Paleothermometer

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A paleothermometer is a methodology that provides an estimate of the ambient temperature at the time of formation of a natural material. Most paleothermometers are based on empirically-calibrated proxy relationships, such as the

tree ring or TEX86 methods. Isotope methods, such as the δ18O method or the clumped-isotope method
, are able to provide, at least in theory, direct measurements of temperature.

Common paleothermometers

δ18O

Main article: δ18O

The

isotopic ratio
of 18O to 16O, usually in foram tests or ice cores. High values mean low temperatures. Confounded by ice volume - more ice means higher δ18O values.

Ocean water is mostly H216O, with small amounts of HD16O and H218O. In

Standard Mean Ocean Water (SMOW) the ratio of D to H is 155.8×10−6 and 18O/16O is 2005×10−6. Fractionation
occurs during changes between condensed and vapour phases: the vapour pressure of heavier isotopes is lower, so vapour contains relatively more of the lighter isotopes and when the vapour condenses the precipitation preferentially contains heavier isotopes. The difference from SMOW is expressed as

;

and a similar formula for δD. δ18O values for precipitation are always negative. The major influence on δ18O is the difference between ocean temperatures where the moisture evaporated and the place where the final precipitation occurred; since ocean temperatures are relatively stable the δ18O value mostly reflects the temperature where precipitation occurs. Taking into account that the precipitation forms above the inversion layer, we are left with a linear relation:

which is empirically calibrated from measurements of temperature and δ18O as a = 0.67‰/°C for Greenland and 0.76‰/°C for East Antarctica. The calibration was initially done on the basis of spatial variations in temperature and it was assumed that this corresponded to temporal variations (Jouzel and Merlivat, 1984). More recently, borehole thermometry has shown that for glacial-interglacial variations, a = 0.33‰/°C (Cuffey et al., 1995), implying that glacial-interglacial temperature changes were twice as large as previously believed.

Mg/Ca and Sr/Ca

Magnesium (Mg) is incorporated into the calcite shells (tests) of planktic and benthic

residence time in the ocean, and so it is possible to largely ignore the effect of changes in seawater Mg/Ca on the signal.[3] Mg/Ca ratios can sometimes underestimate seawater temperatures by way of the dissolution of foraminifer shells, which lowers Mg/Ca values.[4]

Strontium (Sr) incorporates in coral aragonite,[5][6] and it is well established that the precise Sr/Ca ratio in the coral skeleton shows an inverse correlation with the seawater temperature during its biomineralization.[7][8]

Alkenones

Further information: Alkenone and TEX86

Distributions of organic molecules in marine sediments reflect temperature.

Leaf physiognomy

The characteristic leaf sizes,

multivariate approaches integrate multiple leaf characters and climatic parameters. Temperature has been estimated (to varying degrees of fidelity) using leaf physiognomy for Late Cretaceous and Cenozoic leaf floras, principally using two main approaches:[10]

Leaf margin analysis

A

leaf margins (0 ≤ Pmargin ≤ 1) in vegetation varies proportionately with mean annual temperature (MAT[11]).[12] Requires the fossil flora to be segregated into morphotypes (i.e. ‘species’), but does not require their identification. The original LMA regression equation was derived for East Asian forests,[13]
and is:

MAT = 1.141 +(30.6 × Pmargin), standard error ± 2.0 °C

(1)

The error of the estimate for LMA is expressed as the binomial sampling error:[14]

(2)

where c is the slope from the LMA regression equation, Pmargin as used in (1), and r is the number of species scored for leaf margin type for the individual fossil leaf flora. LMA calibrations have been derived for major world regions, including North America,[15] Europe,[16] South America,[17] and Australia.[18] Riparian and wetland environments have a slightly different regression equation, because they have proportionally fewer smooth-margined plants. It is[19]

MAT = 2.223 +(36.3 × Pmargin), standard error ± 2.0 °C

(1′)

CLAMP (Climate leaf analysis multivariate program)

CLAMP is a multivariate approach largely based on a data set of primarily western hemisphere vegetation,

Canonical Correlation Analysis
is used combining 31 leaf characters, but leaf margin type represented a significant component of the relationship between physiognomic states and temperature. Using CLAMP, MAT is estimated with small standard errors (e.g. CCA ± 0.7–1.0 °C). Additional temperature parameters can be estimated using CLAMP, such as the coldest month mean temperature (CMMT) and the warmest month mean temperature (WMMT) which provide estimates for winter and summer mean conditions respectively.

