About
I am a climate scientist who specializes in climate projections, modeling, and data products. Climate projections, modeling and data products are used both by researchers learning more about the weather and climate of Earth, but they are also increasingly used in other ways. For example, a team of researchers may use this information to assess the relationship between local climate and a local ecosystem or stream and the subsequent impacts of a changing climate on that river or stream. Alternatively, local, regional, or even national decision-makers may use this same weather and climate information to understand how people impacted by weather, climate, or climate change as they make decisions for the management of critical systems, such as dams, aquifers, or emergency response. However, these climate projections, models, and data products, collectively climate data products, are all tools to help understand Earth’s climate, the impacts, and make decisions. Would you use a hammer to do a saws job? Of course not! My research focuses on evaluating climate data products, particularly climate projections, for their accuracy, sources of uncertainty, and their appropriate use outside of climate science. Based on this research work, alongside others, I advise users and stakeholders of the appropriate usage for climate data products for their unique needs. The central aspect of my work is that sound scientific research is a critical component needed to inform decision-making and impacts assessments.
I received my PhD and M.S. in Atmospheric Science from the Marine, Earth, and Atmospheric Sciences Department at North Carolina State University in 2016 and 2011 respectively, and my B.S. from North Carolina State University in 2008.
Adrienne Wootten
Climate Scientist
I am a climate scientist who specializes in climate projections, modeling, and data products. Climate projections, modeling and data products are used both by researchers learning more about the weather and climate of Earth, but they are also increasingly used in other ways. Forexample, a team of researchers may use this information to assess the relationship between local climate and a local ecosystem or stream and the subsequent impacts of a changing climate on that river or stream. Alternatively, local, regional, or even national decision-makers may use this same weather and climate information to understand how people impacted by weather, climate, or climate change as they make decisions for the management of critical systems, such as dams, aquifers, or emergency response. However, these climate projections, models, and data products, collectively climate data products, are all tools to help understand Earth’s climate, the impacts, and make decisions. Would you use a hammer to do a saws job? Of course not! My research focuses on evaluating climate data products, particularly climate projections, for their accuracy, sources of uncertainty, and their appropriate use outside of climate science. Based on this research work, alongside others, I advise users and stakeholders of the appropriate usage for climate data products for their unique needs. The central aspect of my work is that sound scientific research is a critical component needed to inform decision-making and impacts assessments.
I received my PhD and M.S. in Atmospheric Science from the Marine, Earth, and Atmospheric Sciences Department at North Carolina State University in 2016 and 2011 respectively, and my B.S. from North Carolina State University in 2008.
Adrienne Wootten
Climate Scientist
Worked With (can change this)
Publications
We will include presentation engagements in this list.
2025
Thompson, Liam; Wootten, Adrienne M.; Corporal-Lodangco, Irenea L.; Nielsen-Gammon, John; Trepanier, Jill
A Review: Communicating Uncertainty within the Global Climate Projections Journal Article
In: 2025, ISSN: 1948-8335.
Abstract | Links | Tags: Journal Article
@article{Thompson2025,
title = {A Review: Communicating Uncertainty within the Global Climate Projections},
author = {Liam Thompson and Adrienne M. Wootten and Irenea L. Corporal-Lodangco and John Nielsen-Gammon and Jill Trepanier},
doi = {10.1175/wcas-d-25-0039.1},
issn = {1948-8335},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
publisher = {American Meteorological Society},
abstract = {<jats:title>Abstract</jats:title>
<jats:p>Uncertainty is inherent to all sciences and can be studied from several different perspectives. However, best practices for climate scientists communicating uncertainty in climate projections are unclear. As anthropogenic greenhouse gas emissions continue to rise, the impacts of human-activity on the climate system have become more apparent. This makes the communication of uncertainty in the climate projections critical to decision-makers. Further, the public often equates science to certainty. Yet, it is critical to understand that climate projections are not the future and that projections themselves contain several sources of uncertainty. As such, this review makes four primary recommendations to guide future research and considerations when communicating uncertainty. The goal is to provide a central place for climate scientists to have crucial conversations on how to communicate this uncertainty to facilitate decision-making. First, a standardized uncertainty communication framework, specific to climate projections, should be developed and implemented. Second, research is needed to determine how to communicate which climate projections are best representing current reality. This is critical given recent funding uncertainties that could impact the ability to make more certain climate projections. Third, there is a lack of research on how different decision-makers perceive uncertainty, which could point to a need to develop industry-specific uncertainty communication practices when developing a larger, universal uncertainty communication framework. Finally, we recommend investigating whether too much emphasis is placed on the potential impacts of climate change (negative framing of uncertainty) rather than on actions the public can take to reduce the projected warming (positive framing).</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title>
<jats:p>Uncertainty is inherent to all sciences and can be studied from several different perspectives. However, best practices for climate scientists communicating uncertainty in climate projections are unclear. As anthropogenic greenhouse gas emissions continue to rise, the impacts of human-activity on the climate system have become more apparent. This makes the communication of uncertainty in the climate projections critical to decision-makers. Further, the public often equates science to certainty. Yet, it is critical to understand that climate projections are not the future and that projections themselves contain several sources of uncertainty. As such, this review makes four primary recommendations to guide future research and considerations when communicating uncertainty. The goal is to provide a central place for climate scientists to have crucial conversations on how to communicate this uncertainty to facilitate decision-making. First, a standardized uncertainty communication framework, specific to climate projections, should be developed and implemented. Second, research is needed to determine how to communicate which climate projections are best representing current reality. This is critical given recent funding uncertainties that could impact the ability to make more certain climate projections. Third, there is a lack of research on how different decision-makers perceive uncertainty, which could point to a need to develop industry-specific uncertainty communication practices when developing a larger, universal uncertainty communication framework. Finally, we recommend investigating whether too much emphasis is placed on the potential impacts of climate change (negative framing of uncertainty) rather than on actions the public can take to reduce the projected warming (positive framing).</jats:p>
<jats:p>Uncertainty is inherent to all sciences and can be studied from several different perspectives. However, best practices for climate scientists communicating uncertainty in climate projections are unclear. As anthropogenic greenhouse gas emissions continue to rise, the impacts of human-activity on the climate system have become more apparent. This makes the communication of uncertainty in the climate projections critical to decision-makers. Further, the public often equates science to certainty. Yet, it is critical to understand that climate projections are not the future and that projections themselves contain several sources of uncertainty. As such, this review makes four primary recommendations to guide future research and considerations when communicating uncertainty. The goal is to provide a central place for climate scientists to have crucial conversations on how to communicate this uncertainty to facilitate decision-making. First, a standardized uncertainty communication framework, specific to climate projections, should be developed and implemented. Second, research is needed to determine how to communicate which climate projections are best representing current reality. This is critical given recent funding uncertainties that could impact the ability to make more certain climate projections. Third, there is a lack of research on how different decision-makers perceive uncertainty, which could point to a need to develop industry-specific uncertainty communication practices when developing a larger, universal uncertainty communication framework. Finally, we recommend investigating whether too much emphasis is placed on the potential impacts of climate change (negative framing of uncertainty) rather than on actions the public can take to reduce the projected warming (positive framing).</jats:p>
E. C. Massoud A. M. Wootten, C. Raymond
“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs Journal Article
In: Wiley Geophysical Research Letters, vol. 52, iss. 13, 2025.
