Kristian Strommen

Abstract:

Sea ice ensemble forecasts can be highly underdispersive, meaning that the ensemble spread is notably lower than the average forecast error. One common strategy to address underdispersion is to add stochastic perturbations to the forecasts. We detail the implementation of a stochastically perturbed parameterisation (SPP) scheme for SI³, the sea ice component used by the Integrated Forecast System (IFS), the forecast model used and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We then evaluate its impact on seasonal forecasts of Northern Hemisphere summer and winter. The inclusion of SPP is found to enhance ensemble spread for sea ice concentration (SIC) and sea ice thickness (SIT) forecasts by around 10 % relative to a forecast with no SPP, which results in a better calibrated probabilistic forecast. Some small but robust changes to the mean state are also found, including a general decrease in the mean SIC and a redistribution of the winter ice from the central Arctic to the ice edge. These changes reduce or increase the mean bias depending on the region. Changes to the mean and spread of the sea ice result in changes to the mean and spread of air temperature up to at least 850 hPa, altering the mean air temperature biases of the model. An apparent consequence of this is a significant increase in the anomaly correlation coefficients of 500 hPa geopotential height (Z500) over the Euro-Atlantic domain in winter, which partially projects onto the North Atlantic Oscillation. We conclude that sea ice stochastic perturbations can be a valuable contribution to increased reliability of seasonal forecasts of the sea ice itself and can impact seasonal forecasts of the atmosphere at high and mid latitudes.

Abstract:

Abstract We correct an error relating to the comparison of precipitation variability in coupled models versus models run with prescribed SSTs (“AMIP models”) and discuss what conclusions to draw from the corrected result.

Abstract:

Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. However, there is still much uncertainty as to how this performance extends to longer timescales, where interactions with slower components of the earth system become important. We take GenCast, a state-of-the-art probabilistic MLWP model, and apply it to the task of seasonal forecasting with prescribed sea surface temperature (SST), by providing anomalies persisted over climatology (GenCast-Persisted) or forcing with observed SSTs (GenCastForced). The forecasts are compared to the European Centre for Medium-Range Weather Forecasts seasonal forecasting system, SEAS5. Our results indicate that, despite being trained at short timescales, GenCast-Persisted produces much of the correct precipitation patterns in response to El Ni˜no and La Ni˜na events, with several erroneous patterns in GenCast-Persisted corrected with GenCast-Forced. The uncertainty in precipitation response, as represented by the ensemble, compares favourably to SEAS5. Whilst SEAS5 achieves superior skill in the tropics for 2-metre temperature and mean sea level pressure (MSLP), GenCast-Persisted achieves higher skill in some areas in higher latitudes, including mountainous areas, with notable improvements for MSLP in particular; this is reflected in a slightly higher correlation with the observed NAO index. Reliability diagrams indicate that GenCast-Persisted has little skill relative to climatology, whilst GenCast-Forced produces forecasts with reliability comparable to SEAS5. These results provide an indication of the potential of MLWP models similar to GenCast for the ‘full’ seasonal forecasting problem, where the atmospheric model is coupled to ocean, land and cryosphere models.