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In every Intergovernmental Panel on Climate Change (IPCC) Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group reports and special reports, as well as their respective chapters. Within the reports, the ambition is to integrate knowledge on possible climate futures across the Working Groups and scientific research domains based on a small set of "framing pathways" such as the so-called representative concentration pathways (RCPs) in the Fifth IPCC Assessment Report (AR5) and the shared socioeconomic pathway (SSP) scenarios in the Sixth Assessment Report (AR6). This perspective, initiated by discussions at the IPCC Bangkok workshop in April 2023 on the "Use of Scenarios in AR6 and Subsequent Assessments", is intended to serve as one of the community contributions to highlight the needs for the next generation of framing pathways that is being advanced under the Coupled Model Intercomparison Project (CMIP) umbrella, which will influence or even predicate the IPCC AR7 consideration of framing pathways. Here we suggest several policy research objectives that such a set of framing pathways should ideally fulfil, including mitigation needs for meeting the Paris Agreement objectives, the risks associated with carbon removal strategies, the consequences of delay in enacting that mitigation, guidance for adaptation needs, loss and damage, and for achieving mitigation in the wider context of societal development goals. Based on this context, we suggest that the next generation of climate scenarios for Earth system models should evolve towards representative emission pathways (REPs) and suggest key categories for such pathways. These framing pathways should address the most critical mitigation policy and adaptation plans that need to be implemented over the next 10 years. In our view, the most important categories are those relevant in the context of the Paris Agreement long-term goal, specifically an immediate action (low overshoot) 1.5 °C pathway and a delayed action (high overshoot) 1.5 °C pathway. Two other key categories are a pathway category approximately in line with current (as expressed by 2023) near- and long-term policy objectives, as well as a higher-emission category that is approximately in line with "current policies" (as expressed by 2023). We also argue for the scientific and policy relevance in exploring two "worlds that could have been". One of these categories has high-emission trajectories well above what is implied by current policies and the other has very-low-emission trajectories which assume that global mitigation action in line with limiting warming to 1.5 °C without overshoot had begun in 2015. Finally, we note that the timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. Under the Paris Agreement, for the second global stocktake, which will occur in 2028, and to inform subsequent development of nationally determined contributions (NDCs) up to 2040, scientific inputs are required by 2027. These needs should be carefully considered in the development timeline of community modelling activities, including those under CMIP7. [ABSTRACT FROM AUTHOR]

Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions, and to understand whether an extremely cold winter, such as the coldest winter in the historical record of Germany in 1963 (−6.3 °C or −3.4 σ seasonal DJF temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): winter as cold as 1963 is still physically possible in Central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extreme cold winter to avoid potential maladaptation and increased vulnerability. [ABSTRACT FROM AUTHOR]

In every IPCC Assessment cycle, a multitude of scenarios are assessed, with different scope and emphasis throughout the various Working Group and Special Reports and their respective chapters. Within the reports, the ambition is to integrate knowledge on possible climate futures across the Working Groups and scientific research domains based on a small set of 'framing pathways', such as the so-called RCP pathways from the Fifth IPCC Assessment report (AR5) and the SSP-RCP scenarios in the Sixth Assessment Report (AR6). This perspective, initiated by discussions at the IPCC Bangkok workshop in April 2023 on the "Use of Scenarios in AR6 and Subsequent Assessments", is intended to serve as one of the community contributions to highlight needs for the next generation of framing pathways that is being advanced under the CMIP umbrella for use in the IPCC AR7. Here we suggest a number of policy research objectives that such a set of framing pathways should ideally fulfil, including mitigation needs for meeting the Paris Agreement objectives, the risks associated with carbon removal strategies, the consequences of delay in enacting that mitigation, guidance for adaptation needs, loss and damage, and for achieving mitigation in the wider context of Societal Development goals. Based on this context we suggest that the next generation of climate scenarios for Earth System Models should evolve towards 'Representative Emission Pathways' (REPs) and suggest key categories for such pathways. These 'framing pathways' should address the most critical mitigation policy and adaptation needs over the next 5-10 years. In our view the most important categories are those relevant in the context of the Paris Agreement long-term goal, specifically an immediate action (low overshoot) 1.5°C pathway, and a delayed action (high overshoot) 1.5°C pathway. Two other key categories are a pathway category approximately in line with current (as expressed by 2023) near- and long-term policy objectives, and a higher emissions category that is approximately in line with "current policies" (as expressed by 2023). We also argue for the scientific and policy relevance in exploring two 'worlds that could have been'. One of these categories has high emission trajectories well above what is implied by current policies, and the other has very low emission trajectories that assume that global mitigation action in line with limiting warming to 1.5°C without overshoot had begun in 2015. Finally, we note that timely provision of new scientific information on pathways is critical to inform the development and implementation of climate policy. For the second Global Stocktake under the Paris Agreement in 2028, and to inform subsequent development of Nationally Determined Contributions (NDCs) up to 2040, scientific inputs are required well before 2028. These needs should be carefully considered in the development timeline of community modelling activities including those under CMIP7. [ABSTRACT FROM AUTHOR]

Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer "what if" questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 °C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2:9 °C accounts for 60 %-80% of the 2021 excess event intensity relative to average pre-industrial heatwave conditions. [ABSTRACT FROM AUTHOR]

The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s. [ABSTRACT FROM AUTHOR]

For sectors like agriculture, hydrology and ecology, increasing interannual variability (IAV) can have larger impacts than changes in the mean state, whereas decreasing IAV in winter implies that the coldest seasons warm more than the mean. IAV is difficult to reliably quantify in single realizations of climate (observations and single-model realizations) as they are too short, and represent a combination of external forcing and IAV. Single-model initial-condition large ensembles (SMILEs) are powerful tools to overcome this problem, as they provide many realizations of past and future climate and thus a larger sample size to robustly evaluate and quantify changes in IAV. We use three SMILE-based regional climate models (CanESM-CRCM, ECEARTH-RACMO and CESM-CCLM) to investigate downscaled changes in IAV of summer and winter temperature and precipitation, the number of heat waves, and the maximum length of dry periods over Europe. An evaluation against the observational data set E-OBS reveals that all models reproduce observational IAV reasonably well, although both under- and overestimation of observational IAV occur in all models in a few cases. We further demonstrate that SMILEs are essential to robustly quantify changes in IAV since some individual realizations show significant IAV changes, whereas others do not. Thus, a large sample size, i.e., information from all members of SMILEs, is needed to robustly quantify the significance of IAV changes. Projected IAV changes in temperature over Europe are in line with existing literature: increasing variability in summer and stable to decreasing variability in winter. Here, we further show that summer and winter precipitation, as well as the two summer extreme indicators mostly also show these seasonal changes. [ABSTRACT FROM AUTHOR]

The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5-95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850-1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20-28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s. [ABSTRACT FROM AUTHOR]

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections. [ABSTRACT FROM AUTHOR]

Single model large ensembles are widely used model experiments to estimate internal climate variability. The underlying assumption is that the internal variability (here: inter-annual variability) of the chosen model is a good approximation of the observed natural (inter-annual) variability. In this study, we test this assumption based on three regional climate model large ensembles (16 members of an EC-EARTH-RACMO ensemble, 21 members of a CESM-CCLM ensemble, 50 members of a CanESM-CRCM ensemble) for four European domains (British Isles, France, Mid-Europe, Alps). Simulated inter-annual variability is evaluated against E-OBS and the inter-annunal variability and its future change are compared across the ensembles. To the knowledge of the authors, this is the first comparison of regional large ensembles over Europe. Analysis comprises seasonal temperature and precipitation, as well as indicators for dry periods and heat waves. Results show a large consistency of all three ensembles with E-OBS data for most indicators and regions, validating the abilities of these ensembles to represent natural variability on the annual scale. EC-EARTH-RACMO shows the highest inter-annual variability for winter temperature and precipitation, whereas CESM-CCLM shows the highest variability for summer temperature and precipitation, as well as for heatwaves and dry periods. Despite these model differences, the sign of the future changes in internal variability is largely the same in all models: for summer temperature, summer precipitation and the number of heat waves, the internal variability increases, while it decreases for winter temperature. Changes of winter precipitation and dry periods are a bit unclear, with a tendency to increase for dry periods. The overall consistency across single model large ensembles and observations strengthens the concept of large ensembles, and underlines their great potential for understanding and quantifying the role of internal climate variability. [ABSTRACT FROM AUTHOR]

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. [ABSTRACT FROM AUTHOR]

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model output rarely fulfills. Most currently used statistical bias adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple, potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model output and observations for many climate related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers, and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty of bias-adjusted hazard indicators due to internal variability and show how inadequate bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modelled climate impacts are associated with substantial uncertainties associated with the choice of bias adjustment. We conclude that, as long as bias adjustment is unavoidable for climate impact assessments, the use of statistical bias adjustment approaches that correct the multivariate dependence structure of drivers is preferable. [ABSTRACT FROM AUTHOR]

