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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Process-oriented diagnosis of tropical cyclone genesis and intensification in high-resolution global models

Despite recent improvements, many global climate models (GCMs) still show strong biases
in the representation of tropical cyclone (TC) activity, especially its frequency and intensity.
These GCM biases limit the reliability of TC sub-seasonal and seasonal predictions and future
projections. A lack of diagnostics that could provide insights into process-level errors in the
model representation of TCs has slowed model improvement.


We propose a project focused on the diagnosis of process-level errors in the model
representation of TC genesis and intensification. Our proposed project will build upon the
success of our ongoing project, during which we have developed process-oriented diagnostics
for TCs by adapting diagnostics that were originally developed for the Madden-Julian Oscillation
(MJO) and convective self-aggregation. No widely-accepted such process-based diagnostics for
global TC modeling existed before our current project. The diagnostics we developed have been
applied to a limited number of high- and low-resolution GCMs, which has allowed us to identify
processes that are key to TC intensification and genesis. In the proposed work, we will extend
the development and implementation of these diagnostics so that they may be fully utilized as
a community tool and may guide NOAA model development:
• First, we will examine key processes associated with TC genesis and intensification in
long term satellite observations and reanalysis products, using multiple observations
to quantify uncertainty. This will provide a “reference” version of our diagnostics
against which the model representation of the same processes can be validated. This
process-level evaluation against observations is crucial to model improvement.
• Second, while the diagnostics developed have been applied to a limited number of
model simulations that have been obtained in an opportunity-based manner, the
diagnostics and comparison with observational results need to be applied to a wider
group of models. We will take advantage of the upcoming model intercomparison
projects (CMIP6/HighResMIP/PRIMAVERA) to evaluate and identify biases in the
model simulations;
• Third, we will use the new NOAA model (GFDL AM4/CM4), which has been already
developed and shows decent capability in simulating TCs globally, to perform targeted
experiments guided by the results of our ongoing project and the proposed
observational analyses. The result of the targeted simulations will help improve the
NOAA model, and also would provide useful information to other modeling groups;
• Lastly, we will translate the existing codes and scripts to open source languages and
implement it into the NOAA MDTF diagnostics package to maximize the accessibility
of the diagnostics.


This project fits well within the MAPP Competition entitled “Addressing Key Issues in
CMIP6-era Earth System Models”, by developing and using process-oriented diagnostics to
identify the source of GCM biases in TC simulation and by providing paths toward the model
improvement. This advancement in our understanding of TC simulation will be extremely
valuable for improving the next generation of climate models, which is vital for making robust
projections of future TC activity and its impacts; a key component of NOAA’s long-term goals.

Principal Investigator (s): Daehyun Kim (University of Washington)

Co-PI (s):Wing, Allison (Florida State University) Camargo, Suzana (Lamont-Doherty Earth Observatory, Columbia University) Zhao, Ming (NOAA/GFDL)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

Process-oriented Model Evaluation for the North American Monsoon

The objective of this proposal is to develop process-oriented diagnostics to evaluate global
model representation of the North American monsoon (NAM) and explore the pathways to model
improvements. The NAM is chosen to be the focus of the project because of its significance to the
United States, and also because it serves as an ideal testing ground for model evacuation and
improvement owing to the important roles of many fundamental physical processes and their
interplay with the large-scale monsoon circulation. We will focus three aspects of the NAM, its
moist thermodynamic perspective, the link between the continental monsoon to the subtropical
northeastern Pacific cloud regime, and the multi-scale nature of the NAM. Process-oriented
diagnostics will be developed in the convective quasi-equilibrium framework to evaluate the
seasonality, structure, intensity and variability of the NAM. The simulated convection and cloud
processes will be evaluated using satellite and site-specific data from the ob4MIPs. In particular,
the synergetic analysis of the CloudSat and MODIS will help to link the deficiencies in simulated
cloud processes to uncertain parameters in microphysics schemes. In addition, two bulk metrics,
which link model performance and physics formulation, will be tested and are expected to provide
insights into model improvement. Although we focus on the NAM, the proposed research
addresses some common issues in climate models and will contribute to improvement of the
overall model performance.


