The recent slowdown in global warming has initiated a reanalysis of temperature data in some mountainous regions for understanding the consequences and impact that a hiatus has on the climate system. Spatiotemporal temperature variability is analyzed over the Tibetan Plateau because of its sensitivity to climate change with a station network updated to 2014, and its linkages to remote sensing-based variability of MODIS daytime and nighttime temperature are investigated. Results indicate the following: 1) Almost all stations have experienced a notable warming in the time interval 1961-2014, with most obvious warming in winter, which depends on the selected time intervals. 2) There is no clear shift from a predominant warming to a near stagnation during the most recent period (2001-present). 3) Uniform altitudinal dependence of temperature change trends could not be confirmed for all regions, time intervals, and seasons, but sometimes an altitude threshold around 3 km is apparent. 4) Most of the meteorological stations are associated with MODIS temperature warming pixels, and thus regional cooling is missing when considering only the locations of meteorological stations. In summarizing, previous studies based on station observations do not provide a complete picture for the temperature change over the Tibetan Plateau. Remote sensing-based analyses have the potential to find early signals of regional climate changes and assess the impact of global climate changes in complex regional, seasonal, and altitudinal environments.
Urbanization induced change of the thermal environment of cities is analyzed using MODIS LST and DMSP/OLS nighttime light data sets (2001-2012) to a) extend previous studies on individual megacities to a city size spectrum; b) investigate the heterogeneous surface thermal environment associated with the urbanization processes in terms of nighttime light intensity and city size; and c) provide insights in predicting how urban ecosystems will respond to urbanization for both a developing and a developed country (China and US-America), and on global scale. The following results are obtained: i) Nighttime light intensities of both countries (and globally) increase with increasing city size. ii) City size dependent annual or seasonal mean temperature tendencies show the urban effect by decreasing daytime and increasing nighttime mean temperatures (particularly in China) while variability can be related to climate fluctuations. iii) Daytime/nighttime seasonal warming tendencies (inferred from regional downscaling within city clusters) show the high light intensity regions to be stable while in low light intensity regions fluctuations prevail.
Vegetation greenness distributions [based on remote sensing normalized difference vegetation index (NDVI)] and their change are analyzed as functional vegetation–climate relations in a two-dimensional ecohydrological state space spanned by surface flux ratios of energy excess (U; loss by sensible heat H over supply by net radiation N) versus water excess (W; loss by discharge Ro over gain by precipitation P). An ecohydrological ansatz attributes state change trajectories in (U, W) space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation greenness interpreted as an active tracer. Selecting the Tibetan Plateau with its complex topographic, climate, and vegetation conditions as target area, ERA-Interim weather data link geographic and (U, W) state space, into which local remote sensing Global Inventory Modeling and Mapping Studies (GIMMS) data (NDVI) are embedded; a first and second period (1982–93 and 1994–2006) are chosen for change attribution analysis. The study revealed the following results: 1) State space statistics are characterized by a bimodal distribution with two distinct geobotanic regimes (semidesert and steppe) of low and moderate vegetation greenness separated by gaps at aridity D ~ 2 (net radiation over precipitation) and greenness NDVI ~ 0.3. 2) Changes between the first and second period are attributed to external (about 70%) and internal (30%) processes. 3) Attribution conditioned joint distributions of NDVI (and its change) show 38.2% decreasing (61.8% increasing) area cover with low (moderate) greenness while high greenness areas are slightly reduced. 4) Water surplus regions benefit most from climate change (showing vegetation greenness growth) while the energy surplus change is ambiguous, because ecohydrological diagnostics attributes high mountainous regions (such as the Himalayas) as internal without considering the heat storage deficit due to increasing vegetation.
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