Seventh Framework Programme Theme 6 Environment
Collaborative Project (Large-scale Integrating Project)
Project no. 212085
Project acronym: MEECE
Project title: Marine Ecosystem Evolution in a Changing Environment
D1.5 Driver response envelope scenarios
Due date of deliverable: 31.08.2010
Actual submission date: 20.09.2010 updated 07.2011
Organisation name of lead contractor for this deliverable: CNRS
Start date of project: 01.09.08
Duration: 48 months
Project Coordinator: Icarus Allen, Plymouth Marine Laboratory
Project co-funded by the European Commission within the Seventh Framework Programme,
Theme 6 Environment
Dissemination Level
PU Public
x
PP
Restricted to other programme participants (including the Commission)
RE Restricted to a group specified by the consortium (including the Commission)
CO Confidential, only for members of the consortium (including the Commission)
EC FP7 MEECE | 212085 | D1.5 | Driver envelopes for ecosystem model scenarios, updated July2011
D1.5 Driver envelopes for ecosystem model scenarios
L Bopp, (CNRS), I Allen (PML), C Smith (HCMR)
Table of Contents
1. Introduction ............................................................................................................. 2
2. Climate Driver Response Envelopes ...................................................................... 3
st
2.1 Projected 21 century changes in marine biogeochemistry: a multi scenario
analysis. .................................................................................................................. 4
2.2.1 Global Scale ........................................................................................... 5
2.2.2 European Seas....................................................................................... 8
3. Anthropogenic response envelopes........................................................................ 9
3.1 Overall approach............................................................................................... 9
3.2 Types Anthropogenic Response Envelopes ................................................... 10
3.2.1 Eutrophication ...................................................................................... 10
3.2.2. Pollution............................................................................................... 10
3.2.3 Fishing.................................................................................................. 11
3.2.4 Coloured Dissolved Organic matter ..................................................... 12
3.2.5 Invasive Species .................................................................................. 12
4. Summary of the scenarios .................................................................................... 13
4.1 Scenarios for WP3 .......................................................................................... 13
4.2 Scenarios for WP4 .......................................................................................... 14
Appendix 1: ELME scenarios ................................................................................... 16
Appendix 2: Definitions and codes for scenarios ...................................................... 18
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1. Introduction
The purpose of this document is to outline the potential response envelopes of climatic and
anthropogenic drivers which act to modify European marine ecosystems. The policy driver for
this work is the Marine Strategy Framework Directive (2008/56/EC) (MSFD) which requires
member states to develop strategies to achieve a healthy marine environment and make
ecosystems more resilient to climate change in all European marine waters by 2020 at the
latest. The strategies must contain a detailed assessment of the state of the environment, a
definition of "good environmental status" at regional level and the establishment of clear
environmental targets and monitoring programmes. The MSFD also identifies a number of
high level descriptors (e.g. biodiversity, commercial fish, eutrophication, foodwebs, pollution
and invasive species) each of which has a defined set of indicators.
The goal of this work is to indentify the range of response of key drivers of marine
ecosystems (e.g. climate change, ocean acidification, pollution, eutrophication) which will
enable us to define and run model scenarios to quantify changes in ecosystem state and
attribute the dominant drivers causing them .
These envelopes of response will used to define the model scenarios for ecosystem response
to climate and anthropogenic forcing (WP3 & 4 respectively). Information from the resulting
simulations will be synthesised into a web based atlas, which will allow the outputs to be
readily interpreted and the implications for marine policy and management to be diagnosed in
WP 5 (Figure 1). The guidelines for scenario definition presented here are based on the
discussions helped at workshop in Crete (Feb 2010) and Bologna (June 2010).
