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.



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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:

Dynamical coupling between the troposphere and stratosphere in Earth System Models

Abstract.
Dynamic coupling between the extratropical tropospheric and stratospheric circulations during
boreal winter and austral spring are primarily mediated on short timescales via vertically
propagating waves generated in the troposphere and their interaction with the stratospheric flow,
but can also be modulated by phenomena that vary on sub-seasonal to decadal timescales. As a
result, stratosphere-troposphere coupling processes need to be taken into account to fully
understand, for example, the occurrence of extreme climate events linked to shifts in the
extratropical storm tracks; climate variability on seasonal time scales such as teleconnections
related to the El Niño-Southern Oscillation (ENSO) and stratospheric Quasi-Biennial Oscillation
(QBO); and the response of the climate system to natural (e.g., solar, and volcanic aerosols) and
anthropogenic (greenhouse gas increase, changes in ozone depleting substances) forcings.
Yet climate models (including those part of CMIP5/6) often lack a proper representation of
stratospheric processes and realistic dynamic stratosphere-troposphere interactions, which can be
related to model-specific configurations and parameterized (e.g., small-scale waves) or absent
(e.g., interactive stratospheric chemistry) processes. These biases are tied to further model biases
in the positions and variability of the tropospheric jets and their connection to extreme events.
We propose to develop process-oriented diagnostics (PODs) to measure the fidelity of key
dynamical stratosphere-troposphere coupling processes and to inform actionable pathways to
future model improvement. Our specific objectives of the proposed project are as follows:
1. Develop PODs that systematically evaluate the two-way stratosphere-troposphere coupling
processes in Earth System Models, and make these PODs publicly available through
implementation in the Model Diagnostics Task Force (MDTF) framework,
2. Apply PODS to CMIP6 simulations (both pre-industrial control and historical) and
simulations of relevant CMIP-Endorsed Model Intercomparison Projects (MIPs) to
diagnose and benchmark how different model configurations impact the representation of
two-way stratosphere-troposphere coupling processes.
This Type 1 proposed project addresses the MAPP competition “Process-Oriented Diagnostics
for Climate Model Improvement and Applications” by developing PODs to benchmark process-
level deficiencies in stratosphere-troposphere coupling in Earth System models. This work would
close a gap in the Model Diagnostics Task Force framework, which currently does not include
PODs that examine these processes and their linkages to related climate phenomena. The
stratosphere-troposphere coupling processes we seek to address are relevant to the Climate
Program Office’s high-priority climate risk areas because they are closely connected with surface
climate extremes driven by changes to the storm tracks and/or teleconnections; and to NOAA’s
mission to advance our understanding of the Earth’s climate system and to use this knowledge to
advance resilience of our Nation. We anticipate our diagnostics and their evaluation to have broad
impacts for better understanding uncertainties in model simulations associated with large-scale
atmospheric dynamics, and for improved representation of coupling processes linked to climate
extremes.

Principal Investigator (s): Amy Butler (NOAA CSL)

Co-PI (s):Zachary Lawrence

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report:

Process-oriented analysis of organized convection and synoptic disturbances in the tropics

Abstract
In recent years, global models have improved such that they exhibit some skill in sea-
sonal tropical cyclone (TC) forecasting and are a useful source of both near-term TC activity
forecast guidance and long-term climate projections. However, many challenges remain, par-
ticularly with regards to TC genesis prediction. Models exhibit a wide spread in their ability
to reproduce the observed TC frequency, with most models typically producing too few TCs
in certain regions, such as the North Atlantic, while producing too many in other regions,
such as the Indian Ocean during monsoon season. These biases contribute to uncertainty
in future projections of TC frequency. While the impacts of mean state biases on the TC
frequency biases have been extensively studied, less attention has been paid to how well
the models represent the synoptic scale disturbances that lead to the formation of TCs. In
particular, it is largely unknown whether and to what extent the degree of synoptic-scale
convective organization affects the probability of synoptic disturbances developing into TCs
and whether the models correctly represent the effect of convective organization. Addressing
this question requires examining how deep convection becomes organized in those distur-
bances and how it interacts with circulation, moisture, and radiation, in both observations
and climate models.

