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Equatorial Atlantic Circulation and Tropical Climate Variability. Peter Brandt. GEOMAR, Kiel, Germany. Equatorial Atlantic Circulation and Tropical Climate Variability. With contributions from : - PowerPoint PPT Presentation
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Equatorial Atlantic Circulation and Tropical Climate Variability
With contributions from:
Richard Greatbatch1, Jurgen Fischer1, Sven-Helge Didwischuss1, Andreas Funk2, Alexis Tantet1,3, William Johns4
1GEOMAR Helmholtz-Zentrum fur Ozeanforschung Kiel, Germany2WTD 71/FWG, Forschungsbereich fur Wasserschall und Geophysik, Kiel, Germany3now at Institute for Marine and Atmospheric Research, Utrecht University, The Netherlands4RSMAS/MPO, University of Miami, USA 2
OutlineIntroduction
• ITCZ and tropical Atlantic variability (TAV)
• TACE observing system
Data & MethodsEUC TransportEUC-TAV Relation
• EUC during warm/cold events
• Shear variability
Equatorial Deep Jets• Equatorial basin modes
• Interaction with EUCSummaryOutlook
Sahel rainfall climatology
MA-Position
JJA-Position
Sahel
Guinea
Guinea rainfall climatology
Atlantic Marine ITCZ ComplexITCZ position and
rainfall intensity affect densely populated regions in West Africa
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Rainfall and SST annual cycleIntroduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets
Summary Outlook
Mechanisms of Tropical Atlantic Variability
Mechanisms influencing Variability of Tropical Atlantic SST
Chang et al., 2006
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Tropical Atlantic Variability (TAV) modes
Zonal mode (Atlantic Nino)Meridional mode (gradient mode)ENSO influenceNAO influence
MERIDIONAL MODE
ZONAL MODE
Strong seasonality of Tropical Atlantic Variability makes understanding and prediction of tropical Atlantic variability a challenge.
Sutton et al. 2000
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Meridional Mode (March-April) During spring the
meridional SST gradient dominates TAV
Underlying mechanism is the Wind-Evaporation-SST (WES) Feedback Mechanism (Saravanan and Chang, 2004)
Kushnir et al. 2006
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Zonal Mode (June-August) Zonal Mode is
associated with rainfall variability, onset and strength of African Monsoon (Caniaux et al. 2011, Brandt et al. 2011)
Underlying mechanism is the Bjerknes feedback that is strong during boreal spring/summer (Keenlyside and Latif 2007)
Kushnir et al. 2006
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Equatorial Atlantic Cold Tongue
Cold tongue develops during boreal summer
Interannual variability of ATL3 SST index (3°S–3°N, 20°W–0°) much smaller than seasonal cycle
10
Brandt et al. 2011
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Onset of Atlantic Cold Tongue and West African Monsoon
WAM onset follows the ACT onset by some weeks.
Significant correlation of ACT and WAM onsets
11
WAM onset – northward migration of rainfall (10°W-10°E.) (Fontaine and Louvet, 2006)ACT onset – surface area (with T<25°C) threshold Caniaux et al. 2011, Brandt et al. 2011
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Regression of SST and Wind onto
12
WAMOnsetSignificantcorrelation with cold tongue SST (zonal mode) andSST in the tropical NE Atlantic(meridional mode)
ACTOnset
Cold tongue
SST;Wind
forcing in the
western equatorial Atlantic
(zonal mode)
Brandt et al. 2011
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
SST Errors in Coupled Climate Models
Jungclaus et al. 2006
Dark gray model too warm
Large errors in the eastern tropical Atlantic
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
2006-2011 Tropical Atlantic Climate Experiment
A focused observational and modeling effort in the tropical Atlantic to advance the predictability of climate variability in the surrounding region and to provide a basis for assessment and improvement of coupled models.
TACE was envisioned as a program of enhanced observations and modeling studies spanning a period of approximately 6 years. The results of TACE were expected to contribute to the design of a sustained observing system for the tropical Atlantic.
TACE focuses on the eastern equatorial Atlantic as it is badly represented in coupled and uncoupled climate models and is a source of low prediction skill on seasonal to interannual time scales. 14
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
TACE observational network
15 Observing system during the TACE period including different process studies, like e.g. the 23°W equatorial moorings
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Equatorial Mooring Array at 23°W
single mooring from June 2005
3 mooringsfrom June 2006 to May 2011
16
Ship Section Mean
Brandt, et al. 2013, submitted
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
EUC from Shipboard Measurements
20 shipboard velocity sections are used to calculate the dominant variability pattern in terms of Hilbert EOFs
Sorted with respect to the seasonal cycle 17
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Reconstruction of Zonal Velocity Sections
Dominant variability pattern from ship sections
Pattern are regressed onto moored time series
Method validation by using the ship sections itself
Alternative: optimal width method
18
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Validation of EUC Transport Calculation using Ship Sections
19
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Eastward EUC TransportGeneral
agreement between different methods
20
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
EUC TransportYears with strong and weak annual cycleShip sections alone are hardly conclusive
about seasonal cycle
21
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Pacific EUC TransportMean EUC
Transport (solid) and EUC transport for strong El Niños (dashed)
Strongly reduced EUC transport during El Niños. EUC disappeared during 1982/83 El Niño (Firing et al. 1983)
22
Johnson et al. 2002
What is the relation between Atlantic EUC transport and tropical Atlantic variability?