Abstract | Links | Tags: Journal Article
@article{nokey,
title = {“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs},
author = {A. M. Wootten, E. C. Massoud, C. Raymond},
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025GL116492},
doi = {10.1029/2025GL116492},
year = {2025},
date = {2025-07-01},
journal = {Wiley Geophysical Research Letters},
volume = {52},
issue = {13},
abstract = {Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.
Tefera, Gebrekidan W.; Ray, Ram L.; Wootten, Adrienne M.
Changes in Plant Hardiness Zones under Climate Change Scenarios in the South-Central United States Journal Article
In: vol. 64, no. 4, pp. 339–351, 2025, ISSN: 1558-8432.
Abstract | Links | Tags: Journal Article
@article{Tefera2025,
title = {Changes in Plant Hardiness Zones under Climate Change Scenarios in the South-Central United States},
author = {Gebrekidan W. Tefera and Ram L. Ray and Adrienne M. Wootten},
doi = {10.1175/jamc-d-24-0080.1},
issn = {1558-8432},
year = {2025},
date = {2025-04-00},
urldate = {2025-04-00},
volume = {64},
number = {4},
pages = {339--351},
publisher = {American Meteorological Society},
abstract = {<jats:title>Abstract</jats:title>
<jats:p>Climate change scenarios have been developed for the south-central United States, indicating a minimum temperature change. These scenarios have combined multiple emission scenarios, global climate model simulations, and statistical downscaling techniques. The scenarios are for the midcentury (2036–65) and far-future (2070–99) period. Based on the USDA definitions, plant hardiness zones were delineated under different climate change scenarios. These zones divide the geography into zones by 10°F (5.56°C) increments from each zone. Climate projections developed from RCP4.5 and RCP8.5 emission scenarios project an increase in minimum temperature, while climate projections under the RCP2.6 emissions’ scenario (RCP2.6; 2070–99) project a decrease in minimum temperature. The increase in minimum temperature triggers an expansion of warm plant hardiness zones (PHZs). In the ensemble mean of climate projections under RCP8.5, 2070–99, 27% of the south-central United States will change from hardiness zones 9a and 9b to 10a and 10b, and 23% will change from PHZ10 (10a and 10b) to PHZ11 (11a and 11b). The most significant transition in all climate change scenarios occurs from PHZ9 to PHZ10. Plant hardiness zone mapping under diverse climate change scenarios corroborates that the southern region of Texas may experience heat stress, while Colorado and Kansas may benefit from an expansion of thermally suitable areas for plant growth.</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title>
<jats:p>Climate change scenarios have been developed for the south-central United States, indicating a minimum temperature change. These scenarios have combined multiple emission scenarios, global climate model simulations, and statistical downscaling techniques. The scenarios are for the midcentury (2036–65) and far-future (2070–99) period. Based on the USDA definitions, plant hardiness zones were delineated under different climate change scenarios. These zones divide the geography into zones by 10°F (5.56°C) increments from each zone. Climate projections developed from RCP4.5 and RCP8.5 emission scenarios project an increase in minimum temperature, while climate projections under the RCP2.6 emissions’ scenario (RCP2.6; 2070–99) project a decrease in minimum temperature. The increase in minimum temperature triggers an expansion of warm plant hardiness zones (PHZs). In the ensemble mean of climate projections under RCP8.5, 2070–99, 27% of the south-central United States will change from hardiness zones 9a and 9b to 10a and 10b, and 23% will change from PHZ10 (10a and 10b) to PHZ11 (11a and 11b). The most significant transition in all climate change scenarios occurs from PHZ9 to PHZ10. Plant hardiness zone mapping under diverse climate change scenarios corroborates that the southern region of Texas may experience heat stress, while Colorado and Kansas may benefit from an expansion of thermally suitable areas for plant growth.</jats:p>
<jats:p>Climate change scenarios have been developed for the south-central United States, indicating a minimum temperature change. These scenarios have combined multiple emission scenarios, global climate model simulations, and statistical downscaling techniques. The scenarios are for the midcentury (2036–65) and far-future (2070–99) period. Based on the USDA definitions, plant hardiness zones were delineated under different climate change scenarios. These zones divide the geography into zones by 10°F (5.56°C) increments from each zone. Climate projections developed from RCP4.5 and RCP8.5 emission scenarios project an increase in minimum temperature, while climate projections under the RCP2.6 emissions’ scenario (RCP2.6; 2070–99) project a decrease in minimum temperature. The increase in minimum temperature triggers an expansion of warm plant hardiness zones (PHZs). In the ensemble mean of climate projections under RCP8.5, 2070–99, 27% of the south-central United States will change from hardiness zones 9a and 9b to 10a and 10b, and 23% will change from PHZ10 (10a and 10b) to PHZ11 (11a and 11b). The most significant transition in all climate change scenarios occurs from PHZ9 to PHZ10. Plant hardiness zone mapping under diverse climate change scenarios corroborates that the southern region of Texas may experience heat stress, while Colorado and Kansas may benefit from an expansion of thermally suitable areas for plant growth.</jats:p>
Sharma, Chetan; Başağaoğlu, Hakan; Yoosefdoost, Icen; Wootten, Adrienne; Chakraborty-Reddy, Debarati; Bertetti, F. Paul; Mirchi, Ali; Chakraborty, Debaditya
Efficacy of mitigation strategies for aquifer sustainability under climate change Journal Article
In: Nat Sustain, vol. 8, no. 1, pp. 44–53, 2025, ISSN: 2398-9629.
