REPORT TO THE PRESIDENT
Extreme Weather Risk in a Changing Climate:
Enhancing prediction and protecting
communities

Executive Office of the President
President’s Council of Advisors on
Science and Technology
April 2023

About the President’s Council of Advisors on Science andTechnology
The President’s Council of Advisors on Science and Technology (PCAST) is a federal advisorycommittee appointed by the President to augment the science and technology advice available to himfrom inside the White House and from the federal agencies. PCAST is comprised of 28 of the Nation’s
thought leaders, selected for their distinguished service and accomplishments in academia,
government, and the private sector. PCAST advises the President on matters involving science,technology, and innovation policy, as well as on matters involving scientific and technologicalinformation that is needed to inform policy affecting the economy, worker empowerment, education,energy, the environment, public health, national and homeland security, racial equity, and othertopics.
For more information about PCAST see www.whitehouse.gov/pcast.

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EXECUTIVE OFFICE OF THE PRESIDENTPRESIDENT’S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGYWASHINGTON, D.C. 20502
President Joseph R. Biden, Jr.
The White House
Washington, D.C.
Dear Mr. President,Your administration has made unprecedented advances in addressing climate change. PCAST
believes advances in climate science and computation can add to this record by creating an effective
and accessible suite of tools that provide individuals, communities, and the federal government with
the information they need to prepare for the risks of extreme weather in a changing climate.
The most immediate danger that Americans face from climate change is the worsening of extreme
weather, including hurricanes, floods, droughts, heatwaves, and wildfires. Extreme weather disasters
have caused over $1 trillion in damages over the last seven years, and in 2022 alone, displaced an
estimated 3.4 million Americans from their homes. Recent scientific studies have found that thelikelihood of certain weather patterns, such as extreme hurricane rainfall over the state of Texas,
have multiplied due to climate change. This type of change makes historical models and long-term
records unreliable guides to future probabilities. Reliable information is critical for American families
choosing where to live, municipal leaders developing flood-control plans, or insurers selling wildfire
coverage. And yet, when it comes to assessing the future risks from extreme weather, America isflying blind.
The good news is that climate modeling and computation are advancing rapidly. Capabilities exist
within federal agencies to make significantly better predictions of the likelihoods of extreme weather– for example the chance that a category five hurricane will hit Miami in any one year, or the chanceof another rainfall event like the one that caused catastrophic flooding in Kentucky in July 2022. Bysharing resources and information, the federal government also can spur an ecosystem of academic
research and private investment that improves and expands access to climate and weather risk
information. This in turn can inform smart government policy and support the American people withinformation they can use to protect themselves.
The households at greatest risk from floods, fires, and heatwaves are often low-income families, anddiscriminatory historical practices such as redlining have contributed to minority populations living
in risk-prone areas. A national effort to improve the quantification of extreme weather risk also
would help to ensure that federal funds, including those designated under the Infrastructure
Investment and Jobs Act (IIJA), are targeted to areas where their benefit will be highest.
This report recommends steps to dramatically improve our quantification of extreme weather risk,
and to share this information with households, businesses, and government agencies. It also
recommends the development of a national adaptation plan that would provide guidance that local
communities need to tackle changing risks from extreme weather events. Taken together, PCAST
believes that these actions can help protect the lives, livelihoods, and property of Americans forgenerations.
Sincerely,
The President’s Council of Advisors on Science and Technology

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The President’s Council of Advisors on
Science and Technology
Co-Chairs
Frances H. Arnold
Linus Pauling Professor of Chemical
Engineering, Bioengineering, andBiochemistry
California Institute of Technology

Arati Prabhakar
Director, Office of Science and Technology
PolicyAssistant to the President for Science andTechnology
The White House

Maria T. Zuber
Vice President for Research and E. A.
Griswold Professor of Geophysics
Massachusetts Institute of Technology

Francis Collins
Former Co-Chair
Former Acting Science Advisor to the
President
The White House

Members
Dan E. Arvizu
Chancellor
New Mexico State University System

Lisa A. Cooper
Bloomberg Distinguished Professor of Equity
in Health and Healthcare and Director of
the Center for Health Equity
Johns Hopkins University

Dennis Assanis
President
University of Delaware

John O. DabiriCentennial Professor of Aeronautics andMechanical Engineering
California Institute of Technology

John Banovetz
Executive Vice President, Chief
Technology Officer andEnvironmental Responsibility
3M Company

William Dally
Chief Scientist and Senior Vice President for
Research
NVIDIA

Ash Carter (1954-2022)
Director, Belfer Center for Science andInternational Affairs and Belfer Professorof Technology and Global Affairs
Harvard University

Sue Desmond-Hellmann
Former CEO
Bill & Melinda Gates Foundation

Frances Colón
Senior Director, International Climate
Center for American Progress

Inez Fung
Professor of Atmospheric Science
University of California, Berkeley

3Andrea Goldsmith
Dean of the School of Engineering andApplied Science and the Arthur LeGrand
Doty Professor of Electrical and Computer
Engineering
Princeton University

William Press
Leslie Surginer Professor of Computer
Science and Integrative Biology
The University of Texas at Austin
Penny Pritzker
Former Member
ChairmanPSP Partners

Laura H. Greene
Chief Scientist, National High Magnetic Field
Laboratory
Florida State University, University of
Florida, and Los Alamos National
Laboratory
Marie Krafft Professor of Physics
Florida State University

Jennifer Richeson
Philip R. Allen Professor of Psychology andDirector of the Social Perception andCommunication Lab
Yale University

Paula Hammond
Institute Professor, Head of the Departmentof Chemical Engineering, and member of theKoch Institute for Integrative
Cancer Research
Massachusetts Institute of Technology

Vicki Sato
Professor of Management Practice (Retired)
Harvard Business School
Lisa Su
President and CEO
Advanced Micro Devices (AMD)Eric Horvitz
Chief Scientific Officer
Microsoft

Kathryn D. Sullivan
Former NASA Astronaut
Former NOAA Administrator

Joe Kiani
Chairman and CEO
MasimoTerence Tao
Professor & the James and Carol Collins
Chair in the College of Letters andSciences
University of California, Los Angeles

Jon LevinPhilip H. Knight Professor and Dean of
the Graduate School of Business
Stanford University

Phil Venables
Chief Information Security Officer
Google Cloud

Steve PacalaFrederick D. Petrie Professor in the
Department of Ecology and
Evolutionary Biology
Princeton University

Catherine Woteki
Visiting Distinguished Institute Professor in
the Biocomplexity Institute
University of Virginia
Professor of Food Science and Human
Nutrition
Iowa State University

Saul PerlmutterFranklin W. and Karen Weber Dabby
Professor of Physics and Director of
the Berkeley Institute for Data
Science
University of California, Berkeley
Senior Scientist
Lawrence Berkeley National Labs

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PCAST Staff
Lara Campbell
Executive Director
Reba Bandyopadhyay
Deputy Executive Director
Bich-Thuy (Twee) Sim
Assistant Director for Transformative
Medicine and Health Innovation
Kevin Johnstun
Research Analyst
Alexia Sare
Policy Analyst
Jonathan Judd
InternAnne-Marie Mazza
Former Executive Director
Sarah Domnitz
Former Principal Deputy Executive Director
Quinn Anex-Ries
Former Intern

5Working Group on Extreme Weather Risk
in a Changing Climate
Working Group members participated in the preparation of this report. The full membership of
PCAST reviewed and approved the report.

Co-Leads
Jon LevinPhilip H. Knight Professor and Dean of
the Graduate School of Business
Stanford University

Steve PacalaFrederick D. Petrie Professor in the
Department of Ecology and
Evolutionary Biology
Princeton University

Members
Dan E. Arvizu
Chancellor
New Mexico State University System

Tapio Schneider
Theodore Y. Wu Professor of Environmental
Science and Engineering
CaltechSenior Research Scientist
Jet Propulsion Laboratory

Clara Deser
Senior Scientist
National Center for Atmospheric Research

Johannes StroebelDavid S. Loeb Professor of Finance
New York University

John O. DabiriCentennial Professor of Aeronautics andMechanical Engineering
California Institute of Technology

Kathryn D. Sullivan
Former NASA Astronaut
Former NOAA Administrator

Darrell Duffie
Adams Distinguished Professor ofManagement and Professor of Finance
Stanford University

Terence Tao
Professor & the James and Carol Collins
Chair in the College of Letters andSciences
University of California, Los Angeles

Kerry EmanuelProfessor Emeritus of Atmospheric Science
Massachusetts Institute of Technology
Inez Fung
Professor of Atmospheric Science
University of California, Berkeley
Bob Litterman
Founding Partner
Kepos Capital

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Executive Summary
Extreme weather has devastating impacts on the American people, our communities, and our
economy. All of us have seen the damage wrought by catastrophic wildfires in western states, byfloods, and tropical cyclones on the Gulf Coast, and by recent severe tornados in southern and easternstates. Extreme weather also reduces property values, raises the costs of insurance, and poses
national and global economic risks from supply chain disruptions and forced migrations. Today,
climate change is changing the patterns and risks of extreme weather, including the frequency andseverity of many hazards.
The challenge of hurricanes and other severe storms, floods, and wildfires is gaining significant
attention and the annual cost of climate and weather disasters has been rising. The National Oceanicand Atmospheric Administration (NOAA) has catalogued over $1 trillion of damages during the last
seven years (2016-2022).1 Industry reports caution that by mid-century, insurance premiums incertain markets could rise substantially, straining U.S. households.2 Last year, the Office ofManagement and Budget (OMB) cited a potential federal revenue loss of $2 trillion per year fromclimate change at the end of the century, along with additional expenditures of $25 to $128 billion on
selected insurance and disaster relief programs.3 In addition, there are deeper costs from loss of life,
negative health impacts, and the destruction of communities.4 The Census Bureau recently estimatedthat in 2022 alone, 3.4 million Americans were displaced from their homes by extreme weather
disasters.5 Moreover, lower-income households are often those at greatest risk from floods, storms,
and wildfires. These households have fewer resources to take actions that will offset a rising risk ofextreme weather.

1 Smith, A. B. (2023, January 10). 2022 U.S. billion-dollar weather and climate disasters in historical context.

NOAA National Centers for Environmental Information. https://www.climate.gov/newsfeatures/blogs/2022-us-billion-dollar-weather-and-climate-disasters-historical-context . The average
disaster costs for 2017-21 are $148 billion per year; the data include only large (> $1bn) disasters.
2 One specific example cited by McKinsey is that (inflation-adjusted) annual flood losses in Florida could riseby 50% by 2050, with corresponding premium increases if the costs are passed to policy-holders.
Grimaldi, A., Javanmardian, K., Pinner, D., Samandari, H., & Strovink, K. (2020, November 18). Climate change
and P&C insurance: The threat and opportunity. McKinsey & Company.
https://www.mckinsey.com/industries/financial-services/our-insights/climate-change-and-p-and-cinsurance-the-threat-and-opportunity
3 The revenue number is from an OMB blog post “Quantifying Risks to the Federal Budget from Climate
Change” (April 4, 2022) that summarizes a set of OMB reports, including “Climate Risk Exposure: An
Assessment of the Federal Government’s Financial Risks to Climate Change” which contains the specificestimates on program costs. Estimates of climate damages 75+ years out have a wide range of associateduncertainty, and OMB’s analysis of costs looks only at a small number of federal programs.
Executive Office of the President (2022, April 4). Quantifying Risks to the Federal Budget from Climate
Change. The White House. https://www.whitehouse.gov/omb/briefing-room/2022/04/04/quantifyingrisks-to-the-federal-budget-from-climate-change/ ; U.S. Office of Management and Budget (2022, April).
Climate Risk Exposure: An Assessment of the Federal Government's Financial Risks to Climate Change.
https://www.whitehouse.gov/wp-content/uploads/2022/04/OMB_Climate_Risk_Exposure_2022.pdf
4 Chapter 9 of the 2023 Economic Report of the President discusses evidence on a wide range of costs fromclimate change and extreme weather. https://www.whitehouse.gov/wp-content/uploads/2023/03/ERP2023.pdf
5 U.S. Census Bureau. (2023, January 15). Week 53 Household Pulse Survey: January 4 - January 16.
https://census.gov. https://www.census.gov/data/tables/2023/demo/hhp/hhp53.html

7This PCAST report investigates how recent scientific and technical advances could be used to provide
more accurate and actionable information to guide decision-making and policy at all levels. PCAST
recommends federal actions to better quantify and disseminate current and future risks of extreme
weather, including risks of human and financial losses caused by flood, fire, storms, and drought.
PCAST also recommends actions to bolster the emerging private ecosystem providing climate risk
information. Finally, PCAST recommends the development of a national adaptation plan to assist
communities in preparing for and adapting to changing risks from extreme weather events.
This report builds on the October 2021 White House report, A Roadmap to Build a Climate-Resilient
Economy, outlining a multi-agency plan to implement Executive Order 14030 on climate-related
financial risk.6 That plan addresses both the rising physical risks from extreme weather, and transition
risks in moving toward a low-carbon economy. PCAST’s recommendations focus on how climate
science and computing can provide significantly better information about the physical risks fromextreme weather to empower households, communities, and companies and enable smart policy.

Recommendations
Recommendation 1: A National Effort to Quantify Extreme Weather Risk.
To prepare for dangerous weather, one needs to know how damaging it is likely to be and how frequently
it will occur. A levee designed for a 100-year storm producing 15 inches of rain is not adequate if the
100-year storm now packs 25 inches. PCAST recommends a focused federal effort to provide estimatesof the risk that a weather event of a given severity will occur in any location and year between now andmidcentury. These include extremes of temperature, rainfall, and wind speed.
1.1.

U.S. climate-modeling centers supported by the National Oceanic and Atmospheric
Administration (NOAA), National Science Foundation (NSF), Department of Energy(DOE), and National Aeronautics and Space Administration (NASA) should enhancetheir high-resolution modeling capabilities and state-of-the-art statistical methods toquantify annual extreme weather risks from the present until mid-century at aresolution of 10 km or finer. This will require both agency prioritization andcollaboration.

