Studies of future extreme weather events often struggle to place those events into context for stakeholders interested in climate risk assessment and adaptation planning. Here we place future extremes in the context of the recent past by examining how the frequency of impactful observed events may change in a warmer climate, and how events with a similar likelihood of occurrence to those seen in the past may change in magnitude. This approach helps users understand potential future impacts from the perspective of past events. Alongside the quantitative results, we developed a qualitative narrative to help decision-makers conceptualise how future extreme weather might differ from familiar events. This narrative describes plausible future events from climate projections, encouraging audiences to draw on their lived experience and consider how future impacts may compare. We apply this framework to three UK case studies: heatwaves across the UK, heavy rain over Cumbria and wildfire over Norfolk. These case studies were selected through engagement with stakeholders focused on national climate risks. All three types of extreme weather are projected to increase in both frequency and magnitude as the climate warms. By grounding future projections in the context of past impactful events, the framework could support improved climate risk assessment and help policymakers explore how increasing severity and frequency of extreme weather may influence future impacts and adaptation needs in a warming world. Together these approaches help translate possible future climate extremes into information that supports practical, informed adaptation decisions.

Solutions including oscillatory responses to monotonic forcing are derived for a generalized energy balance box model, and used to help explore general circulation model (GCM) global surface temperature responses to forcing. Oscillations superposed on the warming following abrupt increases are observed in several GCMs. They are associated with a cooling of the North Atlantic, linked to strong changes in the Atlantic Meridional Overturning Circulation (AMOC) and Arctic sea ice in GCM simulations. Global temperature plateaus lasting for decades in abrupt quadrupling experiments are linked to an AMOC collapse to a weak state of 2-7 Sv. In the abrupt doubling experiment of a long CESM1 run, allowing AMOC to recover, we find oscillations in both temperature and AMOC persisting over multi-century timescales. A box model fitted to this predict oscillations superposed on future long-term warming too, with amplitudes around some tenths of a degree.

Global temperature responses from different abrupt CO2 change experiments participating in Coupled Model Intercomparison Project Phase 6 (CMIP6) and LongRunMIP are systematically compared in order to study the linearity of the responses. For CMIP6 models, abrupt-4xCO2 experiments warm on average 2.2 times more than abrupt-2xCO2 experiments. A factor of about 2 can be attributed to the differences in forcing, and the rest is likely due to nonlinear responses. Abrupt-0p5xCO2 responses are weaker than abrupt-2xCO2, mostly because of weaker forcing. CMIP6 abrupt CO2 change experiments respond linearly enough to well reconstruct responses to other experiments, such as 1pctCO2, but uncertainties in the forcing can give uncertain responses. We derive also a generalised energy balance box model that includes the possibility of having oscillations in the global temperature responses. Oscillations are found in some models, and are connected to changes in ocean circulation and sea ice. Oscillating components connected to a cooling in the North Atlantic can counteract the long-term warming for decades or centuries and cause pauses in global temperature increase.

A collapse of the Atlantic Meridional Overturning Circulation (AMOC) would have substantial impacts on global precipitation patterns, especially in the vulnerable tropical monsoon regions. We assess these impacts using four state-of-the-art climate models with bistable AMOC. Spatial and seasonal patterns of precipitation change are remarkably consistent across models. We focus on the South American Monsoon (SAM), the West African Monsoon (WAM), the Indian Summer Monsoon (ISM) and the East Asian Summer Monsoon (EASM). Models consistently suggest substantial disruptions for WAM, ISM and EASM with shorter wet and longer dry seasons (-29.07\%,-18.76\% and -3.78\% ensemble mean annual rainfall change, respectively). Models also agree on changes for the SAM, suggesting rainfall increases overall, in contrast to previous studies. These are more pronounced in the southern Amazon (+43.79\%), accompanied by decreasing dry-season length. Consistently across models, our results suggest major rearranging of all tropical monsoon systems in response to an AMOC collapse.