Links | Tags: Journal Article
@article{Sharma2024,
title = {Efficacy of mitigation strategies for aquifer sustainability under climate change},
author = {Chetan Sharma and Hakan Başağaoğlu and Icen Yoosefdoost and Adrienne Wootten and Debarati Chakraborty-Reddy and F. Paul Bertetti and Ali Mirchi and Debaditya Chakraborty},
doi = {10.1038/s41893-024-01477-6},
issn = {2398-9629},
year = {2025},
date = {2025-01-00},
urldate = {2025-01-00},
journal = {Nat Sustain},
volume = {8},
number = {1},
pages = {44--53},
publisher = {Springer Science and Business Media LLC},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
2024
Wootten, A. M.; Başağaoğlu, H.; Bertetti, F. P.; Chakraborty, D.; Sharma, C.; Samimi, M.; Mirchi, A.
Customized Statistically Downscaled CMIP5 and CMIP6 Projections: Application in the Edwards Aquifer Region in South‐Central Texas Journal Article
In: Earth's Future, vol. 12, no. 10, 2024, ISSN: 2328-4277.
Abstract | Links | Tags: Journal Article
@article{Wootten2024,
title = {Customized Statistically Downscaled CMIP5 and CMIP6 Projections: Application in the Edwards Aquifer Region in South‐Central Texas},
author = {A. M. Wootten and H. Başağaoğlu and F. P. Bertetti and D. Chakraborty and C. Sharma and M. Samimi and A. Mirchi},
doi = {10.1029/2024ef004716},
issn = {2328-4277},
year = {2024},
date = {2024-10-00},
urldate = {2024-10-00},
journal = {Earth's Future},
volume = {12},
number = {10},
publisher = {American Geophysical Union (AGU)},
abstract = {<jats:title>Abstract</jats:title><jats:p>Climate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>Climate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.</jats:p>
Bertetti, F. Paul; Başağaoğlu, Hakan; Yang, Changbing; Schmidt, Logan; Wootten, Adrienne; Sharma, Chetan; Chakraborty, Debaditya
Geological Society of America, 2024.
Links | Tags: Conference
@conference{2024,
title = {Projecting Recharge and Springflow in a Karstic Aquifer System Under a Changing Climate: Will Current Management Approaches be Adequate?},
author = {F. Paul Bertetti and Hakan Başağaoğlu and Changbing Yang and Logan Schmidt and Adrienne Wootten and Chetan Sharma and
Debaditya Chakraborty},
doi = {10.1130/abs/2024AM-404077},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
publisher = {Geological Society of America},
keywords = {Conference},
pubstate = {published},
tppubtype = {conference}
}
de Moulpied, Michael; Robertson, Clinton R.; Smith, Ryan; Johnson, Matthew; Wootten, Adrienne M.; Martin, Elinor; Lopez, Roel; Randklev, Charles R.
In: Aquatic Conservation, vol. 34, no. 8, 2024, ISSN: 1099-0755.
Abstract | Links | Tags: Journal Article
@article{deMoulpied2024,
title = {Growth and longevity of two imperilled mussel species from the Edwards Plateau of Central Texas and its implications for freshwater mussel conservation and management},
author = {Michael de Moulpied and Clinton R. Robertson and Ryan Smith and Matthew Johnson and Adrienne M. Wootten and Elinor Martin and Roel Lopez and Charles R. Randklev},
doi = {10.1002/aqc.4224},
issn = {1099-0755},
year = {2024},
date = {2024-08-00},
urldate = {2024-08-00},
journal = {Aquatic Conservation},
volume = {34},
number = {8},
publisher = {Wiley},
abstract = {<jats:title>Abstract</jats:title><jats:p><jats:list>
<jats:list-item><jats:p>Life history information such as growth and longevity have been useful for understanding evolutionary relationships and predicting species responses to management and habitat alteration for aquatic species. For unionid mussels, which are globally imperilled, life history information remains unknown for a majority of unionid mussels and because of this has not been broadly used to guide mussel conservation efforts.</jats:p></jats:list-item>
<jats:list-item><jats:p>To address this knowledge gap, growth and longevity were estimated for <jats:italic>Cyclonaias petrina</jats:italic>, Texas pimpleback, and <jats:styled-content style="fixed-case"><jats:italic>Lampsilis bracteata</jats:italic></jats:styled-content>, Texas fatmucket, using thin‐sectioning and validated using cross‐dating. Both species are proposed for listing under the US Endangered Species Act.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates differed between <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.065, 0.086, 0.101; <jats:italic>L∞</jats:italic> = 55.03, 76.44, 94.43) and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.187, 0.208; <jats:italic>L∞</jats:italic> = 61.40, 61.52), and growth for <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> correlated to annual flow indices. Cross‐dating revealed high interseries correlations (<jats:italic>R</jats:italic> = 0.400–0.573), indicating estimates can be viewed with some certainty.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates indicate <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> is positioned near the <jats:italic>K</jats:italic> endpoint and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> near the <jats:italic>r</jats:italic> endpoint along the <jats:italic>r</jats:italic>/<jats:italic>K</jats:italic> life history continuum. This suggests <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> should be favoured in stable habitats where disturbance is minimal, whereas <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> is expected to tolerate habitats with frequent and likely stochastic patterns of disturbance.</jats:p></jats:list-item>
<jats:list-item><jats:p>Knowledge of growth and longevity along with life history position provides a qualitative basis to help scientists and practitioners better anticipate how species will respond to environmental change and management actions. Given the conservation status of <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> the life history findings from this study are timely and should be useful in their conservation.</jats:p></jats:list-item>
</jats:list></jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p><jats:list>
<jats:list-item><jats:p>Life history information such as growth and longevity have been useful for understanding evolutionary relationships and predicting species responses to management and habitat alteration for aquatic species. For unionid mussels, which are globally imperilled, life history information remains unknown for a majority of unionid mussels and because of this has not been broadly used to guide mussel conservation efforts.</jats:p></jats:list-item>
<jats:list-item><jats:p>To address this knowledge gap, growth and longevity were estimated for <jats:italic>Cyclonaias petrina</jats:italic>, Texas pimpleback, and <jats:styled-content style="fixed-case"><jats:italic>Lampsilis bracteata</jats:italic></jats:styled-content>, Texas fatmucket, using thin‐sectioning and validated using cross‐dating. Both species are proposed for listing under the US Endangered Species Act.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates differed between <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.065, 0.086, 0.101; <jats:italic>L∞</jats:italic> = 55.03, 76.44, 94.43) and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.187, 0.208; <jats:italic>L∞</jats:italic> = 61.40, 61.52), and growth for <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> correlated to annual flow indices. Cross‐dating revealed high interseries correlations (<jats:italic>R</jats:italic> = 0.400–0.573), indicating estimates can be viewed with some certainty.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates indicate <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> is positioned near the <jats:italic>K</jats:italic> endpoint and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> near the <jats:italic>r</jats:italic> endpoint along the <jats:italic>r</jats:italic>/<jats:italic>K</jats:italic> life history continuum. This suggests <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> should be favoured in stable habitats where disturbance is minimal, whereas <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> is expected to tolerate habitats with frequent and likely stochastic patterns of disturbance.</jats:p></jats:list-item>
