Skip to content

Downscaled Climate Projections for Heat & Health Applications

[ heat photo - NOAA CPO ] Starting in 2021, GFDL’s Empirical Statistical Downscaling team (ESD Team, part of NOAA/OAR/GFDL) will be working on a new research project involving the statistical downscaling of multi-decadal climate projections for heat and human health applications.  The ESD team will be collaborating with Ellen Mecray (NOAA/NESDIS/NCEI/Regional Climate Services Director for the Eastern Region), Hunter Jones (NOAA/OAR/CPO/NIHHIS Climate and Health Project Manager), and a yet-to-be-selected post doctoral researcher who will be part of the Princeton University Cooperative Institute for Modeling the Earth System (CIMES).

[ Join Our Team! ] We’re Hiring!
See this Position Announcement for the post doctoral researcher position described on this web page.

Heat is one of the leading weather-related killers in the United States, resulting in hundreds of fatalities each year and even more heat-related illnesses. Observations show an upward trend in the frequency and intensity of extreme heat events over the past several decades, and climate modeling studies indicate this trend will continue. Consequently, it is imperative for scientists, health officials, emergency responders, city planners, and others to better understand and properly utilize climate data, so they can make well-informed and risk-reducing decisions.

However, large-scale physical climate data does not meet most decision-making needs.  Stakeholders often are interested in smaller spatial scales and climate-related variables that are closely linked to factors other than just the raw output of the global climate models (GCMs). Translating information about the physical climate into more relevant terms of interest to stakeholders can be accomplished  by incorporating prudent climate data processing into well-designed applied climate impacts studies.  Our project will address both the climate data processing aspect and the translation of research results for informed use by applied researchers and stakeholders in the heat and human heath sector. Accordingly, the partnership will involve people with expertise in different disciplines jointly defining the scope and context of the research problem, developing research questions and experimental designs targeting topics of particular interest, and formulating strategies for the communication and appropriate use of scientific results — a process sometimes referred to as the co-production of actionable science.


Sections:
[ Interdisciplinary Connections: Pursuing “actionable science” ]
[ The Project Team ]
[ More about the GFDL ESD team’s role ]
[ Supporting NOAA’s Mission ]

Interdisciplinary Connections:

Pursuing “actionable science”

A critical component of the project will examine ways to improve the transfer of knowledge between climate modelers, health scientists,  and decision makers.

The process of going from the best available climate science to well-informed planning and decision-making is a challenge. The transfer of knowledge and translation of information involves multiple interdisciplinary exchanges, as highlighted in the figure below.

[ 3 box schematic of data and knowledge exchanges ]

The “upstream” climate science element includes the process by which dynamical modelers and statistical downscalers produce climate projection data products.  Those climate projection data products are frequently used as input to studies conducted by “midstream” researchers (e.g., by epidemiologists or health scientists who are not climate scientists themselves). Climate impact studies conducted by midstream researchers are then used by “downstream” planners and stakeholders to make decisions regarding risk management, adaptation planning, and climate resilience.

Upstream climate researchers typically publish their results in peer-reviewed journals and upload the associated climate projection data products onto a data server or portal.  Although web-based data portals make it easy to transfer climate projection data products from upstream to midstream researchers, often there is little explanation for determining the suitability of the climate projection data product for a particular application, thus leaving room for misuse and misinterpretation.

This project aims to improve that communication and transfer of knowledge by tapping into the expertise of different participants to both develop new, targeted research results and to advance our scientific understanding in ways that strengthen some of the communication pathways depicted by arrows in the above figure.  

The Project Team

  • [Keith Dixon - NOAA/GFDL] Keith Dixon is the Empirical Statistical Downscaling team lead at GFDL. He and other ESD Team members occupy the upstream climate science expertise box depicted above. In prior work, the ESD Team has evaluated the performance characteristics of bias correction and statistical downscaling techniques and reported findings that illustrate and explain underappreciated strengths, weaknesses, and “features” of some commonly used statistical techniques when applied to climate projection data products.
    Two ESD Team journal articles and a poster relevant to this project are:

    • Dixon, K. W., and Coauthors, 2016: Evaluating the stationarity assumption in statistically downscaled climate projections: Is past performance an indicator of future results? Climatic Change. https://doi.org/10.1007/s10584-016-1598-0.
    • Lanzante, J. R., and Coauthors, 2018: Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-17-0046.1.
    • Dixon, K. W., J. R. Lanzante and D. Adams-Smith, 2017: Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products, Poster PA43B-0321, American Geophysical Union, Fall Meeting 2017 [Poster PDF]

