Our understanding of how climate will change in the future is still very limited. There have been many studies conducted in trying to predict the future to obtain a rough idea of how climate will be. Understanding past climate is critical in trying to obtain an idea of how climate will be in the future. However, in order to analyze past climate, more accurate data needs to be generated. Climate surfaces, which are used in almost every environmental subfield, are critical to have. Most of the data available lack appropriate spatial attributes, such as, spatial extent and resolution, temporal resolution, and reduction in uncertainty. Therefore, one the objective of this research is to generate a dataset that will provide the necessary information to determine how climate has changed and the trends that have been experienced. A systematic evaluation was performed using different elevation and remote sensing products to improve the accuracy of climate surfaces. The results confirmed that remote sensing data significantly outperformed the commonly-used elevation product to generate climate surfaces, particularly for precipitation. This leads to determination of the optimal spatial resolution based on currently available weather data. Many high spatial resolution climate surfaces have been created without adequate understanding of how the generation of increasingly fine resolutions influences uncertainty. Findings show that regardless of the ecological zone, eco-region, or elevation zone, there were not any statistically significant differences among the uncertainties of all spatial resolutions. Although this indicates that interpolation of fine scale climate surfaces will generally not result in greater or lesser uncertainties, there will often be practical limits that dictate the logical limits of spatial resolution. For instance, the accuracy of a weather station location is often two or fewer decimal places (about 50% of the data), which makes the derivation of surfaces with resolutions < 1km inappropriate. With the appropriate climate surfaces a test was done to determine the uncertainty that has influence in ecological niche modeling. Ecological niche modeling is a popular tool that provides a probability of distribution for a species based on the connection made with the parameters that are fed in; yet, there has been a lack of consideration for uncertainty, especially for environmental surfaces. An experiment was conducted to determine the impact of uncertainty on species location and environmental surfaces. Utilizing the uncertainty information obtained from the climate surfaces and the uncertainty of species location obtained from GBIF, we will be able to include uncertainty in the ecological niche models. The test species were 43 well-known distributions of mammals in the United States. By running Monte Carlo simulation and sensitivity analyses, the findings were that uncertainty in climate surfaces has a significant difference compare to the base than DEM. Uncertainty in the three aspects (topography, climate surfaces, and point locality) are critical information to be include and consider when generating a species model. Without this information one cannot conclude the probability of any single species.