Thank you for the hit question. Yes, it is true that climatic uncertainty has become the new normal. You see, the hurricane Patricia which lashed in Mexico last week was a surprise in respect of nature and characteristics.But it is not acceptable that we would continue to accept this new normal play havoc on us. We have to redesign our model architecture in a way that the associated risk out of those uncertainties be well represented. While doing so, we need to address the following;
Statistical models built out of longterm historical data and on the basis of probabilistic assumptions may likely to be affected. Generally, multi-model ensemble is generally used to overcome the limitation of various model and reduce model error and uncertainties. But this initiative may not be sufficient. We should remember that the forecast out of probabilistic models are limited by the statistics of fed historical timeseries data which with the progression of time and occurrence of new normal events proves less representative day by day. On the other hand, the numerical models generally provides the forecast output based on the limitation of assumptions in its model physics and dynamics. The numerical weather or climate model physics, dynamics are developed primarily based on temperate regime. Therefore, many efficient model lacks its approximate representative tropical model physics and dynamics and thus add up a high systematic bias, which becomes very difficult to ameliorate later on, due to forecast based on this. Therefore, model physics and dynamics have to be relooked once based on the regional application and model assumptions. Realistic good satellite based predictor variables are required to be identified for the model physics and dynamics so that high spatio-temporal resolution model input data be fed to the region specific model. Satellite data assimilation into the dynamic or numerical model has already been progressed a lot. But there is a need to relook into the tropical region specific model physics and dynamics. The identification of oceanic component in new normal should also be relooked into. The new normal are affecting the ocean also. Or in other way, we can say that the exposure of new normals on the ocean which have a long memory may gradually affect the oceanic dynamics in very different way. This is also being reflected in finding the weak global teleconnection to the regional weather now-a-days. For example, the relationship between ENSO activity and regional and local precipitation is decaying day by day. Most of the global numerical models are built generally strongly as a robust AGCM with nearly no representation of this global teleconnectivity parameter. This has a deep bearing in the model efficiency in predicting the new normals. Thus, we can relook into the issue of new normals in weather and finetune the available models, developing or modifying the associated model physics and dynamics, assimilating the satellite input variable and minimize the model error in forecasting the new normals and come up with a reasonable agricultural strategy for a tropical country like India.