WP3 considers two classes of experiments using coupled physical-biogeochemical-HTL
models: re-analysis forced simulations and climate-scenario forced simulations. The objective
of the former is to hindcast recent history using forcing constrained by observations (such as
ERA-40 and NCEP). This provides reference simulations which can be verified against
contemporary observations to give estimates of uncertainty in the models and feedback to
development/parameter choice (WP2). The second class of model simulations are forced by
global climate simulations, which are unconstrained by observations and represent ‘typical’
conditions both in the past and under various atmospheric composition scenarios (defined in
IPCC-AR4). The computational expense of three-dimensional coupled hydrodynamic requires
the use of ‘time-slice’ experiments for specific periods
The focus is both on a variety of coastal-ocean regions and on a global scale using the range
of coupled hydrodynamic-ecosystem models that have already been applied in each area as
the base line set of simulations. To facilitate this inter-comparison, several common scenarios
to be run in each region need to be defined.
WP4 addresses, the potential response of the marine ecosystem to direct anthropogenic
stress in future climate change scenarios.
The following types of anthropogenic impacts on the marine environment will be considered;
1. Input of pollutant substances with the capability of altering marine ecosystem function
(e.g. eutrophication) or with direct toxic effects on the biota (e.g. heavy metals,
herbicides, antibiotics);
2. Input of optically active dissolved organic matter that may substantially modify the
light climate in the water column and therefore the primary production processes;
3. The exploitation of marine living resources (direct and indirect effects of fishery on
ecosystem structure and functioning);
4. Introduction of invasive alien species.
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Figure 1. Summary diagram of the simulation approach and the linkages to WP5.
2. Climate Driver Response Envelopes
To take into account climate change & ocean acidification drivers scenarios in the different
regional configurations & models, MEECE participants have chosen to use, when possible,
outputs of the IPSL-CM4 / PISCES model. These outputs are used both to force the regional
models (winds, air temperature, precipitation…) but also to initialize their biogeochemical
fields (in particular nutrients, carbon & alkalinity). They are provided on a dods server (see
http: //dods.extra.cea.fr/data/p48bopp/MEECE/IPSL_PISCES/ and deliverable D1.2). The
specific data required for model forcing depends on the boundary condition algorithms of the
individual models. Details of the recommended downscaling methodology used in MEECE
can be found in D3.1
Based on the limited amount of CPUs available for the different regional
models, only one future scenario and a 20-yr time slice have been agreed
on. The following time slice and conditions have been defined:
- Scenario: A1B
- 2080-2100 (optional: 2030-2050)
- Model: IPSL-CM4 / PISCES
Figure 2 displays some of the results of this simulation for the 2080-2100 period, with a focus
on the European Seas.
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Figure 2: Changes between 2080-2100 and 1980-2000 in SST (a, °C), carbonate ion concentrations (b,
micromole/L), nitrate concentration (c, micromole/L) and vertically integrated primary productivity (d, %), from IPSLCM4-PISCES.
In this report, we explore how the MEECE drivers (e.g. climate change and ocean
acidification) depend on the choices of both the future scenario and the global model used.
We first focus, with the same model configuration, on how the response of marine
biogeochemistry depends on the scenario used (A1B, A2 and FP6-ENSEMBLE E1
scenarios). We then show how marine biogeochemistry (i.e. primary productivity) responds to
climate change for the same future scenario (SRES – A2) but for different global oceanbiogeochemistry models.
st
2.1 Projected 21 century changes in marine biogeochemistry: a multi scenario
analysis.
We explored the sensitivity of our results to the choice of the future scenario. Three scenarios
were chosen: among them are the classical SRES scenarios A2 and A1B, which are very
similar in terms of radiative forcing from 2000-2050, but then diverge in the second half of the
st
21 century. We have also chosen a very moderate scenario (E1) (see Figure 3).
Figure 3: Prescribed atmospheric pCO (ppm) as defined by the SRES A2, SRES A1B and E1 scenarios for 20002
2100.
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This E1 scenario was designed for attempting to match the European Union target of keeping
global anthropogenic warming below 2°C above pre-industrial levels. The E1 scenario was
derived within the FP6-ENSEMBLES project by using an ”Integrated Assessment Model”
which includes the energy system, land use, carbon cycle and also a simple climate model. In
terms of atmospheric CO2, it peaks to less than 450ppm in 2050 and then shows a marked
decline to the end of the century.
