biogeochemical modelling corinne le quéré university of east anglia and the british antarctic...
TRANSCRIPT
Biogeochemical modelling
Corinne Le Quéré
University of East Anglia and the British Antarctic Survey
• project the future
• test hypothesis (e.g. CLAW)
• quantify feedbacks
• formalize your ideas e.g. FCO2 = kg·(∆pCO2)
SOLAS Science
Model dimensions:
0D FCO2 = kg·(∆pCO2)
1D depth/height
2D depth/height + latitude
3D depth/height + latitude + longitude
4D depth/height + latitude + longitude + time
Outline of lecture:
1. Introduction
2. Chemical processes
3. Biological processes
4. Physical processes
5. Model evaluation and benchmarking
6. One example (ocean CO2 sink)
7. The modeller’s psychology
Outline of lecture:
1. IntroductionIntroduction
2. Chemical processes
3. Biological processesBiological processes
4. Physical processesPhysical processes
5. Model evaluation and benchmarkingModel evaluation and benchmarking
6. One example (ocean CO2 sink)One example (ocean CO2 sink)
7. The modeller’s psychologyThe modeller’s psychology
SOLAS Science
• known processes
• measured species
• derived rates
Parameterisation of chemical processes are 0-Dimensional:
Typical chemical processes in the atmosphere:
1. ozone
2. NOx
3. hydrocarbon (Volatile Organic Carbon)
4. OH-
5. aerosols
6. CO, CH4
NOx AND VOC processes (D. Jacobs)
Emission
NOh (420 nm)
NO2
HNO3
1 day
NITROGEN OXIDES (NOx) VOLATILE ORGANIC COMPOUNDS (VOC)
Emission
VOC
OHHCHOh (340 nm)
hoursCO
hours
BOUNDARYLAYER
~ 2 km
Deposition
Tropospheric ozone processes (D. Jacobs)
O3
O2h
O3
OH HO2
h, H2O
Deposition
NO
H2O2
CO, VOC
NO2
h
STRATOSPHERE
TROPOSPHERE
8-18 km
!================================================================= !
! The decay for CH4 is calculated by: ! OH + CH4 -> CH3 + H2O ! k = 2.45E-12 exp(-1775/T) ! ! This is from JPL '97. JPL '00 does not revise '97 value. (jsw) !================================================================= DO L = 1, MAXVAL( LPAUSE ) DO J = 1, JJPAR DO I = 1, IIPAR ! Only consider tropospheric boxes IF ( L < LPAUSE(I,J) ) THEN
!jsw Is it all right that I'm using ! 24-hr avg temperature to calc. rate coeff.? KRATE = 2.45d-12 * EXP( -1775d0 / Tavg(I,J,L) )
! Conversion from [kg/box] --> [molec/cm3] ! [kg CH4/box] * [box/cm3] * XNUMOL_CH4 [molec CH4/kg CH4] STT2GCH4 = 1d0 / AIRVOL(I,J,L) / 1d6 * XNUMOL_CH4
! CH4 in [molec/cm3] GCH4 = STT(I,J,L,1) * STT2GCH4
! Sum loss in TCH4(3) (molecules/box) TCH4(I,J,L,3) = TCH4(I,J,L,3)+ & ( GCH4 * BOXVL(I,J,L) * KRATE * BOH(I,J,L) * DT )
! Calculate new CH4 value: [CH4]=[CH4](1-k[OH]*delt) GCH4 = GCH4 * ( 1d0 - KRATE * BOH(I,J,L) * DT )
! Convert back from [molec/cm3] --> [kg/box] STT(I,J,L,1) = GCH4 / STT2GCH4
ENDIF ENDDO ENDDO ENDDO
example of model
code from GEOS-
CHEM
Typical chemical processes in the ocean:
1. C cycle (CO2, CO32-,HCO3
-,CaCO3,H2CO3)
2. pH
3. Si cycle (SiO2 to Si(OH)4-)
4. Fe cycle (Fe3+ to Fe2+)
5. photochemistry (degration of Organic C by light)
The Fe cycle in the oceans
Fe(III)L
Fe2+
Fe3+
pFe
dissolved Fe
hμ
P, B
organic or inorganic
sedimentationCoagulationDissociation
L
growth
hμ
hμ = photoreduction
dissolved, colloidal
carbon cycle
CO2
CO2 + H2O + CO2-3 2HCO-
3
chemical reactions
90
numbers in PgC/yr
atmosphere
ocean
! Set volumetric solubility constants for co2 in mol/l*atm (Weiss, 1974)! ------------------------------------------------------------------------------------! c00 = -58.0931 c01 = 90.5069 c02 = 22.2940 c03 = 0.027766 c04 = -0.025888 c05 = 0.0050578!! ln(k0) of solubility of co2 (eq. 12, Weiss, 1974)! ---------------------------------------------------------! cek0 = c00+c01/qtt+c02*zqtt+sal*(c03+c04*qtt+c05*qtt2) ak0 = exp(cek0) * smicr!! this is Wanninkhof (1992) equation 8 (with chemical enhancement), in cm/h! -------------------------------------------------------------------------! kgwanin(ji,jj) = (0.3*ws*ws + 2.5*(0.5246+ttc*(0.016256+ttc*0.00049946)))!! convert from cm/h to m/s and apply ice cover! --------------------------------------------! kgwanin(ji,jj) = kgwanin(ji,jj) /100./3600. * (1-freeze(ji,jj))
