1. Hadley Centre Evaluating modelled and observed trends and variability in ocean heat content Jonathan Gregory 1,2, Helene Banks 1, Peter Stott 1, Jason Lowe 1 (1) Hadley Centre for Climate Prediction and Research, Met Office, Bracknell, United Kingdom (2) Centre for Global Atmospheric Modelling, Department of Meteorology, University of Reading, United Kingdom Sampling in the World Ocean Database (WOD, Levitus et al., 1998) can be very sparse as figure 2 shows. Levitus et al. (2000) created almost globally complete data using interpolation methods (Levitus et al., 1994). For each month from 1941 to 1996 we have averaged the WOD observations onto the grid of the HadCM3 AOGCM, which is uniform in the horizontal with boxes of 1.25º  1.25º and has 20 layers in the vertical ranging from 10 m to 615 m thickness. Data coverage in the Northern Hemisphere (0-65ºN) on the HadCM3 grid reaches a maximum of 70% after 1970 in the upper 360 m. Coverage declines with increasing depth; below 1193 m it never exceeds 20%. In the Southern Hemisphere (0-65ºS) coverage never exceeds 40% even in the upper 360 m. Figure 2. Number of observations in the top model level from the WOD for 1941- 1996. How can we produce estimates of ocean heat content from sparsely and irregularly sampled temperature fields? Considering each layer individually there are two extreme alternatives: Representative averages: assume that the average temperature of sampled points in any layer is representative of the average over the whole volume of that layer. Zero anomalies: where temperature is not sampled assume that the temperature anomaly is zero and hence does not contribute to the heat content anomaly of that layer. Where data coverage is complete the two estimates will be exactly equal. In other cases the method of representative averages will give larger anomalies. More complex interpolation schemes such as those used by Levitus et al. (2000) should give intermediate results. Figure 3a shows the ocean heat content anomaly calculated from WOD over the top 360 m of the Northern Hemisphere 0-65ºN. After 1970 our two estimates are almost identical and in good agreement with Levitus because of adequate data coverage. Before 1970, they disagree about the sign of the anomaly. Extending the results down to 3000 m (Figure 3b) and to include the Southern Hemisphere 0-65ºS (Figures 3c and 3d) we find increasingly large differences, with substantial disagreement even after 1970. The discrepancies arise from poor data coverage. As expected, the Levitus timeseries lies between our two. The Levitus range in heat content for the upper 3000 m of the global ocean is around 20  10 22 J. With our two different processing methods we find a range of heat content of around 50  10 22 J for the representative averages and 6  10 22 J for the zero anomalies. Figure 3. Timeseries of five-year running-mean ocean heat content anomalies, with respect to the mean of 1970-1989, calculated by the methods of representative averages and zero anomalies, and compared with the Levitus estimates. Note different vertical scales. The interannual RMS deviation from a linear trend to the five-year running means for world ocean heat content above 3000 m for 1957-1994 in the Levitus results is 5.35  10 22 J. In the HadCM3 control run it is 1.93  10 22 J, significantly less at the 1% level. It is important to explain the discrepancy because variability of the size exhibited by the Levitus timeseries would have a substantial influence on the heat balance of the climate system and the interannual variability of climate, possibly affecting the conclusions of climate change detection and attribution studies. For example, the Levitus timeseries shows an increase of 7.5  10 22 J from 1988 to 1994, corresponding to a heat flux averaged over the world of 0.7 W m -2, about half the size of the radiative forcing due to carbon dioxide. Figure 5 (a) The interannual standard deviation of detrended layer heat content as a function of layer number for HadCM3 and the Levitus anomalies. (b) The interannual standard deviation of detrended temperature. In both panels the shaded region indicates the sampling uncertainty obtained by taking a number of 50-year segments of the HadCM3 control simulation. Observational results have previously shown that ocean heat content has increased over the last fifty years. Models are generally