April 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

This post provides an update of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through April 2016 and HADCRUT4 and NCEI (formerly NCDC) through March 2016—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through April 2016.  It also includes a model-data comparison.

INITIAL NOTES:

We recently discussed and illustrated the impacts of the adjustments to surface temperature data in the posts:

The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015).  For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:

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), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post 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 6.5 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 that has been updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.

Most of the graphs in the update 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 chapter 1.25 – Many, But Not All, Climate Metrics Are Presented in Anomaly and in Absolute Forms of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB).

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 April 2016 GISS global temperature anomaly is +1.11 deg C.  It made a relatively large downtick since March 2016, a -0.18 deg C decrease, which should be a response to the decay of the El Niño.

01 GISS Time Series

Figure 1 – GISS Land-Ocean Temperature Index

NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)

NOTE:  The NCEI only produces 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:

And recently:

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 “pause buster” 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 March 2016 NCEI global land plus sea surface temperature anomaly was +1.22 deg C.  See Figure 2. It rose slightly (an increase of +0.03 deg C) since February 2016.

02 NCEI Time Series

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 March 2016 HADCRUT4 global temperature anomaly is +1.06 deg C. See Figure 3.  It’s unchanged since February 2016.

03 HADCRUT4 Time Series

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 6.5 beta through April 2016 are here.

Update:  The April 2016 UAH (Release 6.5 beta) lower troposphere temperature anomaly is +0.71 deg C.  It dropped slightly (a decrease of about -0.02 deg C) since March 2016.

04 UAH TLT Time Series

Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Composite – Release 6.5 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.

Note:  RSS recently release new versions of the mid-troposphere temperature and lower stratosphere temperature (TLS) products.  So far, their lower troposphere temperature product has not been updated to this new version.

Update:  The April 2016 RSS lower troposphere temperature anomaly is +0.76 deg C.  It dropped (a decrease of about -0.09 deg C) since March 2016.

05 RSS TLT Time Series

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…and before the strong El Niño of 2015/16.

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”.  See the June 2015 update for the trends before the adjustments.  But the trends of the revised reconstructions still fall short of the modeled warming rates during the hiatus periods.

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.

AVERAGE

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 Surface and TLT Averages

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, 2015 and now 2016 (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 running-mean filters to reduce the monthly variations.  The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here.   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.  That is, by using the almost the full term of the data, no one with any sense of reality can claim I’ve cherry picked the base years for anomalies with this comparison.

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 difference now between models and data is worst-case, comparable to the difference at about 1910. 

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. (Just in case you’re having trouble seeing the difference in the warming rates during the early warming period, see the model-data comparison using 30-year trends here.) 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.

RECENT RECORD HIGHS

We discussed the recent record-high global sea surface temperatures for 2014 and 2015 and the reasons for them in General Discussions 2 and 3 of my recent free ebook On Global Warming and the Illusion of Control (25MB).   The book was introduced in the post 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, TLT and LOST Updates. Bookmark the permalink.

9 Responses to April 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

  1. Richard Brown says:

    Bob – I have a possibly daft question for you. Have you (or has anyone else) considered the local impact on SST of hydrothermal vent fields? From global maps of SST anomalies, I got the impression that there is a fairly persistent “hot spot” (for example) in the region that coincides roughly with Okinawa, Izu-Ogasawara and Mariana trenches. Read somewhere that “A volume of water approximately equal to the world’s oceans passes through the hydrothermal mid-ocean ridge cycle every 20 million years”, which would mean around 67 million cubic meters of superheated water (up to 400 deg C) emitted per year. I know this would be undetectable if uniformly mixed. But its impact might (just) be detectable if it rises straight through cold depths and forms a skin layer at surface. A gigawatt heater left on permanently – would we notice?

  2. Bob Tisdale says:

    Richard, I don’t think you’ve asked a daft question. Many skeptics have raised the question of subsurface volcanoes on ocean warming. The thought that hydrothermal vents might be contributing is similar.

