Guest Post by Bob Tisdale
We recently discussed and illustrated how the differences between sea surface temperature datasets prevented us from knowing which of the recent strong El Niño events (the 1982/83, 1997/98 or 2015/16 El Niños) was actually strongest. See the post here. That post, of course, was intended to counter all of the nonsense from alarmist bloggers and the mainstream media about the current El Niño being the strongest ever.
In this post, we’re going to illustrate the differences between the monthly long-term (1870 to present) NINO3.4 region sea surface temperature anomalies from 5 datasets to further show that the differences grow considerably as we travel back in time.
The post concludes with a recent comment at NOAA’s ENSO blog about the uncertainties of NINO3.4 sea surface temperature data from a well-known and well-respected ENSO researcher. My thanks to Larry Kummer, Editor of the FabiusMaximus blog, for calling my attention to it on the thread here at WattsUpWithThat.
INTRODUCTION
The sea surface temperature anomaly data for the NINO3.4 region are a commonly used index for the strength, frequency and duration of El Niño and La Niña events. The NINO3.4 region covers a large area of the equatorial Pacific. See Figure 1. It is bordered by the coordinates of 5S-5N, 170W-120W. It covers an area that’s roughly 6.2 million square kilometers (or 2.4 million square miles). As references, Australia covers a surface area of about 7.7 million km^2 and the contiguous United States covers about 8.0 million km^2. So the NINO3.4 region is not small.
Figure 1
Since the early 1990s, NOAA has had moored buoys in place that sample sea surface temperature and a number of other metrics in the tropical Pacific. (I believe there are 20 buoys in the NINO3.4 region.) Prior to then, the number and location of sea surface temperature samples depended on ship traffic. Drifting buoys (not ARGO floats) have also sampled sea surface temperatures in the NINO3.4 region since the early 2000s, but the number of samples and their locations depend on whether the drifters have wandered into the NINO3.4 region. The number of monthly in situ temperature samples for the NINO3.4 region that are available from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) are shown in Figure 2.
Figure 2
The large dip and rebound in 2013 was due to temporary budget cuts by NOAA for TAO project maintenance, if memory serves.
Two long-term reconstructions also include satellite-based data, in addition to the in situ data from various types of buckets, from ship inlets and from moored and drifting buoys.
For their sea surface temperature reconstructions, data suppliers like NOAA and the UK Met Office then make numerous adjustments to the source sea surface temperature data from ICOADS to account for the different types of measuring devices.
MONTHLY NINO3.4 SEA SURFACE TEMPERATURE ANOMALY COMPARISON
Figure 3 compares the sea surface temperature anomalies for the NINO3.4 region from 5 different reconstructions, for three timeframes:
- NOAA ERSST.v3b (NOAA’s former infilled dataset)
- NOAA ERSST.v4 (NOAA’s recently introduced “pause buster” data, infilled)
- NOAA Kaplan/Reynolds OI.v2 (An earlier infilled reconstruction from NOAA spliced onto the NOAA satellite-enhanced Reynolds OI.v2 data)
- UKMO HADISST (UKMO’s interpolated/infilled and satellite-enhanced product)
- Cowtan and Way/HADSST3 (UKMO HADSST3 product infilled by Cowtan and Way)
Note: I used the Cowtan and Way data because the HADSST3 data are not infilled, and there are numerous gaps in the HADSST3 NINO3.4 region data due to the poor sampling in that region, especially during the world wars. Also for you consideration, for the period of Jan 1950 to October 2015, the correlation coefficient of the HADSST3 and the Cowtan and Way NINO3.4 sea surface temperature anomalies is 0.99. [End note.]
The top comparison runs from January 1870 (the start year of the HADISST data) to October 2015 (the last month of HADISST data as of this writing). The anomalies are referenced to the full term of the data (1870 to 2014) so not to skew the later results. The center comparison starts in January 1950 and the bottom comparison starts in 1975.
Figure 3
Because of the overlaps, it’s difficult to see the differences between the reconstructions of NINO3.4 region sea surface temperature anomalies. So I prepared Animation 1, which presents the individual products. As shown, there can be noticeable differences in the strengths of El Niño and La Niña events. Also, the ERSST.v3b data are missing a few events prior to the mid-1880s.
