# Presentation: 531258

## Combining Argo data with other hydrographic data to generate climatologies and nowcasts of physical properties

- C. Schmid
- NOAA/AOML

## Topics

- Data sets and methods.
- Mapping of the mixed layer properties.
- Deriving the heat storage rate.
- Deriving the oceanic heat transport.
- Deriving the heat budget.

## Data sets

- Hydrographic profiles from Argo and pre-Argo floats (various sources).
- Hydrographic profiles from XBTs, CTDs (AOML & NODC).
- Velocity from surface drifters (AOML).
- Sea surface height from altimetry.
- Scatterometer wind.
- Reynolds SST.
- SST from Remote Sensing Systems microwave TMI.
- Surface fluxes from the NCEP reanalysis 1.

**Notes:**
Hydrographic data: 1/1997 – 6/2005
Data for heat transport: January/7/1998-August/18/2004
Surface fluxes: 1/1997 – 5/2005

## Monthly coverage with hydrographic data & Study regions

**Notes:**
Data coverage after objective analysis

## Monthly data coverage 2004

**Notes:**
Data coverage after objective analysis

## Monthly data coverage 2005

**Notes:**
Data coverage after objective analysis

## Monthly data coverage 2006

**Notes:**
Data coverage after objective analysis

## Drifter trajectories (1990 – 3/2005)

**Drifter trajectories (1990 – 3/2005)**

**Notes:**
1990 is for 25S to 25N.

## Processing of hydrographic data

- Remove duplicate profiles.
- Apply automatic QC to all profiles.
- Combine data sets from different sources, sorted by space and time.
- Derive mixed layer properties (T, S, h) using the temperature gradient (limit 0.05oC/dbar).
- Apply statistical QC to mixed layer properties.

**Notes:**
The bottom of the mixed layer was defined as the depth where the temperature gradient exceeds 0.05C/dbar.
An additional quality control is performed on the mixed layer properties themselves. This is a statistical analysis of the mixed layer thickness, for example. If a value is outside of 2 standard deviations around the median of all values in each box for each month it is considered unreliable and excluded from the analysis. If not enough data are available within one box, then the data in immediately adjacent boxes are considered as well.
Salinity is obtained from TS relationship from same month if possible. All data in same box are used otherwise.

## QC procedures for profiles

- Global range tests (e.g. temperature within range of –2.5 to 40oC)
- Spike, vertical gradient and density inversion tests.
- Comparison with Levitus climatology and NCEP ocean reanalysis (GODAS). Rejection if profile outside of 10 standard deviations from both profiles.

## QC procedures for mixed layer properties

- Separate check of mixed layer thickness and temperature.
- Derive median and standard deviation from all values obtained within one month in a box (latitude x longitude: 1.5o x 5o).
- Reject values if they are outside of a 2 standard deviation envelop around the median for each month and box (use adjacent boxes if needed).

## Mixed layer temperature

**Notes:**
Results after objective analysis, not smoothed
Error bar is F*std.dev, to get F:  COR=center of region
COM=center of mass (only grid points with small enough rms)
D=(distance between COM and COR)/(distance between farthest grid point and COR)
P=percent coverage (if 0 make 1e-6 to avoid division by 0)
F=D/P*100

## Mixed layer thickness

**Notes:**
Results after objective analysis, not smoothed

## Mixed layer temperature at 30.5oW

**Notes:**
Can see equatorial cold tongue in most years. Weak because quite far west.

## Mixed layer thickness at 30.5oW

**Notes:**
Can see changes of mixed layer thickness associated with NECC 3-5S.
Large in second half of the year when the current is strong.

## Heat budget equation for mixed layer

- Terms:
-  Heat storage rate
-  Horizontal Advection
-  entrainment (new)
-  total surface fluxes
-  penetrative short-wave radiation (neglect for now)
-  Diffusive flux at the base of the mixed layer is small
-   (not shown, neglect).

- where

## Deriving the heat storage rate (HSR)

- Derive monthly averages of mixed layer properties and positions in each box.
- Perform objective analysis with a grid resolution of 1o by 1o.
- Interpolate time series over short gaps (< 4 months).
- Smooth the time series with a 3-point running mean.
- Derive the time derivative of the temperature and the heat storage rate.

**Notes:**
The mixed layer properties and positions within each box are averaged over the time period of interest (monthly means). Then an objective analysis is performed with a grid resolution of 1\DEGR\ by 1\DEGR. The correlation length scales were set to 20\DEGR\ in the zonal and 4\DEGR\ in the meridional direction. The error of the observations was assumed to be 0.01\DEGC, 0.01~$psu$ and 10~$m$ for temperature, salinity and mixed layer thickness, respectively. The error of the climatological field was assumed to be 1\DEGC, 1~$psu$ and 10~$m$. The rms error from the objective analysis is used to exclude regions with insufficient data coverage from the contour maps and from further analysis. The limits chosen for this purpose are 0.05\DEGC, 0.03~$psu$ and 6~$m$ for the three parameters.
The heat storage rate is derived as $\rho c_p z_{ml} \delta T \delta t^{-1}$ from the gridded mixed layer
properties. Before this is done the time series in each box are smoothed with a three-point running mean after a linear interpolation over short gaps (less than 4 months), which is especially important for the time derivative of the temperature.

