Homogenization of monthly long-term temperature series of mainland Norway

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1 Homogenization of monthly long-term temperature series of mainland Norway Lars Andresen (P.O. Box 43, N-0313 OSLO, NORWAY) ABSTRACT In connection with homogenizing climatologic long-term time series Nordklim has decided to test the homogenization software of the Czech climatologist Petr Štěpánek. In a previous report concerning Southeast Norway, we mainly used recommended settings when running the programs. In this report we have tested some alternative settings by use of the Cost action ES0601 Benchmark data set. With these new settings and by utilizing existing metadata of the series, mainland Norway (Southeast Norway included) is analyzed and detected breaks are automatically adjusted. 231 station series, with minimum length of 30 years, were analysed with the SNHT homogenization method for detection of inhomogeneities in the series. Altogether 80 series, 51 reconstructed (combined) and 29 single, were adjusted according to the documented procedure where the breaks were confirmed by metadata. Another 20 series, 8 reconstructed and 12 single, were adjusted without any metadata. The consequence is that 83 series, 19 reconstructed and 64 single, are without traceable breaks and thus classified as homogeneous without adjustments. Finally, in 48 series, 17 reconstructed and 31 single, there were detected traces of breaks, but with the defined procedure these series were not adjusted. They may contain inhomogeneities, but probably the breaks are of minor importance or small. No. 2 Keywords: Temperature series, Homogenization, Technical Oslo,

2 Nordklim Nordic co-operation within climate activities Homogenization of monthly long-term temperature series of mainland Norway Lars Andresen May

3 Foreword This report is prepared under project 1 of the Nordic NORDKLIM activity: Nordic Co- Operation within Climate Activities. The NORDKLIM activity is a part of the formalised collaboration between the NORDic METeorological institutes, NORDMET. The main objectives of NORDKLIM are: 1) Strengthening the Nordic climate competence for coping with increased national and international competition 2) Improving the cost-efficiency of the Nordic meteorological services (i.e. by improving procedures for standardized quality control & more rational production of standard climate statistics) 3) Coordinating joint Nordic activities on climate analyses and studies on long-term climate variations The NORDKLIM activity consists of three separate projects that are complementary to each other: 1. Nordklim-OBS (Improvement of existing quality control methods by using radar information, organized working tools for manual control (HQC) and interpolation of observations (QC2). Homogenization of long-term time series.) 2. Nordklim-GRID (Production of high-resolution grid data sets based on all available, good quality observations. Creation of a web portal for dissemination of grid data.). This activity is postponed until further notice. 3. Nordklim-ADAPT (Study on climate change impact and adaptation measures needed to avoid unfavorable consequences for the society and correspondingly study how to benefit from positive impacts of climate change.) A detailed description of Nordklim is available at: During the writing of the report plans for reorganizing the future NORDKLIM activities emerged. The addresses of the Nordic Meteorological Institutes are: Denmark: Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark, (www.dmi.dk) Finland: Finnish Meteorological Institute, P.O.Box.503, FI Helsinki, Finland, (www.fmi.fi) Iceland: Vedurstofa Islánds, Bustadavegur 9, IS-150 Reykjavik, Iceland, (www.vedur.is) Norway: Norwegian Meteorological Institute, P.O.Box 43 Blindern, N-0313 Oslo, Norway, (www.met.no) Sweden: Swedish Meteorological and Hydrological Institute, S Norrköping, Sweden, (www.smhi.se) 2

4 Table of contents Foreword... 2 Table of contents Introduction History Automation Area of analysis Data basis Quality control and reconstruction Result of the quality control Reconstruction of monthly data series Long-term temperature series Testing settings of the ProClimDB program Preferred conditions Homogenization procedure Status of the station series after the homogeneity analysis Graphic presentation of some results of the homogeneity analysis References Acknowledgement Appendix Stations used for the reconstruction of long-term temperature series Reference stations used in the homogeneity analysis (short extract) Results of the homogeneity analysis break time and adjustments Metadata used in the homogeneity analysis Status of the break detections through the use of metadata Changes in the homogenization procedure Description of the most common functions in ProClimDB

