Long-term consistent grid data for temperature in Switzerland

Transcription

1 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Long-term consistent grid data for temperature in Switzerland F. A. Isotta, M. Begert and C. Frei 10th September 2015

2 Content Introduction: motivation, method Results and evaluation Conclusion and outlook 2

3 Content Introduction: motivation, method Results and evaluation Conclusion and outlook 3

4 Introduction - Motivation Develop new datasets for monthly temperature and precipitation suitable for climate monitoring (regularly updated) (1864-) (-now) and (-now) Only with homogenized station data Continuous measurements (no gaps) Constant station density (same every time step) The amount of fulfilling all requirements is low Temperature Precipitation

5 Introduction - Method + High-resolution component , ~85 2-km grid dataset Long-term component (continuous) 5

6 RSOI - Overview Reduced Space Optimal Interpolation (Kaplan et al., 1997; Schmidli et al. 2001, 2002; Schiemann et al., 2010; Masson et al., 2015) PCA + + High-resolution component CALIBRATION PERIOD ( ) (anomalies) PC loadings Dimensionality reduction (truncation) Station data (sparse network) CALIBRATION PERIOD ( ) Station data (sparse network) RECONSTRUCTION PERIOD ( ) + OI Reconstruction (sparse network) RECONSTRUCTION PERIOD ( ) 6

7 Content Introduction: motivation, method Results and evaluation Conclusion and outlook 7

8 RSOI Results and evaluation Calibration period: Reconstruction period: Dimensionality reduction (truncation): 20 Evaluation: Tests with changing calibration (length and period), truncation, data quality, amount Use of crossvalidation (leave-one-out) Mean absolute error (MAE) Mean-Squared Error Skill Score (MSESS) 1= perfect reconstruction, 0=no skill Variance Trend 1,, 1,,,, 8

9 PCA PC loading 1 91% PC loading 2 5% PC loading 3-1% PC loading 4 1% 9

10 Reconstruction examples (anomalies ) Tanomalies Tanomalies 10

11 Reconstrucion examples (anomalies ) Direct interpolation Reconstruction July 1976 ALL MSESS MSESS MSESS

12 Reconstrucion examples (anomalies ) Direct interpolation Reconstruction October 1962 ALL MSESS MSESS MSESS

13 MAE Mean absolute error (degc) 1,, MAE calculated at the same 28 (on anomalies) MAE ALL DJF MAM JJA SON Reconstruction with 57 stat. Reconstruction with 28 stat. Direct grid (~85 )

14 Skill: MSESS Explained temporal variance Most of the have MSESS >

15 Skill: MSESS Expl. variance Explained spatial variance calibration period Explained variance fraction (28 ) MSESS ALL DJF MAM JJA SON Median q q

16 Variance Variance in time, 28 Reconstructed Observed 16

17 Trend Non homogenized data Theil-Sen trend estimate (degc/y) Stippling: 0.05 statistical significance (Mann-Kendall) Homogeneous data (28 ) Direct interpolation Homogeneous data (57 ) 17

18 Content Introduction: motivation, method Results and evaluation Conclusion and outlook 18

19 Conclusion and outlook RSOI method RSOI is an attractive method to benefit of short-term high-resolution information to reconstruct longer time scales with less observations available. Method suitable for complex terrain where variations are spatially anchored. Successful reconstruction of time series and spatial distribution of temperature The discrepancies between observations and reconstruction are relatively moderate (MAE 0.25) Reconstruction improves long-term consistency Outlook Reconstruction for the whole period (1864-) Apply same method for precipitation fields (station homogenization ongoing) Develop a regularly updated climate monitoring product at MeteoSwiss Potential for application in the entire Alpine Region 19