Earth Orbital Eccentricity Effect on Global Mean Surface Temperature

I thought it would be an interesting exercise to look at the annual change in global mean surface temperature (GMST) in the atmosphere at 2 meters above ground level versus the annual change in solar radiation incoming (SRI) at the top of the atmosphere (TOA).

The earth’s orbit is slightly elliptical.  Eccentricity is a measure of the departure of an ellipse from a true circle.  The earth’s eccentricity varies over long time scales and is estimated to have ranged from a low of 0.000055 to a high of 0.0679, with a geometric mean of 0.0019.  The present eccentricity is 0.017 and decreasing very slowly.  That doesn’t seem like much, but the earth’s distance from the sun currently increases by 3.4% over the course of a year from the minimum distance in early January to the maximum distance in early July.  That variation in distance causes about a 6.8% increase in TOA SRI relative to the minimum amount at the greatest distance in early July in order to reach the maximum amount at closest distance in early January, assuming a constant output of radiation from the sun.

The US National Aeronautical and Space Administration (NASA) Clouds and the Earth’s Radiant Energy System (CERES) measurements from satellites include TOA SRI.  I downloaded monthly CERES TOA SRI for the period from March 2000 through July 2018.  I also have compiled monthly estimates of GMST from the Climate Forecast System Reanalysis (CFSR) output of initial conditions four times per day, with monthly data prior to 2010 provided by the University of Maine Climate Change Institute (UM CCI) and data from 2010 through 2018 calculated from the Climate Data Assimilation System (CDAS) output of 2-meter surface air temperature.

A time series graph comparing TOA SRI with GMST is provided in Figure 1 below (click to enlarge).  It shows the annual cycle of GMST with a peak in July and minimum in January, whereas the TOA SRI peaks in July with a minimum in January – the complete opposite.  At first, this seems counter-intuitive since increased incoming solar radiation should cause increased temperature and yet the data show the opposite.  My guess is that there is a time lag mediated primarily by the oceans and possibly also affected by the much greater percentage of land in the northern hemisphere, but I have not read up on the subject.  This graph also includes centered running 12-month averages (Run 12) for both TOA SRI and GMST.

Figure 1. Comparison of CERES TOA SRI with CFSR GMST
(click to enlarge)

Figure 2 below compares the CERES TOA SRI with the CFSR Northern Hemisphere (NH) mean surface temperature.  The phase of the annual cycle in temperature is similar to that of the global temperature cycle and is nearly opposite the phase of the TOA SRI cycle.  Notice that the amplitude of the annual temperature cycle is much larger than the global cycle.

Figure 2. Comparison of CERES TOA SRI with CFSR Northern Hemisphere Mean Surface Temperature (click to enlarge)

Figure 3 below compares the CERES TOA SRI with the CFSR Southern Hemisphere (SH) mean surface temperature.  The phase of the annual cycle in temperature is very close to the phase of the TOA SRI cycle, but advanced forward by about a month.  Notice that the annual temperature cycle is larger than the global cycle, but not near as large as the NH cycle.

Figure 3. Comparison of CERES TOA SRI with CFSR Southern Hemisphere Mean Surface Temperature (click to enlarge)

Figure 4 below compares the CERES TOA SRI with the CFSR tropics (30N-30S) mean surface temperature, which covers the 50% of the global surface that receives most of the incoming solar energy.

Figure 4. Comparison of CERES TOA SRI with CFSR Tropics Mean Surface Temperature (click to enlarge)

The not quite linear cyclical nature of the relationship between SRI and GMST is illustrated by the scatter plot in Figure 5 below.

Figure 5. Scatter plot of CFSR GMST versus CERES TOA SRI
(click to enlarge)

By lagging the GMST by 6 months, the relationship to TOA SRI looks more meaningful, as seen in Figure 6 below.

Figure 6. Scatter plot of CFSR GMST lagged 6 months versus CERES TOA SRI
(click to enlarge)

I calculated and compiled annual statistics for each year.  During the study period, the 2001-2017 TOA SRI average of the 17 annual averages was 340.023 watts per meter squared (watts/m2) with a standard deviation of only 0.087 watts/m2.  The monthly averages ranged from 329.113 to 351.516 watts/m2.  The annual range in monthly TOA SRI averaged 21.998 watts/m2 with a standard deviation of only 0.106 watts/m2.  The highest annual range was 21.838 and lowest 22.261 watts/m2.  The average percentage of the annual range relative to the lowest month each year was 6.682%.  Note that I did not bother to weight the annual averages based on number of days per month because I do not expect a significant difference.