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6 1. Introduction To check the homogeneity of time series it is recommended to use one or more acknowledged homogenization methods. Different countries may use different methods. The conditions, e.g. by selecting reference stations and other conditions as well, may be different also when using the same method. That is the reason and background for COST action ES0601 HOME (Mestre, 2006). The intention of this action is to provide a common recommended procedure for homogenizing climatic time series. The results from this action will be presented in Until then we would like to use the Standard Normal Homogeneity Test (SNHT) by Hans Alexandersson (1986) as this test is used in earlier homogenization work at met.no. This gives us the opportunity to compare new results with earlier ones (Andresen, 2010). 1.1 History About 20 years ago the SNHT method was used on some Norwegian annual precipitation series and monthly temperature series. The first results were presented in the DNMI report KLIMA publications (Hanssen-Bauer et al. 1991) and (Førland and Hanssen-Bauer, 1992, in Norwegian). The last one was published in English in 1994 (Hanssen-Bauer and Førland). In the first one 151 annual precipitation series of 75 years length were tested; 52 stations were classified as homogeneous, 99 with at least one break point. In the second one 165 of the longest series of annual precipitation in Norway were tested, 50 classified as homogeneous, 79 as homogeneous after one adjustment. The first results concerning long-term temperature series were the presentation of the Dombås series (Nordli, 1995), the NACD temperature series (Hanssen-Bauer et al. 1996) and Arctic series (Nordli et al. 1996): 11 Arctic stations of which 8 located on Spitsbergen, the others on Jan Mayen, Bjørnøya (Bear Island) and Hopen. The last 3 stations and 4 stations from Spitsbergen were classified as homogeneous. This analysis was extended with 13 stations from the mainland Norway (Nordli, 1997, in Norwegian). 5 stations (Færder, Oksøy, Utsira, Karasjok and Vardø) were classified as homogeneous in the end of the procedure, 7 were tested and adjusted, but with imperfect homogeneity in the end (Dombås, Oslo, Nesbyen, Lærdal, Ona, Glomfjord and Tromsø) and 1 station with several unadjusted breaks (Bergen). This work with precipitation and temperature series was the basis of the Norwegian part of the North Atlantic Climate Dataset (NACD), which was established in 1996 (Frich et al.) for the period In 2001 the dataset was transferred to a Nordklim database and extended by data from several temperature parameters from REWARD (Relating Extreme Weather to Atmospheric circulation using a Regionalised Dataset) data (Tuomenvirta et.al. 2001). In this dataset the data series were updated to the year Concerning the Norwegian monthly mean temperature series the homogeneity tested data are identical with the test described in Nordli (1996 and especially 1997). The data from 1995 to 1999 were added without any further homogeneity testing. The dataset consist of 18 Norwegian temperature series and 11 other stations for other parameters. So concerning homogeneity of temperature series only a few are examined so far. From Nordli (1997, b) we see that 22 stations are tested. However, these are undocumented series tested for use as reference series in the homogeneity analysis (not adjusted; used as guidance in the selection of series). 5

