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Biogeochemical modelling Corinne Le Quéré University of East Anglia and the British Antarctic Survey
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project the future test hypothesis (e.g. CLAW) quantify feedbacks formalize your ideas e.g. F CO2 = k g ·(pCO 2 )
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SOLAS Science
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Model dimensions: 0D F CO2 = k g ·(pCO 2 ) 1D depth/height 2D depth/height + latitude 3D depth/height + latitude + longitude 4D depth/height + latitude + longitude + time
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Outline of lecture: 1.Introduction 2.Chemical processes 3.Biological processes 4.Physical processes 5.Model evaluation and benchmarking 6.One example (ocean CO 2 sink) 7.The modellers psychology
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Outline of lecture:
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SOLAS Science
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known processes measured species derived rates Parameterisation of chemical processes are 0-Dimensional:
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Typical chemical processes in the atmosphere: 1.ozone 2.NOx 3.hydrocarbon (Volatile Organic Carbon) 4.OH - 5.aerosols 6.CO, CH 4
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NO x AND VOC processes (D. Jacobs) Emission NO h (420 nm) NO 2 HNO 3 1 day NITROGEN OXIDES (NO x ) VOLATILE ORGANIC COMPOUNDS (VOC) Emission VOC OH HCHO h (340 nm) hours CO hours BOUNDARY LAYER ~ 2 km Deposition
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Tropospheric ozone processes (D. Jacobs) O3O3 O2O2 h O3O3 OHHO 2 h, H 2 O Deposition NO H2O2H2O2 CO, VOC NO 2 h STRATOSPHERE TROPOSPHERE 8-18 km
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!================================================================= ! ! 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 example of model code from GEOS-CHEM
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Typical chemical processes in the ocean: 1.C cycle (CO 2, CO 3 2-,HCO 3 -,CaCO 3,H 2 CO 3 ) 2.pH 3.Si cycle (SiO 2 to Si(OH) 4 - ) 4.Fe cycle (Fe 3+ to Fe 2+ ) 5.photochemistry (degration of Organic C by light)
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The Fe cycle in the oceans Fe(III)L Fe 2+ Fe 3+ pFe dissolved Fe hμhμ P, B organic or inorganic sedimentation Coagulation Dissociation L growth hμhμ hμ = photoreduction dissolved, colloidal
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carbon cycle CO 2 CO 2 + H 2 O + CO 2- 3 2HCO - 3 chemical reactions 90 numbers in PgC/yr atmosphere ocean
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! 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 CO 2 gas exchange formulation
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Outline of lecture:
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SOLAS Science
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Typical biological processes in the ocean: 1.phytoplankton growth 2.zooplankton grazing 3.bacterial remineralisation 4.particulate dynamics
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poorly known processes some measured rates vertical transport of particles Parameterisation of biological processes are 1-Dimensional:
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carbon cycle 45 34 CO 2 CO 2 + H 2 O + CO 2- 3 2HCO - 3 chemical reactions 90 numbers in PgC/yr biological activity 11 atmosphere ocean
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surface mixed layer depth atmosphere 100 m biological activity
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real surface atmosphere 100 m biological activity
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers Primary Production 45 PgC/y what they do
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers what they do these bloom
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers what they do these form shells
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers what they do these respond to pH
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers what they do these float
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phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers what they need Fe PN P N P N PN PN PNSi
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Respiration 34 PgC/y Primary Production 45 PgC/y pico-heterotrophs bacteria phyto- plankton pico-autotrophs N 2 -fixers calcifiers DMS-producers mixed silicifiers zoo- plankton proto meso macro
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Respiration 34 PgC/y pico-heterotrophsbacteria zoo- plankton proto meso macro what they do
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pico-heterotrophsbacteria zoo- plankton proto meso macro what they do these control blooms
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pico-heterotrophsbacteria zoo- plankton proto meso macro what they do these produce big feacal pellets
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pico-heterotrophsbacteria zoo- plankton proto meso macro what they need FOOD F O O D FOOD F O O D
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time scale a few +1 days a few days
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NO 3 NH 4 Si DIC Fe PO 4 light T predation mortality, sedimentation environment biogeochemistry biology maximum growth rate phytoplankton growth
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growth rate (1/d) Buitenhuis et al., 2006 temperature (˚C) pico phytoplankton diatoms micro zooplankton meso zooplankton
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growth rate (1/d) Buitenhuis et al., 2006 temperature (˚C) pico phytoplankton diatoms micro zooplankton meso zooplankton
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Modelling strategy: diagnostic models (Najjar et al., 1992; OCMIP2 1998-200) biogeochemical models (Maier-Reimer et al., 1990-1993) ecosystem models (Fasham et al., 1993) NP ZD Calcifiers PO 4 Fe Nutrient Phytoplankton Zooplankton Detritus (NPZD) Dynamic Green Ocean Models (DGOM)
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! ! 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
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Outline of lecture:
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SOLAS Science
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Typical physical processes in the atmosphere and ocean: 1.advection 2.diffusion 3.mixing 4.convection
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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:
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convection and horizontal advection
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vertical advection
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Eddies and mixing
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! 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 ! 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 ! example of model code from NEMO ocean physical model
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carbon cycle 45 34 physical transport 11 33 CO 2 CO 2 + H 2 O + CO 2- 3 2HCO - 3 chemical reactions 90 numbers in PgC/yr biological activity 11 atmosphere ocean
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Outline of lecture:
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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.
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4 1.e-5 Example benchmark for marine carbon cycle model: CO 2 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 CO 2 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
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Carbon-cycle model intercomparison Project (OCMIP) visual evaluation of model results
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formal evaluation of model results using a Taylor diagram Carbon-cycle model intercomparison Project (OCMIP)
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Model Bias M: Model Results D: Observational Data
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Cost functions N: Number of Observations D: Observational Data σ D : 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). examples: ERSEM Courtesy of I.Allen
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Model efficiency D: Observational Data D_bar: Mean of Data M: Model Results
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Outline of lecture:
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carbon cycle 45 34 physical transport 11 33 CO 2 CO 2 + H 2 O + CO 2- 3 2HCO - 3 chemical reactions 90 numbers in PgC/yr biological activity 11 atmosphere ocean
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Smith and Reynolds 2005 and IPCC 2007 water energy winds observed warming trend 1979-2005
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physical transport chemical reactions ocean biological activity
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sea-air CO 2 flux anomaly
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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 0.5-1.5 o x2 o resolution 31 vertical levels calculated vertical mixing NCEP daily forcing
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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 0.5-1.5 o x2 o resolution 31 vertical levels calculated vertical mixing NCEP daily forcing for year 1967
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Change in Southern Ocean CO 2 sink in model real forcing
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1967 forcing Change in Southern Ocean CO 2 sink in model changes in winds
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Outline of lecture:
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truth time The modellers psychology
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truth time illusion (everybody is happy)
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truth illusion (everybody is happy) time
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truth time chaos (everybody is happy) illusion (everybody is happy)
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truth illusion (everybody is happy) chaos (everybody is happy) relief (need a new job) time
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truth illusion (everybody is happy) chaos (everybody is happy) relief (need a new job) climate models land ecosystem models ocean biogeochemistry models climate models time
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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:
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