Transcript Climate Modeling

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 I
n red and cold currents are shown in blue.
Role of ocean surface currents
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.
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.
Graph showing a tropical ocean thermocline (depth vs. temperature).
Note the rapid change between 400 and 800 meters.
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 exchange
Albedo
Topography forced momentum change
•Human impact directly change the land surface
Release of CO2 and other GHGs
Release of Aerosol
Change the Land surface cover
UHI 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 time
Source: volcano eruptions, wind lifting of dust, biomass burning,
vegetation
New result and great uncertainty of the effect of aerosol on climate
Small aerosol reflect back the solar radiation
Large 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 programs
http://stommel.tamu.edu/~baum/climate_modelin
g.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
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
http://www.usgcrp.gov/usgcrp/images/ocp2003/ocpfy2003-fig3-4.htm
Definition
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.
Differences between Regional Climate Model (RCM) and Global Climate Model (GCM)
RCM
1. Coverage:
2. Model resolution:
for selected region,
finer resolution,
1 km-10km
3. Model components are different
GCM
for the globe
coarse resolution
60-250km, or larger
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” questions
for 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
Backward
Exact
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Central
x i x i1
Forward
 Φ    
 
 
x
 x 
 Φ    
 
 
x
 x 
i 1
i

x
Forward differences
i
i
i-2
i-1
i
i+1
i+2
i
Definition of derivatives and
approximations
i -1

x
Backward differences
i
i
 Φ    
 
 
2x
 x 
i 1
x
i
Central differences
i -1
i
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
CLM
cpl
POP
CISM
atmosphere
CAM: an improved version of CCM using hybrid coordinates and a Eulerian
dynamical core which is separated from the parameterization package
land
CLM: an successor from NCAR LSM by changing the biogeophysical, carbon cycle
and vegetation dynamics parameterizations in the LSM
ocean
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
ice
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
Hybrid Vertical coordinate
Picture taken from http://www.ccsm.ucar.edu/models/atm-cam/
Model physics in CAM
Radiation
(Moist) precipitation
Deep
Zhang-McFarlane
(1995)
Shallow
Stratiform condensation
Hack
(1994)
Zhang et al
(2003)
Shortwave
Longwave
Cloud fraction
Collins (2001)
Surface Exchange
Atm-Lnd
Atm-Ocn
Turbulence
Atm-Ice
(Monin-Obkhov similarity theory)
ABL
Free atmosphere
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
Needleleaf
Glacier
Vegetation
Grass
Wet-land
Lake
Crop
Bare ground
Water balance in CLM
• Surface evaporation
• TOPMODEL-like runoff scheme
• Canopy water budget
• Soil water budget
• Snow water budget


E  a q  q a / rd
*
Canopy water budget:
Wdew
 f P  Dd  Dr  E w
t
Precipitation
arriving at
canopy top
Direct
drainage
Canopy drip
Evaporation
from canopy
Radiation balance in CLM
Canopy temperature:
Rn,c – Hc – LvEc = 0
Tc
Newton-Raphson method
F(x)+F’(x)(xn-xn-1)=0
Soil and snow temperature:
c
T F

S
t z
T
F  
z
Tsoil, Tsnow
Crank-Nicholson method
model evaluation-Model uncertainty
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
Multimodel ensembles show systematic discrepancies when
comapared with observed mean temperature
Contours are observed
mean surface temperature,
color shading show discrepancy calculated from
multimodel ensembles.
Lack of broad
stratus decks
Typical model error (RMS
error in multi-model ensemble) surface temperature
field.
Calculated from IPCC AR4
participating 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 discrepancy 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
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
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
Source: Fig. 8.5
of IPCC AR4
chapter 8
1. Double ITCZ syndrome
and lack of SPCZ structure
2. systematic southern
hemisphere differences
Zonal mean wind stress on ocean surface is reasonably captured by multi-model ensemble mean quantity
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Source: Fig. 8.7 of IPCC AR4 chapter 8
Zonal mean SST show marginal errors using multimodel ensemble mean quantity
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Source: Fig. 8.8 of IPCC AR4 chapter 8
Different climate projection scenarios suggest unprecedented increasing trend in global mean temperatures
Source: Fig. 10.4 of IPCC AR4 chapter 10