The work of multiple agencies together with an effective leadership framework is critical because, as
explained in Box 2 of the report, this activity does not fit within a single existing administrative unit
within the federal government.
1.2.

The White House should designate a lead agency to maintain an extreme weather
data portal where observations and modeling products are regularly updated andwidely accessible, using an analysis-ready format that enables downstream users andhazard models on smaller scales to assess the local risks of wildfire, flood, drought,
and other weather-related hazards. The portal should also provide guidance andrecommendations related to the reliability of modeling products.

6 The White House. (2021, October 14). A Roadmap to Build a Climate-Resilient Economy.

https://www.whitehouse.gov/wp-content/uploads/2021/10/Climate-Finance-Report.pdf

8Here, we are not focused on predicting weather over the coming days and weeks, a problem adeptly
handled by the National Weather Service, but on predicting the climate, that is the probability of
weather events over years and decades. In particular, the recommendation directly addresses thelack of high-quality estimates of extreme weather probabilities for most locations and types of events.
Current risk assessments are based on older models or historical weather records. Older climate
models typically have a resolution of 25 to 100 km or coarser, limiting their predictive accuracy,
while today’s computational resources and downscaling techniques have the potential to provide
resolutions of 10 km or finer. Historical weather records are generally too short to estimate thechance of once-in-a-lifetime events and are unreliable when there are changes in the climate. Recent
advances in observations, modeling, and computation make it possible to create more accurate andoperational assessment of evolving risks, provided that existing federal computational and humanresources are deployed at the needed scale, and that information is made readily accessible fordownstream uses. The aspiration is that just as Americans today have access to high-quality
operational weather forecasts, the time has come to invest in an operational climate science thatprovides improved tools for risk assessment and management.
Recommendation 2: An Improved Ecosystem for Climate Risk Assessment.
To prepare for changing patterns of extreme weather, the nation will require information beyond theprobabilities of extreme temperature, rainfall, and wind speed. One must be able to predict the severityof resulting weather hazards, (e.g., flood, fires and droughts) and the human and economic losses they
will cause. PCAST recommends steps that will enable the development of high-quality private and public
sector tools to better measure and evaluate extreme weather-related risks across the country.
2.1

Designate an interagency group to inventory and release federal data that are
useful to develop and test weather-hazard models, which predict flood, fire anddrought from extreme weather, and hazard-loss models, which predict human andfinancial losses from hazards or directly from extreme weather such as significant
heat, cold, hail, or wind. This effort should include any relevant and available finescale elevation data and other physical information, as well as data on Federal
disaster and insurance claims needed to validate risk models, with appropriate
safeguards to protect privacy and security. The interagency group will require
expertise about procedures and rules governing the release of federal data.

2.2

The National Oceanic and Atmospheric Administration (NOAA) and the Federal
Emergency Management Agency (FEMA), and other federal agencies as the President
deems necessary, should develop guidelines for measuring the accuracy of
weather-hazard and hazard-loss models with skill scores and promote the use ofskill-scoring among federal agencies that rely on assessments of climate and weather
hazards including flood, drought, storm, and wildfire, and the human and economic
damages they cause.

2.3

Fund research, potentially through multiple agencies, on risk-assessment modelingsystems that use extreme weather probabilities, weather-hazard models, andhazard-loss models to quantify the likelihood and economic costs of extreme weather
events. This effort should aim to foster improved quantification of weather-hazard

9risks in the public and private sector, through improved access to models and data,
and a robust ecosystem of model evaluation and testing.
This recommendation addresses the fact that households, communities, companies, and government
agencies do not have sufficiently reliable and geographically specific information about how they
may be affected by extreme weather. While a burgeoning industry is beginning to provide climate
risk information, much of this is of questionable quality, either because it has not been transparently
skill-scored to show that it can predict past events, or because it relies on methods that have beenshown by the academic literature to have significant bias.7 Also, much of this information is too costly
for most individuals or small municipalities to afford, although some is available at no cost. To
improve the quality of the models, developers need support in overcoming two primary barriers: 1)
the data required to improve models is often siloed or inaccessible, and 2) the field lacks sufficiently
robust mechanisms to allow consumers of the information, including federal agencies, to assess
model skill. Transparent and accessible skill-scoring would promote model improvement and help
standardize nascent industry efforts, especially if federal agencies were to prioritize applications forfunding that rely on relatively skillful models. Addressing these barriers will help foster a stronger
academic and private sector ecosystem of climate risk assessment. This will also leverage priorinvestments, such as the DOE’s HyperFACETS project or the World Climate Research Program’s
CORDEX project.
Recommendation 3: A National Adaptation Plan to Mitigate Extreme Weather Risk
To prepare for changing risks from extreme weather, the provision of information will not be enough,especially given uneven resources across local communities. The federal government can accelerate
preparedness by creating a national plan that guides and supports investment at all levels, and by
funding research that illuminates which policy responses are effective and equitable.
3.1

The White House should develop and publish a National Adaptation Plan toprepare for and mitigate increased risks from extreme weather. The Plan should
include:(a) A systematic approach to mapping near term and longer-term high-danger zones
for each extreme weather hazard, updated as estimates of risk are improved.
(b) Decision frameworks to assist local communities in making plans to mitigate oradapt to extreme weather risk.
(c) Regulatory and legal options to reduce long-term risk exposure, for instancethrough land-use planning, zoning, and building code adoption.
(d) Comprehensive assessment of Federal and State programs in alleviating disasterrisks, including the distribution of federal funds for disaster preparation withattention to whether these programs are accessible to low-income communities.
(e) Guidelines for equitable allocation of disaster relief. These guidelines should
anticipate the potential for recurring extreme weather events, so that relief
programs work to mitigate future dangers to a community.

Condon, Madison (2023). Climate Services: The Business of Physical Risk. Arizona State Law Journal,
Forthcoming. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4396826#
7

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3.2

Fund research on the adaptation of households, real-estate and insurance
markets, and local governments to changing climate and extreme weather risk.
This effort should aim to illuminate the behavior of households and firms in responseto evolving climate and weather risks, and the benefits and costs of alternative
policies aimed at mitigating and adapting to these risks. NSF may be best positioned
to lead this effort, especially if coordinated with mission-oriented agencies such as
NOAA, HUD, EPA, and others.

This final recommendation addresses the current lack of a national adaptation plan to guidefederal and local investments to mitigate extreme weather risk and to improve the safety oflower-income and marginalized communities in greatest peril,8 particularly Black, Latino,
Indigenous and other communities of color. Relevant programs are distributed over a scoreof federal agencies, and in many cases, responsibility lies with communities and local
governments who confront a disjointed array of federal programs and initiatives.9 A coherent
national plan should address issues of equity as well as economic efficiency and resilience,
especially given the disproportionate risks faced by lower-income and marginalized
communities. This adaptation plan also must incorporate current data and modeling thatquantifies the changing climate, avoiding likely significant inaccuracies from exclusive
reliance on the historical record.

8 White House Environmental Justice Advisory Council. (2021, May 21). Final Recommendations: Justice 40

Climate and Economic Justice Screening Tool & Executive Order 12898 Revisions.
https://www.epa.gov/sites/default/files/2021-05/documents/whiteh2.pdf
9 A broad set of federal programs are relevant for disaster recovery. For instance, social-safety net programs(disability, unemployment, Medicare) play an important financial role in areas affected by disasters, although
not specifically designed for this purpose (e.g. https://www.aeaweb.org/articles?id=10.1257/pol.20140296).

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Extreme Weather Risk in a Changing Climate: Enhancing
prediction and protecting communities
A Hypothetical Scenario: The Case of Franklinburg, Georgia
Franklinburg is a town of 30,000 people in northern Georgia. Franklinburg’s mayor, Kimberley Wilson,
is alarmed by the repeated hurricanes and tropical cyclones in recent years. She is also conscious ofextreme rainfall throughout the Southeast not linked to tropical cyclones, including a 20-inch event in
Baton Rouge in 2016, a 16-inch event in Kentucky in 2022, and a 24-inch event in Ft. Lauderdale in 2023.
Franklinburg has never suffered a serious flood over 200 years, but also has not had a rainfall event of
this severity. Mayor Wilson consults several publicly available assessment tools but they do not answer
her questions. One informs her that the chance of a rainfall event greater than 3 inches might haveincreased by as much as 30-50% since 1975 or might not have increased at all. She also knows thatFranklinburg has had multiple rainfall events of more than 3 inches without major flooding. Faced withvague fears but lacking actionable information, Mayor Wilson simply waits and hopes for the best.
A few years on, Mayor Wilson hears of an ongoing federal effort to dramatically improve the predictionof extreme weather risk. She contacts the Climate Navigation Foundation (CNF), which is using dataand standards released by the federal government to develop software that predicts flooding anddamages, down to the level of individual homes and buildings. An assessment by CNF identifies a serious
concern for Franklinburg. Brush Creek, which flows through a low-income neighborhood, is at risk offlooding from an 8-inch or greater rainfall, potentially depositing multiple feet of water into the heartof town, damaging the hospital, middle school, and the town hall, as well as many homes and offices.
Following a series of public meetings that feature the alarming flood maps from CNF, the town
undertakes a hydrological study. It recommends increasing the height of the natural barrier between
Brush Creek and the surrounding neighborhood. The town applies successfully for a grant from FEMA’s
Flood Mitigation Assistance Grant program. Mayor Wilson also convinces the town council to invest in
back-up electricity generation for the hospital and the town community center.
Nearly 3 years after construction, an extreme rain event deposits 14 inches of rain over three days.
The town leadership prudently evacuates the neighborhoods in danger. The community center provides
refuge to the ill, elderly, and other vulnerable people. As designed, the additions to the Brush Creekbarrier keep the water contained, though Mayor Wilson knows that the town would have been
devastated if they had not had the knowledge and resources to prepare. The event never crosses the
President’s desk because a disaster has been prevented.

Introduction
Extreme weather has devastating impacts on the American people, our communities, and our
economy. Severe wildfires that have ravaged parts of the American West, and destructive tropical
cyclones along the Gulf Coast, are salient examples. These types of climate and weather-related
disasters also affect American households by reducing property values and raising the costs of
insurance. Moreover, multiple events of different types are sometimes occurring in rapid succession
and interact synergistically to strain infrastructure and budgets. Now, climate change is affecting the12frequency and severity of extreme weather events, creating new uncertainty about the risks weface.10
Recent scientific studies have shown that the probability of certain extreme weather events, such as
Hurricane Harvey which severely impacted Louisiana and Texas in 2017, or the heat wave that swept
the Pacific Northwest in 2021, have increased dramatically over the last several decades because ofclimate change.11 The risks of future extreme weather-related disasters are amplified both by climatechange and by increased development in areas that are prone to floods, fires, and droughts.12
Moreover, an extensive body of work documents that low-income communities are
disproportionately impacted by extreme weather, often due to discriminatory historical practices
such as redlining that have affected housing patterns.13
Insurance arrangements, both public and private, play a critical role in sharing the financial costs ofclimate and weather disasters. Swiss Re, a global reinsurer, forecasts that global property insurance
premiums will rise by 5.3% annually to 2040, with climate change as the main driver. 14 Rising costs
from extreme weather challenge communities, businesses, and states and local governments. The
federal government often has significant liability because it provides disaster relief following major
events. In the case of flooding, a failure of private insurance arrangements prompted Congress tocreate the National Flood Insurance Program in 1968. As we look ahead to higher costs of climate
and weather disasters, American taxpayers, as well as American families, will benefit by preparing
for and mitigating these risks.15
PCAST identified three obstacles to addressing risks from extreme weather and makes
recommendations for federal action to address each of them.

10 The 2022 IPCC Report assigns high or very high confidence to higher flood/storm induced damages incoastal areas, damages to infrastructure, and damages to key economic sectors due to climate change, and alitany of global risks from climate events, especially in the period from 2040-2100. Intergovernmental Panelon Climate Change (2022). Summary for Policymakers.
https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_SummaryForPolicymakers.pdf
11 Columbia University (Climate Attribution. (2023). Climate Attribution Database.
https://climateattribution.org/) maintains a large database of attribution studies that calculate the likelihoodof specific weather patterns and weather-related events under alternative climate scenarios. For example,“Assessing the present and future probability of Hurricane Harvey’s rainfall” finds that due to climate change
the probability of Harvey-magnitude rainfall may have increased by a multiple of six since the late 20 th
century. See Emanuel, K. (2017). Assessing the present and future probability of Hurricane Harvey's rainfall.
Proceedings of the National Academy of Sciences of the United States of America, 114(48), 12681–12684.
https://doi.org/10.1073/pnas.1716222114
12 US Geological Service (2016, November 29). Effects of urban development on floods [Fact sheet].
https://pubs.usgs.gov/fs/fs07603/
13 For example, see Buchanan, M. K., Kulp, S., Cushing, L., Morello-Frosch, R., Nedwick, T., & Strauss, B. (2020).
Sea level rise and coastal flooding threaten affordable housing. Environmental Research Letters, 15(12),124020. https://doi.org/10.1088/1748-9326/abb266
14 Holzheu, T., Lechner, R., Vischer, A., Bevere, L., Staib, D., Finucane, J., Belgibayeva, A., & Fan, I. (April 2021).
sigma 4/2021 - More risk: the changing nature of P&C insurance opportunities to 2040 | Swiss Re.
https://www.swissre.com/institute/research/sigma-research/sigma-2021-04.html
15 U.S. Office of Management and Budget (2023). Analytical Perspectives: Budget of the U.S. Government: Fiscal
Year 2023. https://www.govinfo.gov/content/pkg/BUDGET-2023-PER/pdf/BUDGET-2023-PER.pdf

13

•

First, we lack high-quality estimates of extreme weather probabilities for most locations andtypes of events. Most current assessments are based on historical records of extreme weather
and do not incorporate rapidly changing
climate. Others rely on climate models that, forBox 1: Terms of Reference
example, underestimate the risks of extreme• Extreme Weather Model: therainfall that causes flooding, the severities of
LIKELIHOOD of extreme weather oftropical cyclones, and other critical extremes.16
X MAGNITUDE
However, recent advances in modeling andcomputation provide a far more accurate• Weather Hazard model: The
capability that could be deployed using existing
SEVERITY of a weather hazard,
such as locations and depth ofhuman and computational resources withinflooding, or the locations andfederal agencies. To prepare for the complex
intensity of wildfire caused by an X
and geographically varying effects of climate
magnitude weather event.
change, it is critical that we have the best
possible understanding of the frequency and• Hazard Loss model: ECONOMIC
severity of current and near future (to midOR HUMAN LOSSES due to a
century) extreme weather. The first set of
magnitude X weather event or arecommendations outlines an effort toseverity Y weather hazard.
dramatically improve the quality of available
estimates.

