Monday, December 11, 2017

Oceans: Abstract Values vs. Measured Values - 5

Fig. 1 Abstract maximum, average, and minimum
Just in case the message is not getting through, today I present some more graphs to show how true the three scientific papers (which I quoted from yesterday) were  (Oceans: Abstract Values vs. Measured Values - 4).

I mean where those papers pointed out how the measurements which scientists have been able to take are not spread out evenly (in terms of latitude & longitude) across the vast oceans of the world.

My argument or discussion about this is that we need to have a way of doing with WOD datasets what the GISTEMP and PSMSL data users have been able to do with those datasets.
Fig. 2a
Fig. 2b
Fig. 2c

That is, to define which WOD layers and/or zones can be used to represent the entirety of the ocean conditions, that is, the oceans as a whole.

Only twenty-three PSMSL tide gauge stations out of about 1,400 tide gauge stations can do that in terms of sea level change.

The GISTEMP is similar in that the global mean average temperature anomaly can be shown in the same manner (a representative subset).

But, as those papers discussing the world oceans point out, the same is not yet accomplished with WOD datasets.

The measurements are too concentrated in certain areas to the exclusion of other areas, are too few, and do not go deep enough into the abyss.

The ARGO automated system of submarine drones is changing that in the upper 2,000 m of the oceans, but that is a relatively recent technological win.

There is no long term in situ set of measurements of ocean temperature and salinity data going back in time for over a century, like there are with the PSMSL and GISTEMP datasets.

To visually point out the measurement aberrations I am speaking of, let's look at the new graphs generated by version 1.7 of the software I am constructing.

The graph at Fig. 1 shows the ABSTRACT (calculated) maximum, average, and minimum thermal expansion and contraction pattern from the years 1880 to 2016.

The three graphs at Fig. 2a - Fig. 2c show what happens when in situ WOD measurements are added to the data stream used to generate those abstract graphs.

Fig. 3 Abstract avg. compared to measured
The patterns made by the in situ measurements are out of sync with the abstract patterns made with the WOD information about valid maximum and minimum temperature and salinity values.

Since those values from the WOD manual define validity at all ocean basins and all depths at those basins, being out of sync with them is a problem, especially when the out-of-sync pattern emerges using any of the three different sets of WOD data which compose three different layer lists.

Those three sets are 1) all layers, 2) 6 selected layers, and 3) 8 selected layers, as shown by the report below.

The software module loading sequence proceeds from 1 through 6 (GISS data loader, ABSTRACT data generator, G6 loader, PSMSL loader, G8 loader, and the WOD all-layers loader.

Those modules load in situ measurement data from SQL tables, as well as WOD maximum / minimum valid values.

The software then organizes the data into annual structures (past to present).

The measured values are converted into TEOS values, according to the TEOS rules, by functions in the TEOS toolkit (e.g. Golden 23 Zones Meet TEOS-10).

I may have to stop using WOD layers, to instead use individually selected WOD Zones.

I want to find locations that stay within the guard rails of the valid WOD maximum / minimum values in Appendix 11 of their user manual (see links here).

I am on the case.

The previous post in this series is here.

A printout of the loading sequence of the module follows:

DREDD BLOG
GISS, PSMSL, WOD & TEOS
Data Analyzer Report
(ver. 1.7)
=======================

(1) GISS Loader
---------------
processed 137 rows


(2) ABSTRACT Calculator

-----------------------
processed:
137 years of data
30 ocean basins
at 33 depths


(3) WOD G6 Loader
-----------------
processing layer 5
processed 118 rows
(59 years) of data

processing layer 7
processed 102 rows
(51 years) of data

processing layer 8
processed 162 rows
(81 years) of data

processing layer 9
processed 176 rows
(88 years) of data

processing layer 10
processed 172 rows
(86 years) of data

processing layer 12
processed 142 rows
(71 years) of data


(4) PSMSL Loader
----------------
processed 10,199 rows


(5) WOD G8 ALT Loader
---------------------
processing layer 3
processed 142 rows
(71 years) of data