Animation 1
MONTHLY NINO3.4 SEA SURFACE TEMPERATURE ANOMALY DIFFERENCE
How large are those differences? Figure 4 presents the differences between the monthly minimum and maximum NINO3.4 sea surface temperature anomalies for those five datasets. That is, I had the spreadsheet determine the monthly maximum and minimum values; then I subtracted the minimums from the maximums. In looking at the differences between datasets, consider that, according to NOAA, the threshold for a weak El Niño is a NINO3.4 sea surface temperature anomaly of +0.5 deg C, for a moderate El Niño the threshold is +1.0 deg C and for a strong El Niño it’s +1.5 deg C. The thresholds are the reverse for weak, moderate and strong La Niñas. See the footnotes in the NOAA ENSO Blog post here. Also, please note that the y-axis in the top graph of Figure 4 is different than the other two.
Figure 4
Note: We’ve already discussed how NOAA’s transition from ERSST.v3b to ERSST.v4 changed which seasons are considered El Niño and La Niña events in their Oceanic NINO Index. See the post Weak El Niños and La Niñas Come and Go from NOAA’s Oceanic NINO Index (ONI) with Each SST Dataset Revision. That is, even updates to sea surface temperature datasets can change what NOAA considers to be a weak El Niño or La Niña season. Most notably, with the ERSST.v3b data, the 2014/15 season registered as a weak El Niño, but with the ERSST.v4 data, the 2014/15 season became ENSO neutral. [End note.]
The 2.5 deg C spike that peaks in boreal winter 1877/78 was caused by the ERSST.v3b data not including the El Niño that happened then. Refer again to Animation 1.
On the other hand, the 3.0 deg C spike in April 1919 is caused by something that’s odd. Figure 5 includes the evolutions of the 1918/19 El Niño from the five datasets. There’s a curious one-month drop-off in the new NOAA ERSST.v4 “pause buster” sea surface temperature data in April 1919.
Figure 5
Makes one wonder how many other anomalous downward spikes appear in the early ERSST.v4 data throughout the global oceans. (No, I’m not going to search for them. You can if you like.)
ENSO RESEARCHER NOTES THE UNCERTAINTIES OF NINO3.4 SST ANOMALY DATA ARE ABOUT +/- 0.3 DEG C
As noted in the opening of this post, Larry Kummer, Editor of the FabiusMaximus blog, called my attention to a comment at the NOAA ENSO Blog about the uncertainties of NINO3.4 sea surface temperature anomaly data. The comment appears on the thread of the NOAA post December El Niño update: phenomenal cosmic powers! by Emily Becker, and the Tue, 2015-12-15 18:24 comment that follows was written by Anthony Barnston, a well-known ENSO researcher from the International Research Institute for Climate and Society (IRI). The two sea surface temperature datasets Anthony Barnston refers to are the NOAA “pause-buster” ERSST.v4 (in-situ only data) and the NOAA Optimum Interpolation SST data version 2 (a.k.a. Reynolds OI.v2), which is satellite-enhanced data. He writes in response to a question about data accuracy (my boldface):
The accuracy for a single SST-measuring thermometer is on the order of 0.1C. It may give a read-out to hundredths, but the last digit would be wobbling around as different water touched the sensor (on a ship, on on a buoy, for example). It could be recorded either to the nearest tenth or the nearest hundredth. But that’s for one thermometer. We’re trying to measure the Nino3.4 region, which extends over an enormous area. There are vast portions of that area where no measurements are taken directly (called in-situ). The uncertainty comes about because of these holes in coverage. Satellite measurements help tremendously with this problem. But they are not as reliable as in-situ measurements, because they are indirect (remote sensed) measurements. We’ve come a long way with them, but there are still biases that vary in space and from one day to another, and are partially unpredictable. These can cause errors of over a full degree in some cases. We hope that these errors cancel one another out, but it’s not always the case, because they are sometimes non-random, and large areas have the same direction of error (no cancellation). Because of this problem of having large portions of the Nino3.4 area not measured directly, and relying on very helpful but far-from-perfect satellite measurements, the SST in the Nino3.4 region has a typical uncertainty of 0.3C or even more sometimes. That’s part of why the ERSSv4 and the OISSTv2 SST data sets, the two most commonly used ones in this country, can disagree by several tenths of a degree. So, while the accuracy of a single thermometer may be a tenth or a hundredth of a degree, the accuracy of our estimates of the entire Nino3.4 region is only about plus or minus 0.3C. Sorry about this big disapointment. It bothers me also. We need thousands of ships evenly spaced across the region to get a truly accurate reading. It’s not worth the money, so it isn’t going to happen. If we improved satellite measurement technology, that could be the key.
