Funding Opportunities & Funded Projects

FY18 Research Opportunities

For all three competitions, Advancing Earth System Data Assimilation, Addressing Key Issues in CMIP6-era Earth System Models, and Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability, LOIs are due June 28, 2017 by 5pm and Full Proposals are due September 25, 2017 by 5pm.

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Advancing understanding of Arctic sea ice variability and diagnostic predictability in ESMs with regional-to-global-scale process- oriented evaluation

Principal Investigator (s): Cecilia M. Bitz (University of Washington)

Co-PI (s):Ed Blanchard-Wrigglesworth (University of Washington), Wei Cheng (University of Washington), Aaron Donohoe (University of Washington)

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

In this proposal for MAPP Competition 2, Addressing Key Issues in CMIP6-era Earth System Models, we propose to characterize and understand biases in CMIP6 Arctic sea ice variability. We will focus on determining relationships between modeled quantities that are observable and quantities that characterize processes, so that we can simultaneously evaluate an observable and sea ice processes. We seek to understand when biases are due to missing physics or poor tuning and what makes some models outliers. We will use this understanding to recommend essential
model physics and future directions in sea ice modeling.

The objectives of this proposal focused on creating metrics are threefold. (1) Categorize the spatial and temporal nature of sea ice variability across the multi-model ensemble, in both the unforced intrinsic variation and forced response. This will give us a basis from which to evaluate the role of ocean-ice and atmosphere-ice processes on the sea ice. (2) Characterize the spatio-temporal variability of atmosphere-ice and ocean-ice interface fluxes associated with sea ice variability (3) Quantify ocean stratification strength, the amplitude and vertical structure of atmospheric meridional energy fluxes into the Arctic and radiative variability associated with clouds and sea ice and how each impacts sea ice variability.

We will develop process-oriented metrics in order to understand inter-model spread in the drivers of sea ice variability and to place the models in the context of observations. The metrics are designed to identify parameterizations and model physics that need improvements. We will test proposed improvements in the developmental Community Earth System Model (CESM) and work with the CESM working groups to communicate necessary changes to other climate model developers.

Relevance to the NOAA MAPP Competition and NOAA’s Long-Term Goal: Our project is about understanding the source of the coupled atmosphere-sea ice-ocean biases that
affect sea ice variability. We propose to develop systematic process-oriented analysis methods and scripts for community use in collaboration with MDTF and SIMIP that can be used with CMIP6 output. By evaluating processes relevant to sea ice variability, and ultimately identifying ways to improve model accuracy, our project is aligned with MAPP’s mission to “enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system”, and NOAA’s long-term goal of “providing the essential and highest quality environmental information vital to our Nation’s safety, prosperity and resilience.”

An Open Framework for Process-Oriented Diagnostics of Global Models

Principal Investigator (s): David Neelin (University of California, Los Angeles)

Co-PI (s):Eric Maloney (Colorado State University), Yi Ming (NOAA Geophysical Fluid Dynamics Laboratory), Andrew Gettelman (National Center for Atmospheric Research), Peter Gleckler (Lawrence Livermore national laboratory)

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

Problem addressed and rationale: There is a need to identify targeted improvements to the fidelity of models for the Earth System and its variability. Process-oriented diagnostics characterize a physical process in a manner related directly to mechanisms essential to its simulation, and thus provide valuable guidance for model improvement. An organizational framework that integrates such diagnostic development projects aids accessibility by modelers.

Work Summary: The proposed Type 1 team will expand an open framework to entrain process- oriented diagnostics developed by multiple research teams into the development stream of the modeling centers. Building on work by the previous Type 1 team project, it will coordinate Type 2 individual projects through an Application Programming Interface (API) for process-oriented diagnostics. Modules under this protocol will compare any development model version to observations, while leveraging analysis of the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble to place these diagnostics in a multi-model context. The CMIP6 ensemble will be used in the framework to aid the model developer in identification of poorly represented physical pathways. The API will permit comparison of multiple model runs from CMIP6 models or perturbation/ensemble runs of individual models. The lead PI team maintains consistency with the previous Type 1 team while expanding representation from the Geophysical Fluid Dynamics Laboratory (GFDL) model development and diagnostics teams and from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) to leverage community data standards and enhance coordination of metrics and diagnostics development across agencies. A task force will be created, modeled on the current Model Diagnostics Task Force, which will emphasize proactively reaching out to PIs of Type 2 proposals funded under this MAPP call. A key ingredient in ensuring that diagnostics are useful to the development teams is feedback from these teams and from other groups. Task Force members will be invited to present their diagnostic development plans early, to coordinate with expansion of the API. The interaction will promote common standards and tools, fostering diagnostics modules that are well targeted and implemented for ease of coordination both within the Task Force and with national and international efforts. Self-documentation and community data and metadata protocols will be included in the API. The task force will also coordinate synthetic publications. The Type 1 Team will also develop tools and additional process-based diagnostics in key areas complementing Type 2 proposals, including tools to assist modelers in navigating trade-offs among multiple observational constraints. Diagnostics for basin-scale heat uptake and sea level change will be standardized. Diagnostics for feedback mechanisms in regional hydroclimate extremes including cloud feedbacks will be developed, complemented by parameter-perturbation experiments with the GFDL model that will be made available to the Type 2 teams. Diagnostics will be brought into the framework for processes affecting temperature and precipitation distribution tails, including advanced convective diagnostics and moist-static energy diagnostics.

