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European Union funding: 821205, 821003, 820829. Wellcome Trust: 205212/Z/16/Z

Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.

Global aviation operations contribute to anthropogenic climate change via a complex set of processes that lead to a net surface warming. Of importance are aviation emissions of carbon dioxide (CO2), nitrogen oxides (NOx), water vapor, soot and sulfate aerosols, and increased cloudiness due to contrail formation. Aviation grew strongly over the past decades (1960–2018) in terms of activity, with revenue passenger kilometers increasing from 109 to 8269 billion km yr−1, and in terms of climate change impacts, with CO2 emissions increasing by a factor of 6.8–1034 Tg CO2 yr−1. Over the period 2013–2018, the growth rates in both terms show a marked increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are calculated for the years 2000–2018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising from them, yields the largest positive net (warming) ERF term followed by CO2 and NOx emissions. The formation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 mW (mW) m−2 (5–95% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m−2), CO2 (34.3 mW m−2), and NOx (17.5 mW m−2). Non-CO2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m−2. Uncertainty distributions (5%, 95%) show that non-CO2 forcing terms contribute about 8 times more than CO2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. CO2-warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO2 emissions alone. CO2 and NOx aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions., Graphical abstract Image 1, Highlights • Global aviation warms Earth's surface through both CO2 and net non-CO2 contributions. • Global aviation contributes a few percent to anthropogenic radiative forcing. • Non-CO2 impacts comprise about 2/3 of the net radiative forcing. • Comprehensive and quantitative calculations of aviation effects are presented. • Data are made available to analyze past, present and future aviation climate forcing.

As the Paris Agreement moves towards implementation, we explore opportunities it presents to move part of the climate change mitigation challenge upstream into supply-side climate policy. Supply-side climate policies have been sparsely employed to date, although interest is growing in approaches that can curtail and end fossil fuel production. Rather than focussing on fully eliminating fossil fuel production, we set out an alternative supply-side approach that involves fossil fuel producers taking progressive action to reduce and eliminate the climate impacts of their products. Our proposal uses the Paris Agreement’s framework for finance and international cooperation to establish supply-side climate action that supplements demand-side climate policies, and thereby enhance climate ambition. The concepts presented rely on large-scale geosequestration technologies that can deposit carbon into geological reservoirs at rates progressively aligned to those at which carbon is being extracted by fossil fuel producers. Convergence of extraction and deposition rates is a necessary condition for achieving net-zero CO2 emissions while still enabling some essential uses of fossil carbon. We propose an evolutionary approach to policy development, starting as a technology-support mechanism using results-based finance before transitioning into systematic measures under, firstly, Article 6 of the Paris Agreement, and, ultimately, decarbonised fuel standards. Collectively these could incentivise fossil fuel resource holders to manage both outflows and inflows of carbon in the geosphere, and in doing so, create, foster, accelerate and maintain an urgently needed market for geological CO2 storage.

A very large ensemble is used to identify subgrid-scale parameter settings for the HadCM3 model that are capable of best simulating the ocean state over the recent past (1980–2010). A simple particle filtering technique based upon the agreement of basin mean sea surface temperature (SST) and upper 700-m ocean heat content with EN3 observations is applied to an existing perturbed physics ensemble with initial conditions perturbations. A single set of subgrid-scale parameter values was identified from the wide range of initial parameter sets that gave the best agreement with ocean observations for the period studied. The parameter set, different from the standard model parameters, has a transient climate response of 1.68 K. The selected parameter set shows an improved agreement with EN3 decadal-mean SST patterns and the Atlantic meridional overturning circulation (AMOC) at 26°N as measured by the Rapid Climate Change (RAPID) array. Particle filtering techniques as demonstrated here could have a useful role in improving the starting point for traditional model-tuning exercises in coupled climate models.

In order to mobilize the volume of mitigation required to reach a global emissions path consistent with 1.5°C, policy instruments need to be much more stringent than they have been to date. They wi...

Researchers have quantified the contributions of industrialized and developing nations’ historical emissions to global surface temperature rise. Recent findings that nearly two-thirds of total industrial CO2 and CH4 emissions can be traced to 90 major industrial carbon producers have drawn attention to their potential climate responsibilities. Here, we use a simple climate model to quantify the contribution of historical (1880–2010) and recent (1980–2010) emissions traced to these producers to the historical rise in global atmospheric CO2, surface temperature, and sea level. Emissions traced to these 90 carbon producers contributed ∼57% of the observed rise in atmospheric CO2, ∼42–50% of the rise in global mean surface temperature (GMST), and ∼26–32% of global sea level (GSL) rise over the historical period and ∼43% (atmospheric CO2), ∼29–35% (GMST), and ∼11–14% (GSL) since 1980 (based on best-estimate parameters and accounting for uncertainty arising from the lack of data on aerosol forcings traced to producers). Emissions traced to seven investor-owned and seven majority state-owned carbon producers were consistently among the top 20 largest individual company contributors to each global impact across both time periods. This study lays the groundwork for tracing emissions sourced from industrial carbon producers to specific climate impacts and furthers scientific and policy consideration of their historical responsibilities for climate change.