I think it would be safe to assume that the +/- 0.3 Deg C accuracies noted are for recent decades, since the early 1990s, when the TAO project buoys were in place. Before then, the sampling was much poorer and the differences between datasets are considerably larger.
CLOSING
Not only do the differences between sea surface temperature datasets and the uncertainties of the data prevent us from knowing the strengths of El Niño or La Niña events, those differences and uncertainties also prevent us from knowing if El Niño or La Niña events have taken place and how long they lasted.
SOURCE
The sea surface temperature data, the Cowtan and Way data and the number of ICOADS sea surface temperature samples are available from the KNMI Climate Explorer.
Bob, Thanks for all you do in reporting the status of El Nino/La Nina conditions.
So far here in NE Calif., this El Niño has not accounted for much more than an average fall/winter precipitation level, so the previous El Niños that you site resulted in more impressive snow and rain fall amounts than the current one.
Definitely no “drought buster” thus far. There is a long road ahead to dig out of this deficit.
Historically speaking, this drought may last for the next 50 years. I’m betting that nobody has figured out why the large variations in drought cycles have occurred in California overr the Holocene.
Ernest Bush: Anthropocene drought. 😉
For anyone who likes to follow actual wind patterns in the Pacific (and everywhere else), this is a very nice website: http://earth.nullschool.net/#current/wind/surface/level/orthographic=197.05,37.30,612/loc=-165.922,40.716
When you click somewhere on the map, the windspeed of that place is shown. You can move the globe to all viewpoints you like. Very interesting also: the view from the North and the South Pole. Furthermore you can get an idea about the heat transport outside the tropics.
You might find this view rather more informative as far as El Nino is concerned.
Yes indeed! Inclusive pressure areas. Thanks!
You have to wonder what’s going on here though 🙂
Or the Sea Surface Temperature Anomalies http://earth.nullschool.net/#current/ocean/primary/waves/overlay=sea_surface_temp_anomaly/orthographic=-151.37,-0.41,466
Look at this monster in the northern Pacific…http://earth.nullschool.net/#current/wind/surface/level/overlay=mean_sea_level_pressure/orthographic=-142.88,37.96,466
@ AJB Jan 14 9:39 am, Hot air from DC and NYC.
@ AJB, 8:31 am.That one gets really interesting when you see what happens when you go from 70 up to the 10 Kpa levels. What I see is a total reversal! Am I correct? And you are right that is a big blocking monster!
Thanks for that link, Wim Röst.
TA
Bob,
This is fantastic and useful work, a valuable contribution to the public’s understanding of these important issues!
Thank you, again, for offering these sober assessments of data. The general public have no idea what “uncertainty” means, and perhaps a lot of the scientific literate population haven’t a clue either. Certainly everyone is led astray by scientists refusing to publish results in the format (best estimate) +/- (uncertainty), and using a standard coverage factor for reporting the uncertainty.
Uncertainty in presenting historical data graphs jumping around as shown in Figure 3, based on the minuscule number of readings shown in Figure 2 covering an area 75% of the US 48 state region.
All in 1/10 degree anomalies.
Thanks, Bob. Very good explanation.
ENSO can be large or small, or not there. ENSO seems to be tripolar and bi-sexual.
No wonder it is impossible to predict its future; we cannot even tell its history.
I think the best one-number index that has been developed is the ESRL-PSD Multivariate ENSO Index (Klaus Wolter, NOAA), at http://www.esrl.noaa.gov/psd/enso/mei/
My favorite topic actually addressed – uncertainty. I would have liked a more extensive discussion about how that large uncertainty means we can’t make a lot of statements that have been made by the press, but at least he acknowledged the uncertainty. It’s a start.
Bob, thanks as always for an interesting exposition.
w.
