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Climate feedback mechanisms are known to substantially affect the surface temperature response to an external forcing. This study aims to advance our physical and quantitative understanding of forcing and feedback contributions to the surface temperature response to an external forcing. The dissertation begins with a comprehensive overview of the climate feedback concept and the frameworks used to interpret the effects of forcing and feedbacks on surface temperature. The climate feedback-response analysis method (CFRAM), a relatively new climate feedback framework whose advantages over the traditional climate feedback analysis framework are delineated, is then used to study the seasonal surface temperature response to a doubling of CO2 in a global warming simulation of the NCAR CCSM4. This allows us for the first time to explain the major features of the seasonal warming structure quantitatively. Polar regions, for example, experience the largest warming and the greatest seasonal variation, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing, and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter. The CFRAM allows for a process-based decomposition of the temperature response into individual contributions by the forcing and non-temperature feedbacks, which implicitly include the thermal-radiative coupling (i.e., temperature feedback) effects between the surface and atmosphere. To uncover this hidden effect in the CFRAM, this study develops and introduces a method known as the surface feedback-response analysis method (SFRAM) to isolate the temperature feedback effects on surface temperature, allowing for a physical and quantitative understanding of the temperature feedback effects. The temperature feedback effect is found to be the most important contributor to the surface temperature change, accounting for nearly 76% of the global mean surface warming. From the CFRAM perspective, the temperature feedback effect is just the indirect effects of the forcing and non-temperature feedbacks. The SFRAM analysis, in conjunction with the CFRAM results, indicates that in general the indirect effects of the forcing and non-temperature feedbacks on the surface temperature change are larger than the direct effects; thus demonstrating the influence and strength of the temperature feedback effect in the CFRAM results. By isolating the temperature feedback loop, an understanding of why the indirect effects are generally larger than direct effects is achieved. The SFRAM also serves as a bridge to the traditional TOA feedback analysis. A comparison of the SFRAM results with those of the traditional TOA feedback analysis indicates the largest disparity in interpretation is given for the lapse-rate feedback, which is shown to just stem from a misinterpretation of the temperature feedback effects on surface temperature. A better and more intuitive explanation is achieved through the surface perspective of the SFRAM than the TOA perspective of the traditional feedback analysis. A reconciliation of the surface and TOA perspectives is achieved once the temperature feedback effects are included with the effects of the forcing and non-temperature feedbacks, as in the CFRAM analysis.