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Monday, August 30, 2010

PRELIMINARY August 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at PRELIMINARY August 2010 SST Anomaly Update
The August 2010 SST data through the NOAA NOMADS website won’t be official for at least another week. Refer to the schedule on the NOAA Optimum Interpolation Sea Surface Temperature Analysis Frequently Asked Questions webpage. The following are the preliminary Global and NINO3.4 SST anomalies for August 2010 presented by the NOMADS website. I’ve also included the weekly data through August 25, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the wiggles are visible.

Also included at the end of the post are a comparison of the evolution of the 2010/11 La Niña to past La Niña events and a comparison of 1998 and 2010 global SST anomalies.


Based on the preliminary data, monthly NINO3.4 SST anomalies are continuing to drop, and the drop has them well into La Niña territory.
Monthly NINO3.4 SST Anomalies

Monthly Global SST anomalies, according to the preliminary data, are stalled, changing only -0.01 deg C this month. This can happen after El Niño events. Refer to the comparison of 1998 and 2010 Global SST anomalies that follows.
Monthly Global SST Anomalies


The weekly NINO3.4 SST anomaly data have dropped significantly over the past week. They are now below -1.5 deg C.
Weekly NINO3.4 SST Anomalies

Weekly Global SST Anomalies are still flat. There are some minor wiggles, but the Global SST anomalies are still lagging the drop in NINO3.4 SST anomalies.
Weekly Global SST Anomalies


As of the week centered on August 25, 2010, the 2010 NINO3.4 SST anomalies have not yet dropped below the low levels of the 1988/89 La Niña.
La Niña Evolution Comparison

The following comparison graph shows that 2010 SST anomalies are not above 1998 levels. The weekly global SST anomalies are noisy, so I’ve also shown them smoothed with a 5-week running-average filter.
1998 And 2010 Global SST Anomaly Comparison

SST anomaly data is available through the NOAA NOMADS website:

Saturday, August 28, 2010

On Lee and McPhaden (2010) “Increasing intensity of El Niño in the central‐equatorial Pacific”

As happens all too often, the press release for a paper has an incorrect title and begins with an unfounded claim. The JPL press release for Lee and McPhaden 2010 “Increasing intensity of El Niño in the central‐equatorial Pacific” is no exception. The title of the press release “NASA/NOAA Study Finds El Niños are Growing Stronger” is wrong. The paper discusses the increase in strength in Central Pacific El Niño events, but does not conclude that El Niño events in general have increased. In fact, as will be illustrated in this post, the strengths of NINO3 and NINO4 based El Niño events, when combined, have actually decreased over the period of the Lee and McPhaden study.

And the press release begins with, “A relatively new type of El Niño…”

New? Central Pacific El Niño events, also known as El Niño Modoki, are not new. Figure 1 is a long-term graph of the El Niño Modoki index, calculated using the method described in Ashok et al (2007) “El Nino Modoki and its Possible Teleconnection.” Link to Ashok et al:

As illustrated, El Niño Modoki events, the wiggles above the red line that marks the threshold of El Niño Modoki, appear throughout the record since 1900. This was discussed and illustrated in the post There Is Nothing New About The El Nino Modoki. The only thing new about Central Pacific El Niño events is the researchers’ new-found interest in them.
Figure 1

The science web pages of newspapers have repeated these bits of misinformation. The New York Times headline of Pacific Hot Spells Shifting as Predicted in Human-Heated World is actually contradicted by the study. Lee and McPhaden note about past studies, “This region has experienced a well‐documented warming tendency for at least a few decades [e.g., Cane et al., 1997; Cravatte et al., 2009], which appears to be consistent with theoretically predicted change of the background SST under global warming scenarios [Cane et al., 1997]. Cravatte et al. [2009] also discussed the implications of warming trend in the warm pool to ocean‐atmosphere interactions and El Niño events. Here we use satellite observations of SST in the past three decades to examine SST in the CP region, distinguishing between the increases in El Niño intensity and changes in background SST.”

But the abstract of Lee and McPhaden reads, “Therefore, the well‐documented warming trend of the warm pool in the CP region is primarily a result of more intense El Niño events rather than a general rise of background SST.” This contradicts the earlier studies with “theoretically predicted change of the background SST under global warming scenarios.”

The Los Angeles Times headline, El Niño has grown more intense and shifted westward in last three decades, data show., is also misleading for the same reason noted above. And they include quotes from Bill Patzert of JPL. “Patzert said the paper was observational rather than conclusive. ‘What will happen if this new type of El Niño becomes permanent? Will it give us wetter or drier El Niños?’ he asked.” [My boldface.]

Again, El Niño Modoki events are not new.

