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A multi‐model ensemble provides useful information about the uncertainty of the future changes of climate. High‐emission scenarios using representative concentration pathways (RCP8.5) of the Fifth Phase Coupled Model Inter‐comparison Project (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) also aids to capture the possible extremity of the climate change. Using the CMIP5 regional climate modelling predictions, this study analyses the distribution of the temperature and precipitation in Bangladesh in the recent years (1971–2000) and in three future periods (2010–2040, 2041–2070 and 2070–2100) considering RCP8.5 scenarios. Climate changes are expressed in terms of 30‐year return values of annual near‐surface temperature and 24‐h precipitation amounts. At the end of the century, the mean temperature increase over Bangladesh among the 11 RCMs will vary from 5.77 to 3.24 °C. Spatial analysis of the 11 RCMs exhibited that the southwest and the south central parts of Bangladesh will experience a greater temperature rise in the future. Possible changes in rainfall are also exhibited both temporally and spatially. Based on the analysis of all the RCMs, a significant increase of rainfall in the pre‐ and post‐monsoon period is observed. It is also evident that monsoon rainfall will not increase in comparison with pre‐monsoon season. Zonal statistics of 64 districts of Bangladesh are also conducted for the 2020s, 2050s and 2080s to find out the most exposed regions in terms of the highest rise in temperature and changes in precipitation.

An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 µm band, a requirement for NDSI; however, the newly proposed Mid-Infrared Sea and Lake Ice Index (MISI) incorporates the reflective component of the 3.9 µm or mid-infrared (MIR) band, which the GOES-13 imager does operate. Incorporating MISI into a sea or lake ice mapping algorithm allows for mapping of thin or broken ice with no snow cover (nilas, frazil ice) and thicker ice with snow cover to a degree of confidence that is comparable to other ice mapping products. The proposed index has been applied over the Great Lakes region and qualitatively compared to the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the National Ice Center ice concentration maps and MODIS snow cover products. The application of MISI may open additional possibilities in climate research using historical GOES imagery. Furthermore, MISI may be used in addition to the current NDSI in ice identification to build more robust ice-mapping algorithms for the next generation GOES satellites.

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