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Monday, March 30, 2009

KNMI Added ERSST.v3b Data To Climate Explorer

I’ve moved to WordPress.  This post can now be found at KNMI Added ERSST.v3b Data To Climate Explorer
On Sunday, March 29, 2009, Geert Jan van Oldenborgh of the Royal Netherlands Meteorological Institute added ERSST.v3b data to Climate Explorer. Many thanks to Dr Van Oldenborgh for his quick response to my email request.

The ERSST.v3b version of NCDC’s Extended Reconstructed Sea Surface Temperature dataset replaced the ERSSST.v2 version, which is no longer being updated.

This post is a quick introduction to the data, with time-series graphs of the SST data on global, hemispheric, and individual ocean bases. I’ve also included the NINO3.4 SST anomaly data. In each graph, the data from January 1854 to February 2009 is illustrated without filtering. The exception to this is one graph, Figure 2, and it has been smoothed with a 37-month running-average filter.


Figure 1 illustrates the ERSST.v3b Global SST Anomaly data. There are some minor differences between it and earlier versions of the ERSST data, which will not be discussed in this post.

(Note that the 1945 shift in the ERSST.v3b data does not appear to have had any additional corrections in response to the Thompson et al (2008) paper “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature”. I discussed that 1945/46 shift in my posts The Large 1945 SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data and Part 2 of The Large SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data. The latter post also illustrates the shift in wind speed data at the same time, but in the opposite direction.)

Figure 1

The ERSST.v3b Global SST anomaly data includes the dip from ~1878 to ~1910 and the rebound from ~1910 to ~1940, similar to earlier versions of the data. Refer to Figure 2. And consistent with the earlier versions, there is no significant difference between the SST anomalies of the 1870s and those of the late 1970s.
Figure 2
I'll end my commentary here and let the graphs speak for themselves.


Northern Hemisphere
Figure 3

Southern Hemisphere
Figure 4


North Atlantic (0 to 75N, 78W to 10E)
Figure 5

South Atlantic (0 to 60S, 70W to 20E)
Figure 6

North Pacific (0 to 65N, 100E to 270E, where 270E=90W)
Figure 7

South Pacific (0 to 60S, 145E to 290E, where 290E=70W)
Figure 8

Indian Ocean (30N to 60S, 20E to 145E)
Figure 9

Arctic Ocean (65N to 90N)
Figure 10

Southern Ocean (60S to 90S)
Figure 11


NINO3.4 (5S to 5N, 170W to 120W)
Figure 12


The ERSST.v3b data, both SST and SST anomalies, are available through the KNMI Climate Explorer webpage:

Friday, March 27, 2009

Part 2 of The Large SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data

And A Look At Two More COADS Datasets - SST & Wind Speed


In The Large 1945 SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data, I illustrated that the 1945/46 shift SST anomalies also appears in Cloud Cover and Marine Air Temperature data. In this post, I provide comparative graphs of HADISST anomaly data from January 1930 to December 1959 and the Marine Air Temperature and Cloud Cover datasets. I’ve also added two more COADS datasets, SST and wind speed. The wind speed data shows a shift in 1945 but of the opposite sign.

Note: I am not the first blogger to note that the shift also occurs in the Marine Air Temperature data. In the ClimateAudit post Nature "Discovers" Another Climate Audit Finding, at comment 120, Fred Moolten writes, “SST measurements are only one means of recording temperature over the oceans. A separate method involves marine air temperature (NMAT) measurements that are less robust but generally match SST quite well. These, of course, are not subject to errors involving insulated or uninsulated buckets or engine intakes. If one examines NMAT records, they faithfully parallel the SST trends at most times during the twentieth century. During the 1940s, this is also true, but only partially. In particular, they exhibit the same dip as the SST measurements, implying that this dip cannot be fully explained by SST artefact. However, the SST dip slightly exceeds the NMAT dip, and it is this small excess, rather than the entire descending limb of temperature, that is most plausibly attributable to artefact. (In passing, one should note that land temperatures also exhibit a peak and decline, but to a much lesser extent).”

And though one might exist, I have found no mention of the shift in cloud cover data at the same time in any blogs.


