Overview of Earth System Modeling
and Fluid Dynamical Issue
Model and Data
Hierarchies for
Simulating and
Understanding Climate
Marco A. Giorgetta
Overview
1. The Earth System – and Earth System Models (ESMs)
2. Research with ESMs
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A GCM study on emission pathways to climate stabilization
3. Fluid dynamical issues in the development of ESMs
1. The Earth System – and Earth System Models (ESMs)
The Earth System
In general terms:
The Earth and everything gravitationally bound to it
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Earth interior
Oceans with sea ice
Land surfaces: soil, ice shields, glaciers
Atmosphere up to ~100 km
Life in all compartments
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Land vegetation and soil organism
Marine biota
Humans!
The Earth System
In climate science:
A relatively new term, chosen to describe:
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The physical climate system …
… and geo-bio-chemical processes …
… as necessary to understand the climate of the past …
… and to “predict” the future climate of the next ~100 years …
… where climate = [T, wind, q, precipitation]
Explicitly account for the interaction of bio-geo-chemical
processes with climate, and anthropogenic influences.
Key for understanding climate: Energy transfer
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Radiation + heat fluxes and storage in A, O, and L
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Distributions of T, q and wind,
Hydrological cycle
Globally averaged
vertical energy transfer
in the atmosphere
Source:
IPCC AR4 WG1 Rep., Ch. 1, FAQ Fig.1
Components of the climate system,
interactions, and changes
(Source: IPCC AR4 WG1 Ch.1, FAQ 1.2, Figure 1)
Earth System Models (ESMs)
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Simplified/idealized descriptions of the ES
[Cf. “Model” in architecture, fashion, engineering, …]
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Formal description, allowing for computational
experiments  What if …
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Test understanding of the functioning of the ES
Explain observed features
Turbulent mixing in oceans was stronger
“Major” volcanic eruptions happened?
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Highly complex models within the model hierarchy
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Fortran code of ~105 lines
The Earth System
History of “Type II” models
1. General circulation models of atmosphere or ocean
 weather, seasonal cycle, …
2. Coupled atmosphere ocean models = “climate model”
 El Niño/La Niña, “small” climate change, …
3. Earth system model = “climate model” +
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Land and ocean bio-geo-chemistry
Clouds/aerosols/chemistry in the atmosphere
Cryosphere: Glaciers, ice shields, shelf ice
 Climate of other periods, “large” climate change
 ESMs are most complex
Schematic view of the ES
Health
Wealth
Food
etc.
Atmosphere
Energy
Momentum
Land
Substance cycles
H2O, C N S P …
Ocean
Use & management
of the environment
Society
Construction of ESMs
1. Decide on spatial and temporal scales, and on
processes, which are scientifically relevant and
practically feasible ( model hierarchies)
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Length of simulations
~102 years
Required turnover rate ~102 years/week
~200 km horizontal resolution
2. Equations for the dynamics of atmosph., ocean, and ice
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200 km  Primitive equations
Numerical methods  discretized, i.e. computable, equations
“Dynamical core”  Christiane’s talk
Construction of ESMs (cont.)
 Transport scheme for the advection of vapor, cloud
particles, … / salt, plankton, …
 “Physics package” for the physical, biological, chemical
and unresolved dynamical processes; atmosphere:
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Radiation
Turbulent vertical fluxes (“vertical diffusion”) of heat, momentum, tracers
Surface (snow cover, albedo, evaporation, transpiration, lateral water flows)
Microphysics
Convection
Cloudiness
Sub-grid-scale orographic effects
Non-orographic gravity wave drag
Construction of ESMs (cont.)
Parameterizations rely on assumptions, e.g.:
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Radiation
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Grid scale << Earth radius
Grid scale >> layer thickness
Local thermal equilibrium
Earth
Gas = air + small variations
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 plane parallel assumption
 neglect fluxes trough lateral boundaries
 valid up to ~70 km in the atmosphere of
 valid for the atmosphere of Earth
2. Research with ESMs
A GCM study on emission pathways to climate stabilization
E. Roeckner, M. Giorgetta, T. Crüger,
M. Esch, and J. Pongratz
Submitted to Climatic Change
Motivation
• United Nations Framework on Climate Change:
– Article 2: ‘... to achieve stabilization of greenhouse gas concentrations ...
that would prevent dangerous anthropogenic interference with the
climate system‘
• Questions
– For a given CO2 concentration pathway into the future:
• What is the climate change?
• What anthropogenic CO2 emissions are allowable?
– What fraction of anthrop. carbon remains in the atmosphere?
– What is the role of feedbacks between climate change and the C-cycle?
