Timmreck, C.: The Arctic polar vortex response to volcanic forcing of different strengths, J. Geophys. Res., 126, e2020JD034450, doi.org/10.1029/2020JD034450, 2021.

Near surface air temperature is a primary variable to track global climate change. While mean temperature is often used to quantify global warming, the diurnal temperature range (DTR) - defined as the difference of daily minimum and maximum temperature - can provide additional information on changes to the diurnal cycle and temperature extrema, which is important for impacts of climate change. Different to increasing global mean temperature, observations have shown a decrease of global mean DTR over recent decades. This trend has been attributed to human emissions of greenhouse gases (GHG) which increase daily minimum temperature (Tmin), usually measured at night, more than daily maximum temperature (Tmax), observed during the day, by trapping outgoing longwave radiation. Aerosol radiative forcing has been associated with absorbing and scattering incoming (solar) shortwave radiation; thus, aerosols are assumed to reduce Tmax more than Tmin, decreasing the DTR. However, historical single and ALL forcing simulations from models that are part of phase 6 of the Coupled Model Intercomparison Project (CMIP6) model a decrease of the DTR for GHG and ALL forcings but show an increase in the DTR for anthropogenic aerosols due to a larger reduction of Tmin than Tmax. We investigate this discrepancy in aerosol contributions to changes in the DTR by applying causal inference methods to quantify the impact aerosols have on the DTR in Europe. To address the various effects aerosols have on the climate system, by interacting with both radiation and clouds, we include Tmin, Tmax, aerosol optical depth (AOD), cloud cover, cloud height, incoming shortwave radiation (SW) and outgoing longwave radiation using observational satellite and gridded station data for the past decade in our analysis. First results agree with the cooling effect of aerosols on Tmax by reducing SW radiation and show a positive effect of AOD on low cloud cover. They further suggest that a decrease of Tmax causes a decrease in Tmin, possibly explaining the CMIP6 model results.

Providing comprehensive regional- and local-scale information on changes observed in the climate system plays a vital role in planning effective and efficient climate change adaptation options, specifically over resource-limited regions. Here, we assess changes in temperature and heat waves over different regions of the African continent, with a focus on spatiotemporal trends and the time of emergence of change in hot extremes from natural variability. We analyse absolute and relative threshold indices. Data sets include temperatures from observations (CRUTS4.03 and BEST) and from three representative state-of-the-art reanalyses (ERA5, MERRA2 and JRA-55) for the common period 1980–2018. Statistically significant warming is observed over all regions of Africa in temperature time series from CRU observations and reanalysis data, although the trend strength varies between data sets. Also, extreme temperatures and heat wave indices from BEST observations and all reanalysis data sets reveal increasing trends over all regions of the African continent. However, there are differences in both trend strength and time evolution of heat wave indices between different reanalysis data sets. Most data sets agree in identifying 2010 as a peak heat year over Northern and Western Africa while Eastern and Southern Africa experienced the highest heat wave occurrence in 2016. Our results clearly reveal that heat wave occurrences have emerged from natural climate variability in Africa. The earliest time of emergence takes place in the Northern Africa region in the early 2000s while in the other African regions emergence over natural variability is found mainly after 2010. This also depends on the respective index metrics, where indices based on more consecutive days show later emergence of heat wave trends. Overall, significant warming and an increase in heat wave occurrence is found in all regions of Africa and has emerged from natural variability in the past one or two decades.

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.

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.