Similarly, the 2001-2017 average of the 17 annual averages of GMST was 287.810 Kelvin (K) with a standard deviation of 0.133 K and a range in monthly averages from 285.505 K to 289.864 K.  The lowest annual average was 287.735 K in 2008 and the highest was 288.051 K in 2016.  The annual range in monthly GMST averaged 3.775 K with a standard deviation of 0.166 K and varied from 3.511 K to 4.128 K.

For 2001-2017, the CFSR monthly surface temperature data shows an average annual range of 12.589 K for the northern hemisphere and an average annual range of 5.227 K for the southern hemisphere. The southern hemisphere cycle is only one month delayed from the TOA SRI cycle. Both of the hemispheric cyclical temperature swings are larger than the global swing and include both seasonal earth tilt plus eccentricity effects. The global swing should be the net remainder resulting from the eccentricity induced TOA SRI effect.

Initial Thoughts

So, if we assume that the annual range in TOA SRI is the primary driver for the annual range in GMST, then we can calculate that GMST rises (or falls) by 3.775/21.998 = 0.172 K per 1 watt/m2 of TOA SRI change.  The implication is that if a doubling of CO2 causes a radiative forcing of 3.7 watts/m2, the corresponding rise in GMST would be 3.7 x 0.172 = 0.635 K and this would include any feedbacks that normally occur in the earth system over an annual cycle.  The main difference is that the ramp up in solar input from orbital eccentricity occurs over only a 6 month period each year, whereas the doubling of CO2 might take on the order of a century.  However, both are responses to changes to the earth’s radiation balance.  So far, I have not been able to think of any compelling reasons why the difference in time scales would make much difference in the resulting effect on GMST.  Possibly the short period cycling of the solar input may not allow enough time to reach full impact on GMST in either direction, leading to a quasi-steady state oscillating result?

I’m afraid this assessment may be too simplistic or that I may have overlooked some important influences.  Thus, I’m not at all certain this approach is a valid method for estimating the effect on GMST from a doubling of CO2.  I will be interested hear what readers have to say and I would be especially interested in learning how well the global climate models handle this annual cycling of both TOA SRI and GMST.

After Further Review

I’m now seeing that the annual cycle in global temperature is dominated by the seasonal cycle related to the earth’s axial tilt.  I thought it might still be possible to estimate the effects of eccentricity on the seasonal cycle in each hemisphere, and from that result, estimate the net effect on the global temperature cycle from eccentricity.  However, I found that backing out a rough estimate of the effect of eccentricity actually increased the global annual temperature range, because it increases the NH annual temperature range more than it decreases the SH annual temperature range.  Consequently it does not appear possible to determine the effect of eccentricity on global temperature without using a climate model to test the effect of varying degrees of eccentricity.  Like many things I’m learning about climate … it is very complex.

References

Eccentricity discussion:
https://en.wikipedia.org/wiki/Milankovitch_cycles

CERES project description:
https://en.wikipedia.org/wiki/Clouds_and_the_Earth’s_Radiant_Energy_System

CERES data download:
https://ceres.larc.nasa.gov/order_data.php

UM CCI Reanalyzer:
https://climatereanalyzer.org/

CDAS monthly average downloads:
https://nomads.ncdc.noaa.gov/modeldata/cfsv2_analysis_monthlymeans_pgb/

Climate sensitivity discussion:
https://en.wikipedia.org/wiki/Climate_sensitivity

Earth’s energy budget discussion:
https://en.wikipedia.org/wiki/Earth%27s_radiation_balance

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Arctic Buoy Decline

There has been a sharp decline in the number of Arctic buoys reporting temperature measurements to the global synoptic weather network the last couple of years, as compared to the previous several years. Below are comparisons of synoptic temperature observations for 1200 Universal Time Coordinates (UTC) on January 1 for each year from 2019 back to 2015 for the Arctic Ocean area. The first set of maps below, Figures 1 through 5,  are standard plots of weather data, including temperature (upper left from station circle), from the National Oceanographic and Atmospheric Administration (NOAA) Weather Prediction Center (WPC) for the southwestern portion of the Arctic Ocean.  Notice the increasing number of open ocean buoys reporting going back each year.  Click on any of the maps to see the full size image.

Figure 1. Synoptic weather observations for 2019 January 1 at 1200 UTC.

Figure 2. Synoptic weather observations for 2018 January 1 at 1200 UTC.

