Guest Post By Walter Dnes
The NOAA(NCDC) January data set update was delayed. It came in during the afternoon/evening of March 6th. With all the January data being in, now is the time for the January Leading Indicator “JLI” algorithm forecast to “put up or shut up”, and make forecasts for 2014. As described here and here, the JLI algorithm is not a “real forecast” per se, but rather a “zero skill baseline” that a “real forecast” has to beat in order to show skill. The only excuse I’ll use for missing the forecasts is a Pinatubo-scale event, i.e. a major volcano (or meteorite/comet impact) that kicks up a significant amount of particulates/sulfates/etc. into the stratosphere.
First, the raw data. Because some of the data sets adjust their past data every month, the algorithm would produce slightly different results each month for the quantitative forecasts. In close cases, even the qualitative forecasts can change. In order to allow reproduction of the results, the January 2014 data sets, as downloaded in February 2014, are attached here, along with the spreadsheet used for the calculations.
The Qualitative Forecasts
- HadCRUT v3
The January 2014 HadCRUT3 monthly anomaly was 0.472 versus 0.392 in January 2013. The 2014 annual mean anomaly is forecast to be warmer than the 2013 annual mean of 0.459.
- HadCRUT v4
The January 2014 HadCRUT4 monthly anomaly was 0.506 versus 0.450 in January 2013. The 2014 annual mean anomaly is forecast to be warmer than the 2013 annual mean of 0.488.
- GISS
The January 2014 GISS monthly anomaly was 0.70 versus 0.63 in January 2013. The 2014 annual mean anomaly is forecast to be warmer than the 2013 annual mean of 0.603.
- UAH v5.6
The January 2014 UAH5.6 monthly anomaly was 0.291 versus 0.497 in January 2013. The 2014 annual mean anomaly is forecast be cooler than the 2013 annual mean of 0.236.
- RSS
The January 2014 RSS monthly anomaly was 0.262 versus 0.439 in January 2013. The 2014 annual mean anomaly is forecast to be cooler than the 2013 annual mean of 0.218.
- NOAA (NCDC)
The January 2014 NOAA (NCDC) monthly anomaly was 0.6480 versus 0.5491 in January 2013. The 2014 annual mean anomaly is forecast to be warmer than the 2013 annual mean of 0.625.
The Quantitative Forecasts
Due to the noisiness of the data it is possible for the qualitative forecast to indicate a warmer value than the previous year, while the quantitative forecast indicates a cooler value (or vice versa). This type of mixed signal occurs for 2014 in the land data sets, where the qualitative forecast is for warmer than the previous year, but quantitative forecast is for a cooler year.
Tab “jan_and_avg_2” of the spreadsheet has some statistics in the block P1:V4, comparing the January anomalies with the annual anomalies. These include slope() and intercept(). Once we have the January anomaly, we can apply the old “y = mx + b” linear equation to get a quantitative prediction for the year.
- HadCRUT v3 * The slope in cell Q3 is 0.81614. The intercept in cell R4 is 0.02345. The Jan 2014 anomaly is +0.472. Applying the standard “y = mx + b” equation, we get a predicted 2014 annual anomaly of +0.409 with an unknown error margin.
- HadCRUT v4 * The slope in cell R3 is 0.77609. The intercept in cell R4 is 0.02637. The Jan 2014 anomaly is +0.506. Applying the standard “y = mx + b” equation, we get a predicted 2014 annual anomaly of +0.419 with an unknown error margin.
- GISS * The slope in cell S3 is 0.81358. The intercept in cell S4 is 0.03062. The Jan 2014 anomaly is +0.70. Applying the standard “y = mx + b” equation, we get a predicted 2014 annual anomaly of +0.600 with an unknown error margin.
- UAH v5.6 * The slope in cell T3 is 0.64062. The intercept in cell T4 is 0.01732. The Jan 2014 anomaly is +0.291. Applying the standard “y = mx + b” equation, we get a predicted 2014 annual anomaly of 0.64062 * 0.291 + 0.01732 = 0.204 with an unknown error margin.
- RSS * The slope in cell U3 is 0.64755 and the intercept in cell U4 is 0.03456. The Jan 2014 anomaly is +0.262 The predicted 2014 annual anomaly is 0.64755 * 0.262 + 0.03456 = 0.204 with an unknown error margin.
- NOAA (NCDC) * The slope in cell V3 is 0.84179 and the intercept in cell V4 is 0.04571. The Jan 2014 anomaly is +0.648 The predicted 2014 annual anomaly is 0.84179 * 0.648 + 0.04571 = 0.591 with an unknown error margin.
