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Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

A Methodology for North American Decadal Climate Prediction

View abstract

We propose to develop and validate a new method for predicting North American (NA) decadal climate. The approach will involve integrating knowledge of the statistics of internal atmospheric decadal climate variability in NA climate with a) the estimate of the North American signal associated with the external radiative forcings, and b) the estimate of the North American response to trajectories of boundary forcings that are initialized from the observed state of the climate system (e.g., sea surface temperature) which may differ from the boundary forcings consistent with the external signal alone.

Three sets of information will be utilized in generating the decadal predictions: (i) an estimate of the North American decadal signal associated with the external radiative forcing based on the uninitialized CMIP5 simulations, (ii) an estimate of the internal component of SST (and the corresponding NA response) for the next decade based on the initialized decadal predictions, and finally (iii) an estimate of the uncertainty in the decadal means of the North American climate due to the atmospheric internal variability, and adding that to the estimate of NA decadal signal estimated as part of steps one and two. This procedure will be repeated for each decade spanning the 1980-2010 period.

This proposal builds upon our prior research that led to the first, experimental probabilistic forecast of North American decadal climate for 2011-2020 (but using uninitialized methods alone). Finally, thru an integration of uninitialized and initialized approaches, this project will also produce a probabilistic decadal forecast for North America for the independent period of 2015-2024, and will be accompanied by an estimate of skill based on the decadal hindcasts for 1980-2004 period. We will also provide a comparison of the skill of decadal predictions based on our approach with that from the CMIP5 uninitialized and initialized decadal predictions.


Principal Investigator (s): Kumar, Arun (NOAA/CPC)

Co-PI (s): Hoerling, Martin (NOAA/ESRL); Hurrell, James (NCAR), Quan, Xiaowei (University of Colorado)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Developing an Optimum Multimodel ENSO Prediction

View abstract

The ENSO prediction plume product, issued monthly by the International Research Institute for Climate and Society (IRI), shows a large set of model forecasts for the ENSO state out to 10 months in advance. Although helpful, the product currently has a number of serious deficiencies. First, it shows predicted SST anomalies with respect to differing climatological base periods, because the modeling centers from which the predictions come do not use identical base periods. Secondly, many modeling centers do not correct systematic errors in their predictions, and these errors are perpetuated in the predictions on the plume. Some of the forecast models, while showing skill in their real-time ENSO forecasts, do not have adequate hindcast histories from which to treat such systematic errors. Finally, only a rudimentary method is used to consolidate the predictions into a logically derived multimodel mean prediction, and no attempt is made to develop a forecast probability distribution about the mean prediction. Thus, users gain some idea of the forecast probability distribution solely from visual inspection of the inter-model ensemble mean forecast disagreement-a by-far suboptimum criterion.

The proposed work will substantially improve the calibration as well as the multimodel ensembling inadequacies, leading to a more accurate multimodel deterministic and probabilistic ENSO prediction. Additionally, the product will have a more user-friendly format such that probabilities of the full range of possible values are provided more explicitly. Several methods will be tested to develop the forecast probability density function, including equally and skill based weighting, and including an ensemble regression method that uses the models' individual ensemble members for those models producing ensemble predictions. The forecast probability distributions from the selected methodology mayor may not closely coincide with the spread of the individual model ensemble means; when they do not, the resulting forecast distribution would be the proper indicator of uncertainty that is missing from the current plume product.

The ENSO prediction plume product will have several versions, one of which will be a multimodel ensemble using only the NOAA models participating in the national multimodel ensemble (NMME) experiment, using the same strategy as for the larger set of models. In examining this smaller model set, the issue of the number of models in a multimodel ensemble will be addressed: How many acceptably skillful models tend to produce the best possible multimodel prediction skill, given the high inter-relatedness of the model ENSO predictions? Experimentation with weighting schemes and number of models wi1llead to new methodological knowledge, and to an optimum version of the all-model and the NOAA NMME model plumes.

