March 2017 Projected Temperature Anomalies from NCEP/NCAR Data

March 2017 Projected Temperature Anomalies from NCEP/NCAR Data

via Watts Up With That?
http://ift.tt/1Viafi3

Guest Post By Walter Dnes

In continuation of my Temperature Anomaly projections, the following are my March projections, as well as last month’s projections for February, to see how well they fared.

Data Set Projected Actual Delta
HadCRUT4 2017/02 +0.817 (incomplete data)
HadCRUT4 2017/03 +0.817 (incomplete data)
GISS 2017/02 +1.02 +1.10 +0.08
GISS 2017/03 +1.03
UAHv6 2017/02 +0.544 +0.348 -0.196
UAHv6 2017/03 +0.351
RSS 2017/02 +0.606 +0.440 -0.166
RSS 2017/03 +0.437
NCEI 2017/02 +0.9849 +0.9782 -0.0067
NCEI 2017/02 +0.9831

The Data Sources

The latest data can be obtained from the following sources

Miscellaneous Notes
At the time of posting 4 of the 5 monthly data sets were available through February 2017. HadCRUT4 is available through January 2017. The NCEP/NCAR re-analysis data runs 2 days behind real-time. Therefore, real daily data From February 28th through March 29th is used, and the 30th is assumed to have the same anomaly as the 29th.

The projections for the surface data sets (HadCRUT4, GISS, and NCEI) are derived from the previous 12 months of NCEP/NCAR anomalies compared to the same months’ anomalies for each of the 3 surface data sets. For each of the 3 data sets, the slope() value (“m”) and the intercept() value (“b”) are calculated. Using the current month’s NCEP/NCAR anomaly as “x”, the numbers are plugged into the high-school linear equation “y = mx + b” and “y” is the answer for the specific data set. The entire globe’s NCEP/NCAR data is used for HadCRUT, GISS, and NCEI.

For RSS and UAH, subsets of global data are used, to match the latitude coverage provided by the satellites. I had originally used the same linear extrapolation algorithm for the satellite data sets as for the surface sets, but the projections for RSS and UAH have been consistently too high the past few months. Given that the March NCEP/NCAR UAH and RSS subset anomalies are almost identical to February, but the linear extrapolations are noticeably higher, something had to change. I looked into the problem and changed the projection method for the satellite data sets.

The Problem
The next 2 graphs show recent UAH and RSS actual anomalies versus the respective NCEP/NCAR anomalies for the portions of the globe covered by each of the satellite data sets. RSS actual (green) anomaly tracked slightly above its NCEP/NCAR equivalant through November 2016 (2016.917). But from December 2016 (2017.000) onwards, it has been slightly below. Similarly, UAH actual anomaly tracked its NCEP/NCAR equivalant closely through November 2016, but fell and remained below it from December 2016 onwards. I’m not speculating why this has happened, but merely acknowledging the observed numbers.


The Response
Since the switchover in December, the actual satellite anomalies have paralleled their NCEP/NCAR subsets, but with a different offset than before. So I take the difference (current month minus previous month) in the NCEP/NCAR subset anomalies, multiply by the slope(), and add to the previous month’s anomaly. E.g. for the March 2017 UAH projection…

  1. subtract the February 2017 UAH subset NCEP/NCAR anomaly from the March number
  2. multiply the result of step 1 by the slope of Mar-2016-to-Feb-2017 UAH anomalies versus the NCEP/NCAR subset anomalies for the UAH satellite coverage area.
  3. add the result of step 2 to the observed February UAH anomaly, giving the March projected anomaly

The graph immediately below is a plot of recent NCEP/NCAR daily anomalies, versus 1994-2013 base, similar to Nick Stokes’ web page. The second graph is a monthly version, going back to 1997. The trendlines are as follows…

  • Black – The longest line with a negative slope in the daily graph goes back to early July, 2015, as noted in the graph legend. On the monthly graph, it’s August 2015. This is near the start of the El Nino, and nothing to write home about. Reaching back to 2005 or earlier would be a good start.
  • Green – This is the trendline from a local minimum in the slope around late 2004, early 2005. To even BEGIN to work on a “pause back to 2005”, the anomaly has to drop below the green line.
  • Pink – This is the trendline from a local minimum in the slope from mid-2001. Again, the anomaly needs to drop below this line to start working back to a pause to that date.
  • Red – The trendline back to a local minimum in the slope from late 1997. Again, the anomaly needs to drop below this line to start working back to a pause to that date.

NCEP/NCAR Daily Anomalies:

http://ift.tt/2nJtRjq

NCEP/NCAR Monthly Anomalies:

http://ift.tt/2nJirw1

via Watts Up With That? http://ift.tt/1Viafi3

March 31, 2017 at 01:00PM

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