Climate Change Prediction (2010)

Impact of Atmosphere and sub-surface ocean data on decadal climate prediction  

N.J.Dunstone and D.M. Smith (1)

Corresponding author : Nick Dunstone

(1) : Met Office Hadley Centre, Exeter, UK

Argo delivers the primary source of data from the ocean interior as needed to run predictive climate models used to inform government policies on climate change mitigation and adaptation. Since the publication of the IPCC’s fourth assessment report, the emphasis of UK climate research has shifted from global to regional spatial scales, in order to deliver the information needed to evaluate the socio-economic impacts of climate change. There is a growing requirement for skilful seasonal-to-decadal forecasts of climate.

Decadal climate prediction aims to predict natural internal variability in addition to the response of the climate system to anthropogenic forcing. The MOC has been linked to Atlantic Multi‐Decadal Variability which is thought to influence many regional climate phenomena, including North American and European summer climate, North Eastern Brazilian and African Sahel rainfall and Atlantic hurricanes [Sutton and Hodson, 2005; Knight et al., 2006; Zhang and Delworth, 2006; Smith et al., 2010 ; Dunstone et al., 2011]. Unlike perfect model experiments, real decadal forecasts do not have instantaneous knowledge of all climate variables at all locations and so we need address the forecast skill given more realistic observations.

Idealised model experiments have investigated the impact of assimilating different amounts of ocean and atmosphere data on decadal climate prediction skill. Assimilating monthly average sub-surface temperature and salinity data successfully initialises the MOC and produces skillful predictions of global ocean heat content. However, when sea surface temperature data is assimilated alone, the predictions have much less skill, particularly in the extra-tropics. The upper 2000m temperature and salinity observations currently provided by Argo are therefore potentially well suited to initialising decadal climate predictions. Assimilating data beneath 2000m further reduces the RMS error, with the most significant improvements in the Southern Ocean. Furthermore, assimilating six hourly atmospheric observations significantly improves the forecast skill within the first year, but has little impact thereafter. Full details of those experiments are given in Dunstone and Smith, 2010.

Data and Method

Idealised model experiments are based around a 50 year control run section of the dynamical global climate model «HadCM3». Our primary focus is to assess the impact of assimilating different amounts of data on decadal forecast skill. The Argo array now provides global coverage of T&S data to depths of up to 2000 m. Important questions are : how does this compare with assimilating full‐depth T & S data; and how does this compare to only assimilating SST?

To address these questions we performed four experiments :

  1. Full Depth : ocean T&S are assimilated to full depth, together with atmospheric variables as by Smith et al. [2007],
  2. 2000m : as Full Depth but only to depth of 2000m,
  3. 2000m NoAt: as 2000m but no atmospheric assimilation,
  4. SST6hr and SST96hr : SST is assimilated with 6 or 96 hour relaxation constant and no atmosphere assimilation.

All experiments assimilate complete monthly fields of ocean data and, where appropriate, six hourly average atmospheric variables (surface pressure, 3-dimensional u and v winds and potential temperature) from the HadCM3 control run output.

Results

During the assimilation phase, the MOC at depth of 1000m and a latitude of 30°N for each of the experiments is calculated and its timeseries are plotted in figure 1. The Full Depth experiment agrees with the original control run (the « truth ») very well. This illustrates that assimilating monthly T & S and atmospheric observations is sufficient to successfully reproduce the MOC. The 2000m experiment also shows a high level of agreement with the truth, shown both by the five year smoothed timeseries and the dashed line which shows the annual MOC. The MOC timeseries for the 2000m NoAt experiment shows that in the absence of wind forcing provided by atmospheric assimilation, the annual MOC timeseries is not reproduced faithfully. However, crucially the low frequency (five year smoothed) MOC follows the truth well. The two SST experiments are based on Keenlyside et al 2008. From figure 1b, it is clear that neither of the experiments that assimilate only SST are able to successfully initialise the MOC.

Figure 1 : Five year running mean MOC of the experiments during the assimilation phase. (a) - Full Depth, 2000m and 2000m NoAt experiments. The solid lines are 5 year running means, the broken lines are annual means. For clarity we do not show the Full Depth annual MOC as it is very similar to the original control and 2000m experiment. (b) - SST assimilation experiments for 6 hr (dot‐dash) and 96 hr (solid) relaxation. Two different versions are shown that start from different model initial conditions (red and blue). Note that forecasts are started from the red set. The evolution when no assimilation takes place is shown by the dashed lines.

Using initial conditions provided by the assimilation runs, forecasts began from seven start dates. The dates were chosen to sample a range of different MOC initial states. Each forecast is a nine member ensemble created by a small random perturbation (5 × 10-4 K) applied to each initial SST grid‐point and is run for 16 years.

