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).

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 horizontal grid cells that are not 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 Temperature November 2016 Preliminary

Climate Forecast System Reanalysis  (CFSR) monthly global surface temperature anomaly estimates for 2014 through November 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 increase from October to November.  The November UM CCI and WxBELL estimates are preliminary and may change slightly when final estimates are released.  Click on the graph below to see a larger copy.

figure-1-global-temp-anom-2014-2016-nov-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 October 2016, except CRUT4 which is final through September 2016.  All estimates have been synced to the latest climatological reference period 1981-2010.

See the Monthly Trends page and the Daily Update page for the latest graphs of 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.

Global Temperature September 2016 Preliminary

The September preliminary Climate Forecast System Reanalysis (CFSR) monthly global surface temperature anomaly estimates from the University of Maine Climate Change Institute (UM CCI) and from WeatherBELL (WxBELL) show a slight drop from August with levels similar to July as can be seen in the graph below.  The graph includes monthly estimates beginning January 2014.  The preliminary UM CCI global average temperature estimate of 15.92C ties the previous highest September 2003 CFSR estimate, for records beginning 1979.

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

Also shown for comparison are monthly global temperature anomaly estimates from six other major sources, including lower tropospheric estimates from the University of Alabama at Huntsville (UAH) and Remote Sensing Systems (RSS), and global surface temperature anomaly 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 August 2016.  All estimates have been synced to the latest climatological reference period 1981-2010.

For longer trend graphs of the latest monthly UM CCI CFSR estimates, see the “Monthly Trends” page from the menu at the top of this post and for more detailed daily estimates, see the “Daily Updates” page.