Progresses in Indo-Pacific Climate Predictions

The climate variation in the Indo-Pacific region plays a very important role in the global climate system. Moreover, the interannual variations in the ocean-atmosphere interactions of this sector give rise to the two most dominant coupled climate phenomena, the El Nino/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). These climate phenomena affect the weather and climate worldwide. The changes in their characteristics under global warming are a matter of concern as more intense and frequent occurrences of these phenomena, and associated climate extremes, are causing huge socio-economic damages. Therefore, research and operational centers are developing better understanding and prediction systems for developing effective mitigation and adaptation measures.

ENSO has been one of the most sought-after climate phenomena for climate prediction systems. Over the years, the modeling for the ENSO prediction has improved considerably compared to earlier empirical models. Operational centers are now using very sophisticated ocean-atmosphere coupled models for their routine seasonal to interannual climate predictions. In addition to the improvements in the dynamical predictions, significant progress has been achieved in statistical climate predictions. Artificial intelligence and machine learning (AI/ML) techniques are now increasingly employed in climate prediction systems, from model initialization to ensemble averages. Therefore, this topic aims to review the present status of the state of the art in climate modeling as well as developments in AI/ML techniques for climate predictability studies in the Indo-Pacific sector.

Submissions are welcomed on the following themes and related areas:

• Progresses in the modeling and predictability studies of intra-seasonal to interannual climate variations in the Indo-Pacific region (such as Madden-Julian Oscillations, ENSO, ENSO Modoki, IOD, Indian summer monsoon, Asian monsoon) and their teleconnections
• Current state of model initializations including artificial intelligence/machine learning (AI/ML)
• Progresses in model parameterizations (e.g., convective parameterizations, ocean mixing, etc.) including the use of AI/ML in those parametrizations
• Understanding model biases and scope of bias corrections
• Progresses in the predictability of decadal Indo-Pacific climate variations