7 1.2 Automation In this report the purpose has been to analyse as many series as possible within a region, and with a minimum of manual interventions. As a matter of fact there as been a higher degree of automation in this report than for the analysis of Southeast Norway (Andresen, 2010). This time we have run the program with a few changes of the settings (conditions) and with more iterations in the end of the procedure. As for the previous report (Andresen, 2010), we have used the programs of the Czech climatologist, Petr Štěpánek*. He has made a software package with 3 different programs, which are useful for homogenization issues. He has his own home page: where the homogenization programs are described. In the programs there are a lot of help documents for the user. For our purpose the installation of the programs at a PC and use of the LoadData program is described by Andresen (2009). As the final result depends on the best possible quality of the data we will start with controlling the monthly data and correct outliers and other errors. Then we make combined series from available time series of stations close to each other in order to get long-term time series as long as possible. In ProClimDB terminology these series are called reconstructed series. This reconstruction is performed before we do the homogenization analysis. The figures of the ProClimDB program in this report are taken from version (November 2010 April 2011). 1.3 Area of analysis In principle, we can do the analysis for the whole country in one step. For practical reasons the analysis is done for 6 individual regions: 1. West Norway (counties: Rogaland, Hordaland and Sogn og Fjordane, with supporting stations from Vest-Agder, Aust-Agder and Telemark (west of LON=8 ), Buskerud and Oppland (west of LON=9 ) and Møre og Romsdal). See Figure 1b. 2. Northwest Norway (counties: Møre og Romsdal and Sør-Trøndelag, with supporting stations from Sogn og Fjordane, Oppland and Hedmark (north of LAT=61 ) and Nord-Trøndelag (south of LAT=65 )). See Figure 1c. 3. Mid Norway (counties: Nord-Trøndelag and Nordland (South), with supporting stations from Møre og Romsdal and Sør-Trøndelag and Nordland (north of 67 )). See Figure 1d. 4. North Norway (counties: Nordland (North), with supporting stations from Nordland (south of 67 ) and Troms). See Figure 1e. 5. North Norway (counties: Troms and Finnmark, with supporting stations from Nordland (north of 67 )). See Figure 1f. 6. Southeast Norway (counties: Østfold, Oslo, Akershus, Hedmark, Oppland, Buskerud, Vestfold, Telemark, Aust-Agder and Vest-Agder, with supporting stations from Rogaland, Møre og Romsdal and Sør-Trøndelag). See Figure 1a. * Petr Štěpánek is the head of the Climatology Department at CHMI (Czech Hydrometeorological Institute), Regional Office Brno. See: 6

8 In retro perspective we see that we could have simplified this by extending region 2 with Nord-Trøndelag, dropping region 3 and extending region 4 with Nordland (South), ending up with totally 5 regions. Other combinations of counties is of course possible. Some series are excluded (Chapter 3). We make some limitations in the selection of stations after the reconstruction procedure (Chapter 4). Figure 1a. Figure 1b. Southeast Norway West Norway In the figures the red squares are the analyzed station series and the green ones the supporting station series. 7

9 Figure 1c. Figure 1d. Møre og Romsdal and Nord-Trøndelag and Sør-Trøndelag Nordland (South) Figure 1e. Figure 1f. Nordland (North) Troms and Finnmark In the figures the red squares are the analyzed station series and the green ones the supporting station series. 8

10 2. Data basis For the total period of the Norwegian time series, , we download monthly average temperature from the table T_MONTH. Geographic information for the climatic stations is taken from the table T_ST_INFO (later changed to V_ST_INFO). Remark These data are regarded as original data series defined by its station id (national station number 5 digits). In the last 40 years or so, when relocations were expected to affect the homogeneity of the time series, the station was regarded as a new station with a new station number. In earlier times there may have been large relocations without any change of the station identity. Fortunately such relocations are normally reported in the metadata. As we combine different series from the same local area, this difference in naming of stations has no effect on the detection of breaks in the series. The station series included here are not homogenized series except for the Oslo series, period (II) and another series (I); both were homogenized separately by Birkeland (1923). In both series there are some parts which are not homogenized ( (II) and , (I)). Since 2010 there has been some reorganizing of the Oslo series, due to an internal project (Histklim) and some details in this information can be corrected later. Notice! For the earliest period there are no neighbours for making reference series. Thus break detection is useless for this period. In this report the Oslo series are joined in another way. The current reconstruction is possibly better than that used in Andresen (2010). Further investigation of the Oslo series will show which reconstruction is the best one. As a matter of fact the observations of the two series (II and I) were done at the same property before 1866 and after 1877, but for the first series with the thermometer in a wall screen at the Observatory building and for the second one with the thermometer in a Wild screen, 22 m from the building. Data Data is selected from the climate database in the same way as in Andresen (2010). When the homogenization procedure is changed and it is necessary to describe the used files, we refer to the folder: C:\AnClim_0111\Oslo 9

