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T. Ansell, R. J. Allan, D. E. Parker, N. Rayner, & P. Brohan Hadley Centre, Met Office

Development of a daily gridded mean sea level pressure dataset over the North Atlantic – European region from 1850 – 2003. T. Ansell, R. J. Allan, D. E. Parker, N. Rayner, & P. Brohan Hadley Centre, Met Office D. Lister & P. D. Jones CRU, University of East Anglia. Outline.

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T. Ansell, R. J. Allan, D. E. Parker, N. Rayner, & P. Brohan Hadley Centre, Met Office

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  1. Development of a daily gridded mean sea level pressure dataset over the North Atlantic – European region from 1850 – 2003 T. Ansell, R. J. Allan, D. E. Parker, N. Rayner, & P. Brohan Hadley Centre, Met Office D. Lister & P. D. Jones CRU, University of East Anglia

  2. Outline • Overview of project • Data sources • Quality control & gridding procedure • Blending and interpolation • Error estimates • Diagnostics • Notes to user

  3. EMULATE Project European and North Atlantic daily to MULTidecadal climATE variability • WP1: To create daily gridded MSLP fields from 1850 • WP2: derive a set of characteristic atmospheric circulation patterns • WP3: relate variations and trends in atmospheric circulation and associated surface climate variability over Europe to SST patterns, particularly North Atlantic SST. • WP4: relate variations and trends in atmospheric circulation patterns to prominent extremes in temperature and precipitation

  4. Daily - sub daily pressure sources • Terrestrial data • EU projects e.g. IMPROVE • WASA • Digitised Russian, UK, French and Middle Eastern. • Marine data • ICOADS (blend of COADS and Met Office’s marine data bank, plus newly digitised Japanese Kobe and US Maury collections) • NCEP – GTS observations • Met Office Northern Hemisphere operational ‘superfiles’ • NCEP-NCAR reanalyses

  5. Monthly pressure sources • ADVICE (station and gridded fields) • HadSLP (gridded fields – global) • Kaplan (gridded fields – marine only) • NCEP/NCAR reanalyses • Needed for: • land station adjustments, • QC background field for gridding marine data and for blending of land and marine fields.

  6. Strategy • Use Superfiles 1881-2003 • Extend back to 1850 with blended land station data and ICOADS obs • Interpolate to obtain complete coverage Progress • Version 1 (complete) • gridded marine obs and 41 stations from 1850-1880 • combined with superfiles 1881-2002 • Version 2 (complete by end May) • gridded marine obs and 82 stations from 1850-2002 and • blended with superfiles • Version 3 (complete by end June) • Additional land station observations

  7. Distribution of land and island stations

  8. Quality control procedure (1) • Land and island stations • corrections • standard gravity & temperature, • mean sea level, • diurnal cycle • correct to GMT • Homogeneity checks (near neighbours) • Limited by a lack of meta data (eg site changes, observation time change, change of instrument). • Adjust daily observations to match reference monthly • means

  9. Homogenisation a) Near neighbours – Gibraltar (red), Cadiz (blue) 1850-1880 b) Gibraltar minus Cadiz 1850-1880

  10. Adjustments Astrakhan (1868-1880) • Form monthly means • Compare to reference • station or grid point value • Difference in the monthly means is the ‘adjustment’ value • Filter adjustments to remove jumps at end of each month • Take adjustment from each daily value. a) unadjusted series (red), adjusted (blue) b) Monthly adjustments

  11. Quality control procedure (2) • Marine • diurnal cycle corrections • duplicate removal • adjust US Maury observations for low MSLP bias • create residuals by removing monthly background field from each observation • screen residuals against intra monthly variability and ‘maximum’ permitted difference to background • - Grid to 1x1 degree grid boxes, applying a ‘smoothing’ and ‘infilling’ procedure • median rather than mean (more robust to outliers) • add back monthly background field • ** assume gravity corrections have been applied at source

  12. 7 degree ‘smoothing’ ‘smoothing’ ‘in filling’ 0 0 0 0 0 0 0 - - - - - - - 0 0 0 0 0 0 0 - - - - - - - 0 0 0 0 0 0 0 - - - - - - - 0 0 0 X 0 0 0 - - - X - - - 0 0 0 0 0 0 0 - - - - - - - 0 0 0 0 0 0 0 - - - - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ‘0’ = data present, ‘-’ = no data, ‘X’ = target box

