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A Collaborative Investigation of the Mechanisms, Predictability, and Climate Impacts of Simulated AMOC Multi-Decadal Variability

Principal Investigator(s): Gokhan Danabasoglu and Joseph J. Tribbia, National Center for Atmospheric Research; Thomas L. Delworth and Anthony J. Rosati, NOAA/Geophysical Fluid Dynamics Laboratory; and John Marshall, Massachusetts Institute of Technology

Year Initially Funded: 2009

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. 

A Collaborative Investigation of the Mechanisms, Predictability, and Climate Impacts of Simulated AMOC Multi-Decadal Variability

Principal Investigator(s): Gokhan Danabasoglu and Joseph J. Tribbia, National Center for Atmospheric Research; Thomas L. Delworth and Anthony J. Rosati, NOAA/Geophysical Fluid Dynamics Laboratory; and John Marshall, Massachusetts Institute of Technology

Year Initially Funded: 2009

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. 

A Collaborative Multi-Model Study: Understanding AMOC Variability Mechanisms and their Impacts on Decadal Prediction

Principal Investigator(s): Gokhan Danabasoglu, NCAR; Thomas Delworth, NOAA/GFDL; Young-Oh Kwon, Woods Hole Oceanographic Institution

Year Initially Funded: 2013

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.

A Global Model Investigation of MJO Initiation for DYNAMO

Principal Investigator(s): Guang Zhang, Scripps Institution of Oceanography

Year Initially Funded: 2011

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The DYNAMO field campaign in the Indian Ocean provides an unprecedented opportunity to study MJO initiation. In this project, the PI proposes to investigate the MJO initiation in the Indian Ocean using the NCAR CAM3 and the DYNAMO observations. The objective is to improve MJO simulation, and ultimately MJO prediction using global models. As part of the DYNAMO modeling effort, the project aims to answer the following scientific questions relevant to Hypotheses I and II in the DYNAMO Science Planning Overview (SPO) document:

I. What are the factors determining the initiation of MJO in the Indian Ocean?
II. How does the cloud population interact with the MJO circulation during MJO initiation? Can the NCAR CAM3 reproduce the observed cloud population?

The basic research tools used in this work are the NCAR CAM3 and the improved Zhang-McFarlane convection scheme. The data used for model evaluation and improvement will be DYNAMO field observations from sounding array, radar and satellites, ECMWF reanalyses products, and other data assembled by the Year of Tropical Convection (YOTC) project. The combination of model and observations will allow us to test new ideas using models and evaluate them using observations. To answer the above questions, we will perform a series of simulations using the CAM3 and its single column version in both prediction mode and the traditional climate simulation mode. Simulation tests will be conducted to determine what factors affect the MJO initiation the most. In particular, shallow convection preconditioning, convective sensitivity to environmental moisture through lateral entrainment, sea surface temperature and surface evaporation, among others, are deemed critical to MJO initiation. We will investigate each of them in the proposed research. The model output will be compared with DYNAMO observations and reanalyses products. For each observed and simulated MJO, budgets of heat and moisture will be computed to determine the sources and sinks of moisture during different stages of MJO initiation over the Indian Ocean. The cloud population, as measured by cloud top heights and optical depth, during the MJO life cycle from model simulations and satellite observations will be compared and related to MJO circulation. We will also participate in the inter-model comparisons to identify sensitivity of different models to parameters in convection scheme.

Intellectual merit: MJO simulation is a challenging scientific problem in GCMs. Using an improved version of the convection scheme developed by the PI, the NCAR CAM3 can simulate MJOs realistically. Thus, the model can be used as a tool together with DYNAMO observations to understand MJO initiation in the Indian Ocean. The work will help improve the MJO simulation and prediction not only in CAM3, but also in other GCMs.

Broader impacts: Poor MJO simulation is a well recognized problem in many GCMs. It negatively impacts the model development efforts in the global modeling community, and affects the simulation of other climate systems. This work will have impacts beyond the MJO dynamics and simulation. MJO simulation can be used as a metric to evaluate model performance.

