Detecting change in UK extreme precipitation using results from the climateprediction.net BBC Climate Change Experiment 11th International Meeting on Statistical Climatology Edinburgh University, UK 15 th July 2010 Hayley Fowler (Newcastle University) Dan Cooley (Colorado State University), Steve Sain (NCAR), Milo Thurston (Oxford University

Aims  To estimate how seasonal extreme precipitation will change for regions across the globe using results from the BBC climateprediction.net experiment  To explore when changes in extreme precipitation due to climate change will be detectable  To explore which parameters of climate models affect extreme rainfall simulation and detectability

Defining “detection”  Detection is the process of demonstrating that climate has changed in some defined statistical sense without providing reasons for the change  Change is detected in observations when the likelihood of an observation (e.g. an extreme temperature) lies outside the bounds of what might be expected to occur by chance  Changes may not be found if the underlying trend is weak compared with the “noise” of natural climate variability; conversely there is always a small chance of spurious detection (e.g. outlier at end of the observational record)

The ‘BBC’ Climate Change Experiment (climateprediction.net) 1.The HadCM3L coupled atmosphere-ocean global climate model was run in ‘grand ensemble’ mode for a control and transient integration (observed forcings applied from 1921 and A1B emissions scenario from 2000) for 1921 to 2080, varying 34 parameter values and initial conditions 2.There are now more than 5000 complete matching control/transient runs available

Data Available  EA Rainfall and Weather Impacts Generator  Developed for EA as a catchment scale Decision Support Tool  Generates series of daily rainfall, T, RH, wind, sunshine and PET on 5km UK grid  Based on UKCIP02 scenarios  Combines NSRP rainfall model with CRU Weather Generator  Increasingly used throughout UK  Being used as part of UKCIP08 scenarios

UK Case Study  8 UK Grid Cells  Extract 1 day monthly maximum precipitation for for 304 model pairs (complete models from 524 model set used at Oxford for mean temperature change analysis)  Calculate time series of seasonal maxima for each model pair and grid cell hl nine ei wm la cwkt

Structure of statistical model To establish the structure of the statistical model needed to fit the data several combinations of the GEV tested: 1.Control has no forcings applied:  time-invariant GEV model fitted 2.Scenario has observed forcings and has SRES A1B forcings applied:  We assume GEV location parameter, μ, most likely to change – but use a model selection exercise that increasingly adds complexity (i.e. no change, level shift, linear trend in μ from 1920, change in linear trend in μ from 2000) – and use AICc criterion to determine best fit  We also test whether allowing σ and ξ to vary improves model fit

Results of model selection  Different behavior between seasons:  winter and spring tend to choose hinge-type models (change in linear trend in μ from 2000)  summer and autumn - common model for the Control and Scenario runs more likely to be selected (less evidence of change in extreme precipitation)  Marginal improvement to model from inclusion trend in σ and ξ – therefore restricted to μ  Best GEV model used in each case to provide estimate of 20y RP for each year of the time series

Projected changes in extreme precipitation  percentage changes to 20y return level of 1d winter extreme precipitation by 2020, 2050 and 2080 from projected by CPDN BBC CCE

Testing for detectibility  Detectible increase in extreme precipitation defined as the year at which we would reject (at the α = 0.05 level) the null hypothesis that the 20-year return levels from the two runs are equal in favor of the alternative hypothesis that the 20-year return level from the Scenario run is greater than that from the Control run (1) Hinge-type model: Detection time = 84 years (2) Linear model: Detection time = 71 years

Results for detectability  For the winter season, more than 50% of the climate model pairs found a detectable difference by 2010  For the spring season, more than 50% of the climate model pairs found a detectable difference by 2030  However, for the summer and fall seasons respectively, only ∼ 30% and 40% of the data sets showed a detectable difference by 2080

Climate model parameter effects GEV shape parameter (ξ)  Not well understood which climate model parameters could possibly affect ξ (affects tail of extreme precipitation distribution)  Ran sequence of simple one-way analysis of variance (ANOVA) tests for each of the 34 different model parameters for Control runs only - ξ serves as our variable and the different parameter settings serve as treatment – looked at p-values and also “importance”  Two climate model parameters, the entrainment coefficient (entcoef) and the icefall speed (vf1), have an important effect on the tail weight in summer, explaining respectively 35% and 9%of the total variability found in the estimates of ξ

Climate model parameter effects GEV shape parameter (ξ)  Previously found to affect climate sensitivity  Entrainment coefficient affects how air is diluted in rising cumulus cloud columns, thus partially controls the amount of convective activity – suggests precipitation efficiency must play a critical role in the occurrence of heavy precipitation (as Wilson and Toumi, 2005 suggest for observations)  Ice fall speed has a major impact on cloud cover and cloud optical properties - reducing this parameter results in increased long-wave clear sky and increased low-level layer clouds, allowing air to remain moister - causes simulation of increases in extreme precipitation in the climate model output

Climate model parameter effects Detectability  Contingency table analysis used to determine effect on detectability - run only for winter season  Two climate model parameters seem to affect the time of detection: “ct” – the accretion constant, and: “anthsca”, which describes the scaling factor for emissions from anthropogenic sulfur aerosols

Conclusions  Climateprediction.net GCMs suggest that most UK regions will experience increases in extreme 1 day precipitation, particularly in winter  Models suggest that changes are likely to be detectable in the near future (by 2050)  changes are more likely to be detectable in winter than in other seasons  Two climate model parameters have an important effect on the tail weight in summer, and two others seem to affect the time of detection in winter  Climate model simulated extreme precipitation has a fundamentally different behavior to observations, perhaps due to the negative estimate of ξ

Fowler, H.J, Cooley, D, Sain, S.R and Thurston, M Detecting change in UK extreme precipitation using results from the climateprediction.net BBC Climate Change Experiment. Extremes, 13(2), , doi: /s y. Fowler, H.J. and Wilby, R.L Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk. Water Resources Research, 46, W03525, doi: /2008WR