Series Lecture of Introduction to Sustainability

京都大學グロバールCOEプログラム2009-2014
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極端気象と適応社会の生存科学
Sustainability/Survivability Science for a Resilient Society Adaptable to Extreme Weather Condition
Extreme weather and its prediction (1)
Dr. Bin HE
[email protected]
Disaster Prevention Research Institute
Kyoto University, Japan
Oct. 28, 2010
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Did anyone experience it?
Heat waves
ens-newswire.com
Droughts
nowpublic.com
Cyclones
spacebeaglenotes.blogspot.com
spacebeaglenotes.blogspot.com
Floods
blogs.msdn.com
Tidal waves
qwickstep.com
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Extreme event:
“an average of a number of weather events over a
certain period of time which is itself extreme (e.g.
Extreme
weather
includes
weather
rainfall
over a season)”
phenomena that are at the extremes of the
Simple historical
extremes:
distribution, especially severe or
unseasonal
weather.
Thevariables
most commonly
“individual
local
weather
exceeding
used definition of extreme weather is
criticalbased
levelsonon aancontinuous
scale”
event's climatological
distribution.
Extreme weather occurs only
Complex
extremes:
5% or less of the time.
“severe weather associated with particular climatic
phenomena, often requiring a critical combination
of variables”
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Hurricanes
Tornadoes
Typhoons
Flooding
Thunderstorms
Monsoons
Lightning
Bizarre Storms
http://www.google.co.jp/i
mghp?hl=ja&tab=wi

Drought
Dust storm
Wild fire

MORE……!!
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
http://www.google.co.jp/i
mghp?hl=ja&tab=wi
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Nonlinear process

. Uncertainty.

. Model's limitation to predict extreme.

Combination

. Snow covers, cloud covers…

. Minimum and maximum temperature.

. Combined high temperature and high humidity .

. Wind speed, cold temperature and wind sheer.

. Precipitation amount and concentration.

. Time, location and etc...
Is outside the normal range of intensity that a region experiences.
Complicated and very difficult to
understand weather patterns fully, but
we can understand it well enough to
make useful decisions for society.
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Severity
large impacts (extreme loss):
◦ Injury and loss of life
◦ Damage to the environment
◦ Damage to ecosystems
90th percentile
Extremeness
large values of variables:
◦ maxima or minima
◦ exceedance above a high threshold
◦ exceedance above all previous
recorded values
Frequency
Source: www6.cptec.inpe.br/caio/talks/cuba-coelho
Longevity
◦ Acute: Having a rapid onset and following a short but severe course
◦ Chronic: Lasting for a long period of time (> 3 months)
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In order to understand climate change, we must have an understanding about
both weather and climate.
What is the difference between
Climate and weather?
‘Climate is what we expect,
weather is what we get.’
Weather is what we experience on a day to
day basis and what guides our daily outfit and
plans for local travel and recreation…
www.lmnts.org
http://www.clipartpal.com/clipart/science/rain_116296.html
Weather adds up to climate over time and
climate informs weather predictions - they are
connected through time and dependent on place. 9
Linking Weather and Climate
“weather” and “climate” treated separately.
(1) How do climate variations and change affect weather
phenomena?
(2) How do weather phenomena affect climate variations
and change?
(3) What are key phenomena and processes that bridge
the time scales between synoptic-scale weather (time
scales of order a few days) and climate variations of a
season or longer?
Real physical system is a continuum:
Fast “weather” processes
Slower “climate” fluctuations
Understanding connections between weather and climate is
required to make progress in addressing important societal
issues:
•Assessing risks
•Abrupt climate change
www.atmos.umd.edu/~martini
/wrfchem
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
Difference between climate change and global warming?
Global Warming involves warming up of the Earth based on its average
temperature, while climate change is more broad and involves the
change in the average weather, such as temperature, wind patterns, and
precipitation, than an area experiences.

Examples of extreme weather events being affected by
global warming?
Some events are floods, droughts, hurricanes, tornadoes, changes in
precipitation and temperature, and more.

