Ben B. B. Booth

Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability

Systematic climate shifts have been linked to multidecadal variability in observed sea surface temperatures in the North Atlantic Ocean. These links are extensive, influencing a range of climate processes such as hurricane activity and African Sahel and Amazonian droughts. The variability is distinct from historical global-mean temperature changes and is commonly attributed to natural ocean oscillations. A number of studies have provided evidence that aerosols can influence long-term changes in sea surface temperatures, but climate models have so far failed to reproduce these interactions and the role of aerosols in decadal variability remains unclear. Here we use a state-of-the-art Earth system climate model to show that aerosol emissions and periods of volcanic activity explain 76 per cent of the simulated multidecadal variance in detrended 1860-2005 North Atlantic sea surface temperatures. After 1950, simulated variability is within observational estimates; our estimates for 1910-1940 capture twice the warming of previous generation models but do not explain the entire observed trend. Other processes, such as ocean circulation, may also have contributed to variability in the early twentieth century. Mechanistically, we find that inclusion of aerosol-cloud microphysical effects, which were included in few previous multimodel ensembles, dominates the magnitude (80 per cent) and the spatial pattern of the total surface aerosol forcing in the North Atlantic. Our findings suggest that anthropogenic aerosol emissions influenced a range of societally important historical climate events such as peaks in hurricane activity and Sahel drought. Decadal-scale model predictions of regional Atlantic climate will probably be improved by incorporating aerosol-cloud microphysical interactions and estimates of future concentrations of aerosols, emissions of which are directly addressable by policy actions.

Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability

The release of carbon from tropical forests may exacerbate future climate change, but the magnitude of the effect in climate models remains uncertain. Coupled climate–carbon-cycle models generally agree that carbon storage on land will increase as a result of the simultaneous enhancement of plant photosynthesis and water use efficiency under higher atmospheric CO2 concentrations, but will decrease owing to higher soil and plant respiration rates associated with warming temperatures. At present, the balance between these effects varies markedly among coupled climate–carbon-cycle models, leading to a range of 330 gigatonnes in the projected change in the amount of carbon stored on tropical land by 2100. Explanations for this large uncertainty include differences in the predicted change in rainfall in Amazonia and variations in the responses of alternative vegetation models to warming. Here we identify an emergent linear relationship, across an ensemble of models, between the sensitivity of tropical land carbon storage to warming and the sensitivity of the annual growth rate of atmospheric CO2 to tropical temperature anomalies. Combined with contemporary observations of atmospheric CO2 concentration and tropical temperature, this relationship provides a tight constraint on the sensitivity of tropical land carbon to climate change. We estimate that over tropical land from latitude 30° north to 30° south, warming alone will release 53 ± 17 gigatonnes of carbon per kelvin. Compared with the unconstrained ensemble of climate–carbon-cycle projections, this indicates a much lower risk of Amazon forest dieback under CO2-induced climate change if CO2 fertilization effects are as large as suggested by current models. Our study, however, also implies greater certainty that carbon will be lost from tropical land if warming arises from reductions in aerosols or increases in other greenhouse gases.

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean–atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

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.

The Multidecadal Atlantic SST—Sahel Rainfall Teleconnection in CMIP5 Simulations

AbstractThis study uses models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to evaluate and investigate Sahel rainfall multidecadal variability and teleconnections with global sea surface temperatures (SSTs). Multidecadal variability is lower than observed in all historical simulations evaluated. Focus is on teleconnections with North Atlantic SST [Atlantic multidecadal variability (AMV)] as it is more successfully simulated than the Indian Ocean teleconnection. To investigate why some models successfully simulated this teleconnection and others did not, despite having similarly large AMV, two groups of models were selected. Models with large AMV were highlighted as good (or poor) by their ability to simulate relatively high (low) Sahel multidecadal variability and have significant (not significant) correlation between multidecadal Sahel rainfall and an AMV index. Poor models fail to capture the teleconnection between the AMV and Sahel rainfall because the spatial distribution of SST ...

Sensitivity of Twentieth-Century Sahel Rainfall to Sulfate Aerosol and CO2 Forcing

AbstractA full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentieth-century remains elusive some 25 yr after the height of the event. Previous studies have suggested that this drying trend may be explained by either decadal modes of natural variability or by human-driven emissions (primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal variability and model formulation. The sizes of these ensembles enable the relative role of human-driven changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic aerosol changes are likely t...

Selecting Ensemble Members to Provide Regional Climate Change Information

AbstractClimate model ensembles, such as the Coupled Model Intercomparison Project, phase 3 (CMIP3), are used to characterize broadscale ranges of projected regional climate change and their impacts. The 17-member Hadley Centre perturbed physics GCM ensemble [Quantifying Uncertainty in Model Predictions (“QUMP”)] extends this capability by including data enabling dynamical downscaling of these ranges, and similar data are now being made available from the CMIP phase 5 (CMIP5) GCMs. These raise new opportunities to provide and apply high-resolution regional climate projections. This study highlights the importance of employing a well-considered sampling strategy from available ensembles to provide scientifically credible information on regional climate change while minimizing the computational complexity of ensemble downscaling.A subset of the QUMP ensemble is selected for a downscaling program in Vietnam using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate modeling system. M...

The Sensitivity of the Rate of Transient Climate Change to Ocean Physics Perturbations

Abstract “Perturbed physics” ensembles of Hadley Centre climate models have recently been used to quantify uncertainties in atmospheric and surface climate feedbacks under enhanced levels of CO2, and to produce probabilistic estimates of the magnitude of equilibrium climate change. The rate of time-dependent climate change is determined both by the strength of atmosphere–surface climate feedbacks and by the strength of processes that remove heat from the surface to the deep ocean. Here a first small ensemble of coupled atmosphere–ocean climate model experiments in which the parameters that control three key ocean physical processes are perturbed is described. It is found that the perturbations have little impact on the rate of ocean heat uptake, and thus have little impact on the time-dependent rate of global warming. Under the idealized scenario of 1% yr−1 compounded CO2 increase, the spread in the transient climate response is of the order of a few tenths of a degree, in contrast to the spread of order ...

Quantifying sources of inter-model diversity in the cloud albedo effect

There is a large diversity in simulated aerosol forcing among models that participated in the fifth Coupled Model Intercomparison Project, particularly related to aerosol interactions with clouds. Here we use the reported model data and fitted aerosol-cloud relations to separate the main sources of inter-model diversity in the magnitude of the cloud albedo effect. There is a large diversity in the global load and spatial distribution of sulfate aerosol, as well as in global mean cloud top effective radius. The use of different parameterizations of aerosol-cloud interactions makes the largest contribution to diversity in modeled radiative forcing (−39%, +48% about the mean estimate). Uncertainty in preindustrial sulfate load also makes a substantial contribution (−15%, +61% about the mean estimate), with smaller contributions from inter-model differences in the historical change in sulfate load and in mean cloud fraction.