July 2013 Global Surface (Land+Ocean) Temperature Anomaly Update

My apologies for the delay. I was waiting for the NCDC to update their data webpage here. As of this morning, the NCDC has not updated it, so I used the 0.61 deg C anomaly for July 2013 published in the NOAA/NCDC State of the Climate Report for July 2013. Lesson learned.

Initial Notes: This post contains graphs of running trends in global surface temperature anomalies for periods of 12+ and 16 years using GISS Land-Ocean Temperature Index (LOTI) data. They indicate that we have not seen a warming hiatus this long since the 1970s.

Much of the following text is boilerplate. It is intended for those new to the presentation of global surface temperature anomaly data.


Introduction: The GISS Land Ocean Temperature Index (LOTI) data is a product of the Goddard Institute for Space Studies. Starting with their January 2013 update, it uses NCDC ERSST.v3b sea surface temperature data. The impact of the recent change in sea surface temperature datasets is discussed here. GISS adjusts GHCN and other land surface temperature data via a number of methods and infills missing data using 1200km smoothing. Refer to the GISS description here. Unlike the UK Met Office and NCDC products, GISS masks sea surface temperature data at the poles where seasonal sea ice exists, and they extend land surface temperature data out over the oceans in those locations. Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies. The data source is here.

Update: The July 2013 GISS global temperature anomaly is +0.54 deg C. It cooled (a drop of about -0.12 deg C) since June 2013.




Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly dataset is a product of the National Climatic Data Center (NCDC). NCDC merges their Extended Reconstructed Sea Surface Temperature version 3b (ERSST.v3b) with the Global Historical Climatology Network-Monthly (GHCN-M) version 3.2.0 for land surface air temperatures. NOAA infills missing data for both land and sea surface temperature datasets using methods presented in Smith et al (2008). Keep in mind, when reading Smith et al (2008), that the NCDC removed the satellite-based sea surface temperature data because it changed the annual global temperature rankings. Since most of Smith et al (2008) was about the satellite-based data and the benefits of incorporating it into the reconstruction, one might consider that the NCDC temperature product is no longer supported by a peer-reviewed paper.

The NCDC data source is usually here. NCDC uses 1901 to 2000 for the base years for anomalies. As noted in the opening, the NCDC has been slow to update that webpage this month, so I used the 0.61 deg C anomaly published in their State of the Climate Report for July 2013 here.

Update: The July 2013 NCDC global land plus sea surface temperature anomaly is +0.61 deg C. It decreased -0.03 deg C since June 2013.


NCDC Global (Land and Ocean) Surface Temperature Anomalies


Introduction: The UK Met Office HADCRUT4 dataset merges CRUTEM4 land-surface air temperature dataset and the HadSST3 sea-surface temperature (SST) dataset. 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 NCDC products, missing data is 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 not included in the global average value of HADCRUT4. The HADCRUT4 dataset is described in the Morice et al (2012) paper here. The CRUTEM4 data is described in Jones et al (2012) here. And the HadSST3 data 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 data source is here.

Update: The July 2013 HADCRUT4 global temperature anomaly is +0.51 deg C. It rose about +0.06 deg C since June 2013.




As noted in my post Open Letter to the Royal Meteorological Society Regarding Dr. Trenberth’s Article “Has Global Warming Stalled?”, Kevin Trenberth of NCAR presented 10-year period-averaged temperatures in his article for the Royal Meteorological Society. He was attempting to show that the recent hiatus in global warming since 2001 was not unusual. Kevin Trenberth conveniently overlooked the fact that, based on his selected start year of 2001, the hiatus has lasted 12+ years, not 10.

The period from January 2001 to July 2013 is now 151-months long. Refer to the following graph of running 151-month trends from January 1880 to May 2013, using the GISS LOTI global temperature anomaly product. The last data point in the graph is the linear trend (in deg C per decade) from January 2001 to the current month. It is basically zero. That, of course, indicates global surface temperatures have not warmed during the most recent 151-month period. Working back in time, the data point immediately before the last one represents the linear trend for the 151-month period of December 2000 to June 2013, and the data point before it shows the trend in deg C per decade for November 2000 to May 2013, and so on.