Thank you Bob for these summaries. They are invaluable – the way things should be done, just as a good professor presents her/his work
I am of the view that wind patterns can tell us a lot – a lot more than water temperature patterns. It is clear that they can be indicative of certain conditions. Here in New Zealand there is a clear signature in regards to Nino/Nina cycles. Mind, we are a range of mountains in the sea, with lovely Antarctica right up our butt and the tradewinds at our flank – and not too much politics in the way
Our Metservice predicted pretty accurately weather patterns over the last 6 months with a 80% certainty that the impact would persist into February. This service has been notably accurate on its predictions on weather over the last few years. Well done Chaps. You are right up there
PS: “no one knows for sure” That’s relates to this entire subject right? Why can’t people realise this? This why we have science and where the great joy of scientific endeavor lies
@ Michael C, not knocking your Met guys but looking at the prevailing weather patterns ( The Westerlies and the other patterns that you mentioned) I think the overall weather pattern for NZ seems to be not as complex as the EU or North America. But hey we envy you guys no matter what! From tropical beaches to skiing in a heart beat?
Tobias. Tis true to a point. What ever strikes NZ is undisturbed. Yet, the climate and weather is very diverse. Within one hours drive you can go from an annual rainfall zone of 200 inches to one of 30 inches. I can never recall one forecast for all of NZ. I think the comparative accuracy comes down to focus on a smaller land mass. When they now say eg “showers developing in the afternoon” they are around 85% of the time spot-on. When they predict 3 days fine they are over 90% accurate (ATME) . We can make hay with confidence. Their long-range is getting very good too. As they get more accurate they grow in confidence.
I remember the joke among farmers before computers and satellite imagery gave forecasters some real teeth: “If you listen to the weather forecast and assume the opposite you have a 50% chance of getting it right” 🙂
Bob’s excellent dissertation should remind us once again that in a fog of uncertainty, most any picture one wishes to paint can be created; even visions of hockey-sticks, anorexic polar bears and deep-fried penguins.
A deep dive La Nina in combination with declining AMO could be interesting. Throw in significant quiet on the solar cycle front for added effect.
Buckets! Neither replication nor random sampling means the .3 C sampling variance is unlikely to be accurate (being nice, since the variance of sample size of 1 = infinity/unknown). When are climate scientists going to perform sampling in the manner required by the rest of field science ? No time like the present. With 10s of billions being spent/wasted, it would seem that there is enough money for quality data collection instead of all this drek and proforma adjustments as if they make the drek smell nicer.
Bob, I’ve been forecasting weather in the SW Pacific for years now specifically for yachts and fishing vessels. As part of my analysis I take note of the currents in the area, mostly from Satellite data. It’s most fascinating to study this data; the entire region is constantly swirling with current which varies day by day. It’s not predictable at all. The main thing I note is that these swirls and vortex’s blanket the region. There is no real length to them; so I have to assume trying to draw temperature conclusions from less than 100 measurements for most of that huge area may not mean very much. Currents that do have staying power I find are in only about three areas, one is along the Australian coat, another the South American coast and lastly the equatorial counter current; the rest of it inbetween is a dogs breakfast.
I always look forward to your posts, keep up the good work.
A moving image: http://earth.nullschool.net/#2015/10/16/0000Z/ocean/surface/currents/overlay=currents/orthographic=-150.69,4.74,654
No global warming evident in this data?
Thanks again Bob, for yet another great explanatory article. We turn to you for accurate reporting of the El Nino/La Nina conditions. We still have heavy rains over Eastern Australia.
Seems that this is just a middling to strong El Niño, not as strong as the 97-98 but stronger than the one in 2010. The reason that records are being set has nothing to do with the El Niño; it’s what it’s superimposed on.
+10
From what I’ve seen on this El Nino its way more diffused than any other reading I’ve seen before. I don’t know if that is actual or a result in equipment upgrade since the previous, but all readings taken from 97-98 the nino appeared much more condensed in the graphic itself. If that water is more chop unlike before a world of differences would result, imo.
PDO destructively interfered.
According to LOD, the 82-83 Nino was stronger than 97.
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“The largest changes ever recorded in LOD and AAM [Atmospheric Angular Momentum] occurred during January and February 1983” [Rosen et al.,1984 and Eubanks et al., 1985].
http://ntrs.nasa.gov/search.jsp?R=20060041727
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The rotation rate isn’t politically adjusted every day.
