Funded Projects

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: GC21-410a, GC21-410b | View Publications on Google Scholar

Field campaigns have been designed to collect in-situ data that both improve process level understanding and form benchmark datasets for experiments with and improvements of reanalysis, satellite data, and models. However, the transition of observations to model, satellite, and reanalysis projects rarely happens in a timely fashion or with full participation between all expert parties involved. This proposed data synthesis effort will produce a more effective and lasting use of ATOMIC field campaign observations for evaluating satellite and reanalysis products and use in numerical models. The ATOMIC 2020 field campaign in the northwestern tropical Atlantic Ocean collected a unique and diverse dataset of air-sea interaction, ocean properties, and clouds from drifting, shipborne, airborne, air-deployed, and uncrewed platforms. The variety of measurement heights and depths, platform motions, and instrument uncertainties is at the same time a strength and a usability barrier. To invite effective and lasting use by modeling, satellite, and reanalysis teams, the observation teams must account for these details and synthesize the measurements in a common, standard, open source database. In the first year of the project we will convene a joint conference to obtain guidance from modelers on critical variables and data formats. The air-sea fluxes, sea surface temperature (SST), and surface waves are the three most important sets of information for coupled weather and climate models to resolve correctly to understand and predict environmental change. Our hypothesis is that the strengths in terms of model, satellite, and observational research applications of a combined, consistent ATOMIC database of SST, fluxes, and waves from multiple platforms far outweighs the strengths of any individual dataset. We will make use of periods of co-located data between in situ and airborne platforms plus satellite overpasses, with the NOAA Ship Ronald H. Brown serving as the standard for comparison. We will draw on ATOMIC and EUREC4A data for the most complete picture to produce and archive a combined, consistently quality controlled, and consistently processed database from all platforms. Databases for SST, air-sea fluxes, waves, plus near surface bulk variables that result from this effort will be posted to NCEI for public distribution. By bringing observations to the modeling and satellite communities and facilitating partnerships across the external and NOAA communities, the proposed data synthesis directly responds to the COM/CVP/GOMO call to “develop an observations-based product for climate monitoring or modeling application” that “increases the use of NOAA’s historical field campaign data” and “enables improved climate modeling or monitoring (e.g., enables future climate model evaluation, validation, process-oriented diagnostics)”, contributing to the first two objectives of NOAA’s long- term climate goals as described in NOAA’s Next-Generation Strategic Plan.

Program (s): Climate Variability & Predictability

Competition: Observing and Understanding Upper - Ocean Processes and Shallow Convection in the Tropical Atlantic Ocean

Award Number: NA19OAR4310374 | View Publications on Google Scholar

The proposed study will investigate the spatial structure of surface fluxes and waves due to organized patterns of low clouds along with ocean mesoscale eddies and fronts. These processes are known to coexist in all tropical oceans, but the details of their spatial variations have not been captured in any available datasets. Waves, trade winds, shallow clouds, and ocean eddies frequently coexist in the tropical northwestern Atlantic, where the ATOMIC field campaign has been planned. We propose an observation-based project as part of ATOMIC, in which we will investigate the spatial structure in the atmospheric and oceanic mixed layers when cloud patterns and ocean mesoscale eddies are present. We will use a distributed array of ten autonomous platforms called SWIFTs, a NOAA research vessel, and the NOAA P3 aircraft to make these observations. The Surface Wave Instrumented Floats with Tracking (SWIFTs) will offer a Lagrangian, distributed view of ocean features as they evolve and clouds as they develop overhead. The specific scientific questions raised about clouds and waves are: 1. How are surface energy fluxes influenced by organized cloud patterns within the trade winds and spatial gradients in SST across eddies? 2. How are surface waves, and in particular wave breaking, modified by ocean mesoscale variations in currents? 3. How are surface fluxes and turbulence in the oceanic and atmospheric mixed layers impacted by coinciding perturbations of cloud and wave conditions? 1-D air-sea interaction has been well-studied with decades of point-measurements collected from ships. That these measurements only cover a single point in space produces the largest gap in our understanding of air-sea interaction as well as the largest limitation of these datasets for use by numerical models. Research is needed on waves and air-sea fluxes, particularly in the tropics where ocean mesoscale features, persistent trade winds, and organized patterns of low-clouds coexist. The overarching theme of this work is to understand how fine-scale patterns in the oceanic and atmospheric mixed layers co-evolve so that, in the future, these processes can be represented in satellite data and predicted in numerical models with greater fidelity. Our project involves multiple observational datasets in the ocean, atmosphere, and at the sea surface. The project methodology systematically sifts through these data with objective analysis focused on physical processes. These steps will efficiently translate data into research results about coupled air-sea interaction that are actionable and relevant for operational environmental monitoring and numerical prediction.