<jats:list-item><jats:p>Knowledge of growth and longevity along with life history position provides a qualitative basis to help scientists and practitioners better anticipate how species will respond to environmental change and management actions. Given the conservation status of <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> the life history findings from this study are timely and should be useful in their conservation.</jats:p></jats:list-item>
</jats:list></jats:p>
<jats:list-item><jats:p>Life history information such as growth and longevity have been useful for understanding evolutionary relationships and predicting species responses to management and habitat alteration for aquatic species. For unionid mussels, which are globally imperilled, life history information remains unknown for a majority of unionid mussels and because of this has not been broadly used to guide mussel conservation efforts.</jats:p></jats:list-item>
<jats:list-item><jats:p>To address this knowledge gap, growth and longevity were estimated for <jats:italic>Cyclonaias petrina</jats:italic>, Texas pimpleback, and <jats:styled-content style="fixed-case"><jats:italic>Lampsilis bracteata</jats:italic></jats:styled-content>, Texas fatmucket, using thin‐sectioning and validated using cross‐dating. Both species are proposed for listing under the US Endangered Species Act.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates differed between <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.065, 0.086, 0.101; <jats:italic>L∞</jats:italic> = 55.03, 76.44, 94.43) and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> (<jats:italic>K</jats:italic> = 0.187, 0.208; <jats:italic>L∞</jats:italic> = 61.40, 61.52), and growth for <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> correlated to annual flow indices. Cross‐dating revealed high interseries correlations (<jats:italic>R</jats:italic> = 0.400–0.573), indicating estimates can be viewed with some certainty.</jats:p></jats:list-item>
<jats:list-item><jats:p>Growth and longevity estimates indicate <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> is positioned near the <jats:italic>K</jats:italic> endpoint and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> near the <jats:italic>r</jats:italic> endpoint along the <jats:italic>r</jats:italic>/<jats:italic>K</jats:italic> life history continuum. This suggests <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> should be favoured in stable habitats where disturbance is minimal, whereas <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> is expected to tolerate habitats with frequent and likely stochastic patterns of disturbance.</jats:p></jats:list-item>
<jats:list-item><jats:p>Knowledge of growth and longevity along with life history position provides a qualitative basis to help scientists and practitioners better anticipate how species will respond to environmental change and management actions. Given the conservation status of <jats:styled-content style="fixed-case"><jats:italic>C. petrina</jats:italic></jats:styled-content> and <jats:styled-content style="fixed-case"><jats:italic>L. bracteata</jats:italic></jats:styled-content> the life history findings from this study are timely and should be useful in their conservation.</jats:p></jats:list-item>
</jats:list></jats:p>
Wootten, Adrienne M.; Dixon, Keith W.; Adams‐Smith, Dennis J.; McPherson, Renee A.
False springs and spring phenology: Propagating effects of downscaling technique and training data Journal Article
In: Intl Journal of Climatology, vol. 44, no. 6, pp. 2021–2040, 2024, ISSN: 1097-0088.
Abstract | Links | Tags: Journal Article
@article{Wootten2024b,
title = {False springs and spring phenology: Propagating effects of downscaling technique and training data},
author = {Adrienne M. Wootten and Keith W. Dixon and Dennis J. Adams‐Smith and Renee A. McPherson},
doi = {10.1002/joc.8438},
issn = {1097-0088},
year = {2024},
date = {2024-05-00},
urldate = {2024-05-00},
journal = {Intl Journal of Climatology},
volume = {44},
number = {6},
pages = {2021--2040},
publisher = {Wiley},
abstract = {<jats:title>Abstract</jats:title><jats:p>Projected changes to spring phenological indicators (such as first leaf and first bloom) are of importance to assessing the impacts of climate change on ecosystems and species. The risk of false springs (when a killing freeze occurs after plants of interest bloom), which can cause ecological and economic damage, is also projected to change across much of the United States. Given the coarse nature of global climate models, downscaled climate projections have commonly been used to assess local changes in spring phenological indices. Few studies that examine the influence of the sources of uncertainty sources in the downscaling approach on projections of phenological changes. This study examines the influence of sources of uncertainty on projections of spring phenological indicators and false spring risk using the South Central United States. The downscaled climate projections were created using three statistical downscaling techniques applied with three gridded observation datasets as training data and three global climate models. This study finds that projections of spring phenological indicators and false spring risk are primarily sensitive to the choice of global climate models. However, this study also finds that the formulation of the downscaling approach can cause errors representing the daily low‐temperature distribution, which can cause errors in false spring risk by failing to capture the timing between the last spring freeze and the first bloom. One should carefully consider the downscaling approach used when using downscaled climate projections to assess changes to spring phenology and false spring risk.</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>Projected changes to spring phenological indicators (such as first leaf and first bloom) are of importance to assessing the impacts of climate change on ecosystems and species. The risk of false springs (when a killing freeze occurs after plants of interest bloom), which can cause ecological and economic damage, is also projected to change across much of the United States. Given the coarse nature of global climate models, downscaled climate projections have commonly been used to assess local changes in spring phenological indices. Few studies that examine the influence of the sources of uncertainty sources in the downscaling approach on projections of phenological changes. This study examines the influence of sources of uncertainty on projections of spring phenological indicators and false spring risk using the South Central United States. The downscaled climate projections were created using three statistical downscaling techniques applied with three gridded observation datasets as training data and three global climate models. This study finds that projections of spring phenological indicators and false spring risk are primarily sensitive to the choice of global climate models. However, this study also finds that the formulation of the downscaling approach can cause errors representing the daily low‐temperature distribution, which can cause errors in false spring risk by failing to capture the timing between the last spring freeze and the first bloom. One should carefully consider the downscaling approach used when using downscaled climate projections to assess changes to spring phenology and false spring risk.</jats:p>
Tefera, Gebrekidan Worku; Ray, Ram L.; Wootten, Adrienne M.
Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA Journal Article
In: Weather and Climate Extremes, vol. 43, 2024, ISSN: 2212-0947.
Links | Tags: Journal Article
@article{Tefera2024,
title = {Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA},
author = {Gebrekidan Worku Tefera and Ram L. Ray and Adrienne M. Wootten},
doi = {10.1016/j.wace.2023.100637},
issn = {2212-0947},
year = {2024},
date = {2024-03-00},
urldate = {2024-03-00},
journal = {Weather and Climate Extremes},
volume = {43},
publisher = {Elsevier BV},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
2023
Wootten, Adrienne M.; Massoud, Elias C.; Waliser, Duane E.; Lee, Huikyo
Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States Journal Article
In: Earth Syst. Dynam., vol. 14, no. 1, pp. 121–145, 2023, ISSN: 2190-4987.
Abstract | Links | Tags: Journal Article
@article{Wootten2023,
title = {Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States},
author = {Adrienne M. Wootten and Elias C. Massoud and Duane E. Waliser and Huikyo Lee},
doi = {10.5194/esd-14-121-2023},
issn = {2190-4987},
year = {2023},
date = {2023-00-00},
urldate = {2023-00-00},
journal = {Earth Syst. Dynam.},
volume = {14},
number = {1},
pages = {121--145},
publisher = {Copernicus GmbH},
abstract = {<jats:p>Abstract. Given the increasing use of climate projections and multi-model
ensemble weighting for a diverse array of applications, this project
assesses the sensitivities of climate model weighting strategies and their
resulting ensemble means to multiple components, such as the weighting
schemes, climate variables, or spatial domains of interest. The purpose of
this study is to assess the sensitivities associated with multi-model
weighting strategies. The analysis makes use of global climate models from
the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their
statistically downscaled counterparts created with the localized constructed
analogs (LOCA) method. This work focuses on historical and projected future
mean precipitation and daily high temperatures of the south-central United
States. Results suggest that the model weights and the corresponding
weighted model means can be sensitive to the weighting strategy that is
applied. For instance, when estimating model weights based on Louisiana
precipitation, the weighted projections show a wetter and cooler
south-central domain in the future compared to other weighting strategies.
Alternatively, for example, when estimating model weights based on New
Mexico temperature, the weighted projections show a drier and warmer
south-central domain in the future. However, when considering the entire
south-central domain in estimating the model weights, the weighted future
projections show a compromise in the precipitation and temperature
estimates. As for uncertainty, our matrix of results provided a more certain
picture of future climate compared to the spread in the original model
ensemble. If future impact assessments utilize weighting strategies, then
our findings suggest that how the specific weighting strategy is used with
climate projections may depend on the needs of an impact assessment or
adaptation plan.
</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Abstract. Given the increasing use of climate projections and multi-model
ensemble weighting for a diverse array of applications, this project
assesses the sensitivities of climate model weighting strategies and their
resulting ensemble means to multiple components, such as the weighting
schemes, climate variables, or spatial domains of interest. The purpose of
this study is to assess the sensitivities associated with multi-model
weighting strategies. The analysis makes use of global climate models from
the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their
statistically downscaled counterparts created with the localized constructed
analogs (LOCA) method. This work focuses on historical and projected future
mean precipitation and daily high temperatures of the south-central United
States. Results suggest that the model weights and the corresponding
weighted model means can be sensitive to the weighting strategy that is
applied. For instance, when estimating model weights based on Louisiana
precipitation, the weighted projections show a wetter and cooler
south-central domain in the future compared to other weighting strategies.
Alternatively, for example, when estimating model weights based on New
Mexico temperature, the weighted projections show a drier and warmer
south-central domain in the future. However, when considering the entire
south-central domain in estimating the model weights, the weighted future
projections show a compromise in the precipitation and temperature
estimates. As for uncertainty, our matrix of results provided a more certain
picture of future climate compared to the spread in the original model
ensemble. If future impact assessments utilize weighting strategies, then
our findings suggest that how the specific weighting strategy is used with
climate projections may depend on the needs of an impact assessment or
adaptation plan.
</jats:p>
ensemble weighting for a diverse array of applications, this project
assesses the sensitivities of climate model weighting strategies and their
resulting ensemble means to multiple components, such as the weighting
schemes, climate variables, or spatial domains of interest. The purpose of
this study is to assess the sensitivities associated with multi-model
weighting strategies. The analysis makes use of global climate models from
the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their
statistically downscaled counterparts created with the localized constructed
analogs (LOCA) method. This work focuses on historical and projected future
mean precipitation and daily high temperatures of the south-central United
States. Results suggest that the model weights and the corresponding
weighted model means can be sensitive to the weighting strategy that is
applied. For instance, when estimating model weights based on Louisiana
precipitation, the weighted projections show a wetter and cooler
south-central domain in the future compared to other weighting strategies.
Alternatively, for example, when estimating model weights based on New
Mexico temperature, the weighted projections show a drier and warmer
south-central domain in the future. However, when considering the entire
south-central domain in estimating the model weights, the weighted future
projections show a compromise in the precipitation and temperature
estimates. As for uncertainty, our matrix of results provided a more certain
picture of future climate compared to the spread in the original model
ensemble. If future impact assessments utilize weighting strategies, then
our findings suggest that how the specific weighting strategy is used with
climate projections may depend on the needs of an impact assessment or
adaptation plan.
</jats:p>
2021
Guckian, Meaghan L.; Markowitz, Ezra M.; Tucker, Clay S.; Kiekebusch, Elsita; Klemm, Toni; Middleton, Lindsey; Wootten, Adrienne; Staudinger, Michelle D.
Assessing the Impact of an Online Climate Science Community: The Early Career Climate Forum Journal Article
In: vol. 13, no. 2, pp. 315–325, 2021, ISSN: 1948-8335.
