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Trends in extremes in the ENSEMBLES daily gridded observational datasets for Europe. Nynke Hofstra and Mark New Oxford University Centre for the Environment. ENSEMBLES dataset. Daily dataset Europe 1950-2006 Precipitation and mean, minimum and maximum temperature Four different RCM grids - PowerPoint PPT Presentation
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Nynke Hofstra and Mark New
Oxford University Centre for the Environment
Trends in extremes in the ENSEMBLES daily gridded
observational datasets for Europe
ENSEMBLES dataset
• Daily dataset• Europe• 1950-2006• Precipitation and mean, minimum and
maximum temperature• Four different RCM grids• Kriging interpolation method for
anomalies, Thin Plate Splines for monthly totals/means
• 95% confidence intervalsHaylock et al. Submitted to JGR
Introduction
• How can this dataset be used for comparison with extremes of RCM output
• Required: ‘true’ areal averages
Introduction
• Several ways to calculate ‘true’ areal averages:– Interpolation of stations within grid (e.g.
Huntingford et al. 2003)– Osborn / McSweeney (1997, 2007) method
using inter-station correlation– More focused on extremes:
• Method of Booij (2002)• Areal Reduction Factors, like Fowler et al. (2005)
• But not enough station data available
Introduction
• Variance of the areal average influenced by amount of stations used
• Density of station network differs in time and space
Introduction
Haylock et al. (submitted JGR) Klok and Klein Tank (submitted Int. J. Climatol.)
Objective
• Understand what the influence of station density is on the distribution and trends in extremes of gridded data
• Focus: – Precipitation– Gamma distribution– Extreme precipitation trends
Contents
• Experiment
• Gamma distribution results
• Trends in extremes results
• Conclusions so far
• Further questions and applications
Experiment
• Similar setup to interpolation done for ENSEMBLES dataset
• One grid with 7 stations in or nearby
• 252 stations with 70% or more data available within a 450 km search radius
Experiment
Experiment
Experiment
• Calculate ‘true’ areal average of 7 stations
• Use Angular Distance Weighting (ADW) interpolation of– 100 random combinations of 4 – 50 stations– all stations
• First interpolate to 0.1 degree grid, then average over 0.22 degree grid
• ADW uses 10 stations with highest standardised weights and needs minimum 4 stations for the interpolation
Experiment
• Calculate the parameters of the gamma distribution– Using Thom (1958) maximum
likelihood method
• Calculate linear trends in extreme indices– Using fclimdex programme
Gamma distribution
α = 0.5
α = 1
α = 2 α = 3
α = 4
β = 0.5
β = 1
β =2 β = 5 β = 10
McSweeney 2007
Gamma distribution
• How well does the gamma distribution fit the data?
N=9051
Gamma distribution
• Dry day distribution and gamma parameters
Gamma distribution
α=0.6, β=4α=0.8, β=7
95th percentile
Gamma distribution
Trends in extremes
Trends in extremes
Conclusions so far
• Gamma scale parameter smaller for interpolated values– Smoothing– Small differences between
interpolated and ‘true’– Small differences using 4 or 50
stations for the interpolation
Conclusions so far
• Trend in interpolated values larger than in station values
• Small differences using 4 or 50 stations for the interpolation
• It seems that local trend is picked up even if the amount of stations used for the interpolation is small
Further questions and applications• Is the smoothing that we have observed over-
smoothing?• What is the distance to the closest station for all
combinations of stations?• What happens to the trend of the grid value if
only stations with a negative trend are used?
• Split the study into two parts: interpolation to 0.1 degree grid and averaging to 0.22 degree grid
• Do a similar experiment for minimum and maximum temperature
Thank you!
Nynke Hofstra
Oxford University Centre for the Environment [email protected]
Questions, ideas and remarks very welcome!