Carbon budgets, which define the total allowable CO2 emissions associated with a given global climate target, are a useful way of framing the climate mitigation challenge. In this paper, we review the geophysical basis for the idea of a carbon budget, showing how this concept emerges from a linear climate response to cumulative CO2 emissions. We then discuss the difference between a “CO2-only carbon budget” associated with a given level of CO2-induced warming and an “effective carbon budget” associated with a given level of warming caused by all human emissions. We present estimates for the CO2-only and effective carbon budgets for 1.5 and 2 °C, based on both model simulations and updated observational data. Finally, we discuss the key contributors to uncertainty in carbon budget estimates and suggest some implications of this uncertainty for decision-making. Based on the analysis presented here, we argue that while the CO2-only carbon budget is a robust upper bound on allowable emissions for a given climate target, the size of the effective carbon budget is dependent on the how quickly we are able to mitigate non-CO2 greenhouse gas and aerosol emissions. This suggests that climate mitigation efforts could benefit from being responsive to a changing effective carbon budget over time, as well as to potential new information that could narrow uncertainty associated with the climate response to CO2 emissions.

Summer weather extremes are often associated with high‐amplitude atmospheric planetary waves (Petoukhov et al., 2013). Such conditions lead to stationary weather patterns, triggering heat waves and sometimes prolonged intense rainfall. These wave events, referred to as periods of Quasi‐Resonant Amplification (QRA), are relatively rare though and hence provide only a few data points in the meteorological record to analyse. Here, we use atmospheric models coupled to boundary conditions that have evolved slowly (i.e., climate), to supplement measurements. Specifically we assess altered probabilities of resonant episodes by employing a unique massive ensemble of atmosphere‐only climate simulations to populate statistical distributions of event occurrence. We focus on amplified waves during the two most extreme European heat waves on record, in years 2003 and 2015 (Russo et al., 2015). These years are compared with other modelled recent years (1987–2011), and critically against a modelled world without climate change. We find that there are differences in the statistical characteristics of wave event likelihood between years, suggesting a strong dependence on the known and prescribed Sea Surface Temperature (SST) patterns. The differences are larger than those projected to have occurred under climate change since the pre‐industrial period. However, this feature of small differences since pre‐industrial is based on single large ensembles, with members consisting of a range of estimates of SST adjustment from pre‐industrial to present. Such SST changes are from projections by a set of coupled atmosphere–ocean (AOGCM) climate models. When instead an ensemble for pre‐industrial estimates is subdivided into simulations according to which AOGCM the SST changes are based on, we find differences in QRA occurrence. These differences suggest that to reliably estimate changes to extremes associated with altered amplification of planetary waves, and under future raised greenhouse gas concentrations, likely requires reductions in any spread of future modelled SST patterns.

The North Atlantic Oscillation (NAO) and eddy-driven jet contain a forced component arising from sea surface temperature (SST) variations. Due to large amounts of internal variability, it is not trivial to determine where and to what extent SSTs force the NAO and jet. A linear statistical–dynamic method is employed with a large climate ensemble to compute the sensitivities of the winter and summer NAO and jet speed and latitude to the SSTs. Key regions of sensitivity are identified in the Indian and Pacific basins, and the North Atlantic tripole. Using the sensitivity maps and a long observational SST dataset, skillful reconstructions of the NAO and jet time series are made. The ability to skillfully forecast both the winter and summer NAO using only SST anomalies is also demonstrated. The linear approach used here allows precise attribution of model forecast signals to SSTs in particular regions. Skill comes from the Atlantic and Pacific basins on short lead times, while the Indian Ocean SSTs may contribute to the longer-term NAO trend. However, despite the region of high sensitivity in the Indian Ocean, SSTs here do not provide significant skill on interannual time scales, which highlights the limitations of the imposed SST approach. Given the impact of the NAO and jet on Northern Hemisphere weather and climate, these results provide useful information that could be used for improved attribution and forecasting.

We investigate the opposing effects of direct radiative forcing and sea surface warming on the atmospheric circulation using a hierarchy of models. In large ensembles of three general circulation models, direct $$\hbox {CO}_2$$ forcing produces a wavenumber 5 stationary wave over the Northern Hemisphere in summer. Sea surface warming produces a similar wave, but with the opposite sign. The waves are also present in the Coupled Model Intercomparison Project phase 5 ensemble with opposite signs due to direct $$\hbox {CO}_2$$ and sea surface warming. Analyses of tropical precipitation changes and equivalent potential temperature changes and the results from a simple barotropic model show that the wave is forced from the tropics. Key forcing locations are the Western Atlantic, Eastern Atlantic and in the Indian Ocean just off the east coast of Africa. The stationary wave has a significant impact on regional temperature anomalies in the Northern Hemisphere summer, explaining some of the direct effect that $$\hbox {CO}_2$$ concentration has on temperature extremes. Ultimately, the climate sensitivity and future changes in the land–sea temperature contrast will dictate the balance between the opposing effects on regional changes in mean and extreme temperature and precipitation under climate change.

A severe drought hit the Greater Horn of Africa (GHA) in 2014, but it remains unclear whether this extreme event was attributable to anthropogenic climate change or part of longer‐term natural cycles. Precipitation patterns are known to be changing across the GHA, but trajectories in land surface variables are much less well known. We simulated the GHA land surface environment to assess the balance between natural cycles and human‐induced climate change. Using a new form of event attribution study where we focused on both climate variables and also directly simulated land surface variables, we combined publicly volunteered distributed computing with land surface simulations to quantify land surface responses. Uncertainty was quantified both for climate model and land surface model outputs. We identified two distinct “drought trajectories” in the GHA bimodal seasonality area during the March–May (Long Rains season) of 2014. Human‐induced climate change may have resulted in regions from Lake Nalubaale (Lake Victoria) to Northern Kenya receiving less precipitation in this season and having up to 20% higher probability of drought‐level evapotranspiration rates (increasing drought). In contrast, the simulated anthropogenic climate change signal for this season induced somewhat wetter conditions and up to 20% lower probability of drought‐level evapotranspiration in Eastern Ethiopia, Southern Somalia, and coastal Kenya (decreasing drought). Uncertainties in our modeling system varied by region and variable of focus, but broadly we found that land surface simulation uncertainty neither added significantly to climate model uncertainty nor significantly reduced it., Journal of Geophysical Research: Atmospheres, 124 (8), ISSN:0148-0227, ISSN:2169-897X

Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, version 3P (HadAM3P), were implemented for the western United States at 25-km resolution. Over 136,000 valid and complete 1-yr runs have been generated to date: about 126,000 for 1960–2009 using observed sea surface temperatures (SSTs) and 10,000 for 2030–49 using projected SSTs from a global model simulation. Ensemble members differ in initial conditions, model physics, and (potentially, for future runs) SSTs. This unprecedented confluence of high spatial resolution and large ensemble size allows high signal-to-noise ratio and more robust estimates of uncertainty. This paper describes the experiment, compares model output with observations, shows select results for climate change simulations, and gives examples of the strength of the large ensemble size.

While cumulative carbon dioxide (CO2) emissions dominate anthropogenic warming over centuries, temperatures over the coming decades are also strongly affected by short-lived climate pollutants (SLCPs), complicating the estimation of cumulative emission budgets for ambitious mitigation goals. Using conventional Global Warming Potentials (GWPs) to convert SLCPs to “CO2-equivalent” emissions misrepresents their impact on global temperature. Here we show that peak warming under a range of mitigation scenarios is determined by a linear combination of cumulative CO2 emissions to the time of peak warming and non-CO2 radiative forcing immediately prior to that time. This may be understood by expressing aggregate non-CO2 forcing as cumulative CO2 forcing-equivalent (CO2-fe) emissions. We show further that contributions to CO2-fe emissions are well approximated by a new usage of GWP, denoted GWP*, which relates cumulative CO2 emissions to date with the current rate of emission of SLCPs. GWP* accurately indicates the impact of emissions of both long-lived and short-lived pollutants on radiative forcing and temperatures over a wide range of timescales, including under ambitious mitigation when conventional GWPs fail. Measured by GWP*, implementing the Paris Agreement would reduce the expected rate of warming in 2030 by 28% relative to a No Policy scenario. Expressing mitigation efforts in terms of their impact on future cumulative emissions aggregated using GWP* would relate them directly to contributions to future warming, better informing both burden-sharing discussions and long-term policies and measures in pursuit of ambitious global temperature goals.

Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM–GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation.

The United Nations Framework Convention on Climate Change (UNFCCC) has established the Warsaw International Mechanism (WIM) to deal with loss and damage associated with climate change impacts, including extreme events, in developing countries. It is not yet known whether events will need to be attributed to anthropogenic climate change to be considered under the WIM. Attribution is possible for some extreme events- a climate model assessment can estimate how greenhouse gas emissions have affected the likelihood of their occurrence. Dialogue between scientists and stakeholders is required to establish whether, and how, this science could play a role in the WIM.

The winter of 2013–14 had unusual weather in many parts of the world. Here we analyze the cold extremes that were widely reported in North America and the lack of cold extremes in western Europe. We perform a statistical analysis of cold extremes at two representative stations in these areas: Chicago, Illinois, and De Bilt, the Netherlands. This shows that the lowest minimum temperature of the winter was not very unusual in Chicago, even in the current warmer climate. Around 1950 it would have been completely normal. The same holds for multiday cold periods. Only the whole winter temperature was unusual, with a return time larger than 25 years. In the Netherlands, the opposite holds: the absence of any cold waves was highly unusual even now, and would have been extremely improbable halfway through the previous century. These results are representative of other stations in the regions. The difference is due to the skewness of the temperature distribution. In both locations, cold extremes are more likely than equally large warm extremes in winter. Severe cold outbreaks and cold winters, like the winter of 2013–14 in the Great Lakes area, are therefore not evidence against global warming: they will keep on occurring, even if they become less frequent. The absence of cold weather as observed in the Netherlands is a strong signal of a warming trend, as this would have been statistically extremely improbable in the 1950s.

The transient climate response (TCR) is a highly policy-relevant quantity in climate science. We show that recent revisions to TCR in the IPCC 5th Assessment Report have more impact on projections over the next century than revisions to the equilibrium climate sensitivity (ECS). While it is well known that upper bounds on ECS are dependent on model structure, here we show that the same applies to TCR. Our results use observations of the planetary energy budget, updated radiative forcing estimates and a number of simple climate models. We also investigate the ratio TCR:ECS, or realised warming fraction (RWF), a highly policy-relevant quantity. We show that global climate models (GCMs) don’t sample a region of low TCR and high RWF consistent with observed climate change under all simple models considered. Whether the additional constraints from GCMs are sufficient to rule out these low climate responses is a matter for further research.