11 3. Quality control and reconstruction In some procedures it is possible to work in ProClimDB with both monthly and daily data processing. Though we are working here with monthly data only, we use the term MON to indicate the monthly profile. The digits following MON refer to the menu point of ProClimDB. We also use menu points in file names to indicate where (and when) the files are made. Be sure that the correct mode is selected (buttons MON to the left of button Change PROFILE). It is smart to define a profile of the present work, e.g. T_MONTH. In Settings (button) Stations selection we fill in 0/10/10 in the 3 fields. From the beginning there are no restrictions of minimum station length. All procedures below are done in the same way as in Andresen (2010) and should be studied there for details. To make the described selection of stations we run MON 1-1 and MON 1-5. The selections can be done by filtering departments (counties) and by ordering latitude and longitude. The new data file_1-1 is input to MON 7-8, the data info file_1-5 input to MON 7-7. Notice! The menu number can be changed when turning to a new version. You will find an explanation of the used functions in Appendix 7. Before we go through the homogenization procedure we do some quality control of the monthly mean values. Outliers and obvious errors are corrected. Then nearby station series are combined (see below). 3.1 Result of the quality control This time we have not manually studied the ordered monthly columns (from highest to lowest). We have only used MON 7-2 and MON 7-4 with the same settings as before with one exception: For some regions we have used Limit-Distance/km = 100 (instead of 200). We delete 2350 Unknown ( ) Non-existing station number 4270 Skedsmo ( ) Many large errors Deleted in both info file and data file. Also some shorter series from stations without any geographic coordinates are excluded. We correct is changed to: Holmestrand 2/ Holmestrand 3/ Tonstad 12/ Tonstad 6/ Slåtterøy fyr 4/ (Bruun, 1957: 6.3) Slåtterøy fyr 5/ (Bruun: 8.1) Bergen-F.berg 12/ Ona 8/ (11.3 looks better from neighbours) Ona 10/ (Bruun: 6.9) 10

12 Other corrections Coordinates of: Modum II (same coordinates as for Modum) Bergen-Birkeland (same coordinates as for Bergen-Konow) Trondheim-Birkeland (same coordinates as for Trondheim-Fester) Changes in the data info file: Ullensvang forsøksgard ( ) deleted Ullensvang Forsøksgard (prestegården) /80 and Ullensvang (Kvitavoll og Ernes) 4/1880-3/ Ullensvang-Helleland 4/ and Ullensvang II (Århus) Reconstruction of monthly data series For reconstruction of monthly data series we use the functions MON 7-7 and MON 7-8. Then we need a data info file for the reconstructed series and use MON 1-1 and MON 1-5, as before. From the data info file_1-5 we take a look at the data coverage to see if some series should be omitted. This type of selection of series may be a combination of data coverage of the single series and the reconstructed ones. Here we have had a preference of the reconstructed series and we have assessed Period_mis = 10% as a general limit (with a few exceptions). From these assessments we have kept the following series (not marked for delete), although part series may have a Period_mis > 10%: 31620_r Møsstrand, 15310_r Bøverdal, 15730_r Bråtå, 25630_r Geilo, 49520_r Ullensvang, 51530_r Voss, 52535_r Fedje/Hellisøy fyr, 57170_2_r Førde i Sunnfjord, 57770_r Ytterøyane, 69360_r Meråker, 69760_r Levanger, 70150_r Verdal, 79400_r Nerdal I Rana, Tennevoll Some station series are supporting series, which have the missing values concentrated on a few years. Thus we allow these stations to contribute as part of reference series for selected periods. In Data Info file_1-5_b we keep the marks for delete (without deleting) Røros lufthavn (we accept period_mis above 10% (16.7%)) Ås (we accept missing years (48 months)) Dagali lufthavn (we accept period_mis above 10% (10.7%)) Dagali II (we accept period_mis above 10% (13.1%)) Lindesnes fyr (we accept missing years (48 months)) Selbu-Bogstad (we accept period_mis above 10% (25.0%)) We have omitted the following series (marked for delete, but not deleted): Glåpen ( ) Røst III ( ) / 85910_r Røst ( ) 11

