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Jason Holt 1 , James Harle 1 , Sarah Wakelin 1 ,

Exploring the drivers of climate change impacts on shelf and coastal marine ecosystems: consequences for downscaling experiment design. Jason Holt 1 , James Harle 1 , Sarah Wakelin 1 , Momme Butenschon 2 , Yuri Artioli 2 , Icarus Allen 2 Jason Lowe 3 , Jonathan Tinker 3

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Jason Holt 1 , James Harle 1 , Sarah Wakelin 1 ,

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  1. Exploring the drivers of climate change impacts on shelf and coastal marine ecosystems: consequences for downscaling experiment design Jason Holt1, James Harle1, Sarah Wakelin1, Momme Butenschon2, Yuri Artioli2, Icarus Allen2 Jason Lowe3, Jonathan Tinker3 1National Oceanography centre, Liverpool, UK 2Plymouth Marine Laboratory 3 Met Office Hadley Centre, UK jholt@noc.ac.uk

  2. Synopsis • Processes • Principles and questions • Choice of experiments • Large scale effects • Process attribution • Timing and Seasonal effects

  3. Physical processes in shelf seas Here focus on broad continental shelves where ocean-shelf coupling is restricted From Holt, Huthnance et al Coastal Ocean Circulation Dynamics in Coupled Coastal Wind-Wave-Current Dynamics, CUP, In Press

  4. Physical processes mediating climate impacts • Many of these can Except: • Bathymetry and f are not expected to change • Taylor-Proudman theorem: “currents follow topography” is a strong constraint • Currents tend to follow direction of (low mode) coastal trapped wave propagation • Barotropic tides; (baroclinic tides are another matter) • Day length • So which processes are important?

  5. Physical controls on phytoplankton growth Meteorology Temperature Zooplankton, HTLs Tides IOP Light Buoyancy Phytoplankton growth Turbulence Shear stress Nutrients Benthic/Pelagic Recycling = dependency Terrestrial/riverine inputs Circulation

  6. Phytoplankton growth: Seasonally stratified The ‘canonical view’ E D C A A: Light levels permit growth B: Reduced mixing triggers rapid bloom C: Surface Nitrate depleted D: Surface Ammonia depleted E: Break down of stratification C-E: Diapycnal mixing fuels mid-water production B Phyto. biomass Nitrate

  7. Phytoplankton growth: Coastal regions • Still well defined growing season • Stages less clear • N drawdown but light limitation dominates throughout year

  8. Two broad views of biophysical control Interplay of turbulence, mixing and nutrient supply How much N is there in the first place LOICZ type approach: No Ns Ns Qo Sverdrup (1953) Bloom model Q+Qr Nr Qr Holt et al 2012 Biogeosciences Huisman et al L&O (1999) Critical turbulence Act on different time/space scales

  9. Speculate on some impacts • Physiological response to warming temperatures: (autotrophic and hetrotrophic) • Increased strat. reduces nutrient supply • Where seasonal strat. is important changes in seasonal warming needed to change strat. (Holt et al PinO 2010) • Changes in cloudiness → changes in light levels • Changes in wind → changes in mixing response Both → changes in bloom timing • Changes in oceanic N → changes in on-shelf N

  10. Large scale and decadal control • C, N, P, Si budgets controlled by ocean-shelf exchange and river/atmos inputs • In this case: • Most water comes from open ocean • Most from south • Time scales 5-10years Holt et al 2012 Biogeosciences, 9, 97-117

  11. Nutrient replenishment on a downwelling shelf Ocean-shelf Nitrate flux Deep winter mixing PAP mooring Wind driven circulation transports on-shelf Replenishes surface nutrient supply Tracer distribution after 24mnths Wakelin et al OD 2009

  12. How to explore with models? Some principles • Model experiments are question dependent • Process understanding is key to understand a system’s response to change • A model cannot make firm statements about processes that are not modelled (however well the model fits the data) • When timescales are long, space scales are large (L~UT)

  13. A question: How might marine ecosystems respond to future climate? • Ecosystem = Primary production • Marine = Northwest European continental Shelf • Respond = What are the drivers of change? • Future = when matters ? • Climate = Large scale global change and variability • Might = Uncertainty is key to relevance of answer

  14. A model: ERSEM POLCOMS 1/6o x1/9o~12 km resolution, 42 s-levels Holt et al GRL 2009 Wakelin et al Ocean Dyn. 2009 Allen et el Sarsia 2001 Blackford et al JMS 2004 Holt et al 2012 Biogeosciences, 9, 97-117

  15. Choices of experiments: • Timeslice experiments • CNTL: 1980-2000 • A1B: 2080-2100 • Perturbation experiments • Manipulation experiments • Transient experiments

