Climate simulations of the industrial era typically start in 1850, using the first fifty years as a baseline for ‘pre-industrial’ climate. However, the period immediately prior to 1850 is of particular interest due to early human influence and heightened volcanic activity, the latter of which led to cooler global temperatures than those observed in the subsequent historical period. In this study, we present a suite of Earth system model simulations (using UKESM1.1) that start in 1750 and span the entire industrial period. We compare these simulations to a new instrumental observation-based dataset, GloSATref, which provides global surface air temperature variations from 1781 onwards. We investigate the climatic changes during the early industrial period, separating the effects of natural and anthropogenic forcings. Model-simulated early-19th-century temperature patterns show substantial cooling relative to the long-term mean, particularly in low latitudes, which agree well with observed patterns. We find significant long-term differences between simulations initialized in 1750 and 1850, with lasting effects well into the 20th century, consistent with differences in vegetation and the substantial ocean cooling driven by high volcanic activity in the 1750 simulations. Our results indicate that an earlier start to historical simulations could lead to more representative climate simulations over the historical period, and deepen our understanding of early anthropogenic warming, natural climate variability, and the climate responses to future volcanic eruptions.

Abstract. Parties to the 2015 Paris Agreement agreed to limit the long-term increase in global average temperature to well below 2 °C and pursue efforts to keep temperatures below 1.5 °C relative to pre-industrial levels. As the world is fast approaching the 1.5 °C warming level on a sustained basis, and with 2024 likely the first year that was over 1.5 °C warmer than 1850-1900, there is ever increasing interest in how we will know whether and when 1.5 °C warming since pre-industrial has been reached or exceeded with respect to a long-term average. This paper represents a comprehensive community methodological overview, building on the IPCC 6th assessment. It explains why there is no straightforward answer and proposes clear and reasoned ways forward. Existing challenges are as follows. Firstly, the Paris Agreement text contains definitional ambiguities around 'pre-industrial', 'global average temperature', whether the assessment should be on realised or long-term human-induced warming, and over what time frame the long-term temperature goal applies. Then, there are intrinsic limitations of observational records which get more uncertain further back in time due to data sparsity and measurement heterogeneity. Finally, in a non-stationary climate, multidecadal mean indicators of global temperature change will either lag behind the change or must rely on expected future temperature changes (based on extrapolation, initialized predictions, or scenario-based and constrained projections). Our analysis shows that knowing 'whether we are there yet' is a multifaceted and inherently probabilistic problem that includes information on the definition of a specific level of global warming, temperature changes over multiple timescales, and also potentially includes unpacking the attribution of human-caused changes from observed variations. Given the policy relevance of understanding where the world stands relative to 1.5 °C, or any other level of global warming since pre-industrial, there are a number of practical steps which could be taken to increase specificity in answering this critical question in a timely manner, and inform future monitoring and assessment activities. This paper reviews a broad range of approaches, identifies the most pragmatic, robust and transparent, and clarifies requirements for use in real time including how to handle and represent remaining uncertainties. We show that it is possible by combining lines of evidence and several methodologies to estimate the present long-term warming level without delay in a manner that is robust both in retrospective validation of crossing past warming levels and, critically, to divergent warming futures including potential wildcard impacts of large volcanoes which can mask underlying warming for several years. Results are benchmarked against historical exceedances of 0.5 °C and 1 °C warming. Long-term warming as assessed using the approaches developed herein and data up to and including 2024 stands at 1.40 [1.23–1.58] °C, and underlying human-caused warming stands at 1.34 [1.18–1.50] °C. In IPCC quantified likelihood language this means that it was unlikely that long-term realised warming had exceeded 1.5 °C by the end of 2024 and very unlikely that human-induced warming had exceeded 1.5 °C.

Over the past 20 years, extreme event attribution has developed rapidly, providing a wide range of methods to attribute weather events – from unconditioned probabilistic to strongly conditioned storyline approaches. Advancing the field now requires combining results from multiple methods, allowing more robust conclusions drawn from various lines of evidence. Yet, doing so remains challenging. I will outline various methods currently used highlighting the benefits and disadvantages of each.I will then present the ‘new kid on the block’: ReBASE. ReBASE (Reanalysis Based Attribution and Storylines of Extremes) is a collaborative project led jointly by the University of Edinburgh and the University of Reading. We use a novel data driven reanalysis based approach to examine real-world weather translated into different climates. We produce a reanalysis product of the current decade. Then, by altering the sea surface temperature, sea-ice concentrations, and forcings, we create storylines of how the weather could play out in both warmer and colder climates. These storylines can be used to attribute impacts and assess amplification of extreme weather events in a warmer climate. We can also provide counterfactuals of past events, translated to the present day climate.This approach allows a global perspective on extreme events and their impacts - the same experiments produce global factual and counterfactual reconstructions of every day in the chosen periods. The data will be made openly available to allow anyone to explore their own choice of extreme event anywhere in the globe. Counterfactuals will also be developed for future warmer climate conditions to understand how extreme events and their impacts will change, and help inform adaptation decisions.

