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Predictions of future climate are of central importance in determining actions to adapt to the impacts of climate change and in formulating targets to reduce emissions of greenhouse gases. In the absence of analogues of the future, physically based numerical climate models must be used to make predictions. New approaches are under development to deal with a number of sources of uncertainty that arise in the prediction process. This paper introduces some of the concepts and issues in these new approaches, which are discussed in more detail in the papers contained in this issue.

A causal link has been invoked between inter-hemispheric albedo, cross-equatorial energy transport and the double-Intertropical Convergence Zone (ITCZ) bias in climate models. Southern Ocean cloud biases are a major determinant of inter-hemispheric albedo biases in many models, including HadGEM2-ES, a fully coupled model with a dynamical ocean. In this study, targeted albedo corrections are applied in the Southern Ocean to explore the dynamical response to artificially reducing these biases. The Southern Hemisphere jet increases in strength in response to the increased tropical-extratropical temperature gradient, with increased energy transport into the mid-latitudes in the atmosphere, but no improvement is observed in the double-ITCZ bias or atmospheric cross-equatorial energy transport, a finding which supports other recent work. The majority of the adjustment in energy transport in the tropics is achieved in the ocean, with the response further limited to the Pacific Ocean. As a result, the frequently argued teleconnection between the Southern Ocean and tropical precipitation biases is muted. Further experiments in which tropical longwave biases are also reduced do not yield improvement in the representation of the tropical atmosphere. These results suggest that the dramatic improvements in tropical precipitation that have been shown in previous studies may be a function of the lack of dynamical ocean and/or the simplified hemispheric albedo bias corrections applied in that work. It further suggests that efforts to correct the double ITCZ problem in coupled models that focus on large-scale energetic controls will prove fruitless without improvements in the representation of atmospheric processes.

The Indian Ocean Dipole is a key mode of interannual climate variability influencing much of Asia and Australia. A Review suggests that in response to greenhouse warming, mean conditions of the Indian Ocean will shift toward a positive dipole state, but with no overall shift in the frequency of positive and negative events as defined relative to the mean climate state. Natural modes of variability centred in the tropics, such as the El Niño/Southern Oscillation and the Indian Ocean Dipole, are a significant source of interannual climate variability across the globe. Future climate warming could alter these modes of variability. For example, with the warming projected for the end of the twenty-first century, the mean climate of the tropical Indian Ocean is expected to change considerably. These changes have the potential to affect the Indian Ocean Dipole, currently characterized by an alternation of anomalous cooling in the eastern tropical Indian Ocean and warming in the west in a positive dipole event, and the reverse pattern for negative events. The amplitude of positive events is generally greater than that of negative events. Mean climate warming in austral spring is expected to lead to stronger easterly winds just south of the Equator, faster warming of sea surface temperatures in the western Indian Ocean compared with the eastern basin, and a shoaling equatorial thermocline. The mean climate conditions that result from these changes more closely resemble a positive dipole state. However, defined relative to the mean state at any given time, the overall frequency of events is not projected to change — but we expect a reduction in the difference in amplitude between positive and negative dipole events.

This Review looks at the state of knowledge on the El Niño/Southern Oscillation (ENSO), a natural climate phenomenon. It discusses recent advances and insights into how climate change will affect this natural climate varibility cycle. The El Niño/Southern Oscillation (ENSO) is the dominant climate phenomenon affecting extreme weather conditions worldwide. Its response to greenhouse warming has challenged scientists for decades, despite model agreement on projected changes in mean state. Recent studies have provided new insights into the elusive links between changes in ENSO and in the mean state of the Pacific climate. The projected slow-down in Walker circulation is expected to weaken equatorial Pacific Ocean currents, boosting the occurrences of eastward-propagating warm surface anomalies that characterize observed extreme El Niño events. Accelerated equatorial Pacific warming, particularly in the east, is expected to induce extreme rainfall in the eastern equatorial Pacific and extreme equatorward swings of the Pacific convergence zones, both of which are features of extreme El Niño. The frequency of extreme La Niña is also expected to increase in response to more extreme El Niños, an accelerated maritime continent warming and surface-intensified ocean warming. ENSO-related catastrophic weather events are thus likely to occur more frequently with unabated greenhouse-gas emissions. But model biases and recent observed strengthening of the Walker circulation highlight the need for further testing as new models, observations and insights become available.

The 2015 Lancet Commission on Health and Climate Change has been formed to map out the impacts of climate change, and the necessary policy responses, in order to ensure the highest attainable standards of health for populations worldwide. This Commission is multidisciplinary and international in nature, with strong collaboration between academic centres in Europe and China.