•

Second, communities and households do not have reliable access to the information necessary
to prepare for and mitigate risks from extreme weather-related disasters. While a burgeoning
industry is beginning to provide climate risk information, details of the products areproprietary and there is little assurance as to their quality, the projected costs of damages
vary widely, and the data required to improve models is often siloed or inaccessible.
Moreover, we lack robust mechanisms to evaluate the quality of models and estimates being
used by government agencies and the public. Our second set of recommendations therefore
aims to stimulate and improve the public and private ecosystem that provides climate risk
analysis.

•

Third, there is currently no national adaptation plan that guides federal and local investments
to mitigate extreme weather risk and secure the safety of communities, particularly thosewith the greatest risk and the least resources. Relevant programs are distributed over a scoreof federal agencies. In many cases, however, responsibility lies with communities and local
governments who lack the personnel and know-how to navigate their options. There are
significant issues of equity as well as economic efficiency with this process. Our final
recommendations are aimed at developing a national plan to help guide decision-making andadvise on steps to protect communities, especially those likely to experience the most harm.

The challenges are, of course, interconnected. High-quality estimates of the probability of extreme
temperatures, and extreme precipitation and wind, are critical to assessing the risks from intense
wildfires, floods, and drought. This information must be distributed widely because decisions about
16 Intergovernmental Panel on Climate Change. (2021). Working Group 1: The Physical Science Basis, Climate

Change 2021: The Physical Science Basis. https://www.ipcc.ch/report/ar6/wg1/

14

how to mitigate these risks are highly decentralized across households, corporations, andgovernments. Finally, federal guidance can both assist local communities in planning, and benefit
from improved risk assessment.

A National Effort to Improve Estimates of Extreme Weather Risk
With recent advances in extreme weather modeling and computing power, it is now possible to focus
a national effort on quantifying the evolving likelihoods of extreme weather events at a resolution ofat least 10 km (Appendix B). Heat waves are the deadliest weather extremes and are becoming more
intense and more frequent worldwide.17,18
Climate models have had some skill in predicting heat waves for more than a decade,19 but until
recently have systematically underestimated extreme precipitation and thus inland floods, which are
the costliest weather-related hazards.20 These models were also unable to accurately represent, forexample, extra-tropical frontal systems or tropical cyclones.21
Recently developed models with 10-25 km resolution can now more accurately predict risks ofextreme rainfall and other extreme weather events and how these have changed with the climate(Appendix B). High-resolution modeling could thus provide more accurate risk estimates of extreme
weather than is possible by relying solely on historical records and older models. Statistical methods
using machine learning techniques may also provide a useful complement to high-resolution climate
models.22

17 NOAA National Weather Service. (2021). Weather Related Fatality and Injury Statistics. NOAA's National

Weather Service. https://www.weather.gov/hazstat/
18 The sixth report of the IPCC states (with confidence level virtually certain): “The intensity of hot extremes(including heatwaves) has increases and those of cold extremes have decreased on the global scale since1950.” Intergovernmental Panel on Climate Change. (2021). Chapter 11: Weather and Climate Extreme
Events in a Changing Climate | Climate Change 2021: The Physical Science Basis.
https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/
19 National Academies of Sciences, Engineering, and Medicine. (2016). Attribution of extreme weather events inthe context of climate change. The National Academies Press.
https://nap.nationalacademies.org/catalog/21852/attribution-of-extreme-weather-events-in-the-context-ofclimate-change ; https://doi.org/10.17226/21852
20 NOAA & FEMA (2010, March). Flooding - Our Nation's Most Frequent and Costly Natural Disaster [Press
release]. https://www.fbiic.gov/public/2010/mar/FloodingHistoryandCausesFS.PDF
21 Srivastava, A., Grotjahn, R., & Ullrich, P. A. (2020). Evaluation of historical CMIP6 model simulations of
extreme precipitation over contiguous US regions. Weather and Climate Extremes, 29, 100268.
https://doi.org/10.1016/j.wace.2020.100268
22 For example, it is possible to project future extreme weather probabilities by starting with high-resolution
historical data (e.g., temperature and precipitation) jointly with models, to learn how to correct biases inmodels and scale output down to impact-relevant scales. See examples in Harris, L., McRae, A. T. T., Chantry,M., Dueben, P. D., & Palmer, T. N. (2022). A Generative Deep Learning Approach to Stochastic Downscaling ofPrecipitation Forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.
https://doi.org/10.1029/2022MS003120 &Ballard, T., & Erinjippurath, G. (2020, November 24). Contrastive Learning for Climate Model Bias Correctionand Super-Resolution. https://s3.us-east-1.amazonaws.com/climate-changeai/papers/aaaifss2022/10/paper.pdf

15

At least four federal agencies and institutions have the expertise and computer resources tocontribute to a national effort to improve the quantification of extreme weather risk: the National
Oceanic and Atmospheric Administration (NOAA), the National Science Foundation (NSF), theDepartment of Energy (DOE), and the National Aeronautics and Space Administration (NASA).
Because robust estimates of risk require ensembles of many runs from multiple models, it makes
sense that a national modeling effort would draw on the collective capabilities of agencies.

Recommendation 1.1
U.S. climate-modeling centers supported by NOAA, NSF, DOE, and NASA should enhance theirhigh-resolution modeling capabilities and state-of-the-art statistical methods to quantify
annual extreme weather risks from the present until mid-century at a resolution of 10 km or
finer. This will require both agency prioritization and collaboration.
The work of multiple agencies together with an effective leadership framework are critical because,
as explained in Box 2 of the report, this activity does not fit within a single existing administrative
unit within the federal government.

Box 2: Agency partitioning of responsibilities – creating operational climatologies
Climate modeling does not fit neatly within any current branch of government. While NOAA has
responsibility for operational weather forecasting, climate research is spread across multiple
agencies and centers, including NOAA, NASA, DOE, and NSF. These groups each have the capacity
to calculate climatologies – which are defined here as relationships between characteristics of aweather event such as temperature, wind speed, and rainfall, and its annual chance of occurrence- for every place on Earth in every year. Climatologies include severe and low-likelihood extreme
weather, such as the chance of a Category 5 hurricane hitting Miami in any one year. But until
recently, these so-called tail risks have not been estimated with enough accuracy to be used
operationally. As a result, anything labeled “weather” or “operational” has been exclusively within
NOAA’s domain. The four climate modeling centers have long recognized that accurate extreme
weather climatologies would be indispensable to plan for and manage the risks of extreme weather,
especially the growing risks caused by climate change. Several have long-standing research
programs to improve extreme weather climatologies and have anticipated the day when these
could become operational. This research has recently surged ahead. PCAST has concluded that themost advanced climate models, when combined with historic weather data and advanced analyticaltechniques, could now produce far better and more accurate estimates of extreme weather risk
than we have today, and also predict the changes in risk that will occur over the next several
decades. Recommendation 1 would launch an operational wing of climate science, which will
require many runs of high-resolution climate models that already exist within each of the four large
climate modeling centers. Thus, the activity will not fit into the existing
operational/nonoperational partitioning.
Ensembles from climate models provide evolving probability distributions of temperature and otheratmospheric conditions (e.g., humidity, precipitation, wind) at different locations, as well asprobabilities of near-future El Niño’s and other natural climate events that contribute to extreme
weather. Models may be global to capture planetary changes in climate, or regional to provide greater

16precision; PCAST envisions that efforts under recommendation 1.1 would use regional models andstatistical methods to downscale the 10-25 km resolution of the global models to 1 km over the U.S.
and its territories.
High-resolution climate modeling would improve the inputs to the downstream models (e.g.,weather-hazard models and hazard-loss models) that provide estimates of economic and humanrisks from weather-related disasters. Weather-hazard models require both weather inputs (i.e.,amount of precipitation for flooding) and other data to predict the severity of weather-related events
such as urban flooding, droughts, and wildfires. These additional data include local topography forflood modeling, water holding capacity of soils for drought modeling, and forestland fuel conditions
for wildfire modeling. Hazard-loss models then predict human and economic losses from the severityof weather-related hazards and information on local populations and infrastructure. Critical toachieving these benefits is to effectively disseminate the resulting improved, high-resolution climate
estimates. Importantly, this is not a one-time action; equally critical is a process to update anddisseminate estimates as climate, infrastructure, and modeling methods evolve.

Recommendation 1.2
The White House should designate a lead agency to maintain an extreme weather data portal
where observations and modeling products are regularly updated and widely accessible,
using an analysis-ready format that enables downstream users and hazard models on smallerscales to assess the local risks of wildfire, flood, drought, and other weather-related hazards.
The portal should also provide guidance and recommendations related to the reliability ofmodeling products.
In addition to their value to the United States, the outputs of Recommendations 1.1 and 1.2 would
create a valuable global public good. Indeed, most analyses find that climate-induced risks fromextreme weather are even larger outside of the United States, for instance in countries such as India,
China, Malaysia, and the Philippines, many of which also host significant U.S. interests (e.g., U.S. Navy
installations, which are vulnerable to coastal flooding).23 This is partly because other countries are
at greater risk from sea-level rise or other compounding effects, and partly because their physicalinfrastructure and populations are highly vulnerable to floods, storms, and other weather-relatedrisks.

An Improved Ecosystem for Climate Risk Assessment
Many of the methods to estimate human and economic risks from extreme weather were originallydeveloped by the insurance industry (Appendix C) and companies that provide insurers and lenders
with sophisticated catastrophe (CAT) modeling capabilities and/or results. However, with thegrowing attention paid to climate change, there is now a growing industry of climate risk assessment
initiatives that include both for-profit companies and NGOs. Some organizations even provide
23 Swiss Re Institute assesses climate risks across 48 countries, finding that the United State risk profile was

7th lowest amongst those assessed. Swiss Re Institute (2021, April). The economics of climate change: noaction is not an option. https://www.swissre.com/dam/jcr:e73ee7c3-7f83-4c17-a2b8-8ef23a8d3312/swissre-institute-expertise-publication-economics-of-climate-change.pdf

17estimates of the potential risks of wildfire, flood, and heat risk at the property level. The federal
government is also taking steps to provide and freely share information, through the new Climate
Mapping for Resilience and Adaptation (CMRA) data portal, the National Risk Index, and the HAZUS
program.24
In principle, granular information about the impending risks from heat, flood, fire, and other hazards
should allow homeowners, companies, and local governments to have risk information tailored totheir situation. This information should be used to pinpoint areas at high risk, so local communities
can engage in robust discussions about risk mitigation.
At this time, however, there is little assurance of the accuracy and reliability of available estimates.
Moreover, the data required to improve and validate risk estimates is often inaccessible. For example,
private insurers generally are reluctant to share historical claims data that would be valuable forquantifying risk, and there is no simple way to access both physical and financial data that resides infederal agencies. Our first recommendation for improving the ecosystem for climate risk assessment,
therefore, is aimed at sharing federal data that would help to stimulate a more robust ecosystem of
accurate, accessible, and reliable climate risk assessment.

Recommendation 2.1
Designate an interagency group to inventory and release federal data that are useful todevelop and test weather-hazard models, which predict flood, fire and drought from extreme
weather, and hazard-loss models, which predict human and financial losses from hazards ordirectly from extreme weather such as significant heat, cold, hail, or wind. This effort should
include any relevant and available fine-scale elevation data and other physical information,
as well as data on Federal disaster and insurance claims needed to validate risk models, withappropriate safeguards to protect privacy and security. The interagency group will require
expertise about procedures and rules governing the release of federal data.
The proliferation of climate risk models raises a further challenge. In areas such as weather modelingthere is a long tradition of skill-scoring, where models are assessed and validated relative tohistorical data. For instance, NOAA’s Climate Prediction Center routinely scrutinizes the accuracy ofshort-term and medium-term weather forecasts. There is no analogous mechanism in place to assessclimate risk models. PCAST repeatedly found it difficult to get a clear statement about the accuracyof a weather-hazard or hazard-loss model from its developer. Nonetheless, skill scoring of these

24 The CMRA data portal aims to provide climate-related information to local communities. It was developedin 2022 as an interagency partnership overseen by the U.S. Global Change Research Program (USGCRP). The
National Risk Index, which is the product of another interagency partnership, hosted by the Department ofHomeland Security, provides county-level estimates of natural hazard risk. Hazus is a collection of hazardloss models produced by the Federal Emergency Management Agency (FEMA).

18

models is both possible and increasingly prevalent in the academic literature.25,26,27 Comparative
shopping made possible by routine, transparent, and accessible skill-scoring would increase the paceof model improvement and help the nation to develop a rigorous climate risk assessment industry.
The federal government could also require or prioritize the use of skillful models in applications forhazard-mitigation funding, which would indirectly incentivize model developers to skill score theirmodels and publish the results.

Recommendation 2.2
NOAA, FEMA, and other federal agencies as the President deems necessary, should develop
guidelines for measuring the accuracy of climate risk models with skill-scores and promote
the use of skill-scoring among federal agencies that rely on assessments of climate andweather hazards including flood, drought, storm, and wildfire, and the human and economic
damages they cause.
Finally, proprietary CAT models in the insurance industry estimate risks of losses by linking together
extreme weather probabilities, weather-hazard models, and hazard-loss models. This industry needs
to rapidly expand beyond its traditional borders to keep pace with the new risks caused by climatechange, the rapid growth of the climate-risk assessment industry, and the diversification of its
customer base to include every public and private entity that faces climate-related risk. For thisreason, PCAST recommends a new federal investment in research and graduate training.

