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Monday, June 29, 2009

Well, There Ya Have It (OI.v2 SST Data Update Schedule)

I’ve moved to WordPress.  This post can now be found at Well, There Ya Have It (OI.v2 SST Data Update Schedule)
NOAA’s schedule for providing real-time OI.v2 SST data is described in their FAQ webpage:

They write:

The real-time weekly fields run Sunday through Saturday and are generated early Monday morning. We have to wait until we can interpolate weekly fields to the last day of the month to generate a complete monthly field. Thus, the full monthly fields are updated according to the following schedule:

It is best to wait until late morning (US Eastern Time) to pick up new fields to give us time to ensure the analysis ran properly.

So it appears I downloaded preliminary data on the first of last month and what I called a correction was really the official data.

In light of this, I will post preliminary Global and NINO3.4 SST anomaly data on or around the first of each month, then wait until the NOAA analysis is complete before posting my monthly updates.

Preliminary June 2009 Global SST Anomaly Data

Preliminary June 2009 NINO3.4 SST Anomaly Data



OI.v2 SST data are available through the NOAA NOMALDS system:

Sunday, June 28, 2009

Contiguous U.S. GISTEMP Linear Trends: Before and After

I’ve moved to WordPress.  This post can now be found at Contiguous U.S. GISTEMP Linear Trends: Before and After
Many of us have seen gif animations and blink comparators of the older version of Contiguous U.S. GISTEMP data versus the newer version, and here’s yet another one. The presentation is clearer than most.


It is based on the John Daly archived data:
and the current Contiguous U.S. surface temperature anomaly data from GISS:

In their presentations, most people have been concerned with which decade had the highest U.S. surface temperature anomaly: the 1940s or the 1990s. But I couldn’t recall having ever seen a trend comparison, so I snipped off the last 9 years from current data and let EXCEL plot the trends:

Before the post-1999 GISS adjustments to the Contiguous U.S. GISTEMP data, the linear trend for the period of 1880 to 1999 was 0.035 deg C/decade. After the adjustments, the linear trend rose to 0.044 deg C/decade.
Thanks to Anthony Watts who provided the link to the older GISTEMP data archived at John Daly's website in his post here:
Anthony Watts has advised that the thanks should go to Michael Hammer who wrote the original post at Jennifer Marohasy's website:
In the thread at WUWT, Steve McIntyre of ClimateAudit provided links to his posts from a couple of years ago:

Friday, June 26, 2009

Part 2 of Comparison of GISTEMP and UAH MSU TLT Anomalies

I’ve moved to WordPress.  This post can now be found at Part 2 of Comparison of GISTEMP and UAH MSU TLT Anomalies
Or The Comparison of GISTEMP Land Surface Temperature Anomalies and the UAH MSU TLT Anomalies for the Same Land Surface Areas


In the first part of this post, Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies, I illustrated that global GISTEMP had a higher linear trend than UAH MSU TLT--nothing earth-shattering there. But that post also divided the globe into 8 subsets and compared the linear trends for those smaller areas of the globe. In this post, we’ll look at Land Surface Temperature and TLT anomalies for each of the continents to further illustrate any differences between the GISTEMP and UAH MSU TLT data.


The KNMI Climate Explorer website provides access to GISS Land Surface Temperature data that is separate from SST data. The UAH MSU TLT data available through KNMI, however, is not separated into land and ocean subsets. And due to the shape of the continents, there is no way to gather the TLT anomalies over the complete continental land masses without also getting TLT anomalies over portions of the oceans. Therefore, for this comparison of Land Surface Temperature anomalies and the TLT anomalies over those same land areas, I subdivided the data into smaller areas of the continents to minimize any influence from ocean data.

