Posts Tagged ‘sea level data’

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The Search for Acceleration, part 5, The Netherlands

July 6, 2013

magnifying glass 145This is part 5 of a series of posts in which I am searching for a large acceleration in sea level rise rate in the latter part of the 20th century that could reconcile the 1.8 mm per year average rise rate for the century attributed to tide gauge data and the approximately 3 mm per year rise rate for the tail end of the century attributed to the satellite data.

The global sea level rise rate is swamped by other effects.  In most locations the yearly rise and fall of the oceans is greater than the 18 cm of sea level rise during the entire 20th century.  Geologic effects (e.g. glacial isostatic adjustment or plate tectonics) add to local and regional rise rates, making them deviate greatly from the global rise rate.

I am working under the theory that by detrending sea level data from individual (local) sites and averaging with other regional sites it should be possible to extract changes in regional sea level rise rates while bypassing the question of what the “true” sea level rise rate is in that region.

The Netherlands

Netherlands elevation

Elevation and tide gauge locations for The Netherlands.

Nobody cares more about sea level rise than the folks in The Netherlands. They have been dealing with the issue long before anybody was worried about global warming, since 20% of the country’s area is below sea level.  They have excellent sea level data spanning nearly 150 years.  This map shows land elevations in the Netherlands as well as the location of seven high quality tide gauge stations.

Here is the PSMSL data for those seven locations…

Netherlands Raw Spread

Tide gauge data for the Netherlands.

As I have mentioned before, I am not concerned with finding the sea level rise rate, but rather the change in sea level rise rate. But this set of data averages out to have a 20th century rise rate very close to the commonly reported tide gauge derived average of 1.8 mm/year. (Click on image if animation does not advance.)

sea level annotated 450ani

These seven stations also have very coherent yearly signals, created from the 2, 3, 4, 6 & 12 month Fourier components.  Note that the magnitude of the yearly signal is nearly the same as the entire average sea level rise for the entire 20th century.

Netherlands Yearly signal

Now, lets consider the weighted, detrended data to derive the relative acceleration. (Click on image if animation does not work.)

Netherlands weighted and detrened 450ani

Conclusion

The Netherlands tide gauge data if of the highest quality and long duration.  All seven stations cover 1870 to the present.  The detrended sea level rise rate does indicate that the overall sea level rise rate for last two decades of the 20th century was a few mm per year greater than the century’s average.  However, this very reliable data also indicates that the sea level rise rate at the beginning of the century was just as high as the end of the century.

____________________________

Sources

20th century rise rate average of 1.8 mm/year

1. Church and White Global Mean Sea Level Reconstruction

2. Links to Church and White sea level data

Satellite data (about 3 mm/year)

CU Sea Level Research Group

RLR tide gauge data

Permanent Service For Mean Sea Level

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Cities Underwater: Miami

July 2, 2013

Perhaps the dolts at Rolling Stone should stick to photos of aging rock stars, because they just embarrass themselves when they stray into science and reason.  Their boneheaded article by Jeff Goodell, “Goodbye, Miami” starts out by looking back from some fictitious hurricane in 2030.  This fantasy breathlessly tells us…

“When the water receded after Hurricane Milo of 2030… A dead manatee floated in the pool where Elvis had once swum. Most of the damage occurred not from the hurricane’s 175-mph winds, but from the 24-foot storm surge that overwhelmed the low-lying city.”

Well, at least they snuck in something about a dead rock-n-roller.  They continue…

The storm knocked out the wastewater-treatment plant on Virginia Key, forcing the city to dump hundreds of millions of gallons of raw sewage into Biscayne Bay. Tampons and condoms littered the beaches, and the stench of human excrement stoked fears of cholera. More than 800 people died, many of them swept away by the surging waters that submerged much of Miami Beach and Fort Lauderdale.

Wait!  Don’t the folks at Rolling Stone think “condoms littering the beaches” are a good thing?  I’m confused.

After another paragraph of blather they really get to the point…

But Hurricane Milo was unexpectedly devastating. Because sea-level­ rise had already pushed the water table so high, it took weeks for the storm waters to recede…And still, the waters kept rising, nearly a foot each decade. By the latter end of the 21st century, Miami became something else entirely: a popular snorkeling spot where people could swim with sharks and sea turtles and explore the wreckage of a great American city.

Well now, Mr. Goodell, I can’t decide if you are dishonest or just plain stupid. Anybody who is going to put his fingers to the keyboard to write an article about sea level rise at a particular coastal city would surely look up the sea level data for the region before indulging in such preposterous fantasies.

I’ll help him out.  Here is a list of sea level tide gauge sites in Florida with long and up-to-date records.  Click on any of then to see the sea level plots from the Permanent Service for Mean Sea Level.

