Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models

An analysis of the retrospective predictions by seven coupled ocean-atmosphere models from major forecasting centres of Europe and USA, aimed at assessing their ability in predicting the interannual variation of the Indian summer monsoon rainfall (ISMR), particularly the extremes (i.e. droughts and excess rainfall seasons) is presented in this article. On the whole, the skill in prediction of extremes is not bad since most of the models are able to predict the sign of the ISMR anomaly for a majority of the extremes. There is a remarkable coherence between the models in successes and failures of the predictions, with all the models generating loud false alarms for the normal monsoon season of 1997 and the excess monsoon season of 1983. It is well known that the El Niño and Southern Oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in the interannual variation of ISMR and particularly the extremes. The prediction of the phases of these modes and their link with the monsoon has also been assessed. It is found that models are able to simulate ENSO-monsoon link realistically, whereas the EQUINOO-ISMR link is simulated realistically by only one model-the ECMWF model. Furthermore, it is found that in most models this link is opposite to the observed, with the predicted ISMR being negatively (instead of positively) correlated with the rainfall over the western equatorial Indian Ocean and positively (instead of negatively) correlated with the rainfall over the eastern equatorial Indian Ocean. Analysis of the seasons for which the predictions of almost all the models have large errors has suggested the facets of ENSO and EQUINOO and the links with the monsoon that need to be improved for improving monsoon predictions by these models

On forecasting the Indian summer monsoon: The intriguing season of 2002

This year, the rainfall over India during the first half of the summer monsoon season was 30 below normal. This has naturally led to a lot of concern and speculation about the causes. We have shown that the deficit in rainfall is a part of the natural variability. Analysis of the past data suggests that there is a 78 chance that seasonal mean rainfall this year will be 10 or more below the long-term average value. We discuss briefly how forecasts for seasonal rainfall are generated, whether this event could have been foreseen, and share our perspective on the problems and prospects of forecasting the summer monsoon rainfall over the Indian region

A modified deep learning weather prediction using cubed sphere for global precipitation

Deep learning (DL), a potent technology to develop Digital Twin (DT), for weather prediction using cubed spheres (DLWP-CS) was recently proposed to facilitate data-driven simulations of global weather fields. DLWP-CS is a temporal mapping algorithm wherein time-stepping is performed through U-NET. Although DLWP-CS has shown impressive results for fields, such as temperature and geopotential height, this technique is complicated and computationally challenging for a complex, non-linear field, such as precipitation, which depends on other prognostic environmental co-variables. To address this challenge, we modify the DLWP-CS and call our technique “modified DLWP-CS” (MDLWP-CS). In this study, we transform the architecture from a temporal to a spatio-temporal mapping (multivariate setup), wherein precursor(s) of precipitation can be used as input. As a proof of concept, as a first simple case, a 2-m surface air temperature is used to predict precipitation using MDLWP-CS. The model is trained using hourly ERA-5 reanalysis and the resulting experimental findings are compared to two benchmark models, viz, the linear regression and an operational numerical weather prediction model, which is the Global Forecast System (GFS). The fidelity of MDLWP-CS is much better compared to linear regression and the results are equivalent to GFS output in terms of daily precipitation prediction with 1 day lag. These results provide an encouraging framework for an efficient DT that can facilitate speedy, high fidelity precipitation predictions.</jats:p

Indian summer monsoon onset forecast skill in the UK Met Office initialized coupled seasonal forecasting system (GloSea5-GC2)

Accurate and precise forecasting of the Indian monsoon is important for the socio-economic security of India, with improvements in agriculture and associated sectors from prediction of the monsoon onset. In this study we establish the skill of the UK Met Office coupled initialized global seasonal forecasting system, GloSea5-GC2, in forecasting Indian monsoon onset. We build on previous work that has demonstrated the good skill of GloSea5 at forecasting interannual variations of the seasonal mean Indian monsoon using measures of large-scale circulation and local precipitation. We analyze the summer hindcasts from a set of three springtime start-dates in late April/early May for the 20-year hindcast period (1992-2011). The hindcast set features at least fifteen ensemble members for each year and is analyzed using five different objective monsoon indices. These indices are designed to examine large and local-scale measures of the monsoon circulation, hydrological changes, tropospheric temperature gradient, or rainfall for single value (area-averaged) or grid-point measures of the Indian monsoon onset. There is significant correlation between onset dates in the model and those found in reanalysis. Indices based on large-scale dynamic and thermodynamic indices are better at estimating monsoon onset in the model rather than local-scale dynamical and hydrological indices. This can be attributed to the model's better representation of large-scale dynamics compared to local-scale features. GloSea5 may not be able to predict the exact date of monsoon onset over India, but this study shows that the model has a good ability at predicting category-wise monsoon onset, using early, normal or late tercile categories. Using a grid-point local rainfall onset index, we note that the forecast skill is highest over parts of central India, the Gangetic plains, and parts of coastal India - all zones of extensive agriculture in India. El Niño Southern Oscillation (ENSO) forcing in the model improves the forecast skill of monsoon onset when using a large-scale circulation index, with late monsoon onset coinciding with El Niño conditions and early monsoon onset more common in La Niña years. The results of this study suggest that GloSea5's ensemble-mean forecast may be used for reliable Indian monsoon onset prediction a month in advance despite systematic model errors

Impact of the moisture transport formulation on the simulated tropical rainfall in a general circulation model

The impact of numerical modeling of moisture transport on the simulation of the seasonal mean pattern of precipitation in the tropics is studied. The NCAR CCM2 with spectral and semi- Lagrangian moisture transport has been used for this purpose. The differences in the numerical modeling of moisture transport are found to have a significant impact on the simulation of the seasonal mean patterns. The major differences while using the spectral method (vis-a-vis the semi-Lagrangian method) are (1) a decrease in rainfall over the Indian monsoon region, (2) a decrease in rainfall over the west Pacific region and (3) an increase in rainfall over the central and east Pacific regions. There are substantial differences in the amount of precipitable water vapor simulated by the two moisture transport techniques. It is shown that the difference in precipitable water vapor between the two simulations is associated with changes in the vertical moist static stability (VMS) of the atmosphere, and differences in the simulated precipitation patterns

Impact of convective downdrafts on model simulations: results from aqua-planet integrations

The role of convective scale downdrafts has been examined, using the NCAR-CAM3.0 aqua-planet configuration. We find that, convective downdrafts make the atmosphere more unstable thus increasing the convective available potential energy (CAPE) of the atmosphere. It is noticed that, although the rate of CAPE consumption increases with the incorporation of downdrafts, the generation of CAPE increases with a higher rate. Also, it is noted that there is a reduction in the deep convective rainfall, with the inclusion of downdrafts, which is primarily due to the re-evaporation of precipitation within the downdrafts. There is a large increase in the low cloud fraction and the shortwave cloud forcing with the inclusion of convective scale downdrafts in the cumulus scheme, which along with the evaporation within the downdraft causes cooling in the troposphere