Program (s): Climate Variability and Predictability

Competition: Improved Understanding of Tropical Pacific Processes, Biases, and Climatology

Award Number: NA13OAR4310163 OR NA13OAR4310164 | View Publications on Google Scholar

Abstract Lack of understanding of MJO initiation, as well as the inability of most global models to adequately simulate the MJO and its initiation, limits our subseasonal to interannual prediction capability, including forecasts of extreme events such as Atlantic hurricanes, West Coast flooding, and ENSO. The model used here, the Ocean-Land-Atmosphere Model (OLAM), has a grid topology that enables local mesh refinement to any degree without the need for special grid nesting algorithms. Hence, although it is a global model, OLAM allows local mesh refinements in the DYNAMO observing area that enables explicit simulation of cloud systems. This allows both the global context of the MJO as well as local processes on the cloud system scale to be handled seamlessly in one model. We will use OLAM hindcasts of the three MJO events during October-December 2011 to examine the importance of environmental moisture, shear, and the evolution of cloud populations to MJO initiation. We address the following:

• How well does OLAM represent the evolution of atmospheric variables compared to DYNAMO observations? The quadrilateral DYNAMO array provided vertical humidity, wind, and temperature soundings as well as advective tendencies that will be compared to OLAM fields during the three initiation events to determine model fidelity. The ability of OLAM to represent both the MJO and convectively coupled equatorial waves (CCEWs) will be assessed. Further, statistics from the suite of centimeter and millimeter DYNAMO radars, as well as surface flux and wind datasets from ships, buoys, and aircraft, will be used to validate OLAM.

• What Processes Contribute to Tropospheric Moistening in Advance of MJO Initiation in OLAM? A comprehensive analysis of OLAM tropospheric humidity evolution as it relates to MJO initiation and CCEWs will be conducted. The column-integrated moisture budget will be used to assess the importance of vertical and horizontal advection, condensational drying, and surface evaporation to the moistening process in advance of MJO initiation. The role of gustiness due to sub-gridscale motions in affecting surface flux will be assessed. Follow-up mechanism denial experiments (e.g. not allowing surface flux feedbacks) or sensitivity experiments will be conducted to determine MJO initiation sensitivity to various moistening processes.

• How Do Cloud Populations Vary in Advance of MJO Initiation, and How Do These Variations Interact with the Large-Scale Environment and CCEWs to Enable MJO Initiation? How cloud populations evolve in advance of the MJO initiation will be examined with OLAM. We will assess how important shallow convection is for moistening the free troposphere in advance of MJO deep convective initiation, and how changing convective organization due to CCEWs and MCSs affect the moisture tendency due to clouds. We will also examine the hypothesis that both a deep moist layer and substantial lower tropospheric shear are necessary to produce long-lived MCSs that mark the genesis of MJO events. We will document how the gross moist stability of the modeled atmosphere varies with cloud regime. OLAM hindcasts will be compared to hindcast experiments with a conventional parameterized model, to suggest where parameterization improvements might improve simulations of MJO initiation.

This proposal directly addresses the CPO DYNAMO competition by using OLAM to analyze the initiation of the MJO during the DYNAMO time period, specifically by validating OLAM using field campaign data. We are particularly interested in determining the processes that most strongly regulate the moisture budget in advance of MJO initiation, as well as the unique contributions of different cloud regimes to the moistening process. This proposal supports NOAA’s NGSP by helping to improve parameterizations that will allow more accurate predictions of future climate, allowing society to better anticipate and respond to the challenges of climate change. This proposal entails research that advances the nation’s core capabilities in understanding and modeling the climate system, a primary goal of the CPO.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

The possibility of making decadal climate predictions has been recognized after the great progress made during last couple of decades in climate system modeling, seasonal to interannual climate predictions, and century-scale climate projections. Determining the sources of predictability within the climate system is still a formidable challenge for decadal climate predictions. Although studies of the subject have suggested that decadal predictability resides in both external forced variability and slow natural variability, further exploration and a better understanding of the sources of decadal predictability are needed. In this project, we propose to investigate the predictability of the Pacific decadal sea surface temperature (SST) variability, which is a major source for decadal climate anomalies over North America. 

Through diagnostic studies of CIMP5 experiments and additional modeling studies, we will examine contributions to the predictability from both slow external forcing and internal dynamics, focusing on the decadal predictability in subsurface heat content and SST variability over the north and tropical Pacific. We will also explore the potential contributions to the decadal predictability from natural or forced changes in ENSO activity. The outcome of this project will add to our understanding of the predictability of Pacific decadal variability, which meets the objectives of the NOAA CVP program. 