More accurate, usable predictions of the ENSO state are the fundamental aims of improving the ENSO prediction plume product. Because the ENSO state is related to seasonal climate, better and more easily understood ENSO predictions are of benefit to users in the U.S. and worldwide. Better ENSO predictions lead to better predictions of seasonal climate in known seasons/locations in the U.S. and the globe. Better seasonal climate prediction, in turn, is relevant to the mission of the MAPP Program, seeking to advance intra-seasonal to decadal climate prediction. This work is also relevant to the Next Generation Strategic Plan, as enhanced climate predictions leads to more valued, relied-upon climate services for the benefit of any climate-sensitive sector (e.g., water management, coastal sustainability). The combination of the effects of climate change and ENSO has potential for the hazard of record-breaking climate extremes.


Principal Investigator (s): Barnston, Anthony (IRI/Columbia University)

Co-PI (s): Tippett, Michael (IRI/Columbia University); van den Dool, Huug (NOAA/CPC)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Application of information theory to measure and increase the skill of long-term forecasting

View abstract

The research program proposed here aims to systematically apply information theory metrics to post-processing and evaluation of long-range forecasts, with the goal of quantifying and increasing their usefulness to end users. To support climate adaptation, NOAA and other national and international forecast centers are providing long-range forecasts based on increasingly sophisticated ensembles and super-ensembles of dynamical model runs. For such forecasts to be useful to end users, post-processing must be applied to move from often biased model outputs to calibrated probability distributions for quantities of interest that calibrate model output based on the observational record and past forecasts or hindcasts. The concept of information gain (IG) from a baseline probability distribution function (PDF) for the quantity of interest to a refined PDF that incorporates model predictions offers an intuitive measure of the skill of models at long-range forecasting that has a solid theoretical basis and provides an objective function for optimally combining multiple dynamical forecasts with climatology and statistical patterns.

The main components of the proposed work are (1) evaluate IG of current and archived forecast products compared to suitable baseline PDFs based on simple statistical models that incorporate persistence and trends; (2) develop and test generally useful methods for constructing maximally informative PDFs from available single-model or multimodel ensemble forecasts; (3) evaluate the statistical uncertainty of expected IG computed from finite available samples; (4) compare IG to other widely used metrics for the ranking of forecast models and post-processing methods in order to understand the behavior and respective advantages of different methods. We will test and demonstrate our methods with existing sets of archived model forecasts and hindcasts, including NOAA's Climate Prediction Center seasonal forecasting product and the new NCEP Climate Forecast System Version 2, focusing on the seasonal (<1 year) prediction timeframe for which more independent calibration data are available. We will deliver not only publications describing our results but an open-source software tool to apply information metrics for the post-processing and evaluation for any given forecast problem that has available historical calibration data, facilitating the adoption and further development of information metrics by diverse research and applications communities.

Our project goals are closely aligned with the "intra-seasonal to decadal climate prediction" MAPP competition's priority area of achieving "an objective comparative evaluation of climate prediction skill . . . to assess optimal prediction methodologies for specific applications". We believe that the research and software tool proposed here will tangibly advance the MAPP Program objective of "developing integrated assessment and prediction capabilities relevant to decision makers", and through it NOAA’s goal of a "Weather-Ready Nation" achieved through delivering relevant environmental information.


Principal Investigator (s): Krakauer, Nir (CCNY)

Co-PI (s): Grossberg, Michae (CCNY); Gladkova, Irina (CCNY)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Best Practices for Estimating Forecast Uncertainty in Seasonal-to-Decadal Predictions

View abstract

Seasonal predictions are increasingly acknowledged by the adaptation community as a valuable resource to inform management of risks and opportunities. There is also growing awareness of the role that decadal variability plays in our experience of climate change. Given that decadal prediction experiments will be part of CMIP5, it is almost certain that the adaptation community will want to add these predictions to their portfolio of climate information. The problem is that predictions across these timescales carry various model biases, including probabilistic unreliability, unless they are recalibrated.