The seasonal response of rainfall over tropical South America to a shutdown in the Atlantic Meridional Overturning Circulation (AMOC) is examined, in HadGEM3 model simulations where freshwater is added to the north Atlantic. Potential biases in these simulations are explored by comparing the unperturbed simulation with observations. In this simulation, in years when the latitude of the model Atlantic Intertropical Convergence Zone (ITCZ) is realistic, the model provides a reasonable simulation of the spatial and seasonal variation in regional-scale rainfall over tropical South America. However, some climatological mean rainfall biases over this region are attributed to the climatological southward bias in the Atlantic ITCZ. Under an AMOC shutdown, the rainfall changes over tropical South America are largely associated with a southward shift of the Atlantic ITCZ. The large seasonal variation in rainfall change over tropical South America is linked primarily with the variation in the location of peak rainfall (itself driven largely by variation in the latitude of peak solar insolation and by the lagged variation in Atlantic ITCZ). The simulated rainfall changes appear to be biased in some months by the southward bias in the Atlantic ITCZ, including a possible overestimation of drying in March and June. In addition, the Atlantic ITCZ in HadGEM3 tends to shift too far in both the seasonal cycle (as reported in other models) and in inter-annual variability. Excessive inter-annual variability may arise because the model ITCZ is too close to the equator, combined with an increase in variability near the equator. Further understanding of what drives the variability in ITCZ latitude, and how that relates to ITCZ shifts under an AMOC shutdown, is suggested as a future research priority.

A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). Here, we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias. A previous study (McNeall et al., 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping, parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to modelling errors such as missing deep rooting in the Amazon in the land surface component of the climate model, to a warm–dry bias in the Amazon climate of the model or a combination of both. In this study, we bias correct the climate of the Amazon in the climate model from McNeall et al. (2016) using an “augmented” Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as it is to changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region. The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors and helping model developers discover and prioritise model errors to target.

Precipitation and atmospheric circulation are the coupled processes through which tropical ocean surface temperatures drive global weather and climate(1-5). Local sea surface warming tends to increase precipitation, but this local control is difficult to disentangle from remote effects of conditions elsewhere. As an example of such a remote effect, El Nino Southern Oscillation (ENSO) events in the equatorial Pacific Ocean alter precipitation across the tropics. Atmospheric circulations associated with tropical precipitation are predominantly deep, extending up to the tropopause. Shallow atmospheric circulations(6-8) affecting the lower troposphere also occur, but the importance of their interaction with precipitation is unclear. Uncertainty in precipitation observations(9,10) and limited observations of shallow circulations(11) further obstruct our understanding of the ocean's influence on weather and climate. Despite decades of research, persistent biases remain in many numerical model simulations(12-)(18), including excessively wide tropical rainbands(14,18), the 'double-intertropical convergence zone problem'(12,16,)(17) and too-weak responses to ENSO15. These biases demonstrate gaps in our understanding, reducing confidence in forecasts and projections. Here we use observations to show that seasonal tropical precipitation has a high sensitivity to local sea surface temperature. Our best observational estimate is an 80 per cent change in precipitation for every gram per kilogram change in the saturation specific humidity (itself a function of the sea surface temperature). This observed sensitivity is higher than in 43 of the 47 climate models studied, and is associated with strong shallow circulations. Models with more realistic (closer to 80%) sensitivity have smaller biases across a wide range of metrics. Our results apply to both temporal and spatial variation, over regions where climatological precipitation is about one millimetre per day or more. Our analyses of multiple independent observations, physical constraints and model data underpin these findings. The spread in model behaviour is further linked to differences in shallow convection, thus providing a focus for accelerated research to improve seasonal forecasts through multidecadal climate projections.

It is important to understand how future environmental policies will impact both climate change and air pollution. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone, and precursor gases, should improve air quality, NTCF reductions will also impact climate. Prior assessments of the impact of NTCF mitigation on air quality and climate have been limited. This is related to the idealized nature of some prior studies, simplified treatment of aerosols and chemically reactive gases, as well as a lack of a sufficiently large number of models to quantify model diversity and robust responses. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using nine state-of-the-art chemistry–climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with “weak” (SSP3-7.0) versus “strong” (SSP3-7.0-lowNTCF) levels of air quality control measures. As SSP3-7.0 lacks climate policy and has the highest levels of NTCFs, our results (e.g., surface warming) represent an upper bound. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface fine particulate matter (PM2.5) and ozone (O3) decrease by -2.2±0.32 µg m−3 and -4.6±0.88 ppb, respectively (changes quoted here are for the entire 2015–2055 time period; uncertainty represents the 95 % confidence interval), over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.25±0.12 K and 0.03±0.012 mm d−1, respectively. Similarly, increases in extreme weather indices, including the hottest and wettest days, also occur. Regionally, the largest warming and wetting occurs over Asia, including central and north Asia (0.66±0.20 K and 0.03±0.02 mm d−1), south Asia (0.47±0.16 K and 0.17±0.09 mm d−1), and east Asia (0.46±0.20 K and 0.15±0.06 mm d−1). Relatively large warming and wetting of the Arctic also occur at 0.59±0.36 K and 0.04±0.02 mm d−1, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet climate mitigation goals.

