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Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time evolution. This makes direct comparison of simulated and observed variability difficult and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near-free-running model simulation with the time evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated and evaluate the extent to which these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the North Atlantic and Europe. The results indicate where initialised simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

In this study we detect and quantify changes in the distribution of the annual maximum daily maximum temperature (TXx) in a large observation-based gridded data set of European daily temperature during the years 1950–2018. Several statistical models are considered, each of which analyses TXx using a generalized extreme-value (GEV) distribution with the GEV parameters varying smoothly over space. In contrast to several previous studies which fit independent GEV models at the grid-box level, our models pull information from neighbouring grid boxes for more efficient parameter estimation. The GEV location and scale parameters are allowed to vary in time using the log of atmospheric CO2 as a covariate. Changes are detected most strongly in the GEV location parameter, with the TXx distributions generally shifting towards hotter temperatures. Averaged across our spatial domain, the 100-year return level of TXx based on the 2018 climate is approximately 2 ∘C (95 % confidence interval of [2.03,2.12] ∘C) hotter than that based on the 1950 climate. Moreover, averaged across our spatial domain, the 100-year return level of TXx based on the 1950 climate corresponds approximately to a 6-year return level in the 2018 climate.

Detection and attribution of climate extremes in Africa has predominantly focused on univariate analysis of drought events and heat waves in different regions of the continent, although there have been unusual extreme events such as the Ethiopian heavy rainfall and flooding events in 2020, the 2022 flood in Nigeria and the 2020-2022 persistent drought event over Eastern Africa. Furthermore, the compounding of such type of extremes has not been investigated on the African continent so far.Decision makers and climate practitioners need context-specific and reliable information about the magnitude of climate risk for humans, socioeconomic and environmental sectors. Compound climate extremes (different combinations of heavy precipitation, floods, heat waves, droughts, and high discharge) are known for their large devastating impacts resulting from their concurrent or in-close-succession occurrence. Furthermore, the conventional univariate risk assessment can lead to under underestimated risk. Although risk analysis for compound extremes is a recent research topic globally, lack of such information is more acute for developing regions.Therefore, in this study, we conduct a detection and attribution study on the recent extreme events using a univariate analysis. In addition, we assess whether there was compounding of multiple extremes events using event coincidence analysis in different regions of the African continent. We use bivariate fraction of attributable risk method to take dependence between compounding events into account. We also assess sector-specific risks of compound climate extremes in the historical period and in the future using climate projections over Africa. Our risk analysis will be based on the recently proposed bottom-up approach and multi-criteria risk assessment, and follow procedures: prioritizing specific aspects of a sector of interest based on national and regional priorities, examining relevant climate and societal drivers, defining compound extremes, vulnerability assessment, preliminary analysis of risk assessment and refining the analysis for a detailed risk assessment. Keywords: detection and attribution, risk and vulnerability assessment, compound climate extremes, heat waves, droughts, floods, climate change

We assess the suitability of unpaired image-to-image translation networks for bias correcting data simulated by global atmospheric circulation models. We use the Unsupervised Image-to-Image Translation (UNIT) neural network architecture to map between data from the HadGEM3-A-N216 model and ERA5 reanalysis data in a geographical area centered on the South Asian monsoon, which has well-documented serious biases in this model. The UNIT network corrects cross-variable correlations and spatial structures but creates bias corrections with less extreme values than the target distribution. By combining the UNIT neural network with the classical technique of quantile mapping (QM), we can produce bias corrections that are better than either alone. The UNIT+QM scheme is shown to correct cross-variable correlations, spatial patterns, and all marginal distributions of single variables. The careful correction of such joint distributions is of high importance for compound extremes research.

The North Atlantic Oscillation (NAO) plays a leading role in modulating wintertime climate over the North Atlantic and the surrounding continents of Europe and North America. Here we show that the observed evolution of the NAO displays larger multi-decadal variability than that simulated by nearly all CMIP6 models. To investigate the role of the NAO as a pacemaker of multi-decadal climate variability, we analyse simulations that are constrained to follow the observed NAO. We use a particle filter data-assimilation technique that sub-selects members that follow the observed NAO among an ensemble of simulations, as well as the El Niño Southern Oscillation and Southern Annular Mode in a global climate model, without the use of nudging terms. Since the climate model also contains external forcings, these simulations can be used to compare the simulated forced response to the effect of the three assimilated modes. Concentrating on the 28 year periods of strongest observed NAO trends, we show that NAO variability leads to large multi-decadal trends in temperature and precipitation over Northern Hemisphere land as well as in sea-ice concentration. The Atlantic subpolar gyre region is particularly strongly influenced by the NAO, with links found to both concurrent atmospheric variability and to the Atlantic Meridional Overturning Circulation (AMOC). Care thus needs to be taken to account for impacts of the NAO when using sea surface temperature in this region as a proxy for AMOC strength over decadal to multi-decadal time-scales. Our results have important implications for climate analyses of the North Atlantic region and highlight the need for further work to understand the causes of multi-decadal NAO variability.

