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Downscaled Climate Projections for Heat & Health Applications

[ heat photo - NOAA CPO ]Beginning in spring 2022, GFDL’s Empirical Statistical Downscaling team (ESD Team, part of NOAA/OAR/GFDL) has been working on a research project involving the downscaling of multi-decadal climate projections for heat and human health applications.  The ESD team is collaborating with Hunter Jones (NOAA/OAR/CPO/NIHHIS Climate and Health Project Manager), Ellen Mecray (NOAA/NESDIS/NCEI/Regional Climate Services Director for the Eastern Region), and Benjamin Le Roy,  a post doctoral researcher with the Princeton University Cooperative Institute for Modeling the Earth System (CIMES).

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.

[ map of study region ] However, large-scale physical climate data does not meet many long-range 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 is addressing 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 involves 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.

Our initial focus has been on a portion of the mid-Atlantic states with an emphasis on the urban heat island of Philadelphia and its connections to heat-related public health issues.  Additional analyses are being performed over a 13 state region stretching from Maine to Virginia. That many vulnerable people live in urban environments whose microclimatic variations may not be well represented in commonly used climate data products poses challenges that are being investigated.

[ Interdisciplinary Connections: Pursuing “actionable science” ]
[ The Project Team ]
[ Presentations ]

[ 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 interdisciplinary climate 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 public health specialists who are not climate scientists themselves.) Subsequently, climate impact studies conducted by midstream researchers often are 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, there typically is little in the way of explanation geared toward supporting attempts to determine the suitability of a 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.

[ SURFEX-modeled minimum nighttime air temperatures simulated for the Philadelphia, PA areas ]

Our Upstream Research Experimental Design

Our climate research focuses on a portion of the mid-Atlantic states, with an emphasis on the urban heat island of Philadelphia (right). Broadly speaking, two complementary research branches are being pursued with the common aim of quantitatively examining strengths / weakness / sources of uncertainty in heat and health-related climate projection information that is communicated to applied researchers and other stakeholders.

The research branch that involves using SURFEX to model conditions for the area shown to the right, is performed at 400m spatial resolution.  SURFEX simulations have been made for a historical period (1991-2020) and for a small ensemble of four future climate change scenarios (2021 – 2085).

Our second research branch studies a larger portion of the northeastern United States and examines data products commonly used climate data products (e.g., station observations, gridded observational-based data sets and reanalyses, and statistically downscaled climate projections.)  Some preliminary results from this work were presented at the 2024 American Meteorological Society Meeting [Abstract].

Additional information on the two research branches soon will be described on a separate web page.

Partnerships & Interdisciplinary Connections

As part of the co-production of actionable science aspect of this project, our team has spoken with and learned from representatives of several organizations (governmental, university & NGOs) whose work is connected to the topic of climate, heat, and health. And we have participated in multiple interdisciplinary workshops. [ Philadelphia Heat Health Emergency sign ] We are now collaborating with the Philadelphia Department of Public Health on research regarding the criteria the city uses to declare Heat Health Emergencies. This includes generating projections of the increased frequency of such events in the coming decades and analyses of uncertainties in the heat index diagnostic calculations used.

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.
    • Lanzante, J. R., and Coauthors, 2018: Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society.
    • 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  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. Philadelphia, which is a focal point of our study, is one of 14 cities selected for the 2022 NOAA Urban Heat Island Campaigns – an effort for which Hunter is a principal, as part of the National Integrated Heat Health Information System (NIHHIS).
    • 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,
    • 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.

  • [ Dennis Adams-Smith ]Dennis Adams-Smith is a data scientist who joined the GFDL ESD team in June 2016. He brings in-depth knowledge of statistical methods, R programming and climate data.  Dennis, as a UCAR CPAESS (Cooperative Programs for the Advancement of Earth System Science) staff member stationed in Princeton, NJ, is supporting the creation of an expanded and improved framework both for statistical downscaling of climate model output and for analyzing the results in a rigorous manner that can be readily communicated to users of climate information, including statistically downscaled climate products. For example, Dennis has developed a workflow to allow the computation of when the Philadelphia Department of Public Health’s Heat Health Emergencies and Heat Caution thresholds would be exceeded using as input observational data from Philadelphia Airport or model-simulated tempearture and humidity time series.

  • [ Benjamin Le Roy ]Benjamin Le Roy joined our team in May 2022 as a postdoctoral research associate with Princeton University’s Cooperative Institute for Modeling the Earth System (CIMES). For this project, Benjamin has been using the SURFEX model and its urban surface energy balance modeling capabilities to examine the urban heat island in Philadelphia. Particular attention is being given to comparing urban climate impact indicators as represented by SURFEX high resolution urban climate simulations, by several observational data products, and by statistical downscaling of climate projections. Attention also 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. Benjamin has returned to his native Europe, and in 2024 we will be submitting his work done with us to journals.

    • Le Roy, B., A. Lemonsu, and R. Schoetter, 2021: A statistical–dynamical downscaling methodology for the urban heat island applied to the EURO-CORDEX ensemble. Climate Dynamics, 56, 2487–2508,
  • [ Nicole Zenes ] Nicole Zenes joined the ESD team in July 2023 as an SAIC contractor scientific programmer. The team benefits from Nickie’s R programming skills and research experience to advance the team’s capabilities.
  • [ John Lanzante ] John Lanzante, a member of GFDL’s Weather and Climate Dynamics Division, collaborates with the team on topics related to his extensive experience in statistics, meteorology, and data analysis techniques.

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2024 American Meteorological Society Meeting:

  • For the Heat Index, It’s about Both the Heat and the Humidity … and about Choices in Calculation Methodologies  [LINK]
    K. W. Dixon, D. Adams-Smith, J.R. Lanzante
  • Comparisons of Climate Indices Based on Several Modeled and Observed U.S. Temperature Data Products Using a Metadata-Preserving, Modular R Coding Framework [LINK]
    N. Zenes, K. W. Dixon, D. Adams-Smith

2023 American Meteorological Society Meeting:

  • On the Representation of the Urban Heat Island in Climate Data Products used in Heat and Health Studies [LINK]
    K. W. Dixon, B. Le Roy, D. Adams-Smith, J.R. Lanzante
  • High-Resolution Urban Climate Simulations to Study Health Impacts of Heat in Philadelphia [LINK]
    B. Le Roy, K. W. Dixon, D. Adams-Smith, J.R. Lanzante, H. Jones, E. Mecray

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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 present 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 project, we are 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, [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.  Advancing climate information and services to promote an improved understanding of, and resilience to, extreme heat is also specifically cited in the October 2021 “Opportunities for Expanding and Improving Climate Information and Services for the Public — A Report to the National Climate Task Force” document, signed by the Director of the White House Office of Science and Technology Policy, the Adminstrator of NOAA (Dr. Richard Spinrad), and the Administrator of the Federal Emergency Management Agency.

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 updated 05/13/2022