Figure 1 illustrates the global grids used in the comparisons of GISTEMP Land Surface Temperature and UAH MSU TLT anomalies. The coordinates are listed on the graphs that follow. Also, an area of Siberia seems to have had elevated surface temperatures in recent years (though it did not make an appearance in the most current map [May 2009] of Surface Temperature Anomalies, Figure 1). I’ve attempted to capture that “Siberian Hotspot” in the area enclosed in purple.

Figure 1


Figure 2 illustrates the Australian GISTEMP Land Surface Temperatures and UAH MSU TLT anomalies for the same land mass area. The GISTEMP linear trend is negative (-0.011 deg C/decade), while the UAH MSU TLT linear trend is positive (0.11 deg C/decade).
Figure 2

For the Central North American data, Figure 3, the GISTEMP linear trend of 0.201 deg C/decade is also less than the UAH MSU TLT linear trend of 0.224 deg C/decade.
Figure 3

The Northwestern North America datasets, Figure 4, include most of Western Canada. The UAH MSU TLT linear trend (0.256 deg C/decade) is greater than the GISTEMP linear trend (0.169 deg C/decade).
Figure 4

It is unfortunate that the UAH MSU TLT data is not separated into ocean and land data on the KNMI Climate Explorer website. I did look for other websites where I could download the separate land and ocean TLT anomalies, but if one exists, it eluded me. The comparison of European land surface temperature and TLT anomalies required that I whittle down of the area covered to minimize any influence of ocean data. Refer again to the map in Figure 1.

The “Eurasian Strip” data, Figure 5, should at least capture the “core” temperatures for Europe. The 0.475 deg C/decade linear trend for GISTEMP is higher than the UAH MSU TLT linear trend of 0.426 deg C/decade.
Figure 5

Travelling east, the shape of Asia allows a comparison of a much larger land surface area. The GISTEMP linear trend (0.438 deg C/decade) in the Asian comparison, Figure 6, is significantly higher than the UAH MSU TLT linear trend (0.237 deg C/decade).
Figure 6

For the “Siberian Hotspot,” Figure 7, the difference in the trends is less that the difference for the Asian datasets with the larger surface area. The GISTEMP linear trend for the "Siberian Hotspot" is 0.451 deg C/decade, while the UAH MSU TLT linear trend is 0.317 deg C/decade.
Figure 7

Figure 8 illustrates the significant difference in the linear trends for the South American data. The UAH MSU TLT linear trend (0.097 deg C/decade) is much less than the GISTEMP linear trend (0.237 deg C/decade). The GISTEMP linear trend is more than twice that of the UAH MSU TLT data.
Figure 8

The difference between the GISTEMP and UAH MSU TLT data for Northern Africa, Figure 9, is also substantial. The linear trend for the UAH MSU TLT data is 0.094 deg C/decade, but the GISTEMP linear trend is more than 3 times greater at 0.306 deg C/decade.
Figure 9

As illustrated in Figure 10, the UAH MSU TLT linear trend for Southern and Central Africa is a relatively low, only 0.031 deg C/decade. The GISTEMP linear trend, on the other hand, is 0.276 deg C/decade.
Figure 10

For the last comparison, I used the “SoPol” land data from the UAH MSU TLT webpage:

Similar to the difference shown in the comparison of the land plus ocean data for the Antarctic, the GISTEMP Antarctic Land Surface Temperature shows a positive trend (0.055 deg C/decade) while the linear trend for the UAH MSU TLT data is negative (-0.113 deg C/decade). Refer to Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies for a further discussion of the differences in the Antarctic data. It’s primarily a presentation of the unusual timing of the mid-1990s rise in the GISTEMP data. That rise and fall is not a result of the 1997/98 El Nino. It precedes the 1997/98 El Nino.
Figure 11

The GISTEMP Land Surface Temperature and UAH MSU TLT data (with the exception of the Antarctic Land TLT data) are available through the KNMI Climate Explorer website:

Wednesday, June 24, 2009

Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies

I’ve moved to WordPress.  This post can now be found at Part 1 of Comparison of GISTEMP and UAH MSU TLT Anomalies
CORRECTION: Many thanks to bloggers timetochooseagain and Mark Nodine for catching the errors in the Arctic and North American Plus trends that I had listed in the text. They found them in the WattsUpWithThat version of this post.

