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Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2016
Task Force:
Final Report:

Developing a Real-Time Multi-Model Sub-Seasonal Predictive Capability

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Principal Investigator (s): Ben Kirtman (University of Miami)

Co-PI (s): Kathy Pegion (GMU/COLA), Tim DelSole (GMU/COLA), Andrew Robertson (IRI/Columbia), Mike Tippett (IRI/Columbia), Robert Burgman (FIU), Hai Lin (Environmental Canada), Jon Gottschalck (NOAA/CPC), Dan Collins (NOAA/CPC)
Year Initially Funded: 2016
Task Force:
Final Report:

Increasing Subseasonal-to-Seasonal (S2S) Forecast Skill through Climate Teleconnections: A Hybrid Statistical-Dynamical Prediction System

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

Co-PI (s): Qin Zhang (NOAA/CPC), Q.J. Wang (CSIRO), Andrew Schepen (CSIRO), Emily Becker (NOAA/CPC)
Year Initially Funded: 2016
Task Force:
Final Report:

Development of ensemble-based sea ice analysis and forecasting in the Climate Forecast System

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Principal Investigator (s): Jim Carton (University of Maryland)

Co-PI (s): Steve Penny (UMD/NCEP), Robert Grumbine (NOAA/EMC), Suru Saha (NOAA/EMC)
Year Initially Funded: 2016
Task Force:
Final Report:

Upgrading the CPC operational ocean monitoring to an eddy-permitting global ocean analysis using the Hybrid Global Ocean Data Assimilation System as a replacement for GODAS

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Principal Investigator (s): Steve Penny (University of Maryland)

Co-PI (s): Jim Carton (UMD), Yan Xue (NOAA/CPC), David Behringer (NOAA/EMC), Laury Miller (NOAA/EMC)
Year Initially Funded: 2016
Task Force:
Final Report:

Seasonal Climate Forecasting Applied to Wildland Fire Management in Alaska

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Principal Investigator (s): Uma Bhatt (International Arctic Research Center)

Co-PI (s): Peter Bieniek (IARC), Alison York (IARC), Peitao Peng (NOAA/CPC), Brian Brettschneider (IARC), Richard Thoman (NOAA/NWS), Gene Petrescu (NOAA, NWS), Randi Jandt (UAF), Robert Ziel (State of Alaska), GaBrielle Branson (BLM), Heidi Strader (BLM), Sharon Alden (BLM)
Year Initially Funded: 2016
Task Force:
Final Report:

Development toward NCEP’s fully-coupled global forecast and data assimilation system: A coupled wave - ocean system

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Principal Investigator (s): Stephen Griffies (NOAA/GFDL)

Co-PI (s): Robert Hallberg (NOAA/GFDL), Alistair Adcroft (NOAA/GFDL & Priceton), Arun Chawla (NOAA/EMC), Suranjana Saha (NOAA/EMC), Steve Penny (UMD/NCEP)
Year Initially Funded: 2016
Task Force:
Final Report:

The Inclusion of Sub-Seasonal to Seasonal Predictions of the Navy's Earth System Model in the North American Multi-Model Ensemble (NMME)

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Principal Investigator (s): Neil Barton (Naval Research Laboratory)

Co-PI (s): Joseph Metzger (NRL), Dan Collins (NOAA/CPC), Jon Gottschalck (NOAA/CPC)
Year Initially Funded: 2016
Task Force:
Final Report:

A Subseasonal Excessive Heat Outlook System for CPC’s Global Tropics Hazards and Benefits Outlook (GTH)

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Principal Investigator (s): Augustin Vintzileos (University of Maryland/ESSIC)

Co-PI (s): Stephen Baxter (NOAA/CPC)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Evaluation of Warm Cloud Microphysical Processes in Global Climate Models with Multi-Sensor Satellite Observations

View abstract
"In this investigation, we will evaluate microphysical processes of warm liquid clouds in global climate models with multi-sensor satellite observations, in response to the Modeling, Analysis, Prediction and Projection (MAPP) competition of “Process-oriented evaluation of climate and Earth system models and derived projections” within Area A “Metrics for climate and Earth system model development” and of Type 2 “Research teams developing process-oriented metrics.” Warm cloud microphysics is one of the most uncertain components in global climate models and is also a major pathway through which aerosols influence the clouds and climate, referred to as the aerosol indirect effect. The objective of the research is to: (i) develop observation-based metrics that dictate key signatures of the warm rain microphysical processes with a combined use of the CloudSat/A-Train multi-sensor satellite observation products, (ii) apply the methodologies to climate models to identify fundamental biases in the representation of key microphysical processes that are crucial for estimates of the aerosol indirect radiative forcing and thus for climate projections, and (iii) propose improvements of microphysical parameterizations in such models for a better representation of warm cloud processes and more reliable estimates of the aerosol indirect radiative forcing.