Connection between global warming and extreme weather:
Global warming changes the circulation of heat around earth and as a result
it changes how energy flows through weather systems. For example,
areas of the ocean will heat differently and air masses will heat
differently as well. Also the evaporation and precipitation patterns will
likely change.
IPCC 2007
12
http://maps.grida.no/go/graphic/trends_in_natural_disasters


The Earth’s surface has warmed about 0.6 degrees in the
past 100 years, with the 10 warmest years all occurring
since 1990.
Other evidence of global warming includes more
heatwaves, warming of the oceans and lower
atmosphere, less snow, and glacial retreat.
Arctic
summer
sea ice
loss:
Prediction
s v reality
IPCC 2007
‘We are basically looking now at a future climate that's beyond
anything we've considered seriously in climate model simulations’:
Christopher Field, Director, Carnegie Institute Department of Global
Ecology, Stanford University, IPCC . Feb 15 2009.
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-
-
-
-
Higher sea levels
Global Warming melts ice caps
and expands water, resulting in a
rising sea level.
Erosion of coastal areas
Effected by Storms, Precipitation,
Sea level rise
Damage to estuaries
Decline in water quality
Increase salinity of
bays, rivers, and
groundwater tables
Decreasing yield for fisheries
Decrease in marine biodiversity/
migration of species
Increase in extreme weather
events
IPCC 2007
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95% of disaster deaths over last 25
years in low- and middle-income
countries
Rapid growth in number of extreme
weather ‘natural’ disasters:
◦ storms, floods and droughts rather than
earthquakes, volcanic eruptions and industrial
accidents
Severe weather affects everyone on our planet!
Impacts individuals, economies, governments, wars…..
http://www.huffingtonpost.com/news/extreme-weather
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


•
•
•
Global Warming is linked to extremes in
weather conditions.
Increase in the number and intensity of
extreme weather events such as
hurricanes, floodings, droughts,
cyclones and other severe storms.
Costs of damage from extreme weather
events linked to global warming are
very high.
Increase in number and severity of
extreme events due to global warming.
More heat waves.
More floods, hurricanes.
http://www.huffingtonpost.co
m/news/extreme-weather
The availability of water plays
a more important role on
these impacts than
temperature itself
IPCC 2007
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Extreme weather has an enormous
impact on people around the world.
 It affects the production of food,
because droughts and floods interfere
with agriculture.
 Severe storms can take lives and
destroy coastal communities.
 The economic impact of lost
buildings, jobs, and homes can be
devastating.

http://www.huffingtonpost.co
m/news/extreme-weather
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Extreme
weather
Damage
Loss
Severe events (extreme loss events) caused by:
 Rare weather events
 Extreme weather events (amenable to EVT)
 Clustered weather events (e.g. climate event)
Extreme loss is not always due to
extreme weather!
http://www.huffingtonpost.co
m/news/extreme-weather
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
Urban populations already facing difficulties with
extreme weather events
◦ High vulnerability of infants & children including impacts
on long term development as well as more immediate
impacts
◦ Disruptions that affect urban livelihoods


Urban centres at risk of sea-level rise - on coasts
with settlements and water sources at risk
Urban populations with the least resilience
◦ How large their impact is so dependent on what is done in
advance regarding preparedness
http://www.huffingtonpost.co
m/news/extreme-weather
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General information
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

