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Thursday, September 30, 2010

PRELIMINARY September 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at PRELIMINARY September 2010 SST Anomaly Update
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Sorry this is late. I’ve been working on a project that’s been occupying my thoughts.

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The September 2010 SST data through the NOAA NOMADS website won’t be official until October 11. 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 September 2010 presented by the NOMADS website. I’ve also included the weekly data through September 22, 2010, but I’ve shortened the span of the weekly data, starting it in January 2004, so that the wiggles are visible.

PRELIMINARY MONTHLY DATA
Based on the preliminary data, monthly NINO3.4 SST anomalies are continuing to drop, and the drop has them at -1.5 deg C.
http://i55.tinypic.com/iwn96d.jpg
Monthly NINO3.4 SST Anomalies

Monthly Global SST anomalies, according to the preliminary data, are still stalled. With the step up in the South Atlantic and its effect on the North Atlantic, it will be interesting to see how much global SST anomalies will decline.
http://i56.tinypic.com/152dser.jpg
Monthly Global SST Anomalies

WEEKLY DATA

The weekly NINO3.4 SST anomaly data have dropped again over the past week. They are still below -1.5 deg C.
http://i53.tinypic.com/a4h07.jpg
Weekly NINO3.4 SST Anomalies

Weekly Global SST Anomalies are still flat. There continue to be some minor wiggles, but the Global SST anomalies are still lagging the drop in NINO3.4 SST anomalies.
http://i51.tinypic.com/2r3l4d4.jpg
Weekly Global SST Anomalies


SOURCES
SST anomaly data is available through the NOAA NOMADS website:
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite=

Monday, September 20, 2010

Mid-September 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at Mid-September 2010 SST Anomaly Update
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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.

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NINO3.4
NINO3.4 SST anomalies for the week centered on September 15, 2010 show that central equatorial Pacific SST anomalies have risen slightly in the past week. Presently they’re at -1.5 deg C.
http://i53.tinypic.com/2zsa2cy.jpg
NINO3.4 SST Anomalies
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http://i51.tinypic.com/2luw95w.jpg
NINO3.4 SST Anomalies - Short-Term

GLOBAL
Weekly Global SST anomalies are still elevated and have continued their decline.
http://i54.tinypic.com/24e3n6w.jpg
Global SST Anomalies
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http://i53.tinypic.com/wnvut.jpg
Global SST Anomalies - Short-Term

COMPARISON TO PAST LA NIÑA EVENTS AND TRANSITIONS
And for those wondering where the present NINO3.4 SST anomalies stack up against past La Niña events, I’ve provided the following comparison. I’ve also provided a comparison of the declines in global SST anomalies in response to the transitions from El Niño to La Niña, using the same years. Note that the first SST anomaly reading for each year has been zeroed, and that all global SST anomalies have been shifted accordingly. The decline in 2010 Global SST anomalies is toward the high side of the mid-range of past events.
http://i51.tinypic.com/n3lz54.jpg
Comparison Of La Niña Evolution – 2010 Versus 1988, 1998, and 2007
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http://i56.tinypic.com/xe3otl.jpg
Comparison Of Global SST Anomalies During La Niña Evolution – 2010 Versus 1988, 1998, and 2007

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

Thursday, September 16, 2010

The Declines In Global Temperatures From El Niño To La Niña

I’ve moved to WordPress.  This post can now be found at The Declines In Global Temperatures From El Niño To La Niña
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Global surface temperature and lower troposphere temperature (TLT) anomalies are dropping this year in response to the transition from El Niño to the La Niña conditions in the tropical Pacific. The temperature anomalies in some datasets are dropping more quickly than others, and I was asked to illustrate the typical declines in global temperatures during these transitions.

In this post, I’ve illustrated the drops in surface temperature and TLT anomalies, starting in January and ending in June of the following year:
--For the five years since 1979 when an “official” El Niño event was followed by a La Niña—1983, 1988, 1995, 1998, 2007,
--For 2010 year to date. The most current month is August, with the exception of the Hadley Centre data, which lags and ends in July.
--For the three surface temperature anomaly datasets--GISS, Hadley Centre, and NCDC, and:
--For the two TLT anomaly datasets--RSS and UAH.

