Projecting global fish stocks and catches up to 2100 Jorn Bruggeman jbr@pml.ac.uk Supporting Blue Growth with innovative applications based on EU e-infrastructures 14-15 February 2018, Brussels

Background Published in 2009, most downloaded FAO report 2018 update expected to influence policy world-wide Outline global projections regional chapters aquaculture implications PML: catch projections per Exclusive Economic Zone 25/10/2019

Fish modelling Process-based modelling with simple rules differentiate fish by size, not species small fish feed on plankton, large fish on fish metabolism is temperature dependent Blanchard et al. 2009. J Animal Ecol 78(1): 270–280 Blanchard et al. 2012. Phil Trans Roy Soc B 367(1605): 2979–2989 25/10/2019

Data requirements Variables Space and time abundance of plankton types of different sizes temperature Space and time domain: global period: present till 2100 temporal resolution: monthly or finer spatial scale: ideally depth-explicit, aggregated per EEZ 25/10/2019

Sourcing climate projections Coupled Model Intercomparison Project: CMIP5 20 climate models historical run and multiple scenarios (RCPs): 1850-2100 gridded data (1°, 2°) monthly data for surface ocean biogeochemistry yearly data for depth-explicit biogeochemistry https://cmip.llnl.gov/cmip5/ 25/10/2019

Workflow CMIP5 archive, 3.3 PB EEZ shapefile FAO FishStat internet esgf-index1.ceda.ac.uk EEZ shapefile www.marineregions.org FAO FishStat www.fao.org 1. select & download models: GFSL/IPSL/CMCC scenarios: RCP 2.6, 8.5 ocean biogeochemistry only local cluster 5. validate catch against historical records time series of stocks and catches per EEZ, 500 MB ± 800 GB 3. average over each Exclusive Economic Zone 8.75 GB 2. extract key variables: temperature, plankton ± 80 GB 4. simulate fish per EEZ from 1850 to 2100 25/10/2019

Workflow CMIP5 archive, 3.3 PB EEZ shapefile FAO FishStat internet esgf-index1.ceda.ac.uk EEZ shapefile www.marineregions.org FAO FishStat www.fao.org UK Janet network 1. select & download models: GFSL/IPSL/CMCC scenarios: RCP 2.6, 8.5 ocean biogeochemistry only Python local cluster 5. validate catch against historical records Python time series of stocks and catches per EEZ, 500 MB ± 800 GB 3. average over each Exclusive Economic Zone 8.75 GB Python + Fortran 3.5 sec/simulated yr parallelized NetCDF Operators 2. extract key variables: temperature, plankton ± 80 GB 4. simulate fish per EEZ from 1850 to 2100 25/10/2019

Projected change in catch 2050 2100 RCP 2.6 RCP 8.5 % change 25/10/2019

FAO global capture production database Present-day catches? correlation: 0.57 Sources of uncertainty and error observations: reporting per fishing nation, not EEZ; underreporting inputs: plankton and temperature from climate models parameterization: present and future fishing mortality (F = 0.2 yr-1) FAO global capture production database 25/10/2019

Role of fishing mortality F = 0.2 yr-1 F = 0.4 yr-1 F = 0.8 yr-1 Why does fishing not have more of an impact? Stock and recruitment are decoupled: production of eggs and juveniles is set by plankton availability, not stock size Predications of relative change are thus robust 25/10/2019

Key findings surface temperature ↑ stratification ↑ surface nutrients ↓ plankton ↓ fish ↓ However: Due to interplay between plankton and temperature, lower latitudes (poorer nations) are disproportionally affected This becomes even more pronounced if we account for habitat change – tropical regions lose the most species 25/10/2019

Towards a service Contract: develop a Marine, Coastal, Fisheries module for European seas phase I: consult stakeholders phase II: implementation and projection delivery Potential follow ups from European domain to global regular updates 25/10/2019

Service design climate drivers meteorology, river run-off, boundary conditions CMIP, EURO-CORDEX, E-HYPE Service developed by PML hydrodynamics and biogeochemistry European Regional Seas Ecosystem Model Physics and biogeochemistry temperature, plankton fish modelling eutrophication ocean fronts Climate Impact Indicator suite Climate Data Store (CDS) Use cases – developed with partners and stakeholders Fisheries, Eutrophication, Marine Spatial Planning, Natural Capital Accounting 25/10/2019

Thank you https://www.surveymonkey.com/r/C3S_MCF_SIS Manuel Barange, FAO Momme Butenschön, Centro Euro-Mediterraneo sui Cambiamenti Climatici William Cheung, University of British Columbia Julia Blanchard, University of Tasmania 25/10/2019