Joe Romm eco-fanaticism shown in Sea-Level Rise claims

The previous post was quite long and involved. But to see why Jo Romm is so out of order in criticizing President Trump’s withdrawal from the Paris Climate Agreement, one only has to examine the sub-heading of his rant  Trump falsely claims Paris deal has a minimal impact on warming. –

It may be time to sell your coastal property.

This follows with a graphic of Florida.

This implies that people in Southern Florida should take in account a 6 metre (236 inch) rise in sea levels as a result of President Trump’s decision. Does this implied claim stack up. As in the previous post, let us take a look at Climate Interactive’s data.

Without policy, Climate Interactive forecast that US emissions without policy will be 14.44 GtCO2e, just over 10% of global GHG emissions, and up from 6.8 GtCO2e in 2010. At most, even on CIs flawed reasoning, global emissions will be just 7% lower in 2100 with US policy. In the real world, the expensive job-destroying policy of the US will make global emissions around 1% lower even under the implausible assumption that the country were to extend the policy through to the end of the century. That would be a tiny fraction of one degree lower, even making a further assumption that a doubling of CO2 levels causes 3C of warming (an assumption contradicted by recent evidence). Now it could be that every other country will follow suit, and abandon all climate mitigation policies. This would be a unlikely scenario, given that I have not sensed a great enthusiasm for other countries to follow the lead of the current Leader of the Free World. But even if that did happen, the previous post showed that current policies do not amount to very much difference in emissions. Yet let us engage on a flight of fancy and assume for the moment that President Trump abandoning the Paris Climate Agreement will (a) make the difference between 1.5C of warming, with negligable sea-level rise and 4.2C of warming with the full impact of sea-level rise being felt (b) 5% of that rise. What difference will this make to sea-level rise?

The Miami-Dade Climate Change website has a report from The Sea Level Rise Task Force that I examined last November. Figure 1 of that report gives projections of sea-level rise assuming the no global climate policy.

Taking the most extreme NOAA projection it will be around the end of next century before sea-levels rose by 6 metres. Under the IPCC AR5 median estimates – and this is meant to be the Climate Bible for policy-makers – it would be hundreds of years before that sea-level rise would be achieved. Let us assume that the time horizon of any adult thinking of buying a property, is through to 2060, 42 years from now. The NOAA projection is 30 inches (0.76 metres) for the full difference in sea-level rise, or 1.5 inches (0.04 metres) for the slightly more realistic estimate. Using the mainstream IPCC AR5 median estimate, sea-level rise is 11 inches (0.28 metres) for the full difference in sea-level rise, or 0.6 inches (0.01 metres) for the slightly more realistic estimate. The real world evidence suggests that even these tiny projected sea level rises are exaggerated. Sea tide gauges around Florida have failed to show an acceleration in the rate of sea level rise. For example this from NOAA for Key West.

2.37mm/year is 9 inches a century. Even this might be an exaggeration, as in Miami itself, where the recorded increase is 2.45mm/year, the land is estimated to be sinking at 0.53mm/year.

Concluding Comments

If people based their evidence on the real world, President Trump pulling out of the Paris Climate Agreement will make somewhere between zero and an imperceptible difference to sea-level rise. If they base their assumptions on mainstream climate models, the difference is still imperceptible. But those with the biggest influence on policy are more influenced by the crazy alarmists like Joe Romm. The real worry should be that many policy-makers State level will be encouraged to waste even more money on unnecessary flood defenses, and could effectively make low-lying properties near worthless by planning blight when there is no real risk.

Kevin Marshall

 

Rignot et al 2011 on ice sheet melt acceleration – reconciling with sea level rise

In this posting I will look at Rignot et al 2011 and compare with the University of Colorado “official” level figures.

The basic conclusions are

  1. Rignot et al 2011 estimate that over an 18 year period the acceleration in polar ice melt contribution around 1.8mm extra to annual sea level rise that either stayed constant at 3.2mm, or showed a slight decline. Examination of the Rignot figures suggests this result is a bit too neat.
  2. Although that acceleration in ice melt are not reflected in average long-term rise, large short-term variations ice-melt in the rate of ice melt do show up in swings in the rate of sea level rise two or three years later. In fact using an 11 month moving average sea level rise, the fit on some of those changes in sea levels are almost a mirror image of that rise.
  3. The mirrored similarities between the graphs would be improved by removing the acceleration trend in the ice-melt.
  4. When I measure the magnitude of the swings in the sea levels they are six to eight times greater than the expected 1mm rise from 365 billion tonnes of water. Considering possible errors makes the problem worse and would reduce the similarities between the two graphs.