In weather forecasting, one generally goes with the model consensus, or at least the majority opinion. The JLI …
- qualitative forecast indicates 4 (surface) data sets warmer and 2 (satellite) data sets cooler
- quantitative forecast indicates all 6 data sets cooler
The “cooler” runs outnumber the “warmer” runs 8 to 4. So I’ll go with a somewhat cooler year overall.
The Met Office 2014 Prediction
19 December 2013 — The global average temperature in 2014 is expected to be between 0.43 C and 0.71 C above the long-term (1961-1990) average of 14.0 C, with a central estimate of 0.57 C, according to the Met Office annual global temperature forecast.
Their forecast is based on the average of HadCRUT4, GISS, and NOAA(NCDC) anomalies. Using the numbers from the JLI quantitative anomaly forecasts, the JLI equivalent forecast is…
( 0.419 + 0.600 + .591 ) / 3 = 0.537
I acknowledge that I have an additional 2 months of data available compared to what UK Met Office had when they made their forecast.
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To be clear, the serious work on the 2020-2030 global cooling forecast came from Paleoclimatologist Tim Patterson of Carleton University.
I was writing an article for the Calgary Herald and phoned Tim and said: “Tim, you and I both believe climate change is natural and cyclical, correct?” Tim immediately agreed. So I said “OK, when is it going to get colder?” He then said, with a pause of just a few seconds, “2020 to 2030”. I asked why, and he explained that he based his answer on his research into the Gleissberg Cycle, which is about 90 years long. I asked Tim if the ~60 year PDO cycle might be a better fit, but he preferred the Gleissberg.
If the PDO governs, then global cooling has probably already begun, but it will take a few more years to be sure.
I am increasingly convinced that CO2 is utterly irrelevant as a driver of global temperature. Wait ten years and this will be the new conventional wisdom in climate science. Some people will say they knew it all along… 🙂
Regards to all, Allan
.
Allan M.R. MacRae says:
> March 7, 2014 at 4:52 pm
>
> Serious Question – HELP please – I need this.
>
> Does ANYONE out there have a strong predictive
> track record, say 3 or 6 or 9 or 12 months in the
> future, for North American winter temperatures?
You can get general forecasts from commercial firms if you’re willing to pay. I can’t personally vouch for anybody’s accuracy.
Latitude says:
March 7, 2014 at 3:54 pm
“Steven Mosher says:
March 7, 2014 at 1:24 pm
What is known
===
whew, what a relief
At least we know we don’t know squat………”
I respectfully disagree with you, Latitude. I think we DO know squat.
We are about to get a La Nina, not el Nino. This will pull temperatures down.
“Due to the noisiness of the data it is possible for the qualitative forecast to indicate a warmer value than the previous year, while the quantitative forecast indicates a cooler value (or vice versa). This type of mixed signal occurs for 2014 in the land data sets, where the qualitative forecast is for warmer than the previous year, but quantitative forecast is for a cooler year.”
…………..
In other words,the average slope of their linear temperature rend continues to be zero. When noise is included, 2014 could either be above or below 2013.
Steven Mosher:
“What is known
1. An El Nino event occurs about once every three to seven years,
2 As the ocean builds up heat in the western Pacific Ocean, some of the heat goes into the atmosphere through evaporation.
3.Right now the El Nino Southern Oscillation is in its “neutral” phase — neither warm nor cool
4. If we get an El nino, temperatures are likely to warm.
5. models make forecasts”
………….
Difference in opinion about what’s “known” may be around certainty. Your statement above is filled with “about, some, if, likely”.
In grad school I knew two profs (a philosopher and and physicist) who were arguing over the certainty of science. The physicist said “if you drop a pencil I can tell you the speed it falls, the distance, the kinetic energy, etc.”. The philosopher said “can your science tell me if I’ll drop the pencil”.
” The Met Office 2014 Prediction
19 December 2013 — The global average temperature in 2014 is expected to be between 0.43 C and 0.71 C above the long-term (1961-1990) average of 14.0 C, with a central estimate of 0.57 C, according to the Met Office annual global temperature forecast.”
Can anyone explain why the Met Office refers its prediction on a long term (1961-1990) average? Why not (1951-1980) or (1971-1990) or the most recent (1981-2010)? Is it because the anomaly may look higher when using 1961-1990 as the base? Or is it because they might find it difficlult to exclude UAH and RSS from their averages if they selected the later periods?
RE Seasonal Weather Forecasting in North America
http://www.cpc.ncep.noaa.gov/products/predictions/long_range/tools/briefing/seas_veri.grid.php
NOAA predicted in November 2013 that this winter (December January February) would be warmer than usual in northeastern and south-central USA. It was actually much colder than usual in these regions.
NOAA’s recent Temperature Forecast Heidke Skill Scores are terrible (near-zero), but still seem inflated given their utter failure.
