# Presentation: 118069

## 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

**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**

**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.**

## 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](http://www.cdc.noaa.gov/Climaterisks/#Regions)_**   (After Wolter et al, 1999,. J. Climate, 12, 3255-3272. )**

## Maps of the US showing increased ENSO-related risks of extreme climate conditions

**La Niña**

** ****http://www.cdc.noaa.gov/Climaterisks/****   (After Wolter et al, 1999,. J. Climate, 12, 3255-3272. )**

**El Niño**

**spring (FMA) ****temperature**

**winter (DJF)****precipitation**

## 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)

**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 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

**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**

**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

**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**

**Extending the Wolter et al (1999) work to generate Global patterns of observed ENSO-related climate extremes**

## 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

## 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 Relative to the 20% ****Climatological Risk by Chance**

**Risk of Warm Extremes**

**Risk of Cold Extremes**

**JFM**

**JJA**

**Notes:**
Simplify by going for JFM/JAS only?  Also: go for only one set of extremes - check conclusions/comments down the road; otherwise you have eight slides instead of 2...

## Global patterns of observed La Niña temperature extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**Risk of Warm Extremes**

**Risk of Cold Extremes**

**JFM**

**JJA**

## Global patterns of observed El Niño precip extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**Risk of Wet Extremes**

**Risk of Dry Extremes**

**JFM**

**JJA**

**Notes:**
Refer to Brad’s results!

## Global patterns of observed La Niña precip extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**Risk of Wet Extremes**

**Risk of Dry Extremes**

**JFM**

**JJA**

## 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 ]. 

**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.**

## 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 150-year control run

**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 cm****2**** /sec****1**** 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**

**NCAR coupled ocean-atmosphere PaleoCSM**

## 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 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.**

## 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

## Observed and simulated El Niño JFM temp extremes

**observed**

**PaleoCSM**

**Risk Relative to the 20% ****Climatological Risk by Chance**

## Observed and simulated La Niña JFM temp extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**observed**

**PaleoCSM**

## Observed and simulated El Niño JFM precip extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**observed**

**PaleoCSM**

## Observed and simulated La Niña JFM precip extremes

**Risk Relative to the 20% ****Climatological Risk by Chance**

**observed**

**PaleoCSM**

## 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**

## 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...