Funding Opportunities & Funded Projects

FY23 Research Opportunities

For both competitions, Science for the 21st Century Western U.S. Hydroclimate and Products for Areas of Climate Risk, and Projections for Societally-Relevant Problems

LOIs are due September 1, 2022 by 5pm and Full Proposals are due November 21, 2022 by 5pm.



   Search   Reset Search  
Enter Search Value:
- without any prefix or suffix to find all records where a column contains the value you enter, e.g. Net
- with | prefix to find all records where a column starts with the value you enter, e.g. |Network
- with | suffix to find all records where a column ends with the value you enter, e.g. Network|
- with | prefix and suffix to find all records containing the value you enter exactly, e.g. |Network|

During this project, we will develop a flash drought monitoring framework using various gridded datasets that together capture the multivariate nature of this high-impact climate phenomenon and its rapid evolution over sub-seasonal timescales. The proposed research includes three tasks that will lead to new insight into the characteristics of flash drought and enhance our ability to detect their onset, determine their severity, and monitor their compound and cascading impacts. First, we will develop a flash drought intensity index (FDII) that can be used to identify flash drought events and to categorize their severity. The development of an FDII is an important advancement for flash
drought monitoring and research because it will provide a more complete measure of flash drought severity than existing methods that focus only on their rate of intensification without considering the severity of the drought itself. The FDII method will then be applied to a set of atmospheric and land surface variables to develop a comprehensive and multivariate climatology of flash drought occurrence and severity across the U.S. The climatology will include variables depicting anomalies in precipitation, evaporative demand, soil moisture, evapotranspiration, and vegetation health that together capture the drivers and impacts of flash drought. Detailed analysis of the climatology will
provide valuable information regarding the timing and severity of flash drought in each dataset. The timing of rapid changes in each variable, whether those changes occur simultaneously or sequentially, and the severity of the drought conditions provide guidance regarding the compound and cascading impacts associated with flash drought. Results from these tasks will then inform
development of the multivariate flash drought monitor during the final part of the project.

The proposed project is directly relevant to the MAPP “New Climate Monitoring Approaches and
Products for Areas of Climate Risk” competition through the development of an experimental flash drought monitor that will provide a comprehensive assessment of the spatial extent and severity of these high-impact climate features using a multivariate monitoring framework. By using datasets
depicting anomalies in meteorological, soil moisture, and vegetation conditions, the flash drought monitor will be able to capture the multivariate linkages between the atmosphere and land surface components of the climate system and also be grounded in the physical drivers of variability and
change. Because flash drought is often accompanied by extreme temperatures and leads to rapid decreases in water resource availability, the proposed research will help address the monitoring needs for MAPP’s extreme heat and hydroclimate high priority climate risk areas. It will also directly benefit the Climate Prediction Center and the authors of the weekly U.S. Drought Monitor analyses through development of a framework that will enhance their ability to monitor the rapid evolution and severity of flash drought. Finally, the multivariate flash drought climatology and FDII monitoring framework will be valuable resources for the authors of the National Climate
Assessment because they will support the generation of regional assessments of projected changes in the occurrence, spatial extent, and severity of flash drought.

Principal Investigator (s): Jason Otkin (UW Madison)

Co-PI (s):Trent Ford (University of Illinois at Urbana-Champaign)

Task Force:

Year Initially Funded:2022

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

Final Report:

Evaluation and development of a Southeast US heat vulnerability index using a wet bulb globe temperature approach

 

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:

Monitoring smoke hazards across the western United States: Tools for fire scientists, policymakers, and stakeholders

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:

Atmospheric and oceanic BL processes over the eastern equatorial Pacific: development of process-oriented diagnostics to identify errors in climate models with implications to ENSO teleconnections over the United States Affiliated Pacific Islands

