Q: Why are there no intra-annual patterns in global temperature anomalies?

A: because they are anomalies.

Climate sceptics want to be taken seriously. They want to hold joint conferences with climate scientists. It is not impossible for climate sceptics to do good research, though whether the odds are better than a monkey typing Hamlet is unclear on the evidence below.

A few weeks ago, Willis Eschenbach posted some CERES total solar irradiance (TSI) data at WUWT that showed that TSI varies from about 330 to 350 W m-2 over the course of a year. Eschenbach wondered why this >20 W m-2 difference was not obvious in the global temperature record.

… where is the effect of the ~ 22 W/m2 annual variation in the amount of sun hitting the earth?

He has some ideas. But not good ones.

To get an idea of the predicted effect of this variation in TSI, using IPCC figures this TSI change of 22 W/m2 is about the same change in forcing that we would get from six doublings of CO2 … that is to say, CO2 going from the current level (400 ppmv) to the extraordinary level of 25,600 ppmv.

In addition, again according to the IPCC, using their central value of 3°C warming per doubling of CO2 (3.7 W/m2 additional forcing), this change in forcing should be accompanied by a change in temperature of no less than 18°C (32°F).

Now, I can accept that this would be somewhat reduced because of the thermal lag of the climate system. But the transient (immediate) climate response to increased forcing is said to be on the order of 2°C per doubling of CO2. So this still should result in a warming of 12°C (22°F) … and we see nothing of the sort.

One would have hoped that Eschenbach had been interested in climate change long enough to remember some of the basic definitions. The transient climate response is not an immediate response. It is defined by the IPCC as.

… the change in the global surface temperature, averaged over a 20-year period, centred at the time of atmospheric carbon dioxide doubling, that is, at year 70 in a 1% yr–1 compound carbon dioxide increase experiment with a global coupled climate model.

The Earth’s climate will no more show an immediate 12°C response to the intra-annual variability in TSI than a kettle will instantly boil when plugged in. The climate system has an enormous thermal inertia, mainly in the oceans.

But shouldn’t there be some response to this variability in TSI? Yes, but the global temperature anomaly is an anomaly. For each January, the mean of the climate normal period (often 1961-1990) for January is subtracted. Ditto for February and the other months. This processing removes any tendency for Januaries to be warmer or colder than Julys. Eschenbach won’t find the pattern he is looking for because he is looking at the wrong data and he really ought to know this.

But what if he looked in the right place – the variability in absolute temperature as discussed by Jones et al (1999)

The annual cycle of global mean temperatures follows that of the land-dominated NH, with a maximum in July of 15.9°C and a minimum in January of 12.2°C.

Perihelion, the Earth’s closest approach to the Sun, currently occurs in early January. This timing probably mutes the annual cycle in global mean temperature by offsetting some of the hemispheric differences.

Not to be out done, Stan Robertson takes up Eschenbach’s theme without realising that it is nonsense.

… why don’t we see some significant annual cyclic variation of global mean temperature? This is a truly profound question! It ought to keep climate modelers awake all night, every night.

Yawn.

We do need a better class of climate sceptics.

Posted in Silliness, WUWT | Tagged , | 19 Comments

Time to recalibrate those radiocarbon dates?

Radiocarbon dating is probably the most important dating technique for palaeoecological research in the late Quaternary. One complication with 14C dating is that the concentration of 14C in the atmosphere has changed over time due to, for example, solar-driven changes in the 14C production rate. Radiocarbon dates need to be calibrated to correct for this using a calibration curve derived from a huge number of 14C dates on material of known age.

Every few years, the INTCAL community generates an updated 14C calibration curve that incorporating new data. This is a fantastic service to the community.

The current version of the calibration curve is INTCAL13, published in 2013. Many palaeoecologists will be working on material (or reviewing manuscripts) where the dates in the age-depth model were calibrated with a previous version of the calibration curve. Should you change to the new version of the calibration curve? This takes some effort, might mean that different papers published on the same core use different calibration curves, but might not make any material difference.

I’m going to explore how much difference it makes below.

Radiocarbon calibration curves

Radiocarbon calibration curves

The main difference between the curves is how far they extend: IntCal13 and IntCal09 go back to 50000 BP; IntCal04 goes back to 26000 BP; and IntCal98 goes back to 24000, but the last 8000 years of this is a straight line.

More detail on the differences between the curves can be see by plotting the difference of each curve from IntCal13.