@article{Guckian2021,
title = {Assessing the Impact of an Online Climate Science Community: The Early Career Climate Forum},
author = {Meaghan L. Guckian and Ezra M. Markowitz and Clay S. Tucker and Elsita Kiekebusch and Toni Klemm and Lindsey Middleton and Adrienne Wootten and Michelle D. Staudinger},
doi = {10.1175/wcas-d-20-0150.1},
issn = {1948-8335},
year = {2021},
date = {2021-04-00},
volume = {13},
number = {2},
pages = {315--325},
publisher = {American Meteorological Society},
abstract = {Abstract Online science communities can serve as powerful platforms for advancing scientific knowledge, capacity, and outreach by increasing collaboration and information sharing among geographically distant peers, practitioners, and the public. Here, we examine the value and role of the Early Career Climate Forum (ECCF), a climate-focused online science community that is based in the United States and is dedicated to training and providing support to the next generation of climate scientists. In a survey of community users and contributors, we find that the ECCF played a unique role in providing users access to career resources as well as climate-related research and insights. Respondents also indicated that the ECCF provides them with a strong sense of community and a sense of hope for the future of climate science research. These findings highlight the importance of online science communities in shaping and supporting the next generation of scientists and practitioners working at the science–management interface on climate change issues. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fovargue, Rachel E; Rezapour, Shabnam; Rosendahl, Derek; Wootten, Adrienne M; Sabzi, Hamed Zamani; Moreno, Hernan A; Neeson, Thomas M
Spatial planning for water sustainability projects under climate uncertainty: balancing human and environmental water needs Journal Article
In: Environ. Res. Lett., vol. 16, no. 3, 2021, ISSN: 1748-9326.
@article{Fovargue2021,
title = {Spatial planning for water sustainability projects under climate uncertainty: balancing human and environmental water needs},
author = {Rachel E Fovargue and Shabnam Rezapour and Derek Rosendahl and Adrienne M Wootten and Hamed Zamani Sabzi and Hernan A Moreno and Thomas M Neeson},
doi = {10.1088/1748-9326/abdd58},
issn = {1748-9326},
year = {2021},
date = {2021-03-01},
journal = {Environ. Res. Lett.},
volume = {16},
number = {3},
publisher = {IOP Publishing},
abstract = {Abstract
Societies worldwide make large investments in the sustainability of integrated human-freshwater systems, but uncertainty about water supplies under climate change poses a major challenge. Investments in infrastructure, water regulation, or payments for ecosystem services may boost water availability, but may also yield poor returns on investment if directed to locations where water supply unexpectedly fluctuates due to shifting climate. How should investments in water sustainability be allocated across space and among different types of projects? Given the high costs of investments in water sustainability, decision-makers are typically risk-intolerant, and considerable uncertainty about future climate conditions can lead to decision paralysis. Here, we use mathematical optimization models to find Pareto-optimal satisfaction of human and environmental water needs across a large drought-prone river basin for a range of downscaled climate projections. We show how water scarcity and future uncertainty vary independently by location, and that joint consideration of both factors can provide guidance on how to allocate water sustainability investments. Locations with high water scarcity and low uncertainty are good candidates for high-cost, high-reward investments; locations with high scarcity but also high uncertainty may benefit most from low regret investments that minimize the potential for stranded assets if water supply increases. Given uncertainty in climate projections in many regions worldwide, our analysis illustrates how explicit consideration of uncertainty may help to identify the most effective strategies for investments in the long-term sustainability of integrated human-freshwater systems. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bowden, Jared H.; Terando, Adam J.; Misra, Vasu; Wootten, Adrienne; Bhardwaj, Amit; Boyles, Ryan; Gould, William; Collazo, Jaime A.; Spero, Tanya L.
In: Intl Journal of Climatology, vol. 41, no. 2, pp. 1305–1327, 2021, ISSN: 1097-0088.
@article{Bowden2020,
title = {High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands},
author = {Jared H. Bowden and Adam J. Terando and Vasu Misra and Adrienne Wootten and Amit Bhardwaj and Ryan Boyles and William Gould and Jaime A. Collazo and Tanya L. Spero},
doi = {10.1002/joc.6810},
issn = {1097-0088},
year = {2021},
date = {2021-02-00},
journal = {Intl Journal of Climatology},
volume = {41},
number = {2},
pages = {1305--1327},
publisher = {Wiley},
abstract = {Abstract The weather research and forecasting (WRF) model and a combination of the regional spectral model (RSM) and the Japanese Meteorological Agency Non‐Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2‐km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20‐year time slices were downscaled for historical (1986–2005) and future (2041–2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne M.; Dixon, Keith W.; Adams‐Smith, Dennis J.; McPherson, Renee A.
Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique Journal Article
In: Intl Journal of Climatology, vol. 41, no. 2, pp. 980–1001, 2021, ISSN: 1097-0088.
@article{Wootten2020c,
title = {Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique},
author = {Adrienne M. Wootten and Keith W. Dixon and Dennis J. Adams‐Smith and Renee A. McPherson},
doi = {10.1002/joc.6716},
issn = {1097-0088},
year = {2021},
date = {2021-02-00},
journal = {Intl Journal of Climatology},
volume = {41},
number = {2},
pages = {980--1001},
publisher = {Wiley},
abstract = {Abstract Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne M.; Dixon, Keith W.; Adams‐Smith, Dennis J.; McPherson, Renee A.
Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique Journal Article
In: Intl Journal of Climatology, vol. 41, no. 2, pp. 980–1001, 2021, ISSN: 1097-0088.
Abstract | Links | Tags: Journal Article
@article{Wootten2020b,
title = {Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique},
author = {Adrienne M. Wootten and Keith W. Dixon and Dennis J. Adams‐Smith and Renee A. McPherson},
doi = {10.1002/joc.6716},
issn = {1097-0088},
year = {2021},
date = {2021-02-00},
journal = {Intl Journal of Climatology},
volume = {41},
number = {2},
pages = {980--1001},
publisher = {Wiley},
abstract = {Abstract Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions. },
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
2020
Wootten, Adrienne; Massoud, Elias; Sengupta, Agniv; Waliser, Duane; Lee, Huikyo
The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation Journal Article
In: Climate, vol. 8, no. 12, 2020, ISSN: 2225-1154.
@article{Wootten2020d,
title = {The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation},
author = {Adrienne Wootten and Elias Massoud and Agniv Sengupta and Duane Waliser and Huikyo Lee},
doi = {10.3390/cli8120138},
issn = {2225-1154},
year = {2020},
date = {2020-12-00},
journal = {Climate},
volume = {8},
number = {12},
publisher = {MDPI AG},
abstract = {Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne; Massoud, Elias; Sengupta, Agniv; Waliser, Duane; Lee, Huikyo
The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation Journal Article
In: Climate, vol. 8, no. 12, 2020, ISSN: 2225-1154.