Since the January start temperatures are different for each year and for each dataset, I’ve also zeroed the Januarys for each dataset to provide a better visual comparison.

Last, I’ve averaged and compared the surface temperature anomalies, Figure 12, for the years prior to 2010 for each dataset to show how the average declines of surface temperature datasets differ from the TLT anomaly datasets. This was the graph that I was most interested in producing, but there may be those wanting to see the other comparisons, so I’ll present those first.

SURFACE TEMPERATURE DATASETS
Figures 1 through 3 show the land plus sea surface temperature anomalies since 1979, for the transition years from El Niño to La Niña. Year-to-date 2010 data are also included. Illustrated are Hadley Centre, NCDC, and GISS datasets. The GISTEMP dataset is their Land-Ocean Temperature Index (LOTI), which is the combined dataset with 1200km smoothing. All three datasets show elevated temperature anomalies during 2010 with respect to other El Niño to La Niña transition years. (More on that later.) What struck me were the two apparent epochs before and after 1997. That is, if we look at months 3 through 8, the March through August data, there appears to have been an upward shift in the data after the 1997/98 El Niño.
http://i52.tinypic.com/2411ruh.jpg
Figure 1
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http://i54.tinypic.com/bdpird.jpg
Figure 2
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http://i55.tinypic.com/2e4lq11.jpg
Figure 3
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In Figures 4 through 6, I’ve shifted the datasets so that the January surface temperature anomalies are zero. This should provide for a better visual comparison of the decays in temperatures for those who are interested. The Hadley Centre and NCDC datasets are both showing 2010 as having a slow decline through July and August respectively, but the GISS data is showing a much faster decline, with the 2010 decline now about mid range of the past events.
http://i52.tinypic.com/j6l94g.jpg
Figure 4
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http://i53.tinypic.com/im34td.jpg
Figure 5
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http://i52.tinypic.com/xasak7.jpg
Figure 6
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LOWER TROPOSPHERE TEMPERATURE DATASETS
Figures 7 and 8 show the UAH and RSS Lower Troposphere Temperature (TLT) anomalies for the El Niño to La Niña transition years. The TLT anomalies in 2010 are closer to 1998 than any other year in both datasets. (More on that later.) If not for the early drop in 2007, it would also appear that there are two epochs in the TLT data, before and after the 1997/98 El Niño.
http://i51.tinypic.com/2dsg5yd.jpg
Figure 7
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http://i56.tinypic.com/n1p5bq.jpg
Figure 8
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In Figures 9 and 10, the January TLT anomalies have been zeroed, shifting the data. Unlike the Hadley Centre and NCDC Surface Temperature datasets (Figures 4 and 5), the 2010 TLT anomalies appear to be dropping at rates that leave them mid to low in the range of past events. What really stands out in Figure 9 and 10 is how little the TLT anomalies dropped during 1995.
http://i52.tinypic.com/zmm8aa.jpg
Figure 9
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http://i56.tinypic.com/1zn8pow.jpg
Figure 10
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But as shown in Figure 11, which compares the NINO3.4 SST anomalies for the same years used in this post, the associated 1994/95 El Niño and 1995/96 La Niña events were not very strong in terms of tropical Pacific sea surface temperatures.
http://i56.tinypic.com/2mrb9ll.jpg
Figure 11

SURFACE TEMPERATURE AND TLT AVERAGES
In Figure 12, the 1983, 1988, 1995, 1998 and 2007 Global land plus sea surface and TLT anomalies for each of the datasets have been averaged. As illustrated, the GISS, Hadley Centre and NCDC surface temperature datasets show gradual declines during the transition from El Niño to La Niña. The RSS and UAH TLT anomaly datasets show slower declines through September, then sharp drops from September to January of the following year.
http://i55.tinypic.com/2637osp.jpg
Figure 12