Program (s): Climate Variability & Predictability

Competition: Pre-Field Modeling Studies in Support of TPOS Process Studies, a Component of TPOS 2020

Award Number: NA18OAR4310399, NA18OAR4310400, GC18-908 | View Publications on Google Scholar

We propose a re-examination of the design of a Pacific Upwelling and Mixing Physics (PUMP) process study in light of available observing technologies, by characterizing the space and time scales and dynamics of upwelling and the process-level connection to mixing. We will develop analysis frameworks around testable hypotheses, and determine the sampling requirements for an efficient and robust observational implementation. We will address this objective by exploiting a combination of output from high-resolution ocean and climate model simulations, and existing long-term observations. To frame our investigation of observing strategies for PUMP, we will address the following scientific questions: 1. What are the dynamics controlling divergence and upwelling? 2. How is upwelling partitioned into adiabatic and diabatic motions? 3. How do the three dimensional meridional circulation cells and their role in the heat budget respond to changes in surface forcing across a range of time scales from synoptic to inter-annual and in different locations within the tropical Pacific? The proposed effort directly responds to the guiding questions in the solicitation by addressing the following experimental design considerations: What zonal and meridional resolution is needed to adequately measure divergence of mass and the exchange of mass and heat between the thermocline and surface ocean? How long of a deployment of initial observations is needed to adequately represent the statistics of the important processes at play? What regions (e.g. 140ï‚°W or 110ï‚°W) of the eastern tropical Pacific should be most intensively measured? What locations would be most representative of the broader context of the eastern Pacific? Are there locations where available observing technologies are better suited to sample the natural scales of the problem? How far north and south of the equator should the observations extend to capture the key dynamics of the meridional cells? What sustained observations are needed to monitor the state of the vertical exchange in the eastern equatorial Pacific? A concurrent assessment of the model solutions against currently available long-term observations and select historical process studies will provide an up-to-date understanding of model biases and elucidate the limitations of our recommendations for observing system design. We view the proposed effort as the beginning of an iterative and sustained integration of talents, experience and resources to advance our ability to observe and simulate the tropical Pacific and its impacts on the global climate system, thereby directly aligning with the overall goals of NOAA CVP.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

We propose a study of the mechanisms and predictability of interannual to interdecadal climate variability. Our general goals are to to understand the underlying mechanisms for climate variability on these timescales, to identify processes that might lead to predictability, and to understand what the intrinsic limits to climate predictability are. Our main tool is a novel hierarchy of climate models, developed using the Flexible Modeling System at GFDL. At the top of the hierarchy is the state-of-the-art, IPCC-class model at GFDL. Our models directly connect to that, but are simplified by using simpler and more economical physics packages, and/or by making simplifications in the geometry. Use of such models allows more experiments to be performed, including ensemble experiments, and mechanisms to be identified. We will focus on extra-tropical variability in the Atlantic sector, including the interannual and decadal variability of the NAO, although somes aspects of the proposed work are more general. The specific topics we propose to investigate include the timescales on which the atmosphere ocean system may be regarded as truly coupled, the timescales on which the atmosphere forces the ocean, the generation and persistence of sea-surface temperature anomalies and the effects of such anomalies on the atmosphere. As appropriate, we shall use still simpler theoretical tools and analyses to try to abstract the mechanisms to their esssentials. We shall also compare and validate our models against the full coupled climate model to ensure that we are in a realistic parameter regime that is relevant to reality.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

This proposed National Oceanographic Partnership Program project is a collaborative effort between RSMAS and NOAA/AOML to perform Observing System Simulation Experiments (OSSEs) to determine optimum observing strategies for monitoring the Atlantic Meridional Overturning Circulation (AMOC). The most accurate possible three-dimensional estimates of the ocean state are realized by optimally combining observations with ocean model dynamics. Optimal estimates of the state of the AMOC and early detection of significant changes should therefore be obtained by constraining a data-assimilative ocean general circulation model with measurements from a cost-effective observing system. To design an efficient system, it is necessary to first identify the critical variables to be measured, the spatial configuration of sensors, and the frequency of measurements necessary to identify and to characterize temporal and spatial fluctuations. OSSE's provide an objective means to quantitatively evaluate different observing system strategies. The PIs therefore propose to use the U. S. Navy ocean nowcast-forecast system based on the Hybrid-Coordinate Ocean Model (HYCOM) to perform OSSEs to evaluate potential AMOC observing systems. 

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

The Atlantic Meridional Overturning Circulation (AMOC) of the ocean is a singular feature of the general circulation thought to play a major role in maintaining the climate of the planet. There is an intense interest in developing nowcasting and projection systems for the AMOC because of i) its association with variations in meridional ocean heat transport, North Atlantic sea surface temperatures and climatic variables such as air temperature, precipitation, drought and severe weather events such as hurricanes, (ii) its potential predictability, iii) its possible role in abrupt climate change particularly in response to anthropogenic forcing. Motivated by this background, here we propose a collaborative study between NCAR, GFDL, and MIT to: 

1. Characterize modeled AMOC variability and its climate impacts: past, present, and future, 

2. Identify the mechanism(s) of AMOC variability in the GFDL, MIT, and NCAR coupled models, 

3. Explore the extent to which the AMOC is predictable by experimenting with prototype predictability systems initialized by ocean state estimates. 