Figure 3. Synoptic weather observations for 2017 January 1 at 1200 UTC.

Figure 4. Synoptic weather observations for 2016 January 1 at 1200 UTC.

Figure 5. Synoptic weather observations for 2015 January 1 at 1200 UTC.

The next set of maps, Figures 6 through 10, show plots of synoptic temperature observations from OGIMET for the entire Arctic Ocean area for January 1 at 1200 UTC for each year from  2019 back to 2015.  Again notice the increasing number of temperature observations from the open Arctic Ocean going back each year.    Click on any of the maps to see the full size image.

Figure 6. Synoptic temperature observations for 2019 January 1 at 1200 UTC for the Arctic Ocean.

Figure 7. Synoptic temperature observations for 2018 January 1 at 1200 UTC for the Arctic Ocean.

Figure 8. Synoptic temperature observations for 2017 January 1 at 1200 UTC for the Arctic Ocean.

Figure 9. Synoptic temperature observations for 2016 January 1 at 1200 UTC for the Arctic Ocean.

Figure 10. Synoptic temperature observations for 2015 January 1 at 1200 UTC for the Arctic Ocean.

Oddly, the most recent International Arctic Buoy Program (IABP) map for 2018 December 27 shows numerous buoys reporting air temperature from the Arctic Ocean area as seen in Figure 11 below.

Figure 11. IABP map of air temperature observations for 2018 December 27.

Why the temperature measurements from these buoys are not being reported to the global synoptic weather network is puzzling, especially considering that much of the recent global warming has been occurring primarily in the winter night-time Arctic area.  I am also not certain whether any of these buoy measurements are being ingested into global weather models and associated reanalyses, separately from synoptic weather data.  It would be a shame if they are not.

The IABP also provides data and graphs of data from the Arctic Buoys here.   In the past I have compared the detailed buoy measurements from IABP to OGIMET plotted synoptic observations and code that were reported from some of the buoys.  I found that in some cases the IABP reported “surface temperature” from underneath the hull of the buoy was being erroneously reported in the synoptic data as “air temperature”.  Graphs of the IABP data indicate that the “surface temperature” (which is typically from ice or water beneath the buoy) is often substantially different than the “air temperature” when both are reported.  I also found flat-lined air temperatures and air temperatures that did not match well with those from nearby buoys in both data sets.  But these are different problems to investigate for another day.

Happy New Year!

Global Temperature January 2018

Notice:  the graph in this post has been moved to the Monthly Trends page accessible in the menu bar at the top of this page, where it will be updated monthly rather than by monthly posts.

Climate Forecast System Reanalysis (CFSR) monthly global surface temperature anomaly estimates for 2014 through January 2018 from the University of Maine Climate Change Institute (UM CCI) and from WeatherBELL (WxBELL) are graphed below along with monthly global temperature anomaly estimates for the lower troposphere derived from satellite measurements provided by the University of Alabama at Huntsville (UAH).  The UM CCI CFSR estimates have been adjusted (UM adj), while the WxBELL CFSR estimates have been left unadjusted to show the difference.  The UM CCI CFSR adjusted monthly estimates for June through December 2017 are based on final daily averages and the estimate for January 2018 is based on preliminary daily averages.  Most likely the UM CCI January estimate will drop by about 0.10C once final daily averages for January are released. The UM CCI January preliminary estimate at +0.36C was down by 0.04C from December while the WxBELL estimate at +0.26C was down by 0.13C and the UAH estimate also at +0.26C was down by 0.15C. Click on the graph below to see a larger copy.

Also shown for comparison are monthly global temperature anomaly estimates from six other major sources, including lower tropospheric estimates from the Remote Sensing Systems (RSS), and surface estimates from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis Interim adjusted (ERAI adj), US National Center for Environmental Information (NCEI), US National Aeronautics and Space Administration (NASA) Goddard Institute of Space Studies (GISS), the UK Hadley Climate Research Unit Temperature (CRUT), and the Berkeley Earth Surface Temperature (BEST), all final through December 2017. All estimates have been shifted to the latest climatological reference period 1981-2010.

Update 2018 February 6

Final January 2018 global temperature anomaly estimates from RSS for lower troposphere and ERAI adj for surface have been added to the graph.   Both showed decreases from December 2017 to January 2018, with RSS down 0.04C,  and ERAI adj down 0.14C.

