Benchmark database based on surrogate climate records

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Benchmark database based on surrogate climate
records

• Victor Venema

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Goals of COST-HOME working group 1

• Literature survey
• Benchmark dataset
• Known inhomogeneities
• Test the homogenisation algorithms (HA)

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Benchmark dataset

• Real (inhomogeneous) climate records
• Most realistic case
• Investigate if various HA find the same breaks
• Good meta-data
• Synthetic data
• For example, Gaussian white noise
• Insert know inhomogeneities
• Test performance
• Surrogate data
• Empirical distribution and correlations
• Insert know inhomogeneities
• Compare to synthetic data test of assumptions

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Creation benchmark Outline talk

• Start with homogeneous data
• Multiple surrogate and synthetic realisations
• Mask surrogate records
• Add global trend
• Insert inhomogeneities in station time series
• Published on the web
• Homogenize by COST participants and third parties
• Analyse the results and publish

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1) Start with homogeneous data

• Monthly mean temperature and precip (France)
• Later also daily data
• Later maybe other variables
• Homogeneous
• No missing data
• Detrended
• 20 to 30 years is enough for good statistics
• Longer surrogates are based on multiple copies
• Larger scale correlations are small
• Distribution well defined with 30a data
• Generated networks are 50, 100 and 200 a long

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2) Multiple surrogate realisations

• Multiple surrogate realisations
• Temporal correlations
• Station cross-correlations
• Empirical distribution function
• Annual cycle removed before, added at the end
• Number of stations between 5 and 20
• Cross correlation varies as much as possible
• Show plot temporal structure of surrogates
• Show plot cross correlations

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One station with annual cycle
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One station anomalies
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Multiple stations 10 year zoom
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Multiple stations 10 year zoom
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IAAFT algorithm smoothes jumps
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3) Mask surrogate records

• Beginning of records jagged (rough)
• Linear increase in number of stations
• Last station after 25 of full time
• End of record all stations are measuring
• Influence of jagged edge on detection and
  correction
• But trend is also increasing in time (i.e.
  different)!
• Is this a problem?

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3) Mask surrogate records
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4) Add global trend

• NASA GISS GISS Surface Temperature Analysis
  (GISTEMP) by J. Hansen
• Global mean surface temperature
• Last year of any surrogate network is 1999

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5) Insert inhomogeneities in stations

• Random breaks (implemented)
• Frequency of breaks 1/20a, 1/40a
• Size constants for temperature 0.25, 0.5, 1.0 C
• Size factors for rain 0.8, 0.9, 1.1, 1.2
• Simultaneous breaks
• Frequency of breaks 1/50a
• In 10 to 50 of network

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5) Insert inhomogeneities in stations

• Outliers
• Frequency 1 3
• Size 99 and 99.9 percentiles
• Local trends (only temperature)
• Linear increase or decrease in one station
• Duration 30, 60a
• Maximum size 0.2 to 1.5 C
• Frequency once in 10 of the stations

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6) Published on the web

• Inhomogeneous data will be published on the
  COST-HOME homepage
• Everyone is welcome to download and homogenize
  the data

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7) Homogenize by participants

• Return homogenised data
• Should be in COST-HOME file format (next slide)
• Return break detections
• BREAK
• OUTLI
• BEGTR
• ENDTR
• Multiple breaks at one data possible

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7) Homogenize by participants

• COST-HOME file format http//www.meteo.uni-bonn.d
  e/ venema/themes/homogenisation/costhome_fileforma
  t.pdf
• For benchmark COST homogenisation software
• One data and one quality-flag file per station
• Filename variable, resolution, quality, station
• ASCII network-file with station names
• ASCII break-file with dates and station names

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COST-HOME file format monthly data
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COST-HOME file format network file
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8) Analyse the results

• Detailed analysis will be performed in the
  working groups
• Detection
• Correction
• Daily data homogenisation
• Synthetic and surrogate data
• RMS Error
• No. breaks detected (function of size)
• Application reduction in the scatter in the
  trends
• Performance difference between synthetic
  (Gaussian, white noise) and surrogate data

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Work in progress

• Monthly precipitation
• Implement some inhomogeneity types
• Daily data other inhomogeneities
• Synthetic data (Gaussian white noise)
• More input data!
• Agree on the details of the benchmark
• Next meeting?
• Set deadline for the availability benchmark
• Deadline for the return of the homogeneous data

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Questions

• Ideas for a better benchmark
• For example, for other inhomogeneities, constants
• Types of inhomogeneities for daily data
• Automatic processing
• In the order of 100 networks

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(No Transcript)
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7) Homogenize by participants

• COST-HOME file format http//www.meteo.uni-bonn.d
  e/ venema/themes/homogenisation/costhome_fileforma
  t.pdf
• For benchmark COST homogenisation software
• Regular ASCII matrix (columns)
• One data and one quality-flag file per station
• Yearly, daily, subdaily data columns for time,
  one for data
• Monthly data year column, 12 columns for data
• Filename variable, resolution, quality, station
• ASCII network-file with station names
• ASCII break-file with dates and station names