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Isoscapes pp 195–219Cite as

Comprehensive Dynamical Models of Global and Regional Water Isotope Distributions

Abstract

Water isotope tracers can be included in comprehensive atmospheric models and can provide deeper insight to isotope distributions than simple physically-based isotope models or statistical methods because of their ability to resolve the underlying processes of interest. Within such models, isotope tracers follow normal “prognostic” water, and differ only in that fractionation is applied during surface evaporation and transpiration, cloud condensation processes, exchange between falling raindrops and environmental air, and when there are any extra water sources. The details of the mass balance that underlies modeling isotopes and the key processes can be represented in models are examined to illuminate how it is the compilation of many simple aspects which give rise to a comprehensive model. One particular advantage of dynamical isotope models is their ability to account for mixing of air masses by resolved larger-scale transport and by smaller scale parameterized turbulence. While the output from comprehensive dynamical isotope models is useful for subsequent applications, adequate accounting for model error is needed. In the past, few observations have been available to validate isotopic models, especially for vapor which is the model state variable of importance, and validating models remains a limitation. Nonetheless, comprehensive models are valuable in mapping water isotope distributions in vapor and precipitation. They are also well suited to diagnostic studies in which model sensitivity tests expose the physical basis for the final isotopic signal. This type of analysis is invaluable in guiding the interpretation of isotopic observations.

Keywords

  • Isotopic Composition
  • Comprehensive Model
  • Isotopic Exchange
  • Convective Cloud
  • Stratiform Cloud

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 10.1
Fig. 10.2
Fig. 10.3
Fig. 10.4
Fig. 10.5

Notes

  1. 1.

    Strictly the isotope ratio is the mole ratio of heavy to light isotopes, so one should use volume mixing ratio, which differs only by a constant (ratio of molecular weight of air and the vapor).

  2. 2.

    Consider the inequality that results from simplest one-dimensional centered finite difference approximation to Eq. (10.4):

    $ \frac{u}{{2\Delta x}}\left({q_i^{+} - q_i^{-} } \right) \ne \frac{u}{{2\Delta x}}\left[ {\frac{q_i}{q}\left({{q^{+} } - {q^{-} }} \right) + q\left({\frac{q_i^{+}}{q^{+}} - \frac{q_i^{-}}{q^{-}}} \right)} \right] $

    where the subscripts + and – denote values evaluated as some position x + Δx and x − Δx, and values without subscripts are evaluated at position x. The error that leads to the inequality is a result of the order of the finite approximation, and the size of the error can be as large as the isotope variations of interest in cases where the changes in q are large relative to q.

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Noone, D., Sturm, C. (2010). Comprehensive Dynamical Models of Global and Regional Water Isotope Distributions. In: West, J., Bowen, G., Dawson, T., Tu, K. (eds) Isoscapes. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3354-3_10

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