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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The relationship of tropical cyclones to MJO and ENSO in the S2S database

"It is very well established that tropical cyclones (TCs) are modulated globally by the Madden-Julian Oscillation (MJO) and the El Niño-Southern Oscillation (ENSO). Due to these relationships, TC activity is to some extent predictable on both intraseasonal and seasonal time scales. Given the recent effort to develop TC forecasts on intraseasonal time scales a comprehensive analysis of the skill of various models in simulating and predicting TC activity at these time scales is warranted. The S2S dataset includes many high-resolution global weather and climate models that have capability to simulate tropical cyclone activity well. It is the ideal dataset to explore in depth the skill of TC forecast models.

First, we will detect and track tropical cyclones (TCs) in the S2S model output to generate TC tracks for this dataset. We will then analyze various aspects of the TC climatology in the S2S models, e.g., genesis, tracks, seasonality, intensity. Given that TC seasonal forecasts still have limited skill, as was apparent in the Atlantic as recently as 2013, it is important to analyze how well the models in the S2S database simulate the ENSO-TC teleconnection. We propose to examine the relationship of ENSO to TCs globally within the S2S dataset. Our focus will be on the response to ENSO of the environmental variables that are relevant for TCs, as well as modulation of the models’ TCs by ENSO. We will also examine the predictability of the S2S models for TC seasonal forecasts on regional scales. The project will also address how well the models in the S2S database simulate the relationship between the MJO and TCs. First, we will examine how well the different models reproduce the observed modulation of TCs in the active phase of the MJO. Second, we will explore various aspects of this modulation in the models, for instance, if the models modulation by the MJO is dependent on the MJO strength or on the MJO Phase.

In all three aspects of this project - TC climatology, ENSO-TC relationship, and MJO-TC relationship - we will focus on the models’ skill in forecasting TC activity on subseasonal to seasonal time scales. Furthermore, we will examine how the individual models’ ability to simulate TC characteristics is dependent on each model’s configuration. Our contribution to the MAPP task force activities will focus on the skill of S2S TC forecasts."

Principal Investigator (s): Suzana Camargo (Lamont-Doherty Earth Observatory, Columbia University)

Co-PI (s):Adam Sobel (LDEO, Columbia University), Chia-Ying Lee (IRI), Fréderic Vitart (ECMWF)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Prediction, Sensitivity, and Dynamics of Subseasonal To Seasonal Phenomena Diagnosed Through Linear Inverse Models, Their Adjoints, and Numerical Weather Prediction Models

"Subseasonal to seasonal (S2S) predictability faces a unique set of forecast challenges related to initialization, parameterization, and development of model bias around which the forecast state must evolve. Furthermore, a wide range of phenomena exist on S2S timescales that require realistic evolution of the forecast state over a wide range of geographical regions and physical processes (e.g. tropical intraseasonal variability, low-frequency mid-latitude wave dynamics, and downward propagation from the stratosphere), which presents a major challenge for numerical weather prediction (NWP) models that aim to forecast out to S2S time scales. Additionally, targeted forecasts of different specified extreme events (e.g. blocking, heat waves, cold snaps) may be associated with very different sensitivities to any of these processes. A general framework is needed for analyzing and evaluating the role of initial conditions, model physics, and development of model bias, in S2S predictability of specific extreme events. The project will utilize linear inverse models (LIMs) that are derived to infer the dynamics of the atmosphere and ocean from a set of observed states; these states can come from analyses (which are largely free of model bias) or from forecast model states (which may contain biases around which a NWP model state must evolve). The feasibility of the LIM for studies of low- frequency variability has been demonstrated in previous studies.

This project will advance predictive capacity for S2S forecasting by (i) developing LIMs separately around the analysis state (analysis-LIM) and around NWP forecast states that include the effects of the evolved model bias (forecast-LIM); (ii) analyze the role of initial conditions, model physics, and development of model bias in event-specific subseasonal prediction; (iii) test findings using LIM-informed data-denial initialization experiments in a NWP model; and (iv) blend NWP and LIM strengths to maximize predictive skill on S2S time scales. The combination of tasks in this project compose a general framework that addresses the unique challenges outlined above, and informs priorities for NWP model development related to S2S prediction.