    We’d notice the gigawatt heater if it was suddenly turned on or off or if there was a time-varying change in its output, but if it was a constant, then wouldn’t it simply be part of the background state of the oceans?

    The big problem is data. When someone raises the question of a subsurface volcanic contribution, I typically ask for data. Of course there is none.

  3. Richard Brown says:

    Thanks Bob – if the premise had any merit, then one would expect a hint of correlation between SST and tectonic activity in the region (i.e. as new vents were formed), but I doubt records of either would be sufficient to look for this.

    “…background state of the oceans?” – yes/no – my thought was there would just be a tendency towards local hot-spots, but strongly modulated by all the other currents, wind patterns, etc. Global average of course would be “steady state” on any timescale we could perceive.

    Approx 500 vent fields have been mapped to date – perhaps interesting to overlay the static map of these with annual animations of SST’s, but needs computing skills I don’t have!

  4. gymnosperm says:

    Geothermal heating of the oceans seems much like CO2 warming of the atmosphere. In both cases the warmed water/air is from the bottom. In both cases the warmed material must rise. In both cases the warmed material is small in relation to the respective total mass.

    There the similarity ends and the enormous differences in specific heat and total mass weigh.

    Geothermal warming of the oceans is predominantly along the linear ocean ridges. Put a thermometer two meters above the ocean floor near a hydrothermal vent and it will read much warmer than the surrounding ocean, yet we see no linear signal at the ocean surface.

    Carbon dioxide warms the atmosphere in a layer about 100 meters tall over the entire surface of the planet, and again in perhaps a 500m poorly understood layer at the tropopause. Put a thermometer at 2 meters and you see a rise corresponding with increasing CO2, but in the upper troposphere there is no signal at all.

  5. David says:

    Bob,

    Re:-

    “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.”
    _________

    Your Figure 11 shows that temperatures over the past few months have been warmer than the CMIP5 multi-model mean projections for those months. The 61 month smooth also reflects an improvement in model performance over the past ~ 5 years.

    Yes, on average the models are running warm over the past decade; but the claim that they are “diverging rapidly from reality” isn’t currently accurate.

  6. Bob Tisdale says:

    David says: “Your Figure 11 shows that temperatures over the past few months have been warmer than the CMIP5 multi-model mean projections for those months. The 61 month smooth also reflects an improvement in model performance over the past ~ 5 years.”

    Yes, you’re right about Figure 11, David. Based on the 61-month smooth, with the “improvement in model performance over the past ~ 5 years”, the model performance has gone from worst-case to a little better than worst-case…comparable to the horrible performance around 1910.

    David says: “Yes, on average the models are running warm over the past decade; but the claim that they are “diverging rapidly from reality” isn’t currently accurate.”

    I guess I’ll have to clarify my statement, and note that I’m basing it on the 61-month curve, not the monthly data. I could also post a model-data comparison using 30-year trends. For example, the following graph was linked within the second paragraph to follow Figure 11:

    Cheers

  7. David says:

    Thanks Bob.

    You wrote “I could also post a model-data comparison using 30-year trends”.

    Why would you use 30-year periods when comparing model – data trends when you’ve been using centred 61-month periods to make the other model – data comparisons?

    For instance, what would a centred 61-month model-data trend chart look like? Alternatively, why not use a 360-month smooth on the model-data anomaly comparisons?

    Thanks again.

  8. Bob Tisdale says:

    David asks, “Why would you use 30-year periods when comparing model – data trends when you’ve been using centred 61-month periods to make the other model – data comparisons?”

    As I noted in the post: The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here.
    http://data.giss.nasa.gov/gistemp/graphs_v3/

    In fact, that has been the case for decades. See Figure 13…

    …from Hansen and Lebedeff (1987):
    http://pubs.giss.nasa.gov/docs/1987/1987_Hansen_Lebedeff_1.pdf

    On the other hand, 5-year trends are greatly impacted by the timing of El Nino and La Nina events, which is what were were avoiding with the 5-year running mean filters, thus the use of the 30-year trends.

    Cheers.

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