lessons from cbodaq campaign. july 2011 heat wave hottest month for baltimore-washington...

Lessons from CBODAQ Campaign


July 2011 heat waveHottest month for Baltimore-Washington metropolitan area in recorded history


1) Organization/Planning (before the campaign): should we do something differently when organizing/planning GoMex? Do you think we missed something, or should have organized certain aspects of the CBODAQ campaign better?

2) On the boat (during the campaign): Any lessons learnt during the campaign that would be helpful for GoMex? For example regarding filtering, deployment of instruments, communication, etc.

3) GEO-CAPE science questions that CBODAQ helped address (after the campaign), … and moving forward

Lessons from CBODAQ campaign (July 2011)


The combination of stationary/drifters/transect sampling approaches allowed us to examine spatial and temporal variability in ocean color and biogeochemical variables – Is this variability due to biological/photo-bio-chemical processes (e.g. vertical migration) or physical processes (e.g. tides/advection) (Mike).

A preliminary scan of the data revealed very low oxygen saturation, perhaps associated with the observed fish kill, a good relationship between TA and salinity - Considerable spatial variability in all mapped data related to salinity, TA, DIC, pCO2 (Joe).

Optical properties of source water considerably different than the bulk properties of the larger (sampled) water mass – We need more data that shows the source waters in sufficient detail. With sufficient resolution of the dynamic range, it will hopefully be possible to understand what processes are controlling the ultimate expression of the parent water mass (e.g., settling, photo-oxidation, etc.) (Stan)

New chlorophyll-a algorithm (red/green band ratio) for the Chesapeake Bay was validated using these data and historical data - From this dataset, variations in Chl far exceeded those in CDOM. As a result, CDOM inversion models developed for Tampa Bay cannot be fully validated from this Chesapeake Bay dataset (Chuanmin)



Not large variability in CDOM optical characteristics in the main stem of the Bay, but large CDOM gradients close to wetlands/marshes (Chuanmin, Antonio, Maria).

Considerable variability in atmospheric properties (remote-sensing of aerosols and trace gases) over the Chesapeake Bay watershed and estuary, in agreement with air-quality model simulations; Strong gradients at the land-water interface due to anthropogenic activities, atmospheric chemistry and meteorological processes (Maria, Jay, Chris)

In-situ aerosol and trace gas measurements (Carolyn)-Shipboard in situ aerosol and trace gas data included water-soluble inorganic ions (WSII), EC/OC, and CH4

- WSII data showed very clean marine conditions were encountered (based on comparison to multi-year data set from the Gulf of Maine)-WSII differed significantly between the lowest altitude of the P3-B and shipboard-Aerosol carbon typically ≥95% OC and ≤5% EC, except for a few samples on the 13th, 14th, 15th, & 16th where EC comprised 7-20% of the total carbon-Atmospheric CH4 (mean 1.83 ppmv) comparable to surface sea sater CH4 (mean 1.88 ppmv), day-to-day variability differed between these two regimes


1) Organization/Planning (before the campaign): should we do something differently when organizing/planning GoMex? Do you think we missed something, or should have organized certain aspects of the CBODAQ campaign better?Overall well prepared

It would be beneficial to have a better understanding of on-board power requirements, especially if the ship has a limited amount of "clean" power, like the R/V SRV-X did.

It would be good to have a better layout of the ship's bench spaces and science areas (and distribute to group as early as possible) to help optimize where/how the equipment gets set up.

Communicate with D-AQ as early as possible to make arrangements for participants and atmospheric instrumentation on boat as early as possible (MPL was put on SRV-X on July 15th).

Some of the samples did not cover enough dynamic range (as needed for algorithm development and variability assessment). Better planning may be achieved through analyzing existing SeaWiFS or MODIS data over the study region to narrow down the sampling locations.

Use existing datasets in study region before the campaign, to get a better idea on expected spatial/temporal variability. For example, NOAA’s CBIBS (Chesapeake Bay Interpretive Buoy System) data in CB; NOAA’s National Oceanographic Data Center (NODC) in GoM.-http://coastwatch.pfeg.noaa.gov/erddap (Cara Wilson/NOAA)-ftp://imars.marine.usf.edu/ (Frank Muller-Karger/USF)

http://coastwatch.pfeg.noaa.gov/erddap

ftp://imars.marine.usf.edu/


From Cara Wilson (NOAA)


From Frank Muller-Karger (USF)


2) On the boat (during the campaign): Any lessons learnt during the campaign that would be helpful for GoMex? For example regarding filtering, deployment of instruments, communication, etc.

Overall, operations were smooth and everyone was professional/cooperated well.

The GPS on the ship failed to log, which was pretty inexcusable. The ship's systems data should be guaranteed. We also lost the conductivity sensor during the cruise, which made many of the corrections at depth difficult. We should have a backup ctd.

Do a better job of sampling source waters and not concentrate too much on the bulk properties of the larger water mass. For the next campaign:

- sample more often in shallower water that is difficult to get good data in. -deploy an inflatable that could sample around the larger boat to determine the heterogeneity of the water mass the larger vessel is sampling in.

