1. Climate Information for Hydrological Outlooks David Wratt, Roddy Henderson, Charles Pearson & James Renwick NIWA, New Zealand Technical Conference on Changing Climate and Demands for Climate Services for Sustainable Development Antalya, Turkey, 16-18 February 2010 Source: NOAA CDC Source: NOAA CPC

2. Outline Methodology overview Seasonal climate Updates & Outlooks Present conditions Predictability Information used for outlooks The probabilistic projections Producing hydrological outlooks Skill Further guidance products Summary

3. Producing Outlooks - Overview

4. Seasonal Climate Updates and Outlooks

5. Climate stations National information base 208 open climate stations (118 automatic) ~70 soil moisture sites (some NIWA, some local government) - combine with modelled soil moisture from climate data Hydrometric network >600 open stations (NIWA + local government)

6. Climate “update” Rainfall anomaly January 2010 Mean T anomaly January 2010 Sunshine anomaly January 2010

7. Soil moisture “update” Historical average deficit At 9am on 1 Feb (mm) Actual deficit at 9am on 1 Feb 2010 (mm) Anomaly at 9am on 1 Feb 2010(mm)

8. Catchment river flows “update” At end January 2010

9. Potential Seasonal Predictability - NZ About 50% of the variance of seasonally-averaged temperature is potentially predictable (less in winter). Madden & Kidson, IJC, 1997 Only 30% or less of the interannual variance of seasonal precipitation is potentially predictable. Madden et al, IJC, 1999. Studies by Madden and colleagues in the 1990s (IJC 1997, 1999) suggest:

10. Seasonal influences - ENSO state

11. “Average” El Niño Summer“Average” La Niña Summer Rain anomaly

12. Information: Current ENSO state NIWA SOI January 2010: SOI - 0.6 (3-month -0.8) CDC SST anomalies (27 Dec 2009 - 23 Jan 2010) http://www.cdc.noaa.gov/map/ images/sst/sst.anom.month.gif

13. Loosely Adapted from http://www.bom.gov.au/climate/ahead/ENSO-summary.shtml Dynamical Statistical Information: Predicted ENSO State

14. Information: SST around New Zealand November 2009 December 2009 January 2010

15. Regional climate projections from modelling centres Precip Feb-April 2010Temperature Feb-April 2010 IRI APEC Climate Centre

16. Six climate forecasting regions Northland, Auckland, Bay of Plenty Nelson, Marlborough Gisborne, Hawkes Bay, Wairarapa Central North Is, Taranaki, Whanganui, Manawatu, Wellington West Coast, Alps and Foothills, Inland Otago, Southland Coastal Canterbury, East Otago

17. Statistical model predictions based on present conditions For Feb - Mar - Apr 2010

18. Summary of Projections For Feb - Mar - Apr 2010

19. Consensus probabilistic outlook tables For Feb - Mar - Apr 2010

20. Moving to Hydrological Outlooks 1-2 hydrologists participate in climate outlook teleconference Material on present state and projections passed to ~8 hydrologists They do individual forecasts, and merge into a consensus

21. Assessment by one hydrologist For FMA 2010

22. Merging into a “consensus” assessment For FMA 2010

23. Include in the “Update” guidance table

24. Presentation on TCU webpage

25. Skill of outlooks - Whole Country

26. Further hydrological product for water supply utility Now Demand: average Climate terciles: 25%;50%;25%

27. Example: hydro-electricity warning La Ni ñ a projections starting May 2007 May 200750% chance of move to LN JuneLN likely to develop JulyLN on its way AugustLN falters but still possible September LN indicators strengthen October LN conditions have developed November LN to stay for summer December LN strengthens January 2008 LN dominates February LN likely to remain until autumn March AprilLN weakens These were significant and serious warnings for both generators and users!

28. The outcome

29. Summary Common seasonal climate outlook products + information on present state together provide sufficient input for producing hydrological seasonal outlooks (for soil moisture, catchment flows) Skill of climate forecasts for NZ is real but modest. Potential level of predictability sets limits NIWA flow outlooks have higher skill levels overall than rainfall outlooks (influence of initial conditions?) Media want climate outlooks, but often don’t (or won’t) understand limits Using probabilistic outlooks requires sophisticated approach by end-users. There is potential for collaborating with users to develop further products targeted to their needs