Posts Tagged ‘temperature’

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The Search for Acceleration, part 10, US Gulf Coast

February 17, 2014

magnifying glass 145This is part 9 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.  Such a rise rate is needed  to 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.

U.S. Gulf Coast

This region  has 4 tide gauge sites with at least 90% data completion between 1950 and 2008.  Three of the sites have data back to 1930 or earlier .  I will analyse this data in my usual manner: detrending, weighting, averaging and derivatives.

This slideshow shows my standard analysis.

This slideshow requires JavaScript.

Conclusion

One thing is certain from the above graphs: the sea level rise rate in the US Gulf Coast region has not shown an acceleration in the last part of the 20th century or the 21st century. The rise rate reached a peak in the 1940s and has been dropping since around 1970.

Keep in mind that there are many factors that contribute to the rise rate in this region.  Subsidence is the primary cause, and subsidence itself has multiple components.

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The Search for Acceleration, part 7, Western North America

July 30, 2013

magnifying glass 145This is part 7 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.  Such a rise rate is needed  to 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.

Western North America

This region  has 13 tide gauge sites with at least 90% data completion between 1950 and 2008.  Seven of the sites have data back to 1920 or earlier (but with some gaps).  I will analyse this data in the same manner as the Australian data.  I will start with the usual detrending, weighting, averaging and derivatives.  Then, I will find the portion of the sea level that is orthogonal to the ENSO3.4 sea surface temperature.

This slideshow shows my standard analysis.

This slideshow requires JavaScript.

 

ENSO

Like Australia, the sea level around the Western coast of North America seems to be related to the El Nino Southern Oscillation.  The following plot shows an overlay of the detrended weighted average of the 13 Western North American tide gauge sites and the NINO3.4 index from the Hadley Centre.  Both are detrended from 1920 to 2008.  Note that the ENSO data scale is inverted.

Enso and Western North America

Now I will  remove the part of the sea level data that correlates to ENSO  by breaking the sea level data down into ENSO correlated and ENSO orthogonal parts. If the ENSO orthogonal part of the sea level is truly independent of ENSO, then it shows what the sea level around Australia would look like without an ENSO effect. Here is the formula for finding the ENSO orthogonal component of the of the sea level data.

 

Conclusion

The highest rise rate during the period covered by this data occurs around 1980.  But that peak was gone before the the beginning of satellite data.  The 1990s and 2000s show some high and low rise rates, but the highs are no higher than the 1930s, and the lows are lower than the 1940s.  Despite some periods of high rise rates in the 1990s and 2000s, the average rise rate does not indicate a large acceleration over the earlier part of the century.  These conclusions are the same whether or not the ENSO correlated part of the sea level is removed.

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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

ENSO/Global warming relationship: Cobb, et. al., Science, 339, 1/4/13

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The Search for Acceleration, part 6, Australia

July 17, 2013

magnifying glass 145This is part 6 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.

Australia

Australia has only 5 tide gauge stations with data sets that are at least 90% complete going back to 1960, but four of those go back to 1940 or earlier.  I will analyse this data in my usual way (detrend, weight, average, and derivative).

Regional sea level rise rates are usually swamped by things other than just global effects.  In the case of Australia we may be able to disentangle one of these effects – the El Nino Southern Oscillation.  I will also consider the component of the Australian sea level data that is orthogonal to the ENSO3.4 sea surface temperature.

The slide show shows my standard analyse.

This slideshow requires JavaScript.

ENSO

The El Nino Southern Oscillation dominates the sea level around Australia.  The following plot shows an overlay of the detrended weighted average of the five Australian tide gauge sites and the NINO3.4 index from the Hadley Centre.  I am including tide gauge data after 1915 which include at least two tide gauge sites at all times and no large data gaps.  The similarities are obvious.

