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Assessing and Understanding Atlantic Multidecadal Variability in a Suite of GFDL Climate Models: Roles of Climate Feedback and Teleconnection

Principal Investigator(s): Zhengyu Liu (The Ohio State University), Thomas L. Delworth (NOAA/GFDL)

Year Initially Funded: 2020

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.

Drivers of Coastal Sea Level Change Along the Eastern US

Principal Investigator(s): Laure Zanna (New York University); Jianjun Yin (University of Arizona); Stephen M. Griffies, Ming Zhao (NOAA/GFDL)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310411, NA20OAR4310412, GC20-210 | View Publications on Google Scholar


Abstract: Risks associated with coastal sea level changes and extremes affect the densely populated US East Coast. Yet, numerical simulations struggle to adequately predict the patterns and magnitudes of regional and coastal sea level rise. The uncertainty in coastal sea level projections arises from both uncertainty in internal ocean-atmosphere variability and imperfect representation of oceanic and atmospheric processes in models. In this project, we aim to sharpen our understanding of coastal sea level change along the Eastern US on interannual to multi-decadal timescales. We aim to identify how atmospheric and oceanic drivers and their representation in climate models impact coastal sea level projections. The main analysis will be performed using state-of-the-art coupled climate models from NOAA/GFDL of different ocean and atmosphere horizontal resolutions. Specifically, we aim to 1) Understand the key drivers and mechanisms of large-scale, open ocean, sea level change on multi-decadal; 2) Quantify changes in sea level and associated extremes along the US East Coast (including the Gulf Coast), and identify physical processes responsible for the changes; 3) Elucidate the connection between ocean interior processes and their response on the shelves to enhance our forecasting capabilities of coastal sea level. Our work will help to guide future model developments by providing robust model diagnostics and observation-based metrics from which to assess climate simulations. In addition, this analysis (the first of its kind) in realistic coupled climate models at eddying resolution will allow us to map risks of sea level changes to time-dependent dynamical drivers vs. time-independent geographical (e.g., bathymetry) drivers. Relevance to NOAA’s long-term goals and CVP call: Our work will enhance our understanding of natural and forced signals of sea level change in the ocean interior and at the coast. By using a suite of state-of-art coupled climate models, together with an in-depth understanding of physical mechanisms, we aim to map risks of future sea level changes along the US East Coast. These goals, results and methodology directly align with NOAA’s long-term goals and CPO program mission, which includes improving “understanding [...] and prediction of climate and its impacts”. More specifically, this work, by focusing on oceanic (including the Atlantic Meridional Overturning Circulation) and atmospheric mechanisms leading to coastal sea level change using historical and future model scenarios, addresses both priority areas set for the CVP FY19 call. Ultimately, our work linking ocean interior signals to coastal sea level change will serve both as a benchmark and a tool for assessing and improving decadal prediction systems.

Examining the Causes of Trends in the Context of a Variable Subpolar North Atlantic Ocean

Principal Investigator(s): Amy Clement, Benjamin Kirtman (University of Miami), Mark Cane (Columbia University)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310400, GC20-204 | View Publications on Google Scholar


The subpolar North Atlantic is the only extensive region of the world ocean where the sea surface temperature (SST) has cooled since 1900 (IPCC 2013). This cooling occurs in the context of strong multi-decadal variability in SST. The causes of these features of the subpolar North Atlantic are currently debated, and include external forcing as well as externally forced and internally generated variability in the atmosphere and ocean circulation. Understanding the causes of past changes is critical for predicting how the ocean will evolve in the near-term future, as well as in long-term projections. Here we will address the causes of the cooling trend (the so-called ‘warming hole’) in the context of a highly variable subpolar North Atlantic. As a first step, we will dive into the causes of the trend itself, and evaluate a previously untested mechanism: that the cooling is the ocean response to a northward shifted North Atlantic jet. We will employ a climate model hierarchy that includes: a fully coupled model, a slab ocean model, a slab ocean with Ekman included, as well as forced ocean experiments. The mechanisms revealed in this hierarchy will also be tested in CMIP5/6 model experiments and observations. As a second step, we will examine fluctuations about this trend, and how the forced component can impact internally driven ocean and atmosphere variability. To do this we will develop a new framework that can applied across models for evaluating the time-evolving ocean conditions that can lead to predictability. We will also use a large-archive of CESM and CMIP5/6 simulations to evaluate the relationship between the ensemble-mean forced signal and the spread due to internally-generated variability. Our proposed work responds to the NOAA Climate Program Office’s "CVP - Decadal Climate Variability and Predictability" competition. In particular, we address the priority area of ‘Investigation of mechanisms that govern variability of the coupled climate system and its predictability on the interannual to multi-decadal timescales within longterm observation data and/or model data (such as, CMIP6), with a focus on either the Atlantic or Pacific Ocean region.’ By using a combination of the CESM model hierarchy, CMIP5/6 data, and observations, we will gain a process-level understanding of the ocean and atmosphere on the interannual to multi-decadal timescales, which will lead to greater confidence in our ability to predict future changes in the North Atlantic and their impacts.