  • Predictability of the MOC :

For each of the forecast start dates , the ensemble mean annual MOC is calculated and the resulting five‐year smoothed MOC timeseries is plotted in figure 2. The Full Depth, 2000m and 2000m NoAt show a similar level of skill in pre- dicting the evolution of the MOC, as illustrated by the small spread in the root mean squared error (RMSE) (figure 2) averaged over all start dates and lead times. However, both SST experiments fail to capture the correct evolution, resulting in a much larger RMSE than the other experiments (2.26 and 0.88 compared to 0.4 Sv). The lack of skill in these fore- casts clearly arises from the poor initial conditions shown in Figures 2d and 2e.

Figure 2 : Five year running mean forecasts of the MOC. The solid black line is the original control run. Different colours represent different forecast start dates. The thick lines show the mean and the thin lines show the 90% confidence interval assessed from the spread of the ensemble members. Five (of seven) start dates are shown for clarity The RMSE of the MOC averaged over all start dates and lead times is given in the top right of each plot.

  • Forecasting Regional Ocean Heat Content :

Forecast skill is further assessed by examining regional forecast errors. Figure 3 shows the RMSE as a function of lead time for the global ocean, and the Pacific, north Atlantic (north of the equator) and Southern (south of 30° S) oceans separately. The 2000m NoAt experiment is not shown in figure 3 because it is never significantly different to the 2000m experiment in predicting five year means. However, annual mean forecast errors (not shown) for the 2000m experiment are significantly more skillful than the 2000m NoAt experiment for the first year. The Full Depth experiment has a smaller RMSE than the other experiments in all ocean basins, with the most significant improvements in skill in the Southern Ocean. In the North Atlantic the SST experiments are initially less skillful than persistence. In the Pacific Ocean the SST experiments both show skill above persistence.

Figure 3 : Forecast skill as a function of lead time for different ocean basins. The RMSE of five year mean anomalies for the upper 360m ocean temperature is shown for each of the experiments. Error bars are 5–95% confidence intervals on the difference between each experiment and the 2000m experiment (see text). The solid black line is a persistence forecast. Note that for clarity we do not plot the 2000m NoAt experiment because it is never significantly different than the 2000m experiment and error bars are not plotted on the SST‐6hr forecasts as they are almost always above the lower limit of the SST‐96hr error bars.

Conclusions

Assimilating atmospheric variables produces signifi- cantly improved skill in forecasting ocean heat content in year one, but thereafter there appears to be little benefit. This likely follows from the fact that assimilating monthly ocean variables alone was sufficient to capture the low frequency variability of the MOC. However, atmospheric assimilation may give improved skill in situations where the sub‐surface information is less well constrained than it is in these idealised experiments.

At all forecast lead times, assimilating SST alone has significantly lower skill than the experiments that assimilated sub‐surface temperature and salinity.

Assimilation of monthly average temperature and salinity in the upper 2000m produces forecasts with similar skill to full depth assimilation. Furthermore, there is remaining skill in ocean heat content, particularly in the North Atlantic and Southern Ocean after 10 years. This is encouraging as it suggests that the data currently provided by the Argo array has the potential to successfully initialise decadal predictions.

Assimilating data beneath 2000m always reduces the RMSE, with the most significant improvements in the Southern Ocean. This is present from year one of the forecast, suggesting that the improved skill is most likely caused locally, probably due to the strong upwelling in the Southern Ocean which can quickly bring the deep ocean into contact with the surface layers. This could be a location where observations deeper than that currently provided by the Argo array may be useful in the future.

References
  • Dunstone, N. J., and D. M. Smith : Impact of atmosphere and sub‐surface ocean data on decadal climate prediction, Geophys. Res. Lett., 37, L02709, doi:10.1029/2009GL041609, 2010.
  • Dunstone, N. J., D. M. Smith and R. Eade : Multi-year predictability of the tropical Atlantic atmosphere driven by the high latitude north Atlantic ocean, Geophys. Res. Lett., 38, L14701, doi:10.1029/2011GL047949, 2011.
  • Keenlyside, N. S., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner : Advancing decadalscale climate prediction in the North Atlantic sector, Nature, 453, 84, 2008.
  • Knight, J. R., C. K. Folland, and A. A. Scaife : Climate impacts of the Atlantic Multidecadal Oscillation, Geophys. Res. Lett., 33, L17706, doi:10.1029/2006GL026242, 2006.
  • Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy : Improved surface temperature prediction for the coming decade from a global climate model, Science, 317, 796–799, 2007.
  • Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. A. Scaife : Skilful multi-year predictions of Atlantic hurricane frequency, Nature Geoscience, DOI: 10.1038/NGEO1004, 2010.
  • Sutton, R. T., and D. L. R. Hodson : Atlantic Ocean forcing of North American and European summer climate, Science, 309, 115–118, 2005.
  • Zhang, R., and T. L. Delworth : Impact of Atlantic multidecadal oscil- lations on India/Sahel rainfall and Atlantic hurricanes, Geophys. Res. Lett., 33, L17712, doi:10.1029/2006GL026267, 2006.