Category Archives: Climate

Climate studies.

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.

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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

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)

Mauna Loa Temperature Trend

Note:  updated graphs are now posted on the MLMW page accessible in the menu bar at the top of this page.

Mauna Loa on the big island of Hawaii is probably more famous for being a large active shield volcano and for carbon dioxide and solar measurements than for temperature measurements.  However, the Mauna Loa Observatory (MLO) has been making continuous temperature measurements since 1977 and the data return is overall very good.  The observatory is run by the Earth System Research Laboratory (ESRL) which is part of the US National Oceanic and Atmospheric Administration (NOAA).  For more information, see this link: Mauna Loa Observatory.

The observatory is at an elevation of 3,397 meters (11,141 feet) above mean sea level about 5 kilometers (3 miles) north of the Mauna Loa summit that reaches to 4,169 meters (13,679 feet) above sea level.  See the map below for the position of the observatory relative to the summit.

Contour map of the summit of Mauna Loa

Contour map of the summit of Mauna Loa.
The observatory is at the location labeled “Weather Station”.

Below is a photograph of the MLO looking north toward Mauna Kea.  The tall tower has temperature sensors at 2 meters, 10 meters, and 35 meters above ground level.

I downloaded hourly meteorological data that is available by FTP from the ESRL web site here: FTP Data Finder.  I loaded the data into an Excel workbook by year and compiled daily, monthly, and annual statistics for temperature at 2 meters above ground level.  The average annual data return for temperature at 2 meters over the period from 1977 through 2015 was 97%, but there were a few fairly large gaps in the data.  The largest gap was from March 29 to April 29 of 1984. Since most of April was missing, I removed it from the data set.  I also compiled annual weighted means using the monthly averages in an attempt to make sure every month was weighted the same from year to year even if there was substantial missing data.  This is not a perfect approach and is subject to increased uncertainty if there is a large data gap near the beginning or end of a month where temperatures are normally rising or falling on average over the course of the month.  So, the incompleteness does add a small amount of uncertainty to the results, especially for the years with the lowest data returns – 1984, 1993, and 2001 with data completeness of 87, 88, and 88 percent respectively.

The annual average temperatures and associated trend are displayed in Figure 1.  The data show a fairly significant upward trend of +0.0264 degrees Celsius (C) per year (Coefficient of Determination R2=0.2537).  This temperature trend corresponds to +1.03C over the 39 year period of record and to +2.64C if it continued for 100 years.

Mauna Loa annual weighted mean temperature for 1977-2015.

Figure 1. Mauna Loa annual weighted monthly mean temperature for 1977 through 2015 (click to enlarge).

I noticed that since the high peak in 1995, the rise looks much weaker than before the peak.  So I made separate graphs for 1977-1994 shown in Figure 2 and for 1995-2015 shown in Figure 3.  The temperature trends in these graphs confirm that the rise was indeed steeper before 1995 than since 1995.

Mauna Loa annual weighted mean temperature 1977-1994

Figure 2. Mauna Loa annual weighted monthly mean temperature 1977 through 1994 (click to enlarge).

The temperature trend for 1977 through 1994 was +0.0345C per year, corresponding to -0.62C over the 18 year period or to +3.45C if that trend continued for 100 years.  The temperature trend for 1995 through 2015 was +0.194C per year, which is +0.41C over the 21 year period and would be only +1.94C if it continued for 100 years.  The statistical confidence in both of these trends is weak with R2=0.1038 for 1977-1994 and R2=0.0539 for 1995 to 2015.

Mauna Loa annual weighted mean temperature 1995-2015

Figure 3. Mauna Loa annual weighted monthly mean temperature 1995 through 2015 (click to enlarge).

Even though MLO is in the tropics at a latitude of 19.5 degrees north of the equator, the temperature shows a pronounce seasonal pattern as can be seen in Figure 4.  On average the highest monthly average temperature is for June and the lowest for February.

Mauna Loa monthly average temperature 2015

Figure 4. Mauna Loa monthly average temperature for 2015 as compared to the most recent climatological reference period of 1981 through 2010 (click to enlarge).

The most recent 1981-2010 standard climatological reference period averages are 5.2C for February and 9.1C for June.  Figures 5 and 6 show the temperature trend graphs for these two months respectively.

Mauna Loa February average temperatures 1977-2015

Figure 5. Mauna Loa February average temperatures 1977 through 2015
(click to enlarge).

The trend for February is only +0.0106C per year, corresponding to +0.41C over the 39 year period and to +1.06C if it continued 100 years.  The trend for June is much higher at +0.0279C per year, or +1.09C over the 39 year period and would be +2.79C if it continued 100 years.  The February graph also shows a larger variability in monthly average temperature from year to year compared to the June graph.

Mauna Loa June average temperatures 1977-2015

Figure 6. Mauna Loa June average temperatures 1977 through 2015
(click to enlarge).

Regardless how the data are sliced, the temperature trend at the MLO is significantly upward, although with a hint of a slower increase since 1995.  There is a slight chance that expansion of the observatory over time could have an effect similar to a miniature urban heat island that increases over time with the expansion.  How much influence the expansion might have on the temperature trend is difficult to determine without additional information about potential heat sources at the observatory and how they may have changed over time.  Even if we assume that such influences are insignificant, the upward temperature trend could be from natural causes and not necessarily from CO2.  From what I recall reading, hypothetical warming induced by CO2 should occur primarily at higher latitudes and not in the tropics.  Consequently, the observed upward trend is likely to be from unknown natural causes or possibly from increasing localized heat influence over time or both.

 

Mount Washington Temperature Trend

Note:  updated graphs are now posted on the MLMW page accessible in the menu bar at the top of this page.

In looking at weather stations around the world, one stands out as a fairly unique and potentially representative indicator of temperatures in the lower troposphere for at least higher latitudes of North America and perhaps also fairly relevant for higher latitudes in all of the North Hemisphere.  This unique weather station is located at the top of Mount Washington in New Hampshire in the Northeastern US at an elevation of 1,917 meters (6,288 feet) above “mean sea level” (which is not so easy to define, but that’s another story).

Below is a US Geological Survey topographic map of the area.  Mount Washington is the highest of several high peaks oriented roughly south to north in the Presidential Range.  This orientation may help to orographically increase wind speeds with high westerly or easterly winds.

Topographic map of the Mount Washington area

Topographic map of the Mount Washington area.

Below is a view of the peak without snow.

Below is a winter view with heavy snow cover on the mountain.

Below is a closer look at the round observatory at the top of the mountain.

Below is a photo of the observatory covered in snow.

And a closer look at the observatory coated in rime icing in 2004.

This location is very remote, far away from any urban areas.  However, the temperature measurements could be subject to microscale effects from the nearby observatory.  Frequent strong winds at the site should help to minimize any such microscale effects.

I made a quick check on the internet for annual temperature data for the site and found some available for 1948 through 2015 from Weather Warehouse.  At first I accepted the data, since it was from what I thought was a reputable source.  However, I was a bit suspicious of it because the annual variability seemed larger than expected and the temperature colder than expected.  So I decided to compare the data with what is available from the Weather Underground back to 1973.  The two data sets did not match and the differences were substantial.

I then looked to see if measurements were available from the National Center for Environmental Information (NCEI) and found they had daily data since 1948, but the entire data set was not available for free.  They did however provide a free PDF monthly copy of the daily minimum and maximum temperature observations which included a monthly average.

I went to the trouble to download every monthly PDF copy since 1948, which took awhile, but was free.  I then hand entered (triple checking) all of the monthly maximum and minimum averages into a spreadsheet and then calculated annual averages.  January 1948 was incomplete, but all other months were complete through November 2015.  For December 2015, I used the hourly measurements available from the Weather Underground, including the reported 6-hour maximums and minimums  to compile preliminary daily and monthly data for December 2015 and preliminary data for 2015.  Figure 1 shows the resulting annual average temperatures and associated linear regression trend (click to enlarge).

Mt Washington NH annual temperature trend 1948-2015

Figure 1. Mount Washington NH annual temperature trend 1948 through 2015.

The annual average temperatures show a linear upward trend of +0.0115 degrees Celsius (C) per year, which corresponds to +0.78C over the 68-year period and to +1.15C if it continues for 100 years.

