October 2018 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update
As you’ll soon see, there was an eye-catching uptick (+0.25 deg C) in the GISS global Land-Ocean Temperature Index (LOTI) data from September to October 2018. We’ll have to wait for next month’s update to see if it also appears in the NOAA and Met Office datasets.
This post provides updates of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through October 2018 and HADCRUT4 and NOAA NCEI (formerly NOAA NCDC) through September 2018—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through October 2018. It also includes a long-term model-data trend comparison, which shows a number of major flaws in the climate models used by the IPCC for their 5th Assessment Report.
NOTE: As noted in the post A Quick Post before the Monthly Global Surface and TLT Temperature Updates, I’ve added two graphs to this post. You’ll find them near the end. [End note.]
This is simply an update, but it includes a good amount of background information for those new to the datasets. Because it is an update, there is no overview or summary for this post. There are, however, simple monthly summaries for the individual datasets. So for those familiar with the datasets, simply fast-forward to the graphs and read the summaries under the headings of “Update”.
I took a hiatus from blogging for almost two years. During my absence, in July 2017, RSS (Remote Sensing Systems) revised their lower troposphere temperature data with their version 4.0 data. See the RSS webpage FAQ about the V4.0 TLT Update for more information. We briefly discussed the impacts of these changes recently in the post The New RSS TLT Data is Unbelievable! (Or Would That Be Better Said, Not Believable?) A quick Introduction (Cross posted at WattsUpWithThat here.) Dr. Roy Spencer also discussed the revised RSS v4.0 TLT data in his post Comments on the New RSS Lower Tropospheric Temperature Dataset, which was cross posted at WattsUpWithThat here.
IMPORTANT NOTE: The recent revisions to the RSS Lower Troposphere Temperature data may have brought the warming rate of their data more into line with climate model projections globally, but in the all-important tropics, the revisions had little impact on the disparity between models and data, as discussed and illustrated in Dr. Roy Spencer’s post Warming in the Tropics? Even the New RSS Satellite Dataset Says the Models are Wrong. [End important note.]
Back in 2016, we discussed and illustrated the impacts of the adjustments to surface temperature data in a number of 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 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.
IMPORTANT NOTE 2: NOAA recently updated their “Pause-Buster” ERSST.v4 sea surface temperature data to the “Pause-Buster2” ERSST.v5. See the post A Very Quick Introduction to NOAA’s New “Pause-Buster 2” Sea Surface Temperature Dataset ERSST.v5. The WattsUpWithThat cross post is here. [End important note 2.]
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 as the paper that documents it has been accepted for publication. 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).
And I’m also continuing to present the model-data 30-year trend comparison using the GISS Land-Ocean Temperature Index (LOTI) data.
We’ll start the updates with the surface temperature-based datasets.
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 had been using the NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) for ocean surface temperature data, the pause-buster reconstruction, which also infills grids without temperature samples. More recently, since September 2017, GISS LOTI has been using the NOAA Pause-Buster2 Extended Reconstructed Sea Surface Temperature version 5 (ERSST.v5). 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 2018 GISS global temperature anomaly is +0.99 deg C. According to the GISS LOTI data, global surface temperature anomalies made a sizeable and very-noticeable uptick since September, a +0.25 deg C increase.
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 NOAA 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 “pause buster2” Extended Reconstructed Sea Surface Temperature version 5 (ERSST.v5) with the new Global Historical Climatology Network-Monthly (GHCN-M) version 3 for land surface air temperatures. The ERSST.v5 “pause buster2” 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 2018 NCEI global land plus sea surface temperature anomaly was +0.78 deg C. See Figure 2. It made a minor uptick (an increase of about +0.04 deg C) since August 2018.
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 2018 HADCRUT4 global temperature anomaly is +0.60 deg C. See Figure 3. It basically remained the same since the prior month, with a teeny +0.01 deg C uptick from August to September 2018.
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. 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 uses the base years of 1981-2010 for anomalies.
The UAH lower troposphere data are now at Release 6. See Dr. Roy Spencer’s post here. Those Release 6.0 enhancements lowered the warming rates of their lower troposphere temperature anomalies. See Dr. 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, both of which were published 3 years ago in 2015. The UAH lower troposphere anomaly data, Release 6.0, through October 2018 are here.
Update: The October 2018 UAH (Release 6.0) lower troposphere temperature anomaly is +0.22 deg C. It rose noticeably since September (an increase of about +0.08 deg C).
Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Composite – Release 6.0
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. See the webpage here. Also see the RSS MSU & AMSU Time Series Trend Browse Tool.
Note: As discussed in the initial notes of this post, RSS also released their version 4 of their lower troposphere temperature anomaly data, the monthly values of which can be found here.
Update: The October 2018 RSS lower troposphere temperature anomaly, Version 4, is +0.53 deg C. It rose a bit (an uptick of +0.04 deg C) since September 2018.
Figure 5 – RSS Lower Troposphere Temperature (TLT) Version 4 Anomalies
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.
I’ve discontinued the comparisons starting in 1998 and 2001. As expected, the global temperature responses to the 2014/15/16 El Niño effectively ended what was known as the global warming hiatus and brought those extremely short-term trends more into line with the models.
