Predictability of weather and climate

Abstract:

There is no known atmospheric phenomenon with a longer horizon of predictability than the quasibiennial oscillation (QBO) of tropical stratospheric circulation. With a mean period of about 28 months, the QBO phase can routinely be predicted at least a year in advance. This predictability arises from internal atmospheric dynamics, rather than from external forcings with long timescales, and it offers the tantalizing prospect of improved predictions for any phenomena influenced by the QBO. Observed QBO teleconnections include an apparent QBO influence on the stratospheric winter polar vortices in both hemispheres, the Madden-Julian Oscillation (MJO), and the North-Atlantic Oscillation (NAO). Yet the degree to which such teleconnections are real, robust, and sufficiently strong to provide useful predictive skill remains an important topic of research. Utilizing and understanding these linkages will require atmospheric models that adequately represent both the QBO and the mechanisms by which it influences other aspects of the general circulation, such as tropical deep convection.

The 2016 QBO workshop in Oxford aimed to explore these themes, and to build on the outcomes of the first QBO workshop, held in March 2015 in Victoria, BC, Canada (as reported in SPARC Newsletter No. 45). This earlier workshop was the kick-off meeting of the SPARC QBOi (QBO Initiative) activity, and its key outcome was to plan a series of coordinated Atmosphere General Circulation Model (AGCM) experiments (the “phase-one” QBOi experiments). These experiments provide a multi-model dataset that can be used to investigate the aforementioned themes. While the focus of the Victoria meeting was primarily on the QBO itself, the Oxford workshop has broadened the scope of the QBOi activity to encompass QBO impacts. Its primary outcome is a planned set of core papers analysing the phaseone QBOi experiments,

Abstract:

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.

Abstract:

Uncertainties in parametrized processes in general circulation models can be represented as stochastic perturbations to the model formulation. The European Centre for Medium-Range Weather Forecasts (ECMWF) has pioneered approaches to represent these model errors in forecasting systems. In particular, the stochastically perturbed physical tendency (SPPT) scheme for the atmosphere is used in their operational ensemble system for medium- and long-range predictions. Recent studies have shown that these stochastic approaches can both increase the reliability of the probabilistic forecasts and reduce long-term mean biases of the model climate. Towards developing a seamless prediction system in the future, these benefits of stochastic parametrization for both short-term and long-term forecasts make it an essential component of the next generation Earth System models. We present results of the impact of different configurations of the SPPT scheme in ECMWF's seasonal forecasting System 4 on the mean and variability in tropical precipitation. Small-scale perturbations in the SPPT scheme play a significant role in reducing the mean biases in tropical precipitation. The stochastic physics also nonlinearly rectify the convection and precipitation during different phases of El Niño Southern Oscillation events and improve the reliability of the ensemble forecasts for the Madden–Julian Oscillation (MJO). They impact the MJO dynamics by modulating the convective and suppressed phases of the MJO. Finally, we discuss some of the caveats to this analysis and some future prospects.