Recommendation 2.3
Fund research, potentially through multiple agencies, on risk-assessment modeling systems
that use extreme weather probabilities, weather-hazard models, and hazard-loss models toquantify the likelihood and economic costs of extreme weather events. This effort should aim
to foster improved quantification of weather-hazard risks in the public and private sector,
through improved access to models and data, and a robust ecosystem of model evaluation andtesting.
Taken together, the purpose of recommendations 2.1-2.3 is to stimulate the creation of a morevibrant ecosystem for climate risk assessment, so that communities, companies, and government
agencies can have the accurate and reliable information they need to prepare for and mitigate rising
extreme weather risks.

25 Johnson, J. M., Fang, S., Sankarasubramanian, A., Rad, A. M., da Cunha, L. K., Clarke, K. C., ... & Yeghiazarian, L.

(2023). Comprehensive analysis of the NOAA National Water Model: A call for heterogeneous formulations
and diagnostic model selection. 10.22541/essoar.167415214.45806648/v1
26 Johnson, J. M., Munasinghe, D., Eyelade, D., & Cohen, S. (2019). An integrated evaluation of the National
Water Model (NWM)–Height Above Nearest Drainage (HAND) flood mapping methodology. Natural Hazards
and Earth System Sciences, 19(11), 2405–2420. https://doi.org/10.5194/nhess-19-2405-2019
27 Wing, O. E. J., Smith, A. M., Marston, M. L., Porter, J. R., Amodeo, M. F., Sampson, C. C., & Bates, P. D. (2021).
Simulating historical flood events at the continental scale: Observational validation of a large-scalehydrodynamic model. Natural Hazards and Earth System Sciences, 21(2), 559–575.
https://nhess.copernicus.org/articles/21/559/2021/

19

The components of recommendations 1 and 2 will both separately and synergistically improve thenation’s capacity to manage risk. For example, a skillful flood model will accurately show how recent
development exacerbates urban flooding caused by an historic 100-year rainstorm and suggest
critical mitigation measures better than a less-skillful model (for example one that does not treat the
impact of impermeable surfaces very well). Together with accurate estimates of the likelihoods ofextreme rainfall both now and through midcentury, skillful flood and flood-loss models can provide
the basis for better cost-benefit decisions when designing new or retrofitted climate resilient
infrastructure.

A National Adaptation Plan to Mitigate Extreme Weather Risk
The final set of recommendations aim to provide communities with decision-making guidelines tohelp them mitigate and adapt to extreme weather risk. Here, we need to recognize that many
important decisions about how to prepare for increased risks from extreme weather will not be madeby federal agencies, but by households, businesses, and local, state, and tribal governments. In a
recent analysis of weather risk, the Council of Economic Advisors noted this and, consistent with ourrecommendations, identified “producing and disseminating knowledge about climate risk,” as thefirst of four pillars that should guide federal adaption efforts.28
However, providing information will not in itself equip at-risk communities to prepare for and adapt
to new risks. Local communities have many concerns, of which disaster planning is only one, andmany areas at high risk from extreme weather are lower-income.29 Moreover, the decisions arecomplex: they may affect property values, or permitting decisions, or local development in ways thatmatter greatly to households and local businesses. Having access to common frameworks anddecision-making tools can made it easier for communities to process newly available risk informationand make appropriate planning decisions in response.
Any serious attempt to protect households and communities from rising extreme weather hazards
must grapple with difficult trade-offs. For instance, in preparing this report, PCAST heard concernsthat growing awareness and measurement of extreme weather risk would result in higher insurance
prices and reduced property values, placing a disproportionate burden on lower-income andmarginalized communities. While federal and state policies could subsidize insurance purchases orprop up home prices in high-risk areas, these policies can encourage people to live in harm’s way.
Relocating individuals and families away from high-risk areas is another way to save lives and reduce
the future costs of disaster relief. However, relocation programs often are criticized for splintering
28 Council of Economic Advisors, (2023, February) “Opportunities for Better Managing Weather Risk in the

Changing Climate,” Chapter 9 of the 2023 Economic Report of the President.
29 See examples: Burke, M., Heft-Neal, S., Li, J., Driscoll, A., Baylis, P., Stigler, M., Weill, J. A., Burney, J. A., Wen, J.,
Childs, M. L., & Gould, C. F. (2022). Exposures and behavioral responses to wildfire smoke. Nature Human
Behavior, 6(10), 1351–1361. https://doi.org/10.1038/s41562-022-01396-6
Wing, O. E. J., Lehman, W., Bates, P. D., Sampson, C. C., Quinn, N., Smith, A. M., Neal, J. C., Porter, J. R., & Kousky,
C. (2022). Inequitable patterns of US flood risk in the Anthropocene. Nature Climate Change, 12(2), 156–162.
https://doi.org/10.1038/s41558-021-01265-6. In addition, a sizeable literature documents historically how
minority populations were pushed into high-risk areas, (e.g. Schell, C.J. Dyson, K., Fuentes, T.L., Des Roches, S.,
Harris, N.C., Miller, D.S., Woelfle-Erskine, C.-A., & Lambert, M.R. (2020) The ecological and evolutionary
consequences of systemic racism in urban environments. Science (New York, N.Y.), 369(6510).
https://doi.org/10.1126/science.aay4497)

20

local communities, or leaving lower-income families with few options to move to nearby housing.
Finally, programs that require or subsidize investments in “hardening” structures against fire or
storms can protect property and lives. However, these programs take time to be effective, especiallywhen they apply only to new construction, and they need careful design to ensure that investments
are truly cost-effective.30
The federal government has a multitude of programs aimed at helping communities prepare for andrespond to different extreme weather risks (see Box 3) and Appendix D.
BOX 3. Federal Programs and Interaction with Community Decisions31•
Flooding: FEMA operates the National Flood Insurance Program, which offers flood
insurance in areas with significant flood risk and is involved in floodplain
management. Local communities choose whether to participate in this program.
•
Wildfires: The Forest Service and Bureau of Land Management manage a vastacreage of forestland. However, much of the responsibility resides outside the federal
government. For instance, property insurance for wildfires is a private market that isregulated at the state level.
•
Hurricanes and Tropical Cyclones: The federal government regularly steps in tofund recovery and rebuilding after a major disaster. However, local and state
governments generally have responsibility for zoning decisions and building codesthat affect the potential for storm damage.
A variety of federal agencies, including FEMA and HUD, also have significant grant
programs to assist communities in preparing for hazards and building resilience.32 These
programs have gained additional heft with the recent passage of the Infrastructure
Investment and Jobs Act (IIJA). The IIJA designates $50 billion of investments in resilienceand western water infrastructure,33 including $6.8 billion in funding for FEMA to allocate
toward climate mitigation programs.34 Recently, FEMA revised its cost-benefit analysis
framework for grant evaluation, with the goal of making grants more accessible for low
income and marginalized communities.35

30 Appendix D discusses and array of policies that have been used to protect communities from historicalenvironmental risks, and that are potential responses to extreme weather risk.
31 Programs include FEMA grant programs for preparedness, hazard mitigation, and resilience, Forest Service
grant programs to support communities at wildfire risk, Bureau of Reclamation grants to support drought
preparation, and many more.
32 FEMA’s grant program includes separate categories for Preparedness, Hazard Mitigation, and Resilience.
https://www.fema.gov/grants
33 The White House. (2021, August 2). Bipartisan Infrastructure Investment and Jobs Act. [Fact sheet]https://www.whitehouse.gov/briefing-room/statements-releases/2021/08/02/updated-fact-sheetbipartisan-infrastructure-investment-and-jobs-act/
34 FEMA. (2021, November 15). Infrastructure Deal Provides FEMA Billions for Community Mitigation
Investments [Press Released]. https://www.fema.gov/press-release/20211115/infrastructure-deal-providesfema-billions-community-mitigation-investments
35 The FEMA grant program has been criticized in the past for favoring higher-income communities better
prepared to develop proposals and access funding – see especially its 2020 National Advisory Council report,
and subsequent annual reports. The new methodology was released in October 2022.

21

However, despite the array of programs, our nation does not have a national adaptation plan thatassesses climate and weather risks and provides guidelines for coordinating the complex andoverlapping patchwork of federal, state, and local efforts. Such a plan would be a valuable step toward
organizing existing programs, and it would build on the more than 20 climate adaption programs
developed by federal agencies at the direction of the Biden-Harris Administration.36

Recommendation 3.1
The White House should develop and publish a National Adaptation Plan to prepare for andmitigate increased risks from extreme weather. The Plan should include:(a) A systematic approach to mapping near term and longer-term high-danger zones foreach extreme weather hazard, updated as estimates of risk are improved.
(b) Decision frameworks to assist local communities in making plans to mitigate or adapt
to extreme weather risk.
(c) Regulatory and legal options to reduce long-term risk exposure, for instance throughland-use planning, zoning, and building code adoption(d) Comprehensive assessment of Federal and State programs in alleviating disaster risks,
including the distribution of federal funds for disaster preparation with attention towhether these programs are accessible to low-income communities.
(e) Guidelines for equitable allocation of disaster relief. These guidelines should
anticipate the potential for recurring extreme weather events, so that relief programs
work to mitigate future dangers to a community.
Subpart (a) would provide a federal categorization of risk levels that could be used by federal
agencies allocating grant funding, or by states regulating insurance practices. There are several
precedents. The National Flood Insurance Program is in the process of creating updated maps
showing flood risks (“Risk Rating 2.0”). The National Risk Index published by FEMA and the Climate
Mapping for Resilience and Adaption website developed by USGCRP both provide ratings of extreme
weather risk for events at varying geographical granularities. These would be natural portals to sharerisk classifications based on updated estimates of the likelihood of heatwaves, hurricanes, floods,
droughts, and wildfires as this information becomes available. Subparts (b), (c), and (d) envision
guidelines that could help to improve and coordinate the decision-making of local communities andfederal agencies. PCAST recommends developing a set of decision guidelines to assist local
communities at risk from extreme weather, inventorying federal grant programs to ensure that funds
are being delivered to areas at greatest risk, and working aggressively to ensure that the grantmechanisms do not disadvantage lower-income communities. After disasters occur, there is often aquestion of whether to rebuild in the affected area. These decisions ultimately are made locally, but
a set of clear federal decision-making guidelines would benefit communities, especially as PCAST
envisions that communities will be making these decisions more frequently in the coming decades.

36 White House (2021, October 7). Biden Administration Releases Agency Climate Adaptation and Resilience

Plans from Across Federal Government [Fact Sheet] https://www.whitehouse.gov/briefing-room/statementsreleases/2021/10/07/fact-sheet-biden-administration-releases-agency-climate-adaptation-and-resilienceplans-from-across-federal-government/

22

While the state of climate and extreme weather modeling is advancing rapidly, there has been lesswork on how increased risks from extreme weather affect housing prices, insurance and credit,
household location choices, and investment, all of which are important for making policy at the stateand local level. Our final recommendation is aimed at addressing this challenge.

Recommendation 3.2
Fund research on the adaptation of households, real-estate and insurance markets, and local
governments to changing climate and extreme weather risk. This effort should aim to
illuminate the behavior of households and firms in response to evolving climate and weather
risks, and the benefits and costs of alternative policies aimed at mitigating and adapting tothese risks. NSF may be best positioned to lead this effort, especially if coordinated withmission-oriented agencies such as NOAA, HUD, EPA, and others.
Research is urgently needed to understand how behavior changes in response to climate and weather
risks. Today, we are just starting to understand how an increase in the frequency and severity offloods, hurricanes, or wildfires will affect housing and credit prices, insurance markets, businesslocation decisions, or migration. These are important variables for federal, state, local, and tribal
governments, and all organizations interested in facilitating climate adaptation.
In closing our report, PCAST also wants to highlight two ways in which a national effort to measureextreme weather risk more accurately would contribute to policies enacted during the Biden-Harrisadministration and to future policy.
Already in response to Executive Order 14030 on climate-related financial risk, more than twenty
federal agencies have developed plans to address climate change, and there are interagency efforts
to monitor climate risks to financial markets as well as to national security.37 The Federal Insurance
Office (FIO) has launched an effort to investigate the potential for major disruptions in insurance
markets and to identify climate-related gaps in the supervision of insurers 38 Virtually all of these
efforts can benefit from more accurate assessment of the risks of extreme weather events.
Second, it is important to consider the all too likely possibility that over time climate change may leadto increasingly more frequent and severe disasters. Some of the worst-case scenarios for the
economic costs of climate change result from this possibility. Improving the quantification of extreme
weather risk would not only inform decisions today, but enable risk mitigation decisions for likely
impending catastrophes of greater scale and frequency in the future.

37 Two examples are the Climate-related Financial Risk Advisory Committee (CFRAC) established in October

2022 by the Financial Stability Oversight Council, and the Climate Working Group established by the
Department of Defense in March 2021.
38 U.S. Department of the Treasury. (2021, August 31). U.S. Department of the Treasury Launches New Effort on
Climate-Related Financial Risks in the Insurance Sector [Fact sheet]. https://home.treasury.gov/news/pressreleases/jy0337. It is important to recognize, however, that FIO has very limited regulatory authority, and insome cases can lack the ability to requisition or share data.

23

Conclusion
The Biden-Harris administration has made addressing climate change a central priority, including
major investments in preparing for and mitigating extreme weather. We can make further advancesby making it a national priority to improve our understanding and quantification of extreme weather
risks, enabling effective policy decisions and providing Americans with the information they need toprotect themselves.

24

Acknowledgments
The members of PCAST wish to thank the staff in the Office of Science and Technology Policy (OSTP)
for their various contributions throughout the preparation of this report. PCAST would like to thank
Alondra Nelson, Deputy Assistant to the President, formerly Performing the Duties of Director of theWhite House Office of Science and Technology Policy. PCAST also expresses its gratitude to Marc
Aidinoff, Kei Koizumi, and Asad Ramzanali in OSTP. PCAST especially thanks the IDA Science andTechnology Policy Institute (STPI) team, especially Emily Grumbling, Todd Ringler, and Margaret
Murphy, and Xueying “Shirley” Han for assistance with analyses and the report. Additionally, PCAST
would like to thank Eric Lander, former Director of OSTP and PCAST Co-Chair, for providing the initial
impetus and energy that inspired pursuit of this topic. Finally, PCAST wishes to express a heartfelt
thanks to the staff at the Department of Energy, particularly Karen Talamini, who have worked
tirelessly in administering PCAST.