A Global View of Tropical Pacific Biases and Their Effect on Connections Between the Southern Hemisphere and the Equatorial Pacific Climate

Principal Investigator(s): Amy Clement, University of Miami, RSMAS

Year Initially Funded: 2014

Program (s): Climate Variability and Predictability

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

Award Number: NA14OAR4310275 | View Publications on Google Scholar


Previous studies have shown that extra-tropical influences from the North Pacific can impact the tropical Pacific climate, offering some additional degree of predictability of El Nino/Southern Oscillation (ENSO). More recent work has also shown that similar physical mechanisms operate in the Southern hemisphere, and further suggest that the connection between the southern extra-tropics and the equatorial climate is even more direct than the north. The reason is that southeasterly trade winds cross the equator and allow signals to propagate from the south deep into the tropics, while in the northern hemisphere signals are essentially ‘blocked’ by the convergence of winds in the northern ITCZ. Simulating this connection from the southern hemisphere is problematic in coupled GCMS in which an erroneous southern ITCZ can potentially block extra-tropical signals originating in the south. Thus, in this project we will test the hypothesis:

That Pacific ITCZ biases in climate models weaken the southern hemisphere influence on the equator and diminish a potential source of ENSO predictability.

We will test this hypothesis using a collection of climate model simulations that offer multiple realizations of the mean state biases in the Pacific ITCZ. We will perform diagnostic studies using CMIP5 model simulations to test the model-dependence of the mean state and its influence on variability propagating from the southern hemisphere to the equator. We will perform a large number of experiments with climate models in which the ITCZ position is altered in two ways: First by externally imposed perturbations to the energy budget of the model, and second by altering the strength of regional radiative feedbacks using a novel approach that we have developed in prior work (funded by NOAA). This dual method approach of altering ITCZ builds upon recent work that suggests that in addition to local processes in the Pacific that can lead to biases in the ITCZ, the mean ITCZ position is also influenced by processes outside the tropics that alter the radiative balance of the planet. This experimental approach has the advantage of being able to (1) test which regions of the globe are key for the simulation of the Pacific ITCZ, and (2) examine mean state interactions with variability in a consistent framework. Further we will use a hierarchy of models including aqua planet models, AGCM-slab ocean mixed layer models, and fully coupled models, which will allow us to identify the fundamental mechanisms which control the position of the ITCZ and impact Pacific climate variability. The ultimate goal of this work is to design an experimental framework in which we can test how potential sources of predictability, particularly from the southern hemisphere, are affected by Pacific ITCZ biases.

This work will contribute to the goals of the ‘ESS - Climate Variability and Predictability (CVP): Improving Understanding of Tropical Pacific’ by identifying processes and regions outside the tropical Pacific that exert a remote influence on the mean climate. This will help to provide a more complete and global context for understanding tropical Pacific biases, and suggest novel ways in which models can be developed to reducing this external influence on these biases. A unique aspect of this work is that we are focused on additional (an perhaps unexploited) sources of predictability in the Pacific climate system, namely signals or precursors from the southern hemisphere. By testing the central hypothesis or this project, our work will enhance NOAA’s core capabilities in both ‘Modeling and understanding’ and in ‘Predictions’ of the Pacific climate system.

A Multi-model Approach Toward the Attribution of U.S. Climate Variation and Change

Principal Investigator(s): James W. Hurrell, National Center for Atmospheric Research; Martin P. Hoerling and Jon Eischeid, NOAA/Earth System Research Laboratory

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


Key aspects of regional U.S. climate variability and change during the past century lack explanation. What, for example, are the processes and causes responsible for the observed strong seasonality in U.S. surface temperature changes as well as for the spatially inhomogeneous warming? The western U.S. has been the epicenter for warming in recent decades, particularly in spring and summer, and this has led to early snowmelt and premature maximum streamflow. At the same time, there has been a lack of warming in the central U.S., especially during summer, in spite of the warming expected in the interior continent from increasing levels of greenhouse gases in the atmosphere. 

Strong decadal variations of U.S. climate during the last century have confounded both the detection and the attribution of regional climate trends. Prominent among these is the relatively abrupt shift in Pacific-North American climate in the mid-1970s. Other features include the decadal swings between U.S wet regimes (1910s, 1980s-90s) and dry regimes (1930s, 1950s, 2000s). Do these events reflect internal atmospheric variability? Are they the response to decadal variations in the state of the global ocean? What has been the role of anthropogenic forcing? Identifying the factors responsible for the observed low frequency variability is a necessary step toward implementing a credible decadal prediction system and for improving climate information for decision makers. 

Our proposal will increase understanding of observed U.S. climate variability and change through parallel development and analysis of observational and model-generated datasets, and through systematic numerical experimentation to allow attribution of observed variability to processes and causes. In particular, we seek to identify those factors driving fluctuations in U.S. surface temperature and precipitation on the regional scale by employing a hierarchy of existing climate model simulations, as well as new experiments targeted specifically to elucidate the role of oceanic variability. We will employ a multi-model architecture and make resulting data available to the broader research community. 