Since there are a multitude of other papers carrying the press release in one form or another, let’s look at the paper itself.

The Los Angeles Times post includes a link to the paper, which was published in Geophysical Research Letters:

Lee and McPhaden use NINO3 and NINO4 SST anomaly data based on Reynolds OI.v2 SST data. The Reynolds OI.v2 dataset can be accessed through the NOAA NOMADS website:
Since Lee and McPhaden used base years other than the NCDC standard climatology, I’ve used the KNMI Climate Explorer for data. It allows users to select the base years. Link to the KNMI Climate Explorer:

Figure 2, below, is Figure 1 from the Lee and McPhaden. It includes the NINO3 and NINO4 SST anomalies plotted separately. Note the difference in the scales.
Figure 2 = Figure 1 from Lee and McPhaden

If plotted together, Figure 3, the magnitudes of the variations in the two NINO SST anomaly subsets are put in perspective. Any increase in NINO4 (Central Pacific) SST anomalies during El Niño events should be easily overcome by the decrease in NINO3 (Eastern Pacific) SST anomalies, as we shall see.
Figure 3

Lee and McPhaden base their “Intensity” analyses on peak readings of El Niño and La Niña events for the NINO4 (Central Pacific) and NINO3 (Eastern Pacific) regions. And the point of the paper is to illustrate that Central Pacific El Niño events are growing in strength. Refer to Figure 4, which is their Figure 3. Note again that the scales are different, and that they do acknowledge this in the text below the graph. But also note the magnitudes and signs of their “Intensity” trends. The NINO4 (Central Pacific) linear trend for El Niño events is said to be 0.20(+/-0.18) deg C/decade. That’s really the whole point of the paper. There has been an increase in the intensity of Central Pacific El Niño events.
Figure 4 = Figure 3 from Lee and McPhaden

The NINO3 (Eastern Pacific) linear trend is said to be 0.39(+/-0.71) deg C/decade, but that has to be a typographical error, since the trend is negative. They appear to be missing the (all-important) minus sign. If the trend value (0.39) is correct, but the minus sign is missing, then the decrease in the intensity of the Eastern Pacific El Niño events is twice that of the increase in Central Pacific intensity. Since the NINO3 region is larger than the NINO4 region, it means the overall intensity of El Niño events for both regions is decreasing.

The linear trend of the “Intensity” of the NINO4 (Central Pacific) La Niña events are shown to be decreasing, which means they’re increasing in strength, but the linear trend of the NINO3 (Eastern Pacific) La Niña event “Intensity” is of the opposite sign and again it’s twice the value. In summary, looking at the graphs presented in Lee and McPhaden, the overall “Intensities” of El Niño and La Niña events appear to be decreasing.

To confirm that, let’s look at NINO3 and NINO4 SST anomalies where the ENSO-neutral data have been deleted. That is, if the positive anomalies were less than 0.5 deg C, the data were deleted, and if they were greater than -0.5 deg C, the negative anomalies were deleted. It’s an awkward looking graph, Figure 5, with all of the mid-range data missing, but it does illustrate the point. The negative linear trend of the El Niño events based on the NINO3 (Eastern Pacific) data is more than twice the positive trend in the NINO4 (Central Pacific) SST anomalies. The opposite holds true for the La Niña events, indicating the Eastern Pacific La Niña events are becoming less intense at a rate that’s twice the rate that Central Pacific La Niña events are increasing in intensity.
Figure 5

And if we combine the two datasets, Figure 6, the linear trend of the El Niño events is decreasing, indicating they’re becoming less intense. And the La Niña trend is basically flat.
Figure 6

Lee and McPhaden include the following in their Concluding Remarks, “Why these changes are occurring and what accounts for them are important questions. Theories have suggested that the intensity of El Niño could be affected by changes in background conditions such as the depth of the thermocline [e.g., Fedorov and Philander, 2000]. More generally, it is important to know if the increasing intensity and frequency of CP‐El Niño events are related to changes associated with natural decadal-to-multi-decadal variability [e.g., McPhaden and Zhang, 2002; Lee and McPhaden, 2008] or whether the changes are due to anthropogenic greenhouse gas forcing [Yeh et al., 2009].”

But the press release and the press have missed this realistic conclusion.

Friday, August 27, 2010

The Global Coverage of NCDC Merged Land + Sea Surface Temperature Data

I’ve moved to WordPress.  This post can now be found at The Global Coverage of NCDC Merged Land + Sea Surface Temperature Data
There are a numerous blogosphere posts about the global coverage, or lack thereof, of the GISS and Hadley Centre land plus sea surface temperature datasets. Few include the NCDC product. This post provides sample maps from 1880 to 2010 comparing NCDC merged land+sea surface temperature data to those of GISS (GISTEMP with 1200km radius smoothing--LOTI) and Hadley Centre (HADCRUT). It also shows GHCN and ERSST.v3b maps, which are the sources for the NCDC merged product. And this post illustrates how NCDC deletes infilled data in early years, discusses why they delete the data, and shows the very limited impact of the NCDC’s deletion of that data in early years.