! Set Schmit constants! --------------------------------------------------------------------------
schmico2 = 2073.1-125.62*ttc+3.6276*ttc**2-0.043126*ttc**3!! compute gas exchange kg in mol/m2/yr/uatm! --------------------------------------------------------------------------
gasex = kgwanin * (660/schmico2)**0.5 kg = gasex * ak0 * 1.e3 * (3600.*24.*365.25)
example of model
code for CO2 gas
exchange
formulation
Outline of lecture:
1. IntroductionIntroduction
2. CChemicalhemical processes processes
3. Biological processes
4. PhysicalPhysical processes processes
5. Model evaluation and benchmarkingModel evaluation and benchmarking
6. One example (ocean CO2 sink)One example (ocean CO2 sink)
7. The modeller’s psychologyThe modeller’s psychology
SOLAS Science
Typical biological processes in the ocean:
1. phytoplankton growth
2. zooplankton grazing
3. bacterial remineralisation
4. particulate dynamics
• poorly known processes
• some measured rates
• vertical transport of particles
Parameterisation of biological processes are 1-Dimensional:
carbon cycle
45
34
CO2
CO2 + H2O + CO2-3 2HCO-
3
chemical reactions
90
numbers in PgC/yr
biological activity
11
atmosphere
ocean
surface
mixed layer depth
atmosphere
100 m
biological activity
real surface
atmosphere
100 m
biological activity
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
Primary Production
45 PgC/y
what they do
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
what they do
these bloom
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
what they do
these form shells
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
what they do
these respond to
pH
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
what they do
these float
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producersmixed
silicifiers
what they need
Fe P N
Fe P N
Fe P NFe P NFe P N
Fe P N Si
Respiration
34 PgC/y
Primary Production
45 PgC/y
pico-heterotrophsbacteria
phyto-plankton
pico-autotrophs
N2-fixers
calcifiers
DMS-producers
mixed
silicifiers
zoo-plankton
proto
meso
macro
Respiration
34 PgC/ypico-heterotrophsbacteria
zoo-plankton
proto
meso
macro
what they do
pico-heterotrophsbacteria
zoo-plankton
proto
meso
macro
what they do
these control blooms
pico-heterotrophsbacteria
zoo-plankton
proto
meso
macro
what they do
these produce big
feacal pellets
pico-heterotrophsbacteria
zoo-plankton
proto
meso
macro
what they need
F O O D
F O O D
F O O D
F O O D
time scale
a few +1 days
a few days
NO3
NH4
Si
DICFe
PO4
light
T
predation
mortality, sedimentation
environment
biogeochemistry
biology
maximum growth rate
phytoplankton growth
gro
wth
rate
(1/d
)
temperature (˚C)
pico phytoplankton
diatoms
micro zooplankton
meso zooplankton
gro
wth
rate
(1/d
)
temperature (˚C)
pico phytoplankton
diatoms
micro zooplankton
meso zooplankton
Modelling strategy:
diagnostic models (Najjar et al., 1992; OCMIP2 1998-200)
Pt
max 0, Pobs Pmod
biogeochemical models (Maier-Reimer et al., 1990-1993)
Pt
r g T g EP2
KP P
ecosystem models (Fasham et al., 1993)
N P
ZD
Calcifiers
PO4Fe
Nutrient Phytoplankton Zooplankton Detritus
(NPZD)
Dynamic Green Ocean Models
(DGOM)