This project is directly relevant to the goals of Competition 2 in the FY16 MAPP funding opportunity, through understanding the predictability and potential to advance prediction of specific phenomena occurring on S2S timescales. The project develops a framework that can be used to investigate how prediction of S2S phenomena is influenced by specific initial states, coupling between different model components and regions, and model physics (and associated evolution of model bias). The framework will be applied to specific S2S prediction efforts and will be provided in real-time for modeling groups to gain familiarity with the process. Our long-term goal is to develop an operational approach for a priori determination of “state-dependent” subseasonal forecast error. The PIs will also contribute to a MAPP Task Force by providing a product that can be used in real-time to diagnose S2S predictions from a variety of different NWP modeling efforts. Finally, this project is also relevant to NOAA’s Next Generation Strategic Plan for a “Weather-Ready Nation”, through increasing understanding of predictability of potentially hazardous extreme weather, as well as phenomena of importance to the energy sector."

Principal Investigator (s): Brett Hoover (University of Wisconsin - Madison)

Co-PI (s):Matt Newmann (NOAA/ESRL), Michael Morgan (University of Wisconsin - Madison), Daniel Vimont (University of Wisconsin - Madison)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Understanding the Sources of Subseasonal Predictability of Extratropical Cyclone Activity and Improving Their Representation in Forecast Systems

"Extratropical cyclones cause much of the high impact and extreme weather conditions over the mid-latitudes, including heavy precipitation, high winds, coastal storm surges, and extreme cold events. On the other hand, lack of extratropical cyclone activity (ECA) in summer is linked to extreme heat. Hence skillful predictions of future cyclone activity will provide policy makers, emergency management, and stakeholders advanced warnings to prepare for mitigation measures. Unfortunately at present the National Weather Service does not provide any such forecast products in the subseasonal to seasonal time range.

The goal of this project is to improve the subseasonal prediction of ECA and its associated weather extremes. It has three specific objectives: i) Improve the understanding of the physical drivers that give rise to ECA predictability; ii) Improve the prediction of ECA and its drivers by focusing on the forecasting system set-up and model convection parameterizations; iii) Improve the forecasting of weather extremes associated with ECA variability. To achieve these objectives, the following tasks will be conducted: 1) Subseasonal prediction of ECA derived from multi-model ensemble hindcasts will be evaluated, and diagnostic and mechanistic model experiments will be conducted, to test the following hypothesis on ECA predictability: ECA predictability depends on the specific combinations of different drivers, such as the combination of the different phases of the Madden-Julian Oscillation and ENSO; 2) The choices of ensemble members, improved convection parameterizations that control diabatic heating and moisture sink profiles, as well as model resolutions will be investigated to improve the set-up of the forecasting system; 3) The impact of model biases and improvements in ECA prediction on the prediction of weather extremes will be quantified.

This project seeks to advance the subseasonal prediction of ECA and its associated weather extremes, thus contributing to NOAA’s goals to develop the capability to bridge weather and seasonal predictions, and to extend the lead times at which extreme events are skillfully predicted, thereby allowing emergency managers, water resource managers, and other stakeholders more time to prepare, hence this project is highly relevant to NOAA’s long term goals. This project seeks to understand the physical basis behind the subseasonal predictability of ECA, explore how the set-up of the prediction system and model convection parameterization impact system skill in predicting ECA and its drivers, and assess ECA predictability in the context of its impacts on weather extremes, thus this project is highly relevant to this competition."

Principal Investigator (s): Edmund Kar-Man Chang (Stony Brook University)

Co-PI (s):Minghua Zhang (Stony Brook University), Hyemi Kim (Stony Brook University), Wanqiu Wang (NOAA/CPC)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Collaborative Research: Assessing Oceanic Predictability Sources for MJO Propagation

