INSTITUTE OF ATMOSPHERIC PHYSICS, CHINESE ACADEMY OF SCIENCES
Meteorologists frequently use ocean reanalysis data to study the evolution of El Niño-Southern Oscillation (ENSO) over time. These datasets are powerful tools and can paint a reliable picture of equatorial Pacific sea surface temperatures throughout the last 40 years. However, particular ocean reanalysis datasets may reproduce the evolution differently. Because of these divergent data, a team of ENSO experts recently published a study in Advances in Atmospheric Sciences that aims to better understand the reanalysis differences regarding equatorial Pacific upper ocean heat content. This critical parameter is frequently used to diagnose the state of ENSO.
“Interestingly, our study finds that such a difference varies within the life cycle of El Niño events.” said Prof. Wen Zhou, the corresponding author of the study from City University of Hong Kong. Her team notes that the difference among data sets grows as the El Niño develops toward peak phase.
Then, shortly after the peak positive ENSO phase, El Niño quickly decays. As ENSO neutralizes, the dataset results begin to converge closer to an agreeable state. However, this process takes longer than El Niño to neutralize. The dataset difference decay is even slower after a strong El Niño compared to a weaker El Niño.
Although a typical El Niño event decays quickly after its peak phase, its subsurface signal lingers within the region. This leads to a slow signal strength decay during the neutralizing phase of El Niño events. As a result, the decay rate of the difference among the ocean reanalysis data sets also slows down. Because of this, researchers determined that equatorial Pacific Ocean signal strength is strongly correlated to the dissimilarity among the ocean reanalysis datasets.
The asymmetry in growth rate and decay rate of the difference among the analyzed datasets leads to lower data consistency during El Niño. This makes analyzing El Niño mechanics during its decay phase inherently more challenging than in its developmental phase.
JOURNAL
Advances in Atmospheric Sciences
DOI
ARTICLE PUBLICATION DATE
12-Jan-2022
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases
The post needed graphs to illustrate what she was claiming.
It needs a lot more than that. It needs to define the terms used in the summary, give units on the graph axes, give more than two descriptions of four lines, and describe what ‘reanalysis’ is (at least). It may be better in the full paper, but I can’t imagine anyone risking 40 bucks to find out.
Tom, according to Wikipedia, “Ocean reanalysis is a method of combining historical ocean observations with a general ocean model (typically a computational model) driven by historical estimates of surface winds, heat, and freshwater, by way of a data assimilation algorithm to reconstruct historical changes in the state of the ocean.”
That definition agrees with definitions by NOAA and other entities.
In other words, where real data does not exist, an ocean reanalysis provides make-believe data created by computer models and “historical estimates” (guesses) to fill in the blanks.
Regards,
Bob
Exactly Bob.
The article could be summed up as, “our models don’t work.”
Check the full article, including supporting graphics, at
s00376-021-1109-8.pdf (springer.com)
Mmmm ….. data sets do not agree. To be candid, that is not news to me.
One wonders how the science could be settled when the “data” (term used loosely) created through reanalysis isn’t.
Regards,
Bob
Can someone translate this into simple English. “Reanalysis data sets” What?
“It’s bad”.
$10 million please.
I wonder if Giggles is the ghost author of this article. It sure sounds like it!
Whew! I’m glad it wasn’t just me who couldn’t make heads nor tails out of this article.!
Reanalysis is basically revisiting sets of real world observations that don’t match a model of the real world that you have constructed. You reanalyze that data with different tools than the original analysis, trying to figure out why the difference exists.
Now, in most fields, this reanalysis will hopefully show where you need to correct the model to better match the real world. In climate “science,” though, the goal is to figure out where you need to “correct” the real world to better match the model.
“You reanalyze that data with different tools”
ummm….. wouldn’t it be better to reanalyze your model ! 😉
Not if the model is giving the ‘right’ answer.
“Reanalysis is basically revisiting sets of real world observations that don’t match a model “
It’s done regularly; there is nothing about not matching a model. It’s basically a follow-up to numerical weather forecasting. For that a series of observations are ingested into the NWP model to create a full climate state (T, P etc at all levels of the atmosphere) to initialise the forecast. Later the same mechanics are used to improve the state estimate with subsequent data. That is the reanalysis part.
For that a series of observations are assimilated into the NWP model to create a full climate state (T, P etc at all levels of the atmosphere) to initialise the forecast. Later the same mechanics are used to improve the state estimate with subsequent data.
If you ingest data, it turns onto poo.
Meisha, I provided a definition (and simple translation) of reanalysis in an answer to Tom’s question above, but I suspect it had too many links and it’s now stuck in moderation. My bad.
FYI, I used to use the outputs of reanalyses for graphs and illustrations in the old days here at WUWT when I was writing posts that explained how El Nino and La Nina events worked and their long-term aftereffects. I also used reanalyses like the one from ECMWF in my free ebook about the processes and long-term effects of El Nino and La Nina events: Who Turned on the Heat, which is a 550 + Page, 23MB .pdf (Free copy here).
Regards,
Bob
This ain’t the first of these ‘oriental’ things we’ve seen on here.
(I wondered if they are coming from folks simply having a laugh or taking the proverbial P)
Noooo: These are ‘little tests’ being sent out to gauge just how dumb stupid and gullible us in the west have actually become.
That we all really seem to understand and believe in Trapped Heat is bad enough. Followed by our sending them all our manufacturing, industry, steel making and intellectual property.
And when the likes of Bojo, Brandon and Trucklode are the ‘creme-de-la-creme’ of our political class and the best leaders we can find, they cannot believe their luck.
Hence these little tests because, from any sane person’s point of view, what’s going on is completely crazy
Well, now that they know about El Nino divergence, they’ll just reanalyze it differently….
I have a serious question about the validity of the peer review status of any paper on climate (and other science that is more political than science).
Given the known collusion among “scientists” to model data beneficial to furthering their own “theoretical” propositions and continued future career within the government funded arena of climate change.
Two, said data’s dissemination, and it’s direct to government policy actions. Including the hand in glove policy actions of top tier global institutions like the World Bank and those UN agencies that also profit from the deceit of perpetuating a global climate crisis. All the while raking in government funding from around the world. ($632Billion 2019-2020)
The global wealth transfer inherent in global climate policy(s) is a tax no government would ever be able to get away with, without the fraudulent science and the adjoining sociopolitical hysteria of CAGW.
Peer review has its hands all over this manufactured crisis of atmospheric CO2 and its resolution through taxation.
Serious questions usally have a question mark. Your post is just yet more ramblings of a conspiracy theorist.
And yours is just empty mutterings, obviously of low comprehension.
Many conspiracy theories become established fact after a few months. The WEF Great Reset and the Wuhan Virus Lab Leak were conspiracy theories not long ago.
As was Hunter Biden’s laptop.
It winds me up when the graph illustrating [presumably] a key point from the text has been rendered illegible by putting an image that adds nothing behind it. One can only assume the graph doesn’t in fact agree with the argument, though I can’t be bothered to get eye strain finding out
When I was an undergrad, I didn’t understand why data mining was a problem. The idea that actual hypotheses beforehand make results more trusted as opposed to just data mining and thinking you stumbled on great insights seemed foolish to me. But climate science is all data mining with no real hypotheses.