Bob Tisdale suggests that the way some folks have combined the PDO and AMO datasets t produce a new curve is wrong, and here is his supporting analysis. – Anthony
Guest post by Bob Tisdale
Including A Discussion Of Its Use In Wyatt el al (2011)
REFER TO THE UPDATE AT THE END OF THE POST
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UPDATE 2 (July 15, 2011): This update clarifies that my comments about Wyatt et al (2011) pertained only to an illustration in the poster and not to the paper itself. The illustration in the poster does not appear in the paper. And the update also provides links to the comments by Marcia Wyatt (the lead author of the paper) on the cross post at WattsUpWithThat. This update begins after Figure 6, under the heading of “WYATT ET AL (2011)”.
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INTRODUCTION
Graphs that illustrate the sum of the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) data, Figure 1, have appeared in blog posts for more than three years. The first example I can find appeared in a pdf document written by Joe D’Aleo of IceCap: US Temperatures and Climate Factors since 1895. It appeared shortly after in the January 25, 2008 post Warming Trend: PDO And Solar Correlate Better Than CO2 at Watts Up With That? and in numerous posts since then. More recently, the AMO+PDO dataset appeared in the May 22, 2011 post Arctic Cycles – AMO+PDO corresponds to Arctic station group and was referred to in the June 2, 2011 post “Earth itself is telling us there’s nothing to worry about in doubled, or even quadrupled, atmospheric CO2″, which was also posted at Jeff Id’s blog The Air Vent as Future Perfect. A description of the “AMO+PDO” dataset and a link to a spreadsheet can be found at the January 25, 2008 at 9:13 pm comment from the first post at WUWT. A variation on the AMO+PDO graph has also recently found its way into a poster for Wyatt et al (2011) paper Atlantic Multidecadal Oscillation and Northern Hemisphere’s climate variability.
Figure 1
The AMO+PDO curve has been compared to a number of surface temperature variables. Unfortunately, the AMO and PDO datasets cannot be summed.
THE AMO IS DETRENDED SST ANOMALY DATA, BUT THE PDO IS NOT
The AMO data through the NOAA Earth System Research Laboratory (ESRL) AMO webpageis detrended North Atlantic Sea Surface Temperature (SST) Anomalies. (For those who would like an explanation of detrending, refer to the discussion of Figure 6 below.) The PDO, on the other hand, is the product of a principal component analysis of detrended North Pacific SST anomalies, north of 20N. Basically, the PDO represents the spatial patterns of the North Pacific SST anomalies that are similar to those created by El Niño and La Niña events. Since the responses of the North Pacific SST anomalies to El Niño and La Niña events are also impacted by Sea Level Pressure, the PDO and El Niño-Southern Oscillation (ENSO) proxies like NINO3.4 SST anomalies can differ at times.
If one were to detrend the SST anomalies of the North Pacific, north of 20N (the same method used to create the AMO data), standardize it, and compare it to the PDO, the two curves (smoothed with an 11-year filter) appear to be inversely related, Figure 2.
Figure 2
In fact, if we invert the PDO data, multiply it by -1, Figure 3, we can see that they are inversely related and that the detrended North Pacific SST anomalies lead the inverted PDO data for much of the time. That inverse relationship indicates that, over decadal time periods, when the PDO is rising, the detrended SST anomalies are falling and vice versa.
Figure 3
In short, the PDO is an abstract form of North Pacific Sea Surface Temperature data that does not represent the Sea Surface Temperature of the North Pacific. For that reason, it cannot be used to determine the impact of the North Pacific SST on Global Temperatures.
THE “AMO+PDO” DATA AND ITS COMPONENTS
There’s another curious thing about the “AMO+PDO” dataset that can be seen if we plot it along with the AMO and the PDO data used to create it. Refer to Figure 4. Notice how the AMO minimum in the early 1900s is much lower than the minimum in the 1970s. It should not be if the North Atlantic SST anomalies have been detrended.
Figure 4
Figure 5 shows the current AMO data from the NOAA/ESRL website smoothed with an 11-year filter. The early 20thCentury minimum should be comparable to the 1970s minimum.
