Mat Collins

Ensembles and probabilities: a new era in the prediction of climate change

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.

High sensitivity of future global warming to land carbon cycle processes

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.

Towards quantifying uncertainty in predictions of Amazon ‘dieback’

Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a ‘business-as-usual’ emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple ‘big-leaf’ approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth.

Multivariate probabilistic projections using imperfect climate models part I: outline of methodology

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 CO2 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.

Southern Ocean albedo, inter-hemispheric energy transports and the double ITCZ: global impacts of biases in a coupled model

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.

Distributed computing for public-interest climate modeling research

The rapid increase in the speed and capacity of commonly available PCs is providing an opportunity to use distributed computing to tackle major modeling tasks such as climate simulation. The CLIMATEPREDICTION.COM project has developed the software necessary to carry out such a project in the public domain. The paper describes the development of the demonstration release software, along with the computational challenges such as data mining, visualization, and distributed database management.

How uncertain are climate model projections of water availability indicators across the Middle East

The projection of robust regional climate changes over the next 50 years presents a considerable challenge for the current generation of climate models. Water cycle changes are particularly difficult to model in this area because major uncertainties exist in the representation of processes such as large-scale and convective rainfall and their feedback with surface conditions. We present climate model projections and uncertainties in water availability indicators (precipitation, run-off and drought index) for the 1961–1990 and 2021–2050 periods. Ensembles from two global climate models (GCMs) and one regional climate model (RCM) are used to examine different elements of uncertainty. Although all three ensembles capture the general distribution of observed annual precipitation across the Middle East, the RCM is consistently wetter than observations, especially over the mountainous areas. All future projections show decreasing precipitation (ensemble median between −5 and −25%) in coastal Turkey and parts of Lebanon, Syria and Israel and consistent run-off and drought index changes. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) GCM ensemble exhibits drying across the north of the region, whereas the Met Office Hadley Centre work Quantifying Uncertainties in Model ProjectionsAtmospheric (QUMP-A) GCM and RCM ensembles show slight drying in the north and significant wetting in the south. RCM projections also show greater sensitivity (both wetter and drier) and a wider uncertainty range than QUMP-A. The nature of these uncertainties suggests that both large-scale circulation patterns, which influence region-wide drying/wetting patterns, and regional-scale processes, which affect localized water availability, are important sources of uncertainty in these projections. To reduce large uncertainties in water availability projections, it is suggested that efforts would be well placed to focus on the understanding and modelling of both large-scale processes and their teleconnections with Middle East climate and localized processes involved in orographic precipitation.

Inferring changes in ENSO amplitude from the variance of proxy records

One common approach to investigating past changes in El Ni no-Southern Oscillation (ENSO) amplitude is through quantifying the variance of ENSO-influenced proxy records. However, a component of the variance of all such proxies will reflect influences that are unrelated to the instrumental climatic indices from which modern ENSO amplitudes are defined. The unrelated component of proxy variance introduces a fundamental source of uncertainty to all such constraints on past ENSO amplitudes. Based on a simple parametric approach to modeling this uncertainty, we present guidelines for the magnitudes of proxy variance change required to robustly infer the following: (i) any change at all in ENSO amplitude and (ii) a change in ENSO amplitude that exceeds the plausible range of unforced variability. It is noted that more extreme changes in proxy variance are required to robustly infer decreases, as opposed to increases, in past ENSO amplitude from modern levels.

Model tropical Atlantic biases underpin diminished Pacific decadal variability

Pacific trade winds have displayed unprecedented strengthening in recent decades1. This strengthening has been associated with east Pacific sea surface cooling2 and the early twenty-first-century slowdown in global surface warming2,3, amongst a host of other substantial impacts4–9. Although some climate models produce the timing of these recently observed trends10, they all fail to produce the trend magnitude2,11,12. This may in part be related to the apparent model underrepresentation of low-frequency Pacific Ocean variability and decadal wind trends2,11–13 or be due to a misrepresentation of a forced response1,14–16 or a combination of both. An increasingly prominent connection between the Pacific and Atlantic basins has been identified as a key driver of this strengthening of the Pacific trade winds12,17–20. Here we use targeted climate model experiments to show that combining the recent Atlantic warming trend with the typical climate model bias leads to a substantially underestimated response for the Pacific Ocean wind and surface temperature. The underestimation largely stems from a reduction and eastward shift of the atmospheric heating response to the tropical Atlantic warming trend. This result suggests that the recent Pacific trends and model decadal variability may be better captured by models with improved mean-state climatologies.Simulation of observed Pacific wind trends is hampered by model limitations in representing variability or the forced response. Improved mean-state climatologies, including the recent Atlantic warming trend, should improve capture of Pacific trends.

Effect of AMOC collapse on ENSO in a high resolution general circulation model

We look at changes in the El Niño Southern Oscillation (ENSO) in a high-resolution eddy-permitting climate model experiment in which the Atlantic Meridional Circulation (AMOC) is switched off using freshwater hosing. The ENSO mode is shifted eastward and its period becomes longer and more regular when the AMOC is off. The eastward shift can be attributed to an anomalous eastern Ekman transport in the mean equatorial Pacific ocean state. Convergence of this transport deepens the thermocline in the eastern tropical Pacific and increases the temperature anomaly relaxation time, causing increased ENSO period. The anomalous Ekman transport is caused by a surface northerly wind anomaly in response to the meridional sea surface temperature dipole that results from switching the AMOC off. In contrast to a previous study with an earlier version of the model, which showed an increase in ENSO amplitude in an AMOC off experiment, here the amplitude remains the same as in the AMOC on control state. We attribute this difference to variations in the response of decreased stochastic forcing in the different models, which competes with the reduced damping of temperature anomalies. In the new high-resolution model, these effects approximately cancel resulting in no change in amplitude.