Figure 1 illustrates Global SST Anomalies [HADISST] and Global Marine Air Temperature [MOHMAT4.3] data from January 1930 to December 1959. One would believe based on the similarities between the two datasets that they were used to infill one another during the war period when data collection was sparse. Note that the shifts in 1945/46 are of similar magnitudes.
Figure 1

The COADS Air Temperature Anomalies have a much greater upward surge in the early 1940s than the Global SST Anomalies [HADISST], Figure 2, with a much greater drop in 1945/46.
Figure 2

Ocean Cloud Cover, Figure 3, also shows an early 1940s surge. The 1945 downward shift Ocean Cloud is exaggerated.
Figure 3


The COADS SST Anomaly dataset available through the KNMI Climate Explorer webpage appears to be the raw data prior to any adjustments. I say this because the COADS SST anomaly data still includes the anomalous offset in the data prior to 1941, which was adjusted for by Folland et al back in the 1990s. Refer to C. K. Folland and D. E. Parker, 1995, CORRECTION OF INSTRUMENTAL BIASES IN HISTORICAL SEA SURFACE TEMPERATURE DATA, Q.J.R. Meteorolol. Soc. 121, 319-367
These adjustments were further discussed by Steve McIntyre of ClimateAudit in 2005:

In looking at the comparison of HADISST Global SST anomalies and the COADS Global SST Anomalies, Figure 4, it’s very clear that a partial correction of the sudden shift in 1945 has already been made.
Figure 4

And that raises the question…


The answer is no for the Hadley Centre’s HADSST2 version of SST anomalies. I had an older copy (my file was dated October 2007) of the HADSST2GL data, one that was published before the Thompson et al paper. Unfortunately, I do not have an older copy of the HADISST data that I’ve used as reference throughout this post. But it seems unlikely that the Hadley Centre would update one SST dataset and not the other. Figure 5 compares the October 2007 HADSST2GL data with the most recent update, February 2009, for the period of January 1930 to December 1959. I had to reduce the weighting of the February 2009 dataset (red curve) in order to show the October 2007 version of the dataset (the blue curve). There are identical.
Figure 5


So far the rises and falls in all of the datasets have been in the same direction (an increase in the early 1940s followed by a sharp decrease in the mid-1940s) and they have all occurred at approximately the same times. This would lead one to believe the all of the datasets may have been relied on to help infill the others during the period of World War II, intermixing them. If not, then the unlikely explanation would be, maybe (BIG MAYBE) there was a statistical quirk that was responsible for all of the shifts. Or those shifts actually occurred.

To confuse matters, there’s the COADS Wind Speed Anomaly dataset. Refer to Figure 6. Note that there is an increase in the Wind Speed anomaly data from January 1930 to December 1959, which is similar to all of the other datasets illustrated in this post. They all have positive trends from 1930 to 1959. But the shift in the Wind Speed anomaly data in the early 1940s is negative—wind speed drops during that time. Then in 1945 the Wind Speed anomaly increases sharply.
Figure 6

The opposing fall then rise is easily visible in the comparative graph of HADISST Global SST Anomaly data and COADS Global Wind Speed data, Figure 7. I’ve scaled and ranged the Wind Speed anomaly data for illustration purposes only.
Figure 7

The fall then rise in the Wind Speed Anomaly data does appear to lag the SST anomaly data, but why would it only happen during that time period. Curious.


Steve McIntyre’s posts and the comments on the subject of SST anomaly adjustments are quite insightful. The following are links to them:

There may be other posts at ClimateAudit about these adjustments.


The HADISST, the MOHMAT3.4, the COADS Air Temperature, the COADS Cloud Cover, the COADS SST, and the COADS Wind Speed datasets are available through the KNMI Climate Explorer website.

And the HADSST2GL data is available here:

Monday, March 23, 2009

The Large 1945 SST Discontinuity Also Appears in Cloud Cover and Marine Air Temperature Data

And A Curious Long-Term Dataset

UPDATE – March 24, 2009

I have acquired and read the Thompson et al (2008) paper “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature”, and the follow-up articles “Hot Questions of Temperature Bias” by Forest and Reynolds and “Climate Anomaly is an Artifact” by Schiermeier. The similarities between the discontinuity in the Sea Surface Temperature data and those same shifts in Cloud Cover and Marine Air Temperature datasets were not addressed in any of the discussions.