Would it be unfair to suggest that NOAA can compete head-to-head with the UK Met office for “worst seasonal weather forecaster”?
Is any organization good at this? Names?
I predict a weak El Nino towards the end of this year based on this graph/predictor
http://climatedatablog.files.wordpress.com/2014/02/uah-tropics.png
Just the facts
Here is what you asked
“Please enlighten us as to “what is known”. Do you think that “we may see a mini global warming phenomenon”?…”
And before
“Thanks for the ENSO 101 there, but if you try some reading comprehension, I never said “we know nothing”, I wrote that, “In summary, we have no idea what’s going to happen”, and per this comment;”
So let me answer your first question again
Of course we have an Idea of what is going to happen. It’s pretty simple. some people have an Idea that El nino may happen. It’s pretty clear what that means. I have an idea that the sun may go down today. The fact that I am uncertain about it doesnt make it any less of an idea.
##############
Next question was
Have you considered entering the legal field? Between you and Bill Clinton you could spend your twilight years parsing out the meaning of the words “may” and “is”…
Parsing out the meaning of the words “May” and “Is” would be Philosophy son not law. I find it ironic with your name “Just the facts” that you dont pay more attention to facts which are about what ‘is’ and ideas which can be about what “may” exist.
funny actually.
################
There is no “probable knowledge”, we are talking about forecasting here, and as I wrote several years ago;”
Of course there is probable knowledge. Further citing yourself to make an argument is really funny.
Uk.
Why are we building windmills?
It would depend. Which “we” are you referring too?
Regardless of which however you are asking for an explanation of motivations. These explanations
Are always probable not certain. So you might have
An idea and you might point to statements
About motivations but in the end you just have probable
Knowledge. Another word for that is belief. Its funny when
Skeptics are not skeptical.
Steve: I am unclear as to what you think will actually happen.
As far as I can tell all you have said is that there ‘may’ be a El Nino.
As you correctly observe, the sun ‘may’ come up tomorrow.
Care to give any actual prediction of how large or small the upcoming El Nino might be? And when it might happen? Just to get off the fence.
Solomon Green says: March 8, 2014 at 1:57 am
Can anyone explain why the Met Office refers its prediction on a long term (1961-1990) average? Why not (1951-1980) or (1971-1990) or the most recent (1981-2010)? Is it because the anomaly may look higher when using 1961-1990 as the base? Or is it because they might find it difficlult to exclude UAH and RSS from their averages if they selected the later periods?
They should be using 1981–2010 as their base period “in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.”
http://www.ncdc.noaa.gov/cmb-faq/anomalies.php
However, HadCRUT4, “time series are presented as temperature anomalies (deg C) relative to 1961-1990.”
http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html
According to the UEA CRU:
“Why are the temperatures expressed as anomalies from 1961-90?
Stations on land are at different elevations, and different countries measure average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90). For stations to be used, an estimate of the base period average must be calculated. Because many stations do not have complete records for the 1961-90 period several methods have been developed to estimate 1961-90 averages from neighbouring records or using other sources of data (see more discussion on this and other points in Jones et al. 2012). Over the oceans, where observations are generally made from mobile platforms, it is impossible to assemble long series of actual temperatures for fixed points. However it is possible to interpolate historical data to create spatially complete reference climatologies (averages for 1961-90) so that individual observations can be compared with a local normal for the given day of the year (more discussion in Kennedy et al. 2011).”
The UEA CRU explanation incoherent, thus as you say, it is probably “because the anomaly may look higher when using 1961-1990 as the base”.
For reference for:
UAH “the global, hemispheric, and tropical LT anomalies from the 30-year (1981-2010) average”;
http://www.drroyspencer.com/2013/04/uah-global-temperature-update-for-march-2013-0-18-deg-c-again/
GISS “anomalies are relative to the 1951-80 base period means”;
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
HadCRUT4 “time series are presented as temperature anomalies (deg C) relative to 1961-1990”;
http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html
RSS “anomalies are computed by subtracting the mean monthly value (averaged from 1979 through 1998 for each channel) from the average brightness temperature for each month”;
http://www.ssmi.com/msu/msu_data_description.html#rss_msu_data_analysis
NOAA NCDC “the global and hemispheric anomalies are provided with respect to the period 1901-2000, the 20th century average.”
However, they also note that, “beginning in December 2010, all lower troposphere, middle troposphere, and lower stratosphere satellite data are reported here with respect to the 1981–2010 base period. Prior to December 2010, data were reported with respect to the 1979–1998 base period. Remote Sensing Systems continues to provide data to NCDC with respect to the 1979–1998 base period; however, NCDC readjusts the data to the 1981–2010 base period so that the satellite measurements are comparable.”