processing layer 5
processed 118 rows
(59 years) of data

processing layer 7
processed 102 rows
(51 years) of data

processing layer 8
processed 162 rows
(81 years) of data

processing layer 9
processed 176 rows
(88 years) of data

processing layer 10
processed 172 rows
(86 years) of data

processing layer 12
processed 142 rows
(71 years) of data

processing layer 14
processed 142 rows
(71 years) of data


(6) WOD Loader (all layers)
---------------------------
processing layer 0
processed 60 rows
(30 years) of data

processing layer 1
processed 118 rows
(59 years) of data

processing layer 2
processed 96 rows
(48 years) of data

processing layer 3
processed 142 rows
(71 years) of data

processing layer 4
processed 188 rows
(94 years) of data

processing layer 5
processed 118 rows
(59 years) of data

processing layer 6
processed 178 rows
(89 years) of data

processing layer 7
processed 102 rows
(51 years) of data

processing layer 8
processed 162 rows
(81 years) of data

processing layer 9
processed 176 rows
(88 years) of data

processing layer 10
processed 172 rows
(86 years) of data

processing layer 11
processed 142 rows
(71 years) of data

processing layer 12
processed 142 rows
(71 years) of data

processing layer 13
processed 144 rows
(72 years) of data

processing layer 14
processed 142 rows
(71 years) of data

processing layer 15
processed 156 rows
(78 years) of data

processing layer 16
processed 102 rows
(51 years) of data

processing layer 17
processed 0 rows
(0 years) of data

Friday, December 8, 2017

Oceans: Abstract Values vs. Measured Values - 4

Fig. 1a
Fig. 1b
I. Background

This series is about what to do about the dearth of in situ measurements of the whole ocean, top to bottom (Oceans: Abstract Values vs. Measured Values, 2, 3).

The issue, including what to do about it, has been addressed in the scientific literature:
"Prior to 2004, observations of the upper ocean were predominantly confined to the Northern Hemisphere and concentrated along major shipping routes; the Southern Hemisphere is particularly poorly observed. In this century, the advent of the Argo array of autonomous profiling floats ... has significantly increased ocean sampling to achieve near-global coverage for the first time over the upper 1800 m since about 2005. The lack of historical data coverage requires a gap-filling (or mapping) strategy to infill the data gaps in order to estimate the global integral of OHC."
(Ocean Science 2016, Cheng et alia, emphasis added; PDF here). Going back a bit further, the issue came up in another paper:
"A compilation of paleoceanographic data and a coupled atmosphere-ocean climate model were used to examine global ocean surface temperatures of the Last Interglacial (LIG) period, and to produce the first quantitative estimate of the role that ocean thermal expansion likely played in driving sea level rise above present day during the LIG. Our analysis of the paleoclimatic data suggests a peak LIG global sea surface temperature (SST) warming of 0.7 ± 0.6°C compared to the late Holocene. Our LIG climate model simulation suggests a slight cooling of global average SST relative to preindustrial conditions (ΔSST = −0.4°C), with a reduction in atmospheric water vapor in the Southern Hemisphere driven by a northward shift of the Intertropical Convergence Zone, and substantially reduced seasonality in the Southern Hemisphere. Taken together, the model and paleoceanographic data imply a minimal contribution of ocean thermal expansion to LIG sea level rise above present day. Uncertainty remains, but it seems unlikely that thermosteric sea level rise exceeded 0.4 ± 0.3 m during the LIG. This constraint, along with estimates of the sea level contributions from the Greenland Ice Sheet, glaciers and ice caps, implies that 4.1 to 5.8 m of sea level rise during the Last Interglacial period was derived from the Antarctic Ice Sheet. These results reemphasize the concern that both the Antarctic and Greenland Ice Sheets may be more sensitive to temperature than widely thought."
(The role of ocean thermal expansion, AGU, emphasis added). Basically, the scientists point out that this exercise is not a picnic:
"The oceans present myriad challenges for adequate monitoring. To take the ocean’s temperature, it is necessary to use enough sensors at enough locations and at sufficient depths to track changes throughout the entire ocean. It is essential to have measurements that go back many years and that will continue into the future.
...
Since 2006, the Argo program of autonomous profiling floats has provided near-global coverage of the upper 2,000 meters of the ocean over all seasons [Riser et al., 2016]. In addition, climate scientists have been able to quantify the ocean temperature changes back to 1960 on the basis of the much sparser historical instrument record [Cheng et al., 2017]."
(The Most Powerful Evidence, Inside Climate News, emphasis added). That quote contains a reference to "Cheng et al. 2017" which contains the following statement:
"In this paper, we extend and improve a recently proposed mapping strategy (CZ16) to provide a complete gridded temperature field for 0- to 2000-m depths from 1960 to 2015.
...
The success of a mapping method can be judged by how accurately it reconstructs the full ocean temperature domain. When the global ocean is divided into a monthly 1°-by-1° grid, the monthly data coverage is [less than]10% before 1960, [less than]20% from 1960 to 2003, and [less than]30% from 2004 to 2015 (see Materials and Methods for data information and Fig. 1)."
(Improved estimates of ocean heat content from 1960 to 2015, Cheng et al, 2017). In other words, it has not yet been accomplished.

