Funded Projects

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”.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

Atlantic multidecadal sea surface temperature variability (AMV) is a prominent phenomenon that is thought to arise from the natural or internal interaction of the atmosphere and ocean (in contrast with the response to anthropogenic forcing). It is also associated with a wide array of significant global impacts. Models of different complexity strongly support the assertion that AMV is related to the variability of the Atlantic Meridional Overturning Circulation (AMOC) and can be thought of as the surface expression of the latter and the communicator of deep ocean variability to the atmosphere. There is also indication that the related AMV/AMOC variability is potentially predictable. Prediction of AMV should be a crucial element of any attempt to predict the evolution of climate in the coming decades even as the major element of change in this period is the effect of anthropogenic greenhouse gas (GHG) emissions. Several modeling centers have already begun to take such action with models of the class used in the IPCC Fourth Assessment. In preparation for a broad community attempt at addressing nearterm climate change prediction we propose a diagnostic analysis of output from a set of IPCC coupled models to systematically catalog the AMV exhibited in these models, its climatic impacts over land, its link to AMOC and atmospheric variability, and its predictability. Within each representative model we will also use three classes of model output: pre-industrial control runs, ensemble integrations with known 20th century external forcing (GHG, aerosols, solar, and volcanoes), and ensemble integration with projected 21st century GHG forcing. The analysis will deploy an array of diagnostic tools, such as optimal methods for detecting and separating between the externally forced signal and internal variability, multivariate techniques to study the spatial and temporal properties of AMV and its instantaneous and time-lagged associations to subsurface and atmospheric variability. To study modeled AMV predictability and to provide alternative insight to its dynamics we propose to use linear inverse modeling (LIM) methodology that fits model output in multivariate fields to explore modes that display non-normal growth. Identifying such modes allows for efficient assessments of error growth and predictability. Application of the analysis on a range of fully coupled modes will directly contribute to the NOAA goal to "understand and describe climate variability and change to enhance society's ability to plan and respond." 

Program (s): Climate Variability & Predictability

Competition: Observation and Modeling Studies in Support of Tropical Pacific Process Studies, Pre-Field-II

Award Number: NA22OAR4310602 | View Publications on Google Scholar

As part of the plan to address uncertainties in processes regulating sea surface temperatures (SSTs) that lead to biases in the eastern tropical Pacific and reduced skill in El Niño / Southern Oscillation (ENSO) predictions, the Tropical Pacific Observing System First Report recommends implementation of two air-sea interaction process studies: The Pacific Upwelling and Mixing Physics (PUMP) and the Eastern Edge of the Warm Pool (EEWP). The two regions are also identified by the Precipitation Prediction Grand Challenge Strategic Plan as “sources of precipitation predictability”. An aim of these process studies is to determine the minimum observations needed, and on what time and space scales they are needed, for monitoring ocean variability and related climate and weather modes, as well as for constraining models. However, process studies require intensive field observations to resolve ALL critical processes. New uncrewed surface vehicles (USVs) offer great promise for intensive observations of phenomena like the EEWP that can migrate zonally tens of thousands of kilometers, and for upwelling studies that require potentially four surface-to-depth current profilers to be able to estimate the current divergence at a central point. The current proposed work uses recent USV observations and model output to test the capabilities of these platforms in PUMP and EEWP process studies. In particular, the objects of the current study are: 1) An analysis of historical USV data from past Tropical Pacific Observing System (TPOS) missions, including exploring approaches to calculate vertical velocity from the surface to 60-100 m in the PUMP domain; 2) The development of a “USV Sampler” that generates synthetic USV tracks within gridded reanalysis and forecast model fields based upon desired way points and the model’s winds and currents; 3) An evaluation of the synthetic USV data in models (existing high-resolution simulations) to assess the representation of the air-sea interface and upper ocean in the model products and to validate the USV Sampler; and, 4) Informed by the results of the assessments in 3), test a range of adaptive sampling strategies for capturing air-sea interaction processes within the PUMP and EEWP regions to aid future field studies. This project will contribute to a process level understanding of the PUMP and EEWP regions that is critical for advancing long standing biases in global forecasting and climate models and improving subseasonal-to-seasonal and ENSO prediction skill, a priority for the CVP program. This project also raises the technological readiness level of USVs for use in TPOS process studies by evaluating and demonstrating their capabilities within the equatorial Pacific environment. A higher technical readiness level is also an important step for being able to include USVs in the Global Ocean Observing System (GOOS).