Our project will aim to evaluate climate model simulation of synoptic-scale tropical dis-
turbances, the organization of tropical convection within them, and associated interactions
among convection, moisture, and radiation. We will develop and apply a new set of di-
agnostics for synoptic-scale tropical disturbances and convective organization within them,
filling a gap in the MDTF framework, in which no diagnostic currently exists for tropical
disturbances. After developing them with reanalysis datasets and applying them to high-
resolution CMIP6 historical simulations (from HighResMIP), we will use the new diagnostics
to answer questions such as “Is a greater degree of convective organization associated with
developing disturbances?” and “Do models capture observed differences between developing
and non-developing disturbances?” The new diagnostics for tropical disturbances will be
implemented into the NOAA Model Diagnostics Task Force (MDTF) software package.
The proposed research fits well within the MAPP - Process-Oriented Diagnostics for
Climate Model Improvement and Applications (2864458) competition, as it develops and
applies process-oriented diagnostics to a clearly-identified gap in the existing MDTF soft-
ware package synoptic disturbances and associated organized convection that may serve
as precursors for TCs. The application of the new diagnostics will identify key physical
processes responsible for skillful simulation of synoptic-scale tropical disturbances and TC
formation and target areas for model improvement. This will advance our understanding
of model biases, and could lead to improvement in model simulation of TC genesis and fre-
quency and eventually enhance our ability to evaluate the current and future risk of coastal
storms. It will also improve our understanding of and ability to simulate the disturbances
themselves, which are important sources of tropical weather variability in addition to their
role as TC precursors.

Principal Investigator (s): Allison Wing (FSU)

Co-PI (s):Daehyun Kim (UW), Suzana J. Camargo (Columbia University)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report:

Process-Oriented Diagnostics for the Western Boundary Current Variability and Midlatitude Air-Sea Interaction

Western boundary currents (WBCs), such as the Kuroshio-Oyashio Extension in the North Pacific
and the Gulf Stream in the North Atlantic, are the regions of largest ocean variability and intense
air-sea interaction. In particular at interannual and longer time scales, the WBC variability
generates strong ocean-to-atmosphere heat fluxes, resulting in anomalous diabatic heating that
can impact the large-scale atmospheric circulation and the poleward heat transport in both the
ocean and atmosphere. Therefore, variability in the WBCs and associated air-sea interaction play
fundamental roles in regulating our climate. In addition, the WBCs variability have significant
impact on extreme weather, coastal ecosystem, and sea-level.
Despite the importance of WBC variability and associated midlatitude air-sea interaction, the
WBCs are the regions with some of the largest and longstanding ocean biases in the state-of-the-
art coupled climate models. There have been numerous studies on the mean state biases in
global climate models, in particular in WBC regions, and on the impact of improved spatial
resolution. However, the influence of climate model spatial resolution on the biases of the WBC
variability and associated air-sea interaction is yet to be systematically examined, despite their
strong climate impacts. Here we propose to investigate the nature and impacts of the main biases
of the WBC variability in state-of-the-art climate models based on a set of process-oriented
model diagnostics, and establish their dependence on model resolution, as well as their links to
main large-scale circulation biases. Our process-oriented diagnostics would lead to: (1) a
systematic quantification of the model biases for the oceanic and atmospheric variability in the
WBCs and resulting air-sea interaction, (2) identification of the key processes responsible for the
model biases, and their sensitivity to the horizontal resolution of the model, and (3) improved
understanding of the links between the WBC biases and the simulated large-scale atmospheric
and oceanic circulations. The diagnostics will be first developed based on various state-of-the-art
observational and reanalysis datasets. Then, they will be applied to the state-of-the-art climate
model simulations at standard resolution as well as higher resolution to investigate the role of
model resolutions in the biases and the representation of the associated processes.
This proposal targets the FY 2021 NOAA Modeling, Analysis, Predictions, and Projections
(MAPP) Program solicitation Process-Oriented Diagnostics for NOAA Climate Model
Improvement and Applications by proposing to better understand and benchmark process-level
deficiencies related to the WBC ocean variability and associated air-sea interaction in the CMIP6
and HighResMIP simulations, with additional in-depth analyses of the GFDL and NCAR models
using the proposed set of process-oriented diagnostics. Our proposed work is also directly
relevant to NOAA’s long-term climate goal of advancing scientific understanding, monitoring, and
prediction of climate and its impacts, to enable effective decisions, especially since the
improvement in the climate model processes related to the WBC variability and associated air-
sea interaction has significant implications for the prediction of our climate and its impacts.

