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Downloading an Entire Dataset and Using Customized Analysis Code Is EXPLORING ANTARCTIC LAND SURFACE TEMPERATURE EXTREMES USING CONDENSED ANOMALY DATABASES by GLENN EDWIN GRANT B.Sc., Southern Oregon University, 1983 M.A., University of Colorado, 2012 A dissertation submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Geography 2017 This dissertation entitled: Exploring Antarctic Land Surface Temperature Extremes Using Condensed Anomaly Databases written by Glenn Edwin Grant has been approved for the Department of Geography ______________________________________________ Professor Mark Serreze (Geography) ______________________________________________ Mr. David Gallaher (National Snow and Ice Data Center) Date: ______________________ The final copy of this dissertation has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. ii Grant, Glenn Edwin (Ph.D., Geography) Exploring Antarctic Land Surface Temperature Extremes Using Condensed Anomaly Databases Dissertation directed by Professor Mark Serreze Satellite observations have revolutionized the Earth Sciences and climate studies. However, data and imagery continue to accumulate at an accelerating rate, and efficient tools for data discovery, analysis, and quality checking lag behind. In particular, studies of long-term, continental-scale processes at high spatiotemporal resolutions are especially problematic. The traditional technique of downloading an entire dataset and using customized analysis code is often impractical or consumes too many resources. The Condensate Database Project was envisioned as an alternative method for data exploration and quality checking. The project’s premise was that much of the data in any satellite dataset is unneeded and can be eliminated, compacting massive datasets into more manageable sizes. Dataset sizes are further reduced by retaining only anomalous data of high interest. Hosting the resulting “condensed” datasets in high-speed databases enables immediate availability for queries and exploration. Proof of the project’s success relied on demonstrating that the anomaly database methods can enhance and accelerate scientific investigations. The hypothesis of this dissertation is that the condensed datasets are effective tools for exploring many scientific questions, spurring further investigations and revealing important information that might otherwise remain undetected. iii This dissertation uses condensed databases containing 17 years of Antarctic land surface temperature anomalies as its primary data. The study demonstrates the utility of the condensate database methods by discovering new information. In particular, the process revealed critical quality problems in the source satellite data. The results are used as the starting point for four case studies, investigating Antarctic temperature extremes, cloud detection errors, and the teleconnections between Antarctic temperature anomalies and climate indices. The results confirm the hypothesis that the condensate databases are a highly useful tool for Earth Science analyses. Moreover, the quality checking capabilities provide an important method for independent evaluation of dataset veracity. iv Acknowledgements Funding for this research was provided by the National Science Foundation through the Polar Cyberinfrastructure Program, Project 1251257, “Condensate Database for Efficient Anomaly Detection and Quality Assurance of Massive Cryospheric Data”, David W. Gallaher and Qin Lv principal investigators. This dissertation was also made possible with the kind help and patient encouragement of many NSIDC staff. v Table of Contents 1 Introduction ..................................................................................................................... 1 1.1 Background ....................................................................................................................... 2 1.1.1 Motivation ............................................................................................................. 2 1.1.2 Antarctic Land Surface Temperature Extremes..................................................... 4 1.1.3 Geography ............................................................................................................. 8 1.1.4 Objectives .............................................................................................................. 9 1.2 Previous Work ................................................................................................................ 12 1.2.1 Definition of Extreme Temperatures ................................................................... 12 1.2.2 Climate Regime Subregions ................................................................................. 15 1.2.3 Recent Climate Change Findings ......................................................................... 16 1.3 Physical Basis .................................................................................................................. 19 1.3.1 Remote Sensing of LSTs ....................................................................................... 23 1.3.2 Energy Balance at the Surface ............................................................................. 24 1.3.3 Uncertainties and Validation ............................................................................... 29 1.3.4 Comparisons between LSTs and Near-Surface Air Temperatures ...................... 33 2 Data ................................................................................................................................ 36 2.1 MODIS/Terra Land Surface Temperatures ..................................................................... 36 2.1.1 Cloud Masking Confidence Level ......................................................................... 39 2.1.2 Discovered Data Errors and Artifacts .................................................................. 40 2.2 Reanalysis Datasets ........................................................................................................ 43 2.2.1 MERRA-2 .............................................................................................................. 43 vi 2.2.2 ERA Interim .......................................................................................................... 44 2.3 Elevation ......................................................................................................................... 44 2.4 Surface Observations ...................................................................................................... 44 2.5 Climate Indices ............................................................................................................... 44 3 Methods ......................................................................................................................... 46 3.1 Anomaly Detection and Condensed Databases ............................................................. 46 3.1.1 Dataset Cleaning .................................................................................................. 48 3.1.2 Creating Baselines ................................................................................................ 50 3.1.3 Anomaly Determination ...................................................................................... 50 3.1.4 Creating and Using Anomaly Databases .............................................................. 51 3.2 Ancillary Analysis Programs ............................................................................................ 53 4 Results and Discussion .................................................................................................... 55 4.1 Baseline Statistics ........................................................................................................... 55 4.1.1 Sample Population ............................................................................................... 55 4.1.2 Percentiles ........................................................................................................... 57 4.1.3 Median, Mean, and Standard Deviation ............................................................. 58 4.1.4 Linear Regressions ............................................................................................... 61 4.1.5 Absolute Maxima and Minima ............................................................................ 65 4.2 Spatial Patterns of LST Extremes .................................................................................... 69 4.2.1 Highest Temperatures ......................................................................................... 69 4.2.2 Lowest Temperatures .......................................................................................... 72 4.3 Temporal Patterns of LST Extremes ............................................................................... 74 vii 4.3.1 Skewness .............................................................................................................. 80 4.4 Teleconnections to Climate Indices ................................................................................ 81 4.4.1 Southern Annual Mode Index .............................................................................. 83 4.4.2 Southern Oscillation Index ................................................................................... 86 4.4.3
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