METR112-Climate Modeling

•Basic concepts of climate system •Numerical method and parameterization in the model•Evaluation and sensitivity study of the model

Question from last week:

Sun Spot are relatively dark areas on the surface of the Sun where intense magnetic activity inhibits convection and so cools the surface. The number of sunspots correlates with the intensity of solar radiation

Foukal et al. (1977) realised that higher values of radiation are associated with more sunspots

because the areas surrounding sunspots are brighter, the overall effect is that more sunspots means a brighter sun

How can you know the future climate and climate change?

Climate system

http://www.usgcrp.gov/usgcrp/Library/nationalassessment/overviewtools.htm

Atmosphere: composition

Even though with small percentage, trace gases such as CO2 and water vapor act as very important gas composition in the atmosphere

Atmosphere: vertical structure

Troposphere: where most weather processes take place

Note: the height of tropopause is not the same everywhere. The tropopause is lower in high latitude than in tropics

Atmosphere: energy budget

(Kiehl and trenberth 1997)

Atmosphere: general circulation

•Hadley cell•Trade wind•Westerlies•ITCZ•Subtropical high•Strom track region•Polar Hadley cell

Ocean: critical roles in climate system

Physical properties and role in climate:

•The biggest water resource on earth

•Low albedo excellent absorber of solar radiation

•One of the primary heat sources for atmosphere

•High heat capacity reduces the magnitude of seasonal cycle of atmosphere

•Important polarward energy transport

•Large reservoir for chemical elements for atmosphere

Ocean: salinity distribution closely relates to precipitation evaporation

From Pickard and Emery: Descriptive Physical Oceanography: An Introduction

Ocean: annual cycle of mixed layer

In winter, SST is low, wind waves are large), mixed layer is deep

In summer, (SST high water stable), mixed layer is shallow.

March is nearly isothermal in upper 100 meters.

March-August, SST increases, (absorption of solar radiation). Mixed layer 30 m.

August-March, net loss of heat, seasonal thermocline eroding due to mixing.

Ocean: surface currents – the gyres

http://www.windows.ucar.edu/tour/link=/earth/Water/images/Surface_currents_jpg_image.html

•Wind drived•Coriolis force and location of land affect current pattern•Clockwise in NH, anticlockwise in SH

The water of the ocean surface moves in a regular pattern called surface ocean currents. The currents are named. In this map, warm currents are shown In red and cold currents are shown in blue.

Surface ocean currents carry heat from place to place in the Earth system. This affects regional climates. The Sun warms water at the equator more than it does at the high latitude polar regions. The heat travels in surface currents to higher latitudes. A current that brings warmth into a high latitude region will make that region’s climate less chilly.

Role of ocean surface currents

Graph showing a tropical ocean thermocline (depth vs. temperature). Note the rapid change between 400 and 800 meters.

Thermocline

The thermocline (sometimes metalimnion) is a thin but distinct layer in a large body of fluid (e.g. such as an ocean or lake), in which temperature changes more rapidly with depth than it does in the layers above or below.

In the ocean, the thermocline may be thought of as an invisible blanket which separates the upper mixed layer from the calm deep water below.

Ocean: thermocline

•When water is sufficiently cooled, at polar latitudes, by cold atmospheric air, it gets denser and sinks •The vertical sinking motion causes horizontal water motion as surface waters replace the sinking water. •The large-scale flow pattern that results from the sinking of water in the Nordic and Greenland Seas and around Antarctica is called the oceanic conveyor belt

Land: where most human impact are applied •Lower boundary of 30% of earth surface lower heat capacity than ocean•Higher variability in interaction with atmosphere than ocean surface

Moisture exchangeAlbedoTopography forced momentum change

•Human impact directly change the land surfaceRelease of CO2 and other GHGsRelease of AerosolChange the Land surface coverUHI effect

The greenhouse gases act as insulation

Land: aerosols

Aerosol: the small particles in the atmosphere which varying in size, chemical composition, temporal and spatial distribution and life timeSource: volcano eruptions, wind lifting of dust, biomass burning, vegetationNew result and great uncertainty of the effect of aerosol on climate

Small aerosol reflect back the solar radiationLarge aerosol can block longwave radiation

Land: Landuse changes

Land-cover changes alter• surface albedo and

emissivity• water uptake by roots• leaf area index• canopy interception

capacity• stomatal resistance• roughness length• ….