Taking a closer look for patterns in the data, I see three distinct periods as shown in Figure 2.  The first period from 1948 through 1968 shows a statistically robust downward trend of -0.0695C per year, or -1.46C over the 21-year period with a fairly high coefficient of determination (R-square=0.5466).  During this period global carbon dioxide (CO2) concentrations were beginning to accelerate higher from human influence increasing from about 310 parts per billion (ppb) to about 322 ppb based on ice core measurements.  This is an increase of about 4%.  In contrast, Mount Washington temperatures decreased.  The mean annual average for this period was -2.8C.

Mt Washington NH temperature trend patterns 1948-2015

Figure 2. Mount Washington NH temperature trend patterns 1948 through 2015.

For the period 1969 through 1997, the Mount Washington annual average temperatures showed a lot of annual variation, but not much trend.  A linear regression fit for this period indicates a statistically low confidence slight upward trend of +0.0077C per year, or +0.22C over the 29-year period.  The mean annual average temperature for this period was also -2.8C.  During this period, annual average CO2 levels measured at the top of the Mauna Loa volcano in Hawaii rose substantially from 325 ppb to 363 ppb, or an increase of about 12%.

In 1998 there was a large high spike in the annual average temperature that may be related to global effects from the El Niño event that peaked in late 1997 and early 1998 in the tropical central and eastern Pacific Ocean.  For the period 1998 through 2015, the Mount Washington annual average temperatures again show large variations and only a statistically weak trend, in this case slightly downward at -0.0122C per year or -0.22C over the 18-year period.  However, the mean annual average temperature for this most recent period was -2.0C, which is 0.8C higher than the previous two periods.  This change appears to be a step jump that is not consistent with the steady rise in CO2.  The Mauna Loa CO2 annual averages increased from 367 ppb in 1998 to 401 ppb in 2015, or an increase of 9%.

I also looked at the trend in average maximum and average minimum temperatures at Mount Washington as shown in Figure 3.  Trends for both are very similar to the annual average trend.  All of these trends are statistically low confidence, but do show an overall small upward rise across the 1948-2015 period.  Interestingly, the temperatures for the last two years are lower than for the first two years.

Mt Washington NH temperature trends 1948-2015 for maximum, average, and minimum.

Figure 3. Mount Washington NH temperature trends 1948 through 2015 for annual average maximum, annual average, and annual average minimum.

The overall temperature pattern at Mount Washington is very similar during the satellite era to the satellite derived estimates of global lower troposphere temperature patterns and to the Climate Forecast System Reanalysis (CFSR) global temperature patterns described in previous posts.  All of these show evidence of a relatively flat period from 1979 to 1997, then a sudden upward jump apparently associated with the 1997-1998 El Niño, followed by another relatively flat period to the present.  These patterns look nothing like the steadily increasing CO2 concentrations and this discrepancy casts a large measure of doubt over predictions of catastrophic global warming caused by man-made “greenhouse” gases dominated by CO2.  If CO2 was the main driver for global temperatures, the patterns should show a consistent match.  The implication is that CO2 is not the main driver of global temperatures.

Uncertainty in Global Temperature Assessments

In trying to assess global temperature anomalies and trends over time, there are a wide variety of considerations that influence the accuracy of our assessments, including:

  • Sensor type and calibration
  • Sensor redundancy and accuracy
  • Sensor shielding and aspiration
  • Sensor height above ground
  • Ground cover
  • Site exposure
  • Proximity to water bodies
  • Orographic influences
  • Spatial coverage and representativeness
  • Number and timing of observations
  • Data completeness
  • Accuracy of site location information
  • Station relocation
  • Changes in any of the above influences over time
  • To adjust or not to adjust?

These issues directly affect the accuracy and representativeness of temperature measurements. Below I will briefly address each of these influences on global temperature measurement uncertainty while attempting to avoid turning this post into a book.

Sensor type and calibration
Mercury thermometers were long considered the reference standard for temperature measurements. However, in recent decades, thermistors have gradually replaced liquid thermometers in most weather station and climatic station networks. Linearity over typical measurement ranges and proper calibration are critical to minimizing potential bias in measurements, but in practice this is usually a small source of uncertainty, ideally less than 0.5 degrees Centigrade (C) but sometimes can cause biases of 1C or more for extended periods. Anytime an instrument is replaced there is a chance of a small change in sensor bias that can affect trends over time.  Typically there is little or no documentation of this information.