That is where the Dredd Blog criticism of "thermal expansion is the major cause of sea level rise in the past century or so" comes from (On Thermal Expansion & Thermal Contraction, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27).

II. An Abstract Observation

Now, let's consider that "gap-filling (or mapping) strategy" exercise (mentioned in the last quote above, Cheng et al, 2017).

The problem has been approached here at Dredd Blog by developing a software program I call an abstract pattern generator. which produces WOD patterns using data in the WOD documentation.
Fig. 2a
Fig. 2b
What I mean by "WOD patterns" can be seen at Fig. 1a and Fig. 1b.

The upper pane of Fig. 1a is a graph generated from data of the Permanent Service for Mean Sea Level (PSMSL).

It details sea level rise (SLR) at the "Golden 23" tide gauge stations (185.157 mm of SLR).

The lower pane is a graph of the abstract pattern of thermal expansion over the same time frame (25.019 mm of SLR).

In other words, the abstract thermal expansion pattern shows that thermal expansion is only 13.5% of total SLR (25.019 ÷ 185.157 = 0.135123166 or 13.5%) during that time frame, which means that it is not a major portion of global sea level rise, because those numbers also mean that 86.5% of SLR is caused by ice sheet and land glacier melt water.

The abstraction calculation is based on World Ocean Database (WOD) data in their official documentation, depicted in part at Fig. 2a and Fig. 2b, which is a portion of "APPENDIX 11. ACCEPTABLE RANGES OF OBSERVED VARIABLES AS A FUNCTION OF DEPTH, BY BASIN" (see Appendix 11, page 132, of The WOD Manual, PDF).

The gist of Appendix 11 is to show maximum and minimum values at all ocean depths in all ocean basins around the globe.

By adding the maximum and minimum values together, then dividing by 2 (at each depth of each ocean basins), the software is then ready for the next step, which is to conform those values to GISTEMP constraints.

By "GISTEMP constraints" I mean adjusting those mean average Appendix 11 values by the GISTEMP anomaly pattern.

That is done by multiplying the WOD values by 0.93 (93% of that GISTEMP anomaly value becomes the temperature anomaly value in each ocean basin at each depth).

That is because scientists tell us that some 93% of heat trapped by green house gases ends up in the oceans.

So, by fusing that GISTEMP anomaly pattern to the abstract temperature pattern made by the WOD data, we have a pattern which we can use to generate Thermodynamic Equation Of Seawater (TEOS) patterns (e.g. Golden 23 Zones Meet TEOS-10).

III. Using Abstract Patterns With TEOS-10

Using abstract WOD data to generate TEOS values is done by the same process as using in situ ocean temperature and salinity measurements (The Art of Making Thermal Expansion Graphs).

To conform either in situ temperature and salinity measurements or abstract temperature and salinity values, one uses the TEOS functions (the difference is that the abstract values have been conformed to the GISTEMP pattern as stated in Section II above).

The graph at Fig. 1b shows the resulting TEOS Conservative Temperature (CT) and Absolute Salinity (SA) patterns that emerge on an annual basis from 1880 - 2016 when one uses this technique.

