Funded Projects

Program (s): Climate Variability and Predictability

Competition:

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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.

Program (s): Climate Variability and Predictability

Competition:

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In order to improve daily to seasonal forecasts, numerical weather prediction models require better representation of the interactions among the upper ocean, the marine atmospheric boundary layer (MABL), and tropospheric convection. Tropical weather and midlatitude teleconnections are driven by latent heat released by precipitating clouds. While individual convective clouds are essentially unpredictable, the eastward-propagating convective envelope of the Madden Julian Oscillation (MJO) is predictable on intraseasonal time scales. The MJO dominates variability of zonal wind and outgoing longwave radiation in the tropics, especially over the Indian Ocean. Though the MJO offers the potential for improved predictability, few numerical weather prediction models presently simulate the eastward propagation of the MJO; even fewer simulate MJO development in the central Indian Ocean. The predictability gap in the developing stage of the MJO coincides with a dearth of observations and a corresponding lack of understanding of air-sea interaction and dynamical and convective processes in the Indian Ocean. The Dynamics of the MJO (DYNAMO) program combines observations and modeling to address these problems.

We propose to make a suite of observations from a ship in the Indian Ocean during DYNAMO, to measure surface air-sea fluxes, MABL turbulent mixing, and cloud and precipitation development. Our strategy seamlessly measures processes, from the surface to the MABL and the free troposphere, contributing to tropical convection constituting the MJO. We will equip a US research vessel with radiative sensors and turbulent flux sensors that observe covariances of near-surface temperature, humidity, and velocity; and measure below-cloud mixing and turbulent velocities in the MABL with a scanning Doppler lidar. We also propose to install a cloud observing system consisting of a lidar ceilometer, a multi-channel microwave radiometer measuring integrated liquid and vapor, and a W-band (3.17 mm) Doppler cloud radar, which provides a sensitive vertical profile of cloud liquid water drops, in-cloud turbulence, and precipitation over the life cycle of the cloud.

Our direct observations of surface evaporation, vertical mixing, and cloud formation will complement mesoscale precipitation structure from a scanning C-band (5.3 cm) radar; and largescale tropospheric heat and moisture budgets from rawinsondes released frequently from an array of stations (proposed separately). Flux, turbulence, and cloud data we collect from DYNAMO will be used for testing gridded flux analyses, MABL mixing and cumulus parameterizations, and air-sea interactions in models.

Program (s): Climate Variability and Predictability

Competition: AMOC-Climate Linkages in NA/SA

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Our recent study showed that there exists a coherent spatial pattern of inter-hemispheric global model sea surface temperature (SST) biases in CMIP5 (Coupled Model Intercomparison Project phase 5) climate models and this global pattern of model SST biases is closely linked to the strength of simulated Atlantic Meridional Overturning Circulation (AMOC). The models with a weaker AMOC are associated with cold SST biases in the entire Northern Hemisphere, and with an anomalous atmospheric pattern that resembles the Northern Hemisphere annular mode. These models are also associated with a strengthening of Antarctic Bottom Water (AABW) formation and warm SST biases in the Southern Ocean. However, in many of these models, the amplitudes of the AMOC agree very well with or are even larger than the observed value of about 18 Sv at 26.5°N, but they still show cold SST biases in the Northern Hemisphere. This suggests that the AMOC strength may not be the only factor that causes the cold SST bias. A common symptom in these models is that the returning flow of the AMOC at depth is too shallow. A shallow returning flow would carry excessive heat southward; thus the net northward heat transport by the AMOC would be weaker than the observed. The shallow returning flow in CMIP5 models should be linked to the bias in the southward pathways of the AMOC at depth. We propose to continue our investigations to (1) diagnose the meridional heat transport and its link to model SST biases in CMIP5 models, (2) perform and analyze “robust diagnostic” simulations of the AMOC to reconstruct realistic southward returning flow pathways of the AMOC, (3) explore AMOC southward returning flow pathways and sources of the shallow returning flow of the AMOC in CMIP5 models, (4) investigate the relationship of North and South Atlantic water masses associated with the AMOC, and (5) examine the impacts of improved AMOC on global SST. We will use available hydrographic observations interpolated into isopycnal surfaces, CMIP5 outputs, and model experiments of NCAR Community Earth System Model and an intermediate complexity model.
The proposed work directly contributes to the priority for NOAA FY2016 CPO/CVP funding: “Solicits projects that will refine the current scientific understanding of the AMOC state, variability, and change. Specifically, projects are sought that use newly deployed and existing observations in combination with modeling experiments to refine our understanding of the present and historical circulation (and related transports of heat and freshwater) in the North and/or South Atlantic. An emerging priority is to provide a more detailed characterization of AMOC flow pathways and their impact on variability.” The main outcome of this study will greatly improve our understanding of the decadal predictability of the AMOC and associated climate impacts, and help improve CMIP5 models.