WHY ARE 2010 SURFACE TEMPERATURE AND TLT ANOMALIES NEAR RECORD LEVELS?
Refer again to Figures 1 though 3 for the Surface temperature anomaly datasets and Figures 7 and 8 for Lower Troposphere Temperature anomaly datasets. It’s quite obvious that 2010 global temperature anomalies are near to record levels. And if we refer to Figure 11, for the years included in this post, we can see that the January 2010 NINO3.4 SST anomalies were third highest since 1979; that is, the strength of the 2009/10 El Niño was a distant third compared to the 1982/83 and 1997/98 El Niño events. Some might take the elevated 2010 global anomalies as proof of the continued impact of anthropogenic global warming.

In reality, much of this rise in global temperature is, of course, caused by the fact that the East Indian and West Pacific Sea Surface Temperature (SST) anomalies can rise in response to El Niño AND La Niña events and the fact that these warmings can be cumulative when El Niño and La Niña events occur in sequence. I’ve discussed this in numerous posts, including “More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND... ...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents,” “More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events”, and with animations of numerous datasets in “La Niña Is Not The Opposite Of El Niño – The Videos.”

Also, after fifteen years of relatively flat Sea Surface Temperature anomalies, the South Atlantic SST anomalies shifted upwards in 2009/10. I discussed this shift in the post The 2009/10 Warming Of The South Atlantic, but I still have not found a paper or webpage that presents an anthropogenic cause for it.

And of course, the TLT anomalies of the mid-to-high latitudes of the Northern Hemisphere shifted upwards in response to the 1997/98 El Niño. This was discussed and illustrated with time-series graphs and Hovmoller plots in the post “RSS MSU TLT Time-Latitude Plots...Show Climate Responses That Cannot Be Easily Illustrated With Time-Series Graphs Alone.”

SOURCES
GISS LOTI data:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

Hadley Centre CRUTEM3+HadSST2 data and the HADSST2 data used for the NINO3.4 anomalies are available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

NCDC Land Plus Sea Surface data is available on or about the 3rd of each month through their ERSST.v3b webpage:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo
Specifically for this post:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo/aravg.mon.land_ocean.90S.90N.asc

RSS MSU TLT anomalies:
http://www.remss.com/data/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_2.txt

UAH MSU TLT anomalies:
http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt

Tuesday, September 14, 2010

An Inverse Relationship Between The PDO And North Pacific SST Anomaly Residuals

I’ve moved to WordPress.  This post can now be found at An Inverse Relationship Between The PDO And North Pacific SST Anomaly Residuals
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I recently posted An Introduction To ENSO, AMO, and PDO -- Part 3, which provided a discussion of the Pacific Decadal Oscillation (PDO) for those new to climate and climate change. On the thread, TallBloke left a comment about my description of one of the figures. Unfortunately, the comment is lost in the ether. TallBloke took exception this part of the post, “Comparing the North Pacific Residual to the PDO, Figure 13, the two datasets have no relationship with one another. This means that the contribution of the North Pacific (north of 20N) to Global SST anomalies is independent of the PDO.” I’ve reproduced Figure 13 here as Figure 1. He noted that the two curves appeared to be negatively correlated.

http://i56.tinypic.com/ipcodt.jpg
Figure 1

Note: The North Pacific SST residuals in this post are for the coordinates of 20N-65N, 100W-100E.

