LOS SENTIMIENTOS EN LA TOMA DE DECISIONES

The UK Climate Projections 2009 (UKCP09) provide climate data and facts, with the aim to help those who need to prepare and adapt in order to mitigate the likely impacts of climate change (UKCP09, 2012d). The information is intended to be suitable to support user decisions in the real world;

methods for projections have therefore been rigorously tested. Using a new methodology designed

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by the Met Office Hadley Centre (MOHC), the UK’s official centre for climate change research, UKCP09 have produced the fifth generation of climate change information for the UK including probabilistic projections, utilising HadCM3. As the term ‘projection’ implies, the climate of the future could be one of many possibilities (Lovejoy and Hannah, 2005), depending on the workings of the climate system.

In comparison to UKCIP02, the previous climate projections, the UKCP09 projections are much more advanced with inclusion of sampling uncertainties of climate system processes in the GCM (Jenkins et al., 2009). UKCP09 considers more feedbacks than previously, and methodically explores the uncertainties related to them. The resolution of UKCIP02 only went as high as 50km, compared to UKCP09’s 25km grid resolution, reflecting greater accuracy of local climate feedbacks.

2.9.1 Model Description

HadCM3 is an AOGCM, which uses perturbed physics ensembles (PPE), discussed in section 2.9.1.1, to generate the projections. Both atmospheric and oceanic processes were accounted for in the modelling, ‘providing a realistic representation of the climatological processes’ (UKCP09, 2012b). The models are also quite advanced in that they consider feedbacks associated with the carbon cycle and the sulphur cycle, as well as some ocean transport processes. As it is unlikely that there will be a drastic change in the Atlantic Ocean MOC this century, only the effects of a gradual weakening of the circulation over time are included in the UKCP09. Variations in external factors like solar activity and volcanic eruptions cannot be predicted, and are not considered in the projections. UKCP09’s models comprehensively sample key uncertainties systematically, in carbon cycle processes and downscaling, as well as internal climate variability and uncertainties in atmospheric and oceanic processes.

Sampling uncertainty in processes affecting oceanic uptake of carbon are not performed in the UKCP09 models, which as discussed in section 2.7.1, is expected to increase over the 21^st century (Cox et al., 2000). Carbonaceous aerosols, non-aerosol atmospheric chemistry and methane cycle feedbacks are also omitted from the simulations, all of which would have an effect on the forcing of the climate (Murphy et al., 2009). There are nonetheless limits to computational power, modelling capacity and current comprehension and all possible future outcomes may not be captured.

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An assumption in UKCP09 (2012a) is that projections of future climate by certain models are deemed to be more reliable if they accurately simulate recent climate observations, at the global scale.

Different weights are then placed on these variants accordingly when simulations are run.

Consideration at the global scale is important as studies have shown UKCP09 (2012a) that ‘large scale processes dominate local responses to forcing’.

2.9.1.1 Perturbed Physical Ensemble

As mentioned in section 2.6.3 parameterisations are used to account for the unknown outplay of some climate system processes. There are considerable uncertainties associated with many parameters, i.e. they are poorly constrained by observations. Parameters regulating principle physical and biogeochemical processes in the HadCM3 AOGCM can be adjusted accordingly to represent different plausible interactions that may occur in earth system processes (Murphy et al., 2009). Practice is therefore to run a set of simulations sampling each relevant parameter combination, giving different possible model variants. This is phrased a Perturbed Physical Ensemble (PPE), which is the method adopted in the UKCP09 projections, with a climate change projection generated for each variant (Murphy et al., 2009). These outcomes are then weighted using historical observations. There are of course parameter errors in its representation of the real climate system.

Exploration of the full range of variability of each model parameter is computationally not possible, but an evaluation of earth system modelling uncertainty and internal climate variability on feedbacks expected to have a considerable effect on climate change over the next century, is quantified through the use of a PPE (Murphy et al., 2007).

Murphy et al. (2007) note that a Bayesian statistical framework underpins the ensemble simulations, whereby prior distributions for uncertain model parameters are approximated by experts based on their knowledge of the relevant physical processes. For more information on the methodology for climate change projections using perturbed physics ensembles the reader is referred to the paper by Murphy et al. (2007).

2.9.1.2 Multi-model Ensemble

The UKCP09 projections incorporate projections from other GCMs, more specifically 12 of the GCMs used in the IPCC’s Fourth Assessment Report, to create a multi-model ensemble. This allows the integration of structural error (the difference between the real world and the model projections) in the projections, giving a more inclusive range of uncertainties than the HadCM3 on its own could

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provide. As the UKCP09 (2012a) methodology states ‘It prevents the models from being too heavily biased by the way in which one model is structured’.

2.9.1.3 Regional Climate Model

Projections were downscaled to a 25km grid over land areas of the UK, using the regional climate model (RCM) HadRM3 to produce high resolution climate change projections (UKCP09, 2012c).

RCMs take into account the smaller scale topographical features not detected by GCMs, and thus the local climate change is projected more realistically and at a scale more preferable for decision making. The RCM is essentially nested into an AOGCM, which has a corresponding simulation with

‘spatial scales skilfully resolved by the latter’ (NERC, 2011), known as the downscaling process. As Denis et al. (2002) states “Nested RCMs have been shown to generate skilful fine-scale information in idealised predictability studies”, exemplifying their practicability for decision making.

Uncertainty is introduced when downscaling projections, but this aims to be captured by the model from an ensemble of 11 different simulations of HadRM3 (NERC, 2011). This is a similar process as performed for HadCM3 whereby parameters are varied to represent the different possible physical processes of the climate system. Greater confidence can then be assigned to the method used to produce the climate change projections.