From that, we then can then generate the thermal expansion coefficient and the thermosteric volume change.

From that thermosteric volume change we can calculate the sea level change (SLC) as shown in Fig. 1a.

Using the WOD manual data for all 30 ocean basins around the globe, and all 33 depths in each of those ocean basins, forms a pattern against which we can judge the general completeness and general accuracy of our in situ measurements.

It also helps us to select a "Golden 23" group of areas that mirror the whole ocean  (On Thermal Expansion & Thermal Contraction - 28).

IV. Comparing In Situ Measurement Patterns
With Abstract Calculated Patterns

So now we can talk about the current techniques of using what is described as skimpy in situ measurements (down to only about 2,000 m depth, when the average ocean basin depth is 3,682.2 m ... ~50% not used) to do the estimations all of the science team authors wrote about.

They pointed out that we have to use estimations in any case, because the datasets are incomplete in various places for various reasons, from dangerous conditions to weaker technology in times past.

To me, incomplete data is a bad place to start, having realized that the in situ measurements, although quite accurate and plentiful, are a patchwork of convenience-based expeditions that can make it difficult to see the entire picture.

I mean the total picture which must be constructed from outside the convenience zone of only expeditions to safe and warm global ocean areas.

That is why I hypothesize that it is better to start with an abstract pattern which matches the pattern made by our historically complete datasets (e.g. GISTEMP & PSMSL).

V. Conclusion

"He say one and one and one is three ... come together ..."



The next post in this series is here, the previous post in this series is here.

Wednesday, December 6, 2017

Proxymetry3 - 7

Fig. 1 Abstract Thermal Expansion
For those who like to keep their own datasets for use on their blogs, updates are available for data in two premier data services.

The Permanent Service For Mean Sea Level (PSMSL) and the World Ocean Database (WOD) have recently updated their datasets.

I updated the WOD datasets in my SQL server, which added about 55 million more in situ measurements to the almost one billion already in it, while the more modest new PSMSL data update only added about 200 new annual sea level records to the mix.
Fig. 2a All WOD Layers
Fig. 2b "Golden 6" WOD Layers
Fig. 2c "Golden 8" WOD Layers

Meanwhile, another type of record keeps being made in more places, and in more ways than one (Rising Seas May Wipe Out These Jersey Towns, but They're Still Rated AAA, Moody's Warns Cities to Address Climate Risks or Face Downgrades, U.S. Disbands Group That Prepared Cities for Climate Shocks).

In tune with Bob Dylan's lyrics ("I used to care, but things have changed"), some agents of officialdom in the real estate business are in denial about the science of things (Real estate industry blocks sea-level warnings that could crimp profits on coastal properties).

So, any of those folks can stop reading now, because I am going to get back to the future and talk about the aforementioned database update and other realities.

For sure, one should keep a close and regular watch on the changing ocean temperatures via WOD data.

The same goes for tide gauge records because, as figure Fig. 2a - Fig. 2c show, there is not only constant change in the oceans, but there are differences in the changing picture depending on which layers of data one uses.

As one can see by those graphs (Fig. 2a - Fig. 2c), the thermal expansion values calculated using the most recent in situ measurements are not as large as those generated by the abstract model (Fig. 1).

In fact, the measurements taken show a decrease in thermal generated ocean sea level rise volume during the time frame shown (1968 - 2016)

That is one reason I constructed the software module that generates an abstract view (Fig. 1) of how ocean thermal expansion would look in a perfect mathematical vacuum, informed of course by historical GISTEMP records.

On the WOD side of things, I have decided to continue using several locations and several combinations of WOD layers (including all ocean depths) in order to determine how thermal expansion and contraction are progressing in world oceans (On Thermal Expansion & Thermal Contraction, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27).

Using different locations, and all depths at those different locations, is a good way to discover how very different things can look, depending on those locations and depths of the measurements taken.

The data that one uses in order to be and stay informed will determine the degree of one's awareness.

So, that type of practice will continue on Dredd Blog until a "Golden xxx" number of WOD locations can be determined to be as useful as the "Golden 23" tide gauge stations selected by scientist Bruce C. Douglas some time ago.