The patterns of changes of CO3 and NO3 in the European seas are very similar for the SRES
A1B and E1 scenarios (see Figure 4). CO sea surface concentrations decrease from -10 to 3
70 micromol/L in the A1B scenario, whereas the decrease if limited to less than -30
micromol/L in the E1 scenario. NO3 concentration show in both scenarios a marked dipole:
NO3 concentrations increase in the Barents Sea in both scenarios but decrease elsewhere.
Figure 4: Changes between 2080-2100 and 1980-2000 in carbonate ion concentration (a and c, micromole/L) and
nitrate concentration (b and d, micromole/L) for the SRES-A1B (upper) and E1 (lower) scenarios, both with IPSLCM4-PISCES.
2.2 Planktonic ecosystem response to climate change1: A multi-model analysis of
projected 21 century changes in marine biogeochemistry.
The projections of the IPSL-CM4-PISCES model have been compared to 4 other models
(BCCR, MPIM, CCSM3 and CSM1.4) for the SRES-A2 scenario. The analysis of these
simulations has been made at the global scale as part of the FP6-EurOceans project
(Steinacher et al 2010). In this report, we share the main conclusions of Steinacher et al.
(2010) (see abstract and Figure 5), then we show how these models behave in the European
seas.
1
This work refers to the planktonic ecosystem response to climate change component of task
1.5. The remainder of T1.5 is reported in D1.4.
2.2.1 Global Scale
Changes in marine net primary productivity (PP) and export of particulate organic carbon (EP)
are projected over the 21st century with four global coupled carbon cycle climate models.
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These include representations of marine ecosystems and the carbon cycle of different
structure and complexity. All four models show a decrease in global mean PP and EP
between 2 and 20% by 2100 relative to preindustrial conditions, for the SRES A2 emission
scenario. Two different regimes for productivity changes are consistently identified in all
models. The first chain of mechanisms is dominant in the low- and mid-latitude ocean and in
the North Atlantic: reduced input of macro-nutrients into the euphotic zone related to
enhanced stratification, reduced mixed layer depth, and slowed circulation causes a decrease
in macronutrient concentrations and in PP and EP. The second regime is projected for parts
of the Southern Ocean: an alleviation of light and/or temperature limitation leads to an
increase in PP and EP as productivity is fuelled by a sustained nutrient input. A region of
disagreement among the models is the Arctic, where three models project an increase in PP
while one model projects a decrease. Projected changes in seasonal and inter-annual
variability are modest in most regions. Regional model skill metrics are proposed to generate
multi-model mean fields that show an improved skill in representing observation-based
estimates compared to a simple multi-model average. Model results are compared to recent
productivity projections with three different algorithms, usually applied to infer net primary
production from satellite observations.
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Figure 5: Productivity (left) and projected changes by 2090–2099 (right). Vertically integrated annual mean primary
-2
-1
production (PP, mgC m day ) derived from ocean colour (a) (SeaWiFS; Behrenfeld et al., 2006; Behrenfeld and
Falkowski, 1997b) and simulated by IPSL (c), MPIM (e), CSM1.4 (g), and CCSM3 (i) under preindustrial conditions
(decadal mean 1860–1869). The Taylor diagram (b) shows the correspondence between model results and the
satellite-based estimates (Taylor, 2001). In this diagram the polar coordinates represent the correlation coefficient R
(polar angle) and the normalized standard deviation (radius). Panels d, f, h, and j show the projected changes by the
end of the 21st century under SRES A2 for the four models. The changes are shown on an exponential scale and
represent the difference between 2090–2099 and 1860–1869 (decadal means).
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2.2.2 European Seas
Figures 6 and 7 are used to draw roughly the same conclusions as those we could achieve
with the model results from IPSL. Nutrient concentrations (nitrate or phosphate) decrease with
climate change for all models and in roughly similar proportions. The BCCR, CSM, CSSM,
and MPIM models do not show however increasing concentrations in the Barents Sea. In
terms of productivity, all models show a strong dipole between a northern zone where
productivity increases substantially (up +100%), and a southern zone, more extensive, where
productivity decreases (from -10 to -50% in the different models).
Figure 6: Changes between 2080-2100 and 1980-2000 in nitrate concentration (micromole/L) from CSM1.4,
CCSM3, BCCR and MPIM.
Figure 7: Changes between 2080-2100 and 1980-2000 in relative net primary productivity or net community
productivity (%) from CSM1.4, CCSM3, BCCR and MPIM
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3. Anthropogenic response envelopes
3.1 Overall approach
The Marine Strategy framework directive indentifies 11 descriptors of Good Environmental
Status. Each of these has a set of potential variables. MEECE has undertaken an exercise
whereby we mapped potential indicators onto model outputs, so we can determine the key
variables to be simulated. A web-based tool has been defined to summarise the results of this
exercise and can be found at (http://www.meece.eu/Library.aspx). This tool allows the user to
search by region; descriptor and attribute to determine which models systems can supply
relevant information.
The scenarios will use the SRES AR4 storylines (Fig. 8, as interpreted by the ELME project –
www.elme-eu.org) to define our scenarios for fishing, nutrients and pollution (see Appendix
1). The storylines follow the four-quadrant approach, whereby the future ‘possibility-space’ is
divided, based on two axes or dimensions. The approach has become commonplace
following its earlier adoption by the Intergovernmental Panel on Climate Change (IPCC). The
basis of the 4-quadrant model is the identification of the two driving forces with the greatest
importance and the highest uncertainty. Many existing scenario exercises seem to have also
chosen similar criteria to define their ‘possibility-space’, with an axis representing ‘local to
global’ and an axis representing ‘community to consumerism’. Summaries of these scenarios
follow.
Figure 8. Schematic of the ELME interpretation of the AR4 storylines.
A1: World markets: Technology and markets fail to deliver sustainable solutions
• People aspire to personal independence, material wealth and greater mobility, all of
which have a negative effect on wider societal and environmental goals.
• Pressure grows to reduce taxes, and more public services are privatized or privately
managed.
• Social and environmental governance is achieved through international legal
frameworks setting minimum standards, and through market-based approaches.
• Marine ecosystems are heavily degraded by human activity.
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•
Increased pressures are placed on marine biological resources, either through
utilization or through increasing levels of ‘stressors’ (for example, loss of habitats and
changes in water quality).
A2: National Enterprise: National identity gets in the way of global sustainability
• People aspire to personal independence and material wealth but within a national
cultural identity.
• The balance of opinion favours increased national isolation and independence in
economic, foreign and defence policy.
• Long-term economic growth is limited by government policies, which protect
important national industries.
• By 2020, marine ecosystems come under greater pressure than at present.
• Efforts to reduce the effects of human activity are abandoned where they conflict with
issues of national self sufficiency.
• Large-scale, environmentally damaging projects such as tidal barrages and widescale oil exploration develop under the Fortress Britain scenario.
• Governments fail to deal with global problems;
B1: Global Community: International co-operation towards global sustainability
• People aspire to high levels of welfare and a healthy environment, the best way to
achieve these aims is through international co-operation.
• Sustainability is seen from a global viewpoint, including: maintaining biodiversity,
protecting global commons and providing fair access to environmental resources.
• Policies are co-ordinated at the European Union and international level. This is a high
taxation scenario.
• Major investment in offshore renewable energy projects
• Internationally agreed control measures reduce the amount of pollution released into
the marine environment
• The health of the oceans across the world improves, although it is necessary to
sacrifice some local areas for development.
B2: Local Responsibility: Tailored solutions for local problems
• Public policies aim to promote economic activities that are small scale and regional.
• Sustainable development is a major aim of this scenario.
• An important focus is on using technology and new ideas to make the best use of
local and regional resources. By 2020, this leads to varied outcomes in different parts
of the UK.
