Jon Robson J.i.robson@reading.ac.uk Rowan Sutton and Doug Smith (Met Office)

1. An analysis of a decadal prediction system Jon Robson J.i.robson@reading.ac.uk Rowan Sutton and Doug Smith (Met Office) Thanks also to Ed Hawkins, Alan Iwi and Andy Heaps

2. Overview • Background and motivation • Introduction to DePreSys • Analysis of DePreSys hindcasts • Hypothesis testing experiments • Conclusions and Implications

3. Projections of climate change • The current rate of observed global mean warming is predicted to continue and may even increase over the coming decade • Decision makers will need the best information available on regional or local scales for adaptation decisions. • Current regional climate projections are dominated by natural variability over the next decade How can we constrain the uncertainty in climate projections over the next decade?

4. Uncertainty Internal • Uncertainty in climate forecasts arise from 3 sources. • Model uncertainty • Scenario uncertainty • Internal variability • Global projections of climate change are dominated by model and scenario error • However for regional scales internal variability can be a very important source of uncertainty over the next two decades Can we reduce the uncertainty caused by internal variability? Scenario Model Internal Scenario Model (Hawkins and Sutton, 2009)

5. Long-time scale variability and predictability • “slower” parts of the earth system could be predictable for many years and could constrain uncertainty over the next decade. • Depends on what you look at and where you look • But there does seem to be some hint of potential predictability in the North Atlantic What is the cause of this predictability? (Boer, 2004)

6. AMOC variability • The AMOC transports heat northward and warms the climate of Western Europe. • Model studies show that the strength of the AMOC is naturally variable on multi-decadal timescales and modulates the northward heat transport • “Perfect” model results suggest the AMOC could be potentially predictable for over a decade (Knight et al, 2005) 1 Sverdrup = 106 m3 s-1 But we do not know how that translates into actual predictability

7. Initialised forecasts - DePreSys • Smith et al, Science, 2007. showed that initialising the ocean in a coupled climate model did improve the skill of global surface temperature forecasts over the next decade compared to forecasts that didn’t assimilate information. Surface temp 113m heat content

8. Motivation for my project • Mean skill scores do not inform you of why the forecasts are performing better, or indeed why forecasts that assimilate information are performing worse in some areas. • What are the mechanisms behind the improved predictability? • Why do some forecasts fail? • Evaluating the climate models against observations at the process level – A new handle on understanding model error.

9. 2. Introduction to DePreSys

10. DePreSys • Fully coupled decadal forecast system, based on HadCM3 • Initialised from the observed climate state in order to constrain predictions over the next decade • Forced by anthropogenic emissions (SRES B2 scenario), previous 11 year solar cycle and volcanic aerosol. Volcanic aerosol is reduced with an e-folding timescale of one year. • There are no future volcanoes in the forecasts Hindcast Set • 4 member ensemble DePreSyshindcasts initialised seasonally (March, June, Sept and December) over the years 1982-2001 • For comparison a second similar ensemble is also initialised, that does NOT assimilate observations – this is called NoAssim • Over 6000 model years

11. Initialisation of DePreSys • Seasonal forecasts typically assimilate the full fields of variables to initialise the model as close to the observed state as possible. • However the model climate and the real climate are not the same, and so the forecast will drift back to the model’s preferred state over the course of the forecast • DePreSys is Initialised close to the model attractor by assimilating anomalies on to the model climate Top 100m average Temperature + Observed anomaly Climatology (Calculated form transient integrations)

12. Anomaly assimilation NoAssim Assimilation Run Global Temp DePreSys Transient Run’s Obs anomaly 1979 2001 time 1960 2010 • Assimilation run is started from a transient run and integrated forward using historical forcing and is constantly relaxed (strongly) toward the model climatology plus the observed anomalies Ocean:- Relaxed to 3D T and S, anomalies calculated from Met Office Ocean analysis. Climatological period = 1941-1996 Atmosphere:- Relaxed to 3D temp, 3D winds and surface pressure calculated from ERA-40. Climatological period = 1979-2001 • DePreSys also has a perturbed physics ensemble of 9 QUMP models

13. 3. Analysis of DePreSys hindcasts • What changes have occurred in the world oceans over the hindcast period?

