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Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models. In this study, the relationship between climate sensitivity and different measures of the present-day climatology is investigated. First, it is shown that 1) a variable proposed earlier that is based on interannual variation of seasonal temperature and 2) the seasonal cycle amplitude are unable to constrain the range of climate sensitivity beyond what was initially covered by the ensemble. Second, it is illustrated how model calibration helps to reveal potentially useful relationships but might also complicate the interpretation of multimodel results. As a consequence, when ensembles are small, when models are neither independently developed nor structurally identical, when observations are likely to have been used in the model development and evaluation process, and when the interpretation of the relationships across models in terms of well-understood physical processes is not obvious, care should be taken when using relationships across models to constrain model projections. This study demonstrates the pitfalls that might occur if emergent statistical relationships are prematurely interpreted as an effective constraint on projected global or regional climate change.

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of ‘model fit’; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections., WIREs Climate Change, 8 (3), ISSN:1757-7780, ISSN:1757-7799

The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above preindustrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. In this study, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2 °C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 and 2 °C levels and an overshoot 1.5 °C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. Here we describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 % greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and 1 in 40 years in the 1.5 °C scenario. Significance of impact differences with respect to multi-model variability is not assessed., Earth System Dynamics, 8 (3), ISSN:2190-4987, ISSN:2190-4979

Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.

Understanding changes in precipitation variability is essential for a complete explanation of the hydrologic cycle’s response to warming and its impacts. While changes in mean and extreme precipitation have been studied intensively, precipitation variability has received less attention, despite its theoretical and practical importance. Here, we show that precipitation variability in most climate models increases over a majority of global land area in response to warming (66% of land has a robust increase in variability of seasonal-mean precipitation). Comparing recent decades to RCP8.5 projections for the end of the 21st century, we find that in the global, multi-model mean, precipitation variability increases 3–4% K−1 globally, 4–5% K−1 over land and 2–4% K−1 over ocean, and is remarkably robust on a range of timescales from daily to decadal. Precipitation variability increases by at least as much as mean precipitation and less than moisture and extreme precipitation for most models, regions, and timescales. We interpret this as being related to an increase in moisture which is partially mitigated by weakening circulation. We show that changes in observed daily variability in station data are consistent with increased variability., Scientific Reports, 7, ISSN:2045-2322

There are large uncertainties associated with the projection of climate extremes. This study shows that the uncertainties are mainly due to internal climate variability. However, model projections are consistent when averaged across regions, allowing robust projection of future extremes.

This is a corrigendum for the article 2015 Environ. Res. Lett. 10 075003

Between about 1998 and 2012, a time that coincided with political negotiations for preventing climate change, the surface of Earth seemed hardly to warm. This phenomenon, often termed the 'global warming hiatus', caused doubt in the public mind about how well anthropogenic climate change and natural variability are understood. Here we show that apparently contradictory conclusions stem from different definitions of 'hiatus' and from different datasets. A combination of changes in forcing, uptake of heat by the oceans, natural variability and incomplete observational coverage reconciles models and data. Combined with stronger recent warming trends in newer datasets, we are now more confident than ever that human influence is dominant in long-term warming.

[1] Complexity and resolution of global climate models are steadily increasing, yet the uncertainty of their projections remains large, particularly for precipitation. Given the impacts precipitation changes have on ecosystems, there is a need to reduce projection uncertainty by assessing the performance of climate models. A common way of evaluating models is to consider global maps of errors against observations for a range of variables. However, depending on the purpose, feature-based metrics defined on a regional scale and for one variable may be more suitable to identify the most accurate models. We compare three different ways of ranking the CMIP3 climate models: errors in a broad range of climate variables, errors in global field of precipitation, and regional features of modeled precipitation in areas where pronounced future changes are expected. The same analysis is performed for temperature to identify potential differences between variables. The multimodel mean is found to outperform all single models in the global field-based rankings but performs only averagely for the feature-based ranking. Selecting the best models for each metric reduces the absolute spread in projections. If anomalies are considered, the model spread is reduced in a few regions, while the uncertainty can be increased in others. We also demonstrate that the common attribution of a lack of model agreement in precipitation projections to different model physics may be misleading. Agreement is similarly poor within different ensemble members of the same model, indicating that the lack of robust trends can be attributed partly to a low signal-to-noise ratio.