Addressing climate change requires knowledge of its impacts on both nature and people. This Review depicts current approaches to the attribution of climate change impacts and potential uses for this information. The discussion covers how impact attribution identifies the drivers of observed changes and events that form links in the causal chain from anthropogenic greenhouse gas emissions and other human-induced climate forcing factors to effects on natural and human systems mediated by changes in climate and weather. Various approaches are presented that use observations and/or model simulations to estimate how a world without climate change could have evolved. In addition, different societal uses of impact attribution results are discussed and how different study designs might support them. This Review also identifies persistent knowledge gaps that call for input from policy experts globally. For example, future tailored designs might enable the attribution of additional impacts and improve quantification of the role of climate change against other drivers, whereas increased transdisciplinary collaboration and organization might provide standardization that benefits data comparability and synthesis. Addressing the remaining challenges is expected to help the impact attribution community to produce targeted answers for well-framed questions that inform development, implementation and operationalization of climate policies. Climate change impact attribution quantifies the observed consequences of climate change through combining climate attribution aspects with impact modelling. Undorf et al. discuss the key steps and methodological choices required, possible societal uses of impact attribution results and ongoing challenges.

Volcanic eruptions substantially impact tropical precipitation over the historical period but they differ in their emission strength, geographical latitude and season of the eruption, which makes it difficult to draw general conclusions. 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 a suite of 100-member ensembles of the MPI-ESM-LR for idealized equatorial and extratropical eruptions of different eruption strengths and an additional 100-member ensemble without forcing. We find that precipitation reduction is primarily energetically constrained by less atmospheric net energy input (NEI). NEI decreases rapidly in the first months after the eruption due to reduced incoming solar radiation and then the circulation weaken as a consequence of less moist static energy (MSE) exported away from the intertropical convergence zone. Only afterwards, when the overturning has already weakened, the MSE, and then the gross moist stability (GMS) contribute stronger to the precipitation reduction. Tropical precipitation over land reacts immediately to forcing changes, while the precipitation response over the ocean and the temperature response have much longer response times. Altered dry-wet pattern (“wet gets drier”) and the decreased monsoon precipitation are strongly tied to the weakening of the regional tropical overturning. Differences related to the geographical locations of the volcanic eruptions will be highlighted.

Typically, climate simulations covering the historical period start in 1850, with the first fifty years used as a baseline to represent a ‘pre-industrial' climate. The period immediately prior to 1850 is however of particular interest, as it had far more volcanic activity than any time during the subsequent historical period, and this is known to have caused large cooling of global temperatures. Exploring the climate of this period could help to better understand early anthropogenic warming, natural climate variability and anticipate the response to large future eruptions.Here we will: (1) highlight the development of a new instrumental observation-based dataset (GloSAT) for temperature variations across the globe from 1781 to present; (2) discuss an ensemble of historical simulations with UKESM1 which were started in 1750, 100 years earlier than typical. These two sources of evidence will be used to identify the long-lasting impacts of the early 19th century volcanism and disentangle it from the response to other forcings and internal variations. Longer term effects of this period are also explored with significant differences found with historical simulations run using the same model initialised in 1850 lasting well into the 20th century. The implications of this discrepancy and the role of large volcanic eruptions on multi-decadal climate will be discussed.