Difference from IntCal13

Difference from IntCal13

Before about 12000 years there are substantial difference between the curves. I would want to recalibrate dates if any were older than this.

Difference from IntCal13 in the Holocene

Difference from IntCal13 in the Holocene

During the Holocene, there are some substantial difference between IntCal98 and IntCal13. I would certainly recalibrate any dates calibrated with IntCal98. The differences between IntCal04/09 and IntCal13 are and generally similar, but deviate by up to 40 years in a few places.

The uncertainty of the calibration curves have also changed.

Uncertainty in the calibration curves

Uncertainty in the calibration curves

The more recent calibration curves generally have a smaller uncertainty than the older curves. This is especially pronounced before the start of the Holocene.

For chronologies with pre-Holocene dates, it is certainly worth recalibrating and using the latest calibration curve. For Holocene chronologies, the differences between IntCal04/09/13 are probably not enough to change any conclusions except when high precision is required.

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Tibetan tree-rings and the sun

Via Maarten Blaauw’s Club du Soleil, I’ve found another paper reporting evidence of solar variability in tree-rings from Tibet. The evidence in the first was dubious, is Duan and Zhang (2014) any better?

Readers familiar with this series critically evaluating palaeoecological evidence for solar effects on climate may already have guessed that this is not going to end well. But have patience.

Duan and Zhang generate a 449-year maximum late wood density (MXD) data from Balfour spruce in the southeastern Tibetan Plateau using standard dendroclimate techniques. They use this record to “to reveal the long-term relationship between solar activity and temperature change in the study area”. They are utterly convinced that signal is there.

The analysis of the solar relationship starts with a Pearson correlation between MXD and sunspot numbers: r =  0.193. This is statistically significant, but the r2 is only 0.037, so this is explaining a tiny proportion of the variance in MXD.

The analysis moves on to the obligatory spectral analysis.

Figure 9. (a) The MTM spectral analysis of the MXD chronology over the period 1563–2011. (b) Thirty year moving correlation coefficients between the MXD chronology and solar sunspot number based on raw values, 11 year moving average and 22 year moving average, respectively.

Figure 9. (a) The MTM spectral analysis of the MXD chronology over the period 1563–2011. (b) Thirty year moving correlation coefficients between the MXD chronology and solar sunspot number based on raw values, 11 year moving average and 22 year moving average, respectively.

The multitaper spectral analysis finds a number of peaks that are significant (presumably against an AR(1) null hypothesis – which might not be full appropriate). The 11.7, 54 and 204 year cycles are claimed as solar variability. Since the time series is only 449 years long, there are only just over two 204-year cycles, so this peak corresponding to the de Vries cycle cannot be considered robust. The 54 year cycle is claimed as a fourth harmonic of the de Vries cycle – the first time I’ve seen anyone claiming to find this – looks like special pleading to me. The 11.7 year cycle matches the 11 year sunspot cycle fairly well, and I don’t blame the authors for getting excited. This result merits further investigation.

After the spectral analysis comes a running correlation. This is an analysis that is often done badly, but I have not seen it done worse than done here.  The 30 year running correlation of  MXD with sunspots is occasionally above the significance threshold used by Duan and Zhang, which appears to be the one-sided p = 0.05 level for 30 observations. There is no allowance for autocorrelation in the time series or for multiple testing. Duan and Zhang proceed to smooth both datasets with either a 11 or 22 year moving average and repeat the 30 year correlation. They believe the high correlations to be physically interesting, forgetting that they have induced a very strong autocorrelation and consequently have nearly no degrees of freedom available. The high correlations are an artefact of the smoothing rather than the importance of solar variability.

The paper reviews the literature for solar cycles in tree-rings. I would find the list more persuasive if I had not already discussed discussed some of the studies . It would be better still if the list was not inflated by papers reporting 14C variability in tree rings, which is of course entirely unrelated to any growth response trees might have to solar variability.

This paper offers at best weak evidence for the importance of solar variability, and many of the results are an artefact of the methods.


Duan and Zhang 2014. A 449 year warm season temperature reconstruction in the southeastern Tibetan Plateau and its relation to solar activity. Journal of Geophysical Research: Atmospheres

Posted in climate, Peer reviewed literature, solar variability | Tagged , | 3 Comments

Fossil fuel loving dinosaur

Anthony Watts suggested today that we could use the Cretaceous Period as an analogue for a 2°C warmer world. Excellent idea, we can use that warm period from the past to test how well climate models perform. If they perform well, it will increase the credibility of the model projections for the 21st Century. It’s is such a good idea that someone has already done it.