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.

More than 100 countries have adopted a global warming limit of 2 degrees C or below (relative to pre-industrial levels) as a guiding principle for mitigation efforts to reduce climate change risks, impacts and damages. However, the greenhouse gas (GHG) emissions corresponding to a specified maximum warming are poorly known owing to uncertainties in the carbon cycle and the climate response. Here we provide a comprehensive probabilistic analysis aimed at quantifying GHG emission budgets for the 2000-50 period that would limit warming throughout the twenty-first century to below 2 degrees C, based on a combination of published distributions of climate system properties and observational constraints. We show that, for the chosen class of emission scenarios, both cumulative emissions up to 2050 and emission levels in 2050 are robust indicators of the probability that twenty-first century warming will not exceed 2 degrees C relative to pre-industrial temperatures. Limiting cumulative CO(2) emissions over 2000-50 to 1,000 Gt CO(2) yields a 25% probability of warming exceeding 2 degrees C-and a limit of 1,440 Gt CO(2) yields a 50% probability-given a representative estimate of the distribution of climate system properties. As known 2000-06 CO(2) emissions were approximately 234 Gt CO(2), less than half the proven economically recoverable oil, gas and coal reserves can still be emitted up to 2050 to achieve such a goal. Recent G8 Communiques envisage halved global GHG emissions by 2050, for which we estimate a 12-45% probability of exceeding 2 degrees C-assuming 1990 as emission base year and a range of published climate sensitivity distributions. Emissions levels in 2020 are a less robust indicator, but for the scenarios considered, the probability of exceeding 2 degrees C rises to 53-87% if global GHG emissions are still more than 25% above 2000 levels in 2020.

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally-constrained projections of future climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include: new historical simulations of aerosols-only, stratospheric-ozone-only, CO2-only, solar-only and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing; future single forcing experiments, allowing observationally-constrained projections of future climate change; and an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing.

Several methods exist to estimate the cumulative carbon emissions that would keep global warming to below a given temperature limit. Here we review estimates reported by the IPCC and the recent literature, and discuss the reasons underlying their differences. The most scientifically robust number — the carbon budget for CO2-induced warming only — is also the least relevant for real-world policy. Including all greenhouse gases and using methods based on scenarios that avoid instead of exceed a given temperature limit results in lower carbon budgets. For a >66% chance of limiting warming below the internationally agreed temperature limit of 2 °C relative to pre-industrial levels, the most appropriate carbon budget estimate is 590–1,240 GtCO2 from 2015 onwards. Variations within this range depend on the probability of staying below 2 °C and on end-of-century non-CO2 warming. Current CO2 emissions are about 40 GtCO2 yr−1, and global CO2 emissions thus have to be reduced urgently to keep within a 2 °C-compatible budget.

Updated models are being used for the new assessment report from the Intergovernmental Panel on Climate Change. This study compares projections from the latest models with those from earlier versions. The spread of results has not changed significantly, and some of the spread will always remain due to the internal variability of the climate system. As models improve, they are able to represent more processes in greater detail, allowing for greater confidence in their projections, in spite of model spread.

As climate models improve, decision-makers' expectations for accurate climate predictions are growing. Natural climate variability, however, limits climate predictability and hampers the ability to guide adaptation in many regions such as North America. Scientists, policymakers and the public need to improve communication and avoid raising expectations for accurate regional predictions everywhere.

This study investigates uncertainties in impact assessments when using climate projections. The uncertainties in health-related metrics combining temperature and humidity are much smaller than if the uncertainties in the two variables were independent. The finding reveals the potential for joint assessment of projection uncertainties in other variables used in impact studies.

