# Presentation: 415699

## Combining Argo Data with Other in Situ and Remote Observations

**Judith Gray**
**U.S. Department of Commerce**
**National Oceanic and Atmospheric Administration**
**Atlantic Oceanographic and Meteorological Laboratory, Miami, FL **

## Objectives of Large-Scale Ocean Observations

- Provide basic description of physical state of the ocean including variability on seasonal and longer time scales
- Reveal processes that influence climate
- Provide large-scale context for regional process studies of shorter duration
- Produce required data for assimilation and (seasonal and longer) model initialization
- Complement satellite remote sensing with data for validation, calibration, and interpretation

## Other Global Observing Systems

- World Ocean Circulation Experiment (WOCE) repeat deep hydrography
- Time Series stations, both buoys and ships
- Surface drifter network
- Broad-scale XBT network, repeat sections; hi-res XBT/XCTD
- Sea-level network (GLOSS calibration & maintenance stndrds
- Acoustic tomography/thermography
- New technologies:  gliders and other autonomous vehicles, addition of compatible biogeochemical sensors, co-evolution with models to enable full integration
- NASA/South Africa Satellite Laser Ranging Station - optical radar, part of the international SLR tracking network

## Research/Operations Interface

- For implementation and maintenance of a complete observing system, a strong partnership between research institutions and operational agencies must be created
- Strong leadership and participatory roles on both sides
- Integration across Observing System platforms
- Integration across instrument development, network design,implementation, data management, scientific analysis, & data assimilation

**Notes:**
OSSE obtaining model info of what needs to be modeled

## Co-evolution of Observations and Modeling

- The roles of observations must be to:
- Provide appropriate data and statistics for data assimilation and model initialization,
- Provide independent information for testing model results and model processes, and
- Discover new phenomena not anticipated in models, thereby stimulating model improvements.
- The role of models must be to:
- Direct enhancements to the observing system, what needs to be measured and where
- Use/assimilate the data to improve weather and climate forecasts

## Argo Floats

- Positions of the floats that have delivered data within the last 30 days

## ARGO Floats used to Validate Upper Ocean Heat Content

**ARGO Floats used to Validate Upper Ocean Heat Content**
**Fields Derived from Satellite Altimetry**

**Notes:**
We compute the upper ocean heat content for hurricane studies.  The global field of heat content to the depth of the 26C isotherm is shown at the top.  These fields are computed using altimetry observations.  Satellite altimetry measure the sea height, which is proportional to the upper ocean heat content.  The higher the sea level, the warmer the upper ocean usually is.  Data from ARGO floats are used to validate these estimates.  The lower panels show where the validations are done in the maps and the scattered plots show you the error of the estimates.  The correlation between the estimates and actual observations is approximately 0.9.

## Upper Ocean Heat Content for Hurricane Studies

- We compute the upper ocean heat content for hurricane studies.  The global field of heat content to the depth of the 26oC isotherm is shown at the top.  These fields are computed using altimetry observations.  Satellite altimetry measures the sea height, which is proportional to the upper ocean heat content.  The higher the sea level, the warmer the upper ocean usually is.  Data from ARGO floats are used to validate these estimates.  The lower panels show where the validations are done in the maps and the scattered plots show you the error of the estimates.  The correlation between the estimates and actual observations is approximately 0.9.

## NOAA/AOML XBT Transects

**NOAA/AOML XBT Transects**

**Notes:**
AOML deploys approximately 10,000 XBTs per year in all basins and in different modes (high density (HD)= 4 transects per year, 30 drops per day during the transect; low density (LD) = 12 times per year, 4 drops per day during the transect).  High density mode is done mainly to study mesoscale ocean features and currents, while low density are done to investigate large scale – long period ocean variability). Some transects are maintained exclusively by AOML, others in collaboration with international partners.  The map shows these transects.  AX15 crosses the Gulf of Guinea.  It will be done with AOML XBTs with the logistical support of IRD/France.

## XBT Transects

- AOML deploys approximately 10,000 XBTs per year in all basins and in different modes (high density (HD)= 4 transects per year, 30 drops per day during the transect; low density (LD) = 12 times per year, 4 drops per day during the transect).  High density mode is done mainly to study mesoscale ocean features and currents, while low density are done to investigate large scale – long period ocean variability. Some transects are maintained exclusively by AOML, others in collaboration with international partners.  The map shows these transects.  AX15 crosses the Gulf of Guinea.  It will be done with AOML XBTs with the logistical support of IRD/France.

