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How Will Climate Change Impact Surface Temperature in Australia, in a Future Where Fossil Fuel Use Continues to Grow Unabated (SSP5-8.5)? Assignment Sample

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How Will Climate Change Impact Surface Temperature in Australia, in a Future Where Fossil Fuel Use Continues to Grow Unabated (SSP5-8.5)?

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In the modern day, data analysis is essential since it facilitates the development of several sectors by enabling the identification of patterns and trends, gaining an understanding of complex systems, and making academic appraisals. Therefore, in the current situation, data analysis is essential to comprehend how different environmental issues affect our world. Here the previous variables include details on the geographical distribution of carbon on land and in lakes and rivers, its primary use across water columns, rainfall, sea surface temperature, salinity levels, and other relevant variables.


After that understanding the complex interactions between the earth systems’s many parts, such as the atmosphere, seas, and land, depends heavily on these factors. Hereafter, this study basically uses the “R Studio” software to analyse the datasets “all_cmip6_data” which mainly discusses or analyses the PH of seawater, sea surface temperature, mass carbon in some products, and others. Therefore, “R Studio” is the perfect platform for analysing complicated data sets, such as those connected to climate change and environmental research, thanks to its wide collection of statistical functions and visualization tools [4]. Information examination assumes a significant part in figuring out complex frameworks and recognizing examples and patterns that influence our reality. This is particularly obvious in natural examination, where information on elements, for example, carbon dissemination ashore and water, essential creation, ocean surface temperature, and saltiness levels are crucial for figuring out the connections between the world's a large number [2]. In this review, R Studio programming was utilized to break down the all_cmip6_data dataset, which incorporates factors connected with seawater pH, mass carbon in items, and ocean surface temperature. R Studio gives a strong stage to examining such datasets and has a large number of measurable capabilities and perception devices to help with information understanding.


Understanding the consequences of environmental concerns in recent days requires considerable data analysis as they have grown more complicated. Therefore, understanding the interactions between various elements of the earth’s system requires knowledge of variables such as carbon diffusion, “primary production”, moisture, “sea surface temperature”, salinity, pH, and other suitable variables. After that researchers frequently use software like “R Studio”, which provides a vast array of data analysis functions and visualization capabilities, to analyse such complex datasets [1]. The main objective of this project is to use “R Studio” to analyse data on the PH of seawater, the temperature of the sea surface, mass carbon in various items, and other environmental parameters. After that, this literature-based approach emphasizes the value of data analysis and “R Studio” in comprehending environmental problems and creating strategies for environmentally friendly resource management that are based on solid evidence.


Library implementations

Figure 1: Library implementations

This figure shows the importance of library implementations in the “R studio” software for data analysis and retrieving some visualization like various types of diagrams.

Details of latitude, longitude, and others

Figure 2: Details of latitude, longitude, and others

The provided code uses the “” method to retrieve four variables from a “netCDF” file. therefore, latitude, longitude, time, and “cLand” are the retrieved variables where the variable “cLand” is changed from Kelvin to Celsius by deducting 273.15 the analyse the sea surface temperature from the datasets. These variables can be applied to the data to aid in additional analysis or data visualization.

Visualization of the first-time step of Phos

Figure 3: Visualization of the first-time step of Phos

The first time step of the cLand variable taken from the netCDF file is plotted using the "raster" package in the provided code. The figure shows a PH of seawater map for the region denoted by the latitude and longitude data [3].

Details of latitude, longitude, time, and others

Figure 4: Details of latitude, longitude, time, and others

GGplot of the Phos mean

Figure 5: GGplot of the Phos mean

The provided code generates a raster display of the mean phosphorus data using the “ggplot2” library. Here the graphic represents the latitude and longitude data as a region, and the average value of phosphorus over that region is shown.

Global mean value

Figure 6: Global mean value

A raster dataset is used to generate the code, which determines the average value and then computes a weighted mean based on the area of each grid cell.

Code for opening the NetCDF file in R studio

Figure 7: Code for opening the NetCDF file in R studio

Here is the R language algorithm to open the “Netcdf” file in tho the software platform.

Details of the NC file

Figure 8: Details of the NC file

Visualization of the time step of tas

Figure 9: Visualization of the time step of tas

Here the figure displays the plot diagram of the first time step of “tas”  where the “tas” is the variable used to analyse the surface temperature of the dataset and the “Netcdf” data file.

GGplot of tas mean

Figure 10: GGplot of tas mean

GGplot mean value of tas

Figure 11: GGplot mean value of tas

The “tas_global2” dataset's global mean temperature data is plotted using the “ggplot” function in the code. Therefore, the “ssp” variable, which indicates various projections of future greenhouse gas emissions, is used to colour the figure.

Plot diagram of the first time step of CLand

Figure 12: Plot diagram of the first time step of CLand

GGplot of CLand mean

Figure 13: GGplot of CLand mean

The code generates a raster plot of the mean value of “cLand” across various longitude and latitude coordinates using the “GGplot” function.


The approach has taught me the value of data analysis and visualization in understanding intricate environmental challenges. Therefore, my ability to obtain and analyse datasets pertaining to variables like sea surface temperature, seawater pH, and mass carbon has been made possible through the usage of the “R Studio” program. After that, the shown graphics give a brief overview of how various libraries, techniques, and packages for data analysis and visualization are used. Overall, the literature evaluation emphasizes the need of using sustainable resource management and evidence-based decision-making to solve environmental issues. In light of my experience, I advise anyone who continues this effort to investigate further visualization approaches and use machine learning algorithms to obtain more insight into the datasets [5].


In conclusion, data analysis is essential for comprehending how different environmental challenges affect our planet. Understanding the intricate interactions between the many elements of the earth's surface requires an understanding of the above-discussed factors, including carbon distribution, primary production, temperature, and salt levels. Thanks to its extensive library of statistical functions and visualization tools, the R Studio software offers a fantastic platform for analysing datasets related to climate change and environmental research.


  • [1]  Ariel de Lima, D., Helito, C.P., Lima, L.L.D., Clazzer, R., Gonçalves, R.K. and Camargo, O.P.D., 2022. How to perform a meta-analysis: a practical step-by-step guide using r software and rstudio. Acta Ortopédica Brasileira, 30.
  • [2] Klinges, D.H., Duffy, J.P., Kearney, M.R. and Maclean, I.M., 2022. mcera5: Driving microclimate models with ERA5 global gridded climate data. Methods in Ecology and Evolution, 13(7), pp.1402-1411.
  • [3] Paul, R., Adeyemi, O. and Arif, A.A., 2022. Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data. American Journal of Industrial Medicine, 65(4), pp.262-267.
  • [4] Lemenkova, P., 2022. Tanzania Craton, Serengeti Plain and Eastern Rift Valley: mapping of geospatial data by scripting techniques. Estonian Journal of Earth Sciences, 71(2), pp.61-79.
  • [5] Amir, A.S. and Tiro, M.A., 2022. Development of R Package for Regression Analysis with User Friendly Interface. ARRUS Journal of Mathematics and Applied Science, 2(1), pp.23-35.
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