Guest Post By Walter Dnes
In continuation of my Temperature Anomaly projections, the following are my May projections, as well as last month’s projections for April, to see how well they fared.
|HadCRUT4 2017/04||+0.606 (incomplete data)||+0.740||+0.134|
|RSS v3.3 2017/04||+0.115||+0.392||+0.277|
|RSS v3.3 2017/05||+0.402|
|RSS v4.0 2017/04||+0.329||+0.480||+0.151|
|RSS v4.0 2017/05||+0.497|
The Data Sources
The latest data can be obtained from the following sources
- HadCRUT4 http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.126.96.36.199.monthly_ns_avg.txt
- GISS https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
- UAH http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/tltglhmam_6.0.txt
- RSS v3.3 http://data.remss.com/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_3.txt
- RSS v4.0 http://data.remss.com/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TTT_Anomalies_Land_and_Ocean_v04_0.txt
- NCEI https://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/p12/12/1880-2017.csv Note: has switched to 2 decimal places of precision as of April 2017
The Latest 12 Months
People are already talking about whether or not 2017 will be “the hottest year ever”. The 2016 mean anomaly can be characterized as the “12-month running mean ending in December 2016”. To get an apples-to-apples comparison, May 2016 to April 2017 is used for a 12-month running mean to compare against the year 2016.
The latest 12-month running mean (pseudo-year “9999”, highlighted in blue in the tables below) ranks anywhere from 2nd to 4th, depending on the data set. The May 2017 NCEP/NCAR anomaly is down slightly from May 2016, implying that the 6 May 2017 anomalies will be slightly lower, further cementing the decline of the 12-month running mean. This will make it even harder for 2017 to beat 2016 as the warmest year ever. May marks the 8th consecutive month with NCEP/NCAR global anomaly lower than 12 months ago. However, that could change in June unless the June 2017 value drops below current daily values near the end of May.
The following table ranks the top 10 warmest years for earch surface data set, as well as a pseudo “year 9999” consisting of the latest available 12-month running mean of anomaly data.
Similarly, for the satellite data sets…
|UAH||RSS v3.3||RSS v4.0|
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:
NCEP/NCAR Monthly Anomalies:
At the time of posting, the 6 monthly data sets were available through April 2017. The NCEP/NCAR re-analysis data runs 2 days behind real-time. Therefore, real daily data from April 30th through May 29th is used, and the 30th is assumed to have the same anomaly as the 29th. For RSS and UAH, subsets of global NCEP/NCAR data are used, to match the latitude coverage provided by the satellites.
This month, I’ve switched the land data set projections to use the same algorithm as the satellite data set projection. I.e. the monthly anomaly difference (current month minus previous month) in the NCEP/NCAR subset anomalies is multiplied by the slope() of the data set (versus NCEP/NCAR) for the previous 12 months, and added to the previous month’s anomaly. April actual anomalies for the land sets were more than 0.1 C° above the projections. My previous method was projecting lower May than April values for the land sets, even though NCEP/NCAR anomaly for May is higher than for April. To quote many bad 1950’s B-grade science fiction movies…”That does not compute”.