IDENTIFICATION OF URBAN BIAS IN TEMPERATURE TRENDS ON JAVA ISLAND 1963-2022
Temperature change is a key indicator of climate change. However, with increasing urbanization, there is a factor of uncertainty in the calculation of temperature trends, especially in urban areas. With this uncertainty, it is necessary to learn more about urban bias in Indonesia, especially i...
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/85184 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | Temperature change is a key indicator of climate change. However, with increasing
urbanization, there is a factor of uncertainty in the calculation of temperature trends,
especially in urban areas. With this uncertainty, it is necessary to learn more about
urban bias in Indonesia, especially in Java Island which has the highest level of
urbanization in Indonesia. This study aims to identify urban bias in temperature trends
on Java Island in the last two climate periods (1962-1992 and 1993-2022) and identify
spatial variations of urban bias in temperature trends on Java Island.
The data used in this study are observational data of surface temperature, nighttime
brightness, population density and ERA5 (The European Centre for Medium-Range
Weather Forecast (ECMWF) Reanalysis 5-th Generation) reanalysis surface
temperature. Urban and rural classifications are calculated based on population
density and nighttime brightness which are then derived into the degree of
urbanization. Calculation of temperature trends using linear regression and
calculation of urban bias with the urban minus rural method.
In the identification of urban bias with observational data, positive urban bias is
identified in the trend of maximum, average and diurnal range temperature anomalies.
In the identification of urban bias with reanalysis data in the first period (1963-1992)
there is only a positive urban bias in the minimum temperature anomaly trend, while
in the second period (1993-2022) the majority of urban bias is positive with urban bias
in the average temperature anomaly trend of 16%. In terms of spatial variation, the
West Java region has the highest urban bias. |
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