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Uncertainty Quantification, data quality, and observing network design in an ocean state of the Tropical Pacific

Principal Investigator(s): Patrick Heimbach (University of Texas at Austin), Matthew Mazloff, University of California San Diego, Scripps; Aneesh Subramanian, University of Colorado Boulder (CU Boulder)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: NA21OAR4310255; NA21OAR4310257; NA21OAR4310253 | View Publications on Google Scholar


This proposal addresses NOAA-OAR-CPO-2021-2006389, Competition #5 “Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities”. Our focus is on assessing observational strategies for the Tropical Pacific Observing System (TPOS) in the context of data assimilation for reanalysis and model initialization. Core elements of the TPOS mission include observing ENSO, advancing scientific understanding of ENSO variability, and identifying effective observational strategies for skillful weather and climate prediction on subseasonal to seasonal (S2S) timescales. To support TPOS, we propose a fundamentally dynamics-based approach to observing system design within a dynamically and kinematically consistent adjoint-based (4DVar) data assimilation framework. Our research proceeds along three main thrusts: (i) We will leverage a now-existing medium-resolution (1/3°) Tropical Pacific Ocean State Estimate (TPOSE) which will be augmented to a higher-resolution, 1/6° product (TPOSE_HR). This state estimate will improve the representation of currents and Tropical Instability Waves (TIWs) and provide a baseline for three study periods, covering an Eastern Pacific El Niño (2015/16), a Central Pacific El Niño or Modoki (2009/10), and an Eastern Pacific La Niña (2010/11). These baseline TPOSE_HRs will enable us to address a number of questions regarding the impact and best practices of assimilating emerging TPOS sensors. Because our adjoint-based framework provides sensitivities to the atmospheric state, we will be able to assess how existing or hypothetical sensors will constrain air-sea fluxes. (ii) In a second thrust, we will conduct dynamical attribution studies for a range of quantities of interest (QoIs) by means of a Green’s functions approach, with the detailed sensitivity kernels provided by the adjoint. The choice of QoIs is guided by TPOS2020 goals, in particular with respect to ENSO monitoring, understanding, and prediction. Recognizing that ENSO diversity is governed by a complex interplay of dynamical processes and that TPOS serves to inform a diverse range of weather and climate phenomena, we will assess the degree to which sensitivity patterns for diverse QoIs to be tested exhibit redundancies. Sensitivity maps, interpreted as “heat maps” quantify variables and regions that impact the QoI as a function of lag time. These maps provide valuable information regarding predictable anomaly propagation and instrument placement. (iii) In a third thrust, use of derivative information will be further refined by quantifying how existing or hypothetical observational assets may reduce uncertainties in the QoIs chosen under (ii). This will be achieved within the context of Hessian-based uncertainty quantification (UQ) and optimal observing network design. The ability to conduct such calculations sets our framework apart from conventional studies based upon observing system [simulation] experiment (OS[S]E) approaches.

Evaluating Earth System Models using apparent relationships rather than spatial-fields-using neural networks as model comparators

Principal Investigator(s): Anand Gnanadesikan (Johns Hopkins University)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: NA21OAR4310256 | View Publications on Google Scholar


While Earth System Models often fail to reproduce biological fields like phytoplankton biomass and chlorophyll, the reasons behind such failures are complex. Because phytoplankton growth rates depend on environmental conditions like nutrients and light, and these in turn depend on the rates of mixing and upwelling, physical biases in models can produce biases in circulation such that a “perfect” biological model will still give imperfect results. For example, an Earth System Model in which the relationship between macronutrients and biomass matches that found in the real North Atlantic will still produce a spatial bias in the distribution of nutrients if the path of the North Atlantic Current is poorly simulated. If we knew that the model had the correct relationship between biomass and nutrients, we could unambiguously tie such an error to model physics. However, the actual apparent relationships (those seen in the real world between environmental drivers and phytoplankton biomass) are far from simple and may deviate from intrinsic relationships based on bench science which are often coded into models. For example, low phytoplankton biomass may be associated with low levels of nutrients in the presence of high levels of light, or high levels of nutrients in the presence of low levels of light. Simply plotting biomass against nutrients will then result in a maximum biomass concentration at intermediate levels of nutrient, capturing the asymptote of biomass at high levels of nutrient may require careful extrapolation. Better constraining the drivers of phytoplankton change and variability is essential to NOAA’s mission to improve the prediction of the Earth System in order to build resilience to changes. The proposal directly addresses the call within the competition to “examine biases in observed and modeled data/products and advance understanding of the causes for large differences between observed and modeled ocean data/products.” We aim to build on recent work showing that machine learning methods (in particular, Neural Network Ensembles) can be used to extract biologically reasonable complex relationships from ESMs and also used to compare the similarity of the biological codes across models. We propose to examine whether such methods can find robust relationships between biomass and observed environmental parameters on regional and global scales, and use the resulting relationships as metrics for evaluating Earth System Model output. We will do this using combinations of remotely sensed data (chlorophyll, carbon biovolume) and in-situ data (phytoplankton biomass, nutrients, Ekman upwelling, light, mixed layer depth). We will also develop a toolkit whereby Earth System Models that are part of the current IPCC process can be compared with observational relationships and to each other.

