Foralps 2nd conference. Assessment of Lombardy's climate in the last century: data analysis, methodologies and indices

Transcription

1 Alessia Marchetti, Angela Sulis Assessment of Lombardy's climate in the last century: data

2 Contents The dataset The daily indices analysis Some preliminary monthly data indices analysis Some key points

3 The secular series of rain and temperature in the GLA A huge work of data rescue, collection from several sources and data organization enables now ARPA Lombardia to count on: -2 long term daily series of rainfall; - 11 series of temperature in the Greater Lombardy Area (GLA, 44 to 47_N, 7 to 12_E). This dataset can be considered representative of the whole region in space and time

4 The secular series of rainfall in the GLA 2 stations 1927 s*station

5 The secular series of temperatures in the GLA 11 stations 1127 s*station

6 Contents The dataset The daily indices analysis Some preliminary monthly data indices analysis Some key points

7 Climate Indices calculation on daily data 5 stations with long term daily series have been chosen with the last 1 s of homogeneous data: Mantova, Milano, Pavia, Lugano, Locarno With a tool written in R language (Rclimdex) All the 27 rainfall and temperature indices based on daily data have been analysed for the last century

8 Temperature indexes Extreme temperatures increase WARM COLD EXTREMES EXTREMES Index name Calculation method Unit of Temperature code meas. s increase Frost days Annual count when TN(daily minimum)<ºc Days FD 1 Summer days Annual count when TX(daily maximum)>25ºc Days SU25 1 Ice days Annual count when TX(daily maximum)<ºc Days ID 1 Tropical nights Annual count when TN(daily minimum)>2ºc Days TR2 Growing season Length Annual (1st Jan to 31 st Dec in NH, 1 st July to 3 th June in SH) count between first span of at least 6 days with TG>5ºC and first span after July 1 (January 1 in SH) of 6 days with TG<5ºC Days GSL Max Tmax Monthly maximum value of daily maximum temp ºC TXx 1 Max Tmin Monthly maximum value of daily minimum temp ºC TNx 1 Min Tmax Monthly minimum value of daily maximum temp ºC TXn 1 Min Tmin Monthly minimum value of daily minimum temp ºC TNn 1 Cool nights Percentage of days when TN<1th percentile Days TN1p 1 Cool days Percentage of days when TX<1th percentile Days TX1p 1 Warm nights Percentage of days when TN>9th percentile Days TN9p 1 Warm days Percentage of days when TX>9th percentile Days TX9p 1 Warm spell duration indicator Annual count of days w ith at least 6 consecutive days w hen TX>9th percentile Days WSDI 1 Cold spell duration indicator Annual count of days w ith at least 6 consecutive days w hen TN<1th percentile Days CSDI 1 Diurnal temperature range Monthly mean difference between TX and TN ºC DTR 1 1 1

9 Precipitation indexes Index name Calculation method Unit of meas. code Precipitation increase EFFECTS ON WET DAYS Max 1-day precipitation amount Monthly maximum 1-day precipitation Mm RX1day 1 Max 5-day precipitation amount Monthly maximum consecutive 5-day precipitation Mm Rx5day 1 Simple daily intensity index Annual total precipitation divided by the number of w et days (defined as PRCP>=1.mm) in the Mm/day SDII 1 Number of heavy precipitation days Annual count of days when PRCP>=1mm Days R1 1 Number of very heavy precipitation days Annual count of days when PRCP>=2mm Days R2 1 Number of days above nn mm (nn=25) Annual count of days w hen PRCP>=nn mm, nn is user defined threshold Days R25 1 Consecutive dry days Maximum number of consecutive days with RR<1mm Days CDD 1 Consecutive wet days Maximum number of consecutive days with RR>=1mm Days CWD 1 Very wet days Annual total PRCP when RR>95 th percentile Mm R95p 1 Extremely wet days Annual total PRCP when RR>99 th percentile mm R99p 1 Annual total wet-day precipitation Annual total PRCP in wet days (RR>=1mm) mm PRCPTOT 1 EFFECTS IN DRY DAYS

10 Swamped by the indices! Choice of the most significant ones -Temperature: GSL (Growing season Length) TR2 (Tropical nights) ID (Ice days) FD(Frost days) -Precipitation: R1 (Number of heavy precipitation days) R2 (Number of very heavy precipitation days) CDD (Consecutive dry days) CWD(Consecutive wet days)

11 Climate Indices calculation Daily data Results of the analysis: Temperature GSL Growing season Length - Annual count between first span of at least 6 days with TG>5ºC and first span after July 1 of 6 days with TG<5º GSL-Growing Season Lenght Milano Mantova Pavia Linear increase for the 3 plain stations: Milano: +4 days per 1 s Mantova: +1 day per 1 s Pavia: +.8 day per 1 s days 3 +4 d/1y +1 d/1y d/1y

