HISTALP Historical Instrumental Time Series for the Greater Alpine Region-

Transcription

1 HISTALP Historical Instrumental Time Series for the Greater Alpine Region- From HIST-ALP to HIST-EU a dataset for European long-term climate variability studies on regional scale Ingeborg Auer

2 Folie 2 Overview The Greater Alpine Region and HISTALP area, network, data requirements Inhomogeneities HISTALP today HISTALP on-line, web-site, applications HISTALP tomorrow HIST-EU CIRCLE Mountain Call

3 Folie 3 km 2, m asl Complicated terrain (littoral, mountain tops, valleys, cold air pools, urban sites, etc. The Greater Alpine Region GAR (4 to 19 E, 43 to 49 deg N) Principal climatological regions: NW-SE, high level stations, climate divide between Atlantic, Mediterranean and continental influences

4 Invitation to a conference in 2011:

5 Folie 5 Some HISTALP characteristics MULTIPLE Air temperature Precipitation Air pressure Sunshine duration Source: Auer I, Böhm R, Jurkovic A, Lipa W, Orlik A, Potzmann R, Schöner W, Ungersböck M, Matulla C, Briffa K, Jones PD, Efthymiadis D, Brunetti M, Nanni T, Maugeri M, Mercalli L, Mestre O, Moisselin JM, Begert M, Müller- Westermeier G, Kveton V, Bochnicek O, Stastny P, Lapin M, Szalai S, Szentimrey T, Cegnar T, Dolinar M, Gajic- Capka M, Zaninovic K, Majstorovic Z, Nieplova E HISTALP Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region. Int. J. Climatol. 27: (2007) LONG-TERM (regularly updated) air pressure anomalies precipitation totals sunshine duration totals temperature means bold: hom series thin:

6 Homogeneity of series - and how to remove inhomogeneities A homogeneous climate series reflects only changes in the real climate not disturbed by inhomogeneities introduced by relocations, instrument changes, sheltering, observation rules, etc. (breaks!) artificial trends by urbanization, changing environment (growing trees) Metadata data about data Statistical tests to find inhomogeneities Methods to adjust inhomogeneities To adjust the mean in monthly data To adjust the whole frequency distribution in daily data (extremes) Examples!

7 Systematic effect: early rain gages, example: monthly Precipitation in Pula Pola, complicated history (Austria-Italy-Yugoslavia- Croatia), a 30% break in 1897 Comparison of mean monthly precipitation Pula (HR) : rooftop (14.5m) versus 1.3m near to ground m/1.3m (% %) kk. HydrographischesAmt: 2 sites (25 years of parallel measurements) JAN FEB MAR APR MAY JUN JUL AUG SEP parallel m easurem ents tower (14.5m above ground) near to ground (1.3m above ground) OCT NOV DEC

8 Folie 8 New: Early bias in instrumental series has been removed (systematic effect) Prior to thermometers were insufficiently sheltered from direct sunlight normally placed on north-facing walls or windows temperatures recorded in the summer half of the year were biased warm and those in the winter half biased cold, with the summer effect dominating, simultaneous measurements taken for eight recent years at the old and modern site at Kremsmünster, differences in the diurnal cycle depending on season, using metadata EI correction of 32 series The historic measuring site for temperature and humidity, 6.9m above ground at the NNE-facing front of the historic astronomical tower of the Monastery Kremsmünster (48 03' 21" N, 14 08' 01" E, 380 m asl.) Source: Böhm R, Jones PD, Hiebl J, Frank D Brunetti, Maugeri M, The Early Instrumental Warm-bias: A Solution For Long Central European Temperature Series , accepted for CLIMATIC CHANGE special issue MILLENNIUM project

9 Homogenization of daily data Differences of parallel measurements in Kremsmünster of Tmax Daily inhomogeneities depend on the weather situation, especially weather extremes Winter Summer inhomogeneities in daily tmax series may depend on the temperature itself

10 CIRCLE Mountain Call Folie 10 HISTALP on-line: - Station Data, 1 x1 grids,

11 Folie 11 HISTALP POSSIBLE FUTURE OF HISTEU - OBJECTIVES network should cover (main parts) of Europe Homogenisation on monthly basis Under the umbrella of EUMETNET with additional national funding?