To better understand variability/error/uncertainty in measurement (e.g. due to filtration procedures, sample storage/handling, or instrumentation), we should collect replicates of samples and analyze following identical procedures; and/or follow a procedure where all but one steps are identical , to assess influence of this step on measurement error/uncertainty (e.g. evaluate ‘freezing’ as a preservation technique of CDOM/DOC/DON/DOP samples)


4) Next steps Data submitted to SeaBASS (update?)

Need more studies focusing on data/instrument inter-comparison

Data synthesis is needed (write a ‘synthesis/overview” manuscript?)

Writing of manuscripts-Tzortziou M., J. R Herman, *C. P Loughner, A.Cede, N. Abuhassan, S. Naik, 2013, " Spatial and temporal variability of ozone and nitrogen dioxide over a major urban estuarine ecosystem", Journal of Atmospheric Chemistry, Special Issue PINESAP, DISCOVER-AQ, DOI: 10.1007/s10874-013-9255-8.

-Reed A., A. M. Thompson, D. E. Kollonige, D. K. Martins, M. Tzortziou, J. R. Herman, T. A. Berkoff, N. K. Abuhassan, A. Cede , 2013, "Effects of Local Meteorology and Aerosols on Ozone and Nitrogen Dioxide Retrievals from OMI and Pandora Spectrometers in Maryland, USA during DISCOVER-AQ 2011", Journal of Atmospheric Chemistry, Special Issue PINESAP, DISCOVER-AQ, DOI: 10.1007/s10874-013-9254-9.

-Stauffer R, Thompson A, Martins D.K., Clark R.D., Loughner C.P., Delgado R., Berkoff T.A., Gluth E.C., Dickerson R.R., Stehr J.W., Tzortziou M., Weinheimer A.J., 2012, "Bay Breeze Influence on Surface Ozone at Edgewood, MD, during July 2011", Journal of Atmospheric Chemistry, DOI: 10.1007/s10874-012-9241-6

- Loughner C.P., M. Tzortziou, M. Follette-Cook, K. E. Pickering, D. Goldberg, C. Satam, A. Weinheimer, J. H. Crawford, A. Mannino, D. J. Knapp, D. D. Montzka, G. B. Diskin, L. T. Marufu, and R. R. Dickerson (In Review), "Impact of bay breeze circulations on surface air quality and boundary layer export", Atmospheric Environment

- Tzortziou M., J. R Herman, Z. Ahmad (In Review), "Variability in Atmospheric NO2 and Impact on Ocean Color Retrievals in Coastal Waters near Polluted Urban Areas", Journal of Geophysical Research.

-Goldberg DL, CP Loughner, M Tzortziou, JW. Stehr, KE Pickering, L Tambaoga Marufua, JH Crawfordd, RC Cohene, and R Dickerson, (In Review), "Surface Ozone Concentrations over the Chesapeake Bay during DISCOVER-AQ", Atmospheric Environment

- Joe et al., “Estimation of aragonite saturation state in plume dominated coastal waters”, (In Prep)

- Le, C., C. Hu, J. Cannizzaro, and H. Duan (2013). Long-term distribution patterns of remote sensed water quality parameters in Chesapeake Bay. Estuarine, Coastal and Shelf Science, accepted.


From Joe

July 17 1500-2200 GMT (D-AQ cruise)


From Stan


– An improved Chl algorithm for Chesapeake Bay (Le et al., 2013)– For GEO-CAPE science questions: the new algorithm resulted in smaller

dynamic range and less variability than shown by the OCx ratio algorithm

1.0

10.0

100.0

1.0 10.0 100.0Measured Chla (mg m-3)

Rrs

der

ived

Chl

a (m

g m

-3)

RGCI OC3 OC4

RGCI: R2=0.75 p<0.01MRE=18.8±12.6%RMSE(%)=22.5%Mean ratio=1.02

OC3: R2=0.09 p=0.45MRE=33.4±19.5%RMSE(%)=38.4%Mean ratio=0.74 OC4: R2=0.09 p=0.53

MRE=43.2±41.5%RMSE(%)=59.4%Mean ratio=1.29

July 2011, Chesapeake Bay (N=32)

1

10

100

1 10 100In situ Chla (mg m-3)

MO

DIS

Chl

a (m

g m-3

)

OC3 RGCI

Chl algorithm using in situ Rrs Chl algorithm using MODIS Rrs

From Chuanmin


From Chuanmin

– CDOM dynamic range is not large enough to validate an inversion model

CDOM model developed for Tampa Bay When applied to C Bay, the range is too small

0.1

1

10

0.1 1 10

Sate

llite

der

ived

ag(

443)

(m

-1)

Measured ag(443) (m-1)

MODIS:R2=0.72 N=33MRE=23.7%RMSE=29.6%Mean ratio=1.07

SeaWiFS:R2=0.83 N=23MRE=17.6%RMSE=25.7%Mean ratio=1.06

0.1

1

10

0.1 1 10

Rrs

der

ived

ag(

443)

(m

-1)

