Seasonal Prediction of Omicron Pandemic

The ongoing coronavirus disease 2019 (COVID-19) pandemic has pushed the world in the face of another huge outbreak. In order to have a better understanding on the fast transmission of Omicron variant, we made seasonal predictions on the development of Omicron pandemic globally, as well as 11 key countries. The results demonstrated that the pandemic has an exponential-like growth rate at the initial stage of the outbreak, and will have small resurgences around April and June in north hemisphere countries and south hemisphere countries, respectively.

Potential predictability of Southwest US rainfall : role of tropical and high-latitude variability

This study explores the potential predictability of Southwest US (SWUS) precipitation for the November-March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden-Julian Oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely.

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.

Seasonal Prediction of Tropical Cyclones over the North Atlantic and Western North Pacific

In this study, Tropical Cyclones (TC) over the Western North Pacific (WNP) and North Atlantic (NA) basins are analysed in seasonal forecasting models from five European modelling centres. Most models are able to capture the observed seasonal cycle of TC frequencies over both basins; however, large differences for numbers and spatial track densities are found. In agreement with previous studies, TC numbers are often underestimated, which is likely related to coarse model resolutions. Besides shortcomings in TC characteristics, significant positive skill (deterministic and probabilistic) in predicting TC numbers and accumulated cyclone energy is found over both basins. Whereas the predictions of TC numbers over the WNP basin are mostly unreliable, most seasonal forecast provide reliable predictions for the NA basin. Besides positive skill over the entire NA basin, all seasonal forecasting models are skillful in predicting the interannual TC variability over a region covering the Caribbean and North American coastline, suggesting that the models carry useful information, e.g. for adaptation and mitigation purposes ahead of the upcoming TC season. However, skill in all forecast models over a smaller region centred along the Asian coastline is smaller compared to their skill in the entire WNP basin.

Seasonal prediction of cyclonic disturbances over the Bay of Bengal during summer monsoon season : Identification of potential predictors

The cyclonic disturbances (CD) over the Bay of Bengal during monsoon season have significant impact on rainfall over India. On many occasions, they cause flood leading to loss of lives and properties. Hence, any early information about the frequency of occurrence of such disturbances will help immensely the disaster managers and planners. However, the studies are limited on the seasonal prediction of CD over the Bay of Bengal unlike other Ocean basins of the world. Hence, a study has been undertaken to find out the potential predictors during the months of April and May for prediction of frequency of cyclonic disturbances over the Bay of Bengal during monsoon season (June – September). For this purpose, best track data of India Meteorological Department and large scale field parameters based on NCEP/NCAR reanalysis data have been analyzed for the period of 1948 – 2007. The linear correlation analysis has been applied between frequency of CD and large scale field parameters based on NCEP/NCAR reanalysis data to find out the potential predictors. The large scale field parameters over the equatorial Indian Ocean, especially over west equatorial Indian Ocean and adjoining Arabian Sea (up to 15° N) should be favourable in April and May with lower mean sea level pressure (MSLP), lower geopotential heights and stronger southerlies in lower and middle levels, along with stronger northerly components at upper level for higher frequency of CD during subsequent monsoon season. Consequently, there should be increase in relative humidity (RH) and precipitable water content and decrease in outgoing longwave radiation (OLR) and temperature at lower levels over this region during April and May for higher frequency of CD during subsequent monsoon season. Comparing the area of significant correlation between frequency of CD and large scale field parameters and its stability from April to September, MSLP and geopotential heights are most influencing parameters followed by OLR, sea surface temperature, air temperature and RH at 850 hPa level.

Seasonal Sea Ice Prediction with the CICE Model and Positive Impact of CryoSat-2 Ice Thickness Initialization

The Los Alamos sea ice model (CICE) is being tested in standalone mode for its suitability for seasonal time scale prediction. The prescribed atmospheric forcings to drive the model are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean in the control experiments are also from CFSR. The simulated sea ice extent in the Arctic in control experiments is generally in good agreement with observations in the warm season at all lead times up to 12 months, suggesting that CICE is able to provide useful ice edge information for seasonal prediction. However, the ice thickness forecast has a positive bias stemming from the initial conditions and often persists for more than a season, limiting the model’s seasonal forecast skill. In addition, thicker ice has a lower melting rate in the warm season, both at the bottom and top, contributing to this positive bias. When this bias is removed by initializing the model using ice thickness data from satellite observations while keeping all other initial fields unchanged, both simulated ice edge and thickness improve. This indicates the important role of ice thickness initialization in sea ice seasonal prediction.