Transcript Document 7518756

Global Patterns of the Risk of
Seasonal Extremes Related to
ENSO
Robert S. Webb, Jon K. Eischeid,
Henry F. Diaz, Klaus Wolter, Catherine
A. Smith, and Randall M. Dole.
NOAA-CIRES Climate Diagnostics
Center, 325 Broadway, Boulder, CO
Outline
• ENSO-related climate extremes in the USA
http://www.cdc.noaa.gov/Climaterisks/
• Global patterns of observed ENSO-related
climate extremes
• http://www.cdc.noaa.gov/spotlight/09262002/
• Global patterns of simulated ENSO-related
climate extremes
A Climate Extremes Focus
(more than just the mean)
• Extreme climate conditions strongly impact
(both positively and negatively) the natural
environment and society.
• Mearns et al. (1984) highlighted the
potential large sensitivity of extreme events
to relatively small changes in the mean
conditions under climate change.
• The natural environment and society have
been, and will continue to be, strongly
impacted by the extreme climate events
associated with the ENSO variability.
• Understanding and documenting the impact
of climate extremes, rather than just mean
climate conditions, thus is an important
focus in studying current climate variability,
paleoclimate, or future climate.
Risk of climate extremes with shift in mean
The idealized example
of a mean climate shift
equal to a 1/2 standard
deviation decrease will
double the likelihood of
dry events expected
under normal
conditions while
halving the likelihood
of wet events expected
under normal
conditions
Defining ENSO Climate Extremes
Wolter et al (1999) focused on relationships between ENSO and
the impact of small shifts in mean temperature (and precipitation)
climate anomalies (typically one-half standard deviation in
sensitive regions of the US) on the frequency of occurrence of
extreme events in the extremes, or tails of seasonal climate
distributions relative to the climatological unshifted distributions.
• define wet/dry or warm/cold climatological extremes as
exceeding the highest or lowest 20% of the 100+ year
instrumental record.
• defined ENSO as the top 20 SOI years (La Niña) and the
lowest 20 SOI years (El Niño).
• Four extreme event years would be expected by chance
under either the 20 years of El Niño or La Niña conditions
• A decrease in the number of years to one extreme event year
(0.25x) or increase to eight extreme event years (2x) are
significant at >95% level
Observational record of extreme seasonal
precipitation anomalies for the US Gulf Coast with
ENSO conditions in the 0 to 3 preceding seasons
El Niño
La Niña
http://www.cdc.noaa.gov/Climaterisks/#Regions (After Wolter et al, 1999,. J. Climate, 12, 3255-3272. )
Maps of the US showing increased ENSOrelated risks of extreme climate conditions
El Niño
La Niña
winter (DJF)
precipitation
spring (FMA)
temperature
http://www.cdc.noaa.gov/Climaterisks/ (After Wolter et al, 1999,. J. Climate, 12, 3255-3272. )
Extending the Wolter et al (1999) work to generate Global
patterns of observed ENSO-related climate extremes
• Temperature and precipitation data based on 7280
terrestrial stations from the VERSION 2 AD spanning the
time period 1896 to 1995
• Vose, R. S., R. L. Schmoyer, P. M. Steurer, T. C. Peterson, R.
Heim, T. R. Karl, and J. K. Eischeid. 1992. The Global
Historical Climatology Network: Long-term monthly
temperature, precipitation, sea level pressure, and station
pressure data. NDP-041. Carbon Dioxide Information
Analysis Center, Oak Ridge National Laboratory, Oak Ridge,
Tennessee
• Data were gridded to the PaleoCSM atmosphere
3.75°x°3.75 grid
• Twelve 3-month seasonal averages (JFM, FMA, ….., DJF)
Extending the Wolter et al (1999) work to generate Global
patterns of observed ENSO-related climate extremes
• Bivariate ENSO Timeseries ( "BEST" Index)
• Smith, C.A. and P. Sardeshmukh, 2000, The Effect of
ENSO on the Intraseasonal Variance of Surface
Temperature in Winter., International J. of Climatology, 20,
1543-1557.