Principal Investigator (s): Young-Oh Kwon (Woods Hole Oceanographic Institution)

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report: Kwon_Y_Process-Oriented_FY20MAPP.pdf

Process-oriented evaluation of oceanic equatorial waves in the Indian and west Pacific Ocean forced by intraseasonal westerly wind events

Equatorial shallow water ocean wave modes (OWMs), such as eastward-propagating Kelvin
waves and westward-propagating equatorial Rossby (ER) waves, help regulate the depth of the thermocline, ocean heat content (OHC), ocean currents, sea surface height (SSH), and sea surface temperature (SST). Their modification of upper-ocean thermal characteristics influences the evolution of important coupled air-sea phenomena including the Madden-Julian oscillation (MJO), the Indian Ocean dipole (IOD), and the El Niño Southern Oscillation (ENSO). In the tropical Indian and Pacific Oceans, OWMs are frequently forced by strong, but short lived intraseasonal westerly wind events (WWEs; occurring every 30-70 days and lasting 3-21 days) acting on the ocean surface. The strength and meridional structure of the WWE forcing and the ocean mean state (including the depth of the thermocline and the stability of the upper ocean) help determine the amplitude and propagation characteristics of the OWMs.
For the first time, diagnosis of intraseasonal WWE-forced OWMs in CMIP models is possible
with daily output of the depth of the thermocline in several CMIP6-member models, which was
not available in previous CMIP archives. Our main goal is to diagnose the fidelity of
intraseasonal WWE-forced OWMs in CMIP6 and other model databases relative to observations and link OWM biases to biases in the WWE forcing, or to biases in the ocean mean state. We will also examine changes to WWEs, tropical OWMs, and the ocean mean state under climate change. Our work plan is to:
1. Diagnose the fidelity of tropical OWM spectra and spatial variance patterns in CMIP6
models and other climate model databases relative to observations.
2. Characterize the frequency, intensity, and meridional structure of intraseasonal WWEs in
models and observations.
3. Assess the realism of intraseasonal WWE-forced OWMs as a function of WWE intensity
and meridional structure in models relative to observations.
4. Evaluate the stability of the ocean mean state in models relative to observations and its
relationship to OWM amplitude and phase speed.
5. Quantify changes in OWM climatology, WWE statistics, and ocean stability under climate
change and relate OWM changes to changes in WWE characteristics and ocean stability.
This work will result in a tropical OWM process-oriented diagnostic (POD) with several
diagnostic components that will be added to the Model Diagnostic Task Force software package.
The OWM POD fills “clearly-identified gaps in the existing MDTF software package” including
“open- and coastal ocean systems'' and advances the evaluation of coupled processes in climate models. Our objectives are highly relevant to one of the main goals of the MAPP Process-Oriented Diagnostics call to “better understand and benchmark process-level deficiencies that result in model performance biases for simulated Earth system and climate phenomena.” More broadly, this work advances NOAA’s long-term goal to “advance [the] understanding of the Earth’s climate system.” Ultimately, understanding the processes that lead to OWM biases is needed to improve OWM representation in models and obtain better predictions of climate modes influenced by OWMs, such as the MJO, the IOD, and ENSO.

Principal Investigator (s): Emily Dellaripa (CIRA)

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report: Dellaripa_E_Process-Oriented_FY20MAPP.pdf

Processed-Oriented Diagnostics of Aerosol-Cloud Interactions in CMP6 Models

Abstract
Aerosols represent a key source of uncertainty in global climate models. Through
the absorption and scattering of shortwave radiation, aerosols reduce the incoming
solar radiation at the surface and thus offset part of the warming resulting from
increases in anthropogenic greenhouse gases. In addition to this direct radiative effect,
certain types of aerosols are known to act as cloud condensation nuclei, altering the
cloud albedo and lifetime. Differences in modeling the effective radiative forcing
from aerosol-cloud interactions (ERFaci) are a substantial source of uncertainty in
predicting climate change.

Aerosol-climate interactions (ACI) play an important role in climate projections
despite the limited ability of models to represent aerosol and cloud processes accurately.
Indeed, climate models can disagree on both the sign and magnitude of the radiative
effects from aerosol-cloud interactions. This disagreement reflects, in part, the absence of
a consistent methodology to quantify their effects in models. Indeed, even the direct
radiative effects of aerosols are rarely calculated explicitly. The lack of a coherent
framework to quantify the radiative impact of aerosol-cloud interactions limits our
ability to compare its importance across different models, or even between
different versions of the same model. This is compounded by the lack of regionally-
resolved observations of ACI on a global scale, that account for the presence of co-
varying meteorological conditions on ACI. Thus, despite their fundamental role in
determining both historical and future climate change, the magnitude of ACI remains
poorly constrained in models.