Investigating the Connection between the Atlantic Meridional Overturning Circulation (AMOC) and the Northwest Atlantic Coastal Sea Level: Connecting the Dots across the Shelf Break

Principal Investigator(s): Ke Chen, Jiayan Yang (WHOI); Jian Zhao (University of Maryland Center for Environmental Science)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310398, NA20OAR4310399, GC20-203 | View Publications on Google Scholar


Two research areas in the Atlantic Ocean have received elevated interests in the community: (1) the stability and variability of the Atlantic Meridional Circulation (AMOC), and (2) the accelerated sea level rise (SLR) along the North American Coast from Cape Hatteras to Nova Scotia. The relationship between them, i.e., whether the observed coastal SLR is resulted from changes in the AMOC and whether a predicted weakening of the AMOC transport will further accelerate coastal SLR, is still being debated. As a circulation system in the open ocean, the AMOC would need to overcome the strong topographic barrier across the continental shelf break in order to influence coastal sea level. The cross-shelf connection is the least understood aspect in any suggested mechanisms linking coastal sea level variability to the AMOC changes. Most present climate models and basin-scale Ocean General Circulation Models (OGCMs), even with increasing resolutions, are not up to the task to address the complex cross-isobath processes near the continental shelf break. This is due to the fact that many coastal processes are often not well represented or adequately treated in global scale models, which are optimized for the large-scale processes like the AMOC and Gulf Stream (GS). Therefore, a well-treated and carefully designed regional model forced by dynamically consistent global model is more desirable for assessing the relationship between coastal sea level and the AMOC. In this project, we propose an integrated approach using both in situ and satellite observations, eddy-resolving global data-assimilative reanalysis, and a hierarchy of numerical models, including a state-of-the-art regional ocean circulation model and a 2-layer process model to study the dynamical linkages between the AMOC and the sea level variability on the Northwest (NW) Atlantic shelf over interannual and decadal time scales. Specifically, we will analyze the AMOC variations on various time scales using available observations and global eddy-resolving, data assimilative simulations, characterize and quantify their impacts on western boundary currents (WBCs) and slope currents, search possible connections with coastal sea level changes, and identify and examine cross-shelf connection processes and mechanisms using models. Our goal is to identify and understand key cross-shelf processes and mechanisms that are important in connecting the AMOC and coastal sea level variability, based on which we can further develop predictive skills for coastal sea level changes. The outcome of this project will also be useful for the improvements of climate models in their representations of coastal processes. Our proposed work directly addresses the Competition of CVP - Decadal Climate Variability and Predictability in the area of investigation of the relationship between the Atlantic Meridional Overturning Circulation (AMOC) and global and regional sea level (historical, current, and/or future), with a focus on understanding sea level extremes and coastal impacts in the United States, for the improved understanding of the ocean-climate system. This project is also responsive to the CPO’s strategy in addressing challenges in the areas of Weather and Climate Extremes, Climate impacts on water resources and Coasts and climate resilience.

Mechanisms of interannual- to decadal-scale predictability for ocean physics and biogeochemistry in the California Current System

Principal Investigator(s): Mercedes Pozo Buil (UC Santa Cruz); Nicole Lovenduski (CU Boulder/INSTAAR); Emanuele Di Lorenzo (Georgia Institute of Technology); Michael Jacox, Steven Bograd, and Elliott Hazen (NOAA/SWFSC)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310404, NA20OAR4310405, NA20OAR4310406, GC20-207 | View Publications on Google Scholar


