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A Lagrangian trajectory and isotopic fractionation (Flexpart-MCIM) approach to modelling the isotopic composition of rainfall over the British Isles.

University of East Anglia
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  • Earth Science
  • Geography


A LAGRANGIAN TRAJECTORY AND ISOTOPIC FRACTIONATION (FLEXPART-MCIM) APPROACH TO MODELLING THE ISOTOPIC COMPOSITION OF RAINFALL OVER THE BRITISH ISLES. A thesis submitted to the School of Environmental Sciences of the University of East Anglia in partial fulfilment of the requirements for the degree of Doctor of Philosophy By Katherine Ann Teresa Eames June 2008 c© This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author’s prior, written consent. Abstract A novel approach to modelling the oxygen and hydrogen isotope ratios of rainfall over the British Isles is presented. The model process involves two stages. First, a Lagrangian particle dispersion model (FLEXPART) that uses European Centre for Medium Range Weather Forecasting Reanalysis (ECMWF ERA-40) fields to produce ensembles of back trajectories of the three–dimensional path of air parcels prior to rainfall events. Second, physical atmospheric parameters along these trajectories are then input in to a Mixed Cloud Isotope Model (MCIM) to predict the isotopic ratio of rainfall. Models of the movement of oxygen and hydrogen isotopes through the hydrological system are vital to gain understanding of the isotopic systems so as to improve the use of isotopes as palaeoclimate proxies to uncover information about the past. A case study comparing daily observed isotopic values with modelled values for the same day is presented for Norwich for raindays in November and December 2005. The re- sults of this comparison are very promising for the simulation of δ18O, δD and deuterium excess for events where more than 3 mm but less than 15 mm of rain fell. A positive relationship is seen between the modelled and observed values, i.e. higher modelled val- ues correspond to higher observed values

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