Our proposal offers a systematic framework to recalibrate seasonal-to-decadal predictions to yield estimates of forecast uncertainty that can be used to inform decisions such as planning and risk management across these timescales. A comprehensive sensitivity study will also provide guidance on the optimal design of prediction systems in the face of limited resources often faced by many modeling and forecast centers. This guidance will allow for informed decisions on trade-offs between, amongst other factors, the frequency and number of historical hindcasts, ensemble sizes, and the complexity of the recalibration scheme. Through this study we will assess the appropriate level of complexity of recalibration scheme for a given prediction problem, quality of ensemble predictions, and design of hindcasts: in the face of small hindcast samples or small ensemble sizes, more complex schemes may actually degrade the predictions through the addition of noise. This work will involve the development and use of mathematical models in order to test the full range of design choices associated with seasonal-todecadal hindcasts and forecasts. The mathematical models synthetically represent the ensemble prediction and observation time series, and the relation between the two. Existing dynamical model data sets of seasonal-to-interannual simulations and hindcasts and seasonal-to-decadal initialized and uninitialized hindcasts will be used both to parameterize the mathematical models and to demonstrate the impact of recalibration on forecast performance.

This work is highly relevant to NOAA's long-term goal of climate adaptation. This work will assist in the effort to create and sustain enhanced resilience in communities and economies by creating climate prediction and projection information, and associated methodologies, that enable society to better anticipate and respond to climate and its impacts. Our work directly addresses two of NOAA's five-year climate objectives: "assessments of current and future states of the climate system that identify potential impacts and inform science, service and stewardship decisions"; and, "adaptation choices supported by sustained, reliable, and timely climate services". Of particular relevance to the MAPP program and Priority Area 1 to advance intraseasonal to decadal climate prediction, the proposed work will "assess the optimal choice of ensemble members, forecast times, and model diversity in order to characterize the impact of initial condition and model uncertainties in climate prediction", and also "improve understanding of the impact of climate model biases and their evolution in forecast time on prediction skill, and the 'best practice' for post-processing predictions".


Principal Investigator (s): Goddard, Lisa (IRI/Columbia University)

Co-PI (s): Ferro, Christopher (University of Exeter); Mason, Simon (IRI/Columbia University); Fricker, T. (University of Exeter); Stephenson, D. (University of Exeter)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Toward Developing A Seasonal Outlook for the Occurrence of Major U.S. Tornado Outbreaks

View abstract

The record-breaking U.S. tornado outbreak in the spring of 2011 prompts the need to identify and understand long-term climate signals that may provide seasonal predictability for intense tornado outbreaks. Currently, seasonal forecast skill for intense U.S. tornado outbreaks, such as occurred in 2011, has not been demonstrated. A recent study by Lee et al. [2011] used both observations and modeling experiments to find that a positive phase of the Trans-Niño (TNI), characterized by cooling in the central tropical Pacific and warming in eastern tropical Pacific, is associated with large-scale processes that may contribute to major tornado outbreaks over the U.S. In particular, they found that seven of the ten most active tornado years during 1950 – 2010, including the top three, are characterized by a strongly positive phase of the TNI, suggesting that if we can predict the TNI, we may be able to issue a seasonal warning (or outlook) for extreme tornado outbreaks over the U.S.

The main goals of this proposal are (1) to refine the potential predictive skill provided by the TNI, (2) to explore other long-term climate signals that may provide additional predictability in seasonal and longer time scales, and (3) to evaluate and potentially improve seasonal forecast skill for intense U.S. tornado outbreaks in the NCEP Climate Forecast System version 2 (CFSv2). With these three goals in mind, our work will be comprised of five tasks: (task-1) reanalyzing the severe weather database (SWD); (task-2) establishing meteorological indices for estimating the occurrence of tornadoes; (task-3) exploring long-term climate signals that may provide predictability of U.S. tornado activity; (task-4) analyzing the CFSv2 reforecasts; and (task-5) exploring the potential of an experimental hybrid dynamical-statistical seasonal forecasting system. Completing task-1 and -2 will result in a bias-corrected SWD and reanalysis based proxy tornado datasets, which will be essential for studying the tornado-climate linkage and thus useful for the wider climate and tornado research community. Completing task-3 will identify long-term climate signals that may provide predictability of U.S. tornado activity. Completing task-4 and -5 may potentially result in an experimental hybrid seasonal forecast system for U.S. tornado activity. If the experimental forecast system is shown to provide skillful seasonal predictability of U.S. tornado activity, it will be used to develop a seasonal tornado outlook at NOAA CPC.

