Utilization of high-resolution EGM2008 gravity data for geological exploration over the Singhbhum-Orissa Craton,India

High-resolution EIGEN6C4 and EGM2008 Bouguer gravity data of 2190 degree spherical harmonic over the Singhbhum-Orissa Craton, India, have been generated from the International Centre for Global Earth Models. The Bouguer gravity anomaly difference maps of (i) in situ and EIGEN6C4, (ii) in situ and EGM2008 and iii) EIGEN6C4 and EGM2008 of the study area are compared. It reveals that EIGEN6C4 has lesser systematic error than EGM2008. However, from different profile plots of Bouguer gravity, east–west horizontal derivative and north–south horizontal derivative anomalies of the in situ, EIGEN6C4 and EGM2008, it is observed that most of the signatures of lithounits and geological structural elements are delineated very well by EGM2008 and match 94–98% with those of EIGEN6C4. Further, the Bouguer gravity, east–west horizontal derivative and north–south horizontal derivative anomalies of EGM2008 data over the study area have been used effectively for identifying various lithounits and geological structural elements.     

Temporal variations in Sr and 87Sr/86Sr of the Ganga headwaters: estimates of dissolved Sr flux to the mainstream

The headwaters of the Ganga (the Alaknanda, Bhagirathi and the Ganga) were analysed for their dissolved major ions, Sr and ⁸⁷Sr/⁸⁶Sr on a biweekly to monthly basis over a period of one year to determine their temporal variations and the factors contributing to them. The concentrations of major ions and Sr show significant seasonal variation with lower values during monsoon period in all the three rivers. A similar trend is also observed for ⁸⁷Sr/⁸⁶Sr and Na*/Ca (Na* = Na_r? Cl_r) suggesting relatively lower contribution of Sr and Na from silicates (which are more radiogenic in Sr) during monsoon. Budget calculations show that silicate derived dissolved Sr (Sr_s) in the river Ganga, Alaknanda and the Bhagirathi varied from 10 ± 4 to 27 ± 11, 7 ± 3 to 30 ± 12, 16 ± 6 to 57 ± 23% of measured Sr respectively with lower values during monsoon. The relative decrease in silicate erosion compared to carbonate during monsoon can result from several factors, these include higher dissolution kinetics of the carbonates, lower water–rock interaction time and availability of larger area for weathering. The annual discharge weighted Sr flux derived from the time series data is higher by ～20% from that based on peak flow Sr, and lower by ～40% compared to that derived from lean flow Sr concentration. The area‐normalized annual flux of dissolved Sr from the Ganga at Rishikesh is about five times its flux at Rajshahi (Bangladesh) and a few other major global rivers, such as the Amazon, indicating higher erosion rate over the Himalaya. Copyright © 2010 John Wiley & Sons, Ltd.     

Medium-range forecast of cyclogenesis over North Indian Ocean with multilayer perceptron model using satellite data

An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum.     

Simulation of Indian summer monsoon intraseasonal oscillations in a superparameterized coupled climate model: need to improve the embedded cloud resolving model

The characteristic features of Indian summer monsoon (ISM) and monsoon intraseasonal oscillations (MISO) are analyzed in the 25 year simulation by the superparameterized Community Climate System Model (SP-CCSM). The observations indicate the low frequency oscillation with a period of 30–60 day to have the highest power with a dominant northward propagation, while the faster mode of MISO with a period of 10–20 day shows a stationary pattern with no northward propagation. SP-CCSM simulates two dominant quasi-periodic oscillations with periods 15–30 day and 40–70 day indicating a systematic low frequency bias in simulating the observed modes. Further, contrary to the observation, the SP-CCSM 15–30 day mode has a significant northward propagation; while the 40–70 day mode does not show prominent northward propagation. The inability of the SP-CCSM to reproduce the observed modes correctly is shown to be linked with inability of the cloud resolving model (CRM) to reproduce the characteristic heating associated with the barotropic and baroclinic vertical structures of the high-frequency and the low-frequency modes. It appears that the superparameterization in the General Circulation Model (GCM) certainly improves seasonal mean model bias significantly. There is a need to improve the CRM through which the barotropic and baroclinic modes are simulated with proper space and time distribution.     

Four-dimensional structure and sub-seasonal regulation of the Indian summer monsoon multi-decadal mode

A better understanding of the drivers and teleconnection mechanisms responsible for the multi decadal mode (MDM) of variability of the Indian summer monsoon rainfall (ISMR) with major socio-economic impacts in the region through clustering of large-scale floods or droughts is key to improving the poor simulation of ISMR MDM by most climate models. Here, using the longest instrumental record of ISMR available (1813–2006) and longest atmospheric and oceanic re-analyses, the global four dimensional (space–time) structures of atmospheric and oceanic fields of the multi-decadal mode of ISMR and sub-seasonal evolution of the teleconnection mechanism are brought out, essential for understanding underlying drivers but lacking so far. The relationships between the spatial structure of winds, Sea Surface Temperature (SST) and thermocline depth with the ISMR MDM indicate that the tropical ocean over the Indo-Pacific domain is passive responding primarily to the surface winds associated with the mode. A close association between the Atlantic Meridional Overturning Circulation (AMOC), north Atlantic (NA) SST, NA sea surface salinity (SSS) and the ISMR MDM indicate a slow oceanic pathway linking NA SST and the ISMR. In addition to strong correlation (~ 0.9) between global spatial patterns of JJAS SST associated with the MDMs of ISMR, NA SST and AMOC, strong temporal coherence (correlations ~ 0.9) between them is suggestive of regulation of the ISMR MDM (T ~ 65-years) by the NA SST associated with the Atlantic Multidecadal Oscillation (AMO) through a ‘fast’ atmospheric bridge. On a seasonal time scale, the atmospheric bridge manifests in the form of a stationary Rossby wave train generated by an anticyclonic (cyclonic) barotropic vorticity located above positive (negative) SST anomaly over NA in two phases of the AMO. That the AMO SST is the driver of the ISMR MDM is further supported when we unravel the sub-seasonal face of the teleconnection between the two. We show that phase locking of active (break) spells with annual cycle during positive (negative) phases of the ISMR MDM are forced by a similar phase locking of barotropic anticyclonic (cyclonic) vorticity over the NA SST with the annual cycle through the generation of a quasi-stationary Rossby wave train with an anticyclonic (cyclonic) vorticity at upper level over the Indian region with the NA columnar vorticity leading Indian monsoon rainfall by about a week. Our findings provide a basis for enhanced predictability of tropical climate through slow modulation by extra-tropical SST.

     

The once and future pulse of Indian monsoonal climate

We present a comprehensive assessment of the present and expected future pulse of the Indian monsoon climate based on observational and global climate model projections. The analysis supports the view that seasonal Indian monsoon rains in the latter half of the 21th century may not be materially different in abundance to that experienced today although their intensity and duration of wet and dry spells may change appreciably. Such an assessment comes with considerable uncertainty. With regard to temperature, however, we find that the Indian temperatures during the late 21st Century will very likely exceed the highest values experienced in the 130-year instrumental record of Indian data. This assessment comes with higher confidence than for rainfall because of the large spatial scale driving the thermal response of climate to greenhouse gas forcing. We also find that monsoon climate changes, especially temperature, could heighten human and crop mortality posing a socio-economic threat to the Indian subcontinent.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.