Mathew Barlow, AER Michael Tippett, IRI

1. The use of large-scale climate information to predict Central Asia river flows at one- and two-season leads Mathew Barlow, AER Michael Tippett, IRI

2. Ideas • Central Asia river flows largely driven by snowmelt. • Relate river flow to preceding cold season’s snow pack. • Routine snow pack measurements are scarce. • Use local precipitation as a proxy? • Local precipitation influenced by large-scale, potentially predictable, climate variability. • Upper level winds • Tropical connection (“Perfect ocean for drought”) • How much information can be extracted from available real-time products?

3. Where is Central Asia?(For the purposes of this analysis) Number of years in 3-year period, Nov 1998 - Oct 2001, where precipitation amounts were in the lowest fifth of the yearly values since 1979. River flow stations in current analysis, 36 years of monthly data, 1950-1985

4. Previous Results and Background Precipitation and drought • Agrawala et al. 2001: Recent drought and societal impact for Central Southwest Asia (CSWA) • Barlow et al. 2002: Drought pattern and west Pacific SSTs + La Nina; precipitation in eastern Indian Ocean • Hoerling and Kumar 2003: West Pacific SSTs + La Nina give global drought pattern in model • Tippett et al. 2003, 2005: Role of upper-level winds. West Pacific model precipitation can be used for CSWA forecasts that have operational skill over recent period. • Barlow et al. (sub): MJO in E. Indian Ocean affects CSWA daily precipitation; Rodwell-Hoskins hypothesis River flow • Schär et al. 2004: High predictability of river flow for a Central Asia river based on ECMWF reanalysis antecedent winter precipitation

5. Data • UEA 0.5x0.5 precipitation (consistent with averages from station data) • NCEP/NCAR reanalysis and CDAS winds, precipitation • Kaplan SSTs • 24 river flow stations, reporting 93-100% of the time, monthly, 1950-1985, from NCAR ds552.1, ds553.2 • No correction for human influence on flows. However, results hold across stations representing a range of flow volumes and elevations, and over full period of record. • Averaging over bulk of flow for a given year (less sensitive to release timing) • Schar results suggest accounting for human influence increases the strength of the relationship; and relationship continues strongly after dissolution of Soviet Union • Results physically consistent

6. Climatology Nov-Apr Precipitation “cold season” Topography (km) Station precipitation May-Oct Precipitation

7. First EOF of DJFM 200 hPa reanalysis winds Correlation: Wind EOF1/Precip EOF1 = .66 Wind EOF1/NE = .58 FirstEOF of DJFM precip Winter precipitation related to upper level winds.

8. East Asia Jet Stream—tropical connection • Negative correlation between Central Asia precipitation and EAJS strength. • Positive correlation between EAJS strength and Maritime Continent precipitation. Yang et al. 2002

9. Mean Normalized River Flow - Coherence

10. Central Asia Seasonal Cycle Seasonal cycles of precipitation, river flow, and vegetation in region

11. Average Normalized River Flow: Antecedent Winter Precipitation and SSTs Repeating correlations separately for each half of the record yields the same patterns, slightly stronger in the recent data Correlation to Antecedent Nov-Mar Precip Covariance to Antecedent Nov-Mar SST Max Correlation = 0.8 Max Correlation = 0.6

12. Local aggregated analysis suggests that local precipitation is a useful predictor of river flow. • Few precipitation observations in real (or recent) time. • Follow Schär and try to use analysis precipitation estimates. • Extract additional information from analysis winds. • Use pattern based regression (CCA).

13. Canonical Correlation Analysis (CCA) Nov-Mar 200hPa U and model precipitation Apr-Aug River Flows (Using reanalysis/CDAS wind and model precipitation for ease and consistency in operational use.) Average correlation = -0.7 CCA time series: Nov-Mar U,P (blue) & Apr-Aug River Flows (green); Nino 4 (red)

14. CCA in Prediction Mode Compute patterns from CCA on historical data Project U,P patterns onto current U,P anomalies to get magnitude This magnitude times the river flow pattern is the river flow forecast. In the current case, essentially predicting the total river flow amount at the start of the season. For cross validation skills, sequentially withhold one season from pattern calculations and forecast for that season. Cross-Validated Skill Scores River Flow Stations

15. Summary • Central Asian river flows can be predicted based on operationally available data, with an average cross-validated skill correlation of 0.43 (including the 3 wayward stations) and 10 stations correlated greater than 0.5. • Considerable spatial coherence in the signal and a relationship to a previously-recognized pattern of large-scale variability. • Possible forecasts refinements include • updating forecast in May and June to account for end of rainy season • using more real-time observed precipitation • targeting individual stations. • Preliminary results suggest that vegetation (NDVI) may be amenable to the same forecasting technique.