June 2017 Temperature Anomaly projections

Guest Post By Walter Dnes

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

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.4.5.0.0.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

The Latest 12 Months

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 June 2017 NCEP/NCAR anomaly is 0.10 to 0.12 lower than for June 2016 (global and satellite data sets). This implies that the June 2017 anomalies will be lower than the corresponding 2016 values, 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. June marks the 9th consecutive month with NCEP/NCAR global and satellite anomalies lower than 12 months ago.

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, i.e. June 2016 to May 2017.

Similarly, for the satellite data sets…

January-through-May of 2017 were all cooler, in all data sets, than the corresponding months in 2016. Therefore, June-through-December 2017 would have to be warmer than the corresponding months in 2016 to beat the 2016 annual values and make 2017 “the warmest year ever”. Here are the numbers…

• HadCRUT4 2016 Jun-Dec was +0.664; 2017 needs to be +0.778
• GISS 2016 Jun-Dec was +0.860; 2017 needs to be +0.995
• UAH 2016 Jun-Dec was +0.384; 2017 needs to be +0.641
• RSS 3.3 2016 Jun-Dec was +0.430; 2017 needs to be +0.688
• RSS 4.0 2016 Jun-Dec was +0.674; 2017 needs to be +0.932
• NCEI 2016 Jun-Dec was +0.837; 2017 needs to be +0.960

The Graphs

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 late May, 2015, as noted in the graph legend. On the monthly graph, it’s June 2015. This is slowly growing ever longer but nothing notable yet. 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:

https://i0.wp.com/wattsupwiththat.files.wordpress.com/2017/06/daily.png

NCEP/NCAR Monthly Anomalies:

https://i2.wp.com/wattsupwiththat.files.wordpress.com/2017/06/monthly.png

Miscellaneous Notes

At the time of posting, the 6 monthly data sets were available through May 2017. The NCEP/NCAR re-analysis data runs 2 days behind real-time. Therefore, real daily data from May 31st through June 28th is used, and June the 29th is assumed to have the same anomaly as the 28th. For RSS and UAH, subsets of global NCEP/NCAR data are used, to match the latitude coverage provided by the satellites.

In search of better results, I’ve tweaked the data set projection algorithm again. The monthly anomaly difference (current month minus previous month) in the corresponding NCEP/NCAR subset anomalies is multiplied by 0.5 and added to the previous month’s anomaly for that data set. This applies to all 6 data sets.