The NOAA CPO Modeling, Analysis, Predictions, and Projections (MAPP) program hosted a webinar on the topic of Seasonal Predictions of Fisheries on Thursday, April 7, 2016. The announcement is provided below.
|April 7, 2016
4:00 PM – 5:30 PM ET
|Seasonal Predictions of Fisheries
|Speakers and Topics:
|Alistair J. Hobdayv (CSIRO Oceans and Atmosphere)
Seasonal forecasting: supporting marine fishers and managers in a changing climate
Michael Alexanderv (NOAA Earth System Research Laboratory)
Samantha Siedleckiv (University of Washington) and Isaac Kaplanv (NOAA National Marine Fisheries Service)
Sarah Gaichasv (NMFS Northeast Fisheries Science Center)
|To view the slideshow:
1. Click the link below or copy and paste the link to a browser: https://cpomapp.webex.com/cpomapp/onstage/g.php?MTID=e35a15e3525250d62be95e777f0d3c665
2. Enter your name and e-mail address, and click “Join Now”. If necessary, enter the event passcode: 20910
To hear the audio:
Utilize the on-screen dial-in instructions visible after logging into webex
Webex and the teleconference line can accommodate only 100 attendees on a first-come, first-served basis. Please try to share a connection with colleagues at your institution to preserve space for others.
(Right click and Save Link As) .mp4
Alistair J. Hobday – The production of marine protein from fishing and aquaculture is influenced by environmental conditions. Ocean temperature, for example, can change the growth rate of cultured animals, or the distribution of wild stocks. In turn these impacts may require changes in fishing or farming practices. In addition to short-term environmental fluctuations, long-term climate-related trends are also resulting in new conditions, necessitating adjustment in fishing, farming and management approaches. Longer-term climate forecasts, however, are seen as less relevant by many in the seafood sector due to more immediate concerns. Seasonal forecasts provide insight into upcoming environmental conditions, and thus allow improved decision making. Forecasts based on dynamic ocean models are now possible and offer improved performance relative to statistical forecasts, particularly given baseline shifts in the environment due to climate change. Seasonal forecasting is being used in marine farming and fishing operations in Australia, including wild tuna and farmed salmon and prawns, to reduce uncertainty and manage business risks. Forecast variables include water temperature, rainfall and air temperature, and are considered useful up to approximately 4 months into the future, depending on the region and season of interest. Species-specific habitat forecasts can also be made by combining these environment forecasts with biological habitat preference data. Seasonal forecasts are useful when a range of options are available for implementation in response to the forecasts. The use of seasonal forecasts in supporting effective marine management may also represent a useful stepping stone to improved decision making and industry resilience at longer timescales.
Michael Alexander – Recently, Stock et al. (2015, Progress in Oceanography) assessed the sea surface temperature (SST) forecast skill in coastal ecosystem regions using two global climate model forecast systems. The models were found to have skill in most large marine ecosystem adjacent to the United States. Here we assess the skill of monthly SST anomaly predictions from the 14 models participating in Phase I of the North American Multi-Model Ensemble (NMME) project. While the individual models vary in skill as a function of lead-time, forecast month and especially by region, the multi-model ensemble mean often provides the best overall forecast.
The forecast skill was examined in greater detail for the California Current System. We found a strong latitudinal gradient in predictability within the CCS: SST forecast skill is highest in the north (off the Washington/Oregon coasts) and lowest in the Southern California Bight. In all cases, SST predictability can largely be attributed to two influences: persistence and ENSO variability. Applications for using the SST forecasts in the CCS and other regions for fishery management will be discussed.
Samantha Siedlecki and Isaac Kaplan – J-SCOPE (JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem, http://www.nanoos.org/products/j-scope/) provides short term (six to nine month) forecasts of ocean conditions for the US Pacific Northwest and southern British Columbia. J-SCOPE features dynamical downscaling of regional ocean conditions, linking NOAA’s Climate Forecast System (CFS) to a high resolution regional model (ROMS). We have examined model performance and predictability for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales, for variables relevant to management decisions for fisheries, protected species and ecosystem health. Forecasting efforts are aided by a relationship with local stakeholders and a real-time observational network.
Sarah Gaichas – Fisheries are affected by seasonal weather patterns, inter-annual variability, and shifting climate in diverse ways. Both global and local changes in temperature and precipitation alter the physical habitat and therefore the ecosystems where fisheries operate. Productivity of fisheries can be changed through direct impacts of temperature on animal physiology and survival as well as changing fish distributions. There are also indirect impacts on fisheries through changing ocean primary production, altered food webs, communities, and habitats. Information on fishery conditions at seasonal and multi-year scales can be used to improve fishery management. Examples from the Northeast US Large Marine Ecosystem demonstrate methods for addressing climate and ecosystem conditions in current analyses to support management decisions. Incorporating seasonal forecasting into fisheries science and management is a continually developing process, which will increase in importance as forecast products and scientific understanding improve.