1. Coupled errors of CMIP5 GMCs in the south-eastern tropical Atlantic
Thomas Toniazzo & Steven Woolnough
University of Reading, UK
10/9/ updates

2. Wind, SST, and ocean errors in tropical coastal upwelling systems
The south-eastern tropical Atlantic (SEA) is associated with warm SST biases in GCM
linked with the equatorial region
impairing the simulated climate in climatically critical land areas s.a. the Amazon basin, and causing misrepresentation of basin-wide ocean-atmosphere feedbacks (e.g. MOC).
problems with Sc cloud-cover and with coastal winds
coastal and equatorial upwelling
sensitive to wind-stress errors, to SW and LH flux errors, and to errors in subsurface ocean water properties.

3. Like CmiP3, the CMIP5 set shows ubiquity of warm & wet error in south Atlantic
[K] / [mm/day]

4. An analysis of error growth in initialised decadal forecasts
We analyse the errors as a function of lead time in the initialised decadal hindcast integrations in CMIP5
These provide the opportunity to document areas of fast error growth and can help disentangle cause-effect relationships linking the coupled atmospheric and oceanic errors.
We focus primarily on the generation of SST errors in the marine coastal region of the SEA
Unfortunately, we are restricted to models with a reasonably large ensemble of full-field initialised hindcasts, and decent high-frequency diagnostics
Grand total of CMIP5 models satisfying the above requirements so far is 2.

5. Three models from CMIP5 CanCM4 (CCCma) HadCM3 (UKMO) CFSv2-2011
(NOAA-NCEP)

6. 1. CanCM4
(a1)
We use composites over 5 hindcasts (starting in 1 Jan , 2003, 2004, and 2006) averaged over all 10 members of the „i1“ ensembl of the CanCM4 model, Figure (a1).
SEA SSTs stay close to observations for about 3 months, after which the errors grow quickly. Within 6-7 months, they reach their climatology. Meridional wind- stress, Figure (a2), is marginally weak but the integrated wind-stress curl, Figure (a3), is accurate, implying a correct oceanic upwelling mass-flux.
(a2)
(a3)

7. (b1)
Upwelling is initially effective in counter- acting the heating by surface fluxes, Figure (b2).
Apart from minor adjustments the initial tendencies, Figure (b1), are correct during the first 2 months. In March, SST fail to cool, and LW losses, Figure (b3), grow until the net heat gain becomes negative in JJA, leading to a zero average over the annual cycle.
This implies that cooling by oceanic advection has stopped.
(b2)
(b3)

8. (c1)
(c2)
In the same month (March) sub-surface ocean temperatures rapidly grows a large positive error, Figure (c1).
It is partly salinity-compensated, Figure (c2), indicating a geostrophic advection error.
Additional surface freshening however, first seen in month 5 in Figure (c2), stabilises the mixed layer and reduces vertical mixing.

9. The origin of the sub-surface anomaly is traced in the monthly- mean tendencies of the vertically averaged ocean temperature in Figures (d1-4). It first develops in the Gulf of Guinea, and spreads from there as a mixed coastal- Kelvin/equatorial- Rossby wave to the west, and as a coastal Kelvin wave to the south.
(d1)
(d2)
(d3)
(d4)

10. High-frequency 3-D ocean diagnostics are unavailable and the initial evolution in the first month cannot be traced, but the surface diagnostics over the Equator suggests the cause. The zonal wind-stress, Figure (e2), has nearly its full climatological error from day 1, unbalancing the equatorial thermocline, which deepens, Figure (e1). Equatorial upwelling is also slowed causing SSTs to rise.
(e1)
(e2)

11. (e3)
Day 1-5
The positive zonal wind error is associated with precipitation errors that also appear immediately over the African and South- American continents, Figure (e3). This type of error growth mechanism is consistent with the results of Richter et al. (2011).

12. (f1)
(f2)
As with the surface heat fluxes, precipitation errors subsequently respond to the changed distribution of SSTs, with a wet error appearing from May over the Equator, Figure (f1). Over the SEA, a small wet error first occurs in April, but it represents a positive feedback on the warm SST error. Surface freshening stabilises the upper ocean and reduces vertical mixing. This allows SSTs to warm even further with the arrival of the second warm season (September), reaching a peak above 28°C the following April and fuelling further heavy precipitation, Figure (f2). Balance is reached by an additional increase in LW cooling, Figure (b4).

