October 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference Update

As one might expect in response to a strong El Niño, the GISS global Land Ocean Temperature Index jumped 0.24 deg C from September to October 2014, raising its value to more than 1.0 deg C above the reference period temperatures for 1951-1980.   

# # #

This post provides an update of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through October 2015 and HADCRUT4 and NCEI (formerly NCDC) through September 2015—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through October 2015.  It also includes a model-data comparison.

INITIAL NOTES (BOILERPLATE):

The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015).

Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update.

The UKMO also recently made adjustments to their HadCRUT4 product, but they are minor compared to the GISS and NCEI adjustments.

We’re using the UAH lower troposphere temperature anomalies Release 6.0 for this post even though it’s in beta form.  And for those who wish to whine about my portrayals of the changes to the UAH and to the GISS and NCEI products, see the post here.

The GISS LOTI surface temperature reconstruction and the two lower troposphere temperature composites are for the most recent month.  The HADCRUT4 and NCEI products lag one month.

Much of the following text is boilerplate…updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.

Most of the update graphs start in 1979.  That’s a commonly used start year for global temperature products because many of the satellite-based temperature composites start then.

We discussed why the three suppliers of surface temperature products use different base years for anomalies in the post Why Aren’t Global Surface Temperature Data Produced in Absolute Form?

Since the July 2015 update, we’re using the UKMO’s HadCRUT4 reconstruction for the model-data comparisons.

GISS LAND OCEAN TEMPERATURE INDEX (LOTI)

Introduction: The GISS Land Ocean Temperature Index (LOTI) reconstruction is a product of the Goddard Institute for Space Studies.  Starting with the June 2015 update, GISS LOTI uses the new NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), the pause-buster reconstruction, which also infills grids without temperature samples.  For land surfaces, GISS adjusts GHCN and other land surface temperature products via a number of methods and infills areas without temperature samples using 1200km smoothing. Refer to the GISS description here.   Unlike the UK Met Office and NCEI products, GISS masks sea surface temperature data at the poles, anywhere seasonal sea ice has existed, and they extend land surface temperature data out over the oceans in those locations, regardless of whether or not sea surface temperature observations for the polar oceans are available that month.  Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies.  The values for the GISS product are found here. (I archived the former version here at the WaybackMachine.)

Update:  The October 2015 GISS global temperature anomaly is +1.04 deg C.  It jumped up considerably (about 0.24 deg C) since September 2015.

01 GISS

Figure 1 – GISS Land-Ocean Temperature Index

NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)

NOTE:  The NCEI produces only the product with the manufactured-warming adjustments presented in the paper Karl et al. (2015). As far as I know, the former version of the reconstruction is no longer available online. For more information on those curious adjustments, see the posts:

Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly reconstruction is the product of the National Centers for Environmental Information (NCEI), which was formerly known as the National Climatic Data Center (NCDC).  NCEI merges their new Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) with the new Global Historical Climatology Network-Monthly (GHCN-M) version 3.3.0 for land surface air temperatures. The ERSST.v4 sea surface temperature reconstruction infills grids without temperature samples in a given month.  NCEI also infills land surface grids using statistical methods, but they do not infill over the polar oceans when sea ice exists.  When sea ice exists, NCEI leave a polar ocean grid blank.

The source of the NCEI values is through their Global Surface Temperature Anomalies webpage.  Click on the link to Anomalies and Index Data.)

Update (Lags One Month): The September 2015 NCEI global land plus sea surface temperature anomaly was +0.90 deg C.  See Figure 2. It rose very slightly (an increase of +0.01 deg C) since August 2015 (based on the new reconstruction).

02 NCEI

Figure 2 – NCEI Global (Land and Ocean) Surface Temperature Anomalies

UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)

Introduction: The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction.  CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre.  Unlike the GISS and NCEI reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product.  That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged.  The HADCRUT4 reconstruction is described in the Morice et al (2012) paper here.  The CRUTEM4 product is described in Jones et al (2012) here. And the HadSST3 reconstruction is presented in the 2-part Kennedy et al (2012) paper here and here.  The UKMO uses the base years of 1961-1990 for anomalies.  The monthly values of the HADCRUT4 product can be found here.

Update (Lags One Month):  The September 2015 HADCRUT4 global temperature anomaly is +0.79 deg C. See Figure 3.  It increased (about +0.05 deg C) since August 2015.

03 HADCRUT4

Figure 3 – HADCRUT4

UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (UAH TLT)

Special sensors (microwave sounding units) aboard satellites have orbited the Earth since the late 1970s, allowing scientists to calculate the temperatures of the atmosphere at various heights above sea level (lower troposphere, mid troposphere, tropopause and lower stratosphere). The atmospheric temperature values are calculated from a series of satellites with overlapping operation periods, not from a single satellite. Because the atmospheric temperature products rely on numerous satellites, they are known as composites. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature composite include the altitudes of zero to about 12,500 meters, but are most heavily weighted to the altitudes of less than 3000 meters.  See the left-hand cell of the illustration here.