The NCDC merged land+sea surface temperature anomaly data is now available through the KNMI Climate Explorer. (Many thanks to Dr. Geert Jan van Oldenborgh.)

Figure 1 compares the July 2010 temperature anomaly map of the NCDC merged Land+Sea Surface Temperature to those of the GISS and Hadley Centre products. I’ve used the base years of 1901 to 2000 for all datasets. These are the base years of the NCDC data, not their dot-covered maps. And the contour levels of the maps were set for a range of -4.0 to 4.0 deg C.

As illustrated, the NCDC does not present data over sea ice. Also, there is a sizeable area of east-central Africa without data during July 2010. And the NCDC does not present Antarctic data. The infilling methods employed by the NCDC provide greater land surface coverage than the Hadley Centre product but less coverage than GISS. The methods used by NCDC are discussed in Smith et al (2008) Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), and in Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997).
Figure 1

GISS includes more Arctic surface station data than NCDC and Hadley Centre. This can be seen in the maps that compare the NCDC GHCN data, the Hadley Centre CRUTEM3 data, and the GISS land surface data with 250km radius smoothing, Figure 2. GISS includes Antarctic surface stations (not illustrated), which are not included in GHCN. And of course, GISS Deletes Arctic And Southern Ocean Sea Surface Temperature Data and extends land surface data out over the oceans to increase coverage in the Arctic and Antarctic.
Figure 2

The GISTEMP combined land plus sea surface temperature dataset with 250km radius smoothing is used to show how little Arctic Ocean sea surface temperature data remains in the GISS product. Refer to the bottom cell in Figure 3. The NCDC and Hadley Centre, on the other hand, include Arctic Ocean Sea Surface Temperature data during seasons with reduced sea ice.
Figure 3

Figures 4 through 8 provide global coverage comparison maps for NCDC, Hadley Centre and GISS surface temperature products from 2010 to 1880. Januarys in 2010, 1975, 1940, 1910, and 1880 are shown. Note how the coverage decreases in early years. The exception is the SST data presented by GISS. Keep in mind that the three SST datasets prior to the satellite era basically use a common source SST dataset, ICOADS. Refer to An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products. The HADSST2 data in the Hadley Centre maps represents the locations of the SST samples. The HADISST and ERSST.v3b datasets used by GISS and NCDC are infilled using statistical methods.
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

The decrease in land surface coverage is not surprising, but the NCDC uses ERSST.v3b SST data for the oceans and that dataset provides complete coverage for the oceans even in early years. This can be seen in Figures 9 through 13. They include the same Januarys as the maps above, but they present the NCDC merged product and the GHCN land surface data and ERSST.v3b sea surface data used by NCDC. The NCDC infilled much of the Sea Surface Temperature data in early years. Why do they then delete so much of it? The answer follows the maps.
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

In Smith et al (2008), Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), the NCDC describes why they delete data from their merged product. On page 6, under the heading of “Sampling cutoffs for large-scale averaging”, they write, “The above results show that the reconstructions can be improved in periods with sparse sampling. However, there can still be damping errors in periods with sparse sampling. Damping of large-scale averages may be reduced by eliminating poorly sampled regions because anomalies in those regions may be greatly damped. In Smith et al. (2005) error estimates were used to show that most Arctic and Antarctic anomalies are unreliable and those regions were removed from the global average computation. Here testing using the simulated data is done to find objectively when regions should be eliminated from the global average to minimize the MSE [global mean-squared error] of the average compared to the full data.” Smith et al then go on to describe the criteria for deleting the data in poorly sampled regions.

Of course, the question that comes to mind is, what impact does deleting the all of that SST data have on the long-term trends? Answer: very little. Figures 14 and 15 compare SST data for the NCDC merged product and the source SST data in the North and South Pacific. The coordinates (illustrated on the graphs) were chosen to capture large portions of those ocean subsets, while making sure they were free of influences from land surface data and sea ice. As shown, the NCDC merged data become much more volatile during periods of reduced coverage, but there is little impact on the long-term trends.
Figure 14
Figure 15

Makes one wonder, doesn’t it?