The GFDL models (CM4, AM4 and fvGFS) will be employed to assist the development and
testing of the diagnostics and metrics. Perturbed-physics ensembles will be carried out using CM4
and AM4 in the weather forecasting mode, and the high-frequency output will be evaluated to
examine fast-physics error growth and constrain parameter uncertainties based on observations.
Climate simulations will be further carried out to examine slow error growth. In addition, the
fvGFS will be run at the seasonal-prediction mode with a configuration similar to the GFDL fvGFS
experimental 10-day forecasts (i.e., 13-km globally uniform resolution with an interactive, refined
grid of 3-km resolution). These simulations will be used to assess climate model errors, especially
in representing multi-scale processes and weather/climate extremes. The simulations will also help
to explore the capability of the fvGFS in seamless prediction from the synoptic to the seasonal
time scales. The diagnostics and metrics will be developed and tested mainly using the GFDL
model simulations, and further testing of robustness will be carried out using the CMIP6 data, in
particular the CFMIP, GMMIP and HighResMIP.


The proposed research falls right into the focal area of the MAPP’s competition on
“addressing key issues in CMIP6-era earth system models”, and is also highly relevant to the
MAPP’s mission to enhance the Nation's capability to predict natural variability and changes in
Earth's climate system.

Climate Risk Area: Water Resources

Principal Investigator (s): Zhuo Wang (University of Illinois)

Co-PI (s):Lucas Harris (NOAA/GFDL)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

Understanding Systematic Model Biases in Simulating the Pacific Dynamic Sea Level Variability and Change

Dynamic sea level (DSL) in the Pacific Ocean is an important indicator of climate variability
and change. Due to the dominant thermosteric effect, the Pacific DSL reflects the vertically
integrated ocean temperature anomalies and temporally accumulated ocean heat uptake/release.
Recently, Peyser et al. (2016) identified an east-west see-saw as the dominant variability mode of
DSL in the tropical Pacific. This see-saw is closely related to the variability and change of global
mean surface temperature. However, climate models tend to show systematic and outstanding
biases in simulating this see-saw variability, potentially influencing the accuracy of future climate
and sea level predictions and projections.

The primary goal of this project is to investigate the mechanisms responsible for the
systematic biases of the new CMIP6 models in simulating the Pacific DSL variability and change,
provide strategies and pathways for model development and improvement, and eventually reduce
model uncertainty in future climate and sea level predictions and projections. More specifically,
the objectives are to: a) analyze observational, reanalysis and modeling data to better understand
internal DSL variability and externally forced DSL changes in the Pacific; b) quantify the biases
of the CMIP6 models in simulating the Pacific DSL variability and change as well as their climate
and coastal impacts, and compare the results with those from CMIP5; and c) use the GFDL high
resolution coupled climate models (CM4, CM2.5 and CM2.6) and ocean model (MOM6) to
systematically study the sources of the model biases and the critical processes that can lead to
model improvement. To achieve the goal, we will perform systematic data analyses and
comparison, and conduct a series of sensitivity experiments. We will focus on various critical
atmospheric and oceanic processes and identify their roles in causing model biases in simulating
the Pacific DSL variability and change.

This proposal is closely relevant to the MAPP competition: Addressing key issues in CMIP6-
era Earth system models. The NOAA’s long-term goals include improved scientific understanding
of the changing climate system and its impacts, and assessments of current and future states of the
climate system that identify potential impacts and inform science, service, and stewardship
decisions. One focus of CPO’s climate research portfolio is on climate intelligence which includes
observations, modeling and prediction. We anticipate that the outcome of this project will meet
NOAA’s goals by deepening our understanding about the causes of the Pacific DSL variability
and change and the mechanisms for systematic and outstanding model biases, thereby helping
reduce model uncertainty and leading to more accurate climate and sea level predictions and
projections including extreme events. During the project, the PIs will closely interact with the
CMIP, FAFMIP and OMIP modeling communities and contribute to the related IPCC assessments.

