Funding Opportunities & Funded Projects

FY23 Research Opportunities

For both competitions, Science for the 21st Century Western U.S. Hydroclimate and Products for Areas of Climate Risk, and Projections for Societally-Relevant Problems

LOIs are due September 1, 2022 by 5pm and Full Proposals are due November 21, 2022 by 5pm.



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Subseasonal to Seasonal Prediction with NCAR’s CESM2-WACCM

The goal of this joint project between NCAR, NOAA ESRL Physical Sciences Division and
NOAA/NCEP Climate Prediction Center (CPC) is to test and demonstrate the utility of NCAR’s
Community Earth System Model, version 2 with the Whole Atmosphere Community Climate
Model as its atmospheric component (CESM2-WACCM) as a subseasonal-to-seasonal (S2S)
prediction system that could improve NCEP’s operational multi-model S2S prediction capability.
CESM2-WACCM will be released by NCAR in Fall 2017. This whole atmosphere-ocean
coupled climate model has several improved features that address sources of predictive skill on
S2S time scale, including proper representation of stratospheric processes (e.g. Quasi-Biennial
Oscillation (QBO) and Stratospheric Sudden Warmings (SSWs)), Madden Julian Oscillation
(MJO), El Niño/Southern Oscillation (ENSO) and land surface processes. Furthermore, these
features address improved multiscale interactions between QBO, MJO and SSWs that can
provide rather untapped source of subseasonal predictability. Finally, using the Data
Assimilation Research Testbed (DART) to initialize WACCM allows for the utilization of high
altitude observations in S2S predictions.


In this proposed NOAA Climate Test Bed project, NCAR and NOAA/ESRL will work closely
with NCEP/CPC to:
1. Demonstrate the utility of a prototype of the CESM2-WACCM S2S system by running a
set of reforecasts for the 1999-2018 time period in compliance with the SubX and CPC
protocols (transition from Readiness Level (RL) 5 to 6).
2. Run CESM2-WACCM in real-time, operational mode (transition from RL 6 to 7).
3. Evaluate inclusion of the real-time, operational environment prototype forecasts from
CESM2-WACCM into CPC forecasts (transition from RL 7 to 8).


Improvements of the CESM2-WACCM system will be determined by comparing the new system
with reforecasts and real time forecasts from operational systems using CPC’s metrics for
evaluation as well as additional process-oriented diagnostics. These process evaluations will
focus on multi-scale/mode interactions between QBO, MJO, extreme stratospheric vortex events
and tropospheric modes of variability. In addition to comparing CESM2-WACCM to current
models available in the SubX database, we will utilize comprehensive reforecast data sets from
previous versions of CESM that will be submitted to the SubX project and differ in their
representation of stratospheric processes and tropospheric physics.


The proposed project directly addresses MAPP competition 3: NOAA Climate Test Bed -
Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability by improving
the SubX multi-model ensemble prediction systems. Specifically, the utility of a new model that
can tap into potential sources of S2S predictive skill is tested. This project addresses NOAA’s
long-term goal: “Weather-Ready Nation Society is prepared for and responds to weather-related
events” by carrying out research on and providing tools for the prediction of extreme events on
the subseasonal time scale.

Principal Investigator (s): Jadwiga Richter (UCAR)

Co-PI (s):Collins, Dan(NOAA/CPC), Perlwitz, Judith(NOAA/ESRL/PSD), Nicholas Pedatella (NCAR), Joseph Tribbia (NCAR/CGD), Julio Bacmeister, (NCAR/CGD)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

Advancing the Effectiveness and Efficiency of GLDAS Assimilation of JPSS Land Data Products for NCEP NWP and Drought Monitoring Operations