The data and maps are available through the KNMI Climate Explorer:

Monday, August 23, 2010

Mid-August 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at Mid-August 2010 SST Anomaly Update
NOTE: The weekly OI.v2 SST data is available in two periods through the NOAA NOMADS website, from November 1981 to 1989, and from 1990 to present. The mid-month posts now include the full term of the NINO3.4 and Global SST anomalies from 1990 to present and a shorter-term view from 2004 to present to make the recent wiggles easier to see.



NINO3.4 SST anomalies for the week centered on August 18, 2010 show that central equatorial Pacific SST anomalies have reached an initial plateau. Presently they’re at -1.1 deg C, which is 0.21 deg C higher than the lowest value that occurred three weeks ago.
NINO3.4 SST Anomalies
NINO3.4 SST Anomalies - Short-Term


Weekly Global SST anomalies are still elevated and are firmly planted in a mid-year stall. They don’t appear to want to rise or fall. Will they make a sizable drop as they had during 2007?
Global SST Anomalies
Global SST Anomalies - Short-Term


And for those wondering where the present NINO3.4 SST anomalies stack up against prior La Niña events, I’ve provided the following comparison.
Comparison Of La Niña Evolution – 2010 Versus 1988, 1998, and 2007

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

Thursday, August 19, 2010

On Liu and Curry (2010) “Accelerated Warming of the Southern Ocean and Its Impacts on the Hydrological Cycle and Sea Ice”

The Liu and Curry (2010) paper has been the subject of a number of posts at Watts Up With That over the past few days. This post should complement Willis Eschenbach’s post Dr. Curry Warms the Southern Ocean, by providing a more detailed glimpse at the availability of source data used by Hadley Centre and NCDC in their SST datasets and by illustrating SST anomalies for the periods used by Liu and Curry. I’ve also extended the data beyond the cutoff year used by Liu and Curry to show the significant drop in SST anomalies since 1999.

Preliminary Note: I understand that Liu and Curry illustrated the principal component from an analysis of the SST data south of 40S, but there are two primary objectives of this post as noted above: to show how sparse the source data is and to show that SST anomalies for the studied area have declined significantly since 1999.

On the Georgia Tech on: “the paradox of the Antarctic sea ice” thread at WUWT, author Judith Curry kindly linked a copy of the paper in manuscript form:

Liu and Curry use two Sea Surface Temperature datasets, ERSST and HADISST. They clarify which of the NCDC ERSST datasets they used with their citation of Smith TM, Reynolds RW (2004) Improved Extended Reconstruction of SST (1854-1997). J. Clim. 17:2466-247. That’s the ERSST.v2 version. First question some readers might have: If ERSST.v2 was replaced by ERSST.v3b, why use the old version? Don’t know, so I’ll include both versions in the following graphs.

Liu and Curry examine the period of 1950 to 1999. Sea surface temperature data south of 40S is very sparse prior to the satellite era. The HADISST data began to include satellite-based SST readings in 1982. Considering the NCDC deleted satellite data from their ERSST.v3 data (making it ERSST.v3b) that dataset and their ERSST.v2 continue to rely on very sparse buoy- and ship-based observations. ICOADS is the ship- and buoy-based SST dataset that serves as the source for Hadley Centre and NCDC. Figure 1 shows typical monthly ICOADS SST observations for the Southern Hemisphere, south of 40S. The South Pole Stereographic maps are for Januarys in 1950, 1960, 1970, 1980, 1990 and 2000. Since I wanted to illustrate locations and not values, I set the contour levels so that they were out of the range of the data. I used Januarys because it is a Southern Hemisphere summer month and might get more ship traffic along shipping lanes.
Figure 1

As you can see, there is very little data as a starting point for Hadley Centre and NCDC, but they do manage to infill the SST data using statistical tools. Refer to Figure 2. It shows that the three SST datasets provided complete coverage in 1950 and 1999, which are the start and end years of the period examined by Liu and Curry. For more information on the ERSST and HADISST datasets refer to my post An Overview Of Sea Surface Temperature Datasets Used In Global Temperature Products.
Figure 2

A question some might ask, why did Liu and Curry end the data in 1999? Dunno.

As noted above, Liu and Curry illustrate data for the latitudes south of 40S. There are differences of opinion about what makes up the northern boundary of the Southern Ocean. Geography.com writes about the Southern Ocean, “A decision by the International Hydrographic Organization in the spring of 2000 delimited a fifth world ocean - the Southern Ocean - from the southern portions of the Atlantic Ocean, Indian Ocean, and Pacific Ocean. The Southern Ocean extends from the coast of Antarctica north to 60 degrees south latitude, which coincides with the Antarctic Treaty Limit. The Southern Ocean is now the fourth largest of the world's five oceans (after the Pacific Ocean, Atlantic Ocean, and Indian Ocean, but larger than the Arctic Ocean).”