Fernandina Beach, Florida: 100 years of data, 2.02 mm/year  (0.8 inches/decade)

Mayport, Florida: 80 years of data, 2.40 mm/year (0.9 inches/decade)

Key West , Florida: 100 years of data, 2.24 mm/year (0.9 inches/decade)

Naples, Florida: 40 years of data, 2.02  mm/year  (0.8 inches/decade)

Fort Myers, Florida:  40 years of data, 2.40 mm/year (0.9 inches/decade)

St. Petersburg, Florida: 60 years of data, 2.36 mm/year (0.9 inches/decade)

Clearwater Beach, Florida: 40 years of data, 2.43 mm/year (1.0 inches/decade)

Cedar Key, Florida: 100 years of data, 1.80 mm/year (0.7 inches/decade)

Apalachicola, Florida:  40 years of data,  1.38 mm/year (0.5 inches/decade)

Panama City, Florida: 40 years of data, 0.75 mm/year (0.3 inches/decade)

Pensacola, Florida: 90 years of data,  2.1 mm/year (0.8 inches/decade)

Look at those numbers.  They don’t exactly look like “nearly a foot each decade,” do they?

OK, Mr. Goodnell, stick with me here – we’re going to do some 5th grade math.  Look at the data above and make an estimate of how much the sea level will rise along the Florida coast by 2030.  How about we go with 2 inches (although that is certainly too high).

Now suppose your fictitious hurricane does bring a “24-foot storm surge.” Oh no!!! with the additional sea level rise that storm surge will be 2 inches higher!!!

 

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The Search for Acceleration, part 4: Mea Culpa

June 30, 2013

magnifying glass 145I had a big mistake in my last two posts…

The Search for Acceleration, part 2: East Coast of North America

and

The Search for Acceleration, part 3: Japan

The error caused my rise rate calculations to be off by a factor of 12! This was because I failed to account for the monthly increments in the RLR data.  This mistake caused large errors on my conclusions, which have now been corrected.

I use National Instruments LabVIEW software for all of my coding.  LabVIEW is an advanced graphical programming platform that makes it possible to write sophisticated code in a completely graphical environment.  That is, no lines of text as in the more traditional languages like Fortran or C.  Instead, various sub-programs (or “sub-VIs” in LabView parlance) can be wired together to create complex programs that would take much longer to write other languages.

The image below shows the mistake I made.  Mea Culpa.

LabView error

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The Search for Acceleration, part 2: East Coast of North America

June 24, 2013

magnifying glass 145

CORRECTION: 6/30/13

The original detrended sea level rise rate graphs for this post was off by a factor of 12!.  This greatly changes my conclusion.  Incorrect information is now crossed out and is followed by corrected information in red.

This is part 2 of a series of posts in which I am searching for a large acceleration in sea level rise rate in the latter part of the 20th century that could reconcile the 1.8 mm per year average rise rate for the century attributed to tide gauge data and the approximately 3 mm per year rise rate for the tail end of the century attributed to the satellite data.

The global sea level rise rate is swamped by other effects.  In most locations the yearly rise and fall of the oceans is greater than the 18 cm of sea level rise during the entire 20th century.  Geologic effects (e.g. glacial isostatic adjustment or plate tectonics) add to local and regional rise rates, making them deviate greatly from the global rise rate.

I am working under the theory that by detrending sea level data from individual (local) sites and averaging with other regional sites it should be possible to extract changes in regional sea level rise rates while bypassing the question of what the “true” sea level rise rate is in that region.

East Coast of North America

Conclusion: There is no sign of an acceleration in the sea level rise rate in the tide gauge data from the East Coast of North America.

Conclusion:  The tide gauge data for the East Coast of North America that covers that satellite sea level data era (1993 to present) does show a rise rate that is significantly higher than the tide gauge data rise rate for the 20th century.  But the sea level rise rate in the 1930s through 1940s and around 1970 was as high or higher.Whether or not this data reconciles the difference between the 20th century tide gauge rise rate average and the satellite rise rate average is still ambiguous.

I have selected the East Coast of North America, for no particular reason, as the first region to analyse.  I looked for tide gauge data along the coast such that it covered at least the period from 1960 to 2008 with 90% of all monthly data accounted for.  Usable sites ranged from Nova Scotia to Georgia.

Click on any animations or graphs to enlarge.
East Coast North America 90p 1960-2008 Map

The following plot shows the qualifying data spread out for easy comparison.  The key at the right shows the associate RLR data files.

East Coast North America 90p 1960-2008 Raw Spread

Averaged data

As I mentioned above, I am not concerned with finding the sea level rise rate, but rather the change in sea level rise rate. However the following data for the East Coast of North America is interesting because it shows an averaged sea level rise rate for the 20th century that is close to the satellite derived sea rate for the end of the 20th century.  This is will not be the case for most regions around the world.  If you squint the right way you can also see the change in rise rate around 1930 that shows up in the various iterations of Church and White’s derivations of 20th century sea levels.

