# Presentation: 443621

## Extreme Weather and Climate Events:What are they and where do they come from?

- Dr. David B. Stephenson
- University of Reading, U.K.

## Plan of the talk

- What are extreme events?
- Point process characterisation
- Example 1: Norwegian megafloods
- Example 2: Clustering of European storms
- How will extremes change?
- Thanks to Chris Ferro, Caio Coelho, Pascal Mailier
**Climate Analysis Group: www.met.rdg.ac.uk/cag**

## What are extremes?

## Examples of wet and windy extremes

**Extra-tropical cyclone**

**Hurricane**

**Polar low**

**Extra-tropical cyclone**

**Convective severe storm**

## Examples of dry and hot extremes

**Drought**

**Wild fire**

**Dust storm**

**Dust storm**

## IPCC 2001 definitions

**Simple extremes:**
- 	“individual local weather variables
- 	 exceeding critical levels on a continuous
- 	 scale”
**Complex extremes:**
- 	“severe weather associated with particular
- 	 climatic phenomena, often requiring
- 	a critical combination of variables”
**Extreme weather event:**
- “an extreme weather event would
- normally be as rare or rarer than
- the 10th or 90th percentile.”
**Extreme climate event:**
- 	“an average of a number of weather events over a certain period of time which is itself extreme (e.g. rainfall over a season)”

## Some properties of extreme events

**Severity**
- large impacts (extreme losses):
- Injury and loss of life
- Damage to the environment
- Damage to ecosystems
**Extremeness**
- large values of meteorological variables:
- maxima or minima
- exceedance above a high threshold
- exceedance above all previous recorded values (*record breaker*)
**Rarity****	**
- small probability of occurrence
**Longevity****	**
**Acute: **Having a rapid onset and following a short but severe course.
**Chronic: **Lasting for a long period of time (> 3 months) or marked by frequent recurrence

## Point process description

## Point process ideas

- A stochastic process that generates discrete space-time events
- RATE of process = probability of event per unit time.
- Characterised by counting events in fixed time intervals or by measuring time intervals between successive events.
**Marked point process****: ****events ****also have a magnitude**

## Rate of the process
The probability that an event occurs per unit time
interval (sometimes called frequency). Note that
even constant rate processes give rise to counts
that can exhibit large amounts of variability. 

Magnitude of the process
The probability distribution of the magnitudes of
the events. This can depend also on the rate of
the process.  

Serial dependency
The statistical dependency of the properties of an 
extreme event on the properties of other recent 
extreme events. Serial dependency in event times 
is known as a clustered process. 

- The probability that an event occurs per unit time
- interval (sometimes called frequency). Note that
- even constant rate processes give rise to counts
- that can exhibit large amounts of variability.
**Magnitude of the process**
- The probability distribution of the magnitudes of
- the events. This can depend also on the rate of
- the process.
**Serial dependency**
- The statistical dependency of the properties of an
- extreme event on the properties of other recent
- extreme events. Serial dependency in event times
- is known as a clustered process.

- Attributes of Extreme Event Processes

## Example: Norwegian floods and megafloods

- Ongoing collaboration with
- Anne-Grete Bøe Pytte, Svein Olaf Dahl

- Jostedalen, 1979

- Elverum, 1934

- Setra,Drivdalen
- Damage from Storofsen, 1789

- Skjåk,
- gullies and landslides
- caused by Storofsen, 1789

## Sediment cores from Lake Butjønna

- Sediment cores from Lake Butjønna

## Flood events since 7264BC

- Events since 1000AD

- All 114 flood events in the sediment record

- Storofsen 1789 
- ... 1819 1903 1915 20??

**Questions:**
-  increasing number of events?
-  increasing intensity of events?
-  clusters of events?

- Megafloods are defined
- as events having
- intensities >= 1

## Rate estimation for all 114 events

- **Mean rate = 1 event every 81.2 years. linear trend +1.90%/century**

- 95% confidence interval
- on rate estimate
- and kernel width

## Time interval between events

- **Decreasing trend in the time between events**

## Rate estimate for the 26 megaflood events

- **Mean rate = 1 event every 361.6 years. linear trend +2.23%/century**
**Similar trend in rate to that for all events**

- 95% confidence interval
- on rate estimate
- and kernel width

## Has the intensity of the events changed? 

- **No evidence for change in intensity distribution before/after 2000BC**

- Before 2000BC 	After 2000BC

- Distribution of intensity of events

## Clustering of Extra-Tropical Cyclones

-  Objective eastward tracks identified using TRACK software
-  NCAR/NCEP 6 hourly 850mb relative vorticity maxima
-  Extended winters (1 Oct-31 Mar) from 1948-2003
-  Identified 355,450 cyclone tracks in Northern Hemisphere