This is where I need people to critically review the figures. Point 1 I believe is beyond doubt. Points 2 and 3 are easy to replicate if you have something like Excel 2010 where you can ghost one graph over another. But on point 4 I have been scratching my head over for a couple of days now, and cannot see any way that will correct the figures in the right direction.

The Rignot Paper Abstract

Rignot et al 2011 et al says the following.

Ice sheet mass balance estimates have improved substantially in recent years using a variety of techniques, over different time periods, and at various levels of spatial detail. Considerable disparity remains between these estimates due to the inherent uncertainties of each method, the lack of detailed comparison between independent estimates, and the effect of temporal modulations in ice sheet surface mass balance. Here, we present a consistent record of mass balance for the Greenland and Antarctic ice sheets over the past two decades, validated by the comparison of two independent techniques over the last 8 years: one differencing perimeter loss from net accumulation, and one using a dense time series of time variable gravity. We find excellent agreement between the two techniques for absolute mass loss and acceleration of mass loss. In 2006, the Greenland and Antarctic ice sheets experienced a combined mass loss of 475 ± 158 Gt/yr, equivalent to 1.3 ± 0.4 mm/yr sea level rise. Notably, the acceleration in ice sheet loss over the last 18 years was 21.9 ± 1 Gt/yr2 for Greenland and 14.5 ± 2 Gt/yr2 for Antarctica, for a combined total of 36.3 ± 2 Gt/yr2. This acceleration is 3 times larger than for mountain glaciers and ice caps (12 ± 6 Gt/yr2). If this trend continues, ice sheets will be the dominant contributor to sea level rise in the 21st century.

In 2006 combined mass loss was 475 billion tonnes, sufficient to raise sea levels 1.3mm. This gives 365 billion tonnes to raise sea levels by 1mm, something that I have verified in other studies. So the paper estimates the sea level rise from Greenland and Antarctic ice sheets mass loss is accelerating by 0.1mm per annum. The authors also claim that from mountain glaciers and ice caps there is a further 0.033mm of acceleration. Multiply that by 18 years means melting ice must be raising sea levels in 2011 by around 2.4mm more than in 1993. We can also backtrack to when the start point of net ice melt by deducting 36.3 from yearly figure. In 1993, the first year of the study, ice melt comes to 3.1 billion tonnes. That is effectively zero.

Further, the 2006 ice mass loss has a calculated uncertainty figure at 158 is quite large. To the nearest whole number it is exactly one-third of 475 central ice-mass loss figure. From this we can then calculate the upper and lower uncertainty bands on the contribution to sea level rise in 1993. The upper band is to assume the highest figure for 2006 (+475+158) and the lowest acceleration (+36.3-2). The lower band is to assume the lowest figure for 2006 (+475-158) and the highest acceleration (+36.3+2).

I have put the results in giga tonnes, and equivalent sea level rise into a couple of tables below.

Rignot2011 implied polar ice melt

The whole structure of figures seems somewhat contrived. However, it does not mean that the figures are outside of the bounds of what could be actually happening. To assess that we must look to the seasonally adjusted sea level rise figures from the University of Colorado.


At this stage, the point to note is the trend line. Since the first satellite data in 1993, sea levels have been rising at about 3.2 mm per year. This I have plotted against the modelled contribution from Rignot.


If you add in the contribution from mountain glaciers and ice caps, in 18 years ice melt is alleged to have increased from 0% to 75% of the sea level rise trend in that period. That does not stack up. A model that fits so conveniently into the study period and nicely rounds to sea level rise equivalents, shows a trend that is completely out of line with the sea level rise.

To probe further, I downloaded the sea level data figures from the University of Colorado.

Replicating the Colorado Sea Level Rise graph

From the website I downloaded the seasonally adjusted figures, as that is what Rignot had used for ice melt.

I only use Excel, not the R statistical package like proper statisticians use. So I had to simplify the data. Please bear with me, as my “dumb” analysis still yields some very interesting results in the next section.

There are currently 710 lines of data, of which the first is “1992.9595 -5.800“. Each data point has a date and a value. The four places of decimals enable not only the actual day, but even the hour do be identified. As such, if readings are irregularly spaced, it will not bias the analysis, despite there only being about three readings a month, or one every ten days. I split the data into calendar months by first multiplying the decimal by days in the year and doing a lookup from here. I then took the average value per month. I know that February is likely to be under represented, by with 245 months of data, it will not make much of a difference to the general picture.

