Predictability of weather and climate

Abstract:

The use of reduced numerical precision to reduce computing costs for the cloud resolving model of superparameterised simulations of the atmosphere is investigated. An approach to identify the optimal level of precision for many different model components is presented and a detailed analysis of precision is performed. This is non-trivial for a complex model that shows chaotic behaviour such as the cloud resolving model in this paper.

results of the reduced precision analysis provide valuable information for the quantification of model uncertainty for individual model components. The precision analysis is also used to identify model parts that are of less importance thus enabling a reduction of model complexity. It is shown that the precision analysis can be used to improve model efficiency for both simulations in double precision and in reduced precision. Model simulations are performed with a superparametrised single-column model version of the OpenIFS model that is forced by observational datasets. A software emulator was used to mimic the use of reduced precision floating point arithmetic in simulations.

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.