Abstract. Over the next few decades, policies that optimally address both climate change and air quality are essential. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone and precursor gases (but not methane), should improve air quality, NTCF reductions will also impact climate. How future policies affect the abundance of NTCFs and their impact on climate and air quality remains uncertain. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using state-of-the-art chemistry-climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with weak versus strong levels of air quality control measures. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface ozone (O3) and fine particulate matter (PM2.5) decrease by −15 % and −25 %, respectively, over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.24 K and 1.1 %, respectively, with similar increases in extreme weather indices. Regionally, the largest warming and wetting trends occur over Asia, including central and north Asia (0.56 K and 2.1 %), south Asia (0.48 K and 4.6 %) and east Asia (0.44 K and 4.7 %). Relatively large warming and wetting of the Arctic also occurs at 0.41 K and 2.1 %, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality, but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet mitigation goals.

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

We treat the output of the simulator y as an uncertain function f () of the simulator inputs x, so that y = f (x).We wish to produce a predictive distribution for y at any model input, conditional on the points already run, or the design (Y, X).Throughout the study, we use a kriging function, similar to a Gaussian process regression emulator, as coded in the package DiceKriging (Roustant et al., 2012) in the statistical programming environment R (R Core Team, 2016), for prediction of

This article reviews recent scientific progress, relating to four major systems that could exhibit threshold behaviour: ice sheets, the Atlantic meridional overturning circulation (AMOC), tropical forests and ecosystem responses to ocean acidification. The focus is on advances since the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The most significant developments in each component are identified by synthesizing input from multiple experts from each field. For ice sheets, some degree of irreversible loss (timescales of millennia) of part of the West Antarctic Ice Sheet (WAIS) may have already begun, but the rate and eventual magnitude of this irreversible loss is uncertain. The observed AMOC overturning has decreased from 2004–2014, but it is unclear at this stage whether this is forced or is internal variability. New evidence from experimental and natural droughts has given greater confidence that tropical forests are adversely affected by drought. The ecological and socio-economic impacts of ocean acidification are expected to greatly increase over the range from today’s annual value of around 400, up to 650 ppm CO2 in the atmosphere (reached around 2070 under RCP8.5), with the rapid development of aragonite undersaturation at high latitudes affecting calcifying organisms. Tropical coral reefs are vulnerable to the interaction of ocean acidification and temperature rise, and the rapidity of those changes, with severe losses and risks to survival at 2 °C warming above pre-industrial levels. Across the four systems studied, however, quantitative evidence for a difference in risk between 1.5 and 2 °C warming above pre-industrial levels is limited.

Peter Good , Jonathan Bamber , Kate Halladay , Anna Harper , Laura Jackson , Gillian 5 Kay, Bart Kruijt, Jason Lowe , Oliver Phillips, Jeff Ridley, Meric Srokosz , Carol 6 Turley, Phillip Williamson 7 8 Met Office Hadley Centre, Exeter, United Kingdom. 9 School of Geographical Sciences, University of Bristol, UK 10 College of Engineering, Mathematics, and Physical Sciences, University of Exeter, UK 11 ALTERRA, Wageningen UR, PO box 47, 6700 AA Wageningen, Netherlands 12 School of Geography, University of Leeds, Leeds, UK 13 National Oceanography Centre, University of Southampton Waterfront Campus, 14 Southampton, UK 15 Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK 16 School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK and 17 Natural Environment Research Council, UK 18 19 20 21

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?

The future of the Amazon rainforest is unknown due to uncertainties in projected climate change and the response of the forest to this change (forest resiliency). Here, we explore the effect of some uncertainties in climate and land surface processes on the future of the forest, using a perturbed physics ensemble of HadCM3C. This is the first time Amazon forest changes are presented using an ensemble exploring both land vegetation processes and physical climate feedbacks in a fully coupled modelling framework. Under three different emissions scenarios, we measure the change in the forest coverage by the end of the 21st century (the transient response) and make a novel adaptation to a previously used method known as dry-season resilience to predict the long-term committed response of the forest, should the state of the climate remain constant past 2100. Our analysis of this ensemble suggests that there will be a high chance of greater forest loss on longer timescales than is realized by 2100, especially for mid-range and low emissions scenarios. In both the transient and predicted committed responses, there is an increasing uncertainty in the outcome of the forest as the strength of the emissions scenarios increases. It is important to note however, that very few of the simulations produce future forest loss of the magnitude previously shown under the standard model configuration. We find that low optimum temperatures for photosynthesis and a high minimum leaf area index needed for the forest to compete for space appear to be precursors for dieback. We then decompose the uncertainty into that associated with future climate change and that associated with forest resiliency, finding that it is important to reduce the uncertainty in both of these if we are to better determine the Amazon's outcome.

