WEATHER AND CLIMATE
EXTREMES
Professor Mark Saunders
Mullard Space Science Laboratory
University College London
9th February 2016
MSSL LECTURE SERIES!
2015/16!
Lecture Structure
1. Definitions.
2. Why weather and climate extremes are
important.
3. Quantifying weather and climate extremes.
4. Sources of uncertainty.
5. Examples of MSSL research.
2
Climate Extremes
Cricket Ground, Worcester July 2007!
(Courtesy Webb Aviation)!
UK Summer 2007 Floods!
(Loss ~ US $ 9 billion)!
2004 Hurricane Season!
(Loss ~ US $ 50 billion)!
Storm Imogen 8th February 2016
High waves batter Porthleven, Cornwall
Photo from Mike Stacey/Barcroft Media
Was this an extreme weather event?
4
1. Definitions
5
Definition of an ‘Extreme’ Event
Extreme events are, in general, easy to recognise but difficult
to define.
-
There is no unique definition of an ‘extreme’ (Stephenson,
2008; IPCC, 2012).
-
The concept of ‘extremeness’ is relative.
-
The words ‘severe’, ‘rare’, and ‘high-impact’ are used
interchangeably with ‘extreme’.
-
An event from the extreme tails of a distribution is not
necessarily extreme in terms of its impact.
6
Distinction between “Extreme Weather
Event” and “Extreme Climate Event”
•
Distinction is not precise but is related to their time scales:
Extreme weather event (or weather extreme) is typically
associated with changing weather patterns; namely with
timescales between 1 day and a few weeks.
Extreme climate event (or climate extreme) happens on
timescales longer than about 1 month.
•
Weather and climate extremes can be defined
quantitatively in two ways:
- Related to their probability of occurrence.
- Related to a specific threshold.
7
Choice of Threshold
The high threshold used to define extreme events can be chosen
in different ways. These may be: (a) impact-related; (b) use of a
constant threshold based on an empirical distribution (for
example the 90th or 95th quantile).
90th quantile threshold
95th quantile threshold
8
NOAA Choice of Threshold
In most cases,
extreme events are
defined as lying in
the outermost 10%
of the places
history (analyses
are done at
national and
regional levels).
http://www.ncdc.noaa.gov/climate-information/extreme-events
9
Types of Weather and Climate Extremes
Main types: Windstorms (tropical and extratropical), floods
(river, storm surge and flash), severe weather
(thunderstorms/tornado/hail), drought, wildfires,
heat waves, extreme cold, and extreme ocean
waves.
Climate oscillations: El Niño Southern Oscillation
North Atlantic Oscillation
Arctic Oscillation
10
2. Why Weather and Climate
Extremes are Important. 11
Global Billion-Dollar Loss Events 2000-2015!
Economic Loss Events
Dominated by weather hazards
Insured Loss Events
Dominated by weather hazards
Source: Aon Benfield!
2015 Most Costly Natural Catastrophe
Global Economic Loss Events
Source: Aon Benfield!
Dominated by weather and climate extremes.
3. Quantifying Weather and Climate
Extremes. 14
Basic Methods for Quantification
1.  A probabilistic description of the hazard process, defined
on a particular domain (region and time interval).
2.  Hazard maps showing the probability of a specified
measure exceeding a specified threshold in the given time
interval.
3.  Probability of exceedance (PE) curves, which can be
applied directly to the features of the hazard, but which
can also be applied to summarise loss. PE curves usually
show the hazard feature or loss along the horizontal axis
and the probability of exceedance on the vertical axis.
15
Why Quantification is Important
It provides the best information for assessing risk
(including loss) and its uncertainty.
Risk managers will want the best information that the risk
assessor can provide, subject to resource constraints. Thus
perforce scientific information (based on testable physical
laws and empirical regularities) should be quantified, if it can
be, because a failure to quantify implies a loss of information.
Additional Reason:
Catastrophe models – the sophisticated modelling technique
used by (re)insurers to assess natural hazard risk and loss –
is underpinned by quantitative data.
Example of probabilistic descriptions:
66 h probabilistic forecast for 15–16 October 1987
Slingo J , and Palmer T Phil. Trans. R. Soc. A 2011;369:4751-4767
Ensemble Set of 100 Tracks & Intensities
Hurricane
Ike
Issued 72hrs
before Texas
landfall.
(TSR Business forecast
product)
18
ECMWF Forecast for Nino 3.4 SST
19
Example of hazard map showing
probability of a specified measure
20
Cat 1 Surface Wind Probabilities
21
Probabilistic Storm Surge
Provides likelihood that
storm surge will exceed
different heights above
normal tide level.!
Hurricane
Earl (2010)!