Climate Risk Area: Coastal Inundation

Principal Investigator (s): Jianjun Yin (University of Arizona)

Co-PI (s):Stephen Griffies (NOAA/GFDL)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

Understanding the Role of Radiative Forcing and Cloud-Circulation Feedbacks on Spatial Rainfall Shifts in CMIP6

A robust prediction of all climate models is for wet regions to become wetter and dry
regions to become drier in response to increased greenhouse gases. The physical
processes that drive this response arise from increased water vapor and are generally
considered to be well understood. In contrast, the processes that govern spatial shifts of
rain belts are less well understood, despite the fact that such changes also have profound
societal consequences. This is particularly relevant in the tropics and sub-tropics due to
their large spatial gradients between wet and dry regions.

Recent research has highlighted the importance of radiative forcing from both greenhouse
gases and aerosols in driving large-scale shifts in rainfall patterns through their influence
on the atmospheric circulation. The instantaneous radiative forcing from greenhouse
gases has been shown to drive large-scale changes in the monsoonal circulations.
Likewise, the strong hemispheric asymmetry in aerosol radiative forcing has been shown
to drive large-scale changes in the meridional circulation. Both of these radiatively-forced
circulation changes have direct impacts in modulating the regional distribution of rainfall.
Unfortunately, recent studies have highlighted significant biases in model calculations of
radiative forcing under identical emission scenarios. Such biases remain largely
undocumented since radiative forcing is rarely calculated or archived, despite its
fundamental role in determining the forced response to anthropogenic emissions.

Due to their strong influence on atmospheric heating rates, clouds play a key role in
regulating the large-scale circulation of the atmosphere and therefore the regional
distribution, frequency and intensity of rainfall. Recent studies suggest that regional
shifts in rainfall may also be amplified through circulation-driven cloud feedbacks that
respond to, and enhance, the radiatively-forced rainfall change. The selection of “Clouds,
Circulation, and Climate Sensitivity” as one of the WCRP Grand Challenges underscores
both the importance and current lack of understanding regarding these processes.

This proposal seeks to exploit model simulations from CMIP6 along with idealized
forcing scenarios from RFMIP and PDRMIP to better quantify and understand the role
of instantaneous radiative forcing and cloud-circulation feedbacks in modulating shifts in
the spatial distribution and intensity of rainfall.

The primary objectives of this proposal are to:
i) Develop and apply process-oriented metrics to quantify and evaluate model simulations
of instantaneous radiative forcing and cloud-circulation feedbacks;


ii) Quantify the impacts of radiatively-forced circulation changes on the regional
distribution of rainfall;


iii) Use historical observations in conjunction with spatial fingerprinting techniques to
better constrain the representation of these radiative and cloud processes in models.
In accomplishing these objectives, we will directly contribute to the NOAA MAPP goal
of developing and applying process-oriented metrics to better understand sources of
model bias involving “cloud and radiative processes” and their impact on “weather and
climate extremes”.

In accomplishing these objectives, we will directly contribute to the NOAA MAPP goal
of developing and applying process-oriented metrics to better understand sources of
model bias involving “cloud and radiative processes” and their impact on “weather and
climate extremes”.