Almost a year ago, the Thompson et al (2008) paper “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature” caused a buzz in the climate change blogs. Thompson et al discussed the believed reasons for the sudden shift in global mean surface temperature in and around 1945.

Data sets used to monitor the Earth's climate indicate that the surface of the Earth warmed from ~1910 to 1940, cooled slightly from ~1940 to 1970, and then warmed markedly from ~1970 onward. The weak cooling apparent in the middle part of the century has been interpreted in the context of a variety of physical factors, such as atmosphere–ocean interactions and anthropogenic emissions of sulphate aerosols. Here we call attention to a previously overlooked discontinuity in the record at 1945, which is a prominent feature of the cooling trend in the mid-twentieth century. The discontinuity is evident in published versions of the global-mean temperature time series, but stands out more clearly after the data are filtered for the effects of internal climate variability. We argue that the abrupt temperature drop of ~0.3 deg C in 1945 is the apparent result of uncorrected instrumental biases in the sea surface temperature record. Corrections for the discontinuity are expected to alter the character of mid-twentieth century temperature variability but not estimates of the century-long trend in global-mean temperatures.

The “abrupt temperature drop” is visible in Figure 1, which illustrates the Global SST Anomalies (HADISST) from January 1930 to December 1959.

Figure 1

In the “The Independent”…
…Science Editor, Steve Connor, wrote, “The scientists point out that the British measurements were taken by throwing canvas buckets over the side and hauling water up to the deck for temperatures to be measured by immersing a thermometer for several minutes, which would result in a slightly cooler record because of evaporation from the bucket.

“The preferred American method was to take the temperature of the water sucked in by intake pipes to cool the ships' engines. Those records would be slightly warmer than the actual temperature of the sea because of the heat from the ship, the scientists said.

“Taking into account the difference in the way of measuring sea-surface temperatures, and the sudden increase in the proportion of British ships taking the measurements after the war, the result was an artificial lowering of the global average temperature by about 0.2C, said Professor Phil Jones of the University of East Anglia in Norwich.”

“The Independent” included a graph illustrating the effect the new corrections would have on the global temperature record. Refer to Figure 2.
Figure 2


And it’s doubtful that the “difference in the way of measuring sea-surface temperatures” (Phil Jones) impacted Cloud Cover and Marine Air Temperature measurements.

Figure 3 illustrates the COADS Global Ocean Cloud Cover data from Jan 1939 to December 1959. Its shift in 1945 is pronounced.
Figure 3

The MOHMAT4.3 Global Marine Air Temperature for the same period contains the same shift, as shown in Figure 4.
Figure 4

As does the COADS air temperature data illustrated in Figure 5.
Figure 5

Did the researchers consider the same shifts in other variables before deciding the SST data was in error?


I always download data for the entire term of the dataset, regardless of the period I’m investigating. It’s very easy then to smooth the additional data and plot the results. The long-term COADS Cloud Cover and the MOHMAT4.3 Global Marine Air Temperature data (smoothed with 37-month running-average filters) are shown in Figures 6 and 7. The magnitudes of the surges in the early-to-mid 1940s are clearly visible.
Figure 6
Figure 7

Now the smoothed COADS Air Temperature data from 1800 to 2007, Figure 8. I had to download the data a second time to assure that I hadn’t made an error. It clearly shows that the COADS Air Temperature data was higher in 1800 than it is today.
Figure 8

The raw COADS Air Temperature data, Figure 9, shows part of the reason for the anomalous curve. The data before 1850 appears unreliable, probably a function of measurement accuracy and data density, or lack thereof.
Figure 9

But if the data from 1800 to 1849 is removed, Figure 10, the COADS Air Temperature data continues to show early anomalies that are higher than present anomalies.
Figure 10


Not if we consider SST temperature reconstructions. The Subtropical South Pacific SST Reconstruction of Lindsey et al (2000) was discussed in my post on SST Reconstructions. It shows similar results with the early years warmer than present, Figure 11.
Figure 11

And the Mann et al NINO3 SST Reconstruction, when smoothed with a 30-year Gaussian-weighted filter as created by Jones et al (2001), shows a similar decrease in SST anomalies until about 1900. Refer to Figure 12. The Jones et al smoothing of the Mann data (and other ENSO Reconstructions and data) was discussed in my post Low Frequency ENSO Oscillations.