East Coast North America 90p 1960-2008 Avg 450ani2

Detrended data

East Coast North America 90p 1960-2008 450ani corrected

The last frame of the detrended data animation is worth repeating (see below).  Notice that there is no evidence of an extreme or consistent increase in the sea level rise rate in the last two decades.  The rise rates were as great or greater in the 1940s, 1950s and 1970s than they were in the 1980s, 1990s and 2000s.  However, at least part of the satellite era (1993 to present) tide gauge data may be more than 2 mm/year greater than the average for the 20th century.  It is safe to say that the tide gauge data from the East Coast of the North America does not reconcile the difference between the 20th century rise rate average (about 1.8 mm/year) and the satellite measured average (about 3 mm/year) Whether or not this data reconciles the difference between the 20th century tide gauge rise rate average and the satellite rise rate average is still ambiguous.

East Coast North America 90p 1960-2008 Detrended Acceleration
corrected East Coast North America 90p 1960-2008 Detrended Acceleration

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The Search for Acceleration, part 1

June 17, 2013

magnifying glass 145A while back I wrote a tongue-in-cheek post alleging that NASA satellites are being used to raise the global sea levels.  The point was that the most commonly quoted 20th century sea level data had the average rise rate at about 1.8 mm per year, but the satellite data since 1993 had the rise rate at about 3 mm per year.

I have always wondered if those two facts could be reconciled.  If they are both true, does that mean there has been an extreme acceleration in sea level rise rates at the tail end of the 20th century?  Shouldn’t such an acceleration be apparent in the tide gauge data?   So I have spent much time recently searching the data for such an acceleration.

For all my searching, the outcome is still somewhat ambiguous.  This is the beginning of a series of posts on that search.  In this post I will cover the basic methods I have used, and present results in subsequent posts.

The data.

The map in figure 1 (click to enlarge) shows all the locations of tide gauge data from the RLR data set, which is maintained by the Permanent Service for Mean Sea Level (PSMSL).  The PSMSL overview notes…

Established in 1933, the Permanent Service for Mean Sea Level (PSMSL) has been responsible for the collection, publication, analysis and interpretation of sea level data from the global network of tide gauges. It is based in Liverpool at the National Oceanograhy Centre  (NOC), which is a component of the UK Natural Environment Research Council(NERC).

The PSMSL data is a comprehensive collection of sea level data.  The map in figure 1 (click to enlarge) shows 1384 RLR data sites.  The circles are 100 km radius and centered on each site.  This seems like a gold mine of data.  But most of the data is not useful for the purpose I have stated, because it either covers an insufficient length of time, or has too many “holes” in the time period that it does cover.

Location of all RLR data sites.

Figure 1. Location of all RLR data sites.

We need sites with good records of sea level for at least the last half century.  This drastically limits the number of adequate sites and their global distribution.  Figure 2 (click to enlarge) shows all the sites with data starting in 1955 at the latest, ending in 2005 at the earliest, and having data for at least 90% of the months between 1955 and 2005.  Of the original 1384 sites, only 112 meet these criteria and their global distribution is greatly reduced.

 Sea level sites with 90% completion between 1955 and 2005.

Figure 2. Sea level sites with 90% completion between 1955 and 2005.

The methods that I will employ to mine the data will start by selecting data from sites that meet various completion criteria. Typically, those criteria will be all the data sites in some region that have 90% complete data over some specified time period.

Needle in a hay stack

Looking for acceleration in sea level data can be like looking for a needle in a haystack.  The average global sea level rise rate for the 20th century was about 1.8 mm per year.  But the fluctuations from year to year or even month to month at any particular site or region can be hundreds of times greater than the yearly average.

Consider the sea level data from the North Sea port city of  Den Helder in the Netherlands. Figure 3, below, (click to enlarge) shows the type of processing I use to analyse sea level data in order to find the needle of acceleration in the haystack of data.  Read the figure caption for details.  Note that 3A through 3F all show a red bar in the upper left corner to represent the amount of globally averaged sea level rise for the entire 20th century, and compare it to the short term fluctuation at Den Helder.  The wide fluctuations of the Den Helder data is typical of all the tide gauge sea level data from around the world.