Figure 5
THE AMO DATA USED IN THE “AMO+PDO” SPREADSHEET IS ACTUALLY NORTH ATLANTIC SST DATA
As noted earlier, the NOAA Earth System Research Laboratory (ESRL) calculates the Atlantic Multidecadal Oscillation (AMO) data by detrending North Atlantic SST anomalies. They use the coordinates of 0-70N, 80W-0 for the North Atlantic. The current version of the ESRL AMO data can be found at ESRL : PSD : Download Climate Timeseries: AMO SST. It’s identified on the webpage as the “AMO (Atlantic Multidecadal Oscillation) Index”.
The AMO data used in the “AMO+PDO” spreadsheet and graphs has not been detrended. In other words, it’s “raw” North Atlantic SST anomaly data. The NOAA ESRL/PSD appears to have changed how they present AMO data sometime between 2007/08 and now. They note on the Atlantic Multi-decadal Oscillation portion of their Climate Indices webpage, “this index is newly computed from a new dataset. Please use it and note that it supersedes the old indices. The data is calculated from the Kalplan SST. See the AMO webpagefor more details.” Or the ESRL had two AMO datasets available online back in 2007/08.
The older version of the ESRL/PSD AMO data used in the AMO+PDO dataset (through 2006) is still available online: AMO(unsmoothed): Standard PSD Format. It’s linked to the AMO – NOAA Earth System Research Laboratorywebpage. They list the source as “Calculated from the HadISST1.” That’s wrong. The linked data is based on Kaplan SST data, not HADISST. They also note that the data is “Area averaged SST in the Atlantic north of 0”. There’s no mention of detrending.
A NOTE ABOUT DETRENDING
For those who are unsure what I’ve meant by detrending, refer to Figure 6. It’s a graph borrowed from my post An Introduction To ENSO, AMO, and PDO — Part 2.The trend of the SST anomalies is determined, and the trend values are subtracted from the SST anomaly data, “flattening” the trend.
Figure 6
WYATT ET AL (2011)
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UPDATE 2 (July 15, 2011): The following discusses an illustration from the poster for Wyatt et al (2011). In an earlier update that I placed at the end of this post, I had noted that the illustration from the poster does not appear in the paper, but since that update is at the end of the post, many readers may have missed the clarification. With this update, I wanted to reinforce that my comments are not about the Wyatt et al (2011) paper; they are about an illustration from the poster that did not appear in the paper.
I also want to call your attention to the comments by Marcia Wyatt (lead author of Wyatt et al) on the WattsUpWithThat cross post of On The AMO+PDO Dataset. Marcia’s first comment appears at June 8, 2011 at 8:52 pm . And for those interested, I replied to Marcia here: June 8, 2011 at 10:26 pm. Refer also to her additional comments at June 11, 2011 at 9:30 am , and recently on the thread starting at July 6, 2011 at 7:47 am .
Back to the original post.
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I was asked to comment on the Wyatt et al (2011) AMO+PDO graph included in their poster, Figure 7. Unfortunately, there’s very little discussion of the graph on the poster and there’s a paywall on the paper, so I have no means of verifying the sources of the data. But…
Figure 7
The same basic problems (the PDO does not represent the SST anomalies of the North Pacific and the PDO is inversely related to the detrended SST anomalies of the North Pacific) apply to the Wyatt et al (2011) AMO+PDO graph.
In addition to that, the note in the poster that “the NH [Northern Hemisphere] surface temperature time series can be nearly perfectly represented as the weighted sum of the AMO and PDO reconstructions” raises a red flag for me. Nearly perfectly? There are significant differences between SST datasets. The Sea Surface Temperature dataset that’s part of the combined surface temperature dataset must be used if a nearly perfect fit is to have any meaning. That is, for example, when referring to the Hadley Centre’s HADCRUT Land+Sea Surface Temperature data, AMO and PDO data based on HADSST2 should be used. Since there’s the paywall on the paper, I can’t confirm if Wyatt et al used the related SST data to create their AMO and PDO data, so the following portion of this discussion (Figures 8 and 9) is for example only.