These changes affect• partitioning of surface

energy fluxes• boundary layer structure• cloud and precipitation

formation• ….

Urbanization is an example of landuse change

General climate model – an approach for the future climate

•Atmospheric GCM is first used in 1950s to predict short-time future weather

•GCM develops and performs continuously improving since then with helps from updating computational resources and better understanding of atmospheric dynamics

•Atmospheric and Oceanic Coupled GCMs (e.g., CCSM, HadCM, GISS, CCCS, CFS) are major ways to predict and project future climate

•A list of GCM and climate modeling programshttp://stommel.tamu.edu/~baum/climate_modeling.html

Regional climate model•The first generation of regional climate model is developed by Dickinson et.al (1989) and Giorgi et. al (1990) due to the coarse resolution of GCM not able to resolve local process•Second generation of RCM (RegCM2) is developed in NCAR (Giorgi et al. 1993) based on MM5 and improved boundary layer parameterizations•Third generation of RCM (RegCM3) (Pal et al. 2007) is developed with various improvements in dynamics and physical parameterizations

http://www.usgcrp.gov/usgcrp/images/ocp2003/ocpfy2003-fig3-4.htm

The past, present and future of climate models

During the last 25 years, different components are added to the climate model to better represent our climate system

Climate Model NASA Earth Observatory Glossary http://earthobservatory.nasa.gov/Library/glossary.php3?mode=alpha&seg=b&segend=d

A quantitative way of representing the interactions of the atmosphere, oceans, land surface, and ice.

Models can range from relatively simple to quite comprehensive. Also see General Circulation Model.

General Circulation Model (GCM) A global, three-dimensional computer model of the climate system which can be used to simulate human-induced climate change. GCMs are highly complex and they represent the effects of such factors as reflective and absorptive properties of atmospheric water vapor, greenhouse gas concentrations, clouds, annual and daily solar heating, ocean temperatures and ice boundaries. The most recent GCMs include global representations of the atmosphere, oceans, and land surface.

Definition

Differences between Regional Climate Model (RCM) and Global Climate Model (GCM)

1. Coverage: for selected region, for the globe2. Model resolution: finer resolution, coarse resolution

1 km-10km 60-250km, or larger

3. Model components are different

RCM GCM

Climate Model:

Equations believed to represent the physical, chemical, and biological processes governing the climate system for the scale of interest

It can answer “What If” questionsfor example, what would the climate be if CO2 is doubled?

what would the climate be if Greenland ice is all melt?what………………………..if Amazon forest is gone?what…………………………if SF bay area

population is doubled?

Numerical method: finite difference method

ii-1 i+1 i+2i-2

.

.. .

.

ix 1ix Forward

Backward

Central

Exact

Definition of derivatives and approximations

ix

i1i

i

xx

Φ

Forward differences

ix

1-ii

i

xx

Φ

Backward differences

ix

1-i1i

i

x2x

Φ

Central differences

Example: CCSM (Community Climate System Model)

Community Climate System Model is a fully coupled climate model of spectral coordinate in the horizontal and 26 layers in the vertical direction. It contains of AGCM(CAM), OGCM(POP), land surface model(CLM) and sea ice model(CSIM). Each model component exchanges information with the others through a flux coupler (cpl)

CAM: an improved version of CCM using hybrid coordinates and a Eulerian dynamical core which is separated from the parameterization package

CLM: an successor from NCAR LSM by changing the biogeophysical, carbon cycle and vegetation dynamics parameterizations in the LSM