Program (s): Climate Variability & Predictability

Competition: Climate Process Teams (CPTs) - Translating Ocean and/or Atmospheric Process Understanding to Improve Climate Models

Award Number: NA19OAR4310363, GC19-402 | View Publications on Google Scholar

Tropical cyclones (TCs), shallow cumuli, and low-level jets (LLJs) are all important phenomena in the climate system, but have been historically difficult to represent in climate models. For instance, even at higher resolution, simulated TCs often exhibit an incorrect relationship between minimum pressure and surface wind speed. Simulated shallow cumuli often exhibit a local maximum ("jet") in the wind profile that too broad and diffuse. Simulated LLJs often suffer from a weak diurnal cycle of surface winds. All three climate model deficiencies may be related in part to inadequate parameterization of subgrid momentum fluxes in the atmospheric boundary layer. The parameterizations of momentum flux in current-generation climate models are crude. Often momentum parameterization suites consists of downgradient diffusion plus a separate cumulus momentum transport scheme. However, the presence of a near-surface jet in the wind profile can sometimes lead to upgradient momentum flux at the top of the jet maximum, even when deep convection is not present. Furthermore, the need to model the complex diurnal evolution of winds in LLJs is difficult when the task of simulating momentum is divided between separate parameterizations. This project proposes to parameterize momentum transport by prognosing subgrid momentum fluxes directly. This approach is quite different from conventional approaches, but it adheres more closely the governing equations, and hence is more flexible and general. For instance, it is capable of predicting upgradient momentum fluxes. In this project, the process of momentum transport will be examined using a comprehensive hierarchy of observations and models. Based on these studies and improved understanding, prognostic equations for momentum fluxes will be refined and tested. The equations will be implemented into two leading climate models, one from GFDL and the other from NCAR. The resulting simulations will be evaluated against observations.

Program (s): Climate Variability & Predictability

Competition: Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region

Award Number: NA17OAR4310258 | View Publications on Google Scholar

Models have difficulty in simulating and predicting the evolution of the MJO as it crosses over the Maritime Continent (MC) because of the intricate convective-environmental interactions over the complex geography of the region. Land and ocean differences in the region lead to a strong diurnal cycle in rainfall over land that varies in character during the propagation of the Madden-Julian Oscillation (MJO). The hypothesis of this work is that the diurnal cycle over land disrupts the convective evolution in the MJO envelope and that the MJO has to overcome this strong diurnal signal to make it through the MC unscathed. The operational Indonesia Meteorological, Climatological and Geophysical Agency (BMKG) radar network consists of more than 30 single-polarization, Doppler C-band radars spread across the Indonesian islands. We will work with BMKG to collect and analyze high=resolution reflectivity and radial velocity observations from this network for all of 2018 to study storm structure variations across the MC during different phases of the MJO. In particular, we will determine whether there are variations in diurnal cycle rain intensity and organization before, during, and after the passage of the MJO convective envelope and how the MJO convective envelope responds to strong versus weak diurnal cycles over land. While these relationships have been studied with radars at individual sites, the tremendous extent of the BMKG radar network allows a much more comprehensive analysis of diurnal-intraseasonal interactions. In addition, results from satellite studies of these relationships are highly dependent on the limited sampling (e.g., once per day swaths) and/or proxy nature (e.g., outgoing longwave radiation) of measurements made from space. Rain mosaics from the radar network will further be used to assess large-scale model output, such as rain statistics throughout the diurnal cycle and over complex topography, both of which vex coarser resolution models, and can be assimilated into high-resolution regional models for enhanced analysis of the atmosphere over the MC. This work will also include science and technology exchange and capacity building with BMKG through interactions with their Division for Remote Sensing Imagery Management. This work is highly relevant to the objective of the Competition “Climate Variability and Predictability Program (CVP) – Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region” by providing detailed observations of the evolution of convection across the Indonesian islands, which will help understand the scale interactions at play as the MJO traverses the complex geography of the MC and the associated land-ocean variability in storm structure and circulations, including very complicated diurnal cycles. It will also provide upstream convective conditions over much of the MC during PISTON. This work supports NOAA’s long-term climate goals by providing information on how detailed “scale-aware” parameterizations need to be to accurately model and predict the MJO propagation over the MC. Improvements in MJO model predictions and their subsequent impact on US circulation patterns can lead to improved prediction of US precipitation at sub-seasonal to seasonal (S2S) timescales.