Den Helder Netherlands sea level
Figure 3. (Click to enlarge.) 20th century sea level data from Den Helder, The Netherlands.  RLR data file #23.  For comparison, the red bar in the upper left corner of 3A through 3F shows the global average sea level rise for the 20th century.
3A.
  Raw data.
3B. Raw data minus the yearly signal.
3C. Yearly signal.  This is constructed from the Fourier components of the raw data for 12, 6, 4, 3 and 2 months.
3D. Overlay of FWHM Gaussian smooths of the raw data minus the yearly signal.  The scale is the same as 3A and 3B for comparison purposes.
3E. Same as 3D, but with expanded scale.
3F. Raw data and 5 year Gaussian smooth.
3G. Sea level rise rates from derivatives of the 1, 3, 5, 7 & 9 year FWHM Gaussian smooths.
3H.  Same as 3G, but for only the 3, 5, 7 & 9 year FWHM Gaussian smooths.  The red line shows the average global rise rate for the 20th century.

Not rise rates, but change in rise rates.

I want to stress that in most cases I will not be concerned the actual sea rise rate, but rather, I am looking for a change in the sea level rise rate.  This is an important distinction, because often times tide gauge data from two or more sites in the same region may have very different rise rates, but very similar changes in sea level rise rates over a long period of time.  For example, consider Wernemunde, Germany and Stockholm, Sweden, both in the Baltic Sea region, about 600 km apart.  Figure 4a shows their sea level data from 1940 to 2010, with a 2 year Fourier long pass filter.   A linear fit to the Wernemunde data over that period gives an overall sea level rise rate of 1.68 mm/year, while Stockholm had an overall rise rate of -3.35 mm/year.  The Wernemunde and Stockholm sea levels are obviously dominated by different local effects.

But if the Wernemunde and Stockholm data are detrended (the best linear fit of the data subtracted  from the data itself) then we can see that there are remarkable similarities (see figure 4b).  Those two data sets are clearly measuring a combination of signals: local, regional, and global.  While their sea level rise rates may be very different, their changes in sea level rise rates are very similar.

Figure 3.  a.) Stockholm and Wernemunde sea level, and (b) detrended version.

Figure 4. a.) Stockholm and Wernemunde sea level, and (b) detrended version.

Mathmatically speaking, let f1(t) and f2(t) be sea level data for two sites in the same region and let

derivative eq

Weighted averages

The example illustrated in figure 4 shows only two sets of tide gauge data from the Baltic Sea region.  When I finally present data for the Baltic Sea region in a later post I will use about 25 tide gauge sites.  The similarity between these 25 data sets after detrending is quite amazing (at least to me).  It leaves me with great confidence that those sets of detrended data are measuring nearly the same regional and global signals while having very different local signals.  But the question still remains: how to combine the data for those sites?

I will typically combine such data sets two different ways: simple unweighted year by year average of the detrended data, and a weighted average of the detrended data.  The weighting will be based on distance between sites.  S0, for example, if I choose a weighting threshold of 200 km, then sites that are more than 200 km away from the next closest site will be weighted as “1.”  A site that is within 200 km of n other sites will be weighted as 1/(n+1).  Figure 3 shows an example of the site weighting.

Figure 3.  Site weighting. Overlapping circles are weight less than non-overlampping circles.

Figure 3. Site weighting. Overlapping circles are weight less than non-overlapping circles.

Data smoothing

I will use a fourier technique to remove yearly signals.  That is, the Fourier transform of a sea level time series will have the components coresponding to 12, 6, 4, 3, and 2 months removed.  Additional long pass smoothing may be applied by removing all Fourier components for periods shorter than some threshold period, but usually I will apply a Gaussian filter after the yearly signal has been removed.  It will always be noted when I apply these techniques.

Coming soon

In later posts I will analyze data and present results from various regions around the world.

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Rahmstorf (2011): Robust or Just Busted (Part 6): Holgate’s sea level data

November 11, 2012

This is part 6 of a multi-part series about “Testing the robustness of semi-empirical sea level projections,” Rahmstorf, et. al., Climate Dynamics, 2011. You can see an index of all parts here. I frequently refer to this paper as R2011.

Recall figure 1 from R2011[1]…

Figure 1 from "Testing the robustness of semi-empirical sea level projections" (Rahmstorf, et. al., Climate Dynamics, 2011)

One of the primary points of this graphic is the quadratic fit of one data set (CW06) overlaid on all the other data sets.  The message that you are to receive is that these various sets of sea level data all tell the same essential story.  The falseness of this claim was discussed in “Quadratic fits of laughter.”

But let’s take Rahmstorf at his word.  Let’s agree with him that these sea level data sets all tell essentially the same story.  R2011’s big point is that the Rahmstorf model is “robust” given a variety of different historical data sources.  So it seems a tad bit strange that after going to all the trouble to point out these various sea level data sources and their similarities, he only gives the projection results of his model for three of them (CW06[2], CW11[3], and JE08[4]).