  13. Gridding & Blending procedure (1) • Gridding land station data and marine obs (1850-2002) • remove the monthly background field value from each daily station observation and each daily marine 1x1 degree grid box value. • Assemble all land observations in each 1x1 degree box and the corresponding marine value and take the median residual value. • Regrid to 5x5 degree grid points (weighted). • Add back the monthly background field value. • Fields have relativelysparse coverage between 1850-1880. Marine observations are sparse during 1860-1870

  14. Fraction of decade with observations present in each grid box 1850-1859 1860-1869

  15. Fraction of decade with observations present in each grid box 1930-1939 1940-1949

  16. Gridding & Blending procedure (2) • Blending • fill missing values in the blended land & marine fields with the aid of superfiles data post 1881. • Blending is done via the solution of Poisson’s equation, where the forcing term is the Laplacian of our less reliable, but complete field (superfiles). Result : near complete fields 1950 onwards • then employ reduced space optimal interpolation (after Kaplan et al, 1997). Result :spatially complete fields.

  17. RSOI theory • Method to reconstruct fields by fitting observed (incomplete) data in EOF space, using classical least squares approach. • Least squares fit constrained to give greatest weight to data with smaller estimated error variance. • Large scale features of variable are recovered, presumed to be of largest climatic importance • Major assumption is that EOFs describe a set of patterns that are dominant throughout the reconstruction period. • Crucial to obtain a reliable estimate of the: • space covariance matrix & • error fields

  18. Grid box uncertainties G = SLP + ES+ EM +EQC + Esys + EEB • Marine observations • sampling and measurement error (ES , EM) • QC error associated with smoothing procedure during gridding (EQC). • Systematic error (Esys) estimated from comparison of charts of observations and model analyses • Land station observations • Elevation related bias (EEB) associated with reduction to mean sea level. Estimated from comparison of Addis Ababa with model analyses at that grid point: EEB= h/1500 • Systematic error (Esys)

  19. Method to estimate EM& ES in gridded data Background • Time series of a gridded data will show variability on a number of time scales (long term trend, decadal, interannual, high frequency). • Latter is partly due to EM and ES • To isolate the variability due to EM & ES, remove the trend in the grid box series • To separate the actual high frequency variability to that due to EM and ES, note that the true MSLP variability will be independent of the number of observations, but the magnitude of EM + ES will not. • Variability increases when number of observations is small.

  20. Methodology • Calculate the variability as function of number of observations • take all the points in the anomaly time series where nobs = 1 and calculate the standard deviation. Repeat for each number of observations. • Fit a curve, following the expression for the standard error of the mean of n partly correlated obs (Jones et al, 1997) :sqrt(v2 (1+(n-1)r)/n), • where v = standard deviation for a single ob and r = correlation between the obs • For n<50, the fit to curve • gives v= 9.1 and r= 0.11 • using v2 = m2 + s2 and • r = s2/v2, the measurement • and sampling error (m) • = 8.6 hPa for one observation • and the ‘true’ MSLP variability • (s) = 3.0 hPa

  21. Measurement and sampling error field for one ob. Measurement and sampling error for n obs :m/sqrt(n)

  22. Procedure to estimate EQC uncertainties • using NCEP/NCAR-reanalyses, withhold data to represent historical sampling and apply gridding and QC procedure. • Repeat with n realisations, randomly withholding data, and compare each realisation with complete fields • White noise has been added to the reanalysis fields, as an examination has shown that NCEP/NCAR fields were too smooth. • Initial results give an estimate of the error associated with the QC and gridding procedure of 2-3 hPa in the midlatitudes

  23. Diagnostics • Comparisons with other MSLP products: • ADVICE, Smith & Reynolds, NCEP/NCAR reanalyses • Variability • Extreme events • ADVICE monthly means & climatologies. • NAO

  24. Comparisons with monthly ADVICE

  25. a) Winter (DJF) NAO index Gibraltar – Reykjavik (1850-2002) Azores – Iceland (1867-2004)

  26. Gibraltar – Reykjavik (1850-2002) a) winter (DJF) b) summer (JJA)

  27. Notes to user • Daily MSLP fields over the North-Atlantic European region on a 5 deg latitude x 5 degree longitude grid from 1850-2003, with associated grid box error estimates. • Available as a spatially complete data set. • Will be available to the wider climate science community in November 2004. • Some problems in the Greenland region (in later period) • Correction applied for low marine MSLP bias in 1850-1860s • EOFS used in RSOI calculated over the modern period. • HadSLP2 (monthly gridded MSLP data set) 1850-2003 available early next year.

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