Climate Risk Area: Water Resources

Principal Investigator (s): Brian Soden (University of Miami)

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

Weather-type based cross-timescale diagnostics of CMIP6-era models

The objective of this project is to perform a process-based multi-timescale diagnostic of CMIP5 and CMIP6-era Earth System Models using a weather-typing dynamical approach. The proposed
work focuses on how accurately extreme rainfall events, both wet and dry, are represented over the US in CMIP5/6 models. Although the project will emphasize the present and next generation
of NOAA/GFDL models, to guarantee robustness other available models will also be diagnosed. This project will develop process-informed cross-timescale tools to diagnose CMIP5/6 historical and climate-change projections over North America based on the methodology of large-scale recurrent, persistent weather types (WTs), also known as large-scale meteorological patterns (LSMPs). These regimes provide a dynamically informative intermediary between the large- scale drivers of climate variability and change from sub-seasonal to decadal timescales, and mid- latitude high-impact weather events, through the mechanism of synoptic control. The proposed work will provide an urgently-needed process-level understanding on rainfall extremes in CMIP5/6 simulations, and develop standard metrics that model developers and users can apply to these models easily. These will allow model developers to quickly assess the impacts of changes in parameters, and will enable users to better assess confidence levels on projections of return intervals of extreme rainfall events.


The proposed work will build on recent previous work by the team demonstrating the effectiveness of the approach to both (1) cross-timescale diagnostics of rainfall over North and South America, and (2) diagnose GCM model performance in a suite of GFDL forecast models.

Expected deliverables of the project include (a) general open-source software package to perform weather-type based cross-timescale diagnostics of climate models, including new process-based metrics that can be added to the MAPP diagnostics Task Force framework, and documentation for the software package; and (b) an online “diagnostic atlas” containing the process-based metrics (e.g., WT spatial patterns and frequencies of occurrences at different timescales, extreme rainfall composite analysis for different thresholds, anomaly correlations to climate drivers) available via the IRI Data Library.

Principal Investigator (s): Angel Munoz (Columbia University)

Co-PI (s):Vecchi, Gabriel (Princeton Unviersity), Ming Zhao (NOAA/GFDL)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

A Hybrid Statistical-Dynamical System for the Seamless Prediction of Daily Extremes and Subseasonal to Seasonal Climate Variability

We propose to demonstrate the skill and suitability for operations of a statistical- dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and subseasonal-to-seasonal temperature and precipitation. We recently demonstrated a Bayesian statistical method for post-processing seasonal forecasts of mean temperature and precipitation from the North American Multi-Model Ensemble (NMME). We now seek to test the utility of an updated hybrid statistical-dynamical prediction system that facilitates seamless subseasonal and seasonal forecasting. Specific updates we intend to implement for the forecast system include: 1) Aggregation of post-processed daily forecasts to enhance the skill of subseasonal forecasts on weekly and biweekly timescales; and 2) Disaggregation of seasonal forecasts to determine the probability of daily extremes. We propose to apply the method developed by the co-PIs of this proposal (Schepen et al., 2017b) to first calibrate climate model daily forecasts through Bayesian joint probability modeling and then relate these calibrated daily forecasts made at di↵erent leads through application of the Schaake Shu✏e approach (Clark et al., 2004). The calibrated and shuffed daily forecasts will then be aggregated for subseasonal and seasonal prediction. Through this approach, forecast skill that exists at shorter subseasonal leads (e.g., weeks 1-2) will be used to improve forecast skill at longer leads (e.g., weeks 3-4). Furthermore, using the methodology developed by the co-PIs (Schepen et al., 2017a), we propose to disaggregate seasonal forecasts from the NMME into distributions of daily values. We will first develop hybrid statistical-dynamical models that use skillful NMME forecasts of large scale climate patterns (e.g., ENSO) in statistical models that relate these remote climate patterns to North American temperature and precipitation variability. Forecasts from these hybrid models first will be used to predict seasonal temperature and precipitation, and then will be statistically disaggregated to generate consistent, seamless forecasts of the distribution of daily temperatures or precipitation amounts. The probability of daily extremes of temperature or precipitation during a seasonal forecast period will be produced, taking full advantage of the enhanced predictability o↵ered by interannual models of variability, such as ENSO, the Arctic Oscillation, or climate change. Importantly, this method allows for the representation of daily extremes consistent with climate conditions.