Of those three input sea level data sets, only two of them give similar sea level projections for the 21st century.  The outlier which results from CW11 shows significantly lower sea level projections.  Because of this, the outlier must be rejected (according to R2011), even though Church and White, the authors of both CW06 and CW11, clearly think the CW11 data is an improvement over their Cw06 data.

What about some of the other sea level rise data sets shown in R2011’s figure 1?  What type of 21st century sea level projections do they yield when inserted into Rahmstorf’s model?

Holgate’s sea level data

Let’s consider the sea level rise data of Simon Holgate.    The above image shows Holgate’s 2004 data[5], labeled HW04.  As I have previously pointed out, R2011 oddly includes Holgate’s 2004 data but ignores his 2007 data[6], H07.  I will consider both.  In my previous post I showed the results of Rahmstorf’s model when either CW06 and CW11 are input with six different combinations of reservoir storage and ground water depletion inputs.  The following two graphs show the results in the same format using HW04 and H07 (instead of CWo6 and CW11) with the same combination of reservoir storage and ground water depletion inputs.  I have kept the horizontal axis scaling the same as in the previous post to highlight the different results when Church and White data is used and when Holgate data is used.  Data files with all the specifics of this data are at the bottom of the post.

FIGURE 2. Sea level rise projections for the 21st century based on my implementation of Rahmstorf’s model under the RCP45 emissions scenario (Moss, 2010)[7] for Holgate sea level data coupled with various combinations of reservoir storage and groundwater depletion data inputs.
FIGURE 3. Sea level rise projections for the 21st century based on my implementation of Rahmstorf’s model under the RCP85 emissions scenario (Moss, 2010)[7] for Holgate sea level data coupled with various combinations of reservoir storage and groundwater depletion data inputs.

For comparison, here are the previously posted results using Church and White sea level data…

 RCP45

 RCP85

Hmmm…

Didn’t R2011 imply that those various sea level data sets shown if figure 1, above, told the same essential story?  Yes, I believe he did!  That is why they overlaid the same quadratic fit onto all of them.

And didn’t R2011 say that their model was “robust?”  Yes, I am quite certain that they did!  In fact the word “robust” was in the title of their paper, and they said…

“We determine the parameters of the semiempirical link between global temperature and global sea level in a wide variety of ways…We then compare projections of all these different model versions (over 30) for a moderate global warming scenario for the period 2000–2100. We find the projections are robust

and

“we will systematically explore how robust semi-empirical sea level projections are with respect  to the choice of data sets”

So, they claim to use “a wide variety of ways” to look at “all these different model versions (over 30).”  They show plots of seven different sea level data sets and imply their similarity.  But they only show projections based on three of them.  Then they reject the projections based on one of the three, even though it is arguably the best sea level data of the bunch.

What do they say about their model’s projections based on the “wide variety” other sea level data sets that look so good overlaid with the same quadratic fit…?

Cricket. Cricket.

How would R2011 reject the projections based on the Holgate data?

How would R2011 reject the projections based on the Holgate data that I have shown above in figures 2 and 3?  Well they would undoubtedly point out that the fit parameter, To (the so called baseline temperature, is way too low.  Recall, R2011 finds To to be on the order of -0.4 °C (below the 1950 to 1980 global average).  When Holgate’s sea level data is used, To is on the order of -4.0 °C.  Hey Rahmstorf, don’t blame me, its your model!

Maybe one of these days I will write a justification for a large negative To.  It is really quite simple.  But I am going to conclude for today.

Which of the many projections do I endorse?

Which projections are better – the ones based on CW06, CW11, JE08, HW04, or H07?  None of them.  As I have pointed out over and over, the Rahmstorf model is bogus, bogus, bogus.  I have now shown, again, that it is also not robust.  It is only marginally better than a random number generator.  HIgher temperatures would likely lead to higher sea levels, but Rahmstorf’s model is useless in determining how much.

Data files with specifics of of my implementation of Rahmstorf’s model using Holgate sea level data

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Chao 2oo8
Ground water depletion: none
Result files…
Summary: vr-summary-121110-165152.doc
Inputs: vr-input-image-121110-165152.png
Fit: vr-fit-image-121110-165152.png
Projections: vr-projections-image-121110-165152.png

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2010 extrapolated to 1880
Result files…
Summary: vr-summary-121029-132349.doc
Inputs: vr-input-image-121029-132349.png
Fit: vr-fit-image-121029-132349.png
Projections: vr-projections-image-121029-132349.png

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2010
Result files…
Summary: vr-summary-121029-132148.doc
Inputs: vr-input-image-121029-132148.png
Fit: vr-fit-image-121029-132148.png
Projections: vr-projections-image-121029-132148.png

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2012
Result files…
Summary: vr-summary-121105-230616.doc
Inputs: vr-input-image-121105-230616.png
Fit: vr-fit-image-121105-230616.png
Projections: vr-projections-image-121105-230616.png