The monthly UAH lower troposphere temperature composite is the product of the Earth System Science Center of the University of Alabama in Huntsville (UAH). UAH provides the lower troposphere temperature anomalies broken down into numerous subsets.  See the webpage here.  The UAH lower troposphere temperature composite are supported by Christy et al. (2000) MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons.  Additionally, Dr. Roy Spencer of UAH presents at his blog the monthly UAH TLT anomaly updates a few days before the release at the UAH website.  Those posts are also regularly cross posted at WattsUpWithThat.  UAH uses the base years of 1981-2010 for anomalies. The UAH lower troposphere temperature product is for the latitudes of 85S to 85N, which represent more than 99% of the surface of the globe.

UAH recently released a beta version of Release 6.0 of their atmospheric temperature product. Those enhancements lowered the warming rates of their lower troposphere temperature anomalies.  See Dr. Roy Spencer’s blog post Version 6.0 of the UAH Temperature Dataset Released: New LT Trend = +0.11 C/decade and my blog post New UAH Lower Troposphere Temperature Data Show No Global Warming for More Than 18 Years. The UAH lower troposphere anomalies Release 6.3 beta through October 2015 are here.

Update:  The October 2015 UAH (Release 6.0 beta) lower troposphere temperature anomaly is +0.43 deg C.  It rose (an increase of about +0.18 deg C) since September 2015.

04 UAH TLT Release 6.3

Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Composite – Release 6.3 Beta

RSS LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (RSS TLT)

Like the UAH lower troposphere temperature product, Remote Sensing Systems (RSS) calculates lower troposphere temperature anomalies from microwave sounding units aboard a series of NOAA satellites. RSS describes their product at the Upper Air Temperature webpage.   The RSS product is supported by Mears and Wentz (2009) Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records from the MSU and AMSU Microwave Sounders. RSS also presents their lower troposphere temperature composite in various subsets. The land+ocean TLT values are here.  Curiously, on that webpage, RSS lists the composite as extending from 82.5S to 82.5N, while on their Upper Air Temperature webpage linked above, they state:

We do not provide monthly means poleward of 82.5 degrees (or south of 70S for TLT) due to difficulties in merging measurements in these regions.

Also see the RSS MSU & AMSU Time Series Trend Browse Tool. RSS uses the base years of 1979 to 1998 for anomalies.

Update:  The October 2015 RSS lower troposphere temperature anomaly is +0.44 deg C.  It rose (an increase of about +0.07 deg C) since September 2015.

05 RSS TLT

Figure 5 – RSS Lower Troposphere Temperature (TLT) Anomalies

COMPARISONS

The GISS, HADCRUT4 and NCEI global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 6 compares the five global temperature anomaly products starting in 1979.  Again, due to the timing of this post, the HADCRUT4 and NCEI updates lag the UAH, RSS and GISS products by a month. For those wanting a closer look at the more recent wiggles and trends, Figure 7 starts in 1998, which was the start year used by von Storch et al (2013) Can climate models explain the recent stagnation in global warming?  They, of course, found that the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) models could NOT explain the recent slowdown in warming, but that was before NOAA manufactured warming with their new ERSST.v4 reconstruction.

Figure 8 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming slowdown in his RMS article Has Global Warming Stalled?

Because the suppliers all use different base years for calculating anomalies, I’ve referenced them to a common 30-year period: 1981 to 2010.  Referring to their discussion under FAQ 9 here, according to NOAA:

This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.

The impacts of the unjustifiable adjustments to the ERSST.v4 reconstruction are visible in the two shorter-term comparisons, Figures 7 and 8.  That is, the short-term warming rates of the new NCEI and GISS reconstructions are noticeably higher during “the hiatus”, as are the trends of the newly revised HADCRUT product.  See the June update for the trends before the adjustments.  But the trends of the revised reconstructions still fall short of the modeled warming rates.

06 Comparison 1979 Start

Figure 6 – Comparison Starting in 1979

#####

07 Comparison 1998 Start

Figure 7 – Comparison Starting in 1998

#####

08 Comparison 2001 Start

Figure 8 – Comparison Starting in 2001

Note also that the graphs list the trends of the CMIP5 multi-model mean (historic and RCP8.5 forcings), which are the climate models used by the IPCC for their 5th Assessment Report.

AVERAGES

Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly reconstructions and the average of the RSS and UAH lower troposphere temperature composites.  Again because the HADCRUT4 and NCEI products lag one month in this update, the most current average only includes the GISS product.

09 Average TLT v Surface

Figure 9 – Average of Global Land+Sea Surface Temperature Anomaly Products

MODEL-DATA COMPARISON & DIFFERENCE

Note: The HADCRUT4 reconstruction is now used in this section.  [End note.]

