HEAT WAVES

Gregorio GomezRobert SauermannBen Lynton

Introduction: What is a heat wave? 2010 Russian Heat Wave

1. Dole et al. (2011): Natural Variability & “Omega” Blocking

2. Rahmstorf & Coumou (2011): Warming & Frequency

3. Otto et al. (2012): Reconciling 1 & 2

4. Samenow article (2012): Public Perception

Debate: Has global warming had an effect on heat waves?

http://www.youtube.com/watch?v=hl9WYhYJjqQ

Presentation Outline

Introduction

• 1858, London’s Great Stink• 1936, North America• Great Depression, drought, dust storms• Record temperatures in 12 states, clearing 120oF• 5,000 US and 1,100 Canadian deaths

• 1995, Chicago• 106oF = average Arizona temperatures• 700 deaths in 5 days, infrastructure break down

• 2003, Western Europe• Hottest summer since 1500 A.D.• 40,000 deaths + forest fires, glacier floods, crop

destroyed

Historical Heat Waves

History Channel: http://www.history.com/news/history-lists/heat-waves-throughout-history

• 55,000 deaths• 25% annual crop production

decrease• 15 billion USD loss to the Russian

economyhttp://www.youtube.com/watch?v=eCb0pNY5jeE

2010 Russian Heat Wave

Average climatological seasonal cycleAbove average temperatureBelow average temperatures

*National Climatic Data Center: Global Summary of the Day

Tem

pera

ture

Ano

mal

ies

(°C)

Off

of a

vera

ge

tem

pera

ture

11/

1/09

-10

/31/

10

The World Meteorological Organization considers a climatic event a heat wave if the local maximum daily temperature exceeds the historical average by 5°C for 5 consecutive days.

Heat “Wave/Dome”

High pressure in mid/upper atmosphere (5km) diverts jet stream stifles circulation traps heat on surface

Magnified by sun angle,clear skies, and drought (latent heat)

What is a Heat Wave?

Understanding Statistics: Mean and Variability

An increase in the mean raised heat wave frequency

Increases in the variability raise hot and cold event frequencies

Changes in mean or variability affect heat wave frequency differently

slide 7: 'comparison' is showing a change in shape (there's a change in mean and I believe a change in the skew) -- would label correctly, or not include.

1. Dole et al. (2011)

Randall Dole• Fellow of the American Meteorological Society• Division Director for the CIRES and IPCC member

Motivation

Cause of 2010 Russian Heat Wave:What were the primary causes for 2010 Western Russian heat wave?

Predictability of Russian 2010 Heat Wave:Based on natural and human forcings and observed regional climate trends, could the heat wave have been predicted?

Methodology: Data & Models• Data– Observations• Western Russian mean July temperatures• Extreme temperature event frequency/variability

– Datasets• NOAA, GHCN, NASA, GISTEMP

• Model Experiments1. Simulations to observe trends in heat wave frequency• IPCC CMIP3 model

2. Evaluate potential effects of July 2010 boundary conditions• AM2.1, MAECHAM5

3. Future global warming effects on heat waves• IPCC CMIP3 model

Western Russia Mean July Temperatures since 1880

No significant temperature change in Western Russia from 1880-2009Mean regional July temp trend unlikely to have caused 2010 heat wave

Western RussiaJuly temp change= -0.1°C / 130yrs=.0008 °C/yr

= July Mean T2009 – July Mean T1880

(°C/130yrs)Western EuropeLarge temp change(Recall 2003 heat wave)

Dole finds no shift in the mean

Western Russia Shows No Increase In Temperature Variability

(+) anomalies

(-) anomalies

Light Grey: simulated temperature anomalies (normalized)Dark Grey: simulated temperature anomalies (non-normalized)Both are based on 22 CMIP3 model simulations

No observable trends in Western Russia temperature extremes Temperature variability trend unlikely to have caused 2010 heat wave

Mean Temp1880-2009

2010 ##: year of top 10 (+) anomaly

Dole finds no change in variance

Statistical Summary: neither mean nor variability explain 2010 heat wave

“no statistically significant long-term change is detected in either the mean or variability of Western Russia July temperatures”

• No significant difference in Western Russia temp mean over last 65 years than previous 65 years (t-test)