Relevance to competition: This proposal directly addresses the call for the “Modeling, Analysis, Predictions, and Projections (MAPP) Competition 2: Addressing Key Issues in CMIP6-era Earth System Models” by developing a Type 1 core team to lead integration of projects on process oriented diagnostics. It proposes a code and data sharing framework that facilitates integration of these into the development path of modeling centers, scientific development of new process- oriented diagnostics, and protocols to engage and synthesize the efforts of Type 2 projects in model evaluation, as well as plans for the dissemination of this information. It addresses NOAA's long-term climate goals by strengthening foundational capabilities, combining observations with modeling and prediction, and communication of scientific understanding.

ENSO-induced persistence of droughts and storms over the U.S. Affiliated Pacific Islands: development of process-oriented diagnostics to identify errors in climate models

Principal Investigator (s): H Annamalai (University of Hawaii)

Co-PI (s):Yi Ming (NOAA.GFDL), Gill Martin (Hadley Center), Richard Neale (NCAR)

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

Abstract
During the space-time evolution of El Niño–Southern Oscillation (ENSO), the insular
U.S. Affiliated Pacific Islands (USAPI) experience drought-like conditions that persist for
3-4 seasons. Furthermore, environmental conditions favor frequent formation of cyclones
with evidence during the recent 2015-16 El Niño. In CMIP6-era models, therefore, to
represent weather and climate extremes over the USAPI during ENSO, a prerequisite lies
in models’ fidelity in translating equatorial Pacific sea surface temperature (SST)
anomalies into diabatic processes. Our goals are to develop process-oriented diagnostics
(POD) and relevant metrics at a level close enough to model formulations (e.g., at the
parameterization levels) that will identify the origin of biases and inform model
improvement decisions.

With a particular focus on model formulations that determine vertical processes, we
will assess parameterization schemes’ fidelity over different convective regimes along the
equatorial Pacific, as well as during different environmental conditions that exist during the
life cycle of ENSO. This represents a rigorous, targeted test bed for objectively diagnosing
the response of parameterization schemes. To identify model formulations that correspond
to the biases, we target four objectives: (i) During ENSO, examine parameterizations’
response to time-varying large-scale forcing and identify processes that lead to drought
persistence over the USAPI; (ii) Identify processes that determine tropical cyclone statistics
during ENSO; (iii) Examine upper troposphere vorticity budgets and their dependence on
the vertical gradient of Q1; (iv) Diagnose model formulations that account for vertical
profiles of cloud properties, Q1, moisture and vertical velocity. Before applying the POD to
study extremes over the USAPI, it will be employed along the equatorial Pacific, the source
region for predictability of global climate variations during ENSO. Thus, outcomes are not
region-specific. The POD can be transitioned to ENSO teleconnection studies, in general.

Our proposed research targets the MAPP competition that focuses “Addressing Key
Issues in CMIP6-era Earth System Models”, and specifically, the competition identified
research topic – “representation of model processes relevant to Weather and climate
extremes, including drought”. The PODs will be applied to CMIP6-era models, and other
relevant solutions performed within CMIP6 framework. Deliverables include a set of
metrics that illustrate models’ fidelity in representing ENSO-teleconnection, and
identify sources of model biases that reveal model formulation deficiencies. Our
proposed research will enhance the POD framework that is being developed under the
auspices of MAPP sponsored Model Diagnostics Task Force. Importantly, our POD will
be user accessible, flexible and adaptable such that it can be transitioned to any group
of process-level evaluations during the model developments.