Relevance:
The proposed project is directly relevant to Competition 3 focus area 1 in that the primary deliverable will be a hybrid statistical-dynamical prediction system, applying post-processing techniques developed in the broader community for operational purposes. The project is relevant to NWS goal 3 to “Complete the seamless suite of NCEP weather and climate products by filling the week 3-4 gap.” This project also addresses NOAA’s long- term goal of a “Weather-Ready Nation: Society is prepared for and responds to weather- related events,” by providing information on the potential for daily extreme events related to climate forecasts.

Principal Investigator (s): Dan Collins (NOAA/CPC)

Co-PI (s):Q.J. Wang (University of Melbourne), Andrew Schepen (CSIRO Land and Water)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

A New Technique for Improved MJO Prediction

Predicting the Madden-Julian Oscillation (MJO) is key to global prediction on subseasonal- to-seasonal (S2S) timescales. The Real-time Multivariate MJO (RMM) index is commonly used to measure MJO prediction skill and used as a predictor for predictions of other parameters over the globe. This index has proven to be very useful in providing information of the planetary-scale circulation pattern associated with the MJO and eastward propagation of the MJO in a statistical sense. But it is known to be ineffective in accurately identifying longitudinal locations of convection centers for individual MJO events. This shortcoming of the RMM index has hindered its applications in predicting remote influences of the MJO, which sensitively depend on longitudinal locations of MJO convection centers.

Recently, we have developed a new method that identifies individual MJO events by tracking eastward motion of large-scale precipitation anomalies along the equator. This method allows several key parameters to be quantitatively and accurately defined for individual MJO events. The parameters include the longitude and time of MJO initiation and termination, speed and range of MJO propagation, life span and mean strength of the MJO, and intervals of neighboring MJO events. Prediction of these parameters is important in capturing the tropical forcing of subtropical and extratropical circulations at intraseasonal timescales, but is difficult to derive from the RMM index. The new MJO tracking method has been used successfully in quantifying the barrier effect on MJO propagation by the Indo-Pacific Maritime Continent and interpreting
the issue of MJO simulations by global models. With suitable minor adjustment, this method can be applied to real-time MJO forecast and provide an alternative technique for monitoring MJO events and measuring their prediction skill.

The objective of this proposed project is to revise and test the new method of MJO tracking for real-time application in enhancing our ability to predict the MJO and its related extremes. We plan to take the existing MJO tracking method and develop it into one that can be used in real-time forecast. The proposed work includes:

(1) Adjust the existing MJO tracking method to make it applicable to real-time forecast.

(2) Apply the real-time tracking method to CFSv2 historical reforecast to develop a statistical
base of MJO forecast skill as a function of the season and the MJO (initiation location, etc.).

(3) Compare MJO prediction measurement based on this new tracking method and other EOF-
based methods (RMM index, OLR index) and seek possible ways to complement each other.

(4) Test the new MJO tracking method or tracking-EOF hybrid method in real-time environment.
These steps will be applied to CFSv2 forecast only. If successful, we plan to expand this work to
S2S Prediction products or NMME in a follow-up project.

Relevance to the Competition:
This proposed research will target solicited area (1) of the Climate Test Bed: Testing and demonstration of an experimental prediction methodology (e.g. new calibration or post-
processing techniques, verification techniques) or system (e.g., experimental multi-model combinations, hybrid statistical/dynamical systems, merging of systems across timescales to advance subseasonal prediction) developed in the broader community for operational purposes.