Here we investigate how uncertainties in the solar and volcanic forcing records of the past millennium affect the large-scale temperature response using a two-box impulse response model. We use different published solar forcing records and present a new volcanic forcing ensemble that accounts for random uncertainties in eruption dating and sulfur injection amount. The simulations are compared to proxy reconstructions from PAGES 2k and Northern Hemispheric tree ring data. We find that low solar forcing is most consistent with all the proxy reconstructions, even when accounting for volcanic uncertainty. We also find that the residuals are in line with CMIP6 control variability at centennial timescales. Volcanic forcing uncertainty induces a significant spread in the temperature response, especially at periods of peak forcing. For individual eruptions and superposed epoch analyses, volcanic uncertainty can strongly affect the agreement with proxy reconstructions and partly explain known proxy–model discrepancies.

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.

Although the West Antarctic surface climate has experienced large changes over the past decades with widespread surface warming, an overall increase in snow accumulation and a deepening of the Amundsen Sea Low, the exact role of human activities in these changes has not yet been fully investigated, which limits confidence in future projections. Here, we perform a detection and attribution analysis using instrumental and proxy-based reconstructions, and two large climate model simulation ensembles to quantify the forced response in these observed changes. We show that surface climate changes since the 1950s were driven by anthropogenic forcing, in particular the greenhouse gas forcing and stratospheric ozone depletion. Therefore, our results indicate that the 21st century changes will depend on both the greenhouse gas emissions and the ozone layer recovery.

Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time-evolution. This makes direct comparison of simulated and observed variability difficult, and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El-Niño Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near free running model simulation with the time-evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated, and evaluate to what extent these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the north Atlantic and Europe. The results indicate where initialized simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El-Niño Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

The adverse impacts of marine heatwaves (MHWs) on marine ecosystems and human activities are well-documented, yet observational studies tend to largely rely on recent records. Long-term records of MHWs can put the recent increase in frequency and intensity of MHWs in the context of past variability. We used long-term monthly sea surface temperature (SST) data and night marine air temperatures to characterise past MHW activity. A persistent increase in the global extent of MHWs is demonstrated, beginning around 1970. The average annual MHW extent post-2010 is estimated to be increased at least four fold compared to that pre-1970. A strong correlation between spatial variance of recorded average monthly SSTs and the average inverse number of monthly observations implies both frequency and amplitude of MHWs is overestimated when the number of monthly observations is low. Nevertheless, many identified early MHWs appear genuine, such as a multi-month event in the North Atlantic in 1851–1852. MHWs are also affected by poorer sampling during the world wars. The most extensive MHW years globally coincide with El Niño years, and MHW extent in the North Atlantic is correlated with the Atlantic Multidecadal Oscillation.

Extreme heat, particularly if combined with humidity, poses a severe risk to human health. To estimate future global risk of extreme heat with humidity on health, we calculate indicators of heat stress that have been commonly used: the Heat Index, the Wet-Bulb Globe Temperature and the Wet-Bulb Temperature, from the latest Climate Model Intercomparison Project (CMIP6) projections. We analyse how and where different levels of heat stress hazards will change, from severe to deadly, and how results are sensitive to the choice of the index used. We evaluate this risk at country-level and use population and GDP | PPP growth scenario to estimate the vulnerability of each nation. Consistent with previous studies, we find that South and East Asia, and the Middle-East, are highly exposed to heat stress hazards, and that this exposure increases by 20%–60% with global mean temperature change from 1.5 to 3 ∘C. However, we also find substantial increases in heat health risk for some vulnerable countries with less adaptive capacity, such as West Africa, and Central and South America. For these regions, about 20 to more than 50% of the population could be exposed to severe heat stress each year on average, independent of the index used. For global warming of 3∘, European countries and the USA will also be exposed several times per year to conditions with daily mean heat stress level equal to the maximum heat stress of the 2003 heat wave.