Atmospheric modes of variability relevant for extreme temperature and precipitation events are evaluated in models currently being used for extreme event attribution. A 100 member initial condition ensemble of the global circulation model HadAM3P is compared with both the multi-model ensemble from the Coupled Model Inter-comparison Project, Phase 5 (CMIP5) and the CMIP5 atmosphere-only counterparts (AMIP5). The use of HadAM3P allows for huge ensembles to be computed relatively fast, thereby providing unique insights into the dynamics of extremes. The analysis focuses on mid Northern Latitudes (primarily Europe) during winter, and is compared with ERA-Interim reanalysis. The tri-modal Atlantic eddy-driven jet distribution is remarkably well captured in HadAM3P, but not so in the CMIP5 or AMIP5 multi-model mean, although individual models fare better. The well known underestimation of blocking in the Atlantic region is apparent in CMIP5 and AMIP5, and also, to a lesser extent, in HadAM3P. Pacific blocking features are well produced in all modeling initiatives. Blocking duration is biased towards models reproducing too many short-lived events in all three modelling systems. Associated storm tracks are too zonal over the Atlantic in the CMIP5 and AMIP5 ensembles, but better simulated in HadAM3P with the exception of being too weak over Western Europe. In all cases, the CMIP5 and AMIP5 performances were almost identical, suggesting that the biases in atmospheric modes considered here are not strongly coupled to SSTs, and perhaps other model characteristics such as resolution are more important. For event attribution studies, it is recommended that rather than taking statistics over the entire CMIP5 or AMIP5 available models, only models capable of producing the relevant dynamical phenomena be employed. ISSN:0930-7575 ISSN:1432-0894

Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, ‘Was the event caused by anthropogenic climate change?’ Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer's home computer. Such a set-up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events. This article introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere-only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteers' home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe. This builds confidence in the applicability of the weather@home system for event attribution studies.

The crucial question in the public debate of extreme events is increasingly whether and to what extent the event has been caused by anthropogenic warming. In this study we investigate this question using extreme summer precipitation events in England and Wales as an example for probabilistic event attribution using very large ensembles of regional climate model (RCM) simulations within the weather@home.net project. This allows us to analyse the statistics of high precipitation events in England and Wales, a region with a high quality precipitation observational dataset. Validating the model simulations against observations shows a credible shape of the distribution of 5-day precipitation, and thus confidence in the results. While the risk of extreme July precipitation events has at least doubled due to anthropogenic climate change in the modelling framework, no significant changes can be detected for the other two summer months. This study thus highlights the challenges of probabilistic event attribution of complex weather events and identifies the need to further decompose atmospheric features responsible for an event to occur for quantitative attribution analysis.

The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2 doubling in a 1% yr−1 CO2 increase experiment, ranges from 0.8 to 2.4 K EgC−1 in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)—a somewhat broader range than that found in a previous generation of carbon–climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%–95% range of 0.7–2.0 K EgC−1.

[1] This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El NiQ no Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes.

A number of studies have set out to obtain a range of atmosphere and ocean model behavior by perturbing parameters in a single climate model (perturbed physics ensemble: PPE). Early studies used shallow layer slab ocean or flux-adjusted coupled ocean-atmosphere models to obtain a broad range of behavior as characterized by climate sensitivity. A recent study reports a relatively narrow range of sensitivities (2.2–3.2°C) in a PPE of 35 coupled models without flux adjustment, raising the question whether previous broad ranges were an artifact of the use of models that were not in top-of-atmosphere (TOA) energy balance. Moreover, no PPE experiment has reported a large spread of behavior of the ocean compared to that exhibited in a multi-model ensemble (MME) such as Coupled Model Intercomparison Project phase 3 (CMIP3). In this work, we randomly perturb model parameters of a coupled ocean-atmosphere general circulation model using a space-filling design containing 10,000 combinations. The ensemble is run over the distributed computing platform of climateprediction.net under fixed pre-industrial forcing without flux adjustment. We resample a second, 20,000-member, ensemble with perturbations conditioned on the TOA fluxes from the first ensemble to not drift significantly from a realistic base state while targeting a range of behavior. Models within the targeted ensemble show realistic regional control climates when compared to the CMIP3 ensemble, although there is a bias in global mean surface temperature. The range of predicted equilibrium climate sensitivities of the targeted ensemble is substantially smaller than that obtained with flux adjustment, but larger than the range in the CMIP3 ensemble or in the 35-model un-flux-adjusted PPE in a recent study mentioned above. The Atlantic meridional overturning circulation in the targeted ensemble exhibits a spread in strength as wide as that found in the CMIP3 ensemble. We conclude that flux adjustment is not a pre-requisite for obtaining a broad spread of behavior in a perturbed physics ensemble.

This paper addresses some fundamental methodological issues concerning the sensitivity analysisof chaotic geophysical systems. We show, using the Lorenz system as an example, that anai¨ve approach to variational (‘‘adjoint’’) sensitivity analysis is of limited utility. Applied totrajectories which are long relative to the predictability time scales of the system, cumulativeerror growth means that adjoint results diverge exponentially from the ‘‘macroscopic climatesensitivity’’ (that is, the sensitivity of time-averaged properties of the system to finite-amplitudeperturbations). This problem occurs even for time-averaged quantities and given infinite computingresources. Alternatively, applied to very short trajectories, the adjoint provides an incorrectestimate of the sensitivity, even if averaged over large numbers of initial conditions, becausea finite time scale is required for the model climate to respond fully to certain perturbations.In the Lorenz (1963) system, an intermediate time scale is found on which an ensemble ofadjoint gradients can give a reasonably accurate (O(10%)) estimate of the macroscopic climatesensitivity. While this ensemble-adjoint approach is unlikely to be reliable for more complexsystems, it may provide useful guidance in identifying important parameter-combinations to beexplored further through direct finite-amplitude perturbations. DOI: 10.1034/j.1600-0870.2000.01137.x

