From the UNIVERSITY OF TEXAS AT AUSTIN and the “what if it’s only El Niño, then what?” department:
Scientists at The University of Texas Institute for Geophysics (UTIG) have found that a devastating combination of global warming and El Niño is responsible for causing extreme temperatures in April 2016 in Southeast Asia.
The research, published on June 6 in the journal Nature Communications, shows that El Niño triggered the heat, causing about half of the warming, while global warming caused one-third and raised the heat into record-breaking territories, according to the team’s analysis. El Niño is a climate pattern that impacts the tropical Pacific, and usually brings warmer temperatures to Southeast Asia in April.
In April 2016, high temperatures in mainland Southeast Asia broke all previous records, exacerbating energy consumption, disrupting crop production and causing severe human discomfort in Cambodia, Thailand and other countries in the region. The especially high temperatures of 2016 made the researchers interested in investigating the factors behind such extreme heat, including the impact of the record-breaking El Niño of 2015 and whether ongoing global warming played a significant role in the event.
The researchers used computer model simulations designed to disentangle the natural and human-made causes of the extreme heat. They also used observations from land and ocean monitoring systems and found that long-term warming has played an increasing role in rising April temperatures in Southeast Asia. Since 1980, this trend has caused a new temperature record each April following an El Niño.
“The El Niño system primes mainland Southeast Asia for extremes, although long-term warming is undoubtedly exacerbating these hot Aprils,” said UTIG postdoctoral fellow Kaustubh Thirumalai, who led the study. UTIG is a research unit of the UT Austin Jackson School of Geosciences.
The researchers used statistical techniques to quantify the contributions from El Niño and from long-term warming. Their analysis looked at the 15 hottest April temperatures over the past 80 years. All of them occurred after 1980, and all of them but one coincided with El Niño. They found that while the impact of El Niño fluctuated over the years, the impact of global warming has steadily increased over time.
“Though almost 50 percent of the April 2016 event was due to the 2015-16 El Niño, at least 30 percent of the anomaly was due to long-term warming, and there’s definitely more to come in the future,” Thirumalai said.
Looking at the model predictions for the next 50 years, the researchers found that the impact of climate change could amplify the effects of each El Niño, leading to temperature records being broken more often.
“Because of long-term warming, even a weaker El Niño than the 2015-16 event in the mid-to-late 21st century could cause bigger impacts,” said co-author Pedro DiNezio, who is a research associate at UTIG.
Despite all the evidence pointing to worsening extremes, the researchers emphasized that preparedness could allow societies in this region to cope with climate change.
“The silver lining is that these can be predicted a few months in advance since they happen after the peak of an El Niño,” Thirumalai said.
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Just a couple things:
- Only back to 1980? Hmm seems like cherry picking to me, While El Niño is a only recently known phenomenon, there are proxies that could indicate previous past ENSO strengths, which may have been even greater. To assume that 2016 is the largest because of a supposed [contribution] of global warming is pure folly, on many levels.
- They don’t know if the increase in SST is due to the previous Super El Nino and subsequent ones adding heat to the ocean in that area, or not. They don’t know if it’s a contribution of cloudiness (lack of it) or other factors. As many have observed, getting the atmosphere to heat the ocean is quite the trick. Direct solar insolation change is a far more likely candidate, as is wind patterns changing the surface albedo due to roughness. Have they considered algae, and turbidity too? How about population and infrastructure/land use changes in that area affecting temperature measurements over time?
- Here is what April 2016 looks like in that area, compared to April 2017, followed by the global map. Looks like SST cooling is imminent.
Now, look at the latest:
Source: http://www.ospo.noaa.gov/Products/ocean/sst/anomaly/
Bob Tisdale suggests that changes in the Pacific Warm Pool might be a factor:
https://bobtisdale.wordpress.com/2008/10/10/the-pacific-warm-pool-vs-enso/
This is what I don’t understand:
There is not a temperature on that graph below 0.7-0.8 deg C, so what is the zero point mean? The contribution of El Nino is (coincidentally) 0.7 deg C. Why not move the zero point up 0.7 deg C?
In fact, if you look at the temperature from 1980-2014, you would assume that it is +1.0C +/- 0.4C for that entire period. 2016 is simply an anomaly. The pink/red bars are an optical illusion created by the author. Has climate change increased temperatures by 0.7 deg C in Southeast Asia in 25 years? (why isn’t 2015 on the graph?)
35 years — not 25 years. Either way, the typical estimates seem to be an estimate of 0.1 deg C per decade with the tropics being much lower than the poles.
Ellllllllll ….. Ninyooohhhhhhhhhhh! It’s Ayyyyyyyyyy Geeeeeeeeee Doublllllllllle Yooouuuuuuuuuu!!!!!
