2019
DOI: 10.1007/s41748-019-00121-0
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Downscaling and Projection of Spatiotemporal Changes in Temperature of Bangladesh

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Cited by 39 publications
(40 citation statements)
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“…4). This rise of temperature is consistent with CMIP5 data analysis over South Asia under RCP8.5, which is in the range 3.2-5.1 °C for the period 2070-2099 (Alamgir et al 2019). Overall, the change in the future temperature over the northern Peninsula is bigger (exceeding 6.0 °C) than over the southern Peninsula, which is in line with the CMIP3 and CMIP5 projections, as shown in Almazroui et al (2016Almazroui et al ( , 2017b.…”
Section: Changes In Temperature For the Near And Far Futuressupporting
confidence: 87%
“…4). This rise of temperature is consistent with CMIP5 data analysis over South Asia under RCP8.5, which is in the range 3.2-5.1 °C for the period 2070-2099 (Alamgir et al 2019). Overall, the change in the future temperature over the northern Peninsula is bigger (exceeding 6.0 °C) than over the southern Peninsula, which is in line with the CMIP3 and CMIP5 projections, as shown in Almazroui et al (2016Almazroui et al ( , 2017b.…”
Section: Changes In Temperature For the Near And Far Futuressupporting
confidence: 87%
“…This study found an increase in annual mean temperature from 2.6 to 4.8 °C by 2100, relative to the reference period. Alamgir et al (2019) used eight CMIP5 GCMs to statistically downscale over Bangladesh and reported an increase in temperature by 2.7 to 4.7 °C under RCP 8.5 at the end of this century.…”
Section: Discussionmentioning
confidence: 99%
“…Large-scale features can be represented in different ways in ESD, either based on the nearest grid-box values from reanalyses and GCMs (e.g., Liu et al, 2016), or by an index that represents some spatially aggregated quantity or a statistic describing the spatio-temporal patterns in a larger region (e.g., Benestad, 2011). Common Empirical Orthogonal Function (common EOF) analysis applied to GCM and reanalysis data can be used to represent the large-scale predictors (Flury, 1988;Barnett, 1999;Benestad, 2001Benestad, , 2011. This means that EOF analysis is applied to an object combining the GCM and reanalysis data, which decomposes the combined data into a common set of spatial patterns, eigenvalues representing the relative importance of each pattern, and principal components (PCs) describing the temporal variations associated with the spatial patterns for the GCM and reanalysis data separately (see schematic in Fig.…”
Section: Introductionmentioning
confidence: 99%