Funding Opportunities & Funded Projects

With losses mounting from coastal flooding and associated impacts, better information on
present and future coastal flood risk is needed by stakeholders ranging from the military to state
and local governments who collectively oversee trillions of dollars of coastal infrastructure;
small to large businesses in coastal economies; and roughly half of the US population who live in
coastal areas. Available flood risk information is largely limited to present day 1% and 0.2%
annual chance flood levels from the FEMA National Flood Insurance Program, does not take
into account ongoing Sea Level Rise (SLR), and is insufficient for future planning. For example,
due to SLR the national rate of minor, but disruptive, high tide flooding has doubled since about
2000 and is accelerating in over 40 coastal locations (Sweet et al, 2020). An authoritative source
of coastal flood likelihood information is needed that will support assessments of contemporary
(baseline) risk of various coastal ocean and flood hazard levels (e.g., 10%, 20%, 99% annual
chance events; NOAA NWS minor, moderate and major coastal flood thresholds) along U.S.
coastlines sufficient to inform a variety of decisions. The basis for such an assessment requires a
multi-decadal reanalysis of coastal water levels that is well validated to NOAA tide gauge
records.
This project will compute a sequence of multi-decade (1979-2019) coastal water reanalyses
using the ADCIRC storm surge, tide, and wind-wave model, driven by long-term meteorological
reanalyses from NOAA and ECMWF. Predicted coastal water levels along the US eastern and
Gulf of Mexico coasts will be compared to NOAA NWLON tide gauge observations and used in
a recently developed data assimilation scheme for ADCIRC that corrects for unmodeled, low
frequency contributions to the total coastal water level. To account for missing tropical cyclone
energetics in the global meteorological reanalysis products, we will use a blending approach to
insert tropical cyclones into the background meteorology using ADCIRC’s Generalized
Asymmetric Holland Model on a moving, nested, high resolution grid. The ADCIRC-based
coastal water level reanalysis will form the basis for examining future water level statistics that
factor in sea level rise and future meteorological conditions. The water level reanalysis results
will be made publicly available on THREDDS Data Servers for access and dissemination to the
broader research community.

Principal Investigator (s): Brian Blanton

Co-PI (s):Rick Luettich, William Sweet, Greg Dusek

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

Introduction to the Problem: It is well understood that stress on the human body from high temperatures is exacerbated by high humidity. The National Weather Service uses a familiar metric (the heat index) that incorporates temperature and humidity to quantify this effect. However, long-term U.S. monitoring of heat stress indicators relevant to human health has been limited to the period from around the middle of the 20th century to present because of a lack of digitally available humidity data prior to 1948. This omits some of the most important heat events in U.S. history; namely, those associated with the 1930s Dust Bowl. On the basis of temperature alone, the Dust Bowl era includes some of the most intense and frequently occurring events across much of the eastern two-thirds of the country over the last 120 years. Extending the availability of heat stress metrics that combine both temperature and humidity over the past century is critically important for providing historical context to current heat wave trends.

Rationale: There is an opportunity to remedy this critical data gap. The Climate Database Modernization Program (CDMP), which ran from 2000 to 2011 at the former National Climatic Data Center (now the National Centers for Environmental Information [NCEI]), funded the keying of early hourly weather observations going back to the late 19th century. This set of data has never been publicly available but is now being incorporated into the new Global Historical Climatology Network-hourly (GHCNh) of NCEI. This study will provide the scientific basis for new heat wave monitoring products by characterizing, for the first time, the features of heat
waves in the contiguous United States over a century-plus time frame that includes the 1930s Dust Bowl era and uses human health–relevant heat metrics that incorporate humidity.

Brief Summary of Work: Human heat stress metrics will be created (as time series) using the new hourly humidity station data. These station time series will then be aggregated to create regional and national indicators that will be used to characterize long-term trends, decadal-scale variability, and individual event features. The Twentieth Century Reanalysis version 3 (20CRv3) will be used to fill in missing data, supporting the computation of indicators back through 1895.
This will match the period of record of NCEI’s climate division dataset. Our project will address how early 20th-century heat waves, particularly those during the 1930s, compare to modern events with regard to human health stress metrics. The 20CRv3 will be used to characterize the synoptic-scale features of heat events, including regional lower-tropospheric wind flow patterns that affect heat and moisture advection and hemispheric patterns of mid-tropospheric geopotential height anomalies, including blocking high occurrences.