To validate this, I created a simplified version of the cumulative sea level rise graph. It still shows a cumulative rise of 64mm in 20 years. It shows the data points and a 5 month centred moving average.


The problem with the graph is that it visually under represents significant short term fluctuations. The rise in the last twelve months is obtained by subtracting figure from 12 months previous from the current month. This generated some enormous fluctuations. I therefore created 11 month and 35 month centred moving averages.


Even though smoothed, there are some pretty large fluctuations in the rate of sea level rise, particularly in 1997 to 2000 and after the start of 2010.

The 35 month centred is much smoother and it is possible to see a distinct decline from 2001 to 2007 by more than 50%.

Rignot graph compared

The Rignot et al 2011 paper posts three nice graphs of estimated ice losses. There are separate ones for Greenland and Antarctica, then a combined graph. It is this graph C that I reproduce below.


The annualised rate takes each month reading and multiplies by 12. So change is exaggerated?

My graph smooth the peaks, as it is a moving average. However, the Rognot graph appears to be almost a mirror of the sea level rise 11-month centred, except ice mass balance has a slope. For instance

  • Rognot has a peak of ice loss around the end of 1994, with an opposite peak of ice gain by a low at the end of 1994. The rate of sea level rise peaked in early 1996, followed by sharp change to small decline in sea levels at the end of 1998.
  • Rognot has a peak of ice loss at the end of 2007, with an opposite low in ice loss in early 2009. The rate of sea level rise peaked in early in late 2009, followed by sharp change to significant decline in sea levels in early 2012.

The peaks and troughs are so similar I flipped over the Rignot graph and aligned it up with rate of sea level rise graph.


Not only do major turning points correspond, but whole sections as well. The delay in the ice sheet melt is reflected in sea rise about 20 to 36 months after, so it not that exact. Also, a much better fit would be obtained if Rignot had not built in a slope in the graph. It seems the acceleration is anomalous.

I have uploaded a file here that contains the images. In Excel try moving the upper image over the lower one, without releasing. I have also included another graph with a centred 13 month moving average. It obtains similar results. It seems that in Rignot et al polar ice melt model (modified for slope) would appear to be an excellent short-run predictive model of the rate sea level rise.

There is a slight snag with my numbers. Before doing the above comparison, I looked at various movements between high and low points.


I have had to estimate the sea ice changes and dates of changes from the graph. In light blue is the number of gigatonnes of ice/water that is apparently needed to raise see levels by 1mm. The figure in deeper blue is how many times more effective Rignot ice melt appears to be in raising sea levels. I cannot see where I have made an error in this table. Or, at least, everything I can think of makes the problem worse. Here are some possibilities.

  1. Annualised data on ice-melt. Each monthly figure is multiplied by twelve. But smoothing would not only make the movements less of a fit between graphs but would exaggerate the problem.
  2. Moving average figure for sea level rise. But de-smoothing would not only make the movements less of a fit between graphs but would exaggerate the problem. Further, I would expect a lagged response to smudge the sharpness of the initial impulse, note make it more acute.

Please look at the figures carefully, as there is bound to be an issue. After all, one (slightly) manic beancounter is more likely to be wrong that four leading experts in their field.

Kevin Marshall

NB. I have comment moderation set for anyone who has not previously commented. If you would like to contact, but do not wish publication, please use the comments, making this clear. I will respect any non-threatening request.

Rignot, E., I. Velicogna, M. R. van den Broeke, A. Monaghan, and J. Lenaerts (2011), Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise, Geophys. Res. Lett., 38, L05503, doi:10.1029/2011GL046583.,

Two Comments on Antarctic Ice Accumulation

Jo Nova blogs on a study that claims the Antarctic continent is accumulating ice mass at a rapid rate. I have made two comments. One is opposing someone who claims that Antarctica is actually losing ice. The other is that the claimed rate of ice accumulation does not make sense against known data on sea levels.

Manicbeancounter

April 17, 2013 at 6:27 am · Reply

John Brooks says

I’m also interested that the mass of antarctic land ice follows solar irradiance. This makes perfect sense. However I can’t see why the effective of an increase in the greenhouse effect wouldn’t have exactly the same result.