The intensity of precipitation events is expected to increase in the future. The rate of increase depends on the strength or rarity of the events; very strong and rare events tend to follow the Clausius-Clapeyron relation, whereas weaker events or precipitation averages increase at a smaller rate than expected from the Clausius-Clapeyron relation. An often overlooked aspect is seasonal occurrence of such events, which might change in the future. To address the impact of seasonality, we use a large ensemble of regional and global climate model simulations, comprising tens of thousands of model years of daily temperature and precipitation for the past, present, and future. In order to make the data comparable, they are quantile mapped to observation-based time series representative of the Aare catchment in Switzerland. Model simulations show no increase in annual maximum 1-day precipitation events (Rx1day) over the last 400 years and an increase of 10 %-20% until the end of the century for a strong (RCP8.5) forcing scenario. This fits with a Clausius-Clapeyron scaling of temperature at the event day, which increases less than annual mean temperature. An important reason for this is a shift in seasonality. Rx1day events become less frequent in late summer and more frequent in early summer and early autumn, when it is cooler. The seasonality shift is shown to be related to summer drying. Models with decreasing annual mean or summer mean precipitation show this behaviour more strongly. The highest Rx1day per decade, in contrast, shows no change in seasonality in the future. This discrepancy implies that decadalscale extremes are thermodynamically limited; conditions conducive to strong events still occur during the hottest time of the year on a decadal scale. In contrast, Rx1day events are also limited by other factors. Conducive conditions are not reached every summer in the present, and even less so in the future. Results suggest that changes in the seasonal cycle need to be accounted for when preparing for moderately extreme precipitation events and assessing their socioeconomic impacts [ABSTRACT FROM AUTHOR]

This study investigates the global response of the midlatitude atmospheric circulation to 1.5 and 2.0 °C of warming using the HAPPI (Half a degree Additional warming, Prognosis and Projected Impacts) ensemble, with a focus on the winter season. Characterising and understanding this response is critical for accurately assessing the near-term regional impacts of climate change and the benefits of limiting warming to 1.5 °C above pre-industrial levels, as advocated by the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC). The HAPPI experimental design allows an assessment of uncertainty in the circulation response due to model dependence and internal variability. Internal variability is found to dominate the multi-model mean response of the jet streams, storm tracks, and stationary waves across most of the midlatitudes; larger signals in these features are mostly consistent with those seen in more strongly forced warming scenarios. Signals that emerge in the 1.5 °C experiment are a weakening of storm activity over North America, an inland shift of the North American stationary ridge, an equatorward shift of the North Pacific jet exit, and an equatorward intensification of the South Pacific jet. Signals that emerge under an additional 0.5 °C of warming include a poleward shift of the North Atlantic jet exit, an eastward extension of the North Atlantic storm track, and an intensification on the flanks of the Southern Hemisphere storm track. Case studies explore the implications of these circulation responses for precipitation impacts in the Mediterranean, in western Europe, and on the North American west coast, paying particular attention to possible outcomes at the tails of the response distributions. For example, the projected weakening of the Mediterranean storm track emerges in the 2 °C warmer world, with exceptionally dry decades becoming 5 times more likely. [ABSTRACT FROM AUTHOR]

This study investigates the global response of the midlatitude atmospheric circulation to 1.5°C and 2.0°C of warming using the HAPPI Half a degree Additional warming, Projections, Prognosis and Impacts ensemble, with a focus on the winter season. Characterizing and understanding this response is critical for accurately assessing the near-term regional impacts of climate change and the benefits of limiting warming to the 1.5°C above pre-industrial levels, as advocated by the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC). The HAPPI experimental design allows an assessment of uncertainty in the circulation response due to model dependence and internal variability. Internal variability is found to dominate the multi-model mean response of the jet streams, storm tracks and stationary waves across most of the midlatitudes; larger signals in these features are mostly consistent with those seen in more strongly forced warming scenarios. Signals that emerge in the 1.5°C experiment are a weakening of storm activity over North America, an inland shift of the North American stationary ridge, an equatorward shift of the North Pacific jet exit, and an equatorward intensification of the South Pacific jet. Signals that emerge under an additional 0.5°C of warming include a poleward shift of the North Atlantic jet exit, an eastward extension of the North Atlantic storm track, and an intensification on the flanks of the Southern Hemisphere storm track. Case studies explore the implications of these circulation responses for precipitation impacts in the Mediterranean, western Europe and the North American west coast, paying particular attention to possible outcomes at the tails of the response distributions. For example, the projected weakening of the Mediterranean storm track emerges in the 2.0°C world, though the ensemble spread still allows for both wetting and drying responses. [ABSTRACT FROM AUTHOR]