• Local communities manage the marine environment
• There are fewer invasive species, oil spills and less damage due to port development.
• Action is taken to reduce the effects of human activity at a local level, and this results
in a cleaner marine environment overall.
3.2 Types Anthropogenic Response Envelopes
3.2.1 Eutrophication
Eutrophication scenarios will draw on ELME. The existing river inputs will be modified
according the trends identified in Appendix 1. The scenarios will assess the consequences for
eutrophication related drivers, e.g. nutrient concentrations and ratios, changes in chlorophyll.
3.2.2. Pollution
Pollution scenarios will draw on ELME. The baseline pollution inputs will be modified
according the trends identified in Appendix 1. The scenarios will assess the consequences for
eutrophication related drivers, e.g. pollutant concentrations and impacts on the growth of key
functional types (e.g. phytoplankton, benthic suspension feeders)
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3.2.3 Fishing
There is a tension with the fishing scenarios between defining ‘scenarios’ as fisheries
management objectives and defining fisheries scenarios in terms of the ELME interpretation
of the millennium assessment scenarios. The scenarios will be defined in terms of changes in
fishing effort (F). In the following discussion we use a number of reference points for values of
F which are defined as follows (Figure 9).
•
•
•
•
•
Fmax: F-value for a maximum yield of fish
F0.1: F-value where Y/R is 10% of the maximum Y/R slope
Fmsy: F at maximum sustainable yield
Flim: maximum value beyond which the stock will not be able to self-renewal
Fpa: F-value for a precautionary approach (can estimated as a function of Flim).
Figure. 9: Common reference and target F values with respect to catch/yield
The scenarios will assess the consequences in terms of changes in populations of all
commercially exploited fish, foodwebs and changes in habitat state as appropriate to the
model in question.
Considering the A1 and B1 scenarios some potential impacts on fisheries are as follows:
A1 World Markets - Markets First
• Decommissioning subsidies reduced
• Fewer legal and technical restrictions
• Fish from the cheapest sources
• Only a few high-tech boats
• Heavily depleted fish stocks
• Rapid expansion of fish farming
This means that at the least, food species require management with an overall approach
using Fpa (could also be seen as where we have been coming from). It was noted that in
theory Fpa is a limit and not a target.
B1: Global Community - Sustainability First
• Yield vs. environmental impact
• Fish from sustainable sources worldwide
• Effective effort-based control system
• EU/international marine strategy
• Resources allocated to ‘natural’ predators
• Seabed set aside for nature conservation
All species should be treated sustainably; with an overall approach using Fmsy (this can also
be seen as to where the current policy is taking us to).
Scenarios
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•
•
•
Standard: Business As Usual (existing F values: perhaps should be Fmsy but have
been traded off to higher levels for particular species in particular areas)
A1: Fpa
B1: Fmsy
Considerations
In data poor areas (e.g. Mediterranean and Black Sea) we may need to use qualified
estimates: if only one value is known, then we can apply a known ratio from a similar species
to get the other value (e.g. Fpa can be approximated as 0.47-0.61.Flim), or use expert opinion.
It should be noted that Fpa and Fmsy are quite close and might not offer enough contrast for the
models (scenario runs might not differentiate well), but they are still seen to the important
parameter values to run and base the 2 fishing scenario
In addition we encourage the modellers to run additional scenarios:
•
•
•
•
runs with higher F values e.g. Flim, max historic F,
runs with differential F-values, e.g. apply Fmsy to main food species and a high F
value (Flim) to all other species - this is equivalent to managed food stocks with quotas
and non quotas on industrial fishing stocks
runs with zero F to represent no fishing or recovery scenarios (although it Is accepted
that this is not what a “pristine” system might be like)
Where possible, Fmsy model outputs from runs of future (e.g. climate) scenarios
3.2.4 Coloured Dissolved Organic matter
There is no available information as to what the likely shifts in terrestrial CDOM sources will
be in response to a combination of changes in land use and climate related impacts. MEECE
will explore the sensitivity of the habitat state (in terms of optical properties) and hence the
foodweb (in terms of primary and secondary production) by perturbing the observed satellite
IOP fields used to constrain the model optics.