!! Evolution of Mesozooplankton! ------------------------! trn(ji,jj,jk,jpmes) = trn(ji,jj,jk,jpmes) & & +mesoge(ji,jj,jk)*gramet(ji,jj,jk) & & -tortz2(ji,jj,jk)-respz2(ji,jj,jk)!! Evolution of DOC! ----------------! trn(ji,jj,jk,jpdoc) = trn(ji,jj,jk,jpdoc) & & +rn_sigpoc*orem(ji,jj,jk)-olimi(ji,jj,jk) & & +grarem(ji,jj,jk)*(1.-rn_sigmic)+grarem2(ji,jj,jk) & & *(1.-rn_sigmes)-xaggdoc(ji,jj,jk)-xaggdoc2(ji,jj,jk)& & +depdoc(ji,jj,jk)!! Evolution of POC! ------------------------------------------------------------------! trn(ji,jj,jk,jpgoc) = trn(ji,jj,jk,jpgoc) & & +grapoc2(ji,jj,jk)+resphy(ji,jj,jk,jpdia,1)+xagg(ji,jj,jk) & & +tortz2(ji,jj,jk)-orem2(ji,jj,jk)-grazgoc(ji,jj,jk) & & +xaggdoc2(ji,jj,jk) & & +(sinking2(ji,jj,jk)-sinking2(ji,jj,jk+1))/e3t_0(jk)!! Evolution of dissolved IRON! ------------------------------------------------------------------! trn(ji,jj,jk,jpfer) = trn(ji,jj,jk,jpfer)- & & xbactfer(ji,jj,jk)+ferat3*( & & respz2(ji,jj,jk)+respz(ji,jj,jk))+grafer(ji,jj,jk) & & +grafer2(ji,jj,jk)+ofer(ji,jj,jk) & & +(1.-rn_siggoc)*ofer2(ji,jj,jk) & & -xscave(ji,jj,jk)+irondep(ji,jj,jk) & & +depfer(ji,jj,jk)-xaggdfe(ji,jj,jk)!
example of model
code from PlankTOM
ecosystem model
Outline of lecture:
1. IntroductionIntroduction
2. CChemicalhemical processes processes
3. BiologicalBiological processes processes
4. Physical processes
5. Model evaluation and benchmarkingModel evaluation and benchmarking
6. One example (ocean CO2 sink)One example (ocean CO2 sink)
7. The modeller’s psychologyThe modeller’s psychology
SOLAS Science
Typical physical processes in the atmosphere and ocean:
1. advection
2. diffusion
3. mixing
4. convection
• well known processes with physical equations
• difficult to represent because of size of grid
• sub-grid scale parameterisations developed and tuned to give reasonable physical transport
Parameterisation of physical processes are 3-Dimensional:
convection and horizontal advection
vertical advection
Eddies and mixing
! Horizontal advective fluxes ! ----------------------------- ! ! =============== DO jk = 1, jpkm1 ! Horizontal slab ! ! =============== DO jj = 1, jpjm1 DO ji = 1, fs_jpim1 ! vector opt. ! upstream indicator zcofi = MAX( zind(ji+1,jj,jk), zind(ji,jj,jk) ) zcofj = MAX( zind(ji,jj+1,jk), zind(ji,jj,jk) ) ! volume fluxes * 1/2
zfui = 0.5 * e2u(ji,jj) * pun(ji,jj,jk) zfvj = 0.5 * e1v(ji,jj) * pvn(ji,jj,jk)
! centered scheme zcenut = zfui * ( tn(ji,jj,jk) + tn(ji+1,jj ,jk) ) zcenvt = zfvj * ( tn(ji,jj,jk) + tn(ji ,jj+1,jk) ) zcenus = zfui * ( sn(ji,jj,jk) + sn(ji+1,jj ,jk) ) zcenvs = zfvj * ( sn(ji,jj,jk) + sn(ji ,jj+1,jk) ) END DO END DO ! Tracer flux divergence at t-point added to the general trend ! -------------------------------------------------------------- DO jj = 2, jpjm1 DO ji = fs_2, fs_jpim1 ! vector opt.
zbtr = btr2(ji,jj) ! horizontal advective trends
zta = - zbtr * ( zwx(ji,jj,jk) - zwx(ji-1,jj ,jk) & & + zwy(ji,jj,jk) - zwy(ji ,jj-1,jk) ) zsa = - zbtr * ( zww(ji,jj,jk) - zww(ji-1,jj ,jk) & & + zwz(ji,jj,jk) - zwz(ji ,jj-1,jk) ) ! add it to the general tracer trends ta(ji,jj,jk) = ta(ji,jj,jk) + zta sa(ji,jj,jk) = sa(ji,jj,jk) + zsa END DO END DO !
example of model
code from NEMO
ocean physical model
carbon cycle
45
34
physical transport
11
33
CO2
CO2 + H2O + CO2-3 2HCO-
3
chemical reactions
90
numbers in PgC/yr
biological activity
11
atmosphere
ocean
Outline of lecture:
1. IntroductionIntroduction
2. CChemicalhemical processes processes
3. BiologicalBiological processes processes
4. PhysicalPhysical processes processes
5. Model evaluation and benchmarking
6. One example (ocean CO2 sink)One example (ocean CO2 sink)
7. The modeller’s psychologyThe modeller’s psychology
validation: process of checking if something satisfies
a certain criterion
evaluation: systematic determination of merit, worth
and significance of something using criteria against a
set of standards
benchmarking: process of comparing the quality of a
product to another that is widely considered to be a
standard. Benchmarking provides a snapshot of the
performance of your model, and helps to keep track
of model evalution.