Sensor redundancy and accuracy
Ideally, multiple independent sensors should be collocated to better assess the temperature measurement precision and to provide a better data return if an individual sensor failure occurs. However, most weather and climate stations have only one temperature sensor. Ideally, temperature sensors should be calibrated or at least compared to a reference standard periodically to ensure optimal accuracy, but in practice there is a wide variation in quality control for temperature measurements and this is yet another small source of uncertainty.

Sensor shielding and aspiration
Proper shielding is critical to avoid high bias during periods of intense sunshine and low bias during periods of rain. Likewise adequate sensor aspiration important to ensure that ambient air is being sampled as opposed to stagnant air inside the measurement housing that may have a different temperature from outside air if not properly ventilated. If unaddressed, these influences can cause high biases on the order of 1C to 3C during sunny conditions and can cause similar low biases if the sensor gets wet from fog or rain.

Sensor height above ground
Another important influence is the sensor height above ground. The recommended measurement height is 1.25 to 2 meters above ground, which is typically met for most weather and climate stations. However, a few stations are sometimes located on the tops of buildings and this situation can introduce a high bias at night during temperature inversion conditions as compared to nearby stations at the ideal measurement height. This influence could cause a high bias of as much as 2C to 4C or more on many nights, depending on the height of the building and frequency and intensity of ground-based temperature inversions.

Ground cover
Ground cover near and below the sensor location can greatly influence the measurements. Ideally the nearby ground should be covered in uniform low-growing vegetation, such as routinely mowed grass. But many sensors are sited over or near concrete, asphalt, buildings, bare soil, or rocks and these types of ground cover can introduce high biases during periods of intense sunshine as compared to the ideal ground surface. They can also add heat to the air at night after sunny days. These effects may add uncertainty on the order of 1C to 3C depending on the type, proximity, and extent of ground cover variations.

Site exposure
The overall site exposure, including the proximity of buildings, trees, and localized terrain can influence the representativeness of measurements at the site. If wind flow is obstructed, wind aspirated temperature measurements may more frequently be biased high during sunny conditions. Wind flow obstruction may not be as much of a factor for properly shielded motor-aspirated sensors, but may still influence heat build-up in the air over less than ideal ground surfaces in the immediate area. This influence can also be a factor in site representativeness for a larger area. Site exposures that are not typical of the larger surrounding area may not represent it very well.

Proximity to water bodies
For land measurements, nearby ponds, lakes, creeks, rivers, and oceans can exert considerable influence on temperature measurements, depending on the size and proximity of the water body. Most water bodies more than about a kilometer across can cause lake-breeze or sea-breeze effects during the day and land-breeze effects at night that affect temperatures at sites in close enough proximity. Since water temperature changes much more slowly than air temperature and water effectively stores more heat than air, it will impact nearby air temperatures in ways that will not be seen at locations farther from the water. This influence is mainly a concern for representativeness of the site measurement on a larger scale and may affect trends if a site is moved to a greater or lesser distance from a nearby water area.

Orographic influences
Terrain can drive large effects on temperature from both elevation differences and slope orientation relative to sun angle. In morning sunshine, eastern facing slopes will heat up faster than flat or western facing slopes. Since warm air rises, this effect cause a local updraft to occur, also called up-slope wind. Later in the day the same effect occurs on westward facing slopes. At night with clear skies, as air cools near the ground because of heat radiating into space, the cold air near the surface is denser and will sink to lower elevations if the terrain is not flat. This effect will cause down-slope or drainage flows that cause cold air to pool at lower elevation and will leave warmer air at higher locations. Thus stations in valley locations will tend to be much colder with clear-sky radiation cooling at night than hillside or hilltop locations, whereas during the day or with cloudy windy conditions the temperatures may be very similar if all other considerations are equal. If the elevation difference is large enough, higher elevation sites can be significantly colder during windy weather with near neutral atmospheric lapse rate. These terrain influences are mainly a concern for representativeness of the site measurement on a larger scale and can add considerable uncertainty for trends if a site is relocated from hilltop to valley or vice versa.