The El Niño–Southern Oscillation is a naturally occurring fluctuation that originates in the tropical Pacific region and affects the lives of millions of people worldwide. An overview of relevant research suggests that progress in our understanding of the impact of climate change on many of the processes that contribute to El Niño variability is considerable, but projections for the phenomenon itself are not yet possible. The El Niño–Southern Oscillation (ENSO) is a naturally occurring fluctuation that originates in the tropical Pacific region and affects ecosystems, agriculture, freshwater supplies, hurricanes and other severe weather events worldwide. Under the influence of global warming, the mean climate of the Pacific region will probably undergo significant changes. The tropical easterly trade winds are expected to weaken; surface ocean temperatures are expected to warm fastest near the equator and more slowly farther away; the equatorial thermocline that marks the transition between the wind-mixed upper ocean and deeper layers is expected to shoal; and the temperature gradients across the thermocline are expected to become steeper. Year-to-year ENSO variability is controlled by a delicate balance of amplifying and damping feedbacks, and one or more of the physical processes that are responsible for determining the characteristics of ENSO will probably be modified by climate change. Therefore, despite considerable progress in our understanding of the impact of climate change on many of the processes that contribute to El Niño variability, it is not yet possible to say whether ENSO activity will be enhanced or damped, or if the frequency of events will change.

Determining how El Niño and its impacts may change over the next 10 to 100 years remains a difficult scientific challenge. Ocean–atmosphere coupled general circulation models (CGCMs) are routinely used both to analyze El Niño mechanisms and teleconnections and to predict its evolution on a broad range of time scales, from seasonal to centennial. The ability to simulate El Niño as an emergent property of these models has largely improved over the last few years. Nevertheless, the diversity of model simulations of present-day El Niño indicates current limitations in our ability to model this climate phenomenon and to anticipate changes in its characteristics. A review of the several factors that contribute to this diversity, as well as potential means to improve the simulation of El Niño, is presented.

The link between cross-equatorial energy transport, the double-intertropical convergence zone (DI) problem and biases in tropical and extratropical albedo and energy budgets in climate models have been investigated in multiple studies, though DI biases persist in many models. Here, a coupled climate model, HadGEM2-ES, is used to investigate the response to idealised energy perturbations in the tropics and extratropics, in both the northern and southern hemispheres, through the imposition of stratospheric aerosols that reflect incoming radiation. The impact on the tropical climate of high and low latitude forcing strongly contrasts, with large changes in tropical precipitation and modulation of the DI bias when the tropics are cooled as precipitation moves away from the cooled hemisphere. These responses are muted when the extratropics are cooled, as the meridional energy transport anomalies that are excited by these energy budget anomalies are partitioned between the atmosphere and ocean. The results here highlight the persistence of the DI bias in HadGEM2-ES, indicating why little progress has been made in rectifying these problems through many generations of climate models. A highly linear relationship between cross-equatorial atmospheric energy transport, tropical precipitation asymmetry and tropical sea surface temperature biases is also demonstrated, giving some suggestion as to where improvements in these large scale, persistent biases may be achieved.

Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climate-carbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climate-carbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change.

We demonstrate a method for making probabilistic projections of climate change at global and regional scales, using examples consisting of the equilibrium response to doubled CO 2 concentrations of global annual mean temperature and regional climate changes in summer and winter temperature and precipitation over Northern Europe and England-Wales. This method combines information from a perturbed physics ensemble, a set of international climate models, and observations. Our approach is based on a multivariate Bayesian framework which enables the prediction of a joint probability distribution for several variables constrained by more than one observational metric. This is important if different sets of impacts scientists are to use these probabilistic projections to make coherent forecasts for the impacts of climate change, by inputting several uncertain climate variables into their impacts models. Unlike a single metric, multiple metrics reduce the risk of rewarding a model variant which scores well due to a fortuitous compensation of errors rather than because it is providing a realistic simulation of the observed quantity. We provide some physical interpretation of how the key metrics constrain our probabilistic projections. The method also has a quantity, called discrepancy , which represents the degree of imperfection in the climate model i.e. it measures the extent to which missing processes, choices of parameterisation schemes and approximations in the climate model affect our ability to use outputs from climate models to make inferences about the real system. Other studies have, sometimes without realising it, treated the climate model as if it had no model error. We show that omission of discrepancy increases the risk of making over-confident predictions. Discrepancy also provides a transparent way of incorporating improvements in subsequent generations of climate models into probabilistic assessments. The set of international climate models is used to derive some numbers for the discrepancy term for the perturbed physics ensemble, and associated caveats with doing this are discussed.