For MAPP program Competition 1: Advancing Earth System Data Assimilation, we
propose to advance the effectiveness and efficiency of assimilation of JPSS land data products
through the Global Land Data Assimilation System (GLDAS) for NCEP NWP and drought
monitoring operations. Satellite remote sensing land surface data products have been generated
and made available for various applications in the past decades. The data products of soil
moisture, land surface temperature, albedo, vegetation indices/green vegetation fraction, surface
type are currently operationally generated from NOAA JPSS VIIRS or GCOM-W1/AMSR2
satellite sensors for use in NOAA NCEP numerical weather, climate and hydrological
predictions. Several algorithms have also been introduced to assimilate these data products into
land surface models to improve NCEP weather, climate and hydrological forecasts. However,
none of those JPSS land data products and none of those data assimilation algorithms were used
in NCEP operational NWP models, drought monitoring system, or national water model. The
situation might have been caused by the low efficiency and effectiveness of the currently used
data assimilation algorithms. Based on the findings of the land data assimilation research
community in the past decades, we propose to: 1) implement the dual-pass data assimilation
algorithm by Yang et al (2007) into GLDAS of NCEP without the need to use the so-called
“bias-correction” in the conventional land data assimilation algorithms; 2) inter-compare the
efficiency and effectiveness of the dual-pass data assimilation algorithm with conventional
algorithm (i.e., the EnKF using CDF-matching for bias correction); 3) evaluate the impact of the
dual-pass algorithm in NCEP NWP model simulations and forecasts; 4) examine the drought
monitoring capability using soil moisture profile output from GLDAS with or without
assimilating the satellite land data products; 5) streamline the dual-pass data assimilation
algorithm in GLDAS to enable a straightforward transition of the algorithm to NCEP operation
environment and finally deliver the improved drought monitoring products to NCEP.

Through advancing CPO’s capability of “modeling and prediction” and meeting NOAA’s
long-term climate goal, this proposed project attempts to enhance the effectiveness and
efficiency of assimilation of NOAA JPSS data products in NCEP’s modeling and prediction
systems of GLDAS, Global Forecast System (GFS), Climate Forecast System (CFS) and drought
monitoring operations. This attempt meets CPO’s research objective of focusing on climate
intelligence and climate resilience as stated in the MAPP FY18 information sheet.

Climate Risk Area: Water Resources

Principal Investigator (s): Xiwu Zhan (NOAA/NESDIS/STAR)

Co-PI (s):Yin, Jifu (UMD-ESSIC/NOAA-NESDIS-STAR) Zheng, Weizhong (NOAA-NWS-NCEP) Dong, Jiarui (NOAA-NWS-NCEP) Xia, Youlong (NOAA-NWS-NCEP)

Task Force:

Year Initially Funded:2018

Competition: Advancing Earth System Data Assimilation

Final Report:

Joint NOAA-NASA Development of a Data Assimilation System for Aerosol Reanalysis and Forecasting

Not only are atmospheric aerosols pivotal contributors to air pollution but through impact on atmospheric radiation and clouds they affect weather and climate. Because of complexity of origin and interactions with environment, modeling aerosol concentrations and quantification of their impact on climate and weather remain highly uncertain. Data assimilation constrains these uncertainties by correcting model errors and filling observation voids thus providing an estimate of the aerosol state that is uniform in space and time.

Here, we propose a joint NOAA-NASA partnership to develop a new global aerosol data assimilation system for reanalysis and forecasting. This partnership has a long and fruitful history of collaboration on atmospheric modeling and assimilation and the proposed work is its natural continuation.

The system will rely on the following components: NASA’s extension to Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol parameterization that includes nitrates; assimilation of multi-channel Aerosol Optical Depth (AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellites, and AErosol RObotic NETwork (AERONET) direct sun measurements; new Ensemble Kalman Filter (EnKF) methodology using a chemical model with stochastic perturbations to emissions, chemical backgrounds and parameters/tendencies. Computer codes will build upon those employed operationally at NCEP, starting with the current NEMS GFS Aerosol Component (NGAC) implementation and evolving into a system based on the FV3-GFS version with the enhanced GOCART and the GFS physical parameterizations. Aerosol data assimilation will be based on an extension of the meteorological Ensemble Kalman Filter (EnKF) for aerosols. Software development will be coordinated with the Joint Effort for Data assimilation Integration (JEDI) so that it adheres to the architecture and object oriented framework emerging from this initiative.

The proposed scope of work is as follows:
● Adapt NASA’s aerosol observation processing system and quality control for the
NOAA environment plus contributing interfaces to NOAA specific instruments.


● Develop ensemble perturbation strategies specific for aerosols and evaluate the
quality of the ensemble.


● Deploy a cycled aerosol data assimilation system at NOAA/ESRL/GSD in hindcast
mode.

