A. K. Sahai

Objective identification of nonlinear convectively coupled phases of monsoon intraseasonal oscillation: implications for prediction

Abstract The nonlinear convectively coupled character of the summer monsoon intraseasonal oscillation (ISO) that manifests in its event-to-event variations is a major hurdle for skillful extended-range prediction of the active/break episodes. The convectively coupled character of the monsoon ISO implies that a particular nonlinear phase of the precipitation ISO is linked to a unique pattern of the large-scale variables. A methodology has been presented to capture different nonlinear phases of the precipitation ISO using a combination of a sufficiently large number of dynamical variables. This is achieved through a nonlinear pattern recognition technique known as self-organizing map (SOM) involving six daily large-scale circulation indices. It is demonstrated that the nonlinearly classified states of the large-scale circulation isolated at the SOM nodes without involving any information on rainfall are strongly linked to different phases of evolution of the rainfall ISO, including the active and break phas...

A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall

An ensemble mean and probabilistic approach is essential for reliable forecast of the All India Summer Monsoon Rainfall (AIR) due to the seminal role played by internal fast processes in interannual variability (IAV) of the monsoon. In this paper, we transform a previously used empirical model to construct a large ensemble of models to deliver useful probabilistic forecast of AIR. The empirical model picks up predictors only from global sea surface temperature (SST). Methodology of construction implicitly incorporates uncertainty arising from internal variability as well as from the decadal variability of the predictor-predictand relationship. The forecast system demonstrates the capability of predicting monsoon droughts with high degree of confidence. Results during independent verification period (1999-2008) suggest a roadmap for generating empirical probabilistic forecast of monsoon IAV for practical delivery to the user community.

Possible role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2 coupled model

Reasonably realistic climatology of atmospheric and oceanic parameters over the Asian monsoon region is a pre-requisite for models used for monsoon studies. The biases in representing these features lead to problems in representing the strength and variability of Indian summer monsoon (ISM). This study attempts to unravel the ability of a state-of-the-art coupled model, SINTEX-F2, in simulating these characteristics of ISM. The coupled model reproduces the precipitation and circulation climatology reasonably well. However, the mean ISM is weaker than observed, as evident from various monsoon indices. A wavenumber–frequency spectrum analysis reveals that the model intraseasonal oscillations are also weaker-than-observed. One possible reason for the weaker-than-observed ISM arises from the warm bias, over the tropical oceans, especially over the equatorial western Indian Ocean, inherent in the model. This warm bias is not only confined to the surface layers, but also extends through most of the troposphere. As a result of this warm bias, the coupled model has too weak meridional tropospheric temperature gradient to drive a realistic monsoon circulation. This in turn leads to a weakening of the moisture gradient as well as the vertical shear of easterlies required for sustained northward propagation of rain band, resulting in weak monsoon circulation. It is also noted that the recently documented interaction between the interannual and intraseasonal variabilities of ISM through very long breaks (VLBs) is poor in the model. This seems to be related to the inability of the model in simulating the eastward propagating Madden–Julian oscillation during VLBs.

Interdecadal variations in AGCM simulation skills

Global climate models forced by sea surface temperature are standard tools in seasonal climate prediction and in projection of future climate change caused by anthropogenic emissions of greenhouse gases. Assessing the ability of these models to reproduce observed atmospheric circulation given the lower boundary conditions, and thus its ability to predict climate, has been a recurrent concern. Several assessments have shown that the performance of models is seasonally dependent, but there has always been the assumption that, for a given season, the model skill is constant throughout the period being analyzed. Here, it is demonstrated that there are periods when these models perform well and periods when they do not capture observed climate variability. The variations of the model performance have temporal scales and spatial patterns consistent with decadal/interdecadal climate variability. These results suggest that there are unmodeled climate processes that affect seasonal climate prediction as well as scenarios of climate change, particularly regional climate change projections. The reliability of these scenarios may depend on the time slice of the model output being analyzed. Therefore, more comprehensive model assessment should include a verification of the long-term stability of their performance.

Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is de...

