Impacts of land surface properties on temperature trends over the United States: Assessment using the US historical climate network and North American regional reanalysis datasets

Souleymane Fall, Purdue University

Abstract

Temperature trends result from natural and anthropogenic factors. The latter was first seen as the result of radiative forcings, mainly the increasing concentrations of greenhouse gases. However, the increasing evidence that some non-radiative forcings such as land use/land cover (LULC) change may also be major factors contributing to climate change has prompted the National Research Council (NRC, 2005) to recommend the broadening of the climate change issue to include LULC processes as an important climate forcing. In addition, at the station level, increasing attention has been given to non-climatic biases that affect temperature records due to changes of the local environment at the vicinity of the station, changes in instrumentation and/or observations practices. This study (i) uses comparisons between in-situ observations and reanalysis datasets as an independent method to estimate temperature trends and variability and evaluate adjustments made to temperature records to correct non-climatic biases, (ii) uses the Observation Minus Reanalysis (OMR) method to investigate the impacts of sensitivity of surface temperature trends to LULC change over the conterminous United States and (iii) compares temperature and equivalent temperature (which is a variable that combines both temperature and moisture) and analyzes their respective correlation to vegetation properties. The comparison between the reanalysis and in-situ temperature observations shows that the reanalysis faithfully captures the intraseasonal and interannual variability of the station observations and also provides valuable information about the effects of individual station location (well or poorly sited) on temperature observations. Moreover, the comparison between surface observations and the North American Regional Reanalysis (NARR) using the Mean Square Difference (MSD) method is efficient in detecting LULC changes that took place at the vicinity of stations or changes related to observation practices, and in evaluating the impacts of adjustments performed on raw observations. OMR trends were found to be sensitive to land cover types and results indicate that land use conversion often results in more warming than cooling. Overall, our results confirm the robustness of the OMR method for capturing patterns of LULC changes at local and regional scales. The comparison between temperature and equivalent temperature demonstrates that atmospheric heat content may help to quantify the differences between surface and tropospheric trends, and hence the impact of land cover types on the surface temperature changes. Moreover, equivalent temperature is more correlated to biomass increase, vegetation transpiration and other surface moisture characteristics. Overall, this study suggests that in addition to considering the greenhouse gases-driven radiative forcings, multi-decadal and longer climate models simulations must further include LULC changes.

Degree

Ph.D.

Advisors

Niyogi, Purdue University.

Subject Area

Physical geography|Climate Change|Atmospheric sciences

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