Models and scenarios on which climate projection are based vary between IPCC reports. To facilitate meaningful comparison, this study provides probabilistic climate projections for different scenarios in a single consistent framework, incorporating the overall consensus understanding of the uncertainty in climate sensitivity, and constrained by the observed historical warming.

The Arctic sea ice cover declined over the last few decades and reached a record minimum in 2007, with a slight recovery thereafter. Inspired by this the authors investigate the response of atmospheric and oceanic properties to a 1-yr period of reduced sea ice cover. Two ensembles of equilibrium and transient simulations are produced with the Community Climate System Model. A sea ice change is induced through an albedo change of 1 yr. The sea ice area and thickness recover in both ensembles after 3 and 5 yr, respectively. The sea ice anomaly leads to changes in ocean temperature and salinity to a depth of about 200 m in the Arctic Basin. Further, the salinity and temperature changes in the surface layer trigger a “Great Salinity Anomaly” in the North Atlantic that takes roughly 8 yr to travel across the North Atlantic back to high latitudes. In the atmosphere the changes induced by the sea ice anomaly do not last as long as in the ocean. The response in the transient and equilibrium simulations, while similar overall, differs in specific regional and temporal details. The surface air temperature increases over the Arctic Basin and the anomaly extends through the whole atmospheric column, changing the geopotential height fields and thus the storm tracks. The patterns of warming and thus the position of the geopotential height changes vary in the two ensembles. While the equilibrium simulation shifts the storm tracks to the south over the eastern North Atlantic and Europe, the transient simulation shifts the storm tracks south over the western North Atlantic and North America. The authors propose that the overall reduction in sea ice cover is important for producing ocean anomalies; however, for atmospheric anomalies the regional location of the sea ice anomalies is more important. While observed trends in Arctic sea ice are large and exceed those simulated by comprehensive climate models, there is little evidence based on this particular model that the seasonal loss of sea ice (e.g., as occurred in 2007) would constitute a threshold after which the Arctic would exhibit nonlinear, irreversible, or strongly accelerated sea ice loss. Caution should be exerted when extrapolating short-term trends to future sea ice behavior.

The formal detection of climate warming and its attribution to human influence has so far relied on the differences between natural and anthropogenic warming patterns. An alternative and entirely independent attribution method that relies on the principle of conservation of energy instead, confirms greenhouse gas warming by 0.85 °C since the mid-twentieth century, half of which was offset by aerosol cooling.

Regional projections of future climate with associated uncertainty estimates are increasingly being demanded. Generally, such scenarios rely on a finite number of model projections and are accompanied by considerable uncertainties which cannot be fully quantified. Consequently, probabilistic climate projections are conditioned on several subjective assumptions which can be treated in a Bayesian framework. In this study, a recently developed Bayesian multi-model combination algorithm is applied to regional climate model simulations from the ENSEMBLES project to generate probabilistic projections for Switzerland. The seasonal temperature and precipitation scenarios are calculated relative to 1980–2009 for three 30-year scenario periods (centred at 2035, 2060, and 2085), three regions, and the A1B emission scenario. Projections for two further emission scenarios are obtained by pattern scaling. Key to the Bayesian algorithm is the determination of prior distributions about climatic parameters. It is shown that the prior choice of model projection uncertainty ultimately determines the uncertainty in the climate change signal. Here, we assume that model uncertainty is fully sampled by the climate models available. We have extended the algorithm such that internal decadal variability is also included in all scenario calculations. The A1B scenarios show a significant rise in temperature increasing from 0.9–1.4 °C by 2035 (depending upon region and season), to 2.0–2.9 °C by 2060, and to 2.7–4.1 °C by 2085. Mean precipitation changes are subject to large uncertainties with median changes close to zero. Significant signals are seen towards the end of the century with a summer drying of 18–24% depending on region, and a likely increase of winter precipitation in Switzerland south of the Alps. The A2 scenario implies a warming of 3.2–4.8 °C, and a summer drying of 21–28% by 2085, while in case of the mitigation scenario RCP3PD, climate change could be stabilized to 1.2–1.8 °C of warming and 8–10% of drying. Copyright © 2011 Royal Meteorological Society

About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution. These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.