## derive v∙T and v ́∙  T ́ from data set compiled by Rick
Mean velocities are from drifters.
Time variant velocities are from altimetry (geostrophic) and scatterometer winds (Ekman).
Mean temperature is from Reynolds SST.
Time variant temperature comes from Remote Sensing Systems microwave TMI.
Derive cph from monthly mean of mixed layer properties.
Integrate around region to get the transport.

- Mean velocities are from drifters.
- Time variant velocities are from altimetry (geostrophic) and scatterometer winds (Ekman).
- Mean temperature is from Reynolds SST.
- Time variant temperature comes from Remote Sensing Systems microwave TMI.
- Derive cph from monthly mean of mixed layer properties.
- Integrate around region to get the transport.

**Deriving horizontal heat transport**

**Notes:**
At this time only based on <v>*grad(<T>)  and v’*grad(T’).
<T> is from Reynolds SST (product with the smallest bias according to Rick)
T’ is from Remote Sensing Systems microwave TMI
<v> climatology from drifter data
v’ from AVISO altimetry and scatterometer wind (mesoscale – yes, seasonal – yes, at least in NECC region) to get geostrophic and Ekman components (model of Ralph and Niiler, 2003 provides the latter).

## Temperature (T) and velocity (v) fields were provided by Rick (the same data were used to derive the horizontal heat transport).
The mixed layer properties (, cp, h ) are derived from the hydrographic profiles.
Estimate mean for region to get the entrainment.

- The mixed layer properties (, cp, h ) are derived from the hydrographic profiles.
- Estimate mean for region to get the entrainment.

**Deriving entrainment of heat into mixed layer**

**Notes:**
At this time only based on <v>*grad(<T>)  and v’*grad(T’).
<T> is from Reynolds SST (product with the smallest bias according to Rick)
T’ is from Remote Sensing Systems microwave TMI
<v> climatology from drifter data
v’ from AVISO altimetry and scatterometer wind (mesoscale – yes, seasonal – yes, at least in NECC region) to get geostrophic and Ekman components (model of Ralph and Niiler, 2003 provides the latter).

## Surface flux maps

## Surface flux maps

**Surface flux maps**

## Heat storage maps

**Heat storage maps**

## Heat storage rate map

## Surface fluxes in NEC-W

**Surface fluxes in NEC-W**

**Notes:**
NEC-W
Nice seasonal cycle and nice correlation between HSR and NSF, in phase.
NT small and noisy -> most of the time not much impact; no improvement of heat balance.
HSR:
Linear interpolation of time series (T, rho, cpp, z) at every grid point (excluding gaps > 3)
Running mean over 3 values
Derive time derivative of T and HSR.

## Surface fluxes in TNA-SE

**Surface fluxes in TNA-SE**

**Notes:**
Annual and semi-annual cycle dominated by short-wave. Most years indicate an annual cycle of latent heat flux exists (reason: annual migration of ITCZ, southernmost in April-May, northernmost in July-August). This variability is masking the semi-annual period. Variability of the other two terms insignificant.

## Surface fluxes in TSA-NE3

**Notes:**
As in NEC-W, but the sensible component has a much smaller amplitude -> insignificant annual cycle.

## Surface fluxes in U-S3

**Surface fluxes in U-S3**

**Notes:**
As in NEC-W, but the sensible component has a much smaller amplitude -> insignificant annual cycle.

## HSR, NSF and NT in NEC-W

**HSR, NSF and NT in NEC-W**

**Notes:**
NEC-W
Nice seasonal cycle and nice correlation between HSR and NSF, in phase.
NT small and noisy -> most of the time not much impact; no improvement of heat balance.
HSR:
Linear interpolation of time series (T, rho, cpp, z) at every grid point (excluding gaps > 3)
Running mean over 3 values
Derive time derivative of T and HSR.

## HSR, NSF and NT in TNA-SE

**HSR, NSF and NT in TNA-SE**

**Notes:**
TNA-SW
Nice seasonal cycle of  HSR, NSF quite noisy (due to latent flux). Only partial balance between HSR and NSF.
NT noisy -> magnitude large enough to dominate the balance, no improvement of heat balance but maybe after smoothing. NT reduces differences at some times and increases them at other times.

## HSR, NSF and NT in TSA-NE3

**HSR, NSF and NT in TSA-NE3**

**Notes:**
Phase shift between HSR and NSF, and differences in amplitude.
Adding NT yields smaller phase shift and amplitude differences in most years.

## HSR, NSF and NT in U-S3

**HSR, NSF and NT in U-S3**

**Notes:**
HSR and NSF are in phase, offset between them is about 50 W/m^2.
NT noisy and large.
Adding NT to NSF essentially eliminates annual cycle. Difference to HSR increases to 100 W/m^2.
Need upwelling to cool it ...
Impact of aerosol/Sahara dust on surface flux could be partially responsible (not well represented in
NCEP surface flux).

## TNA-SE

- Foltz et al, 2003

**TNA-SE**

## TNA-SE

- Foltz et al, 2003

**TNA-SE**

## TSA-NE3

- Foltz et al, 2003

**TSA-NE3**

## TSA-NE3

- Foltz et al, 2003

**TSA-NE3**

## HSR in TSA-NE3, model and observations

**HSR in TSA-NE3, model and observations**

**Notes:**
Phase shift between HSR and NSF, and differences in amplitude.
Adding NT yields smaller phase shift and amplitude differences in most years.