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Modeling and Data Assimilation of Ocean Biology

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Title: Modeling and Data Assimilation of Ocean Biology


1
Modeling and Data Assimilation of Ocean
Biology Using MODIS Data Watson
Gregg NASA/GMAO Surface Spectral
Irradiance Model Validation Data Assimilation
2
NASA Ocean Biogeochemical Model (NOBM)
Winds, ozone, relative humidity, pressure,
precip. water, clouds (cover, tc), aerosols (ta,
?a, asym)
Sea Ice
Dust (Fe)
Winds SST
Radiative Model (OASIM)
Ed(?) Es(?)
Ed(?) Es(?)
Layer Depths
IOP
Biogeochemical Processes Model
Circulation Model (Poseidon)
Temperature, Layer Depths
Advection-diffusion
Global model grid domain 84?S to 72?N1.25?
lon., 2/3? lat.14 layers
3
OASIM (Ocean-Atmosphere Spectral Irradiance Model)
33 spectral bands, 25 nm resolution 350-700nm
Water vapor
CO2
Ozone
Oxygen
Molecules, aerosols
LwN
Ed
Es
? Ed, Es
Ed
Es
? Ed, Es
air
sea
(1 - ?)
Es
Eu
(1 - ?)
Ed
4
Spectral absorption and scattering coefficients
m-1 (water) m2 mg-1(others)
Wavelength nm
5
Data sets needed for OASIM and dates available.
ISCCP is the International Satellite Cloud
Climatology Project MODIS is the Moderate
Resolution Imaging Spectroradiometer (Terra and
Aqua are spacecraft) AVHRR is the Advanced Very
High Resolution Radiometer TOMS is the Total
Ozone Mapping Spectrometer (Nimbus-7 and
EP (Earth Probe) are spacecraft) and MERRA is
the Modern-Era Retrospective analysis for
Research and Applications . 1981
1983/06 1993/05 1996/07 2000/03
2002/07 Clouds Cover ISCCP climatology
ISCCP LWP ISCCP climatology ISCCP
re MODIS climatology MODIS-Terra
MODIS-TerraAqua Aerosols ta AVHRR
MODIS-Terra MODIS-TerraAqua ?a MODIS
climatology MODIS-Terra
MODIS-TerraAqua asymmetry MODIS climatology
MODIS-Terra MODIS-TerraAqua Ozone
Nimbus-7 TOMS Climatology
EP-TOMS Sfc Pressure MERRA Wind
Speed MERRA Rel. humidity MERRA Precip.
water MERRA
6
In situ observations of total surface irradiance.
(gt2000 observations)
7
W m-2
8
OASIM
ISCCP
N 2249 Slope 0.894 Y-int 37.31 r
0.932 RMS 18.96 Bias 12.69
N 2322 Slope 1.017 Y-int 0.212 r
0.888 RMS 20.91 Bias 4.19
NCEP
ISLSCP II
N 2333 Slope 0.604 Y-int 99.61 r
0.696 RMS 29.78 Bias 7.456
N 270 Slope 1.032 Y-int 10.43 r 0.969 RMS
23.06 Bias 17.66
9
Model Validation
Most common use of MODIS data How well does the
model perform?
10
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11
Seasonal Correlation
North Pacific
North Atlantic
North Central Pacific
North Central Atlantic
North Indian
Equatorial Indian
Equatorial Pacific
Equatorial Atlantic
Chlorophyll (mg m-3)
South Indian
South Pacific
South Atlantic
Antarctic
Day of Year
Statistically positively correlated (P lt 0.05)
all 12 basins
Red model Diamonds MODIS monthly mean
12
Data Assimilation
Incorporation of data into a model via constant
confrontation Now becoming established in ocean
biology gt50 papers using data, 12 using satellite
data (Gregg et al., Journal of Marine Systems,
2009) In ocean biology, Two Classes Variational
(e.g., adjoint, 4DVar) Sequential (e.g., Kalman
Filter) Here we used Sequential
Methodologies, Conditional Relaxation Analysis
Method Ensemble Kalman Filter
13
1
Data Assimilation NASA Ocean Color Sensors
(SeaWiFS and MODIS-Aqua) Assimilation variable
chlorophyll Conditional Relaxation Analysis
Method (sequential method) Comparison with in
situ data
Bias Uncertainty N SeaWiFS
-1.3 32.7
2086 Assimilation Model 0.1 33.4 4465
14
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15
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16
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17
Assimilation of Satellite Chlorophyll
1
mg m-3
Data assimilation improves a model by forcing
agreement with data and reducing model biases.
It also improves the data by filling in
missing data and reducing sampling biases.
18
Daily ocean coverage by MODIS-Aqua and SeaWiFS.
clouds
aerosols
sun glint
inter-orbit gap
sensor tilt change
high solar zenith angle
19
MODIS Sampling Aug 2003
Assimilation Aug 2003
mg m-3
Difference (MODIS-assimilation) Aug 2003
No. Days Sampled by MODIS Aug 2003
Number

20
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21
pCO2atm
pCO2sw
PIC
DIC
DOC
443
N/C Detritus
bPIC
POC
443
bd
bchl
Carbon
22
  • Summary
  • MODIS data find multiple uses for ocean biology
    modeling, from radiative transfer to ocean
    modeling validation to data assimilation
  • MODIS atmospheric and ocean color data are useful
    for ocean biology modeling
  • Data assimilation not only improves models but
    also improves data representations
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