This post provides an update of the data for the three primary suppliers of global land+ocean surface temperature data—GISS through May 2015 and HADCRUT4 and NCEI (formerly NCDC) through April 2015—and of the two suppliers of satellite-based lower troposphere temperature data (RSS and UAH) through May 2015.
I’m still using Release 5.6 of the UAH lower troposphere temperature anomalies for this post, because Release 6.0 is only in beta form. I’ve included the UAH release 6.0 data as a supplemental graph, though. Also, the global land+ocean surface data represented by the Karl et al. (2015) paper have not yet been released by NCEI.
I’ve eliminated the graphs of the long-term running trends (see sample here from the March 2015 update). I’ve been presenting them for a few years and I can’t recall one comment about them. I replaced them with a model-data comparison which shows the growing difference between model simulations of global surface temperatures and data.
For discussions of the annual GISS and NCEI data for 2014, see the posts:
- Does the Uptick in Global Surface Temperatures in 2014 Help the Growing Difference between Climate Models and Reality?
- On the Biases Caused by Omissions in the 2014 NOAA State of the Climate Report
GISS LOTI surface data, and the two lower troposphere temperature datasets are for the most recent month. The HADCRUT4 and NCEI data lag one month.
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 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 of surface temperature data 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 in early 2013, GISS LOTI uses NCEI ERSST.v3b sea surface temperature data. The impact of that 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 NCEI 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 May 2015 GISS global temperature anomaly is +0.71 deg C. It is unchanged since April 2015.
Figure 1 – GISS Land-Ocean Temperature Index
NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)
NOTE: As of this writing, the NCEI has not published the data associated with the “pause busting” adjustments presented in the paper Karl et al. (2015). For more information on those curious adjustments, see the posts:
- NOAA/NCDC’s new ‘pause-buster’ paper: a laughable attempt to create warming by adjusting past data
- More Curiosities about NOAA’s New “Pause Busting” Sea Surface Temperature Dataset
- Open Letter to Tom Karl of NOAA/NCEI Regarding “Hiatus Busting” Paper
Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly dataset is a product of the National Centers for Environmental Information (NCEI), which was formerly known as the National Climatic Data Center (NCDC). NCEI 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 NCEI 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 NCEI temperature product is no longer supported by a peer-reviewed paper.
The NCEI data source is through their Global Surface Temperature Anomalies webpage. Click on the link to Anomalies and Index Data.)
Update (Lags One Month): The April 2015 NCEI global land plus sea surface temperature anomaly was +0.74 deg C. See Figure 2. It dropped (a decrease of -0.11 deg C) since March 2015.
Figure 2 – NCEI 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 NCEI 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 April 2015 HADCRUT4 global temperature anomaly is +0.66 deg C. See Figure 3. It decreased slightly (about -0.03 deg C) since March 2015.
Figure 3 – HADCRUT4
UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY DATA (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. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature data 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 lower troposphere temperature data are calculated from a series of satellites with overlapping operation periods, not from a single satellite. The monthly UAH lower troposphere temperature data is the product of the Earth System Science Center of the University of Alabama in Huntsville (UAH). UAH provides the data broken down into numerous subsets. See the webpage here. The UAH lower troposphere temperature data 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 data updates a few days before the release at the UAH website. Those posts are also cross posted at WattsUpWithThat. UAH uses the base years of 1981-2010 for anomalies. The UAH lower troposphere temperature data are for the latitudes of 85S to 85N, which represent more than 99% of the surface of the globe.
Update: The May 2015 UAH (Release 5.6) lower troposphere temperature anomaly is +0.32 deg C. It rose (an increase of about +0.16 deg C) since April 2015.
Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Data – Release 5.6
UAH recently released a beta version of Release 6.0 of their atmospheric temperature data. Those enhancements lowered the warming rates of their lower troposphere temperature data. 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. When the release 6.0 becomes the official dataset from UAH, we’ll include it in the comparisons shown later in this post. The UAH lower troposphere Release 6.0 beta data through May 2015 are here.
Update: The May 2015 UAH (Release 6.0 beta) lower troposphere temperature anomaly is +0.27 deg C. It rose (an increase of about +0.21 deg C) since April 2015.
Figure 4 Supplement – UAH Lower Troposphere Temperature (TLT) Anomaly Data – Release 6.0 Beta
RSS LOWER TROPOSPHERE TEMPERATURE ANOMALY DATA (RSS TLT)
Like the UAH lower troposphere temperature data, Remote Sensing Systems (RSS) calculates lower troposphere temperature anomalies from microwave sounding units aboard a series of NOAA satellites. RSS describes their data at the Upper Air Temperature webpage. The RSS data are 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 data in various subsets. The land+ocean TLT data are here. Curiously, on that webpage, RSS lists the data 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 May 2015 RSS lower troposphere temperature anomaly is +0.31 deg C. It rose (an increase of about +0.14 deg C) since April 2015.
Figure 5 – RSS Lower Troposphere Temperature (TLT) Anomaly Data
A QUICK NOTE ABOUT THE DIFFERENCE BETWEEN RSS AND UAH TLT DATA
THIS NOTE WILL BE REMOVED AS SOON AS THE UAH RELEASE 6.0 DATA ARE MADE FINAL.
There is a noticeable difference between the RSS and UAH lower troposphere temperature anomaly data. Dr. Roy Spencer discussed this in his November 2011 blog post On the Divergence Between the UAH and RSS Global Temperature Records. In summary, John Christy and Roy Spencer believe the divergence is caused by the use of data from different satellites. UAH has used the NASA Aqua AMSU satellite in recent years, while as Dr. Spencer writes:
…RSS is still using the old NOAA-15 satellite which has a decaying orbit, to which they are then applying a diurnal cycle drift correction based upon a climate model, which does not quite match reality.
I updated the graphs in Roy Spencer’s post in On the Differences and Similarities between Global Surface Temperature and Lower Troposphere Temperature Anomaly Datasets.
While the two lower troposphere temperature datasets are different in recent years, UAH believes their data are correct, and, likewise, RSS believes their TLT data are correct. Does the UAH data have a warming bias in recent years or does the RSS data have cooling bias? Until the two suppliers can account for and agree on the differences, both are available for presentation.
Roy Spencer has recently updated his discussion on the RSS and UAH differences in the post Why Do Different Satellite Datasets Produce Different Global Temperature Trends?
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 data lag the UAH, RSS and GISS products by a month. Because the three surface temperature datasets share common source data, (GISS and NCEI 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 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.
Figure 6 – Comparison Starting in 1979
Figure 7 – Comparison Starting in 1998
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.
Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly products and the average of the RSS and UAH lower troposphere temperature data. Again because the HADCRUT4 and NCEI data lag one month in this update, the most current average only includes the GISS product.
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 in the surface temperature data. 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 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, dropping slowly throughout most of that region, until the El Niño of 2009/10, when the surface temperatures of that 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.
MODEL-DATA COMPARISON & DIFFERENCE
Considering the uptick in surface temperatures this year (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, GISS Land-Ocean Temperature Index (LOTI) data are 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 data and model outputs have been smoothed with 61-month filters to reduce the monthly variations. Also, the anomalies for the data and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results.
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 data 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 GISS global Land-Ocean Temperature Index (LOTI) 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 near full term of the data (1880 to 2013) were 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.
The greatest difference between models and data occurs in the 1880s. The 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 nearer to a zero difference until the 1990s, 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 since about 1910…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 data (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases. We discussed the recent record-high global sea surface temperatures and the reasons for them in the post On The Recent Record-High Global Sea Surface Temperatures – The Wheres and Whys.