"The body of evidence from decades of work suggests a paradigm of the Madden Julian Oscillation (MJO) as a primarily atmospheric disturbance whose initiation, maintenance, and propagation characteristics may be flavored by surface turbulent fluxes that are modulated by sea surface temperature (SST) variations. The longer-than-synoptic timescale of the MJO and its impact on a variety of high-impact global weather phenomena implies an opportunity to increase global weather prediction skill if the MJO can be reliably predicted. Impediments to MJO predictions include 1) poor predictions of whether a given convective event will propagate from the Indian Ocean to the West Pacific Ocean, where the global teleconnection response is strongest; and 2) a lack of understanding of the processes that initiate MJO convection. The onset of MJO convection can be preceded by a variety of atmospheric precursor signals whose individual expressions can vary from event to event, and can influence prediction skill. Once the MJO initiates, forecast skill tends to be highest for high amplitude events. Both climate and forecast simulations of the MJO are improved in coupled atmosphere–ocean models, suggesting a source of predictability from ocean feedbacks. Leveraging these potential sources of predictability for MJO forecasts remains challenging in light of low amplitude or decaying MJO events, and the variable influence of ocean feedbacks on MJO development. These prediction challenges are rooted in our limited understanding of key underlying physical processes involving MJO evolution and ocean–atmosphere interactions.

To improve our understanding, we propose a multi-pronged approach based on separating observed MJO events into three classes: strong, weak, and “eastward-decaying” (i.e., those that terminate before crossing the Maritime Continent). Factors that differentiate these classes of MJO events and their predictability will be explored as follows:
1. The Subseasonal-to-Seasonal (S2S) database will be used to analyze the predictability of and prediction skill for the three MJO classes, focusing on atmospheric and oceanic precursor signals. Ocean reanalysis data will provide additional context.
2. Specific ocean feedback processes will be tested in hindcast simulations using coupled models with a demonstrated ability to simulate the MJO. Particular attention will be given to MJO events associated with high-impact weather.
3. An ad hoc set of air-sea interaction diagnostics currently being developed in conjunction with WCRP S2S and WGNE MJO Task Force members (and others) for climate simulations will be expanded for application to hindcast simulations.

The proposed work is relevant to MAPP Competition 2 because:
1) it will improve the understanding of MJO predictability,
2) it has the potential to advance predictions of subseasonal and seasonal phenomena,
3) it uses existing (S2S database) and new model experiments to explore how the MJO is influenced by coupling between Earth system components (ocean–atmosphere),
4) it addresses predictability in the context of key underlying physical processes,
5) it actively seeks collaborative activities with WCRP S2S and WGNE MJO Task Force scientists to deliver a set of MJO air–sea interaction diagnostics.

This research will advance core capabilities in 1) understanding and modeling and 2) predictions and projections. It addresses NOAA’s long-term climate goals for “improved scientific understanding of the changing climate system” and “assessment of current and future states of the climate system that identify potential impacts and inform science.”"

Principal Investigator (s): Charlotte DeMott (Colorado State University)

Co-PI (s):Nicholas Klingaman (University of Reading, Earley Gate)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Forecasting North Pacific Blocking and Atmospheric River Probabilities: Sensitivity to Model Physics and the MJO

"Atmospheric rivers (ARs) are intense synoptic-scale plumes of tropospheric water vapor that can lead to extreme precipitation and flooding when they make landfall. These features cause extreme flooding events not only along the west coast of the contiguous United States (CONUS), but also in Canada and Alaska. The ability to forecast ARs would provide society with advanced knowledge of their extreme impacts. Recent work by the our team demonstrates an inverse relationship between winter-time ARs hitting Alaska and CONUS, driven by the presence of a blocking anticyclone over the east Pacific that acts to divert the ARs away from CONUS and into the Gulf of Alaska. The potential exists to forecast the probabilities of North Pacific blocking and AR occurrence through knowledge of the Madden-Julian oscillation (MJO).

Specifically, additional recent work by our team demonstrates that the pattern of blocking leading to an increase in Alaskan ARs (and subsequent decrease in CONUS ARs) is driven, at least in part, by phase 8 of the MJO – a phenomenon that is potentially predictable on timescales of 4 weeks or longer. Thus, while ARs are synoptic features unlikely to be forecast explicitly for lead times beyond 10 days, great promise exists for forecasting their probabilities based on knowledge of the MJO. However, many climate models and numerical weather prediction models cannot simulate the MJO with fidelity. The overarching goal of the proposed work is to quantify the extent to which east Pacific blocking and AR probabilities can be skillfully
forecast at lead times of multiple weeks through their dynamical link with the MJO, including an explicit investigation of how AR prediction skill varies with a model’s ability to forecast the MJO.