ENSO sea level overlay

Let’s try to remove the ENSO effect from the sea level around Australia. I will do that by breaking the sea level data down into an ENSO correlated and ENSO orthogonal parts. If the ENSO orthogonal part of the sea level is truly independent of ENSO, then it shows what the sea level around Australia would look like without an ENSO effect.  Here is the formula for finding the ENSO orthogonal component of the of the sea level data.

orthogonal formula440

The top of each of the following slides shows the weighted, detrended, averaged Australian sea level (white), ENSO3.4 sea surface temperature (blue),  and the component of sea level data that is orthogonal to the ENSO3.4 data (red).  The bottom of each slide shows the corresponding relative rise rates associated with sea level (white) and with the ENSO orthogonal component of the sea level (red).  Each successive slide shows the same original data with increasing Gaussian smoothing.

The most important thing to notice is that when the ENSO influence is removed the sea level rise rate at the end of the century is significantly reduced.

ENSO and global warming

If the higher relative rise rates at the end of the century are due to ENSO, then it is interesting to ask whether ENSO fluctuations are greater now (because of global warming?) than in the past.  The best answer to this question can be found in Highly Variable El Niño-Southern Oscillation Throughout the Holocene (Cobb, et. al., Science, 339, 1/4/13).

The abstract states…

Twentieth-century ENSO variance is significantly higher than average fossil coral ENSO variance but is not unprecedented. Our results suggest that forced changes in ENSO, whether natural or anthropogenic, may be difficult to detect against a background of large internal variability.

and the body of the paper mentions…

[T]he detection (and attribution) of any changes in ENSO properties would require very long time series spanning many centuries, to the extent that detection of such changes is even possible.

[M]uch of the observed differences in ENSO variance over the past 7 ky reflect strong internal variability… Relatively robust 20th-century ENSO variability may reflect a sensitivity to anthropogenic greenhouse forcing, but definitive proof of such an effect requires much longer data sets than are currently available, given the large range of natural ENSO variability implied by the available fossil coral data.

Conclusion

According to my usual analysis the rise rate at the end of the century was clearly higher than the average (from 1940 to present), but no higher than the 1940s.   Does the reconcile the satellite data and tide gauge data?  Yes.

But, when the part of the detrended sea level that is correlated to ENSO3.4 is removed, the remaining orthogonal part of the rise rate appears to be lower at the end of the century than during the 1940s, and not particularly high compared to the rest of the century. So if my removal of the ENSO effect is correct, then there was nothing “unusual” about the rise rate at the end of the century
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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

ENSO/Global warming relationship: Cobb, et. al., Science, 339, 1/4/13

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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

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Library of data for testing “robustness” of Rahmstorf models

September 5, 2012

This is part 3.5 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 have finally published my small library of temperature, sea-level and sea-level modifier (reservoir storage, groundwater depletion, etc.)  data from various sources.

All of these data files have a consistent format which can be read by my code that calculates fit parameters for the Rahmstorf model relating sea level to temperature.  However, not all of the time series are long enough to be useful in that model.

You can see the data files here.

I am open to suggestions for additions to this list.  If you have any criticisms of the files, such as accuracy of the data, format, selection, anything – please leave a comment.  I will give due attention to any legitimate criticism that is aimed at improving the data.

Coming soon…

I am a slow worker, but I try to be thorough.

The first output from my code, using Rahmstorf’s preferred inputs (GISS temperature, Church and White 2006 sea level data, and the Chao reservoir correction) will be presented soon.  The goal of that presentation will be two-fold: to verify that of my model implementation are consistent with Rahmstorfs; to have a simple format for presenting those result.  That format can then be applied to the results of other input data.

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Rahmstorf: Is it OK to call him an “alarmist” now?

May 9, 2012

Some folks never give up.  In the following video Stefan Rahmstorf says…

To me a tipping point in the climate system is like a sweet spot in the climate system, where a small perturbation can have a major, even qualitative effect.  It’s like a small change in temperature moving, for example, the Greenland Ice sheet beyond the point where eventually it will melt down all together…from about 2 degrees global warming there would be a risk of the complete meltdown of the Greenland Ice sheet…I think this two degree limit agreed in Cancun by the politicians may not be enough to prevent a dangerous interference in the climate system.

Now let’s be clear about this: a “complete meltdown” of the Greenland ice sheet would raise the planet’s sea level 7 meters (7000 mm).  The sea level rise rate today is about 3 mm per year and decreasing according to satellite data.  A rational reading the tide gauge data is even less.