Project Summary: The California Current System (CCS) is a highly productive eastern boundary upwelling system, in which seasonal upwelling fuels primary production that supports a thriving marine ecosystem and socioeconomically valuable services including fisheries and tourism. The CCS and resources derived from it are strongly driven by changes in the physical and biogeochemical environment, both of which experience considerable variability on timescales ranging from days to centuries. Prognostic information on this variability is therefore highly desirable for marine resource users, for example managers of fisheries whose target populations are sensitive to variations in the climate system. With this motivation, a number of recent and ongoing efforts have begun to explore predictability and forecast skill in the CCS on seasonal timescales (~1-12 months), and to project long-term (~50-100 years) influences of climate change. However, near-term (2-20 year) predictions have received relatively little attention, at least in part because predictable signals are often obscured by intrinsic climate variability on these critical timescales. Nonetheless, potential for skillful near-term forecasts of the physical and biogeochemical ocean state has been demonstrated for the CCS, and could in turn provide actionable information to marine resource managers. The overarching goal of this project is to quantify the predictability of the physical and biogeochemical CCS variability on interannual to decadal timescales, to understand the physical mechanisms that drive predictability, and to evaluate the ability of current decadal forecast systems to realize that predictability as forecast skill. Key elements of the proposed work plan are to (1) identify from historical data the physical mechanisms that drive interannual to decadal variability in CCS temperature, salinity, pH, oxygen, nutrient concentration, and marine productivity, (2) quantify the predictability and forecast skill of these quantities using the Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE), and (3) identify sources of any differences between predictability and forecast skill in CESM-DPLE (i.e., potential forecast skill that is not being realized in modern decadal forecast systems). These tasks will be carried out using a suite of model and observational datasets including multi-decadal high-resolution ocean reanalyses, in situ ocean biogeochemical observations, and retrospective decadal forecasts from CESM-DPLE. Relevance to the Competition and NOAA’s Long-Term Climate Goal: The proposed research will directly address the goal of the competition, to “identify state, mechanisms, and sources of predictability on the interannual to decadal timescale, which will help to lead to future improvements in skillful decadal prediction systems for climate (ocean and atmosphere)”. Key physical and biogeochemical variables identified in the proposal will be addressed, as will the physical mechanisms that govern their predictability on interannual to decadal timescales. Finally, comparison of this predictability with realized forecast skill in modern decadal predictions will allow us to identify key limitations on forecast skill and areas where predictability in the ecosystem can be exploited to improve it. The proposed project will also support NOAA’s long-term climate goals, particularly by advancing scientific understanding of variability and predictability in the North Pacific in a way that can support effective decision making about marine resources that are sensitive to that variability, thereby improving resilience of US ecosystems and economies.

Oceanic Mechanisms of Tropical Pacific Climate Variability Involving the Subtropical-Tropical Cells (STCs)

Principal Investigator(s): Antonietta Capotondi, Prashant D. Sardeshmukh (NOAA/ESRL/PSD)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: GC20-202 | View Publications on Google Scholar


Tropical Pacific decadal variability (TPDV) plays an important role in the global climate, as evident from its influence on the recent slowdown of the global surface temperature trend. Decadal variations of tropical Pacific background conditions also affect the amplitude, frequency, and spatial pattern of El Niño Southern Oscillation (ENSO) events on inter-annual timescales, whose global impacts can be sensitive to the distinctive evolution and structure of individual events. For both of these reasons it is important to assess the predictability of TPDV through a better understanding of its underlying mechanisms. Despite extensive research, however, a clear understanding of those mechanisms remains elusive. Some studies have suggested that TPDV may originate purely by chance, while others have emphasized the role of slow oceanic adjustment processes and their possible feedbacks on the atmosphere. Such processes involve upper-oceanic meridional overturning circulations in both hemispheres known as Subtropical-Tropical Cells (STCs), as well as oceanic Rossby waves that mediate the STC adjustment and the evolution and structure of tropical Pacific heat content. A connection between STC strength and equatorial SST anomalies has been shown in several studies using relatively short observational and model datasets. However, many questions remain unanswered. The primary goal of the proposed research is to elucidate the role of oceanic dynamical processes in TPDV using a combination of observations, oceanic and coupled atmosphere-ocean reanalysis products, and model outputs from the Climate Model Inter-comparison Project phase 6 (CMIP6). Our specific objectives are 1) to examine the role of the northern and southern STCs and oceanic Rossby waves in altering the tropical upper-oceanic heat content and tropical SSTs at decadal timescales, and to assess the nature of the wind anomalies forcing these processes; 2) to evaluate the representation of TPDV in pre-industrial and historical simulations of the CMIP6 models; and 3) to use the CMIP6 future scenario simulations to examine projected changes in TPDV and its associated processes. The proposed research will directly address the first priority area of the competition: “Investigation of mechanisms that govern variability of the coupled climate system and its predictability on the interannual to multi-decadal timescales within long-term observation and/or model data (such as, CMIP6)” by examining the mechanisms of decadal/multi-decadal variability in the tropical Pacific, the low-frequency modulation of interannual variability, and the degree of predictability associated with those mechanisms.