Oh wait, that not what he means. Watts thinks we can use the Cretaceous as an analogue for what it will be like to live in a 2°C warmer world.

Was the Cretaceous too warm for Earth’s diverse species? Absolutely not – the Cretaceous hosted a bounty of life and biodiversity, the emergence of the first flowering plants, the first appearance of our mammal ancestors. The Dinosaurs dominated the warm Cretaceous for 80 million years, a long period during which life flourished.

Cretaceous species had millions of years to adapt to warm conditions. Our current biota has perhaps a century. Species will need to adapt to the changing climate or migrate potentially hundreds of kilometres, often across human dominated landscapes, to remain in their ecological niche. Rates of climate change can be more important that the magnitude of change for biodiversity.

The dinosaurs didn’t care much about the high eustatic sea levels in the Cretaceous as they hadn’t built cities and nuclear power stations by the sea. Has Watts not noticed that the only dinosaurs are those that continue to promote the burning of fossil fuels, labouring under the delusion that they emit “harmless CO2 emissions.”

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Lasers, biomarkers and the Sun

The earliest work on Holocene palaeoecology focused on megafossils such as Pinus stumps. Then macrofossils such as hazel shells were used to reconstruct species distributions and climate. Then pollen analysis became important, complementing rather than supplanting the larger fossils. The end of this progress towards smaller and smaller proxies is found with biomarkers, chemical tracers of species presence and their environment.

Not only can the proxies used become smaller, the size of the sample required for the proxy can become smaller. This is hugely advantageous, allowing high resolution work and reducing competition for mud from a core. Perhaps the ultimate in small samples has been achieved with the nanogram samples in a new paper in PNAS by Wörmer et al (2014).

Wörmer et al shine a laser onto their sediment which causes organic biomarkers to be released from the sediment so they can be measured by a fancy mass spectrometer. The laser makes sub-millimetre spots on the surface of a core (as small as 10 µm) allowing extremely high resolution sampling, even in systems with low sedimentations rates.

Wörmer et al analyse some biomarkers produced by Archaea. These biomarkers are used by the Archaea in their cell membranes – the ratio of the different biomarkers changes with the temperature at which the cells grew to maintain membrane fluidity. Consequently,  the ratio of the different biomarkers can be used to infer past temperature. The ratio used by Wörmer et al is similar to the TEX86 temperature proxy (there are technical reasons why TEX86 cannot be used).

Obviously, if you are going to work on sediment cores at a very high resolution, you need cores that are undisturbed. If, for example, animals have burrowed through the sediment, mixing it, the high frequency variability will have been smeared out. Animals need oxygen to live, so sediments deposited in anoxic conditions will have little or no bioturbation, preserving the high frequency variability.

To demonstrate their methodology, Wörmer et al analysed a section of core from a period in the early Holocene when the eastern Mediterranean was anoxic. This anoxia occurred because higher precipitation in the Nile catchment led to fresher surface conditions. As fresh water is less dense than salty water, this made it more difficult to mix the surface and deep waters, so not enough oxygen could be supplied to the deep water. The resulting anoxic sediments are known as sapropels. Several are known from the Mediterranean; Wörmer et al analyse the most recent with a resolution of about 4 years.

Their sea surface temperature (SST) reconstruction is surprisingly dynamic. Thirty year SST means range between 24.1 and 30.7 °C. The authors note that this variability might be partially due to factors other that changes in SST, including changes in the ecotype of Archaea present, changing seasonality of production and changing depth (and hence temperature) of the chemocline where enhanced production of the biomarkers occurs.

Part of the problem is that Archaea ecology is poorly constrained. When early Scandinavian palaeoecologists found Pinus stumps in peat bogs the interpretation was obvious – Pinus once grew here, and we know a lot about Pinus ecology. Interpretation of pollen data is more complex – different species have different production rates and disperse pollen over different distances, but these factors are well know and fairly well constrained. Biomarkers produced by little known microbes give lots of great data, but the interpretation is hard.

Wörmer et al run a spectral analysis on their reconstruction and declare that they find evidence of solar variability in the data. This is where, as you might imagine if you have read my previous posts on palaeoecological evidence of solar variability, I cease to praise the paper.