The estimated range of climate sensitivity, the equilibrium warming resulting from a doubling of the atmospheric carbon dioxide concentration, has not decreased substantially in past decades. New statistical methods for estimating the climate sensitivity have been proposed and provide a better quantification of relative probabilities of climate sensitivity within the almost canonical range of 2–4.5 K; however, large uncertainties remain, in particular for the upper bound. Simple indices of spatial radiation patterns are used here to establish a relationship between an observable radiative quantity and the equilibrium climate sensitivity. The indices are computed for the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset and offer a possibility to constrain climate sensitivity by considering radiation patterns in the climate system. High correlations between the indices and climate sensitivity are found, for example, in the cloud radiative forcing of the incoming longwave surface radiation and in the clear-sky component of the incoming surface shortwave flux, the net shortwave surface budget, and the atmospheric shortwave attenuation variable β. The climate sensitivity was estimated from the mean of the indices during the years 1990–99 for the CMIP3 models. The surface radiative flux dataset from the Clouds and the Earth’s Radiant Energy System (CERES) together with its top-of-atmosphere Energy Balanced and Filled equivalent (CERES EBAF) are used as a reference observational dataset, resulting in a best estimate for climate sensitivity of 3.3 K with a likely range of 2.7–4.0 K. A comparison with other satellite and reanalysis datasets show similar likely ranges and best estimates of 1.7–3.8 (3.3 K) [Earth Radiation Budget Experiment (ERBE)], 2.9–3.7 (3.3 K) [International Satellite Cloud Climatology Project radiative surface flux data (ISCCP-FD)], 2.8–4.1 (3.5 K) [NASA’s Modern Era Retrospective-Analysis for Research and Application (MERRA)], 3.0–4.2 (3.6 K) [Japanese 25-yr Reanalysis (JRA-25)], 2.7–3.9 (3.4 K) [European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim)], 3.0–4.0 (3.5 K) [ERA-40], and 3.1–4.7 (3.6 K) for the NCEP reanalysis. For each individual reference dataset, the results suggest that values for the sensitivity below 1.7 K are not likely to be consistent with observed radiation patterns given the structure of current climate models. For the aggregation of the reference datasets, the climate sensitivity is not likely to be below 2.9 K within the framework of this study, whereas values exceeding 4.5 K cannot be excluded from this analysis. While these ranges cannot be interpreted properly in terms of probability, they are consistent with other estimates of climate sensitivity and reaffirm that the current climatology provides a strong constraint on the lower bound of climate sensitivity even in a set of structurally different models.

Paleoclimate evidence and climate models indicate that certain elements of the climate system may exhibit thresholds, with small changes in greenhouse gas emissions resulting in non-linear and potentially irreversible regime shifts with serious consequences for socio-economic systems. Such thresholds or tipping points in the climate system are likely to depend on both the magnitude and rate of change of surface warming. The collapse of the Atlantic thermohaline circulation (THC) is one example of such a threshold. To evaluate mitigation policies that curb greenhouse gas emissions to levels that prevent such a climate threshold being reached, we use the MERGE model of Manne, Mendelsohn and Richels. Depending on assumptions on climate sensitivity and technological progress, our analysis shows that preserving the THC may require a fast and strong greenhouse gas emission reduction from today's level, with transition to nuclear and/or renewable energy, possibly combined with the use of carbon capture and sequestration systems.