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

A control simulation in present-day conditions with the NCAR Community Climate System Model version 3 (CCSM3), a major contributor to the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (AR4), shows two regimes of Atlantic meridional overturning circulation (AMOC) variability, with an abrupt transition between them. We will first focus on the differences and the rapid transition between the two regimes of AMOC variability, i.e. a period with very regular and strong decadal variability, and one with irregular and weak multi-decadal variability, in terms of the mechanisms and associated global climate impact. We will then establish whether there are also multiple regimes and rapid transitions in the AMOC variability of the newly developed CCSM4 climate model, the CMIP5 participating version, and investigate and compare their mechanisms. 

CCSM3 exhibits a pronounced decadal variability of the AMOC in the present-day control integrations as well as global warming integrations. Two distinct regimes of decadal AMOC variability are apparent in the 700-yr long CCSM3 control integration with T85 atmospheric resolution (CCSM3-T85): a strong 20-year periodicity is seen for 300 years before an abrupt transition to a red noise-like variability lasting for the last 250 years. In the former regime, the decadal signal is also seen in the atmosphere, while there seems to be much less climatic impact in the latter. Regime transitions have been found in many coupled climate models, but they have not been considered explicitly other than in simplified models. Such non-stationarity exists in nature (as for ENSO and NAO) and may critically influence the predictability of the system. Hence, understanding what controls them and developing a methodology to do so is important. The analysis will be based on advanced statistical methods and complemented by numerical model experiments to elucidate the findings from the statistical analysis. In addition, we propose to use linear inverse modeling to assess the predictability of the AMOC. When possible, the findings will be compared with statistical signatures derived from the observations and reanalyses, so that the reliability of the model simulations can be assessed. 

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

A control simulation in present-day conditions with the NCAR Community Climate System Model version 3 (CCSM3), a major contributor to the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (AR4), shows two regimes of Atlantic meridional overturning circulation (AMOC) variability, with an abrupt transition between them. We will first focus on the differences and the rapid transition between the two regimes of AMOC variability, i.e. a period with very regular and strong decadal variability, and one with irregular and weak multi-decadal variability, in terms of the mechanisms and associated global climate impact. We will then establish whether there are also multiple regimes and rapid transitions in the AMOC variability of the newly developed CCSM4 climate model, the CMIP5 participating version, and investigate and compare their mechanisms. 

CCSM3 exhibits a pronounced decadal variability of the AMOC in the present-day control integrations as well as global warming integrations. Two distinct regimes of decadal AMOC variability are apparent in the 700-yr long CCSM3 control integration with T85 atmospheric resolution (CCSM3-T85): a strong 20-year periodicity is seen for 300 years before an abrupt transition to a red noise-like variability lasting for the last 250 years. In the former regime, the decadal signal is also seen in the atmosphere, while there seems to be much less climatic impact in the latter. Regime transitions have been found in many coupled climate models, but they have not been considered explicitly other than in simplified models. Such non-stationarity exists in nature (as for ENSO and NAO) and may critically influence the predictability of the system. Hence, understanding what controls them and developing a methodology to do so is important. The analysis will be based on advanced statistical methods and complemented by numerical model experiments to elucidate the findings from the statistical analysis. In addition, we propose to use linear inverse modeling to assess the predictability of the AMOC. When possible, the findings will be compared with statistical signatures derived from the observations and reanalyses, so that the reliability of the model simulations can be assessed. 

Program (s): Climate Variability & Predictability

Competition: Climate and Changing Ocean Conditions: Research and Modeling to Support the Needs of NOAA Fisheries