I had listed the annual values but identified them as decadal. These errors have been corrected.

I originally started this comparison by looking at the differences between OI.v2 SST and the UAH MSU Lower Troposphere Temperature (TLT) anomalies for the same ocean segments. Since OI.v2 SST data has been used by GISS for their GISTEMP product since December 1981, I decided to add another comparison: GISS Land Surface Temperature (LST) versus UAH MSU TLT for the same continental land segments. Then I added one last comparison, which is the subject of this post.

Note 1: The data illustrated in the following graphs are as I downloaded them from the KNMI Climate Explorer website. I made no effort to offset either dataset in the comparative graphs so that the two curves rested on one another. The graphs will show that GISTEMP anomalies are higher than UAH MSU TLT anomalies. This is a function of base years. Focus on the trends and the shapes of the curves, not the location of the curves.

Figure 1 is a comparison of Global GISS Surface Temperature (GISTEMP) and UAH MSU Lower Troposphere Temperature (TLT) anomalies. Both datasets have been smoothed with 12-month running-average filters.
Figure 1

Similar graphs always create speculative comments about the basis for the differences between GISS and UAH data. In this post, I’ve segmented the globe, Figure 2, to locate the areas with the largest differences, in an effort to narrow the possible reasons for those divergences. The coordinates used are listed on the graphs. I’ve plotted the data and added the linear trends, but I have not speculated about the causes for the differences in the data for the smaller global areas.
Figure 2

Note 2: GISTEMP data through the KNMI Climate Explorer is available with 250 km and 1200km smoothing. The graphs in the post use the 1200 km smoothing, which is the smoothing presented by GISS in their GISTEMP product. Figure 2, however, is the May 2009 GISS Global Temperature Anomaly map with 250km smoothing. Grey areas indicate locations with no data. These are the areas infilled by the 1200 km smoothing.

Note 3: Also keep in mind that the MSU TLT data reaches to 82.5N and 70S. The approximate locations of those latitudes are shown in Figure 2. UAH also fills in the polar data. On the other hand, MSU data has better global coverage in other areas where surface station data is lacking.

Note 4: And for the last note before looking at graphs and EXCEL-calculated trends, keep in mind that GISTEMP and UAH MSU TLT represent datasets made up of different variables. GISTEMP is composed of Sea Surface Temperature (SST) and of Land Surface Temperature data based on surface station readings. The UAH MSU TLT data represents the temperature of the lower troposphere.


Figure 3 illustrates GISTEMP and UAH MSU TLT anomalies for the Arctic, 65N to 90N. The GISTEMP linear trend for the period is 0.595 deg C/decade while the UAH MSUTLT data has a linear trend of 0.461 deg C/decade. Note how the GISS data exaggerates (or the UAH MSU data suppresses) the variations, especially after mid-2004.
Figure 3

The North America Plus datasets, Figure 4, also include the Eastern North Pacific and the majority of the North Atlantic. The trends are significantly lower than the Arctic datasets, as would be expected. The linear trend for the UAH MSU TLT data (0.185 deg C/decade) is greater than the trend for the GISTEMP data (0.159 deg C/decade).
Figure 4

The South America Plus datasets, Figure 5, also show a UAH MSU TLT linear trend (0.063 deg C/decade) that is higher than the GISTEMP trend (0.05 deg C/decade). These datasets also include major portions of the eastern South Pacific and western South Atlantic. Both linear trends are again significantly lower than the North American Plus datasets. Note the dominance of the ENSO signal in the South American Plus data.
Figure 5