Multimodel combination is a pragmatic approach to estimating model uncertainties and to making climate projections more reliable. The simplest way of constructing a multimodel is to give one vote to each model (“equal weighting”), while more sophisticated approaches suggest applying model weights according to some measure of performance (“optimum weighting”). In this study, a simple conceptual model of climate change projections is introduced and applied to discuss the effects of model weighting in more generic terms. The results confirm that equally weighted multimodels on average outperform the single models, and that projection errors can in principle be further reduced by optimum weighting. However, this not only requires accurate knowledge of the single model skill, but the relative contributions of the joint model error and unpredictable noise also need to be known to avoid biased weights. If weights are applied that do not appropriately represent the true underlying uncertainties, weighted multimodels perform on average worse than equally weighted ones, which is a scenario that is not unlikely, given that at present there is no consensus on how skill-based weights can be obtained. Particularly when internal variability is large, more information may be lost by inappropriate weighting than could potentially be gained by optimum weighting. These results indicate that for many applications equal weighting may be the safer and more transparent way to combine models. However, also within the presented framework eliminating models from an ensemble can be justified if they are known to lack key mechanisms that are indispensable for meaningful climate projections.

Global climate policy is increasingly debating the value of very low warming targets, yet not many experiments conducted with global climate models in their fully coupled versions are currently available to help inform studies of the corresponding impacts. This raises the question whether a map of warming or precipitation change in a world 1.5 °C warmer than preindustrial can be emulated from existing simulations that reach higher warming targets, or whether entirely new simulations are required. Here we show that also for this type of low warming in strong mitigation scenarios, climate change signals are quite linear as a function of global temperature. Therefore, emulation techniques amounting to linear rescaling on the basis of global temperature change ratios (like simple pattern scaling) provide a viable way forward. The errors introduced are small relative to the spread in the forced response to a given scenario that we can assess from a multi-model ensemble. They are also small relative to the noise introduced into the estimates of the forced response by internal variability within a single model, which we can assess from either control simulations or initial condition ensembles. Challenges arise when scaling inadvertently reduces the inter-model spread or suppresses the internal variability, both important sources of uncertainty for impact assessment, or when the scenarios have very different characteristics in the composition of the forcings. Taking advantage of an available suite of coupled model simulations under low-warming and intermediate scenarios, we evaluate the accuracy of these emulation techniques and show that they are unlikely to represent a substantial contribution to the total uncertainty., Environmental Research Letters, 13 (5), ISSN:1748-9326, ISSN:1748-9318

Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal—for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate “good” and “bad” models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.

Climatic Change, 103 (3-4), ISSN:0165-0009, ISSN:1573-1480

Imagine you are hosting a garden party tomorrow and you are trying to decide whether or not to put up a tent against the rain. You read the weather forecast in the newspaper and you ask the farmer next door, and you look at the sky (knowing that persistence is often not a bad weather forecast). So you get three predictions, but how would you aggregate them? Would you average them with equal weight? You might trust the forecast model more (or less) than the farmer, not because you understand how either of them generates their prediction, but because of your past experience in similar situations. But why seek advice from more than one source in the first place? We intuitively assume that the combined information from multiple sources improves our understanding and therefore our ability to decide. Now having read one newspaper forecast already, would a second and a third one increase your confidence? That seems unlikely, because you know that all newspaper forecasts are based on one of only a few numerical weather prediction models. Now once you have decided on a set of forecasts, and irrespective of whether they agree or not, you will have to synthesize the different pieces of information and decide about the tent for the party. The optimal decision probably involves more than just the most likely prediction. If the damage without the tent is likely to be large, and if putting up the tent is easy, then you might go for the tent in a case of large prediction uncertainty even if the most likely outcome is no rain. Although it may seem far-fetched at first, the problem of climate projection is in fact similar in many respects to the garden party situation discussed above. So far, projections from multiple climate models were often aggregated into simple averages, standard deviations and ranges. One example is the recent Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC)

Climate Dynamics, 35 (4), ISSN:0930-7575, ISSN:1432-0894

Why should we believe predictions of climate change over the next century if we cannot predict the weather 3 days ahead?Reto Knutti explains why scientists have confidence in climate change models, how statistical methods can help to quantify their uncertainties—and why uncertainty is not an argument for delaying efforts to reduce the damage.

