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Benchmark and Pseudo-Reality Dataset for Climate Validation

This report discusses the compilation of benchmark and pseudo-reality datasets for climate validation, including observational data sets and gridded observations. The report also addresses the challenges and considerations for data quality, temporal resolution, and spatial coverage. Pilot regions, such as maritime, continental, alpine, and Mediterranean climates, are suggested for the validation exercise.

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Benchmark and Pseudo-Reality Dataset for Climate Validation

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  1. VALUE WG2Benchmark data set &pseudo-reality (year 1-2)Report, Trieste Meeting Sep.12Sven Kotlarski, José Gutiérrez

  2. 13 Members Chair / co-Chair: Kotlarski (CH) Gutiérrez (E)WG Members: Bärring (S) Bosshard (CH) Brands (E) Brzóska (PL) J.H. Christensen (DK) Coppola (I) Jaczewski (PL) Jones (UK) Maraun (D) Pagé (FR) Pianko (PL)

  3. Overview (MoU) “Observational benchmark data sets and pseudo realities will be set up for the validation by WG2. These will comprise climatically distinct regions, e.g., maritime, continental, alpine and Mediterranean climates. The daily station data set will build upon the ECA&D data set publicly available from the Dutch weather service KNMI. Sub-daily station data will be provided by the Swedish Meteorological and Hydrological Institute SMHI for Sweden, MeteoSwiss for the Alps and the Regional Agency for Prevention and Environment ARPA of the Emilia Romagna for a Mediterranean climate. Station data may require the user to individually sign an agreement with the data provider to use the data for scientific purposes only. Additionally, the benchmark data set will contain daily gridded observations for the validation of SDS and in particular RCMs. The latter simulate area averages and are not directly comparable with station observations. As gridded observations, the EOBS data set will be employed, which is the gridded version of the ECA&D station data set developed within the ENSEMBLES project. Several researchers have noted potential weaknesses of the EOBS data set in regions of sparse data (e.g., Herrera et al, 2010), such that the validation will be restricted to selected regions, where a high data quality is ensured. Currently, a gridded precipitation data set with sub-daily resolution for the Alps, based on a combination of station and radar data, is under development by MeteoSwiss and might be used in a later stage of VALUE. Further data sets which might be available in the future or by further partners joining VALUE at a later stage will be considered additionally. The pseudo reality …”

  4. Tasks, Milestones, Deliverables T5: compile benchmark data set; upload benchmark data set to website 1st half of year 2 T6: compile pseudo reality; upload pseudo reality to website1st half of year 2 D3: observational benchmark data set and a pseudo reality for the validation (upload to website)1st half of year 2 M3: observational benchmark data set and pseudo reality compiled 1st half of year 2

  5. Observational Benchmark Dataset (1) 2 Regions 1 4 3 Validation SD Validation SD and DD Gridded obs. data(e.g. EOBS) Station-based obs. data(e.g. ECA&D) Parameters and required temporal resolution depend on target(i.e. on WG) Parameters and required temporal resolution depend on target(i.e. on WG)

  6. Observational Benchmark Dataset (2) Open issues (to be discussed this meeting) • Which pilot regions? Example: maritime -> UK, continental -> PL, alpine -> CH, Mediterrenean -> E / IP • Is quality of observational data appropriate? • Is temporal resolution appropriate? • Is spatial coverage appropriate?Concerns especially network density for gridded observations • Are all required parameters available? • Is length of observed period appropriate?30 years at least? Depends ontarget / WG

  7. Dataset inventory • Compiled by WG2 with input from further WGs • But: Not much feedback, inventory probably not complete yet(especially wrt. regional/national station data sets) • Please add data from your country! • Data availability and terms of use still need to be checked

  8. E-OBS (1) • Standard reference data set for RCM evaluation • Entire European land surface, 1950 - present, daily resolution, available on standard RCM grids • Would allow for a “fair” comparison of SD and DD performance • BUT • Poor data quality in regions of low network density(over-smoothing of the spatial field) • Smoothing particularly affects tails of dictribution(reduction of daily extremes) • Temporal inhomogeneities due to changes in contributing station network • Hofstra et al. 2009, Hofstra et al. 2010, Kysely and Plavcova 2010,Maraun et al. 2012, Herrera et al. 2012

  9. E-OBS (2)

  10. E-OBS (3) ? ? ? ? Restrict validation exercise to regions of high network density orapply different regional / national gridded datasets

  11. Suggestions for pilot regions • Covers both atlantic and mediterranean climates • Publicly available high-quality datasets at daily resolutionSpain02: daily P, Tmax, Tmin at 0.2° for 1950-2008 • Several downscaling studies already exist, including validation ofE-OBS and ENSEMBLES RCMs • Besides daily data: hourly data available for some stations Spain / IB Open for discussion! • Alpine climate • High-quality gridded (2 km) and station-based data available • Gridded hourly precipitation up from 2003, sub-daily station data available • Large number of previous downscaling studies (SD and DD) Switzerland • Continental climate • Gridded 25km daily data, quality probably superior to E-OBS Poland • Maritime to alpine climate • Safran analysis available (8 km res., 1958-2010, hourly to daily) France (?)

  12. Further Issues • Selection of predictors (ERA40? Identical for each SD method? Include predictors with high uncertainty, e.g. humidity?) • Data distribution / data serverSantander group set up a THREDDS dataserver that could host observational referencedata and predictor datasetshttp://www.meteo.unican.es/thredds/catalog/VALUE/ • Involvement of WGs 3 to 5Selection of reference data and selection of predictors: This meeting?Further discussions: One / two representatives of each WG? • Scenarios • Downscale CMIP5 projections? • Data server could host GCM predictors • GCM selection -> Paper Brands et al. Median of the absolute SLP bias/std values alongthe lateral boundaries of the Euro-CORDEX domains(1 Re-analysis and 7 CMIP5 GCMs)

  13. Thanks …

  14. Suggestion for pilot region Spain / IP • Publicly available high-quality datasets at daily resolution(> 100 stations, Spain02 / IB02 gridded datasets) • Spain02: daily P, Tmax, Tmin at 0.2° for 1950-2008 • Covers both atlantic and mediterranean climates • Several downscaling studies already exist, including validation ofE-OBS and ENSEMBLES RCMs • Besides daily data: hourly data available for some stations Herrera et al. 2012

  15. E-OBS: Temporal coverage

  16. E-OBS: Distribution of T stations (1)

  17. E-OBS: Distribution of T stations (2)

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