Combining Argo Data with Other in Situ and Remote Observations

1
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
2
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

3
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

4
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

5
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

6
Argo Floats
Positions of the floats that have delivered data
within the last 30 days
7
ARGO Floats used to Validate Upper Ocean Heat
Content Fields Derived from Satellite Altimetry
8
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.

9
NOAA/AOML XBT Transects
10
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.

11
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
12
Animations of monthly mean currents and SST from
drifters (time mean field shown here)
13
(No Transcript)
14
Monitoring Currents in Real-Time
http//www.aoml.noaa.gov/phod/altimetry/cvar/index
.php
15
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.

16
Access CoastWatch Global Satellite Data and
Products
Joaquin Trinanes and Gustavo Goni
17
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.
18
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
19

•    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

20
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 cant reproduce a natural
  climate cycle like the AMO because the AMO is
  probably governed by changes in the MOC which the
  models 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.

21
A multidecadal oscillation of SST found mainly
in the North Atlantic the Atlantic multidecadal
oscillation (AMO)
22
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.

23
Composites of the Atlantic Warm Pool (AWP)
1950-2000
5 Largest AWPs
5 Smallest AWPs
Dark contour gt SST 28.5C

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

24
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
25
Correlation of AMO vs. July-September rainfall
26
Correlation of AMO with U.S. divisional rainfall
(1895-1999) Enfield et al. (2001)
27
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.

28

• Lake Okeechobee inflow vs. AMO

29
Gray et al. (2004) AMO reconstruction
Eastern US and European tree rings have been
calibrated to give 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 regime intervals of the
AMO (c).
Strong evidence that the AMO is a natural climate
mode, not anthropogenic.
30
Spectral randomization Ebusuzaki (1997)
31
We then fit a statistical
distribution to the interval data
By doing a Monte Carlo resampling of regime
intervals in the Gray et al. extended AMO index,
we get a histogram of AMO regime intervals
(blue), which can be successfully fit by a Gamma
(?) distribution (PDF, red).
We repeat this many times for the resamplings
32
Let t1 years since
last shift t2 years until the next shift
We now compute the conditional
probability for t2 given t1
33

• 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.