12 Results of the analysis: Temperature Climate Indices calculation Daily data TR2 number of tropical nights - Annual count when TN (daily minimum) >2 TR2-Tropical Nights Mantova Milano Pavia Lineare (Milano) 1 Linear increase for the 3 plain stations: 9 8 Milano: +2 days per 1 s 7 Mantova: +1 day per 1 s Pavia: +1 day per 1 s days d/1y 3 +1 d/1y d/1y

13 Climate Indices calculation Daily data Results of the analysis: Temperature FD number of frost days - Annual count when TN (daily minimum) <ºC FD-Frost Days Mantova Milano Pavia Lineare (Milano) Linear decrease for the 3 plain stations: Milano: -3 days per 1 s Mantova: -2 day per 1 s Pavia: -4 day per 1 s days d/1y -2 d/1y 2-3 d/1y

14 Climate Indices calculation Daily data Results of the analysis: Precipitation R1 number of heavy precipitation days - Annual count of days when PRCP>=1mm R2 number of very heavy precipitation days - Annual count of days when PRCP>=2mm 6 R1-Number of heavy precipitation days Mantova Milano Pavia 35 R2-Number of very heavy precipitation days Mantova Milano Pavia Lineare (Milano) days 3 days Milano: -.5 day per 1 s Mantova: +1 day per 1 s Pavia: -2 days per 1 s No significant change for the stations in the plain Milano: +3 days per 1 s Mantova: +1 day per 1 s Pavia: +1 day per 1 s

15 Climate Indices calculation Daily data Results of the analysis: Precipitation R1 number of heavy precipitation days - Annual count of days when PRCP>=1mm R2 number of very heavy precipitation days - Annual count of days when PRCP>=2mm 9 R1-Number of heavy precipitation days Locarno Lugano 7 R2-Number of very heavy precipitation days Locarno Lugano days 5 days Locarno: -4 days per 1 s Lugano: -5 days per 1 s Locarno: -3 days per 1 s Lugano: -3 days per 1 s No significant change for the stations at higher altitudes extra Lombardy

16 Climate Indices calculation Daily data Results of the analysis: Precipitation CDD number of consecutive dry days - Maximum number of consecutive days with RR<1mm 9 CDD-Consecutive Dry Days Mantova Milano Pavia 9 CDD-Consecutive Dry Days Locarno Lugano days 5 days Milano: +4 days per 1 s Mantova: +1 day per 1 s Pavia: +2 day per 1 s No significant changes for all the stations Locarno: -2 days per 1 s Lugano: +5 days per 1 s

17 Climate Indices calculation Daily data Results of the analysis: Precipitation CWD number of consecutive wet days - Maximum number of consecutive days with RR>=1mm 18 CWD-Consecutive Wet Days Mantova Milano Pavia Lineare (Milano) 2 CWD-Consecutive Wet Days Locarno Lugano days days Milano: -.6 day per 1 s Locarno: -.6 day per 1 s Mantova: -.6 day per 1 s Lugano: +.2 day per 1 s Pavia: -.9 day per 1 s No significant change for all the stations

18 Climate Indices calculation Daily data Results of the analysis: Temperature and Precipitation the example of Milano Temperature TR2 number of tropical nights - Annual count when TN (daily minimum) >2 ID count of ice days - Annual count when TX(daily maximum)<ºc Precipitation CDD number of consecutive dry days CWD number of consecutive wet days 1 Milano ID TR2 9 Milano CWD CDD 9 8 Due to the urban heat island d/1y days 5 days Significant increase of TR2 2 days per 1 s Decrease of ID day per 1 s -.2 d/1y No significant change for both precipitation indices

19 Contents The dataset The daily indices analysis Some preliminary monthly data indices analysis Some key points

20 Climate Indices calculation Monthly data Some simple indices based on monthly, seasonal and annual data were calculated for 4 stations representative of Lombardy s four different climatological areas: Sondrio (Alpine), Bergamo (Pre-Alpine), Milano (high plain), Mantova (low plain and Oltrepo) Temperature: T max (warmest month) T min (coldest month) average annual T Precipitation: max monthly P min monthly P annual total P All the indices were calculated using the homogeneous series and the observed ones for all stations A comparison between the trends in the indices calculated using the homogeneous series and the observed ones was also done

21 Climate Indices calculation Monthly data Homogeneous series: Temperature Increase for all stations (1-2 C C per 1 s) Sondrio T max T min Bergamo T max T min Average annual T 3 Average annual T T max T max 3 Milano T min Average annual T 3 Mantova T min Average annual T C C C C

