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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.

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.