It sounds straightforward. As the Earth warms due to the increased concentration of greenhouse gases in the atmosphere, global temperatures rise and so heatwaves become warmer as well. This means that a fixed temperature threshold is passed more often: the probability of extreme heat increases. However, land use changes, vegetation change, irrigation, air pollution, and other changes also drive local and regional trends in heatwaves. Sometimes they enhance heatwave intensity, but they can also counteract the effects of climate change, and in some regions, the mechanisms that impact on trends in heatwaves have not yet been fully identified. Climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales. However, changes in annual daily maximum temperatures do not follow global warming over some regions, including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability. Also, temperature variability is unrealistic in many models, and can fail standard quality checks. Therefore, reliable attribution and projection of change in heatwaves remain a major scientific challenge in many regions, particularly where the moisture budget is not well simulated, and where land surface changes, changes in short-lived forcers, and soil moisture interactions are important., Earth's Future, 10 (6), ISSN:2328-4277

In the United Kingdom, where 90% of residents are projected to live in urban areas by 2050, projecting changes in urban heat islands (UHIs) is essential to municipal adaptation. Increased summer temperatures are linked to increased mortality. Using the new regional U.K. Climate Projections, UKCP18-regional, we estimate the 1981–2079 trends in summer urban and rural near-surface air temperatures and in UHI intensities during day and at night in the 10 most populous built-up areas in England. Summer temperatures increase by 0.45°–0.81°C per decade under RCP8.5, depending on the time of day and location. Nighttime temperatures increase more in urban than rural areas, enhancing the nighttime UHI by 0.01°–0.05°C per decade in all cities. When these upward UHI signals emerge from 2008–18 variability, positive summer nighttime UHI intensities of up to 1.8°C are projected in most cities. However, we can prevent most of these upward nighttime UHI signals from emerging by stabilizing climate to the Paris Agreement target of 2°C above preindustrial levels. In contrast, daytime UHI intensities decrease in nine cities, at rates between −0.004° and −0.05°C per decade, indicating a trend toward a reduced daytime UHI effect. These changes reflect different feedbacks over urban and rural areas and are specific to UKCP18-regional. Future research is important to better understand the drivers of these UHI intensity changes.

The sea surface temperature (SST) contrast between the Northern Hemisphere (NH) and Southern Hemisphere (SH) influences the location of the intertropical convergence zone (ITCZ) and the intensity of the monsoon systems. This study examines the contributions of external forcing and unforced internal variability to the interhemispheric SST contrast in HadSST3 and ERSSTv5 observations, and 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) from 1881 to 2012. Using multimodel mean fingerprints, a significant influence of anthropogenic, but not natural, forcing is detected in the interhemispheric SST contrast, with the observed response larger than that of the model mean in ERSSTv5. The forced response consists of asymmetric NH–SH SST cooling from the mid-twentieth century to around 1980, followed by opposite NH–SH SST warming. The remaining best-estimate residual or unforced component is marked by NH–SH SST maxima in the 1930s and mid-1960s, and a rapid NH–SH SST decrease around 1970. Examination of decadal shifts in the observed interhemispheric SST contrast highlights the shift around 1970 as the most prominent from 1881 to 2012. Both NH and SH SST variability contributed to the shift, which appears not to be attributable to external forcings. Most models examined fail to capture such large-magnitude shifts in their control simulations, although some models with high interhemispheric SST variability are able to produce them. Large-magnitude shifts produced by the control simulations feature disparate spatial SST patterns, some of which are consistent with changes typically associated with the Atlantic meridional overturning circulation (AMOC).

There is no uniform definition of heat waves and many climate indices can be derived from the surface temperature. When considering the impact of heat on human health, heat stress needs to be considered. Several indicators of heat stress have are commonly used, such as the Heat Index (HI), the Wet-Bulb Globe Temperature (WBGT) or the Wet-Bulb Temperature (Tw), all take into account the temperature and humidity. Each of these indices can be computed from non-linear empirical formula but they all use different scales which make results difficult to compare. Here we performed a comparative study using these 3 indices by defining corresponding levels of heat stress between the different metrics. We analyzed where sever, dangerous and deadly heat stress hazards will become more frequent, using climate model projections from CMIP6, and where the choice of the index makes a difference. For each index, we use a filtering techniques to remove models that cannot reproduce realistic extreme values during the current period (using a set of 4 different reanalyses as a reference). Following, we translated this risk in terms of country exposure and vulnerability, using population and GDP growth scenario.We show that South and East Asia and Middle-East, as previously pointed out by many studies, are highly exposed to heat stress hazards. But more vulnerable countries with less resources for mitigation are also highlighted such as West Africa and Central and South America. For all these regions, about 20 to more than 50% of the population would be exposed to sever heat stress each year no matter the heat stress index chosen. European countries and USA will also be exposed several time per year to conditions of similar heat stress level than the 2003 heat wave. When going to more extreme hazards, especially when considering the “survivability threshold” of 35°C for Tw, different indices lead to more discrepancies in the results but similar regions can be identified as the most vulnerable.

This paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on an ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991–1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed preindustrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1). The results show overall good agreement between the different models on the global and hemispheric scales concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP's experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes, especially in the tropics, that preliminary analyses link with minor differences in forcing implementation; model physics, notably aerosol–radiation interactions; the simulation and sampling of El Niño–Southern Oscillation (ENSO); and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modelling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocol with greater focus on the evolving state of ENSO in the pre-eruption period.

Extreme temperature events causing significant environmental and humanitarian impacts are expected to increase in frequency and magnitude due to global warming. The latest generation of climate model projections, Coupled Model Intercomparison Project Phase Six (CMIP6), provides a new and improved database to investigate change in future daily scale extreme temperature events. This study examines the changes in 1, 3, and 5 day averaged annual maximum temperature in four large CMIP6 ensembles. It analyses, using a generalized extreme value (GEV) method, the change in extreme daily mean temperatures at 1.5 and 2°C of global warming, levels highlighted by the 2016 Paris Agreement, and additionally at 3°C. Extremely hot events are characterized using the annual maxima of daily near surface air temperature in the SSP370 scenario. Global changes in the mode of the distributions (location parameter) follow long‐term summer warming and show very similar spatial patterns. Changes in variability (scale parameter) show a clear trend of increases over the tropics and decreases over higher latitudes, while changes to the tails of distributions (shape parameter) show less globally consistent trends but clear signals over the Arctic sea ice, behaviour also seen in variability. Risk ratios (RRs) indicating the change in probability of hot daily extremes that currently have a 10 year return period increase globally with mean temperature change, with greater increases over the tropics. Globally averaged changes in RR over land range from 3.1–3.6 to 7.9–8.3 for 1.5 and 3°C of warming, respectively. For the latter case, this indicates previously rare, once‐in‐a‐decade summer extremes will occur almost annually in the future under high warming.

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.

In this paper we develop a method to quantify the accuracy of different pattern extraction techniques for the additive space–time modes often assumed to be present in climate data. It has previously been shown that the standard technique of principal component analysis (PCA; also known as empirical orthogonal functions) may extract patterns that are not physically meaningful. Here we analyze two modern pattern extraction methods, namely dynamical mode decomposition (DMD) and slow feature analysis (SFA), in comparison with PCA. We develop a Monte Carlo method to generate synthetic additive modes that mimic the properties of climate modes described in the literature. The datasets composed of these generated modes do not satisfy the assumptions of any pattern extraction method presented. We find that both alternative methods significantly outperform PCA in extracting local and global modes in the synthetic data. These techniques had a higher mean accuracy across modes in 60 out of 60 mixed synthetic climates, with SFA slightly outperforming DMD. We show that in the majority of simple cases PCA extracts modes that are not significantly better than a random guess. Finally, when applied to real climate data these alternative techniques extract a more coherent and less noisy global warming signal, as well as an El Niño signal with a clearer spectral peak in the time series, and more a physically plausible spatial pattern.

The detection and attribution (D&A) of paleoclimatic change to external radiative forcing relies on regression of statistical reconstructions on simulations. However, this procedure may be biased by assumptions of stationarity and univariate linear response of the underlying paleoclimatic observations. Here we perform a D&A study via regression of tree ring width (TRW) observations on TRW simulations which are forward modeled from climate simulations. Temperature and moisture-sensitive TRW simulations show distinct patterns in time and space. Temperature-sensitive TRW observations and simulations are significantly correlated for northern hemisphere averages, and their variation is attributed most closely to volcanically forced simulations. In decadally smoothed temporal fingerprints, we find the observed responses to be significantly larger and/or more persistent than the simulated responses. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the two years following major volcanic eruptions. We can for the first time attribute this spatiotemporal fingerprint in moisture limited tree-ring records to volcanic forcing. These results suggest that use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies, and evaluation of the climate sensitivity to external radiative forcing, than has previously been possible.