Figure 12

Unless the datasets were used to infill one another during the 1940s, or unless the "bucket adjustments" also somehow magically apply to Marine Air Temperature and Cloud Cover data,
the similarities in the shifts of the SST , the Cloud Cover and the Marine Air Temperature datasets would make one question the conclusions of the Thompson et al (2008) paper “A large discontinuity in the mid-twentieth century in observed global-mean surface temperature”.


The HADISST, COADS Ocean Cloud Cover, MOHMAT4.3 Marine Air Temperature, and COADS Air Temperature data are available through the KNMI Climate Explorer website:

Refer to the linked posts for the sources of the reconstruction datasets.

Sunday, March 22, 2009

The Latest Revisions to Ocean Heat Content Data

I’ve moved to WordPress.  This post can now be found at The Latest Revisions to Ocean Heat Content Data
The November 5, 2008, NASA Earth Observatory article “Correcting Ocean Cooling”… http://earthobservatory.nasa.gov/Features/OceanCooling/…contained the graph of the newly revised Ocean Heat Content on page 4 of the article. It compares the original OHC data to the newly revised data. Figure 1 is that graph.

Figure 1

Using the coordinate capabilities of MS Paint, I “duplicated” the graph of the revised data in Figure 2 so that I could run a few quick comparisons in my post “Revised Ocean Heat Content.”

In the Recent Ocean Heat and MLO CO2 Trends thread at WattsUpWithThat, blogger DJ provided a link to the NOAA National Oceanic Data Center’s upcoming Levitus et al paper on Ocean Heat Content to be published in Geophysical Research Letters.
Figure 3 is the OHC graph from that webpage. Note the Annual data since 2004.5. What happened to the significant drop in 2007 in Figure 1? Have trouble seeing the difference between Figures 1 and 3?
Figure 3

Following the “basin time series” links will bring you to the NODC’s listing of “WORLD” Yearly Ocean Heat Content from 1955 to 2007.

Figure 4 is a comparative graph of the data I created from the Earth Observatory article and the data used in the Levitus et al paper. The two datasets track well until 2007.5.
Figure 4


As illustrated in Figure 4, the depiction of Ocean Heat Content varies from month to month even from the same data supplier. But there are other recent papers that illustrate Ocean Heat Content. These are illustrated in the manuscript of the Levitus et al (2009) paper “Global Ocean Heat Content 1955-2008 in light of recently revealed instrumentation problems”. Refer to Figure 5, which is Figure S9 in the Levitus et al paper. [Note that the Levitus et al data (red curve) includes the 2008 data in this graph.] Levitus et al, Ishii and Kimoto, and Dominguez et al were all published within a year of one another. All three papers illustrate the same variable, but the data varies significantly between the three datasets. Note the divergence of the Levitus et al data (red curve) in 2003.
Figure 5

A decade from now, when researchers sort out the problems of measuring Ocean Heat Content, when they agree on the methodologies to be used to calculate it, it may serve as a worthwhile measure of climate change. At present it does not.

UPDATE – May 7, 2009

Let me clarify my closing comment about the OHC reconstructions.

Given: El Nino events redistribute heat from the tropical Pacific to the high latitudes so that it can be radiated into space more readily. Let’s say I wanted to analyze the 1997/98 El Nino to the determine how much of that heat was released to the atmosphere and how much was simply redistributed to the extratropical North and South Pacific and to other ocean subsets. Refer to the following graph. It’s the comparative graph of Levitus et al, Ishii and Kimoto, and Domingues et al OHC datasets, Figure S9 in the Levitus et al paper. I’ve highlighted 1997 and 1998. In 1997, the OHC in all three datasets increased, and in 1998, they all decreased. BUT look at the differences in the magnitudes of the changes in 1998. Which dataset depicts the changes correctly? Right now, I don’t have enough confidence in any of them to do the study I’ve suggested.


Saturday, March 21, 2009

Oceanic Volcanism and Sea Surface Temperature

I’ve moved to WordPress.  This post can now be found at Oceanic Volcanism and Sea Surface Temperature
Many times when Sea Surface Temperature (SST) anomalies are discussed on blogs, someone mentions ocean volcanoes, stating their belief that the subsurface volcanism causes large areas of positive SST anomalies. When an oceanic volcano becomes active or erupts, bloggers find maps of SST anomalies and latch onto hotspots, believing the new-found volcanism is responsible for the anomalous warmth. They rarely if ever research the hotspot areas to determine if the anomalies are parts of patterns or if they are simply weather noise.