The proposed work contributes directly to a high-priority topic for NOAA FY2012 MAPP funding Priority Area-1 Advance Intra-seasonal to Decadal Climate Prediction: "(i) Achieve an objective comparative evaluation of climate prediction skill from dynamical, statistical, and hybrid or consolidated systems to assess optimal prediction methodologies for specific applications." This proposed work will be conducted under the auspices of the Cooperative Institute of Marine and Atmospheric Science program at the University of Miami’s Rosenstiel School of Marine and Atmospheric Science, and addresses CIMAS Theme: (Climate Research and Impacts). This work is relevant to the NOAA goals: (Weather-Ready Nation: Society is prepared for and responds to weather-related events, and Climate Adaptation and Mitigation: An informed society anticipating and responding to climate and its impacts) in support of NOAA’s Strategic Plan.


Principal Investigator (s): Lee, S.-K. (University of Miami/CIMAS)

Co-PI (s): Weaver, Scott (NOAA/CPC); Atlas, Robert (NOAA/AMOL); Wang, Chunzai (NOAA/AMOL); Enfield, David (NOAA/AMOL)
Year Initially Funded: 2012
Task Force:
Drought Task Force
Final Report:

Exploring best practice procedures for optimal use of climate forecast for regional hydrological applications

View abstract

Decisions regarding water resource management, agricultural practice, and energy allocation often require information about future climate conditions weeks to months in advance. Skillful and reliable seasonal climate prediction can significantly facilitate and benefit the decision making process. However, it is a challenge to make skillful predictions about the climate system at these time scales because the processes that contribute to the seasonal predictability are not fully understood thus not adequately represented in climate models. Research is needed to assess the current prediction skills from the state-of-the-art climate forecast systems, and to develop "best practice" procedures for optimal use of climate forecasts in applications that are directly relevant to decision making. This proposed project addresses those very issues.

In this project, we will carry out research activities to 1) evaluate the state-of-theart climate forecast systems, quantify their seasonal prediction quality, and assess factors that contribute to the prediction skill; 2) assess the optimal choice of ensemble members and scales, and to develop best practice procedures for combining and post-processing multiple forecasts to achieve better forecast quality; and 3) demonstrate the usefulness of seasonal climate prediction and evaluate the new post-processing procedures with seasonal drought prediction. The innovation of the proposed research is mainly reflected in the second activity. We will develop two innovative methods (multiscale Bayesian merging and structured output regression) in parallel to combine forecast information across multiple characteristic spatial and temporal scales. These methods will address outstanding issues like spatial and temporal dependence (or correlation structure) that is practically ignored when combining forecast members in an ensemble or multimodel ensemble system currently. It is our intention to make comparative evaluation of these two methods that grew out of two research communities. These statistical methods have the potential to significant advance the seasonal climate forecast skills. We will demonstrate the improvement in prediction skills and usefulness of climate prediction in regional hydrological applications by performing seasonal drought forecast for selected drought events in the US using these new methods.

We have assessed the feasibility of the project and have a clear understanding of possible difficulties. The proposed methods are new to seasonal climate prediction, but have been used in research fields of data assimilation, data mining and machine learning. The research team has experience in dealing with these methods, so we expect this project will progress smoothly. The project is relevant to MAPP program because it directly responds to the first priority area solicited by MAPP for FY2010, i.e., advance intro-seasonal to decadal climate prediction. In particular, this proposed research focuses on objective assessment of climate prediction skill from state-of-the-art climate forecast systems, and development of "best-practice" procedures for post-processing the predictions for hydrological applications. The outcome of this research will contribute to NOAA's operation in seasonal climate forecasting.