For more information on "layers" see: The Layered Approach To Big Water, 2, 3, 4, 5, 6, 7, 8.

The previous post in this series is here.









Monday, December 4, 2017

Banker Jekyll Will Hyde Your Money - 13

Progress: It's that way ! ... No, it's this way !
I. One Person's Progress is
Another Person's Regression

I began this series in August of 2009 (Banker Jekyll Will Hyde Your Money).

It has covered a lot of ground since then (Banker Jekyll Will Hyde Your Money, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12).

I thought I would add another post to this series, since major tax legislation is in the works; one version passed in the House of Representatives and one version passed in the Senate.

The two bills are contrary to one another, so, as with any such bill it must go to a conference where elected politicians will struggle to make it unified.

If that takes place, then both houses will have to vote on it again before it can be sent to the president for final approval or disapproval (Another delicate challenge for Republicans: Reconciling House and Senate tax bills).

Members of both houses have spoken of "progress" when discussing this now bifurcated legislation, as have the bankers and citizen commentators (all Democrats in the Senate voted against this "progress" while all but one Republican in the Senate voted in favor of this "progress").

There has been "progress" ("a movement toward a goal") by Banker Jekyll who continues to want to Hyde our money, and there has also been "progress" by honesty-and-justice seeking working class members.

In other words, as I have said before, the word "progress" is a meaningless dog whistle,  a doublespeak word, which like a "coffee cup" can hold a refreshing beverage or a poisonous mixture that will kill anyone who drinks it.

The ambiguity in the word "progress" manifests because the word is bound, by definition, to "a goal."

As the dictionary link above reveals, in its core definition "progress" is "a movement toward a goal" (I sense that "one person's progress is another person's regression").

So, since the number of goals that exist in our universe is most likely infinite, the word has no real absolute meaning unless and until it is transparently linked to a goal.

The goal of the tax bill is not a goal that everyone seeks, thus there are several types of "progress" involved (The House and Senate tax bills, explained, From Tax Rates To Deductions: Comparing The House & Senate Bills To Current Law).

II. Deficit Considerations

The Congressional Budget Office (CBO) indicates that the tax cuts to corporations and the
Senators Hatch up a Grassley story
wealthy will generate an increase in the federal debt by some 1.4 trillion dollars (PRELIMINARY: 8:49PM, December 1, 2017, SUMMARY OF THE DEFICIT EFFECTS, PDF; cf. this and this).

According to Senators Hatch and Grassley, the reason the taxpayer money is being taken from the poor and given to the rich is because the poor spend all their money on booze and women. and, like Americans of Puerto Rico will not lift a finger to help themselves (The Guardian).

III. Corporate Tax Rate

The "progress" according to one group indicates that corporate rates will drop to 20% from 35%, ostensibly because corporations are better persons than poor people are (now that the Supremes have made a corporation a person).

A past Labor Secretary had a different view of this "progress" and its goal:
"But the tax plan gives American corporations a $2 trillion tax break, at a time when they’re enjoying record profits and stashing unprecedented amounts of cash in offshore tax shelters. And it gives America’s wealthiest citizens trillions more, when the richest 1 percent now hold a record 38.6 percent of the nation’s total wealth, up from 33.7 percent a decade ago.

The reason Republicans give for enacting the plan is “supply-side” trickle-down nonsense. The real reason is payback to the GOP’s mega-donors.

A few Republicans are starting to admit this. Last week, Gary Cohn, Trump’s lead economic advisor, conceded in an interview that 'the most excited group out there are big CEOs, about our tax plan.' Republican Rep. Chris Collins admitted that 'my donors are basically saying, ‘Get it done or don’t ever call me again.’ Republican Sen. Lindsey Graham warned that if Republicans failed to pass tax reform, 'the financial contributions will stop.' "
(Robert Reich). I am reminded of the song with the lyrics "freedom is just another word for nothing left to loose" by Janis "Pearl" Joplin.

IV. Conclusion

"What we gonna do when the money runs out?" - David Gray (@Nightblindness)






The previous post in this series is here.