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Souleymane Fall, Dev Niyogi, and Fredrick H. M. Semazzi

Abstract

This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant correlation is found between the temperature index for Senegal and the Pacific sea surface temperatures during the January–April period, especially in the El Niño zone. In contrast to earlier regional-scale studies, precipitation does not show a negative trend and has remained largely unchanged, with a few locations showing a positive trend, particularly in the northeastern and southwestern regions. This study reveals a need for more localized climate analyses of the West Africa region because local climate variations are not always captured by large-scale analysis, and such variations can alter conclusions related to regional climate change.

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Laure M. Montandon, Souleymane Fall, Roger A. Pielke Sr., and Dev Niyogi

Abstract

The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures, it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased toward urban and cropland (>50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban areas that have remained uncultivated in the past century (14.2% stations versus 43.2% land). Results from the temperature trends over the different landscapes confirm that the temperature trends are different for different LULC and that the GHCNv.2 stations network might be missing on long-term larger positive trends. This opens the possibility that the temperature increases of Earth’s land surface in the last century would be higher than what the GHCNv.2-based GAST analyses report.

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Roger Pielke Sr., John Nielsen-Gammon, Christopher Davey, Jim Angel, Odie Bliss, Nolan Doesken, Ming Cai, Souleymane Fall, Dev Niyogi, Kevin Gallo, Robert Hale, Kenneth G. Hubbard, Xiaomao Lin, Hong Li, and Sethu Raman

The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement.

A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover, rather than providing additional independent information, the use of the data from poorly sited stations provides a false sense of confidence in the robustness of the surface temperature trend assessments.

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Rezaul Mahmood, Roger A. Pielke Sr., Kenneth G. Hubbard, Dev Niyogi, Gordon Bonan, Peter Lawrence, Richard McNider, Clive McAlpine, Andres Etter, Samuel Gameda, Budong Qian, Andrew Carleton, Adriana Beltran-Przekurat, Thomas Chase, Arturo I. Quintanar, Jimmy O. Adegoke, Sajith Vezhapparambu, Glen Conner, Salvi Asefi, Elif Sertel, David R. Legates, Yuling Wu, Robert Hale, Oliver W. Frauenfeld, Anthony Watts, Marshall Shepherd, Chandana Mitra, Valentine G. Anantharaj, Souleymane Fall, Robert Lund, Anna Treviño, Peter Blanken, Jinyang Du, Hsin-I Chang, Ronnie Leeper, Udaysankar S. Nair, Scott Dobler, Ravinesh Deo, and Jozef Syktus
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