Principal Investigator (s): Luo, Lifeng (Michigan State University)

Co-PI (s): Tan, Pang-Ning (Michigan State University)
Year Initially Funded: 2012
Task Force:
Drought Task Force
Final Report:

Enhancing Seasonal Drought Prediction Capabilities for the US and the Globe Using the National Multi-Model Ensemble

View abstract

As articulated in the National Integrated Drought Information System implementation plan (NIDIS; NOAA 2007), decision-makers in drought-sensitive sectors would gain substantial utility from enhanced and objectively derived drought outlooks which allow stakeholders to test a variety of scenarios across different time scales to guide their decision-making. One of the two adopted strategies for the enhancement of seasonal drought outlooks under the NIDIS plan is the prediction of widely-used, meteorologically based drought indicators, and common goals of both the climate test bed program (CTB) and NIDIS is to enhance skill of seasonal drought forecasts via multi-model ensemble techniques. The new National Multi-Model Ensemble (NMME) brings an important new tool to this effort. And given its global coverage, the NMME further allows for the development and test bedding of a prototype global drought prediction system, a core component of a global drought early warning system (GDEWS) recently endorsed by the World Climate Research Program (WCRP).

Here we propose to build on probabilistic seasonal drought prediction capabilities recently developed by members of the research team by incorporating NMME forecasts into that framework. The overall goal of the project is to enhance current seasonal drought prediction efforts over the US while also developing a prototype drought prediction system for the globe. The work will blend observed drought conditions with the dynamical model precipitation (and temperature) forecasts to predict multiple "drought" indicators. The focus will be on generating objectively derived probabilities of future drought conditions (i.e., drought indicator values) given the current drought state at lead times of 1 to 8 months. Web-based tools will be developed for the interactive display of drought forecast information as well as historical drought conditions. The overall strategy is envisioned to be an intermediate complexity approach that can easily transition to near real time operations to support the NIDIS Drought Early Warning system.

This proposal responds to the MAPP program priority area 1: "Advance intra-seasonal to decadal climate prediction" while tied to priority area 2: "Develop an experimental National Multi model ensemble climate prediction system." Specifically, the proposed research will contribute to the priority area 1 objectives to "assess optimal prediction methodologies for specific applications" and "achieve an improved understanding of the impact of initializing select components of the Earth system for climate prediction at a particular timescale." Products from this proposal will contribute to the CPC drought briefing and the NMME drought indicator predictions will provide drought forecasters a valuable tool for their operational monthly and seasonal Drought Outlook. The work also relates to the NOAA Next-Generation Strategic Plan (NGSP) by addressing its objective for "improved scientific understanding of the changing climate system and its impacts", which includes a new generation of climate predictions and increased confidence in assessing and anticipating climate impacts. It also provides necessary information for addressing the NGSP objective of "mitigation and adaptation choices supported by sustained, reliable, and timely climate services" by which national, state, local, and tribal governments and water resource managers are better able to prepare for, adapt, and respond to drought and flooding and can more confidently manage water resources.

Principal Investigator (s): Lyon, Bradfield (IRI/Columbia University)

Co-PI (s): Mo, Kingtse (NOAA/CPC); Barnston, Anthony (IRI/Columbia University)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

A US National Multi-Model Ensemble ISI Prediction System

View abstract

The proposed research leverages an existing National Multi-Model Ensemble (NMME) team that has already formed and is already producing routine real-time intra-seasonal, seasonal and interannual (ISI) predictions and providing them to the NOAA Climate Prediction Center (CPC) on an experimental basis for evaluation and consolidation as a multi-model ensemble ISI prediction system. The experimental prediction system developed by this NMME team is as an "MME of opportunity" in that the ISI prediction systems are readily available and each team member has independently developed the prediction protocol.