GOES-16 Preview

The first next generation US Geostationary Operational Environmental Satellite (GOES) was recently launched by the US National Aeronautic and Space Administration (NASA) on November 19th of 2016, designated as GOES-R.  This new series of satellites will provide 34 meteorological, solar and space weather products.  They orbit above a fixed point at the earth’s equator at a distance of about 22,300 miles out in space.   As with all US meteorological satellites, the US National Oceanic and Atmospheric Administration (NOAA) has taken over operation of the satellite and designated it GOES-16.  Below is a link to a visible composite color high resolution full-disk test image from midday January 15th of 2017 provided by NOAA.  To see the image in full resolution, click on the reduced image below which will take you to NOAA’s web site to view the full resolution image (use scroll bars or browser magnification tool to navigate) and you can return here by using your browser back button.

Additional test images can be seen here:
GOES-16 Image Gallery

Here is an animation showing the 16 different imagery channels available:

Below is a description of the satellite and its uses.

In May 2017, NOAA will announce the new location for GOES-16.  It will replace either GOES-East or GOES-West and will become operational in November 2017.  The next satellite in the series, GOES-S, is scheduled for launch in spring 2018 and should be operational by a year later.

Information about data access can be found here:
GOES-R User Systems

NASA also has a useful web page for viewing real-time and archived high resolution imagery from three polar orbiting satellites here:
NASA Worldview

2016 Precipitable Water Animation

I ran into this animation on the interwebs.  It’s a great visualization of atmospheric water vapor in the atmosphere and how it moves from the tropics to the poles.  Water in its various forms, including oceans, lakes, water vapor, clouds, rain, snow, ice, and glaciers, is a major player in weather and thus climate.  It is perhaps the most dominant player besides incoming solar radiation which is the main driver of the weather-climate heat engine.

Keep in mind that this animation does not show liquid water, as in clouds and fog, which are also very important in the weather-climate energy budget.  The air typically has very low water vapor content in the polar regions, allowing other greenhouse gases to have more of an effect than where water vapor is much more abundant, as in the tropics.  However, because of the very cold polar temperatures, clouds, fog, and precipitation still occur there, which somewhat limits the effect of other greenhouse gases in the polar regions.

The Earth web site where this video originated is also a great visualization tool for looking at current, past, and forecast weather conditions, as well as some ocean conditions.  Click on the link below for an example showing the current wind flow and temperature.

earth

When you visit the above link, click on the “earth” label in the bottom left corner to pop up a menu with many options to select.  Also, the J and K keys will step the selected display forward or backward one time step (3 hours).  The weather data displayed is from the Global Forecast System (GFS).  Be sure to give the globe a spin by clicking and dragging.  If you have a mouse, use the mouse roller bar to zoom in and out.

CFSR Adjustment

As discussed in the previous post, Weather Model Reanalysis Comparisons, the US National Center for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) shows a shift in global temperature anomaly estimates apparently associated with the switch from Version 1 to Version 2 (CFSV2) that occurred in early 2011.  The version upgrade shift is apparent when the CFSR estimates are compared with the older and unmodified  NCEP reanalysis performed in conjunction with the National Center for Atmospheric Research (NCEP/NCAR R1), as can be seen in Figure 1 (click on any of the graph images below to see a larger copy).

Figure 1. Comparison of NCEP/NCAR R1 and NCEP CFSR/CFSV2 monthly global temperature anomaly estimates for 1979-2015.

Figure 1. Comparison of NCEP/NCAR R1 and NCEP CFSR/CFSV2 monthly global temperature anomaly estimates for 1979-2015.

A linear regression of the NCEP CFSR/CFSV2 versus the NCEP/NCAR R1 for the period of discrepancy that runs from 1979 through April 2010 shows a good correlation with R-square of 0.83, as seen in Figure 2.

Figure 2. Scatter plot and linear regression of NCEP/NCAR R1 versus NCEP CFSR/CFSV2 for 1979 through April 2010.

Figure 2. Scatter plot and linear regression of NCEP/NCAR R1 versus NCEP CFSR/CFSV2 for 1979 through April 2010.

The resulting slope of 0.865 and intercept of -0.2 was applied to the NCEP CFSR/CFSV2 monthly global temperature anomaly estimates for the regression period.  Estimates for May 2010 through November 2016 were not adjusted since they compared well with the NCEP/NCAR R1 estimates during that period.  The adjusted NCEP CFSR/CFSV2 estimates are compared to the NCEP/NCAR R1 estimates in Figure 3 (click to enlarge), and show a good agreement.  If only it were so easy to adjust all the CFSR parameters to match.

