Utskifting av bakgrunnsbilde: -Høyreklikk på lysbildet og velg «Formater bakgrunn» -Under «Fyll», velg «Bilde eller tekstur» og deretter «Fil…» -Velg ønsket bakgrunnsbilde og klikk «Åpne» -Avslutt med å velge «Lukk» Homogenization in Norway E. Lundstad, H. M. Gjelten, O.E. Tveito Norwegian Meteorological Institute

Outline - Challenges TEMPERATURE - Daily homogenization of temperature data from 5 cities in Norway. PRECIPITATION -Homogeneity testing of precipitation data: -Seasonal/monthly data. Have finished testing 1 of 13 regions. - Objectives and Methods -Problems and Opportunities -Conclusion 2

Norwegian Meteorological Institute Challenges in Norway 3

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Daily homogenization 5

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Norwegian Meteorological Institute Reasons for breaks, from metadata (%) 14 Source: Ø. Nordli (1997) & L. Andresen (2011), MET NO TEMPERATURE

Norwegian Meteorological Institute Temperature 15

Norwegian Meteorological Institute Oslo 16 The Oslo temperature series 1837 – 2012 – Homogeneity testing and climate analyses (Ø. Nordli et al)

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Norwegian Meteorological Institute Reasons for breaks, from metadata (%) 18 Source: I. Hanssen-Bauer & E. Førland (1994), MET NO PRECIPITATION

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Norwegian Meteorological Institute Færder

Norwegian Meteorological Institute Challenges with the methods 21 - especially precipitation HOMER

Norwegian Meteorological Institute Precipitation ·Original goal: Homogenize monthly precipitation data. ·Current goal: Homogenize seasonal precipitation data. Monthly: too much noise? ·Software: Homer for quality control, MASH for homogenization. ·Recently finished the testing of the first of thirteen regions. 22

Norwegian Meteorological Institute The southeastern region ·94 stations, all with length > 70 years 23

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Norwegian Meteorological Institute Mash - Breaks ·1926: 1.02/ 1928: 1.02/ 1929: 1.01/ 1930: 1.03/ 1931: 0.97/ 1932: 0.96/ 1936: 0.94/ 1941: 1.04/ 1946: 1.04/ 25

·MASH often detects breaks in consecutive years of equal but opposite value. These are most likely outliers and are not considered as breaks. ·Even though the most obvious outlier breaks were removed, there are still clusters of breaks with approximately opposite value. See example of results for one station for spring: ·This gives many unexplained breaks. Have not yet decided how to go about these breaks. 26

Norwegian Meteorological Institute The southeastern region, results 27

Norwegian Meteorological Institute Opportunities ·Wind screens were intoduced around 1907 at many stations  might not detect this break  possible underestimation of precipitation in the first part of the period 28

Norwegian Meteorological Institute Strategy for homogenization of daily temperature ·would recommend the following approach; ·1) QC daily tx and tn (see **) ·2) Produce from them monthly means of TN, TX ·3) Detect inhomogeneities over annual and seasonal (DJF, MAM, JJA, SON) averages of monthly TX and TN (see *) ·4) Adjust, trying to keep a similar pattern for TX and TN (sometimes breaks are obvious in one variable and not in the other; you may need to use different correction patterns, but whenever is possible, try to use the same one) ·5) Approach daily homogenization: ·5.1) If you meet the conditions for SPLIDHOM, do SPLIDHOM ·5.2) As most likely your SPLIDHOM dataset will be shorter in time and space than the one you homogenized for the monthly, use the Vincent method to homogenize the whole dataset. ·5.3) QC your homogenized daily data, specially looking from oversshoting (tx < tn) ·6) From the homogenized daily dataset, you can derived a homogenized monthly TX, TN, DTR and TM homogenized datasets. The one coming from 5.1 will match the one comming from 4) ·* You can use monthly means derived from (tx+tn)/2 and DTR (tx-tn) to help you in the detection process. · ** I don't start with the mean, as it can be derived from tx and tn. Doing separate procedures will lead to means which do not agree with tx an tn and lots ot troubles down the road. Of course, if you need a mean defined different than the standard (tx-tn)/2 homogenized, you can do it, but be aware of my comments. 29

Norwegian Meteorological Institute Summary and Conclusion ·Daily homogenization is a challenge ·Homer is good for temperature ·Mash gives to many breaks and we can not find these breaks in the Meta data ·Some breaks are easy to find ·Urbanization is important to investigate ·Around 50 % of the precipitation stations are homogeneous 30

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Norwegian Meteorological Institute T. E. Twitter. Thanks for your attention!

Norwegian Meteorological Institute