Program (s): Climate Variability and Predictability

Competition:

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We propose to investigate the role of atmospheric noise (due to internal dynamics) at the air-sea interface on the limit of decadal predictability of tropical and North Pacific regions using the NOAA-NCEP Climate Forecast System (CFS). There is increasing evidence from observations and modeling studies that the Earth’s climate system possesses natural variability on decadal timescales. Numerous physical mechanisms have been proposed for decadal variability in the tropical and North Pacific areas. However, it is not well understood which of these mechanisms underpins the decadal predictability and if the state-of-the-art climate models show any decadal forecast skill. One of the ingredients of the physical mechanisms is the stochastic weather noise (due to internal atmospheric dynamics) randomly forcing the ocean through the surface turbulent fluxes. From a climate modeling perspective, the problem is further complicated because it has to be understood as a problem of separating the predictable signal from the unpredictable background noise. We propose to use the interactive ensemble coupling strategy, which is designed to filter out the noise, to investigate the role of noise on the limit of decadal predictability. 

The CFS has been exploited mostly as a monthly and seasonal forecast tool. It has also great potential for forecasts of the longer timescales, which recommends it as a suitable candidate of a multi-model ensemble forecast system. This proposed project has the following main objectives: 

1. investigate the role of weather noise on the internal decadal predictability of tropical and North Pacific SST; 

2. produce a set of ensemble decadal hindcasts with CFS between 1981 and 2001; 

3. evaluate the effects of systematic errors on the decadal forecast skill. 

We expect that the results of this study will unify the three elements currently competing to explain factors which limit the decadal predictability of the SST variations. Initial conditions, boundary conditions and weather noise might all be required to explain the reality. The proposed directly contributes to the Climate Variability and Predictability (CVP) in the main priority areas of (i) understanding the limits of decadal predictability, and (ii) developing a decadal climate prediction system. 

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: NA22OAR4310608 NA22OAR4310609 NA22OAR4310610 | View Publications on Google Scholar

Tropical cyclones (TCs) are a major source of extreme precipitation in tropical and subtropical regions. Accurate TC forecasts are key to predicting precipitation at the subseasonal time scale in the CONUS as landfalling TCs often bring extreme rain, especially to the coastal regions. Given the potentially catastrophic societal impacts of torrential TC precipitation and their potentially negative influence on a model’s subseasonal precipitation prediction skill, if not simulated correctly, there is a clear need to evaluate subseasonal TC prediction and understand its skill in models. We propose a project focused on the subseasonal prediction of TCs and their associated precipitation in the CONUS in the Unified Forecast System (UFS) and other models. We aim to identify and understand model biases and systematic errors in the representation of the Madden-Julian Oscillation (MJO)-TC relationship, a key source of predictability for subseasonal TC prediction. Under the proposed research, we will first conduct performance-based analyses to objectively evaluate the performance of UFS and other models at predicting the MJO and its circulation anomalies during boreal summer, as well as the modulation of TC precursor disturbances and TC activity at subseasonal timescales in the Northeast Pacific and North Atlantic basins. We will then perform process-based analyses targeting the dynamics and thermodynamics of precursor disturbances and their conversion into TCs (i.e., tropical cyclogenesis) and precipitation associated with TCs and TC remnants in the CONUS coastal and inland regions, which will provide insights into the origins of precipitation forecast error. This will include a diagnosis of errors in the subseasonal modulation of TC precursors and TCs, even if a model is able to produce good MJO predictions.