Measured ag(443) (m-1)

R2=0.4 N=24MRE=20.4%RMSE=25.2%Mean ratio=1.14

Tampa Bay

Chesapeake Bay


0 10 20 30 40 50 60 70

0.60.81.01.21.41.61.82.0

SR

# Station

0 10 20 30 40 50 60 70

0.016

0.018

0.020

0.022

0.024

SC

DO

M

0 10 20 30 40 50 60 700

10

20

30

40

50

a CD

OM(3

00

)

T1 S1 T4 D1 D2 T5 T2 D3 T3 S3 T2-2 ChR UBay Pat Ubay Mbay T2-2 BW marsh UBay

From Maria


50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

0.60.81.01.21.41.61.82.0

SR

# Station

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

0.016

0.018

0.020

0.022

0.024

SC

DO

M

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 650

10

20

30

40

50a C

DO

M(3

00

)marshes

CB main stem

From Maria

-76.5 -76.4 -76.3 -76.2 -76.1

38.4

38.8

39.2

-76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1

Lat

itu

de

Longitude

0.10.20.30.40.50.60.70.80.91.0

38.4

38.8

39.2

39.6

-76.5

-76.4

-76.3

-76.2

-76.1

0.51.01.52.02.5

Instr4074

Instr3125

CIMEL SERC

D-AQ overpass D-AQ overpass

D2-Drifter (Patuxent) D1-Drifter (Upper Bay) T4-Transect (Choptank) S1-Stationary (T2-2)

7/11 7/12 7/13 7/14 7/15

AO

T (

440)

T1-Transect

Lo

ng

itu

de

Lat

Lat

itu

de

Long

An

gst

rom

Bad air quality days at the beginning, improving later in the week, gets worse during the last few daysLarge variability in AOT, by more than a factor of 2, in most days Good agreement with SERC when we were close to SERC, larger differences as we move further away (AOT lower over the water)The Angstrom exponent was highly variable, but always > 1.0, showing the large influence of urban aerosols over the Bay

> 0.7

< 0.1

AOT(440)


From Maria

0.10.20.30.40.50.60.70.80.91.0

38.4

38.8

39.2

39.6

-76.5

-76.4

-76.3

-76.2

-76.1

0.51.01.52.02.5

Instr 4074

Instr 3125

CIMEL SERC

D-AQ overpass D-AQ overpass D-AQ overpass

S3- Stationary (Upper Bay) T3 - Blackwater D3-Drifter (T2-2) T2-Transect (Mid Bay)

7/16 7/17 7/18 7/19 7/20

AO

T (

440)

T5-Transect (Upper Bay)

Lat

Lo

ng

itu

de

Lat

itu

de

Long

An

gst

rom

-76.5 -76.4 -76.3 -76.2 -76.1

38.4

38.8

39.2

-76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1 -76.5 -76.4 -76.3 -76.2 -76.1

Lati

tud

e

Longitude

Bad air quality days at the beginning, improving later in the week, gets worse during the last few daysLarge variability in AOT, by more than a factor of 2, in most days Good agreement with SERC when we were close to SERC, larger differences as we move further away (AOT lower over the water)The Angstrom exponent was highly variable, but always > 1.0, showing the large influence of urban aerosols over the Bay

> 0.7

< 0.1

AOT(440)


From Maria

Diurnal Variability in Tropospheric NO2 and O3 over the Chesapeake Bay

CMAQ NO2 (Surface to 250 hPa) CMAQ O3 (surface to 250 hPa)

Significant spatial and temporal variability, of the order of 0.5 to 1.0 DU, in tropospheric NO2 over the Chesapeake Bay estuarine waters High NO2 in the north Chesapeake Bay , lower in the mid-Bay with larger variability Significant Variability in column O3 as well

Variability was also found on surface NO, NOy and O3 (up to 60-80 ppbv), both spatially (transect measurements) and temporally (day-to-day and diurnal), both in measurements and model simulations



Observed (left) and WRF simulated (right) 2 m temperature and 10 m wind speed at 0900 UTC (05:00 AM EDT) on 11 July 2011 (Loughner et al., 2013).


From Maria (more on NO2 at Tzortziou et al poster)

- Bay breeze circulation develops- Weak winds over the bay reveal areas of stagnation due to winds changing direction associated with the bay breeze.

Stagnation and low deposition rates result in pollutant buildup over the bay.

Observed (left) and WRF simulated (right) 2 m temperature and 10 m wind speed at 1800 UTC (02:00 PM EDT).on 11 July 2011 (Loughner et al., 2013).

Total atmospheric nitrogen near the surface at 1:00 PM EST as calculated by CMAQ


From Carolyn (more on C. Jordan poster)

Water-soluble Na+ (top panel) and SO4=

(bottom panel) ions in total aerosols observed during CBODAQ. Total carbon (TC, top

panel), organic carbon (OC, middle panel), and

elemental carbon (EC, bottom panel) (in µg m-3 ) observed during CBODAQ

lessons from cbodaq campaign. july 2011 heat wave hottest month for baltimore-washington...

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