Cluster analysis of contemporary and future climate of Latvia

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1 Cluster analysis of contemporary and future climate of Latvia Juris Senņikovs, Uldis Bethers, Ilze Klints Laboratory for mathematical modelling of environmental and technological processes, University of Latvia What are the patterns of spatial variability of temperature and precipitation climate in Latvia and Baltic states in reference period and in the future? We considered bias-corrected set of runs of regional climate models from ENSEMBLES projects. Principal component and cluster analysis has been performed for reference climate and future climate of Latvia and Baltic states

2 Contents RCM data and bias-correction Monthly average temperature (T) and precipitation (p) and T/p climate distance (metric) and normalization Correlation properties of monthly averages and principal component analysis (PCA) Cluster analysis of T/P climate Spatial variability of regional T/p climate and its change in the future.

3 Regional climate model data 22 RCM model runs from ENSEMBLES project for the A1B scenario were considered, biascorrection applied Annual average temperature and precipitation from ensemble median of biascorrected RCM model runs, reference period Annual average temperature and precipitation from ensemble median of biascorrected RCM model runs, future period Temperature +3-4 C Precipitation mm/year Bias-correction method: Sennikovs J, Bethers U (2009) Statistical downscaling method of regional climate model results for hydrological modelling

4 Monthly average temperature and precipitation, East-West gradient of monthly temperatures during period October- February North-South gradient of monthly temperatures during spring Local maximums of precipitation North-South gradient of monthly precipitation during April-June East West gradient of monthly precipitation during October- January

5 Quantifying temperature and precipitation climate We consider 24 variables 12 monthly average temperatures (T m ) and 12 monthly precipitation amounts (p m ) for each point (i) Variable transformation (e.g. standardization) necessary to be able to compare temperature and precipitation parts Variable matrix X ( 24 x N points ) p 1 p 2... p 12 T 1 T 2... T 12 i=1, 2, 3,...,N points pp mmii TT mmii = pp mmii pp 0mm pp mm = TT mmii TT 0mm TT mm variable offset norm Monthly averages of each variable over all considered points - offsets. Fixed norms for temperature and precipitation, determined as overall standard deviation, separately for temperature and precipitation. That allows comparison between different time periods. T=0.8 C, p=7.2 mm/month NN pppppppppppp TT 0mm = 1 NN TT mmmm ii=1 TT mm = TT = ssssss ii,mm (TT mmmm TT 0mm

6 Correlations between T/p climatic variables Precipitation Temperature Strong correlation between temperatures of months in cold half of the year, e.g. locations with higher temperatures in November have also higher temperatures in January Strong correlation between winter month precipitations Correlation between spring month precipitation and temperature Anti-correlation between spring month temperature and autumn precipitation Precipitation Temperature

7 Principal component analysis Find linearly uncorrelated new variables (principal components) from original set of 24 variables, new variables are linear combinations of original ones. Find which original variables constitute new variables (principal directions) Quantify fraction of total variance that each component represents Separate temperature and precipitation parts of the components, estimate their relative importance Principal directions Precipitation Temperature PC1: winter precipitation and winter temperature (+) PC2: summer/autumn precipitation (-), annual temperature (+) PC3: annual precipitation (+) Precipitation Temperature

8 Principal components of climatic T/p variables PC1: 47% PC2: 31% PC3: 14% Total variance: 18.5 T variance: 9.7 P variance: PC1: 55% PC2: 25% PC3: 10% Total variance: 19.6 T variance: 7.3 P variance: 12.3 (1) East-West winter precipitation and winter temperature (2) South-East - summer/autumn precipitation, annual temperature (3) Annual precipitation, local topography East-West component relatively more dominant especially near the sea in the future climate

9 Quantifying temperature and precipitation climatic distance between points 12 iiii = TT mmmm TT mmmm ββ 12 + pp mmmm pp mmmm ββ NN PPPP PPPP ββ iiii = PPPP mmmm PPPP mmmm mm=1 mm=1 mm=1 Hierachical clusterization Find separate climatic regions based on temperature and precipitation distance between observation points or gridpoints Final cluster shapes depend on selection of metrics and clusterization method Climate variables vary gradually over the region, therefore, cluster boundaries could be somewhat arbitrary