Award Number: NA20OAR4310482 OR GC20-303 | View Publications on Google Scholar

The Northeast U.S. Continental Shelf Large Marine Ecosystem (NES LME) is arguably one of the most oceanographically dynamic marine ecosystems. As such, managing fish stocks that respond to this dynamic environment has become increasingly challenging due to the synergistic impacts of fisheries and climate change. Many fishery stock assessments are single species models that do not include environmental variables, which may lead to increased retrospective patterns of stock estimates. Incorporating environmental variables into population models for stock assessment and subsequent forecasts could improve model performance and reduce uncertainty in future population size, as there is ample evidence that environmental variability affects fish populations. Improved understanding of the processes affecting the predictability of the physical environment on the NES and better modeling strategies for the region are critical components of climate-ready fisheries management in the region.Here, we propose to investigate the 1-5 year predictability of physical ocean conditions on the NES and the associated large-scale climate and coastal ocean processes, using a new state-of-the-art, coupled ocean-atmosphere regional model for the NES in combination with statistical analyses of global climate model simulations and observational datasets. In particular, we will investigate how large-scale climate phenomena, such as the Pacific Decadal Oscillation, North Atlantic Oscillation, Atlantic meridional overturning circulation, and Gulf Stream variability drive physical ocean conditions on the NES, and how the improved understanding of those physical mechanisms and predictability can improve multi-year predictions for the region.This proposal targets the FY 2020 NOAA Climate Variability and Predictability (CVP) Program solicitation CVP - Climate and Changing Ocean Conditions - Process Research and Modeling to Support the Needs of NOAA Fisheries by proposing to investigate the physical processes linking large-scale climate phenomena with physical conditions on the NES LME, and associated multi-year predictability. Our proposed research will be a valuable contribution to the newly initiated Northeast Climate Integrated Modeling (NCLIM) effort to support the needs of NOAA Fisheries, which is an interdisciplinary community collaboration aimed at developing a modeling framework that integrates across climate, regional, fishery, and human system models to advance research and enable responsive fisheries and marine resource management. Our proposed work is also directly relevant to the NOAA’s long-term climate goal of advancing scientific understanding, monitoring, and prediction of climate and its impacts, to enable effective decisions.

Program (s): Climate Variability & Predictability

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

Award Number: NA17OAR4310262 | View Publications on Google Scholar

Atmospheric convection in the Maritime Continent (MC) region undergoes substantial multi-scale variability on the diurnal, synoptic, intraseasonal, and seasonal scales. The processes governing these multiscale variabilities and interactions are essential for predicting high-impact weather and climate, especially the barrier effect of the MC on the Madden-Julian Oscillation (MJO). These processes are not well understood and are key issues motivating the planned YMC and PISTON programs. Among all the challenges, uncertainties in current global analysis products and prediction regarding temporal and spatial variability of atmospheric convection, its diurnal cycle, triggering, propagation and upscale growth, and distribution over water and land in the MC are critical factors limiting the application of such analyses to understanding these processes. The available global analysis and reanalysis products cannot accurately represent the detailed features of convective systems in the MC either temporally or spatially because of their coarse resolution (~50 km to 100 km and 6 hourly) and deficiencies in cumulus parameterization schemes. The overarching goal of this proposed project is to produce an hourly regional analysis over the MC region during the whole period of YMC at a cloud-permitting scale (~ 3 km horizontal grid spacing) using the community mesoscale Weather Research and Forecasting (WRF) model and the NCEP Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble-variational data assimilation system with the assimilation of all available in-situ observations, radar data, and satellite data products during YMC, including PISTON. It is anticipated that the proposed regional analysis will reveal detailed properties of atmospheric convection and its environmental conditions that are not available from global analysis products and thus will enhance our ability to answer the following science questions: • What are the spatial and temporal distributions and variabilities of mesoscale atmospheric convective systems, their large-scale environmental conditions, and associated physical and dynamical processes during their triggering, propagation, and upscale growth, associated with the MJO? • What is the role of local-scale land-sea breezes and orography effects in convective system initiation, evolution, and propagation? How do these local-scale effects contribute to the diurnal cycle and the multiscale variability over the MC region? In addition, what are the major controlling factors that enhance their interactions with large-scale dynamic and thermodynamic conditions? What are the processes controlling the offshore diurnal migration of precipitating systems? • What are the major causes of the barrier effect of the MC on the MJO? How is the MJO affected by local-scale flows and thermodynamic conditions, such as sea-land breezes, orography effects, storm outflows, etc., in the context of the MC barrier effect? • Compared with the regional reanalysis, what are the major uncertainties and limitations of available global reanalyses in representing the physical processes of zonal propagation of the MJO and the barrier effect of the MC? • Based on the verification and validation of the quality of regional analysis in various locations, what types of observations are the most useful for better representation of the atmospheric processes and conditions critical to MJO propagation in the MC region? The proposed research directly responds to the NOAA Climate Variability and Predictability Program solicitation for proposals “that aim to improve understanding of processes that affect the propagation (speed, intensity, disruption, geographic placement) of intraseasonal oscillations in the Maritime Continent and broader region by using a combination of in situ and remote observations, data analysis, modeling, and/or theoretical understanding of local and remote processes.”

