NOAA's winter update: about the weird warmth and this El Niño

Summary: While journalists too often turn reporting about the weather into clickbait and climate porn the IPCC and major climate agencies produce clear and accurate articles. Why we choose to be poorly informed despite access to the information superhighway is a mystery for future historians to solve. For those that prefer the red pill, here are three articles from NOAA explaining this winter’s weather and the 2015-16 El Niño.

Why we’re misled; how to get good info.
Explaining our warm winter: “July in Christmas”.
Was this a record strong El Niño?
Here’s the missing key: uncertainty of measurements
Other posts about this El Niño.
For More Information.

(1) Why we’re misled; how to get good info

Journalists report the weather and climate because it provides a stream of lurid stories (always a disaster or record in some form, somewhere) that we enjoy reading. They increasingly rely on activists (often amateur activists) for alarming — entertaining but often misleading — sound bites. Which is why the news media are among our least-trusted institutions, and their profits are melting like this winter’s snow.

Journalists — and citizens — interested in accurate information can turn to reliable and clear articles from NOAA. NOAA had a good 2015. They accurately predicted this would be among the 3 strongest on record, contrary to the hysterical predictions of a “monster” or “Godzilla” El Niño. And it appears to have peaked as their models predicted in early December, although the strongest impacts on the weather lie ahead in January and February.

The following three excerpts explain key things about this winter’s weather. What’s causing it? (Spoiler: as usual, there are several factors at work.) How strong is this El Niño (using an alternative measure)? And they explain the key detail missing in almost every story about weather records: the uncertainty of these measurements.

(2) Excerpt from “July in Christmas“

By Michelle L’Heureux
At NOAA’s website, 8 January 2016

{W}hat on earth was going on with the weather? Let’s zoom out and look at November and December together because they were fairly similar. As you can see below, temperatures were strongly above-average across much of North America (shown by the yellow/orange/red shading), with the exception of the western U.S. which was either near or slightly below average (shown by the blue shading).

November and December 2015 average temperature anomaly in degrees Celsius (°C). {Temperature difference from the 1982-2014 monthly mean.}

This blog certainly won’t provide a definitive answer to the question of what’s behind such warmth — we will all have to wait for the more authoritative, peer-reviewed attribution studies that will be forthcoming. But I will try to address some aspects of the last two months using some simple linear statistics — a poor (wo)man’s attribution of sorts. More advanced statistical methods and models, which are run on super computers, will eventually provide more credibility, fine-tuning these answers further.

The goal of attribution is to uncover which mechanisms resulted in certain weather and climate events. Said another way, it’s the process of figuring out how much a given influence contributed to a particular outcome. If two cars meet head on and collide on a narrow road at 3 a.m. on a snowy night, an attribution study of the crash would try to figure out how much of the crash was due to the narrowness of the road, how much due to the darkness, how much to the weather, how much to the drivers’ sleepiness, etc.

Of course, given this is the ENSO blog, one question has to be whether the warmth over North America was influenced by the strong El Niño. At least in part! Below is a map showing the typical November and December (which are averaged together) temperature departures related to El Niño. I’m weighting this map by the observed Niño-3.4 value from the last two months of 2015, so the temperatures you see are a statistical estimate of the portion related to El Niño.

The November and December 2015 reconstruction of the temperature anomalies related to ENSO. The reconstruction is created by taking the linear regression map and multiplying the observed November-December average Niño-3.4 SST index value (+2 standard deviations).

But El Niño is never the only cause. There was also a strong Arctic Oscillation (AO) or what is more popularly called the polar vortex these days (2), but it wasn’t bringing cold air to the United States, but rather trapping it up near the polar cap. The AO is in a positive state if cold air is trapped near the Arctic and it is in a negative state if cold air is moving away from the pole, toward more central and southern portions of the hemisphere (U.S., Europe, Asia). Tony has previously noted that the AO is less predictable than ENSO. So, what portion of the November–December warmth was associated with the AO?

The November and December 2015 reconstruction of the temperature anomalies related to the Arctic Oscillation (AO). The reconstruction is created by taking the linear regression map and multiplying the observed November-December average AO value (+1.5 standard deviations).

Then we can add up the AO and ENSO maps and obtain a temperature pattern based on the contribution from both climate phenomena. Clearly the appearance of the ENSO+AO map (top right panel below) is pretty similar to what we saw during November-December 2015 (top left panel below).

Upper left: November–December 2015 average temperature anomaly in °C. Upper right: the AO & ENSO reconstruction maps of temperature anomalies summed together. Bottom: observed anomalies with the AO+ENSO reconstruction subtracted out (the leftover or residual not linearly explained by AO+ENSO reconstructions).