Many things impact regional or local SST anomalies: from changes in cloud cover, to variations in precipitation, to changes in wind speed. SST anomalies in certain areas of the oceans vary inversely to the SST anomalies of the eastern equatorial Pacific during ENSO events. They warm during La Ninas or cool during El Ninos. These patterns are reflected as the Pacific Decadal Oscillation (PDO), the 1st EOF of North Pacific SST anomalies north of 20N; or the Interdecadal Pacific Oscillation (IPO), the 3rd EOF of global SST anomalies; or the North Pacific Gyre Oscillation (NPGO), the 2nd EOF of North Pacific Sea Surface Height (SSH). Many times these warm SST areas form along the South Pacific Convergence Zone (SPCZ). Refer to Figure 1 for the location of the SPCZ. The illustration is courtesy of the NASA Global Tropical Experiment webpage:
Figure 1

Figure 2 is the OI.v2 Global SST anomaly map for the February 2009. Note the line of warm SST anomalies along the SPCZ. The warm anomalies extend from the Pacific Warm Pool past South America to the South Atlantic.
Figure 2

Yet there are those who wrongly attribute the warm spots along the SPCZ to the recent volcanism off the South Pacific island of Tonga.

The following is a Global SSH video from JPL (tpglobal.mpeg) from October 1992 to August 2002 that I’ve placed on YouTube. The video is available in .mpg format through:
Another listing of videos:

As you will note in the video, Sea Surface Height and Sea Surface Temperature anomalies vary greatly over the course of days, weeks, months, years, and decades. These continuous perturbations are not likely caused by an infrequent awakenings of oceanic volcanoes. They’re caused by coupled ocean-atmosphere interactions.

A link to the corresponding YouTube webpage:

Wednesday, March 18, 2009

March 2009 Mid-Month ENSO Update

I’ve moved to WordPress.  This post can now be found at March 2009 Mid-Month ENSO Update
During the week centered on Wednesday March 11, 2009, NINO3.4 SST anomalies have risen above the threshold for a La Nina to -0.41 deg C.
NINO3.4 SST Anomalies

On the other hand, NINO3 SST anomalies have dropped to -0.84 deg C.
NINO3 SST Anomalies

The cool water appears to be working its way westward toward the NINO3.4 region. Refer to the following map…
Central Pacific SST Anomalies (Week Centered on March 11, 2009)

And refer to the animation in the following link:

To add another perspective, the Southern Oscillation Index (SOI) for February 2009 shows no sign of weakening. Note that the SOI data has been inverted so that it corresponds to the variations in NINO SST anomalies.
Inverted Southern Oscillation Index


The map and the SST anomaly data used to create the graphs are available through the NOAA NOMADS system:

Southern Oscillation Index data available from the Australian Bureau of Meteorology webpage:

Sunday, March 15, 2009

Has Global Warming Accelerated?

I’ve moved to WordPress.  This post can now be found at Has Global Warming Accelerated?
CORRECTION: In agreement with my post The Atlantic Multidecadal Oscillation - Correcting My Mistake, I edited the sentence in this post that read, “North Atlantic SST anomalies vary in a semi-periodic cycle that is normally expressed as a residual, the Atlantic Multidecadal Oscillation (AMO).” The AMO is not a residual. It is detrended North Atlantic SST anomalies, according to the NOAA ESRL. The sentence now reads, “North Atlantic SST anomalies vary in a semi-periodic cycle that is normally illustrated as the Atlantic Multidecadal Oscillation (AMO). The AMO is created by detrending the North Atlantic SST anomalies.”

In The Impact of the North Atlantic and Volcanic Aerosols on Short-Term Global SST Trends, I illustrated the significant effects of two natural variables, the Atlantic Multidecadal Oscillation and volcanic aerosols, on the global SST anomaly trends for the OI.v2 SST dataset. The conclusion of that post was that natural variables caused rate of rise in SST anomalies to appear to be accelerating in recent times, when, in fact, it has not accelerated. OI.v2 SST is a short-term dataset, lasting only from November 1981 to present. This post examines the effects of those natural variables on a longer-term SST anomaly dataset, ERSST.v2, Figure 1, and also extracts the immediate impacts of ENSO and solar variations.
Figure 1

Most analyses of this type examine combined land and sea surface temperatures. I’ve elected to illustrate sea surface temperature (SST), which represent approximately 70% of the surface of the globe, because:
-It contains much less noise,
-Therefore, it requires less smoothing to dampen that noise, and
-The immediate effects of the North Atlantic can be removed simply.