Yup – within my 40 years memory as a grassland farmer, there has never been a NZ summer even remotely like it. It had real grunt. This ones a pup by comparison
If you want infilled , used hadISST 😉
I would not use Cowtan & Way’s warmist manipulated data for anything.
“So, while the accuracy of a single thermometer may be a tenth or a hundredth of a degree, the accuracy of our estimates of the entire Nino3.4 region is only about plus or minus 0.3C”.
would not the fact that day after day readings in related thermometers help to confirm the overall trend and temperature range? I understand one thermometer may be out but a hundred days of readings is the same as having 100 thermometers spread out in time?
In other words would the accuracy not be slightly improved by having hundreds of days of readings?
It makes one wonder if we could not be just as accurate by taking all temperature measurements (historical and current), making no adjustments, and rounding everything to the nearest degree C
I am not a statistician but my gut feeling tells me that it would just as accurate over the long term and a damned sight more efficient. Genuine trends would show after reasonable amounts of time. It would stop the bullshit involving tenths of degrees. Little chance 🙁
Yes, Michael C. All one has to do is visit the library and retrieve newspaper data from the weather section. All temperatures given there are non adjusted. I know that in NZ one can do this easily, just an investment in time. There is also a book, can get the name if you want but its out of print; this book gives the temperature readings in major areas of NZ going back nearly 100 years. This data is vastly different from the adjusted temperatures after NIWA gets done with it.
Someone in this post talked about NZ Met Service accuracy in the long term. Most of this sort of analysis comes from NIWA, not the Met Service and I beg to differ on their skill scores.
Another surmised that its easier to forecast weather in NZ than the EU and that what comes is undisturbed. Not so. As any meteorologist knows nothing much occurs on the surface without being driven at upper levels. It is the coastal ranges of Australia which start the undulations of both the jet at 200hPa and the short waves at 500hPa; these are the weather makers. We joke that if one were to remove Australia, NZ would have much different weather!
Thanks Bob. Uncertainty is very rarely publicised.
Using SOI as a metric shows this recently peaked El Nino to have been moderately strong, as I reported in early December at
https://kenskingdom.wordpress.com/2015/12/03/how-significant-is-this-el-nino/
My conclusions:
“The current El Nino event is not going to break any records, unless it continues for several years!
It is nowhere near the most intense, nor the longest, nor the strongest.
It cannot compare with the intensity of previous El Ninos, as measured by three month average values, such as in 1896, 1905, or 1983.
It cannot compare with the length of previous El Ninos, such as the 1941-42 event, or the series of years of El Nino and neutral conditions in the 1980s and 1990s.
Depending on the measure used, it is fourth or sixth strongest for this stage of the cycle.”
I know without a shadow of doubt that you are probably right 🙂 Exceptional events that approach records occur less than 10% of the time. Following this logic the cyclone in the Atlantic will probably die at sea. It was no big deal. There has probably been many more, unnoticed. Also, following this logic there is a 90% chance that ‘average’ global temperatures will splutter along at around last century levels for several decades (given the usual up and downs from noise). Both sides in the war will need to continue to split straws for weapons. The warmers had to predict that the current Nino would be the strongest
Working with probabilities saves an awful lot of wasted time. Given what we now know, AGW is most probably not occurring within tolerances of 0.5 c over a century and is almost entirely overridden by natural forcings such that its influence should be discounted. If I knew that reporting will be unbiased I would happily put down $20,000 on that to collect in 30 years.
Every time it rains in California the media call it “El Nino Rain.” It’s their way of implying that the current heavy rains are an oddity.
“So, while the accuracy of a single thermometer may be a tenth or a hundredth of a degree, the accuracy of our estimates of the entire Nino3.4 region is only about plus or minus 0.3C. Sorry about this big disapointment. It bothers me also. We need thousands of ships evenly spaced across the region to get a truly accurate reading. It’s not worth the money, so it isn’t going to happen. If we improved satellite measurement technology, that could be the key”.
That being the case what is the margin of error over global marine measurements? How many thousands of ships were spread across the globe taking measurements from mid nineteen to mid twentieth centuries? What does this infer in regards to land surface measurement?
To a large extent global warming is a bedtime story, a fairy tale that has been made up as we go along
Skeptics want to ,make their point? Just keep asking: “What is your calculated margin of error?”