  • [ Ellen Mecray photo ] Ellen Mecray  is the National Oceanic and Atmospheric Administration’s (NOAA) Regional Climate Services Director for the Eastern Region. She focuses on the delivery and interpretation of information using networks across several critical economic sectors. In addition to providing climate expertise for her 16-state region, she also served as the Coordinating Chapter Author for the Northeast regional chapter of the U.S. Global Change Research Program’s fourth National Climate Assessment, and is a leader for several federal interagency partnerships. Recently, Ellen has applied her interdisciplinary systems-level science to a model for Service Delivery to state and local decision-makers.
    • Link To Video of Ellen’s presentation
      “Climate Services: Application to Heat Health and Barriers to Implementation” (presented October 19, 2020 at the Aspen Global Change Institutes’ virtual workshop Advancing the Theory and Practice of Urban Heat Resilience)
    • Mecray, E., and Coauthors, 2017: Regional Engagement Workshop Summary Report: Northeast Region. U.S. Global Change Research Program. 22 pp. [PDF]

  • Hunter Jones is the program manager and lead of the Climate Program Office’s Extreme Heat Climate Risk Area. The topic of Extreme Heat is one of four societally important Climate Risk Areas that NOAA CPO identified in 2020 as part of a new integrative and interdisciplinary initiative.  Hunter fosters and manages connections between scientists and stakeholders (the arrows above), with a very specific focus on climate change and the health risk of heat in urban environments.
    • Jones, H. M., E. L. Mecray, and Coauthors, 2019: Understanding Decision Context to Improve Heat Health Information. Bulletin of the American Meteorological Society, 100, ES221–ES225, https://doi.org/10.1175/BAMS-D-19-0042.1.
    • Google Book link for the book chapter
      Jones, H. M. (2018). Climate Change and Increasing Risk of Extreme Heat. In Human Health and Physical Activity During Heat Exposure (pp. 1-13). Springer.

  • [ Question mark ]The yet-to-be-selected postdoctoral research associate will be part of the Princeton University’s Cooperative Institute for Modeling the Earth System (CIMES). The postdoc will sit with the GFDL ESD Team and conduct research involving the analysis of statistically refined climate projections and their use in heat and health studies. Details remain to be determined, but special attention will be given to the cascade of uncertainties from emissions scenarios to dynamical climate models to statistically downscaled climate projections and derived products that subsequently are used  in studies that inform climate risk reduction and resilience activities.
    ⇒ See this Position Announcement for the post doctoral researcher position described on this web page.

[ ⇑ Back to Top of Page ]


More about the GFDL ESD Team’s role

In this project partnership, GFDL’s ESD Team is particularly interested in strengthening the link between the upstream climate science (such as that conducted at NOAA GFDL) and midstream researchers — those “savvy practitioners” who select statistically refined climate model projections for use in their studies of the potential impact of future climate change on heat and human health. A common question we hear from practitioners is, “what is the best set of statistically refined climate projections to use?”  Unfortunately, there is no one-size-fits-all answer to that question, because the data requirements and sensitivities of different applications to different weather and climate measures  varies greatly.

In our journal publications and conference presentations, GFDL’s ESD team members and collaborators have presented research results that illustrate and explain underappreciated strengths, weaknesses and other performance characteristics of bias correction and statistical downscaling techniques in somewhat general mathematical, meteorological, and climatic terms, because that’s where our expertise lies. But we typically have left it to the midstream practitioners who use statistically refined climate projections as input to their climate impacts studies to explore whether, and to what extent, the factors identified in our research findings may affect the assessment of uncertainties in their applied research work (i.e., in studies of the potential effect of climate change on    fill in the blank  .) In this upcoming project, we look forward to forging connections so that the results of research we co-produce may be incorporated more readily into decision-making relevant activities.

The motivation for this kind of project is certainly not new, as is evident in the following excerpts from an article by Barsugli et al., (2013), which remain true today:

…the “practitioner’s dilemma” is no longer the lack of downscaled projections; it is how to choose an appropriate data set, assess its credibility, and use it wisely.
…A central issue faced by practitioners is the uncertainty of climate information, and evaluation has a role to play here too. To characterize climatic uncertainty, current scientific practice recommends using ensembles of climate projections that account for various sources of uncertainty: different emissions scenarios, global models, or downscaling methods. A comprehensive assessment of downscaling methods and resulting data sets will provide objective criteria for inclusion of downscaled climate projections in climate change analyses and lead to a better understanding of the uncertainty contributed by downscaling.
Barsugli, J. J., and Coauthors, 2013: The Practitioner’s Dilemma: How to Assess the Credibility of Downscaled Climate Projections. Eos, Transactions American Geophysical Union, 94, 424–425, https://doi.org/10.1002/2013EO460005. [PDF]

Supporting NOAA’s Mission

In supporting NOAA’s mission, this project aligns with two out of three of NOAA’s 2020-2026 Research and Development Vision Areas [PDF]:

  • Reducing societal impacts from hazardous weather and other environmental phenomena
  • A robust and effective research, development, and transition enterprise

Additionally, this project addresses the topic of extreme heat, which is one of four societally important Climate Risk Areas identified by NOAA’s Climate Program Office in 2020.

[ ⇑ Back to Top of Page ]


 updated 06/25/2021