41.e-5
Example benchmark for marine carbon cycle model:
• CO2 sink in 1990 between 1.8-2.6 PgC/y
• export of carbon between 9-12 PgC/y
• primary production between 40-70 PgC/y
• CO2 variability in equatorial Pacific between 0.6-1.0 PgC
• mezo-zooplankton grazing << micro-zooplankton grazing
• all phytoplankton biomass > 0.02 PgC
• no phytoplankton biomass dominate globally
Carbon-cycle model intercomparison Project (OCMIP)
visual evaluation of model results
formal evaluation of model results using a Taylor
diagram
Carbon-cycle model intercomparison Project (OCMIP)
Model Bias
100*
D
MDPbias
M: Model Results
D: Observational Data
-50
-40
-30
-20
-10
0
10
20
30
40
50
% M
odel
Bia
s Excellent
Excellent
Very Good
Very Good
Good
Good
Poor
Poor
Cost functions
D
MD
nCF
1
N: Number of Observations
D: Observational Data
σD: Standard deviation Data
CF < 1 = very good,1–2 = good,
2–5 = reasonable,>5 = poor
OSPAR Commission (1998).
CF < 1 = very good, 1–2 = good,
2–3 = reasonable, >3 = poor
Radach and Moll (2006).
0
0.2
0.4
0.6
0.8
1
1.2
Cos
t Fun
ctio
n
Very Good
Good
examples: ERSEMCourtesy of I.Allen
Model efficiency
2
2
1
DD
MDME
D: Observational Data
D_bar: Mean of Data
M: Model Results
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Mod
el E
ffici
ency
Excellent
No Skill
Poor
Very Good
Good
Outline of lecture:
1. IntroductionIntroduction
2. CChemicalhemical processes processes
3. BiologicalBiological processes processes
4. PhysicalPhysical processes processes
5. Model evaluation and benchmarkingModel evaluation and benchmarking
6. One example (ocean CO2 sink)
7. The modeller’s psychologyThe modeller’s psychology
carbon cycle
45
34
physical transport
11
33
CO2
CO2 + H2O + CO2-3 2HCO-
3
chemical reactions
90
numbers in PgC/yr
biological activity
11
atmosphere
ocean
Smith and Reynolds 2005 and IPCC 2007
water
energy
winds
observed warming trend 1979-2005
physical transport
chemical reactions
ocean
biological activity
sea-air CO2 flux anomaly
• PISCES-T ecosystem model • 2 phyto, 2 zoo., 2 sinking particles• limitation by Fe, P, and Si• initialise with observations in 1948
(Buitenhuis et al., GBC 2006)
OPA model
• OPA General Circulation model • 0.5-1.5ox2o resolution• 31 vertical levels • calculated vertical mixing• NCEP daily forcing
• PISCES-T ecosystem model • 2 phyto, 2 zoo., 2 sinking particles• limitation by Fe, P, and Si• initialise with observations in 1948
(Buitenhuis et al., GBC 2006)
OPA model
• OPA General Circulation model • 0.5-1.5ox2o resolution• 31 vertical levels • calculated vertical mixing• NCEP daily forcing for year 1967
Change in Southern Ocean CO2 sink in model
real forcing
real forcing
1967 forcing
Change in Southern Ocean CO2 sink in model
changes in winds
Outline of lecture:
1. IntroductionIntroduction
2. CChemicalhemical processes processes
3. BiologicalBiological processes processes
4. PhysicalPhysical processes processes
5. Model evaluation and benchmarkingModel evaluation and benchmarking
6. One example (ocean COOne example (ocean CO22 sink) sink)
7. The modeller’s psychology
truth
time
The modeller‘s psychology
truth
time
illusion (everybody is happy)
truth
illusion (everybody is happy)
time
truth
time
chaos (everybody is
happy)
illusion (everybody is happy)
truth
illusion (everybody is happy)
chaos (everybody is
happy)
relief (need a new job)
time
truth
illusion (everybody is happy)
chaos (everybody is
happy)
relief (need a new job)
climate modelsland ecosystem
modelsocean biogeochemistr
y models
climate models
time
• do your best, but simplify to answer your question
• use benchmarking to
• i) validate, and
• ii) follow improvements in your model
• EVERYTHING must make sense
Putting it all together: