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Causes for the Variability and Change of Physical Ocean Conditions over the Northeast U.S. Shelf: Impacts of ENSO and NAO in a Changing Climate

Principal Investigator(s): Weiqing Han (University of Colorado); Michael Alexander (NOAA/ESRL); Sang-Ik Shin (NOAA/ESRL, CIRES)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

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

Award Number: NA20OAR4310480, GC20-301 | View Publications on Google Scholar


The marine ecosystems on the northeast United States (NEUS) shelf are particularly vulnerable to climate variability and change, because the physical ocean conditions (e.g., temperature, salinity, currents and sea level) in this region are strongly influenced by the cold Labrador Current from the north, warm Gulf Stream from the south, and open ocean variability from the east, with each being strongly affected by the changing climate. The NEUS shelf is also subject to strong local forcing (e.g., wind, surface buoyancy fluxes, river runoff), which dominates interannual variability and is linked to the North Atlantic Oscillation (NAO) during recent decades. The effect of remote (relative to local) forcing on interannual variability of NEUS coast, however, has never been quantified. On decadal and interdecadal (collectively referred to as “decadal” hereafter) timescales, modeling studies suggested that variability of the NEUS shelf is most susceptible to the variability and change in Atlantic Meridional Overturning Circulation (AMOC), including its weakening due to anthropogenic warming. The coarse resolutions of the models, however, cannot resolve the complex bathymetry of the continental shelf and slope; yet, the shelf dynamics differ from that of the open ocean, and the continental slope acts as a dynamical barrier for the exchange between coastal and open ocean. Inadequate resolution of the shelf and slope can cause artificially strong impacts from the open ocean, including the effect of the AMOC. Recent observational analyses suggest that decadal variability of the NEUS coast is linked to both the NAO and El Niño – Southern Oscillation (ENSO). The mechanisms for ENSO to affect the physical ocean conditions and therefore the large marine ecosystem (LME) of the NEUS shelf remain unknown.The overall goal of this proposal is to: quantify the remote and local forcing of coastal ocean conditions (e.g. temperature, salinity, current) on the NEUS shelf since the 1960s on interannual and decadal timescales; investigate the associated mechanisms, and assess the impacts of the NAO and ENSO on this region. We will carry out a hierarchy of high-resolution modeling experiments using the Regional Ocean Modeling System (ROMS) in a domain that covers the entire US east coast. By using a high-resolution model, we can resolve the bathymetry of the NEUS shelf and slope. The domain of the model is fairly large to more accurately depict signals coming from the open ocean, including properly representing the Gulf Stream and the impact of the Labrador Current. To extract the NAO and ENSO signals, we will apply the Bayesian Dynamical Linear Model (DLM), which can capture the non-stationary impacts of climate modes, as demonstrated by our recent studies. To confirm the DLM results, we will also use Linear Inverse Modeling (LIM) to extract the ENSO/nonENSO-related signals. The model results will be analyzed in conjunction with available satellite and in situ observations. The Mid-Atlantic Bight (MAB) and the Gulf of Maine (GOM) of the NEUS coast are quite densely observed compared to coastal oceans globally.

Regional multi-year prediction for the Northeast U.S. Continental Shelf

Principal Investigator(s): Young-Oh Kwon, Hyodae Seo, and Ke Chen (WHOI); Paula Fratantoni, Vincent Saba (NOAA/NMFS/NEFSC); Michael Alexander (NOAA/ESRL)

Year Initially Funded: 2020

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.

Testing the relationship between NAO and Atlantic Multidecadal Variability over recent centuries using paleoclimate proxy data to improve decadal-scale climate predictions for fisheries management

Principal Investigator(s): Kelly Halimeda Kilbourne (University of Maryland Center for Environmental Science)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