This proposal aims to fill this gap by developing a set of diagnostics for evaluating
aerosol-cloud interactions in models that can be derived from existing
CMIP6 simulations, or from standard model performed by labs runs during
the model development cycle, and can be applied to both historical and future
emission scenarios. The model diagnostics will be compared to observationally-
constrained estimates of ERFaci for low (warm) marine clouds which are the
dominant source of uncertainty of ACI in models. These estimates use satellite
measurements to provide observational constraints on the cloud susceptibility to
aerosols within a framework that accounts for the role of varying environmental
factors in modulating the strength of aerosol–cloud interactions.
Through these diagnostics, we aim to both quantify and better constrain
the representation of aerosol-cloud processes in CMIP6 models. This will directly support
the MAPP program goal to “advance understanding of biases generally affecting CMIP6-
era and next-generation models and to identify targeted model improvements that
can improve model fidelity.”

Principal Investigator (s): Brian Soden (UM)

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report: Soden_B_Process-Oriented_FY20MAPP.pdf

Subtropical to Subpolar Atlantic Model Biases Addressed through Process-Level Diagnostics (Sub2Sub)

The proposed work is a collaborative project between the University of Wisconsin Madison and
the National Center for Atmospheric Research (NCAR) to understand how the North Atlantic
Current (NAC) path influences the Atlantic meridional overturning circulation (AMOC) in climate
models of varying configurations and resolutions. The goal of this project is a thorough model
comparison of NAC pathway biases – a longstanding issue in ocean and climate models – and their
relationship with thermohaline biases from the subtropics into the subpolar North Atlantic. As part
of this proposal, a set of process-oriented diagnostics (PODs) will be applied to output from the
Coupled Model Intercomparison Project version 6 (CMIP6), Ocean Model Intercomparison
Project (OMIP), High-Resolution Model Intercomparison Project (HiResMIP), and to experiments
from an in-development version of the Community Earth System Model (CESM) coupled to the
Modular Ocean Model version 6 (MOM6). The most novel of the PODs to be implemented are
two NAC pathway identification algorithms, one applying image processing techniques on
currents and the other drawing from the atmospheric thermal front identification literature.
Analysis will focus on combining the NAC pathway POD with surface-forced water mass
formation, allowing for a dynamically-defined decomposition of surface formation between
subtropical and subpolar regions, as delineated by the NAC. Decomposing formation further by
individual surface fluxes and by the overlap of density classes with surface fluxes will allow
process-based characterization of ocean and climate model biases. Sources of pathway-related
biases will be identified through comparisons of models from the multimodel ensembles described
above, focusing on the effect of horizontal resolution in models with MOM. An expected outcome
of this project is a clearer understanding of sources of NAC pathway and subtropical-to-subpolar
formation model biases, identifying potential directions for ocean model development.
Relevance: Through its analysis of ocean and coupled model simulations of historical climate,
this proposal supports both NOAA’s mission to “advance our understanding of the Earth’s climate
system” and the CPO MAPP program’s overall mission to “enhance the Nation's and NOAA's
capability to understand, predict, and project variability and changes in Earth's climate system.”
The proposed work aligns with the MAPP program’s primary objectives of “improving Earth
System models”, “supporting an integrated Earth System analysis capability” and “improving
methodologies for global to regional scale climate analysis, predictions, and projections” by
identifying sources of ocean model biases in the subtropical to subpolar North Atlantic. The current
Model Development Task Force (MDTF) diagnostic software have a large gap in open ocean
model diagnostics. The North Atlantic Current pathway identification and water mass
transformation PODs described in this proposal would partly fill this gap. Through close
collaboration with NCAR, opportunities for MOM6 improvement will be identified in CESM-
MOM6 development simulations. Because MOM6 is also the ocean component model in NOAA
ESMs, the CESM-MOM6 diagnosis described here will indirectly contribute to the improvement
of NOAA models. In collaboration with NCAR, POD software will be contributed to MDTF for
further dissemination and to CESM community diagnostic packages.

Principal Investigator (s): Elizabeth Maroon (UW-Madison)

Co-PI (s):Stephen Yeager (NCAR), Gokhan Danabasoglu (NCAR)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2021

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

Final Report: Maroon_E_Process-Oriented_FY20MAPP.pdf

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:

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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:

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