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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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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

[email protected]

  • Processes
  • Principles and questions
  • Choice of experiments
  • Large scale effects
  • Process attribution
  • Timing and Seasonal effects
physical processes in shelf seas
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

physical processes mediating climate impacts
Physical processes mediating climate impacts
  • Many of these can


  • 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?
physical controls on phytoplankton growth
Physical controls on phytoplankton growth












Shear stress




= dependency

Terrestrial/riverine inputs


phytoplankton growth seasonally stratified
Phytoplankton growth: Seasonally stratified

The ‘canonical view’





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


Phyto. biomass


phytoplankton growth coastal regions
Phytoplankton growth: Coastal regions
  • Still well defined growing season
  • Stages less clear
  • N drawdown but light limitation dominates throughout year
two broad views of biophysical control
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:





Sverdrup (1953) Bloom model




Holt et al 2012 Biogeosciences

Huisman et al L&O (1999)

Critical turbulence

Act on different time/space scales

speculate on some impacts
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
large scale and decadal control
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

nutrient replenishment on a downwelling shelf
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

how to explore with models
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)
a question
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
a model
A model:



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

choices of experiments
Choices of experiments:
  • Timeslice experiments
    • CNTL: 1980-2000
    • A1B: 2080-2100
  • Perturbation experiments
  • Manipulation experiments
  • Transient experiments
transient simulations
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

timeslice experiments
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


perturbation experiments
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

consideration of uncertainty
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

but of what
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
manipulation experiments
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
approach to manipulation experiments
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
example of two views of the future
Example of two views of the future



18yrs+5yr spinup,

timeslice forcing
Timeslice forcing




River inputs; future boundary nut. from IPSL; IOPs


6hrly atmos.

Monthly ocean

large scale control
Large scale control

ERA40 reference



Annual N uptake

Change in N uptake

Winter N

Change in winter N

process attribution experiments
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

process attribution netpp
Process attribution: netPP


Boundary N

Useful for bulk dependencies

‘Fast Settling’

Shortwave radiation

‘Fast Settling’

Shortwave radiation

process attribution total n
Process attribution: Total N

Boundary N


DIN+DON+benthic N

‘Fast Settling’

Shortwave radiation

views of the system
Views of the system

Change in bulk properties

Change in diatom fraction



Fraction :



changes in key times in seasonal cycle
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
changes in bloom production
Changes in bloom production

Change in bloom duration

Change in

pre-strat. bloom duration

Change in netpp after


Change in netpp during bloom

  • 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