Climate variability in the last millennium (past 1000 years) is dominated by the effects of large-magnitude volcanic eruptions; however, a long-standing mismatch exists between model-simulated and tree-ring-derived surface cooling. Accounting for the self-limiting effects of large sulfur dioxide (SO2) injections and the limitations in tree-ring records, such as lagged responses due to biological memory, reconciles some of the discrepancy, but uncertainties remain, particularly for the largest tropical eruptions. The representation of volcanic forcing in the latest generation of climate models has improved significantly, but most models prescribe the aerosol optical properties rather than using SO2 emissions directly and including interactions between the aerosol, chemistry, and dynamics. Here, we use the UK Earth System Model (UKESM) to simulate the climate of the last millennium (1250–1850 CE) using volcanic SO2 emissions. Averaged across all large-magnitude eruptions, we find similar Northern Hemisphere (NH) summer cooling compared with other last-millennium climate simulations from the Paleoclimate Modelling Intercomparison Project Phase 4 (PMIP4), run with both SO2 emissions and prescribed forcing, and a continued overestimation of surface cooling compared with tree-ring reconstructions. However, for the largest-magnitude tropical eruptions in 1257 (Mt. Samalas) and 1815 (Mt. Tambora), some models, including UKESM1, suggest a smaller NH summer cooling that is in better agreement with tree-ring records. In UKESM1, we find that the simulated volcanic forcing differs considerably from the PMIP4 dataset used in models without interactive aerosol schemes, with marked differences in the hemispheric spread of the aerosol, resulting in lower forcing in the NH when SO2 emissions are used. Our results suggest that, for the largest tropical eruptions, the spatial distribution of aerosol can account for some of the discrepancies between model-simulated and tree-ring-derived cooling. Further work should therefore focus on better resolving the spatial distribution of aerosol forcing for past eruptions.

It is well-established that explosive volcanic eruptions typically lead to cooler surface temperatures in summer, but the picture in Northern Hemisphere winter is much more uncertain. Recent large, low-latitude eruptions have been followed by warm anomalies across Eurasia in winter and cold anomalies near Greenland, hypothesized to be part of a dynamical response to the volcanic forcing that drives a positive North Atlantic Oscillation (NAO). But the evidence for a dynamical, winter warming response is inconclusive because internal variability is large, many climate models do not simulate a dynamical response like this, and there are few such eruptions to study.New datasets that allow additional eruptions from the early 19th century to be studied are therefore particularly valuable and we will present new analyses of the winters following four large eruptions in 1809, 1815, 1831 and 1835 (alongside four later eruptions in 1883, 1902, 1982 and 1991). This analysis is made possible by a new gridded instrumental dataset combining marine and land air temperatures from the 1780s onwards developed in the ongoing GloSAT project. It is supplemented by analysis of an ensemble of historically-forced simulations with UKESM1.1 initialised in 1750, also from the GloSAT project, and by two reanalyses (20CRv3 from 1806 and ModE-RA from 1421).For the instrumental and reanalysis datasets, warming in Europe was found in the first post-eruption winter following six out of the eight cases studied, and in the second post-eruption winter in five. Similar results were found for cold anomalies near Greenland and for a positive winter NAO index. The anomaly magnitudes for individual cases were mostly within the range of internal variability but the consistency of the response across eruptions and datasets was significant in comparison with non-volcanic winters. The UKESM1.1 simulations showed a significant response (with Eurasian winter warming, Greenland cooling and positive NAO) for only the largest eruption (Tambora), suggesting a response may require a minimum forcing strength to occur.

AbstractBritain's power system has shifted towards a major contribution from wind energy. However, wind is highly variable, and exceptionally low wind events can simultaneously occur with cold conditions, which increase demand. These conditions can pose a threat for the security of energy supply. Here we use bias‐corrected wind supply data and the estimated temperature‐related part of demand to analyse events of potential weather‐related energy shortfall based on the historic meteorological record. We conduct sensitivity studies with varying scenarios of Britain's total wind energy capacity and the temperature sensitivity of national demand. These scenarios are estimates for present‐day conditions as well as potential future changes of the power system. We apply a new methodology to estimate the potential severity of an event for the power system, and analyse the atmospheric conditions associated with the most severe events. We find that events of potentially severe shortfall are relatively rare and short‐lived, and often occur with an atmospheric pattern broadly resembling a negative North Atlantic Oscillation. This broad tendency emerges from a wide range of individual daily weather patterns that cause cold and still conditions. With an increase in wind capacity, it is likely that severe events will become rarer, although the most severe days of the record are relatively insensitive to changes in wind supply and temperature sensitivity of demand under our assumptions.

Climate over land—where humans live and the majority of food is produced—is changing rapidly, driving severe impacts through extreme heat, wildfires, drought and flooding. Our ability to monitor and model this changing climate is being transformed through new observational systems and increasingly complex Earth system models. But fundamental understanding of the processes governing land climate has not kept pace, weakening our ability to interpret and utilize data from these advanced tools. Here we argue that for land-climate science to accelerate forwards, an alternative approach is needed. We advocate a parallel scientific effort, one emphasizing robust theories, that aims to inspire current and future land-climate scientists to better comprehend the processes governing land climate, its variability and extremes and its sensitivity to global warming. Such an effort, we believe, is essential to better understand the risks people face, where they live, in an era of climate change. Accelerating progress in land-climate science requires a renewed focus on developing theory to complement and underpin Earth system models and observations.