Figure 3. Comparison of monthly global temperature anomaly estimates for NCEP/NCAR R1 and adjusted NCEP CFSR/CFSV2 for 1979-2016.

Figure 3. Comparison of monthly global temperature anomaly estimates for NCEP/NCAR R1 and adjusted NCEP CFSR/CFSV2 for 1979-2016.

The adjusted NCEP CFSR/CFSV2 estimates also compare well with the recently adjusted European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis Interim (ERAI) as can be seen in Figure 4.

Figure 4. Comparison of monthly global temperature anomaly estimates for NCEP adjusted CFSR/CFSV2 and adjusted ERAI for 1979-2016.

Figure 4. Comparison of monthly global temperature anomaly estimates for NCEP adjusted CFSR/CFSV2 and adjusted ERAI for 1979-2016.

Figure 5 shows a closeup view of monthly global temperature anomaly estimates for the 21st Century so far, including those from NCEP/NCAR R1, NCEP CFSR/CFSV2, and ERAI.  Note the reference period was shifted to 1981-2010.

Figure 5. Comparison of monthly global temperature anomaly estimates for the 20th Century so far.

Figure 5. Comparison of monthly global temperature anomaly estimates for the 20th Century so far.

It is interesting that the adjusted NCEP CFSR/CFSV2 shows little trend for the 20th Century portion of the period covered as can be seen in Figure 6, despite rapidly rising global atmospheric carbon dioxide levels during this time.

Figure 6. Adjusted NCEP CFSR/CFSV2 trend for 1979-2000.

Figure 6. Adjusted NCEP CFSR/CFSV2 trend for 1979-2000.

For most of the 21st Century so far, there also has been little rise in global temperature as indicated in Figure 7, with the exception of the large high spike associated with the 2016 El Niño event at the end of this most recent period.

Figure 7. Adjusted NCEP CFSR/CFSV2 trend for 2001-2016.

Figure 7. Adjusted NCEP CFSR/CFSV2 trend for 2001-2016.

The adjusted NCEP CFSR/CFSV2 trend for 1979 through 2015 is +0.00130 degrees Celsius (C) per month, or equivalent to +1.56C per 100 years if it were to continue that long, as compared to +1.52C/100 years for NCEP/NCAR R1 and +1.67C/100 years for ERAI projected from the same period.  The next year or two should be very telling as to whether global temperature returns the the level before the El Niño or steps up to a higher trend.  A flat or higher trend would definitely be more preferential than the beginning of a decline into the next glacial period.

Here’s hoping everyone has a great new year!

Weather Model Reanalysis Comparisons

Introduction
Global numerical weather prediction (NWP) models provide the best available framework for assessing global variability and trends for a variety of weather-climate parameters over time, including temperature.  The most advanced NWP models include the Global Forecast System (GFS) model run by the US National Center for Environmental Predictions (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) model.  These models provide output on rectangular latitude-longitude grids with grid cells that are not all equal in surface area.  Another approach for global weather modeling is to use icosahedral horizontal grids with grid cells that are approximately equal in surface area, such as the Finite-volume Icosahedral Model (FIM) being developed by the US National Oceanographic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL).  The FIM approach may eventually become predominant, but for now, the older GFS and ECMWF models are in the forefront.

Reanalysis
Reanalyses have been performed using both the GFS and ECMWF models in conjunction with additional input data not available in real-time for weather forecast model runs, plus the original input data used for the forecast runs, but with additional data quality filters.  The GFS reanalysis performed by NCEP is called the Climate Forecast System Reanalysis (CFSR) and the latest ECMWF reanalysis is called the ECMWF Interim Reanalysis (ERAI).  The weather models have been evolving over the years, including higher spatial resolution, and thus changes have been made to the reanalysis efforts to take advantage of more advanced model features.  The oldest NOAA reanalysis effort is being maintained and updated by the National Center for Atmospheric Research (NCAR) and is called the NCEP/NCAR Reanalysis I (NCEP/NCAR R1).  It has been run backward to 1948, but has a relatively low spatial resolution.  However, it uses a consistent model and methodology over the entire period from 1948 to present, in contrast to changing methodologies that complicate other reanalysis efforts for comparing longer time periods.  The NCEP/NCAR R1 has output horizontal grid resolution of 2.5/2.5 degrees latitude/longitude.  The NCEP CFSR and more recent NCEP CFSR Version 2 (CFSV2) provide horizontal output of 0.5/0.5 degrees latitude/longitude.  The CFSV2 became operational beginning April 2011.  The ERAI has horizontal output resolution of 0.75/0.75 degrees latitude/longitude.  The CFSR and ERAI were both run back to 1979 to provide historical analyses.  Recently, the ERAI has been adjusted in an effort to compensate for possible bias differences resulting from model input changes that began in 2002.