Wörmer et al figure 3 (A) Downcore profile of CCaT and seven-point running average (red line). Data points are mean of ∼15 measurements. (B) Spectral analysis for the downcore CCaT values, theoretical red noise (dashed line) and 99% false alarm level (dotted line). (C) Mean-subtracted downcore profile of CCaT overlaid with band-pass filtered signals centered at a frequency of 0.79 cycles cm−1 (blue line).

Wörmer et al figure 3 (A) Downcore profile of CCaT and seven-point running average (red line). Data points are mean of ∼15 measurements. (B) Spectral analysis for the downcore CCaT values, theoretical red noise (dashed line) and 99% false alarm level (dotted line). (C) Mean-subtracted downcore profile of CCaT overlaid with band-pass filtered signals centred at a frequency of 0.79 cycles cm−1 (blue line).

There is one statistically-significant spectral peak with a periodicity of 212 years, similar to the de Vries ~200 year cycle. There is no evidence of a ~90 year Gleissberg cycle – there is a trough in the spectrum at the relevant frequency (1.8/cm). This might be thought a little odd – why should the proxy be sensitive to solar variability at one frequency but not another? Wörmer et al obviously thought so to, for they cite a modelling study, Seidenglanz et al 2012, as showing “the potential of the ∼200-y de Vries cycle, but not of the ∼90-y Gleissberg cycle, to impact both surface and middepth water temperature in the Mediterranean Sea.”

Seidenglanz et al run a climate model forced by either 200 or 90 year periodicity in solar irradiance and identify regions of the modelled ocean that have coherent temperature variability. It is an interesting paper. However there are problems for Wörmer et al. First, however useful climate models are at a global level, they are less reliable at a regional scale. Seidenglanz et al use a low resolution model (3.6°). This is minimal for work in the Eastern Mediterranean where there are very few grid boxes. Such a low resolution model cannot be expected to fully capture the oceanographic process in the eastern Mediterranean. Second, zooming right in to the figures in Seidenglanz et al, it would appear that the coherence at the surface is only significant for the 90 year cycle, and not for either cycle at mid-depths. All told, the Eastern Mediterranean results from Seidenglanz et al do not help Wörmer et al, but the ideas they present that different periodicities of solar variability might be detected in different proxy records is.

Any spectral analysis of proxy data is dependent on the precision of the chronology. However, I will not criticise the age-depth model for core GeoB 15103–1 analysed by Wörmer et al. There isn’t one. This is why the spectrum in figure 3 has the axis label cm-1 rather than the expected yr-1. Details of how Wörmer et al construct a chronology for a core with no dates are given in the supplementary material.

The unoxidized layer of sapropel 1 at our station is about 19 cm thick according to element concentrations obtained by XRF scanning (7, 8) (Fig. S9) and thus slightly thicker than most sapropel 1 layers (e.g., ref. 9). The pore water profile of Mn clearly indicates that the postdepositional oxidation (burn down) is still ongoing. The formation of sapropel 1 in the eastern Mediterranean is considered a basinwide synchronous event (10), and the unoxidized sapropel has been described to cover an average of 3,000 y of sediment deposition (e.g., ref. 11). Assuming a continuous deposition, this translates into a linear sedimentation rate of about 6.3 cm ky-1. This value is in general agreement with a sedimentation rate of 6.6 cm ky-1 for the interval between 6.31 and 7.60 ky [23 and 32 cm below seafloor (cmbsf); corresponding to the upper section of sapropel 1] derived from the age determination by Paterne [data deposited in Pangaea database, www.pangaea.de (doi: 10.1594/PANGAEA.407609)] for sediment core MD84-641 taken at immediate proximity to our study site.

There are problems here that mean that the mean sedimentation rate of 6.3 cm ky-1 is uncertain and hence the cycle length of 200 years is uncertain.

Rossignol-Strick (1999) (ref 11) argue that the deposition of the sapropel took place over about 3000 years (from 9000 to 6000 BP). However the sapropel can be oxidised after deposition and the remaining unoxidised sapropelic sediment can represent a shorter interval. At one site in the Levantine basin the top of the sapropel is at 7650 BP.

At MD84-641, adjacent to GeoB 15103–1, the top of the sapropel is 6500 BP. That would make the sapropel 2500 years long, a mean sedimentation rate of 7.6 cm ky-1, and a cycle length of 175 years.

The reference to Paterne for MD84-641 sends us right down the rabbit hole. The Pangaea.de page for this core links to a paper by Paterne discussing cores from the Tyrrhenian Sea (the other side of Italy). I’ve not managed to find the correct reference for the chronology of MD84-641, but it might be Fontugne et al (1989) which I have not managed to find online.