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Broader Impacts and Relevance to Competition: This proposal will target the high-priority climate risk area of extreme heat using a dataset that was previously unavailable for climate analysis. It will provide a new monitoring indicator suite (heat index and related metrics and circulation features) extending back to 1895. The longer period of analysis, encompassing the epic 1930s heat waves, will advance understanding about recent trends in heat waves and the nature of multidecadal variability.

Principal Investigator (s): Kenneth Kunkel (NCSU)

Co-PI (s):Brooke Stewart, Laura Stevens, Ronald Leeper (NCSU)

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

 

The 4th US National Climate Assessment (2018) identified extreme heat as one of the  Southeast’s most pressing human health climate risks in urban areas and is exacerbated by  an aging population, warming climate, and rapid urbanization. Much of the work in the  National Climate Assessment on extreme heat is based on apparent temperature (e.g., heat  index) extremes, which largely do not measure the physiological impact of heat stress on  the human body. Furthermore, at-risk groups (e.g., low income communities and elderly  populations) may lack sufficient cooling or have underlying health conditions. These  groups are especially threatened by warm and humid nighttime temperatures, neither of  which are measured appropriately by traditional methods. For human health applications,  wet bulb globe temperature (WBGT) is a better measure of how heat affects humans, and is  currently used in operational settings (e.g., military and athletics). However, WBGT has not  been used widely in observational climate studies, due to the lack of observational datasets.  Further complicating matters, many methods exist for calculating WBGT, some of which  may not be suitable for the Southeast US.  

Broader Impacts and Relevance to the Competition & NOAA’s Climate Program Office 

Heat is the deadliest weather-related hazard.  While we propose a rigorous evaluation of  WBGT, we recognize the limitations of the measure when interfacing with the public.  WBGT values are not intuitive, and a fatal WBGT (i.e., 94 ̊F) may be perceived as safe when  assumed to be on par with traditional heat index values. In response to the NOAA Climate  Program Office competition for MAPP: New Climate Monitoring Approaches and Products for  Areas of Climate Risk, we propose to evaluate WBGT formulas and calculate climatologies  and trends across the Southeast US, with a focus on urbanized and “seasonally-urban”  areas. Per the solicitation, we will develop a new climate monitoring product. This product  will be a Heat Vulnerability Index (HVI) based off WBGT analyses with an exposure,  sensitivity, and adaptive capacity component. We will test the HVI with four National  Weather Service Weather Forecast Offices and one Military partner.  

We will develop a real-time HVI monitor, similar to the US Drought Monitor, for operational  use. NOAA’s Climate Program Office has identified extreme heat in urban regions as an area  of focus. Our results will help support NWS’ Weather Ready Nation initiative by identifying areas of vulnerability useful for successful prediction and preparation of extreme heat  events. Furthermore, this index can be used in future National Climate Assessment  activities as a more accurate snapshot of extreme heat in the Southeast US.  

We will address the project goals through five tasks: (1) Gather observations and evaluate  WBGT estimation formulas; (2) Develop WBGT climatologies and perform trend analysis;  (3) Build a WBGT based Heat Vulnerability Index (HVI); (4) Test gridded WBGT data and the HVI with project partners; (5) Participation on MAPP Task Force. 

 

Principal Investigator (s): Kathie Dello (NCSU)

Co-PI (s):Jared Rennie (NCSU)

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

Introduction to the problem: Among weather-related hazards, extreme heat is one of the leading causes of death worldwide, and in future decades a changing climate is likely to disproportionally increase the frequency of extreme heat events, leading to a greater potential for heat-related mortality in the future. Extreme low temperatures can also lead to increased mortality, and as recent events have shown, may not disappear entirely in a  warming world. There is a challenge in assessing extreme temperature events spatially, as human vulnerability is location-dependent, and thus, no specific temperature values are universally relevant for such an analysis. This concern has led to myriad ways in assessing the occurrence of extreme temperature events. 

