sinjaesinjae yoo yoo, , jisoojisoo parkpark kordi ... · ((guoguo, 1993), 1993) • wind stress...
TRANSCRIPT
PrimaryPrimary productivity productivity of the Yellow Seaof the Yellow Sea
SinjaeSinjae YooYoo, , JisooJisoo ParkParkKORDIKORDI
AnsanAnsan, South Korea, South Korea
OutlineOutline
BackgroundsBackgrounds• Uncertainty in PP estimation in the YS• Env. characteristics
Results of our previous studyResults of our previous studyThings to consider furtherThings to consider furtherCurrent efforts & future directionsCurrent efforts & future directions
(PICES NPESR, 2004)
The Yellow Sea is the most productive sea in NP, or is it?
Primary productivity of YS Primary productivity of YS (limitation of current knowledge)(limitation of current knowledge)
Point measurements vary Point measurements vary in the range of 11.78 in the range of 11.78 ∼∼ 3,175 3,175 ㎎㎎ C mC m--22 dd--11 depending on time and depending on time and space. space. Some of Some of inin--situ estimates situ estimates on annual production on annual production are ~150 are ~150 gCgC mm--22 yy--11, which is small compared , which is small compared to fish landings.to fish landings.Uncertainty is Uncertainty is significantsignificant
~600 gC m-2 yr-1
Annual Global Primary Production(IMCS, Rutgers)
Comparison of CHL by OC4 (standard) algorithm and in-situ CHL
Some Some envenv. characteristics of the YS . characteristics of the YS Major forcing of hydrographic condition Major forcing of hydrographic condition ((GuoGuo, 1993), 1993)• Wind stress (monsoon) and heat exchange with
atmosphere• Strong tides (max. ~11 m excursion)• Fresh water input
Extreme temperature range (seasonally and Extreme temperature range (seasonally and vertically)vertically)Extreme turbidity range (Extreme turbidity range (SecchiSecchi transparency: transparency: >12 m ~ <0.2m) >12 m ~ <0.2m)
Bathymetry and vertical temp. structureBathymetry and vertical temp. structure
Xia et al. (2006)
FEB
FEB APR
APR
JUL
JUL OCT
OCT
Better algorithm is needed for:Better algorithm is needed for:
Better retrieval of CHL and KPARBetter retrieval of CHL and KPAREstimation of photosynthesis rate in extreme Estimation of photosynthesis rate in extreme turbidity range.turbidity range.Vertical structure of chlorophyllVertical structure of chlorophyll• Error of PP estimation when ignoring subsurface
chlorophyll maximum = 17.7%~30.1% (Park, 2000)
Dep
th (m
)
Chlorophyll-a (mg m-3)
Our previous approach Our previous approach (Son et al., 2005)(Son et al., 2005)
We divided the YS into three areas We divided the YS into three areas and got averages from each. and got averages from each. • P-I parameters (141 sets)• Vertical CHL profiles were parameterized
(86 profiles).Smith model (1936) was used for Smith model (1936) was used for photosynthesis.photosynthesis.KPAR was derived from nLw555KPAR was derived from nLw555Empirical local algorithm (Empirical local algorithm (AhnAhn, 2004) , 2004) was used to retrieve chlorophyllwas used to retrieve chlorophyll
y = 0.267x0.7472
R2 = 0. 5964
(this study )
y = 0.2249 x0.7269
(Son et al ., 20 05)
0 .01
0.1
1
10
0.1 1 10
SeaWi FS l evel2 nLw5 55
KP
AR
(m
-1)
Primary production in the 3 regions of the Yellow SeaPrimary production in the 3 regions of the Yellow Sea
Sub-regionArea×103
km2
Mean Primary Production Rate
mgC m-2 d-1 ×104 tonC d-1
May Sep May Sep
CCW 58.9 590.3 589.3 3.5 3.5
MYW 147.4 946.5 722.6 13.9 10.6
KCW 28.9 734.2 553.7 2.1 1.6
mean 835.6 672.4
total 235.2 19.7 15.8
Limitations of the previous studyLimitations of the previous studyEstimation was possible only for two seasons Estimation was possible only for two seasons (spring and autumn).(spring and autumn).Regions were fixed geographically but not Regions were fixed geographically but not optically.optically.• Averages of P-I parameters were used.• Averages of CHL profile parameters were used.
PP in the turbid region was not modeled PP in the turbid region was not modeled accurately.accurately.CHL algorithm needs improvements.CHL algorithm needs improvements.
Seasonal changes of KPAR (m-1)
Seasonal changes of depth-integrated PAR (mE m-2 s-1)
Two types of production systems Two types of production systems in YSin YS
TidallyTidally--mixed zonemixed zoneLight-limitedDark-adaptedTyco-pelagic species
Seasonally stratified Seasonally stratified regionregion
Nutrient-limited in the upper layerSCM production is significant
Zone-1
Zone-2
Zone-3
Zone-4
Optical characteristicsOptical characteristics
May, 1998(Yoo & Park, 1998)
PP--I curves in the turbid zone I curves in the turbid zone
Yoo & Shin (1995)
Current effortsCurrent efforts
Better parameterization of physiological Better parameterization of physiological parametersparameters• In turbid environment
More inMore in--situ measurements are planned.situ measurements are planned.• Turbid waters• Winter
Better estimation of CHL vertical structureBetter estimation of CHL vertical structureChlorophyll algorithmChlorophyll algorithm• YOC workshop (YS Ocean Color Database)
Can we retrieve/estimate CHL structure parameters Can we retrieve/estimate CHL structure parameters from surface properties (CHL, KPAR, SST)?from surface properties (CHL, KPAR, SST)?
y = 53.97x0.889
R² = 0.9081
10
100
0.01 0.1 1 10
B_t
ot (m
g m
-3)
B_pd (mg m-3)
y = 1.140x1.070
R² = 0.9170.01
0.1
1
10
0.01 0.1 1 10
B_p
d (m
g m
-3)
B0 (mg m-3)
y = 1.076x + 0.005R² = 0.764
0
0.3
0.6
0.9
1.2
1.5
0 0.5 1
B_p
d (m
g m
-3)
B0 (mg m-3)
y = 0.573x + 4.456R² = 0.664
0
10
20
30
40
50
0 20 40 60 80
Zm (m
)
Z_eu (m)
y = 1.005x + 8.365R² = 0.596
010203040506070
0 50
Z_eu
(m)
MLD (m)
y = 3.659x-0.92
R² = 0.725
05
101520253035404550
0 0.1 0.2 0.3 0.4 0.5
Zm (m
)
KPAR (m-1)
Yellow Sea Ocean Color DatabaseYellow Sea Ocean Color Database(Bio(Bio--optical measurements)optical measurements)
ChinaChina• TANG Junwu
National Satellite Ocean Application Service, SOA, Beijing
JapanJapan• KAWAMURA Hiroshi
Tohoku University• ISHIZAKA Joji
Nagasaki University
KoreaKorea• AHN Yu-Hwan, YOO Sinjae
KORDI• KIM Sang-Woo
NFRDI
Thank You!Thank You!