Principal Investigator (s): Chidong Zhang (NOAA/PMEL)

Co-PI (s):Wang, Wanqiu (NCEP/CPC)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

Operational transition of novel statistical–dynamical forecasts for tropical subseasonal-to-seasonal drivers

Subseasonal-to-seasonal (S2S) has emerged as one of the great frontiers for atmospheric
predictability. These time scales of weeks-to-months are at the heart of the mission for NOAA’s
Climate Prediction Center (CPC), which has been particularly focused on expanding and
improving their 3–4-week forecasts. Dynamical S2S models have improved significantly over
recent years, but they have yet to fully tap the potential predictability of coherent tropical modes
like the Madden–Julian Oscillation (MJO).


A unique approach to this problem has been implemented on NCICS.org/mjo. This website takes
recent observations and appends them with 45-day forecasts from the Climate Forecast System
version 2 (CFSv2). The combined data are then Fourier filtered in space and time for some of the
dominant modes of S2S variability in the tropics: the MJO, convectively coupled equatorial waves,
and low-frequency variability like the El Niño–Southern Oscillation (ENSO). This filtering
highlights the most predictable aspects of the S2S system. The website includes numerous maps,
Hovmöllers, and indices for identifying and predicting these modes. It has been updating daily
since 2011 with several upgrades and iterations over the years. These diagnostics have become
routine inputs for CPC’s Global Tropical Hazards (GTH) outlook.


NCICS.org/mjo has reached a level a maturity (Readiness Level 7) where it is a prime candidate
to be transitioned fully into operations at CPC. This proposal outlines that transition. It also
includes some modest development work that will use proven methods to further tailor these
diagnostics to CPC’s goals. Once these processes have been transferred to CPC, future work could
easily expand them to other models that CPC is already ingesting.


The proposed project is directly to the MAPP/NOAA Climate Test Bed competition because it
takes a demonstrated research product and transitions it into operations at NOAA’s CPC. In
particular, the proposal leverages a newly proved methodology to improve key forecast products
like the Weeks 3-4 outlooks. Transitioning this product to operations will help NOAA fulfill its
long-term climate goals by expanding CPC’s capabilities for monitoring and prediction using
existing models and observations. These diagnostics are already used by a number of public,
private, academic, and international users to provide them the climate intelligence they need for
resilience against climate variability.

Principal Investigator (s): Carl Schreck (North Carolina State University)

Co-PI (s):Stephen Baxter (NOAA/CPC), Matthew Janiga (UCAR)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

Sensitivity of NMME Seasonal Predictions to Ocean Eddy Resolving Coupled Models

The research proposed here is based on the hypothesis that the presence of oceanic
mesoscale features, that is fronts and eddies, significant modify local air-sea coupling, which in
turn affects the local representation of the predictable large scale climatic features. For example,
preliminary results from retrospective forecast experiments with a global ocean eddy resolving
coupled prediction system indicate that rainfall forecasts along the west coast of North America
are significantly affected by eddy activity in the Kuroshio region, and comparisons with the
current North American Multi-Model Ensemble (NMME) forecasts indicate that this effect is
absent in the current operational system.


The proposed research leverages current efforts in the NMME project and the newly
developing SubX project to test the above hypothesis. Specifically, we propose to repeat the
NMME retrospective predictions with a version of CCSM4 that utilizes significantly higher
resolution in the ocean (and ice) component model (i.e., 0.1 degree vs. 1 degree) and increased
atmospheric component model resolution (0.5 degree vs. 1 degree). The initialization strategy
follows the approach currently in use for both NMME and SubX; and the preliminary
retrospective forecasts provide proof of concept, and indicate that this approach could be used
for the operational NMME.


In addition to implementing the NMME retrospective forecast protocol, the proposed
work includes a detailed large-scale forecast quality assessment using both deterministic and
probabilistic measures. While the proposed research focuses on one particular model, we will
examine how the inclusion of this model affects the multi-model forecast quality. The analysis of
retrospective forecasts will extend beyond monthly and seasonal means to consider extremes and
the shifts in the weather statistics within seasonal and monthly time-scales (and even week 3-4).
Following on some of the preliminary results presented here, we critically examine how the
resolved oceanic mesoscale features change the air-sea coupling, and the local and remote
forecast evolution and quality.