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Pokhrel 2012 extrapolated back to 1900
Ground water depletion: Pokhrel 2012 extrapolated back to 1900
Result files…
Summary: vr-summary-121029-133403.doc
Inputs: vr-input-image-121029-133403.png
Fit: vr-fit-image-121029-133403.png
Projections: vr-projections-image-121029-133403.png

Sea level data: Holgate and Woodworth 2004
Reservoir storage: Pokhrel 2012
Ground water depletion: Pokhrel 2012
Result files…
Summary: vr-summary-121029-132906.doc
Inputs: vr-input-image-121029-132906.png
Fit: vr-fit-image-121029-132906.png
Projections: vr-projections-image-121029-132906.png

Sea level data: Holgate 2007
Reservoir storage: Chao 2008
Ground water depletion: none
Result files…
Summary: vr-summary-121029-133753.doc
Inputs: vr-input-image-121029-133753.png
Fit: vr-fit-image-121029-133753.png
Projections: vr-projections-image-121029-133753.png

Sea level data: Holgate 2007
Reservoir storage: Chao 2008
Ground water depletion: Wada 2010 extrapolated to 1880
Result files…
Summary: vr-summary-121029-135519.doc
Inputs: vr-input-image-121029-135519.png
Fit: vr-fit-image-121029-135519.png
Projections: vr-projections-image-121029-135519.png

Sea level data: Holgate 2007
Reservoir storage: Chao 2008
Ground water depletion: Wada 2010
Result files…
Summary: vr-summary-121029-134334.doc
Inputs: vr-input-image-121029-134334.png
Fit: vr-fit-image-1209121029-134334.png
Projections: vr-projections-image-121029-134334.png

Sea level data: Holgate 2007
Reservoir storage: Chao 2008
Ground water depletion: Wada 2012
Result files…
Summary: vr-summary-121029-135834.doc
Inputs: vr-input-image-121029-135834.png
Fit: vr-fit-image-121029-135834.png
Projections: vr-projections-image-121029-135834.png

Sea level data: Holgate 2007
Reservoir storage: Pokhrel 2012 extrapolated to 1900
Ground water depletion: Pokhrel 2012 extrapolated to 1900
Result files…
Summary: vr-summary-121029-175833.doc
Inputs: vr-input-image-121029-175833.png
Fit: vr-fit-image-121029-175833.png
Projections: vr-projections-image-121029-175833.png

Sea level data: Holgate 2007
Reservoir storage: Pokhrel 2012
Ground water depletion: Pokhrel 2012
Result files…
Summary: vr-summary-121029-140159.doc
Inputs: vr-input-image-121029-140159.png
Fit: vr-fit-image-121029-140159.png
Projections: vr-projections-image-121029-140159.png

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[1]  Rahmstorf, S., et. al., “Testing the robustness of semi-empirical sea level projections” Climate Dynamics, 2011

[2] Church, J. A., and N. J. White, “A 20th century acceleration in global sea-level rise“,  Geophys. Res. Lett., 33, 2006

[3] Church, J. A. and N.J. White, “Sea-level rise from the late 19th to  the early 21st Century“, Surveys in Geophysics, 2011

[4] Jevrejeva, S., et. al. “Recent global sea level acceleration started over 200 years ago? ,”  Geophys. Res. Lett., 35, 2008

[5] Holgate, S. J. and Woodworth, P.L., “Evidence for enhanced coastal sea level rise during the 1990s,” Geophys. Res. Lett., 31, 2004

[6] Holgate, S.J., “On the decadal rates of sea level change during the twentieth century,” Geophys. Res. Lett., 34, 2007

[7] Moss, et. al., “The next generation of scenarios for climate change research and assessment,” Nature, 463, 2010

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Rahmstorf (2011): Robust or Just Busted (Part 5): Why a paper about “robustness”

September 29, 2012

This is part 5 of a multi-part series about “Testing the robustness of semi-empirical sea level projections,” Rahmstorf, et. al., Climate Dynamics, 2011. You can see an index of all parts here. I frequently refer to this paper as R2011.

I will refer to Stefan Rahmstorf’s ”Testing the robustness of semi-empirical sea level projections” as R2011 [1].

What does R2011 mean by “robust?”

What does Rahmstorf mean when he says his model linking sea level to temperature is “robust?”  Simply this: when the inputs that he deems acceptable are inserted into his model, he gets the results he likes.

How does he decide which inputs are acceptable?  Easy – if they yield the results he likes, then they are acceptable.  It is a very simple and efficient system of logic!

Why a paper about “robustness?”