Abstract
Climate models’ limitations in representing the bottom-heavy vertical circulation over the
eastern equatorial Pacific result in erroneously simulated ENSO–induced teleconnections. Over Hawaii and USAPI, direct impacts of this weakness include errors in representing multi-
seasonal persistence of droughts/floods. Our overarching hypothesis is: The eastern equatorial Pacific cause and effect relationship for variations in convection can be determined from a
comprehensive process-based diagnosis (down to the individual parameterization level) of
systematic changes in vertical structure in response to changes in ocean-atmosphere surface
characteristics. This includes meridional SST gradient-induced surface convergence.
Our objective, towards identifying the initial source(s) of model errors in climatological
mean states, is to develop process-oriented diagnostics (PODs) that: (i) Assess “co-occurring
parameterized processes in models; (ii) Include heterogeneous observational sources; (iii) Can
be applied to daily and shorter timescales (model time steps at 30 minutes) to identify errors
due to fast processes; and (iv) Quantify model development progress in CAM7/AM5. The
proposed PODs target processes related to: (a) Atmospheric boundary layer and near surface
interactions; (b) Vertical distributions in the troposphere and (c) Upper-ocean mixing.
Crucially PODs developed here will be used interactively during CAM7/AM5 development,
where each successive prototype simulation can be objectively assessed. This analysis
workflow will improve the model development processes considerably, in that performance
changes can be linked directly to parameterization improvements.

Our proposed research targets the MAPP competition that focuses on “key issues in the
representation of Earth system processes in CMIP6-era and developmental models to improve
model fidelity”, with a particular focus on “clearly-identified gaps in the existing MDTF
software package”. Continuing assessment in moist convection processes, our proposed PODs
branch-off from the ongoing efforts with primary focus on processes related to climatological
basic-states, atmospheric and oceanic boundary layer, and co-located column processes.
Recognizing that in data-sparse regions native model biases can dominate in reanalysis, we
employ currently under-utilized in-situ, field, and radiosonde observations (taken and
maintained by NOAA), to develop PODs based on ground observations. Process-based
diagnosis of CFSv2 (NOAA operational model) is lacking, and the PODs developed here will
be applied to forecasts. Our proposed work has close synergy with NOAA strategic plan for
improved understanding and model applications relevant to high-priority climate risk areas.
Specific to CPO are extreme droughts and heat, and coastal flooding over Hawaii and USAPI.
In their studies, the PIs have extensively employed most of the PODs. Implementing them into the MDTF framework will therefore be straightforward. Deliverables include a set of process-based metrics and post-processed data from models and observations that aid in assessing the improvements in recent model versions.

Principal Investigator (s): H Annamalai (University of Hawai`i)

Co-PI (s):Richard Neale (NCAR), Kelvin Richards (Univ. of Hawaii), Arun Kumar (CPC/NCEP/NOAA)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

Competition: Process-Oriented Diagnostics for Climate Model Improvement and Applications

Final Report:

Combined Land and Ocean Drivers of U.S. Drought Determined from Information Theoretic Evaluation of Observations and Coupled Models

Anomalous atmospheric circulation features are a prime source of drought over the US and can
often be associated with remote SST influence, often modulated by other factors. Once drought
is underway, drying soil and withering vegetation may exacerbate drought locally and regionally
by reinforcing conditions inimical to precipitation. The proposed project will investigate the
holistic drivers and responses to drought over the US in an Earth system context using
observations, reanalyses, subseasonal forecasts and global model sensitivity studies. Given
that probabilistic predictability is derived from both land and ocean components on subseasonal
to seasonal time scales, there is great potential to harvest more forecast skill in a complete
approach. Three questions are posed: (1) What are the combined roles of ocean and land in the
initiation and maintenance of drought over the US as suggested by observationally based data
sets? (2) Do forecast models employed on time scales relevant for drought onset and demise
(subseasonal to seasonal) replicate the relationships indicated in observational data? (3) Can the
interplay between remote ocean and near-field land anomalies as drivers of drought be
diagnosed in sensitivity studies using the latest global forecast model?

The proposed research has three objectives: (1) Characterize the roles of ocean and land in
drought by examining the coupled processes each have with the atmosphere in a systems
approach using established metrics, updated with techniques from information theory that
minimize arbitrary assumptions; (2) Diagnose operational and research models that currently
provide subseasonal forecasts, portraying their coupled ocean-land-atmosphere performance,
and potentially attributing drought forecast skill to specific model behaviors; (3) Specifically
apply and diagnose the behavior of the Unified Forecast System (UFS) in the area of drought
prediction, including sensitivity studies to isolate drivers.