A Multiscale Diagnostics Hierarchy for Detecting, Source-Tracking, Understanding, and Reducing Model Biases in the US Warm Season S2S Precipitation Variability

Principal Investigator(s): Yi Deng (Georgia Institute of Technology), Yi Ming (Boston College and NOAA/GFDL)

Year Initially Funded: 2022

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310606 | View Publications on Google Scholar


Precipitation processes are multi-scale in nature. A faithful representation of precipitation in a model relies on its capability to capture 1) large-scale atmospheric circulation patterns that trigger the development of a precipitating weather event such as cyclones and thunderstorms, and 2) local, smaller scale physical processes (including convection, radiation, cloud physics, air-sea interaction, etc.) that determine the lifecycle of a weather event through their interactions with large-scale flow. In the central United States, warm season (March-August) precipitation is mainly associated with Mesoscale Convective Systems (MCS), a form of “layered” overturning circulations that is often poorly resolved or parameterized in a global climate model. The failure of such parameterizations to realistically account for scale-interactions, together with model intrinsic biases in reproducing large-scale forcing of MCSs, poses a major challenge in our effort to simulate and predict warm season precipitation, particularly across the S2S timescales. In response to this challenge, here we propose a multi-scale diagnostics hierarchy for detecting, source-tracking, understanding and reducing model biases in the US warm season S2S precipitation variability. The cornerstone of this hierarchy is the partitioning of MCS processes into two components: large-scale forcing and local, smaller scale physics. Teasing out large-scale forcing from a myriad of interacting scales of an MCS allows one to potentially trace the origin of MCS variability and identify remote sources of predictability for MCS precipitation. By integrating data diagnosis with numerical modeling, the PIs will develop the diagnostics hierarchy targeting processes of MCS initiation, growth and decay. Specific tasks to be carried out include 1) constructing new evaluation metrics to quantify the S2S variability in the U.S. warm season precipitation, 2) statistical mapping of MCS variability onto S2S precipitation variability, 3) partitioning the GFDL AM4’s MCS biases into components associated with large-scale forcing and model physics, 4) multi-scale diagnostics and idealized modeling to reveal the dynamical nature of model biases in MCS large-scale forcing, 5) experimenting with new packages of model physics to further understand the contribution of local processes to MCS biases, and 6) connecting model biases in MCS large-scale forcing with modes of climate variability and exploring remote sources of S2S predictability for MCSs with NOAA-funded field campaign observations. The proposed project is a direct response to the joint competition to “advance process understanding and representation of precipitation in models”. Aiming at the longstanding problem of MCS simulation, we will develop, test, and deliver to the community an innovative multiscale diagnostic framework that encompasses process-level metrics development, scale-resolving diagnostics, error partitioning, source tracking, and generation of dynamics-based guidance for model optimization and update. This work contributes directly to the goal of “Focus Area A: Identifying and understanding key processes that influence model biases and systematic errors in the simulation of precipitation at the subseasonal to seasonal (S2S) timescale”. The insights gained from the scale-resolving bias attribution will also pave the way for formulating and testing (with NOAA field campaign observations) hypotheses regarding remote sources of S2S predictability of precipitation from the tropical Indo-Pacific and Atlantic. Given the significance of S2S precipitation forecasts for hazards mitigation and water resource management, the proposed project will ultimately contribute to the objective of the NOAA CPO - “advancing scientific understanding, monitoring, and prediction of climate and its impacts to enable effective decisions”.

A Nudging and Ensemble Forecasting Approach to Identify and Correct Tropical Pacific Bias-Producing Processes in CESM

Principal Investigator(s): Aneesh Subramanian, Regents of the University of California; Art Miller, Regents of the University of California

Year Initially Funded: 2014

Program (s): Climate Variability and Predictability

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

Award Number: NA14OAR4310276 | View Publications on Google Scholar


Current short-term tropical climate forecasts (e.g, of the Madden Julian Oscillation (MJO) and of El Niño/Southern Oscillation (ENSO) events) experience both a systematic error (climate drift) that results in sustained biases of the model tropical climatology and an error in representing the space-time scales of the transients (e.g., phase speed errors, etc.) We propose to identify the physical mechanisms that lead to the seasonal biases in the tropical Pacific by isolating the parameters and parameterizations that influence the development of biases in short-term climate forecasts. Our overarching scientific objective is to identify, explain, and correct the climate biases in the Pacific ocean that occur in the Community Earth System Model (CESM). We are currently using the Community Atmospheric Model (CAM3) in MJO forecast experiments and tests of convective parameterization improvements. We propose to extend these MJO forecast studies to include (a) the fully coupled CESM system, and (b) ENSO timescale forecasts.

