I, the copyright holder of this work, hereby publish it under the following license:

This file is licensed under the Creative Commons Attribution 3.0 Unported license.

You are free:

• to share – to copy, distribute and transmit the work
• to remix – to adapt the work

Under the following conditions:

• attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

https://creativecommons.org/licenses/by/3.0CC BY 3.0 Creative Commons Attribution 3.0 truetrue

The graph shows global monthly near-surface air temperature anomaly estimates since 1970 from eight sources spanning three different estimation approaches. The plotted values are temperature anomalies — the difference between a month’s temperature from a given series and the mean for that month of the year for that series across a reference interval, here taken as 1951 – 1980. The overlaid trend is a 4th order polynomial fit to the mean of the plotted series.

1. NASA GISTEMP global temperature series^[1]

Data from: http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt.

2. HadCRUT4 global temperature series^[2]

Data from: http://www.cru.uea.ac.uk/cru/data/temperature/HadCRUT4-gl.dat.

3. HadCRUT + Cowtan & Way global temperature series (version 2)^[3]

Data from: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_v2_0_0.txt.

4. NOAA National Climate Data Center global temperature series^[4]

Data from: ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_ocean.90S.90N.df_1901-2000mean.dat.

5. Berkeley Earth land + ocean global temperature series^[5]

Data from: http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt.

6. NCEP / NCAR reanalysis (version 1) global average surface air temperatures^[6][7]

Data from: http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Air+Temperature&level=2000&lat1=-90&lat2=90&lon1=0&lon2=360&iseas=0&mon1=0&mon2=0&iarea=1&typeout=1&Submit=Create+Timeseries.

Satellite MSU remote sensing cannot measure meteorological surface air temperature (typically defined as that at 2 m height). The best that can be obtained is a temperature estimate across the lower troposphere,^[8] about the bottom 5000 m of atmosphere^[8] That temperature is of course much colder than the surface temperature and varies somewhat differently with climatic influences. Nevertheless it is often compared with surface temperatures.^[9] The satellite series do not extend across the reference interval adopted for the graph. Instead they are each rebased so that their mean across the 30 year interval 1979 – 2008 equals the mean of the five instrumental series across the same interval.

7. RSS temperature lower troposphere global temperature series (RSS TLT version 3.3)^[10]

Data from: http://www.remss.com/data/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_3.txt.

8. UAH temperature lower troposphere global temperature series (UAH TLT version 5.6)^[11]

Data from: http://vortex.nsstc.uah.edu/data/msu/t2lt/tltglhmam_5.6.txt.

For comparison, the Intergovernmental Panel on Climate Change Fifth Assessment Report^[12] central temperature estimates for 2050 for representative concentration pathways RCP6 (mid range) and RCP8.5 (high) are also shown.

The Microsoft Excel spreadsheet that produced this graph can be downloaded here: http://gergs.net/?attachment_id=1246.