Predictability of weather and climate

Abstract:

<p><strong>Abstract.</strong> As the dominant mode of variability in the tropical stratosphere, the Quasi-Biennial Oscillation (QBO) has been subject to extensive research. Though there is a well developed theory of this phenomenon being forced by wave-mean flow interaction, simulating the QBO adequately in global climate models (GCMs) still remains difficult. This paper presents a set of metrics to characterise the QBO using a number of different reanalysis datasets and the FU Berlin radiosonde observation dataset. The same metrics are then calculated from CMIP5 and CCMVal-2 intercomparison project simulations which included a representation of QBO like behaviour to evaluate which aspects of the QBO are well captured by the models and which ones remain a challenge for future model development.</p>

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:

In recent years, the idea of geoengineering, artificially modifying the climate to reduce global temperatures, has received increasing attention due to the lack of progress in reducing global greenhouse gas emissions. Stratospheric sulfate injection (SSI) is a geoengineering method proposed to reduce planetary warming by reflecting a proportion of solar radiation back into space that would otherwise warm the surface and lower atmosphere. We analyze results from the HadGEM2-CCS climate model with stratospheric emissions of 10 Tg yr-1 of SO2, designed to offset global temperature rise by around 1°C. A reduction in concentrating solar power (CSP) output of 5.9% on average over land is shown under SSI compared to a baseline future climate change scenario (RCP4.5) due to a decrease in direct radiation. Solar photovoltaic (PV) energy is generally less affected as it can use diffuse radiation, which increases under SSI, at the expense of direct radiation. Our results from HadGEM2-CCS are compared to the GEOSCCM chemistry-climate model from the Geoengineering Model Intercomparison Project (GeoMIP), with 5 Tg yr-1 emission of SO2. In many regions, the differences predicted in solar energy output between the SSI and RCP4.5 simulations are robust, as the sign of the changes for both the HadGEM2-CCS and GEOSCCM models agree. Furthermore, the sign of the total and direct annual mean radiation changes evaluated by HadGEM2-CCS agree with the sign of the multi-model mean changes of an ensemble of GeoMIP models over the majority of the world.