Comparisons
Four different reanalysis estimates of monthly global surface temperature anomalies are compared below for the period from 1979 through 2015, with two of them extending through November 2016.  The NCEP/NCAR R1, NCEP CFSR/CFSV2, and ERAI Unadjusted data sets were obtained from the University of Maine Climate Change Institute (UM CCI) and the ERAI Adjusted data set was obtained from the ECMWF Copernicus web site.  Since there were changes in methodologies in the ERAI beginning 2002 and in the NCEP CFSR/CFSV2 beginning April 2011, the estimates are compared using two different reference periods, 1979-1998 and 2011-2015.

Figures 1 and 2 provide overviews of the full 1979-2016 time series of monthly global surface temperature anomaly estimates referenced to 1979-1998 and 2011-2015 respectively (click on any graph image for a larger view).

Figure 1. Overview of global surface temperature anomalies referenced to 1979-1998.

Figure 1. Overview of global surface temperature anomalies referenced to 1979-1998.

Figure 2. Overview of global surface temperature anomalies referenced to 2011-2015.

Figure 2. Overview of global surface temperature anomalies referenced to 2011-2015.

A closer look at the 20th Century estimates referenced to 1979-1998 is shown in Figure 3.  All four of the estimates are in close agreement during this period, with the spread generally near or less than 0.1 degrees Celsius (C).  Most of the largest outliers are from the coarser resolution NCEP/NCAR R1.

Figure 3. Global surface temperature anomalies for 1979-2000, referenced to 1979-1998.

Figure 3. Global surface temperature anomalies for 1979-2000, referenced to 1979-1998.

Figure 4 provides a closer view of the estimates for the 21st Century so far, as referenced to 1979-1998.  During this period, the estimates diverge substantially, with a spread closer to 0.2C most months.  The NCEP CFSR/CFSV2 is generally near the top of the spread for 2001-2009, but then suddenly drops to the bottom of the spread beginning April 2010, a year before the CFSV2 became operational.  The NCEP/NCAR R1 and ERAI Adjusted show the closest and most consistent match during this period.

Figure 4. Global surface temperature anomalies for 2001-2016, referenced to 1979-1998.

Figure 4. Global surface temperature anomalies for 2001-2016, referenced to 1979-1998.

For an alternative perspective, the same estimates have been graphed relative to a 2011-2015 reference period, as shown in greater detail in Figures 5 and 6.  From this reference perspective, there is excellent agreement among all four estimates for 2011-2015.  The largest spread is in the 20th Century portion, shown in Figure 5.  Oddly, the NCEP CFSR/CFSV2 separates high during the period from about 1999 through March 2010, while the other three estimates are much closer together but lower.  For the period 1979-1998, the ERAI and NCEP/NCAR R1 match well, with the ERAI Unadjusted slightly higher and the NCEP CFSR/CFSV2 the highest.

Figure 5. Global surface temperature anomalies for 1979-2000, referenced to 2011-2015.

Figure 5. Global surface temperature anomalies for 1979-2000, referenced to 2011-2015.

Figure 6. Global surface temperature anomalies for 2001-2016, referenced to 2011-2015.

Figure 6. Global surface temperature anomalies for 2001-2016, referenced to 2011-2015.

The corresponding global temperature trends for 1979-2015 projected to 100 years range from +1.25C for ERAI Unadjusted, to +1.32C for NCEP CFSR/CFSV2, to +1.52C for NCEP/NCAR R1, to +1.67C for ERAI Adjusted.  These trends are independent of any reference period and the range in these trends illustrates some of the uncertainties involved in trying to determine global temperature trends.

Conclusions
The four global surface temperature anomaly reanalysis data sets examined showed good agreement for 1979-1998 when referenced to that period and showed excellent agreement for 2011-2015 when referenced to that period.  However, trends indicated by the four data sets differed significantly, mainly because of apparent reanalysis modifications during the period from 1999 through 2010.  The differing trends indicate a small part of the uncertainty involved in trying to assess global temperature trends over long time periods.

Data Sources
UM CCI (NCEP/NCAR R1, NCEP CFSR/CFSV2, and ERAI Unadjusted)
ECMWF Copernicus (ERAI Adjusted)