The Half A degree additional warming, Prognosis and Projected Impacts (HAPPI) experimental protocol provides a multi-model database to compare the effects of stabilizing anthropogenic global warming of 1.5 °C over preindustrial levels to 2.0 °C over these levels. The HAPPI experiment is based upon large ensembles of global atmospheric models forced by sea surface temperature and sea ice concentrations plausible for these stabilization levels. This paper examines changes in extremes of high temperatures averaged over three consecutive days. Changes in this measure of extreme temperature are also compared to changes in hot season temperatures. We find that the differences between the two stabilization scenarios in extreme high temperatures over land ranges from about 0.25 to 1.0 °C depending on location and model. Results from the HAPPI models are consistent with similar results from the one available fully coupled climate model. However, a complicating factor in interpreting extreme temperature changes across the HAPPI models is their diversity of aerosol forcing changes. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (>50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006-2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario. [ABSTRACT FROM AUTHOR]

Robust appraisals of climate impacts at different levels of global-mean temperature increase are vital to guide assessments of dangerous anthropogenic interference with the climate system. The 2015 Paris Agreement includes a two-headed temperature goal: "holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C". Despite the prominence of these two temperature limits, a comprehensive overview of the differences in climate impacts at these levels is still missing. Here we provide an assessment of key impacts of climate change at warming levels of 1.5 °C and 2 °C, including extreme weather events, water availability, agricultural yields, sea-level rise and risk of coral reef loss. Our results reveal substantial differences in impacts between a 1.5 °C and 2 °C warming that are highly relevant for the assessment of dangerous anthropogenic interference with the climate system. For heat-related extremes, the additional 0.5 °C increase in global-mean temperature marks the difference between events at the upper limit of present-day natural variability and a new climate regime, particularly in tropical regions. Similarly, this warming difference is likely to be decisive for the future of tropical coral reefs. In a scenario with an end-of-century warming of 2 °C, virtually all tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90 % in 2050 and projected to decline to 70 % by 2100 for a 1.5 °C scenario. Analyses of precipitation-related impacts reveal distinct regional differences and hot-spots of change emerge. Regional reduction in median water availability for the Mediterranean is found to nearly double from 9 % to 17 % between 1.5 °C and 2 °C, and the projected lengthening of regional dry spells increases from 7 to 11 %. Projections for agricultural yields differ between crop types as well as world regions. While some (in particular high-latitude) regions may benefit, tropical regions like West Africa, South-East Asia, as well as Central and northern South America are projected to face substantial local yield reductions, particularly for wheat and maize. Best estimate sea-level rise projections based on two illustrative scenarios indicate a 50 cm rise by 2100 relative to year 2000-levels for a 2°C scenario, and about 10 cm lower levels for a 1.5 °C scenario. In a 1.5 °C scenario, the rate of sea-level rise in 2100 would be reduced by about 30 % compared to a 2 °C scenario. Our findings highlight the importance of regional differentiation to assess both future climate risks and different vulnerabilities to incremental increases in global-mean temperature. The article provides a consistent and comprehensive assessment of existing projections and a good basis for future work on refining our understanding of the difference between impacts at 1.5 °C and 2 °C warming. [ABSTRACT FROM AUTHOR]

