Funding Opportunities & Funded Projects

FY21 Research Opportunities

For both competitions, New Climate Monitoring Approaches and Products for Areas of Climate Risk, and Process-Oriented Diagnostics for Climate Model Improvement and Applications

LOIs are due August 17, 2020 by 5pm and Full Proposals are due November 30, 2020 by 5pm.



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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

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.

 

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
modelling 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.

 

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.

 

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:

Droughts over Hawaii and the U.S-affiliated Pacific Islands: a framework to understand processes and feedbacks, assess predictability and reduce uncertainties.

Undoubtedly, the phase and intensity of El Niño-Southern Oscillation (ENSO) exert
detectable imprints on the onset, severity and maintenance of droughts over Hawaii and the
U.S. affiliated Pacific Islands (USAPI). However, apart from ENSO characteristics, local
air-sea interactions, remotely forced teleconnections, phase and intensity of other natural
modes of variability [Pacific Meridional Mode (PMM) and/or Pacific decadal variability],
are also expected to contribute to observed drought characteristics. Observations show
occurrences of unprecedented persistence beyond ENSO timescales implying factors other
than ENSO are involved, and until now, no studies have attempted to explain the processes
and feedbacks that led to such drought persistence. Only as of May 2019, USAPI drought
information has been included in the U.S. Drought Monitor (USDM). In consultation with
local stakeholders, there is a need in the Insular Pacific to identify precursors to
monitor and predict the severity and persistence of droughts. Thus, we will devise a
framework with a focus on understanding processes and feedbacks, assessing causality
and reliably quantify uncertainties in their prediction.

Our overarching goal is to work towards the development of a Drought Early Warning
System (DEWS) in the Pacific and contribute to the Drought Task Force. To accomplish
this goal, our objectives are: (i) based on specific stakeholder-relevant thresholds, study
characteristics of severe and prolonged droughts from a multitude of observations and
reanalysis products; (ii) assess the predictability of drought life cycle, and develop a robust
system for drought monitoring; and (iii) work closely with various regional stakeholders and
National Integrated Drought Information System (NIDIS), and link the research results to
improve drought prediction. Three inter-linked tasks will be carried out, and they are: (1)
assess the role of multi-scale climate modes of variability, remote teleconnections in
conjunction with ENSO in determining droughts and their persistence, and study processes
and feedbacks; (2) apply skill metrics in the suite of hindcasts and real-time forecasts
performed with NMME models, and devise methods to reliably quantify uncertainties; and
(3) seek frequent feedbacks from stakeholders and NIDIS, and develop a process-based
monitoring system for drought life cycle (onset, amplification, persistence and withdrawal).
Our proposed research targets the MAPP competition “Characterizing and Anticipating
U.S. Droughts’ Complex Interactions” and specifically, the competition identified research
topics – “Identify the array of complex interactions that lead to US droughts, focusing on
key processes and feedbacks, explain why extreme or prolonged droughts occurs”, and
“Examine the predictability of US droughts considering the intervening processes and their
multi-scale evolution, focusing on identifying precursor mechanisms, methodologies for
prediction, with an overall reduction of uncertainties”. Project deliverables will enhance
USDM efforts for the USAPI through the development of a Drought Early Warning
System (DEWS) in the Pacific focused on the tropical Pacific. We will also contribute to
the Drought Task Force by sharing the developed approaches and metrics.

Award Announcement: https://www.soest.hawaii.edu/soestwp/announce/news/new-funding-supports-drought-predictions-in-hawaii-and-pacific-islands/

Principal Investigator (s): H Annamalai

Co-PI (s):Arun Kumar, John Marra

Task Force: Drought Task Force

Year Initially Funded:2020

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

Final Report:

Regional Influences of Vegetation on Complex Droughts in North America

Problem Introduction — Extreme droughts diminish freshwater availability, with significant
consequences for the wellbeing of people and ecosystems. Surface vegetation is central to drought
because of its large influence on water and energy fluxes at the land-atmosphere boundary and
thus on soil moisture and streamflow. A key unknown is the extent to which vegetation and
droughts jointly shape each other: how vegetation specifically shapes regional drought
characteristics, and how vegetation-drought interactions evolve with increasing CO2 and climate
change. The mechanisms by which vegetation and drought are coupled and how that coupling
interacts with changes in climate and CO2 to alter future droughts must therefore be understood to
best inform risk management decisions in drought-prone regions.