The sedimentation rate between 23 cm and 32 cm in core MD84-641 is not constant. It varies between 2.3 and 11.7 cm ky-1. Some of this variance will be due to uncertainty in the dates, but I think some is real and that the sedimentation rate is not constant. There is certainly enough uncertainty about the sedimentation rate of the small section of the sapropel in GeoB 15103–1 to be cautious that the cycle length matches the de Vries cycle. (NB – the dates are probably not calibrated, but that won’t change the argument much)

Enough with the chronology. Wörmer et al estimate their spectrum with REDFIT, a method suitable for unevenly spaced samples (although because they assume a constant sedimentation rate their samples are actually evenly spaced). The significance test in REDFIT assumes that the proxy comes from an AR(1) process. If this is not true, REDFIT cannot be expected to give unbiased estimates of the significance of spectral peaks. There is a test in REDFIT of whether a proxy record comes from an AR(1) process. Wörmer et al don’t use it. To be fair, I don’t think anybody has ever used it. Yes, most if not all of the papers using REDFIT to identify solar cycles in proxy data have not tested whether the assumptions of REDFIT are met. Oops. The spectrum in Wörmer et al looks suspiciously like what one would expect if the underlying process is not an AR(1) process. I’ll write more about this in the future – a couple of manuscripts are in preparation.

One last point. Wörmer et al identify a de Vries cycle in their proxy data during the late phase of sapropel 1, sometime between 8000 and 6000 BP. This is a period when there is no significant variability at the de Vries time scale in the cosmic isotope record of solar activity by Steinhilber et al (2012). It would seem unlikely that archaeal biomarkers would record a solar cycle not detectable by cosmic isotopes.

Comparison of solar activity (blue) and δ18O from Dongge cave, China (green). both records are detrended.  (A) Time series of solar activity (TSI) and δ18O. (B) Wavelet of solar activity (TSI).  Black boundaries mark 95% significance level. (C) Wavelet coherence of solar activity (TSI) and δ18O. De Vries cycle at approximately 210 y and Eddy cycle at approximately 1,000 y are marked with horizontal, gray dashed lines. Arrows pointing to the right indicate that the records are in phase. Black boundaries mark the 95% significance level.

Comparison of solar activity (blue) and δ18O from Dongge cave, China (green). both records are detrended. (A) Time series of solar activity (TSI) and δ18O. (B) Wavelet of solar activity (TSI). Black boundaries mark 95% significance level. (C) Wavelet coherence of solar activity (TSI) and δ18O. De Vries cycle at approximately 210 y and Eddy cycle at approximately 1,000 y are marked with horizontal, gray dashed lines.
Arrows pointing to the right indicate that the records are in phase.

The analytical biogeochemistry in Wörmer et al is excellent. However, the claim of solar variability in the biomarker record is not credible. I strongly suspect that the spectral peak is an artefact of the methods used, and in any case its period is too uncertain to assign to solar variability with confidence.

You can expect this paper to be cited by the next edition of the anti-IPCC report, the NIPCC.


Fontugne et al (1989) Initiation de la stratification de la Mediterrane orientale et debit du Nil a l’Holocene, in Past and Future Evolution of Deserts, Actes Colloque Prog. Int. Corr. Geol.(IGPC) 252, Jerba, Tunisia

Wörmer et al 2014. Ultra-high-resolution paleoenvironmental records via direct laser-based analysis of lipid biomarkers in sediment core samples. PNAS

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Dealing with transfer function outliers

Many transfer functions for inferring environmental conditions from species assemblages include observations with a large difference between the observed and estimated value of the environmental variable after cross-validation. These are the outliers: they make the performance of the transfer function worse than it would otherwise be.

What to do about them? My preference is to first check for transcription errors and then try to understand what is special about these observations.

  • Is there something unusual about the site that the outlier comes from? Perhaps the lake is unusually deep, affecting the proportion of planktonic diatoms in a pH calibration set. Or perhaps a lake receives snow-melt throughout the summer, decoupling the relationship between air temperature and chironomids.
  • Is it possible that the microfossil assemblage does not represent the modern community? Some ocean cores lack any Holocene deposition, so the core tops are from cold glacial conditions. Alpine lakes with low sedimentation rates might have surface sediments deposited in the little ice age.
  • Have taphonomic processes including transport and degradation of microfossils affected the assemblage?