This paper presents an update on the detection and attribution of global annual mean surface air temperature changes, using recently developed climate models. In particular, it applies a new methodology that permits the inclusion of many more general circulation models (GCMs) into the analysis, and it also includes more recent observations. This methodology involves fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. Despite considerable spread in estimated EBM parameters, characteristics of model performance, such as the transient climate response, appear to be more constrained for each of the forcings. The resulting estimated response patterns are provided as input to the standard fingerprinting method used in previous studies. The estimated GCM responses to changes in greenhouse gases are detected in the observed record for all of the GCMs, and are generally found to be consistent with the observed changes; the same is generally true for the responses to changes in stratospheric aerosols from volcanic eruptions. GCM responses to changes in tropospheric sulfate aerosols and solar irradiance also appear consistent with the observed record, although the uncertainty is larger. Greenhouse gas and solar irradiance changes are found to have contributed to a best guess of ∼0.8 and ∼0.3 K warming over the 1901–2005 period, respectively, while sulfate aerosols have contributed a ∼0.4 K cooling. This analysis provides an observationally constrained estimate of future warming, which is found to be fairly robust across GCMs. By 2100, a warming of between about 1.5 and 4.5 K can be expected according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B emissions scenario. These results indicate an emerging constraint for global mean surface temperature responses to external forcings across GCMs, which is corroborated in the observed record. This implies that observationally constrained estimates of past warming and predictions of future warming are indeed becoming robust.

The estimated range of climate sensitivity has remained unchanged for decades, resulting in large uncertainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most probabilistic estimates derived from the observed twentieth-century warming. The current generation of general circulation models are within that range but do not sample the highest values.

The leading mode of variability of the lower atmosphere circulation in the Northern Hemisphere is a largely zonally symmetric mode known as the Arctic Oscillation. We calculate Arctic Oscillation (AO) indices on a range of levels from 1000 to 10 hPa by means of a principal component analysis of National Centers for Environmental Prediction daily geopotential height anomalies. We find the apparent downward propagation of anomalies noted by other authors to be statistically significant compared to a red noise model. By examining histograms of these indices for each month, we note that the distribution of the index is generally close to Gaussian in the troposphere. In the stratosphere, however, the index is negatively skewed in the winter and positively skewed in the spring. We conclude that the positive skewness in April results from the coexistence of distinct summer and winter circulation states, and by examining polar stratospheric temperatures, we conclude that the negative skewness in January may be due to the radiatively determined limit on the vortex strength. This radiative limit responds relatively slowly to anthropogenic forcing, whereas changes in planetary wave forcing could have a much faster impact on the number of warm events. This suggests a hypothesis that the vortex strength may respond nonlinearly to anthropogenic forcing, which is supported by an observed change in the shape of the histograms of 20-200 hPa AO indices in January over the past 40 years. Copyright 2001 by the American Geophysical Union.

There is increasingly clear evidence that human influence has contributed substantially to the large-scale climatic changes that have occurred over the past few decades. Attention is now turning to the physical implications of the emerging anthropogenic signal. Of particular interest is the question of whether current climate models may be over- or under-estimating the amplitude of the climate system's response to external forcing, including anthropogenic. Evidence of a significant error in a model-simulated response amplitude would indicate the existence of amplifying or damping mechanisms that are inadequately represented in the model. The range of uncertainty in the factor by which we can scale model-simulated changes while remaining consistent with observed change provides an estimate of uncertainty in model-based predictions. With any model that displays a realistic level of internal variability, the problem of estimating this factor is complicated by the fact that it represents a ratio between two incompletely known quantities: both observed and simulated responses are subject to sampling uncertainty, primarily due to internal chaotic variability. Sampling uncertainty in the simulated response can be reduced, but not eliminated, through ensemble simulations. Accurate estimation of these scaling factors requires a modification of the standard "optimal fingerprinting" algorithm for climate change detection, drawing on the conventional "total least squares" approach discussed in the statistical literature. Code for both variants of optimal fingerprinting can be found on http://www.climateprediction.net/detection .

A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of twentieth-century warming. These estimates are then projected forward in time using a linear, compact relationship between twentieth-century warming and twenty-first-century warming. This relationship is established from a large ensemble of energy balance models. By varying the energy balance model parameters an estimate is made of the error associated with using the linear relationship in forecasts of twentieth-century global mean temperature. Including this error has very little impact on the forecasts. There is a 50% chance that the global mean temperature change between 1995 and 2035 will be greater than 1.5 K for the Special Report on Emissions Scenarios (SRES) A1FI scenario. Under SRES B2 the same threshold is not exceeded until 2055. These results should be relatively robust to model developments for a given radiative forcing history.

[1] A methodology for constraining climate forecasts, developed for application to the multithousand member perturbed physics ensemble of simulations completed by the distributed computing project ClimatePrediction.net, is here presented in detail. The methodology is extended to produce constrained forecasts of mean surface temperature and precipitation within 21 land-based regions and is validated with climate simulations from other models available from the IPCC (AR4) data set. The mean forecasted values of temperature and precipitation largely confirm prior results for the same regions. In particular, precipitation in the Mediterranean basin is shown to decrease and temperature over northern Europe is shown to increase with comparatively little uncertainty in the forecast (i.e., with tight constraints). However, in some cases the forecasts show large uncertainty, and there are a few cases where the forecasts cannot be constrained at all. These results illustrate the effectiveness of the methodology and its applicability to regional climate variables.

Tropical instability waves in a primitive equation model of the tropical Pacific Ocean, forced with analyzed wind stresses updated daily, show unexpectedly close phase correspondence with observation through the latter half of 1992. This suggests that these waves are not pure instabilities developing from infinitesimal disturbances, but that their phases and phase speeds are at least partially determined by the wind stress forcing. To quantify and explain this observation, we perfomed several numerical experiments, which indicate that remotely forced Rossby waves can influence both the phase and phase speed of tropical instability waves. We suggest that a remote wind forcing determines the high model/observation phase correspondence of tropical instability waves through a relatively realistic simulation of equatorial Kelvin and Rossby wave activity.