http://www.wmo.int/
pages/prog/gcos/scX
VI/09.4_WCAS_Kolli
No specific tools or procedures in generally.
Necessary to improve model forecast accuracy.
Numerical model based probabilistic forecast.
Increase model's predictability to extreme.
Seasonal dependence.
Experiences of forecasters
Statistical model based probabilistic forecast.
Ensemble or single forecast.
Multi-methods
Considering the ratio of cost/loss
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Dependence of socio-economic activities
on weather and climatic factors
Reliability of climate products including
awareness of associated uncertainties
and their implications to decisionmaking
Accessibility of useful weather/climate
information for decision making
Ability of users to act on the basis of
climate information
http://www.wmo.int/
pages/prog/gcos/scX
VI/09.4_WCAS_Kolli
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Modified from
www.wmo.int/pages/prog/g
cos/scXVI/09.4_WCAS_Kolli
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Source:
http://www.cgd.ucar.edu/cas/Trenberth/P
resentations/ClimForecastsTrenberth
www.casc.org/meetings/aug07/Buja
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 Forecasting
was a subjective art
◦ Based on surface observations
 Forecasts
of extreme events
were poor beyond 12 hours
Source:
www.nws.noaa.gov/com/files/5
0thsymposium3animation
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
Based on sophisticated global and regional
numerical models
◦ Initialized with global observations, satellites, aircraft, ships,
buoys, radar
◦ Produce accurate forecasts of extreme events 5-7 days in
advance
◦ Including “hazards assessment”
product to day 14
www.cpc.noaa.gov/products
Receives Over 116 Million Global
Observations Daily
Sustained Computational Speed: 450
Billion Calculations/Sec
Generates More Than 5.7 Million Model
Fields Each Day
Global Models (Weather, Ocean, Climate)
Regional Models (Aviation, Severe
Weather, Fire Weather)
Hazards Models (Hurricane, Volcanic Ash,
Dispersion)
Quick updating
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opencongress.org
http://wirelessbroadband.seesaa.net/a
rticle/131781801.html
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http://www.google.co.jp/images
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Some figure from commons.wikimedia.org
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In-situ Data
• Monthly means/extremes of temp. & total precip
• Daily max/min mean temperatures
• Hourly data
• Marine surface observations (ships/buoys)
•Aircraft observations
e.g. 1850 or 1900-current
Satellite Data
• Polar Orbiting Environmental Satellites (POES)
1978-current
• Geostationary Operational Environmental Satellites (GOES)
1978-current 300,000GB
Radar Data
• Radar (NEXRAD) – U.S.
http://www.dfompo.gc.ca/media/backfiche/2003/mar10-eng.htm
1995-current 360,000GB
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Description
 One of NCDC’s most popular web pages
 More than 100K accesses per month
 Central NOAA web page for information/links
on hurricanes, tornadoes, storm events, drought,
extreme temperatures, heavy precipitation, etc.
 Billion dollar weather disasters
Applications
 Natural hazards mitigation
 Insurance claims
http://lwf.ncdc.noaa.gov/oa/climate/severeweather/extremes.htm
Hurricane Mitch
 Agriculture
 Many others
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New probe to help predict extreme weather
A water tracking satellite launched by the
European Space Agency is designed to help
give faster predictions of floods and other
extreme weather incidents caused by climate
change.
The 315 million euro Soil Moisture and
Ocean Salinity (SMOS) probe was carried into
space on a Russian Rockot launcher from the
Plesetsk cosmodrome in northern Russia on
Monday, local time, and is now orbiting 760
km above Earth from where it will gauge the
impact of climate change on the movement
of water across land, air and sea.
http://www.cosmosmagazine.co
m/news/3108/probe-willmonitor-climate-impact-water
By providing precise measures of soil moisture
and ocean surface salt levels, SMOS will fill
important gaps in scientific knowledge about the
water cycle and help meteorologists make more
accurate forecasts in near-real time, say experts.
The general conclusion
that emerges of the
diagnostics of the IPCC
AR4 simulations: Asian
summer monsoon
rainfall is likely to be
enhanced.
From Kumar et al.
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 We
can’t make accurate predictions
about the rate of extreme weather
because climatic patterns are too
complex and have too many variables.
 Predictions are based on computer
models that predict how phenomena
such as temperature, rainfall patterns, &
sea level will be affected.
 Computer models are becoming more
reliable as more data are available,
additional factors are considered, &
faster computers are built.
Source:
www.nws.noaa.gov/com/files/5
0thsymposium3animation.ppt
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10th percentile
90th percentile
Shift in the frequency distribution towards larger values
• Frequency of extremely cold days and nights has decreased
• Frequency of extremely hot days and nights has increased
No trends found in many stations
Only a few stations show statistically significant trends
• Some stations are getting drier
• Longest dry spells are getting longer for a few stations