The quest to determine climate sensitivity has been going on for decades, with disturbingly little progress in narrowing the large uncertainty range. But fascinating new insights have been gained that will provide useful information for policy makers, even though the upper limit of climate sensitivity will probably remain uncertain for the near future.

Predictions of future climate are based on elaborate numerical computer models. As computational capacity increases and better observations become available, one would expect the model predictions to become more reliable. However, are they really improving, and how do we know? This paper discusses how current climate models are evaluated, why and where scientists have confidence in their models, how uncertainty in predictions can be quantified, and why models often tend to converge on what we observe but not on what we predict. Furthermore, it outlines some strategies on how the climate modelling community may overcome some of the current deficiencies in the attempt to provide useful information to the public and policy-makers.

A limited number of complex numerical models that simulate the Earth's atmosphere, ocean, and land processes are the primary tool to study how climate may change over the next century due to anthropogenic emissions of greenhouse gases. A standard assumption is that these climate models are random samples from a distribution of possible models centered around the true climate. This implies that agreement with observations and the predictive skill of climate models will improve as more models are added to an average of the models. In this article we present a statistical methodology to quantify whether climate models are indeed unbiased and whether and where model biases are correlated across models. We consider the simulated mean state and the simulated trend over the period 1970–1999 for Northern Hemisphere summer and winter temperature. The key to the statistical analysis is a spatial model for the bias of each climate model and the use of kernel smoothing to estimate the correlations of biases across di...

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.

The soils of the northern hemispheric permafrost region are estimated to contain 1100 to 1500Pg of carbon. A substantial fraction of this carbon has been frozen and therefore protected from microbial decay for millennia. As anthropogenic climate warming progresses much of this permafrost is expected to thaw. Here we conduct perturbed model experiments on a climate model of intermediate complexity, with an improved permafrost carbon module, to estimate with formal uncertainty bounds the release of carbon from permafrost soils by the year 2100 and 2300CE. We estimate that by year 2100 the permafrost region may release between 56 (13 to 118)PgC under Representative Concentration Pathway (RCP) 2.6 and 102 (27 to 199)PgC under RCP 8.5, with substantially more to be released under each scenario by the year 2300. Our analysis suggests that the two parameters that contribute most to the uncertainty in the release of carbon from permafrost soils are the size of the non-passive fraction of the permafrost carbon pool and the equilibrium climate sensitivity. A subset of 25 model variants are integrated 8000 years into the future under continued RCP forcing. Under the moderate RCP 4.5 forcing a remnant near-surface permafrost region persists in the high Arctic, eventually developing a new permafrost carbon pool. Overall our simulations suggest that the permafrost carbon cycle feedback to climate change will make a significant contribution to climate change over the next centuries and millennia, releasing a quantity of carbon 3 to 54% of the cumulative anthropogenic total., Biogeosciences, 13 (7), ISSN:1726-4170

The world's governments agreed to limit global mean temperature change to below 2-derees C compared with pr-industrial levels in the years following the 2009 climate conference in Copenhagen. This 2-degrees C warming target is perceived by the pulic as a universally accepted goal, identified by scientists as a safe limit that avoids dangerous climate change. This perception is incorrect: no scientific assessment has clearly justified or defended the 2-degrees C target as a safe level of warming, and indeed, this is not a problem that science alone can address. We argue that global temperature is the best climate target quantity, but it is unclear what level can be consiered safe. The 2-degrees C target is useful for anchoring discussions, but has been ineffective in triggering the required emission reductions; debates on considering a lower target are strongly at odds with the current real-world level of action. These debates are moot, however, as the decisions that need to be taken now to limit warming to 1.5 or 2 degrees C are very similar. We need to agree how to start, not where to end mitigation.