• Use Earth system model including the carbon cycle
– simulate the carbon flux between atmosphere, and ocean or land
• Use two scenarios for the future until 2100:
– “SRES A1B” scenario
• No mitigation
– “E1” scenario developed for ENSEMBLES (Van Vuuren et al., 2007)
• Agressive mitigation scenario E1
• Limit global change in surface air temperature to 2°
• (implies stablization of CO2 concentration in 22nd century at ~450 ppmv
• European ENSEMBLES project
– Other models  multi model ensemble
Methodology
Method proposed for the future CMIP5 experiments, i.e. experiments for
the 5th IPCC assessment of climate change (Hibbard et al., 2007):
Policies
Story lines
Impacts
(Mitigation) Scenario
Emissions
2B
Concentrations
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2A
Carbon cycle - climate model
Surface
temperature
Experiments
1860 1900 1950 2000 2050 2100
Control
“1860”
1000 yr
Historic
1860-2005
SRES A1B
E1 450 ppm
: full coupling;
: C-cycle decoupled
Ensembles of
5 realizations
Scenarios for CO2 concentration
CO2 concentration in ppmv
• 1860-2005: observations
• 2005-2100: scenarios
Others: CH4, N2O, CFCs
CO2 [ppmv]
2050
2100
A2
522
836
A1B-S1
522
703
B1
482
540
A1B-450/E1
435
421
… and of the model used here
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(no feedback)
A: ECHAM
Energy
Momentum
L: JSBACH
Substance cycles
H2O, C
O: MPIOM +
HAMOCC
Prescribed BCs from
observations+scenarios
Society
Pre-industrial control simulation
Global annual mean surface air temperature (°C) and CO2 concentration (ppmv)
Pre-industrial conditions, thick lines: 11-year running means
Surface air temperature
(left scale, °C)
Atmospheric CO2
concentration
(right scale, ppmv)
• Climate of undisturbed system stable over 1000 years
Global mean surface air temperature
Global annual mean surface air temperature anomalies w.r.t. 1860-1880 (°C)
5 year running means
simulated (5 realizations)
observed (Brohan et al., 2006)
• Simulated surface air temperature less variable than observed.
• Natural sources of variability like volcanic forcing or the 11 year
solar cycle are excluded from the experiment.
• Simulated warming in 2005 slightly underestimated.
Global mean CO2 emissions 1860 to 2005
CO2 emissions from fossil fuel combustion and cement production (GtC/yr)
Global annual mean; 11-year running means
Implied emissions
from simulations
Observed
(Marland et al., 2006)
• Model allows for relatively higher emissions before 1930.
• Minimum in 1940s
• Similar emissions in 2000.
Simulated carbon uptake 1860 to 2005
Simulated carbon uptake (GtC/yr)
11-year running means
Simulated ocean uptake
Simulated land uptake
• Ocean carbon uptake very similar to land uptake
• Reduced uptake in 1950s
Carbon uptake by ocean and land
Fraction of simulated fossil fuel emissions (%)
Remaining in the atmosphere
Absorbed by ocean
Aborbed by land
• 50% of simulated fossil fuel emissons remain in the atmosphere
• In 2000: simulated ocean uptake = ~2 x simulated land uptake
Global surface air temperature anomalies
Global annual mean surface air temperature anomalies w.r.t. 1860-1880 (°C)
Historic 1950-2000
A1B 2001 – 2100
E1 2001 – 2100
• Initially stronger warming in E1 than in A1B because of faster
reduction in sulfate aerosol loading, hence less cooling.
• Reduce warming in E1 after 2040
• Warming in 2100: ~4°C in A1B and ~2°C in E1
 Climate – carbon cycle feedback differs after 2050
Implied CO2 emissions 1950 to 2100
Implied CO2 emissions with and without climate – carbon cycle feedback (GtC/yr)
Historic 1950 – 2000
A1B 2001 – 2100
E1 2001 – 2100
without feedback
with feedback
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Implied CO2 emissions of E1 scenario drop sharply after ~2015 (unlike
emissions for A1B scenario)
Implied emissions are reduced by feedback
In 2100: -2 GtC/yr in E1 and -4.5 GtC/yr in A1B
Implied emissions of E1 close to 0 in 2100.
Accumulated C emissions: Coupled – Uncoupled
Reduction in accumulated C emissions by climate – carbon cycle coupling (GtC)
(11-year running means)
Historic 1860 – 2000
A1B 2001 – 2100
E1 2001 – 2100
• Climate – carbon cycle feedback reduces implied carbon
emissions until 2100 by 180 (E1) to 280 (A1B) GtC.
Conclusions
• The E1 scenario fulfills the EU climate policy goal of limiting the
global temperature increase to a maximum of 2°C.
• In the 2050s (2090s) the allowable CO2 emissions for E1 are about
65% (17%) of those of the 1990’s.
• As in previous studies, a positive climate-carbon cycle feedback is
simulated.
 Climate warming reduces the ability of both land and ocean to take
up anthropogenic carbon.
 Climate – carbon cycle feedback reduces the allowable emissions by
about 2 GtC/yr in the E1 scenario.
3. Fluid dynamical issues in the development of ESMs
Conservation properties of numerical models
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The discretized system shall have the same
conservation properties as the underlying continuous
system
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Mass and tracer mass – consistent continuity and transport eq.
Momentum – “Radiation upper boundary condition”
Energy – Energy conversion due to wave dissipation
Adaptivity
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Grid refinement
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Dynamical core
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Adjust scheme to expected errors ( FE schemes)
Parameterizations:
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static or dynamic?
Redistribute grid points or create/destroy grid points?
2d or 3d?
Single time integration scheme or recursive schemes?
Conservation properties?
Submodels: embedded dynamical models – “super-parameterizations”
Cost function
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How to predict the need for refinement, and what for?
How to confine cost?
High performance computing
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Parallelization:
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From ~102 cores to 105 cores
Model integration, data handling, post processing
Hardware and software reliability
Data
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Storage capacity grows less than computing power
Limited bandwidth for data access
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