04 GISS 151-Month Running Trends

151-Month Linear Trends

The highest recent rate of warming based on its linear trend occurred during the 151-month period that ended in late 2003, but warming trends have dropped drastically since then. Also note that about the early 1970s was the last time there had been a 151-month period without global warming—before recently.


In his RMS article, Kevin Trenberth also conveniently overlooked the fact that the discussions about the warming hiatus are now for a time period of about 16 years, not 10 years—ever since David Rose’s DailyMail article titled “Global warming stopped 16 years ago, reveals Met Office report quietly released… and here is the chart to prove it”. In my response to Trenberth’s article, I updated David Rose’s graph, noting that surface temperatures in April 2013 were basically the same as they were in June 1997. We’ll use June 1997 as the start month for the running 16-year trends. The period is now 194-months long. The following graph is similar to the one above, except that it’s presenting running trends for 194-month periods.

05 GISS 194-Month Running Trends

194-Month Linear Trends

The last time global surface temperatures warmed at the minimal rate of 0.06 deg C per decade for a 194-month period was the late 1970s.

The most widely used metric of global warming—global surface temperatures—indicates that the rate of global warming has slowed drastically and that the duration of the hiatus in global warming is unusual during a period when global surface temperatures are allegedly being warmed from the hypothetical impacts of manmade greenhouse gases.


There is very little difference in the end point trends of 12+-year and 16+-year running trends if HADCRUT4 or NCDC products are used in place of GISS data. The major difference in the graphs is with the HADCRUT4 data and it can be seen in a graph of the 12+-year trends. I suspect this is caused by the updates to the HADSST3 data that have not been applied to the ERSST.v3b sea surface temperature data used by GISS and NCDC.


The GISS, HADCRUT4 and NCDC global surface temperature anomalies are compared in the next three time-series graphs.  All three of those comparison graphs present the anomalies using the base years of 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 first graph compares the three global surface temperature anomaly products starting in 1979. The graph also includes the linear trends. Because the three datasets share common source data, (GISS and NCDC also use the same sea surface temperature data) it should come as no surprise that they are so similar.

06 Comparison 1979 Start

Comparison Starting in 1979

For those wanting a closer look at the more recent wiggles and trends, the second graph 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.

07 Comparison 1998 Start

Comparison Starting in 1998

The third comparison graph starts with Kevin Trenberth’s chosen year of 2001, as discussed above.

07 Comparison 2001 Start

Comparison Starting in 2001


The last graph presents the average of the GISS, HADCRUT and NCDC land plus sea surface temperature anomaly products. The flatness of the data since 2001 is very obvious, as is the fact that surface temperatures have rarely risen above those created by the 1997/98 El Niño.

08 Average

Average of Global Land+Sea Surface Temperature Anomaly Products


Last month, I presented a post here about the impacts of base years on the presentation of global surface temperature anomalies. I used 1979 as the start year. It’s a commonly used start year because the satellite era of global temperature products starts then.

According to a recent blog post by a global warming enthusiast, here, my using the start year of 1979 is “hiding the incline”.

I can categorically state that I’m not hiding the incline. It’s not me. The data are doing a great job all on their own of hiding the incline, because there hasn’t been one to any great extent for the past 12 to 16 years. The data don’t need any help from me.

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.
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1 Response to July 2013 Global Surface (Land+Ocean) Temperature Anomaly Update

  1. tomwys says:

    No model that I’m aware of can “explain” the recent flatline, and the hiatus has left the “noise” or error bars of all of them. There’s nothing wrong with admission of such by the modelers, and what’s needed is to publicly inform policymakers that their CO2 “management” proposals have little basis in actual DATA.

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