The Paris Agreement sets a long-term temperature goal of holding the global average temperature increase to well below 2 °C, and pursuing efforts to limit this to 1.5 °C above pre-industrial levels. Here, we present an overview of science and policy aspects related to this goal and analyse the implications for mitigation pathways. We show examples of discernible differences in impacts between 1.5 °C and 2 °C warming. At the same time, most available low emission scenarios at least temporarily exceed the 1.5 °C limit before 2100. The legacy of temperature overshoots and the feasibility of limiting warming to 1.5 °C, or below, thus become central elements of a post-Paris science agenda. The near-term mitigation targets set by countries for the 2020-2030 period are insufficient to secure the achievement of the temperature goal. An increase in mitigation ambition for this period will determine the Agreement's effectiveness in achieving its temperature goal.

Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an ‘ensemble of opportunity’, are discussed in detail.

A Bayesian uncertainty analysis of 12 parameters of the Bern2.5D climate model is presented. This includes an extensive sensitivity study with respect to the major statistical assumptions. Special attention is given to the parameter representing climate sensitivity. Using the framework of robust Bayesian analysis, the authors first define a nonparametric set of prior distributions for climate sensitivity S and then update the entire set according to Bayes’ theorem. The upper and lower probability that S lies above 4.5°C is calculated over the resulting set of posterior distributions. Furthermore, posterior distributions under different assumptions on the likelihood function are computed. The main characteristics of the marginal posterior distributions of climate sensitivity are quite robust with regard to statistical models of climate variability and observational error. However, the influence of prior assumptions on the tails of distributions is substantial considering the important political implications. Moreover, the authors find that ocean heat change data have a considerable potential to constrain climate sensitivity.

The soils of the Northern Hemisphere permafrost region are estimated to contain 1100 to 1500 Pg of carbon (Pg C). A substantial fraction of this carbon has been frozen and therefore protected from microbial decay for millennia. As anthropogenic climate warming progresses much of this permafrost is expected to thaw. Here we conduct perturbed physics experiments on a climate model of intermediate complexity, with an improved permafrost carbon module, to estimate with formal uncertainty bounds the release of carbon from permafrost soils by year 2100 and 2300. We estimate that by 2100 the permafrost region may release between 56 (13 to 118) Pg C under Representative Concentration Pathway (RCP) 2.6 and 102 (27 to 199) Pg C under RCP 8.5, with substantially more to be released under each scenario by year 2300. A subset of 25 model variants were projected 8000 years into the future under continued RCP 4.5 and 8.5 forcing. Under the high forcing scenario the permafrost carbon pool decays away over several thousand years. Under the moderate scenario forcing a remnant near-surface permafrost region persists in the high Arctic which develops a large permafrost carbon pool, leading to global recovery of the pool beginning in mid third millennium of the common era (CE). Overall our simulations suggest that the permafrost carbon cycle feedback to climate change will make a significant but not cataclysmic contribution to climate change over the next centuries and millennia.

Recently, assessments have robustly linked stabilization of global-mean temperature rise to the necessity of limiting the total amount of emitted carbon-dioxide (CO2). Halting global warming thus requires virtually zero annual CO2 emissions at some point. Policymakers have now incorporated this concept in the negotiating text for a new global climate agreement, but confusion remains about concepts like carbon neutrality, climate neutrality, full decarbonization, and net zero carbon or net zero greenhouse gas (GHG) emissions. Here we clarify these concepts, discuss their appropriateness to serve as a long-term global benchmark for achieving temperature targets, and provide a detailed quantification. We find that with current pledges and for a likely (>66%) chance of staying below 2 °C, the scenario literature suggests net zero CO2 emissions between 2060 and 2070, with net negative CO2 emissions thereafter. Because of residual non-CO2 emissions, net zero is always reached later for total GHG emissions than for CO2. Net zero emissions targets are a useful focal point for policy, linking a global temperature target and socio-economic pathways to a necessary long-term limit on cumulative CO2 emissions., Environmental Research Letters, 10 (10), ISSN:1748-9326, ISSN:1748-9318

