Global Patterns of the Risk of Seasonal Extremes Related to ENSO

1
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

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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

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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.

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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

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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

6
Observational record of extreme seasonal
precipitation anomalies for the US Gulf Coast
with ENSO conditions in the 0 to 3 preceding
seasons
La Niña
El Niño
http//www.cdc.noaa.gov/Climaterisks/Regions
(After Wolter et al, 1999,. J. Climate, 12,
3255-3272. )
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Maps of the US showing increased ENSO-related
risks of extreme climate conditions
La Niña
El Niño
winter (DJF)precipitation
spring (FMA) temperature
http//www.cdc.noaa.gov/Climaterisks/ (After
Wolter et al, 1999,. J. Climate, 12, 3255-3272. )
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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
  3.75x3.75 grid
• Twelve 3-month seasonal averages (JFM, FMA, ..,
  DJF)

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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 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 0.96

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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 3-month 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

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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
12
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).
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Global patterns of observed El Niño temperature
extremes
Risk of Warm Extremes
Risk of Cold Extremes
JFM
JJA
Risk Relative to the 20 Climatological Risk by
Chance
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Global patterns of observed La Niña temperature
extremes
Risk of Warm Extremes
Risk of Cold Extremes
JFM
JJA
Risk Relative to the 20 Climatological Risk by
Chance
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Global patterns of observed El Niño precip
extremes
Risk of Wet Extremes
Risk of Dry Extremes
JFM
JJA
Risk Relative to the 20 Climatological Risk by
Chance
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Global patterns of observed La Niña precip
extremes
Risk of Wet Extremes
Risk of Dry Extremes
JFM
JJA
Risk Relative to the 20 Climatological Risk by
Chance
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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_p
  df_pr.htmlhttp//www.cdc.noaa.gov/rwebb/ensorisk
  /pdfs/ext_pdf_tp.html

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• 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 .

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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.

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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, 35873607.
• 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 3.75x3.75
  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 150-year control run

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PaleoCSM simulated ENSO variability
Figure 5 from Otto-Bliesner and Brady. Time
series of simulated Niño 12, Niño 3, Niño 4, and
an equatorial version of the SOI
22
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
  5N5S,13080W and the western Pacific
  5N5S, 90140E),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

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Global patterns of simulated ENSO-related 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.

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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
25
Observed and simulated El Niño JFM temp extremes
observed
PaleoCSM
Risk Relative to the 20 Climatological Risk by
Chance
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Observed and simulated La Niña JFM temp extremes
observed
PaleoCSM
Risk Relative to the 20 Climatological Risk by
Chance
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Observed and simulated El Niño JFM precip extremes
observed
PaleoCSM
Risk Relative to the 20 Climatological Risk by
Chance
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Observed and simulated La Niña JFM precip extremes
observed
PaleoCSM
Risk Relative to the 20 Climatological Risk by
Chance
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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

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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