Model and Data Hierarchies for Simulating and Understanding Climate

1
Overview of Earth System Modeling and Fluid
Dynamical Issue

• Model and Data Hierarchies for Simulating and
  Understanding Climate
• Marco A. Giorgetta

2
Overview

• The Earth System and Earth System Models (ESMs)
• Research with ESMs
• A GCM study on emission pathways to climate
  stabilization
• Fluid dynamical issues in the development of ESMs

3

• 1. The Earth System and Earth System Models
  (ESMs)

4
The Earth System

• In general terms
• The Earth and everything gravitationally bound
  to it
• Earth interior
• Oceans with sea ice
• Land surfaces soil, ice shields, glaciers
• Atmosphere up to 100 km
• Life in all compartments
• Land vegetation and soil organism
• Marine biota
• Humans!

5
The Earth System

• In climate science
• A relatively new term, chosen to describe
• 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.

6
Key for understanding climate Energy transfer

• Radiation heat fluxes and storage in A, O, and
  L
• Distributions of T, q and wind,
• Hydrological cycle

Globally averaged vertical energy transfer in the
atmosphere SourceIPCC AR4 WG1
Rep., Ch. 1, FAQ Fig.1
7
Components of the climate system, interactions,
and changes
(Source IPCC AR4 WG1 Ch.1, FAQ 1.2, Figure 1)
8
Earth System Models (ESMs)

• Simplified/idealized descriptions of the ESCf.
  Model in architecture, fashion, engineering,
• Test understanding of the functioning of the ES
• Explain observed features
• Formal description, allowing for computational
  experiments ? What if
• Turbulent mixing in oceans was stronger
• Major volcanic eruptions happened?
• Highly complex models within the model hierarchy
• Fortran code of 105 lines

9
The Earth System

• History of Type II models
• General circulation models of atmosphere or
  ocean? weather, seasonal cycle,
• Coupled atmosphere ocean models climate
  model? El Niño/La Niña, small climate change,
  
• Earth system model climate model
• 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

10
Schematic view of the ES
11
Construction of ESMs

• 1. Decide on spatial and temporal scales, and on
  processes, which are scientifically relevant and
  practically feasible (? model hierarchies)
• 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
• 200 km ? Primitive equations
• Numerical methods ? discretized, i.e. computable,
  equations
• Dynamical core ? Christianes talk

12
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
• 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

13
Construction of ESMs (cont.)

• Parameterizations rely on assumptions, e.g.
• Radiation
• Grid scale ltlt Earth radius ? plane parallel
  assumption
• Grid scale gtgt layer thickness ? neglect fluxes
  trough lateral boundaries
• Local thermal equilibrium ? valid up to 70 km
  in the atmosphere of Earth
• Gas air small variations ? valid for the
  atmosphere of Earth

14

• 2. Research with ESMs

15

• A GCM study on emission pathways to climate
  stabilization
• E. Roeckner, M. Giorgetta, T. Crüger, M. Esch,
  and J. Pongratz
• Submitted to Climatic Change

16
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?

17

• 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

18
Methodology

• Method proposed for the future CMIP5 experiments,
  i.e. experiments for the 5th IPCC assessment of
  climate change (Hibbard et al., 2007)

19
Experiments
Control18601000 yr
Historic1860-2005
SRES A1B
Ensembles of 5 realizations
E1 450 ppm
full coupling C-cycle
decoupled
20
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
21
and of the model used here
X (no feedback)
A ECHAM
Substance cyclesH2O, C
EnergyMomentum
Society
L JSBACH
O MPIOM HAMOCC
Prescribed BCs fromobservationsscenarios
22
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

23
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.

24
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.

25
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

26
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

27
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 4C in A1B and 2C in E1
• Climate carbon cycle feedback differs after 2050

28
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

• Implied CO2 emissions of E1 scenario drop sharply
  after 2015 (unlike emissions for A1B scenario)
• Implied emissions are reduced by feedbackIn
  2100 -2 GtC/yr in E1 and -4.5 GtC/yr in A1B
• Implied emissions of E1 close to 0 in 2100.

29
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.

30
Conclusions

• The E1 scenario fulfills the EU climate policy
  goal of limiting the global temperature increase
  to a maximum of 2C.
• In the 2050s (2090s) the allowable CO2 emissions
  for E1 are about 65 (17) of those of the
  1990s.
• 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.

31

• 3. Fluid dynamical issues in the development of
  ESMs

32
Conservation properties of numerical models

• The discretized system shall have the same
  conservation properties as the underlying
  continuous system
• Mass and tracer mass consistent continuity and
  transport eq.
• Momentum Radiation upper boundary condition
• Energy Energy conversion due to wave
  dissipation

33
Adaptivity

• Grid refinement
• static or dynamic?
• Redistribute grid points or create/destroy grid
  points?
• 2d or 3d?
• Single time integration scheme or recursive
  schemes?
• Conservation properties?
• Dynamical core
• Adjust scheme to expected errors (? FE schemes)
• Parameterizations
• Submodels embedded dynamical models
  super-parameterizations
• Cost function
• How to predict the need for refinement, and what
  for?
• How to confine cost?

34
High performance computing

• Parallelization
• From 102 cores to 105 cores
• Model integration, data handling, post processing
• Hardware and software reliability
• Data
• Storage capacity grows less than computing power
• Limited bandwidth for data access

35

• END

36
Title

• text

37
Title

• text

38
Title

• text

39
Title

• text

40
Title

• text