The climate of the last glacial period was extremely variable, characterized by abrupt warming events in the Northern Hemisphere, accompanied by slower temperature changes in Antarctica and variations of global sea level. It is generally accepted that this millennial-scale climate variability was caused by abrupt changes in the ocean thermohaline circulation. Here we use a coupled ocean–atmosphere–sea ice model to show that freshwater discharge into the North Atlantic Ocean, in addition to a reduction of the thermohaline circulation, has a direct effect on Southern Ocean temperature. The related anomalous oceanic southward heat transport arises from a zonal density gradient in the subtropical North Atlantic caused by a fast wave-adjustment process. We present an extended and quantitative bipolar seesaw concept that explains the timing and amplitude of Greenland and Antarctic temperature changes, the slow changes in Antarctic temperature and its similarity to sea level, as well as a possible time lag of sea level with respect to Antarctic temperature during Marine Isotope Stage 3.

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.

The Environmental Research Letters focus issue on 'Cumulative Emissions, Global Carbon Budgets and the Implications for Climate Mitigation Targets' was launched in 2015 to highlight the emerging science of the climate response to cumulative emissions, and how this can inform efforts to decrease emissions fast enough to avoid dangerous climate impacts. The 22 research articles published represent a fantastic snapshot of the state-or-the-art in this field, covering both the science and policy aspects of cumulative emissions and carbon budget research. In this Review and Synthesis, we summarize the findings published in this focus issue, outline some suggestions for ongoing research needs, and present our assessment of the implications of this research for ongoing efforts to meet the goals of the Paris climate agreement.

Mesoscale eddies are ubiquitous features in the Southern Ocean, yet their phenomenology is not well quantified. To tackle this task, we use satellite observations of sea level anomalies and sea surface temperature (SST) as well as in situ temperature and salinity measurements from profiling floats. Over the period 1997–2010, we identified over a million mesoscale eddy instances and were able to track about 105 of them over 1 month or more. The Antarctic Circumpolar Current (ACC), the boundary current systems, and the regions where they interact are hot spots of eddy presence, representing also the birth places and graveyards of most eddies. These hot spots contrast strongly to areas shallower than about 2000 m, where mesoscale eddies are essentially absent, likely due to topographical steering. Anticyclones tend to dominate the southern subtropical gyres, and cyclones the northern flank of the ACC. Major causes of regional polarity dominance are larger formation numbers and lifespans, with a contribution of differential propagation pathways of long-lived eddies. Areas of dominance of one polarity are generally congruent with the same polarity being longer-lived, bigger, of larger amplitude, and more intense. Eddies extend down to at least 2000 m. In the ACC, eddies show near surface temperature and salinity maxima, whereas eddies in the subtropical areas generally have deeper anomaly maxima, presumably inherited from their origin in the boundary currents. The temperature and salinity signatures of the average eddy suggest that their tracer anomalies are a result of both trapping in the eddy core and stirring.

Limiting global warming to any level requires limiting the total amount of CO2 emissions, or staying within a CO2 budget. Here we assess how emissions from short-lived non-CO2 species like methane, hydrofluorocarbons (HFCs), black-carbon, and sulphates influence these CO2 budgets. Our default case, which assumes mitigation in all sectors and of all gases, results in a CO2 budget between 2011–2100 of 340 PgC for a >66% chance of staying below 2°C, consistent with the assessment of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Extreme variations of air-pollutant emissions from black-carbon and sulphates influence this budget by about ±5%. In the hypothetical case of no methane or HFCs mitigation—which is unlikely when CO2 is stringently reduced—the budgets would be much smaller (40% or up to 60%, respectively). However, assuming very stringent CH4 mitigation as a sensitivity case, CO2 budgets could be 25% higher. A limit on cumulative CO2 emissions remains critical for temperature targets. Even a 25% higher CO2 budget still means peaking global emissions in the next two decades, and achieving net zero CO2 emissions during the third quarter of the 21st century. The leverage we have to affect the CO2 budget by targeting non-CO2 diminishes strongly along with CO2 mitigation, because these are partly linked through economic and technological factors., Environmental Research Letters, 10 (7), ISSN:1748-9326, ISSN:1748-9318