13 We have deleted the following series, not acceptable for combination, or single series/combined series with poor data coverage: 23500_1 Løken i Volbu ( ) A lot of missing values 26900_r Drammen ( ) A lot of missing values Midtlæger ( ) A lot of missing values Sognefjellhytta ( ) A lot of missing values Lindesnes fyr ( ) Needless for the analysis. Missing values Vangsnes ( ) Missing values Kråkenes ( ) A lot of missing values Rana-Båsmoen ( ) A lot of missing values In the end we will analyse only series longer than 30 years. Notice! Due to an error of the geographical coordinates of the station Trondheim-Møllerup, this series was not combined with other Trondheim series. The correct values are: LAT= , LON= , UTM_E=271438, UTM_N= Due to an error in the program the station series Dalen i Telemark I ( ) and Dalen i Telemark II ( ) were not reconstructed. The reconstructed series are presented in Appendix 1, Tables A Altogether there are 95 reconstructed series. Remark Strictly, the station Røros does not belong to the defined region Southeast Norway, Table A1.1. However, the best correlated stations are located within the defined region. Only the station Trondheim from Sør-Trøndelag (neighbour county to the north) is used as a reference station (second best out of two) for the period and station Selbu (third best out of five) for the periods and The other reference stations are Dombås, Lillehammer, Rena, Alvdal, Fokstugu, Drevsjø, Vågå and Skåbu. This means that Røros climatologically belongs to the actual region. We see from Figure 1a that there are sufficient stations left in Sør-Trøndelag to use as neighbours if they had been better correlated than the mentioned ones from Hedmark and Oppland. That is the reason for presenting the analysis (break detection and adjustments) of Røros as the 38 th reconstructed series of Southeast Norway. 12

14 3.3 Long-term temperature series Figure 3.1. Left: Monthly temperature series with length 30 years, still running in 2007 (72 series). Right: Monthly temperature series with length 100 years, still running in 2007 (22 series). Red dot is the Bergen series. It was not reconstructed because of an error in the program. It should be possible to reconstruct a series of Bergen for the period Presenting stations running in 2007 means that if some station series ended in 2007 there is an acceptable gap of 4 years of missing data if one can find a nearby location to continue the observation series from However, most of the series in Figure 3.1 (right) are still running in When reconstructing series we allow up to a max gap of 4 consecutive years and a max station height difference of 150 m. Max distance between series is 10 km. We see from the figures above that 30-year series are relatively well distributed all over the country, except for county Nordland and some other minor areas. 100-year series are well distributed along the coast, except for a few gaps in North Norway. There are a lot of inland series in Southeast Norway, otherwise such series are sparse. 13