Program (s): Climate Variability & Predictability

Competition: Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region

Award Number: NA18OAR4310300, NA18OAR4310299 | View Publications on Google Scholar

The Madden-Julian oscillation (MJO) and the boreal summer intraseasonal oscillation (BSISO) are the dominant modes of tropical intraseasonal variability (ISV), providing a primary source of predictability on intraseasonal timescales. Many global climate models (GCMs) suffer from poor representations of the MJO and BSISO, especially their propagation over the Maritime Continent (MC). The role of the strong MC diurnal cycle on the propagation of the ISV has remained poorly understood due the lack of in-situ observations. The Years of Maritime Continent (YMC) and Propagation of Intra-Seasonal Tropical Oscillations (PISTON) field campaigns will collect in-situ observations of the diurnal variation of the atmospheric state, among many other things, which will provide a unique opportunity to enhance our understanding of the interactions among the diurnal cycle, the mean state, and the propagation of ISV over the MC. The proposed work is organized around the following two hypotheses: 1) The diurnal cycle of convection in the MC islands and over the adjacent water destructively interferes with convection of the MJO and BSISO, weakening their intraseasonal convective envelopes, and disrupting their MJO/BSISO; and 2) The diurnal cycle over the MC plays a key role in determining/shaping the seasonal mean basic state. Biases in the MC diurnal cycle in GCMs deteriorate the basic state, which in turn prevents the model from simulating a realistic propagation of intraseasonal variability over the broader MC area. To test these hypotheses, the proposed research aims to use YMC and PISTON field campaign observations together with global and regional models to enhance our understanding of the role of the MC on the propagation of the MJO and BSISO. High resolution cloud system resolving simulations with a regional climate model will be conducted targeting observed ISV events. A series of long uncoupled and coupled simulations will be made with a GCM that exhibits superior skill in simulating the ISV. The YMC and PISTON observations together with the satellite observations will be used to evaluate the MC diurnal cycle in the model simulations. The model simulations will be repeated with the MC diurnal cycle suppressed to examine the direct and indirect effect of the MC diurnal cycle on ISV propagation. Short-term hindcast experiments will be conducted with the GCM after the YMC and PISTON field campaigns to examine the role of the diurnal cycle on ISV propagation in the context of events that occurred during the field campaigns. Lastly, the NCEP operational model hindcast dataset will be analyzed, to understand the relationship among the biases in the diurnal cycle, the mean state, and the ISV propagation. Relevance to the competition and NOAA’s long-term climate goal: The proposed research strongly addresses the objective of the competition: “CVP - Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region” as it focuses on the propagation of the tropical ISV through the MC using a combination of in situ and remote observations, modeling, and data analysis. The expected outcome of the proposed research will advance understanding of MJO dynamics and will provide key information for improving ISV prediction. By contributing to advancing our ability to predict the tropical ISV, which affects high-impact weather events over the US, our proposed project is also relevant to NOAA’s long-term climate goal of “providing the essential and highest quality environmental information vital to our Nation’s safety, prosperity and resilience.”

Program (s): Climate Variability and Predictability

Competition:

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El Niño / Southern Oscillation (ENSO) variability represents the leading source of interannual variability in the tropical Pacific and globally. Our understanding of ENSO developed rapidly in the 1980’s and 1990’s with the development of intermediate coupled models in which ENSO variability operates around a prescribed mean state. This was a useful approach, as it has been found that ENSO characteristics are very sensitive to details of the tropical Pacific mean state and seasonal cycle. At the same time, global climate models (GCMs) have improved to the point that ENSO variability exists, in some form, in many of the current generation of GCMs. Unfortunately, large, and even small, biases in GCM simulations of the tropical mean state lead to large biases in simulations of ENSO variability. While attempts have been made to relate biases in ENSO variability to biases in the mean state of the tropical climate, analysis has been limited to analysis of existing GCM output, qualitative comparisons between GCM output and coupled dynamical theory, and analysis of modal characteristics using very simple models. 