25

Appendix A. External Experts Consulted
PCAST sought input from a diverse group of additional experts and stakeholders. PCAST expresses
its gratitude to those listed here who shared their expertise. They did not review drafts of the report,
and their willingness to engage with PCAST on specific points does not imply endorsement of theviews expressed herein. Responsibility for the opinions, findings, and recommendations in thisreport and for any errors of fact or interpretation rests solely with PCAST.

Natalie Ambrosio-Preudhomme
Senior Vice President
Moody’s

Jay Burns
Senior Climate Policy Advisor
Washington State Insurance Commission

Mike Amodeo
Head of Data Science
First Street Foundation

Matthew Eby
Founder & Executive Director
First Street Foundation

Todd Anderson
Acting Associate Director
Office of Biological & Environmental
Research
DOE

Eli Fenichel
Assistant Director for Natural Resource
Economics & Accounts
OSTP
Craig Fugate
Chief Resilience Officer
Once Concern

Ko Barrett
Senior Climate Advisor
NOAA

Justina Gallegos
Senior Policy Advisor
White House

Asmeret Berhe
Director of the Office of Science
DOE

Gregory Garner
Climate Change Data Scientist
Gro Intelligence

Steve Binkley
Former Acting Director, Office of Science
DOE
Heather Boushey
MemberCouncil of Economics

Burcu Guner
Europe & Africa Practice Leader for Risk
& Finance Solutions
Moody’s/Analytics

David Bresch
Professor of Weather & Climate Risks
ETH Zurich

Kelly Hereid
Director
Catastrophe R&D, Liberty Mutual

26

Alice Hill
David M. Rubenstein Fellow for Energy &Environment
Council on Foreign Relations

Aeisha LeGrande
Senior Executive Assistant
U.S. Department of the Treasury
Lai-yung Ruby Leung
Chief Scientist
Energy Exascale Earth System Model(E3SM)
DOE

Maria Honeycutt
Former Assistant Director for Resilience
Science & Technology
OSTP
Solomon Hsiang
Chancellor’s Professor
UC Berkely

Jane Lubchenco
Deputy Director for Environment andClimateOSTP
Divya Manikakar
Global Head of ESG Market Engagement
S&P Global

Paul Haung
Assistant Administrator, Insurance
FEMA
Ayana Johnson
Co-Founder & Executive Director
The All We Can Save Project

Carlos Martin
Project Director of the Remolding Futures
Program & David M. Rubenstein Fellow
Harvard and The Brookings Institute

Walter Eric Juzenas
Counselor to the Under Secretary for
Domestic Finance
U.S. Department of the Treasury

Richard Mattison
Chief Product Officer ESG
S&P Global

Sarah Kapnic
Chief Scientist
NOAA

Emilie Mazzacurati
Senior Vice President Government
Relations
Moody’s/GPRA

Ed Kearns
Chief Data Officer
First Street Foundation

Sean McNabb
Emergency Management Specialist
FEMA

Carolyn Kousky
Executive Director
Wharton Risk Center

Sara Menker
Founder and CEO
Gro Intelligence

Harriet Kung
Deputy Director of Science Programs in
the Office of Science
DOE

Christos Mitas
Vice President Model Development
Moody’s/RMS

27

Robert Muir-Wood
Risk Program Director and Senior
Scientist
RMS

Michael Simonettim
SVP, Climate & Index Products
Gro Intelligence
Paul Smith
COO
State Farm

Sella Nevo
Senior Information Scientist
RAND Corporation

Michael Tipsord
CEOState Farm

Kevin O’Donnell
CEO
RenaissanceRe
Laura Petes
Chief of Staff, C&E and AD for Climate
Resilience
OSTP

Michael Wara
Research Fellow
Steyer-Taylor Center for Energy Policy &Finance, Stanford Law School

Marius Poppa
Senior Index Quantitative Researcher
Gro Intelligence

Sharlene Weatherwax
Biological & Environment Division
DOE

Ben Preston
Consultant
OSTP

Scott Weaver
Executive Dir, Interagency Meteorological
Coordination Office
OSTP

Jesse Rozelle
Program Manager
FEMA

Karen White
Member of the Board, Varo Bank
Former CEO RMS (Risk Management
Solutions

Nicholas Samuels
Global Head of Climate Solutions
Moody’s/ESG Solutions

Lynne Yowell
Vice President, Secretary & Counsel
State Farm

Chris Schwalm
Risk Program Director & Senior Scientist
WoodwellCasey Zuzak
Senior Risk Analyst
FEMA

Truman Semans
CEO
OS Climate

28

Appendix B. Improved Estimates of Extreme Weather
Probabilities
Current assessments of climate and weather risks are based predominantly on historical event
statistics accumulated over the past 50 to 100 years.39 But long-term losses are dominated by “tailrisks” (i.e., rare, high-impact events, typically with recurrence intervals around 100 years). This posesa challenge for using historical records to assess important tail risks, because the weather records
are generally much too short to provide accurate estimates. Moreover, climate change is increasing
the probability of events that once were rare, such as the devastating rainfall during Hurricane
Harvey in Texas.40 Therefore, it is imperative in assessing current risks and risks in the coming
decades to avoid exclusive reliance on the historical record and to take the changing climate into
account.

High-resolution Climate Ensembles
A central opportunity to improve the assessment of extreme weather risks in a changing climate
comes from exploiting the historical record jointly with advances in numerical modeling of weather
and climate, enabled by the exponential increase in supercomputer performance. Models of thephysics of the atmosphere, oceans, and the hydrological and biological processes on land underlie
weather forecasts and are the most reliable sources of information about future changes in weather
probabilities. As the climate system is nonlinear and chaotic, large ensembles of model simulations
are needed to capture the probabilities of any event. Models have advanced to the point thatensembles of century-long simulations with resolutions around 10–25 km are now possible. Climate
models divide the earth into three-dimensional grids, with finer grids permitting higher accuracy andprecision. With a 15 km resolution, each cell is roughly the area of an average U.S. zip code.

39 Climate is the statistical distribution of weather, often characterized by summary statistics such as themean temperature. Climate is analogous to the probabilities associated with numbers on a die (each hasprobability ⅙) while weather is analogous to the number that comes up in a single roll. A substantial fractionof R&D programs at NOAA are focused on prediction of individual weather events, with relevant timescales ofdays to at most a season. Here, we are interested in longer time scales – up to the multiple decades relevant
for infrastructure planning – which means estimating the climate, i.e., the probabilities of different kinds ofweather. Climate change complicates the picture by progressively changing the climate, which is analogous toprogressively loading the dice.
40 Emanuel, K. (2017). Assessing the present and future probability of Hurricane Harvey's rainfall.
Proceedings of the National Academy of Sciences of the United States of America, 114(48), 12681–12684.
https://doi.org/10.1073/pnas.1716222114
Risser, M. D., & Wehner, M. F. (2017). Attributable Human‐Induced Changes in the Likelihood and Magnitudeof the Observed Extreme Precipitation during Hurricane Harvey. Geophysical Research Letters, 44(24),
12,457-12,464. https://doi.org/10.1002/2017GL075888
van Oldenborgh, G. J., van der Wiel, K., Sebastian, A., Singh, R., Arrighi, J., Otto, F., Haustein, K., Li, S., Vecchi, G.,& Cullen, H. (2017). Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environmental
Research Letters, 12(12), 124009. https://doi.org/10.1088/1748-9326/aa9ef2
29

Such simulations require the most advanced supercomputing facilities, such as those of the
Department of Energy, which have consistently led the rankings of the world’s fastest computers.41
Leveraging newly developed AI tools and established methods for downscaling, the simulations canbe combined with historical observations to correct biases that remain in even the most advanced
simulations and to obtain information on geographically even more granular kilometer scales.
A coordinated modeling effort by the U.S. climate modeling centers can significantly improve current
estimates of extreme weather probabilities based on the historical record. As a recent review aptly
stated, “calls for the integration of climate science into risk disclosure and decision-making
across many levels of economic activity has leap-frogged the current capabilities of climate
science and climate models by at least a decade.”42
At resolutions in the range of 10-25 km, extreme temperatures, extreme precipitation events, andsevere storms are captured much more effectively than in existing climate simulations with courser
scales.43 For example, Figure 1, below, shows the improved performance of recent high-resolution
modeling for extreme rainfall patterns in the U.S. Northeast.44

41 In the 2022 Top500 list of the world’s supercomputers, the Frontier system at Oak Ridge National

Laboratory was judged to be the fastest, achieving over a quintillion operations per second.
42 Fiedler, T., Pitman, A. J., Mackenzie, K., Wood, N., Jakob, C., & Perkins-Kirkpatrick, S. E. (2021). Business risk
and the emergence of climate analytics. Nature Climate Change, 11(2), 87–94.
https://doi.org/10.1038/s41558-020-00984-6
43 Vecchi et al.Tropical cyclone sensitivities to CO2 doubling: roles of atmospheric resolution, synoptic
variability and background climate changes. Clim Dyn 53, 5999–6033(2019). https://doi.org/10.1007/s00382-019-04913-y
T. Delworth et al., SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction
and projection, Journal of Advances in Modeling Earth Systems,12, e2019MS00189. See
also https://www.gfdl.noaa.gov/spear/
44 T. Delworth personal communication using data from the large ensemble of NOAA’s SPEAR model
described in T. Delworth et al., SPEAR: The next generation GFDL modeling system for seasonal tomultidecadal prediction and projection, Journal of Advances in Modeling Earth Systems,12, e2019MS00189.
See also https://www.gfdl.noaa.gov/spear/30

Figure 1. Probabilities of 24-hour rainfall produced by four models against observations for the
Northeastern U.S. from 1948-2020. Ninety-nine percent of simulated or observed days have rainfall lessthan the value shown by the appropriate vertical line in the left graph. CPC-OBS shows the observations.
CESM-LE is an NCAR model and SPEAR-LO is a NOAA model with horizontal atmospheric resolutions of
1 degree of latitude, which is about 100 km. SPEAR-MED has a resolution of one-half degree (~50 km),while SPEAR-HI has a resolution of 0.25 degrees (25 km).42
Extreme heat waves are the deadliest weather events, and floods from extreme rainfall and storm
surge are associated with the highest financial damages.45,46 Newer, higher-resolution climate models
capture, for example, precipitation extremes better than previous models with roughly fourfold
lower resolution (Figure 1).47,48,49 Thus, the latest climate models provide a timely andunprecedented opportunity to estimate risks of the most deadly and costly extreme events, all ofwhich have been impacted by climate change.
State-of-the-art climate models rely on ensembles of multiple models, each run multiple times to
produce different possible realizations of the weather everywhere on the globe and for the duration
45 NOAA & FEMA (2010, March). Flooding - Our Nation's Most Frequent and Costly Natural Disaster [Fact

sheet]. https://www.fbiic.gov/public/2010/mar/FloodingHistoryandCausesFS.PDF
46 NOAA National Weather Service. (2021). Weather Related Fatality and Injury Statistics. NOAA's National
Weather Service. https://www.weather.gov/hazstat/
47 Harris, L., McRae, A. T. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022). A Generative Deep Learning
Approach to Stochastic Downscaling of Precipitation Forecasts. Journal of Advances in Modeling Earth Systems,
14(10), e2022MS003120. https://doi.org/10.1029/2022MS003120
48 Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research.
Cambridge University Press. https://www.cambridge.org/core/books/statistical-downscaling-and-biascorrection-for-climate-research/4ED479BAA8309C7ECBE6136236E3960F
https://doi.org/10.1017/9781107588783
49 Groenke, B., Madaus, L., & Monteleoni, C. (2020, August 11). ClimAlign: Unsupervised statistical
downscaling of climate variables via normalizing flows. Climate Informatics 2020: 10th International
Conference on Climate Infomatics. https://arxiv.org/pdf/2008.04679
31

of the run. The data can be combined to generate estimated probabilities of any type of extreme
weather in any location, for historical climate conditions (e.g. starting in 1950), the present, and thefuture. A coordinated effort to produce ensembles of climate simulations and evaluate them against
historical observations will allow the field to close the gap between the climate information needs
for risk disclosure and decision making and the information that is currently available.