Considering the uptick in surface temperatures in 2014 (see the posts here and here), government agencies that supply global surface temperature products have been touting record high combined global land and ocean surface temperatures. Alarmists happily ignore the fact that it is easy to have record high global temperatures in the midst of a hiatus or slowdown in global warming, and they have been using the recent record highs to draw attention away from the growing difference between observed global surface temperatures and the IPCC climate model-based projections of them.

There are a number of ways to present how poorly climate models simulate global surface temperatures.  Normally they are compared in a time-series graph.  See the example in Figure 10. In that example, the UKMO HadCRUT4 land+ocean surface temperature reconstruction is compared to the multi-model mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. The reconstruction and model outputs have been smoothed with 61-month filters to reduce the monthly variations. Also, the anomalies for the reconstruction and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results.

10 Model-Data Comparison

Figure 10

It’s very hard to overlook the fact that, over the past decade, climate models are simulating way too much warming and are diverging rapidly from reality.

Another way to show how poorly climate models perform is to subtract the observations-based reconstruction from the average of the model outputs (model mean). We first presented and discussed this method using global surface temperatures in absolute form. (See the post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison.)  The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.

In this example, we’re illustrating the model-data differences in the monthly surface temperature anomalies. Also included in red is the difference smoothed with a 61-month running mean filter.

11 Model-Data Difference

Figure 11

The greatest difference between models and reconstruction occurs now.

There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s.  The reason:  the models do not properly simulate the observed cooling that takes place at that time.  Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until 1940. That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again.  The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming.

It’s very easy to see the recent record-high global surface temperatures have had a tiny impact on the difference between models and observations.

See the post On the Use of the Multi-Model Mean for a discussion of its use in model-data comparisons.

MONTHLY SEA SURFACE TEMPERATURE UPDATE

The most recent sea surface temperature update can be found here.  The satellite-enhanced sea surface temperature composite (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.  We discussed the recent record-high global sea surface temperatures in 2014 and the reasons for them in the post On The Recent Record-High Global Sea Surface Temperatures – The Wheres and Whys.

NEW EBOOK

Early Edition Cover2 - On Global Warming and the Illusion of Control

Just in case you haven’t encountered it, I recently published my new ebook On Global Warming and the Illusion of Control (25 MB .pdf).  IT’S FREE. The post that introduces it is here (cross post at WattsUpWithThat is here).

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About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in Global Temperature Update, Lower Troposphere Temperature, TLT and LOST Updates. Bookmark the permalink.

11 Responses to October 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference Update

  1. Chaam Jamal says:

    i think too much can be made of 30-year patterns in temperature data because the “inherent chaos” of nature’s signals can generate these kinds of patterns at random and without an external cause. the human instinct to look for patterns and come up with causal explanations can go wrong in such cases. often, apparent patterns in nature contain no information.
    link#1 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689425
    link#2 https://www.researchgate.net/profile/Demetris_Koutsoyiannis

  2. Thanks, Bob.
    This is a good way to follow the effects of the ongoing El Niño.

    NOAA-NWS Climate Prediction Center – CPC thinks “El Niño will likely peak during the Northern Hemisphere winter 2015-16, with a transition to ENSO-neutral anticipated during the late spring or early summer 2016.”
    See http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_disc_nov2015/ensodisc.html

  3. Chaam Jamal says:

    i think too much can be made of 30-year patterns in temperature data because the “inherent chaos” of nature’s signals can generate these kinds of patterns at random and without an external cause. the human instinct to look for patterns and come up with causal explanations can go wrong in such cases. often, apparent patterns in nature contain no information.
    link#1 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689425
    link#2 https://www.researchgate.net/profile/Demetris_Koutsoyiannis

  4. Chaam Jamal says:

    the “inherent chaos” of nature’s signals can generate patterns at random and without an external cause. the human instinct to look for patterns and come up with causal explanations can go wrong in such cases.
    link#1 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689425
    link#2 https://www.researchgate.net/profile/Demetris_Koutsoyiannis

  5. tomwys1 says:

    As usual, a clear, cogent set of explanations!

    THANKS!!!

  6. Werner Kohl says:

    Hi Bob,
    thanks again for this information update. You do so much valuable work for us.

  7. Pingback: The Differences between Sea Surface Temperature Datasets Prevent Us from Knowing Which El Niño Was Strongest According NINO3.4 Region Temperature Data | Bob Tisdale – Climate Observations

  8. Pingback: The Differences between Sea Surface Temperature Datasets Prevent Us from Knowing Which El Niño Was Strongest According NINO3.4 Region Temperature Data | Watts Up With That?

  9. Pingback: The Differences between Sea Surface Temperature Datasets Prevent Us from Knowing Which El Niño Was Strongest According NINO3.4 Region Temperature Data | Daily Green World

  10. Pingback: November 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference Update. | Bob Tisdale – Climate Observations

  11. Pingback: November 2015 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly & Model-Data Difference Update | Watts Up With That?

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