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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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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 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]


Synopsis

Synopsis

  • 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

    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?


Physical controls on phytoplankton growth

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


Phytoplankton growth seasonally stratified

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


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:

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


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:

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


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

30yrs


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

www.meece.eu/documents/deliverables/WP3/D3.1.pdf


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

HadCM3

IPSL CM4

18yrs+5yr spinup,


Timeslice forcing

Timeslice forcing

HadCM3

IPSL CM4

Common:

River inputs; future boundary nut. from IPSL; IOPs

Frequnecy:

6hrly atmos.

Monthly ocean


Large scale control

Large scale control

ERA40 reference

HadCM3

IPSL

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

Temperature

Boundary N

Useful for bulk dependencies

‘Fast Settling’

Shortwave radiation

‘Fast Settling’

Shortwave radiation


Process attribution total n

Process attribution: Total N

Boundary N

Temperature

DIN+DON+benthic N

‘Fast Settling’

Shortwave radiation


Views of the system

Views of the system

Change in bulk properties

Change in diatom fraction

A1B

IPSL

Fraction :

-1

CNTRL


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

bloom

Change in netpp during bloom


Conclusions

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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