For AMO data, Wyatt et al refer to Enfield (2001), which used detrended Kaplan SST anomaly data for the North Atlantic. But there are no surface temperature datasets that use Kaplan SST. (This was one of the problems that Tamino had encountered in his AMO post.) ERSST.v3b data is used by NCDC. GISS uses HADISST and Reynolds OI.v2 SST data for their combined surface temperature products. And HADSST2 is used in the Hadley Centre’s HADCRUT. And the differences between Kaplan and the three SST datasets used in Surface Temperature data can be significant, as shown in Figure 8. (For those wondering why the AMO minimums in the 1970s is lower than the early 20thCentury minimums in this graph, I’ve detrended the data starting in 1900.)
Figure 8
If the PDO could be combined with the AMO data (it can’t), using the correct PDO dataset would also be important. Unfortunately, the PDO data was not referenced in the Wyatt et al poster or their guest post at Roger Pielke Sr’s blog. The most-often-used PDO dataset referred to and used in climate studies is the one available through the JISAO website. In fact, Marcia Wyatt’s co-authors Tsonis and Kravtsov referred to the JISAO PDO data in their 2007 paper A new dynamical mechanism for major climate shifts. But, the JISAO PDO data is based on two obsolete SST datasets (UKMO and Reynolds OI.v1) from 1900 to 2001. None of the current surface temperature datasets use UKMO SST or the obsolete Reynolds OI.v1 SST data. And there are again significant differences between the JISAO PDO data and the 1stPrincipal Components of the detrended North Pacific SST data used in the Surface Temperature products, Figure 9.
Figure 9
The other curiosity about the Wyatt et al “AMO+PDO” graph is the weighting: the AMO is multiplied by 0.83 and the PDO by 0.44. The surface area of the North Pacific (20N-65N, 100E-100W) used for the PDO is slightly larger than the North Atlantic (0-70N, 80W-0) surface area used in the AMO. Based on the surface areas, one would expect a weighting of 53% Pacific and 47% Atlantic, or some similarly weighted factors. Again, since the paper is paywalled, I have no idea how Wyatt et al explain the graph, its components or the weighting.
Wyatt et al could replace the PDO data in their AMO+PDO graph with an SST-based ENSO index like NINO3.4 or Cold Tongue Index (CTI) SST anomalies and wind up with similar results. An AMO+ENSO Proxy curve may not fit nearly perfectly with the Northern Hemisphere Temperature anomalies, but that combination should better represent the two indices that impact Northern Hemisphere surface temperature anomalies.
CLOSING QUESTION
The Pacific Decadal Oscillation is a well-established climate index that’s used for many variables other than surface temperature. Unfortunately, many people mistakenly believe it is calculated the same as (and is therefore comparable to) the Atlantic Multidecadal Oscillation. Do we need a new index to represent the multidecadal variability of North Pacific Sea Surface Temperatures? The amplitude of the multidecadal variations in detrended North Pacific SST anomalies is less than the variations in the North Atlantic, Figure 10. And the frequencies are somewhat different, meaning the two datasets can run in and out of synch.
Figure 10
UPDATE (June 8, 2011)
Here’s a curiosity: I just checked all of the illustrations for Wyatt et al (2011). Figure 2 from the poster, which is Figure 7 in this post, the graph that includes the weighted AMO+PDO dataset, does not appear in the paper. I double checked by having Adobe Acrobat do a word search and the phrases “weighted sum” and “AMO and PDO reconstructions” do not appear in the paper.
And for those interested, Wyatt et al used the ESRL AMO data, the JISAO PDO data, and HADCRUT NH surface temperature data.