POP: almost identical to LANL’s POP1.4.3, only minor changes are made to facilitate the original version server as ocean model component of CCSM2.0.1

CSIM: consists an elastic-viscous-plastic dynamics scheme, and ice thickness distribution, energy-conserving thermodynamics, a slab ocean mixed layer model, and the ability to run using prescribed ice concentrations

cpl

CISMPOP

CLMCAM

atmosphere

land

ocean

ice

Picture taken from http://www.ccsm.ucar.edu/models/atm-cam/

Hybrid Vertical coordinate

Model physics in CAM

Surface Exchange

Atm-Lnd Atm-Ocn Atm-Ice

(Moist) precipitation

Deep Shallow Stratiform condensation

Radiation

Shortwave Longwave

Turbulence

ABL Free atmosphere

Zhang-McFarlane(1995) (1994)

Hack Zhang et al(2003) Cloud fraction

Collins (2001)

(Monin-Obkhov similarity theory)ABL depth ( Vogelezang and holtslag 1996)

CLM: combination of BATS, LSM &Common Land Model

• 10 soil layers, up to five snow layers

• Prognostic variables are: canopy temperature, intercepted water by canopy, soil or snow temperature, water and ice mass in the soil or snow layer and snow layer thickness

• Mosaic land-cover

• Same surface data with LSM2, and similar parameterizations with Common Land Model

Mosaic sub-grid land-cover treatment

Glacier

Wet-land

Vegetation

Lake

Grass

Bare ground

Crop

Needleleaf

Water balance in CLM

• Surface evaporation

• TOPMODEL-like runoff scheme

• Canopy water budget

• Soil water budget

• Snow water budget

da*

a r/qqE

Canopy water budget:

wrdfdew EDDPt

W

Precipitation arriving at canopy top

Direct drainage

Canopy dripEvaporation from canopy

Canopy temperature:Rn,c – Hc – LvEc = 0

Newton-Raphson method

Tc

Sz

F

t

Tc

z

TF

Soil and snow temperature:

Tsoil, Tsnow

Crank-Nicholson method

F(x)+F’(x)(xn-xn-1)=0

Radiation balance in CLM

Verify the predictions and statistics of predictions• Compatibility with observations • Various simulations to assure the agreement with basic theoretical understanding

Model Inter-comparison studies• Compare different models

model evaluation-Model uncertainty

Multimodel ensembles show systematic discrepancies when comapared with observed mean temperature

Lack of broad stratus decks

Contours are observedmean surface temperature,color shading show discre-pancy calculated frommultimodel ensembles.

Typical model error (RMS error in multi-model ensem-ble) surface temperature field.Calculated from IPCC AR4participating models.

Source: Fig. 8.2 of IPCC AR4 chapter 8

Multimodel ensemble show significant errors in standard deviations of surface temperatures

Contours are observed surface temperature variability,color shading show that of discre-pancy calculated from multimodel ensembles from observations.

Source: Fig. 8.3 of IPCC AR4 chapter 8

Short and longwave radiation budgets show dominant RMS errors in tropical and subtropical regions based on 12 month climatology

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Curves show RMS errors in short wave (left panel) and long wave (right panel) radiation

Source: Fig. 8.4 of IPCC AR4 chapter 8

Simulated precipitation show systematic biases

Observed annual mean precipitation in cm

Multimodel ensemble of annual mean precipitation in cm

1. Double ITCZ syndrome and lack of SPCZ structure2. systematic southern hemisphere differences

Source: Fig. 8.5of IPCC AR4 chapter 8

Zonal mean wind stress on ocean surface is reasona- bly captured by multi-model ensemble mean quantity

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Source: Fig. 8.7 of IPCC AR4 chapter 8

Zonal mean SST show marginal errors using multi-model ensemble mean quantity

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Source: Fig. 8.8 of IPCC AR4 chapter 8

Different climate projection scenarios suggest unprece-dented increasing trend in global mean temperatures

Source: Fig. 10.4 of IPCC AR4 chapter 10