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4.1 HISTORIC DATA OF TEMPERATURE CHANGES
Analysing past trends in climate is a dificult task due to gaps in data sets and consistency of past data. Past
data sets, wherever available require considerable cleaning up due to vintage instruments used for
collection of data as well as data gaps. According to a new Berkeley Earth study, the average temperature of
the Earth’s land has risen by 1.5°C over the past 250 years. The good match between the new temperature
record and historical carbon dioxide records suggests that the most straightforward explanation for this
warming is human greenhouse gas emissions Berkeley Earth Website 2012.
4.1.1 Downscaled Climate variability and change analysis
The climate data past and future from Climate Systems Analysis Group CSAG, Indian Institute of
Tropical Meteorology IITM, Indian Meteorological Department IMD and Global Historical Climate
Network GHCN were analysed and their results discussed within this report. The CSAG data was
downloaded from University of Cape Town web site accessed between December 2009 and March 2010.
CSAG has taken data from nine large-scale general circulation models GCMs -listed in Table below and
down-scaled the scenario results to a scale more relevant to the cities. Information regarding data is
presented below:
CSAG data was used for analysing the climate variability and change for two cities namely Surat
and Indore. The data have not gone through any bias corrections
- sometimes they represent the historical climate as being more wetdry or hotcold than actually
happened. These biases were corrected within this study.
The data from CSAG which are modelled for only one greenhouse gas emissions scenario, A2 and for only
one future time range: 2046-2065. Whereas the data from PRECIS included three emission scenarios A1B
2021-2050, A2 2071-2100 and B2 2071-2100.
The CSAG data are currently available as station point data and PRECIS as gridded data.
The Indian Meteorology Department’s daily station data has many days of missing data, especially for
precipitation. Therefore, the information from GHCN was used to compare and correlate with the model’s
base data to identify the level of bias in the models. Daily rainfall and temperature data, data from GHCN
was used for bias correcting.
Name of Research Institute Name of the Model
Abbreviation used within this document
Canadian Centre for Climate Modelling Analysis CCCMa Coupled Global Climate Model
CGCM3 Centre National de Recherches Meteorologiques, Meteo
France, France CNRM-CM3
CNRM-CM3 CSIRO, Australia
CSIRO Mark 3.0 CSIRO
Geophysical Fluid Dynamics Laboratory, NOAA CM2.0 - AOGCM
GFDL NASA Goddard Institute for Space Studies NASAGISS,
USA AOM 4x3
GISS Institut Pierre Simon Laplace IPSL, France
IPSL-CM4 IPSL
Meteorological Institute of the University of Bonn Germany, Institute of KMA Korea, and Model and Data
Group ECHO-G = ECHAM4 + HOPE-G
MIUB Max Planck Institute for Meteorology, Germany
ECHAM5MPI-OM MPI
Meteorological Research Institute, Japan Meteorological Agency, Japan
MRI-CGCM2.3.2 MRI
Indian Institute of Tropical Meteorology, Pune and Hadley Research Center UK
Providing Regional Climates for Impacts Studies
PRECIS
Table 4.1: Name of Research Institute, Model and Abbreviation
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measurement by the bias correction factor. This enabled in comparing the results with respect to the
observed information.
4.2 ExTREME EVENT ANALySIS