Guest Post By Walter Dnes
In continuation of my Temperature Anomaly projections, the following are my July projections, as well as last month’s projections for June, to see how well they fared. Note that I’ve changed to a different NCEP/NCAR reanalysis dataset as of the July 2017 projections. More details below.
|RSS v3.3 2017/06||+0.486||+0.344||-0.142|
|RSS v3.3 2017/07||+0.354|
|RSS v4.0 2017/06||+0.539||+0.389||-0.150|
|RSS v4.0 2017/07||+0.446|
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.188.8.131.52.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
Switching to a different NCEP reanalysis data set
Up til now, I’ve been using air.sig995.YYYY.nc data files from ftp directory
where YYYY is the year the data represents. As of this month I’m switching to air.YYYY.nc files from ftp directory: ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.dailyavgs/pressure/
(Citation: Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.).
As its name suggests, the sig995.YYYY.nc data is valid at the 995 mb level, which is a good proxy for surface temperatures. Unfortunately, it has not worked well as a proxy for the satellite data sets. The air.YYYY.nc data has 17 pressure levels of data; 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 millibars. A bit of experimentation indicates a very good correlation between satellite data sets, and the 700 mb pressure level data, when taking the appropriate global subset corresponding to the satellites’ coverage.
The 700 millibar data will be used for the satellite projections, until/unless something better comes along. To reduce the amount of files, downloading, etc, the 1000 millibar level data from the air.YYYY.nc files will be used as a proxy for surface temperatures. Thus, my surface data will no longer be identical to that on Nick Stokes’ web page, but it will probably still track closely. As with the 995 millibar data, GISS has a good correlation (0.836) with the 1000 millibar data, but HadCRUT and NCEI are both below 0.45.
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 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. July 2016 to June 2017.
Similarly, for the satellite data sets…
|UAH||RSS v3.3||RSS v4.0|
January-through-June of 2017 were all cooler, in all 6 data sets, than the corresponding months in 2016. Therefore, July-through-December 2017 would have to be noticably warmer than the corresponding months in 2016 to beat the 2016 annual values and make 2017 “the warmest year ever”. “Never say never”, but it’s looking more difficult with each passing month.
The graph immediately below is a plot of recent NCEP/NCAR daily anomalies, versus 1994-2013 base. 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:
NCEP/NCAR Monthly Anomalies:
At the time of posting, the 6 monthly data sets were available through June 2017. The NCEP/NCAR reanalysis data runs 2 days behind real-time. Therefore, real daily data from July 1st through July 29th is used, and July the 30th and 31st are assumed to have the same anomaly as the 29th. For HadCRUT, GISS, and NCEI, the 1000 millibar data is used as a proxy. For RSS and UAH, subsets of the 700 millibar reanalysis are used, to match the latitude coverage provided by the satellites. In all cases the projection for a specific data set is obtained by
* subtracting the previous month’s NCEP/NCAR proxy anomaly value from this month’s value (1000 mb or 700 mb as appropriate)
* multiplying the result by the slope() of the data (previous 12 months) of the specific data set versus NCEP
* adding that result to the previous month’s value of the data set