The present proposal outlines a research plan aimed at quantitatively estimating the influence of mean state biases on ENSO biases in the present generation of GCMs. This will be accomplished by development and application of a linearized version of the intermediate coupled models described above. This linear ocean / atmosphere model (LOAM) can be tuned around observed or modeled mean states, and once having done so, has been shown to reproduce characteristics of the respective observed or modeled ENSO variability (e.g. amplitude, stability, period, seasonal phase-locking, regularity). An advantage to the linear model is that it can be used to investigate the sensitivity of ENSO characteristics to specific features in the mean state by tuning model parameters to, say, observations, and substituting individual parameters derived from a model. A research strategy is described that uses this model to: 1. Characterize (quantitatively) the spatial and temporal structure of modeled ENSO variability, and ENSO characteristics when the LOAM is linearized around each model’s mean state. 2. Using the LOAM, conduct sensitivity studies to quantify how mean state biases affect bias in ENSO simulation. 3. Using the LOAM, conduct sensitivity studies to understand changes in ENSO behavior under future climate scenarios. 

This proposal directly addresses CVP’s focus area in two ways: (1) it proposes a strategy for understanding the source of bias in simulated interannual ENSO variability, and (2) it identifies specific biases in the mean state that produce those biases. By identifying ENSO sensitivity to specific mean state biases, the work will provide quantitative guidance for modeling groups trying to improve ENSO simulation. 

Program (s): Climate Variability & Predictability

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

Award Number: NA18OAR4310408, NA18OAR4310409 | View Publications on Google Scholar

Due to the far-reaching societal impacts, developing models and observing systems that enable reliable forecasts of the tropical Pacific Ocean in general and the Equatorial Cold Tongue (ECT) in particular are a high priority. However, global numerical models used for this purpose have significant deficiencies. Several of these deficiencies may result from poorly-constrained parameterizations in the ocean model and/or coarse grid resolution (usually 10-100 km in the horizontal and 10 m in the vertical). For example, upwelling and vertical mixing are two processes that are crucial components of the heat budget of the ECT, but these processes have traditionally been difficult to observe and depend significantly on physics that occurs at scales much smaller than a typical model grid cell. In addition, previous studies have demonstrated that these processes are sensitive to model resolution and parameterization scheme. This proposal is to support a team of modelers and observationalists in conducting process-oriented numerical experiments designed to reveal how small-scale (< 500 km, subannual) processes contribute to upwelling, mixing and thereby the heat budget of the ECT. The ECT is a “pacemaker” of global climate, and therefore obtaining improved forecasts and observations of the ECT is a high priority for NOAA. The proposed work contributes to this broader objective, and more specifically aids the scientific community and the broader public by addressing the goals of this competition: ● New state-of-the-art high-resolution simulations of the ECT will be shared with the scientific community, which will facilitate scientific discovery via future analysis. ● By quantifying the contribution of different small-scale processes in the models to upwelling and vertical heat fluxes, the proposed work will clarify the benefits associated with observing various processes and scales, so that observational process studies can focus on the spatial and temporal scales and processes that are most important. ● By identifying aspects of regional model solutions of the ECT that are most biased in their representation of vertical mixing and accompanying heat fluxes, the results of the proposed work will be used to help design a sampling plan for observational process studies that will constrain later parameterization and model development efforts and more efficiently improve model solutions and forecasts. ● By conducting observing system simulation experiments, the proposed effort will identify optimal observational tools for future process studies. ● By supporting the career development of early career scientists (Whitt, Bachman, and a to-be-named postdoc), the proposal supports the development of a globally-competitive STEM workforce.