Glen R. Harris

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 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 ...

The Met Office Hadley Centre climate modelling capability: the competing requirements for improved resolution, complexity and dealing with uncertainty

Predictions of future climate change require complex computer models of the climate system to represent the full range of processes and interactions that influence climate. The Met Office Hadley Centre uses ‘families’ of models as part of the Met Office Unified Model Framework to address different classes of problems. The HadGEM family is a suite of state-of-the-art global environment models that are used to reduce uncertainty and represent and predict complex feedbacks. The HadCM3 family is a suite of well established but cheaper models that are used for multiple simulations, for example, to quantify uncertainty or to test the impact of multiple emissions scenarios.

A review of recent developments in climate change science. Part I: Understanding of future change in the large-scale climate system:

This article reviews some of the major lines of recent scientific progress relevant to the choice of global climate policy targets, focusing on changes in understanding since publication of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Developments are highlighted in the following major climate system components: ice sheets; sea ice; the Atlantic Meridional Overturning Circulation; tropical forests; and accelerated carbon release from permafrost and ocean hydrates. The most significant developments in each component are identified by synthesizing input from multiple experts from each field. Overall, while large uncertainties remain in all fields, some substantial progress in understanding is revealed.

What do recent advances in quantifying climate and carbon cycle uncertainties mean for climate policy

Global policy targets for greenhouse gas emissions reductions are being negotiated. The amount of emitted carbon dioxide remaining in the atmosphere is controlled by carbon cycle processes in the ocean and on land. These processes are themselves affected by climate. The resulting ‘climate‐carbon cycle feedback’ has recently been quantified, but the policy implications have not. Using a scheme to emulate the range of state-of-the-art model results for climate feedback strength, including the modelled range of climate sensitivity and other key uncertainties, we analyse recent global targets. The G8 target of a 50% cut in emissions by 2050 leaves CO2 concentrations rising rapidly, approaching 1000 ppm by 2300. The Stern Review’s proposed 25% cut in emissions by 2050, continuing to an 80% cut, does in fact approach stabilization of CO2 concentration on a policy-relevant (century) timescale, with most models projecting concentrations between 500 and 600 ppm by 2100. However concentrations continue to rise gradually. Long-term stabilization at 550 ppm CO2 requires cuts in emissions of 81 to 90% by 2300, and more beyond as a portion of the CO2 emitted persists for centuries to millennia. Reductions of other greenhouse gases cannot compensate for the long-term effects of emitting CO2.

Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations

We describe results from a 57-member ensemble of transient climate change simulations, featuring simultaneous perturbations to 54 parameters in the atmosphere, ocean, sulphur cycle and terrestrial ecosystem components of an earth system model (ESM). These emissions-driven simulations are compared against the CMIP3 multi-model ensemble of physical climate system models, used extensively to inform previous assessments of regional climate change, and also against emissions-driven simulations from ESMs contributed to the CMIP5 archive. Members of our earth system perturbed parameter ensemble (ESPPE) are competitive with CMIP3 and CMIP5 models in their simulations of historical climate. In particular, they perform reasonably well in comparison with HadGEM2-ES, a more sophisticated and expensive earth system model contributed to CMIP5. The ESPPE therefore provides a computationally cost-effective tool to explore interactions between earth system processes. In response to a non-intervention emissions scenario, the ESPPE simulates distributions of future regional temperature change characterised by wide ranges, and warm shifts, compared to those of CMIP3 models. These differences partly reflect the uncertain influence of global carbon cycle feedbacks in the ESPPE. In addition, the regional effects of interactions between different earth system feedbacks, particularly involving physical and ecosystem processes, shift and widen the ESPPE spread in normalised patterns of surface temperature and precipitation change in many regions. Significant differences from CMIP3 also arise from the use of parametric perturbations (rather than a multimodel ensemble) to represent model uncertainties, and this is also the case when ESPPE results are compared against parallel emissions-driven simulations from CMIP5 ESMs. When driven by an aggressive mitigation scenario, the ESPPE and HadGEM2-ES reveal significant but uncertain impacts in limiting temperature increases during the second half of the twenty-first century. Emissions-driven simulations create scope for development of errors in properties that were previously prescribed in coupled ocean–atmosphere models, such as historical CO2 concentrations and vegetation distributions. In this context, historical intra-ensemble variations in the airborne fraction of CO2 emissions, and in summer soil moisture in northern hemisphere continental regions, are shown to be potentially useful constraints, subject to uncertainties in the relevant observations. Our results suggest that future climate-related risks can be assessed more comprehensively by updating projection methodologies to support formal combination of emissions-driven perturbed parameter and multi-model earth system model simulations with suitable observational constraints. This would provide scenarios underpinned by a more complete representation of the chain of uncertainties from anthropogenic emissions to future climate outcomes.

Narrowing the Range of Future Climate Projections Using Historical Observations of Atmospheric CO2

AbstractUncertainty in the behavior of the carbon cycle is important in driving the range in future projected climate change. Previous comparisons of model responses with historical CO2 observations have suggested a strong constraint on simulated projections that could narrow the range considered plausible. This study uses a new 57-member perturbed parameter ensemble of variants of an Earth system model for three future scenarios, which 1) explores a wider range of potential climate responses than before and 2) includes the impact of past uncertainty in carbon emissions on simulated trends. These two factors represent a more complete exploration of uncertainty, although they lead to a weaker constraint on the range of future CO2 concentrations as compared to earlier studies. Nevertheless, CO2 observations are shown to be effective at narrowing the distribution, excluding 30 of 57 simulations as inconsistent with historical CO2 changes. The perturbed model variants excluded are mainly at the high end of th...