developing npp algorithms for the arctic

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Developing NPP algorithms for the Arctic

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Developing NPP algorithms for the Arctic. 1. Empirical chlorophyll based algorithm. Chukchi Sea. ANCOVA H 0 – means between light levels are equal. P < 0.00 , H 0 is rejected. 1. Empirical chlorophyll based algorithm. Chukchi Sea. 1. Empirical chlorophyll based algorithm. Resolute Bay. - PowerPoint PPT Presentation

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Page 1: Developing NPP algorithms  for the Arctic

Developing NPP algorithms for the Arctic

Page 2: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Chlorophyll (mg m-3)

0.001 0.01 0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.01

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Chukchi Sea

Page 3: Developing NPP algorithms  for the Arctic

ANCOVA H0 – means between light levels are equal

SourceSum-of-

Squaresdf Mean-Square F-ratio P

LIGHT_CH 170.6 5 34.1 15.1 0.000

DAILY_PP_CH 3052.7 1 3052.7 1354.8 0.000

Error 1428.6 634 2.3

1% 5% 15% 30% 50% 100%

1% 1.000

5% 0.000 1.000

15% 0.000 0.912 1.000

30% 0.000 0.921 1.000 1.000

50% 0.000 0.780 1.000 0.999 1.000

100% 0.000 0.931 1.000 1.0000 0.99 1.000

P < 0.00 , H0 is rejected

1% 5% 15% 30% 50% 100%

1% 1.000

5% 0.000 1.000

15% 0.000 0.912 1.000

30% 0.000 0.921 1.000 1.000

50% 0.000 0.780 1.000 0.999 1.000

100% 0.000 0.931 1.000 1.0000 0.99 1.000

Page 4: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Chlorophyll (mg m-3)

0.001 0.01 0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.01

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Chlorophyll (mg m-3)

0.001 0.01 0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.01

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Chukchi Sea

Page 5: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Resolute Bay

Chlorophyll (mg m-3)

0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Page 6: Developing NPP algorithms  for the Arctic

ANCOVA H0 – means between light levels are equal

Source Sum-of-Squares df Mean-Square F-ratio P

LIGHT_CH 1027.1 5 205.4 11.7 0.000

DAILY_PP_CH 8696.4 1 8696.4 495.6 0.000

Error 2913.1 166 17.5

P < 0.00 , H0 is rejected

1% 5% 15% 30% 50% 100%

1% 1.000

5% 0.000 1.000

15% 0.000 0.581 1.000

30% 0.000 0.322 0.998 1.000

50% 0.000 0.205 0.988 0.999 1.000

100% 0.000 0.502 1.000 1.0000 0.995 1.000

Page 7: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Chlorophyll (mg m-3)

0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Resolute Bay

Chlorophyll (mg m-3)

0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

0.1

1

10

100

1000

10000

100% 50% 30% 15% 5% 1%

Page 8: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Barents Sea

Chlorophyll (mg m-3)

0.1 1 10 100

Dai

ly P

P (

mg

C m

-3 d

ay-1

)

1

10

100

1000

Page 9: Developing NPP algorithms  for the Arctic

1. Empirical chlorophyll based algorithm

Log Chlorophyll (mg m-3)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Lo

g D

aily

PP

(m

g C

m-3

da

y-1)

-2

-1

0

1

2

3

4

Resolute BayChukchi BayBarents

Combined dataset

Page 10: Developing NPP algorithms  for the Arctic

ANCOVA H0 – means between regions are equal

Source Sum-of-Squares df Mean-Square F-ratio P

LIGHT_CH 2.28 2 1.14 18.55 0.000

DAILY_PP_CH 136.48 1 136.48 2220.18 0.000

Error 33.93 552 0.061

P < 0.00 , H0 is rejected

Resolute Bay

Chukchi Sea Barents Sea

Resolute Bay 1.000

Chukchi Sea 0.000 1.000

Barents Sea 0.361 0.000 1.000

Page 11: Developing NPP algorithms  for the Arctic

Log Chlorophyll (mg m-3)

