Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Climate models and assessment of climate change
Document related concepts
Transcript
Introduction to climate modeling Peter Guttorp University of Washington [email protected] http://www.stat.washington.edu/peter Acknowledgements ASA climate consensus workshop Kevin Trenberth Ben Santer Myles Allen IPCC Fourth Assessment Reports Steve Sain NCAR IMAGe/GSP Weather and climate Climate is –average weather WMO 30 years (1961-1990) –marginal distribution of weather temperature wind precipitation –classification of weather type state of the climate system Weather is –current activity in troposphere Models of climate and weather Numerical weather prediction: –Initial state is critical –Don’t care about entire distribution, just most likely event –Need not conserve mass and energy Climate models: –Independent of initial state –Need to get distribution of weather right –Critical to conserve mass and energy The heat engine Greenhouse effect A simple climate model What comes in Sr 2 (1 a) Solar constant 1367 W/m2 Earth’s albedo 0.3 must go out 4r 2 T 4 Effective emissivity (greenhouse, clouds) 0.64 Stefan’s constant 5.67×10-8 W/(K4·m2) Solution Average earth temperature is T=285K (12°C) One degree Celsius change in average earth temperature is obtained by changing solar constant by 1.4% Earth’s albedo by 3.3% effective emissivity by 1.4% But in reality… The solar constant is not constant The albedo changes with land use changes, ice melting and cloudiness The emissivity changes with greenhouse gas changes and cloudiness Need to model the three-dimensional (at least) atmosphere But the atmosphere interacts with land surfaces… …and with oceans! Historically mid 70s Atmosphere models mid-80s Interactions with land early 90s Coupled with sea & ice late 90s Added sulphur aerosols 2000 Other aerosols and carbon cycle 2005 Dynamic vegetation and atmospheric chemistry The climate engine I If Earth did not rotate: tropics get higher solar radiation hot air rises, reducing surface pressure and increasing pressure higher up forces air towards poles lower surface pressure at poles makes air sink moves back towards tropics The climate engine II Since earth does rotate, air packets do not follow longitude lines (Coriolis effect) Speed of rotation highest at equator Winds travelling polewards get a bigger and bigger westerly speed (jet streams) Air becomes unstable Waves develop in the westerly flow (low pressure systems over Northern Europe) Mixes warm tropical air with cold polar air Net transport of heat polewards Modeling the atmosphere Coupled partial differential equations describing Conservation of mass Conservation of momentum Conservation of water Thermodynamics Hydrostatic equilibrium Boundary values Radiative forcings The effect of gridding Parameterization Some important processes happen on scales below the discretization Typically expressed in terms of resolved processes (statistically) or data Examples: dry and moist convection cloud amount/cloud optical properties radiative transfer planetary boundary layer transports surface energy exchanges horizontal and vertical dissipation processes Can data force parametrizations? Experiment with simple climate model Realistic priors on forcings Using several data sets on hemispheric annual mean temperature oceanic heat content Markov chain Monte Carlo analysis Goal: Estimate climate sensitivity (temperature response to CO2 doubling) Hemispheric model Schlesinger, Jiang & Charlson 1992 NH mixed layer SH atmosphere SH mixed layer NH interior ocean SH interior ocean NH bottom SH bottom Vertical heat transport by upwelling and diffusion Atmosphere in equilibrium with ocean SH polar ocean NH polar ocean NH atmosphere Stochastic model Observation Y Model output M(,) Truth Z parameters SOI E forcings , Y Y Z Z M,,,E Missing data treated as additional parameters to be estimated Mixed layer Upwelling velocity Vertical heat diffusivity Air-ocean exchange Polar parameter Ocean hemispheric exchange SOI coeff, SH SOI coeff, NH Comparison of Mean Simulation Properties Simulated Land Temp Difference: Sim- Observed Sources of uncertainty Forcings Sea surface temperature is uncertain, especially for early years Greenhouse gases vague estimates for early part Data Global mean temperature is not measured Uncertainty in estimates may be as big as 1°C Greenhouse gases Anthropogenic CO2 from fossil fuel and land use change Methane from agriculture and fossil fuels 1/3 of NOx from agricultural sources Historical data Sensitivity Reasonable climate models must reproduce El Niño Pacific Decadal Oscillation Dust bowl, Sahel drought etc. El Niño simulations El Niño simulations “obs” temp precip slp simulations Cloud (OLR) Anomalies and ENSO Observed Simulated Hack (1998) More Cloud Less Cloud Regional models Dynamic downscaling: Higher resolution models driven by lower resolution global models Statistical downscaling: Regression model using global model, terrain etc. Stochastic downscaling: Stochastic model for subgridscale processes driven by global model Dynamic downscaling of a GCM Comparing RCM to data Regional climate model RCM3 from SMHI Forced by ERA40 Need to compare distributions Data observed minimum daily temperatures at Stockholm Observatory How well does the climate model reproduce data? Resolution in a regional climate model 50 x 50 km Where is the problem? Regional model corresponds to grid square average average over land cover type 3 hr resolution Data correspond to point measurement open air continuous time Model problems with cloud representation constrain to lower resolution model? Data issues Need for high quality climate data repository (Exeter workshop) Reanalysis not only needed for met data Lots of satellites are deteriorating– many are not being replaced Some countries will not make data available to the international community Homogenization Historical SST data issues Ocean surface temperature record Data from buoys, ships, satellites, floats Arctic ice pack