www6.cptec.inpe.br/caio
/talks/cuba-coelho
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www.nws.noaa.gov/com/files/50
thsymposium3animation
http://www.cgd.ucar.edu/cas/Trenberth/P
resentations/ClimForecastsTrenberth
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Detection of extreme weather changes…
changes in mean
changes in variance
http://www.cgd.ucar.edu/cas/Trenberth/P
resentations/ClimForecastsTrenberth
Statistical tests
detection of trend
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Detection of extreme weather changes…
Spatio-temporal exploratory methods
and probability models
Analysis of extremes with covariates
indices of large scale flow regimes.
Characterize completely extremes
properties
Improve the methods to compare
observed extremes to simulated ones.
Percentile analysis, use of extreme
indices (number of frost days, number
wet days, etc.)
Multivariate extreme analysis
Software: Statistica, R, SPSS, etc.
http://www.cgd.ucar.edu/cas/Trenberth/P
resentations/ClimForecastsTrenberth.
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Non-parametric test
which does not make assumptions
about the population distribution;
Parametric test
Which is based upon the assumption
that the data are sampled from a
Gaussian distribution.
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1) The first step in any hypothesis testing is to state the
relevant null (H0) and alternative hypotheses (H1) to be
tested;
2) The second step is to consider the assumptions being
made in doing the test;
3) Compute the relevant test statistic (the distribution of
such a statistic under the null hypothesis can be derived
from the assumptions);
4) Compare the test-statistic (S) to the relevant critical
values (CV) ;
5) Decide to either fail to reject the null hypothesis or
reject it in favor of the alternative. The decision rule is to
reject the null hypothesis (H0) if S > CV and vice versa.
http://www.atmosphere.mpg.de/enid/1__Weat
her___Fronts/-_Weather_and_Climate_15x.html
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
Internationally accepted convention
recommended by the World
Meteorological Organization (WMO) that
the 30-year period is a basic climatic
time scale, and the statistical properties
calculated for the consecutive 30-year
periods 1901-1930, 1931-1960, and
most frequently used 1961-90. These are
called limatologically standard normals.
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He and Takara, et al. 2010
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1910-2009
He and Takara, et al. 2010
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Detection of long term trend
positive and significant trends
positive and significant trends
He and Takara, et al. 2010
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Changes in annual precipitation a),
soil moisture (b) For the period 20802099 respect to 1980-1999, A1B
( source IPCC,2007)
Probability density functions from
different studies for global Tmean
change for the different SRES
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5. Concluding remarks
Extreme weather events: mostly large impact to
society, natural and etc…
There is still limited use of climate information for
decision making within extreme weather events.
Model forecast ability:
-Limitation to predict extreme events.
Major challenges :
–Spatial and temporal scale
–Micro-climates
–Long-range predictions
The statistical Mann-Kendall test can be used to
detect trends.
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 The
ppt-file of this lecture can be
downloaded from
http://ars.gcoe.kyoto-u.ac.jp/index.php?id=134
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IPCC, 2007: Climate change 2007 .
Buser, C.M., H.R. Künsch, D. Lüthi, M. Wild and C. Schär, 2009: Bayesian multi-model projection of
climate: Bias assumptions and interannual variability. Climate Dynamics, 33 (6) 849-868
Cook, K. H. and Vizy, E. K. (2006), 'Coupled model simulations of the west African monsoon system:
Twentieth- and Twenty-First-century simulations', Journal of Climate, 19 (15), 3681-703.
Dessai, S. and M. Hulme (2004). "Does climate adaptation policy need probabilities?" Climate Policy
4(2): 107-128.
Fischer, E.M. and C. Schär, 2009: Future changes in daily summer temperature variability: driving
processes and role for temperature extremes. Clim. Dyn., 33 (7), 917-935
Murphy, J. M., D. M. H. Sexton, et al. (2004). "Quantification of modelling uncertainties in a large
ensemble of climate change simulations." Nature 430: 768 - 772.
Stainforth, D. A., T. Aina, et al. (2005). "Uncertainty in predictions of the climate response to rising
levels of greenhouse gases." Nature 433(7024): 403-406.
Tebaldi, C., Smith, R. L., Nychka, D., and Mearns, L. O. (2005), 'Quantifying uncertainty in projections
of regional climate change: A Bayesian approach to the analysis of multimodel ensembles', Journal
of Climate, 18 (10), 1524-40.
http://www.cgd.ucar.edu/cas/Trenberth/Presentations/ClimForecastsTrenberth
http://www.clipartpal.com/clipart/science/rain_116296.html
http://www.huffingtonpost.com/news/extreme-weather
http://www.atmosphere.mpg.de/enid/1__Weather___Fronts/-_Weather_and_Climate_15x.html
www.nws.noaa.gov/com/files/50thsymposium3animation
google.com/image,
etc…
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Thank you.
Questions?
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