The Europe Plus datasets show the highest trends of those examined in this post. This should be due to the impact of the North Atlantic on Europe. As illustrated and discussed in my post “Putting The Short-Term Trend Of North Atlantic SST Anomalies Into Perspective”, the linear trend of the North Atlantic SST anomalies is more than 2.5 times the dataset with the next highest trend. The GISTEMP trend (0.429 deg C/decade) for the Europe Plus dataset is slightly higher than the UAH MSU trend (0.379 deg C/decade).
Figure 6

The difference in linear trends is greatest in the Africa Plus datasets, Figure 7. The GISTEMP linear trend at 0.194 deg C/decade is more than twice the linear trend of 0.093 deg C/decade for the UAH MSU data.
Figure 7

For the Asia Plus subsets, Figure 8, the GISTEMP linear trend (0.256 deg C/decade) is also higher than the UAH MSU linear trend (0.179 deg C/decade). The Asia Plus datasets have the second highest linear trends of the areas illustrated in this post.
Figure 8

The comparison of the Australia Plus datasets, Figure 9, illustrates another occasion when the GISTEMP linear trend (0.076 deg C/decade) is less than the USH MSU linear trend (0.096 deg C/decade).
Figure 9

The first thing that stands out in the comparison of Antarctic datasets is the difference in the signs of the linear trends. The GISTEMP data show a positive trend of 0.048 deg C/decade, while the UAH MSU data show a negative trend, -0.091 deg C/decade.
Figure 10

The Antarctic datasets are also the noisiest of those illustrated in this post. But the real curiosity is the timing of the mid-to-late 1990s spike in the GISTEMP Antarctic data. At first glance, it appears to be a result of the 1997/98 El Nino. But the spike is more than a year early. In Figure 11, scaled NINO3.4 SST anomalies have been added to the comparative graph of Antarctic Plus GISTEMP and UAH MSU TLT data. The spike in the GISTEMP Antarctic data is not a response to the 1997/98 El Nino.
Figure 11

Figure 12 illustrates the GISTEMP Surface Temperature and the two components of it: GISTEMP Land Surface Temperature, and OI.v2 SST data for the Southern Ocean. The source of the anomalous spike in the mid-1990s is the GISTEMP Land Surface Temperature data, not the SST data.
Figure 12

Note 5: The GISTEMP Surface Temperature data from 90S to 60S is clearly dominated by the GISTEMP Land Surface Temperature data, though the surface area of the Southern Ocean (20.3 million sq km) is greater than the land mass of Antarctica (14.0 million sq km). This appears to be a function of Southern Hemisphere sea ice area, which can vary from 1.5 to 16.5 million sq km over the course of a year. During the winter, sea ice area increases. The land surface area then becomes greater than the sea surface area, making it the dominant dataset.


I do not recall any discussions of a 1996 spike in the GISTEMP Antarctic surface temperature data. I have double-checked to assure I downloaded the data correctly. However, I have not tried to confirm whether or not the 1996 spike occurs in the individual Antarctic surface station data available from GISS:

A gif animation of the annual GISTEMP maps, Figure 13, does show elevated Antarctic surface temperatures in 1996.
Figure 13


THE GISTEMP Surface Temperature, GISTEMP Land Surface Temperature, UAH MSU TLT, and OI.v2 SST data are available through the KNMI Climate Explorer website:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Monday, June 22, 2009

Mid-June 2009 NINO3.4 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at Mid-June 2009 NINO3.4 SST Anomaly Update
A quick post. Weekly NINO3.4 SST anomalies for the week centered on June 17, 2009 are above 0.5 deg C, well into the range of El Nino temperatures.

NINO3.4 SST Anomalies

OI.v2 SST anomaly data is available through the NOAA NOMADS system:

Tuesday, June 16, 2009

The USGCRP Report “Global Climate Change Impacts in the United States” Fails To Acknowledge the Multiyear Effects of ENSO on Global Temperature

The USGCRP report “Global Climate Change Impacts in the United States" was released today. Link to report:

As noted in the title, it fails to address the multiyear effects of El Nino-Southern Oscillation (ENSO) events on global temperature.