• No significant difference in Western Russia temp variability over last 65years than previous 65years (F-Test)

1880-1944 1945-2009 1880-1944 1945-2009

“Omega” Blocking Pattern

Omega Blocking is a common cause of heat waves

Pressure: Low-High-Low• High pressure over large

latitude

Disturbs Jet Stream• Difficult for air flow to move

from west to east over high pressure hump traps heat

Region under Ω Block• dry weather, light wind

Ω troughs• Rain and clouds

Typical Western Russia Heat Wave Conditions

Top 10 Heat Waves exhibit classic “omega” blocking pattern

Top 10 Composite

Height of pressure bar anomalies off of 5000m(in 10s of meters)

Temp anomalies off of local average surface temperature (°C)

##: year of top 10 (+) anomaly

Comparing 2010 with Top 10

2010 Heat Wave Consistent with Top 10 Composite2010 Heat Wave exhibits classic “omega” blocking

2010 Heat WaveTop 10 CompositeHeight of pressure bar anomalies off of 5000m(in 10s of meters)

Temp anomalies off of local average surface temperature in (°C)

Height of pressure bar anomalies off of 5000m(in 10s of meters)

Temp anomalies off of local average surface temperature (°C)

Moreover, the Blocking Pattern was not Predictable

July 2010 climate conditionsGDFL AM2.1 and MAECHAM5• natural and human forcings• e.g. SSTs, arctic sea ice

50-member ensemble• Inconsistent with “Ω” blocking

Single Model Simulation• Qualitatively similar to

observations• Reflect internal atmospheric

variability rather than systematic response to boundary conditions

Boundary Conditions could not predict 2010 Blocking Pattern

Height of pressure bar anomalies off of 5000m(in 10s of meters)

Temperature anomalies off of local average surface temp in (°C)

Height of pressure bar anomalies off of 5000m(in 10s of meters)

Temperature anomalies off of local average surface temp in (°C)

Boundary Conditions Forcing GDFL AM2.1 ~ ~

MAECHAM5NOAA CFS

Not mean shift, Not increased variability, Not Boundary Conditions2010 Heat Wave not predictable, and likely due to natural variability

Models predict global increase in the probability of future heat waves

Probability of Future Heat Waves on Earth

% of 22 CMIP3 models that simulate ≥ 10% probability of heat wave occurrence

CMIP3 models show increase in heat wave frequency, with uncertain timing due to sensitivityin greenhouse gas concentration predictions

Conclusions

Cause of 2010 Russian Heat Wave:Internal atmospheric variability created an “omega” blocking period which caused the heat wave.

Predictability of 2010 Russian Heat Wave:The 2010 Western Russian heat wave could not have been predicted as there have not been observed changes in mean temperatures or extreme temperature variability, and boundary conditions could not have predicted the ‘omega’ blocking event.

2. Rahmstorf & Coumou (2011)

Stefan Rahmstorf: Paper 2•German climate change advisory council member•Author of paleo-climate chapter in 4th IPCC report

Dim Coumou: Paper 2• Potsdam Institute of Climate Impact Research

member

Motivation

• Heat Wave Frequency : How do warming trends influence the expected number

of record breaking and threshold breaking heat waves?

• Cause of Moscow Heat Wave: What is the probability that local warming trends caused the 2010 Moscow Heat Wave?

Methodology

• Data: - NASA Goddard Institute for Space Studies (GISS) annual

global temperature (0.09°C variability, + 0.70°C / last 100 years)- Moscow Weather Station mean July temperature (1.7°C

variability, + 1.8°C / last 100 years)

• Simulations / Calculations: 1. Generated Gaussian distributions of stationary climate,

linearly increasing climate, and non-linearly increasing climate2. Applied theoretical results to GISS data and Moscow data3. Calculated expected probability and number of heat

records in past decade

Understanding Statistics: Mean and Variability

An increase in the mean raised heat wave frequency

Increases in the variability raise hot and cold event frequencies

Changes in mean or variability affect heat wave frequency differently

slide 7: 'comparison' is showing a change in shape (there's a change in mean and I believe a change in the skew) -- would label correctly, or not include.

