Category Archives: Weather

Weather studies.

Global Temperature 2017 February Preliminary

Climate Forecast System Reanalysis (CFSR) monthly global surface temperature anomaly estimates for 2014 through February 2017 from the University of Maine Climate Change Institute (UM CCI) and from WeatherBELL (WxBELL) are graphed below.  The UM CCI CFSR estimates have been adjusted (UM adj), while the WxBELL CFSR estimates have been left unadjusted to show the difference.  Both of these estimates showed increases from January to February in 2017.  The UM CCI CFSR adjusted monthly estimates for August 2016 through January 2017 are based on final daily averages and for February 2017 are based on preliminary daily averages, and thus these preliminary monthly estimates may change slightly when the final monthly estimates are released.  Click on the graph below to see a larger copy.

figure-1-global-temp-anom-2014-2017-feb-prel

Also shown for comparison are monthly global temperature anomaly estimates from seven other major sources, including lower tropospheric estimates from the University of Alabama at Huntsville (UAH) and 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 version 4 (CRUT4), and the Berkeley Earth Surface Temperature (BEST), all  final through January 2017, except for CRUT4 which is final through December 2016.  All estimates have been shifted to the latest climatological reference period 1981-2010.

The graph above shows that the various global temperature estimates converged in early 2016 and then diverged considerably later in 2016 and have remained divergent in early 2017.  The convergence seems to be associated with the strong El Niño event that peaked in early 2016.  It will be interesting to see what happens in the remainder of 2017.

Global Temperature 2017 January Preliminary

Climate Forecast System Reanalysis (CFSR) monthly global surface temperature anomaly estimates for 2014 through January 2017 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 (CFSRadj), while the WxBELL CFSR estimates have been left unadjusted to show the difference.  All three of these estimates showed slight increases from December to January.  The UM CCI CFSR adjusted monthly estimates for August through December 2016 are based on final daily averages and for January 2017 are based on preliminary daily averages, and thus these preliminary estimates may change slightly when the final monthly estimates are released.  Click on the graph below to see a larger copy.

figure-1-global-temp-anom-2014-2017-jan

Also shown for comparison are monthly global temperature anomaly estimates from six other major sources, including lower tropospheric estimates from Remote Sensing Systems (RSS), and surface estimates from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis Interim adjusted (ERAIadj), 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 version 4 (CRUT4), and the Berkeley Earth Surface Temperature (BEST), all  final through December 2016.  All estimates have been shifted to the latest climatological reference period 1981-2010.

The graph above shows that the various global temperature estimates converged in early 2016 and then diverged considerably later in 2016.  The convergence seems to be associated with the strong El Niño event that peaked in early 2016.  It will be interesting to see what happens in 2017.

See the Monthly Trends page and the Daily Update page for the latest graphs of the latest monthly and daily trends for the UM CCI CFSR estimates (access from the menu at the top of this page).

Update 2017 February 6

Final January 2017 global temperature anomaly estimates for WxBELL, RSS, and ERAIadj have been added to the graph.

Update 2017 February 9

The graph has been updated to show the UM CCI January 2017 monthly estimate based on final daily estimates for January released today.

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.

Global Temperature December 2016 Preliminary

Climate Forecast System Reanalysis (CFSR) monthly global surface temperature anomaly estimates for 2014 through December 2016 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 (CFSRadj), while the WxBELL CFSR estimates have been left unadjusted to show the difference.  All three of these estimates showed decreases from November to December.  The UM CCI CFSR adjusted monthly estimates for August through December are based on final daily averages and may change slightly when the final monthly estimates are released.  Click on the graph below to see a larger copy.

figure-1-global-temp-anom-2014-2016-dec-prev

Also shown for comparison are monthly global temperature anomaly estimates from six other major sources, including lower tropospheric estimates from Remote Sensing Systems (RSS), and surface estimates from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis Interim (ERAI), 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 version 4 (CRUT4), and the Berkeley Earth Surface Temperature (BEST), all  final through November 2016.  All estimates have been shifted to the latest climatological reference period 1981-2010.

See the Monthly Trends page and the Daily Update page for the latest graphs of the latest monthly and daily trends for the UM CCI CFSR estimates (access from the menu at the top of this page).

Global Temperature October 2016 Preliminary

Climate Forecast System Reanalysis  (CFSR) monthly global surface temperature anomaly estimates for 2014 through October 2016 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).  All three of these estimates showed a slight decrease from September to October.  The October UM CCI and WxBELL estimates are preliminary and may change slightly when final estimates are released.  Click on the graph to see a larger copy.

figure-1-global-temp-anom-2014-2016-oct-prel

Also shown for comparison are monthly global temperature anomaly estimates from five other major sources, including lower tropospheric estimates from Remote Sensing Systems (RSS), and surface estimates from the 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 version 4 (CRUT4), and the Berkeley Earth Surface Temperature (BEST), all  final through September 2016.  All estimates have been synced to the latest climatological reference period 1981-2010.

Mount Washington Temperature Trend

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.