We present a detailed analysis of mechanisms underlying the evapotranspiration response to increased \(\hbox {CO}_2\) in HadGEM2-ES, focussed on western Amazonia. We use three simulations from CMIP5 in which atmospheric \(\hbox {CO}_2\) increases at 1% per year reaching approximately four times pre-industrial levels after 140 years. Using 3-hourly data, we found that evapotranspiration (ET) change was dominated by decreased stomatal conductance (\(g_s\)), and to a lesser extent by decreased canopy water and increased moisture gradient (specific humidity difference between surface and near-surface). There were large, non-linear decreases in ET in the simulation in which radiative and physiological forcings could interact. This non-linearity arises from non-linearity in the conductance term (includes aerodynamic and stomatal resistance and partitioning between the two, which is determined by canopy water availability), the moisture gradient, and negative correlation between these two terms. The conductance term is non-linear because GPP responds non-linearly to temperature and GPP is the dominant control on \(g_s\) in HadGEM2-ES. In addition, canopy water declines, mainly due to increases in potential evaporation, which further decrease the conductance term. The moisture gradient responds non-linearly owing to the non-linear response of temperature to \(\hbox {CO}_2\) increases, which increases the Bowen ratio. Moisture gradient increases resulting from ET decline increase ET and thus constitute a negative feedback. This analysis highlights the importance of the \(g_s\) parametrisation in determining the ET response and the potential differences between offline and online simulations owing to feedbacks on ET via the atmosphere, some of which would not occur in an offline simulation.

Abstract. nonlinMIP aims to quantify and understand, at regional scales, climate responses that are non-linear under CO2 forcing (mechanisms for which doubling the CO2 forcing does not double the response). Non-linear responses can be large at regional scales, with important implications for understanding mechanisms and for GCM emulation techniques (e.g. energy balance models and pattern-scaling methods). However, these processes are hard to explore using traditional experiments, explaining why they have had little attention in previous studies. Some single model studies have established novel analysis principles and some physical mechanisms. There is now a need to explore robustness and uncertainty in such mechanisms across a range of models. nonlinMIP addresses this using a simple, small set of CO2-forced experiments that are able to separate linear and non-linear mechanisms cleanly, with a good signal/noise ratio – while being demonstrably traceable to realistic transient scenarios. The design builds on the CMIP5 and CMIP6 DECK protocols, and is centred around a suite of instantaneous atmospheric CO2 change experiments, with a ramp-up-ramp-down experiment to test traceability to gradual forcing scenarios. In all cases the models are intended to be used with CO2 concentrations rather than CO2 emissions as the input. The understanding gained will help interpret the spread in policy-relevant scenario projections. Here we outline the basic physical principles behind nonlinMIP, and the method of establishing traceability from abruptCO2 to gradual forcing experiments, before detailing the experimental design and finally some analysis principles. The test of traceability from abruptCO2 to transient experiments is recommended as a standard analysis within the CMIP5 and CMIP6 DECK protocols.

In both the observational record and atmosphere-ocean general circulation model (AOGCM) simulations of the last \(\sim\)150 years, short-lived negative radiative forcing due to volcanic aerosol, following explosive eruptions, causes sudden global-mean cooling of up to \(\sim\)0.3 K. This is about five times smaller than expected from the transient climate response parameter (TCRP, K of global-mean surface air temperature change per W m−2 of radiative forcing increase) evaluated under atmospheric CO2 concentration increasing at 1 % yr−1. Using the step model (Good et al. in Geophys Res Lett 38:L01703, 2011. doi: 10.1029/2010GL045208), we confirm the previous finding (Held et al. in J Clim 23:2418–2427, 2010. doi: 10.1175/2009JCLI3466.1) that the main reason for the discrepancy is the damping of the response to short-lived forcing by the thermal inertia of the upper ocean. Although the step model includes this effect, it still overestimates the volcanic cooling simulated by AOGCMs by about 60 %. We show that this remaining discrepancy can be explained by the magnitude of the volcanic forcing, which may be smaller in AOGCMs (by 30 % for the HadCM3 AOGCM) than in off-line calculations that do not account for rapid cloud adjustment, and the climate sensitivity parameter, which may be smaller than for increasing CO2 (40 % smaller than for 4 × CO2 in HadCM3).