Anthropogenic aerosols (AER) have been found to impact both Earth’s energy and water cycle. Like greenhouse gases (GHG) they are an anthropogenic climate forcing, which will play an important role in shaping Earth’s future climate. To improve future predictions, it is, therefore, fundamental to understand and quantify the individual impacts these two forcings have on the climate system. This can be achieved by using detection and attribution methods facilitating the differentiation of the response of the climate system to different forcings.Separating the signal of individual anthropogenic effects related to greenhouse gas and aerosol emissions is hindered by large uncertainties in the response to aerosol forcing in different climate models. Thus, in this study we investigate a joint change in temperature and precipitation to reduce the signal-to-noise ratio and better constrain the impact of anthropogenic aerosols since 1979. Building on previous findings on how aerosols affect climate, we focus on shifts in tropical precipitation by tracking wet/dry regions as well as changes in the interhemispheric temperature asymmetry (ITA) and the diurnal temperature range (DTR), due to its unique response to different radiative forcings. Individual fingerprints are derived from large-ensembles of historical single-forcing simulations from three models that are part of phase 6 of the Coupled Model Intercomparison Project (CMIP6).We find inter-model agreement in the trends for wet regions, ITA, and DTR in single-forcing and historical (all-forcing) runs. Contrasting trends in these time series derived for AER-only and GHG-only simulations suggest that aerosols have offset some of the greenhouse gas induced precipitation and temperature changes in the past. While a drying in the dry regions can be observed for GHG-only simulations, inter-model agreement is not found for aerosols. Early results show an improved constraint on the detection of a greenhouse gas signal when investigating a joint change in wet and dry regions, which is refined by including the other variables and indicators.

The North Atlantic Oscillation (NAO) is the leading mode of climate variability over the North Atlantic region, affecting temperature and rainfall over timescales from days through to seasons and decades. Previous studies have shown that variations in the NAO yield significant multi-decadal trends in European rainfall, especially in winter, and the magnitude of past multi-decadal NAO variability is generally not reproduced by CMIP class models This has important implications for deriving observational constraints, and the application of these scaling factors to projections of future European rainfall.We have constructed two sets of multi-model-mean spatiotemporal fingerprints of European rainfall: one set that retains NAO variability, and another set that excludes the variability associated with the NAO (removing it using a simple regression technique). Following the so-called Allen-Scott-Kettleborough ‘ASK’ method, we conduct total-least-squares regressions using the two different sets of fingerprints against the observations in order to analyse the impact of removing the NAO in potentially enhancing the signal-to-noise. The derived scaling factors shed light on the ability of CMIP6 models to reproduce the magnitude of the forced response in precipitation, and confidence intervals for each of the scaling factors describes the range of magnitudes of the model response that are consistent with the observed signal.Here we focus on one clear example, northern European rainfall, although we have also analysed additional European regions and surface air temperature across all of the seasons. There is an increasing trend in observed rainfall anomalies associated with the NAO over northern Europe, most pronounced in winter, which is not replicated in models. Once the variability associated with the NAO is removed the magnitude of the observed trends is reduced, and the scaling factors (derived in the ASK framework) are similarly reduced.Along with a shift in the magnitude that comes from the modified observations, the constraint also tightens (the spread in consistent scaling factors narrows) due to the increased signal-to-noise in the modelled response once the NAO is removed from the simulations. This has important implications for the use of scaling factors to constrain future projections of European climate. The observed decadal to multi-decadal trends resulting from known modes of internal variability should be accounted for in the derivation of scaling factors to better capture the forced signal and bolster confidence in the constrained projections.

Precipitation changes are notoriously highly variable, and climate models misplace circulation features, making it difficult to evaluate if mechanisms of precipitation change are well reproduced in climate models. Several methods have been developed to detect externally forced precipitation change tracking circulation features rather than specific locations. For example, analysis of monthly ascending and descending regions in reanalysis show the increase of rainfall in ascending regions. Analysis of wet and dry regions in GPCP blended data shows that if the locations of wet and dry regions are tracked from month to month then trends over the past 3-4 decades can be attributed to a combination of human influences and the recovery from drying associated with the Mount Pinatubo eruption in wet regions. In response to volcanic eruptions, wet regions tend to dry and dry regions may get wetter, indicating a reduced moisture transport to the wettest regions of the tropics under strong volcanic forcing. However, this is also impacted by the hemispheric characteristics of the eruptions.