But isolating the Southern Ocean for climate studies really isn’t that simple. The Antarctic Circumpolar Current (ACC) is said to isolate the Southern Ocean from the Atlantic, Indian and Pacific Oceans. Unfortunately, the northern boundary of the ACC varies as it circumnavigates the ocean surrounding Antarctica. Refer to the University of Miami Antarctic CP current webpage.

In this post, I’ll illustrate the SST anomalies of the area south of 40S that was used by Liu and Curry. They capture additional portions of the ocean within the Antarctic Circumpolar Current. (They also capture small areas north of the ACC.) And I’ll identify that data as the Mid-to-High Latitudes of the Southern Hemisphere (90S-40S).

I’ll also illustrate the SST anomalies of the Southern Ocean, as defined above (south of 60S), because they capture the Sea Surface Temperature anomalies of the Southern Ocean most influential on and influenced by Sea Ice. Let’s look at that data first.


Figure 3 compares the three versions of Southern Ocean (90S-60S) SST anomalies, from January 1950 to December 1999, the same years used by Liu and Curry. Included are ERSST.v2, which Is used in Liu and Curry, ERSST.v3b which is the current version of that dataset, and the HADISST data, also used in Liu and Curry. All three datasets are globally complete. And as shown in Figure 1, the Hadley Centre and NCDC have to do a significant amount of infilling to create spatially complete data for those latitudes. The data has been smoothed with a 13-month running-average filter to reduce the noise. Also shown are the linear trends. Again, this is not the full area of the Southern Hemisphere SST data used by Liu and Curry. I’ve provided it because it presents data that is more impacted by (and has more of an impact on) Sea Ice. The linear trend of the ERSST.v2 is almost twice that of the HADISST data. Note also the change in the variability of the HADISST data after the late 1970s. HADISST has used satellite data since 1982 and this helps capture the variability of the Southern Ocean SST anomalies.
Figure 3

Figure 4 shows the Southern Ocean SST anomalies for the ERSST.v2, ERSST.v3b, and HADISST from January 1950 to December 2009, with the data smoothed with a 13-month filter. The HADISST data peaked in the early 1990s and has been dropping since. This was not easily observed with the shortened dataset. The two ERSST datasets peaked in the early 1980s. For all three datasets, the recent declines in the SST anomalies have caused their linear trends to drop sharply from the values presented in Figure 3. In fact, the HADISST is now basically flat.
Figure 4


Figure 5 is a comparison graph of the SST anomalies for the latitudes (90S-40S) and years (1950-1999) used by Liu and Curry. Note how there are two distinctive periods when there are sharp rises in SST anomalies: from 1966 to 1970 and from 1974 to 1980. Then from 1980 to 1999 the SST anomalies for the mid-to-high latitudes of the Southern Hemisphere flattened considerably. The HADISST data flattened more than the ERSST datasets.
Figure 5

In Figure 6, I’ve included the data through December 2009. Note the significant drops in the SST anomalies in all three datasets. All three peaked in 1997 (curiously before the peak of the 1997/98 El Niño), and have been dropping sharply since then.
Figure 6

The title of Liu and Curry (2010) “Accelerated Warming of the Southern Ocean and Its Impacts on the Hydrological Cycle and Sea Ice” contradicts the SST anomalies of the latitudes used in the paper. The SST anomalies are not warming. They are cooling and have been for more than a decade.


The Maps were created using, and the data is available through, the KNMI Climate Explorer:

Monday, August 16, 2010

An Introduction To ENSO, AMO, and PDO -- Part 2

I’ve moved to WordPress.  This post can now be found at An Introduction To ENSO, AMO, and PDO — Part 2
An Introduction To ENSO, AMO, and PDO – Part 1 provided a detailed description the El Niño-Southern Oscillation (ENSO). This post, Part 2, discusses the Atlantic Multidecadal Oscillation (AMO), its impact on Northern Hemisphere surface temperatures, and a disagreement on the cause. The Pacific Decadal Oscillation (PDO) is discussed in An Introduction To ENSO, AMO, and PDO -- Part 3.

The NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage refers readers to the Wikipedia Atlantic Multidecadal Oscillation webpage, so we’ll start there. Wikipedia initially defines the AMO as, “The Atlantic multidecadal oscillation (AMO) is a mode of variability occurring in the North Atlantic Ocean and which has its principal expression in the sea surface temperature (SST) field.” In other words, it’s a variation in the sea surface temperatures of the North Atlantic Ocean.

Figure 1 compares Global and North Atlantic Sea Surface Temperature anomalies from January 1870 to May 2010. The data has been smoothed with a 37-month filter to reduce the signal noise. The Sea Surface Temperature anomalies of the North Atlantic appear to exaggerate the rises and falls of the global data. Smoothing both datasets with a 121-month filter, Figure 2, helps to show the extra variability of the North Atlantic.