The interplay between sea level and Atlantic Meridional Overturning Circulation: Cause and effect relationships, predictability, and coastal implications

Principal Investigator(s): Denis Volkov, Marlos Goes (University of Miami/CIMAS); Hong Zhang (UCLA/JIFRESSE)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310407, GC20-208 | View Publications on Google Scholar


The global mean sea level is rising as the result of ocean warming and melting of terrestrial glaciers and ice sheets. Regional sea level changes can deviate significantly from the global average change, which is largely the result of the spatial redistribution of heat and freshwater by ocean circulation. The latter is often simplified by a zonal integral of meridional velocities known as the Meridional Overturning Circulation (MOC). The MOC-modulated meridional divergence of heat and freshwater drives the large-scale steric (due to density changes) sea level changes. Near the coast these changes provide background conditions that, when combined with the effect of tides and with synoptic sea level fluctuations due to the variable atmospheric pressure and winds, can result in nuisance flooding and storm surge events that oftentimes affect densely populated urban areas. The goal of this proposal is to establish the relationships between the MOC and the gyre-scale and coastal sea level changes throughout the Atlantic Ocean basin, and identify the key mechanisms responsible for these relationships. The main focus will be at understanding how the large-scale sea level patterns influenced by the MOC may affect coastal sea level in the Atlantic Ocean and the U.S. East Coast, in particular. The proposed research will utilize a suite of observational data collected by Atlantic MOC observing arrays (e.g., RAPID/MOCHA/WBTS, MOVE, SAMBA, OSNAP, and a combination of altimetry and hydrography, etc.); satellite measurements of sea surface height (SSH), temperature (SST), salinity (SSS), and winds; hydrographic data (Argo, XBT, CTD); coastal tide gauges; and atmospheric re-analyses. Statistical techniques (e.g. Empirical Orthogonal Functions (EOFs), including joint and complex EOFs, wavelet and cross-wavelet transforms, wavelet coherence, etc.) will be used to identify the leading modes of variability, how these modes evolve in space and time, and lag-lead relationships between the modes and other variables. The established relationships will be investigated in more detail using an eddy-permitting Estimating Circulation and Climate of the Ocean Version 5 (ECCOv5) state estimate. The use ECCOv5 will allow budget closures and better process understanding. In addition, the ECCOv5 adjoint sensitivities will be used to quantitatively evaluate the causal mechanisms of regional (including coastal) sea level and heat content variability, as well as changes in the Atlantic MOC. The proposed research directly addresses the second priority area of the FY19 CVP Decadal Climate Variability and Predictability call: “Investigation of the relationship between the Atlantic Meridional Overturning Circulation (AMOC) and global and regional sea level (historical, current, and/or future), with a focus on understanding sea level extremes and coastal impacts in the United States, for the improved understanding of the ocean-climate system”. It also addresses NOAA’s goals by (i) contributing to understanding and predicting changes in climate, weather, oceans and coasts; and (ii) collecting and analyzing information critical for conservation and management of coastal and marine ecosystems and resources.

Understanding the relative roles of Atlantic vs. Pacific coupled dynamics in initialized decadal predictions

Principal Investigator(s): Stephen Yeager, Gokhan Danabasoglu, Elizabeth Maroon (NCAR); Ping Chang (Texas A&M University); Wei Cheng (University of Washington)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310408, NA20OAR4310409, NA20OAR4310410, GC20-209 | View Publications on Google Scholar