Rahmstorf and his associates have a pressing need to defend their sea level rise projections.  I have presented a host of reasons why his model is bogus.  One of the most embarrassing is that one of his fit parameters, that he expected to be positive, is in fact negative for every combination of input tried.  This leads to all kinds of bizarre results (see here, here and here , for example).  The other is that his sea level projections dropped dramatically when his preferred source of 20th century historical input data updated their data set.

This “robustness” paper (R2011) is a stumbling attempt to dismiss the revised sea level data from the source that he had previously enthusiastically used.

A quick recap

Rahmstorf’s model, which I will refer to as the VR2009[2] model, attempts to relate global sea level rise to global temperature through the following formula…

where H is sea level and T is temperature.  Insert historical data for H and T,  and solve to a, b, and To.  Then insert projected temperatures for the 21st century and calculate projected sea level rises for the 21st century.  The VR2009 model and approach have an amazing number of problems and the list just keeps getting longer.  There is a whole family of realistic temperature scenarios for the 21st century that cause this model to yield ridiculous results (see here).  The root of most of these problems comes from the fact that every set of historical sea level inputs and temperatures that Rahmstorf and associates have tried result in a negative b.  That includes every set of input data considered in R2011 (see figure 1, below).

Model inputs and projections in R2011

(click to enlarge) …

FIGURE 1. R2011’s projections of 21st century sea level rise and baseline temperatures under the RCP45 emissions senario (Moss, 2010)[3] for various temperature and sea level input data sets.

I have circled the results R2011 likes.  As you can see, nothing involving the Church’s and White’s 2011 sea level data (CW11)[4] meets R2011’s  quality standard.  R2011 has determined that Church’s and  White’s 2006 sea level data (CW06)[5] is better than Church’s and White’s 2011 data, despite the fact that Church and White obviously think their updated 2011 data is better.

It comes down to To

Why does R2011 think the 2006 sea level data is better than the improved 2011 sea level data?  Well, I have already explained that – the 2006 Church and White sea level data gives the results that R2011 wants – higher sea level rise projections for the 21st century!

But they can’t really say that.  Instead they say that the 2011 Church and White data leads to a baseline temperature, To, that they insist is too low.  To is the steady-state temperature deviation from the 1950-1980 average temperature at which Rahmstorf’s model says the sea level would be unchanging.

Look at the right side of figure 1.  It shows the baseline temperature that R2011 derived with the various sets of input data.  The values of To that meet with R2011’s approval average out to about -0.43 degrees.  But those based on CW11 average out to about -0.62 degrees C.  A difference of less than two tenths of a degree.

If you were to ask the authors of R2011 what other evidence do they have that To must be about -0.43 degrees, they will refer you to “Climate related sea-level variations over the past two millennia[6],” which used evidence from two salt marshes in North Carolina to corroborate this global value.  And they have great confidence in this independent confirmation (because two out of three of the R2011 authors were also authors on this paper).  Hmmm.

I will have more to say about R2011’s preference for To in a later post.

A few input combinations that R2011 did not show you

R2011 implies that it has tried some vast universe of input sea level and temperature data combinations in their model. They say “We then compare projections of all these different model versions (over 30)…”  Wow! Count them – over 30!

But there are many more possible combinations than that.  R2011 has picked a few cherries from a very prolific tree.

In figures 2 and 3, below, I have run several temperature and sea level input data sets in my implementation of Rahmstorf’s model.  In some cases my input combinations are the same as some found in figure 1.  In some cases they are different.  I have arranged the input combinations in chronological order, with older versions of input data on the bottom.  Notice a trend?  Figure 2 and figure 3 give projections based on the RCP45  and RCP85 emission scenarios, respectively.

FIGURE 2. Sea level rise projections for the 21st century based on my implementation of Rahmstorf’s model under the RCP45 emissions scenario (Moss, 2010) for various temperature and sea level input data sets.
FIGURE 3. Sea level rise projections for the 21st century based on my implementation of Rahmstorf’s model under the RCP85 emissions scenario (Moss, 2010) for various temperature and sea level input data sets.

As you can see, newer sea level data (whether it is actually sea level (CW06 vs CH11, or reservoir storage (RS) or ground water depletion (GWD)  modifiers) tends to lead to lower 21st century projections when inserted into Rahmstorf’s model.

Which projection do I endorse? None of them.  Make no mistake – the Rahmstorf model is bogus, no matter what the inputs are.  I am just playing games with it.  The Rahmstorf model is an illusion that hooks you with a simple truth: It is a pretty good bet that higher temperatures lead to higher sea levels.  But the Rahmstorf model is not much better than a Ouija board for quantifying how much.

There is much to be said about the results in figures 2 and 3.  The 48 files below give the long story that is summarized in figures 2 and 3.