Metrics of surface-atmosphere interaction will be used to determine the spatial and temporal
variation of process chains that link surface anomalies to drought. Methods from information
theory will provide novel extensions to convention linear/Gaussian based statistics, giving
nonparametric estimates of active process networks in the Earth system. Observational data
will be used to provide a model-free estimate of drought driver relationships and responses,
which will be compared to reanalysis-based estimates to provide an independent verification
data set for forecast model evaluation. Existing retrospective subseasonal forecasts will then be
evaluated for process fidelity, including lagged responses important for operational forecasting.
The Unified Forecast System (UFS) will also be evaluated and used for sensitivity studies to
explore specific US drought cases.

The research will address all three drought research priority areas by (1) identifying surface-
atmosphere interactions and their related processes that lead to drought; (2) identifying key parameters,
applying methodologies and pathways to use coupling metrics that can contribute
to the capacity of NIDIS to identify situations of elevated drought predictability and risk; (3)
Associating processes and feedbacks between land, ocean and atmosphere that contribute to
drought predictability, prediction and warning.

Principal Investigator (s): Paul Dirmeyer

Co-PI (s):Bohua Huang, Chul-Su Shin

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

Identifying alternatives to snow-based streamflow predictions to advance future drought predictability

For large populations across the western U.S., water supply prediction relies centrally on
knowledge of spring snow conditions, where anomalous snowpack can provide critical early
warning of drought. Yet, a warmer future portends for reduced snowpacks, presenting a major
challenge to the current paradigm of snow-based forecasting. The water management landscape
across the west is variable, with some smaller systems relying exclusively on snow information
to make local statistical forecasts, while others use operational forecast information from more
sophisticated statistical or dynamic forecasts that also leverage snow information.
Therefore, a comprehensive evaluation of the expected changes for snow-based prediction
techniques under historical and future climate conditions is essential. To overcome potential
shortcomings from current methods, there is a need for evaluation of possible alternatives to
snow-based predictions to better inform management and planning for key parts of the western
U.S., like the Intermountain West (IMW) and Pacific Northwest (PNW) Drought Early Warning
System (DEWS) regions.

We propose to develop and evaluate new techniques for drought prediction that will suit the
needs of western U.S. water management entities, considering regional characteristics and shifts
to a warmer, less snow-dominated future climate. The proposed analysis includes four elements:
1) An assessment of how changes in snowpack will impact drought predictability under future
climate. We focus on analyzing operational-type statistical forecasts, as well as conducting
new simulations with the state-of-the-art National Water Model (NWM), a possible
companion or successor model to the current River Forecast Center prediction models.
2) An evaluation into whether drought predictive skill can be recovered by including additional
non-snow predictors (e.g. soil moisture, geophysical, and extreme indicator data, etc.) with a
pipeline of machine learning algorithms informed by rigorous feature selection and tested
across alternative predictive model structures.
3) Obtaining direct input from water entities, via an online survey and in-person workshop, to
ensure that our experiments include current techniques and are relevant to decision makers.
Support letters have been received from regional DEWS partners (Colorado Climate Center
in the IMW and CIG in the PNW), and with other partners that work closely on issues of
water supply, the Colorado Basin RFC, the Western Utilities Climate Alliance (WUCA), and
Seattle Public Utilities to provide input on regionally dependent characteristics.
4) An integration of Elements 1-3 to identify the best drought indicators for each region,
considering current and future conditions, and constraints for adaptation by decision makers.

We address MAPP Priority Area C by evaluating drought predictability using current operational
techniques in the western U.S., focusing on the intervening process of snowpack evolution and
precursor mechanisms. We also address Priority Area B by using machine learning to develop
new/improved methods for drought early warning, applying probabilistic ensemble techniques
for two DEWS regional pilots, where a workshop will focus on pathways for future adoption.