Maybe you should look at the period covered by the graph John. There is an 800 year correlation of mass of antarctic land ice with solar irradiance, with the biggest movements in both prior to 1800. Insofar as the greenhouse effect is significant, it is nearly all after 1945.

And for some reason, I’ve got the idea in my head that antarctic land ice is decreasing.

Sure enough from the Carbon Brief link, this quote

Measurements from the Gravity Recovery and Climate Experiment (GRACE) satellite since 2002 have shown that the mass of the Antarctic ice sheet is decreasing at an average rate of 100 cubic kilometres every year – the size of a small UK city.

(emphasis mine)
The size of a city is usually measured in area, not volume. The ancient City of York, for instance, has an area of 272 square kilometres (105 square miles) and a population of 125,000. Or maybe they mean the volume of the buildings in a city? A famous building in New York is the Empire State Building. Not only is it quite tall it also has quite a large volume. Around 1,040,000 cubic metres or 0.001 cubic kilometres in fact. So does the Carbon Brief claim that a small UK city have a volume of buildings equivalent to 100,000 Empire state buildings? Or each average person in a small UK city occupies a building volume greater than Buckingham Palace?
Alternatively, does John Brooks quote a source that does not have a clue about basic maths?

Manicbeancounter

April 17, 2013 at 8:01 am · Reply

I think this paper does not stack up. I worked as a management accountant in industry for 25 years. One thing I learnt early on when estimating or forecasting was to sense-check the estimates. No matter how good your assumptions are, when estimating or extrapolating well beyond the data trend (where there is potential for error), the best check on the data is by reconciling with other data.
From the above

“The SMB of the grounded AIS is approximately 2100 Gt yr−1, with a large interannual variability. Those changes can be as large as 300 Gt yr−1 and represent approximately 6% of the 1989–2009 average (Van den Broeke et al., 2011).”

A gigatonne of ice is equivalent to a cubic kilometre of water. If the land ice volume is increasing, the water must come from somewhere. Nearly all of that water needs to come from the oceans.
Now for some basic maths. A gigatonne is a billion tonnes. As water has a relative density of 1.0, a tonne of water (1,000 litres) is a cubic metre. Therefore a gigatonne of water is a cubic kilometre (1000^3 = 1,000,000,000 = one billion).
A further factor to consider is the area of the oceans. According to my Times Concise Atlas, the total area of the oceans and seas (excluding the enclosed waters like the Dead Sea and Lake Baykal) is 325,000,000km^2. A cubic kilometre of water added to an enclosed sea of one million square kilometres, would raise the sea level by just 1mm (1000mm x 1000m = 1,000,000mm in a kilometre). So 325km^3 = 325Gt-1 of new ice accumulation above sea level in Antarctica would reduce sea levels by 1mm, or 2100GT-1 by 6.5mm.
Some of the ice accumulation will be on ice shelves, so the impact of 2100GT-1 extra ice per annum extra ice might be to reduce sea levels by just 5mm per annum. Also sea levels might be rising by a little less than the 3.2mm a year that official figures claim, but there is no evidence that sea levels are falling. Further, any net ice melt elsewhere (mostly Greenland) is only adding 1mm to sea level rise. So the rest must be mostly due to thermal expansion of the oceans. I think that the evidence for the oceans heating is very weak and of insignificant amounts. Even Kevin Trenberth in his wildest flights of fantasy would not claim the missing heat (from the air surface temperatures) adds more than 1-2mm to sea level rise.
What this study does show is that by honestly looking at data in different ways, it is possible to reach widely different conclusions. It is only by fitting the data to predetermined conclusions (and suppressing anything outside the consensus) that consistency of results can be achieved.

My scepticism on global warming stems from a belief that scientific evidence is strengthened by being corroborated from independent sources. Honest and independent data analysis means that wildly different conclusions can be reached. Comparing and contrasting these independent sources leads me to believe that the public face of the global warming climate change consensus massively exaggerates the problem.

Kevin Marshall

Cold water on sea level rise alarmism

The new article in Nature on “Recent contributions of glaciers and ice caps to sea level rise” (Jacob et al. 2012) is in stark contrast to what has gone before. It is far from the previous claims.