Internal climate variability will play a major role in determining change on regional scales under global warming. In the extratropics, large-scale atmospheric circulation is responsible for much of observed regional climate variability, from seasonal to multidecadal timescales. However, the extratropical circulation variability on multidecadal timescales is systematically weaker in coupled climate models. Here we show that projections of future extratropical climate from coupled model simulations significantly underestimate the projected uncertainty range originating from large-scale atmospheric circulation variability. Using observational datasets and large ensembles of coupled climate models, we produce synthetic ensemble projections constrained to have variability consistent with the large-scale atmospheric circulation in observations. Compared to the raw model projections, the synthetic observationally-constrained projections exhibit an increased uncertainty in projected 21st century temperature and precipitation changes across much of the Northern extratropics. This increased uncertainty is also associated with an increase of the projected occurrence of future extreme seasons. Future projections of northern hemisphere extratropical climate based on climate model simulations substantially underestimate the uncertainty that originates from large-scale atmospheric circulation variability, suggest synthetic ensemble projections constrained with observations.

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution. All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. Peer Reviewed "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer"

Skillful, reliable and seamless climate information for the next 1-40 years is crucial for policy- and other decision makers to develop suitable planning strategies. This poses a challenge for the scientific community, which is split up into the prediction community (developing initialized predictions up to multi-annual time scales, e.g. 10 years), and the climate projection community (providing long-term projections). As predictions are initialized with the observed climate state at the start of the integration, they are often more skillful for lead times of a few years (depending on variable and region) compared to uninitialized climate projections, which can provide information beyond 10 years. Thus, most useful climate information for the next 1-40 years would likely need to draw upon information from both sources. However, temporal merging from different sources is challenging, e.g., it can lead to discontinuities in the central estimates at the respective transition points, which pose problems for interpretation and communication alike.The aim of this study is to explore if skillful and seamless climate information can be provided by applying a model weighting scheme to initialized decadal predictions and projections. The model specific weights are based on the respective past model performance compared to observations. Whereas for climate projections each model is assigned a single weight, for initialized decadal predictions these weights are calculated for each forecast year separately. Here, we apply the weighting technique to CMIP6 decadal predictions and climate projections from 8 different models.

In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.Keywords: climate change, temperature, extreme events, attribution, CMIP6 Acknowledgement: This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)

Quantifying and reducing the uncertainty of climate projections will benefit both mitigation and adaptation decisions. Observed climate change provides evaluation of climate model simulated change, but the contribution by different external forcing factors needs to be reliably separated in order to use observational constraints. We revisit this ASK (for Allen et al., 2000; Stott and Kettleborough, 2002) approach to use attributed responses to greenhouse gas forcing to constrain future predictions.We derive constraints on the projected near-surface summer temperature change over Europe as well as over three European subregions. The temperature responses to different external forcings (natural and greenhouse-gas (GHG) or combined anthropogenic) are estimated as the multi-model means of historical simulations from the Coupled Model Intercomparison Project 5 and incoming CMIP6, and the range of factors by which they can be scaled and still be consistent with observations since 1950 (E-OBS) given internal variability is calculated and applied to future RCP8.5 simulations.Results show that both the response to GHG-only and to the combined anthropogenic (including aerosols etc.) forcing are detectable in the observed temperature change over Europe, and that the response over the Mediterranean region might be underestimated. Observed precipitation changes over Europe are also detected over some regions, although the confounding effects of the North Atlantic Oscillation need to be considered carefully. The results demonstrate the successful application of the ASK method for constraining projections of regional change over Europe.

The sensitivity of global tropical cyclone (TC) activity to changes in a zonally symmetric sea surface temperature (SST) distribution and the associated large-scale atmospheric circulation are investigated. High-resolution (~50-km horizontal grid spacing) atmospheric general circulation model simulations with maximum SST away from the equator are presented. Simulations with both fixed-SST and slab ocean lower boundary conditions are compared. The simulated TCs that form on the poleward flank of the intertropical convergence zone (ITCZ) are tracked and changes in the frequency and intensity of those storms are analyzed between the different experiments. The total accumulated cyclone energy (ACE) increases as the location of the maximum SST shifts farther away from the equator. The location of the ITCZ also shifts in conjunction with changes to the SST profile, and this plays an important role in mediating the frequency and intensity of the TCs that form within this modeling framework.