Abstract | Links | Tags: Journal Article
@article{Wootten2020,
title = {The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation},
author = {Adrienne Wootten and Elias Massoud and Agniv Sengupta and Duane Waliser and Huikyo Lee},
doi = {10.3390/cli8120138},
issn = {2225-1154},
year = {2020},
date = {2020-12-00},
urldate = {2020-12-00},
journal = {Climate},
volume = {8},
number = {12},
publisher = {MDPI AG},
abstract = {<jats:p>Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.</jats:p>
Eschliman, Carley M.; Kuster, Emma; Ripberger, Joseph; Wootten, Adrienne M.
Preparing to adapt: are public expectations in line with climate projections? Journal Article
In: Climatic Change, vol. 163, no. 2, pp. 851–871, 2020, ISSN: 1573-1480.
Links | Tags:
@article{Eschliman2020,
title = {Preparing to adapt: are public expectations in line with climate projections?},
author = {Carley M. Eschliman and Emma Kuster and Joseph Ripberger and Adrienne M. Wootten},
doi = {10.1007/s10584-020-02830-2},
issn = {1573-1480},
year = {2020},
date = {2020-11-00},
journal = {Climatic Change},
volume = {163},
number = {2},
pages = {851--871},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2019
Rosendahl, Derek; McPherson, Renee; Wootten, Adrienne; Mullens, Esther; Blackband, Jessica; Bryan, Alex
Making Sense of Local Climate Projections Journal Article
In: Eos, vol. 100, 2019, ISSN: 2324-9250.
@article{Rosendahl2019,
title = {Making Sense of Local Climate Projections},
author = {Derek Rosendahl and Renee McPherson and Adrienne Wootten and Esther Mullens and Jessica Blackband and Alex Bryan},
doi = {10.1029/2019eo136493},
issn = {2324-9250},
year = {2019},
date = {2019-11-14},
journal = {Eos},
volume = {100},
publisher = {American Geophysical Union (AGU)},
abstract = {Hands-on training, collaboration with scientists, and practice using real-world challenges give planners and decision-makers confidence to work with climate model information. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2018
Bhardwaj, Amit; Misra, Vasubandhu; Mishra, Akhilesh; Wootten, Adrienne; Boyles, Ryan; Bowden, J. H.; Terando, Adam J.
Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model Journal Article
In: Climatic Change, vol. 147, no. 1-2, pp. 133–147, 2018, ISSN: 1573-1480.
Links | Tags:
@article{Bhardwaj2018,
title = {Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model},
author = {Amit Bhardwaj and Vasubandhu Misra and Akhilesh Mishra and Adrienne Wootten and Ryan Boyles and J. H. Bowden and Adam J. Terando},
doi = {10.1007/s10584-017-2130-x},
issn = {1573-1480},
year = {2018},
date = {2018-03-00},
journal = {Climatic Change},
volume = {147},
number = {1-2},
pages = {133--147},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
Wootten, A.; Terando, A.; Reich, B. J.; Boyles, R. P.; Semazzi, F.
Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques Journal Article
In: vol. 56, no. 12, pp. 3245–3262, 2017, ISSN: 1558-8432.
@article{Wootten2017b,
title = {Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques},
author = {A. Wootten and A. Terando and B. J. Reich and R. P. Boyles and F. Semazzi},
doi = {10.1175/jamc-d-17-0087.1},
issn = {1558-8432},
year = {2017},
date = {2017-12-00},
volume = {56},
number = {12},
pages = {3245--3262},
publisher = {American Meteorological Society},
abstract = {Abstract In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, A.; Terando, A.; Reich, B. J.; Boyles, R. P.; Semazzi, F.
Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques Journal Article
In: vol. 56, no. 12, pp. 3245–3262, 2017, ISSN: 1558-8432.
Abstract | Links | Tags: Journal Article
@article{Wootten2017,
title = {Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques},
author = {A. Wootten and A. Terando and B. J. Reich and R. P. Boyles and F. Semazzi},
doi = {10.1175/jamc-d-17-0087.1},
issn = {1558-8432},
year = {2017},
date = {2017-12-00},
urldate = {2017-12-00},
volume = {56},
number = {12},
pages = {3245--3262},
publisher = {American Meteorological Society},
abstract = {<jats:title>Abstract</jats:title><jats:p>In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.</jats:p>},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.</jats:p>
2016
Wootten, A.; Bowden, J. H.; Boyles, R.; Terando, A.
The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging Journal Article
In: vol. 55, no. 10, pp. 2263–2281, 2016, ISSN: 1558-8432.
@article{Wootten2016,
title = {The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging},
author = {A. Wootten and J. H. Bowden and R. Boyles and A. Terando},
doi = {10.1175/jamc-d-16-0121.1},
issn = {1558-8432},
year = {2016},
date = {2016-10-00},
volume = {55},
number = {10},
pages = {2263--2281},
publisher = {American Meteorological Society},
abstract = {Abstract The sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales (<4 km) and grid nudging at non-convective-permitting scales (>4 km) improves the island average correlation of precipitation by 0.05–0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective-permitting scales with grid nudging. Projected changes in summer precipitation between 2040–42 and 1985–87 using WRF to downscale CCSM4 range from a 2.6-mm average increase to an 81.9-mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2014
Wootten, Adrienne; Boyles, Ryan P.
Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States Journal Article
In: vol. 53, no. 12, pp. 2848–2862, 2014, ISSN: 1558-8432.