Figure 1
Figure 2

Wikipedia continues, “While there is some support for this mode in models and in historical observations, controversy exists with regard to its amplitude, and in particular, the attribution of sea surface temperatures in the tropical Atlantic in areas important for hurricane development.” Hurricane development will not be discussed in this post. And the phrase “some support” does not display a high level of confidence.

Back at the ESRL Atlantic Multidecadal Oscillation webpage, they describe how they calculate the Atlantic Multidecadal Oscillation (AMO) data. Basically, they detrend the sea surface temperature anomalies of the North Atlantic. To detrend the North Atlantic Sea Surface Temperature anomalies, the monthly values of the linear trend are subtracted from the North Atlantic SST anomalies. Refer to Figure 3.
Figure 3

Note the ESRL webpage also provides the AMO data smoothed with a 121-month filter, so my use of a filter of that length is not unusual.

The ESRL uses Kaplan SST data for their AMO dataset. In this post, I’ve used HADISST data because it is available through the KNMI Climate Explorer from 1870 to present. The Kaplan data there ends in 2003, and, therefore, I would not have been able to create many of the graphs in this post that run to May 2010 using it. There are some minor differences between the Kaplan and HADISST-based presentation of the AMO, Figure 4, but they will have no effect on this post.
Figure 4

Wikipedia further defines the AMO: “The AMO was identified in 2001 by Goldenberg et al and named the ‘Atlantic multidecadal mode’.”

So the AMO has been studied for less than a decade.

Wikipedia continues, “The AMO signal is usually defined from the patterns of SST variability in the North Atlantic once any linear trend has been removed. This detrending is intended to remove the influence of greenhouse gas-induced global warming from the analysis.

The detrending has been discussed. The assumption in the second of those two sentences is that anthropogenic greenhouse gases have a measurable effect on Sea Surface Temperatures. Big assumption, considering that longwave radiation can only penetrate the top few millimeters of the ocean surface.

Wikipedia further states, “However, if the global warming signal is significantly non-linear in time (i.e. not just a smooth increase), variations in the forced signal will leak into the AMO definition. Consequently, correlations with the AMO index may alias effects of global warming.”

Let’s discuss the linear versus non-linear global warming signals. Global and North Atlantic SST anomalies were compared in Figures 1 and 2. In Figure 5, the difference between the two datasets is also illustrated, using the 121-month filter, with the Global SST anomalies being subtracted from the SST anomalies of the North Atlantic. The global signal is non-linear, but the difference between the Global SST anomalies and North Atlantic data (the green curve) shows multidecadal variability.
Figure 5

Figure 6 compares the AMO and the difference between the Global and North Atlantic SST anomalies. The two signals show similar variability over the term of the data, but the changes in the AMO data have higher amplitudes. The difference between the Global and North Atlantic data also flattens from approximately 1975 to 1990, indicating that the global and North Atlantic SST anomalies are changing at the same rate during that period. This is not captured by the AMO data.
Figure 6

RealClimate defines the Atlantic Multidecadal Oscillation (“AMO”) as, “A multidecadal (50-80 year timescale) pattern of North Atlantic ocean-atmosphere variability whose existence has been argued for based on statistical analyses of observational and proxy climate data, and coupled Atmosphere-Ocean General Circulation Model (“AOGCM”) simulations. This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and some, but not all, of the high-latitude warming observed in the late 20th century. The term was introduced in a summary by Kerr (2000) of a study by Delworth and Mann (2000).”

“[S]ome, but not all” is not very helpful. Let’s start by comparing the SST anomalies of the North Atlantic to the Global SST anomalies that have had the North Atlantic data removed, Figure 7. To remove the North Atlantic SST anomalies, we’ll assume the surface area of the North Atlantic represents 15% of the global oceans. The data starts in 1975 to capture the “warming observed in the late 20th century”. As illustrated, the linear trend of the North Atlantic SST anomalies from January 1975 to May 2010 is approximately 3.4 times the linear trend of the remaining global oceans. In other words, the additional rise of the North Atlantic SST anomalies caused by the AMO represents a significant portion of the rise in global temperatures.
Figure 7

RealClimate limits their discussion to the high latitudes of the Northern Hemisphere. But let’s examine the Land Plus Ocean Temperature anomaly data for the entire Northern Hemisphere (0-90N). It should provide a good comparison with the North Atlantic Sea Surface Temperature anomaly data. The GISTEMP LOTI dataset will be used. That’s the GISTEMP land plus ocean surface temperature data with 1200km radius smoothing. Refer to Figure 8, which compares those two datasets from 1975 to present. Combined GISTEMP land plus sea surface temperature anomalies mimic the sea surface temperature anomaly variations. The combined land plus ocean dataset exaggerates the variations in the North Atlantic Sea Surface Temperature data, but the linear trend of the combined land plus sea surface temperature data is only (approximately) 20% higher than the trend of the North Atlantic SST anomalies.
Figure 8