MEECE has prepared a set of IOP forcing data to cover this.
(see D1.1;
http://www.meece.eu/datasets.html). The Inherent Optical Properties (IOP) database contains
a series of netCDF data with the monthly IOP of the world oceans in the period 1998-2005.
The IOP are calculated from Ocean Colour data (MODIS satellite) using the algorithm
described in Smyth et al., Applied optics, 2006.
3.2.5 Invasive Species
We will use a different approach for planktonic alien invasive species. In WP2 we have
developed an approach to modelling the impacts alien invasive plankton species as emergent
properties of a PFT model seeded with randomly assigned species (see D2.13). This model
coupled to a GOTM water column model will be applied in selected regions (North Sea,
Barents Sea, Baltic, Black Sea and Adriatic Sea) is seeded with ‘random alien invasive
species’ and the theoretical; changes in community structure, diversity indices and other
measures of vulnerability assessed. The modelling will be forced with climate changes form
IPSL. In the Baltic/North Sea we will also use a climate envelope approach to determine shifts
in habitat of key species (e.g. P minimum) which is again described in D2.13.
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4. Summary of the scenarios
We also need to consider the timeframes of the scenarios: In WP3 we focus on climate
impacts. Climate impacts are only clearly demonstrated statistically in the forcing beyond
2040/2050, therefore we focus on time-slices for the period 2080-2100 to ensure we see a
climate signal. In WP4 we focus more on anthropogenic drivers. The window of policy
relevance is the next 20-30 years hence we focus on a time-slice 2030-2040.
4.1 Scenarios for WP3
The objective of WP3 is to define the envelope of response to climate and circulation drivers
of marine ecosystem function both on a global and a regional scale and to analyze the impact
on ecosystems end to end of changes in: temperature, circulation, mixing, acidification, and
light, focusing on physics, biogeochemistry and ecosystem productivity and on higher trophic
levels
The sub objectives are
• · To define common metrics and scenarios
• · To run/analyze base-line and ensemble scenarios
• · To synthesize results
• · To contribute to knowledge transfer activities in WP6
These simulations are run with the drivers defined in WP4 set to their ‘present-day values’, so
match the WP 4 reference simulation.
WP3.3 Plankton models (+ OA module from WP2)
Hindcast simulation (ERA40, IOP data forcing, MEECE river database)
Time slices scenarios: IPSL climate model (see www.meece.eu)
Recommended: 1980-2000; 2080-2100; A1B, A2, E1 (LUA2R2)
Minimum: 1980-2000; 2080-2100; A1B (use member LUA1B2)
Key outputs as defined in D3.2 common metrics
WP3.4 End to End (physics-plankton-HTL model) see table
Hindcast simulation (ERA40, IOP data forcing, MEECE river database)
Time slices scenarios: IPSL climate model (see www.meece.eu)
Recommended: 1980-2000; 2080-2100; A1B, A2, E1 (LUA2R2)
Minimum: 1980-2000; 2080-2100; A1B (use member LUA1B2)
Key outputs as defined in D3.2 common metrics
The climate forcing data can be downloaded at
http://dods.extra.cea.fr/data/p48bopp/MEECE/IPSL_PISCES/
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4.2 Scenarios for WP4
WP4 OBJECTIVES
•
•
To define the envelope of response to combinations of direct anthropogenic drivers
on marine ecosystem on a regional scale.
Impact on ecosystems end to end of changes in pollution, fishing effort, fluvial nutrient
and CDOM inputs.
The MEECE direct anthropogenic drivers (i.e. Drivers originating from a direct human
pressure on the Marine environment are eutrophication (nutrients), pollutants (e.g.:
herbicides, antibiotics), optically active substances (CDOM), fishing pressure and invasive
species. The focus is on physics, biogeochemistry, ecosystem productivity, higher trophic
levels. The drivers to be addressed in each region are given in table 4.
