| Operational attribution |
| Myles Allen | |
| Department of Physics, University of Oxford | |
| myles.allen@physics.ox.ac.uk | |
| With thanks to: | |
| Pardeep Pall & D‡ith’ Stone, University of Oxford | |
| Peter Stott, The Met Office |
| Why should we develop operational attribution? |
| Policy-makers, lawyers, the public and the media: | ||
| ÒWas this flood/drought/... due to climate change/ENSO/É?Ó | ||
| No consensus on response: question is ambiguous. | ||
| Essential element in costing climate change impacts. | ||
| The insurance industry and its customers: | ||
| Currently withdrawing from risks that may be influenced by climate change, even risks that may in fact be insurable. | ||
| Understanding of the origins of current risks allows rational choices of what risks we accept in a changing climate. | ||
| The scientific community: | ||
| Because we want to know what is going on. | ||
| How can we answer the question ÒIs climate change to blame for this flood?Ó |
| Making sense of the question |
| First formalize the question as one we can actually answer and convince the questioner that that is what they actually meant. | ||
| Treat weather events as indivisible, like deaths. | ||
| Use Òattributable riskÓ methods from epidemiology. | ||
| P1: probability of event under current conditions. | ||
| P0: probability of similar event with external driver removed. | ||
| Fraction Attributable Risk FAR = 1 - P0/P1 | ||
| Using coupled models to estimate attributable warming. | ||
| Statistical methods to estimate FAR (Stott et al, 2004). | ||
| GCM-based methods to estimate FAR (Pall et al, 2007). | ||
| Extensions to other regions and variables (Hoerling, 2007). | ||
| The European heat-wave of 2003 |
| Modelling Southern European area-averaged June-July-August summer temperatures |
| How we attribute different contributions to an observed change in expected temperatures |
| Expected temperatures are not a random variable. | |
| Treat this as a standard regression problem, with contributions from model-simulated patterns of response to different external drivers as the unknown parameters. | |
| Conventional likelihoods for uncertainty estimates. | |
| Complications arising from noise in model-simulated patterns: use a Total Least Squares or Errors in Variables approach. |
| An example: attribution of hemispheric temperature changes with HadCM3 |
| An example: attribution of hemispheric temperature changes with GFDL |
| An example: attribution of hemispheric temperature changes with PCM |
| External contributions to European summer temperatures, relative to pre-industrial |
| Inferring external contributions to risk from external contributions to expected temperatures |
| Stott et al (2004): assumed internal variability in large-area-averaged temperatures is unaffected by changing mean climate (no evidence to contrary). | |
| Fit EV distribution to model-simulated variability, allowing for uncertainty in EV parameters. | |
| Subtract possible offsets (change location parameter) due to anthropogenic influence to generate possible temperature distributions. | |
| Only applies to variables like large-scale temperatures for which we can defensibly assume no change in variability. |
| Changing risks of European
heatwaves (Stott et al, 2004) |
| Anthropogenic contribution to the risk of the 2003 heat-wave |
| Conclusions of Stott et al, 2004, study of the 2003 European heat-wave |
| Human influence very likely increased the risk of such a heat-wave by at least a factor of two, with a best guess factor of 6-10. | |
| But this study was based on a relatively coarse-resolution model (~250km grid over Europe), so conclusions only apply to large-scale, seasonally averaged temperatures. | |
| Still work to be done for small-scale risks & for variables other than temperature. |
| Autumn 2000 floods in the UK |
| A template for operational
attribution (Pall et al, 2007) |
| Aim: to quantify the role of increased greenhouse gases in precipitation responsible for 2000 floods. | |
| Challenge: relatively unlikely event even given 2000 climate drivers and sea surface temperatures (SSTs). | |
| Approach: large (multi-thousand-member) ensemble simulation of April 2000 – March 2001 using forecast-resolution global model (80km resolution near UK). | |
| Identical Ònon-industrialÓ ensemble removing the influence of increased greenhouse gases, including attributable SST change. | |
| Use several coupled models for SST pattern to allow for uncertainty. |
| What, in climate research, is a relatively high-resolution model |
| Patterns of SST signal removed: 1990s seasonal surface temperatures relative to control |
| Large ensembles required, so we use distributed computing: http://attribution.cpdn.org |
| Autumn 2000 as observed
(ERA-40 reanalysis)É Éand in one of the wetter members of our ensemble. |
| Return-times versus total Sept-Nov precipitation in the ÒAutumn 2000Ó ensemble |
| Return-times after removing estimated signal of greenhouse warming |
| Return-times after removing estimated signal of greenhouse warming |
| Contribution of greenhouse warming to risk of Autumn 2000 3-month precipitation anomalies |
| Implications of Pall et al study |
| Early studies with coarse-resolution driving models gave diametrically opposing conclusions re: anthropogenic trend in flood risk. | |
| Large ensemble simulations with higher-resolution models give more modest, but still substantial, anthropogenic increase in flood risk. | |
| We need large ensembles to capture events of interest, and we need relatively high resolution to simulate them realistically: ideal application for novel (ÒgridÓ) computing resources. |
| Changing drought risk in Tripoli due to greenhouse gas increase 1900-2000 |
| Link to work on attribution of trends in African rainfall (Hoerling et al, 2007) |
| Impact of Indian Ocean warming on Southern African rainfall |