This post will serve as February 2014 Global Surface (Land+Ocean) Temperature Anomaly Update
First GISS: Global surface temperatures, as represented by the GISS Land-Ocean Temperature Index (LOTI) data, dropped about 0.25 deg C from January to February 2014. See Figure 1. While month-to-month variations of that magnitude are not unusual, it stands out like a sore thumb sitting there on the end…and helps to draw the eye to the absence of warming since the turn of the century.
Figure 1 – GISS Land-Ocean Temperature Index
The following .gif animation presents the January and February 2014 global surface temperature anomaly maps from the GISS map-making webpage. The new option of Robinson projections does a reasonable job of reducing the relative importance of the poles.
AND NOW BACK TO YOUR REGULARLY SCHEDULED UPDATE
Initial Notes: This post contains graphs of running trends in global surface temperature anomalies for periods of 13+ and 16+ years using NCDC global (land+ocean) surface temperature data. They indicate that we have not seen a warming halt (based on 13 years+ trends) this long since the early-1970s or a warming slowdown (based on 16-years+ trends) since the late-1970s.
Much of the following text is boilerplate. It is intended for those new to the presentation of global surface temperature anomaly data.
Most of the update graphs in the following start in 1979. That’s a commonly used start year for global temperature products because many of the satellite-based temperature datasets start then.
We discussed why the three suppliers use different base years for anomalies in the post Why Aren’t Global Surface Temperature Data Produced in Absolute Form?
GISS LAND OCEAN TEMPERATURE INDEX (LOTI)
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, GISS LOTI 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 February 2014 GISS global temperature anomaly is +0.45 deg C. It cooled (a decrease of about -0.25 deg C) since January 2014. See Figure 1, above.
NCDC GLOBAL SURFACE TEMPERATURE ANOMALIES
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. (Note: the NCDC has been slow with updating the normal data source webpage, so I’ve used the value listed on their State of the Climate Report for February 2014.)
Update: The February 2014 NCDC global land plus sea surface temperature anomaly was +0.41 deg C. See Figure 2. It also dropped considerably (a decrease of -0.24 deg C) since January 2014.
Figure 2 – NCDC Global (Land and Ocean) Surface Temperature Anomalies
UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)
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 (Lags One Month): The January 2013 HADCRUT4 global temperature anomaly is +0.51 deg C. See Figure 3. It increased a slight amount (about +0.02 deg C) since December 2013.
Figure 3 – HADCRUT4
13-YEARS+ (158-MONTH) RUNNING TRENDS
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 halt in global warming since 2001 was not unusual. Kevin Trenberth conveniently overlooked the fact that, based on his selected start year of 2001, the halt at that time had lasted 12+ years, not 10.
The period from January 2001 to January 2014 is now 158-months long—more than 13 years. Refer to the following graph of running 158-month trends from January 1880 to January 2014, using the GISS LOTI global temperature anomaly product.
An explanation of what’s being presented in Figure 4: The last data point in the graph is the linear trend (in deg C per decade) from January 2001 to February 2014. It is basically zero. That, of course, indicates global surface temperatures have not warmed during the most recent 157-month period. Working back in time, the data point immediately before the last one represents the linear trend for the 158-month period of December 2000 to January 2013, and the data point before it shows the trend in deg C per decade for November 2000 to November 2013, and so on.
Figure 4 – 158-Month Linear Trends
The highest recent rate of warming based on its linear trend occurred during the 158-month period that ended about 2004, but warming trends have dropped drastically since then. There was a similar drop in the 1940s, and as you’ll recall, global surface temperatures remained relatively flat from the mid-1940s to the mid-1970s. Also note that the early-1970s was the last time there had been a 158-month period without global warming—before recently.
16-YEARS+ (201-Month+) RUNNING TRENDS
In his RMS article, Kevin Trenberth also conveniently overlooked the fact that the discussions about the warming halt 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 201-months long. The following graph is similar to the one above, except that it’s presenting running trends for 201-month periods.