● Perform a systematic evaluation of this hindcast system against NASA’s MERRA-2, ECMWF’s CAMSiRA (previously MACC), and independent in-situ and satellite observations and produce year-long reanalysis for 2016.

The proposal work addresses MAPP Program objective to develop a new DA-based approach to monitor atmospheric composition. It aims to provide tools to produce global scale aerosol reanalysis to study climate, its change and prediction. By virtue, it addresses NOAA goals to “advance understanding of Earth’s climate system and to foster the application of this knowledge to improve the resilience of our Nation and its partners”.

Principal Investigator (s): Mariusz Pagowski (University of Colorado)

Co-PI (s):da Silva, Arlindo (NASA/GMAO) Lu, Cheng-Hsuan (Sarah) (SUNY Albany) Grell, Georg (NOAA/ESRL/GSD)

Task Force:

Year Initially Funded:2018

Competition: Advancing Earth System Data Assimilation

Final Report:

Multi-Platform CO data assimilation for chemistry climate interaction and air quality prediction

Carbon Monoxide (CO) plays a central role in tropospheric chemistry. As the primary sink of the
hydroxyl radical (OH), CO indirectly controls Methane (CH4) lifetime and CO is also a main
precursor of tropospheric ozone. Thus, capturing its spatio-temporal variability is important for
both short-term Air Quality monitoring and forecasting, and chemistry-climate applications.
The project aims to improve CO forecasting capabilities using the Suomi National Polar-orbiting
Partnership (SNPP) and the Joint Polar Satellite System (JPSS) Cross-track Infrared
Sounder (CrIS) within a data assimilation (DA) framework. CrIS CO retrievals products are
operationally produced by NOAA/NESDIS/STAR by means of the NOAA Unique Combined
Atmospheric Processing System (NUCAPS). The products include geophysical a priori and
quality check processing, required for DA. NUCAPS averaging kernels are available upon request
and will soon become part of the operational suite of products. The proposed work is relevant to
this solicitation in that the assimilation of NUCAPS SNPP and JPSS CrIS CO profiles will improve
CO predictability across different scales, with significant benefits for air quality forecasting and
Earth system modelling.


A 3-year reanalysis (2015 to 2017) will be performed by using several state-of-the-art DA methods
and models developed at the NOAA-ESRL, NCAR-ACOM and NOAA-STAR laboratories. This
effort aims to assess the sources of modelling uncertainties in the CO budget by analyzing different
meteorological forcings, as well as different chemistry and emissions inventories (biomass
burning, anthropogenic and chemical sources including oxidation of biogenic hydrocarbons). The
deliverables from this proposed effort are the 3-year CO emissions and four-dimensional CO
fields with fully characterized model uncertainties. This 3-year simulation will elucidate the
impacts of the 2015 El Niño wildfires on atmospheric composition, as well as the feedback of CO
to CH4 lifetime and growth rate. We will use high quality in-situ observations from aircraft and
tower measurements made in North America and the available global network to objectively
evaluate the posterior CO concentration fields and emissions fluxes.


This project addresses NOAA’s long-term goal to understand changing atmospheric composition
and its impacts. It will provide guidance for best use of the suite of satellite CO observations from
AIRS (2004) to the last JPSS-CrIS (to be launched in 2031) with regards to understanding the CO
budget and its emissions, and impact on air pollution and climate change.

Principal Investigator (s): Benjamin Gaubert (UCAR)

Co-PI (s):Basu, Sourish (University of Colorado CIRES)

Task Force:

Year Initially Funded:2018

Competition: Advancing Earth System Data Assimilation

Final Report:

Near-real time data assimilation for land vegetation and carbon cycle using JPSS space-based observations and in-situ data

Satellite remote sensing has been providing observations of the terrestrial biosphere for decades.
For instance, the NDVI index since the 1980s using NOAA’s AVHRR, followed by NASA’s
MODIS, and more recently the JPSS VIIRS instrument onboard the Suomi NPP (SNPP) satellite
that provides a suite of high quality land-surface observations. There is an opportunity to
significantly enhance the utility of the JPSS observations by bringing models and other datasets
in a coherent and comprehensive framework. This proposal will apply a land-vegetation-carbon
Data Assimilation (DA) system to satellite and in-situ observations, with the aim of providing an
assimilated data-model ‘best-analysis’ monitoring product of vegetation and carbon cycle.