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Lo

g D

aily

PP

(m

g C

m-3

day

-1)

-2

-1

0

1

2

3

4

Resolute BayChukchi BayBarents

1. Empirical chlorophyll based algorithm

Combined dataset

ANCOVA

•Chl 0.8 – 32 mg m-3

•P < 0.01

Average Chlorophyll•Resolute Bay 8.1 mg m-3

•Barents Sea 4.2 mg m-3

•Chukchi Sea 1.2 mg m-3

Log PP = 1.36 + 1.0.Log Chl

Page 12: Developing NPP algorithms  for the Arctic

1.1 Surface chlorophyll vs. Euphotic zone chlorophyll

Chlorophyll (mg m-3)

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Dep

th (

m)

0

20

40

60

80

100

Primary production P (mg C m-3 h-1)

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Spring

2 4 6 8 10 120

20

40

60

80

1000.5 1.0 1.5 2.0

ChlorophyllProductivity

Summer

Page 13: Developing NPP algorithms  for the Arctic

1.1 Surface chlorophyll vs. Euphotic zone PP

Surface Chlorophyll (mg m-3)

0.01 0.1 1 10 100

Eu

ph

oti

c zo

ne

intr

egra

ted

p

rim

ary

pro

du

ctiv

ity

(mg

C m

-2 d

-1)

1

10

100

1000

10000

Chukchi Sea Spring 2002 & 2004

Chukchi Sea Summer 2002 & 2004Resolute Bay

Chukchi Spring r2=0.51

Chukchi Summer r2=0.01

Resolute Bay r2=0.63

Page 14: Developing NPP algorithms  for the Arctic

1.1 Rrs vs. Euphotic zone PP

Rrs 444>490>510/555

1 10

Wat

er c

olu

mn

in

teg

rate

d

pro

du

ctiv

ity

(mg

C m

-2)

0.01

0.1

1

10

100

SpringSummer

Page 15: Developing NPP algorithms  for the Arctic

2. Model based on C:Chl ratios

Behrenfeld et al. (2005) developed a productivity model based on Chl:C ratios and chlorophyll concentrations derived from ocean color satellite observations.

1. Carbon (POC) is retrieved from backscatter2. Chlorophyll is retrieved from Rrs ratios (i.e OC4V4, OC3M or OC3Arc)

These are coupled with mixed layer light levels from surface PAR and K490 observations and growth rates estimated from the literature. The final equation is:

NPP = C . µ . Zeu . h(Io)

Where C is carbon, µ is growth rate, Zeu is the euphotic depth and h(Io) describes how changes in surface light influence the depth dependent profile of carbon fixation

Page 16: Developing NPP algorithms  for the Arctic

2. Model based on C:Chl ratios

Chlorophyll vs Rrs n = 791

y = -3.229x + 0.4986

R2 = 0.7958

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Log Rrs (443>490>510/555)

Log

Chl

orop

hyll

(mg/

m3)

Lab00Lab97Lab96Res98Res96Res95Res94Goa97Ber96Ber95Arc00Arc02-1Arc02-2OC4v4Arc04-1Arc04-2allGreenland SeaLinear (all)

Page 17: Developing NPP algorithms  for the Arctic

2. Model based on C:Chl ratios

Model Input

1. Carbon (POC) is retrieved from backscatter – Behrenfeld global relationship2. Chlorophyll is retrieved from Rrs ratios - OC3Arc3. Surface PAR – estimated from observations4. Mixed layer light levels – estimated from observations5. Growth rate – literature global, 0.5 – 2 divisions d-16. h(Io) taken from Behrenfeld and Falkowski (1997)

NPP = C . µ . Zeu . h(Io)

Model output

1. NPP euphotic zone integrated - g C m-2 day-1

Page 18: Developing NPP algorithms  for the Arctic

2.1 Results

Measured euphotic zone daily productivity (g C m-2 d-1)

0.001 0.01 0.1 1 10

Mo

del

led

eu

ph

oti

c zo

ne

dai

ly

pro

du

ctiv

ity

(g C

m-2

d-1

)