Rationale and objectives: The overarching objective of this proposal is focused on the development of excess temperature event identifications that are based on systematic percentile thresholds of apparent temperature across North America, which also incorporate the acclimatization of the population to prior conditions. These events will include both hot and cold, as well as relative hot and relative cold, where relative refers to the time of year. These events will be developed into prospective monitoring products. The robustness of these products will be assessed through different data sets. Using the event definitions that are developed, we then aim to transition these to become operational monitoring products and real-time forecast products.  

Summary of the work to be completed: Spanning North America, we will first calculate daily values of multiple extreme temperature products for each grid cell across four different reanalysis products, as well as the ISD historical station observation network. We will analyze the historical frequency, duration, spatial extent, and population impacted by these events, and cross-validate the results between the station data and the reanalyses. We will then work with our NOAA collaborator and consult with an advisory board of likely end users to transition all research products above into real-time monitoring products. We will also set up a webpage for real-time forecasting of all products for up to 60-day lead times.  

Relevance to the competition and NOAA: The proposed research will develop new daily-scale monitoring products that identify absolute and relative extreme temperature events. These products will be used to help monitor the occurrence, duration, areal extent and total population impacted by extreme temperatures. A standard set of products can unify and simplify information used by local National Weather Service offices and public health leaders to issue excessive heat and cold warnings across climatologically diverse areas of North America. The competition specifically calls for products that monitor extreme heat, that may be able to fill gaps in the National Climate Assessment and the USGCRP  Indicators Platform, and those that utilize the underused reanalysis datasets and operational climate analyses – all of which are objectives that this research would meet.

Principal Investigator (s): Cameron Lee (Kent State University)

Co-PI (s):Karin Lynn Gleason (NOAA), Scott Sheridan (Kent State University)

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

Fire activity across the United States has increased dramatically across the western United States in recent decades. For example, California experienced a fivefold increase in annual burned area from 1972-2018. Drivers of these trends include warming temperatures and drought, as well as  decades of fire suppression that have allowed fuel to accumulate, leading to a “fire deficit.”  Whatever the drivers, the scientific consensus is that anthropogenic climate change will bring warmer and drier conditions to the West, providing more fuel for fires to consume and further enhancing fire activity. These trends are concerning in part because emerging evidence shows that smoke from fires, like other airborne particles, has a deleterious effect on human health. Improved monitoring of the magnitude of the smoke exposure currently experienced by populations across the western United States would help policymakers and stakeholders plan for present-day wildfires and pave the way toward strategies for future wildfires.  

Here we propose to improve understanding and update the monitoring of smoke hazards resulting from wildfire activity in the western United States. By combining long-term climate records,  observations from satellites, new fire emissions inventories, and models of land cover and  atmospheric chemistry, we will address the following questions:  

1. Can we quantify the impact of anthropogenic climate change on current smoke exposure in the West? 

2. Which regions are especially vulnerable to the long-term fire deficit and would benefit the most from prudent land management? 

3. Which fire-prone regions, in addition to those identified in #2, have the greatest potential to expose large populations to smoke pollution? 

4. Using a machine learning approach, can we update the monitoring of smoke plumes in GOES satellite data? 

Our proposed research would lead to two monitoring products. First, we would construct a smoke risk index, identifying those regions where potential fires could lead to the greatest smoke exposure among populations downwind, allowing government agencies to more wisely deploy scarce resources. Second, we would devise a machine-learning algorithm to streamline the process by which smoke plumes are detected in satellite data. The current method to detect such plumes involves human analysts, but machine learning promises to make that method more efficient,  accurate, and reliable.  

The project targets the NOAA MAPP call for New Climate Monitoring Approaches and Products for Areas of Climate Risk. It promises to develop climate modeling capabilities and applications relevant to decision-makers based on climate analyses, predictions, and projections. Throughout our project, we will work closely with NOAA scientists Heath Hockenberry (NIFC) and John  Simko and Wilfrid Schroeder (NESDIS). We will also engage Christine Wiedinmyer (CIRES),  who is lead developer of the Fire Inventory from NCAR (FINNv2).