This system includes the data assimilation core Local Ensemble Transform Kalman Filter
(LETKF), an advanced DA system that has been successfully applied in a variety of atmosphere,
ocean, and land applications including at NCEP and ECMWF. This assimilator will be applied to
two land-vegetation-carbon models: (1) VEGAS, a UMD-led model widely used in carbon cycle
and dynamic vegetation community, (2) NCEP/EMC’s operational land model Noah with its
dynamic vegetation model version Noah-MP. Weighing observation errors vs. model errors, the
DA system gives an optimized estimate of the state variables such as vegetation biomass, soil
carbon, and leaf area index (LAI). Key model parameters such as light use efficiency (LUE),
respiration dependence on temperature Q10, and phenological cold/drought tolerance are treated
as augmented state variables to be assimilated side-by-side. Gross Primary Productivity (GPP)
and surface fluxes of heat, H2O and CO2 will also be a product of this assimilation.

Our assimilation will include two strands of effort: (1) a historical 37-year period using a
NESDIS/STAR dataset based on harmonized operational global NOAA/AVHRR and 7-year
SNPP/VIIRS data, (2) a shorter current near-real time (NRT) period using the JPSS EDR land
product from VIIRS. In the historical strand, we will build on several datasets/experiences with a
broader suite of datasets, including AVHRR/VIIRS vegetation indices (VIs) and phenology,
FLUXNET carbon and water fluxes across ~200 sites around the world, and the novel remotely
sensed Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory 2
(OCO-2). In the NRT monitoring product, we will focus on the VIIRS vegetation indices
NDVI/EVI and OCO-2 SIF. The historical strand will provide a longer and historical background
that is useful for understanding interannual-decadal variability, as well as validate the system and
establish the best methodology, while the latter will provide an NRT monitoring product of
vegetation health, ecosystem dynamics, fire, agriculture and the global carbon cycle.

The proposal targets at MAPP Competition 1: Advancing Earth System Data Assimilation and
its Objective 2, by developing a new monitoring product on vegetation and carbon cycle. The
deliverable will be a monitoring product of vegetation dynamics and terrestrial carbon cycle at
0.5°×0.5° spatial resolution and hourly time step, provided as a best estimate analysis of blended
satellite and in-situ observations with predictive model. Underlying is a vegetation data
assimilation system that has the potential to provide real-time analysis to initialize vegetation
state for Earth system model prediction, thus contributing to NOAA’s long-term climate and
Earth system modeling goal.

Principal Investigator (s): Ning Zeng (University of Maryland)

Co-PI (s):Kleist, Daryl (NOAA/NCEP/EMC) Kalnay, Eugenia (University of Maryland College Park) Kogan, Felix (NESDIS/STAR) Zhan, Xiwu (Jerry) (NESDIS/STAR) Ek, Michael (NCEP/EMC) Dong, Jiarui (NCEP/MC) Zheng, Weizhong (NCEP/EMC)

Task Force:

Year Initially Funded:2018

Competition: Advancing Earth System Data Assimilation

Final Report:

Towards an evolutionary data assimilation system: the value of JPSS Land data in drought monitoring