This proposal (“Understanding the relative roles of Atlantic vs. Pacific coupled dynamics in initialized decadal predictions”) is targeting the NOAA Climate Variability and Predictability Program competition NOAA-OAR-CPO-2019-2005530 (Decadal Climate Variability and Predictability). This collaborative effort between NCAR, Texas A&M University, and the University of Washington/JISAO will advance decadal prediction science by deepening our understanding of the regionally-dependent coupled dynamics at work in the Atlantic and Pacific in initialized decadal prediction ensembles using full-complexity coupled general circulation models. The motivation for this proposal is that current state-of-the-art decadal prediction systems, such as the CESM Decadal Prediction Large Ensemble (DPLE), show areas of tantalizing promise, but the coupled mechanisms explaining the variations in skill across different regions and fields remain poorly understood. We will analyze the DPLE (in conjunction with its uninitialized counterpart--the CESM Large Ensemble) with the aim of developing improved mechanistic understanding of the origins of skill in interannual-to-decadal predictions of sea surface temperature, sea-ice, atmospheric circulation and moisture transport, and high-impact near-term climate change over populated regions. Our overarching goal is to contribute to improved decadal predictions by elucidating the relative roles of the Atlantic and Pacific Oceans in giving rise to predictable climate signals; investigating the ramifications of model drift; improving our understanding of initialization shock; identifying the origins of low signal-to-noise in key climate fields; and developing process-level understanding of the factors governing skill for a selection of high-impact regional climate variations including tropical cyclones, precipitation over land, and Arctic sea-ice. We will synthesize our findings to develop some specific recommendations for new systems that will be in development during the lifetime of this proposal. This research will help to fulfil NOAA’s climate research goal of advancing scientific understanding and prediction of climate and its impacts, to enable effective decisions. The proposed work is directly relevant to the Competition priority to support investigation of mechanisms that govern variability of the coupled climate system and its predictability on the interannual to multi-decadal timescales with a focus on either the Atlantic or Pacific Ocean region. We have chosen to focus on both Atlantic and Pacific regions, because teleconnections between the two basins are hypothesized to play a significant role in decadal variability and predictability. Hence, understanding DPLE will require consideration of both basins together. Our overarching project goal coincides with the Competition goal to identify state, mechanisms, and sources of predictability on the interannual to decadal timescale, which will help lead to future improvements in skillful decadal predictions systems for climate. This research is intended to serve as a bridge of new understanding connecting the DPLE to the next generation of decadal prediction systems to be developed for CMIP6 and beyond.

What sets the predictability timescales of SST and upper-ocean heat content in the Atlantic and Pacific basins?

Principal Investigator(s): Martha W. Buckley (George Mason University); Laure Zanna (New York University)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310396, NA20OAR4310397, GC20-201 | View Publications on Google Scholar


Understanding the extent to which sea surface temperatures (SSTs) are predictable is important due to the strong impact of SSTs on climate variables, such as temperature and rainfall over adjacent landmasses. For example, low-frequency variability of North Atlantic SSTs, termed the Atlantic Multidecadal Variability, impacts temperatures over North America and Europe, precipitation over the Sahel, and the strength of Atlantic hurricanes. As a result, decadal predictability experiments have received much attention, in particular CMIP5 and CMIP6 include decadal prediction experiments. However, models differ substantially on both the magnitude and spatial pattern of predictability timescales of SST and upper-ocean heat content (UOHC). The overarching goal of this work is to determine the processes that set predictability timescales for SST and UOHC in the Atlantic and Pacific basins and to ascertain how well these processes are represented in models. Predictability timescales will be calculated from both gridded observations and CMIP5/CMIP6 models, and the results will be compared quantitatively. In order to do this, we will calculate predictability timescales from observations and both subsampled CMIP preindustrial control integrations and CMIP historical runs, in which the observational data and model data are temporally sampled and processed (e.g., removal of anthropogenic signal) in the same way. This will enable us to isolate regions where models differ from observations. We will address the underlying mechanisms that lead to the predictability timescales for SST and UOHC in both observations and models. In order to determine the degree to which stochastic atmospheric forcing integrated over the oceanic mixed layer can explain spatial variations on predictability timescales, we will (1) determine the extent to which spatial variations in the wintertime mixed layer depth can explain spatial variations in predictability timescales for SST and UOHC and (2) compare observed predictability timescales for SST to those predicted by an idealized red noise model with no ocean dynamics. We will isolate regions where active ocean dynamics (rather than thermodynamics) play a role in setting predictability timescales. The relevant ocean dynamics in these regions will be determined by (1) analyzing lagged correlations between time series in these regions and atmospheric forcing and ocean dynamical variables and (2) computing heat budgets in the regions of interest. Our proposal is targeted at the first priority area of NOAA’s CVP-Decadal Climate Variability and Predictability Competition. Our proposal is directly relevant to the program objectives to investigate the mechanisms that govern variability and predictability of the ocean on interannual to decadal timescales. We will be using both long-term observational data and models (e.g., CMIP5/CMIP6) and our regional focus will be both the Atlantic and Pacific basins. A main focus of the proposal is a rigorous model-data comparison of predictability timescales. This intercomparison will enable us to assess the realism of models currently used for decadal predictions and provide model developers with both regions and processes that need to be improved in order to better predict decadal climate variability. This is directly relevant to program’s overarching goal to “...identify state, mechanisms, and sources of predictability on the interannual to decadal timescale, which will help to lead to future improvements in skillful decadal prediction systems for climate (ocean and atmosphere).” More broadly, our proposal is relevant to NOAA’s mission to help society “cope with, and adapt to, today’s variations in climate and to prepare for tomorrow’s”.

