On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon

The paper is devoted to examine the ability of a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 Atmospheric General Circulation Model (AGCM), for exploring its utility for long-range dynamical prediction of seasonal Indian summer monsoon rainfall (ISMR) based on 5-members ensemble for the hindcast mode 20-year (1985-2004) period with observed global sea surface temperatures (SSTs) as boundary condition and 6-year (2005-2010) period in the forecast-mode with NCEP Coupled Forecast System (CFS) SSTs as boundary condition. ISMR simulations are examined on five day (pentad) rainfall average basis. It is shown that the model simulated ISMR, based on 5-members ensemble average basis had limited skill in simulating extreme ISMR seasons (drought/excess ISMR). However, if the ensemble averaging is restricted to similar ensemble members either in the overall run of pentad-wise below (B) and above (A) normal rainfall events, as determined by the departure for thethreshold value given by coefficient of variability (CV) for the respective pentads based on IMD observed climatology, or during the season as a whole on the basis of percentage anomaly of ISMR from the seasonal climatology, the foreshadowing of drought/excess monsoon seasons improved considerably. Our strategy of improving dynamical seasonal prediction of ISMR was based on the premise that the intra-seasonal variability (ISV) and intra-annual variability (IAV) are intimately connected and characterized by large scale perturbations westward moving (10-20 day) and northward moving (30-60 day) modes of monsoon ISV during the summer monsoon season. As such the cumulative excess of B events in the simulated season would correspond to drought season and vice-versa. The paper also examines El Niño-Monsoon connections of the simulated ISMR series and they appear to have improved considerably in the proposed methodology. This strategy was particularly found to improve for foreshadowing of droughts. Based on results of the study a strategy is proposed for using the matched signal for simulated ISMR based on excess B over A events and vice-versa for drought or excess ISMR category. The probability distribution for the forecast seasonal ISMR on category basis is also proposed to be based on the relative ratio of similar ensemble members and total ensembles on percentage basis. The paper also discusses that extreme monsoon season are produced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modes in a combined manner and hence stresses to improve prediction of IOD mode in ocean-atmosphere coupled model just as it has happened for the prediction ENSO mode six to nine months in advance.

Machine learning-based dynamical seasonal prediction of summer rainfall in China

<p>The seasonal prediction of summer rainfall is crucial for regional disaster reduction but currently has a low prediction skill. This study developed a machine learning (ML)-based dynamical (MLD) seasonal prediction method for summer rainfall in China based on suitable circulation fields from an operational dynamical prediction model CAS FGOALS-f2. Through choosing optimum hyperparameters for three ML methods to reach the best fitting and the least overfitting, gradient boosting regression trees eventually exhibit the highest prediction skill, obtaining averaged values of 0.33 in the reference training period (1981-2010) and 0.19 in eight individual years (2011-2018) of independent prediction, which significantly improves the previous dynamical prediction skill by more than 300%. Further study suggests that both reducing overfitting and using the best dynamical prediction are imperative in MLD application prospects, which warrants further investigation.</p>

A Framework for Dynamical Seasonal Prediction of Precipitation over the Pacific Islands

A three-step approach to develop a framework for dynamical seasonal prediction of precipitation over the U.S. Affiliated Pacific Islands (USAPI) is adopted. First, guided by the climatological features of basic variables, a view that climates of the USAPI are connected by large-scale phenomena involving the warm pool, South Pacific convergence zone, tropical monsoons, and subtropical anticyclone is proposed. Second, prediction skill in ensemble hindcasts performed with the Climate Forecast System, version 2 (CFSv2), is evaluated with the hypothesis that ENSO is the leading candidate for large and persisting precipitation departures. Third, moist static energy budget diagnostics are performed to identify physical processes responsible for precipitation anomalies. At leads of 0–6 months, CFSv2 demonstrates useful skill in predicting Niño-3.4 SST and equatorial Pacific precipitation anomalies. During El Niño, positive precipitation anomalies along the central (eastern) equatorial Pacific are anchored by net radiative flux (Frad) and moist advection (evaporation and Frad). The model’s skill in predicting precipitation anomalies over South Pacific (Hawaiian) islands is highest (lowest). Over the west Pacific islands, the skill is low during the rainy season. During El Niño, skill over the USAPI, in particular predicting dryness persistence at long leads is useful. Suppressed precipitation over the Hawaiian and South Pacific (west Pacific) islands are determined by anomalous dry and cold air advection (reduced evaporation and Frad). These processes are local, but are dictated by circulation anomalies forced by ENSO. Model budget estimates are qualitatively consistent with those obtained from reanalysis, boosting confidence for societal benefits. However, observational constraints, as well as budget residuals, pose limitations.