To achieve this goal, the tasks outlined in this proposal address two main objectives:

Objective I: Quantification of the predictability and prediction skill of North Pacific blocking and atmospheric river probabilities through knowledge of the MJO.

Objective II: Assessment of the sensitivity of forecast skill to MJO skill and model setup.

To address Objective I, the proposed work will develop statistical forecast models to predict blocking and AR probabilities using knowledge of the MJO and other predictors. Then, we will quantify the ability of operational models to forecast AR and blocking probabilities by analyzing data from two hindcast data sets: the S2S and ISVHE databases. The second objective of the proposed work will address the extent to which AR forecast skill is sensitive to the prediction system setup by further analyzing the S2S and ISVHE databases and performing a series of simulations with two GCMs. Specifically, we will address the extent to which the forecast skill of blocking and AR probabilities are sensitive to: the model’s MJO forecast skill, physics (i.e. cloud parameterization), resolution and forecast lead time.

Relevance to NOAA and Task Force Contributions: This proposal directly addresses the
FFO “MAPP – Research to Advance Prediction of Subseasonal to Seasonal Phenomena” by improving the understanding of how formulation of the prediction system, including physics and resolution, impacts the forecast skill of extreme events (i.e. atmospheric rivers and blocking) and what physical processes lead to their predictability. We will aid NOAA’s NGSP by improving process-level understanding and prediction skill of persistent flow regimes associated with blocking, and extreme rainfall associated with atmospheric rivers, to provide more accurate “assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions.” The proposed efforts will contribute to the new MAPP S2S Task Force by (1) providing scientific leadership of the Task Force (2) linking international efforts on advancing S2S prediction, (3) contributing new understanding of the influence of prediction system setup on forecasts of extreme events (4) delivering a database of extreme events, blocking and atmospheric river occurrence and statistical forecast models."

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

Co-PI (s):Eric Maloney (Colorado State University)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Madden Julian Oscillation - the Maritime Continent barrier and seamless verification

"The Madden Julian Oscillation (MJO) is of central importance in subseasonal to seasonal forecasts but remains difficult to predict. An outstanding problem is that models have difficulty simulating or predicting the propagation of the MJO across the Maritime Continent. This deficiency results in a “prediction barrier.” Overcoming this barrier is a challenge because its precise cause or causes are unknown. Proposed causes include poor representation of th diurnal cycle, biases in mean climate and failure to capture precursor signals. Our project seeks to improve both understanding and prediction of the MJO, focusing on the relation of the MJO to the Maritime Continent. We propose a systematic analysis of forecast and reforecast ensembles from the Seasonal-to-Subseasonal (S2S) prediction project dataset. Success in forecasting MJO propagation across the Maritime Continent varies between different runs in each ensemble as well as across models. Relating forecast success, as well as MJO characteristics, to other variables across the ensembles will identify which variables and processes are most important for determining the success of the forecast and for achieving a good representation of the MJO itself.

Interactions between the diurnal cycle, the MJO, and the mean climatology in structuring deep convection over the Maritime Continent are of particular interest. The moist static energy budget will be adopted to interpret results from a thermodynamic prospective. Dynamic precursors pertinent to the MJO will be extensively explored to evaluate their roles in the MJO forecasts and relation to model biases. All the analysis of these variables will be carried out based on a “seamless” verification approach by which variables are averaged with varying time windows to facilitate smooth transition from daily weather forecast to seasonal climate prediction time scales.

Relevance to NOAA’s goal and to the competition: Our proposal targets Competition 5: Research to Advance Prediction of Subseasonal to Seasonal Phenomena. This competition focuses on the predictability and prediction of S2S phenomena in the context of key underlying physical processes and dynamical processes. Our proposed statistical analysis specifically addresses this issue. Our project is well within the scope of the NOAA’s MAPP program, whose goal is to advance understanding and prediction of variability and changes in Earth's climate system and infuse research advances into NOAA’s service. One particular focus of the MAPP is to improve intraseasonal and interannual climate prediction. Our proposed research is designed to improve understanding of the MJO prediction barrier over the Maritime Continent, and to help identify ensemble forecast error, and sources of that error, in NOAA’s forecast systems. By transforming knowledge learned from the basic research to operational practice, our project will benefit the general public by improving subseasonal to seasonal forecasts and providing better weather service."