 

Principal Investigator (s): Loretta J. Mickley (Harvard University)

Co-PI (s):

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

Coastal flooding is caused by oceanic and atmospheric processes that interact on a range of timescales to affect the local, regional, and global sea level. Major flooding during storms may be caused by large waves, high seas, and heavy rainfall. Minor flooding sometimes occurs without a storm, if the tides and sea level are higher than normal. For example, daily record high water levels in Miami, Florida were set nearly every month during 2019, which caused flooding nuisances such as road closures. Most of the high water levels occurred during fair weather,  without obvious physical explanations for elevated sea level. Recent sea-level extremes are affecting not only Miami but many low-lying regions of the U.S. Coast and Pacific Islands. With ongoing sea-level rise, and the likelihood of future increasing seasonal-to-interannual variability,  coastal sea level anomalies are likely to cause more frequent and severe flooding.  

The University of Hawaii Sea Level Center developed a web product to monitor recent and past water levels, compared to the long-term climatology and records at tide gauge stations. The visualization product seeks to inform how tide gauge observations relate to what is typical for the time of year, or to compare with extremes during past years. It was inspired by similar types of graphical displays of weather and climate extremes, such as heat waves, widely available on the web but uncommon for coastal water levels. Missing from this product is near-real-time cataloging and the ability to provide an understanding of sea-level extremes, which stakeholders desire to improve situational awareness and help inform about future occurrences.  

We will develop a monitoring product for tracking and understanding daily, monthly, and seasonal sea level extremes for the U.S. Coast. The product will describe the characteristics of sea level anomalies as they occur using tide gauges, satellite observations, and climate analyses.  A diagnostic of the physical contributions and forcing mechanisms will be included to explain why sea levels are elevated, as outreach suggested this is of interest. Our methodology will be adapted from existing diagnostic tools, which have been applied to understand the physics of past high sea-level events. We will expand and generalize established methodologies for assessing sea-level variability to be applicable nationally and in near real-time.  

Our research is relevant to the MAPP competition “New Climate Monitoring Approaches and  Products for Areas of Climate Risk”. The primary objective will be to develop a sea-level monitoring product that communicates how daily-to-seasonal fluctuations of coastal water levels evolve with respect to the longer-term climatology. In addition to monitoring extreme sea levels,  the product will also aid in understanding the processes contributing to sea level anomalies.  Deliverables will include a near-real-time assessment of high sea-level events affecting the U.S.  Coast. Product development will complement NOAA environmental monitoring and reporting efforts such as the NOS CO-OPS seasonal High Tide Bulletin and Coastal Inundation  Dashboard, as well as the NESDIS NCEI Climate Monitoring page; the latter of which currently does not include a coastal sea level component. To aid in the transition-to-operations process, we will utilize common datums and epochs established by NOS, as well as indicate location-specific water level thresholds associated with coastal hazards established by NOAA. The new monitoring product will support NOAA’s long-term goals to increase climate intelligence concerning “weather and climate extremes” as well as “coasts and climate resilience”.

Principal Investigator (s): Matthew Widlansky (University of Hawaii Sea Level Center (UHSLC)

Co-PI (s):Gregory Dusek ((NOS-CO-OPS)

Task Force:

Year Initially Funded:2022

Competition: New Climate Monitoring Approaches and Products for Areas of Climate Risk

Final Report:

Increased resolution, complexity, and accuracy of Earth System Models (ESMs) has led to direct use of model projections to inform regional climate change impact assessments, including studies on changes in hydrology, water resources, and water security at sub-continental scales. However, ESMs often exhibit substantial regional biases in hydroclimate mean states, fluxes and - importantly - sensitivities. Biases in land model sensitivity project onto hydroclimate change signals under warming, undermining their accuracy and adding uncertainty to the already broad spread in climate change projections from model experiments. 