The main estimates before Jacob et al. 2012 were:-

  • The Himalayan Glaciers will disappear by 2035. (UNIPCC AR4 2007) Changed to the Himalayan Glaciers may disappear by 2350. (UNIPCC 2010)
  • The Grace Satellite data shows that the polar ice caps are not only melting, but the melt rate is accelerating. Velicogna 2009 claimed that the acceleration in Greenland was −30 ± 11 bnt/yr2 to 286 bnt/yr-1 in 2007 to 2009, and in Antarctica was −26 ± 14 bnt/yr2 to 246 bnt/yr-1 in 2007 to 2009. Concentrating on the period from 2006 to early 2009 for Antarctica only , Chen et al. 2009 estimated that the continent was losing ice at the rate of 190 ± 77 bnt/yr-1, two-thirds is of which comes from West Antarctica, covering about a quarter of the total land surface area. By 2010, the loss from both polar caps would, by Veligona’s estimate be 600 to 650 bnt/yr-1.
  • The average of these two articles was that in 2010 there would be around 600 bnt/yr-1 loss per year.
  • One of the articles’ authors, Prof John Wahr of University of Colarado, Boulder, had previously stated that the Grace measurements indicate an accelerating trend in Greenland. The current graph at Wahr’s website for Greenland shows a distinct accelerating trend through to the start of 2010.

    Mass variability summed over the entire Greenland Ice Sheet, monthly Gravity Recovery and Climate Experiment (GRACE) results (black line; the orange line is a smoothed version) April 2002 and December 2009.

    Prof John Wahl’s graph of Greenland Ice sheet loss, indicating a doubling of the rate of loss over the period to around 150 bnt/yr-1 in 2009.

  • In Zwally and Giovinetto 2011, using three separate estimation techniques, and including the pre-satellite data from 1992 to 2002, estimated the range of +27 to -40 bnt/yr-1.

The new paper in Nature:-

  • Estimates no net loss from the Himalayas in the period 2003 to 2010. When the claim that the Himalayas would lose their glaciers by 2035, Rajendra Pauchari, head of the UNIPCC said the doubts were “voodoo science”. Now even the more moderate claim of melting over hundreds of years looks to be in doubt. Josh has penned a cartoon to illustrate this point.

  • Velicogna 2009, seems somewhat extreme. The Nature paper would estimates a loss of 50% to 75% Velicogna estimate for 2010.
  • Most importantly, there is no mention of acceleration of ice melt from the polar ice caps. This sudden turn-around might be to a sudden change in the data. The sea level rise appears to have stalled in the last 18-24 months, so the sea ice melt (which the Nature paper estimates accounts for 40% of the sea level rise) may have stalled as well. (See Appendix 2). It is necessary to re-run the Nature paper numbers for 2011 data to confirm if this is the case.

In conclusion, it looks that the new nature paper reaches a more moderate position than previous papers using the GRACE satellite data, as it uses a longer period, and subjects the data to a more detailed breakdown. However, in terms of the polar ice melt, it still more extreme than a paper that uses a longer timeframe and three distinct methods of calculation.

Appendix 1 – Leo Hickman in the Guardian has a breakdown of the figures, that nicely puts the issue in context.

Glaciers
Ignore Region Rate (Gt yr-1)
1 Iceland -11.±.2
2 Svalbard -3.±.2
3 Franz Josef Land 0.±.2
4 Novaya Zemlya -4.±.2
5 Severnaya Zemlya -1.±.2
6 Siberia and Kamchatka 2.±.10
7 Altai 3.±.6
8 High Mountain Asia -4.±.20
8a Tianshan -5.±.6
8b Pamirs and Kunlun Shan -1.±.5
8c Himalaya and Karakoram -5.±.6
8d Tibet and Qilian Shan 7.±.7
9 Caucasus 1.±.3
10 Alps -2.±.3
11 Scandinavia 3.±.5
12 Alaska -46.±.7
13 Northwest America excl. Alaska 5.±.8
14 Baffin Island -33.±.5
15 Ellesmere, Axel Heiberg and Devon Islands -34.±.6
16 South America excl. Patagonia -6.±.12
17 Patagonia -23.±.9
18 New Zealand 2.±.3
19 Greenland ice sheet.+.PGICs -222.±.9
20 Antarctica ice sheet.+.PGICs -165.±.72
  Total -536.±.93
  GICs excl. Greenland and Antarctica PGICs -148.±.30
  Antarctica.+.Greenland ice sheet and PGICs -384.±.71
  Total contribution to SLR -1.48.±.0.26
  SLR due to GICs excl. Greenland and Antarctica PGICs -0.41.±.0.08
  SLR due to Antarctica.+.Greenland ice sheet and PGICs -1.06.±.0.19

 

Appendix 2 – University of Colarado Sea level Rise Estimates