  16. Transient simulations • Full downscaled transient simulations • Use high resolution region models to match the OA-GCM experiments Potential Energy Anomaly (stratification) 5-year running mean e.g. POLCOMS forced by HADRM3 downscaled from HADCM3 6x 150yr simulation performed (Physics only) Perturbed atmos. physics ensembles Model config. used in Holt et al PinO 2010

  17. Timeslice experiments • Run for conditions in future and conditions in present and look at difference • Assumes system adjusts during a spin-up period • Must be long enough to ‘average out’ natural variability • Assumes statistics are stationary during timeslice In this case: Natural variability a real issue Spin-up ok for temp., maybe not for nutrients/benthic etc 30yrs

  18. Perturbation experiments • Simplest form - change present day conditions according to some assessment of future conditions: the classical sensitivity analysis, • Perturbation that take into account temporal and spatial variations: • The D change approach: • Advantage of not requiring high frequency OAGCM data • Not suited to transient simulations e.g. Skogen et al JMS 2011 www.meece.eu/documents/deliverables/WP3/D3.1.pdf

  19. Consideration of uncertainty • Is a long transient simulation the ideal for coupled hydrodynamic ecosystem simulations? • How to encompass the uncertainty? • Scenario • Driving models: Ocean, ecosystem, atmosphere • Regional model: hydrodynamic, ecosystem Two many components to hope to build a comprehensive PDF • Aim at an upper - lower bound approach …… Hawkins and Sutton BMS, 2009

  20. but of what…..? • If we want to select a small number of forcing/model scenarios that ‘envelop’ the uncertainty – how do we choose? • Some judgement of forcing model quality (e.g. Overland and Wang, 2007 for Arctic)? • Need a deeper understanding of systems response and drivers • To select a ‘high’ case and a ‘low’ case

  21. Manipulation experiments • To ABRIBUTE which driver or processes is responsible for a response • Can try to diagnose from model fluxes • Soon run into difficulty with cyclic connections • Instead manipulate components of the model and run experiments: • Forcing • Structure • Parameter values

  22. Approach to manipulation experiments • Climate change effect variable V attributable to process P: DVp=DV – DVp’ • e.g. P = • Temperature dependence • Boundary forcing • Met forcing • Settling values • Etc.. Standard pair of timeslices Effect on change in V due to P, including all non-linearities Pair of time slices with process P absent Ideally would have an independent drivier/process attribution i.e.: DV=SDVp, unlikely… • Advantage that it can remain reasonably dynamically consistent and can pick up non-linearities • requires many experiments – changes to model structure required re-running control

  23. Example of two views of the future HadCM3 IPSL CM4 18yrs+5yr spinup,

  24. Timeslice forcing HadCM3 IPSL CM4 Common: River inputs; future boundary nut. from IPSL; IOPs Frequnecy: 6hrly atmos. Monthly ocean

  25. Large scale control ERA40 reference HadCM3 IPSL Annual N uptake Change in N uptake Winter N Change in winter N

  26. Process attribution experiments What fraction of the climate change signal is attributable to: • Changes in temperature • Vp’=remove temperature dependence in ERSEM • Changes in oceanic nutrients • Vp’=replace A1B ocean N b.c.’s with CNTRL N b.c.’s • Change in SWR • Vp’=swap CNTRL light forcing for A1B • Changes in settling • Vp’= changing fast settling rates to match slow • Only for IPSL for now alaTaucher & Oschlies, GRL 2011

  27. Process attribution: netPP Temperature Boundary N Useful for bulk dependencies ‘Fast Settling’ Shortwave radiation ‘Fast Settling’ Shortwave radiation

  28. Process attribution: Total N Boundary N Temperature DIN+DON+benthic N ‘Fast Settling’ Shortwave radiation

  29. Views of the system Change in bulk properties Change in diatom fraction A1B IPSL Fraction : -1 CNTRL

  30. Changes in key times in seasonal cycle • Growth Start netpp > 0.1gCm-2d-1 • Strat. Start = max dN/dt • Bloom stop N>20% wint N • Growth Stop netpp < 0.1gCm-2d-1 • Bloom starts earlier in A1B • Strong stratification starts at about same time • Longer pre-stratification bloom • More efficient use of winter nutrients

  31. Changes in bloom production Change in bloom duration Change in pre-strat. bloom duration Change in netpp after bloom Change in netpp during bloom

  32. Conclusions • Seasonal processes controlled by vertical mixing • Longer time scales by ocean-shelf exchange • Some climate change impacts can be related to these independently • Others are a complex interplay • E.g. Changes in timing → changes in community composition (e.g. more efficient use of winter Si) → changes in local biogeochemistry at odd with mixing model • Downscaling experiment design is far from straightforward

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