Rationale for proposed work — Our proposed work is motivated by the need to advance
understanding of complex vegetation-drought coupling, which is critical for improving model
representations of processes affecting near- and long-term drought predictions.
Summary of Proposed Work — Our proposed research tests two hypotheses: (1) historical
terrestrial vegetation trends and variability have influenced the seasonal-scale characteristics of
historical extreme droughts in North America; and (2) Earth System Model (ESM) projections of
North American vegetation greening and land-surface drying are inconsistent with—and thus need
to be constrained by—the observed influence of vegetation on historical extreme droughts in North
America. The first hypothesis will be tested with observations and models for historical extreme
droughts, such as the recent California (2011-15), Texas (2010-11), and Great Plains-Midwest
(2011-2012) droughts. We will trace vegetation-drought feedbacks and how these complex
interactions shaped previous drought characteristics and influenced their predictability. We will
specifically quantify what role, if any, vegetation played in the onset, evolution, and termination
of historical severe seasonal-to-interannual droughts. The second hypothesis requires the use of
the latest generation of coupled ESMs from CMIP6, along with insights gleaned from our historical
analysis to characterize the influence of vegetation-drought interactions on future North American
drought risks.

Relevance to NOAA’s goal and to the competition — Our proposal targets “Competition 3,”
which seeks to improve our “understanding of how climate affects drought processes, [...] the
relevant processes and feedbacks, and [link] this understanding [to] a more integrated
characterization of droughts and improved probabilistic predictions from seasons to decades.”
We will assess the role of vegetation in complex North American droughts using observations and
CMIP6 models. Our approach will identify the real-world role of vegetation in historical complex
droughts and the systematic model biases in droughts arising from vegetation to improve our
estimates of drought risks over the coming decades and enable drought stakeholders to mitigate
such risks. This goal aligns tightly with the efforts of the NOAA Drought Task Force (DTF), as
well as the NIDIS objectives of improving model predictions, and understanding drought
processes, causes, and associated complex interactions. We will also collaborate on this research
with Brad Udall of the Colorado Water Center and the USGS Southwest Climate Adaptation
Science Center, a key partner of NIDIS.

 

Award Announcement: https://news.dartmouth.edu/news/2020/08/justin-mankin-lead-national-drought-task-force

 

Principal Investigator (s): Justin Mankin

Co-PI (s):Jason E. Smerdon, Richard Seager

Task Force: Drought Task Force

Year Initially Funded:2020

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

Final Report:

Disentangling complex interactions and feedbacks among droughts, fires, and snowpack in the western U.S. by integrating observations and models

United States (U.S.) droughts have significant impacts on climate and human systems, causing
substantial damages to the environment and socio-economic development. Droughts can trigger
chains of complex interactions among climate elements, which in turn can further affect drought
evolution. Snowpack and fires, two key elements affecting hydroclimatic and socio-economic
systems, can interact with and feed back to droughts in complicated ways, particularly in the
western U.S. For example, observations (e.g., Abatzoglou and Kolden, 2013) showed strong
correlations between burned area and drought index, snow water equivalent, and soil moisture over
Rocky Mountains in the past decades. Moreover, the western U.S. droughts are shown to increase
in the past and expected to become more frequent/intense in the future, along with increasing fires
and declining snowpack. However, our current understanding of the interactions and feedbacks
among droughts, fires, and snowpack is still very limited, which hinders accurate predictions and
projections of the U.S. droughts and related hydroclimatic and socio-economic effects. Thus, it is
imperative to have an integrated understanding and quantification of these complex interactions.
The overarching project goal is to disentangle and quantify the complex interactions and feedbacks
among droughts, fires, and snowpack in the western U.S. by integrating observations and models
to improve predictions and projections of drought characteristics and impacts. We propose to
address three key scientific questions and tasks:
1. What are the quantitative characteristics and relationships of droughts, fires, and snowpack
evolution in the western U.S. based on observations? We will integrate remote sensing and in-
situ observations to quantify the characteristics (e.g., intensity and duration) and relationships
of droughts, fires, snowpack, and environmental variables (e.g., soil moisture and precipitation)
in the western U.S., with a focus on extreme/prolonged drought events and DEWS regions.
2. How well do state-of-the-art models capture the characteristics and relationships of droughts,
fires, and snowpack evolution in the western U.S.? We will enhance and validate the Noah-MP
land surface model coupled with the Weather Research and Forecasting with Chemistry (WRF-
chem) model by implementing fire-related processes (e.g., fire occurrence, spread, heat release,
vegetation change, and aerosol emissions) that interact with droughts and snowpack.
3. What are the mechanisms for drought-fire-snowpack interactions and feedbacks in the western
U.S.? We will conduct simulations using the enhanced Noah-MP-WRF-chem model to quantify
drought-fire-snowpack interactions (e.g., through effects of fire-induced aerosol and vegetation
changes on precipitation and snow), and compare with the observational analysis in Task 1.
This project will have the following deliverables:
1. Improved understanding of interactions and feedbacks among droughts, fires, and snowpack in
the western U.S.;
2. Enhanced version of the community Noah-MP land surface model coupled with WRF-chem
that is able to capture the key interactions among droughts, fires, and snowpack processes;
3. A drought prediction model based on observations and machine learning tools for future
application and advancement in drought monitoring, warning, and prediction systems.

