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PhD seminar talk on August 27 at 2pm
August 27, 2019 @ 2:00 pm - 3:00 pm
Title: Improved Statistical Approach for Climate Projection over Bangladesh using Downscaling of Global Climate Model (GCM) Outputs
Speaker: Md. Bazlur Rashid
Abstract:
Global Climate Model (GCM) has evolved from the Atmospheric General Circulation Models
(AGCMs) widely used for daily, seasonal and long term weather prediction. The most widely
recognized application is the projection of future climate states under various scenarios of
increasing atmospheric elements. Over the last few decades, GCMs have been developed to
emulate the present climate system and to project future climate scenarios. Despite notable
development, GCMs do not provide perfect simulations of reality and cannot provide the details
on very small spatial scales due to incomplete scientific understanding and limitations of
available observations in our country. For bridging the gap between the scale of GCMs and
required resolution for practical applications, downscaling provides climate change information
at a suitable spatial and temporal scale from the GCM data. No downscaling for Bangladesh of
detail temperature, precipitation, wind speed, pressure and humidity has been undertaken.
Current research in Bangladesh has not addressed thermal comfort under climate change. In
addition, current studies for Bangladesh do not look at seasonality clearly. Extreme events
(rainfall and temperature), thermal comfort, humidity and wind characteristics, along with
seasonality, under future climate in Bangladesh represent a further research gap and opportunity
for this research. The main object of study will attempt to find and/or develop efficient statistical
methods/tools for climate projection. The specific objectives are (i) to identify suitable model
with bias corrections for climate projection using climate model outputs; (ii) to explore the
effectiveness of the bias correction statistical downscaling method in addressing the model-
related uncertainties involved in future climate predictions; (iii) to identify a suitable
downscaling approach for climate model data to allow daily/ monthly or seasonal meteorological
climate impact studies; (iv) to develop modify algorithms for quantifying the time-variant
uncertainty associated with meteorological extreme weather systems and persistent events under
future climate scenarios and (v) to cross check between available statistical downscaling
techniques for future climate projections and scenarios generation over Bangladesh. So, GCM
information can be enhanced for better representation of the conditions in specific places by
(AGCMs) widely used for daily, seasonal and long term weather prediction. The most widely
recognized application is the projection of future climate states under various scenarios of
increasing atmospheric elements. Over the last few decades, GCMs have been developed to
emulate the present climate system and to project future climate scenarios. Despite notable
development, GCMs do not provide perfect simulations of reality and cannot provide the details
on very small spatial scales due to incomplete scientific understanding and limitations of
available observations in our country. For bridging the gap between the scale of GCMs and
required resolution for practical applications, downscaling provides climate change information
at a suitable spatial and temporal scale from the GCM data. No downscaling for Bangladesh of
detail temperature, precipitation, wind speed, pressure and humidity has been undertaken.
Current research in Bangladesh has not addressed thermal comfort under climate change. In
addition, current studies for Bangladesh do not look at seasonality clearly. Extreme events
(rainfall and temperature), thermal comfort, humidity and wind characteristics, along with
seasonality, under future climate in Bangladesh represent a further research gap and opportunity
for this research. The main object of study will attempt to find and/or develop efficient statistical
methods/tools for climate projection. The specific objectives are (i) to identify suitable model
with bias corrections for climate projection using climate model outputs; (ii) to explore the
effectiveness of the bias correction statistical downscaling method in addressing the model-
related uncertainties involved in future climate predictions; (iii) to identify a suitable
downscaling approach for climate model data to allow daily/ monthly or seasonal meteorological
climate impact studies; (iv) to develop modify algorithms for quantifying the time-variant
uncertainty associated with meteorological extreme weather systems and persistent events under
future climate scenarios and (v) to cross check between available statistical downscaling
techniques for future climate projections and scenarios generation over Bangladesh. So, GCM
information can be enhanced for better representation of the conditions in specific places by
using historically observed local climate information from weather stations. Statistical
Downscaling of climate model can include independent components of climatic signals like El
Nino-Southern Oscillations (ENSO), Indian Ocean Dipole (IOD) and Pacific Decadal
Oscillations (PDO) for a long-lead weather forecast. So, this information can also be used to
improve the future climate projections to assess potential impacts and guide climate-smart
decisions about climate resilience in Bangladesh.
Downscaling of climate model can include independent components of climatic signals like El
Nino-Southern Oscillations (ENSO), Indian Ocean Dipole (IOD) and Pacific Decadal
Oscillations (PDO) for a long-lead weather forecast. So, this information can also be used to
improve the future climate projections to assess potential impacts and guide climate-smart
decisions about climate resilience in Bangladesh.