• A monthly hybrid ocean/atmosphere ENSO index
calculated as an average of the normalized/standardized
Jones et al. CRU SOI and Nino 3.4 SSTs filtered with a 5month running mean and then re-standardized
• La Niña and El Niño conditions exist for a given month in
the timeseries in which the BEST index exceeds ±0.96
Extending the Wolter et al (1999) work to generate Global
patterns of observed ENSO-related climate extremes
• Climate Extremes analyses were made at each grid box for each of
twelve 3-month seasonal averages if missing data did not exceed
25 percent of the 110 years.
• The 20 and 80 percentile values for each of the 3-month seasonal
averages defined the climatological extreme threshold
• The risk associated with El Niño or La Niña was calculated as the
ratio of the 20 percent expected for a given month for both tails of
the distribution versus the actual number of years for each 3month seasonal average that exceeded the 20 and 80 percentile
climatological extreme threshold.
• Boot-strap resampling with replacement test for significance with
a sample size of 10,000 was used and only results that were
significant at >95% confidence interval are presented
Number of months in the 110 year
instrumental record under El Niño and
La Niña conditions and the expected
number of extremes due to chance
Number of
El Ni–os
Number of
El Ni–o
Extremes
Number of
La Ni–as
Number of
La Ni–a
Extremes
January
February
March
April
May
June
July
August
September
October
November
December
19
21
18
17
18
19
17
16
16
17
19
21
4
4
4
3
4
4
3
3
3
3
4
4
12
13
10
9
10
9
10
14
14
12
11
12
2
3
2
2
2
2
2
3
3
2
2
2
Histogram distribution of seasonal NDJ precipitation in the
east coast of Australia for 109 climatology years (grey) and
for the subset of 11 La Niña years (red). The two vertical lines
demark 20 and 80 percentiles of the climatological range.
Since by chance one would expect only two of the La Niña years to be
extreme (11 years multiplied by 0.2), then the change in risk was 4.5
(9 La Niña extreme years divided by the 2 expected extreme years).
Global patterns of observed El Niño temperature extremes
Risk of Warm Extremes
Risk of Cold Extremes
JFM
JJA
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
Global patterns of observed La Niña temperature extremes
Risk of Warm Extremes
Risk of Cold Extremes
JFM
JJA
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
Global patterns of observed El Niño precip extremes
Risk of Wet Extremes
Risk of Dry Extremes
JFM
JJA
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
Global patterns of observed La Niña precip extremes
Risk of Wet Extremes
Risk of Dry Extremes
JFM
JJA
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
Mean versus Extremes
To illustrate how a change in risk associated with El Niño
or La Niña relates to shifts in the mean and extreme
temperature and precipitation values, we selected a
subset of cases with exceptional increases in the risk
for extreme conditions. For these cases we generated
empirical probability density functions [PDFs] for the
complete temperature or precipitation records [all years]
and for the subset of years under El Niño or La Niña
conditions.
http://www.cdc.noaa.gov/~rwebb/ensorisk/pdfs/ext_pdf_pr.html
http://www.cdc.noaa.gov/~rwebb/ensorisk/pdfs/ext_pdf_tp.html
In many cases the shift [increase/decrease] in the risk of climate extremes is
associated with large shifts in mean climate [e.g., the east coast of Australia ]
In some cases significant increases or decreases in the risk of climate extremes
can occur with only minor changes in the mean value [e.g., along the Pacific coast
of South America ].
Conclusions, Part I
• Understanding and documenting the impact of climate extremes,
rather than just mean climate conditions, is an important area of
study when considering the impacts of current climate variability,
paleoclimate conditions, or future climate
• ENSO variability resulting in small shifts in mean temperature
and precipitation values can have a significant impact on the
frequency of occurrence of extreme events
• Although not discussed in much detail, there is not always a
symmetric response in increased or decreased risk for wet/warm
or dry/cold extremes under El Niño or under La Niña conditions.
• The global pattern of El Niño and La Niña impacts on seasonal
observed temperature and precipitation extremes is a useful guide
for assessing the regional probability of an extreme climate event
in association with an individual ENSO event, interpreting
reconstructions of past climate from paleoenvironmental proxies,
and realism of simulated response in global climate model.
NCAR coupled ocean-atmosphere
PaleoCSM
• Otto-Bliesner, B. L., and Brady E. C. (2001). Tropical Pacific
Variability in the NCAR Climate System Model., Journal of
Climate 14, 3587–3607.