## Quality Controlled Drifting Buoy Observations

- Nov 1989-early 2006:
- 887 drifters in S. Atlantic
- (826 with drogues to
- Measure mixed-layer
- currents
- 688 drifter-years of data

**Notes:**
There is an area of divergence “equatorial divergence between 5 and 10 S, so that drifters deployed outside the region tend not to be retained in that region (G. Guinea)

## Animations of monthly mean currents and SST from drifters (time mean field shown here)

## Slide 13

## Monitoring Currents in Real-Time

**http://www.aoml.noaa.gov/phod/altimetry/cvar/index.php**

**Monitoring Currents in Real-Time**

## Surface Currents

- Surface Currents can be monitored in near-real time (2 day delay) using sea height anomalies derived from altimetry.  NOAA/AOML is currently developing web pages that show time series of the variability of several currents, such as the Agulhas Current, the North Brazil Current, the Yucatan Current, and the Florida Current.  The figure at the top shows the time series of the transport of the Agulhas Current (across the transect shown in the map in the left) since 1993.  This time series is updated once a month.  The small circles indicate annual mean values.  The figure at the bottom right shows a space time diagram of  the sea height anomaly values along a corridor of 5 degrees wide parallel to the coast of South Africa.  The high values (reds) indicate warm rings transporting warm and salty waters from the Indian into the Atlantic Ocean.

## Access CoastWatch 

**Access CoastWatch **
**Global Satellite Data and Products**

- Joaquin Trinanes and Gustavo Goni

**Notes:**
More Information?

## CoastWatch Products

**SST Anomalies:** View 5-day (pentad) SST anomaly maps
- for the Caribbean Region. Spatial resolution is 9.28 km.

**Atlantic SST maps:**  Display and retrieve daily and
- pentad Sea Surface Temperature maps for the Atlantic
- Ocean. These maps are created using data from the
- POES satellites.

**Near Real Time Wind Data: ** Display and retrieve
- surface wind data from a variety of sensors (QuikSCAT,
- SSMI, TMI, ERS-2, TOPEX, Jason-1, GFO and Drifters

**Upper Ocean Heat Content:**  Upper ocean thermal
- structure derived from the Sea Surface Height and
- Sea Surface Temperature fields. Updated daily.

**Notes:**
Upper Ocean Heat Content is validated by the ARGO floats. Comparisons are available at ...

##           Is the AMO a Natural Climate Mode and 

**How Does it Affect Hurricanes?**

**David Enfield**
**NOAA Atlantic Oceanographic & Meteorological Lab Miami, Florida**

**Luis Cid-Serrano**
**Dept. Statistics, Universidad de Concepción, Chile**

**Notes:**
AMO is literally .....

##                 Global warming model w/ greenhouse gases &  
                solar forcing (red)
...residual fluctuations (blue) not explained by GHGs (red)
...implies that residual reflects natural fluctuations in SST

-                 solar forcing (red)
- ...residual fluctuations (blue) not explained by GHGs (red)
- ...implies that residual reflects natural fluctuations in SST

- NOAA Atlantic Oceanographic & Meteorological Laboratory

**Notes:**
Typical global warming models force the climate system externally, in this case with solar variations and greenhouse gases (red curve). However, the model can’t reproduce a natural climate cycle like the AMO because the AMO is probably governed by changes in the MOC which the model’s mixed layer slab ocean cannot emulate (Delworth and Mann, 2000). The observed NH air temperatures are influenced by the AMO-related SSTs in the North Atlantic and North Pacific (blue curves, smoothed and unsmoothed) and they show the slow variation of the AMO about the model curve. One of the reasons driving D2M research is the need to identify the natural signals so as to reduce the uncertainty in the global warming projections.

## AMO & Global Warming

- Typical global warming models force the climate system externally, in this case with solar variations and greenhouse gases (red curve). However, the model can’t reproduce a natural climate cycle like the AMO because the AMO is probably governed by changes in the MOC which the model’s mixed layer slab ocean cannot emulate (Delworth and Mann, 2000). The observed Northern Hemisphere air temperatures are influenced by the AMO-related SSTs in the North Atlantic and North Pacific (blue curves, smoothed and unsmoothed) and they show the slow variation of the AMO about the model curve. One of the reasons driving Decadal-Millenial research is the need to identify the natural signals so as to reduce the uncertainty in the global warming projections.

## ... A multidecadal oscillation of SST found mainly in the North Atlantic — the Atlantic multidecadal oscillation (AMO)

## Atlantic Multidecadal Oscillation

- The largest and most influential mode of decadal-to-multidecadal (D2M) climate variability appears to be the AMO. The AMO index (top panel) is defined to be the average of SST over the entire North Atlantic from the equator to 70N (Enfield et al. 2001). Typically it is detrended and smoothed with a 10-year running mean (as shown). If you then correlate that with SST anomalies everywhere, you get the map in the lower panel. It shows that the AMO permeates not only the North Atlantic but much of the North Pacific as well, thus explaining why it dominates the Nortnern Hemisphere temperatures. It is probable that the AMO signal gets into the North Pacific through the atmosphere, most likely by exciting the circumpolar circulation.