 

Principal Investigator (s): Cenlin He

Co-PI (s):Michael Barlage, Fei Chen, Wenfu Tang

Task Force: Drought Task Force

Year Initially Funded:2020

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

Final Report:

Observed and modeled interactions between droughts and heat waves for the Northeast US

 Drought and heat waves both have a range of severe societal and ecosystem impacts and often
share the characteristics of hot, dry days. The links and potential feedbacks between the two
phenomena, however, are not well understood and can vary regionally, seasonally, and relative
to the definitions used for both types of events. Moreover, there are complicated differences in
time and spatial scales between the two phenomena, adding further complexity to their
interactions. Investigating the relationships between droughts and heat waves through
observational and modeling analyses has the potential to improve mechanistic understanding,
subseasonal to seasonal (S2S) predictions, and climate projections of both phenomena.

The goal of the proposed work is to understand the interactions between droughts and
heatwaves, and assess current model skill in simulating and predicting these phenomena and
their interactions. We will use machine learning and moisture tracking techniques to objectively
identify and classify these events into various “types” based on daily circulation data, as a
foundation for dynamically-based investigation. We will then use these observed results as a
basis for assessing the predictability of the relationships and the ability of current climate models
to reproduce the relationships. We focus on the Northeast US warm season (May - Sep), when
heat waves have the largest absolute magnitudes and impacts. This focus allows for a detailed
examination of the underlying drivers of and interactions between the phenomena, and we expect
the techniques developed in the project to be directly applicable to other regions.

The primary scientific objectives are to: 1) Identify and investigate the characteristic daily
circulation patterns for droughts, heat waves, and their interactions, using the "machine learning"
techniques of Self Organizing Maps (SOMs) and K-Means Clustering (KMC) applied to a suite
of reanalysis data for the Northeast US warm season; 2) Identify and investigate the
characteristic moisture pathways and their relationships to circulation patterns for droughts, heat
waves, and their interactions via a set of moisture tracking methods, including Lagrangian back
trajectories and a climate model with integrated (online) water tracers; 3) Investigate the
medium-range and S2S predictability of the circulation patterns and their relationships, and
analyze the dynamical implications; 4) Examine the ability of CMIP6 climate models to
reproduce the key circulation patterns and relationships.

The proposed research is relevant to all three priority areas in the MAPP call by considering a
key process and potential feedback for drought - heat waves - and how they might relate to
extremes or onset of drought; by considering new methodologies, including drought analysis
based on daily circulation patterns identified via "machine learning;" and by considering
predictability and climate projections. The proposed work considers complex interacting
processes ranging from synoptic to seasonal timescales, and will provide the necessary
framework for an informed and practical assessment of S2S predictions and climate projections
for these processes, which are necessary to advance NOAA’s goal of addressing climate-related
societal challenges.

 

Principal Investigator (s): Matthew Barlow

Co-PI (s):Christopher Skinner

Task Force: Drought Task Force

Year Initially Funded:2020

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

Final Report:

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MAPP


FY20-Footprint

Contact

Dr. Annarita Mariotti
MAPP Program Director
P: 301-734-1237
E: annarita.mariotti@noaa.gov

Dr. Daniel Barrie
MAPP Program Manager
P: 301-734-1256
E: daniel.barrie@noaa.gov

Amara Huddleston*
MAPP Communications & Program Analyst
P: 301-734-1218
E: amara.huddleston@noaa.gov

Courtney Byrd*
MAPP Program Assistant
P: 301-734-1257
E: courtney.byrd@noaa.gov

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