However, there is some part that is not well described by the AO+ENSO map. So what does that look like? What is the leftover or residual part that is not explained by our estimate of the El Niño or the positive AO temperature pattern? That map is shown above (bottom panel), and you can see that even after we subtract the combined influence of the AO and El Niño, much of North America is covered mostly by a big splotch of red. Basically, an indecipherable mass of …. warmth. What caused that?

When I first saw this, I thought to myself, well – duh – some part of this is the well-documented trend in temperatures we’ve seen over the past 33 years and what our blogging friends over at NCEI and Beyond the Data have written about. Sure enough, if I compute the linear trend pattern, I obtain the map below.

The change in temperature during November-December from 1982-2014 average in °C. Calculation is based on the linear trend (°C/years) times the number of years.

Of course it’s not a perfect match; nothing in the climate is ever so simple! But clearly, there is overlap between that leftover part and the predominantly positive trends over North America temperature during November-December.

Now for some caveats: The method here is a statistical method that assumes linearity and that past historical relationships continue to hold. We find in climate science that it is often a decent estimate, but it’s definitely not perfect.

Certainly there was other stuff going on…

…such as the Madden Julian Oscillation (MJO) toward the end of December, which was in a position that favors above-average temperatures over the eastern United States. While the MJO likely explains a chunk of short-term or week-to-week temperature fluctuations, it is more difficult to blame it for warmth over two months.

There were also the well above-average sea surface temperatures in the Gulf of Mexico and off the East Coast. I don’t have a handy index to quantify this, but it might be worth looking at further, especially for the near coastal regions. There was also a slightly positive Pacific-North America pattern, but it is often associated with El Niño and is not necessarily distinct. As we say in this biz, random stuff happens and we can’t explain everything.

So El Niño, the positive AO, and long-term climate trends are the leading culprits for the temperature pattern over the U.S. during November and December.

Now that January is underway, what is next?

Keep your eyes on the tropics: we’re now entering the seasons with the strongest El Niño impacts for North America. And, as a special bonus for weather and climate lovers, we have a MJO simultaneously occurring, which is also anticipated to have a ripple effect over the U.S. and regions of the globe (check out CPC’s experimental Week 3-4 outlook). If there were ever an ideal time to be weather ready and climate smart, it would be now.

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(3) Another way NOAA measures the strength of an El Niño

The other metric NOAA often uses to measure the strength of an El Niño is the Multivariate ENSO Index (MEI). Here NOAA explains what this index says about the 2015-16 El Niño, as of January 5. First, what is the MEI?

El Niño/Southern Oscillation (ENSO) is the most important coupled ocean-atmosphere phenomenon to cause global climate variability on interannual time scales. Here we attempt to monitor ENSO by basing the Multivariate ENSO Index (MEI) on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C).

Second, how does this cycle compare with past El Ninos?

Compared to last month, the updated (November-December) MEI has dropped slightly (by 0.19) to +2.12, continuing at the 3rd highest ranking, and about 0.3 sigma behind 1982 and 1997 for this season. The August-September 2015 value of +2.53 remains the third highest overall at any time of year since 1950. The evolution of the 2015 El Niño remains very similar to 1997, as monitored by the MEI, including a first peak in August-September and subsequent weakening during the remainder of the calendar year. In 1998, this was followed by a fairly strong rebound that peaked in late boreal winter 0.4 sigma higher than in Novemeber-December {sic}.

What comes next?

Looking at the nearest 6 rankings (+2/-4) in this season gives us four ‘analogues’ already identified three months ago: 1965, 1972, 1982, and 1997, plus 1987 and 1991. Three of these six analogues evolved into La Niña events one year later (1973, ’88, and ’98), while two of them dropped back into ENSO-neutral conditions (’66 and ’83), and one hung on to weak El Niño rankings one year later (’92). While the odds for La Niña a year from now are higher (50%) than climatological odds (30%), it is obviously not a guaranteed outcome. Meanwhile, strong El Niño conditions (top 10% ranking) are very likely through January-February, while general El Niño rankings (top 30%) are the most likely outcome for the next six months.

(4) Margin of error: the seldom-mentioned key information

Journalists’ announcements of weather records make their measurement sound as easy as counting apples. They seldom (almost never) mention the margin of error of these measurements, something vital to understand what’s happening. Here NOAA’s Anthony Barnston adds a comment about error bars in sea surface temperature (SST) measurements to their “December El Niño update“. Red emphasis added.

“The accuracy for a single SST-measuring thermometer is on the order of 0.1C. … 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.“