10 Clusterization on observation locations - Latvia

11 Clusters reference period versus future period Coastal regions are the most different from the rest they form separate cluster

12 Clusters reference period versus future period

13 Clusters reference period versus future period

14 Clusters reference period versus future period

15 Clusters reference period versus future period

16 Summary Most of the spatial variability of monthly average temperature and precipitation over the Baltic countries could be represented by 3 principal components both in reference and future periods Spatial variability of temperature and precipitation climate will slightly increase over the Baltic countries in period comparing to reference period This increase will mainly come from increase of spatial variability of precipitation, while spatial variability of temperature will decrease Climatic clusters determined from monthly average temperature and precipitation will change only slightly, mainly in the areas closer to the sea

17 ENSEMBLES The ENSEMBLES project (contract number GOCE-CT ) is supported by the European Commission's 6th Framework Programme as a 5 year Integrated Project from under the Thematic Sub-Priority "Global Change and Ecosystems"....develop an ensemble prediction system for climate change based on the principal state-of-the-art, high resolution, global and regional Earth System models developed in Europe, validated against quality controlled, high resolution gridded datasets for Europe, to produce for the first time, an objective probabilistic estimate of uncertainty in future climate at the seasonal to decadal and longer timescales. Model data sets for the A1B scenario are given for the time period We applied bias correction based on the observation statistics for the reference period model runs were considered. RCM data Institution GCM RCM C4I HadCM3Q16 RCA3 CNRM ARPEGE Aladin CNRM ARPEGE_RM 5.1 Aladin DMI ARPEGE HIRHAM DMI ECHAM5-r3 DMI-HIRHAM5 ETHZ HadCM3Q0 CLM GKSS IPSL CLM HC HadCM3Q0 HadRM3Q0 HC HC HadCM3Q16 HadCM3Q3 HadRM3Q16 (high sensitivity) HadRM3Q3 (low sens.) ICTP ECHAM5-r3 RegCM KNMI ECHAM5-r3 RACMO KNMI ECHAM5-r3 RACMO KNMI MIROC RACMO METNO BCM HIRHAM METNO HadCM3Q0 HIRHAM MPI ECHAM5-r3 REMO SMHI BCM RCA SMHI ECHAM5-r3 RCA SMHI HadCM3Q3 RCA UCLM HadCM3Q0 PROMES VMGO HadCM3Q0 RRCM

18 Annual average temperature and sum of precipitation RCM ensemble model median, reference period Original RCM ensemble model median, reference period Bias-corrected Bias-correction method: Sennikovs J, Bethers U (2009) Statistical downscaling method of regional climate model results for hydrological modelling

19 East-West gradient of monthly temperatures during period October- February North-South gradient of monthly temperatures during spring

20 Local maximums of precipitation North-South gradient of monthly precipitation during April-June East West gradient of monthly precipitation during October-January

21 Principal directions and components of climatic T/p variables, reference period Principal component maps Principal directions Precipitation Temperature (1) East-West (47% of total variance) winter precipitation and winter temperature (2) South-East (31%) summer/autumn precipitation, annual temperature (3) Annual precipitation, spring temperature (14%) Total variance: 18.5, T variance: 9.7, P variance: 8.8

22 Principal directions and components of climatic T/p variables, future period Principal component maps Principal directions Precipitation Temperature (1) East-West (55% of total variance) autumn/winter precipitation, winter temperature (2) South-East (25%) summer/autumn precipitation, annual temperature (3) Annual precipitation (10%) East-West component relatively more dominant escpecially near the sea in the future climate Total variance: 19.6, T variance: 7.3, P variance: 12.3

23 Precipitation Temperature Precipitation and temperature parts of principial components, Precipitation and temperature parts in PC2 have different dominant directions

24 Precipitation Temperature Precipitation and temperature parts of principial components, Precipitation more dominant in PC1 comparing to reference period

25 Clusters reference period versus future period

Danish Meteorological Institute

Danish Meteorological Institute Ministry of Climate and Energy Danish Climate Centre Report -3 Weighted scenario temperature and precipitation changes for Denmark using probability density functions for ENSEMBLES regional climate models