14. Rapid warming of the North Atlantic • The rapid warming of the North Atlantic was largely a lagged response to the positive NAO forcing of the 80s and 90s • Evidence that spin up of the AMOC and a surge in the heat transport causes the warming Inverted NAO in black Temp anomaly of Subtropical gyre (60W-10W,50N-66N) from Levitus, ECMWF and Met Office

15. How skillful is DePreSys for the rapid warming? Top 500m average ocean temp for the subpolar gyre (60w-10w, 50n-66n) Black = Observation Red = DePreSys hindcast • DePreSys Exhibits remarkable levels of skill for the 1995 rapid warming of the subpolar gyre

16. However it doesn’t get it right all the time…. • After 1990 DePreSys hindcasts become very eager to warm rapidly in the subpolar gyre region. • What is the cause of these early warmers?

17. What’s happening in the initial conditions? • Need to look at density in order to deduce changes in initialised circulation • In HadCM3 high density in the subpolar gyre due to NAO forcing leads to an increase in overturning, and hence increase the Northward heat transport Correlation of 0-1000m density anomaly leading the AMOC index by 5 years from HadCM3 control run Normalised 150-1000m density anomalies

18. Density errors occur in the assimilation run • Large density errors occur across the whole ocean but occur frequently in the North Atlantic • In the early 90s large density errors occur in the deep convective regions of the North Atlantic • Hypothesis A:- The early warming hindcasts are caused by the presence of errors in the assimilated density anomalies that cause an increase in the AMOC that is too early or too large

19. The Response of the AMOC All of the DePreSys hindcasts show a rapid and large collapse of the AMOC at 50N

20. Drifts present in DePreSys Subpolar gyre 0-500 density Mean Atlantic Stream function evolution as a function of time over all DePreSys hindcasts minus DePreSys climatology 1980 1990 2000 2010 Mean 0-113m T bias in the Gulf Stream Extension 0.0 What is the cause of this Drift? 0.4 Forecast season

21. Drift in the HadCM3 control run Antarctic Bottom Water overturning index • The first transient run was initialised in yr 1859 from the control run (year 9) • Each subsequent transient run was initialised 100 years after the one before • The DePreSys climatology comes from a transient run that was initialised from an unstable state in the control run and is drifting • Hypothesis B:- The background state for the assimilation is unstable and causes the DePreSys hindcasts to drift Sverdrups Temp

22. Can relaxation to just T and S cause further problems? • The model is being relaxed strongly to the background state plus the observed anomalies • If there are no observed anomalies the model will be stuck firm to the climatological state. • However the background state for DePreSys is • It is not clear that this background state will be stable even if all the intervening states are

23. Aside:- The effect of climatology error on mean skill scores • The skill scores are calculated by evaluating forecast anomalies against the observed anomalies • The NoAssimhindcasts are initialised from transient runs with a different climatology to DePreSys NoAssim (trans1) – DePreSys 113m RMSE NoAssim (sep) – DePreSys 113m RMSE

24. 4. Hypothesis testing experiments

25. Hypotheses A. The early warming hindcasts are caused by the presence of errors in the assimilated density anomalies that cause an increase in the AMOC that is too early or too large • Perturb the assimilated density so that the density anomalies are the same as observed, by perturbing salinity anomalies B. The back ground state for the assimilation run is unstable and causes the DePreSys hindcasts to drift? • Use a new climatology calculated from an ensemble of 6 transient runs, initialised 1500 years into the control run. • Thanks to Alan Iwi for supplying the Climatology! • There have been a few changes to DePreSys since the original hindcast experiment. We re-run new unperturbed forecast first to compare with. • Re-run the December 1991 hindcast

26. The effect of density Errors Control – Perturbed Salinity overturning stream function as a fn of Latitude and time Subpolar gyre 0-500m Temp 2nd year SST forecast difference control – perturbed Salinity.

27. The effect of a new climatology Subpolar gyre 0-500m Temp 2nd year SST forecast difference control – new clim .

28. 5. Conclusions and Implications

29. Conclusions • Moving past mean skill scores to looking at individual hindcasts for case studies is an important route for improving decadal prediction systems • Hindcasts can be very sensitive to the choice of climatology used for the anomaly assimilation. • The non-linear equation of state means that some imbalance may be inevitable when climatologies are derived from time mean temperature and salinity • Non-linearities also lead to errors in the assimilated density anomalies that can have a significant effect upon the hindcasts

30. Future of decadal climate forecasting • Decadal forecasting included in CMIP5 (includes HiGEM DPS) • More work required on assimilation and initialisation strategies • Balanced initialisation techniques • Assimilate density directly • Strategies to minimise assimilated density anomaly error • Ensemble design • Understanding the mechanisms that give rise to the improved predictions • Assessing the models against observations at the process level to tackle model error’s Thank you!!