Principal Investigator (s): Shuguang Wang (Columbia University)

Co-PI (s):Adam Sobel (Columbia Universit), Michael Tippett (Columbia University)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Investigating the Underlying Mechanisms and Predictability of the MJO-NAM Linkage in the NMME Phase-2 Models

"Skillful weather predictions with 10- to 30-day lead times for the Northern Hemisphere (NH) extratropics remain a major challenge for the forecast community. Skillful predictions of extratropical NH subseasonal weather ultimately depend on knowledge of the position and strength of the polar jet stream, commonly represented by the Northern Annular Mode (NAM). Avenues forward to narrowing the subseasonal-to-seasonal (S2S) prediction gap with respect to the NAM seek to exploit interactions of intraseasonal modes of climate variability with the NAM. One such mode is the Madden-Julian Oscillation (MJO), the leading mode of subseasonal variability in the tropics. Another source of extended predictability for the NH extratropics is the polar stratosphere, whereby the state of the polar vortex exerts a downward influence on the tropospheric jet stream and thereby alter weather patterns and the tropospheric waveguide. Whether or not this stratospheric influence can influence MJO-related teleconnections remains unresolved.

The proposed project has three main objectives: (1) Enhance our knowledge about the dynamical links between the MJO and the NAM by considering the modulating influence of the extratropical stratosphere; (2) Evaluate these mechanisms of MJO-NH extratropical atmospheric teleconnections in the North American Multi-Model Ensemble Phase-2 (NMME-2) system; and (3) Connect and apply our findings and evaluations to predictions of atmospheric blocking and extreme weather events. Three synergistic research tasks will accomplish these goals. First, we will use observations to quantify and dynamically understand the MJO-stratosphere modulation effect. We will demonstrate how the strength of the polar vortex can modulate Rossby wave trains associated with the MJO and how this modulation affects NH wintertime blocking frequency. Next we will evaluate the MJO-NAM teleconnections in the NMME-2 models with a focus on blocking episodes and document performance as a function of (a) dynamical stratosphere-troposphere coupling and its relation to jet stream variability, and (b) the stratospheric resolution of the model. The final task will be an assessment of the MJO-stratosphere modulation effect in the NMME-2 models whereby we will quantify its ability to affect forecast skill of MJO-related blocking events.

The proposed work is submitted for consideration for the NOAA Modeling, Analysis, Predictions, and Projections (MAPP) Competition 2: Research to Advance Prediction of Subseasonal to Seasonal Phenomena. The proposed work satisfies NOAA MAPP’s mission “to enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system”. Quantifying the MJO-stratosphere modulation effect offers enhanced “process-level understanding” and an “understanding of predictability and the potential to advance the prediction of phenomena occurring on [subseasonal] timescales.” The described tasks collectively “address the predictability and prediction of S2S phenomena in the context of key underlying…dynamical processes (e.g., Rossby wave forcing, wave-mean flow interactions, and troposphere-stratosphere interactions)” and “explore how prediction of S2S phenomena is influenced by various aspects of the prediction system set-up, including: (i) model resolution…(ii) initialization of, and coupling between, Earth system components; [and] (iii) model physics.” Finally, narrowing the S2S predictability gap through a process-based understanding and evaluation of the simulation of the MJO-NAM link heightens societal awareness and preparation for potential extreme weather events, which contributes to NOAA’s “Weather Ready Nation” goal established in the Next Generation Strategic Plan (NGSP). Findings of this work will be shared with the proposed new NOAA S2S Task Force as well as the World Climate Research Programme (WCRP) S2S Prediction project and Working Group on Numerical Experimentation (WGNE) MJO Task Force via planned presentations, co-organized sessions at international conferences, and direct discussions with the committee members."

