Climate Modelling PerspectivesMarco GiorgettaMax Planck Institute for Meteorology

ESA CCI project integration meetingECMWF, 14-16 March 2011

Overview

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

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

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)

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

Components of the climate system, interactions, and changes

(Source: IPCC AR4 WG1 Ch.1, FAQ 1.2, Figure 1)

Schematic view of the Earth system

Atmosphere

Land

Ocean

EnergyMomentum

Substance cyclesH2O, C N S P … Society

Use & managementof the environment

HealthWealthFoodetc.

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

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

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 hPa

Vorticity, 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

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, …

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

Simulations

1. Monthly simulations for global energy balance (~1 day)

2. 30 year AMIP simulations for climatology of atmospheric circulation – ps, Z500, T and U in upper troposphere, water vapor integrals, precipitation

3. Pre-industrial coupled simulation over 50 to 200 years– Modifications change global energy balance … 1.

– … and other properties 2.

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

• …

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)

Examples for phenomena not well simulated in atmospheric GCMs (cf. IPCC AR4, Ch.8)

1. 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

2. 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

Observation: OLR and 850 hPa u-wind

Example 1: Madden-Julian Oscillation

T127L95/TP04L40(rar0008)

T127L95/TP04L40(rar0007)

• 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

Observations

The quasi-biennial oscillation in U in MAECHAM5 T42L90

U(20hPa)=0

Wind Fluctuations at Equator Wavenumber frequency spectrum

n1IG

n1ER Kelvin

Wavenumber frequency spectrum|k| <= 15Freq. <= 1 cpd

Fluctuations in zonal wind (u’, m/s) in June 1993 at 52 hPa, 1.4°N,12 hourly data

u’ (m/s)

QBO forcing by Kelvin waves

E W E W E W

W

E

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 +

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

Example: Precipitation AND evaporation

• HOAPS-3 climatological mean precipitation and evaporation over ice free ocean for 1988-2005(www.hoaps.org)

• Land?

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

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.

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