The possibility that a drying of the tropical upper troposphere might accompany tropospheric warming, substantially reducing the climate's sensitivity to a change in radiative forcing, has been subject to extensive debate. A simple one-dimensional model of tropical convection and humidity originally proposed by Lindzen (1990a) and Sun (1990) is used to explore mechanisms for “cumulus-induced drying,” and a narrowband radiation model to investigate its radiative implications. Low-level moistening, which accompanies warming in all cases, significantly offsets the radiative effects of any upper-level drying, making the net effect on radiative forcing critically sensitive to undetermined parameters in the model. When the model is tuned to reproduce mean relative humidities from Pacific radiosonde data, no substantial drying is observed to accompany a 1 K tropospheric warming. However, the clear-sky moisture feedback is in all cases substantially less than that suggested by a scheme in which relative humidity is held constant, illustrating the importance of upper tropospheric relative humidity for climate sensitivity. Finally, the feasibility of placing constraints on critical parameters through satellite observations of interannual variability of the tropical clear-sky radiation field is investigated.

Realistic simulation of the internal variability of the climate system is important both for climate change detection and as an indicator of whether the physics of the climate system is well-represented in a climate model. In this work zonal mean atmospheric temperatures from a control run of the second Hadley Centre coupled GCM are compared with gridded radiosonde observations for the past 38 years to examine how well modelled and observed variability agree. On time scales of between six months and twenty years, simulated and observed variability of global mean temperatures agree well for the troposphere, but in the equatorial stratosphere variability is lower in the model than in the observations, particularly at periods of two years and seven to twenty years. We find good agreement between modelled and observed variability in the mass-weighted amplitude of a forcing-response pattern, as used for climate change detection, but variability in a signal-to-noise optimised fingerprint pattern is significantly greater in the observations than in a model control run. This discrepancy is marginally consistent with anthropogenic forcing, but more clearly explained by a combination of solar and volcanic forcing, suggesting these should be considered in future `vertical detection' studies. When the relationship between tropical lapse rate and mean temperature was examined, it was found that these quantities are unrealistically coherent in the model at periods above three years. However, there is a clear negative lapse rate feedback in both model and observations: as the tropical troposphere warms, the mid-tropospheric lapse rate decreases on all the time scales considered.

Current approaches to the detection and attribution of an anthropogenic influence on climate involve quantifying the level of agreement between model-predicted patterns of externally forced change and observed changes in the recent climate record. Analyses of uncertainty rely on simulated variability from a climate model. Any numerical representation of the climate is likely to display too little variance on small spatial scales, leading to a risk of spurious detection results. The risk is particularly severe if the detection strategy involves optimisation of signal-to-noise because unrealistic aspects of model variability may automatically be given high weight through the optimisation. The solution is to confine attention to aspects of the model and of the real climate system in which the model simulation of internal climate variability is adequate, or, more accurately, cannot be shown to be deficient. We propose a simple consistency check based on standard linear regression which can be applied to both the space-time and frequency domain approaches to optimal detection and demonstrate the application of this check to the problem of detection and attribution of anthropogenic signals in the radiosonde-based record of recent trends in atmospheric vertical temperature structure. The influence of anthropogenic greenhouse gases can be detected at a high confidence level in this diagnostic, while the combined influence of anthropogenic sulphates and stratospheric ozone depletion is less clearly evident. Assuming the time-scales of the model response are correct, and neglecting the possibility of non-linear feedbacks, the amplitude of the observed signal suggests a climate sensitivity range of 1.2–3.4 K, although the upper end of this range may be underestimated by up to 25% due to uncertainty in model-predicted response patterns.

We investigate the atmospheric response to doubled CO2 and stratospheric ozone depletion in three versions of a general-circulation model with differing vertical resolution and upper-boundary heights. We find that an approximate doubling of the vertical resolution below 10 hPa reduces the temperature response to a doubling of CO2 from 3.4 K to 2.5 K. Much of this difference is associated with changes in the cloud response. All model versions show an increase in the Arctic Oscillation index in response to a doubling of CO2, but the increase is no larger in the model with an upper boundary at 0.01 hPa than in the standard model with a top level at 5 hPa. All models also show general stratospheric cooling in response to doubling CO2. However, unlike some other authors, we find no cooling in the Arctic winter vortex below around 10 hPa in the stratosphere-resolving model, and a weakening of the zonal winds throughout this region. This effect is due to enhanced upward propagation of planetary waves from the troposphere, and is an effect found only in the northern hemisphere, probably because of its larger zonal asymmetries. All models show a small but significant surface cooling in response to a reconstruction of 1998 stratospheric ozone depletion, and an increase in the Antarctic Oscillation index in the southern summer. The cooling extends through most of the atmosphere, and reaches a maximum in the region of the Antarctic ozone hole in November and December. © Royal Meteorological Society, 2003. K. D. Williams's contribution is Crown copyright.