FWIW — it took me a while to find this paper — https://www.nature.com/articles/ncomms15531
There are a few things that are odd about the data:
1) When plotted in a histogram, most of the temperature data from 1901-2014 is a normal distribution. In other words, it is random variation. They also compare two time periods which varies depending on the source data. For the most part, the data in the first part of the century overlaps the data in the second part of the century. In other words, the data does not support that global warming is occurring in this region.
Histograms
2) The data does show a difference in the histograms from “Post el Nino” when compared to “Post La Nina”. In other words, those are distinct populations.
3) The computer model for the global warming trend is a best fit of the quadratic equation after subtracting out the input of the El Nino using data since 1940. They use 1940 as a start date because that is the time when the station density increased. The r for this analysis is 0.83 (an r2 of 0.69), but they don’t give us any error terms. I would love to see a correlation with the temperature station adjustments. I bet it’s pretty high. the numbers are independent of carbon dioxide concentration or any other factor which could affect temperature. NOTE: This r value is comparing the projected data with the ENSO taken out to GISTEMP, but the projected temperature curve is only based on year. This is an odd way to develop a computer model.
4) The computer model is calculated with data that includes the final very high data point in 2016. Anyone who had conducted a quadratic correlation with data like this will confirm that a final data point will add a significant amount to the curvature of the t2 term. (The model is April Surface Air Temperature = 0.014t + (9.57e-5)t2 + 0.44T – 0.03 where t is the year and T is the ENSO 3.4 anomaly. The t2 term may look small, but 2016 squared is a pretty big number — I can’t imagine that it uses calendar year. It must use some number of years greater than a base year. I can’t figure out what the base year is
5) They use April data because that is the month with the lowest cloud cover and rainfall. That means that it is also the month with the highest temperature variation from year to year.
6) Assuming their analysis is correct, figure three of the supplementary data shows a huge hotspot only in South East Asia “with the ENSO component removed.” In other words, this analysis only applies to SouthEast Asia — it is not “Global Warming” in the least. It is only a local phenomenon.
7) The Zero for the graph is the average temperature from 1951-1980. The data is basically flat from 1940 to 1980, then there is an increase in 1981 to 2015 with the huge increase in 2016. In other words, had they not put the pink bars “estimating” the climate change component, then only 2016 would look like an anomaly. Anomaly
A simple inspection of the anomalous temperatures shown over the study areas shows how ‘nuggety’ this heating is on a global scale. You can imagine selection of a point in the central part of this SE Asia map, then a correlation of temperature with distance from the center. Like a bulls eye, in concept.
I will remain a skeptic of global warming explanations until an explanation is given for this lumpiness of global temperatures. Why does the shape of the land appear to interact with the shape of the anomaly? Ab initio, one would expect global warming from CO2, if correct, to exert a more uniform rise all over the globe, not a selective jump from one location to another every couple of years. (Especially after subtraction of El Nino effects).
I do wish researchers would
(a) get the basics correct before waffling on about detailed minor aspects; and
(b) derive and show proper, formal error envelopes, showing both accuracy and precision, with all data.
Geoff.
Can somebody explain to me why our three thermometers (digital weather station and two mercury) consistently show as much as 7c lower than the UK met office official figures for our area during winter yet match them quite well in summer? It seems the colder the actual weather, the bigger the variance between our thermometers and the MO figures.
We live in rural area. At first I thought it may be caused by met office including UHI effect due to urban location of their measuring stations which would have a more pronounced effect in winter when everybody has heating on. However I have taken my own measurements outside the nearest station and it was only 2c warmer than our rural location in winter and still 1c warmer in summer.
Is there any independent verification that the data is reliable? I also personally know of several instances where measuring stations were relocated and yet people seem to be comparing the new data with the old, which should now be null and void for comparison purposes as it was a different location. Same is true for some stations that were historically rural but have since had thousands of houses and businesses built up around them. How can temperature data from today be compared with data from 30 years ago when the immediate environment has completely changed? Is this valid science? I was always taught to use controls in my school science, if the control was contaminated then the whole experiment was null and void.
Average unadjusted temperature of 10 stations in Thailand and Laos.
Yeah, a few warm El Nino months but there is no “global warming” signal here. Highly correlated to the ENSO. This whole science has devolved into cherry-picking and data adjusting.
http://climexp.knmi.nl/data/tlist_temperature_all_105:_:15_10_1_-1_:___mean_a.png
Randall Reeves 415 999 7698 rreeves0802@gmail.com
http://www.figure8voyage.com
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I live in Bangkok and NO it has not been ‘hot’ the last few years. Most of the time it’s cloudy so it may be warm outside but not ‘very hot’. The cool seasons are getting longer not shorter, we get at least a month of cool air from the north in Dec-Jan despite being so close to the equator. I have been living here for 20 years ad I know it used to be hotter here in the late 1990’s and early 2000’s-there were many more ‘hot’ days. This is almost certainly more data manipulation bullshit.