Other than explosive volcanic eruptions, El Nino-Southern Oscillation events have the greatest impacts on global climate on annual and multiyear bases. The year-to-year global temperature impacts of ENSO events are clearly visible in a comparative time-series graph, Figure 1. Also visible are the overriding effects of the 1982 El Chichon and 1991 Mount Pinatubo volcanic eruptions.
Figure 1

The multiyear impacts of the 1986/87/88 and 1997/98 El Nino events on Northern Hemisphere Lower Troposphere Temperature (TLT) are clearly visible in the TLT Time-Latitude Plot available from Remote Sensing Systems (RSS). Refer to Figure 2 and 3, which are from my post “RSS MSU TLT Time-Latitude Plots...Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone.”
Figure 2
Figure 3

A seldom-discussed, naturally occurring oceanic process called Reemergence (Refer to my post “The Reemergence Mechanism”) provides the mechanism by which the global oceans integrate the effects of ENSO events. And it only takes the cumulative effect of a very small portion (0.0045 or less than ½ of 1%) of the monthly ENSO signal, as shown in Figure 4, to reproduce the Global Sea Surface Temperature (SST) anomaly curve.
Figure 4


The USGCRP mentions "El Nino" nine times in the body of the 196-page report, but those references only pertain to global temperature on one occassion. The first reference, however, states that ENSO is independent of human activities.

On page 16, during a discussion Natural Influences, they wrote, “The climate changes that have occurred over the last century are not solely caused by the human and natural factors described above. In addition to these influences, there are also fluctuations in climate that occur even in the absence of changes in human activities, the Sun, or volcanoes. One example is the El Niño phenomenon, which has important influences on many aspects of regional and global climate.” [My emphasis.]

They acknowledged that ENSO is independent of anthropogenic, solar, and volcanic aerosol influence. That’s significant, but they provide no sources to back the statement.

On page 17, in the text of the comparative graph of “Global Temperature and Carbon Dioxide”, they wrote, “These year-to-year fluctuations in temperature are due to natural processes, such as the effects of El Niños, La Niñas, and the eruption of large volcanoes.” [My emphasis.]

Yet they fail to note the multiyear and cumulative effects of ENSO.

Page 36, during a discussion of Pacific Hurricanes, they write, “The total number of tropical storms and hurricanes in the eastern Pacific on seasonal to multi-decade time periods is generally opposite to that observed in the Atlantic. For example, during El Niño events it is common for hurricanes in the Atlantic to be suppressed while the eastern Pacific is more active. This reflects the large-scale atmospheric circulation patterns that extend across both the Atlantic and the Pacific oceans.” [My emphasis.]

That quote is important in many contexts. Much can be inferred from it. Yet they fail to acknowledge the multidecadal epochs when El Nino or La Nina are dominant. These epochs are visible in a time-series graph of smoothed NINO3.4 SST anomalies, Figure 5.
Figure 5

On page 38, under the heading of Snowstorms, they wrote, “The northward shift in storm tracks is reflected in regional changes in the frequency of snowstorms. The South and lower Midwest saw reduced snowstorm frequency during the last century. In contrast, the Northeast and upper Midwest saw increases in snowstorms, although considerable decade-to-decade variations were present in all regions, influenced, for example, by the frequency of El Niño events.” [My emphasis.]

And again, they infer multidecadal influences of ENSO, but the USGCRP have failed to account for it in their attribution of global temperature change.

There are further references of El Nino and La Nina events on pages 81, 147, 148, and 152, as they pertain to tuna stock, droughts, coral reefs, and coastal currents. No need to repeat those in this post.


Like the IPCC, the USGCRP either fails to accept the significant multiyear and cumulative impacts of ENSO on global temperatures or they chose to ignore them in their presentation of the causes of global temperature change.