Agency Coordination and Collaboration
Several federal agencies have the capacity to participate in such an ensemble modeling effort,
including NASA, DOE, NOAA, and the National Center for Atmospheric Research (NCAR), which is
supported by the National Science Foundation. Rather than restrict the modeling to a single agency
or center, the report above recommends a multi-agency effort to improve estimates of extreme
events.
Building on Recommendation 1.1 in the report, the primary agencies and institutions with climatemodeling expertise (NOAA, NCAR, DOE, and NASA) should enhance their high-resolution modelingcapabilities and state-of-the-art statistical methods to quantify the current and near-future risks ofextreme weather at 10km resolution or finer. Indeed, 1 km resolution could be possible in the US,
given the current state of the art. To accomplish this, large multi-model ensembles (> 100 members)of climate simulations at 10-25 km resolution spanning the 20th century and extending through2050, could be made by these four agencies. In this case each modeling center would use its own
models, but all the centers would follow a common simulation protocol. Using the results of theensemble runs, advanced statistical and machine-learning methods could then enable agencies andinstitutions with climate modeling expertise to estimate extreme weather probabilities from existing
weather data and ensemble model projections.
Current operational federal efforts to predict weather rely on information about the current state of
the atmosphere and planet’s surface to forecast a short distance into the future (i.e. hours to days fora particular hurricane or a season for the expected number of named storms). This is the science ofmeteorology. In contrast, climatology is the longer-term probability distribution of the weather, suchas the underlying likelihood of a hurricane of a given strength hitting Miami in any given decade. In
technical terms, a climatology approach is dominated by boundary conditions such as the greenhousegas concentrations in the atmosphere, rather than by the initial conditions that inform a shorter-term
forecast. Also, a series of evolving climatologies is required to plan infrastructure and to adapt toclimate change, while shorter-term forecasting is required to allocate disaster relief.
Recommendation 1.1 in the report above would add the first U.S. operational effort to produce
evolving climatologies for extreme weather to the list of priorities of NOAA, NCAR, DOE, and NASA.
The four organizations could carry out the ensemble simulations with their existing climate models,with funding for staff of around $10M plus supercomputer allocations, but only by re-allocating
computer resources from other priorities, such as fundamental research.
PCAST notes that a steering body with authority over budget allocations would be needed to oversee
the effort, to maintain coordinated activity, and to promote a genuine reprioritization. Existing efforts
like the USGCRP are steered by agency leadership and may not provide the needed external push. An
alternative would be a new steering body created by Congress or the White House.
32Sampling and Downscaling Techniques
The large ensembles produced by this initiative will enable quantification of how often events suchas the Western North America heat wave of 2021, which led to hundreds of excess deaths in
Washington and Oregon, or Hurricane Ian, which devastated Florida in 2022, may occur as a resultof natural atmospheric variability superimposed on climate change. These ensembles will also allow
quantification of uncertainties in risk assessments due to two sources:
1. Model uncertainty, arising from imperfections in models (e.g., because not all clouds can be
explicitly resolved, even at 10 km resolution, different models produce different cloud cover,with the differences affecting other aspects of climate such as precipitation).
2. Internal variability of the chaotic climate system, which can lead to different outcomes becauseof small changes in atmosphere or ocean conditions (e.g. the “butterfly effect”).
These two types of uncertainty dominate uncertainties in outcomes for projections extending two to
three decades into the future.50 This is the most relevant timescale for incorporating extreme weather
hazards into infrastructure planning, the analysis of financial system risks, and disaster
preparedness. On longer time scales, an additional source of uncertainty, called scenario uncertainty,
stemming from imperfect knowledge of how greenhouse gas emissions and land cover change will
evolve, becomes relevant.51 This scenario uncertainty is not captured by the recommended ensemble
simulations, which focus on past greenhouse gas emissions and likely near-future emissions.
To sample the space of possible weather trajectories broadly, it will be important to explore broad
variations of slowly varying ocean conditions. This can be done through sampling of ocean conditions
from very long (thousands of years) coarser-resolution climate simulations or targeted variations ofocean initial conditions, such as those associated with El Niño. The modeling centers involved should
develop a joint protocol for the ensemble simulations to produce the broadest possible sampling of
weather extremes over the period of the simulations.
Producing ensembles of hundreds of simulations from the four modeling centers at resolutions ofaround 10-25 km will substantially improve the basis for quantitative assessments of climate andweather disaster risks. However, these simulations will not be free of systemic biases, for example,
in the regional distribution of rainfall or the severity of extreme thunderstorms. Moreover, the scaleon which impacts of extreme events manifest themselves is smaller yet (1 km or less). Additionally,
even 300-400 carefully designed simulations may not suffice to sample and quantify, for example,
the risks of hurricanes making landfall along the Gulf Coast: while the simulations will explore a muchbroader space of possible weather events than are available observationally, sufficiently rare events
may still not be adequately sampled.
The shortcomings of relying on simulations alone in risk assessments can be mitigated by combining
the simulations with available data, including data generated computationally in targeted high50 Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.:

Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam.,
11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
51 Phenomena such as sea level rise also add uncertainty over longer (>50 year) time scales, because we do
not know exactly how patterns of ice melt will respond to climate change.
33

resolution simulations (e.g., of hurricanes) and the large volume of Earth observations available fromthe ground and from space. Such additional data can be used to (a) downscale impacts such as wind
and precipitation severity to scales of a few kilometers or less and (b) provide estimates of rare event
risks, such as that of landfalling hurricanes, which extend beyond what can be directly obtained fromthe ensemble of climate simulations.
Downscaling has been done by dynamical and statistical means:• Dynamical downscaling consists of embedding finer computational meshes in limited
subdomains of a global model, driving the smaller, more finely resolved domains withboundary conditions supplied from a global model. For example, severe thunderstorms canbe simulated today in computational domains that might span one or a few of the U.S. Plains
States. The grids need not be fixed in space and can move with the hazard. For example, finescale grids can automatically center themselves on hurricanes and follow them as they move.
This is done routinely today in hurricane forecast models. Increasing computer performance
will accelerate such dynamical downscaling and will enable ensembles of downscaled
simulations.
• Statistical downscaling methods are computationally more efficient than dynamical
downscaling methods and allow harnessing observational data. Traditionally, they haverelated the statistics of simulations from a global model to historical observations on finerscales.
As noted earlier, potential for advances in downscaling exist by using machine learning (ML)methods used in image processing and generation, and by combining dynamical downscaling and ML
techniques. For example, it is possible to use ML to refine mappings from coarser-resolution climate
simulations to local impact metrics, leveraging techniques similar to those that provide superresolution in optical imaging. Moreover, generative ML models used in image generation can betrained on samples of dynamically downscaled events together with coarser-resolution ensemble
simulations, providing computationally efficient and reliable statistical downscaling that can also
correct some biases in climate simulations.52,53,54
To accomplish Recommendation 1.1, PCAST recommends that a downscaling and bias-correction
research program be created to scale weather events from the 10-25 km resolution of the ensemble
simulations to scales of 1 km over the U.S. and 10 km globally, with a focus on tail risks of extreme
events.
The downscaling program should consider a call for proposals by NOAA and the NSF, open to the
public and private sector, to deploy recent advances in the mathematical and computational sciences
Harris, L., McRae, A. T. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022). A Generative Deep Learning
Approach to Stochastic Downscaling of Precipitation Forecasts. Journal of Advances in Modeling Earth Systems,
14(10), e2022MS003120. https://doi.org/10.1029/2022MS003120
53 Groenke, B., Madaus, L., & Monteleoni, C. (2020, August 11). ClimAlign: Unsupervised statistical
downscaling of climate variables via normalizing flows. Climate Informatics 2020: 10th International
Conference on Climate Infomatics. https://arxiv.org/pdf/2008.04679
54 Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research.
Cambridge University Press. https://www.cambridge.org/core/books/statistical-downscaling-and-biascorrection-for-climate-research/4ED479BAA8309C7ECBE6136236E3960F
https://doi.org/10.1017/9781107588783
52

34

and machine learning to generate physics-constrained granular weather events from the large
ensemble of high-resolution climate simulations.
Finally, it is critical that the data generated by Recommendations 1.1, 2.1, and 2.2 be easily findable,
accessible, and analyzable by a broad range of stakeholders. PCAST envisions these data serving as
an input for weather-hazard and catastrophe-loss models and for a wide range of private and public
sector planning decisions. Indeed, their use will extend outside the U.S. because the extreme weather
estimates in Recommendation 2.1 will be global.
Large ensembles of high-resolution climate simulations generate data at volumes that are not easilytransferable via Internet downloads. A more effective approach is to store the data on the cloud andmake them accessible and analyzable through an application programming interface (API) and webbased interactive user interfaces for exploration. These interfaces will allow data analysis on thecloud and the effective integration into ML workflows, for example, for downscaling climate model
output. The climate data products made easily and freely available under Recommendation 1.2
should include metadata with information from the model ensembles. Data distribution efforts aslarge as these are currently handled within the private sector, but not within the government. To
facilitate effective action on these data efforts, OSTP could convene a panel of experts to plan andbudget the needed effort before the end of 2023, including its administrative structure.

35

Appendix C. Weather-Hazard and Catastrophe-Loss
ModelsWeather-Hazard Models
Weather-hazard models combine information about weather with information about topography
and surface conditions to predict weather-related hazards including:
1. Inland flooding that occurs when extreme rainfall causes rivers and streams to overflow theirbanks.
2. Coastal flooding due to storm surge and sea level rise.
3. Fire that occurs when an ignition source finds sufficient fuel in dry, hot, and windy conditions.
4. Drought caused by low rainfall, high temperatures, low humidity, insufficient water holding
capacity of soils (for agricultural drought), and insufficient water storage in lakes, reservoirs,
and rivers (for the low streamflow that prevents industrial cooling).
Weather-hazard models are used for two different purposes: (1) predicting the real-time behavior of
an individual event to help manage emergency response, and (2) predicting the probabilities ofhazards to help manage risk. Usually, a model is tailored to one purpose or the other, but some aredesigned to address both. This report focuses only on models targeting the second purpose, whichallows one to compute the likelihood that a hazard of a given severity will occur, for all possible
severities, given the evolving probabilities of extreme weather conditions now and in the future.
Agencies throughout the federal government, academia, and the private sector have operational
flood, fire, and drought models that are constantly being updated. A selection of these is listed inTable 2.1. The table contains 4 flood models, 5 wildfire models, and 3 drought models that are usedby federal agencies. These models play an essential role in the federal programs identified in the
climate resilience Roadmap,55 and in supporting private and public sector decision making. They areoften geographically granular, sometimes at the scale of individual buildings.

55 The White House. (2021, October 14). A Roadmap to Build a Climate-Resilient Economy.

https://www.whitehouse.gov/wp-content/uploads/2021/10/Climate-Finance-Report.pdf
36Extreme Weather Type

Models Used or Developedby One or More Federal
Agencies
Model Description
RASPLOT

A computer program, created by
FEMA, that allows the user to create
flood profiles through the automatic
extraction of data from the U.S. Army
Corps of Engineers’ HydraulicEngineering Centers River Analysis
System(HEC-RAS)
hydraulicmodeling files.

National Streamflow Statistics

Provides regression equations toestimate the frequency of floods andother streamflow statistics.

ADCIRCA system of computer programs forsolving time dependent, free surface
circulation and transport problems
in two and three dimensions.

HEC-RAS

A computer program used in the
preparation of studies and restudies
for the National Flood Insurance
Program.

FlamMap

A quasi-empirical fire spread model,
widely used among forest and fire
managers in operational settings

Flood

Wildfire

FSim-Wildfire Risk Simulation The FSim simulation process
Softwareinvolves assembly of geospatial
landscape and terrain data, assemblyand processing of historical fireoccurrence data, and assembly andprocessing of historical weather
observations.
Wildfire Hazard Potential Map

37

A raster geospatial product that canhelp to inform evaluations of wildfire
hazard or prioritization of fuels
management needs across very large
landscapes.

Extreme Weather Type

Models Used or Developedby One or More Federal
Agencies
Model Description
Wildfire Risk to Communities

A model designed to help community
leaders such as elected officials,
community planners, and fire
managers understand how risk
varies across a state, region, or
county and prioritize actions to
mitigate risk.

Rothermel Fire Surface Spread This model has been used in fire andModelfuels management systems since1972. It is generally used with othermodels including fireline intensityand flame length.

Drought

Standardized
Indices (SPI)

Precipitation A universal meteorological drought
index
for
effective
drought
monitoringandclimaterisk
management.

National
Ensemble(NMME)

Multi-Model The NMME Experiment seeks further
Experiment improvements in intraseasonal toseasonal climate and drought
prediction. The NMME project uses
an ensemble of leading national
climate models to provide climate
forecasts for research purposes.

In general, weather-hazard modeling is an active area of academic, private, and governmental
research and development. Most weather-hazard models are publicly accessible, unlike mostcatastrophe-loss models (see below). The historic record contains many examples of severe weather
hazards that can be used to develop and evaluate weather hazard models. These models remain valid
despite climate change in the frequency of the severe weather that causes them, although
unprecedented weather will obviously be beyond any data set used to develop a weather-hazard
model. Despite these advantages, PCAST repeatedly heard concerns from experts from multiple
sectors regarding needed improvements in hazard modeling.
Meteorology and climatology have a well-established methodology for discarding obsolete models
and evaluating if a new model represents a useful advance. A collection of widely used skill scores38

measure how well each model would have predicted historical data (called hindcasting).56,57,58 Models
without skill are simply not used, and new versions of models are typically not released until they
demonstrate competitive skill.
In contrast, PCAST repeatedly heard that there is not a sufficiently healthy tradition of skill scoring
for weather-hazard modeling. As a result, it is very difficult to obtain a simple clear answer to thequestion: “How well does your model work?” Without clear metrics that cause underperforming
models to be discarded, the number of models in current use by the federal government and others
continues to increase, and at an accelerating pace because of the growth of private firms sellingclimate-related risk analysis. Moreover, quantitative analysis of predictive power is required to
produce quantitative estimates of prediction uncertainty. This is vital in risk analyses, which mustaccount for all known risks, including modeling error risks. Recommendations 2.1 and 2.2 in the main
text of the report aim to correct this problem through the development of data sets that can be used
to skill-score weather-hazard models and to promote skill-scoring within federal agencies, including
when evaluating proposals for funding and private climate risk disclosures. Although few publicly
available hazard-loss (described below) models exist today, these should also be skill-scored, alongwith any proprietary models used within the federal government, or in applications for government
funding.
As systematic quantitative assessment of model skill becomes common, researchers who work on
weather-hazard models will inevitably uncover critical shortcomings in both existing models and test
data. This will provide an opportunity to refocus priorities for federal research funding so as toreduce or eliminate the shortcomings. In the meantime, it is critical that federal funding of basic
research aimed at improving weather-hazard modeling be given high priority. PCAST spoke withmany researchers who were particularly concerned about the research needs summarized in Box 4,
precisely because weather-hazard models are already in common operational use, despite theirshortcomings.