Note: we are ONLY considering these scenarios to define direct anthropogenic
perturbations. The basic emissions scenario we are using remains A1B (see
below). We can justify using a fixed emission scenario on the basis that the SRES
scenarios do not diverge strongly during the time frame of the WP4 experiments.
Table 4 (DOW)
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4.1. Hindcast scenarios to test driver sensitivity
Reference climate scenario for anthropogenic drivers: IPSL-CM4 A1B + Present
Day drivers: 2030-2040.
http://dods.extra.cea.fr/data/p48bopp/MEECE/IPSL_PISCES/
Hindcast simulations of driver sensitivity (as appropriate for each region see table 1)
•
•
•
•
Eutropication: ERA40 + range of driver sensitivity: 5 years
Pollution: ERA40 + range of driver sensitivity: 2 years (copper, herbicides etc.)
Fishing: ERA40 + range of driver sensitivity: 5-20 years
CDOM: ERA40 + range of driver sensitivity: 2 years?
These hind cast simulations should be where possible for the most data rich period for each
region. A subset of the main 45 yr hindcast should be run to explore the sensitivity of each
region to specific drivers. The timescale have been chosen to reflect expected scales of
response for each driver. These may need to be iterated.
4.2: Multiple Driver Scenarios
Climate + all regional drivers: 2030-2040 Compulsory scenarios in bold.
IPSL-CM4 A1B + SC1 world market (A1)
IPSL-CM4 A1B + SC2 global commons (B1)
IPSL-CM4 A1B + SC3 Fortress nation (A2)
IPSL-CM4 A1B + SC4 local stewardship (B2)
The exact perturbation used for each driver is to be guided by the ELME interpretations of
each driver under the above scenarios (http://www.elme-eu.org/ELME_Results.pdf). The sign
of the perturbation should follow the trend of the relevant pressure/driver evidenced by the
outcomes of ELME (the synoptic tables of the regional BBN models and/or the extended
tables showing regional trends for the drivers in the different scenarios; see annex 1) while
the magnitude of the perturbation could to be defined according to historical trends and/or
natural variability.
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Appendix 1: ELME scenarios
Summary tables of the outcomes of the ELME interpretation of the 4 scenarios follow. The
signs of these responses should be used to define the direction of change in the multiple
driver scenarios. Full details can be found at http://www.elme-eu.org/ELME_Results.pdf.
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Appendix 2: Definitions and codes for scenarios
ERA40: ERA-40 is a ECMWF re-analysis of the global atmosphere and surface conditions for
45-years, over the period from September 1957 through August 2002. Many sources of the
meteorological observations were used, including radiosondes, balloons, aircraft, buoys,
satellites, scatterometers. This data was run through the ECMWF computer model at a 40km
resolution. As the ECMWF's computer model is one of the more highly-regarded in the field of
forecasting, many scientists take its reanalysis to have similar merit. The reanalysis was done
in an effort to improve the accuracy of historical weather maps and aid in a more detailed
analysis of various weather systems through a period that was severely lacking in
computerized data.
AR4 scenario definitions: There were 4 SRES scenario families defined by the IPCC 4th
assessment report; A1 World Markets; A2, National Enterprise; B1 Global Community and B2
Local Responsibility, each of which gives a range of response for global average surface
warming (A1 1.4-6.4 C, A2 2.0 – 5.4 C, B1 1.1 – 2.9 C, B2 1.4-3.8 C). Within the A1 scenario
there are several sub-groups, A1T, A1B, A1F1). Each of these scenarios was run in
ensemble mode to quantify the range of response. In MEECE we use the ensemble member
LUA1B2 of the A1B scenarios run using the IPSL climate model. This is chosen because it
represents a mid range warming response. There is an additional scenario E1 which
represents stabilization of atmospheric CO2 at 450 ppm. The forcing data can be downloaded
at http://dods.extra.cea.fr/data/p48bopp/MEECE/IPSL_PISCES/
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