Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Creating new near-surface air temperature datasets to understand elevation-dependent warming in the Tibetan Plateau

© 2020 by the authors. The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002-present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9-0.95 and 1-2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000-3000 m and 4000-5000 m, whereas the elevation interval at 6000-7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Reconstruction of multiple climate variables at high spatiotemporal resolution based on Big Earth data platform

University of Technology Sydney. Faculty of Science.Reconstruction of climate variables with high spatio-temporal resolution is important when the meteorological observations required for environmental monitoring and modelling do not cover the study area. In addition, climate model reanalysis datasets suffer from coarse spatio-temporal resolutions, which fails to capture the complex variability of climate at fine scales. This thesis mainly reconstructed four climate datasets including: mountainous solar radiation, near-surface air temperature datasets over rugged terrain, five distinct metrics of long-term heat wave datasets, an updated database of water and wind erosion. For further use in practice, these datasets are freely accessible and online web application has been developed for academic research on climate change under accelerated global warming. The main findings of this thesis are: (1) A GIS‐based solar radiation model that incorporates albedo, shading by surrounding terrain, and variations in cloudiness was developed to address the spatial variability of these factors in mountainous terrain. (2) The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high‐quality near‐surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we create new near-surface air temperature datasets to understand elevation-dependent warming in the Tibetan Plateau. (3) Under ongoing global warming due to climate change, heat waves in Australia are expected to become more frequent and severe. A Google Earth Engine-based toolkit named heat wave tracker (HWT) is developed, which can be used for dynamic visualization, extraction, and processing of complex heat wave events. The datasets, toolkit, and findings we developed contribute to global studies on heat waves under accelerated global warming. (4) Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. This erosion has resulted in increasing areas of land degradation which threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the trends, magnitude, and location of soil erosion. We apply the water-wind erosion model to produce monthly and annual water, and wind erosion estimation at high spatial resolution (up to 90 m, 500 m) for Australia from 2000 to 2020

Linkages between Atmospheric Circulation, Weather, Climate, Land Cover and Social Dynamics of the Tibetan Plateau

The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. In recent decades, the TP has undergone significant changes due to climate and human activities. Since the 1980s anthropogenic activities, such as the stocking of livestock, land cover change, permafrost degradation, urbanization, highway construction, deforestation and desertification, and unsustainable land management practices, have greatly increased over the TP. As a result, grasslands have undergone rapid degradation and have altered the land surface which in turn has altered the exchange of heat and moisture properties between land and the atmosphere. But gaps still exist in our knowledge of land-atmosphere interactions in the TP and their impacts on weather and climate around the TP, making it difficult to understand the complete energy and water cycles over the region. Moreover, human, and ecological systems are interlinked, and the drivers of change include biophysical, economic, political, social, and cultural elements that operate at different temporal and spatial scales. Current studies do not holistically reflect the complex social-ecological dynamics of the Tibetan Plateau. To increase our understanding of this coupled human-natural system, there is a need for an integrated approach to rendering visible the deep interconnections between the biophysical and social systems of the TP. There is a need for an integrative framework to study the impacts of sedentary and individualized production systems on the health and livelihoods of local communities in the context of land degradation and climate change. To do so, there is a need to understand better the spatial variability and landscape patterns in grassland degradation across the TP. Therefore, the main goal of this dissertation is to contribute to our understanding of the changes over the land surface and how these changes impact the plateau\u27s weather, climate, and social dynamics. This dissertation is structured as three interrelated manuscripts, which each explore specific research questions relating to this larger goal. These manuscripts constitute the three primary papers of this dissertation. The first paper documents the significant association of surface energy flux with vegetation cover, as measured by satellite based AVHRR GIMMS3g normalized difference vegetation index (NDVI) data, during the early growing season of May in the western region of the Tibetan Plateau. In addition, a 1°K increase in the temperature at the 500 hPa level was observed. Based on the identified positive effects of vegetation on the temperature associated with decreased NDVI in the western region of the Tibetan Plateau, I propose a positive energy process for land-atmosphere associations. In the second paper, an increase in Landsat-derived NDVI, i.e., a greening, is identified within the TP, especially during 1990 to 2018 and 2000 to 2018 time periods. Larger median growing season NDVI change values were observed for the Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grassland regions, in comparison to the other three regions studied. Land degradation is prominent in the lower and intermediate hillslope positions in comparison to the higher relative topographic positions, and change is more pronounced in the eastern Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grasslands. Geomorphons were found to be an effective spatial unit for analysis of hillslope change patterns. Through the extensive literature review presented in third paper, this dissertation recommends using critical physical geography (CPG) to study environmental and social issues in the TP. The conceptual model proposed provides a framework for analysis of the dominant controls, feedback, and interactions between natural, human, socioeconomic, and governance activities, allowing researchers to untangle climate change, land degradation, and vulnerability in the Tibetan Plateau. CPG will further help improve our understanding of the exposure of local people to climate and socio-economic and political change and help policy makers devise appropriate strategies to combat future grassland degradation and to improve the lives and strengthen livelihoods of the inhabitants of the TP

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

The fluid dynamics of climate: General Circulation Models and applications to past, present and future climatic changes

La presente tesi di ricerca riguarda l’ottimizzazione e l’utilizzo di modelli climatici globali, ed in modo particolare del modello climatico globale ad alta risoluzione EC-Earth, per affrontare una serie di problemi di interesse nel contesto della dinamica del clima e del cambiamento climatico. In particolare, l’attività di ricerca ha riguardato il lavoro di ottimizzazione, ovvero di tuning, del modello climatico globale EC-Earth, appartenente alla categoria degli Earth System Models, e nello specifico della componente atmosferica del modello. Lo studio del cosiddetto “Equable Climate” dell’Eocene, un periodo caldo verificatosi circa 50 milioni di anni fa caratterizzato da una bassa differenza di temperatura tra equatore e poli e ridotto ciclo annuale alle alte latitudini. Gli “Equable Climates” sono un problema tutt’ora irrisolto nelle scienze del clima e la loro comprensione potrebbe avere importanti implicazioni circa la nostra comprensione ed interpretazione dei cambiamenti climatici in corso. L'analisi delle caratteristiche della precipitazione invernale nella regione montuosa dell’Hindu-Kush Karakoram, nell’Himalaya occidentale, e delle sue teleconnessioni, con particolare riferimento all’Oscillazione Nord Atlantica. Lo studio é stato condotto mediante l’utilizzo congiunto di dati osservativi, rianalisi atmosferiche e simulazioni climatiche realizzate con il modello EC-Earth. Lo studio del cambiamento climatico nelle regioni montane, ed in particolare della dipendenza dalla quota dell’aumento delle temperature superficiali terrestri registrato durante il corso del XX secolo e previsto per le prossime decadi (Elevation-Dependent Warming, EDW). Lo studio si é focalizzato principalmente sulla regione montuosa dell’Himalaya- Tibetan Plateau ed é stato condotto mediante l’utilizzo di un ensemble di modelli climatici globali che hanno partecipato al Coupled Model Intercomparison Project Phase 5 (CMIP5) e all’analisi dei dati osservativi disponibili

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects