Climate Modelling Perspectives

1. Climate Modelling Perspectives Marco Giorgetta Max Planck Institute for Meteorology ESA CCI project integration meetingECMWF, 14-16 March 2011

2. Overview • What is a climate model? • A typical development cycle • Examples: MJO and QBO • Wish list • Summary

3. What is a climate model? • In general, models represent the essential characteristics of an object that is too small, too large, or too complex for a view on the real object  models = simplifications • Climate models describe the part of the climate system that is understood well enough to be described by equations and algorithms, and that is computationally affordable. • Earth system model = most complex variant of a climate model • Prerequisites: Observations and derivatives, theory, applied mathematics and IT

4. The purpose of a climate model • To test the understanding of the climate system, as encoded in a the climate model by simulating the past for comparison with the observed climate • By exploring consequences, what if … • other process formulations, • additional processes changing or adding feedback mechanisms • other boundary conditions, e.g. increasing greenhouse gases • Near surface temperature [°C] in observations and CMIP5 simulations, using MPI-ESM • Observations: CRU • Control • Historical 1850-2005 • RCP4.5 2006-2100 • RCP8.5 2006-2100 • +1%CO2/yr to 4xCO2 • (COMBINE project)

5. 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 Source:IPCC AR4 WG1 Rep., Ch. 1, FAQ Fig.1

6. Components of the climate system, interactions, and changes (Source: IPCC AR4 WG1 Ch.1, FAQ 1.2, Figure 1)

7. Schematic view of the Earth system HealthWealthFood etc. Atmosphere Substance cyclesH2O, C N S P … EnergyMomentum Society Land Ocean Use & management of the environment

8. The atmospheric GCM of a CMIP5 Earth system model • Primitive equations at a resolution of ~100 km, trop+strat, ( prognostic variables) • Wind components (or equivalent) • Temperature • Surface pressure • Water vapor, cloud water, cloud ice • CO2 • Parameterizations ( diagnostic variables) • SW and LW radiative heating/cooling • Turbulent mixing in boundary layer and above (“vertical diffusion”) • Moist convection • Cloud microphysics • Gravity wave drag, orographic and non-orographic • Photosynthesis, carbon allocation in vegetation and soil, vegetation dynamics • External data • Concentrations of CH4, N2O, CFCs, O3 • Aerosol parameters (optical properties) • Land surface properties excluding vegetation maps, leaf area • Sea surface and sea ice properties (coupled to ocean model) • Anthropogenic emissions of CO2 and land use change data

9. Climate model development cycle • Systematic errors of “old” model with respect to climate observations • Top of atmosphere energy budget • Near surface climate: T, psurface, … • Hydrological cycle: Precipitation • Variability: El Nino, storm tracks, … • Model development • Refine model components: • Resolution • Parameterizations dynamics, transport, parameterization • Add process models • For carbon cycle: land vegetation and soil processes, marine biogeochemistry • For atmospheric composition: chemistry, micro-phyiscs • Replace model components: • Dynamical core and transport scheme • Parameterization: Convection, cloud microphysics, … • Reference experiments (AMIP, CFMIP, C4MIP, PMIP, CMIP5, …) • Metrics: standards for quantification of the model quality  Veronika Eyring

10. Example: MPI-ESM • Model the general circulation of atmosphere and ocean, and vegetation dynamics related to the cycling of energy, water and carbon • Atmosphere: ECHAM6 • Resolution: T63 or T127, 47 or 95 levels up to 0.01 hPaVorticity, divergence, surface pressure, temperature, water vapor, cloud water, cloud ice • Land: JSBACH • Processes for photosynthesis, C-allocation in plants and soil, dynamic natural vegetation • Land use change • Heat, water, momentum and CO2 coupling to atmosphere • Water discharge to oceans • Ocean: MPIOM • Resolution: 1.5° or 0.4°, 40 levels • Biogeochemistry in water and sediment • Heat, water, and CO2 coupling to atmosphere

11. Observations for development and tuning of MPI-ESM • Energy fluxes: • Top of atmosphere radiative balance (CERES EBAF)All sky and clear sky • Atmosphere: • psurface, T, Z500, (u,v) in troposphere and lower stratosphere, vertically integrated moisture (ERA40 / ERA-interim) • Near surface temp: CRU • Ocean: • Sea surface temperature and ice cover (HADISST), • Atlantic meridional overturning circulation from RAPID/MOCHA Array at 26°N • Precipitation (GPCP, …) • Climate variability indices: NAO, Nino 3/4, QBO, …

12. Tuning goals • Pre-industrial equilibrium near surface temperature: 13.7 C • Net LW from CERES-EBAF: ~240 W/m2(TSI from SORCE/TIM ~1361 W/m2) • Cloud cover: 60-65% • Precipitation preferably less than 3.0 mm/day • Integrated water vapor less than 24.5 g/m2 • Liquid water path less than 70 g/m2 • ‘Good’ representation of seasonal mean-state of atmosphere • Atlantic meridional overturning circulation: ~18 Sv • ‘Good’ El-Nino, Madden-Julian oscillation and quasi-biennial oscillations • Arctic ice cover

13. Simulations • Monthly simulations for global energy balance (~1 day) • 30 year AMIP simulations for climatology of atmospheric circulation • ps, Z500, T and U in upper troposphere, water vapor integrals, precipitation • Pre-industrial coupled simulation over 50 to 200 years • Modifications change global energy balance …  1. • … and other properties  2.