Smoothing both the North Pacific Residuals (North Pacific SST anomalies minus Global SST anomalies) and the scaled PDO with 121-month filters, Figure 2, helps to illustrate this. They do appear to be inversely related on decadal timescales.
http://i52.tinypic.com/ipaxjr.jpg
Figure 2

And if we invert the PDO data by using a negative scaling factor (-0.2), Figure 3, the two curves definitely show similar variations over similar time periods.
http://i52.tinypic.com/15oz3eo.jpg
Figure 3

Why would the North Pacific warm faster than Global SST anomalies during periods when the PDO is negative? (This discussion of course relates to the multidecadal variations in both signals as illustrated in Figure 2 and 3. It may not be visible in the yearly variations.) First, for the PDO to be negative over decadal periods, the frequency and magnitude of La Niña events have to exceed the frequency and magnitude of El Niño events, and this is because the PDO represents the ENSO-like pattern of the SST anomalies in the North Pacific, north of 20N. (See note below.) During La Niña events, Pacific trade winds strengthen, which reduces cloud cover over the tropical Pacific. This increases the amount of Downward Shortwave Radiation (visible light) reaching the ocean surface and, in turn, warms the tropical Pacific. The warmer water is pushed to the west by the trade winds and is carried northward by the western boundary current, the Kuroshio Current. Then the warm water is carried eastward by the western boundary current extension, the Kuroshio Extension. This is why there is the area of warm SST anomalies east of Japan during La Niña events. During El Niño events, the trade winds decrease or reverse and less warm water than normal is carried from the tropics up to the Kuroshio Extension.

Note: The PDO also appears to be impacted by changes in sea level pressure. Refer to Is The Difference Between NINO3.4 SST Anomalies And The PDO A Function Of Sea Level Pressure? Would sea level pressures also impact the “gyre spin up” of warm waters from the tropics to the Kuroshio Extension? One would think this could impact the duration of the PDO.

There is also another phenomenon that allows SST anomalies in the Kuroshio Extension to persist for periods longer than ENSO, and it’s called the reemergence. Refer to The Reemergence Mechanism. I’ll also have to add a short sentence about it in the post An Introduction To ENSO, AMO, and PDO -- Part 3

Thanks, TallBloke.

SOURCE

The HADISST data used in this post is available through the KNMI Climate Explorer:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere
The PDO data from JISAO is available through the KNMI Climate Explorer "Climate Indices" webpage, but I used the data directly from the JISAO website for this post:http://jisao.washington.edu/pdo/PDO.latest

Saturday, September 11, 2010

The Multivariate ENSO Index (MEI) Captures The Global Temperature Impacts Of ENSO Differently Than SST-Based Indices

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But Those Differences Are Subtle

I was recently asked to comment on the Multivariate ENSO Index (MEI). (Thanks, d.) This post compares the MEI to HADSST2-based NINO3.4 SST anomalies. It also removes the linear effects of ENSO from the Global Temperature Record to show the effects of the differences when performing that type of analysis. And since I’d brought the analysis that far, I thought I’d carry the post a step farther and show the opposing effects of ENSO that exist in global temperature anomaly data.

HADISST-based NINO3.4 SST anomalies also show similar results, though I have not included them in this post. I used HADSST2 data here because I will reference this post in an upcoming one about a paper in press, and that paper uses HADCRUT and HADSST-based NINO3.4 data in its analysis. (The paper attempts to perpetuate a myth I’ve discussed before.)

INTRODUCTION

The Multivariate ENSO Index (MEI) is a calculated dataset that illustrates the timing and magnitude of El Niño and La Niña events. Other ENSO indices use Sea Surface Temperature (SST) Anomalies of the central and eastern Equatorial Pacific or the sea level pressure difference between Tahiti and Darwin, Australia. The MEI, on the other hand, uses additional variables that are part of the coupled ocean-atmosphere ENSO processes. Wolter and Timlin (1998) in “Measuring the strength of ENSO events - how does 1997/98 rank?” note in the abstract, “The Multivariate ENSO Index (MEI) is favoured over conventional indices, since it combines the significant features of all observed surface fields in the Tropical Pacific.” Link to Wolter and Timlin (1998):
http://www.esrl.noaa.gov/psd/people/klaus.wolter/MEI/WT2.pdf

The MEI is maintained by Klaus Wolter of NOAA. He explains why he believes the MEI is “better for monitoring ENSO than the SOI or various SST indices” on the NOAA MEI timeseries data webpage. (Scroll down to the FAQs.) He writes, “In brief, the MEI integrates more information than other indices, it reflects the nature of the coupled ocean-atmosphere system better than either component, and it is less vulnerable to occasional data glitches in the monthly update cycles.”