Future tropical cyclone activity is a topic of great scientific and societal interest. In the absence of a climate theory of tropical cyclogenesis, general circulation models are the primary tool available for investigating the issue. However, the identification of tropical cyclones in model data at moderate resolution is complex, and numerous schemes have been developed for their detection. The influence of different tracking schemes on detected tropical cyclone activity and responses in the Hurricane Working Group experiments is examined herein. These are idealized atmospheric general circulation model experiments aimed at determining and distinguishing the effects of increased sea surface temperature and other increased CO2 effects on tropical cyclone activity. Two tracking schemes are applied to these data and the tracks provided by each modeling group are analyzed. The results herein indicate moderate agreement between the different tracking methods, with some models and experiments showing better agreement across schemes than others. When comparing responses between experiments, it is found that much of the disagreement between schemes is due to differences in duration, wind speed, and formation-latitude thresholds. After homogenization in these thresholds, agreement between different tracking methods is improved. However, much disagreement remains, accountable for by more fundamental differences between the tracking schemes. The results indicate that sensitivity testing and selection of objective thresholds are the key factors in obtaining meaningful, reproducible results when tracking tropical cyclones in climate model data at these resolutions, but that more fundamental differences between tracking methods can also have a significant impact on the responses in activity detected.

The decay of underdense meteor trails in the polar mesopause region is thought to be predominantly due to ambipolar diffusion, a process governed by the ambient temperature and pressure. Hence, observations of meteor decay times have been used to indirectly measure the temperature of the mesopause region. Using meteor observations from a SKiYMET radar in northern Sweden during 2005, this study found that weaker meteor trails have shorter decay times (on average) than relatively stronger trails. This suggests that processes other than ambipolar diffusion can play a significant role in trail diffusion. One particular mechanism, namely electron-ion recombination, is explored. This process is dependent on the initial electron density within the meteor trail, and can lead to a disproportionate reduction in decay time, depending on the strength of the meteor.

A season of operational cell and track data from Darwin, Australia, has been analyzed to explore the statistical characteristics of the convective cell heights. The statistics for the monsoon and break regimes are significantly different with the break season cells being higher for a given reflectivity threshold. The monsoon cells produce more rain, but there are fewer intense cells and there is a much larger contribution from stratiform rain. The monsoon cells are also slightly larger, but shorter lived than the breaks. The shorter lifetime may reflect a more rapid transition to a longer-lived stratiform character. The monsoon regime is shown to be associated with large-scale ascent and higher humidity that may lead to more frequent, but weaker cells. Within regimes, the subset of intense cells generally reach near the tropopause or overshoot. However, there is little evidence in the data for a multimodal distribution of cell heights, except perhaps for the intense monsoon cases. Instead, the picture is a continuous distribution of cell heights with the peak of the distribution shifting to higher values as the distributions are conditioned on higher reflectivity.

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.

Climate models predict a strengthening contrast between wet and dry regions in the tropics and subtropics (30 °S–30 °N), and data from the latest model intercomparison project (CMIP6) support this expectation. Rainfall in ascending regions increases, and in descending regions decreases in climate models, reanalyses, and observational data. This strengthening contrast can be captured by tracking the rainfall change each month in the wettest and driest third of the tropics and subtropics combined. Since wet and dry regions are selected individually every month for each model ensemble member, and the observations, this analysis is largely unaffected by biases in location of precipitation features. Blended satellite and in situ data from 1988–2019 support the CMIP6-model-simulated tendency of sharpening contrasts between wet and dry regions, with rainfall in wet regions increasing substantially opposed by a slight decrease in dry regions. We detect the effect of external forcings on tropical and subtropical observed precipitation in wet and dry regions combined, and attribute this change for the first time to anthropogenic and natural forcings separately. Our results show that most of the observed change has been caused by increasing greenhouse gases. Natural forcings also contribute, following the drop in wet-region precipitation after the 1991 eruption of Mount Pinatubo, while anthropogenic aerosol effects show only weak trends in tropic-wide wet and dry regions consistent with flat global aerosol forcing over the analysis period. The observed response to external forcing is significantly larger (p > 0.95) than the multi-model mean simulated response. As expected from climate models, the observed signal strengthens further when focusing on the wet tail of spatial distributions in both models and data.