Our study is of particular importance because, as the community embarks on an ambitious program of study of Atlantic climate variability, a theoretical underpinning analogous to that which motivated modeling and observations of ENSO, is still lacking. It is hoped that by capitalizing on the very significant efforts in coupled global climate modeling and state estimation methodologies at NCAR, GFDL, and MIT and drawing together their complementary strengths, we will make significant progress in each of the above foci areas. 

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

The Atlantic Meridional Overturning Circulation (AMOC) of the ocean is a singular feature of the general circulation thought to play a major role in maintaining the climate of the planet. There is an intense interest in developing nowcasting and projection systems for the AMOC because of i) its association with variations in meridional ocean heat transport, North Atlantic sea surface temperatures and climatic variables such as air temperature, precipitation, drought and severe weather events such as hurricanes, (ii) its potential predictability, iii) its possible role in abrupt climate change particularly in response to anthropogenic forcing. Motivated by this background, here we propose a collaborative study between NCAR, GFDL, and MIT to: 

1. Characterize modeled AMOC variability and its climate impacts: past, present, and future, 

2. Identify the mechanism(s) of AMOC variability in the GFDL, MIT, and NCAR coupled models, 

3. Explore the extent to which the AMOC is predictable by experimenting with prototype predictability systems initialized by ocean state estimates. 

Our study is of particular importance because, as the community embarks on an ambitious program of study of Atlantic climate variability, a theoretical underpinning analogous to that which motivated modeling and observations of ENSO, is still lacking. It is hoped that by capitalizing on the very significant efforts in coupled global climate modeling and state estimation methodologies at NCAR, GFDL, and MIT and drawing together their complementary strengths, we will make significant progress in each of the above foci areas. 

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

We propose a collaborative study between GFDL, NCAR, and WHOI to greatly advance our understanding of simulated AMOC variability, the impact of that variability on the atmosphere (and climate), and the relevance of that variability to our ability to make decadal climate predictions. Our work is motivated by the role that AMOC is thought to play in decadal climate variability and prediction, and by the critical need to improve our understanding of mechanisms and assessing the fidelity and robustness of simulated AMOC variability against limited observations. A major facet of this proposal is the synergy achieved through the coordinated efforts between the three institutions involved, building upon our existing, strong collaborations. In particular, the development of common metrics and the coordinated design and analysis of focused, sensitivity experiments using suites of models from NCAR and GFDL, the two leading U.S. climate modeling centers, and WHOI’s contribution in analysis of mechanisms and climate impacts are critical aspects of the proposed work. This coordination and synergy will provide an accelerated pathway to assessing robustness of model results and underlying mechanisms that, we hope, will lead to improved decadal prediction capabilities. Our goals include investigating impacts of model resolution, parameterizations, biases, and mean states on AMOC variability; determining the impact of ocean eddies on simulated AMOC and its variability; investigating AMOC variability and mechanisms in the recent past; improving our understanding of how particular physical processes and climate state information may give rise to predictive skill related to AMOC variability and evaluating how model differences in simulating AMOC variability affect related decadal predictability. We will use our findings to evaluate the realism of proposed mechanisms and assess the applicability of our results to other IPCC AR5 models.

Relevance: The proposed research is of high relevance to the NOAA CPO. Specifically, we seek to improve scientific understanding of the changing climate system and its impacts through evaluating and advancing climate prediction methodologies used in decadal climate prediction. Thus, we directly address one of the objectives of NOAA’s five-year climate goals outlined in NOAA’s Next Generation Strategic Plan (NGSP), namely improved scientific understanding of the changing climate system and its impacts. Furthermore, we will access the past, current, and future states of the climate system with a focus on AMOC through reconstruction of its behavior during the past century as well as through potential prediction of its future states. These efforts largely address another NGSP objective, i.e., assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions. Our proposed research will advance core capabilities in understanding and modeling and predictions and projections, both aimed at understanding and advancing decadal prediction capabilities. Relevance to the Competition: This proposal directly addresses one of the ESS program solicitation areas, namely AMOC – Mechanisms and Decadal Predictability. In particular, we seek to improve our understanding of the AMOC variability mechanisms, their model dependencies, and their effects on decadal predictability and prediction through focused multi-model analyses and experimentation. As requested in the solicitation, in addition to in-depth analyses of the GFDL and NCAR models, we will make use of the outputs from the other IPCC AR5 models.