Principal Investigator (s): Jason Furtado (University of Oklahoma)

Co-PI (s):Michelle L’Heureux (NOAA/CPC), Elizabeth Barnes (Colorado State University), Adam Allgood (NOAA/CPC)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Exploring Pathways for Improving MJO Predictions

"The Madden-Julian Oscillation (MJO) is the dominant mode of tropical convection variability on the intraseasonal time scale. The recurrent nature of the MJO with a period of 30-60 days offers an opportunity to bridge the gap between weather forecasting and seasonal prediction. Over the recent years, significant improvements have been made in MJO prediction skill in operational forecasts. However, the MJO prediction performance still differs greatly among the operational systems and efforts are continually underway to seek further improvements. Furthermore, a general guidance on what would be most beneficial developmental pathways to improve MJO simulation and prediction skill remains unclear. Addressing the issues for improving MJO forecasts requires quantifying the sensitivity of MJO predictions to different factors that affect the prediction, and quantifying the relative importance of individual factors. Even though the existing subseasonal to seasonal (S2S) prediction data sets provide an opportunity to evaluate the current capability in predicting atmospheric and oceanic variability at S2S time scale, isolating influences of individual factors based on the existing datasets alone is not sufficient, because various models that were used to produce these databases have different resolutions, initialization procedures and model physics. The objective of this project is to study the influence of different aspects of forecast configurations and their relative importance on the MJO prediction based on a perfect model framework with the CFSv2.

To achieve the objective, the following steps will be taken: 1) A set of long-term control simulations with different model configurations for the atmospheric resolution, convection, and ocean component will first be performed. From them, a control configuration that produces the most realistic MJO simulation will be selected. 2) Potential predictability of the MJO will be assessed for the selected control configuration based on prediction runs with slight perturbations to atmospheric initial conditions taken from its long-term simulation. 3) A suite of forecast experiments will be done with changes to various aspects to the control configuration to determine (and understand) the sensitivity of the MJO predictions to those changes. Our focus will be on the changes in model resolution, convective parameterization, and representation of air-sea coupling. We will work toward isolating the most important factors governing MJO prediction performance.

We anticipate that the proposed research will identify the beneficial development pathways to further improve MJO predictions. It will provide guidance for improving the next generation CFS and other coupled forecast system models in the climate community. Results from this project will also help improve our understanding of the processes related to the MJO dynamics and how to properly represent them in the coupled climate models."

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

Co-PI (s):Wanqiu Wang (NOAA/CPC), Jieshun Zhu (NOAA/CPC)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

Improving subseasonal to seasonal forecast skill of North American precipitation and surface air temperature using multi-model strategy

"This proposal responds to the 2016 solicitation for CPO’s Modeling, Analysis, Prediction and Projection (MAPP) program Competition 2: “Research to Advance Prediction of Subseasonal to Seasonal Phenomena.” The proposed project focuses on some of MAPP’s primary objectives, namely, “improving methodologies for global to regional-scale analysis, predictions, and projections” and “developing integrated assessment and prediction capabilities relevant to decision makers based on climate analyses, predictions, and projections.”

A large number of forecasts from a suite of models are routinely provided by the Subseasonal to Seasonal (S2S) Prediction Project and the North American Multi-Model Ensemble (NMME) Project. To develop a reliable and timely climate product from these datasets, we propose a new methodology to assess an individual model’s forecast skill, generate statistical weights based on the skill of member model forecasts of slowly-varying surface states, and use aforementioned weights to produce an optimized single forecast. We will compare this methodology to traditional multi-model combination techniques. The new methodology has unique advantages: a) It provides an ideal framework for regional analyses and prediction; b) It allows the combined atmospheric forecast to rely more on models with superior forecast skill of surface anomalies, which are the main drivers of the S2S forecast skill; c) Calculations of forecast skill and weights for each model are highly flexible, and the methodology has many potential applications. The weight of each model member can be calculated from the latest evaluation of the model’s forecast performance and may evolve over time.

Preliminary results show that the new methodology outperforms individual models and can increase the one-month lead forecast skill of surface air temperature by 50% over the simple multi-model average across much of the area of focus. Even though the forecast skill improvement of precipitation (P) and surface air temperature (T2m) over North America is our primary target, the effects are expected to reach all forecast variables over the globe. We propose to identify regions where there is significant forecast skill of North American P and T2m and diagnose the dominant factors influencing such skill. We seek to understand how these factors contribute to the forecast skill of P and T2m, especially the role of land surface processes in achieving S2S forecast skill, through crafted numerical experiments with the Climate Forecast System (CFS). The project will also explore the potential to improve S2S forecast skill by improving the quality of land surface initial states in CFS and examine impacts of land initialization on S2S forecast skill. The overall goal of the proposal is to enhance the Nation’s capability to predict variability on S2S time scales. By performing our analysis with the NMME and S2S forecast datasets and adapting it to operational settings, this proposal directly contributes to the NOAA Next-Generation Strategic Plan objectives of “an improved scientific understanding of the changing climate system and its impacts” and “mitigation and adaptation choices supported by sustained, reliable, and timely climate services.”"

