Climate & Models
Katharine is the TTU CC's co-director and an Endowed Professor in Public Policy and Public Law in the Department of Political Science. She is an atmospheric scientist with a talent of expertly communicating facts about climate change. Examples of questions are: Isn't climate change part of Earth's natural cycle? Isn't it the sun causing warming? Wasn't there a time when temperatures were warmer than today? Do scientists fabricate data? Is it part of a hoax? Katharine has vast expertise in analyzing observations, comparing future scenarios, evaluating global and regional climate models, and building and assessing statistical downscaling models. This makes her ideally equipped to translate the science of climate projections to information relevant to agriculture, ecosystems, energy, infrastructure, public health, and water resources. Moreover, Katharine, as an evangelical Christian has a unique perspective and standing among those of faith. Her work has been featured in the top-journal Science, she has participated in documentaries, such as the Emmy award-winning, Years of Living Dangerously, the PBS Frontline report, Climate of Doubt, and the film, Merchants of Doubt. Because of her dedication to communicate the science behind climate projections and the associated risks of climate change to a broad audience, Fortune Magazine named her one of the World's Greatest Leaders in 2017 and Time Magazine listed her among the 100 Most Influential People. One such example is the Global Weirding video series, is a fantastic series of short videos to inform us about why we know climate is changing.
Victor is an Associate Professor of computer science and the Founding Director of Data Analytics Laboratory at Texas Tech University. His research interests focus on data science, specifically on crowdsourcing, data mining, machine learning, big data analytics, deep learning, natural language processing, spatial database and information retrieval, and related applications, such as software engineering, business intelligence, and medical informatics. He has published more than 180 papers. Most papers are published in top journals and conferences in knowledge discovery and data management. In addition, we won the test-of-time research award from the 26th ACM SIGKDD (2020), the best paper award from the International Conference on Cloud Computing and Security (2018), the best student paper award finalist from the 16th International Conference on Web Information System Engineering (2015), the best paper award from the 11th Industrial Conference on Data Mining (2011), and the best paper award runner-up from the 14th ACM SIGKDD (2008).
Shuo's research focuses on the quantification of uncertainties of hydrologic predictions using data assimilation (i.e. using data to inform models). He develops quantitative tools to assess how climate change is predicted to affect hydrology, particularly in regards to hydrologic extremes, and to assess management solutions at a wide range of spatial and temporal scales. For example, he used geostatistical tools to determine the changes to precipitation regime (i.e. when and where precipitation occurs) in regions in Canada.
Anne's research focuses on using a suite of statistical downscaling models to produce high-resolution daily projections of various climate variables to station locations or gridded regions. The climate projections she generates are often used for further research in a wide variety of fields ranging from agriculture and ecological processes to engineering projects. This includes quantifying climate change impacts on infrastructure and how to integrate these assessments into city planning.
Natasja is a global change ecologist. She explores the effects of climate on soil microbial processes and plant physiology. She uses several quantitative approaches in her research, including data assimilation, meta-analyses, Bayesian and multivariate analyses. Data assimilation is an approach that uses data to constrain a model. She used this approach to constrain a soil carbon model to assess how land carbon predictions of Earth System Model compare to observations from field warming experiments. Land contains far more carbon than the atmosphere. Earth System Models predict that land will lose more C with warming than that they gain. If so, then warming will result in a positive feedback to land C loss, leading to faster rates of atmospheric warming. Ascertaining the strength and direction of this feedback is therefore important.
Zhe is a land change scientist. He combines remote sensing with other sources of information. Using a combination of field measurements, carbon modeling and remote sensing, he quantified changes to ecosystem carbon gains or losses following a change in land use from rural to urban. How landscapes change over time, why they change, and how the shift in land use influences carbon fluxes are his forté.