I guess in Greenland ice must melt at -25°C.  Here is today’s temperature outlook…

Oh, I know, the scientifically sophomoric sophisticated will tell us all about the rapidly accelerating glaciers.  Well, their favorite journal, Science, throws a little icy cold water on their dreams of catastrophic nirvana.  In 21st-Century Evolution of Greenland Outlet Glacier Velocities ( T. Moon, et. al., Science, 4 May 2012, Vol. 336, pp. 576-578)  Moon et. al. produced “a decade-long (2000 to 2010) record documenting the ongoing velocity evolution of nearly all (200+) of Greenland’s major outlet glaciers.”  They found that in some regions there was a glacier acceleration (SEE! SEE!), but not very consistently over the last 10 years.  Here is their conclusion

Our observations have implications for recent work on sea level rise. Earlier research (33) used a kinematic approach to estimate upper bounds of 0.8 to 2.0 m for 21st-century sea level rise. In Greenland, this work assumed ice-sheet–wide doubling of glacier speeds (low-end scenario) or an order of magnitude increase in speeds (high-end scenario) from 2000 to 2010. Our wide sampling of actual 2000 to 2010 changes shows that glacier acceleration across the ice sheet remains far below these estimates, suggesting that sea level rise associated with Greenland glacier dynamics remains well below the low-end scenario (9.3 cm by 2100) at present. Continued acceleration, however,may cause sea level rise to approach the low-end limit by this century’s end. Our sampling of a large population of glaciers, many of which have sustained considerable thinning and retreat, suggests little potential for the type of widespread extreme (i.e., order of magnitude) acceleration represented in the high-end scenario (46.7 cm by 2100). Our result is consistent with findings from recent numerical flow models (34).

So, Rahmstorf is worried about a “complete meltdown of the Greenland ice sheet” which would lead to 7 meters (7000 mm) of sea level rise, but the data shows “sea level rise associated with Greenland glacier dynamics remains well below the low-end scenario (9.3 cm by 2100)” (93 mm by 2100).  Does being off by a factor of 75 (7000/93) qualify as “alarmist?”

By the way, when Moon says “Earlier research (33) used a kinematic approach to estimate upper bounds of 0.8 to 2.0 m for 21st-century sea level rise” he is talking about Kinematic Constraints on Glacier Contributions to 21st Century Sea-Level Rise (Pfeffer, et. al., Science, 5 September 2008, Vol. 321. no. 5894, pp. 1340 – 1343).  I discussed this paper at length two years ago in my “Reply to John Mashey.” (Still feeling smug, John?) 

And finally,  Moon’s last sentence says “Our result is consistent with findings from recent numerical flow models (34).”  He is talking about Committed sea-level rise for the next century from Greenland ice sheet dynamics during the past decade (Price, et. al., PNAS, 31 May 2011, vol. 108 no. 22 pp. 8978-8983).    Price, et. al. say

The modeling conducted here and some reasonable assumptions can be used to make approximate upper-bound estimates for future SLR from GIS [Greenland Ice Sheet] dynamics, without accounting for future dynamical changes explicitly. As discussed above, numerous observations indicate that the trigger for the majority of dynamic thinning in Greenland during the last decade was episodic in nature, as the result of incursions of relatively warm ocean waters. By assuming that similar perturbations occur at regular intervals over the next century and that the ice sheet responds in a similar manner, we can repeatedly combine (sum) the cumulative SLR [sea level rise] curve from Fig. 4B to arrive at additional estimates for SLR by 2100. For example, if perturbations like those during the last decade recur every 50, 20, or 10 y during the next 100 y, we estimate a cumulative SLR from GIS dynamics by 2100 of approximately 10, 25, and 45 mm, respectively…Addition of the estimated 40 mm of SLR from changes in SMB [surface mass balance] by 2100 would result in a total SLR from Greenland of 85 mm by 2100.

Holy cow! Rahmstorf is telling us to be worried about 7000 mm of sea level rise due to the “complete meltdown of the Greenland ice sheet,” but Price et. al. say maybe 85 mm due to Greenland by 2100.

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