0.001

0.01

0.1

1

10

Chukchi Sea SpringChukchi Sea Summer

Spring r2 =0.32Slope = 2.6P > 0.05

Summer r2 =0.00Slope = 0.02P > 0.1

Measured euphotic zone daily productivity (g C m-2 d-1)

0.001 0.01 0.1 1 10

Mo

del

led

eu

ph

oti

c zo

ne

dai

ly

pro

du

ctiv

ity

(g C

m-2

d-1

)

0.001

0.01

0.1

1

10

Chukchi Sea SpringChukchi Sea Summer

Spring r2 =0.32Slope = 2.6P > 0.05

Summer r2 =0.00Slope = 0.02P > 0.1

Page 19: Developing NPP algorithms  for the Arctic

2.1 Results – adjusted model

Measured 1st optical depth daily productivity (g C m-2 d-1)

0.0001 0.001 0.01 0.1 1 10

Mo

de

lle

d 1

st o

pti

cal

de

pth

da

ily

pro

du

cti

vity

(g

C m

-2 d

-1)

0.0001

0.001

0.01

0.1

1

10Chukchi Sea SpringChukchi Sea Summer

Summer r2 =0.60Slope = 1.28P < 0.01

Spring r2 =0.81Slope = 0.82P < 0.01

Page 20: Developing NPP algorithms  for the Arctic

Euphotic zone integrated 1st optical depth

Data Slope r2 P Slope r2 P

Chukchi Spring 0.36 0.61 < 0.01 0.82 0.81 < 0.01

Chukchi Summer 0.18 0.00 > 0.10 1.28 0.60 < 0.01

Page 21: Developing NPP algorithms  for the Arctic

2.2 Sensitivity to model input

Measured euphotic zone daily

productivity (g C m-2 d-1)

0.01 0.1 1 10

Mo

del

led

eu

ph

oti

c zo

ne

dai

ly

pro

du

ctiv

ity

(g C

m-2

d-1

)

0.01

0.1

1

10

Mean Ig_0.8 Mean - 1stdev Ig_0.26 Mean + 1 stdev Ig_1.34

Sensitivity to changes in mixed layer light levels

Page 22: Developing NPP algorithms  for the Arctic

2.2 Sensitivity to model input

Mixed layer light levels Surface PAR Growth rates

r2 Slope RMS r2 Slope r2 slope

Mean 0.75 0.36 0.76 0.75 0.36 0.75 0.49

- 1 stdev 0.75 0.22 0.69 0.75 0.29 0.75 0.33

+ 1 stdev 0.75 0.57 0.88 0.75 0.64 0.75 0.65

Page 23: Developing NPP algorithms  for the Arctic

3.0 Comparison of both methods

Measured Surface NPP (g C m-3 d-1)

0.0001 0.001 0.01 0.1 1

Pre

dic

ted

Su

rfac

e N

PP

(g

C m

-3 d

-1)

Pre

dic

ted

1st

op

tica

l d

epth

NP

P (

g C

m-2

d-1

)

0.0001

0.001

0.01

0.1

1

Summer 2000 Summer 2000Summer 2004 Summer 2004

Chl based Behrenfeld Model

Measured 1st optical depth NPP (g C m-2 d-1)

Chl basedr20.76slope 0.6

Chl basedr20.46slope 0.62

Page 24: Developing NPP algorithms  for the Arctic

Surface Chlorophyll - Spring

Page 25: Developing NPP algorithms  for the Arctic

Surface Chlorophyll - Summer

Page 26: Developing NPP algorithms  for the Arctic

Surface chlorophyll - Fall

Page 27: Developing NPP algorithms  for the Arctic

Surface chlorophyll – Winter

Page 28: Developing NPP algorithms  for the Arctic

Surface PP - Spring

Page 29: Developing NPP algorithms  for the Arctic

Surface PP - Summer

Page 30: Developing NPP algorithms  for the Arctic

Surface PP - Fall

Page 31: Developing NPP algorithms  for the Arctic

Surface PP - Winter

Page 32: Developing NPP algorithms  for the Arctic

Kd PAR – Spring + Summer