Erratum to: Climate change projections over India by a downscaling approach using PRECIS

The author “Bhaski Bhaskaran” and his affiliation “Fujitsu Laboratory of Europe, Middlesex, UK” should be replaced by “Balakrishnan Bhaskaran”, “Fujitsu Laboratories of Europe Limited, Hayes Park, Middlesex, UK”, respectively.The corrected name and affiliation are shown in this erratum.     

Simulations of the Indian summer monsoon using a nested regional climate model: domain size experiments

Seasonal simulations of the Indian summer monsoon using a 50-km regional climate model (RCM) are described. Results from three versions of the RCM distinguished by different domain sizes are compared against those of the driving global general circulation model (AGCM). Precipitation over land is 20% larger in the RCMs due to stronger vertical motions arising from finer horizontal resolution. The resulting increase in condensational heating helps to intensify the monsoon trough relative to the AGCM. The RCM precipitation distributions show a strong orographically forced mesoscale component (similar in each version). This component is not present in the AGCM. The RCMs produce two qualitatively realistic intraseasonal oscillations (ISOs) associated respectively with monsoon depressions which propagate northwestward from the Bay of Bengal and repeated northward migrations of the regional tropical convergence zone. The RCM simulations are relatively insensitive to domain size in several respects: (1) the mean bias relative to the AGCM is similar for all three domains; (2) the variability simulated by the RCM is strongly correlated with that of the driving AGCM on both daily and seasonal time scales, even for the largest domain; (3) the mesoscale features and ISOs are not damped by the relative proximity of the lateral boundaries in the version with the smallest domain. Results (1) and (2) contrast strongly with a previous study for Europe carried out with the same model, probably due to inherent differences between mid-latitude and tropical dynamics.     

Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles

Ensembles of climate model simulations are required for input into probabilistic assessments of the risk of future climate change in which uncertainties are quantified. Here we document and compare aspects of climate model ensembles from the multi-model archive and from perturbed physics ensembles generated using the third version of the Hadley Centre climate model (HadCM3). Model-error characteristics derived from time-averaged two-dimensional fields of observed climate variables indicate that the perturbed physics approach is capable of sampling a relatively wide range of different mean climate states, consistent with simple estimates of observational uncertainty and comparable to the range of mean states sampled by the multi-model ensemble. The perturbed physics approach is also capable of sampling a relatively wide range of climate forcings and climate feedbacks under enhanced levels of greenhouse gases, again comparable with the multi-model ensemble. By examining correlations between global time-averaged measures of model error and global measures of climate change feedback strengths, we conclude that there are no simple emergent relationships between climate model errors and the magnitude of future global temperature change. Algorithms for quantifying uncertainty require the use of complex multivariate metrics for constraining projections.     

Climate change projections over India by a downscaling approach using PRECIS

This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre’s 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.     

Application of wave model for weather routing of ships in the North Indian Ocean

Weather routing of ships is used to establish the shortest time route or the most economical route from a departure to arrival point by applying available information of the weather condition viz. wind, wave and current. Information on ship speed loss due to these effects is pre-computed using sea-keeping computing tools, which are then suitably employed in the optimum ship routing algorithm. This developed algorithm was investigated using the wave height information from GEOSAT altimeter records. Dijkstra’s path optimization scheme, which employs optimal control theory and dynamic programming technique, is used to obtain reliable optimum route in a given random sea-state.     

Consistency of wave power at a location in the coastal waters of central eastern Arabian Sea

Ocean Dynamics - Wave energy assessment at a regional scale is required for planning installation of wave energy converters. Based on continuously measured wave data at 14-m water depth in the...     

Rule-based classification of high-resolution imagery over urban areas in New York City

This paper describes the integration of results from different feature extraction algorithms using spectral and spatial attributes to detect specific urban features. Methodology includes segmentation of IKONOS data, computing attributes for creating image objects and classifying the objects with fuzzy logic and rule-based algorithms. Previous research reported low class accuracies for two specific classes – dark and grey roofs. A modified per-field approach was employed to extract urban features. New rule-sets were used on image objects having similar or near-similar spectral and spatial characteristics. Different algorithms using spectral and spatial attributes were developed to extract specific urban features from a time-series of Multi-Spectral Scanner (MSS) (4 m × 4 m) IKONOS data. The modified approach resulted in a remarkable improvement in the accuracy of classes that registered low spectral seperability and therefore low accuracy. The spectral and spatial based classification model may be useful in mapping heterogeneous and spectrally similar urban features.