Process-Based Evaluation of the Representation of Lake-Effect Snowstorms in the Great Lakes Region Among CMIP6 Earth System Models

Principal Investigator (s): Michael Notaro (University of Wisconsin)

Co-PI (s):

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

Abstract: The vast socio-economic importance of the Great Lakes cannot be understated. They
contain 95% of the U.S.’ freshwater supply and impact power production, navigation, industry,
commerce, recreation, agriculture, and ecosystems. Their basin has been a regional hotspot of
pronounced climate change impacts, including rising air temperatures, more frequent heavy
precipitation events, rapid summer warming of lake surfaces, declining lake ice cover, enhanced
lake evaporation, and increase in lake-effect snowfall. Extreme weather events have drawn
increased attention, due to their acute societal impacts, improved modeling capabilities, and
climate change concerns. While the Intergovernmental Panel on Climate Change (IPCC) reports
and National Climate Assessments summarize existing research on extreme events, they give
minimal attention to lake-effect snowstorms, despite their dramatic socio-economic and
environmental impacts. It remains unclear how the frequency of these cold season extremes will
change during this century. The insufficient investigation of projected changes in these cold
season extremes is partly due to the general lack of suitable modeling tools that properly
represent the Great Lakes and associated lake-atmosphere interactions, at a sufficient spatial
resolution. The CMIP6 High Resolution Model Intercomparison Project (HighResMIP)
represents an unprecedented multi-institutional effort to generate global simulations down to a
median resolution of 30 km and a unique opportunity to assess the capability of high-resolution
GCMs to accurately represent lake-atmosphere interactions and resulting lake-effect snowstorms.

A process-based evaluation is proposed of the representation of lake-atmosphere
interactions and resulting lake-effect snowstorms in the Great Lakes region among CMIP6 Earth
System Models. Analysis will primary focus on HighResMIP runs and their likely advances
over coarse DECK historical runs. Analyzed observational datasets will include: station
snowfall from NCDC and Environment Canada; CloudSat and Global Precipitation
Measurement cloud/snowfall estimates; wind, temperature, and sea-level pressure from North
American Regional Reanalysis; Great Lakes Evaporation Network over-lake evaporation and
turbulent flux measurements; buoy water temperature, air temperature, and wind from National
Data Buoy Center; Great Lakes Surface Environmental Analysis lake-surface temperature; Great
Lakes Environmental Research Laboratory (GLERL) vertical lake temperature data; GLERL
lake ice thickness; over-lake precipitation, lake evaporation, and drainage basin runoff from
GLERL Great Lakes hydrologic dataset; and NOAA Great Lakes Ice Atlas. The following
meteorological and limnological variables, considered as essential mechanistic ingredients in
lake-effect snow forecasting, will be evaluated in HighResMIP runs in terms of lake-effect
snowfall occurrence and intensity: temperature difference between the lake surface and 850-hPa;
direction and speed of the sub-700-hPa steering wind; lower tropospheric vertical directional
shear of the steering wind; existence, height, and strength of a low-level subsidence inversion;
over-lake vapor pressure gradient; and lake ice cover. The study is highly relevant to MAPP
competition objectives of addressing key issues in CMIP6 ESMs in terms of climate extremes,
through the design and application of process-oriented metrics for evaluating and improving the
representation of lake-atmosphere interactions and resulting lake-effect snowstorms in CMIP6
models. The project addresses NOAA’s goals to attain “improved...understanding of the
changing climate system” and perform “assessments of current and future states of the climate
system that identify...impacts and inform...decisions.”

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

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)

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

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.

Process-oriented Model Evaluation for the North American Monsoon

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

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

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

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.

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

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

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

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

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.

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

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

Co-PI (s):

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

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”.

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

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

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

Year Initially Funded: 2018

Task Force: Model Diagnostics Task Force

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

Final Report:

Abstract:

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.

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

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

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

Year Initially Funded: 2018

Task Force:

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

Final Report:

Abstract:

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.

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MAPP

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

Emily Read*
MAPP Program Assistant
P: 301-734-1257
E: emily.read@noaa.gov

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Climate Program Office
1315 East-West Hwy, Suite 1100
Silver Spring, MD 20910

CPO.webmaster@noaa.gov

ABOUT OUR ORGANIZATION

Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather. In 2017, the United States experienced a record-tying 16 climate- and weather-related disasters where overall costs reached or exceeded $1 billion. Combined, these events claimed 362 lives, and had significant economic effects on the areas impacted, costing more than $306 billion. Businesses, policy leaders, resource managers and citizens are increasingly asking for information to help them address such challenges.