## Composites of the Atlantic Warm Pool (AWP) 1950-2000

** **Interannual variability of the AWP is large
-  Large AWPs are almost three times larger than small ones

- 5 Largest AWPs

- 5 Smallest AWPs

- Dark contour ==> SST = 28.5°C

## 54 Years of Atlantic Hurricanes (1950-2003)

*Busy hurricane years*
- = years for which the number of late-season hurricanes fall within the top tercile of all years

**Notes:**
Large warm pools and warm TNA are associated with a greater frequency of hurricanes. This is consistent with the findings of Knaff (1997). 

## Correlation of AMO vs. July-September rainfall

- Correlation of AMO vs. July-September rainfall

**Notes:**
Optional slide.

This just shows that the continental US is not the only region in which rainfall is affected by the AMO. Brazil, Europe, the Sahel and southern Alaska are also affected. Gridded rainfall data from Eischeid et al.

## Correlation of AMO with U.S. divisional rainfall (1895-1999)

- Enfield et al. (2001)

## AMO & Rainfall

- Top panel is repeated from the earlier slide. If you now correlate the AMO index with running 10-year averages of US precipitation you get the map below. Over most of the US, a warm AMO (North Atlantic) is associated with reduced rainfall over most of the US. The extended period of positive AMO from 1930-1965 includes two megadroughts, the famous 1930s dust bowl and the 1950s drought. Florida goes the opposite way, and gets more frequent droughts when the AMO is negative. Lake Okeechobee, the hydrological flywheel for South Florida water supplies, receives virtually all of its water from the catchment north of the Lake, climate division #4 (yellow, inset). The difference in the inflow to the lake between AMO(+) and AMO(-) periods is about 40% of the long term mean. This has enormous consequences for South Florida water management.

## Lake Okeechobee inflow  vs. AMO

- NOAA Atlantic Oceanographic & Meteorological Laboratory

## Gray et al. (2004) AMO reconstruction

**Gray et al. (2004) AMO reconstruction**

- The extended AMO proxy (b) correlates highly with the instumental index (a) and allows us to identify long and short regime intervals of the AMO (c).

- Strong evidence that the AMO is a natural climate mode, not anthropogenic.

- NOAA Atlantic Oceanographic & Meteorological Laboratory

**Notes:**
North American tree rings have been “calibrated” to emulate the AMO and give us an extended 425-year index of the AMO. The extended AMO proxy (b) correlates highly with the instumental index (a) and allows us to identify long and short regimes of the AMO. It also confirms that the AMO is a natural climate mode, not anthropogenic.

Best of all, this gives us the possibility of estimating the risk (probability) of future regime shifts. 

## Spectral randomization

**Spectral randomization**
**Ebusuzaki (1997)**

##             We then ‘fit’ a statistical distribution to the interval data 

-             We then ‘fit’ a statistical distribution to the interval data

**We repeat this many times for the resamplings**

- NOAA Atlantic Oceanographic & Meteorological Laboratory

**Notes:**
Under the assumption that the AMO is nonstationary, we can “reample” the 425-year tree ring proxy for the AMO, and “collect” a larger number of regime intervals. Doing that we get the empirical (blue) distribution shown here. A gamma PDF (red curve) will typically fit the empirical data well, giving us shape and scale parameters similar to those shown here. From these parameters we can calculate the probability of seeing future regime shifts within given intervals (next slide). 

Knowing that a regime shift is imminent (or not) can readily be converted into policies and actions where activities impacted by the AMO are concerned. In South Florida, for example, water managers can redirect more or less surface drainage into underground aquifer storage for future availability. Similarly, more (or fewer) long-term water leases may be conceded to industrial or agricultural users.

##                             Let t1 = years since last shift;   t2 = years until the next shift 

-                             Let *t**1* = years since last shift;   *t**2* = years until the next shift

-               We now compute the conditional probability for *t**2* given *t**1*

- NOAA Atlantic Oceanographic & Meteorological Laboratory

**Notes:**
The AMO last shifted in about 1994, from negative to positive (X = 11). The risk of a reverse shift to negative in the next Y = 5 years is only about 30%. But the risk of a change in the next 20 years is 85% The implication for South Florida water availability is that there is a greater than 50% probability that we will begin to see more frequent droughts some time beyond about 10 years from now. On the plus side, there is an equal probability that major hurricanes will return to pre-1994 levels over the same time frame. Will we see our windstorm rates go down accordingly? (rhetorical question)

Knowing that a regime shift is imminent (or not) can readily be converted into policies and actions where activities impacted by the AMO are concerned. In South Florida, for example, water managers can redirect more or less surface drainage into underground aquifer storage for future availability. Similarly, more (or fewer) long-term water leases may be conceded to industrial or agricultural users.

##                 Contributions sought

**1.****	****Provision of platforms for deployment.**
**Provision of facilitation and local logistic support.**
**Provision of ARGO floats.**
**4.****	****Provision of available T and S profile data for ARGO calibration and QC purposes.**
**5.****	****Provision of data services (centralized metadata base management).**
**6.****	****Provision of data products.**
**7.****	****Capacity building (including cross-training and technology transfer).**
**8.****	****Ensuring that data scarce areas are covered through guidance from the Regional Center.**