The activity proposed here is to develop a more “purposeful MME” in which the requirements for operational ISI prediction are used to define the parameters of a rigorous reforecast experiment and evaluation regime. The NMME team will design and test an operational NMME protocol (i.e., a purposeful MME) that is to guide the future research, development and implementation of the NMME beyond what can be achieved based on an "MME of opportunity."

The proposed activity will:

i. Build on existing state-of-the-art US climate prediction models and data assimilation systems that are already in use in NMME-1 and ensure interoperability so as to easily incorporate future model developments.

ii. Take into account operational forecast requirements (forecast frequency, lead time, duration, number of ensemble members, etc.) and regional/user specific needs. A focus of this aspect of the work will be the hydrology of various regions in the US and elsewhere in order to address drought and extreme event prediction.

iii. Utilize the NMME system experimentally in a near-operational mode to demonstrate the feasibility and advantages of running such a system as part of NOAA's operations.

iv. Enable rapid sharing of quality-controlled reforecast data among the NMME team members, and develop procedures for timely and open access to the data, including documentation of models and forecast procedures, by the broader climate research and applications community. The proposed activity will also include several NMME research themes:

i. The evaluation and optimization of the NMME system in hindcast mode (e.g., assessing the optimal number of ensemble members from each model, how to best combine the multi-model forecasts, sources of complementary prediction skill, etc.),

ii. Ocean and land initial condition sensitivity experiments.

iii. The application of the NMME forecasts for regional downscaling and hydrological prediction.


Principal Investigator (s): Kirtman, Benjamin (University of Miami/CIMAS)

Co-PI (s): Kinter, James (COLA); Paolino, Dan (COLA); DeWitt, David (Columbia University); Tippett, Michael (IRI/Columbia University); Barnston, Anthony (IRI/Columbia University); Rosati, Anthony (NOAA/GFDL); Pegion, Kathy (CIRES/University of Colorado); Schubert, Siegfried (NASA Goddard); Reinecker, Michele (NASA Goddard); Suarez, Max (NASA Goddard); van den dool, Huug (NOAA/CPC); Mendez, Malaquias Pena (NOAA/EMC); Huang, Jin (NOAA/CPC); Weaver, Scott (NOAA/CPC); Tribbia, Joe (NCAR); Wood, Eric (Princeton University)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Understanding climate variations in the Intra-Americas Seas and their influence on climate extremes using global high-resolution coupled models

View abstract

We propose to use a hierarchy of GFDL high-resolution climate models to improve our understanding of the climate of the Caribbean Sea and Gulf of Mexico ("Intra-Americas Seas", or "IAS"), including its influence on climate-scale variations and changes in Atlantic hurricane activity and North American drought. Because of the complex, mutli-scale oceanographic, atmospheric and coupled air-sea phenomena that characterize the IAS region, we will focus on both atmospheric and oceanic climate, and their interactions. We will explore the sensitivity of the simulation of the mean climate and climate variations in the IAS to changes in resolution and parameterization in the context of the coupled GFDL high-resolution models. The role of remote influences on climate in the IAS will be explored, assessing oceanic and atmospheric teleconnections by performing "data override" and "partial coupling" experiments with the climate models. Analogous perturbations to the coupled model will be used to explore the influence of the IAS on remote climate through atmospheric and oceanic processes. We will focus particularly on the influence of the IAS on North Atlantic hurricanes and on drought over North America. Predictability of the climate variations and teleconnections from the IAS will be explored using initialized prediction experiments using the GFDL high-resolution modeling system.