**Example: Cyclone tracks crossing Greenwich meridian**
**1 Oct 1989-31 Mar 1990**

## Monthly transits of North Atlantic storms

**West 45N 60W**

**East 55N 0E**

**More cyclones per**
**month than random**

-  regular (in west) and clustered (in east) random processes

**Less cyclones **
**per month than **
**random**

## Overdispersion of storm transit counts

**Units: %**

**under**

**under**

**over**

**over**

**over**

-  Clustering of storms over Europe due to flow-dependence

## Can large-scale flow variations explain the overdisperion?

**n = number of storms crossing a 20****0**** N-S barrier**
**μ = flow-dependent rate**
**x****1****, x****2****, ... , x****k**** = teleconnection indices**
**Maximum likelihood estimation of ß****0****, ß*****i***** **

**Quasi-Poisson regression:**

## Teleconnection patterns

**Polar-Eurasian**

**     ****NAO        **

**Scandinavian    **

**        ****PNA            **

**East Atlantic **

**E. Atl/W. Russian**

**Leading rotated**
**EOFs of 700mb**
**geopotential height **

## Estimated ß regression parameters 

-  all teleconnection patterns are important for cyclone rates

- Green lines outline area with >5 storms/month passing 20° N-S line

## Residual overdispersion

-  Now only underdispersion in baroclinic waveguide (regular waves)

## The Clustering of UK buses

**Is this because bus drivers really**
**love each other?**

**Don’t think so! More to do with rate of arrival depending on time**
**varying background traffic flow. **

## How will extremes change?

## Has the rate of extreme events changed?

## Yes! Step increase in rate by 20%

-  Difficult to detect changes in rate of rare events!

**Data produced by**
**simulation from**
**Poisson distribution**
**with means of 5 and 6**

## How to deal with this uncertainty?

**Get hold of more data:**
-  longer historical series (e.g. paleoclimate)
-  multi-model ensembles of simulations
-  spatial pooling of extremes over regions
**Imaginative inference:**
-  extreme value theory: infer extremal     properties by fitting appropriate statistical  models to large values
-  relate changes in extremes to changes
- in the centre of the distribution
-  relate extremes to other factors and use these to make predictions about extremes (e.g. global mean temperature,  SSTs, NAO)
**Physical insight about key processes**

## Explaining changes in the extremes

- 	Describe changes in quantiles in terms of changes in the location, the scale, and the shape of the parent distribution:

- The *quantile shift*  is the sum of:
-  a location effect (shift in median)
-  a scale effect (change in IQR)
-  a shape effect
**Ferro, C.A.T., D.B. Stephenson, and A. Hannachi, 2005:**
**Simple non-parametric techniques for exploring changing **
**probability distributions of weather, J. Climate, (in press).**** **

**change in**
**location**

**change in**
**scale**

**change in**
**both**

## Regional Model Simulations of daily Tmax

-  Changes in scale and shape both very important

- T90

- ΔT90

- ΔT90-Δm

- ΔT90-Δm-(T90-m) Δs/s

## European Union Project: ENSEMBLES WP4.3 Understanding Extreme Weather/Climate Events 

-  	Provision of statistical methods for identifying extreme events and the climate regimes with which they are associated. More robust assessments of the effects of climate change on the probability of extreme events and on the characteristics of natural modes of climate variability.

- How can we best estimate future
- 	possible changes in return values
- 	for extreme events?
**	****multi-model ensemble ******** tail probabilities**
- Do we understand the key processes that
- 	lead to these changes?
- Which factors are most important
- 	for determining  changes in extreme events?

**http://www.ensembles-eu.org**

## Processes that cause extremes

**1. Rapid growth due to instabilities**
- Fast growth of weather systems caused by positive feedbacks
- e.g. convective instability, baroclinic growth, etc.
**2. Displacement**
- Survival of a weather system into a new spatial region or time period
- e.g. transition of a tropical cyclone into mid-latitudes.
**3. Conjunction **
- Simultaneous supposition of several non-rare events
- e.g. freak waves.
**4. Intermittency**
- Varying variance of a process in space or time
- e.g. precipitation.
**5. Persistence or frequent recurrence:**
- Chronic weather conditions leading to a climate extreme
- e.g. drought, unusually stormy wet season, persistent blocking.
**6. Natural variation**
- Chance occurrence due to natural variations.

- 1

- 3

- 4

## Summary

- Extremes are fascinating but have no single unique definition
- Simple extremes can be treated as marked point processes: rate, magnitude, clustering
- Rarity leads to large sampling uncertainty so statistical and physical inference has to be used

## The End