There is growing concern with evidence that droughts have been intensified due to climate variation and with ongoing land development driven by population growth. This has correspondingly aggravated water scarcity, which threatens the long-term sustainability of water resources. To mitigate the drought vulnerability, an effective drought monitoring system is critical for decision makers. Estimation of key environmental variables such as soil moisture, and evapotranspiration are of paramount importance due to their strong influence on many water resources applications including agricultural production which control the behavior of the climate system. Reliable simulation of land surface properties is highly dependent on the initial and boundary conditions, quality of forcing data, accuracy of measured or calibrated model parameters, and proper estimation of prognostic and diagnostic variables. These can be addressed through Data Assimilation (DA) as a means of merging observations (satellite or in-situ) with model outputs. DA methods based on sequential Bayesian estimation seem better able to take advantage of the temporal organization and structure ofinformation, so that better compliance of the model outputs with observations can be achieved. This proposal responds to competition 1: Advancing Earth System Data Assimilation-objective 2 by proposing a 3-year collaborative project conducted by investigators from Portland State University and NOAA-NESDIS Center for Satellite Applications and Research. The proposed project aligns well with the NOAA’s long‐term climate goal as described in NOAA CPO’s strategy to “address challenges in the area of Weather and climate extremes, and effectively coordinate across these components through the development and deployment of end‐to-end research‐based integrated information systems that address needs of high societal relevance”. The proposed investigation will be conducted by employing Noah- Multiparameterization (Noah-MP) land surface model and the ensemble data assimilation is implemented by means of a novel sequential Bayesian method, the evolutionary particle filter (PF). An optimal assimilation method is needed to maximize information content from observations and model simulations. PF data assimilation has shown to have strong theoretical foundation providing full probabilistic characterization of prognostic variables which are robust and not prone to violation of mass conservation, and Gaussian assumption of noises in the observations. In this proposal, we plan to utilize available global satellite data products of soil moisture (i.e. JPSS/GCOM-W1/AMSR2 and other products available from Soil Moisture Operational Product System (SMOPS)). In addition, other land surface variables (i.e. JPSS surface type, land surface temperature, albedo, and vegetation indices) will be used as needed during the assimilation process. We determine seasonal and regional systematic and random errors in model forcing using the best available resources and reduce systematic errors in the proposed assimilation framework to enhance monitoring capability. The study enhances the use of remotely sensed satellite soil moisture, evapotranspiration data as complementary sources of observation to improve prediction of prognostic and diagnostic hydrologic variables. In this project, we will use the USDM, the USDA’s disaster declaration, and the drought economic loss as references to assess the open loop and DA drought monitoring skill. In addition, drought characteristics such as mean duration, areal extent, total magnitude, intensity, and severity– area–duration curves will be quantified and verified for the recorded droughts across the CONUS.

Climate Risk Area: Water Resources

Principal Investigator (s): Moradkhani, Hamid (University of Alabama)

Co-PI (s):

Task Force:

Year Initially Funded:2018

Competition: Advancing Earth System Data Assimilation

Final Report:

Testing, refinement and demonstration of probabilistic multi-model, calibrated sub-seasonal global forecast products

Sub-seasonal to seasonal (S2S) forecasting (lead times between 2 weeks and 2 months) is a new area of climate prediction that occupies the time range between medium range weather forecasts and seasonal climate prediction. The S2S range is currently the focus of intense research effort through the NOAA MAPP program and WWRP/WCRP S2S project, whose aims include improving forecast skill and understanding, and developing well-calibrated probabilistic forecast products in the sub-seasonal range. Since 2015 NOAA has been issuing experimental sub-seasonal probabilistic week 3–4 outlooks of precipitation and temperature every Friday, and there is a need and opportunity to demonstrate the potential of ongoing research results for improving the guidance used in these NOAA Climate Prediction Center (CPC) forecasts.

Under NWS NGGPS funding we have recently developed and tested a multi-model, calibrated, probabilistic sub-seasonal forecasting methodology for weekly-averaged precipitation (weeks 1–4), as well as week 3–4 two-week averages. The method has been evaluated using hindcasts from three models from the S2S database (CFSv2, ECMWF and CMA) over the U.S. (Vigaud et al. 2017a), and over boreal summer monsoon regions (Vigaud et al. 2017b). In these results, both the calibration and subsequent multi-model ensembling (MME) of the forecast probabilities were found to achieve probabilistically reliable forecasts with skill in some cases. It is this methodology that we propose to refine, further test and to transition to CPC operations in this CTB project.

NOAA MAPP’s S2S activities include the “SubX” sub-seasonal forecasting experiment, that combines multiple global models from NOAA, NASA, Environment and Climate Change Canada (ECCC), the Navy, and National Center for Atmospheric Research to produce once-a-week real- time experimental forecasts of at least 32 days in length, together with 17 years of reforecasts (1999–2015). SubX began a one-year pilot period of real-time predictions in July 2017, as additional guidance to CPC for their week 3–4 outlooks. The SubX project will test the skill of individual prediction systems as well as multi-model combinations. While SubX is limited to a 1-year real- time pilot period, it includes models from both NCEP and ECCC which are operational centers and will continue to issue real-time forecasts after SubX ends. The SubX forecast and reforecast data
is being archived in the IRI Data Library4

Principal Investigator (s): Andrew Robertson (Columbia University)

