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!

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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.

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.

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.

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 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.

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.

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.

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

A closer look at the 20^th 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 4 provides a closer view of the estimates for the 21^st 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.

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 20^th 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 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.
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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

El Niño Comparison 1997-98 versus 2015-16

Note: updated graphs are included in the ENSO page accessible at the top of this page.

The current El Niño that started in 2015 appears to have peaked and to be slowly declining now as can be seen in Figure 1.

Figure 1. Multivariate ENSO Index comparison for 1997-98 versus 2015-16.

This figure compares the Multivariate El Niño Southern Oscillation (ENSO) Index (MEI) provided by the US National Atmospheric and Ocean Administration (NOAA) for the current 2015-16 El Niño versus the 1997-98 El Niño.  Since the satellite global temperature estimates typically show the largest response to El Niño events, global estimates of the temperature of the lower troposphere (TLT) estimates from Remote Sensing Systems (RSS) and the University of Alabama at Huntsville (UAH) are presented in Figures 1 and 2.  Both figures compare the TLT estimates for the 1997-98 El Niño versus the 2015-16 El Niño so far.

Figure 2. RSS global TLT anomaly comparison for 1997-98 versus 2015-16.

Satellite peak global TLT estimates for El Niño events often lag the peak MEI and that appears to be happening with the current El Niño event.  Both the RSS and UAH global TLT estimates through January 2016 are still rising.

Figure 3. UAH global TLT anomaly comparison for 1997-98 versus 2015-16.

If the current El Niño follows a similar pattern to the 1997-98 El Niño, the global TLT estimates may not peak until somewhere in the February to April range.  The 1997-98 El Niño, as well as the 2010-11 El Niño were both followed by strong La Niña cooling events as can be seen in Figure 4 (click to enlarge).  Thus, it seems likely that the current El Niño will also be followed by a strong La Niña, although time will tell.

Figure 4. UAH global TLT anomalies vs Multivariate ENSO Index 1996 through 2016 so far.

Figures 5 shows the current Sea Surface Temperature  (SST) anomalies for today (February 7, 2016) which can be compared to the Figure 6 map of SST anomalies for the same date in 1998.  Both maps were provided by the University of Maine Climate Change Institute.  Click on these figures to enlarge.

Figure 5. Global SST anomalies for 2016 February 7.

Figure 6. Global SST anomalies for 1998 February 7.

These maps indicate that in 1998 the El Niño was more intense in the far eastern equatorial Pacific Ocean, as compared 2016 where the highest SST anomalies are farther west, in the central portions of the equatorial Pacific Ocean, and slightly weaker.  Interestingly, both years exhibit a cold SST anomaly pool in the North Central Pacific Ocean.

For monthly updates to key figures, see the ENSO page accessible from the menu bar at the top of this page.

Global Satellite Temperature versus ENSO

With the current El Niño possibly peaking now it will be interesting to see when the satellite derived global temperature anomalies peak. If past history during the satellite era is any indication, the satellite indicated global lower tropospheric temperature anomaly could peak as much as 3 to 6 months after the El Niño Southern Oscillation (ENSO) peak. The latest monthly Multivariate ENSO Index (MEI) from the US National Oceanic and Atmospheric Administration (NOAA) indicates a slight downturn in the MEI for October compared to September, which could possibly indicate the peak was in September. However, we will have to wait another month or two to be more confident. If the peak was in September, this El Niño will rank as the third most intense of the satellite era based on the MEI, after the 1997-98 and 1982-83 events.

The University of Alabama at Huntsville (UAH) satellite derived monthly global temperature for the lower troposphere (TLT) anomaly estimates showed a sharp rise from September to October as shown with the MEI in Figure 1. Also shown are the Mauna Loa apparent sunlight transmission monthly averages to indicate significant volcanic effects on sunlight transmission through the atmosphere. Effects from reduced sunlight transmission are evident after the El Chichón and Pinatubo volcanic eruptions in April 1982 and June 1991 respectively, after which sharp drops can be seen in the TLT anomaly estimates. Most of the strongest El Niños have been followed by La Niñas with corresponding drops in TLT for as much as a year or two following.

Figure 1. Comparison of monthly UAH satellite temperature for the lower troposphere (TLT) anomaly estimates, NOAA Mulitivariate ENSO Index, and NOAA Mauna Loa Apparent Transmission (MLAT) of sunlight (click to enlarge)

For the latest monthly ENSO updates, see the ENSO page accessible from the menu bar at the top of this post.