Climate models – prediction and projection
Nils Gunnar Kvamstø
Geophysical Department
University of Bergen
Climate models
• Best tool for projections
• The best tool for attribution of observed climate change
• Potential for realistic regional and local projections
• The reality of global warming is based on much more than climate model results
Climate model results used for
• Local climate change• Mitigation studies, e.g.
emissions corresponding to two degrees GW
• Effects of climate change (e.g. extinction, food supply, climate refugees, water management, economy)
Vilhelm Bjerknes 1862-1951 – proposed weather prediction models in
1904
• Doctor deg. 1892• Ass. Prof. in
mechanics Stockholm 1893
• Prof. Stockholm 1895• Kristiania 1907• Leipzig 1912• Bergen 1917• Oslo 1926
Painting: Rolv Groven
Bjerknes’ vision on scientific weather forecasting
1. The state of the atmosphere must be known for a specific time (from observations)
2. Then future states might be computed from conservation laws for mass, energy and momentum
Bjerknes’ vision
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tdt
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p
Udt
d
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Numerical methods:
Finite differences
Spectral methods
Model equations
One set of prognosticvariables in each grid box
Parametrisation of sub-grid scale processes
ZV kP
Resolved topography
Sub-grid topography
Sub-grid processes for parameterisation
Sub-grid processes are prameterized, often as a function ofgrid-point values. Horizontal derivatives are not involved =>Easier to parallellize these computations.
1. State at a specific time S0 (wind, temperature, pressure, humidity clouds) determined from observations.
2. Future stated by solving (non-linear) conservation laws:
Numerical Weather Prediction Models
tSHS
dtSHS
SHdt
dS
t
t
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01
01
0
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Lorenz attractor(analogy to weather behaviour)
Ed Lorenz (1918-2008)
Predictability for weather forecasting
y
x
Limitation in predictability of the weather
score
predictability (days)
Theoretical limit
Today’s limit
Tomorrow’s limit
Limit for useable prediction
Predictability for climate change different from that of weather forecasting
• Weather forecasting: predictability of first kind (the actual weather)
• Prediction of climate change: predictability of second kind (statistical properties of the weather over several years)
• Actual weather models determines: present limits for weather prediction
• Actual climate models determine: present ability to predict climate change
Source: IPCC AR4 WG1
Increase in complexity of climate models
FAR: First Assessment Report (IPCC 1990)SAR: Second Assessment report (IPCC 1996)TAR: Third Assessment Report (IPCC 2001)
CLM
CAM
CICE
HAMOCCMICOM
Atmospheric chemistry
Components in blue communicate trough a coupling component. Components in red are subroutines of blue components.
River routing
NorESM framework and model components
Computer platforms
Shared memory Distributed memory
Combination
memory
network
node
• gridur/embla (2002), 2 nodes, 384 + 512 = 896 cores, 1.0 Tflop
• njord (2006), 62 nodes x 16 cores = 992 cores, 7.5 Tflop
• stallo (2007), 704 nodes x 8 cores = 5632 cores, 60 Tflop
• hexagon (2008), 1388 nodes x 4 cores = 5552 cores, 50 Tflop
Dipole grid (default CCSM) Tripole grid
Specs Bergen Climate Model
Res atmosphere (1.9x 2.5 deg 20 layers) 96*172*20
Res ocean: (1deg, 20 layers) 180*360*90
90 procs Hexagon 6.7 yr/d
Output 6h, d, mon -> 5Tb per 100 years
Emission scenarios from IPCC, includes also air pollution giving aerosols
ppm
EXPERIMENT TYPES
Projections of global temperature change
Norges mål:2 grader
IPCC
IPCC 2007
Changes in precipitation (percent) by the end of the century
Winter Summer
Conclusions• Climate models solve well known physical equations
from hour to hour, from day to day, from year to year• Climate models have no tuning to fit observations• Climate models simulate the observed global warming
during the latest decades• Best tool for future projections and attribution of sources
for climate change• Decadal prediction with models a large research area• Climate drift a problem• Next generation models will include the carbon cycle• Feed-back from clouds and effects of aerosols a
notorious problem
Questions
• Why are there 'wiggles' in the output? • What is tuning? • What is robust in a climate projection and
how can I tell? • Are the models complete? That is, do they
contain all the processes we know about? • Do models have global warming built in? • What is the difference between a physical
model and a statistical model?