We plan to study the spatiotemporal structures of bias development in CESM forecasts, launched from numerous initial states and during which random ENSO and MJO events occur, to determine the relative importance of poor mean-state representation versus the integrated impacts of the transient flows. This bias development will be studied as a function of season to account for significant changes in the background state of the coupled oceanatmosphere system in the tropical Pacific. We will also seek to ascribe these effects to wellknown physical processes for the specific climate modes of variability. We will test the sensitivity of the bias development to changes in coupled model resolution and model parameter selection. We will also implement nudging experiments (towards observations) to pinpoint where the worst parts of the biases develop apart from the nudged variables.

We expect this research to result in identification of the physical processes that lead to the mean biases in the model system and an improvement in parameterizations used in CESM and CAM for forecasts of the climate-scale processes in the tropical Pacific.

This proposal contributes to the CVP component of the NOAA ESS Program by attempting to improve our understanding of the processes contributing to tropical Pacific biases in global climate models (CESM, in this case). Specifically, our work involves (i) short-term forecast experiments, from weeks to a year, to isolate the time scales of bias development and the responsible processes, (ii) development of metrics for coupled GCMs that help to elucidate the main processes contributing to biases, (iii) atmosphere-only and ocean-only models to isolate the sources and amplifiers of biases, and (iv) intercomparison of model parameterizations within CESM. Our overall research will thereby contribute significantly to NOAA’s Next Generation Strategic Plan by improving our scientific understanding of the climate system that will result in better identifying potential climate impacts.

A Pre-Field Modeling Study of Scales, Variability and Processes in the Near Surface Eastern Equatorial Pacific Ocean in Support of TPOS

Principal Investigator(s): Frank Bryan (NCAR), LuAnne Thompson (Univ. of Washington), William Kessler (NOAA/PMEL)

Year Initially Funded: 2018

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.

A Quantitative Analysis of Convective Mass Flux Parameterizations Using Direct Observations from the DYNAMO Field Program

Principal Investigator(s): Christopher Fairall, NOAA/ESRL; Alan Brewer, NOAA/ESRL

Year Initially Funded: 2013

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar


The 2011 DYNAMO investigation of the Madden Julian Oscillation (MJO) included an elaborate, multi-platform observation field study with ships, islands, and aircraft in the Indian Ocean. The R/V Revelle was a primary platform for surface-based near-surface, boundary-layer, cloud, and precipitation observations. Observations from platforms (and sets of platforms) must be integrated for the next stage of research. We propose to address this (partly) using observations made on Revelle, with narrow focus on direct analysis of a specific DYNAMO hypothesis for MJO initiation – pre-moistening of the lower free troposphere by shallow convection. The mechanism for this pre-moistening is vertical transport of water (vapor plus liquid) by shallow convective clouds. Mass flux approximations form the core of must cumulus parameterizations (see Lappen and Randall, ‘Toward a Unified Parameterization of the Boundary Layer and Moist Convection’, Parts I, II, and III) but the application to shallow convection has historically been neglected because of the observational difficulty – conventional scanning precipitation radars are not suitable for non-or weakly-precipitating clouds.

We propose a project that can be completed with existing data from DYNAMO -focusing on two unique NOAA ship-based remote sensors: the 94-GHz cloud Doppler radar and the high resolution Doppler lidar – but also drawing on other sources of data (microwave radiometer, ceilometer, surface fluxes, rawinsondes, and the C-band radar). The time series of radar in-cloud turbulence profiles will be combined with time series of lidar clear-air turbulence profiles. This will allow – for the first time – direct observations of updraft/downdraft structure with sufficient time/space resolution to measure profiles of convective velocity distributions with the shallow convective cloud explicitly partitioned in the time series. Creation of combined Doppler turbulence retrievals will have synergies with area average statistics from scanning precipitation radar (S. Rutledge). The C-band data will define a larger-scale convective context for our analysis. Data from the NOAA P-3 aircraft flux runs will give additional information on profiles of cloudy vs ‘environment’ moisture concentration. Characterization of the convective mass flux profiles will then allow us to address directly the role of shallow convection in the transport of moisture from the boundary layer into the lower troposphere.



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