A Note about the Smoothing: All graphs in this post represent data that has been smoothed with a 37-month running-average filter, but the adjustments have been made to the raw data, before smoothing. Scaling factors have been selected from the cited references and due their visual impacts on the smoothed data. Adjustments to raw data or to data that has been smoothed differently may use different scaling factors.


It’s well known that Global SST anomalies rose during two separate periods since 1900. To show the difference, if any, between the trends for those two periods, I identified the timing of the minimums and maximums in the smoothed SST anomalies illustrated in Figure 1. I plotted them individually and had EXCEL determine the trends. Figures 2 and 3 illustrate nearly identical linear trends during the two warming periods of January 1910 to May 1943 (Linear Trend = ~0.116 Deg C/Decade) and April 1975 to July 2007 (Linear Trend = ~0.115 Deg C/Decade). As illustrated, over the two periods when global SST anomalies have risen, there is NO difference in the rates of rise. There has been NO acceleration in recent years.
Figure 2

Figure 3

If natural variables are eliminated, would there be an increase in recent years? No, but the linear trends for both periods do change—and they decrease significantly.


North Atlantic SST anomalies vary in a semi-periodic cycle that is normally illustrated as the Atlantic Multidecadal Oscillation (AMO). The AMO is created by detrending the North Atlantic SST anomalies. The simplest way to account for its immediate effect on global SST anomalies is to remove the North Atlantic data from the global data. If we assume the Atlantic Ocean surface area is approximately 30% of the global ocean surface area, and assume the North Atlantic represented 50% of the Atlantic, the North Atlantic SST anomaly data can be scaled by a factor of 0.15 and subtracted from the Global SST data.

Figure 4 compares Global SST anomalies and Global SST anomalies with the North Atlantic data removed. While it has been argued at blogs that the AMO is a cycle and should, therefore, have no impact on long-term trends, it is clear that the timing of the cycles raised global SST anomalies in recent decades and lowered them in the early 1900s. The result would be an increase in trend over the 20th Century. In fact, over the term of the data, as illustrated, the variations in the North Atlantic add approximately 0.007 deg C/decade to the global SST anomaly trend.
Figure 4


Estimates of the drop in global temperature for the first year following the Mount Pinatubo eruption vary from 0.2 to 0.5 deg C. Using the Sato Index of Stratospheric Aerosol Optical Thickness to determine the impact of volcanic eruptions on global SST anomalies, the Mount Pinatubo eruption appears to have lowered global SST anomalies approximately 0.15 deg C during the initial year. This coincides with the peak mean optical thickness value (~0.15) of the Sato Index for that eruption, so I have not scaled the Sato Index data. To remove the impacts of volcanic aerosols from the adjusted global SST anomaly dataset, the Sato Index data was simply added (without scaling). The result is illustrated in Figure 5. Note how there is no visible effect on the adjusted global SST anomaly trend, but there were short-term periodic effects on the SST record.
Figure 5


In “Evolution of El Nino-Southern Oscillation and global atmospheric surface temperatures” (2002), Trenberth et al identified a regression coefficient for global temperature of 0.094 deg C for a 1 deg C change in NINO3.4 SST anomaly. I’ve used a scaling factor (0.096) in this post that is not significantly different that the Trenberth et al coefficient. The cited Trenberth et al paper can be found here:
I also shifted the NINO3.4 data by three months to account for the lag between an ENSO event and the response in global temperature.

Figure 6 illustrates the impact of removing the scaled NINO3.4 SST anomaly data from the global SST anomalies that have already been adjusted for North Atlantic variability and volcanic aerosols. It clearly shows that the three peaks in recent years are ENSO related, as was the deep minimum around 1910. By adjusting these, the long-term global SST anomaly trend decreases slightly.
Figure 6

Also note how the peak in the early 1940s has shifted and become exaggerated. This appears to reflect the anomalous response of the Indian Ocean to the multiyear El Nino at that time. Refer to Figure 7, which is a long-term graph of Indian Ocean SST anomalies.
Figure 7


There are no long-term monthly Total Solar Irradiance (TSI) datasets, so I’ve used monthly sunspot numbers as a proxy for TSI.