SOURCES
The current NOAA/ESRL AMO data is available here:
http://www.esrl.noaa.gov/psd/data/timeseries/AMO/
Specifically:
http://www.esrl.noaa.gov/psd/data/correlation/amon.us.long.data
The PDO data is available through the JISAO PDO website:
http://jisao.washington.edu/pdo/
Specifically:
http://jisao.washington.edu/pdo/PDO.latest
All other data is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
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I recall Tsonis writing on this subject:
“Atlantic Multidecadal Oscillation And Northern Hemisphere’s Climate Variability” by Marcia Glaze Wyatt, Sergey Kravtsov, And Anastasios A. Tsonis
http://pielkeclimatesci.wordpress.com/2011/04/21/guest-post-atlantic-multidecadal-oscillation-and-northern-hemisphere%E2%80%99s-climate-variability-by-marcia-glaze-wyatt-sergey-kravtsov-and-anastasios-a-tsonis/
Time-integrated cross-correlation algorithms have utility as initial exploratory tools, but look at the mess Charles Perry (USGS) got himself into by not recognizing the pull of temporally-global extrema on cross-correlation lags. It’s a good place to start, but complex [as in complex numbers, not as in complicated (actually quite simple once one understands)] methods with temporally-local capabilities are needed to avoid nonsensical misinterpretation of lags.
My apologies to Marcia Wyatt, who is the lead author on the paper I referenced (not A. Tsonis).
@SABR Matt (June 10, 2011 at 11:15 am)
“Apart from all other reasons, the parameters of the geoid depend on the distribution of water over the planetary surface.” — from:
Sidorenkov, N.S. (2003). Changes in the Antarctic ice sheet mass and the instability of the Earth’s rotation over the last 110 years. International Association of Geodesy Symposia 127, 339-346.
“The purpose of this paper is to call attention to a close correlation of the decade variations in the Earth rotation with the mass changes in the Antarctic ice sheets.”
“The redistribution of water masses on the Earth entails changes in the components of the Earth’s inertia tensor and causes the motion of poles and changes of the Earth’s rotation speed.”
=
What the mainstream appears to have overlooked is very simple:
Solar max interrupts the semi-annual heat pump. The frequency of pump outages controls multidecadal oscillations (via hydrology). Interannual spatiotemporal chaos makes this difficult or impossible to see using linear methods.
Graphically:
1. http://wattsupwiththat.files.wordpress.com/2010/09/scl_northpacificsst.png
2. http://wattsupwiththat.files.wordpress.com/2010/12/vaughn_lod_fig1b.png
3. http://wattsupwiththat.files.wordpress.com/2010/08/vaughn_lod_amo_sc.png
SCL’ = solar cycle deceleration
=
To develop conceptual understanding:
a) Exposition of p. 433 [pdf p.10]:
Sidorenkov, N.S. (2005). Physics of the Earth’s rotation instabilities. Astronomical and Astrophysical Transactions 24(5), 425-439.
http://images.astronet.ru/pubd/2008/09/28/0001230882/425-439.pdf
(When you have more time, the whole paper deserves careful reading.)
b) Figures 8, 11, 13, & 15 (study them comparatively VERY carefully to “spot the differences” between the pictures…):
Leroux, Marcel (1993). The Mobile Polar High: a new concept explaining present mechanisms of meridional air-mass and energy exchanges and global propagation of palaeoclimatic changes. Global and Planetary Change 7, 69-93.
http://ddata.over-blog.com/xxxyyy/2/32/25/79/Leroux-Global-and-Planetary-Change-1993.pdf
c) Semi-Annual Solar-Terrestrial Power
http://wattsupwiththat.com/2010/12/23/confirmation-of-solar-forcing-of-the-semi-annual-variation-of-length-of-day/
Seminal paper that facilitated dot connection:
Le Mouël, J.-L.; Blanter, E.; Shnirman, M.; & Courtillot, V. (2010). Solar forcing of the semi-annual variation of length-of-day. Geophysical Research Letters 37, L15307. doi:10.1029/2010GL043185.