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.
Figure 6 – Comparison Starting in 1979
Note also that Figure 6 lists the trend of the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report. The metric presented for the models is surface temperature, not lower troposphere.
Figure 7 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. Because the HADCRUT4 and NCEI products lag one month in this update, I’ve only updated this graph through September. What’s really obvious is that the recent update to the RSS TLT data has brought the trend of the average TLT data more into line with the trend of the average of the surface temperature data. See the graph here for a full trend comparison with the older version of the RSS TLT data. Sorry, that’s the last one I have on file.
Figure 7 – Averages of Global TLT Anomaly and Land+Sea Surface Temperature Anomaly Products
MODEL-DATA COMPARISON – 30-YEAR RUNNING TRENDS
One of the best ways to show how poorly climate models simulate surface temperatures is to compare 30-year running trends of global surface temperature data and the model-mean of the climate model simulations of it. See Figure 8. In this case, we’re using the global GISS Land-Ocean Temperature Index for the data. For the models, once again we’re using the model-mean of the climate models stored in the CMIP5 archive with historic forcings to 2005 and worst case RCP8.5 forcings since then. See the post On the Use of the Multi-Model Mean for a discussion of its use in model-data comparisons.
There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades. This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v5 data used by GISS.) Fourth, yet that high 30-year warming ending about 1945 occurred without being caused by the forcings that drive the climate models. That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally? Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.
One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.
MONTHLY SEA SURFACE TEMPERATURE UPDATE
I haven’t published a monthly sea surface temperature update since October 2016, with the most recent update found here. The satellite-enhanced sea surface temperature composite (Reynolds OI.2) used to be presented in global, hemispheric and ocean-basin bases in those posts. I may start updating them again regularly in the near future. Then again, I may not. It depends on if there’s enough call for it by visitors.
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 most recent free ebook On Global Warming and the Illusion of Control (25MB). (And, of course, the record highs in 2016 are lagged responses to the 2014/15/16 El Niño.) That book was introduced in the post here (cross post at WattsUpWithThat is here).
TWO NEW GRAPHS
I’ve added two graphs to these updates. The new graphs are being added for a simple reason: to provide different perspectives on the increases in global temperatures since 1979.
The graphs are of Berkeley Earth global land+ocean surface temperature data and RSS global lower troposphere temperature data, both in absolute (not anomaly) form. That way they include the annual cycles in temperatures, which are far greater than the warming that’s occurred since 1979, based on their linear trends.
I’ve added the new graphs as follow ups to the post Do Doomsters Know How Much Global Surface Temperatures Cycle Annually? (WattsUpWithThat Cross post is here). I knew the RSS TLT data existed in absolute form, but, sadly, I forgot to include it in the “Doomster” post, so I will be including it in the monthly updates along with the Berkeley Earth surface temperature data.
Figure 9 presents the Berkeley Earth global land+ocean surface temperatures in absolute form, with land surface air temperature data for Arctic sea ice (data here). This is created by adding the “Estimated Jan 1951-Dec 1980 monthly absolute temperature (C)” listed there to their respective monthly anomalies. The trend line is as determined by EXCEL for the absolute data, but the trend value is determined by EXCEL from the anomaly data (not illustrated). I use the anomaly data for the trends for a simple reason: with anomalies, I don’t have to worry about the start and end months to account for the impacts of the seasonal cycles on the trends. The 3.6-deg C span of the average annual cycle (for the period of 1979 to 2017) is listed in the title block.
Figure 10 presents the RSS global lower troposphere temperature data in absolute form. That data are available to the public in easy-to-use formats from the KNMI Climate Explorer, on their Monthly Observations webpage, specifically the RSS MSU 4.0 TLT, webpage. There, the TLT data are presented in K, not deg C, the latter of which we are much more familiar, so I added 273.15 to the monthly K values to convert to deg C. As listed in the title block, the span of the average annual cycle (for the period of 1979 to 2017) is 2.6-deg C. The trend for the RSS TLT data is included in the trend comparison graph, not included in this post (example here).
Of course, in both cases, as mentioned above, the new perspective shows that the average annual cycles in the global surface temperature data and in the global lower troposphere temperature data are much greater that their respective rises in global temperature from January 1979 to October 2018, based on the linear trends. And as is shown in the post Do Doomsters Know How Much Global Surface Temperatures Cycle Annually?, the annual cycle in the Berkeley Global Land-Ocean Surface Temperature data is much greater than the long-term rise in global surface temperatures, based on the linear trend, from 1850 to present. Yet, according to the newly published and written-by-foolish-politicians Summary For Policymakers of the IPCC’s SR15, a 1.5 deg C rise in global average surface temperatures from the new “pre-industrial” base period of 1850 to 1900 is somehow supposed to cause gloom and doom for the Earth and its occupants. How pathetic can those bureaucrats get in their efforts to scare global residents into submitting to their political agendas?!!!! It’s not working here. We can only laugh at their pitiful efforts.
STANDARD CLOSING REQUEST
Please purchase my recently published ebooks. As many of you know, this year I published 2 ebooks that are available through Amazon in Kindle format:
via Watts Up With That? https://ift.tt/1Viafi3