Spatial coverage and representativeness
One of the largest problems in estimating global temperature anomalies is the severe lack of spatial coverage over large areas of the globe, mainly over the oceans and in remote uninhabited areas including deserts, jungles, and polar areas. My guess is that poor spatial representativeness is likely to account for the largest uncertainty among all of the various sources of uncertainty. This issue is also related to representativeness of existing measurements to larger areas that do not have measurements.

Number and timing of observations
Early temperature measurements were spot observations no more than a few times a day without the advantage of min/max thermometers or continuous electronic data acquisition that came later. Much of the temperature record used for estimating global temperature anomalies uses measurements that were made once per day using a min/max thermometer with the resulting minimum and maximum temperature averaged to estimate the daily average temperature. This method is subject to biases depending on the time of day that the observation is made, the so-called “time of measurement bias”. The largest biases occur when the time of measurement is near either the minimum temperature (low bias) or maximum temperature (high bias). This influence is especially important for trends if the time of observation changes or when the measurement method changes from min/max thermometer to continuous electronic thermistor measurement.

Data completeness
Many historical temperature records are incomplete and methods for infilling missing data can introduce uncertainty. Greater amounts of infilled data result in greater uncertainty in the data and trends.

Accuracy of site location information
Accurate site location information is important for determining site characteristics when evaluating site representativeness for an area.  It is also important for evaluating what impacts a station move may have on temperature trends. An error of only 0.1 degree latitude corresponds to about 7 miles or 11 kilometers and that can make a big difference in site characterization.

Station relocation
In the historical climate record, there are many cases where measurement locations are moved, sometimes only a few meters and other times as much as a kilometer or more. As mentioned previously these moves can introduce a variety of uncertainties caused by changes in ground cover, site exposure, terrain, and nearby water influences. Attempts to correct for these changes may introduce further error if incorrect assumptions are made.

Changes in any of the above influences over time
Over time, even stations that initially began with ideal siting may gradually become less ideal over time with changes in nearby ground cover, vegetation, buildings, and associated wind flow. Sometimes sudden changes occur, as when a building is constructed close to the site or a nearby grassy field becomes a parking lot. Since many stations are located in urban and suburban environments, effects from increasing urbanization over time may be very significant on long-term temperature trends. Also, sites that were once rural often become suburban or urban over time and are thus subject to a high bias in the temperature trend caused by increasing urban heat island effects over time.

To adjust or not to adjust?
Ideally, if temperature data are going to be adjusted, the adjustment should be carefully documented and justified on a case-by-case basis, including an explanation of what correction was applied to what period and why. However, this approach is very tedious and time consuming and often there is not enough information to determine what, if any, adjustment should be made. Consequently, most organizations that attempt to estimate global temperature trends resort to complex automated algorithms that “homogenize” the data. This approach could actually add to the uncertainty rather than decrease it if incorrect assumptions are applied. My preference would be to adjust the data only when well documented information justifies the adjustment on a case-by-case basis and when the adjustment itself is well documented. Otherwise, data should be left with all of its warts and blemishes since most of the time we cannot be sure what is a blemish and what is real.

Ocean temperatures
About 70% of the earth’s surface is covered by oceans and actual air temperature measurements there are relatively sparse, especially before the buoy and satellite era.  Prior to deployment of weather and ocean measurement buoys and satellites, all ocean related measurements came from ships, including air temperature and water temperature.  These measurements are subject to many of the same problems as those from land stations.

Most of the ships were moving, so there are not any long-term measurement records from fixed locations.  Furthermore, most of the ships follow shipping lanes so that large areas away from the shipping lanes have little or no coverage, especially in the southern hemisphere. A few ship weather stations were deployed to fixed locations from the 1940’s into the 1970’s and were replaced by more numerous fixed buoy weather stations since the 1970’s.  By far the greatest number buoys at present are drifting buoys. However, the number of both fixed and drifting buoys is much smaller than the number of land stations, despite the much larger surface area.  Thus spatial and temporal coverage of air temperature measurements over the oceans remains poor even today.  Consequently, many assessments of global surface air temperature use ocean surface water temperature as a proxy for surface air temperature, which adds another degree of uncertainty to a very large area of the globe.

