Snow Trends in Northern Spain. Analysis and Simulation with Statistical Downscaling Methods

1. Snow Trends in Northern Spain. Analysis and Simulation with Statistical Downscaling Methods María Rosa Pons Thanks to: Daniel San Martín, Sixto Herrera, Carmen Sordo, José Manuel Gutiérrez Universidad de Cantabria – IFCA mariona@inm.es AEMET, Delegación en Cantabria Seminario CLIVAR–ES,“Clima en España: pasado, presente y futuro”, 11th february 2009, Madrid

2. Motivation • Increasing interest in observed and future trends of different meteorological variables. • Most of the studies analyse continuous variables such as temperature and precipitation. Binary events, such as snow occurrence, require a different approach. Can we observe significant trends in snow occurrence during the 20th century? How good are we at forecasting snow events with statistical downscaling models? Are we able to reproduce the observed trends in a robust way? Can we expect similar future trends?

3. Area of study and available data • 33 stations in northern Spain from a binary snow dataset (AEMET) • - Station height ranges from 60m – 1353m • ERA40 Reanalysis (1957-2002) • MPEH5 & CNRM (1960-2100) (ENSEMBLES) • Less than 7% of missing data during the observ. period (1957-2002) and less than 5% per year for annual frequency analysis

4. Snow observations. Trend analysis • A great interannual variability is observed, as well as a significant decreasing trend in the mean annual number of snow days since the mid 70s. • On average, there has been a decrease of around 13 days of snow per year (23 days for high stations) during the period 1975-2002. • This trend represents a relative annual decrease of around 2% (50% for the whole period).

5. Snow observations. Correlations with other variables

6. Snow observations. Correlations with other variables The correlation coefficient between the mean annual temperature and the annual frequency of snow days is -0.72. In some cases, for example high stations in winter, the correlation with precipitation occurrence has the same magnitude as the correlation with temperature. => More complex patterns in the downscaling method.

7. Analog set PC2 PC1 Weather Type (cluster) Analog and weather-typing downscaling approaches The probabilistic local prediction is obtained from the relative frequency of snow occurrence (binary variable) in the analog set or cluster.

8. Limit point for U > 0 Climatological Frequencies (0.03 and 0.09) Method description and calibration Z500,12,24 Z1000,12,24 T500,12,24 T850,12,24 RH850,12,24 02-Sep-1957 30-Aug-2002 • We use a standard analog method: • Euclidean distance • 30 analogs optimized by a trial and test procedure. • Probabilistic forecast with relative frequency.

9. Validation: daily forecasts with this method are only skillful for stations with high climatological frequencies. Statistical downscaling: Daily occurrence The resulting probabilistic forecasts are converted to binary snow occurrence predictions using a certain threshold U for the probability. Calibration:U is chosen in order to fit the predicted climatology (annual snow frequency) to the observed one (a bias-like correction).

10. Sensitivity studies. Downscaling method We compare the results obtained varying the downscaling method (considering a weather type approach). In this case (100 weather types, the analog method exhibits better results). We also analyzed the sensitivity of the results to the resolution of the circulation pattern and its static or dynamic character. Static: 24 Dynamic: 12+24

11. Statistical Downscaling: Annual Frequencies We compared the observed and predicted annual frequencies for the whole period. Both interannual variability and trend are very well reproduced. As expected, the results are better for the stations with higher climatological frequencies/altitudes. Tests were carried out with simpler patterns (T850) obtaining worse results.

12. Simulations under non-stationary conditions Training period Two new training periods with different means were used: • 1970-1980 (cold) • 1992-2002 (warm) The rest of the series was used to test the results. The method seems to be robust and even 10 training years produce good results. * Similar non-stationary experiments are being carried out for other variables (ENSEMBLES project)

13. Downscaling from Global Climate Models

14. Downscaling from Global Climate Models

15. Downscaling from Global Climate Models

16. Conclusions • The annual number of snow days has suffered a significant decreasing trend during the period 1975-2002 (50% relative decrease). • The correlation with annual mean temperature is very high and it seems to be the driving factor. Nevertheless, in some cases correlation with precipitation is also important. • The analog method is skillful for predicting daily snow occurrence for the higher stations. • It reproduces well both annual trend and interannual variability for all stations. • -The method seems robust to simulate trends even under non-stationary conditions. • - First results with global climate models are shown.

17. Thank you for your attention! http://www.meteo.unican.es mariona@inm.es