AN OVERVIEW OF THE MEI

The NOAA MEI home page provides a further description of the Multivariate ENSO Index (MEI): “El Niño/Southern Oscillation (ENSO) is the most important coupled ocean-atmosphere phenomenon to cause global climate variability on interannual time scales. Here we attempt to monitor ENSO by basing the Multivariate ENSO Index (MEI) on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C).”

With the exception of the surface wind components, all of the above variables should be self explanatory. NOAA describes the U and V surface wind components in their Transport Winds webpage: “The meridional component of the wind, V, is considered positive when the wind [is] blowing from south to north. A south wind has a positive meridional component while a north wind has a negative meridional component. The zonal component of the wind, U, is considered positive when the wind is blowing from west to east. Thus, a west wind has a positive zonal component and an east wind a negative zonal component.” They continue, “For example, a wind that is blowing from the northeast would have a negative meridional component, V, and a negative zonal component, U. Such a wind would have a direction of 45 degrees.”

COMPARING THE MEI TO AN SST-BASED ENSO INDEX
Figure 1 illustrates the MEI data from January 1950 through July 2010. Like the SST-based ENSO indices, El Niño events are represented by positive values and La Niña events are negative. The 1982/83 El Niño event is shown to peak higher than the 1997/98 event. And the 1997/98 El Niño shows a double peak. The NOAA MEI timeseries data webpage presents the MEI data in bimonthly form.

http://i54.tinypic.com/2gwaulv.jpg
Figure 1

Note: The MEI data in this post was downloaded from the KNMI Climate Explorer. I’ve used MEI “anomalies” since they will have the same base years as the NINO3.4 SST anomalies and allow for a more direct comparison in the following. The use of MEI anomalies shifts the MEI data slightly, but that shift has no effect on this post.

Figure 2 compares the MEI data to NINO3.4 SST anomalies based on the HADSST2 dataset. Since the MEI is presented bimonthly, the NINO3.4 SST anomalies were smoothed with a lagging 2-month filter for this illustration. That is, for example, the average of January and February SST anomalies are displayed in February. The major variations in both datasets are similar in timing but they differ in magnitude for each event. Note, also, that the MEI data seems to shift upwards around 1976.
http://i56.tinypic.com/8yzwv7.jpg
Figure 2

If we subtract the NINO3.4 SST anomaly data from MEI, that shift becomes more obvious. Refer to Figure 3. From 1976 to 1980, there is additional rise in the MEI that is not present in the NINO3.4 SST anomalies. There is also some obvious additional variability in the MEI.
http://i51.tinypic.com/vhs1ap.jpg
Figure 3

Averaging the differences between the MEI and NINO3.4 SST anomalies over the periods before and after 1976, Figure 4, provides an idea of the magnitude of that additional variation in the MEI.
http://i53.tinypic.com/15805de.jpg
Figure 4

It appears the MEI should account for some of the rise in global temperatures caused by the 1976/77 Pacific Climate Shift.

REMOVING THE LINEAR EFFECTS OF ENSO FROM GLOBAL TEMPERATURES

The common method used by bloggers and climate scientists to remove the effects of ENSO from the global temperature record is to scale the ENSO index data so that the change in the ENSO Index agrees with the resulting change in global temperatures and to lag the ENSO Index data a few months. Then the ENSO Index data is simply subtracted from the global temperature data. It seems to make sense. Unfortunately, it only accounts for the linear effects of ENSO and does not account for the fact that El Niño and La Niña events can warm parts of the global oceans and that these warmings can be cumulative. I’ve discussed this in numerous posts, including “More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND... ...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents,” “More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events”, and with animations of numerous datasets in “La Niña Is Not The Opposite Of El Niño – The Videos.”