13. 2. HadCM3 (DePreSys)

14. Associated with simltaneous sub-surface warm bias...
SETA SST bias developing from March, strengthening in year 2.
WS curl OK, Qnet~0
Linked to Equatorial wave activity excited by zonal wind biases

15. Year-2 amplification due to convectively coupled atmospheric response during the warm season
The surface warm bias can be eliminated via a narrow wind-stress bias correction (reinforced by the feedback)

16. 3. CFSv2 (NCEP) Coupled Forecasting System operational forecasts
employs a coupled version of the assimilation system used for GFS/AFS with full-field initialisation from homogeneous reanalyses
3- and 6-hourly diagnostics on 3 grids for atmosphere, surface and ocean.
Decadal hindcasts only submitted to CMIP5, with 1st Nov start date for 15 (disparate) years.
Extremely rapid growth of moderate warm bias.

17. As with CanCM4, wind-forcing of ocean upwelling, which is slightly too strong, is not the cause of the bias.

18. Large & immediate warming bias from (SW) surface heat fluxes

19. GFS's lack of Sc cloud is over-balanced by evaporation to give near-zero annual-average surface heat gain

20. A shallow warm error with increased near-surface stratification

21. A coastal downwelling is associated with geostrophic zonal convergence bias

22. Downwelling apparently forced from Equatorial region in slow poleward propagation of thermocline deepening. Effect of Equatorial wind-stress biases not clear but favouring deepening in GoG

23. Anticlockwise wind circulation bias across the Equator associated with shallow anticyclone from BL warming.
Large-scale convectively coupled circulation changes are less important

24. Summary of ocean bias developments
CFSv2-2011
CanCM4
HadCM3
time
lat
lon
Combined Hovmueller diagrams (lat-time, left, along African coast, plus lon-time, right, along the Equatorial Atlantic) for the biases of the 16C isotherm depth (colours) and of the near-surface wind (contours: meridional component on left, zonal on right; black for positive values, white for negative values) for each of the three decadal hindcast systems analysed for initial error development from the CMIP5 ensemble in the tropical Atlantic. CFSv2 shows a centre of development mainly in the Gulf of Guinea, which however is triggered by excessive surface SW all along the eastern seaboard; before that couples, winds are mostly OK. CM4 has large initial zonal wind errors over the Equator, and thrmcl depth anomalies propagate into the Benguela area from there. CM3 has negative initial meridional wind errors in the Benguela which triggers a local warming; this later couples with the Equatorial winds generating additional thermocline errors that intensify the warming.

25. Conclusions
Significant warm SEA biases present in all the CMIP5 CGCMs, with error maximum near the Benguela upwelling and accompanied by warm eastern equatorial bias (at least relative i.e. in terms of gradient)
Error-growth analysis in full-field initialised decadal hindcasts used to get insight on mechanisms
Three models analysed: CanCM4, HadCM3 and CFSv2-2011
Biases arise from different processes in the three cases, but always display
Ocean biases, at least in part remotely forced, driving local surface heat budget to a zero annual mean
Subsequent amplification by coupling with the atmospheric BL and convection

26. Final remark – the role of ocean biases
Positive upward surface heat flux biases apparently ubiquitous in CMIP5 (historical), implying reduced oceanic (cold) advection

27. (g1)
(g2)
Horizontal, geostrophic advection biases represent a possible mechanism. These can be approximated by the perturbation in -(g/ρf)SxT
(Toniazzo et al. 2008).
In the region around the salinity front in the south-eastern Atlantic such anomalies can be large.
In CanCM4, the time-average fields over the first month of ensemble „i1“, Figures (g1), (g2), show small T,S errors that imply warm advection in the SEA by this mechanism, Figure (g3). These are exacerbated as the error grows.
Similar biases are seen in integrations with the Hadley- centre models.
(g3)

28. Resolution (in)dependence of errors
(a)
(b)
AMIP integration
aL=1.5º
oL=1º
aL=1.5º
oH=1/3º
aH=1º
oH=1/3º
aH=1º
oL=1º
HadISST
Coupled Model
(c1)
(c2)