Since the late 1970’s satellites have been measuring ocean surface water temperatures, but even these measurements are only possible when skies are clear and can be subject to calibration uncertainties related to clarity of the air column as well as instrument calibration drift.  Spatial coverage of water temperatures has been greatly improved by satellite observations, but temporal coverage affected mainly by cloud cover still adds some uncertainty.

Considering all of the problems noted above, estimating surface air temperatures over the oceans might be the largest source of uncertainty associated with estimates of global surface temperature, especially before the buoy/satellite era beginning in the late 1970’s.

Estimated uncertainties in global temperature assessments
The HadCRUT data set provided by the Climatic Research Unit at the University of East Anglia in conjunction with the Hadley Centre at the United Kingdom Meteorological Office includes estimates of uncertainty with their data. The graph of annual global temperature anomalies in Figure 1 includes the estimated uncertainty ranges provided with the HadCRUT4 data set.

Figure 1.  Estimated annual global temperature anomalies relative to a 1961-1990 baseline in red with upper and lower 95% confidence interval bounds shown in blue, as provided by the UK Hadley Centre.

Figure 1. Estimated annual global temperature anomalies relative to a 1961-1990 baseline in red with upper and lower 95% confidence interval bounds shown in blue, as provided by the UK Hadley Centre.

According to information provided with the HadCRUT4 data set, the trend in Figure 1 represents the “medians of regional time series computed for each of the 100 ensemble member realizations” and the “uncertainties are computed by integrating across the distribution described by the 100 ensemble members, together with additional measurement and sampling error and coverage uncertainty information”. The total uncertainty provided for the estimated 2014 global temperature anomaly of 0.56C is plus or minus 0.09C.

These uncertainties subjectively seem much too low to me, considering all of the various types of uncertainty previously described.  So I took the HadCRUT4 estimated annual uncertainties and multiplied them by a factor of three to produce the graph in Figure 2. I believe this adjustment of the uncertainty results in a conservative estimate of the full uncertainty in the data, which could be as much a factor of five higher rather than the factor of three that I selected for the graph.

Figure 2.  Estimated annual global temperature anomalies relative to a 1961-1990 baseline in red with upper and lower 95% confidence interval bounds shown in blue but multiplied by three from what was provided by the UK Hadley Centre.

Figure 2. Estimated annual global temperature anomalies relative to a 1961-1990 baseline in red with upper and lower 95% confidence interval bounds shown in blue but multiplied by three from what was provided by the UK Hadley Centre.

I do not believe these estimates of global temperature anomalies have enough accuracy to clearly and confidently resolve trends in this data set over the temperature anomaly ranges that have occurred so far. At best, they provide only a somewhat uncertain hint at possible trends over periods of decades to a century or more.  Most of the year-to-year variation is easily in the noise range.

Update 2016 January 26

Global temperature is a concept and not something we can measure directly.  The standard surface temperature measurement height in the US is 2 meters above the ground. In England it is 1.25 meters above the ground. So, what is the surface temperature anyway? Is it the surface air temperature 1.25 to 2 meters above:

  1. The ground in the shade of my backyard?
  2. My concrete patio?
  3. The roof of my house?
  4. A suburban city street?
  5. The roof of a skyscraper?
  6. The street in a downtown skyscraper street canyon?
  7. A paved parking lot?
  8. The top of 2 meters of snow on the ground?
  9. A glacier?
  10. Desert sand?
  11. The ground under the canopy of a jungle or forest?
  12. An open field of grass?
  13. The tops of the highest mountain peaks?
  14. A pond?
  15. A river?
  16. A lake?
  17. An ocean?
  18. The troughs between waves in an ocean storm with 20 meter waves?
  19. The top of each wave in an ocean storm with 20 meter waves?
  20. This list could go on and on.

In reality, when looking at the earth from straight above, all of these locations are at the “surface” and locations around the globe together combine to represent the “global surface temperature”. The surface of the earth is about 510 million square kilometers. That means we would need 510 million temperature sensors evenly spaced around the globe just to have one measurement per square kilometer. Even then, there are many areas with complex terrain and/or highly variable surface features where a single measurement is not likely to represent the average temperature over a square kilometer area very well. Likewise in the middle of a large city. This situation is part of why I believe that our best estimates of global temperature and global temperature anomalies are woefully inadequate and come with a large uncertainty.