22
Probability of Loss Exceedance: Hurricane Wilma
Modelling output from TSR (Tropical Storm Risk)
23
Return Period
Return Period, R, for an event of specified magnitude is the mean
recurrence interval for that event during a period of one year. R has
a unit of year(s).
R is related to the probability of exceedance, pE, where pE is the
probability that the event of specified magnitude will be equalled or
exceeded during a one year period.
R and pE are inversely related:
R = 1 / pE
and thus
pE = 1 / R
Example: A 50-year return-period wind speed:
- has an probability of exceedence of 0.02 in any one year or an
average rate of exceedence of 1 in 50 years.
- should not be interpreted as occurring regularly every 50 years.
24
Example 1: Probability of exceedance curve:
Peak 3-sec wind gust at Heathrow
Exceedance probability in 1 year (%)
100
90
HEATHROW
WMO NR: 03772
Lat: 51° 28' 48" N; Long: 0° 27' W; Height: 25m
Climatology period: 1981-2010 (30 years)
Data completeness: 99.6%
Number modelled gust values: 401,300
80
70
60
50
40
30
20
10
0
20
21
22
23
24
25
26
27
28
29
30
31
Gust speed [m/s] ≥
32
33
34
35
36
37
38
39
Example 2: Return Period Curves for
Annual Precipitation in Bangkok, Thailand
26
Example 3:
Probability of
Exceedance
Curves for
Thailand
Monsoon Rainfall
Shown as a function
of ENSO sign and
ENSO event duration.
Taken from Gale
(2016)
4. Sources of Uncertainty
28
Sources of Uncertainty
All three sources of information are imperfect and hence introduce
uncertainty.
1. Physical models
--- These models inevitably require approximations of complex
processes and can often exclude processes either because they
are unknown or poorly understood. Some hydrometeorological
hazards (eg precipitation) are more challenging than others.
--- Inputs and outputs of the physical model may be incompatible
with the system observations (due to different spatial and temporal
scales), making the model parameters hard to calibrate.
--- Non-stationarity due to the infuence of either natural of
anthropogenic forcings occurs within the Earth System. Nonstationarity should be accounted for, where present, in the
statistical analysis of observational records.
29
Sources of Uncertainty (2)
2. Hazard Data
--- Historical hazard data can be limited especially for large
hazards (tropical cyclones, European windstorms and US
tornadoes), and extend back only 30-40 years. For some
variables (eg temperature extremes the record length can be
150 years or more).
--- Historical hazard data can be of poor quality. There are
often issues of under-recording, data bias and incomplete
datasets.
30
Sources of Uncertainty (3)
3. Experts
--- Experts frequently disagree even under careful elicitation to
avoid ambiguity.
--- Experts can be wrong.
31
Atlantic Multidecadal Oscillation
AMO time series
AMO link to U.S. hurricane
landfalls
Goldenberg et al.,
Science, 2001.
Sutton and Hodson,
Science, 2005.
32
400
350
350
300
300
250
250
200
200
150
150
100
100
50
50
0
0
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
400
Nr of stations
Nr of Wind speed obs.
Nr of Gust obs.
2,000,000
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
Nr of stations
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2,000,000
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
Limited nature of UK peak gust data
Nr of Wind speed obs.
Nr of Gust obs.
Number of stations, wind speed and max. gust observations at UK stations in
MIDAS WH (left panel) and WM (right panel) hourly datasets
Example 1 of Poor
Data Quality
Until recently (~2010) annual hurricane
numbers in the North Atlantic were
underestimated prior to the mid 1960s
due to lack of satellite monitoring. This
was a particular problem for systems
forming/tracking a long way from land.
Careful analyses have now corrected for
the missing hurricane numbers.
See the top panel opposite for the
original hurricane numbers and time
series 1880-2010, and the bottom panel
for the corrected numbers.
(Figure from Vecchi and Knutson, 2011).
Example 2 of Poor Data Quality
Downward trend in
ratio of hurricanes to
intense hurricanes,
and of tropical storm
to intense hurricanes,
but not of tropical
storms to hurricanes
Intense hurricane numbers too
low in 1970’s and early 1980’s
35
Example 3 of Poor Data Quality
Correlation (running 30-yr)
between Aug-Sep tropical
Atlantic sea surface
temperatures (SSTs) and North
Atlantic hurricane activity
disappears centered on the
1940s.
This suggests the physical link
may not be temporally stable.
However, quantification of the
uncertainties in this SST shows
the drop-out in correlation is
likely related to a large
increase in SST uncertainty in
the 1940s (which implies the
link is likely stable after all).
36
Experts Disagree and Can be Wrong
37
5. Examples of MSSL Research
38
5.1 Solar Link to UK/European
Winter Climate
Objective:
To improve understanding of the drivers and
predictability of UK/European winter climate
Motivation:
1.  Recent years have seen considerable variability in winter
climate. Why is this?
