Recent Advances in Climate Extremes Science AVOID 2 FCO-Roshydromet workshop, Moscow, 19 th March...

Recent Advances in Climate Extremes ScienceAVOID 2 FCO-Roshydromet workshop, Moscow, 19th 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

DJ

FJ

JA

12kmModel bias Future change Model bias Future 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

Rain

fall

rate

mm

/h

Spell duration hr

E Kendon & S Chan


Understanding changes in extremes


Natural variability and changing extremes

Met O press release 3/1/2013


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

Retu

rn valu

e

Location Scale R

eturn

value

Return period

1 01 where ln 1

ln 0y

Pq P

P

Return period

Ret

urn

val

ue

R

etu

rn v

alu

e


Allow GEV parameters to depend on NAO and time

0

0

0

T NAO

T NAO

t t

t t

I NAO

I NAO

• 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


Tools to characterise extremes

Metrics that are more relevant to impacts and adaptation


“Reconciling two approaches to attribution of the 2010 Russian heat wave” Otto et al 2012 GRL.

bla

Russian heatwave 2010 - A natural phenomenon enhanced by humans

• But only monthly mean temperatures

• Limited usefulness to impact studies



• 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


bla

Date

Date

Tem

pera

ture

Tem

pera

ture


• Daily temperatures show multi day dependence

• Need extremal tail dependence

Day 1

Day 1

Day 1

Day

2

Day

3

Day

4

Daily temperatureDaily temperature

Daily temperature


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 o

r Loc

ation

2


Spatial dependence through time - HadGHCND gridded daily temperatures (2.5° by 3.75°)

• bla

No Lag 1 Day 2 Day

3 Day 4 Day 5 Day


Change in spatial dependence due to ENSO - 2010 ENSO vs Neutral ENSO

- 2010 ENSO increased size and duration of heatwave

• bla

No Lag 1 Day 2 Day

3 Day 4 Day 5 Day


Time dependency of heatwaves

• Conditional likelihoods - Given a heatwave has occurred

bla


Relative return level curves

• Probabilities conditional on a heatwave having occurred (of any type)• No accounting for spatial dependency

bla


Change in relative return levels for +2 °C

• Every 2 °C warming increases the frequency by factor of ~10.• 90th percentile warms 3.6°C by 2050 (HadGEM2-ES RCP 8.5)• No accounting for spatial dependency

bla



Dealing with climate model uncertainty for extremes


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

1 01 where ln 1

ln 0y

Pq P

P

Return period

Towards probabilistic prediction of future extremes

t

t

00

11 where ln 1 & 0t Gt G

Tq T years

Future 100 year return level for surface Tmax

Global model t

Global model t

Regi

onal

mod

el

tRe

gion

al m

odel

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 yearsNeed 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


50 year return levels for London in 2050s

RCM1:1Regression

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


End

Recent Advances in Climate Extremes Science AVOID 2 FCO-Roshydromet workshop, Moscow, 19 th March...

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