In terms of “why do some of the products use different reference periods?” NOAA NCDC states that:
“the maps show temperature anomalies relative to the 1981–2010 base period. This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average. For the global-scale averages (global land and ocean, land-only, ocean-only, and hemispheric time series), the reference period is adjusted to the 20th Century average for conceptual simplicity (the period is more familiar to more people, and establishes a longer-term average). The adjustment does not change the shape of the time series or affect the trends within it.”
http://www.ncdc.noaa.gov/cmb-faq/anomalies.php
“NOAA’s Climate Prediction Center has already changed their Normals to the 1981 – 2010 base period? Why are those Normals not available?
Many organizations, including NOAA’s Climate Prediction Center (CPC), develop their own averages and change base periods for internal use. However, NCDC’s climate Normals are the official United States Normals as recognized by the World Meteorological Organization and the main Normals made available for a variety of variables.”
http://www.ncdc.noaa.gov/cmb-faq/anomalies.php
NOAA Climate Prediction Center’s CAMS station temperature anomaly dataset. “CAMS” is an acronym for the “Climate Anomaly Monitoring System” in use at the Climate Prediction Center (CPC). “CAMS station surface air temperature anomalies for the globe with respect to the 1971-2000 climatological base period.”
http://iridl.ldeo.columbia.edu/maproom/Global/Atm_Temp/Monthly_stn_anom.html
“They should be using 1981–2010 as our base period “in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.””
You can align all the data sets in their overlap period of 1979-today and thus remove the base period sampling problem and get them all on the same page.
You can go from
http://climatedatablog.files.wordpress.com/2014/02/hadcrut-giss-rss-and-uah-global-annual-anomalies-with-gaussian-annual-and-15-year-low-pass-filters-from-sources1.png
to
http://climatedatablog.files.wordpress.com/2014/02/hadcrut-giss-rss-and-uah-global-annual-anomalies-aligned-1979-2013-with-gaussian-low-pass-and-savitzky-golay-15-year-filters1.png
Steven Mosher says: March 8, 2014 at 7:13 am
So let me answer your first question again
Of course we have an Idea of what is going to happen. It’s pretty simple. some people have an Idea that El nino may happen. It’s pretty clear what that means. I have an idea that the sun may go down today. The fact that I am uncertain about it doesnt make it any less of an idea.
So now you want to argue about the meaning of the word “idea”? The point is that during February to May our predictive capacity as it relates to ENSO is essentially nil, i.e. “The period from February through May is commonly referred to as the spring barrier. During this time, models generally have the least skill to predict the coming season.”
http://iri.columbia.edu/news/la-nina-still-hanging-on/
Parsing out the meaning of the words “May” and “Is” would be Philosophy son not law.
That’s funny, and I am even more amused by the thought of Bill Clinton as a modern day philosopher, but having dealt with contract law, I’d argue that a good attorney could hold their own against any philosopher in arguing about the meaning of a word ad nauseam.
I find it ironic with your name “Just the facts” that you dont pay more attention to facts which are about what ‘is’ and ideas which can be about what “may” exist.
funny actually.
I am glad that you can amuse yourself. Perhaps you could set aside a bit of time to do some research on what’s going to happen with ENSO this year? You are clearly outwordsmithing me, but in terms of our lack of predictive capacity, you haven’t presented any evidence to the contrary.
Of course there is probable knowledge.
You seem to think you’ve stumbled on to some philosophy thread, we’re taking about “probable knowledge” about what’s going to happen with ENSO and global temperatures this year. Your perception of “probable knowledge” seems to encompass laying out the options, i.e. it might get warmer, it might get colder or it might stay the same. Show us a shred of evidence that anyone has any predictive capacity as to what ENSO will do this year.
Further citing yourself to make an argument is really funny.
I am not citing myself, I am citing the dozens of quotes and references within the article and thread that support the fact that we cannot accurately predict global temperatures a few months into the future, much less a few years or decades…
Apart from Nino4 things are running in the cool side currently:
http://www.bom.gov.au/climate/enso/indices.shtml
The QBO should move into the easterly phase around July, which is more conducive for La Nina:
http://www.esrl.noaa.gov/psd/data/correlation/qbo.data
I’m looking at a trend for a more positive AO July through Oct which again would indicate a La Nina bias like last summer.
I see a La Nina coming that will last around 3 years, breaking back to El Nino in 2016/17. It should be close to a -2 event in scale. The end of this year might see a short small El Nino.
My thanks to Just The Facts for answering my questions. Which leaves me with only one more question. How can Climate Scientists really believe in their fudged data?
Thanks, also for your reponses to Steven Mosher. It would be interesting to learn just what percentage of medium term (3-12) months have been accurate. For the UK many of us have noticed that the Met Office’s predictions have a very high degree of correlation with the outcomes. Unfortunately it is negative correlation.