15 4. Testing settings of the ProClimDB program Period with overlapping years There is a lot of settings in the ProClimDB program which may influence the homogeneity analysis. In function MON 8-2, where we find the reference stations based on correlation with neighbours, we have earlier used 40-year periods with 10 years overlap. From Andresen (2010, Appendix 5) there was an indication that it was difficult for the SNHT method to detect smaller breaks near the edge of the series and the chance of fault detections was increasing for smaller breaks. We want to test 10 years versus 20 years overlap on Cost Benchmark data series without regard to the distribution of the breaks in the series. Possibility for a real break There are important settings in function MON 9-3 and MON Y_POSSIBLE is a measure on the probability for a real break, based on the weighting of year, season and month in MON 9-2. We have used the recommended weighting 5;2;1. The maximum possible score is then 25 (1 year *5 + 4 seasons * months *1) which means 100%. A detection of breaks in 5 months and 2 seasons gives a score of 9 ~ 9/25=36 %. The philosophy is: the higher the value of Y_POSSIBLE, the higher the probability of a real break. If metadata confirm a detected break, we allow lower values of Y_POSSIBLE. It should be harder to get approval of a break without metadata than a break with metadata. We want to test different values of Y_POSSIBLE on Cost Benchmark data series. Before we make an adjustment of a break we have the opportunity to check the correlation of the series before and after the adjustment. If the correlation does not improve as much as 0.5 or 0.0 %, we ca decide not to adjust. Or we can skip the correlation criterion (None). We want to test these alternatives on Cost Benchmark data series. Figure 4.1 Network no.3 (Austria). Monthly mean surrogate temperature series, , containing 55 break points of which 45 are realistic with the defined settings defined below. 14

16 We have used the monthly mean surrogate temperature series of network no.3, see Figure 1.4. In original, approximately homogeneous series, there are introduced artificial breaks. Network no.3 contains 15 station series with 45 detectable breaks when we do not accept breaks closer than 4 years from edge nor breaks closer than 4 years from another break. Table 4.1. Number of breaks with different settings of MON 8-2, MON 9-3 and MON 10-2, with emphasis on 10 versus 20 overlapping years. No metadata are available Correct None False None y_p= y_p= y_p= y_p= y_p= y_p= Correct None False None y_p= y_p= y_p= y_p= y_p= y_p= From Table 4.1 we see that 40-year periods with 20 years overlap gives a few more correct breaks than 10 years, but also a few more false ones. But with corr+0.5% the difference is only 1 break. When using the increase in the number of false breaks are less than the increase in the number of correct ones compared with the alternative. We accept 20 years overlap as a better alternative than 10 years. Table 4.2. Number of breaks with different settings of MON 9-3 and MON 10-2, with emphasis on the probability criterion Y_POSSIBLE (y_p). No metadata are available Correct False y_p= y_p= y_p= y_p= y_p= y_p= y_p= y_p= y_p=30 28 y_p=30 8 y_p=40 24 y_p=40 5 From Table 4.1 and 4.2 we see that a strict correlation criterion reduces the number of correctly detected breaks. When corr+ increases from 0.5% to 1.5% the number of correct breaks are reduced with 20-30%. It is no good idea to use a higher corr+ than 0.5%. With the 20 years overlap criterion a high y_p ( 30%) gives about the same number of correct break detection for the alternatives: None, corr+0.0% and corr+0.5%. But the decline in false detections from corr+0.0% to corr+0.5% is significant. We also see from the tables that there is a clear decline in the number of false breaks from y_p=20 to y_p=25. With no metadata we will not use Y_POSSIBLE below 25%. 15

17 Table 4.3 Number of breaks with different settings of MON 9-3 and MON Metadata are available Correct None False None y_p=15; y_p=15; y_p=20; y_p=20; y_p=25; y_p=25; y_p=30; y_p=30; y_p=35; y_p=35; y_p=40; y_p=40; y_p=45; y_p=45; y_p=50; y_p=50; When the detection file of the Benchmark data series is known, we can use this as metadata. Y_p=x;10 means that x is used for a break without metadata and 10 means that break point is confirmed by metadata. A comparison of the tables 4.1 and 4.3 shows that the number of correct detected breaks increases significantly with y_p 30. It does not appear that the number of false detections is influenced by the metadata (y_p=20, 30, 40 (Table 4.1) versus y_p=20;10, 30;10, 40;10 (Table 4.3)). Metadata increases the number of break detections significantly. 4.1 Preferred conditions From this small study we have got some clues for choosing reasonable settings in the functions MON 8-2, MON 9-3 and MON The philosophy is to catch the most extensive breaks in the first two runs (high y_p) to avoid adverse influence on the less extensive breaks in the iterations. We decide to use: years overlap in MON The following procedure for running the functions MON 9-3 and MON 10-2: a. y_p=45;25 in the first run and no correlation criterion b. y_p=45;25 in the first iteration and no correlation criterion c. y-p=40;20 in the second iteration and corr+0.5% d. y_p=30;15 in the third iteration and corr+0.5% e. y_p=25;10 in the fourth iteration and corr+0.5% We choose this procedure on the Benchmark temperature series of network no.3, for metadata available and not available. Table 4.4 Result of homogenizing network no.3 with the defined procedure and metadata available. Metadata available 1.run 1.it. 2.it. 3.it. 4.it Total Detected breaks Correct detection False detection