The role of stratospheric ozone changes in determining the climate response to solar forcing is investigated using a version of the Unified Model from the UK Meteorological Office which includes a mixed-layer ocean of constant depth (HadSM3). Two experiments have been performed, both of which include a wavelength-dependent reduction in total solar irradiance (TSI) of 7.5 W m(-2) (0.55%). The second experiment includes, in addition, an estimate of the accompanying stratospheric ozone changes. A large change in TSI is used (approximately a factor of two greater than the 'best-guess' change between present mean levels and the Maunder Minimum) to demonstrate the sensitivity of the climate system to this forcing. Results show that in the annual mean, the temperature response of the model is enhanced by the inclusion of the ozone changes, by approximately 15-20%. We compare results from our TSI and ozone experiment to those of Shindell et al. [Science 294 (2001) 2149] who performed a similar study with the GISS GCM. Temperature changes are greater in our simulation, as expected from the larger magnitude forcing, however the circulation response is very different: our results do not resemble the Arctic Oscillation, whilst those of Shindell et al. [loc. cit.] project strongly onto this leading mode of variability. The lack of a fully resolved stratosphere in our model is a potential reason for this distinction. To test this possibility, we repeated our combined irradiance and ozone experiment using identical model formulations, but with different vertical extents: the first extends to a height of 5 hPa, the second to 0.01 hPa. Both simulations produce a relatively weak surface pressure response to solar forcing that does not strongly resemble the Arctic Oscillation. (C) 2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

We present a method for constraining key properties of the climate system that are important for climate prediction (climate sensitivity and rate of heat penetration into the deep ocean) by comparing a model's response to known forcings over the twentieth century against climate observations for that period. We use the MIT 2D climate model in conjunction with results from the Hadley Centre's coupled atmosphere-ocean general circulation model (AOGCM) to determine these constraints. The MIT 2D model, which is a zonally averaged version of a 3D GCM, can accurately reproduce the global-mean transient response of coupled AOGCMs through appropriate choices of the climate sensitivity and the effective rate of diffusion of heat anomalies into the deep ocean. Vertical patterns of zonal mean temperature change through the troposphere and lower stratosphere also compare favorably with those generated by 3-D GCMs. We compare the height-latitude pattern of temperature changes as simulated by the MIT 2D model with observed changes, using optimal finger-print detection statistics. Using a linear regression model as in Allen and Tett this approach yields an objective measure of model-observation goodness-of-fit (via the residual sum of squares weighted by differences expected due to internal variability). The MIT model permits one to systematically vary the model's climate sensitivity (by varying the strength of the cloud feedback) and rate of mixing of heat into the deep ocean and determine how the goodness-of-fit with observations depends on these factors. This provides an efficient framework for interpreting detection and attribution results in physical terms. With aerosol forcing set in the middle of the IPCC range, two sets of model parameters are rejected as being implausible when the model response is compared with observations. The first set corresponds to high climate sensitivity and slow heat uptake by the deep ocean. The second set corresponds to low sensitivities for all magnitudes of heat uptake. These results demonstrate that fingerprint patterns must be carefully chosen, if their detection is to reduce the uncertainty of physically important model parameters which affect projections of climate change.

We assess the most probable causes of late twentieth century (1960-1994) tropospheric temperature changes. Optimal detection techniques are used to compare observed spatio-temporal patterns of near-surface and tropospheric temperature change with results from experiments performed with two different versions of the Hadley Centre climate model. We detect anthropogenic forcings, particularly well-mixed greenhouse-gases, with a less certain sulfate aerosol cooling influence. More limited evidence exists for a detectable volcanic influence. Our principal results do not depend upon the choice of model. Both models, but particularly HadCM3, appear to overestimate the simulated climate response to greenhouse gases (especially at the surface) and volcanoes. This result may arise, at least in part, due to errors in the forcings (especially sulfate) and technical details of our approach, which differs from previous studies. We use corrected and uncorrected versions of the radiosonde record to assess sensitivity of our detection results to observational uncertainties. We find that previous corrections applied to the radiosonde temperature record are likely to have been sub-optimal in only taking into account temporal consistency. However, the choice of corrected or uncorrected version has no systematic effect upon our main conclusions. We show that both models are potentially internally consistent explanations of observed tropospheric temperatures.

Changes in near surface air temperature (ΔT) in response to anthropogenic greenhouse gas forcing are expected to show spatial heterogeneity because energy and moisture fluxes are modulated by features of the landscape that are also heterogeneous at these spatial scales. Detecting statistically meaningful heterogeneity requires a combination of high spatial resolution and a large number of simulations. To investigate spatial variability of projected ΔT, we generated regional, high-resolution (25-km horizontal), large ensemble (100 members per year), climate simulations of western United States (US) for the periods 1985 – 2014 and 2030 – 2059, the latter with atmospheric constituent concentrations from the Representative Concentration Pathway 4.5. Using the large ensemble, 95% confidence interval sizes for grid-cell-scale temperature responses were on the order of 0.1 °C, compared to 1 °C from a single ensemble member only. In both winter and spring, the snow-albedo feedback statistically explains roughly half of the spatial variability in 'T. Simulated decreases in albedo exceed 0.1 in places, with rates of change in T per 0.1 decrease in albedo ranging from 0.3 to 1.4 °C. In summer, ΔT pattern in the northwest US is correlated with the pattern of decreasing precipitation. In all seasons, changing lapse rates in the low-to-middle troposphere may account for up to 0.2 °C differences in warming across the western US. Near the coast, a major control of spatial variation is the differential warming between sea and land.