Award Announcement: https://cires.colorado.edu/news/team-innovate-new-ways-predict-drought

Climate Risk Area: Water Resources

Principal Investigator (s): Ben Livneh

Co-PI (s):Joseph Kasprzyk, Benet Duncan

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

Advancing understanding of drought prediction from environmental stressors

Plants and their strategies to deal with heat and moisture stress may play significant roles
in the evolution, maintenance and severity of drought. Water and carbon are exchanged through
plant stomates, and plants can regulate these exchanges in response to increased moisture stress,
becoming more efficient at using water and leading to less transpiration and increased retention of
soil moisture at root depth, thus dampening the effect of drought on the plants, but possibly
intensifying it in the atmosphere. On the other hand, during short dry periods trees can deliver soil
moisture from deeper roots to the atmosphere, moderating atmospheric dryness. Current methods
to monitor and forecast droughts have, at best, highly simplified representation of these and other
vegetation feedback mechanisms.

Mechanistic land surface process models with detailed representations of vegetation have
the advantage of being able to explicitly diagnose which plant responses alter stomatal regulation
and ecosystem function under varying environmental conditions. This project aims to better
understand plant-drought feedbacks using such a model, the Simple Biosphere Model v4.2 (SiB4).
Our working hypothesis is that a detailed biogeophysical land surface model that couples energy
and biogeochemical fluxes with explicit treatment of soil hydrology, canopy conductance and
turbulent transfer will do better job capturing precursor conditions that end up being classified
as drought. We propose to analyze cases of North American droughts from the satellite era and
(a) examine whether they evolved as characterized by drought outlook monitors, and (b) identify
the possible positive or negative feedbacks from vegetation response to climate conditions. In situ
radiation budget and trace gas measurements from NOAA GMD’s measurement networks will be
used to evaluate and improve SiB4 simulations, and a novel aspect of this project is to incorporate
new data constraints that have not been previously considered for drought monitoring. Carbon and
water fluxes are intrinsically linked, and vegetation responses to drought conditions are observable
in atmospheric carbon measurements. We will also make use of a variety of space-based
observations such as solar induced fluorescence (SIF), which reflects dynamic photosynthetic
responses to heat and water stress, and can be simulated by SiB4.

Understanding process-based plant responses to climate stressors such as high temperature
and deficits of vapor pressure and soil moisture has the potential to help predict the occurrence
and severity of a drought. This project is directly responsive to CPO’s strategy to address
challenges in the area of “Climate impacts on water resources” and climate intelligence capabilities
regarding “Observations and monitoring” and “Earth system science and modeling”. We directly
address CPO MAPP Competition #3 (Characterizing and Anticipating U.S. Droughts’ Complex
Interactions) Priority Area A: “Identify the array of complex interdisciplinary interactions that lead
to US drought and intervene during the evolution of drought, focusing on key processes and
feedbacks”, with emphasis on land cover and environmental effects, and Priority Area B: “Identify
key parameters and develop new/improved methodologies to more integrally characterize drought
occurrence”, where the proposal will demonstrate this methodology for a test drought case. These
will add to NIDIS’ resources for monitoring and predicting drought from a new perspective.

Climate Risk Area: Water Resources

Principal Investigator (s): Ian Baker

Co-PI (s):Lori Bruhwiler, Aleya Kaushik

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

An improved understanding of the interacting factors that influence the evolution and severity of droughts in the USA in present and future climates

The increasing demands that will be placed on the water and agricultural resources of the
USA in the future as populations rise and the climate changes, make an improved understand-
ing of drought, its predictability, severity and projected future changes, more critical than
ever. The unfortunate truth is that our ability to accurately forecast individual droughts will
always be limited by the important role played by internal atmospheric variability, which is
inherently unpredictable on the timescales of relevance for seasonal to decadal drought. One
area, where we could still make substantial progress toward improving drought predictability,
is in our understanding of the role of complex and non-linear feedback processes in altering
drought severity and impacts. If we can understand all the relevant feedback processes that
play a role in drought evolution, faithfully represent them in our models, and understand
how they are projected to change in a warmer climate, this will go a long way to enhancing
our capacity to predict impacts arising from drought events.