Putting that aside, let’s use the method to illustrate two points: the additional portion of the aftereffects of the 1976 Pacific Climate Shift accounted for by the MEI, and the opposing effects of ENSO events.

For those who have never attempted to remove the linear effects of ENSO from the global temperature record, I’ll run through the process. As discussed above, first we need to scale the ENSO index data so that the change in the ENSO Index agrees with the resulting change in global temperatures. Figure 5 illustrates the Hadley Centre’s HADCRUT land plus sea surface temperature anomalies from 1950 to present. It also illustrates scaled NINO3.4 SST anomalies, which are being used as the reference ENSO Index in this example. To scale them, the NINO3.4 SST anomalies are simply multiplied by a factor, and in this example, I used a scaling factor of 0.18. I’ve also shifted the NINO3.4 SST anomalies upwards 0.12 deg C so that they will align with Global Temperature anomaly data during the evolution phase of the 1997/98 El Niño. I’ve used the 1997/98 El Niño event as reference since it is the most significant El Niño event that was unaffected by volcanic aerosols, and its SST anomalies were measured by satellites and in situ buoys.

The NINO3.4 SST anomalies have also been shifted back in time (lagged) two months to account for the delayed response of global temperatures to the change in tropical Pacific SST. In other words, it takes global temperatures a few months to respond fully to the ENSO event. As you can see, the leading edges of the two datasets align well. This makes sense since the central and eastern tropical Pacific (20S-20N, 180 to 80W) represent a major portion of the globe, about 9%. The magnitudes of the two variations from trough to peak are also similar during the evolution phase with the scaling factor I’ve used.
http://i54.tinypic.com/2h7jgbb.jpg
Figure 5

In Figure 6, I’ve started the graph in 1995 to show how well the scaled NINO3.4 SST anomalies and the Global Temperature anomalies align during the ramp up of the 1997/98 El Niño.
http://i56.tinypic.com/29w1i86.jpg
Figure 6

The next step is to subtract the NINO3.4 SST anomalies from the Global Temperature anomaly data. The remainder is shown in Figure 7. I’ve highlighted the periods that include the impacts of the explosive eruptions of El Chichon and Mount Pinatubo.
http://i51.tinypic.com/9r2lix.jpg
Figure 7

A Stratospheric Aerosols dataset was introduced in a 1993 paper (Stratospheric aerosol optical depth, 1850-1990) by Sato et al. It can be used to account for the impacts of these eruptions (and those of other volcanic eruptions from 1950 to 1999). Estimates of the peak impact on global temperatures from the Mount Pinatubo eruption vary from 0.2 to 0.5 deg C. I’ve scaled the Sato Index data so that it accounts for approximately 0.35 deg C.

Figure 8 illustrates the Hadley Centre’s HADCRUT Global Temperature anomaly data after it has been adjusted for the impacts of volcanic aerosols and the linear effects of ENSO (using the NINO3.4 SST anomalies). Most of the dips and rebounds from the eruptions of the El Chichon and Mount Pinatubo eruptions have been eliminated. I’ve highlighted the apparent step changes caused by the multiyear aftereffects of the 1986/87/88 and 1997/98 El Niño events. Also note the gradual ramp up in temperatures after the 1976 Pacific Climate Shift. There are no other 5-year periods with a gradual rise similar to that. Does this represent the time required for global temperatures to respond to the sudden 1976 upward shift in eastern Pacific Sea Surface Temperatures?
http://i53.tinypic.com/k46kqc.jpg
Figure 8

In Figure 9, the average adjusted global temperature anomalies for the periods before and immediately after 1976 are shown. The average adjusted global temperature before 1976 is 0.12 deg lower than it is for period of 1977 to 1986. And for those who are interested, I’ve also illustrate the average temperatures for the two periods after the 1986/87/88 and 1997/98 El Niño events.
http://i51.tinypic.com/1zpo11u.jpg
Figure 9