22 Homogeneous series: Precipitation Climate Indices calculation Monthly data Annual total P between -.7 and -.4 mm/ - max monthly P between -.3 and -.7 mm/ - min monthly P between -.8 and -.2 mm/ No significant change for all stations 16 Sondrio Max monthly precipitation Min monthly precipitation Annual total precipitation Lineare (Max monthly precipitation) 2 Bergamo Max monthly precipitation Min monthly precipitation Annual total precipitation Lineare (Max monthly precipitation) mm/ mm/ mm 8 mm mm/ mm/ mm/ mm/ 18 Milano Max monthly precipitation Min monthly precipitation Annual total precipitation Lineare (Max monthly precipitation) 12 Mantova Max monthly precipitation Min monthly precipitation Annual total precipitation Lineare (Max monthly precipitation) mm -.4 mm/ mm mm/ mm/ -.5 mm/ mm/ mm/

23 Climate Indices calculation Monthly data Comparison between the trends: Temperature - Milano In the homogeneous series T max increases more than in the observed one In the observed series T min increases more than in the homogeneous one 31 MILANO homogeneous observed 26 T (warmest month) 21 C 16 Average annual T T (coldest month) T max Average T T min Homogeneous +.9 C/1s +1 C/1s +2 C/1s Observed +.3 C/1s +1 C/1s +2.3 C/1s

24 Climate Indices calculation Monthly data Comparison between the trends: Temperature - Bergamo In the homogeneous series T min increases more than in the observed one In the observed series T max increases more than in the homogeneous one 31 BERGAMO homogeneous observed 26 T (warmest month) 21 C 16 Average annual T 11 6 T (coldest month) T max Average T T min Homogeneous +1.3 C/1s +1.3 C/1s +1.8 C/1s Observed +1.7 C/1s +1.4 C/1s +1 C/1s

25 Climate Indices calculation Monthly data Comparison between the trends: Temperature - Mantova In the homogeneous series T max increases more than in the observed one In the homogeneous series T min increases more than in the observed one In the homogeneous series average T increases more than in the observed one 31 MANTOVA homogeneous observed 26 T (warmest month) 21 C 16 Average annual T 11 6 T (coldest month) T max Average T T min Homogeneous +1.1 C/1s +1 C/1s +1.5 C/1s Observed -.6 C/1s -.3 C/1s -.1 C/1s

26 Climate Indices calculation Monthly data Comparison between the trends: Temperature - Sondrio In the homogeneous series T max increases more than in the observed one In the homogeneous series T min increases more than in the observed one In the homogeneous series average T increases more than in the observed one 31 SONDRIO homogeneous observed 26 T (warmest month) 21 C 16 Average annual T 11 6 T (coldest month) T max Average T T min Homogeneous +1.4 C/1s +1.3 C/1s +1.7 C/1s Observed +.1 C/1s +.2 C/1s +.1 C/1s

27 Climate Indices calculation Seasonal data (3 months) Comparison between the trends: Precipitation the example of Mantova No significant change Homogeneous Observed Homogeneous Observed Spring -2 mm/1s -2 mm/1s Autumn +.2 mm/1s -.3 mm/1s Summer +.4 mm/1s +.2 mm/1s Winter +.5 mm/1s -.2 mm/1s homogeneous homogeneous Spring (March-April-May) observed Autumn (September-October-November) observed mm mm homogeneous homogeneous Summer (June-July-August) observed Winter (December-January-February) observed Lineare (observed) mm mm Salzburg, 28th November

28 Climate Indices calculation Seasonal data (6 months) Comparison between the trends: Precipitation the example of Mantova homogeneous December-May observed mm 3 2 No significant change 1 Homogeneous Observed Dec-May Jun-Nov -1 mm/1s +.6 mm/1s -3 mm/1s -.3 mm/1s 7 June-November homogeneous observed mm

29 Climate Indices calculation Monthly data Comparison between the trends: Temperature Average temperature between the four stations 29 Average T max homogeneous observed 6 Average T min homogeneous observed C C In the homogeneous series temperature increases more than in the observed

30 Climate Indices calculation Monthly data Comparison between the trends: Precipitation 16 Average annual precipitation homogeneous observed mm No significant change? Homogeneous: -3 mm per 1 s Observed: +5 mm per 1 s

31 Contents The dataset The daily indices analysis Some preliminary monthly data indices analysis Some key points

32 Conclusions Interesting points: The climate isn t really stationary, at least in the last century. Strong signals can be detected in temperature data, which is increasing. In rainfalls the signals are not so clear. Consequently, the comparison between homogeneous and observed series show that: -temperatures are sensitive to homogenisation, and some trends are increased by the process -rainfalls aren t so sensitive, in fact the difference between the trends is lower than the measures precision. Only good continuous measurements provide good climate analysis.

33 Thanks for your attention My name s Nicola and I m swamping my napkin! Cheers to all the foralpers!