From Boundary Layer to Deep Convection: The Multi-Plume Eddy-Diffusivity/Mass-Flux (EDMF) Fully Unified Parameterization

Principal Investigator(s): Joao Teixeira (UCLA/JPL); Rong Fu and Mikael Witte (UCLA); Georgios Matheou (University of Connecticut); Leo Donner (NOAA/GFDL); Julio Bacmeister (NCAR)

Year Initially Funded: 2019

Program (s): Climate Variability & Predictability

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

Award Number: GC19-401 | View Publications on Google Scholar


The key objective of this project is to reduce critical systematic biases in the GFDL model related to the boundary layer, convection and clouds by implementing, and evaluating, in the GFDL model, a new fully unified boundary layer and deep convection parameterization based on the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) approach. Turbulence and convection in the atmosphere are at the core of key climate prediction problems. For example: i) to reduce uncertainties in climate projections, it is essential to improve predictions of cloud feedbacks (how clouds respond to, and influence, climate change), which are controlled by the interactions between a turbulent flow with water phase transitions and radiation; ii) to improve extreme weather prediction for the next few decades as climate changes, it is essential to improve our understanding of how moist convection responds to a warmer world. It is increasingly clear that to realistically represent the different manifestations of turbulence and convection in the atmosphere, new unified parameterizations that consider all types of sub-grid flow in one single scheme, are needed. In this context, a parameterization such as EDMF that unifies boundary layer with moist convection (both shallow and deep) is a promising approach. EDMF is based on the unification of concepts generally used for the parameterization of turbulence in the boundary layer (ED) and of moist convection (MF). Studies have shown the potential of EDMF to represent dry and moist convective boundary layers. In the last few years the Lead PI’s group has developed a new version of EDMF that is particularly well suited to simulate moist convective boundary layers and is able to represent in a realistic manner the dry boundary layer, stratocumulus, shallow and deep cumulus convection. This new version uses a multi-plume approach and the probability density function (PDF) of updraft properties in the surface layer is sampled in a Monte-Carlo manner to start a variety of updraft plumes, with a stochastic lateral entrainment parameterization. The current EDMF version is a turbulence and convection parameterization that can be considered as fully unified, since it is able to represent convective processes from boundary layer convection (dry and with clouds) to deep moist convection. In this project, we will implement and evaluate the new EDMF parameterization in the GFDL model. Initially we will evaluate the new EDMF implemented into the GFDL SCM versus a variety of LES case-studies and results from field experiments. For the full 3D implementation, we will focus our evaluation on cloud and convection variables as observed by satellite instruments during present climate. In particular, we will evaluate how the new EDMF version of the GFDL model is able to simulate key boundary layer and convection transitions such as (i) from stratocumulus, to cumulus and to deep convection (over the tropical and sub-tropical oceans) and (ii) the diurnal cycle of tropical convection over land from a stable boundary layer to dry convection, shallow convection and deep precipitating convection. We will also investigate in detail the impact of the new EDMF GFDL simulations in present and future climate. By developing and implementing a new fully unified boundary layer, cloud and convection parameterization in the GFDL model, and reducing key biases in GFDL’s climate predictions, this project will improve NOAA’s Climate Program Office (CPO) capabilities in Earth system science and modeling, will address CPO’s strategic challenges in the areas of (1) Weather and climate and (2) Climate impacts on water resources, and will ultimately advance the scientific understanding and prediction of climate and its impacts, to enable effective decisions.



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