Box 4 – Priority Areas for Research and Development of Weather Hazard Models
There is a critical need to address inadequacies in current generation weather-hazard models toimprove their reliability and skill. These needs motivated Recommendation 2.3 in the main text
and include:
1) Fire59
56 Hargreaves, J. C. (2010). Skill and uncertainty in climate models. Wiley Interdisciplinary Reviews: Climate

Change, 1(4), 556–564. https://doi.org/10.1002/wcc.58
57 Hausfather, Z., Drake, H. F., Abbott, T., & Schmidt, G. A. (2020). Evaluating the Performance of Past Climate
Model Projections. Geophysical Research Letters, 47(1). https://doi.org/10.1029/2019GL085378
58 NOAA National Weather Service Climate Prediction Center. (December 2005). CPC Verification Summary.
https://www.cpc.ncep.noaa.gov/products/verification/summary/index.php?page=tutorial
59 Shuman, J. K., Balch, J. K., Barnes, R. T., Higuera, P. E., Roos, C. I., Schwilk, D. W., Stavros, E. N., Banerjee, T.,
Bela, M. M., Bendix, J., Bertolino, S., Bililign, S., Bladon, K. D., Brando, P., Breidenthal, R. E., Buma, B., Calhoun,
D., Carvalho, L. M. V., Cattau, M. E., . . . Zhang, X. (2022). Reimagine fire science for the anthropocene. PNAS
Nexus, 1(3), pgac115. https://doi.org/10.1093/pnasnexus/pgac115
39

•
•

•

•

Investment in laboratory and field infrastructure to produce long-term, openaccess, and multi-disciplinary datasets for improved understanding and modeling.
Development of models that capture fuel heterogeneity, including the physiologicaldynamics that influence vegetation fuel loading, fuel, and the flammability of live
and dead vegetation.
Development of fire models within land surface models that integrate fire behavior
and effects representative of the social and ecological environment within whichhumans interact with fires and subsequently influence impacts to terrestrial
energy, water, and carbon cycles.
Development of models that predict and quantify the air pollution hazard fromwildfire.

2) Flood60•

3)Application of the latest high-performance computing techniques to inundation
modeling for efficient high-resolution numerical solutions with sub-grid scale river
channels.
• Improved understanding of soil infiltration in complex landscapes (highly variable
terrain, soil structures, and land cover).
• Improved fine-scale models of urban flooding, including realistic representationsof impermeable surfaces and water control measures.
• Improved input data on flood defenses, flood control structures and channel
bathymetry. Accelerate and extend access to LiDAR data, and routinely update its
coverage. Continue support of NOAA and the USGS to collect data from high quality
measuring sites to accurately determine non-stationary probability-magnituderelationships.
• Model validation data: better observations of flood extent and water level throughtime during floods, particularly on floodplains and in urban areas. The forthcoming
NASA Surface Water Ocean Topography61 satellite mission may help with this.
• Improved weather inputs to the flood models for present-day and future climate
modeling.
Drought62• Reduce modeling uncertainties in precipitation projections, including extremes andintermittency.
• Improve understanding of land-vegetation coupling, including soil moisture,
evaporation, and plant physiological responses to heat and water stress. This is anurgent need, motivated by the finding that no existing model (CMIP5 or CMIP6)
reproduces the magnitude of the observed downward trend in atmospherichumidity (water vapor deficit) over arid regions of the U.S. and elsewhere.

60 Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G.,

Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis‐Gruss, S., Freeman, N. B., Houser, T.,
Delgado, M., . . . Krajewski, W. F. (2021). Combined Modeling of U.S. Fluvial, Pluvial, and Coastal Flood Hazard
Under Current and Future Climates. Water Resources Research, 57(2), e2020WR028673.
https://doi.org/10.1029/2020WR028673
61 NASA – Jet Propulsion Laboratory (2023) Surface Water and Ocean Topography. https://swot.jpl.nasa.gov/
62 Simpson, I.R., K. A. McKinnon, D. Kennedy, D. M. Lawrence, F. Lehner and R. Seager, 2023: Humidity trends
in dry regions are inconsistent with climate models and theoretical expectations. PNAS, Submitted Feb 16,
2023.
40

Hazard-Loss and Weather Catastrophe Models
The most advanced models that predict financial and human losses from the severity of a weather
hazard are catastrophe (CAT) models produced by a small number of firms servicing mostly the
insurance and reinsurance industries (e.g., AIR and RMS). A CAT model for estimating the risks ofextreme weather has three components: the probabilities of extreme weather as a function ofseverity, weather-hazard models, and hazard-loss models that predict financial and human losses
from the severity of a weather hazard or caused directly by weather (heat, cold, hail or wind).
Hazard-loss models are typically based on engineering equations and fine-scale information aboutbuildings and infrastructure and are calibrated with a large body of insurance claims data.
Private CAT models suffer important drawbacks, including:• Lack of transparency (the models are largely proprietary and closed source), which reduces
the usefulness of model outputs in a variety of dimensions.63• Reliance on historical hazard records that are usually too short to define the all-important
tail risks that dominate long-term losses and which are based on a climate state that has
already changed appreciably, particularly in the tails.
• Current approach to quantifying risk uncertainty, which is often ignores methodologicaluncertainty.
• Lack of focus on assessing health and mortality risks (existing CAT models are largely focusedon predicting losses to private and commercial property).
• High cost, which is prohibitive for many users, such as municipalities interested in assessing
their current and future weather risks.
In addition to CAT modeling efforts in the private sector, FEMA, through its development of “Risk
Rating 2.0,” has invested in capabilities to better estimate economic damages from flooding. FEMA’s
Hazus program is building a portfolio of hazard-loss models and aspires to estimate human impacts
such as health effects, lost wages, and productivity declines caused by weather hazards. Models
estimating these human impacts are now primarily in the academic literature. 64,65 Finally, in recent
years, a collection of open-source weather-hazard models, have also contributed transparent hazardloss models that assess the risks of economic damage caused by flood and fire.

63 As an example, the National Association of Insurance Commissioners’ “Catastrophe Computer Modeling

Handbook” describes the challenges of using existing CAT modeling in the pricing of insurance:
Consumer representatives are skeptical about the application of computer models to property insurance
rates and underwriting decisions. There has been a perception that the insurers are hiding behind a cloak ofmystery that is held by the catastrophe modelers. The consumer representatives know that a great deal of
public information goes into the models, yet the modelers claim the models are confidential and will not show
consumer representatives how they work. This cloak of mystery and the varying results produced bycatastrophe models tend to make consumer representatives wary. They often oppose including a catastropherate based on confidential models.
64 Grant, E., & Runkle, J. D. (2022). Long-term health effects of wildfire exposure: a scoping review. The Journalof Climate Change and Health, 6, 100110.
65 Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., ... & Houser, T. (2017). Estimating economic
damage from climate change in the United States. Science, 356(6345), 1362-1369.
41

Data Needs
A key barrier to the development of reliable open-source models of damages from extreme weather
is the availability of information on past financial and non-financial losses from extreme weather
events. These data are essential to calibrate new models and to assess the reliability of legacy models.
Much of the loss data held by commercial insurance and CAT modeling firms from past insurance
claims are proprietary and closely held, providing a strong barrier for new private-sector entrants aswell as academic and other non-profit modeling efforts. The inaccessibility of these data also reduces
the ability of outsiders, including regulators and consumer advocacy groups, to fully assess thequality of the industry models.
Fortunately, federal agencies hold a wealth of economic loss data from weather hazards, which, if
made available to the modeling community, would support the development of more robust anddiverse modeling capabilities by a wide range of entities. Perhaps most importantly, data held by
FEMA on claims paid by the federal government after a Presidential Major Disaster Declaration
contain information on property-level financial losses following a wide range of extreme weather
events. Data from FEMA’s National Flood Insurance Program also contain valuable information on
financial damages from floods at the property level. Similarly, data from USDA’s crop insurance
program as well as loss data from damages to federal buildings and property would be helpful for
estimating a range of financial losses from extreme weather. Data on health outcomes held within
the Medicare system, public hospitals, and elsewhere might be similarly helpful for estimating a
range of the health effects of extreme weather hazards.
PCAST believes that immediate efforts to make such data accessible in a privacy-preserving way
would have substantial and immediate benefits by enhancing and enriching the nation’s hazard-loss
modeling capabilities. This motivated Recommendation 2.1 in the main text. Data released underRecommendation 2.1 would also facilitate the development and skill-scoring of weather-hazard
models (previous section), but this need is less acute because substantial data on historic hazards
and weather is already in the public domain.
Recommendation 2.1 will require that the government develops protocols to make these data widely
accessible to the research, non-profit, and private-sector modeling communities. Loss data at thecensus block or census tract level are likely sufficiently aggregated to be shared widely without
access restrictions (e.g., similar to the American Community Survey).
However, property-level loss data will also be important for estimating how financial damages varywith a range of features of the properties, such as efforts to mitigate climate damages from floods
through raising foundations or reducing the hazards from wildfires through using flame retardantmaterials. As a result, PCAST also recommends that the government develop access protocols forproperty-level loss data available to the research community through existing Federal Statistical
Research Data Centers (FSRDCs), which already allow qualified researchers to access restricted-use
microdata from a variety of federal agencies to address important research questions.

42Given its long and successful history of making privacy-protected data from a variety of agencies
available to researchers, PCAST recommends that the U.S. Census Bureau be part of the InterAgency
Working Group called for in Recommendation 2.1.

Research and Training Needs
The recommendations thus far would, if enacted, improve all of the components required to assess
current and near-future risks of human and financial losses from extreme weather: extreme weather
probabilities, weather-hazard, and hazard-loss models. Given the limitations of private models andthe explosive growth in new efforts that offer climate risk assessments, PCAST also recommends thatthe federal government invest in expanded academic research and training in weather hazard andhazard-loss modeling, which is Recommendation 2.3 in the main text. Over time this would produce
a critical mass of openly available information that would spur improvements in all the tools needed
to plan for climate-related risk, foster a responsible private risk-assessment industry, and provide
the skilled personnel needed by that industry.
Once this effort is underway, the interagency group that manages the research and training program
should fund the cooperative development of an open-source climate and weather risk assessment
system (a CAT model) that integrates the best available extreme weather probabilities, weather
hazard models, and hazard loss models. An open-source system with these components would allow
federal, state, and local agencies, private companies, and citizens to estimate and manage their own
climate-related risks. However, to be useful, the system would have to be easy to tailor to the needsof a specific local region or group. This would not happen without oversight by the funder or anadvisory body appointed by the funder, and would probably require the participation of for-profit
companies able to produce a user-friendly interface. The University Corporation for AtmosphericResearch (UCAR) has the experience required to manage this kind of multifaceted project.
PCAST notes that UCAR could coordinate the development of a public risk assessment system thatallows for the estimation of monetary and health damages from extreme climate and weather events.
Such a public effort should have several features. Its products should:
1. Be made freely available to anyone, including insurance companies, regulators, states,
municipalities, financial services firms, federal agencies, and commercial CAT modeling firms,
which could use it as a baseline and benchmark for their own modeling efforts.
2. Be modular so that it can easily incorporate improved estimates of extreme weather hazards,
exposure, damage, and health risks as they become available. This would also allow users to
explore the sensitivity of estimates by using their own extreme weather models.
3. Be sufficiently transparent in its modeling assumptions that a wide range of end users may
become comfortable in using the model itself as well as its outputs for a variety of
applications.
4. Focus on estimating health and mortality risks in addition to economic damages fromextreme weather and weather-related hazards. (As described above, those outcomes are notcurrently the focus of the private CAT modeling industry.)
5. Be developed in close coordination with the weather and hazard models proposed inprevious sections of this report, to ensure that the output from the hazard models can beeasily incorporated into exposure and economic loss modules. In doing so, the proposed
43

effort should lead to a new data standard that facilitates ongoing parallel development ofthese different modules by different teams.
6. Aim to propagate all the uncertainties inherent in risk modeling–from uncertainties inclimate and hazard models to exposure and damage–right down to quantified uncertainty infinancial and health damages, the latter of which are not handled by current industry CAT
models.
The public system could be developed in partnership with both the commercial CAT modelingindustry and non-profit risk modeling efforts. While a publicly available system could be viewed ascompetitive with existing industry CAT models, it will also serve as a valuable baseline for suchmodels, freeing the private sector to focus on tailored risk estimates for paying clients, just as private
weather forecasting enterprises profit from the free availability of global and regional numerical
weather prediction output.
More generally, given the importance of modeling damages from extreme weather hazards, PCAST
believes that having public, non-profit, and commercial models developed in parallel is central tobuilding and supporting a broader and more diverse national modeling capability. Indeed, a publiccapability could also help catalyze research across the federal, academic, and private sectors and, inparticular, would bring together the robust but largely non-overlapping research enterprises in (i)physical modeling of weather hazards, (ii) understanding economic and health risks of extreme
weather, and (iii) forecasting the effect of federal, state, and local policies and policy changes onresilience and adaptation to climate and weather hazards (Recommendation 3.1 in the main text).
The development of a public climate and weather risk assessment capability would also be freely
accessible to communities that cannot afford commercially developed models, and ensure theindependence and continued availability of support from existing non-profit organizations. While apublic capability would have the important benefits as described above, PCAST cautions against
advancing it as the only or primary benchmark climate and weather risk assessment. Rather, an open
source, public effort should work to encourage further model development in both the academic andprivate sectors. By rigorously and transparently testing the performance of different models,preferred models will naturally arise for different applications, and those preferred models maychange over time as model performance evolves.

