14 research outputs found
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Forecasting annual maximum water level for the Negro river at Manaus using dynamical seasonal predictions
Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts
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Drivers and physical processes of drought events over the State of São Paulo, Brazil
The State of São Paulo, Brazil (SSP) was impacted by severe water shortages during the intense austral summer drought of 2013/2014 and 2014/2015 (1415SD). This study seeks to understand the features and physical processes associated with these summer droughts in the context of other droughts over the region during 1961–2010. Thus, this study examines the spatio-temporal characteristics of anomalously low precipitation over SSP and the associated large-scale dynamics at seasonal timescales, using an observation-based dataset from the Climatic Research Unit (CRU) and model simulation outputs from the Met Office Hadley Centre Global Environment Model (HadGEM3-GA6 at N216 resolution). The study analyzes Historical and Natural simulations from the model to examine the role of human-induced climate forcing on droughts over SSP. Composites of large-scale fields associated with droughts are derived from ERA-20C and ERA-Interim reanalysis and the model simulations. HadGEM3-GA6 simulations capture the observed interannual variability of normalized precipitation anomalies over SSP, but with biases. Drought events over SSP are related to subsidence over the region. This is associated with reduced atmospheric moisture over the region as indicated by the analysis of the vertically integrated moisture flux convergence, which is dominated by reduced moisture flux convergence. The Historical simulations simulate the subsidence associated with droughts, but there are magnitude and location biases. The similarities between the circulation features of the severe 1415SD and other drought events over the region show that understanding of the dynamics of the past drought events over SSP could guide assessment of changes in risk of future droughts and improvements of model performance. The study highlights the merits and limitations of the HadGEM3-GA6 simulations. The model possesses the skills in simulating the large-scale atmospheric circulations modulating precipitation variability, leading to drought conditions over SSP