It has been widely reported that anthropogenic warming is detectable with high confidence after the 1950s. However, current palaeoclimate records suggest an earlier onset of industrial-era warming. Here, we combine observational data, multiproxy palaeo records and climate model simulations for a formal detection and attribution study. Instead of the traditional approach to the annual mean temperature change, we focus on changes in temperature seasonality (that is, the summer-minus-winter temperature difference) from the regional to whole Northern Hemisphere scales. We show that the detectable weakening of temperature seasonality, which started synchronously over the northern mid–high latitudes since the late nineteenth century, can be attributed to anthropogenic forcing. Increased greenhouse gas concentrations are the main contributors over northern high latitudes, while sulfate aerosols are the major contributors over northern mid-latitudes. A reduction in greenhouse gas emissions and air pollution is expected to mitigate the weakening of temperature seasonality and its potential ecological effects.

The local aerosol emissions effect is investigated by comparing two numerical simulations (1982‐2016) with the UK HadGEM3‐GA6 model nudged to the same ERA‐Interim circulation. One includes full historical CMIP5 RCP4.5 aerosol emission changes while the second uses a monthly aerosol climatology from 1982. At global scale, the emission scenario does not change the mean surface energy balance but it shows strong regional contrasts. Thus we focus on regions where the change in emission has been the largest: North America, Europe, India and China.No clear impact on temperature trends is found over China although aerosol emissions have increased in recent decades. This could be explained by a stronger role of meteorology in this region rather than direct surface heating, and also by a more limited change in AOD compared to regions such as Europe. Other regions show clearer responses to aerosol effect, consistent with previous studies: Cooler maximum temperatures (with historical emission compared to fixed emissions) where emissions have increased (North‐East of India) and warmer maximum temperatures where emissions have decreased (Europe). However, in each region, the interannual variability in temperatures is strongly controlled by the circulation. Precipitation is also locally decreased (0.5 mm.day‐1) over North India during summer due to a reduction of moisture convergence in the boundary layer (where no nudging is applied).Based on these simulations, we suggest that radiation‐driven aerosol emission impacts on local surface temperature and precipitation is not linear and can be mitigated or cancelled by the local dynamics.

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"

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

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)

L. Lücke. was supported by a studentship from the Natural Environment Research Council (NERC) E3 Doctoral training partnership (grant number NE/L002558/1). A. P. Schurer and G.Hegerl. were supported by NERC under the Belmont forum, Grant PacMedy (NE/P006752/1). The authors acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for CMIP, and thank all the climate modeling groups for producing and making available their model output. The authors acknowledge the Northern Hemisphere Tree‐Ring Network Development (N‐TREND) and the Past Global Changes (PAGES) project for providing publicly available data. Insolation changes caused by the axial precession induce millennial trends in last millennium temperature, varying with season and latitude. A characteristic seasonal trend pattern can be detected in both insolation and modeled surface temperature response. In the extratropical Northern Hemisphere, the maximum insolation trend occurs around April/May, while the minimum trend occurs between July and September. The temperature trend lags behind insolation trend by around a month. Hence orbital forcing potentially affects long‐term trends in proxy data, which are often sensitive to a distinct seasonal window. We find that tree‐ring reconstructions based on early growing season dominated records show different millennial trends from those for late summer dominated proxies. The differential response is similar to that seen in pseudo proxy reconstructions when considering proxy seasonality. This suggests that orbital forcing has influenced long‐term trends in climate proxies. It is therefore vital to use seasonally homogeneous data for reconstructing multicentennial variability. Publisher PDF

In a warming world, temperature extremes are expected to show a distinguishable change over much of the globe even at 1.5 °C warming, and in many regions this change has already been detected in observations. Although many studies predict an increase in heat extreme events, the magnitude of the change varies greatly among different models even for the same mean warming. This uncertainty has been linked to differences in land–atmosphere feedback across models. Here we show that a significant constraint for future projections can be based on the ability of climate models to accurately simulate the present day variability of daily surface maximum temperature. An emergent constraint on Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6) models, applied to ERA5 reanalysis, indicates that the best estimate in hot extreme changes by the end of the century could be worse than previously estimated, mostly for tropical and subtropical regions as well as South and East Asia. Present-day variability of daily maximum temperature at the surface can serve as an emergent constraint on the frequency of heat extremes in a warmer climate, according to tests with a large multi-model ensemble of climate models.

Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contributions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demonstrate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals. Quantifying the temperature impacts of anthropogenic emissions helps monitor proximity to the Paris Agreement goals. Human activities warmed global mean temperature during the past decade by 0.9 to 1.3 °C above 1850–1900 values, with 1.2 to 1.9 °C from greenhouse gases and −0.7 to −0.1 °C from aerosols.

The increased availability of daily model output in the latest generation of climate models should allow us a greater understanding of, and an improved ability to predict, future trends in short du...

We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO 2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6–3.9 K for our Baseline calculation and remains within 2.3–4.5 K under the robustness tests; corresponding 5–95% ranges are 2.3–4.7 K, bounded by 2.0–5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.

The severe drought of the 1930s Dust Bowl decade coincided with record-breaking summer heatwaves that contributed to the socio-economic and ecological disaster over North America’s Great Plains. It remains unresolved to what extent these exceptional heatwaves, hotter than in historically forced coupled climate model simulations, were forced by sea surface temperatures (SSTs) and exacerbated through human-induced deterioration of land cover. Here we show, using an atmospheric-only model, that anomalously warm North Atlantic SSTs enhance heatwave activity through an association with drier spring conditions resulting from weaker moisture transport. Model devegetation simulations, that represent the wide-spread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world., In the 1930s, the US was hit by a severe drought and record-breaking heatwaves in a period known as the Dust Bowl. Here, the authors present model experiments that suggest that warm North Atlantic temperatures and human devegetation played key roles in making these heatwaves particularly strong.

Substantial warming occurred across North America, Europe and the Arctic over the early twentieth century1, including an increase in global drought2, that was partially forced by rising greenhouse gases (GHGs)3. The period included the 1930s Dust Bowl drought4–7 across North America’s Great Plains that caused widespread crop failures4,8, large dust storms9 and considerable out-migration10. This coincided with the central United States experiencing its hottest summers of the twentieth century11,12 in 1934 and 1936, with over 40 heatwave days and maximum temperatures surpassing 44 °C at some locations13,14. Here we use a large-ensemble regional modelling framework to show that GHG increases caused slightly enhanced heatwave activity over the eastern United States during 1934 and 1936. Instead of asking how a present-day heatwave would behave in a world without climate warming, we ask how these 1930s heatwaves would behave with present-day GHGs. Heatwave activity in similarly rare events would be much larger under today’s atmospheric GHG forcing and the return period of a 1-in-100-year heatwave summer (as observed in 1936) would be reduced to about 1-in-40 years. A key driver of the increasing heatwave activity and intensity is reduced evaporative cooling and increased sensible heating during dry springs and summers. The United States experienced two of its hottest recorded summers in 1934 and 1936, amplified by drier soils associated with the Dust Bowl drought. A large regional climate model ensemble estimates present-day GHGs would cause similarly extreme, 1-in-100-year heatwaves to occur about every 40 years.

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. Different approaches to constrain, filter, or weight climate model simulations into probabilistic projections have been proposed to provide such estimates. Here six methods are applied to European climate projections using a consistent framework in order to allow a quantitative comparison. Focus is given to summer temperature and precipitation change in three different spatial regimes in Europe in the period 2041-2060 relative to 1995-2014. The analysis draws on projections from several large initial condition ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods included are diverse in their approach to quantifying uncertainty, and include those which apply weighting schemes based on baseline performance and inter-model relationships, so-called ASK (Allen, Stott and Kettleborough) techniques which use optimal fingerprinting to scale the scale the response to external forcings, to those found in observations and Bayesian approaches to estimating probability distributions. Some of the key differences between methods are the uncertainties covered, the treatment of internal variability, and variables and regions used to inform the methods. In spite of these considerable methodological differences, the median projection from the multi-model methods agree on a statistically significant increase in temperature by mid-century by about 2.5°C in the European average. The estimates of spread, in contrast, differ substantially between methods. Part of this large difference in the spread reflects the fact that different methods attempt to capture different sources of uncertainty, and some are more comprehensive in this respect than others. This study, therefore, highlights the importance of providing clear context about how different methods affect the distribution of projections, particularly the in the upper and lower percentiles that are of interest to 'risk averse' stakeholders. Methods find less agreement in precipitation change with most methods indicating a slight increase in northern Europe and a drying in the central and Mediterranean regions, but with considerably different amplitudes. Further work is needed to understand how the underlying differences between methods lead to such diverse results for precipitation.