Co-PI (s): Collins, Dan (NOAA/CPC), Michael Tippett (Columbia University), Nicholas Vigaud (Columbia University)

Task Force:

Year Initially Funded:2018

Competition: Climate Test Bed - Advancing NOAA's Operational Subseasonal to Seasonal Prediction Capability

Final Report:

Development and evaluation of a seasonal-to-interannual statistical forecasting system for oceanographic conditions and living marine resources on the Northeast U.S. Shelf

The Northeast U.S. Shelf (NES) Large Marine Ecosystem (LME) supports some of the most commercially valuable fisheries in the world and has experienced dramatic ecosystem change in response to fishing pressure, climate variability and climate change, the combined effects of which create a huge challenge for the fisheries stock assessment in this region. Fisheries stock assessments describe the past and current population abundance of a fish population and importantly, develop a forecast for future population growth. Most stock assessment forecasts are used to set Annual Catch Limits over a 6–36 month scale. This forecast has been typically based on the biology of the fish and is highly uncertain. Incorporating physical environmental variables into the stock assessment population model and subsequent forecast could improve model performance and reduce uncertainty in future population size.

Therefore, a reliable prediction of the NES environmental variables, such as the ocean temperature, could lead to a significant improvement of the fisheries stock assessment. However, the current generation climate model-based seasonal-to-interannual predictions exhibit a limited prediction skill in the coastal environment. On the other hand, recent studies by the PIs reported statistically significant correlations between the NES temperature and the multiple large-scale climate features, such as the Gulf Stream path variability, with some lead time up to a few years, which indicates predictability.

Here, we propose to develop a seasonal-to-interannual statistical prediction system for ocean temperatures on the NES, which will be tailored to the needs of the National Marine Fisheries Service (NMFS) Northeast Fisheries Science Center (NEFSC) for fisheries stock assessment. The measures of uncertainty and predictability skill of the prediction product will be rigorously evaluated against three independent long-term regional hindcast simulations based on probabilistic skill metrics. Our first goal is to use previously described statistical relationships linking shelf ocean temperature to atmospheric circulation, Gulf Stream path, and coastal sea-level to develop a prediction system at the 3–36 month time scale. Our second goal is to evaluate this system in the context of selected stock assessments performed by NOAA Fisheries. Our third goal is to clarify the dynamical basis for the statistical relationships using ocean hindcast models and coupled ocean-atmosphere models.

This proposal is targeting the FY 2017 NOAA Modeling, Analysis, Predictions, and Projections (MAPP) Program solicitation Competition 2: Research to explore seasonal prediction of coastal high water levels and changing living marine resources and its third sub-element: Develop and evaluate experimental probabilistic-based prediction products tailored to the needs of the NOS and/or NMFS, as appropriate, by proposing to develop and test a new statistical seasonal-to-interannual prediction system of NES temperature specifically tailored to the needs of NMFS stock assessment, by the team of PIs including the scientists from NMFS. Our proposed work addresses every element of the NOAA’s long-term climate goal of advancing scientific understanding, monitoring, and prediction of climate and its impacts, to enable effective decisions.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Young-Oh Kwon (Woods Hole Oceanographic Institution)

Co-PI (s):Ke Chen (Woods Hole Oceanographic Institution), Glen Gawarkiewicz (Woods Hole Oceanographic Institution), Terry Joyce (Woods Hole Oceanographic Institution), Janet Nye (Stony Brook University), Jon Hare (NOAA/NMFS), Paula Fratantoni (NOAA/NMFS), Vincent Saba (NOAA/NMFS), Tim Miller (NOAA/NMFS)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report: NA17OAR4310111_Final Report.pdf

Downscaled Seasonal Forecasts for Living Marine Resource Management off the US West Coast