Nearest living relative analogy / coexistence analysis

Certain plants prefer certain temperatures; if their pollen is found one can work out the approximate temperature.

13C-18O bonds in carbonates

There is a slight thermodynamic tendency for heavy isotopes to form bonds with each other, in excess of what would be expected from a

"clumped-isotope" geochemistry.[23][24]

See also

References

  1. doi:10.1016/j.epsl.2013.09.037
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  2. doi:10.1016/0016-7037(73)90091-4
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  3. doi:10.1016/S0016-7037(02)00941-9
    .
  4. doi:10.1017/S1089332600002564
    . Retrieved 14 July 2023.
  5. doi:10.2475/ajs.296.1.1
    .
  6. hdl:1834/4103
    .
  7. doi:10.1016/0016-7037(73)90015-X
    .
  8. doi:10.1016/0016-7037(94)90457-X
    .
  9. ^ Bailey, I.W. & Sinnott, E.W. 1916. The climatic distribution of certain kinds of angiosperm leaves. American Journal of Botany 3, 24 - 39.
  10. ^ Greenwood, D.R. 2007. North American Eocene Leaves and Climates: From Wolfe and Dilcher to Burnham and Wilf. In: Jarzen, D., Retallack, G., Jarzen, S. & Manchester, S. (Eds.) Advances in Mesozoic and Cenozoic Paleobotany: studies in celebration of David L. Dilcher and Jack A. Wolfe. Courier Forschungsinstitut Senckenberg 258: 95 – 108.
  11. ^ often written as 'annual mean temperature'; the mean of the monthly mean daily air temperatures for a location.
  12. ^ Wolfe, J.A. 1979. Temperature parameters of Humid to Mesic Forests of Eastern Asia and relation to forests of other regions of the Northern Hemisphere and Australasia. United States Geological Survey Prof. Paper 1106, 1 - 37.
  13. ^ Wing, S.L. & Greenwood, D.R. 1993. Fossils and fossil climates: the case for equable Eocene continental interiors. Philosophical Transactions of the Royal Society of London B 341, 243-252.
  14. ^ Wilf, P. 1997. When are leaves good thermometers? A new case for Leaf Margin Analysis. Paleobiology 23, 373-90.
  15. ^ Miller, I.M., Brandon, M.T. & Hickey, L.J. 2006. Using leaf margin analysis to estimate the Mid-Cretaceous (Albian) paleolatitude of the Baja BC block. Earth & Planetary Science Letters 245: 95–114.
  16. ^ Traiser, C., Klotz, S., Uhl, D., & Mosbrugger, V. 2005. Environmental signals from leaves – A physiognomic analysis of European vegetation. New Phytologist 166: 465–484.
  17. ^ Kowalski, E.A., 2002. Mean annual temperature estimation based on leaf morphology: a test from tropical South America. Palaeogeography, Palaeoclimatology, Palaeoecology 188: 141-165.
  18. ^ Greenwood, D.R., Wilf, P., Wing, S.L. & Christophel, D.C. 2004. Paleotemperature estimates using leaf margin analysis: Is Australia different? PALAIOS 19(2), 129-142.
  19. S2CID 54015435
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  20. ^ Wolfe, J.A. 1993. A method of obtaining climatic parameters from leaf assemblages. U.S. Geological Survey Bulletin, 2040, 73pp.
  21. ^ Spicer, R.A., 2008. CLAMP. In: V. Gornitz (Editor), Encyclopedia of Paleoclimatology and Ancient Environments. Springer, Dordrecht, pp. 156-158.
  22. ^ CLAMP online. "CLAMP1.HTM". Archived from the original on 2011-08-13. Retrieved 2011-05-18.
  23. doi:10.1016/j.epsl.2007.08.020
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  24. doi:10.1016/j.apgeochem.2011.03.080
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