I’ve used the GISS combined land and sea surface data in this example because the Atlantic Multidecadal Oscillation FAQ webpage of the NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) Physical Oceanography Division (PhOD) illustrates a correlation between the AMO and large portions of the Pacific Ocean. That correlation map is shown in Figure 9. I’ve provided only the lower half of the illustration from their webpage linked here:

Under the question “How much of the Atlantic are we talking about?” NOAA AOML writes, “Most of the Atlantic between the equator and Greenland changes in unison. Some area of the North Pacific also seem to be affected.”
Figure 9

Wikipedia notes, “In models, AMO-like variability is associated with small changes in the North Atlantic branch of the Thermohaline Circulation, however historical oceanic observations are not sufficient to associate the derived AMO index to present day circulation anomalies.”

That is a curiously written sentence. It is open to numerous interpretations, and that is not helpful, especially for a technical resource. Are they implying that additional forcings from anthropogenic greenhouse gases would be “sufficient to associate the derived AMO index to present day circulation anomalies”? They don’t state that. Or does it mean the models can’t explain all of the variability because causes and effects are not well understood?

Wikipedia provides a description of Thermohaline Circulation. There’s no reason to repeat it for this post, since it fails to provide a detailed description of the impact of Thermohaline Circulation on the AMO.

Back to the Wikipedia statement: As noted above, they write, “In models, AMO-like variability is associated with small changes in the North Atlantic branch of the Thermohaline Circulation.”

In an earlier post, Atlantic Meridional Overturning Circulation Data, I illustrated what appears to be a rough correlation between ENSO and the North Atlantic surface and subsurface flow based on a reconstruction of Atlantic Meridional Overturning Circulation at 26N. Refer to Figure 10 (which is Figure 6 in the post Atlantic Meridional Overturning Circulation Data). Note that the AMOC data was inverted (multiplied by -1) in Figure 9 to show how the AMOC flow appears to slow in response to El Niño events.
Figure 10

So ENSO events appear to be capable of impacting the rate at which North Atlantic Ocean currents transport water northward as part of Thermohaline Circulation, yet I have not found a paper that discusses this.

Foltz and McPhaden (2008) discussed the interaction between Sahel precipitation, Saharan dust, downward shortwave radiation (visible light from the sun), and their impact on Sea Surface Temperatures of the North Atlantic (and the AMO). Link to Foltz and McPhaden (2008) “Trends in Saharan dust and tropical Atlantic climate during 1980–2006”:

Foltz and McPhaden write in their Abstract, “Trends in tropical Atlantic sea surface temperature (SST), Sahel rainfall, and Saharan dust are investigated during 1980–2006. This period is characterized by a significant increase in tropical North Atlantic SST and the transition from a negative to a positive phase of the Atlantic multidecadal oscillation (AMO). It is found that dust concentrations over western Africa and the tropical North Atlantic Ocean decreased significantly between 1980 and 2006 in association with an increase in Sahel rainfall. The decrease in dust in the tropical North Atlantic tended to increase the surface radiative heat flux by 0.7 W/m^2 which, if unbalanced, would lead to an increase in SST of 3 deg C. Coupled models significantly underestimate the amplitude of the AMO in the tropical North Atlantic possibly because they do not account for changes in Saharan dust concentration.”

In other words, studies that fail to account for the multiple interactions between North Atlantic Sea Surface Temperature (AMO), Sahel precipitation, Saharan Dust, and Downward Shortwave Radiation may not be able to properly account for the rise in North Atlantic Sea Surface Temperature.


Figure 11 is a comparison graph of North Atlantic and scaled NINO3.4 SST anomalies from January 1975 to May 2010. Note that the NINO3.4 SST anomaly data were scaled by a factor of 0.15 and they were shifted back in time (lagged) so that the changes in NINO3.4 SST anomalies align with the response by the North Atlantic SST anomalies. It is obvious that North Atlantic SST anomalies rise and fall in response to ENSO events. North Atlantic Sea Surface Temperature anomalies vary due to the changes in atmospheric circulation caused by the ENSO events. This is discussed in more detail in An Introduction To ENSO, AMO, and PDO – Part 1.