Using a state-of-the-art Earth System Model (ESM), the community Earth System Model
(CESM), we will fully characterize the role of land-atmosphere coupling processes
in governing drought severity. Capitalizing on recent land- and atmosphere-model im-
provements, we will use a constrained circulation ensemble approach to systematically
quantify how the inclusion of a variety of land-atmosphere processes impact on
the evolution and severity of drought events and how we expect these impacts
to change in a warmer climate. These processes include plant hydraulics, dust emis-
sions, irrigation, wildfires, and dynamic vegetation. We will also compare with a variety of
observational data sets and case studies to examine the fidelity of the model’s representation
of drought events. This will lead to improved understanding of the origins and projected
changes in drought and will, for the first time, quantify in a controlled and systematic
manner, the impact of land-atmosphere coupling processes that are at the forefront of our
modeling capabilities.

Broader impacts and relevance to competition: This research will advance our funda-
mental understanding of the complex interacting feedbacks that influence drought severity
and determine their impacts, both in the present and future climate. This will improve our
capacity to predict drought in the near and long term using ESM’s and will inform model
development efforts worldwide by the identification of important interacting processes that
should be included for accurate drought simulation. This research is therefore of direct
relevance to the call and to MAPP in general. The information gained has the potential
to inform agricultural and water resource management, ultimately benefiting society as a
whole. It will provide a new dimension to drought monitoring and attribution studies, such
as those performed by the NOAA Drought Task Force, by increasing our capacity to isolate,
and understand, the role of land-atmosphere coupling processes in drought severity.

Climate Risk Area: Water Resources

Principal Investigator (s): Isla Simpson

Co-PI (s):David Lawrence

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

A probabilistic characterization of the interaction between large-scale atmosphere, land surface and fire to enable improvement of drought early warnings over the Great Plains and California

We propose to characterize and understand the integral effect of the coupling between
atmosphere, land surface and fire generated aerosols on drought triggers and persistence for a
seasonal scale, and its interannual and decadal variability over the United States (US) Great
Plains (GP) and California. These two regions consist the first and second largest economy in
the US, but are prone to extreme droughts. Yet, we cannot predict such droughts over a seasonal
or longer time scale, including the recent extreme droughts in both regions. Statistical models
based on observed persistence of the past droughts appear to provide better skill for seasonal
predictions than the dynamic models in both regions. Yet, we do not fully understand the cause
behind these apparent skills and whether they represent real predictability. We have investigated 
the effect of multi-scale coupling between land surface, shallow and deep convection and large-
scale circulation on the onset and persistence of summer droughts over the US GP, in
collaboration with the Texas Water Development Board (TWDB). In addition, we have started
research on California droughts and experimental seasonal prediction for winter rainfall in
collaboration with the California Department of Water Resources (CDWR). As a logical next
step, we propose to investigate the coupling between atmosphere, land surface/vegetation and
fires, and its impact on drought onset and persistence over the US GP and California with
emphasis on non-local land surface feedbacks and the effects of biomass burning aerosol on
clouds, rainfall and thermodynamic stability of the atmospheric boundary layer, focusing on the
following science questions:
• What large-scale anomalous circulation patterns are responsible for triggering seasonal
drought memory and influence its climate variability?
• How does the coupling between drought and heatwaves influence drought onsets and
intensity regionally?
• Can fires intensify droughts?

We will jointly use machine learning tools, such as Self-Organizing Map (SOM), to provide
a probabilistic observational characterization of the circulation patterns and associated land
surface and fire conditions that contribute to droughts, and detailed process studies to understand
the interplay between droughts and warm surface temperature anomalies in triggering and
amplifying drought through their impacts on heat low, the low-level jets, vegetation response and
aerosols impacts on clouds and precipitation. We will use a suite of interdisciplinary datasets,
including in situ and satellite observations and global and regional reanalysis products, including
those provided by NOAA, the Climate Forecast System Reanalysis Version 2 (CFSRv2), the
North American Regional Reanalysis (NARR) and the North American Land Data Assimilation
System (NLDAS) products. The anticipated results will enable us to provide better seasonal
predictions of rainfall anomalies for TWDB and CDWR. In doing so, we will provide improved
information to support drought early warning for two major stakeholders of the National
Integrated Drought Information System (NIDIS) Southern GP and California/Nevada regions.

