ALBERT

Description: We present a new method for the statistical downscaling of coarse-resolution General Circulation Model (GCM) fields to predict local climate change. Most atmospheric variables have strong seasonal cycles. We show that the prediction of the non-seasonal variability of maximum and minimum daily surface temperature is improved if the seasonal cycle is removed prior to the statistical analysis. The new method consists of three major steps. First, the average seasonal cycles of both predictands and predictors are removed. Second, a principal component-based multiple linear regression model between the deseasonalized predictands and predictors is developed and validated. Finally, the regression is used to make projections of future changes in maximum and minimum daily surface temperature at Shearwater, Nova Scotia. This projection is made using the local grid-scale variables of the Canadian General Circulation Model Version 3 (CGCM3) climate model as predictors. Our statistical downscaling method indicates significant skill in predicting the observed distribution of temperature using GCM predictors. Projections suggest minimum and maximum temperatures at Shearwater will be up to about five degrees warmer by 2100 under the current “business-as-usual” scenario. RÉSUMÉ [Traduit par la rédaction] Nous présentons une nouvelle méthode pour la réduction d'échelle statistique des champs des modèles de circulation générale (MCG) à faible résolution pour prévoir les changements du climat local. La plupart des variables atmosphériques ont des cycles saisonniers bien marqués. Nous démontrons que la prédiction de la variabilité non saisonnière de la température de surface quotidienne minimum et maximum est meilleure si on retranche le cycle saisonnier avant de procéder à l'analyse statistique. Voici les trois grandes étapes de cette nouvelle méthode. D'abord, nous retirons les cycles saisonniers moyens des prédictants et des prédicteurs. Ensuite, nous concevons et validons un modèle de régression linéaire multiple sur composantes principales entre les prédictants et les prédicteurs désaisonnalisés. Enfin, nous nous servons de la régression afin d'établir des projections pour les changements à venir dans la température de surface quotidienne minimum et maximum à Shearwater en Nouvelle-Écosse. Cette projection est établie au moyen des variables locales à l'échelle du maillage de la troisième version du modèle canadien de circulation générale (MCCG3). Notre méthode de réduction d'échelle statistique se révèle très efficace pour prédire la répartition observée de la température au moyen des prédicteurs du MCG. D'après les projections, les températures minimum et maximum à Shearwater connaîtront une augmentation d'environ cinq degrés d'ici 2100 dans le scénario actuel de type « statu quo ».

Description: Current climate models systematically underestimate the strength of oceanic fronts associated with strong western boundary currents, such as the Kuroshio and Gulf Stream Extensions, and have difficulty simulating their positions at the mid-latitude ocean’s western boundaries1. Even with an enhanced grid resolution to resolve ocean mesoscale eddies—energetic circulations with horizontal scales of about a hundred kilometres that strongly interact with the fronts and currents—the bias problem can still persist2; to improve climate models we need a better understanding of the dynamics governing these oceanic frontal regimes. Yet prevailing theories about the western boundary fronts are based on ocean internal dynamics without taking into consideration the intense air–sea feedbacks in these oceanic frontal regions. Here, by focusing on the Kuroshio Extension Jet east of Japan as the direct continuation of the Kuroshio, we show that feedback between ocean mesoscale eddies and the atmosphere (OME-A) is fundamental to the dynamics and control of these energetic currents. Suppressing OME-A feedback in eddy-resolving coupled climate model simulations results in a 20–40 per cent weakening in the Kuroshio Extension Jet. This is because OME-A feedback dominates eddy potential energy destruction, which dissipates more than 70 per cent of the eddy potential energy extracted from the Kuroshio Extension Jet. The absence of OME-A feedback inevitably leads to a reduction in eddy potential energy production in order to balance the energy budget, which results in a weakened mean current. The finding has important implications for improving climate models’ representation of major oceanic fronts, which are essential components in the simulation and prediction of extratropical storms and other extreme events3, 4, 5, 6, as well as in the projection of the effect on these events of climate change.

Description: We revisit the challenges and prospects for ocean circulation models following Griffies et al. (2010). Over the past decade, ocean circulation models evolved through improved understanding, numerics, spatial discretization, grid configurations, parameterizations, data assimilation, environmental monitoring, and process-level observations and modeling. Important large scale applications over the last decade are simulations of the Southern Ocean, the Meridional Overturning Circulation and its variability, and regional sea level change. Submesoscale variability is now routinely resolved in process models and permitted in a few global models, and submesoscale effects are parameterized in most global models. The scales where nonhydrostatic effects become important are beginning to be resolved in regional and process models. Coupling to sea ice, ice shelves, and high-resolution atmospheric models has stimulated new ideas and driven improvements in numerics. Observations have provided insight into turbulence and mixing around the globe and its consequences are assessed through perturbed physics models. Relatedly, parameterizations of the mixing and overturning processes in boundary layers and the ocean interior have improved. New diagnostics being used for evaluating models alongside present and novel observations are briefly referenced. The overall goal is summarizing new developments in ocean modeling, including: how new and existing observations can be used, what modeling challenges remain, and how simulations can be used to support observations.