Assessing Predictability of Seasonal Precipitation for May-June-July in Kazakhstan

1. Assessing Predictability of Seasonal Precipitation for May-June-Julyin Kazakhstan Tony Barnston, IRI, New York, US

2. Possible sources of seasonal climate predictability: 1. Tropical sea surface temperature (SST) anomalies such as El Nino and La Nina, or tropical Atlantic or Indian Ocean SST anomalies 2. Land surface anomalies (up to 1-2 months influence) 3. Persistent extratropical atmospheric circulation anomalies, such as the Arctic Oscillation

3. (uses CRU precipitation) ENSO-based Teleconnections: May-Jun-Jul El Nino El Nino Probability of above normal precipitation

4. (uses CRU precipitation) ENSO-based Teleconnections: May-Jun-Jul La Nina La Nina Probability of above normal precipitation

7. Seasonal precipitation forecasts for May-June-July for northern Kazakhstan

8. Using field of 500 hPa height as predictor of Kazakhstan rainfall in May-Jun-Jul Lagged in time: March-April 500 hPa is used to predict May-Jun-Jul rainfall

9. Using earlier (Mar-Apr) 500 hPa height as predictor for MJJ rain Cross-validation: 5 years held out, middle one predicted skill Distribution of Skill using Mar-Apr 500 hPa ht Correlation of precip at point X with predictor 500 hPa ht x

10. Using observed tropical SST field as predictor of Kazakhstan rainfall in May-Jun-Jul Lagged in time: March-April SST is used to predict May-Jun-Jul rainfall

11. Using earlier March SST as predictor Mode 1 Mode 1 March SST Time Series MJJ Kaz precip Mode 2 Mode 2 March SST Time Series MJJ Kaz precip

12. Cross-validation: 5 years held out, middle one predicted x May-Jun-Jul skill Distribution of skill using March tropical SST

13. Current dynamical model climate predictions for May-June-July 2014

14. North American national multi-model ensemble forecast For May-Jun-Jul 2014 rainfall x

15. Precipitation May-June-July North American National Multi-model Ensemble Anomaly Correlation x skill

16. European national multi-model ensemble forecast For May-Jun-Jul 2014 rainfall x

17. North American national multi-model ensemble forecast For May-Jun-Jul 2014 temperature x

18. Temperature May-June-July North American Multi-model Ensemble Anomaly Correlation x skill

19. European national multi-model ensemble forecast For May-Jun-Jul 2014 temperature x

20. Precipitation Skill IRI Forecasts 1998-2013 May-June-July 0.5-month lead x Heidke hit skill score

21. Using autocorrelations of precipitation In the 3 states in northern part of Kazakhstan, autocorrelations for precipitation are generally weak. However, autocorrelations of July  August are at least 0.3, and >0.4 at some stations. Lag correlations of temperature  precipitation are very weak during the growing season.

22. Global warming trend gives opportunity for some skill in seasonal temperature predictions: With base period in the past, positive temperature anomalies are often a correct forecast.

23. Time series of monthly anomaly of maximum temperature at station 28698 (Omsk, Russia) warming?

24. Time series of annual anomaly of maximum temperature at station 28698 (Omsk, Russia) Warming trend is evident near Northern Kazakhstan

25. Conclusions Tropical SST anomalies during months earlier than May-June-July have almost no relationship with rainfall or temperature in northern Kazakhstan in May-June-July. Upper air geopotential height (500 hPa) in preceding months is related only weakly to Kazakhstan precipitation and temperature in May-June-July. A connection with the Arctic Oscillation is weak. Autocorrelation statistics for precipitation in northern Kazakhstan show some July-to-August anomaly persistence. Dynamical model predictions for Kazakstan show very slight skill for May-June-July precipitation. For temperature, skill is present due to warming trends. An upward temperature trend exists in observations for northern Kazakhstan for the May-June-July season.