The experience and expertise of the research team is ideally suited to implement the
proposed research, and the project is relevant to the MAPP CTB program. In particular, the call
for proposals seeks efforts that will examine “improving multi-model prediction system such as
the North American Multi-Model Ensemble by testing and demonstrating the utility of new or
higher resolution models...” The PI is the science lead for both the NMME and SubX projects,
and fully understands the issues and challenges in integrating a new model into the operational
NMME. In terms of “readiness levels,” the project is currently at RL5 (the final level of
development) and upon completion of the project we anticipate being at RL7 (well into the
demonstration phase).

Principal Investigator (s): Kirtman, Benjamin(University of Miami-RSMAS)

Co-PI (s):Burgman, Robert (Florida International University), Leo Siqueira (University of Miami)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

Skillfully Predicting Atmospheric Rivers and Their Impacts in Weeks 2-5 Based on the State of the MJO and QBO

With the advent of Week 3-4 forecasts beginning in September 2015, the NOAA Climate Prediction Center (CPC) now provides a continuous suite of precipitation outlooks targeting mean conditions spanning from the extended range (i.e. Days 6-10 and 8-14) through approximately a year. While the majority of these outlooks are skillful, Week 3-4 precipitation outlooks only exhibit skill similar to that expected from random chance. Of interest to this proposal is improving CPC precipitation forecasts for Week 3-4 and beyond by improving forecasts of relatively short duration (~day), high-impact, extreme precipitation events associated with atmospheric rivers (ARs) along the west coast of North America.

Skillful forecasts of ARs on subseasonal-to-seasonal (S2S) timescales would support many aspects of society, e.g. emergency management, water managers, shipping route designation, and agricultural practices. While current dynamical model forecast skill of AR events decays rapidly beyond Week 2, recent work by the PIs (published in Mundhenk et al., 2017) demonstrates that AR activity can be skillfully predicted into Week 5 with an empirical forecast model based on two prominent modes of tropical variability: the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). This empirical model is based on the joint MJO and QBO probability distributions and largely follows the recently operational method of Johnson et al. (2014) to forecast 2-meter temperature anomalies.

Given CPC’s existing lack of AR-related guidance, the methodology of Mundhenk et al. (2017) to skillfully predict AR frequencies and precipitation at S2S timescales will be transitioned into CPC operations. Refinement and extension of this methodology will be explored and transitioned into operations, pending demonstrable skill. The goals of the proposed activities are threefold: (1) successfully transition the AR frequency forecast tool to operations, (2) refine and extend the methodology of Mundhenk et al. (2017) to maximize skill of AR activity and AR-related variables, and (3) leverage additional predictors, including dynamical model MJO forecasts, to extend the skillful forecasts beyond Week 5. The tasks will be performed by scientists at the Climate Prediction Center (CPC) and the Department of Atmospheric Science at Colorado State University (CSU), who have extensive experience working together and transitioning similar empirical tools into CPC operations.

Relevance and Suitability for NOAA
The proposed project addresses the CTB goal of testing, demonstration, and implementation of tools for S2S predictions relating to atmospheric rivers. The competition Information Sheet explicitly encourages the prediction of heavy precipitation, and the proposed work will directly address these extremes by improving the prediction of atmospheric rivers and their impacts. This project leverages the PIs past experiences in weather and climate extremes, empirical guidance development, and transition of guidance into forecast operations. The proposed work initially meets NOAA Readiness Level 5, which is the final stage of development where methodologies and products that have been successfully evaluated in experimental settings (i.e. Mundhenk et al., 2017) are tested and prototyped in their relevant environment.

Principal Investigator (s): Elizabeth Barnes (Colorado State University)

Co-PI (s): Eric Maloney (Colorado State University), Daniel Harnos, (NOAA/NWS/NCEP/CPC), Laura Ciasto (INNOVIM, LLC)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

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