18 We found all breaks in 13 out of 15 series (42 breaks). In the remaining 2 series we found 1 break, 2 breaks were not found. Totally we found 43/45 ~ 96% of the breaks. False breaks were found in 5 series. In two of the series, and 11803, we got 5 (out of 8) false ones. These stations are the most remote ones in the western part of Austria, and with few neighbours. In the remaining 3 series there were 1 false detection in each. There were traces of breaks in 6 series, of which 1 break were real. But none of the breaks would have been adjusted. Table 4.5 Result of homogenizing network no.3 with the defined procedure and no metadata. Metadata not available 1.run 1.it. 2.it. 3.it. 4.it Total Detected breaks Correct detection False detection We found all breaks in 7/15 of the series (18 breaks). In the remaining 8 series we found 17 breaks, 10 were not found. Totally we found 35/45 ~ 78% of the breaks. False breaks were found in 6 series. In three of the series, 05904, and 11803, we got 6 (out of 9) false ones. In the remaining 3 series there were 1 false detection in each. There were traces of breaks in 8 series, of which half of the breaks were real. But none of the breaks would have been adjusted. Notice! After the performance of this analysis, network no.3 was excluded together with four other networks because the original series (before manipulation) were not quite homogeneous ( ). We consider this as not crucial for the chosen settings in the actual functions. Conclusion The chosen settings of the functions MON 8-2, MON 9-3 and MON 10-2 look out successful. When using these in the homogenization procedure on network no.3 of the Benchmark data set without any metadata we find 78% of the breaks and introduce 9 false ones. If metadata, confirming all the breaks, are available, we find 96% of the breaks, but still we introduce 8 false ones. This case study confirms that metadata increases the detection rate significantly. Near the boundary of an analyzed region, where there is a shortage of reference series, there is a higher risk of introducing false breaks. Notice! Be aware that we in this study has used a perfect metadata set. In real life a lot of metadata are missing, and in some cases metadata of possible inhomogeneity do not result in any inhomogeneities. 17

19 5. Homogenization procedure We start the homogenization procedure by making a Data Info file for the reconstructed series and add geography information, as described in Ch.3.2. Then we make a batch file with the following functions: MON 2-5 month/season/year file MON 4-3 correlation file MON 8-2 reference info file for reference series (see Appendix 2) MON 8-4 reference series and test series for the homogenization analysis in AnClim MON 9-1 homogeneity result file (HomResults) MON 9-2 homogeneity result file (HomResults2), introducing weighting of season/year MON 9-3 homogeneity result file (InhomAll), introducing Y_POSSIBLE and metadata (see Appendix 4) MON 9-4 checked inhomogeneity file for near breaks/near edge of series MON 9-6 reference info file for adjustments of breaks MON 8-4 reference series for adjustments of breaks MON 10-2 adjustment info file (see Appendix 3) MON 10-3 adjusted series We run this batch file and do changes according to the defined procedure in Chapter 4 before each iteration. The settings and input/output files of each function are described in Andresen (2010). Changes in the software program or in the settings compared with the previous analysis are shown in Appendix 6. The output file of function 9-4 gives all the possible breaks in the series detected by the SNHT method, assuming breaks at least 4 years from the edges of the series and to the nearest break point, with the conditions defined in Ch.4.1. Concerning the adjustments we have used the recommendations from Petr Stepanek (2010) with smoothing of the monthly adjustments and with requirements of meeting the correlation criteria. The result of this procedure is given in Table 5.1. Table 5.1. Number of homogenized series of each region (Southeast, West, Møre og Romsdal and Sør- Trøndelag, Nord-Trøndelag and Nordland (South), Nordland (North) and Troms and Finnmark) and totally, and for reconstructed series and single ones. Region Homogeneous without adjust. Adjusted with metadata Adjusted w.o. metadata Not adjusted, traces of breaks Total Rec. Sing. Sum Rec. Sing. Sum Rec. Sing. Sum Rec. Sing. Sum Rec. Sing. Sum SE W MRST NTN N TF Total The adjusted breaks and the size of the adjustments are given in Appendix 3. 18