Figure 5 – 201-Month Linear Trends
The last time global surface temperatures warmed at this low a rate for a 201-month period was the late 1970s. Also note that the sharp decline is similar to the drop in the 1940s, and, again, as you’ll recall, global surface temperatures remained relatively flat from the mid-1940s to the mid-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 halt in global warming is unusual during a period when global surface temperatures are allegedly being warmed from the hypothetical impacts of manmade greenhouse gases.
A NOTE ABOUT THE RUNNING-TREND GRAPHS
There is very little difference in the end point trends of 13+ year and 16+ year running trends if HADCRUT4 or NCDC or GISS data are used. The major difference in the graphs is with the HADCRUT4 data and it can be seen in a graph of the 13+ 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. Figure 6 compares the three global surface temperature anomaly products starting in 1979. Again, due to the timing of this post, the HADCRUT4 data lags the GISS and NCDC products by a month. 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. 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 halt in warming.
Figure 8 starts in 2001 which was the year Kevin Trenberth chose for the start of the warming halt in his RMS article mentioned and linked earlier. 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
Figure 7 – Comparison Starting in 1998
Figure 8 – Comparison Starting in 2001
Figure 9 presents the average of the GISS, HADCRUT and NCDC land plus sea surface temperature anomaly products. Again because the HADCRUT4 data lags one month in this update, the most current average only includes the GISS and NCDC products.
Figure 9 – Average of Global Land+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. There is a very simple reason for this: the 1997/98 El Niño released enough sunlight-created warm water from beneath the surface of the tropical Pacific to permanently raise the temperature of about 66% of the surface of the global oceans by almost 0.2 deg C. Sea surface temperatures for that portion of the global oceans remained relatively flat until the El Niño of 2009/10, when the surface temperatures of the portion of the global oceans shifted slightly higher again. Prior to that, it was the 1986/87/88 El Niño that caused surface temperatures to shift upwards. If these naturally occurring upward shifts in surface temperatures are new to you, please see the illustrated essay “The Manmade Global Warming Challenge” (42mb) for an introduction.
MONTHLY SEA SURFACE TEMPERATURE UPDATE
The most recent sea surface temperature update can be found here. The satellite-enhanced sea surface temperature data (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.
I notice that Antarctica is getting colder even though it was Summer down there. Also, the Central Pacific and the warm pool seemed to cool. I wouldn’t be surprised to see March continue this downward trend.
As I look at that Jan-Feb global temperature simulation I can’t help but wonder if it was really cold in the Arctic in 1951-1980?
OT, I happened to be poking around the web for anything published about AMO in the last day and found this, published yesterday
Click to access osd-11-943-2014.pdf
Thanks, Alec, aka daffy duck.
Thanks, Bob. An even better article than your always excellent monthly update.
Let us hope last month is not the beginning of a downward trend in temperature.
Hi Bob, this is way off-topic but I can’t locate your email so using this route instead.
I’m a Melbourne, Australia-based journalist writing on climate for quadrant.org.au, also my blog is tthomas061.wordpress.com.
I read your Climate Models Fail with great interest and was struck by your comments that the AR5 models have over-estimated Australian decadal temperature trends by 400%. This has never been publicised here, to my knowledge, and I’m doing a piece on it. I would like to illustrate the piece with the relevant graphs of the 5AR model ensemble vs actual CRUTEM temp data, but can’t extract the graphs from the e-book. Would you be able to email me at [email address deleted by Bob Tisdale] attaching graphs of Australian modelled temps vs reality, and a graph or two from your book of modelled global temps vs reality?
Also, what are the implications of Australian temps being over-forecast, as distinct from global temps? I assume our local CSIRO and academic climate people would use the Australian forecast as inputs to their secondary modelling, but maybe you can explain to me what the implications might be. Thanks, I enjoy stirring things up. My other piece this week is http://quadrant.org.au/opinion/tony-thomas/2014/03/finally-real-climate-science/
(there’s an ambiguity in it, I call the workshop a ‘panel’, I’m correcting that right now).
Thanks and looking forward to some assistance, cheers Tony Thomas
tonythomas061, check your email.
I like the running trends! This seems a much better approach than having to cherry pick a start date, though one still has to pick an interval.I think I have seen Lucia plot trends where the value on the x axis is “years before present” and the y axis is the value of the trend at x (from t-x to t).