able to reproduce the observed trend but not the substantial decadal variability seen in the observed timeseries, as shown for instance in figure 1 for the HadCM3 AOGCM (Gordon et al., 2000). If such variability occurs it is important that climate models should simulate it. We have analysed the variability as a function of depth in the Levitus timeseries and HadCM3 control, both detrended. We used a large number of portions of the control, each of the length of the Levitus timeseries, in order to evaluate the uncertainty in the estimate of the model variability. Figure 5a shows the variability in heat content as a function of layer. In the upper 50 m, the simulated and observed datasets agree rather well. Figure 5b shows that variability in the model generally declines monotonically with increasing depth, but in the Levitus dataset there are marked peaks in the approximate ranges 40-100 m and 200-600 m. Since the source of variability is at the surface, we find these subsurface maxima intriguing. Around 500 m Levitus has twice the variability of the model. Below 200 m, imposing the observed data mask for 1947-1994 on the simulated data has a strong influence on the estimated variability, doubling it at some levels. This sensitivity to the data mask leads us to suggest that the sparseness of the observed data could have made a contribution to inflating the subsurface variability in the Levitus timeseries. Figure 4 shows that in the upper 360m of the Northern Hemisphere ocean (0-65ºN) we find that the model is able to reproduce both the trends and variability when HadCM3 includes both anthropogenic and natural forcings (ALL ensemble). While the trend is reproduced reasonably well in the ANTHRO ensemble, the variability appears to be underestimated. The GHG ensemble overestimates the overall observed warming whereas the NATURAL ensemble shows a slight decreasing trend in ocean heat content, although the enhanced variability seen in the ALL ensemble is also seen in this ensemble. Figure 1. World ocean heat content as estimated by Levitus et al. (2000, hereafter referred to as the “Levitus” results) and from the HadCM3 ensembles: GHG (greenhouse gases only), ANTHRO (greenhouse gases plus sulphate aerosols and tropospheric and stratospheric ozone changes), NATURAL (changes in solar output and stratospheric volcanic aerosols) and ALL (all the forcings included in ANTHRO and NATURAL). Solid lines show the mean of 4 ensemble members and the blue dashed lines show the values for individual members of the ALL ensemble. Figure 4. Heat content anomalies in the upper 360m of the Northern hemisphere ocean (0-65ºN) from Levitus (black lines) and from the four ensembles, with ensembles shown as dashed coloured lines and the ensemble mean as a solid line. References Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns and J. F. B. Mitchell, The Simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clmate Dyn, 16, 147-168, 2000. Levitus, S., and T. P. Boyer, World Ocean Atlas 1994, Volume4: Temperature, NOAA/ESDIS E/OC21, US Department of Commerce, Washington DC, 1994, 117pp. Levitus, S., T. P. Boyer, M. E. Conkright, T. O’Brien, J. Antonov, C. Stephens, L. Stathoplos, D. Johnson, and R. Gelfield, World Ocean Database 1998, no 18 in NOAA Atlas NESDIS, US Department of Commerce, 1998. Levitus,S., J. I. Antonov, T. P. Boyer, and C. Stephens, Warming of the world ocean, Sciemce, 287, 2225-2229, 2000. Conclusions When both natural and anthropogenic forcings are included, HadCM3 simulations and the World Ocean Database have similar trends in global ocean heat content anomalies, but variability in the latter is larger, having a strong subsurface peak around 500 m, which is not present in HadCM3 simulations. Observations of ocean temperature are sparse outside the Northern Hemisphere (0-65ºN) upper 360 m. Within this region with best data coverage, HadCM3 and Levitus have similar variability. Comparison of global ocean heat content with observational estimates has been used as a test of coupled climate models; we suggest that such comparisons should be applied with caution outside the well-observed region. We recommend that continuing subsurface ocean observations should be supported as it is only with sufficient data that we can begin to understand decadal variability in ocean heat content which could be as large as trends over the past 50 years.