14. Tuning parameters • Moist convection • Cloud mass flux across level of neutral buoyancy • Efficiency of of cloud water to precipitation conversion • Entrainment rate for shallow convection • Entrainment rate for penetrative convection • Terminal velocity of ice crystals • Cloud optics • Cloud inhomogeneity factor • Sub grid-scale orographic drag • Active area • Efficiency parameters • Non-orographic gravity wave drag • Source strength • Ocean water color • …

15. Is tuning a problem? • Must remain within the conceptual limits of the parameterization • Often these parameters are not observable • Can give insight in functioning of processes • But good result may occur for wrong reasons (compensation of errors)

16. Examples for phenomena not well simulated in atmospheric GCMs (cf. IPCC AR4, Ch.8) • Madden-Julian Oscillation • Dominant mode of tropical variability on intra-seasonal time scales (30-60 days) • large-scale coupled patterns of circulation and deep convection • propagating eastward across the Indian and Pacific Ocean (high SSTs) • strong precipitation events • Quasi-biennial oscillation • Dominant mode of zonal wind in the equatorial stratosphere, global • Slowest atmospheric oscillation, 22-36 months, average ~28 months • Transport effects on ozone and other substances in the stratosphere

17. Example 1: Madden-Julian Oscillation Observation: OLR and 850 hPa u-wind

18. T127L95/TP04L40 (rar0008) T127L95/TP04L40 (rar0007) Observations • The two simulations differ in a single parameter of the convection scheme (“cloud water to rain conversion efficiency”) • Coupling of convection and horizontal dynamics leads to large scale effect, seen here in the MJO

19. The quasi-biennial oscillation in U in MAECHAM5 T42L90 U(20hPa)=0

20. u’ (m/s) Wind Fluctuations at Equator  Wavenumber frequency spectrum n1IG n1ER Kelvin Fluctuations in zonal wind (u’, m/s) in June 1993 at 52 hPa, 1.4°N, 12 hourly data Wavenumber frequency spectrum |k| <= 15 Freq. <= 1 cpd

21. QBO forcing by Kelvin waves W E W E W E W E

22. What is missing to understand better the MJO and QBO? • Both phenomena depend on a broad spectrum of tropical waves triggered originally by convection, which in turn is organized by waves. • Needed observations: • Wind temperature and moisture collocated • Resolving the diurnal cycle • Resolving mesoscale structures (or better) • Whole tropics • A few years +

23. Wish list for climate modelling • Essential variables should allow the validation of • Prognostic variables (or their equivalents) • Fluxes essential for the energy budget, the hydrological cycle and substance cycles (C) • And the description of external data • O3, aerosol characteristics, GHG, sea and land surface properties, … • Radiation budget at TOA and surface, all sky and clear sky • Precipitation and evaporation • Wind and T at high resolution for spectra and eddy fluxes (3 hr, ~10 km) • Water vapor, cloud water, cloud ice, cloud cover • Sea ice area and volume • Ocean currents, temperature and salinity, surface and subsurface • CO2 net flux at surface (ocean and land) • Gridded, resolving the diurnal cycle, 20+ years

24. Example: Precipitation AND evaporation • HOAPS-3 climatological mean precipitation and evaporation over ice free ocean for 1988-2005(www.hoaps.org) • Land?

25. Re-analyses • Re-Analyses are in many aspects close to climate model data • Global, gridded in time and space • “Best” merge of observations and model • “Essential climate variables” should be fed into a continued re-analysis for atmosphere (e.g. ERA-interim) and ocean (e.g. NEMOVAR)  David Tan

26. Summary • Climate studies rely on high quality observations and derived “products” • To build models of the climate system (campaigns, research satellites) • To evaluate the knowledge encoded in climate models (climate variables) • Key: Cycle of energy, water and C coupled to the general circulation in atmosphere and ocean and vegetation dynamics on land • My ECV wish list • Water vapor • Energy budget at TOA and surface • Satellite based observations, if calibrated and evaluated, and continued over time, are indeed very useful for climate change research.

27. ECVs of CCI phase 1 • Oceanic Domain • O.1 Sea-Ice • O.2 Sea-Level • O.3 Sea-Surface Temperature • O.4 Ocean Colour • Terrestrial Domain • T.2.1 Glaciers & Ice caps • T.5.1 Land Cover • T.9 Fire Disturbance • Atmospheric Domain • A.4 Cloud Properties • A.7 Ozone • A.8 Aerosol Properties • A.9 Greenhouse Gases