 

Principal Investigator (s): Rong Fu

Co-PI (s):

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

Understanding the Mechanisms Leading to Early Warning of Meteorological and Hydrological Drought in the U.S. Caribbean

Introduction and Rationale: In groundwater-limited settings, such as the U.S. Caribbean,
societal, ecological, and agricultural water needs are largely supplied by regular rainfall.
Consequently, these islands, Puerto Rico and the U.S. Virgin Islands, are vulnerable to even
short, rapid-onset, dry spells, known as “flash drought,” and drought early warning is immensely
valuable for civil authorities on the islands. In the wake of the 2015 drought, precipitation
deficits were linked to the early arrival of an elevated hot, dry, dust-laden feature, termed the
Saharan air layer (SAL). The SAL increased static stability, largely suppressing convective
precipitation during a typically rainy time of year. The SAL is a precursor of Caribbean drought.

Summary: This project will first examine and diagnose drought through a suite of
hydrometeorological variables, drought indices, and drought definitions, such as the Palmer
Drought Severity Index, Standardized Precipitation Evaporation Index, etc. Episodes of low
drought metrics will be compared to in-situ hydrologic measurements, such as USDA Soil
Climate Analysis Network data, in the U.S. Caribbean to infer their ability to capture flash
drought onset. Next, concurrent meteorological fields from renanalysis products will be
examined during the flash drought periods to identify the local meteorological conditions driving
flash drought and how these differ from conventional drought. Third, drought frequency will be
characterized as a function of SAL activity over the U.S. Caribbean. Self-organizing maps, a
machine learning technique, will mine historical 2D fields of the Galvez-Davison Index, a
recently developed tool well-suited for detecting SAL outbreaks, to determine common historical
SAL behavior during the hydrologically critical early rainfall season in the U.S. Caribbean.
Teleconnection indices and seasonal numerical weather forecasts will be analyzed for their
ability to provide early warning of SAL, and therefore drought, in the U.S. Caribbean.

Broader Impacts: This project provides critical monitoring and drought early warning
improvements in U.S. Caribbean islands with limited water resources for human populations,
unique drought-vulnerable ecosystems, and a recent history of economic hardship and natural
hazards, including a major drought in 2015. The project includes a co-PI located in the region,
and one of the objectives of this project is to actively engage with local stakeholders who have
recently asked for improved drought information. This project will serve a large population that
is an ethnic and linguistic minority in the U.S., and the project will actively recruit and mentor
students who are under-represented in climate science.

Relevance: This project is relevant by engaging Priority Area C by examining the predictability
of U.S. droughts, as well as their multi-scale evolution. Seasonal model forecasts are mined for
the presence of a precursor mechanism that can inform contextualized forecasts of drought
likelihood and severity. The project establishes a new methodology for prediction by identifying
the flash drought indices which correspond most strongly to parameters that can be derived from
existing seasonal forecast models. On a broader, programmatic level, this project advances the
MAPP primary objective #3 to improve methodologies for global to regional scale analysis,
predictions, and projections.

Climate Risk Area: Water Resources

Principal Investigator (s): Thomas Mote

Co-PI (s):Grizelle González, Paul Miller, Craig Ramseyer

Task Force: Drought Task Force

Year Initially Funded:2020

Competition: Characterizing and Anticipating U.S. Droughts’ Complex Interactions

Final Report:

Page 1  of  9 First   Previous   [1]  2  3  4  5  6  7  8  9  Next   Last  


MAPP


Follow us

Contact

Dr. Annarita Mariotti
MAPP Program Director, on detail to EOP/OSTP
P: 301-734-1237
E:

Dr. Daniel Barrie
Acting MAPP Program Director
P: 301-734-1256
E:

Courtney Byrd
MAPP Program Specialist
P: 301-734-1257
E:

«September 2022»
MonTueWedThuFriSatSun
2930311234
567891011
12131415161718
19202122232425
262728293012
3456789

ABOUT US

Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather. 

CPO HEADQUARTERS

1315 East-West Highway Suite 100
Silver Spring, MD 20910