Excerpts from the paper that should be helpful for newcomers to EOP (Earth Orientation Parameters):
a) “The zonal winds contributing to lod seasonal variations are dominantly low altitude winds.”
b) “[…] solar activity can affect the radiative equilibrium of the troposphere in an indirect way, which cannot be simply deduced from the magnitude of TSI variations.”
c) “The semi-annual oscillation extends to all latitudes and down to low altitudes, as does the annual term. But, unlike the annual term, the main part of the oscillation is symmetrical about the equator; the partial cancellation of the angular momentum of the two hemispheres, which occurs for the annual oscillation, does not happen there [Lambeck, 1980]. Thus, we have here a measure of the seasonal variation of the total angular momentum of the atmosphere of the two hemispheres at the semi-annual frequency.”
d) “When considering separately monthly averages rather than annual ones, differences in the net radiative flux distribution appear, due to the seasonal variation in insulation which is asymmetric with respect to the equator. Seasonal variations of insulation result in seasonal variations of poleward meridional transport, hence of averaged zonal wind.” [Typo: “insulation” should read “insolation”.]
e) “The argument above serves to show that the semiannual variation in lod is linked to a fundamental feature of climate: the latitudinal distribution and transport of energy and momentum.” [Pole-equator contrast drives pumping.]
f) “The solid Earth behaves as a natural spatial integrator and time filter, which makes it possible to study the evolution of the amplitude of the semi-annual variation in zonal winds over a fifty-year time span. We evidence strong modulation of the amplitude of this lod spectral line by the Schwabe cycle (Figure 1a). This shows that the Sun can (directly or undirectly) influence tropospheric zonal mean-winds over decadal to multi-decadal time scales. Zonal mean-winds constitute an important element of global atmospheric circulation. If the solar cycle can influence zonal mean-winds, then it may affect other features of global climate as well […]“ [Typos: 1) “evidence” should read “observe”. 2) “undirectly” should read “indirectly”.]
Bob has answered my above question about HADISST vs. HADSST2 here [ http://bobtisdale.wordpress.com/2011/06/08/on-the-amopdo-dataset/#comment-1976 ].
Bob, the ~1910-1920 discrepancies here [http://i44.tinypic.com/23rk2sh.jpg ] & here [http://i47.tinypic.com/2a4ocqu.png ] roused my suspicions and got me digging here:
Rayner, N.A.; Parker, D.E. ; Horton, E.B.; Folland, C.K.; Alexander, L.V.; Rowell, D.P.; Kent, E.C.; & Kaplan, A. (2003). Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research 108(D14), 4407. doi:10.1029/2002JD002670.
http://www.metoffice.gov.uk/hadobs/hadisst/HadISST_paper.pdf
Figure 3 immediately caught my eye. And:
“[102] The intermonthly autocorrelations in HadISST1 are weakest in 1910-1945 (not shown). A contributing factor may be the very data-sparse periods 1914-1920 and 1940-1945. Coverage in 1871-1909 was often sparse also, but no year had as few data as 1918 (Figure 3). In addition, the El Nino-Southern Oscillation phenomenon, which engenders strong monthly persistence in the tropics and some extra-tropical regions, was strong and coherent in the late 19th century and generally weak and less coherent between roughly 1920 and 1940 [e.g., Allan et al., 1996].” [my emphasis added]
This explains the misleading appearance of linearly detrended North Pacific HADISST [e.g. http://i53.tinypic.com/a0gf2g.jpg ].
Figures 9 & 13 here also shed light on the issue:
Rayner, N.A.; Brohan, P.; Parker, D.E.; Folland, C.K.; Kennedy, J.J.; Vanicek, M.; Ansell, T.J.; & Tett, S.F.B. (2006). Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: the HadSST2 dataset. Journal of Climate 19, 446-469.
http://www.metoffice.gov.uk/hadobs/hadsst2/rayner_etal_2005.pdf
Thanks for the informative reply Bob — appreciated.
corrected links:
[…] ~1910-1920 discrepancies here [ http://i44.tinypic.com/23rk2sh.jpg ] & here [ http://i47.tinypic.com/2a4ocqu.png ] roused my suspicions […]
I took a quick peek at the entries and wanted to reply to those questions directed to me.