One of the greatest challenges in fisheries management is balancing environmental and economic interests by maintaining productive fisheries while limiting bycatch. Static closure rules are often inefficient in this regard, and momentum is building to employ management strategies informed by environmental conditions and predicted species distributions. One such example off the US west coast is the California Current System (CCS) drift gillnet fishery (DGN), whose regulations are enacted by NOAA/NMFS. The DGN targets sustainable stocks of commercially valuable swordfish, but accidental take of non-targeted species (e.g., turtles, cetaceans, sea lions) has led to large-scale fishery closures and declining productivity of the DGN fleet. Recently, dynamic management strategies for the DGN have been explored using observations and statistical models to predict target- by-catch species distributions in near real time, allowing managers to reexamine specific closure rules. Extending these efforts to seasonal forecasts would enhance their utility by giving managers additional time for adaptive responses, but no forecast system currently exists that offers sufficient spatial resolution to capture key physical processes in the CCS and the broad spatial coverage needed to manage wide-ranging fish, turtles, and marine mammals. We propose to produce and validate downscaled seasonal reforecasts for ~3 decades of CCS physical conditions as well as species distributions for target- and by-catch species of interest to US west coast fisheries. Key elements of the proposed work plan are (1) extract and bias-correct global NMME fields, use them to force downscaled reforecasts (i.e., retrospective forecast experiments predicting what happened in the past) of CCS physics, and validate CCS reforecasts with observations, (2) run and validate species distribution reforecasts for target- and by-catch species in the CCS, and (3) determine the added value of an ensemble approach to forecasting living marine resources. Environmental and fisheries data for validating 30+ years of reforecasts is already in hand. The proposed project will provide (i) a set of downscaled seasonal climate reforecasts that can be applied to diverse science and management questions, (ii) target-and by-catch species distribution reforecasts that can be used to reexamine DGN closure rules in collaboration with NOAA/NMFS partners, and (iii) a seasonal forecasting framework that can be applied in fisheries management off the US west coast and elsewhere.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Michael Jacox (University of California, Santa Cruz)

Co-PI (s):Michael Alexander (NOAA/ESRL), Steven Bograd (NOAA/SFSC, Christopher Edwards (University of California, Santa Cruz), Jerome Fiechter (University of California, Santa Cruz), Elliott Hazen (NOAA/SFSC), Samantha Siedlecki (University of Washington)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report: NA17OAR4310105.pdf

Experiments with Seasonal Forecasts of ocean conditions in the Pacific Northwest to aid the crab fishery

The Dungeness crab (Metacarcinus magister) fishery is the most valuable on the US West Coast, with landed values ranging from $100 million to $250 million dollars per year for 2013-2015. In the Pacific Northwest the states and tribes co-manage this fishery and must make critical decisions on seasonal timescales. There is strong interest from crab fishery managers to inform decisions with seasonal forecasts of ocean conditions that are known to affect Dungeness crabs: temperature, oxygen concentrations, aragonite saturation states, and lower-trophic level production rates. We propose to augment an existing forecast system to address the needs of the Dungeness crab fishery managers and stakeholders. This forecast system, JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE, http://www.nanoos.org/products/j-scope/) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). We will tailor and test the ability of J-SCOPE to forecast the autumn period critical to crab fishery managers, and determine whether forcing with a multi-model ensemble (North American Multi-Model Ensemble or NMME) reduces bias and better conveys uncertainty, relative to our previous efforts using CFS alone. Using the J-SCOPE  forecasts, we will quantify the relationship between local ocean conditions and three decision- oriented metrics from the crab fishery: (1) meat quality index, (2) spatial and interannual variability in the spatial distribution of crab catch and abundance, and (3) likelihood of summertime hypoxic events. Forecasts for the crab fishery will be delivered via the existing J- SCOPE app on the NANOOS portal (Northwest Association of Networked Ocean Observing Systems) in partnership with state and tribal managers on our Advisory Council. This project addresses directly the second of MAPP’s competitions on advancing the prediction of subseasonal to seasonal phenomena. This project will address objectives 1 and 3 of the MAPP Program.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Samantha Siedlecki (University of Connecticut)

Co-PI (s):Isaac Kaplan (NOAA/NWFSC), Nicholas Bond (University of Washington), Al Hermann (University of Washington), Jan Newton (University of Washington), Mike Alexander (NOAA/ESRL), Simone Alin (NOAA/PMEL) Advisory Council: Joe Schumacker (Quinault Department of Fisheries), Dan Ayers (Washington Department of Fish and Wildlife), Kelly Corbett (Oregon Dept Fish and Wildlife)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report:

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ABOUT US

Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather. 

CPO HEADQUARTERS

1315 East-West Highway Suite 100
Silver Spring, MD 20910