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

zw

yv

xu

tdt

d

gz

p

Udt

d

Pdt

d

PpVkfdt

VdV

1

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

)(

)(

)(

01

01

0

1

0

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?