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Thu Dec 13, 2018, 10:37 PM

A Discussion of the Intensity of El Nino/La Nina Events Under the Volatile Climate Conditions. [View all]

The paper I'll briefly discuss in this post is this one: Increased variability of eastern Pacific El Niño under greenhouse warming (Cai et al, Nature 564, pages 201–206 (2018))

There's a large number of abbreviations in the text: SST = Sea Surface Temperature; CP = Central Pacific; EP = Eastern Pacific; ENSO = El Nino Southern Oscillation.

From the introduction:

Alternating between El Niño and La Niña events, the ENSO affects extreme weather events, ecosystems and agriculture around the world1,2,3,4,5,6,7. ENSO events vary greatly8,9,10,11,12,13,14,15: the EP-ENSO is associated with strong El Niño events and weak cold SST anomalies, and is characterized by the maximum SST anomaly (the SST anomaly centre) being located in the eastern equatorial Pacific (the ‘Niño3’ region: 5° S–5° N, 150°–90° W); the CP-ENSO is associated with strong or moderate La Niña events and modest El Niño events, and is characterized by the SST anomaly centre being located in the central equatorial Pacific (5° S–5° N, 160° E–150° W). EP-El Niño events are the strongest and most destructive El Niño events. During such events, SST warming in the Niño3 region leads to flooding in southwest USA, Ecuador and northeast Peru, and to droughts in regions that border the western Pacific1,4. In extreme cases, the disruption includes substantial loss of marine life in the eastern Pacific, mass bleaching of corals across the Pacific and beyond2, and movement of the intertropical convergence zone7 and of the South Pacific convergence zone towards the equator5,16, inducing catastrophic floods and droughts across the Pacific region5,7. Because of these severe effects, determining how EP-El Niño SST variability responds to greenhouse warming is one of the most important issues in climate science.

Imagine that...

...Important issues in Climate Science.

It appears that computational modeling of these events has proven problematic and the authors set out to improve the situation by the use of sophisticated statistical analysis, principle component (PC) analysis and empirical orthogonal function (EOF) analysis.

A graphic touching on the subject:

The caption:

Nonlinear relationship between the first and second principal components (PC1 and PC2) of SST anomalies averaged over December–February (black dots; see also Extended Data Fig. 1) from five observational reanalysis products. Grey dots indicate monthly data. The nonlinearity is determined by fitting these monthly data with the quadratic function PC2(t) = α[PC1(t)]2 + βPC1(t) + γ. The red curve shows the same fit, but using the December–February average (black points). b, SST anomaly patterns associated with EP-ENSO in two models, highlighting the large difference in the longitude of EP-ENSO anomaly centres (132.25° W for CESM1-CAM5; 101.75° W for IPSL-CM5A-LR) that can occur between climate models. c, The parameter α determined using the monthly data versus the skewness of the E-index and C-index for all models analysed (symbols). This parameter is a measure of the contrast between the CP-ENSO and EP-ENSO and of the size of the skewness of the corresponding C-index and E-index. Models with greater |α| systematically produce larger negative skewness in the C-index and larger positive skewness in the E-index. The large black filled circles indicate the observed value αobs (dashed line; the mean of the five observational reanalysis products). The 17 models that produce |α| < |αobs|/2 (above the dash-dotted line) are denoted by stars and referred to as ‘non-selected’; the other 17 models are shown using various symbols and correspond to the 17 models that we select for further analysis. Details of all models can be found in ref. 29. The linear fits (solid lines) between α and the E-index or C-index are displayed together with the correlation coefficient R, slope and P value from the regression. d, e, Nonlinear relationship between the December–February-average principal components for the selected (d) and non-selected (e) models, with the red curves showing quadratic fits.

The statistical analysis involves all sorts of cool mathematics but this graphic cuts to the chase with modeling of the last century and this century, the one in which we bet the whole farm, well all the farms and all of the planet on the hope that so called "renewable energy" would save the day without interfering with our lifestyle including trips to our favorite restaurant. The statistical output is related to an "E-index" which refers to the relative amplitude of the El Nino Southern Isolation, and thus the size of the swings in the intensity of the destruction it causes Take a peak and weep:

The caption:

a, Comparison of the standard deviation of the E-index over the present-day (1900–1999) and future (2000–2099) 100-year periods in the 17 selected models. 15 of the 17 selected models (88%) simulate a greater variance in the E-index in the future period (red bars) than in the present-day period (blue bars); the two models that simulate a reduction in variance are greyed out. b, Number of strong EP-El Niño events (E-index > 1.5 s.d.) that occurred in the two 100-year periods. The multi-model mean is also shown in a and b; error bars in the multi-model mean correspond to the 95% confidence interval. The differences between the present-day and future multi-model-mean E-index (s.d.) and between the present-day and future multi-model-mean number of strong events are statistically significant at more than the 95% confidence level. The increase in EP-ENSO SST variance (E-index variance) generally translates to more EP-El Niño events for a given E-index intensity.

So called "renewable energy" didn't save the day, and clearly it isn't saving the day, and it won't save the day, but let's not let that change our minds about anything. It's not like data and results count.

While we tend to focus on California, its droughts, fires and floods, in terms of feedback loops the droughts in South Eastern Asia, owing to the carbon release associated with fires there are far more significant, since they involve rain forests. The worst such El Nino event there in the 20th century probably occurred in 1998, a remarkable year in the history of our destruction of the planetary atmosphere. In that year, carbon dioxide concentrations rose by an astounding 2.93 ppm in a single year! Well it was outstanding...

...We're doing a great job at solving the problem. 2.93 in a single year isn't such a big deal any more. In the whole 20th century we had four years, including 1998, that had single year increases greater than 2.00 ppm, this from 1958 to 1999, if I have my math right, 41 years. In the first 18 years of the 21st century, we've had 11 such years, two of which were greater than 1998. In 2015 carbon dioxide concentrations rose 3.05 ppm; in 2016 they rose 2.98 ppm.

(This data can all be found on the website of the NOAA carbon dioxide observatory at Mauna Loa.)

If you find any of this depressing, don't worry, be happy. Someday you might be able to afford to own a Tesla car, in which case none of this will be your fault; it will be the fault of those Chinese people who make the crap, you know, the Santa Claus place mats and inflatable plastic snowmen for your lawn, stuff like that, that you buy when you drive your wonderful Tesla car to the mall at Christmas time.

We wish you a Merry Christmas and a Happy New Year.

I hope your Christmas shopping is going well. Don't forget to pick up a "Sierra Club" Calendar.

Enjoy the coming solstice; it's only 8 days until the days start growing longer, which is great for all those wonderful solar cells in the Northern Hemisphere.

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Reply A Discussion of the Intensity of El Nino/La Nina Events Under the Volatile Climate Conditions. [View all]
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