Previous studies by the PI devised new methodologies for analyzing the CloudSat/A-Train multi-sensor satellite observations to “fingerprint” warm cloud microphysical processes and also applied them to cloud-resolving and climate models to identify fundamental model biases in representing the processes. This investigation will extend such model diagnostic approaches to systematic analysis of global climate models. For this purpose, we plan to: (i) integrate the new PI-developed methodologies as a unified set of observation-based, process-oriented metrics that “fingerprint” the fundamental process signatures of warm cloud microphysics, and (ii) apply the metrics to results from multiple climate models including GFDL Climate Model version 3 (CM3) and NCAR Community Atmosphere Model version 5 (CAM5) for their process-oriented evaluations. Furthermore, (iii) systematic sensitivity experiments with GFDL CM3 or its atmospheric component, AM3, will be conducted to examine how different assumptions and configurations in microphysics parameterization schemes influence the model representation of the process in the form of the new metrics. Through these analyses, we intend to offer a process-based constraint on fundamental uncertainty in climate models in an attempt to improve the microphysical process representations. A particular emphasis will be placed on mitigation of the dichotomy found in the investigators’ previous study between such a process-based model constraint and the historical temperature reproducibility in GFDL CM3.

The proposed research will directly contribute to the specific objective of the Competition aiming at “process-oriented evaluations of climate and Earth system models and derived projections”. In particular, we intend to substantially mitigate the uncertainties in aerosol indirect radiative forcing arising from fundamental uncertainties in model representation of cloud microphysical processes for more reliable climate projections of global temperature and precipitation. The proposed research will thus contribute to NOAA’s long-term climate goal through addressing the core activities of “understanding and modeling” and “predictions and projections” and the societal challenges of “climate impacts on water resources”."

Principal Investigator (s): Suzuki, Kentaro (University of Tokyo)

Co-PI (s): Chris Golaz (NOAA/GFDL), Huan Guo (NOAA/GFDL)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Process Oriented Diagnostics of Tropical Cyclones In Climate Models

View abstract
"The simulation of tropical cyclone (TC) activity in climate models is still a challenging problem. While some models are able to simulate TC activity with characteristics very similar to those observed, many models have very strong biases. While increasing horizontal resolution often improves the characteristics of model TCs, resolution alone is not sufficient for high skill in simulating TC activity. We propose here a suite of process-based diagnostics to identify model characteristics that are responsible for a good simulation of TCs in global atmospheric and ocean-atmosphere coupled climate models, including high-resolution global models which have become increasingly used for studies of the relationship between TCs and climate.

First, we will examine the role of large-scale environmental variables, such as vertical wind shear and potential intensity as well as various integrated genesis indices, with standard TC activity measures. Second, we will develop and test process-based diagnostics to investigate the influence of model physics on TC formation in the models. As there are some similarities between the process involved in the Madden-Julian Oscillation (MJO) and tropical cyclogenesis, our proposed diagnostics are heavily influenced by the process-based diagnostics that were developed for the MJO simulation. These diagnostics focus on how convections, moisture, clouds and related processes are coupled at individual grid points, and give information about how the convective parameterizations interact with resolved model dynamics. Our working hypothesis is that similar interactions are important for tropical cyclone genesis and intensification in models, and we will test that hypothesis. Third, we will use standard wave diagnostics to identify the Madden-Julian Oscillation and various convectively coupled tropical in the models, and related these to standard TC activity diagnostics, to determine how well the models simulate the association between TCs and these disturbances which are known to modulate TC genesis in observations. In the fourth and final part of our project, we will perform parameter sensitivity studies in which we will modify the physics of a climate model, the NASA Goddard Institute for Space Studies (NASA GISS) model, and analyze how the simulated TC activity is affected. This approach will allow us to explore the physics parameter space in a more controlled fashion than is possible with existing model ensembles of opportunity. In the first three phases, the project will use existing multi-model databases, including those form the Coupled Model Intercomparision Project Phase 5 (CMIP5) and US CLIVAR Hurricane Working Group, as well as additional simulations and model output from our collaborators from NASA GISS, National Oceanographic Atmospheric Administration - Geophysical Fluid Dynamics Laboratory (NOAA-GFDL), in Princeton, NJ and the Istituto Nazionale di Geofisca e Vulcanologia - Centro Euro-Mediterraneo sui Cambiamenti Climatici (INGV-CMCC), in Bologna, Italy.

This project fits well within the MAPP process-oriented evaluation of climate models, by developing new diagnostics to evaluate model perforce in simulating tropical cyclones. The knowledge gained with these diagnostics can then be used in improving the next generation of climate models. This project fits well with NOAA’s long-term climate goals, given the large impacts of tropical cyclones in the U.S. and the importance of making robust projections of future tropical cyclone activity."

Principal Investigator (s): Camargo, Suzana (Columbia University/LDEO)

Co-PI (s): Adam Sobel (Columbia University/LDEO), Daehyun Kim (University of Washington), Anthony Del Genio (NASA Goddard)
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The Modeling, Analysis, Predictions, and Projections (MAPP) Program's mission is to enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system. The MAPP Program supports development of advanced climate modeling technologies to improve simulation of climate variability, prediction of future climate variations from weeks to decades, and projection of long-term future climate conditions. To achieve its mission, the MAPP Program supports research focused on the coupling, integration, and application of Earth system models and analyses across NOAA, among partner agencies, and with the external research community.

Learn more...

Download our program brochure (pdf).