Use of Argo data to detect and understand ocean climate change

1. Use of Argo data to detect and understand ocean climate change Richard Wood, Sheila Stark, Helene Banks, Michael Vellinga and Peili Wu Hadley Centre for Climate Prediction and Research Met Office Exeter, UK

2. Overview • Three important uses for Argo data in climate research • Initialise predictions (daily, seasonal, decadal,…) • Detect and attribute changes/trends • Water mass changes in the South Indian Ocean • Changes in the Atlantic MOC • Provide observational constraints on key processes (to help filter good models from bad) • Creation and propagation of fresh water anomalies • Overarching messages • Need temporal continuity and spatial coverage to answer key questions on a sensible timescale (Argo!) • Community needs to articulate what scientific questions Argo will answer and how soon

3. Salinity changes at 32°S, Indian Ocean Observed (Bindoff & McDougall 2000) 1962 1987 HadCM3 Anthropogenic forcings 1959-69 1989-99 (Banks et al, GRL 2000)

4. Indian Ocean Mode Water freshening simulated in HadCM3model (with various forcings) Persistent mode water freshening is a signal of anthropogenic forcing, in the model. If model internal variability is strong enough, observed freshening is probably anthropogenically forced We only have two snapshots, so how can we quantify the internal variability in the real world? 1960 2000 2040

5. indicated by Argo floats? See B. King later isopycnal freshening isopycnal salting What’s new since 2000? • Part of 32°S section re-sampled in 1995 as part of WOCE. Whole section re-sampled in 2002. Both samples have saltier SAMW than in 1962 (Bryden et al Science 2003, McDonagh et al. J. Climate 2005). Argo floats deployed 2002. • More forcings modelled (natural only and all forcings) and more ensemble members run to sample initial condition uncertainty +

6. SAMW in the Indian Ocean • HadCM3 – a coupled ocean atmosphere model without flux adjustment. • 20th century simulations (4-member ensembles) compared with a parallel control run: • NAT – solar irradiance and volcanism. • ANT – time varying anthropogenic GHGs, sulphate aerosol, ozone. • ALL – both natural and anthropogenic forcing

7. Forced Ensembles Salinity anomalies relative to the mean of the control simulation. No evidence of a return to more saline conditions in late 20th Century

8. Can we make confident attribution of the observed changes? Subsequent 15 year salinity change Initial 25 year salinity change All we have from observations is a single + Can’t rule out internal variability / model internal variability may be too low.

9. - + core SAMW isopycnal What might happen in the 21st Century? Long term freshening trend plus decadal fluctuations High chance of observing ‘+’ in bottom left half Only moderate chance of observing ‘+’ outside ellipse Occasional observation is useless for detecting trends in this water mass

10. A fingerprint of anthropogenic climate change in the Indo-Pacific? Observed salinity changes 1960s -1990’s (Wong et al. Nature 1999) Modelled salinity changes 1990s-2020s (HadCM3 B2, Banks & Bindoff 2003)

11. Timeseries of 30-year changes in fingerprint HadCM3 control 5% sig Adding spatial fingerprint improves signal to noise. But with occasional sampling still need to assume model internal variability is correct. 5% sig HadCM3 B2 1960-90 (Banks & Bindoff J. Climate2003)

12. Monitoring the Atlantic MOC at 26°N Joint NERC RAPID / NSF project Continuous monitoring of MOC 2004-2008 Deployed Feb/Mar 04, refreshed May 05 (Hirschi et al. GRL 2003) Florida Strait transport Ekman Thermal wind Spatially constant correction for the velocity field to ensure mass balance

13. Simulated MOC at 26°N What’s the chance of detecting a trend with 10 years of observations? With 40 years?

14. Observing additional variables… (Vellinga & Wood GRL 2004)

15. …enhances detection skill MOC at 26°N ‘Optimal MOC index’

16. Approach • Started from the observing system of Vellinga and Wood (2004): 26N and 15 hydrographic pseudo-observations (d). • Looked for redundancy in d and the 3 pseudo-observation solution with the best signal to noise ratio. • Reconstructed overturning much less noisy = improved signal to noise ratio and detection times

17. The hydrographic pseudo-observations annual data low pass filtered All 3 can potentially be measured using ARGO

18. Optimal signal to noise ratio Best STNR (and detection time) for a 5 variable d which also includes the salinity on the meridional section and on the 27.9 isopycnal in the Greenland Norwegian Sea.

19. Some freshwater transport processes important for Atlantic MOC stability MOC shutdown (yrs. 20-30)- control: Precipitation (m/yr) Ocean circulation transports tropical salinity anonalies to high latitudes (timescale t) Weakened MOC leads to shift of ITCZ rainfall to south of equator Global warming leads to increased atmospheric freshwater transport from Atlantic to Pacific (Thorpe et al. J. Climate 2001, Vellinga & Wood Climatic Change 2002, Vellinga and Wu J. Climate 2004)

20. Conclusions • Occasional hydrographic sections cannot detect or attribute anthropogenic trends in the ocean • Adding spatial information (model-derived fingerprints) can help, but still need to know if model variability is right (and have to assume fingerprint is broadly correct) • Examples: Indo-Pacific water mass changes, Atlantic MOC • Continuous ocean observations can also help to quantify key processes (e.g. for MOC stability) that are currently uncertain in models • All this points to Argo. But to make the case for sustained observations we need to be more specific about payoff times When can we expect to detect anthropogenic MOC change? How long will it take to quantify tropical freshwater feedbacks?