These questions can be difficult to answer, so an alternative strategy is to delete any outliers above a certain threshold. The value of this threshold is critical as it will strongly affect the apparent performance of the transfer function. Because of this Juggins and Birks (2012)

 prefer to take a conservative approach to sample deletion and initially remove only outliers that have a standardised residual (under internal cross-validation (CV)) that is greater in absolute value than 2 or 2.5. This corresponds to an expected distribution of about 5% and 1% of observations, respectively.

I want to test the effect of this threshold on transfer function performance.

I’ve analysed the SWAP calibration set in the rioja package in R. I’ve fitted a weighted average (WA) model to the data, found the absolute value of the cross-validation residuals for each observation and then refitted the model omitting the n observations with the largest residual, where n is between 1 and 150. I’ve used the root mean square error of prediction (RMSEP) as my metric of model performance.

Upper panel shows the ranked absolute WA residuals of the SWAP calibration set. Lower panel shows how WA RMSEP changes when the n observations with the largest absolute residuals are deleted.

Upper panel shows the ranked absolute WA residuals of the SWAP calibration set. Horizontal lines mark one and two RMSEP. 
Lower panel shows how WA RMSEP changes when the n observations with the largest absolute residuals are deleted.

The pattern is clear. As the threshold for deleting observations is lowered, the RMSEP declines. This holds for a surprising long time – about 140 of the 167 observations can be deleted before the performance starts to degrade – and the model performance becomes excellent, less than a third of the original RMSEP.

A third of the original RMSEP. Who wouldn’t want a model that performed three times better than the original.

So is it a good idea to have a low threshold for deleting outliers? NO - it is cheating. It is a means to artificial improve the performance of the transfer function, deceiving the reader (and probably the author) as to how accurate and precise reconstruction made with the model will be.

The problem is obvious – it is not possible to clean the fossil observations used in the reconstruction in the same way as the modern calibration set was cleaned.

The guidelines set by Juggins and Birks (2012) are reasonable. Excessive data cleansing may be harmful to the prospects of your manuscripts in peer review.

 

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An honest view of sea level change?

Writing for the National Parks Traveller, ecologist  Daniel Botkin claims

the sea level has been rising since the end of the last Ice Age, starting about 14,000 years ago as the continental and mountain glaciers have melted and sea water has expanded with the overall warming. The average rate has been about a foot or two a century (about 23-46 cm per century). Data suggest that the rate was much greater until about 8,000 years ago.

Time to look at the evidence, conveniently published by Lambeck et al a couple of weeks ago.

Solution for the ice-volume Equivalent Sea Level (esl) function and change in ice volume. (A) Individual esl estimates (blue) and the objective estimate of the denoised time series (red line). Inset gives an expanded scale for the last 9,000 y. (B) The same esl estimate and its 95% probability limiting values. Also shown are the major climate events in the interval [the Last Glacial Maximum (LGM), Heinrich events H1 to H3, the Bølling-Allerød warm period (B-A), and the Younger Dryas cold period (Y-D)] as well as the timing of MWP-1A, 1B, and the 8.2 ka BP cooling event. (C) The 95% probability estimates of the esl estimates. (D) Estimates of sea-level rate of change.

Solution for the ice-volume Equivalent Sea Level (esl) function and change in ice volume. (A) Individual esl estimates (blue) and the objective estimate of the denoised time series (red line). Inset gives an expanded scale for the last 9,000 y. (B) The same esl estimate and its 95% probability limiting values. Also shown are the major climate events in the interval [the Last Glacial Maximum (LGM), Heinrich events H1 to H3, the Bølling-Allerød warm period (B-A), and the Younger Dryas cold period (Y-D)] as well as the timing of MWP-1A, 1B, and the 8.2 ka BP cooling event. (C) The 95% probability estimates of the esl estimates. (D) Estimates of sea-level rate of change.

At no time during the last 14 kBP has the sea level rise been 23-46 cm/century. Before 7 kBP, the sea level rose much faster than this. Since 7 kBP, the rate is much lower than this. In the last 3 kBP, the period of most relevance for comparing the modern rise, there was only minor (<20 cm) variability is sea level. Botkin’s numbers are without foundation, designed to distract from the anthropogenic contribution to sea-level rise.

Botkin goes on to cite Ross McKitrick paper approvingly

An important scientific paper published September 1 this year states that Earth’s surface temperature has not changed for the past 19 years, and 16-26 years for the lower atmosphere.

Well at least he gets the publication date correct.

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