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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 Painting: Rolv Groven • Doctor deg. 1892 • Ass. Prof. in mechanics Stockholm 1893 • Prof. Stockholm 1895 • Kristiania 1907 • Leipzig 1912 • Bergen 1917 • Oslo 1926 Bjerknes’ vision on scientific weather forecasting Bjerknes’ vision 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 Model equations Numerical methods: 1 dV fk V p PV dt d P dt d U dt p g z Finite differences Spectral methods d u v w dt t x y z One set of prognostic variables in each grid box Parametrisation of sub-grid scale processes Resolved topography Sub-grid topography PV k Z Sub-grid processes for parameterisation Sub-grid processes are prameterized, often as a function of grid-point values. Horizontal derivatives are not involved => Easier to parallellize these computations. Numerical Weather Prediction Models 1. State at a specific time S0 (wind, temperature, pressure, humidity clouds) determined from observations. 2. Future stated by solving (non-linear) conservation laws: dS H (S0 ) dt t1 S1 H ( S 0 ) dt t0 S1 H ( S 0 ) t Predictability for weather forecasting Lorenz attractor y (analogy to weather behaviour) Ed Lorenz (1918-2008) x Limitation in predictability of the weather Theoretical limit score Today’s limit Limit for useable prediction Tomorrow’s limit predictability (days) 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 Increase in complexity of climate models FAR: First Assessment Report (IPCC 1990) SAR: Second Assessment report (IPCC 1996) TAR: Third Assessment Report (IPCC 2001) Source: IPCC AR4 WG1 NorESM framework and model components Atmospheric chemistry CAM CICE CLM River routing MICOM HAMOCC Components in blue communicate trough a coupling component. Components in red are subroutines of blue components. Computer platforms Shared memory Distributed memory memory network Combination 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 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 Dipole grid (default CCSM) Tripole grid EXPERIMENT TYPES Emission scenarios from IPCC, includes also air pollution giving aerosols ppm 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?