Recent Advances in Climate Extremes Science AVOID 2 FCO-Roshydromet workshop, Moscow, 19 th March 2015 Simon Brown, Met Office Hadley Centre.

© Crown copyright Met Office Outline Modelling the physics of extremes The role of convection in extreme rainfall Understanding changes in extremes Natural variability vs trends Tools to characterise extremes Metrics that are more relevant to impacts and adaptation Dealing with climate model uncertainty for extremes

Modelling the physics of extremes - Benefits of resolving convection Short intense storms can lead to flash flooding important in urban areas and small steep catchments Boscastle flood, August 2004

Hourly rainfall rates from radar 1km model forecast Model forecasts (a) 12km (b) 4km (c) 1km Case study: Boscastle flood, August 2004 E Kendon & S Chan

Resolved convection leads to heavier summer downpours with climate change DJF JJA 12km Model biasFuture changeModel biasFuture change 1.5km mm/h First climate change experiments with a very high resolution (1.5km) model have been carried out for a region of UK. 1.5km model simulates realistic hourly rainfall characteristics including extremes, unlike coarser resolution climate models (Fig 1). Fig 2. Model biases and future changes in heavy rainfall at the hourly timescale in the 12km (left) and 1.5km (right) models, for winter (top) and summer (bottom). We find evidence of a future intensification of hourly rainfall in summer in the 1.5km model, which is not seen in a coarser 12km resolution model (Fig 2). The benefits of the 1.5km model are largely confined to summer, with the 1.5km and 12km models showing similar future changes in hourly rainfall in winter. E Kendon & S Chan

Future change in hourly rainfall characteristics Model bias Future change 12km (DJF) 1.5km (DJF) 12km (JJA) 1.5km (JJA) Wet spell duration versus peak intensity 1.5km model gives a much more realistic representation of the duration-peak-intensity characteristics of rainfall For the first time the 1.5km model shows evidence of an intensification of short-duration rainfall in summer in future Rainfall rate mm/h Spell duration hr E Kendon & S Chan

© Crown copyright Met Office Understanding changes in extremes

© Crown copyright Met Office Natural variability and changing extremes Met O press release 3/1/2013

© Crown copyright Met Office Generalised Extreme Value distribution - The distribution of the maxima within set of blocks of n samples - one of a number of distributions describing extremal properties Quantile expected to be exceeded once every τ years Return value   Location Scale Return value Return period   Return value

© Crown copyright Met Office Allow GEV parameters to depend on NAO and time Location and scale to have a trend and depend on NAO

Annual cycle of location and scale dependence on NAO and trend Location-trend Location-NAO Scale-trend Scale-NAO Vertical bars bootstrapped uncertainty

Annual cycle of the impact on 50 year return levels for 'lowland' UK by NAO & trend +NAO reduces extreme rainfall for most of the year Strongest effect in summer After taking account for NAO, still residual trend of ~10% over period of obs 5-95 range in % change in return level derived from EV fits with randomised covariates

NAO induced change in storm tracks DJF JJA ∆Track density∆Track speed More storms in north fewer in south, all faster for +NAO DJF More storms for -NAO in JJA

© Crown copyright Met Office Tools to characterise extremes Metrics that are more relevant to impacts and adaptation

© Crown copyright Met Office “Reconciling two approaches to attribution of the 2010 Russian heat wave” Otto et al 2012 GRL. bla Russian heatwave A natural phenomenon enhanced by humans But only monthly mean temperatures Limited usefulness to impact studies

© Crown copyright Met Office Tools to characterise extremes Most detection and attribution studies have looked at either long time means (monthly/seasonal) or individual days (hottest day) Extreme temperature events that have the greatest impact are ~10 days This is problematic – each event is different different duration different spatial extent distribution of temperatures within event are all different How to compare events with such different characteristics? Statistical model of extreme space-time weather phenomena Capture dependency in time Capture dependency in space Model extreme temperature distribution -> multidimensional Markov Chains of extremes (Collaboration - Jon Tawn, Hugo Winter, Lancaster University) Characterising real heatwave events

© Crown copyright Met Office bla Date Temperature

© Crown copyright Met Office Daily temperatures show multi day dependence Need extremal tail dependence Day 1 Day 2 Day 3 Day 4 Daily temperature

© Crown copyright Met Office Extremal dependence – dependence in the tail For a given extreme on day 1, what is the likely value on day 2 (or location) Can extend to greater time dependency, and/or space bla Day 1 or Location 1 Day 2 or Location 2

© Crown copyright Met Office Spatial dependence through time - HadGHCND gridded daily temperatures (2.5° by 3.75°) bla No Lag 1 Day 2 Day 3 Day4 Day5 Day

© Crown copyright Met Office Change in spatial dependence due to ENSO ENSO vs Neutral ENSO ENSO increased size and duration of heatwave bla No Lag 1 Day 2 Day 3 Day4 Day5 Day

© Crown copyright Met Office Time dependency of heatwaves Conditional likelihoods - Given a heatwave has occurred bla

© Crown copyright Met Office Relative return level curves Probabilities conditional on a heatwave having occurred (of any type) No accounting for spatial dependency bla

© Crown copyright Met Office Change in relative return levels for +2 °C Every 2 °C warming increases the frequency by factor of ~ th percentile warms 3.6°C by 2050 (HadGEM2-ES RCP 8.5) No accounting for spatial dependency bla

© Crown copyright Met Office Tools to characterise extremes Dealing with climate model uncertainty for extremes

© Crown copyright Met Office Generalised Extreme Value distribution - The distribution of the maxima within set of blocks of n samples - one of a number of distributions describing extremal properties Quantile expected to be exceeded once every  years Return value   Location Scale Return value Return period

Towards probabilistic prediction of future extremes tt tt Future 100 year return level for surface Tmax Global model  t Global model  t Regional model  t Regional model  t Use a perturbed physics global model to sample parameter space (200+ models) Sample the unsampled areas of parameter space with an emulator trained on the 200+ Downscale these parameters using parallel ensemble of regional models Distribution of regional climate dependant extreme value parameters  t &  t Want the temperature at a given point that will be exceeded on average say once every 100 years Need to know how this will change in the future but this is uncertain Use these together with distribution of future global temperatures to produce distribution of future changes Combine these with observed extremes to produce “future observed” extremes

© Crown copyright Met Office 50 year return levels for London in 2050s RCM 1:1 Regression 10% chance 50y return level increase greater than 6 °C 10% chance 50y return level increase greater than 4.3 mm/day 50 year return value for hottest summer day 50 year return value for wettest summer day

Conclusions Modelling the actual physical process that causes the extremes is important Parameterisations were never designed to represent extremes Low frequency internal modes of variability modulate the risk of extremes Trend in UK extreme rainfall for most of year even when NAO taken into account Real extreme weather events need their spatial and temporal characteristics to be accounted for The frequency of damaging heatwaves is projected to increase substantially Probabilistic prediction approaches can be applied to some extreme types to provide more suitable input to risk based adaptation measures

© Crown copyright Met Office End