12 (Präsentation) Folie 12 What is needed data recovery data rescue data search and digitization (including metadata) in national data bases, national archives, historic year books or printed data collections, requests to ongoing or completed research projects, digitization of monthly data if necessary. in parallel, metadata search to support and justify homogenization. Szegedin =today in Hungary Cilli = today Celje in Slovenia

13 (Präsentation) Folie 13 What is needed? Homogenisation Homogenization on a monthly basis An approved software and recommendations delivered by COST ES0601 metadata to justify homogenization averaging of series does diminish inhomogeneities, but does not remove them completely.

14 (Präsentation) Folie 14 EUMETGRID Climate EUMETNET Programmes PEP 725 Collaboration HIST-EU CLIMATE INFORMATION MED-CLIVAR HYMEX, PAGES, MEDARE, GHCN, ERA- CENTURY, CRU, GCOS, ETC. ECA&D

15 Folie 15 possible application: Derived elements based on SENSITIVITY STUDIES : Relationship between air temperature (t m ) and frost days (% of month length) by tan h modelling FF(t m ) = p 3,m (1 tanh(p 2,m (t m p 1,m ))), m=1,.,12 frost frequency (%) Jan frost frequency (%) Apr FROSTDAYS Jul 100 Oct 100 frost frequency (%) frost frequency (%) FF(t m ) = p 3,m (1 tanh (p 2,m (t m p 1,m ))), m=1,.,12

16 Folie 16 Application: Long-term series of frost days To overcome deficiencies in frost days series when no minimum temperature measurements when series carry non removable inhomogeneities when series are too short Source: Auer I, Matulla C, Böhm R, Ungersböck M, Maugeri M, Nanni T, Pastorelli R Sensitivity of Frost Occurrence to Temperature Variability in the European Alps. International Journal of Climatology 25:

17 CIRCLE Mountain Call Folie 17 Application: reconstruction of high resolution precipitation fields Source: Efthymiadis, D., P. D. Jones, K. R. Briffa, I. Auer, R. Böhm, W. Schöner, C. Frei, and J. Schmidli, Construction of a 10-min-gridded precipitation data set for the Greater Alpine Region for , J. Geophys. Res., 111, D01105, doi: /2005jd006120

18 Folie 18 Application: reconstruction of high resolution temperature fields Construction of a new HR absolute grid-3 temperature dataset (high resolutiontemperature climatology of ECSN GAR-HRT) merged with HISTALP-T01 merge the 12 monthly high resolution climatologies (based on 1700 stations in the GAR) with the available coarser resolution Alpine homogenized temperature series (132 stations) to a high resolution long-term temperature variability grid

19 Folie 19 Application in progress. Splitting HISTALP grid-3 precip into the solid and liquid components % solid tanh-model for JANUARY data: 80 Austrian 30 climate stations monthly mean temperature Modeled solid precipitation % solid data: 80 Austrian climate stations tanh-model for JULY monthly mean temperature WIN Hohe Tauern N 1500m WIN Hohe Tauern N 2500m WIN Hohe Tauern N 2000m WIN Hohe Tauern N 3100m snow of special interest for the Alpine region: ecological importance, snow and water balance Economic importance winter tourism % WINTER (DJF)

20 Folie 20 HISTALP FOR GLACIER MB RECONSTRUCTION Glacier mass balance model using HISTALP temperature and precipitation, A statistical mass balance model for reconstruction of LIA ice mass for glaciers in the European Alps, Schöner et al., Annals for Glaciology, 2007

21 Folie 21 COMING TO THE END highpotential of hidden datain archives, yearbooks, computers, projects, Regularly update is needed, EUMETNET European monthly grids before EUMETGRID Thank You!