Air-Sea Fluxes Derived from Global Surface and Field Campaign Observations: A Key Product for Improving Earth System and Climate Models

Principal Investigator(s): Shuyi Chen (University of Washington), Chris Fairall (NOAA/ESRL/PSL)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: NA21OAR4310263 | View Publications on Google Scholar


This proposed study will focus on the first program priority: Developing new observation-based (in situ, satellite) ocean synthesis datasets or products (physical and/or biogeochemical) for climate monitoring or modeling applications through applying existing methods or developing new, state-of-art, innovative methods and approaches (e.g. ocean state estimation, data assimilation, and quantification of observational uncertainty). Fluxes of mass, energy, and momentum between the oceans and the atmosphere have a profound impact on global weather and climate.The air-sea fluxes are of critical importance as they represent and control the energy and water cycle of the Earth system. It plays a key role the global hydrological cycle and precipitation, which are difficult for the current Earth system and climate models to accurately represent. However, our current ability of observing the air-sea fluxes globally is limited and remains as an unmet challenge. This proposed project will aim at 1) increasing the use of NOAA’s field campaign data (e.g., DYNAMO, YMC, ATOMIC and others) and emerging observations from sustained observing networks and systems, which will use a combined with non-NOAA observations that are available, and 2) enabling Earth system and climate model evaluation, validation, process-oriented diagnostics, and/or satellite calibration and validation. We will develop an observation-based air-sea fluxes data product, which will integrate observations from existing surface-based network (e.g., moorings, ships and saildrones), selected satellite measurements, and field campaigns from 1990-2020. This air-sea fluxes data product with an interactive user interface (leverage an existing software that will be adapted for the air- sea flux datasets in this project) will be particularly useful the broad modeling community, not only the modeling centers but also research community including students who are not familiar with field observations. We will engage and work in collaboration with Earth system and climate modeling community from NOAA, NCAR, and others. We plan to help developing model evaluation methods that are based on the observations of a wide range of temporal and spatial scales that can address both model physical process and model performance. The PI Chen and Co-I Fairall have worked with field campaign and ship and surface-based observation in producing air sea sensible and latent heat fluxes (e.g., Fairall et al. 2003, 2010; Chen et al. 2016). The air-sea momentum flux will be derived using the observations and a high- resolution coupled atmosphere-wave-ocean model to access and refine the flux in high wind conditions (e.g., Fairall et al. 2009, Chen et al. 2013). The air-sea fluxes product produced in the project will be made available through a user interface already developed by PI Chen and a research scientist Dr. Brandon Kerns at UW (http://dynamo.ml-ext.ucar.edu/dynamo_legacy/). This will ensure a rapid and easy access for a broader user community.

An air-sea flux, SST, wave database from the ATOMIC field program

Principal Investigator(s): Elizabeth J. Thompson (NOAA/PSL), Darren Jackson (CU CIRES / NOAA PSL), Christopher W. Fairall (NOAA PSL), Dongxiao Zhang (UW CICOES / NOAA PMEL)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: GC21-410a, GC21-410b | View Publications on Google Scholar


