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Professor Myles Allen CBE FRS

Statutory Professor

Research theme

  • Climate physics

Sub department

  • Atmospheric, Oceanic and Planetary Physics
Myles.Allen@physics.ox.ac.uk
Telephone: 01865 (2)72085,01865 (2)75895
Atmospheric Physics Clarendon Laboratory, room 109
  • About
  • Publications

Attribution of twentieth century temperature change to natural and anthropogenic causes

CLIMATE DYNAMICS 17:1 (2001) 1-21

Authors:

PA Stott, SFB Tett, GS Jones, MR Allen, WJ Ingram, JFB Mitchell
More details from the publisher

Identifying signals from intermittent low-frequency behaving systems

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 53:4 (2001) 469-480

Authors:

A Hannachi, MR Allen
More details from the publisher

Natural and Anthropogenic Causes of Recent Climate Change

Chapter in Detecting and Modelling Regional Climate Change, Springer Nature (2001) 275-290

Authors:

DC Hill, MR Allen, NP Gillett, SFB Tett, PA Stott, GS Jones, W Ingram, JFB Mitchell
More details from the publisher

The influence of subseasonal wind variability on Tropical Instability Waves in the Pacific

GEOPHYSICAL RESEARCH LETTERS 28:10 (2001) 2041-2044

Authors:

RE Benestad, RT Sutton, MR Allen, DLT Anderson
More details from the publisher

Application of hierarchical estimation methods to interpolation of SST data

European Space Agency, (Special Publication) ESA SP (2000) 21-25

Authors:

MJ Murray, MR Allen, PW Fieguth

Abstract:

Estimation methods for the interpolation of sea surface temperature (SST) were discussed. SST data was collected using the Along Track Scanning Radiometer (ATSR). Dynamic estimation of SST was carried out using a recursive estimation algorithm which emulates the Kalman filter. Key steps in the estimation process included removal of underlying mean SST field, characterization of spatial and temporal statistics appropriate to SST in Tropical Pacific, establishment of suitable treatment of daytime and nightime data, and the assimilation of the data over time. The capability of assimilating data over time and dealing with spatially-varying prior model was demonstrated by the study.

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