The coupled atmosphere/ocean/sea-ice models used for prediction on seasonal and decadal time scales typically exhibit a cold bias in North Atlantic sea surface temperature (SST) due to the misplacement of the Gulf Stream and the North Atlantic Current (see Figure 1). The cold bias is known to impact the overlying atmospheric circulation, leading to a bias in the atmospheric circulation over the North Atlantic as well as affecting the atmospheric variability in the Euro-Atlantic sector, including Germany.
The first aim of ATMOS-MODINI is to alleviate the cold bias in the MiKlip prediction system by introducing a correction to the path of the Gulf Stream and the North Atlantic Current in the model and then to test the impact of this correction on the variability and forecast skill of the model, the latter by carrying out retrospective forecasts (also known as hindcasts).
The second aim of ATMOS-MODINI is to explore ways to improve the initialisation of the MiKlip system in the tropics. It is well known that the tropics are a source of predictability for the extratropical atmosphere. However, the initialisation of a forecast system in the tropics is more difficult than at other latitudes because of the vanishing at the equator of the Coriolis force due to the Earth’s rotation.
In the first phase of MiKlip we explored the MODINI initialisation technique – a simple technique that uses the time series of observed wind stress applied to the ocean model component to initialise a coupled forecast model. We showed that hindcasts initialised using MODINI have skill in the Pacific sector out to decadal time scales with implications for forecasting global mean surface air temperature as well as the atmospheric circulation.
Figure 2 shows a forecast initialised on January 1, 2015, using MODINI indicating warmer temperatures in the tropical Pacific that persist at least to 2024. The forecast also accurately predicted that 2015, globally, would be the warmest year ever recorded. ATMOS-MODINI will continue to the explore MODINI initialisation as well as ways to combine the MODINI initialisation with other initialisation procedures used in MiKlip.
With respect to the first aim, a high resolution ocean model (NEMO at 1/20 degree resolution, VIKING20), that simulates a realistic path of the North Atlantic Current, has been analysed (Wang et al., 2017). The potential energy term (associated with the JEBAR term) is found to play a major role, pointing to the importance of the interaction between the deep circulation and the variable bottom topography in the dynamics of the North Atlantic Current. Furthermore, an implementation of the flow field correction in the MPI-ESM-LR, based on Drews et al. (2015), has been successfully carried out. The North Atlantic cold bias has been largely removed in a pre-industrial control simulation, with no need for surface freshwater flux correction. There are hints that the correction slightly improves the winter atmospheric mean state, e.g. the strength of the westerlies and the blocking frequency over the Atlantic sector. However, the Atlantic low-frequency variability in the ‘corrected’ MPI-ESM-LR is strongly reduced compared to the ‘uncorrected’ version, in contrast to results by Drews and Greatbatch (2016) using the Kiel Climate model.
Concerning the second aim, a suite of hindcasts has been completed, initialised from an initialisation run using reanalysis wind stress anomalies added to the model climatological wind stress (MODINI), similar to Thoma et al. (2015). Here, the ERA-40 reanalysis has been used for the period 1958-1989 (ERA-Interim for 1990-2016). The hindcasts have been carried out as part of the joint effort within MiKlip II for the intercomparison of initialisation methods (Polkova et al., submitted). Results show that MODINI cannot fully overcome issues inherited from the pre-operational MIKLIP system in its low-resolution (PreOp-LR) in correctly initialising the Pacific decadal variability, contrary to the results of Thoma et al. (2015). Still, there are some noteworthy improvements in correlation skill on the 2-5 year lead-time scale compared to PreOp-LR in the eastern Pacific. The relatively poor overall performance by MODINI is found even when only considering the same hindcast period (1990-2006) that was also used by Thoma et al. (2015), again pointing to the sensitivity of decadal hindcasts of the wind stress product used for initialisation (see also Pohlmann et al., 2017).
Drews, A., Greatbatch, R. J., Ding, H., Latif, M. and Park, W. (2015) The use of a flow field correction technique for alleviating the North Atlantic cold bias with application to the Kiel Climate Model, Ocean Dynamics, 65, 1079-1093. doi: 10.1007/s10236-015-0853-7.
Drews, A., Greatbatch, R. J. (2016), Atlantic Multidecadal Variability in a model with an improved North Atlantic Current, Geophys. Res. Lett., 43 (15), 8199-8206. doi: 10.1002/2016GL069815
Pohlmann, H., Kröger, J., Greatbatch, R. J., Müller, W. A. (2017), Initialization shock in decadal hindcasts due to errors in wind stress over the tropical Pacific, Clim. Dyn., 49 (7-8), 2685-2693. doi: 10.1007/s00382-016-3486-8.
Polkova, Y., Brune, S., Kadow, C., Romanova, V., Gollan, G., Baehr, J., Glowienka-Hense, R., Greatbatch, R. J., Hense, A., Illing, S., Köhl, A., Kröger, J., Müller, W. A., Pankatz, K., Stammer, D., Initialization and ensemble generation for decadal climate predictions: A comparison of different methods, submitted to J. Adv. Model. Earth Syst.
Thoma, M., Greatbatch, R.J., Kadow, C., Gerdes, R. (2015): Decadal hindcasts initialised using observed surface wind stress: Evaluation and Prediction out to 2024, Geophys. Res. Lett., 42 (15), 6454-6461. doi: 10.1002/2015GL064833.
Wang, Y., Claus, M., Greatbatch, R. J., Sheng, J. (2017): Decomposition of the mean barotropic transport in a high-resolution model of the North Atlantic Ocean, Geophys. Res. Lett. 44 (22), 11,537-11,546. doi: 10.1002/2017GL074825.
GEOMAR Helmholtz Centre for Ocean Research Kiel
Prof. Dr. Richard J. Greatbatch
Gollan, G. | Bastin, S., Greatbatch, R.J.
Polkova, I | Brune, S., Kadow, C., Romanova, V., Gollan, G., Baehr, J., Glowienka-Hense, R., Greatbatch, R.J., Hense, A., Illing, S., Köhl, A., Kröger, J., Müller, W.A., Pankatz, K., Stammer, D.
Hansen, F. | Kruschke, T., Greatbatch, R.J., Weisheimer, A.
Scaife, A. | Ferranti, L., Alves, O., Athanasidis, P., Baehr, J., Deque, M., Dippe, T., Dunestone, N., Fereday, D., Gudgel, R.G., Greatbatch, R., Hermanson, L., Imada, Y., Jain, S., Kumar, A., MacLachlan, C., Merryfield, W., Müller, W.A., Ren, H.-L., Smith, D., Takaya, Y., Vecchi, G., Yang, X.
Xinyu Li | Gereon Gollan, Richard J. Greatbatch, Riyu Lu