Field campaigns have been designed to collect in-situ data that both improve process level understanding and form benchmark datasets for experiments with and improvements of reanalysis, satellite data, and models. However, the transition of observations to model, satellite, and reanalysis projects rarely happens in a timely fashion or with full participation between all expert parties involved. This proposed data synthesis effort will produce a more effective and lasting use of ATOMIC field campaign observations for evaluating satellite and reanalysis products and use in numerical models. The ATOMIC 2020 field campaign in the northwestern tropical Atlantic Ocean collected a unique and diverse dataset of air-sea interaction, ocean properties, and clouds from drifting, shipborne, airborne, air-deployed, and uncrewed platforms. The variety of measurement heights and depths, platform motions, and instrument uncertainties is at the same time a strength and a usability barrier. To invite effective and lasting use by modeling, satellite, and reanalysis teams, the observation teams must account for these details and synthesize the measurements in a common, standard, open source database. In the first year of the project we will convene a joint conference to obtain guidance from modelers on critical variables and data formats. The air-sea fluxes, sea surface temperature (SST), and surface waves are the three most important sets of information for coupled weather and climate models to resolve correctly to understand and predict environmental change. Our hypothesis is that the strengths in terms of model, satellite, and observational research applications of a combined, consistent ATOMIC database of SST, fluxes, and waves from multiple platforms far outweighs the strengths of any individual dataset. We will make use of periods of co-located data between in situ and airborne platforms plus satellite overpasses, with the NOAA Ship Ronald H. Brown serving as the standard for comparison. We will draw on ATOMIC and EUREC4A data for the most complete picture to produce and archive a combined, consistently quality controlled, and consistently processed database from all platforms. Databases for SST, air-sea fluxes, waves, plus near surface bulk variables that result from this effort will be posted to NCEI for public distribution. By bringing observations to the modeling and satellite communities and facilitating partnerships across the external and NOAA communities, the proposed data synthesis directly responds to the COM/CVP/GOMO call to “develop an observations-based product for climate monitoring or modeling application” that “increases the use of NOAA’s historical field campaign data” and “enables improved climate modeling or monitoring (e.g., enables future climate model evaluation, validation, process-oriented diagnostics)”, contributing to the first two objectives of NOAA’s long- term climate goals as described in NOAA’s Next-Generation Strategic Plan.

Developing a capability for the real-time comparison of near surface ocean

Principal Investigator(s): Arun Kumar (NOAA/CPC), Meghan Cronin (NOAA/PMEL)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: GC21-411a, GC21-411b | View Publications on Google Scholar


Ocean observations are used for initializing and validating sub seasonal to seasonal forecasts using coupled models; creating synthesis products, for monitoring evolution of ocean conditions; developing climate data records to monitor the influence of slow trends; and for improving understanding of the physics affecting the climate system. With continued investments in ocean observations, and upcoming enhancements in the tropical Pacific observing system (TPOS), increasing the utilization of ocean observations has been identified as one of the key challenges. There is also a consensus in the community that there is a longstanding disconnect between the investments in observations and their utilization in model-based analysis and prediction systems. A real-time comparison between observations and model-based analysis products will represent a direct use of ocean observations for which a need has been long perceived but remains to be realized. Towards bridging the gap between the observation and modeling communities, the goal of this project is to develop a capability for the real-time comparison of in situ ocean observations with operational analysis products. The scope of the project will focus on the observations from moorings in the tropical Pacific. This is because El Niño-Southern Oscillation (ENSO) is one of the most important modes of coupled variability with largest societal impacts, and further, because the TPOS is currently going through evolutionary changes. To accomplish the goals of the project outlined above, the following tasks will be completed: (a) Identify ocean and atmosphere mooring data to be used in the real-time model assessment and set up procedures to update the observational database in real-time. (b) For atmospheric and ocean analyses, set up corresponding procedures to update the model database in real-time. (c) Develop procedures to compare time-series of model analysis with observations and develop a web interface to disseminate the information to the community. (d) Utilization of assembled observational and modeled data bases to address science questions of relevance in understanding coupled climate variability in the tropics. The goals of the proposal are highly relevant to the focus of the present call to “...develop an observations-based product for climate monitoring or modeling application” that “enables improved climate modeling or monitoring (e.g., enables future climate model evaluation, validation, process-oriented diagnostics)”. The project will also address other foci of the call for “Evaluating current methods and approaches for ocean observing and modeling, and the ability of observed and modeled data/products to reproduce physical or biogeochemical processes, climate phenomena, or interactions between Earth System components on different timescales.”

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.



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