Tim Palmer

Abstract:

In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent-McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%-25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.

Abstract:

Many global atmospheric models have too little precipitation variability in the tropics on daily to weekly time scales, and also poor representation of tropical precipitation extremes associated with intense convection. Stochastic parameterisations have the potential to mitigate this problem by representing unpredictable subgrid variability that is left out of deterministic models. We evaluate the impact on the statistics of tropical rainfall of two stochastic schemes, the stochastically perturbed parameterization tendency scheme (SPPT) and stochastic kinetic energy backscatter scheme (SKEBS), in three climate models: EC-Earth, the Met Office Unified Model and the Community Atmosphere Model, version 4 (CAM4). The schemes generally improve the statistics of simulated tropical rainfall variability, particularly by increasing the frequency of heavy rainfall events, reducing its persistence and increasing the high-frequency component of its variability. There is a large range in the size of the impact between models, with EC-Earth showing the largest improvements. The improvements are greater than those obtained by increasing horizontal resolution to ∼20km. Stochastic physics also strongly affects projections of future changes in the frequency of extreme tropical rainfall in EC-Earth. This indicates that small-scale variability that is unresolved and unpredictable in these models has an important role in determining tropical climate variability statistics. Using these schemes, and improved schemes currently under development, is therefore likely to be important for producing good simulations of tropical variability and extremes in the present day and future.

Abstract:

The impact of the stochastic schemes Stochastically Perturbed Parametrisation Tendencies (SPPT) and Stochastic Kinetic Energy Backscatter Scheme (SKEBS) on the representation of interannual variability in the Asian summer monsoon is examined in the coupled climate model CCSM4. The Webster–Yang index, measuring anomalies of a specified wind-shear index in the monsoon region, is used as a metric for monsoon strength, and is used to analyse the output of three model integrations: one deterministic, one with SPPT, and one with SKEBS. Both schemes show improved variability, which we trace back to improvements in the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). SPPT improves the representation of ENSO and through teleconnections thereby the monsoon, supporting previous work on the benefits of this scheme on the model climate. SKEBS also improves monsoon variability by way of improving the representation of the IOD, in particular by breaking an overly strong coupling to ENSO.

Abstract:

The Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme is used at weather and climate forecasting centres worldwide to represent model uncertainty that arises from simplifications involved in the parametrisation process. It uses spatio-temporally correlated multiplicative noise to perturb the sum of the parametrised tendencies. However, SPPT does not distinguish between different parametrisation schemes, which do not necessarily have the same error characteristics. A generalisation to SPPT is proposed, whereby the tendency from each parametrisation scheme can be perturbed using an independent stochastic pattern. This acknowledges that the forecast errors arising from different parametrisations are not perfectly correlated. Two variations of this ‘independent SPPT’ (iSPPT) approach are tested in the Integrated Forecasting System (IFS). The first perturbs all parametrised tendencies independently while the second groups tendencies before perturbation. The iSPPT schemes lead to statistically significant improvements in forecast reliability in the tropics in medium range weather forecasts. This improvement can be attributed to a large, beneficial increase in ensemble spread in regions with significant convective activity. The iSPPT schemes also lead to improved forecast skill in the extra tropics for a set of cases in which the synoptic initial conditions were more likely to result in European ‘forecast busts’. Longer 13-month simulations are also considered to indicate the effect of iSPPT on the mean climate of the IFS.