2. Skillful long-range predictability would provide beneficial
impact.
Background
Winter 2009/10
Winter 2013/14
Porthleven, Cornwall: 4 Jan 1998
(Courtesy, Simon Burt)
UK Big Freeze: Jan 2010
(Image courtesy of NASA).
Porthleven, Cornwall – Storm of 5th February 2014
(Photograph courtesy of Matt Clark, Met Office)
Recent winters have varied from ‘freezes’ to ‘mild, wet and stormy’. Are
these changes caused by natural variability or are they predictable?
North Atlantic Oscillation
+ve NAO!
-ve NAO
(Figures Courtesy of Martin Visbeck, Columbia University)!
Winter NAO Time Series 1825-2013
CRU NAO Index (Iceland – Gibraltar)
Figure courtesy of Tim Osborn, University of East Anglia
Potential Drivers of Winter Variability
•
North Atlantic sea surface temperature:
Rodwell and Folland (2002), Saunders and Qian (2002).
•
Prior summer Eurasian/Northern Hemisphere snow cover
extent:
Bojariu and Gimeno (2003), Saunders et al. (2003).
•
Quasi-Biennial Oscillation (QBO):
Kuroda and Kodera (1999), Castanheira and Graf (2003),
Barriopedro et al. (2008) and others.
•
Solar cycle:
Labitzke (1987), Frame and Gray (2010), Roy and Haigh
(2011) and others. !
Link Between Winter NAO and Solar Activity
Winter NAO Probability of Exceedance by JAS
Solar Activity Quartile Sign 1950/1-2013/4
Link Between Winter NAO and Solar Activity
Red = Winter NAO at least 1.0.
Blue = Winter NAO less than -1.0.
8 out of 10 winters with NAO less than -1.0 have a JAS solar flux < 100 sfu.
(p-value = 0.02).
17 out of 22 winters with NAO greater than 1.0 have a JAS solar flux > 100
sfu (p-value = 0.04).
Implication 1: Predicting Extreme Winters
•  The p-value for the combined low solar flux and strong
easterly QBO years having a mean DJF NAO value ≤ -1.52
is highly significant.
•  Four of the six most –ve NAO winters 1950/1-2013/4
occurred under these conditions. These include 2009/10
and 1962/3.
•  Since solar activity and the QBO are both cyclical and
hence predictable several months ahead this model offers
promise for the long-range predictability of cold winters.
Implication 2: Occurrence of Next Cold Winters?
The current (Jan 2016) projection for the next solar minimum is for
2018/9. Thus I would anticipate a heightened probability of further cold
winters in 2-3 years time.
5.2 Drivers and predictability of
Atlantic hurricane activity and
European winter climate
Objective:
To improve understanding of the drivers and
predictability of weather and climate extremes.
Motivation:
1.  Recent years have seen considerable variability in
hurricane activity and European winter climate. Why?
2. Skillful long-range predictability would provide beneficial
impact.
A. Unexpected quiet 2013 hurricane season
•  Quietest hurricane season since 1982.
•  Research team from eight institutes formed
to explore cause.
•  Explanation eventually determined by MSSL.
Physical mechanism: NAO switched from –ve to +ve in
April 2013. This strong change in the spring NAO affected North Atlantic
atmospheric circulation and tropical SSTs where hurricanes form.
B. Anticorrelation between hurricane activity
and upcoming European winter climate
•  Hurricane main season: Aug-Oct.
•  European winter main season: Dec-Feb.
•  European winter climate strongly linked
to sign of North Atlantic Oscillation (NAO)
+ve NAO
-ve NAO
Significant anticorrelation (i.e.
stormy winter season follows quiet
hurricane season and vice versa)
observed when:
(a) hurricane activity is in upper
or lower tercile.
(b) summer ENSO is neutral.
Significant anticorrelation may be
anticipated by hurricane season
mid point in early September.
Physical mechanism: Cool SST waters in the tropics favour a
quiet hurricane season. This cooling is part of a larger-scale ‘tripole’ SST pattern
which persists, slowly evolves, and by Oct-Nov-Dec is associated with an enhanced
latitudinal temperature gradient at ~50°N which favours a stormy European winter.
C. WMO assessment of performance of
seasonal hurricane outlooks 2003-2013
TSR/MSSL outperforms the two leading US forecasters (NOAA and CSU)
Storm Imogen 8th February 2016
Peak recorded gusts and their return periods
(MSSL analysis):
Needles:
Culdrose:
Pembrey:
Avonmouth:
Heathrow:
Gatwick:
95 mph
79 mph
76 mph
75 mph
57 mph
45 mph
1 year
1-2 years
1 year
3 years
1 year
<1 year.
Conclusion: Imogen was not that unusual.
Historically we expect a storm like Imogen once a year.55