Principal Investigator (s): Zhichang Guo (GMU/COLA)

Co-PI (s):Paul Dirmeyer (GMU/COLA)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

North American Heat Wave Predictability: Assessing the Role of Land Surface Initialization on S2S and NMME Model Forecasts

"This research addresses the critical need to improve our understanding of how land surface initialization and land-atmosphere interactions influence subseasonal to seasonal (S2S) predictability of extreme heat and heat waves over North America. Accurate forecasting of extreme heat events, particularly on S2S timescales, is important for public health preparation as vulnerability to extreme heat has increased over time. Soil moisture anomalies, through their control on the partitioning of sensible and latent heat fluxes, are linked to temperature extremes. Dry soils limit evapotranspiration and can establish and perpetuate extreme heat events through atmospheric heat accumulation (e.g., Miralles et al. 2014). Therefore it is not surprising antecedent soil moisture deficits are found to correspond strongly with extreme temperatures in most regions of the world. Recent studies have demonstrated large spread in model forecasts and simulation of heat wave events over Europe and North America. Significant inter-model variability is purported to be a potential consequence of different boundary layer and convective parameterizations, land surface treatments, and coupled land-atmosphere model sensitivity. To date, few studies have explicitly evaluated the influence of the land surface and its initialization on model predictions of heat waves at S2S time scales.

The influence of antecedent drought conditions is particularly important in North America as past heat wave events may be established and prolonged by both advection of warm, dry air and limitation of local moisture recycling due to dry soils. The strong connection between the land surface and subsequent extreme heat offers promise that realistic soil moisture initialization could improve model forecast skill. Indeed, previous results over the contiguous United States suggest the land surface has a significant impact on extreme heat forecasts, particularly during boreal summer (Ford and Quiring, 2014). However, there is still a lack of consensus about: (1) the role of antecedent drought conditions in forcing heat waves over North America (2) the ability of numerical forecast models to predict extreme heat events at S2S time scales, and (3) the importance of realistic land surface initialization and model fidelity for accurate and timely extreme heat predictions. The goal of this project is to enhance our understanding of the connection between droughts and heat waves in the United States, as well as evaluating the ability of a suite of climate forecast models to predict heat wave occurrence. This goal will be achieved by addressing three main objectives:

(1) Evaluate the ability of numerical forecast models included in the Sub-seasonal to Seasonal (S2S) Prediction and North American Multi-Model Ensemble (NMME) Phase II projects to predict heat waves following drought events in the United States
(2) Relate model forecast performance to parameterization of land surface variables, coupled land-atmosphere metrics and initialization of land surface conditions
(3) Assess how more realistic land surface initialization in forecast models influences their ability to predict and simulate heat wave events in the United States

This project specifically addresses the MAPP Competition 2 priority area of addressing the predictability of S2S phenomena in the context of extremes and their key underlying physical processes. We will be using reforecast datasets from the S2S Prediction Project and the North American Multi-Model Ensemble project. Our project goals are closely aligned with the mission of the Climate Prediction Task Force of achieving significant new advances in current capabilities to understand and predict intra-seasonal to inter-annual climate variability. In addition, the objectives of this research addresses the NOAA high priority of the S2S prediction gap (NWS Goal 3, element 1.20 of the Strategic Plan) as well as NOAA’s goals for leadership in science and innovation. Finally the contributions of this project to the MAPP S2S Task Force address the important issues of S2S predictability and prediction of extreme heat in the context of land-atmosphere coupling and model land surface initialization."

Principal Investigator (s): Trent Ford (Southern Illinois University)

Co-PI (s):Paul Dirmeyer (GMU/COLA)

Task Force: S2S Prediction Task Force

Year Initially Funded: 2016

Competition:

Final Report:

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