Aiming to provide comprehensive information for climate change at regional level, we assess temperature and heat waves and their spatiotemporal trend and time of emergence over different regions of the African continent. We analyze observational data of Climate Research Unit Time Series version 4.03 (CRU TS) and the three state-of-the-art reanalysis datasets; European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), National Oceanic Atmospheric Administration’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) and the Japanese Meteorological Agency’s 55 years reanalysis (JRA-55). We assess changes in monthly mean temperature and the agreement between observations and reanalyses. Changes in heat waves are analyzed based on reanalysis datasets because of their high temporal resolution. Heat waves are defined using absolute and relative thresholds, the number of summer days, tropical nights, the percentage of days with maximum and mean temperature above the 90th percentile, the warm nights and the warm spell duration index. The results show increasing trends in monthly mean temperature in all four regions of Africa with different rate of change. A statistically significant trends in heat waves is found in all the regions. Years of highest heat wave occurrence are identified in 2010 for Northern and Western Africa and 2016 for Eastern and Southern Africa. Minimum-temperature based indices, tropical nights and warm nights, show the highest increase in decadal trends and earliest time of emergence, respectively.Key words: climate change; temperature; heat waves; time of emergence; reanalysis; Africa

This paper provides an overview of the scientific background and the research objectives of the Research Unit “VolImpact” (Revisiting the volcanic impact on atmosphere and climate – preparations for the next big volcanic eruption, FOR 2820). VolImpact was recently funded by the Deutsche Forschungsgemeinschaft (DFG) and started in spring 2019. The main goal of the research unit is to improve our understanding of how the climate system responds to volcanic eruptions. Such an ambitious program is well beyond the capabilities of a single research group, as it requires expertise from complementary disciplines including aerosol microphysical modelling, cloud physics, climate modelling, global observations of trace gas species, clouds and stratospheric aerosols. The research goals will be achieved by building on important recent advances in modelling and measurement capabilities. Examples of the advances in the observations include the now daily near-global observations of multi-spectral aerosol extinction from the limb-scatter instruments OSIRIS, SCIAMACHY and OMPS-LP. In addition, the recently launched SAGE III/ISS and upcoming satellite missions EarthCARE and ALTIUS will provide high resolution observations of aerosols and clouds. Recent improvements in modeling capabilities within the framework of the ICON model family now enable simulations at spatial resolutions fine enough to investigate details of the evolution and dynamics of the volcanic eruptive plume using the large-eddy resolving version, up to volcanic impacts on larger-scale circulation systems in the general circulation model version. When combined with state-of-the-art aerosol and cloud microphysical models, these approaches offer the opportunity to link eruptions directly to their climate forcing. These advances will be exploited in VolImpact to study the effects of volcanic eruptions consistently over the full range of spatial and temporal scales involved, addressing the initial development of explosive eruption plumes (project VolPlume), the variation of stratospheric aerosol particle size and radiative forcing caused by volcanic eruptions (VolARC), the response of clouds (VolCloud), the effects of volcanic eruptions on atmospheric dynamics (VolDyn), as well as their climate impact (VolClim).

The impact of North American and European (NAEU) anthropogenic aerosol emissions on Eurasian summer climate during the twentieth century is studied using historical single- and all-forcing (including anthropogenic aerosols, greenhouse gases, and natural forcings) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Intermodel agreement on significant linear trends during a period of increasing NAEU sulfate emissions (1900–74) reveals robust features of NAEU aerosol impact, supported by opposite changes during the subsequent period of decreasing emissions. Regionally, these include a large-scale cooling and associated anticyclonic circulation, as well as a narrowing of the diurnal temperature range (DTR) over Eurasian midlatitudes. Remotely, NAEU aerosols induce a drying over the western African and northern Indian monsoon regions and a strengthening and southward shift of the subtropical jet consistent with the pattern of temperature change. Over Europe, the temporal variations of observed temperature, pressure, and DTR tend to agree better with simulations that include aerosols. Throughout the twentieth century, aerosols are estimated to explain more than a third of the simulated interdecadal forced variability of European near-surface temperature and more than half between 1940 and 1970. These results highlight the substantial aerosol impact on Eurasian climate, already identifiable in the first half of the twentieth century. This may be relevant for understanding future patterns of change related to further emission reductions.