Calculating theoretical probability of record eventsStationary Climate (blue)

•Probability of record = (1/n)(n = # of data points)

•Defined as a climate with no long term deviation from T[

Non-stationary Climate•Results from either shifting long term mean (T[), changing the distribution of T, or both•Linear warming trend → approx. linear increase in expected probability of record events

Stationary v. Non-stationary probability trends

More extreme events are expected in Non-Stationary climates (+ or -)

Monte-Carlo simulations match actual temp distributions

A) Simulated noise with stationary trend

B) Simulated noise with ↑ linear trend = 0.078/year

C) Simulated noise with ↑ non-linear smoothed NASA GISS trend

D) Normalized global annual NASA GISS temp. + non linear trend (1911 – 2010)

E) Normalized Moscow station July temp. + non linear trend (1911 – 2010)

Simulated Gaussian noise v. actual temperatures

Normalized non-linear trends are the best approx. of actual conditions

Increases in warming lead to more Unprecedented and Threshold Breaking Events

Unprecedented Events•Approx. linear increase

Threshold Breaking Events•Non-linear increase

# Re

cord

eve

nts

in p

ast d

ecad

e

# th

resh

old

brea

king

eve

nts

in p

ast d

ecad

e(.078,1.4)(.078,~7)

(.078,~3)

Exp. Records v. warming trend Exp. Events v. warming trend

±3

±4

Cold events approach 0

Warm events increase

Both record frequency and threshold breaking frequency increase with warming, but at different rates

Observed trend (GISS) Observed trend (GISS)

Smooth non-linear climate trend is best model of both Global and Moscow temperatures

Noise is not exactly Gaussian but is stationary and reasonably close

NASA GISS (1911 – 2010) Moscow Station (1911 – 2010)•SDG = .088°C•Autocorrelation: r = .17

•SDMS = 1.71°C (19x SDG)•Autocorrelation: r = -0.04

To accept a non-linear trend in temperature, temperature deviations (residuals) should exhibit near Gaussian distribution and no autocorrelation

Results of applying Monte-Carlo simulations to increasing GMT observed in NASA GISS data

• Note: Increased inter-annual variability → increased # of extreme events, but decreased # of unprecedented events

If a non-linear trend (or recent linear trend) is used, more Global unprecedented heat extremes are expected

012> 2

19%

39%28%

13%

Linear trend (100 yr)•Predicted extremes = 1.4•Trend = .0078°C/yr

Linear trend (30 yr)•Predicted extremes = 2.4•Trend = .017°C/yr

Non-Linear trend•Predicted extremes = 2.8

Percentage of simulations v. # of expected heat extremes

(100 year linear trend)

*predicated extremes for 2000-2010

Results of applying Monte-Carlo simulations to increasing July Moscow temperatures

Expe

cted

Unp

rece

dent

ed

Hea

t Ext

rem

es /

Dec

ade

Predicted Record Heat Extremes In Stat. & Non-Stat. Climates

• Given linear warming trend of .011/yr, sim predicts .29 unprecedented heat extremes in past decade

• Given non-linear warming trend, sim predicts .85 unprecedented heat extremes in past decade

• In stationary climate .105 unprecedented heat extremes are expected

Blue = Red = Non linear Moscow Trend

If a non-linear trend (or recent linear trend) is used, more Global unprecedented heat extremes are expected

Increased probability of Russian heat record in last decade is attributable to warming trend

• Rahmstorf and Coumou find post 1980 warming trend most relevant

• Relies on notion that warming trends will directly increase the number of record heat waves (i.e. perfect causality)

Linear Non-Linear (2010 omitted)•Rnon = .47•Prec = 78% •Prec = 80% (1880 – 2009)

Concludes w/ 80% prob. the Moscow Heat Wave is a result of warming

Non-Linear•Rnon = .29•Prec = 64%

•Rnon = .85•Prec = 88%

(Rnon – Rstat) / Rnon = Prec

Determines probability that the 2010 heat record is result of warming by measuring % diff. between stationary and non stationary predictions

Potential influence of Moscow urban heat island

Local warming trend observed in Western Russia is largely a result of anthropogenic greenhouse warming

