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Qiaozhen Mu, Maosheng Zhao, John S. Kimball, Nathan G. McDowell, and Steven W. Running

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000–11) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (correlation coefficient r = 0.43) with the precipitation-based Palmer drought severity index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite-based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely sensed global terrestrial DSI enhances capabilities for nearreal-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.

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D. J. Mildrexler, M. Zhao, W. B. Cohen, S. W. Running, X. P. Song, and M. O. Jones

Abstract

Measurements that link surface conditions and climate can provide critical information on important biospheric changes occurring in the Earth system. As the direct driving force of energy and water fluxes at the surface–atmosphere interface, land surface temperature (LST) provides information on physical processes of land-cover change and energy-balance changes that air temperature cannot provide. Annual maximum LST (LSTmax) is especially powerful at minimizing synoptic and seasonal variability and highlighting changes associated with extreme climatic events and significant land-cover changes. The authors investigate whether maximum thermal anomalies from satellite observations could detect heat waves and droughts, a melting cryosphere, and disturbances in the tropical forest from 2003 to 2014. The 1-km2 LSTmax anomalies peaked in 2010 when 20% of the global land area experienced anomalies of greater than 1 standard deviation and over 4% of the global land area was subject to positive anomalies exceeding 2 standard deviations. Positive LSTmax anomalies display complex spatial patterns associated with heat waves and droughts across the global land area. The findings presented herein show that entire biomes are experiencing shifts in their LSTmax distributions driven by extreme climatic events and large-scale land surface changes, such as melting of ice sheets, severe droughts, and the incremental effects of forest loss in tropical forests. As climate warming and land-cover changes continue, it is likely that Earth’s maximum surface temperatures will experience greater and more frequent directional shifts, increasing the possibility that critical thresholds in Earth’s ecosystems and climate system will be surpassed, resulting in profound and irreversible changes.

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J. S. Kimball, M. Zhao, A. D. McGuire, F. A. Heinsch, J. Clein, M. Calef, W. M. Jolly, S. Kang, S. E. Euskirchen, K. C. McDonald, and S. W. Running

Abstract

Northern ecosystems contain much of the global reservoir of terrestrial carbon that is potentially reactive in the context of near-term climate change. Annual variability and recent trends in vegetation productivity across Alaska and northwest Canada were assessed using a satellite remote sensing–based production efficiency model and prognostic simulations of the terrestrial carbon cycle from the Terrestrial Ecosystem Model (TEM) and BIOME–BGC (BioGeoChemical Cycles) model. Evidence of a small, but widespread, positive trend in vegetation gross and net primary production (GPP and NPP) is found for the region from 1982 to 2000, coinciding with summer warming of more than 1.8°C and subsequent relaxation of cold temperature constraints to plant growth. Prognostic model simulation results were generally consistent with the remote sensing record and also indicated that an increase in soil decomposition and plant-available nitrogen with regional warming was partially responsible for the positive productivity response. Despite a positive trend in litter inputs to the soil organic carbon pool, the model results showed evidence of a decline in less labile soil organic carbon, which represents approximately 75% of total carbon storage for the region. These results indicate that the regional carbon cycle may accelerate under a warming climate by increasing the fraction of total carbon storage in vegetation biomass and more rapid turnover of the terrestrial carbon reservoir.

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