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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
© 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
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
© Crown copyright Met Office
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
© 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 2010 - 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
Date
Tem
pera
ture
Tem
pera
ture
© Crown copyright Met Office
• 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
© 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 o
r Loc
ation
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 Day 4 Day 5 Day
© Crown copyright Met Office
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
© 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 ~10.• 90th 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
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
© Crown copyright Met Office
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
© Crown copyright Met Office
End