We can run through the same process using the Multivariate ENSO Index (MEI) as the ENSO Index. Refer to Figures 10, 11, 12 and 13. The scaling factor used with MEI data was 0.16, as shown in Figure 10.
http://i55.tinypic.com/dcvk9l.jpg
Figure 10
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http://i51.tinypic.com/2zsy49j.jpg
Figure 11
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Figure 12
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http://i55.tinypic.com/ad2e04.jpg
Figure 13
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Figure 14 shows the averages of the adjusted global temperature anomalies for the periods before and immediately after 1976. Recall that the shift was 0.12 deg C, using the NINO3.4 SST anomalies to remove the linear effects of ENSO events. Using the MEI dataset, that shift decreases to 0.05 deg C. This was a very simple comparison. Referring back to Figure 3, the period averages are strongly impacted by how the MEI addresses the 1982/83 El Niño. But it’s a starting point for anyone interested in evaluating this further.
http://i53.tinypic.com/15nvxmq.jpg
Figure 14

Figure 15 compares the HADCRUT global temperature anomaly datasets after they have been adjusted for ENSO using the MEI and NINO3.4 SST anomalies. Both datasets have also been adjusted for volcanic aerosols using the same Sato Index scaling factors. Another major difference appears to be how the MEI removes the extra El Niño peaks. Both datasets show the ENSO-induced shifts in global temperature anomalies. And both show the multiyear ramp-up from 1976 to 1981, but as illustrated in Figures 4, 9, and 14, the MEI accounts for more of that ramp-up than NINO3.4 SST anomalies.
http://i51.tinypic.com/j5im9h.jpg
Figure 15

And now, the second reason for this post.

WHAT CAUSES THE ADDITIONAL VARIATIONS?

If we look again at Figure 15, there are still large year-to-year and multiyear variations in the global temperature anomaly datasets after they’ve been adjusted for ENSO and volcanic aerosols. What causes those additional variations?

Detailed analyses of ENSO, like Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures", have shown that parts of the globe warm with a rise in NINO3.4 SST anomalies and others cool. Refer to Figure 16, which is the color version of Figure 8 from Trenberth et al (2002). The correlations with a 0 month lag is shown highlighted in red. Link to Trenberth et al:
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf

http://i47.tinypic.com/261e1lf.png
Figure 16 (Figure 8 from Trenberth et al 2002)

One would think that after the positively correlated impacts of the ENSO events are removed from the global surface temperature record, as we’ve just done, the remainder would include variations from those areas that are negatively correlated.

This can be shown if we invert either ENSO Index dataset and compare it to what’s left over after the linear effects of ENSO and the volcanic eruptions have been removed from global temperature anomalies. Refer to Figures 17 and 18. Much of the additional yearly and multiyear variability can be explained as warming during La Niña events, and cooling during El Niño events. Note how some of the global responses to the variations in the inverted NINO3.4 SST anomalies are exaggerated while others are suppressed. Why?
http://i51.tinypic.com/315zk1l.jpg
Figure 17
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http://i53.tinypic.com/1zfnf5k.jpg
Figure 18

CLOSING
In summary, the MEI accounts for more of the 1976 Pacific Climate Shift than the HADSST2-based NINO3.4 SST anomalies. This also holds true for the NINO3.4 SST anomalies based on other SST datasets (HADISST and ERSST.v3b) as shown in Figure 19. Note: The Oceanic NINO Index (ONI) is based on ERSST.v3b data.
http://i53.tinypic.com/11qi6q9.jpg
Figure 19

Using the simple analysis in this post, the MEI appears to account for more of the aftereffects of the 1976 Pacific Climate Shift (0.07 deg C) than the SST-based ENSO Indices. And there are some additional subtle differences in the MEI data.

And as shown in Figures 17 and 18, when the linear effects of ENSO are removed from Global Temperature anomalies, the remainder logically shows variations that reflect the opposing effects of ENSO.

The post title is The Multivariate ENSO Index (MEI) Captures The Global Temperature Impacts Of ENSO Differently Than SST-Based Indices. It would be up to you as a user of the MEI to determine if the subtle differences mean it’s better.