It is therefore necessary to understand and reduce not just absolute biases, but also sensitivity biases, in order to increase the credibility and robustness of climate change impact assessments based on ESMs. Recent research on runoff sensitivity - the change in runoff as a function of changes in precipitation and temperature - showed a large potential for uncertainty reduction in model projections through the use of observational constraints. However, sensitivity biases remain inadequately measured and their causes poorly understood, thereby impeding model improvement, because process-oriented diagnostics (PODs) that target hydroclimate sensitivities and important features of their climatology, rather than simply mean states and fluxes, are missing from major diagnostics packages. 

We propose to develop and refine a suite of PODs characterizing the fidelity of the hydrologic components - primarily runoff - of land surface models, to be applicable to models in the Coupled Model Intercomparison Project 5 and 6 (CMIP5/6). For the development of the PODs we will leverage several observational products and a range of existing and forthcoming simulations with the Community Earth System Model 2 (CESM2) and Community Land Model 5 (CLM5), including a Perturbed Parameter Ensemble, as well as Land Surface, Snow and Soil moisture MIP (LS3MIP) simulations as part of CMIP6. We will conduct additional sensitivity experiments with CLM5 to reveal model parameters relevant for runoff sensitivity and to refine the PODs.

The outcomes of this research will be a suite of PODs that provide process-based understanding of the origin of hydrologic sensitivity biases in ESMs, which will uncover opportunities for targeted model improvement. We will implement the new PODs in the NOAA Model Diagnostics Task Force (MDTF) Diagnostics Package and, with potential additional support from the Department of Energy, in the International Land Model Benchmarking (ILAMB) package (see Section 4.6 for more details).

 

Principal Investigator (s): Flavio Lehner (Cornell University)

Co-PI (s):Andrew Wood, David Lawrence (NCAR)

Task Force:

Year Initially Funded:2022

Competition:

Final Report:

Statement of the problem: As climate models improve in resolution, and their output is
increasingly integrated into risk assessment, planning, and adaptation efforts, it is critical to
assess their ability to represent coastal processes. In particular, the representation of dynamic sea
level (DSL) is of considerable societal interest, due to the substantial vulnerability of economic,
cultural, and ecological resources to sea level change and variability. Preliminary analysis
indicates that time-mean dynamic sea level (MDSL) gradients are often poorly represented at the
coast in current-generation climate models, calling into question their ability to robustly project
future changes and variability.
Rationale: Emerging models, especially those with higher horizontal resolution (≤0.25o), show
an improved representation of coastal ocean dynamics. Simultaneously, new observational
products are becoming available that better resolve DSL near coastlines, and recent theoretical
advances permit a physical interpretation of DSL gradients. These elements provide a strong
basis for: 1) development of coastal sea level diagnostics; and 2) interpretation of the processes
underlying model-data differences.
Summary of Work: This project will develop mean dynamic sea level (MDSL) diagnostics near
coastlines that allow the representation of key underlying processes to be assessed, prioritized,
and improved in the next generation of climate models. Four activities will be pursued:
(1) Regional MDSL reference products, with improved representation near coastlines, will be
developed by merging altimeter and tide gauge datasets in key boundary current regions;
(2) Biases in modeled MDSL fields (relative to reference products) will be analyzed and
interpreted, using output from a hierarchy of GFDL ocean and climate models spanning a
range of horizontal resolutions;
(3) Key processes underlying GFDL model MDSL biases will be identified using alongshore
and area-integrated momentum balances;
(4) Targeted, process-oriented, MDSL diagnostics will be integrated into the MDTF
framework and applied to the modern ocean state of CMIP6 simulations, providing
context for ongoing ocean model development at GFDL.
Relevance to NOAA, MAPP, the competition, and society:
The improved understanding of processes influencing MDSL, and their representation in a
hierarchy of climate models, will: 1) inform priorities for model development efforts at GFDL
and NCAR (through the MOM6 ocean model) and 2) lead to an improved basis for assessments
of coastal flooding in a changing climate (a CPO high-priority climate risk). The proposed model
diagnostics directly address gaps in the area of open- and coastal ocean systems within the
MDTF software package. For the larger scientific community, these diagnostics will be
integrated into the MTDF and applied across a wide range of CMIP6 models, allowing a broad
understanding of the processes responsible for climate model biases in different regions of the
global ocean. The project will also develop improved MDSL products along the coastlines near
boundary currents. In total, this project extends NOAA's core capabilities of understanding and
modeling the changing climate system, developing projections of impacts, and providing
decision support to meet the broad societal challenge of coastal and climate resilience.