20 5.1 Status of the station series after the homogeneity analysis In the tables below are presented the station series considered homogeneous because of no detected breaks and those series not adjusted but with traces of breaks. _r refer to reconstructed series. _1 and _2 means original part series (non-reconstructed). Table series with no traceable inhomogeneity breaks. SE: 10010_1 Tynset-Støen ( ), Venabu ( ), 13540_r Vinstra ( ), Skåbu-Storslåen ( ), 1400 Brekke Sluse ( ), Vågå-Klones ( ), 1500 Krappeto ( ), 15310_r Bøverdal ( ), Jeløy ( ), Dønski ( ), 200 Trysil ( ), Kutjern ( ), Holmestrand ( ), 27450_1 Melsom ( ), 2840_r Høland ( ), Veggli ( ), Gaustatoppen ( , usikker pga stor høyde), Dalen i Telemark I ( ), 34130_r Jomfruland ( ), 3420_r Eidsberg ( ), Vefall i Drangedal ( ), 37230_2 Tveitsund ( ), 38140_1 Landvik ( ), 39170_1 Kristiansand S-Eg ( ), Austad ( ), Sirdal-Tjørhom ( ), 4870 Egnerfjell ( ), 5500 Åbogen ( ), 6020_r Flisa ( ), Lesjaskog ( ), Sæter i Kvikne ( ), 8710 Sørnesset ( ). Sum: 32 (6 rec.) W: Slirå ( ), 44640_2 Stavanger ( ), 45870_r Fister ( ), Svandalsflona ( ), Ullensvang ( ), 50130_r Omastrand ( ), 50310_r Kvamskogen ( ), 51550_1 Voss Jernbanestasjon ( ), Reimegrend ( ), Raundal ( ), 52290_r Modalen ( ), 53100_2 Vangsnes ( ), 54110_r Lærdal ( ), Fanaråken ( ), Bjørkehaug i Jostedal ( ), Oppstryn ( ), Kråkenes Fyr ( ). Sum: 17 (5 rec.) MRST: Svinøy Fyr ( ), Runde ( ), 64250_1 Kristiansund N I ( ), Tingvoll-Hanem ( ), Kongens Grube ( ), Trondheim-Møllerup ( ), 68170_1 Trondheim-Tyholt ( ), 68860_1 Trondheim-Voll ( ), Vennafjell ( ), 71990_r Buholmråsa ( ). Sum: 10 (1 rec.) NTN: 70340_r Verdal ( ), 73450_r Nordli ( ), Majavatn III ( ), Hattfjelldal ( ), Vardefjell ( , usikker pga stor høyde), 80102_r Solvær ( ), Glomfjord ( ). Sum: 7 (3 rec.) N: Kletkovfjell ( , usikker pga. stor høyde), 82070_r Fauske ( ), Bodø ( ), Helligvær ( ), Finnøy i Hamarøy ( ). Sum: 5 (1 rec.) 19

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