Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 archive

Nagesh Kumar, D. ; Sonali, P. (2016) Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 archive Journal of Water and Climate Change, 7 (1). pp. 83-102. ISSN 2040-2244

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Official URL: http://doi.org/10.2166/wcc.2015.072

Related URL: http://dx.doi.org/10.2166/wcc.2015.072

Abstract

This study analyzes the change in annual and seasonal maximum and minimum temperature (Tmax and Tmin) during the period 1950–2005 (i.e., second half of the 20th century). In-depth analyses have been carried out for all over India as well as for five temperature homogenous regions of India separately. First, the temporal variations of annual and seasonal Tmax and Tmin are analyzed, employing the trend free pre-whitening Mann-Kendall approach. Secondly, it is assessed whether the observations contain significant signals above the natural internal variability determined from a long ‘piControl’ experiment, using Monte Carlo simulation. Thirdly, fingerprint based formal detection and attribution analysis is used to determine the signal strengths of observed and model simulations with respect to different considered experiments. Finally, these signal strengths are compared to attribute the observed changes in Tmax and Tmin to different factors. All the model simulated datasets are retrieved from the CMIP5 archive. It is noticed that the emergence of observed trends is more pronounced in Tmin compared to Tmax. Although observed changes are not solely associated with one specific causative factor, most of the changes in Tmin lie above the bounds of natural internal climate variability.

Item Type:Article
Source:Copyright of this article belongs to IWA Publishing.
Keywords:Climate change signal; CMIP5 dataset; Formal detection-attribution; Natural internal climate variability; Seasonal temperature; Trend detection
ID Code:125669
Deposited On:17 Oct 2022 06:34
Last Modified:14 Nov 2022 10:14

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