Climate Risk Area: Coastal Inundation

Principal Investigator (s): Christopher Little

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

Competition: MDTF

Final Report:

Problem addressed and rationale: Process-oriented diagnostics (PODs) aim to characterize physical processes in a manner that relates directly to mechanisms essential to their simulation, providing guidance for improvement of a climate/weather model or assessment of its ability to address a specific research question. The predecessor Team project (PIs of which form part of the current team) advanced an initial bare-bones framework into a community-based software framework that brings process-oriented diagnostics into the diagnostic suite for modeling centers at the Geophysical Fluid Dynamics Laboratory (GFDL) and the National Center for Atmospheric Research (NCAR). Experience with the recent development suggests multiple areas where refinement and expansion would be beneficial for both modeling centers and POD developers.

Work Summary: The proposed work will build on the previous Model Diagnostics Task Force (MDTF) framework and coordinate with the Type I individual proposals to expand the open frame-
work to entrain PODs developed by multiple research teams into the development stream of the modeling centers. The framework developed over the previous two phases specifies POD protocols
for the target model version and the comparison to observations and permits diagnostics to be placed in a multi-model context using results from the CMIP6 archive. The Type II framework team main-
tains consistency with the previous lead team while expanding the coordination with the diagnostic streams at GFDL and NCAR, and formalizing common standards with the Department of Energy
(DOE) Coordinated Model Evaluation Capabilities (CMEC) effort to further coordinate US-based model evaluation efforts. The proposed work will include the following elements. (1) The current
Team project has established GitHub-based documentation, setup and configuration protocols. With the successful incorporation of a substantial number of PODs, developments on the software side
proposed for the next phase expand on this process, emphasizing maintainability, interoperability portability, provenance and usability. (2) PODs targeting related phenomena and on similar time-
scales will be identified and grouped to coordinate development teams and assist navigation of the results. This organization will also help model developers assess the output frequency requirements
for PODs targeting phenomena on different climate timescales. (3) A task force will be led by the Type II team, modeled on the current MDTF with regular teleconferences, facilitated scientific con-
ference sessions, and coordinated publications. New community-building activities planned by the Type II team include “Developer days” to facilitate communication between climate model and POD
developers and tutorials to familiarize diagnostic developers with coding best practices in the context of the framework. (4) The Type II team will explore ways to include mean-state and variability
diagnostics as context for PODs. Both GFDL and NCAR have expressed the need to modernize their legacy diagnostic suites and the MDTF Framework can help prevent redundancy. Enhancements to
the framework will be implemented to handle common functions, such as atmospheric pressure-level sub-setting and ocean depth range integrals. (5) Similar to the previous Team Proposal, the team will
develop tools and additional prototype PODs in key areas.

Relevance to competition: This proposal addresses the call for the “Modeling, Analysis, Predictions, and Projections (MAPP) Competition 2: Process-Oriented Diagnostics for NOAA Climate Model
Improvement and Applications” for a Type II proposal that advances the model diagnostics software package led by the MDTF and a synergetic process for integrating results of individual projects on process-oriented diagnostics. It proposes infrastructure for code and data sharing that engages researchers in model evaluation and facilitates integration of their research products into the diagnostics packages used by modeling centers, as well as dissemination of this information. It addresses NOAA's long-term climate goals by strengthening foundational capabilities, combining observations with modeling and prediction, and communication of scientific understanding.

Principal Investigator (s): David Neelin (UCLA)

Co-PI (s):John Krasting (GFDL), Andrew Gettelman (NCAR), Eric Maloney (CSU), Paul Ullrich (UC Davis)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

Competition: Modeling, Analysis, Predictions, and Projections (MAPP) Competition 2: Process- Oriented Diagnostics for NOAA Climate Model Improvement and Applications

Final Report: Neelin_D_Process-Oriented_FY20MAPP.pdf