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

[1] Prediction of interannual variability (IAV) of Indian summer monsoon (ISM) rainfall is limited by “internal” dynamics, and the monsoon intraseasonal oscillations (MISOs) seems to be at the heart of producing internal IAV of the ISM. If one could find an identifiable way through which these MISOs are modulated by slowly varying “external” forcing, such as El Nino–Southern Oscillation (ENSO), the uncertainty in the prediction of IAV could be reduced, leading to improvement of seasonal prediction. Such efforts, so far, have been inconclusive. In this study, the modulation of MISOs by ENSO is assessed by using a nonlinear pattern recognition technique known as the Self‐Organizing Map (SOM). The SOM technique is efficient in handling the nonlinearity/event‐to‐event variability of the MISOs and capable of identifying various shades of MISO from large‐scale dynamical/thermodynamical indices, without providing information on rainfall. It is shown that particular MISO phases are preferred during ENSO years, that is, the canonical break phase is preferred more in the El Nino years and the typical active phase is preferred during La Nina years. Interestingly, if the SOM clustering is done by removing the ENSO effect on seasonal mean, the preference for the break node remains relatively unchanged; whereas, the preference reduces/vanishes for the active node. The results indicate that the El Nino–break relationship is almost independent of the ENSO‐monsoon relationship on seasonal scale whereas the La Nina–active association seems to be interwoven with the seasonal relationship.

Development and Evaluation of an Objective Criterion for the Real-Time Prediction of Indian Summer Monsoon Onset in a Coupled Model Framework

AbstractThis study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK...

Moisture dynamics of the northward and eastward propagating boreal summer intraseasonal oscillations: possible role of tropical Indo-west Pacific SST and circulation

Boreal summer intraseasonal oscillation (BSISO) has complex spatial structure due to the co-existence of equatorial eastward and off-equatorial northward propagation in the equatorial Indian Ocean. As a result, equatorial Indian Ocean convection has simultaneous northward and eastward (NE), northward only (N-only) and eastward only (E-only) propagations. It is well established that the convection propagates in the direction of increasing moist static energy (MSE). The moisture and MSE budget analysis reveals that the horizontal advection of anomalous MSE contributes to positive MSE tendency, which is in agreement with the horizontal advection of column integrated moisture anomaly. Northward movement of warm SST and the anomalous moisture advected by zonal wind are the major initiative for the northward propagation of convection from the equatorial Indian Ocean in both NE and N-only category. At the same time warm SST anomaly in the equatorial west Pacific along with moisture advection caused by anomalous meridional wind is important for the equatorial eastward branch of NE propagation. As these anomalies in the west Pacific moves northward, equatorial Indian Ocean convection establishes over the equatorial west Pacific. The absence of these processes confines the BSISO in northward direction for N-only category. In the case of E-only movement, warm SST anomaly and moisture advection by zonal component of wind causes the eastward propagation of convection. Boundary layer moisture convergence always remains east of convection center in E-only propagation, while it coincides with convection centre in other two categories. Thus the present study concludes that the difference in underlying SST and atmospheric circulation in tropical Indo-west Pacific oceanic regions encourage the differential propagation of BSISO convection through moisture dynamics.

A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map

If a coarse resolution dynamical model can well capture the large-scale patterns even if it has bias in smaller scales, the spatial information in smaller domains may also be retrievable. Based on this hypothesis a method has been proposed to downscale the dynamical model forecasts of monsoon intraseasonal oscillations in the extended range, and thus reduce the forecast spatial biases in smaller spatial scales. A hybrid of clustering and analog technique, used in a self organizing map (SOM)-based algorithm, is applied to correct the bias in the model predicted rainfall. The novelty of this method is that the bias correction and downscaling could be done at any resolution in which observation/reanalysis data is available and is independent of the model resolution in which forecast is generated. A set of composite pattern of rainfall is identified by clustering the high resolution observed rainfall using SOM. These set of composite patterns for the clustered days in each cluster centers or nodes are saved and the model forecasts for any day are compared with these patterns. The closest historical pattern is identified by calculating the minimum Euclidean distance between the model rainfall forecast and the observed clustered pattern and is termed as the bias corrected SOM-based post-processed forecast. The bias-corrected and the SOM-based reconstructed forecasts are shown to improve the annual cycle and the skill of deterministic as well as probabilistic forecasts. Usage of the high resolution observational data improves the spatial pattern for smaller domain as seen from a case study for the Mahanadi basin flood during September 2011. Thus, downscaling and bias correction are both achieved by this technique.

Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001–2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3–5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.