The sunspot data was first scaled to reflect the current consensus that, for the past three solar cycles, global temperatures changed approximately 0.1 deg C as the solar cycles varied from minimum to maximum. Then I subtracted the scaled sunspot data from the global SST anomaly data that’s been adjusted for the effects of the North Atlantic, volcanic aerosols and ENSO. Refer to Figure 8. In addition to a minor reduction in trend, removing the impacts of solar irradiance produces noticeable changes in curve. It minimizes the peaks in the data during recent decades, but adds to the troughs in the late 1940s through the 1950s. Then again, it was recently discovered that the SST data during the mid-1940s contained and still contains a large discontinuity. The discontinuity was due to the approach researchers used to account for changes in sampling methods. So I’m not necessarily concerned about any overreaction during the 40s and 50s.
Figure 8


Figure 9 compares the unadjusted Global SST anomaly data with the dataset that’s been adjusted for the North Atlantic variability, volcanic aerosols, ENSO, and solar contributions. The long-term trend drops considerably when the natural variables have been removed, from ~0.041 deg C/decade to ~0.028 deg C/decade, or about 32% [1-(0.028/0.041)]. Note also how the SST anomalies in the mid-1940s did not change but SST anomalies at all other times did. The previously discussed discontinuity occurred in 1945/46. Curious.
Figure 9


Figure 10 illustrates the Global SST anomaly curve with all the previously discussed adjustments. If you, the reader, were to follow the recipe I’ve provided in the preceding, you could create your own spreadsheet. If you do, allow yourself the ability to adjust all of the variables so you can see how the curve can be altered and how the adjustments interrelate. I could illustrate that, but I wouldn’t have any bases for those adjustments.
Figure 10

A few things struck me about Figure 10. How do we interpret the curve? Does it still represent, over the 20th century, a warming period, followed by a period of cooling, then another warming period? Or is it a long continuous warming with an anomalous rise and fall in the 1930s and 40s? And the last question, how would GCMs duplicate it. For example, without the volcanic aerosols forcing, the GISS Model E output, Figure 11, simply looks like an exponential curve.
Figure 11

The GISS Model E was discussed further in my two posts:
GISS Model E Climate Simulations, and
GISS Model E Climate Simulations Part 2
The posts also contain links and the instructions for downloading the Model E output data.


As noted at the beginning of this post, removing the effects of natural variables on global SST anomalies reduces the trends during the two warming periods. Referring back to Figure 10, there’s now a peak instead of a trough at 1975. Therefore, using 1975 as a starting point for the recent period of warming would result in a lower trend. I wouldn’t want to be accused of cherry-picking dates. So I’ll use the minimum at October 1957 as a start time. I also shifted the beginning and end months of the earlier period to accommodate the shifts in peaks and valleys. As shown in Figures 12 and 13, the trends for the two periods of warming are again nearly identical, so based on the adjusted SST data, there has been no acceleration in recent years.
Figure 12
Figure 13

Recall, also, that the trends for the warming periods in the unadjusted data were ~0.115 deg C/decade. Refer to Figures 2 and 3. After removing the natural variables, the trends of the two warming periods plummeted to ~0.065 deg C/decade. That’s a decrease of approximately 43% [1-(0.065/0.115)]. Trends of 0.065 deg C/decade are far short of the 0.2 deg C/decade rise projected by the IPCC for the next two decades.


Monthly Sunspot data from January 1749 to October 2008 is available through NASA’s Marshall Space Flight Center.http://solarscience.msfc.nasa.gov/greenwch/spot_num.txt

Sato Index data is available from GISS:

The ERSST.v2 data are available at the KNMI Climate Explorer website.


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Comment Policy, SST Posts, and Notes

Comments that are political in nature or that have nothing to do with the post will be deleted.
The Smith and Reynolds SST Posts DOES NOT LIST ALL SST POSTS. I stopped using ERSST.v2 data for SST when NOAA deleted it from NOMADS early in 2009.

Please use the search feature in the upper left-hand corner of the page for posts on specific subjects.
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