Monday, June 15, 2009

Putting The Short-Term Trend Of North Atlantic SST Anomalies Into Perspective

I’ve moved to WordPress.  This post can now be found at Putting The Short-Term Trend Of North Atlantic SST Anomalies Into Perspective
The following quote is from my post “The Impact of the North Atlantic and Volcanic Aerosols on Short-Term Global SST Trends”:
“The disparity between the North Atlantic SST anomaly trend…and the rest of the subsets was striking. The North Atlantic SST anomaly linear trend for the period of November 1981 (the start of the OI.v2 SST dataset) and January 2009 is ~0.264 deg C/decade, while the global linear trend is ~0.0948 deg C/decade. The North Atlantic linear trend is approximately 2.8 times the global linear trend, driven by Atlantic Meridional Overturning Circulation and El Ninos, (yes, El Ninos).”

Figure 1 illustrates that disparity. It shows the linear trends for the North Atlantic and the other ocean subsets that I normally include in the monthly SST anomaly updates. Even though North Atlantic SST anomalies have plummeted in the past four months, the difference in the trends is still substantial.
Figure 1

And to reinforce the relative importance of the North Atlantic on the global SST anomaly linear trend since 1979, refer again the my post “The Impact of the North Atlantic and Volcanic Aerosols on Short-Term Global SST Trends”. It showed that by removing the (naturally elevated) North Atlantic SST anomalies, the global SST anomalies dropped from ~0.095 deg C/decade to ~0.055 deg C/decade. And by removing the impacts of the eruptions of El Chichon and Mount Pinatubo, the global SST anomaly linear trend is further reduced to ~0.037 deg C/decade. Refer to Figure 2.
Figure 2


The linear trends illustrated in Figure 1 were created from the time-series graphs of the individual ocean SST anomaly graphs that follow.
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9


OI.v2 SST Anomaly data is available through the NOAA NOMADS system:http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

Saturday, June 13, 2009

The Matthews Factor

I’ve moved to WordPress.  This post can now be found at The Matthews Factor

The press release for Matthews et al (2009) “The Proportionality of Global Warming to Cumulative Carbon Emissions” includes the statement, “These findings mean that we can now say: if you emit that tonne of carbon dioxide, it will lead to 0.0000000000015 degrees of global temperature change.” For the sake of simplicity, I’ll refer to that 13-figure multiplier as the “Matthews Factor”, though they refer to it as the carbon-climate response (CCR). Matthews Factor has a much nicer ring to it than the acronym CCR.

Link to Matthews et al abstract at “Nature”:

Link to press release:

The Matthews et al (2009) abstract reads, “Here we generalize these results and show that the carbon–climate response (CCR), defined as the ratio of temperature change to cumulative carbon emissions, is approximately independent of both the atmospheric CO2 concentration and its rate of change on these timescales. From observational constraints, we estimate CCR to be in the range 1.0–2.1 deg C per trillion tonnes of carbon (Tt C) emitted (5th to 95th percentiles), consistent with twenty-first-century CCR values simulated by climate–carbon models.” [Bold Face Added]

So the Matthews factor is approximately the midpoint of the Carbon-Climate Response range of 1.0–2.1 deg C per trillion tonnes of carbon (Tt C) emitted.

My Figure 1 is Matthews et al Figure 4, available here:

The description of the Matthews et al graph reads, “CCR was estimated for each decade of the twentieth century after 1910 by scaling an observationally constrained estimate of greenhouse-gas-attributable warming relative to 1900–09 by the ratio of CO2 forcing to total greenhouse gas forcing, and dividing by cumulative anthropogenic carbon emissions over the same period. This observationally constrained estimate of CCR is both stable in time and consistent with the estimates derived from model simulations.”

