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Warren Tennant


An assessment of 13-yr simulations of three atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) is presented. The National Centers for Environmental Prediction (NCEP) reanalysis data are used as a baseline for the comparisons. Daily circulation characteristics and interannual variability are investigated in order to improve understanding of the causes of systematic model errors. The focus is to determine the utility of these models in the field of seasonal forecasting.

Daily circulation statistics are well represented by the Hadley Centre Atmospheric Climate Model (HADAM3) but the specific versions of the Center for Ocean–Land–Atmosphere Studies (COLA) and Commonwealth Scientific and Industrial Research Organization (CSIRO9) models examined here produce flow patterns biased toward atmospheric archetype modes characteristic of low spatial variability. All three models show relatively large errors in kinetic energy fields of the vertical mean and shear flow, both in latitudinal placement of the midlatitude jet and geographical location of energy maxima. Evidence suggests that model resolution and model physics affect the accuracy of these simulations.

AGCM interannual variability as forced by sea surface temperatures is realistic in terms of a quasi-SOI (Southern Oscillation index) series and reproduces the El Niño–Southern Oscillation (ENSO) signal above noise levels that are determined from simulations using climatological SSTs. However, rainfall fields over southern Africa show little skill in interannual variability and daily rainfall characteristics indicate that some models are producing too many rain days by up to a factor of 2. Notwithstanding these difficulties, AGCMs, if used carefully, do provide sufficient skillful information for guidance in seasonal forecasting.

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Matt Hawcroft, Sally Lavender, Dan Copsey, Sean Milton, José Rodríguez, Warren Tennant, Stuart Webster, and Tim Cowan


From late January to early February 2019, a quasi-stationary monsoon depression situated over northeast Australia caused devastating floods. During the first week of February, when the event had its greatest impact in northwest Queensland, record-breaking precipitation accumulations were observed in several locations, accompanied by strong winds, substantial cold maximum temperature anomalies, and related wind chill. In spite of the extreme nature of the event, the monthly rainfall outlook for February issued by Australia’s Bureau of Meteorology on 31 January provided no indication of the event. In this study, we evaluate the dynamics of the event and assess how predictable it was across a suite of ensemble model forecasts using the Met Office numerical weather prediction (NWP) system, focusing on a 1-week lead time. In doing so, we demonstrate the skill of the NWP system in predicting the possibility of such an extreme event occurring. We further evaluate the benefits derived from running the ensemble prediction system at higher resolution than used operationally at the Met Office and with a fully coupled dynamical ocean. We show that the primary forecast errors are generated locally, with key sources of these errors including atmosphere–ocean coupling and a known bias associated with the behavior of the convection scheme around the coast. We note that a relatively low-resolution ensemble approach requires limited computing resources, yet has the capacity in this event to provide useful information to decision-makers with over a week’s notice, beyond the duration of many operational deterministic forecasts.

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Warren J. Tennant, Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson


An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.

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