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

Award Number: NA20OAR4310481 | View Publications on Google Scholar


Competition Relevance: One objective in the Northeast Regional Action Plan of NOAA’s Fisheries Climate Science Strategy is to improve medium-term (year to decade) climate forecast products for living marine resources. A key component of medium-term climate prediction is predicting ocean circulation. Two major ocean currents are involved in the Northeast U.S. Shelf Large Marine Ecosystem fisheries management sector, the southward Labrador Current and the northward Gulf Stream. Both are connected to the complex North Atlantic circulation and Atlantic Meridional Overturning Circulation (AMOC). Decadal-scale climate predictability of the state of the North Atlantic Ocean is strongly dependent on AMOC predictability, which requires an understanding of the climate variables that influence and are influenced by AMOC. This is what our proposal is focused on. Scientific Rational: Investigations into the forcing factors driving decadal-scale AMOC variability have been hampered by the relatively short length of direct AMOC observations, difficulties in identifying and modeling the key physical mechanisms, and the convolution of anthropogenic radiative forcing with natural variability during the era of instrumental climate records. This project aims to test a recent hypothesis about the driving mechanism of AMOC decadal variability, using high-resolution paleoclimate archives that provide long (multiple centuries) records of Earth’s climatic behavior, pre-dating significant anthropogenic forcing. The idea is to identify the natural physical relationships between North Atlantic climate variables to test if they are consistent with underlying physical theories developed from modeling studies. Summary of Work: We will specifically gather the highest possible temporal resolution paleoclimate proxies of sea surface temperature from the North Atlantic with a recent multi-proxy reconstruction of North Atlantic Oscillation (NAO) to test if the NAO is associated with heat convergence at high latitudes and if the signal is propagated to lower latitudes. The mechanism we will be testing is laid out by Wills et al. (2018) who find evidence that AMOC and NAO are coupled on decadal to multidecadal timescales. They describe the consequences of that coupling in terms of surface warming, a quantity that can be reconstructed from the highest resolution paleoclimate proxies. Scientific and Broader Impacts: The results of the proposed analysis will provide observational evidence of the relationship between NAO and ocean temperatures in key regions of the North Atlantic that give insight into the mechanistic connections between the atmosphere and ocean circulation in this region on interannual to decadal time scales. Using paleoclimate data, we will test the hypothesis that NAO and AMOC are linked on decadal scales through oceanic heat convergence and buoyancy fluxes based on observational evidence. If the basic hypothesis is rejected, our analysis will provide alternative relationships between NAO and temperature in particular regions that can be further explored in future modeling efforts. In effect, we will be identifying relationships between key variables in a long-term observational dataset that can be used to improve the physical representation of AMOC in climate models used for making climate projections and forecast products in support of fisheries management. Such climate intelligence contributes directly to U.S. prosperity and resilience by helping to maintain healthy fisheries and the communities of people who are dependent on those fisheries.

Understanding dramatic warming and altered fisheries on the US Continental Shelf through observations and multi-scale models

Principal Investigator(s): Jaime Palter, Kelton McMahon and Christopher Kincaid (University of Rhode Island); Paula Fratantoni and Kevin Friedland, (NOAA/NMFS/NEFSC)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

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

Award Number: NA20OAR4310483, GC20-304 | View Publications on Google Scholar


Statement of the Problem: The warming trend observed in the NEUS Continental Shelf Large Marine Ecosystem during recent decades is one of the strongest in the global ocean and has impacted regional fisheries. This warming pattern was accompanied by significant changes in the distribution, productivity, and trophic interactions of many commercially important species. Yet, the oceanographic drivers of these temperature changes have not been identified. The proposed work aims to advance our understanding of these physical processes and their connection with fisheries, ultimately leading to better predictions and preparations for future change. Methods and Summary of Work to be Completed: Our guiding hypothesis is that the increased presence of the Gulf Stream at the Tail of the Grand Banks (TGB) restricts the southwestward transport of the Labrador Current along the NEUS slope, thereby increasing the fraction of subtropical waters on the continental shelf. Because these subtropical waters substantially warm and deoxygenate the shelf, such circulation changes would strongly impact the marine ecosystem. If this hypothesis is correct, then knowing the conditions at the TGB could translate to substantial predictability for temperature-linked fisheries impacts, given that anomalies likely propagate along the slope at relatively slow advective time scales. Despite substantial preliminary evidence, a robust test of the hypothesized connection between circulation at the TGB and anomalous properties on the NEUS slope and shelf has been lacking. Thus, our proposed work will characterize the fluctuations of the Gulf Stream position relative to the TGB and the connection with shelf property and fisheries fluctuations through the following 3 objectives: 1. Reconstruct and compare historical variability in water masses, ecosystem characteristics, and fisheries at the TGB and along the NEUS slope and shelf through the coordinated analysis of satellite, hydrographic, isotopic, and fisheries data. 2. Use the observational record, alongside a numerical model, to expose the mechanisms that lead to co-variability between TGB and slope anomalies, as well as quantify the alongslope propagation time scales for these anomalies. This goal is timely given that models are only recently capable of faithfully simulating dynamics in this complex region. 3. Run a regional ocean model to explore how anomalies propagating along the slope are exchanged across the shelf. This step is necessary to understand when and how alongslope anomalies come to influence the shelf, potentially providing lead time to anticipate changes in the shelf physical environment that are crucial to ecosystems and fisheries. Relevance to the competition: The proposed work directly responds to CVP’s priority to combine observational data analysis with ocean model process studies to better quantify and understand physical changes on the NEUS continental shelf. It also evaluates the impacts of these physical changes on the distribution and migration phenology of the economically important fish and invertebrate species that are part of the Large Marine Ecosystem.

