Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics
Shanah Sharpe - Jackson State University
Co-Author(s): Zhenbu Zhang, Jackson State University, Jackson, MS
Being able to predict the future climate states may help in the determination of more opportune times for the planting and harvesting of crops, as well as give appropriate warning of possible human harm to the environment from various human activities. Although they are very simple, energy balance models (EBMs) play an important role in the study of the climate.
The objective of this project is to solve and improve the parameters of a very general EBM with new available data. Data of the annual average surface temperature of each latitude belt ranging from 0 to 90 degrees for the year 2014 was collected from the NASA Global Modeling and Assimilation Office MERRA website. Linear, quadratic, exponential and logarithmic regressions were run to get the best value of the parameters of the equation. The regressions were run using R programming software.
The two best fit regressions were the quadratic regression and the linear regression. The functions, f1(x) and f2(x), derived from the latter regressions were used as initial temperature distributions. These initial temperature distributions and Newmann boundary conditions were used to solve the initial-value problem for the temperature T(t,x). The effect of the initial values on T(t,x) were compared. Further-more, the obtained T(t,x) was used to predict the climate change for the future. Future work includes finding out the best fit formulas for Ein, Eout and ϕ for the EBM being investigated. Additionally, higher order dimensional models will be examined and visualized.Shanah_Sharpe_ERN.docx
Funder Acknowledgement(s): NSF Bridge to Doctorate Program
Faculty Advisor: Zhenbu Zhang, email@example.com