ABSTRACT:We investigate the sensitivity of surface temperature trends to land use land cover change (LULC) over the conterminous United States (CONUS) using the observation minus reanalysis (OMR) approach. We estimated the OMR trends for the 1979-2003 period from the US Historical Climate Network (USHCN), and the NCEP-NCAR North American Regional Reanalysis (NARR). We used a new mean square differences (MSDs)-based assessment for the comparisons between temperature anomalies from observations and interpolated reanalysis data. Trends of monthly mean temperature anomalies show a strong agreement, especially between adjusted USHCN and NARR (r = 0.9 on average) and demonstrate that NARR captures the climate variability at different time scales. OMR trend results suggest that, unlike findings from studies based on the global reanalysis (NCEP/NCAR reanalysis), NARR often has a larger warming trend than adjusted observations (on average, 0.28 and 0.27°C/decade respectively).OMR trends were found to be sensitive to land cover types. We analysed decadal OMR trends as a function of land types using the Advanced Very High Resolution Radiometer (AVHRR) and new National Land Cover Database (NLCD) 1992-2001 Retrofit Land Cover Change. The magnitude of OMR trends obtained from the NLDC is larger than the one derived from the 'static' AVHRR. Moreover, land use conversion often results in more warming than cooling.Overall, our results confirm the robustness of the OMR method for detecting non-climatic changes at the station level, evaluating the impacts of adjustments performed on raw observations, and most importantly, providing a quantitative estimate of additional warming trends associated with LULC changes at local and regional scales. As most of the warming trends that we identify can be explained on the basis of LULC changes, we suggest that in addition to considering the greenhouse gases-driven radiative forcings, multi-decadal and longer climate models simulations must further include LULC changes.

Temperature (T ) and equivalent temperature (T E ) trends over the United States from 1979 to 2005 and their correlation to land cover types are investigated using National Centers for Environmental Prediction North American Regional Reanalysis data, the Advanced Very High Resolution Radiometer (AVHRR) land use/cover classification, the National Land Cover Database (NLCD) 1992-2001 Retrofit Land Cover Change and the Normalised Difference Vegetation Index (NDVI) derived from AVHRR. Even though most of the magnitude of T E is explained by T , the moisture component induces larger trends and variability of T E relative to T . The contrast between pronounced temporal and spatial differences between T and T E at the near-surface level (2 m) and minor-to-no differences at 300-200 mb is a consistent pattern. This study therefore demonstrates that in addition to temperature, atmospheric heat content may help to quantify the differences between surface and tropospheric heating trends, and hence the impact of land cover types on the surface temperature changes. Correlations of T and T E with NDVI reveal that T E shows a stronger relationship to vegetation cover than T , especially during the growing season, with values that are significantly different and of opposite signs (−0.31 for T vs NDVI; 0.49 for T E vs NDVI). Our results suggest that land cover types influence both moisture availability and temperature in the lower atmosphere and that T E is larger in areas with higher physical evaporation and transpiration rates. As a result, T E can be used as an additional metric for analysing near-surface heating trends with respect to land cover types. Moreover, T E can be tested as a complementary variable for assessing the impact of land surface and boundary layer processes in re-analysis and weather/climate model studies.

No abstract

Holistic approaches are needed for understanding and addressing a wide range of environmental issues that require multidisciplinary studies of complex and interlocking systems. The writers' vision of a cyberinfrastructure for end-to-end environmental exploration ͑C4E4͒ that combines data and modeling tools in an integrated environment across different spatial and temporal scales is presented. The overall goal behind C4E4 is to enable a broad environmental research and remediation community to address the challenges of environmental data management and integration in real-world settings. The St. Joseph Watershed in northern Indiana is chosen as a test bed in this effort. The C4E4 framework will allow researchers to combine heterogeneous data resources with state-ofthe-art modeling and visualization tools through a user-friendly web portal. By engaging TeraGrid resources, C4E4 will have the computational resources to store, manipulate, and query large data sets, thereby facilitating new science. C4E4 will serve as a prototype, and provide valuable experience for scaling up to larger observatories at the national level. This paper presents the writers' vision and goals, initial efforts, and briefly describes how C4E4 can benefit the environmental community.

No abstract