Regarding the methods used to remove the linear effects of ENSO, Trenberth et al (2002) write in the paper linked above, “Although it is possible to use regression to eliminate the linear portion of the global mean temperature signal associated with ENSO, the processes that contribute regionally to the global mean differ considerably, and the linear approach likely leaves an ENSO residual.”

And as shown in the posts linked earlier, those residuals can be considerable.

SOURCE

The data presented in this post are available through the KNMI Climate Explorer:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

Monday, September 6, 2010

August 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at August 2010 SST Anomaly Update
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MONTHLY SST ANOMALY MAP

The map of Global OI.v2 SST anomalies for August 2010 downloaded from the NOMADS website is shown below. The central equatorial Pacific SST anomalies are continuing their drop.

http://i53.tinypic.com/161nbqd.jpg
August 2010 SST Anomalies Map (Global SST Anomaly = +0.22 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies are well below the -0.5 deg C threshold of a La Niña. The Monthly NINO3.4 SST Anomaly is -1.2 deg C. Weekly data has dropped below -1.5 deg C (-1.58 deg C).

Global SST anomalies dropped very little this month, -0.006 deg C. For all intents and purposes, there was no change. The slight decline in the Southern Hemisphere (-0.022 deg C) was greater than the rise in the Northern Hemisphere (+0.014 deg C).
http://i54.tinypic.com/35b7fb5.jpg
Global
Monthly Change = -0.006 deg C
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http://i51.tinypic.com/osgvop.jpg
NINO3.4 SST Anomaly
Monthly Change = -0.22 deg C

EAST INDIAN-WEST PACIFIC

The SST anomalies in the East Indian and West Pacific made a slight rise this month. Will they continue to rise, noticeably, in response to the La Niña as they have in the past?

I’ve added this dataset in an attempt to draw attention to what appears to be the upward step responses. Using the 1986/87/88 and 1997/98 El Niño events as references, East Indian-West Pacific SST Anomalies peak about 7 to 9 months after the peak of the NINO3.4 SST anomalies, so we shouldn’t expect any visible sign of a step change for almost 18 to 24 months. We’ll just have to watch and see.
http://i53.tinypic.com/2ijrae1.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.029 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA

The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to August 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES
http://i54.tinypic.com/rsgbpt.jpg
Northern Hemisphere
Monthly Change = +0.014 deg C
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http://i56.tinypic.com/2a85awo.jpg
Southern Hemisphere
Monthly Change = -0.022 deg C
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http://i55.tinypic.com/2dt501i.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = +0.120 deg C
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http://i53.tinypic.com/33de1de.jpg
South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = -0.120 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic.

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http://i56.tinypic.com/fk5r82.jpg
North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.030 Deg C
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http://i51.tinypic.com/1zgay3a.jpg
South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.036 deg C
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http://i54.tinypic.com/2hgh1qh.jpg
Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = +0.023 deg C
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http://i56.tinypic.com/9jq5on.jpg
Arctic Ocean (65 to 90N)
Monthly Change = +0.137 deg C
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http://i56.tinypic.com/xkynnb.jpg
Southern Ocean (60 to 90S)
Monthly Change = +0.044 deg C

WEEKLY NINO3.4 SST ANOMALIES

The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.158 deg C.
http://i56.tinypic.com/2aes3l3.jpg
Weekly NINO3.4 (5S-5N, 170W-120W)

Weekly NINO3.4 SST anomalies are now lower than the values for the same week during the previous transitions to major satellite-era La Niña events.
http://i53.tinypic.com/4volcg.jpg
La Niña Evolution Comparison

SOURCE

The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

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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.
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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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NOTE: I’ve discovered that some of the links to older posts provide blank pages. While it’s possible to access that post by scrolling through the history, that’s time consuming. There’s a quick fix for the problem, so if you run into an absent post, please advise me. Thanks.
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