Impacts of land cover data quality on regional climate simulations

Abstract

ABSTRACT:The land surface influences local, regional and global climate across many time scales. Accurate representation of land surfaces is an important factor for climate modelling studies because land surfaces control the partitioning of available energy and water. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper (ETM+) images into the Weather Research and Forecasting (WRF) model. We used several image processing techniques to create accurate land cover data from Landsat sensor images obtained between 2001 and 2005. By comparing the new land cover data with the default WRF land cover data, we found that there are two types of error in WRF land cover data that caused misrepresentation of the study region. WRF uses Global Land Cover Characteristics (GLCC) data created from images acquired during 1992 and 1993 and it does not reflect current land cover. And the GLCC includes misclassifications. As a result of these errors, GLCC data do not represent urban areas in the cities of Istanbul, Izmit and Bursa and there are spectral mixing problems between classes, e.g. croplands, urban areas and forests. We used WRF land cover and our new land cover data to conduct numerical simulations. Using meteorological station data within the study area, we found that simulation with the new land cover dataset produces more accurate temperature simulations for the region, thus demonstrating the importance of accurate land cover data.

A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

“…Because of the importance of land surface processes in regional dynamic downscaling, a number of studies have investigated the impact of specification of initial soil moisture and land conditions from data assimilation systems and satellite products (e.g., Pielke et al, 1997;Hong et al, 2009;Moufouma-Okia and Rowell, 2010;Sertel et al, 2010;Panegrossi et al, 2011). Using high resolution soil moisture data derived from ENVISAT/ASAR observations as the initial conditions for the MM5 simulation of the Tanaro flood event of April 2009, the ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis and significantly improved the simulation in timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas (Panegrossi et al, 2011).…”

Impacts of land use change on climate

“…Most of the studies examine regional impacts of land surface states on climate (Costa and Pires, 2010;Fall et al, 2010aFall et al, , 2010bGe, 2010;Kishtawal et al, 2010;Mishra et al, 2010;Moore et al, 2010;Petchprayoon et al, 2010;Sertel et al, 2010;Takahashi et al, 2010;Tokairin et al, 2010;Xiao et al, 2010). However, several studies take a global perspective of land-cover consequences Kvalevåg et al, 2010;Lawrence and Chase, 2010;Strengers et al, 2010).…”

“…The observational studies use in situ climate data (Petchprayoon et al, 2010;Xiao et al, 2010), satellite measurements (Ge, 2010;Kishtawal et al, 2010) and data from regional reanalyses (Fall et al, 2010a(Fall et al, , 2010b. The modelling studies use regional atmospheric models (Moore et al, 2010;Sertel et al, 2010;Takahashi et al, 2010;Tokairin et al, 2010;Xiao et al, 2010), global climate models Costa and Pires, 2010;Kvalevåg et al, 2010;Lawrence and Chase, 2010;Strengers et al, 2010) and one uses a land surface model run offline (Mishra et al, 2010).…”

MODIS land cover uncertainty in regional climate simulations

“…Ge et al (2007) reported that input land cover data with accuracies lower than 80% would have strong impacts on RAMS precipitation simulation in East Africa. Sertel et al (2010) replaced the default Global Land Cover Characteristics dataset in the WRF model with a newly developed land cover map from recent Landsat ETM+ and achieved better temperature simulations for Northwest Turkey. MCD12Q1 products are used widely as input for climate models, but data quality is rarely fully considered.…”