Unfortunately, due to the differences in the slopes of the two curves in Figure 11, some divergences are difficult to see.
Figure 11

Figure 12 is a comparison graph similar to Figure 11, but the North Atlantic SST anomalies have been detrended in Figure 12. The similarities and differences between the variations in NINO3.4 and North Atlantic SST anomalies are more obvious. North Atlantic SST anomalies respond to some ENSO events but not others. At times the North Atlantic SST anomalies exaggerate an ENSO signal, at times (especially during La Niña events) it fails to respond fully to the ENSO event, and at times the North Atlantic SST anomalies can be out of synch with NINO3.4 SST anomalies.
Figure 12

In Figures 13, 14, and 15, I’ve added three sets of notes to Figure 12. The differences between the scaled NINO3.4 and North Atlantic SST anomalies from 1982 to about 1986 and from 1991 to approximately 1996 are highlighted in Figure 13. These divergences are explained by the explosive volcanic eruptions of El Chichon and Mount Pinatubo.
Figure 13

In Figure 14, there are a number of periods circled. Prior to 1976, the North Atlantic SST anomalies do not appear to have dropped significantly in response to the 1973/74/75/76 La Niña. But immediately after, from 1979 to 1981, there is a significant rise in North Atlantic SST anomalies that appears to be an exaggerated response to a minor warming in the NINO3.4 region—a warming that is not strong enough to register as an El Niño. Then, after 2002, the year-to-year variations in North Atlantic SST anomalies are out of synch with the NINO3.4 SST anomalies. Why? These divergences are possibly the result of sea level pressure variations, which can have a strong impact on Sea Surface Temperatures.
Figure 14

Figure 15 highlights the two periods when North Atlantic SST anomalies failed to respond fully to La Niña events. If there are epochs when North Atlantic SST anomalies rise in response to El Niño events, but fail to respond fully to La Niña events, North Atlantic SST anomalies will increase. And with the rise in North Atlantic SST anomalies comes the corresponding rise in Northern Hemisphere land surface temperatures and, based on the correlation map shown above in Figure 9, a corresponding rise in North Pacific SST anomalies.
Figure 15

Figure 16 is a graph taken from the post Reproducing Global Temperature Anomalies With Natural Forcings. Basically, in that post I showed how the underlying curve of Global Land and Sea Surface Temperature anomalies can be reproduced using a simple integral (a scaled running total) of NINO3.4 SST anomalies. The assumption is that the oceans integrate the effects of El Niño and La Niña events.

The curiosity: An AMO signal was not needed to reproduce the global temperature anomaly curve. Does this imply that the AMO is an aftereffect of ENSO in the North Atlantic--that the North Atlantic integrates ENSO?
Figure 16

And for those wondering if I had cherry-picked the Hadley Centre data for the post Reproducing Global Temperature Anomalies With Natural Forcings, the post also illustrates the ability to reproduce the GISS and NCDC global temperature anomaly curves. Refer to Figures 15 and 16 in that post.

A final note about the AMO: Many bloggers will write that the AMO has been positive since 1995. They also imply the AMO contributes to the rise in global temperatures only after that year. And they conclude it will be 30 years after 1995, assuming a 60-year AMO cycle, before the AMO “turns negative” again. But the actual basis for the additional contribution of the North Atlantic is the fact that the North Atlantic SST anomalies are rising faster than global SST anomalies, not that the AMO is positive. Refer again to the green curve in Figure 5.

Note: The warm and cold phases of the AMO are, however, used by climate scientists to explain shifts in North American precipitation patterns.

Looking back at all of the graphs of North Atlantic SST anomalies with 13-month and 37-month filters, it is difficult to tell if the North Atlantic SST anomalies and AMO have recently peaked. The North Atlantic SST anomaly data is simply too volatile. However, there is a dataset that represents, in part, the temperature of the top 700 meters of the oceans, and that dataset is much more stable. It is the National Oceanographic Data Center (NODC) Ocean Heat Content (OHC) data. Refer to the NODC Global Ocean Heat Content webpage. Breaking the oceans down into the individual ocean basins, Figure 17, reveals that the drop in the North Atlantic Ocean Heat Content since 2004/05 is the major cause of the recent decline in Global Ocean Heat Content. Is this an indication that the AMO has recently peaked?
Figure 17

The Atlantic Multidecadal Oscillation is a recently discovered mode of Sea Surface Temperature variability for a significant portion of the global oceans. Climate studies provide different causes for the additional strength of the changes in North Atlantic SST anomalies: some blame the Atlantic branch of Thermohaline Circulation, while another discusses the multiple interactions between Saharan dust, Sahel precipitation, solar radiation, and Atlantic Sea Surface Temperature. While cause may be debatable, its impact on Northern Hemisphere sea surface and land surface temperature is clear.

HADISST data is available through the KNMI Climate Explorer:


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