• Atmospheric model is the latest version of the NCAR
Community Climate Model (CCM3)
• CCM3 is a spectral model run with 18 levels in the vertical
and at T31 resolution ~ 3.75°x°3.75 grid
• Ocean model is the NCAR CSM Ocean Model (NCOM) with
25 levels run with ocean background vertical diffusivity set to
0.1 cm2 /sec1 resulting in enhanced ENSO variability
• Ocean grid longitude ~ 3.6° and variable latitude ~0.8 at the
equator increasing to ~1.8° at the pole
• Temperature, precipitation, sea surface pressure, and sea level
pressure data from the last 110 years of a pre-industrial 150year control run
PaleoCSM simulated ENSO variability
Figure 5 from Otto-Bliesner and Brady. Time series of simulated
Niño 1+2, Niño 3, Niño 4, and an equatorial version of the SOI
Global patterns of PaleoCSM simulated
ENSO-related climate extremes
• Following Smith and Sardeshmukh (2000) calculated
monthly hybrid ocean/atmosphere ENSO index
calculated as an average of the
normalized/standardized an equatorial Southern
Oscillation index (EQSOI; Bell and Halpert 1998: the
difference of the normalized sea level pressures
between the eastern Pacific [5°N–5°S,130°–80°W] and
the western Pacific [5°N–5°S, 90°–140°E]),and Nino 3.4
SSTs filtered with a 5-month running mean and then
re-standardized
• La Niña and El Niño conditions exist for a given month
in the timeseries in which the BEST index exceeds ±1
Global patterns of simulated ENSOrelated climate extremes
• As with the observational dataset, the Climate Extremes
analyses for the PaleoCSM were made at each grid box for
each of twelve 3-month seasonal averages if missing data did
not exceed 25 percent of the 110 years.
• The 20 and 80 percentile values for each of the 3-month
seasonal averages defined the climatological extreme
threshold.
• The risk associated with El Niño or La Niña was calculated as
the ratio of the 20 percent expected for a given month for both
tails of the distribution versus the actual number of years for
each 3-month seasonal average that exceeded the 20 and 80
percentile climatological extreme threshold.
• No bootstrap resampling was used to test for significance.
Number of months in the 110 year
simulated record under El Niño and La
Niña conditions and the expected
number of extremes due to chance
Number of
El Ni–os
Number of
El Ni–o
Extremes
Number of
La Ni–as
Number of
La Ni–a
Extremes
January
February
March
April
May
June
18
15
14
15
17
17
4
3
3
3
3
20
20
18
13
4
4
4
3
July
August
September
October
November
December
18
22
21
20
21
23
3
4
4
4
4
4
5
13
11
16
16
17
18
20
17
3
2
3
3
3
4
4
3
PaleoCSM
observed
Observed and simulated El Niño JFM temp extremes
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
PaleoCSM
observed
Observed and simulated La Niña JFM temp extremes
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
PaleoCSM
observed
Observed and simulated El Niño JFM precip extremes
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
PaleoCSM
observed
Observed and simulated La Niña JFM precip extremes
0
.25
.5 .75 1.25 1. 5 2
2.5 >2.5
Risk Relative to the 20%
Climatological Risk by Chance
Conclusions, Part II
• The simulated global pattern of El Niño and La Niña impacts on
seasonal temperature and precipitation extremes in the 110 year
of the NCAR PaleoCSM captures some of the observed changes
in the likelihood of extreme climate events.
• The best match is between the simulate and observed patterns of
winter temperature extremes in North America, South America,
and Africa, although the lack of observation in the latter two
continents cautions against overinterpretation.
• The apparent mismatches for other seasons and for
precipitation are probably due to a combination of factors
including:
model resolution and inadequate topographic complexity
pre-industrial trace gas forcing in the PaleoCSM simulation
location of the modeled regions of enhanced convection
QuickTime™ and a TIFF (U ncompressed) decompressor are needed to see this picture.
March 20th,
2003, near the
top of Coal
Creek Canyon,
20SW Boulder,
9000’; snow
depth on the left
close to actual
depth of 1.6m…