Changes in European near-surface air temperature variability are projected in response to anthropogenic climate change, due to changes in atmospheric circulation patterns and thermal advection ('dynamics') and changes in local radiative and land surface processes ('thermodynamics'). Statistical techniques have been developed to quantify the relative importance of dynamics on regional temperature variability by empirically determining the relationship between sea level pressure (SLP) and temperature. These techniques can be used to explore whether dynamically-driven temperature variability changes result from changes in SLP or solely from changes in the SLP - temperature relationship due to differential midlatitude-pole and land-sea warming rates. Removing the effect of dynamics on temperature also reveals regions where changes in land surface feedbacks associated with snow cover or soil moisture may contribute to variability change in future climate. In this study, two such techniques, machine learning dynamical adjustment (Sippel et al., submitted) and circulation analogue dynamical adjustment (e.g. Deser et al. 2016, Lehner et al. 2017), are applied on monthly timescales to sets of four 20-member ensembles constructed from multi-century control runs subject to preindustrial, 2, 4, and 8 x CO2 forcing levels. The use of large ensembles in this study is a necessity to robustly determine an estimate of internal variability, a baseline from which one can ascertain forced change. The frequency and spatial extent of atmospheric circulation patterns are compared within and between ensemble sets. Dynamical temperature variability is computed in each ensemble by training on/selecting analogues from 1) the preindustrial ensemble and 2) the respective N x CO2 ensemble (i.e. dynamically adjust 2 x CO2 ensemble with 2 x CO2 training/analogues etc.) to assess the stationarity of the SLP - temperature relationship. Preliminary results suggest that changes in the frequency of winter circulation patterns emerge and contribute to European temperature variability change at 8 x CO2. In contrast, variability change at lower levels of CO2 forcing is primarily due to advection of warmer temperature anomalies by circulation patterns that fall within the range of preindustrial variability. Seasonal differences in dynamic vs. thermodynamic drivers of temperature variability change are also investigated. [ABSTRACT FROM AUTHOR]

In spring and summer 2018, Central Europe experienced a record-breaking drought and heat wave with extreme temperatures, wildfires in Northern Europe and a shortage of water supply over many parts of the continent. One of the main causes leading to this exceptionally hot weather was a weaker jet stream, which allowed a quasi-stationary high pressure system to persist for several weeks. Given the important consequences of such circulation characteristics, the question arises whether the frequency and/or intensity of these events will be affected by global climate change. Against this background, we investigate future changes in the frequency and persistence of Central European circulation types in large ensemble global general circulation models (GCMs) mostly originating from the CMIP5 project, and we validate our data against the ERA-40/-Interim reanalysis for the reference period 1980 – 2018. In accordance with previous works, we find indications for a significant future increase in the frequency of westerlies in winter, and several significant changes in the persistence of circulation types, most notably in summer (more persistent) and winter (less persistent). Our results help to understand future changes in synoptic-scale weather regimes over Europe and their implications in terms of extreme temperature and precipitation events. [ABSTRACT FROM AUTHOR]

Stakeholders express a strong need for reliable information on changes in climate extremes in the recent past and coming decades as a basis for adaptation strategies. However, extremes are very rare by definition and occur irregularly due to internal climate variability. In order to maximize the sampling period design of critical infrastructure is often based on return period estimates across the entire observational record. Based on a set of different large initial condition ensemble I demonstrate that thereby the risk of climate extremes may be seriously underestimated particularly if return periods are estimated based on stationary extreme value analysis. In many cases the probability of extremes in the climate of today and of the near future is substantially higher than averaged across the observational period.Due to internal variability, local to regional trends in extremes may deviate strongly from the forced response in individual simulations and in the real world. This can lead to multi-decadal warming pauses or stagnation periods in extremes, similar to the hiatus or warming holes of hot extremes. I will show that such stagnation periods, during which the forced response is hidden by large variability, may lead to a serious underestimation of the changes in the probability of extremes even if non-stationary extreme value analysis is applied. This is particularly problematic as stagnation periods are often followed by a surge event during which extremes break previous records by a large margin. Large initial condition ensembles provide important guidance on how to robustly assess the risk of extremes in the present-day and near-future climate taking into account the forced climate change signal. [ABSTRACT FROM AUTHOR]

Obtaining reliable estimates of the forced response in climate model projections is important for many applications in climate science. Depending on the variable it is, however, challenging to separate the forced signal from internal variability, particularly for seasonal to daily and regional to local scales. Various methods have been suggested in the literature, ranging from simple linear or polynomial fits, utilizing initial-condition members to machine learning approaches such as dynamical adjustment.Based on a large initial-condition ensemble we develop and test a novel, straight-forward yet reliable approach to separate internal variability and forced response based on LOWESS filtering. It estimates the forced response from climate projections making use of all initial condition members available as well as the pre-industrial control simulations. Our approach hence utilizes the information provided by multiple initial-condition members but is also applicable for models with only one realization.We demonstrate that our approach can reliably isolate the forced response on different spatial scales and for different variables using the ensemble mean of a large initial condition ensemble as reference. We show results for global and regional temperature and precipitation and compare our approach to other methods previously suggested in the literature. Overall, our approach aims to optimize between complexity, a priori parametrizations, and efficient use of all initial condition available for a given model. [ABSTRACT FROM AUTHOR]