Much more to come in later posts

Sea level data: Church and White 2006
Reservoir storage: Chao 2oo8
Ground water depletion: none
Result files…
Summary: vr-summary-120923-091214.doc
Inputs: vr-input-image-120923-091214.png
Fit: vr-fit-image-120923-091214.png
Projections: vr-projections-image-120923-091214.png

Sea level data: Church and White 2006
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2010 extrapolated to 1880
Result files…
Summary: vr-summary-120923-091326.doc
Inputs: vr-input-image-120923-091326.png
Fit: vr-fit-image-120923-091326.png
Projections: vr-projections-image-120923-091326.png

Sea level data: Church and White 2006
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2010
Result files…
Summary: vr-summary-120923-091413.doc
Inputs: vr-input-image-120923-091413.png
Fit: vr-fit-image-120923-091413.png
Projections: vr-projections-image-120923-091413.png

Sea level data: Church and White 2006
Reservoir storage: Chao 2oo8
Ground water depletion: Wada 2012
Result files…
Summary: vr-summary-120923-091517.doc
Inputs: vr-input-image-120923-091517.png
Fit: vr-fit-image-120923-091517.png
Projections: vr-projections-image-120923-091517.png

Sea level data: Church and White 2006
Reservoir storage: Pokhrel 2012 extrapolated back to 1900
Ground water depletion: Pokhrel 2012 extrapolated back to 1900
Result files…
Summary: vr-summary-120923-091643.doc
Inputs: vr-input-image-120923-091643.png
Fit: vr-fit-image-120923-091643.png
Projections: vr-projections-image-120923-091643.png

Sea level data: Church and White 2006
Reservoir storage: Pokhrel 2012
Ground water depletion: Pokhrel 2012
Result files…
Summary: vr-summary-120923-091727.doc
Inputs: vr-input-image-120923-091727.png
Fit: vr-fit-image-120923-091727.png
Projections: vr-projections-image-120923-091727.png

Sea level data: Church and White 2011
Reservoir storage: Chao 2008
Ground water depletion: none
Result files…
Summary: vr-summary-120923-091904.doc
Inputs: vr-input-image-120923-091904.png
Fit: vr-fit-image-120923-091904.png
Projections: vr-projections-image-120923-091904.png

Sea level data: Church and White 2011
Reservoir storage: Chao 2008
Ground water depletion: Wada 2010 extrapolated to 1880
Result files…
Summary: vr-summary-120923-091956.doc
Inputs: vr-input-image-120923-091956.png
Fit: vr-fit-image-120923-091956.png
Projections: vr-projections-image-120923-091956.png

Sea level data: Church and White 2011
Reservoir storage: Chao 2008
Ground water depletion: Wada 2010
Result files…
Summary: vr-summary-120923-092105.doc
Inputs: vr-input-image-120923-092105.png
Fit: vr-fit-image-120923-092105.png
Projections: vr-projections-image-120923-092105.png

Sea level data: Church and White 2011
Reservoir storage: Chao 2008
Ground water depletion: Wada 2012
Result files…
Summary: vr-summary-120923-092202.doc
Inputs: vr-input-image-120923-092202.png
Fit: vr-fit-image-120923-092202.png
Projections: vr-projections-image-120923-092202.png

Sea level data: Church and White 2011
Reservoir storage: Pokhrel 2012 extrapolated to 1900
Ground water depletion: Pokhrel 2012 extrapolated to 1900
Result files…
Summary: vr-summary-120923-092330.doc
Inputs: vr-input-image-120923-092330.png
Fit: vr-fit-image-120923-092330.png
Projections: vr-projections-image-120923-092330.png

Sea level data: Church and White 2011
Reservoir storage: Pokhrel 2012
Ground water depletion: Pokhrel 2012
Result files…
Summary: vr-summary-120923-094501.doc
Inputs: vr-input-image-120923-094501.png
Fit: vr-fit-image-120923-094501.png
Projections: vr-projections-image-120923-094501.png

_________________________________

[1]  Rahmstorf, S., Perrette, M., and Vermeer, M., “Testing the robustness of semi-empirical sea level projections” Climate Dynamics, 2011

[2] Vermeer, M., Rahmstorf, S., “Global sea level linked to global temperature,” PNAS, 2009

[3] Moss, et. al., “The next generation of scenarios for climate change research and assessment,” Nature, 463, 2010

[4] Church, J. A. and N.J. White, “Sea-level rise from the late 19th to  the early 21st Century“, Surveys in Geophysics, 2011

[5] Church, J. A., and N. J. White, “A 20th century acceleration in global sea-level rise“,  Geophys. Res. Lett., 33, 2006

[6] Kemp, Horton, Donnelly, Mann, Vermeer & Rahmstorf,  “Climate related sea-level variations over the past two millennia,” PNAS, 2011