The science of climate change and the effect of anaesthetic gas emissions.

The dedication of the international anaesthetic community to reducing the environmental impact of healthcare is important and to be celebrated. When this is underpinned by robust science, it has the potential to make a real difference. However, volatile anaesthetic agents have been widely promoted in the medical literature as damaging to the climate, leading to a drive to remove them from clinical practice. This is based on notional 'CO2 -equivalent' values created using the simple emission metric known as the global warming potential. Here, we assert that when proper consideration is given to the science of climate change, volatile anaesthetic gas emissions cannot be simply equated to real carbon dioxide emissions, and that their climate impact is vanishingly small. This paper gives anaesthetists a framework to make informed choices founded on climate science and calls for attention to be refocused on the urgent need to reduce the real carbon dioxide emissions associated with healthcare.

Interpretation of snow-climate feedback as produced by 17 general circulation models.

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates.

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.