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310403, GC20-206 | View Publications on Google Scholar

The Atlantic Multidecadal Variability (AMV) is one of the most prominent decadal variability modes and has a worldwide climate impact. Among all the decadal variability modes, AMV likely has the greatest potential of predictability because of its hypothesized close association with deep ocean dynamics, in particular, the Atlantic Meridional Overturning Circulation (AMOC). In spite of some advances in the last three decades, however, many fundamental questions on the mechanism of AMV remain unclear, in particular, regarding the roles of climate feedback and oceanic teleconnections. What are the roles of climate feedbacks of different regions on AMV, and do those feedbacks contribute to different mechanisms of variability in the subpolar and subtropical North Atlantic? What are the roles of various oceanic teleconnections on AMV and how is the time scale of the AMV associated with ocean dynamics? We propose to study the mechanism of AMV in the Atlantic in a suite of GFDL climate models using a combined statistical and dynamic approach, with focus on the roles of climate feedback and oceanic teleconnections. First, we will perform statistical analyses on the available model simulations, including newly available multi-millennial simulations, to assess climate feedbacks and climate teleconnections. Climate feedback will be assessed with various lead-lag feedback analysis methods, including the multi-variate Generalized Equilibrium Feedback Analysis (GEFA). The temporal evolution of the AMV, especially in the subsurface ocean, will be examined using lead-lag correlation analyses and the Linear Inverse Modeling (LIM) analysis. The interaction between the tropical and subpolar North Atlantic will also be examined using LIM. Second, we will perform systematic modeling surgery sensitivity experiments in the newly developed GFDL model “SPEAR”. The goal of the sensitivity experiments will be to explicitly assess the roles of ocean-atmosphere feedback and oceanic teleconnections on the characteristics of simulated AMV. We will perform “Partial Coupling” (PC) experiments by suppressing ocean atmosphere coupling in specific regions, notably the global ocean outside the North Atlantic region, and then, the subtropical North Atlantic, to explicitly assess the roles of ocean-atmosphere feedback outside the North Atlantic and subpolar North Atlantic, respectively. The role of cloud feedback will be studied in experiments that isolate cloud feedbacks. We will also perform “Partial Blocking” (PB) experiments by blocking oceanic teleconnections with a sponge wall in the ocean component model. Westward teleconnection associated with planetary waves will be assessed by a set of PB experiments with meridional PB walls across the middle North Atlantic in the subtropical and subpolar regions, while southward teleconnection along the western boundary will be assessed in a set of PB experiments with zonal PB walls across the western boundary current at several latitudes. Our proposed work will be a close collaboration between OSU and GFDL. Our research will also be coordinated closely with the ongoing modeling activity at GFDL and forms a part of a comprehensive research strategy at GFDL on decadal climate variability and predictability, including decadal climate prediction and global warming research.

Program (s): Climate Variability & Predictability

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

Award Number: NA17OAR4310260 | View Publications on Google Scholar

We propose to conduct a comprehensive study of the hypothesis that the sensitivity of deep convection to column moisture is reduced over the Maritime Continent (MC), which leads to the Madden-Julian Oscillation (MJO) propagation barrier. It will include the following two components. 1. The first is an observational component that will make novel use of data obtained through Global Positioning System (GPS) measurements, currently the only all-weather water vapor measurements, from both satellite Radio Occultation missions and surface GPS stations. In particular, data collected by a network of 60 surface GPS stations over Sumatra for earthquake studies will be processed and introduced to the meteorological community. This will provide a unique multi-year dataset with high temporal resolution (5 minutes) and a spatial layout that is well suited for studying how major islands in the Maritime Continent modulate moist convection. We will then combine the GPS data, radiosonde data, and other available water vapor measurements, as well as in situ and satellite rainfall measurements, to characterize the sensitivity of convection to column moisture. Given that the strong diurnal cycle over land is likely a key process for the reduced sensitivity of convection to column moisture, we will further produce the first all-weather characterization of the diurnal cycle of column moisture over the Maritime Continent and its modulation by the MJO. 2. The observational results will be extended using cloud resolving model simulations with the Weather Research Forecast (WRF) model. Simulations with a range of model configurations, including resolution, will be evaluated against the observational results. The best model configurations will then be used to examine the reasons behind the reduced sensitivity of convection to column moisture and the MJO propagation barrier through detailed diagnostics and mechanism-denial experiments. The improved understanding can then be used to interpret behaviors of forecast models. Our work will produce a unique dataset that contributes to the observations of processes affecting the propagation of the MJO in the Maritime Continent region, as well as an improved understanding of these processes, thus directly address the objectives of this NOAA CVP program call. Given the importance of the MJO, this project will also support core capabilities of NOAA in understanding and modeling of the climate system.