Category Archives: Climate Change

Investigating the nature of our changing climate.

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)

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.

Global Satellite Temperature versus ENSO

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.

21st Century Global Temperature Trends

This analysis focuses on global temperature anomaly trends for our current 21st century so far, beginning in 2001, as we approach the first 15 years. The previous post covered trends over the entire satellite weather data era back to 1979.

As mentioned in my previous post, there are three main relatively independent sources of global surface temperature anomaly estimates available, derived from:  the Global Historical Climate Network (and sometimes including additional land stations) coupled with sea surface temperature measurements (GHCN), global weather forecast model input data, and satellite estimates of lower tropospheric temperatures.  There are multiple groups that compile and publish estimates from these sources, but for simplicity, estimates from four of these groups are presented here.  For the GHCN related estimates, this analysis uses the US National Center for Environmental Information (NCEI) estimates and the Berkeley Earth Surface Temperature (BEST) estimates.  These are compared to the satellite estimates from the University of Alabama Huntsville (UAH) and global forecast system (GFS) estimates provided by the University of Maine (UM) Climate Change Institute (CCI).

I compiled and graphed monthly global temperature anomaly estimates from each of these four groups for the period 2001 through September 2015 and calculated linear regression trends to indicate the overall change over this period. The UM CCI GFS estimates in Figure 1 show the lowest trend at -0.0014 degrees Celsius (C) per month over the period of nearly 15 years, which corresponds to -1.68C per century if maintained. The next lowest trend, shown in Figure 2, is +0.00005C per month from the UAH satellite estimates, which corresponds to +0.06C per century if maintained. The second highest trend is +0.0006C per month for the BEST GHCN estimates shown in Figure 3, which corresponds to +0.72C per century. The highest trend of 0.0009C per month is from the NCEI GHCN estimates shown in Figure 4 and corresponds to +1.08C per century if maintained.

Global temperature anomaly trend for 1979-2015 based on UM CCI GFS monthly estimates

Figure 1. Global temperature anomaly trend for 1979-2015 based on UM CCI GFS monthly estimates.

Global temperature anomaly trend for 1979-2015 based on UAH satellite monthly estimates

Figure 2. Global temperature anomaly trend for 1979-2015 based on UAH satellite monthly estimates.

Global temperature anomaly trend for 1979-2015 based on BEST GHCN monthly estimates

Figure 3. Global temperature anomaly trend for 1979-2015 based on BEST GHCN monthly estimates.

Global temperature anomaly trend for 1979-2015 based on NCEI GHCN monthly estimates

Figure 4. Global temperature anomaly trend for 1979-2015 based on NCEI GHCN monthly estimates.

The variation in these trends underscores the uncertainty in all of these approaches. I suspect that the satellite derived estimate of +0.06C per century is the best compromise among these estimates. I find it very interesting that the GFS derived estimate of -1.68C per century contrasts so greatly with the GHCN derived trends. This discrepancy provides further evidence that ongoing adjustments to the GHCN based estimates are pushing them farther away from reality. We have no way of knowing whether these trends will be maintained for a full century, but there is certainly no clear evidence of an upward trend so far this century despite continued rapid increases in carbon dioxide (CO2). The implication is that CO2 is not at all the driver for global temperature trends and does not warrant expensive efforts to control.

Satellite Era Global Temperature Trends

This analysis focuses on global temperature anomaly trends for the satellite era beginning in 1979. Prior to this time, data coverage over oceans was much more sparse and this problem is worse moving farther back in time. Since the oceans cover 71 percent of the earth’s surface, poor coverage over the oceans leads to great uncertainty in global temperature anomaly estimates and trends prior to the satellite era. Consequently, I have little confidence in any global temperature trend analyses that include estimates from before the satellite era.

As mentioned in my previous post, there are three main relatively independent sources of global surface temperature anomaly estimates available, derived from:  the Global Historical Climate Network (and sometimes including additional land stations) coupled with sea surface temperature measurements (GHCN), global weather forecast model input data, and satellite estimates of lower tropospheric temperatures.  There are multiple groups that compile and publish estimates from these sources but for simplicity, estimates from four of these groups are presented here.  For the GHCN related estimates, this analysis uses the US National Center for Environmental Information (NCEI) estimates and the Berkeley Earth Surface Temperature (BEST) estimates.  These are compared to the satellite estimates from the University of Alabama Huntsville (UAH) and global forecast system (GFS) estimates provided by the University of Maine (UM) Climate Change Institute (CCI).

I compiled and graphed monthly global temperature anomaly estimates from each of these four groups for the period 1979 through September 2015 and calculated linear regression trends to indicate the overall change over this period. The UAH satellite estimates in Figure 1 show the lowest trend at +0.0009 degrees Celsius (C) per month over the period of nearly 37 years, which corresponds to +1.08C per century if maintained. The next lowest trend, shown in Figure 2, is +0.0011C per month from the UM CCI GFS estimates, which corresponds to +1.32C per century if maintained. The second highest trend is +0.0013C per month for the NCEI GHCN estimates shown in Figure 3, which corresponds to +1.56C per century. The highest trend of +0.0014C per month is from the BEST GHCN estimates shown in Figure 4 and corresponds to +1.68C per century if maintained.

Global temperature anomaly trend

Figure 1. Global temperature anomaly trend for 1979-2015 based on UAH satellite monthly estimates.

Global temperature anomaly trend

Figure 2. Global temperature anomaly trend for 1979-2015 based on UM CCI GFS monthly estimates.

Global temperature anomaly trend

Figure 3. Global temperature anomaly trend for 1979-2015 based on NCEI GHCN monthly estimates.

Global temperature anomaly trend

Figure 4. Global temperature anomaly trend for 1979-2015 based on BEST GHCN monthly estimates.

My best guesstimate of uncertainty for global temperature anomaly estimates is at least plus or minus 0.3C to 0.5C and thus these trends appear to be significant, although with a fairly low confidence. I suspect that the satellite derived estimate of +1.08C per century is the most accurate of these estimates, followed by the GFS estimate of +1.32C per century, neither of which is especially alarming considering evidence of much larger century scale changes in temperatures in the past during our current Holocene epoch as previously described in this blog. Also, we have no way of knowing whether these trends will be maintained for a full century and so far for the first 15 years of the 21st century, there is evidence that the upward trend could be ending or at least slowing. My next post will take a closer look at global temperature trends for the 21st century so far.