44

Appendix D. Federal Response to Environmental Hazards
As we identify areas at heightened risk from flood, fire, storms, or drought, how might the federal
government respond? This question will become critically important if new analyses reveal thatclimate change has exacerbated extreme weather risk in ways that outpace response from current
private insurance and government programs.
Historically, when the country has become aware of a significant environmental hazard, Congress hasenacted legislation to address it, although often only after significant harms have accrued. This
Appendix starts by reviewing six examples, as well as lessons learned, that may be applicable incrafting a national adaptation plan for extreme weather, as PCAST recommends, and future policy
directions. The goal of the national adaptation plan should be to identify and avoid potential risks,
especially to disadvantaged communities, in advance.
Historical Examples•

Hazardous Waste. The discovery of serious health problems from hazardous waste sites inLove Canal, near Niagara Falls, and other areas, spurred Congress to pass the Comprehensive
Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). Under the act,
the EPA identifies priority hazardous waste sites (“Superfund sites”) and manages both shortterm removal and longer-term remediation actions.66 As of February 2023, 453 Superfund
sites had been completely remediated, and there were 1,335 sites on the national priority
list.67•

Drinking Water Quality. Studies showing that many public water systems failed to meet
public health standards led to the passage of the Safe Water Drinking Act in 1974 (SWDA). 68
Under the act, the EPA regulates drinking water in both privately and publicly owned water
systems, other than those of very small size. The EPA sets standards for maximum
contaminant levels and for treatment of drinking water. Today, more than 92% of the US
population has access to drinking water meeting all relevant health standards.69

66 U.S. EPA. (2022). Summary of the Comprehensive Environmental Response, Compensation, and Liability Act(Superfund). https://www.epa.gov/laws-regulations/summary-comprehensive-environmental-responsecompensation-and-liability-act
67 U.S. EPA. (2023). Superfund: National Priorities List (NPL). https://www.epa.gov/superfund/superfundnational-priorities-list-npl
68 Weinmeyer, R., Norling, A., Kawarski, M., & Higgins, E. (2017). The Safe Drinking Water Act of 1974 and Its
Role in Providing Access to Safe Drinking Water in the United States. AMA Journal of Ethics, 19(10), 1018–
1026. https://doi.org/10.1001/journalofethics.2017.19.10.hlaw1-1710
Harold M. Schmeck Jr. Special to The New York Times (1974, November 9). E.P.A. Orders a National Study ofChemical Contaminants in Drinking Water. The New York Times.
https://www.nytimes.com/1974/11/09/archives/epa-orders-a-national-study-of-chemical-contaminantsin-drinking.html
U.S EPA. (December 1999). 25 Years of the Safe Drinking Water Act: History and Trends.
69 U.S. EPA. (2022). Safe Drinking Water Act (SDWA). https://www.epa.gov/sdwa
45•Surface Water Quality. A series of incidents in the 1960s, including rivers in Buffalo and
Detroit catching fire, helped to spur the passage of the Clean Water Act in 1972 (CWA).70
Under the act, the EPA creates regulatory requirements limiting effluent discharges that
constrain the behavior of companies and municipalities. The CWA also authorizes federal
assistance for municipal sewage plant construction.71 Many US streams and wetlands are notprotected by the CWA; however, studies have shown that the CWA prevents 700 billion
pounds of toxic pollutants from entering US waterways.72•

Air Quality. Pervasive industrial smog in areas such as Los Angeles, and an incident inDonora, PA in 1948 where smog killed 20 people, helped to spur the Clean Air Act of 1970
(CAA).73 Under the Act, the EPA sets National Ambient Air Quality Standards (NAAQS) for six
pollutants: carbon monoxide, lead, ozone, nitrogen dioxide, sulfur dioxide, and particulatematter.74 The EPA reports that amendments to the CAA have been responsible for preventing
over 230,000 deaths and reducing the frequency of respiratory diseases.75 However, theAmerican Lung Association found in 2022 that more than 40% of Americans, a
disproportionate number of whom are people of color, live in areas that have failing gradesfor particulate matter or ozone.76•

Lead. Concerns about lead poisoning from pipes, paint, and drinking water, and from gasoline
fumes stretch back decades.77 In 1970, the CAA required the development of standards for
air quality.78 In 1986, an amendment to the SWDA banned the use of lead pipes in newplumbing systems.79 The Lead-Based Paint Poisoning Prevention Act of 1971 directed HUD

70 Grant, M. (2022, October 18). The Clean Water Act At 50: How We Got Here and Where We Need to Go.

Food & Water Watch. https://www.foodandwaterwatch.org/2022/10/18/clean-water-act-at-50-how-wegot-here-and-where-we-need-to-go/
71 Copeland, C. (2016, October). Clean Water Act: A Summary of the Law.
https://sgp.fas.org/crs/misc/RL30030.pdf
72 National Wildlife Federation (2022). Five Decades of Clean Water: The Clean Water Act's Incredible
Successes, its Current Limitations, and its Uncertain Future. https://www.nwf.org//media/Documents/PDFs/NWF-Reports/2022/Five-Decades-of-Clean-Water1
73 Jacobs, E. T., Burgess, J. L., & Abbott, M. B. (2018). The Donora Smog Revisited: 70 Years After the Event
That Inspired the Clean Air Act. American Journal of Public Health, 108(S2), S85-S88.
https://doi.org/10.2105/AJPH.2017.304219
74 U.S. EPA. (2022). Lead (Pb) Air Quality Standards. https://www.epa.gov/naaqs/lead-pb-air-qualitystandards
75 UN Environment Programme (Tue, 2020, March 17). The United States Clean Air Act turns 50: is the air any
better half a century later? United Nations Environment Programme. https://www.unep.org/news-andstories/story/united-states-clean-air-act-turns-50-air-any-better-half-century-later
76 America Lung Association. (2022). State of the Air: Key Findings.
https://www.lung.org/research/sota/key-findings
77 Dignam, T., Kaufmann, R. B., LeStourgeon, L., & Brown, M. J. (2019). Control of Lead Sources in the UnitedStates, 1970-2017: Public Health Progress and Current Challenges to Eliminating Lead Exposure. Journal of
Public Health Management and Practice, 25(1), S13-S22. https://doi.org/10.1097/PHH.0000000000000889
78 U.S. EPA. (2022). National Ambient Air Quality Standards (NAAQS) for Lead (Pb).
https://www.epa.gov/lead-air-pollution/national-ambient-air-quality-standards-naaqs-lead-pb
79 Congressional Research Service (2022, February). Controlling Lead in Public Drinking Water Supplies.
https://crsreports.congress.gov/product/pdf/IF/IF11302
46

to eliminate lead-based paint hazards in all public housing and certain private housing.80 Leadhazards continue to affect parts of the population: for example, around 20 million Americans
live in homes where water is delivered at least partly through lead pipes.81 However, levelsof lead detected in the air decreased 97% between 1980 and 2010,82 and blood lead levels inchildren have declined 94% since the 1970s.83•

Asbestos. A landmark 1964 study linked asbestos exposure to cancer (mesothelioma),showing that asbestos installation workers had a 7x higher death rate than expected in thegeneral population.84 Several pieces of legislation, including the Occupational Health and
Safety Act of 1970 authorize aspects of asbestos regulation.85 The EPA and OSHA set stringent
limits on asbestos exposure, and the last asbestos mine in the U.S. closed in 2002.86 However,asbestos is still imported and used in building materials and the number of mesothelioma
deaths remains substantial according to the CDC.87

Lessons Learned•

In each of case, Congress acted to protect the American people as a response to accumulating
evidence, although it often took several decades for the evidence to become sufficiently
persuasive.

•

The laws that were passed measurably improved health outcomes. Air quality, surface water,and drinking water quality have improved substantially. Exposure to lead, asbestos, andhazardous waste have decreased.

•

Exposure to these six hazards persists today and disproportionately affects underserved
communities. The hazardous waste sites that remain to be remediated are more likely to belocated near vulnerable communities. Non-compliance with drinking water standards occurs
more often in small communities. Air pollution disproportionately affects communities withlower-income or minority populations.

•

The methods used to address the hazards included both standards (for air quality, drinking
water, etc.) and federal funding (for hazardous waste cleanup, sewage treatment, etc.). The

80 Yen, J. H. (2013, January). Lead-Based Paint Poisoning Prevention: Summary of Federal Mandates andFinancial Assistance for Reducing Hazards in Housing.
https://crsreports.congress.gov/product/pdf/RS/RS21688/20
81 Olson, E., & Stubblefield, A. (2021, July 8). Lead Pipes Are Widespread and Used in Every State. National
Resources Defense Council. https://www.nrdc.org/resources/lead-pipes-are-widespread-and-used-everystate
82 U.S. EPA. (2022). Integrated Science Assessment (ISA) for Lead. https://www.epa.gov/isa/integratedscience-assessment-isa-lead
83 Congressional Research Service (2022, February). Controlling Lead in Public Drinking Water Supplies.
https://crsreports.congress.gov/product/pdf/IF/IF11302
84 Zimmer, A. T., & Ha, H. (2017). People, planet and profit: Unintended consequences of legacy building
materials. Journal of Environmental Management, 204(Pt 1), 472–485.
https://doi.org/10.1016/j.jenvman.2017.09.026
85 Ibid.
86 Whitmer, M. (2023). Asbestos Mining and Exposure. https://www.asbestos.com/occupations/mining/
87 U.S. Geological Survey (2021, January). Mineral Commodity Summaries: Asbestos.
https://pubs.usgs.gov/periodicals/mcs2021/mcs2021-asbestos.pdf
47federal government also ensures information sharing – whether directly about air quality andwater quality, or by mandating disclosure requirements for instance around the presence oflead paint.

Policies to Address Impacts of Extreme Weather
In writing this report, PCAST spoke to multiple experts to understand the range of policies that might
be deployed to assist communities vulnerable to wildfires, floods, droughts, climate, and weather
risk.
A frequent concern was that successfully quantifying climate risk across the country is likely to reveal
communities that are at very high risk from extreme weather. If this risk was previously underappreciated, the new information could lead to reductions in property value or breakdowns ininsurance provision. This is not an argument against accurate risk assessment. PCAST’s view is that
if families and communities face a climate risk and they deserve to know about it so they can have an
opportunity to reduce the risk and/or prepare for it. However, the potential to identify communities
at risk means it will be important to understand, develop, and deploy policies that can help.
In the case of extreme weather, current federal programs largely focus on provision of federal funds
supporting mitigation and disaster relief. Flooding is an exception because the federal government isdirectly involved in insurance provision under the National Flood Insurance Program (NFIP). The
federal government also provides crop insurance through the USDA Risk Management Agency,
protecting agricultural producers from drought, high rainfall, and freezes, as well as unexpectedvariation in agricultural prices. These programs, and the historical examples above, suggest a
portfolio of approaches that might be considered over time to address heightened risks from extreme
weather.
•

Insurance: As noted above, the NFIP provides flood insurance to roughly 23,000
communities across the country, and the USDA provides crop insurance. Historically, the
NFIP has lost money, suggesting that the premiums charged have been less than actuarily
fair rates. Our working group heard suggestions that the federal government should provide
insurance subsidies for other types of climate- and weather-related risk, such as wildfires,
perhaps means-tested, in order to help vulnerable communities. While such an approach
would assist property owners in high-risk areas, subsidizing insurance also creates
incentives for households and businesses to remain in these areas, and indeed for further
development. If subsidies are to be used, PCAST views it as preferable to condition premium
reductions or premium support on requirements that property owners make investments
or meet certain standards to reduce exposure or potential damage. This approach, which has
been used in both the NFIP and in private insurance markets, has the benefit of reducing
liability and ideally overall expected costs for insurers and taxpayers, as well as supporting
property owners. It is also important to recognize that the federal government provides
implicit insurance against major hazards through its disaster relief programs. This creates
an incentive for property owners and communities to under-insure. Some countries, such as
France and New Zealand, have mandatory national hazard insurance to address this.

48

•Mitigation grants: The federal government, especially through FEMA, administers a rangeof grant programs to help communities prepare for and mitigate climate disasters. IIJA
includes substantial new funds for this purpose. Improved risk models, in conjunction withsocio-economic data, can help target mitigation grants to ensure that federal funds flow tocommunities in greatest need. One concern however, is that lower-income communities may
not have the resources to develop adaptation and mitigation plans or prepare compelling
grant applications, giving these needy communities less access to the resulting federal funds.
To this end, it would be beneficial to track grant allocations against the type of risk maps
recommended in Recommendation 3.1(a), and if the evidence shows that lower-income
communities are systematically missing out on grant funding, consider providing financial
support to prepare applications, or to NGOs serving those communities. One lesson appears
to be that relocation programs need careful design and execution, as well as extensive
community engagement. A national adaption plan, as recommended in the report, willgrapple with these sorts of difficult trade-offs, which may become increasingly important incoastal and flood-prone areas.

•

Relocation. In recent years, both FEMA and HUD have initiated programs to relocate
communities at high flood risk.88,89 These programs follow an older tradition of buying anddestroying homes that have been badly damaged by flooding. The new “managed retreat”
programs have been criticized on the grounds of equity (for impacting lower-income areas
disproportionately) and for destroying the fabric of existing communities. At the same time,
relocation can move families or even whole communities out of harm’s way, and be costeffective because it reduces future federal liability under the NFIP.

•

Building standards. In July 2022, the Biden-Harris launched the National Initiative to
Advance Building Codes with the aim of helping state, local, tribal, and territorial
governments “adopt the latest, current building codes and standards, enabling communities
to be more resilient to hurricanes, flooding, wildfires, and other extreme events that are
intensifying due to climate change.”90 The initiative builds on FEMA’s Building Codes
Strategy that was released in March 2022.91 As documented in the FEMA and White Houseplans, building codes generally are set at the state and local level, but the federal government
can provide assistance in a variety of ways to help ensure up-to-date codes. Improved risk
quantification has clear value in helping to identify areas where current codes create
vulnerabilities.

88 FEMA fact sheet on support for Community Driven Relocation includes links to relevant grant programs:

https://www.fema.gov/fact-sheet/fema-efforts-advancing-community-driven-relocation
89 HUD Real Estate Acquisition and Relocation Program,
https://www.hud.gov/program_offices/comm_planning/relocation
90 Executive Office of the President (2022, June 1). Biden-Harris Administration Launches Initiative toModernize Building Codes, Improve Climate Resilience, and Reduce Energy Costs [Fact Sheet]https://www.whitehouse.gov/briefing-room/statements-releases/2022/06/01/fact-sheet-biden-harrisadministration-launches-initiative-to-modernize-building-codes-improve-climate-resilience-and-reduceenergy-costs/
91 FEMA (2022, March). Building Codes Strategy.
https://www.fema.gov/sites/default/files/documents/fema_building-codes-strategy.pdf
49•Planning assistance / guidance. Historically, FEMA has played a large role in helping statesand municipalities plan for disasters. For instance, FEMA provides a suite of risk modelingtools through its HAZUS program that are freely available. These tools continue to be widelyused. However, both the underlying climate and risk data and the user interface are
outdated. The types of quantification and data products recommended in our report would
address this current challenge.

Consistent with this final point, our main report has proposed a comprehensive approach topromote the sharing of accurate risk information. PCAST views this as a crucial step to help
households, businesses, and government agencies make sound decisions now, as well as to provide
the necessary foundation for future policy.

50