In 2013 the Warsaw International Mechanism (WIM) for loss and damage (L&D) associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). For scientists, L&D raises questions around the extent that such impacts can be attributed to anthropogenic climate change, which may generate complex results and be controversial in the policy arena. This is particularly true in the case of probabilistic event attribution (PEA) science, a new and rapidly evolving field that assesses whether changes in the probabilities of extreme events are attributable to GHG emissions. If the potential applications of PEA are to be considered responsibly, dialogue between scientists and policy makers is fundamental. Two key questions are considered here through a literature review and key stakeholder interviews with representatives from the science and policy sectors underpinning L&D. These provided the opportunity for in-depth insights into stakeholders’ views on firstly, how much is known and understood about PEA by those associated with the L&D debate? Secondly, how might PEA inform L&D and wider climate policy? Results show debate within the climate science community, and limited understanding among other stakeholders, around the sense in which extreme events can be attributed to climate change. However, stakeholders do identify and discuss potential uses for PEA in the WIM and wider policy, but it remains difficult to explore precise applications given the ambiguity surrounding L&D. This implies a need for stakeholders to develop greater understandings of alternative conceptions of L&D and the role of science, and also identify how PEA can best be used to support policy, and address associated challenges. Policy relevance The WIM was established to address the negative impacts of climate change, but whether attribution evidence will be required to link impacts to climate change is yet to be determined, and also controversial. Stakeholders show little awareness of PEA and agreement on its role, which raises important questions for policy. Dialogue between policymakers, practitioners and scientists could help to build a broader understanding of PEA, to determine whether it is relevant, and facilitate both its development and WIM high level decision-making processes.

In response to their respective opinion articles, I argue that Kevin Trenberth's proposal to reverse the burden of proof in attribution studies is misguided, but Judith Curry's counter proposal to abandon hypothesis tests as useless is worse still. Some observed weather events will have been made more likely by human influence on climate, some less likely, and it is a legitimate and very important field of scientific enquiry to work out which are which. The appropriate null hypothesis to use in such studies is that human influence has not increased the probability of occurrence of a particular weather event unless the evidence suggests otherwise. WIREs Clim Change 2011, 2:931–934. doi: 10.1002/wcc.145 For further resources related to this article, please visit the WIREs website.

Key mechanisms important for the simulation and better understanding of the precipitation of the North American monsoon (NAM) were analyzed in this paper. Three experiments with the Providing Regional Climates for Impacts Studies (PRECIS) regional climate model, the Hadley Centre Regional Model version 3P (HadRM3P), driven by different boundary conditions were carried out. After a detailed analysis of the moisture and low-level winds derived from the models, the authors conclude that the Gulf of Mexico (GoM) moisture and the Great Plains low-level jet (GPLLJ) play an important role in the northern portion of the NAM. Moreover, the realistic simulation of these features is necessary for a better simulation of precipitation in the NAM. Previous works suggest that the influence of moisture from the GoM in Arizona–New Mexico (AZNM) takes place primarily via the middle- and upper-tropospheric flow (above 700 mb). However, it is shown here that if the GoM does not supply enough moisture and the GPLLJ at lower levels (below 700 mb) does not reach the AZNM region, then a dry westerly flow dominates that area and the summer precipitation is below normal. The implications of these findings for studies of climate change are demonstrated with the analysis of two general circulation models (GCMs) commonly used for climate change prediction, which are shown not to reproduce correctly the GPLLJ intensity nor the moisture in the GoM. This implies that the precipitation in AZNM would not be correctly represented by a regional model driven by these GCMs.

A tropospheric energy budget argument is used to analyze twentieth-century precipitation changes. It is found that global and ocean-mean general circulation model (GCM) precipitation changes can be understood as being due to the competing direct and surface-temperature-dependent effects of external climate forcings. In agreement with previous work, precipitation is found to respond more strongly to anthropogenic and volcanic sulfate aerosol and solar forcing than to greenhouse gas and black carbon aerosol forcing per unit temperature. This is due to the significant direct effects of greenhouse gas and black carbon forcing. Given that the relative importance of different forcings may change in the twenty-first century, the ratio of global precipitation change to global temperature change may be quite different. Differences in GCM twentieth- and twenty-first-century values are tractable via the energy budget framework in some, but not all, models. Changes in land-mean precipitation, on the other hand, cannot be understood at all with the method used here, even if land–ocean heat transfer is considered. In conclusion, the tropospheric energy budget is a useful concept for understanding the precipitation response to different forcings but it does not fully explain precipitation changes even in the global mean.

A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully. The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation. The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.

Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas–induced climate change, since a substantial fraction of the variance in these reconstructions from the beginning of the analysis in the late thirteenth century to the end of the records can be attributed to external forcing. Results from a detection and attribution analysis show that greenhouse warming is detectable in all analyzed high-variance reconstructions (with the possible exception of one ending in 1925), and that about a third of the warming in the first half of the twentieth century can be attributed to anthropogenic greenhouse gas emissions. The estimated magnitude of the anthropogenic signal is consistent with most of the warming in the second half of the twentieth century being anthropogenic.

The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques.

A combined modeling and observational study suggests that the persistent rainfall deficit during the 2014 rainy season in southern Levant was made more likely due to anthropogenic climate change.

A significant influence of anthropogenic forcing has been detected in global- and continental-scale surface temperature, temperature of the free atmosphere, and global ocean heat uptake. This paper reviews outstanding issues in the detection of climate change and attribution to causes. The detection of changes in variables other than temperature, on regional scales and in climate extremes, is important for evaluating model simulations of changes in societally relevant scales and variables. For example, sea level pressure changes are detectable but are significantly stronger in observations than the changes simulated in climate models, raising questions about simulated changes in climate dynamics. Application of detection and attribution methods to ocean data focusing not only on heat storage but also on the penetration of the anthropogenic signal into the ocean interior, and its effect on global water masses, helps to increase confidence in simulated large-scale changes in the ocean.To evaluate climate change signals with smaller spatial and temporal scales, improved and more densely sampled data are needed in both the atmosphere and ocean. Also, the problem of how model-simulated climate extremes can be compared to station-based observations needs to be addressed.