Figure 1


The Global CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring (1751-2006) is available from the Carbon Dioxide Information Analysis Center (CDIAC):

Let’s limit the period in question to a good portion of the last 30 years. The reason for limited time period is the length of a dataset used later in this post. Figure 2 illustrates the CDIAC Total Annual Global CO2 Emissions from 1979 to 2006. (CDIAC is lax in updating their data files.) As you can see, the CO2 emissions have been accelerating lately, or at least through 2006, so relying on an extension of the trend for 2007 and 2008 should be conservative.
Figure 2

And plotting a running total of the total annual CO2 emissions illustrates the Cumulative Carbon Emissions, Figure 3, which are the focus of Matthews et al.
Figure 3

To show the impact of the Cumulative Carbon Emissions on global temperature, simply multiply that data by the Matthews factor of 0.0000000000015 deg C/Ton of CO2 Emissions. Refer to Figure 4. There can only be one reason for the simple multiplier and that is to place cause and effect in very easy-to-understand terms.
Figure 4

Figure 5 compares the Impact of the Cumulative Carbon Emissions, based on the Matthews Factor, to RSS MSU TLT anomalies. It illustrates the substantial impact Matthews et al are attempting to attribute to cumulative carbon emissions.
Figure 5

At first glance, it almost appears logical. Until one remembers that…

Of course, this is something that Matthews et al account for. Refer again to their description of their Figure 4. In it, they wrote, “CCR was estimated for each decade of the twentieth century after 1910 by scaling an observationally constrained estimate of greenhouse-gas-attributable warming relative to 1900–09 by the ratio of CO2 forcing to total greenhouse gas forcing, and dividing by cumulative anthropogenic carbon emissions over the same period.”

So let’s examine the difference between CO2 and total greenhouse gas forcings.

THE NOAA Annual Greenhouse Gas Index (AGGI) lists the Annual Radiative Forcing (watts/meter^2) from 1979 to 2007 for all anthropogenic greenhouse gases, not simply CO2. This is the dataset that dictated the period of time used in this post. Refer to their Table 2:

Figure 6 shows the Annual Radiative Forcings for CO2 and all anthropogenic greenhouse gases as listed in Table 2 of the AGGI webpage. CO2 does represent a major portion of the anthropogenic total, according to the AGGI.
Figure 6

And for those interested, the percentage of the AGGI represented by CO2 is shown in Figure 7. It’s risen in recent years, due in part to the increases in CO2 emissions, and due in part, as NOAA states, to “the slowdown in the methane growth rate and the decline in the CFCs”.
Figure 7


Dividing the annual Total AGGI by the annual AGGI for CO2 provides the ratio of the total AGGI radiative forcing to the AGGI radiative forcing for CO2. We can then use these ratios as annual multipliers for the Matthews Factor and determine the total impact of all anthropogenic greenhouse gases on global temperature anomaly, assuming the Matthews Factor is correct. In Figure 8, I’ve again compared it to RSS MSU TLT anomalies. It appears that Matthews et al are attempting to attribute all of the warming since 1979 to greenhouse gases.
Figure 8

In an earlier post, The Impact of the North Atlantic and Volcanic Aerosols on Short-Term Global SST Trends, I discussed the effects of volcanic aerosols and of the natural variability of the North Atlantic on Global SST anomaly trends from November 1981 to January 2009. By removing them, the linear trend plummeted from 0.095 deg C/decade to 0.037 deg C/decade. In Figure 9, I’ve combined Figures 1 and 5 from that earlier post to create a comparison graph with linear trends. It illustrates the impacts of those two natural variables on Global SST anomaly trends. Note the substantial drop in the trends.
Figure 9

Note: The post "The Impact of the North Atlantic and Volcanic Aerosols on Short-Term Global SST Trends" also ran at WattsUpWithThat:
If we assume that removing the impacts of volcanic aerosols and the natural variations of the North Atlantic would cause a proportional decrease in the trend of Global RSS MSU TLT anomalies, then the GCMs used by Matthews et al have grossly overestimated the significance of CO2 on global temperature.


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