The principal hypotheses to be tested are i) increased resolution and high-order numerics in global coupled climate models improve simulation of mean climate and variations of the Intra-Americas Seas, ii) remote, large-scale factors (e.g., ENSO and the Atlantic Meridional Overturning Circulation) drive variations and changes in the IAS through atmospheric and oceanic bridges, iii) changes in oceanic circulation and atmospheric convection in the IAS have a detectable influence on remote oceanic and atmospheric conditions, iv) modeled climate variations in the IAS modulate North American drought and North Atlantic tropical cyclone activity in the North Atlantic, v) the improved representation of drivers of IAS variability (e.g., ENSO and AMM) and the mean climate of the IAS in higher resolution models leads to enhanced predictive capacity for regional climate due from initialization and response to radiative forcing. The proposed work should improve our understanding and ability to model a key area of the global climate system, and the model simulations performed in this study and analysis of them will be beneficial to the high-resolution climate model development.

Relevance to NOAA's long-term goal and to the competition: This work will contribute to NOAA's long-term goal of climate adaptation and mitigation through improving our ability to model, predict and understand climate extremes over North America. The IAS is a principal moisture source for rainfall over much of the southeastern and central US, provides a warm water energy source to tropical cyclones and is key in the development of tornadic activity over the US. Therefore improved understanding, modeling and prediction of this key region is necessary to understanding likely changes in droughts, landfalling tropical cyclones and tornadic activity over the US, and help inform adaptation strategies. Though the IAS is influential to climate and extremes, "state-of-the-art global models have very large mean bias and erroneous variability over the [IAS] region," according to the IAS Climate Processes (IASCliP) Modeling Working Group (Misra et al. 2010). This proposal seeks to use higher resolution models to help remedy this important limitation to our current modeling capability.

Principal Investigator (s): Vecchi, Gabriel (NOAA/GFDL)

Co-PI (s): Delworth, Thomas (NOAA/GFDL); Rosati, Anthony (NOAA/GFDL)
Year Initially Funded: 2012
Task Force:
Climate Prediction Task Force
Final Report:

Climate Variability of the Tropical Western Atlantic Storms: Is it hinged to Intra-Americas Seas climate processes?

View abstract

The proposal seeks to understand the low frequency variability of the Tropical Western Atlantic Storms1 (TWAS) and its relationship with Intra-Americas Seas (IAS) climate processes. Traditionally simulations and predictions beyond the NWP range for tropical Atlantic storms have largely been on their frequency of occurrence throughout the basin over the 6-month period from June through November. This type of forecast or simulation has limited application although they have demonstrated admirable skill on seasonal time scales and on longer time scales in their rendition of the 20th century variability. The success of this study could be a harbinger for attempting predictions of a subset of the tropical Atlantic storms that are geographically more limited in the basin (western Atlantic). Furthermore a majority of TWAS make landfall over continental North America. In addition, the TWAS climatologically has a characteristic dominance of genesis in June and November, which could also be potentially exploited if we understand their causality. Thus this proposal is relevant to NOAA's NGSP mission on climate adaptation and mitigation to the threat of potential land falling tropical storms in North America.

The proposed work will employ high-resolution (~10km) coupled oceanatmosphere model centered over the IAS in an attempt to resolve the TWAS, the Caribbean Low Level Jet (CLLJ), air-sea fluxes in the IAS, the diurnal variations in the region and capture the associated, intricate sub-surface ocean structure. In addition several sensitivity experiments are also proposed to understand the influence on the genesis and lifecycle of TWAS. This framework of regional coupled ocean-atmosphere modeling is deliberately chosen to afford the high resolution for multi-decadal integrations and limit the model drift by forcing the regional system with credible largescale reanalysis (NCEP CFSR). The coupled GCM’s have shown acute climatological bias in the IAS region with poor depiction of the associated variability in the boreal summer season.

The central objective of the proposal is to understand whether (and followed by how) IAS climate processes like the evolution of the IAS SST and sub-surface ocean evolution from the prior seasons, variability of CLLJ, air-sea fluxes in IAS, overlying atmospheric meridional cell can influence TWAS. The basis for this investigation is buttressed by several related observational evidence, a clear working knowledge of the models to be employed with its demonstration of use over another domain, and availability of computing resources to conduct the proposed integrations.


Principal Investigator (s): Misra, Vasubandhu (Florida State University)

Co-PI (s):
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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.

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