Paul Vaughan asked me to clarify the index AT. AT stands for atmospheric-mass transfer. AT relates to large-scale prevailing wind direction over the North Atlantic-Eurasian region. Annual anomalies of predominant wind directionbetween 30 to 80 degrees north are determined from daily examination of pressure maps (cyclonic and anti-cyclonic systems). Positive anomalies reflect a more zonal (easterly/westerly) character; negative ones, a more meridional (northerly/southerly) one. Many of you might be more familiar with the cumulative-sum version of AT, the ACI – Atmospheric Circulation Index (Girs 1974). King et al. 1998 introduced a Pacific-centered analogue to the ACI – the Pacific Circulation Index (PCI) – that reflects integrated behavior of the PDO-related Aleutian Low.
A second point asked was regarding my thoughts on why NHT leads AMO by ~4years. That is the 64,000-dollar question! First, there is no assumption of causality; the observation only indicates lagged covariability. With that said, it is helpful to regard the climate indices – NHT, AMO, AT, NAO, PDO, etc.- as not just the raw climate variables (SSTs, surface Ts, or SLPs) from which the indices are constructed, but rather as a subset or collection of dynamics represented by the climate indices. As an example, recent research suggests AMO is connected to multidecadal variability in frequency of sudden-stratospheric-warmings, which are related to both tropical convective processes and to the integrity of the polar vortex – both features wielding hemispheric influence on the climate. In addition, longitudinal and latitudinal placements of the atmospheric centers-of-action shift with multidecadal variations in AMO, as does the meridional placement of the mean intertropical convergence zone (ITCZ), along with associated changes in Atlantic hurricane activity and in frequency of occurrence of Atlantic-NINOs. Likewise, with multidecadal variability in PDO come changes in placement and strength of atmospheric centers-of-action and in placement and strength of associated oceanic gyres and in meridional mean location of the Pacific ITCZ. These and low-frequency variations in NINO are associated with variations of direction and volume of ocean-flow through the Bering Strait and the Indonesian Through Flow, with cascading influence on the Arctic and Indian Oceans, respectively, thereby influencing salinity and density values of both, with both pathways ultimately influencing the salinity of the Atlantic, which, in turn, influences the vigor of the Meridional Overturning Circulation (MOC). Concomitant phase-related changes in thermocline structure, ocean-heat-flux from the western-boundary currents, and the heat-flux’s effect on overlying storm tracks, in addition to other dynamics (only a handful described here), all play roles in the evolution of NHT.
The list of features associated with these modes and with all the other members within the stadium wave is long. Thus, one can see that the PDO and AMO are not merely interesting or climatologically influential in terms of their SST structure. It is the index-associated dynamics that likely hold the answer to the observed lagged covariance between NHT and AMO.
I hope this is helpful.
Marcia
P.S. I forgot to include the following regarding NHT leading AMO. In our study, we found that the “stadium-wave” climate signal (leading two modes of variability) accounts for a substantial fraction of variance in the Atlantic SST dipole (Keenlyside et al. 2008). MSSA showed the SST-dipole reconstructed index (RC) to be statistically identical to that of the NHT. Both lead a same-signed AMO by about four years. The SST-dipole is considered (albeit not without controversy) to be a proxy for the Atlantic Meridional Overturning Circulation (AMOC).
Paul, I think you quite fairly invited justification of the AMOC-AMO relationship in one of your posts. Our paper discusses this, but a short version follows: As far as controversy linking AMOC to AMO (at a lag), it is true that inconsistent modeling results confuse the issue. It is noteworthy that models with periodicities and boreal-winter atmospheric projections closest to observation seem to require a deep or interactive ocean (ex: Knight et al. 2005; Msadek et al. 2010b). Viewing the matter from a paleoclimate perspective, G. bulloides abundance in the Cariaco Basin is considered to be a proxy for the dipole and for the AMOC (Black et al. 1999) and is supportive of AMO-associated changes co-varying with its abundance. Again, our climate signal accounts for a high fractional variance in the G. bulloides record and its RC coincides with the Atlantic SST dipole and the NHT in our study. Thus, an AMOC-AMO relationship is supported by several lines of evidence, but continues to be an area of active research.