@article{Wootten2014b,
title = {Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States},
author = {Adrienne Wootten and Ryan P. Boyles},
doi = {10.1175/jamc-d-14-0034.1},
issn = {1558-8432},
year = {2014},
date = {2014-12-00},
volume = {53},
number = {12},
pages = {2848--2862},
publisher = {American Meteorological Society},
abstract = {Abstract Gauge-calibrated radar estimates of daily precipitation are compared with daily observed values of precipitation from National Weather Service (NWS) Cooperative Observer Network (COOP) stations to evaluate the multisensor precipitation estimate (MPE) product that is gridded by the National Centers for Environmental Prediction (NCEP) for the eastern United States (defined as locations east of the Mississippi River). This study focuses on a broad evaluation of MPE across the study domain by season and intensity. In addition, the aspect of precipitation type is considered through case studies of winter and summer precipitation events across the domain. Results of this study indicate a north–south gradient in the error of MPE and a seasonal pattern with the highest error in summer and autumn and the lowest error in winter. Two case studies of precipitation are also considered in this study. These case studies include instances of intense precipitation and frozen precipitation. These results suggest that MPE is less able to estimate convective-scale precipitation as compared with precipitation variations at larger spatial scales. In addition, the results suggest that MPE is subject to errors related both to the measurement gauges and to the radar estimates used. Two case studies are also included to discuss the differences with regard to precipitation type. The results from these case studies suggest that MPE may have higher error associated with estimating the liquid equivalent of frozen precipitation when compared with NWS COOP network data. The results also suggest the need for more analysis of MPE error for frozen precipitation in diverse topographic regimes. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne; Smith, Kara; Bowden, Jared; Otte, Tanya; Boyles, Ryan
Regional Climate Variations and Change for Terrestrial Ecosystems Workshop Review Journal Article
In: Bulletin Ecologic Soc America, vol. 95, no. 1, pp. 96–97, 2014, ISSN: 2327-6096.
Links | Tags:
@article{Wootten2014c,
title = {Regional Climate Variations and Change for Terrestrial Ecosystems Workshop Review},
author = {Adrienne Wootten and Kara Smith and Jared Bowden and Tanya Otte and Ryan Boyles},
doi = {10.1890/0012-9623-95.1.96},
issn = {2327-6096},
year = {2014},
date = {2014-01-00},
journal = {Bulletin Ecologic Soc America},
volume = {95},
number = {1},
pages = {96--97},
publisher = {Wiley},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne; Smith, Kara; Bowden, Jared; Otte, Tanya; Boyles, Ryan
Regional Climate Variations and Change for Terrestrial Ecosystems Workshop Review Journal Article
In: Bulletin Ecologic Soc America, vol. 95, no. 1, pp. 96–97, 2014, ISSN: 2327-6096.
Links | Tags:
@article{Wootten2014,
title = {Regional Climate Variations and Change for Terrestrial Ecosystems Workshop Review},
author = {Adrienne Wootten and Kara Smith and Jared Bowden and Tanya Otte and Ryan Boyles},
doi = {10.1890/0012-9623-95.1.96},
issn = {2327-6096},
year = {2014},
date = {2014-01-00},
journal = {Bulletin Ecologic Soc America},
volume = {95},
number = {1},
pages = {96--97},
publisher = {Wiley},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick
Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications Bachelor Thesis
2014.
Links | Tags: Journal Article
@bachelorthesis{Wootten2014d,
title = {Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications},
author = {Adrienne Wootten and Kara Smith and Ryan Boyles and Adam Terando and Lydia Stefanova and Vasru Misra and Tom Smith and David L. Blodgett and Fredrick Semazzi},
doi = {10.3133/ofr20141190},
year = {2014},
date = {2014-00-00},
urldate = {2014-00-00},
publisher = {US Geological Survey},
keywords = {Journal Article},
pubstate = {published},
tppubtype = {bachelorthesis}
}
2011
Garcia, Valerie C.; Gego, Edith; Lin, Shao; Pantea, Cristian; Rappazzo, Kristen; Wootten, Adrienne; Rao, S. Trivikrama
An evaluation of transported pollution and respiratory–related hospital admissions in the state of New York Journal Article
In: Atmospheric Pollution Research, vol. 2, no. 1, pp. 9–15, 2011, ISSN: 1309-1042.
Links | Tags:
@article{Garcia2011,
title = {An evaluation of transported pollution and respiratory–related hospital admissions in the state of New York},
author = {Valerie C. Garcia and Edith Gego and Shao Lin and Cristian Pantea and Kristen Rappazzo and Adrienne Wootten and S. Trivikrama Rao},
doi = {10.5094/apr.2011.002},
issn = {1309-1042},
year = {2011},
date = {2011-01-00},
journal = {Atmospheric Pollution Research},
volume = {2},
number = {1},
pages = {9--15},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2010
Wootten, A.; Raman, S.; Sims, A.
Diurnal variation of precipitation over the Carolina Sandhills region Journal Article
In: J Earth Syst Sci, vol. 119, no. 5, pp. 579–596, 2010, ISSN: 0973-774X.
Links | Tags:
@article{Wootten2010,
title = {Diurnal variation of precipitation over the Carolina Sandhills region},
author = {A. Wootten and S. Raman and A. Sims},
doi = {10.1007/s12040-010-0045-2},
issn = {0973-774X},
year = {2010},
date = {2010-10-00},
journal = {J Earth Syst Sci},
volume = {119},
number = {5},
pages = {579--596},
publisher = {Springer Science and Business Media LLC},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2008
Eager, Rebecca E.; Raman, Sethu; Wootten, Adrienne; Westphal, Douglas L.; Reid, Jeffrey S.; Mandoos, Abdulla Al
A climatological study of the sea and land breezes in the Arabian Gulf region Journal Article
In: J. Geophys. Res., vol. 113, no. D15, 2008, ISSN: 0148-0227.
@article{Eager2008,
title = {A climatological study of the sea and land breezes in the Arabian Gulf region},
author = {Rebecca E. Eager and Sethu Raman and Adrienne Wootten and Douglas L. Westphal and Jeffrey S. Reid and Abdulla Al Mandoos},
doi = {10.1029/2007jd009710},
issn = {0148-0227},
year = {2008},
date = {2008-08-16},
journal = {J. Geophys. Res.},
volume = {113},
number = {D15},
publisher = {American Geophysical Union (AGU)},
abstract = {This study focuses on observations of the sea and land breeze circulations in the southern Arabian Gulf. During the summer months, the Indian monsoon creates light northwesterly winds over the Arabian Gulf, allowing for the formation of thermally driven circulations. Observations from a network of stations are used to develop a wind climatology for the Arabian Gulf region. Characteristics of the sea and land breeze circulations, such as onset time, duration, and horizontal and vertical extent are described. The dense network of surface stations in the United Arab Emirates (UAE) allows for a fine‐scale observational study in this region. It is found that the sea breeze occurs during all seasons of the year in this region. It occurs in the late afternoon and continues through the evening. A land breeze sets in during the night. Surface offshore winds in the land breeze are strong probably due to drainage flow down the inland hills. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Talks
Contact
All messages are screened before being received. All legitimate enquiries will receive a reply within 7 business days.