Using the past to improve future predictions requires an understanding and quantification fi cation of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fi ngerprints into a joint study. We fi nd that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fi ngerprint). These results are robust across fi ngerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([-0.86,-0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010-19 relative to 1850- 1900 using the combined signals of GMT and GMLP.

Observations are increasingly used to constrain multi-model projections for future climate assessments. This study assesses the performance of five constraining methods, which have previously been applied to attempt to improve regional climate projections from CMIP5-era models. We employ an out-of-sample testing approach to assess the efficacy of these constraining methods when applied to “pseudo-observational” datasets to constrain future changes in the European climate. These pseudo-observations are taken from CMIP6 simulations, for which future changes were withheld and used for verification. The constrained projections are more accurate and broadly more reliable for regional temperature projections compared to the unconstrained projections, especially in the summer season, which was not clear prior to this study. However, the constraining methods do not improve regional precipitation projections. We also analysed the performance of multi-method projections by combining the constrained projections, which are found to be competitive with the best-performing individual methods and demonstrate improvements in reliability for some temperature projections. The performance of the multi-method projection highlights the potential of combining constraints for the development of constraining methods.

The intensity and frequency of extreme heat events is increasing due to climate change, resulting in a range of societal impacts. In this paper, we use temporal analogues to analyse how past UK heatwave events, such as during the summer of 1923, may change if they were to occur under different global warming scenarios. We find that the six most intense early heat events are caused by circulation patterns similar to that of 1923, which can cause intense heat over the UK and parts of NW Europe. Circulation analogues for the 1923 heatwave are also linked to intense heat events in the future, although not all analogues are anomalously hot. At 4 degrees C of global warming, mean summer temperatures in England over the duration of the 1923 heatwave are between 4.9 and 6.4 degrees warmer than pre-industrial levels across the three models used. At that global mean warming level, future heat events with similar circulation as 1923 over England are estimated to be on average 6.9 degrees C-10.7 degrees C hotter than those at pre-industrial levels. Exploring how the intensity of events similar to past events may change in the future could be an effective risk communication tool for adaptation decision making, particularly if past events are stored in society's memory, for example, due to high impacts.

Large explosive volcanic eruptions cause short-term climatic impacts on both regional and global scales. Their impact on tropical climate variability, in particular El Nino-Southern Oscillation (ENSO), is still uncertain, as is their combined and separate effect on tropical and global precipitation. Here, we investigate the relationship between large-scale temperature and precipitation and tropical volcanic eruption strength, using 100 -member MPI-ESM ensembles for idealized equatorial symmetric Northern Hemisphere summer eruptions of different sulfur emission strengths. Our results show that for idealized tropical eruptions, global and hemispheric mean near -surface temperature and precipitation anomalies are negative and linearly scalable for sulfur emissions between 10 and 40 Tg S. We identify 20 Tg S emission as a threshold where the global ensemble -mean near -surface temperature and precipitation signals exceed the range of internal variability, even though some ensemble members emerge from variability for lower eruption strengths. Seasonal and ensemble mean patterns of near -surface temperature and precipitation anomalies are highly correlated across eruption strengths, in particular for larger emission strengths in the tropics, and strongly modulated by ENSO. There is a tendency to shift toward a warm ENSO phase for the first postvolcanic year as the emission strength increases. Volcanic cooling emerges on a hemisphere -wide scale, while the precipitation response is more localized, and emergence is mainly confined to the tropics and subtropics. SIGNIFICANCE STATEMENT: The purpose of this study is to investigate at which strength the climate responses of volcanic forcing can be distinguished from the internal climate variability and whether the responses will linearly increase as the emission strengths become stronger. We ran 100 -member MPI-ESM ensembles of idealized equatorial volcanic eruptions of different sulfur emission strengths and find that seasonal and ensemble mean patterns of nearsurface temperature and precipitation anomalies are distinguishable and linearly scalable for sulfur emissions from 10 to 40 Tg S if their forcing patterns are similar. The identification of volcanic fingerprints is important for seasonal to decadal forecasts in the case of potential future eruptions and could help to prepare society for the regional climatic consequences of such an event.

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.

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.

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. Here we discuss the use of a particle filter data-assimilation technique in a global climate model, that sub-selects members among an ensemble of simulations, to follow the observed Northern Atlantic Oscillation, El Niño Southern Oscillation and Southern Annular Mode, without the use of nudging terms. We investigate the role of these three modes of climate variability, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. 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. We will show that the NAO variability leads to large multi-decadal trends in temperature, and sea-ice concentration, and that constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

<p>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.</p> <p>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.</p> <p> References:</p> <p>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.</p>