Climate & Models
Katharine is the TTU CSC's director and the founder and CEO of ATMOS Research. 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.
Sharmistha works across disciplines and integrates them expertly: Geospatial Information Science, Hydro-climatology, and Environmental Science. Sharmistha's research focuses on evaluating the impacts of extreme climate events (e.g. drought or heat waves) on water supplies, food security and public health. She uses observations from weather stations and satellite data, which she then integrates with model simulations and spatial analysis in Geographic Information Systems (GIS) to evaluate which indicators best measure the impacts of drought and heat waves on humans and the environment. For example, some drought indicators relevant to agriculture would be ground-level observations of streamflow, groundwater level, soil moisture, temperature, precipitation, and satellite data that provide information on vegetation and water. She also uses satellite images to develop spatial analysis techniques for environmental planning (e.g. quantifying crop water use, invasive species detection, and quality monitoring of lakes and rivers). Her interests are broad: from quantifying public health risks due to the urban heat island effect (cities are hotter than surrounding rural areas), to helping farmers to assess root zone moisture from information that relates to vegetative health, to predictions of seasonal changes in precipitation based on Earth System Models.
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.
Jung Hee is an atmospheric scientist. One of her objectives was to compare observations to CMIP5 simulations in regard to atmospheric processes that are relevant in generating weather extremes, such as the extreme drought. She also investigates sources of bias in models, such as precipitation bias (i.e. why are certain areas predicted to receive more rain?). One of the main sources of uncertainty for weather patterns relate to how clouds will behave in the future as atmospheric water vapor is predicted to increase. The position of clouds within the atmosphere (low or high) has very different outcomes with respect to temperature and precipitation patterns. Her modeling expertise aided in the improvement and validation of cloud behavior. Other model projects involve predictions of changes the variability of temperature and precipitation at the seasonal and longer-term scales.
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.