Blue = Moscow Station

• Note their previous analysis does not address causes of warming trend

• Claim 1/3 of Moscow warming due to local urban heat island effect (2°C / last 30 yrs in city, 1.4°C / last 30 yrs in region – 2X Global avg)

• Rest of warming result of continental warming due to ↑ Global temp

Red = Moscow region

Tem

p An

omal

y (°

C)

Years (1979 – 2009)

Microwave sounding Satellite Data •Western Russia is similar to other continental interiors, and models predict similar results in greenhouse gas forced scenarios (cites 2007 IPCC report)

Conclusions

• Rising mean annual global temperatures and mean July Moscow temperatures have increased the expected probability of an unprecedented heat wave

• Statistical models show an approximate 80% probability that the 2010 Russian heat wave would not have occurred without global warming

• The Local warming trend observed in Western Russia is largely a result of anthropogenic greenhouse warming

3. Otto et al. (2012)

Friederike Otto• Research fellow at the ECI global climate

science program• Work primarily based on improving climate

models with emphasis on extreme events

Motivation

Reconciling Paper 1 & 2:Do Dole et al.(2011) & Rahmstorf and Coumou (2011) really contradict each other?

Global Warming’s role in 2010 Russian Heat Wave:“Whether and to what extent this event is attributable to anthropogenic climate change?”

Methodology

Ensemble Simulations

Definition: The results of mass model simulations using various different initial conditions are compiled to create a probability function with which observed data can be analyzed. Applicability: Climate models rely on a vast number of variables and so one stand alone simulation (especially for the less complex models) can be unreliable. Otto et al., ran simulations for years with high resolutions that would lead to the best data to base their findings off of.

Modelled and Observed Temperature Anomalies• 2010 heat wave was far above the 95th quantile which seen as an

extreme anomaly.• Warming in Western Russia is 1.9±0.8 times that of the global trend.• The function creating the red line is assuming that the probability

density function has not changed shape but rather it’s shifted to a higher mean.

The 2010 heat wave is far above any reasonable projections

Nor

mal

ized

Tem

p An

omal

y

Using Simulated 500 hPa Altitudes to Test Model Accuracy

• Otto et al., test the accuracy of their model by comparing simulated 500 hPa altitudes in (a) to observed reanalysis data in (b)

• Part (b) contains more variability than (a) due to its data set being far smaller that that of the model.

Comparing their model to observations lends credibility to its accuracy

Height anom

alies (km)

off of height of 5km

Modelled vs. Observed 500 hPa Anomaly Altitudes

Reasonably strong correlation between 500 hPa altitudes and mean July temperatures

a) Scatter plot of mean Russian temperatures Vs 500 hPa mean geopotential heights.

b) Corrected for bias and including a line representing a 1 to 1 relationship, the second graph shows that their regression pattern is promisingly accurate.

The 2010 heat wave follows the 1 to 1 trend between geopotential height and temp.

2010 heat wave

2010 Heat Wave

Temp°C

Comparing Conclusions from Paper 1 & 2

• Return time is a theoretical measure of how often an event of a particular magnitude will occur.

• They used mean temperatures from the 1960’s and the 2000’s to produce two different curves illustrating the difference in heat wave frequency.

The figure suggests that both high natural variability and increased heat wave frequency could have caused the 2010 heat wave.

Return Time Vs. Magnitude

+1°C

33 (Years)

°C

Conclusions• Paper 1 claims the heat wave could not have been anticipated

because it was caused principally by natural variability in the West Russian climate.

• Paper 2 fits a trend to Russian temperatures to show that the recent warming has raised the predicted frequency of extreme events.

• Having checked empirical data and conducted numerous simulations, both of the conclusions proposed by the different authors could be true.

• Analysis of large Ensemble Simulations suggest that it is possible for the 2010 heat wave to have been caused by both natural variability or by increased risk caused by a local warming trend.

Epic March — The Washington Post (2012)

Public opinion is varied, yet potentially influential

Record Highs in Midwest, Great Lakes, NortheastLink to Global Warming? • U of Utah, TWC representatives attribute global warming• Accuweather representatives ask for more data

Jason Samenow:• Weather editor for the Washington Post

• Chief Meteorologist for the Capital Weather Gang Blog