Advancing Decadal Predictions by Optimally Detecting Differences in Causal Relations

Principal Investigator(s): Timothy DelSole (George Mason University); Michael K. Tippett (Columbia University)

Year Initially Funded: 2020

Program (s): Climate Variability & Predictability

Competition: Decadal Climate Variability and Predictability

Award Number: NA20OAR4310401, NA20OAR4310402, GC20-205 | View Publications on Google Scholar


Project Summary: The goal of this project is to improve climate predictions and advance our process-level understanding of the ocean and atmosphere on interannual to multi-decadal time scales. To accomplish this goal, we propose to test the consistency of models and observations using a comprehensive, multivariate framework, and then construct a multi-model prediction system based on the subset of models whose internal predictability and climate change signals are consistent with observations. By selecting only models whose variabilities, and presumably physics, are consistent with observations, the resulting multi-model predictions are expected to perform better than predictions based on models with inconsistent variabilities. Also, the nature of the model inconsistencies will be diagnosed in detail. The climate models analyzed in this project will come primarily from the CMIP5/CMIP6 archive. We will select variables that can be validated observationally, such as sea surface temperature, salinity, height, and sea-level pressure, and will focus on the Atlantic and Pacific Oceans, areas where there are outstanding questions regarding the correct physics. Although numerous inconsistencies may be found in any model, inconsistencies in variables that have strong causal links to internally predictable components are the most problematic for prediction. Therefore, variables with the strongest causal relations with predictable components will be identified. Optimization techniques will be used to find the combination of variables and spatial and temporal information that (1) maximizes predictability, (2) maximizes the causal relation to that predictability, or (3) maximizes the ability to discriminate between models. Inconsistencies revealed by this analysis will elucidate how differences in process-level mechanisms between models impact internal variability and predictability. Simple or dynamically-meaningful metrics of these inconsistencies will provide model developers with new tools for model evaluation that will be of immediate relevance to improving predictability. To compare model internal variability to observations, the model’s climate change signal will be removed from observations using optimal fingerprinting techniques. If the internal variabilities are consistent, then they can be pooled in a multivariate test for the consistency of climate change signals between models and observations. If inconsistencies in climate change signals are found, then these will be diagnosed in simple or dynamically-meaningful ways. Models that are found to be consistent in both their internal predictability and climate change signals will then be combined to construct a multi-model prediction system. Empirical prediction models for multi-year prediction will be derived and used to make multi-year predictions of Atlantic and Pacific sea surface temperatures.Relevance to Competition: This proposal responds directly to all priorities in the funding call. Specifically, the proposed research will rigorously quantify “how well our models perform at simulating” decadal climate variability. The proposed research will “improve climate models and predictions” by explicitly constructing a multi-model prediction based on a subset of models whose predictability and causal relations are consistent with observations. The optimized discriminant functions derived in this research will provide a new tool to “enhance our process-level understanding of the climate system” that will be of immediate relevance to improving predictability. We will specifically examine predictability of the Pacific and Atlantic Oceans, and analyze both observations and the CMIP5/CMIP6 data set, consistent with the funding announcement.

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.

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