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Interannual Variability: SST ATL3 and Wind West Atlantic
Richter et al. (2012): canonical events have strong/weak winds prior to cold/warm events
Canonical cold event: 2005
Canonical warm event: 2008
Noncanonical cold event: 2009 (warmest spring with weak winds, but coldest SST in August)
23
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Interannual Variability: SST ATL3 and EUC Transport
Canonical cold/warm events are associated with strong/weak EUC
EUC during 2009 was weak and shows no variation during the strong cooling from May to July
24
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Interannual Variability: SST ATL3 and April/May 2009 Anomalies
According to Richter et al.(2012) noncanonical events are driven by advection from northern hemisphere during strong meridional mode events
SST and wind anomalies during April/May 2009 (Foltz et al. 2012)
25
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Regression MapsStrong June EUC associated with anomalous cold
Cold Tongue and southerly wind anomalies in the northern hemisphere early onset of the West African Monsoon
26Brandt, et al. 2013, submitted
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
June EUC – Wind/SST Relation
27
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
June EUC – Wind/SST Relation
28
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
June EUC – Wind/SST Relation
29 Regression maps reflect a canonical behavior according to Richter et al. (2012)
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Monthly Regressions of Zonal Velocity onto EUC Transport
During all months: strengthening of the eastward EUC associated with strengthening of westward surface flow (strongest shear enhancement in June)
February: weak near surface flow variability, stronger changes in the south
30
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Seasonal Cycle of Upper Ocean Diapycnal Heat Flux
Strongest shear (1/s2) and diapycnal heat flux (W/m2) during June
31Hummels et al. 2013
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Deep Velocity Observations along 23°W
Equatorial Deep Jets are a dominant flow feature below the Equatorial Undercurrent and oscillate with a period of about 4.5 years (Johnson and Zhang 2003, Brandt et al. 2011)
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Equatorial Deep Jets and Basin Mode Oscillations
Downward phase and upward energy propagation
EDJ are excited at depth and propagate toward the surface
update from Brandt et al. 2011
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Equatorial Deep Jets
Excitation of equatorial basin modes (Cane and Moore, 1981)
Vertical Mode DecompositionEquatorial Deep Jets
Harmonic analysis
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Equatorial Deep JetsGreatbatch et al. (2012): EDJ can be
described by high-baroclinic, equatorial basin modes.
How are the Jets forced?1. Inertial Instability (Hua et al. 1997, d’Orgeville et
al. 2004, Eden and Dengler 2008)2. Destabilization of Rossby-gravity waves (Ascani
et al. 2006, d’Orgeville et al. 2007, Hua et al. 2008, Ménesguen et al. 2009)
Upward energy propagation toward the surface hindered by the EUC (e.g. McPhaden et al. 1986) or tunneling through the shear zone (Brown & Sutherland 2007)? 35
Deep Ocean Dynamics | Introduction Equatorial Deep Jets
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Surface Geostrophic Velocity4.5-year cycle of the geostrophic equatorial
zonal surface velocity (from sea level anomalies 15°W-35°W)
Corresponding signal of the ATL3 SST index (3°S–3°N, 20°W–0°)
36
Eastward surface flow anomaly corresponds to warm eastern equatorial Atlantic.
Brandt et al. 2011
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
EDJ interaction with the EUC?Consistent downward phase propagation below the
EUC4.5-year cycle also North, South and above the EUC
core Phases suggest meridional displacement of the EUC
core with the EDJ cycle
37
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
EDJ interaction with the EUC?Consistent downward phase propagation below the
EUC4.5-year cycle also North, South and above the EUC
core Phases suggest meridional displacement of the EUC
core with the EDJ cycle
38
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
SummaryInterannual EUC transport variability largely in agreement with zonal mode variabilityThere are noncanonical events likely associated with meridional mode events during boreal spring4.5-yr EDJ oscillations dominate depth range below the EUC: high-baroclinic, equatorial basin modesPossible interaction of basin mode and EUC (time series are hardly long enough) Improved numerical simulations are required for the understanding of physical processes responsible for EDJ affecting SST and TAV
39
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Summer (JJA) Sea Surface temperature bias pattern for CMIP5White stipples indicate where models are consistently wrong
Persistent errors in climate models with little sign of reduction
Toniazzo and Woolnough, 2013
Despite improved process understanding, model errors remained large resulting in poor TA climate prediction.
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Climate Modelling/PredictionState-of-the-art climate models still show
large errors in the SE AtlanticPossible sources: atmospheric convection,
clouds, aerosols, but similarly oceanic processes (Xu et al. 2013) like:• Advection from equatorial region, too weak stratification
• Not resolved coastal upwelling processesSeveral initiatives to improve ocean data
base in the SE Atlantic and to reduce model bias• EU PREFACE (PI Noel Keenlyside) • German SACUS (PI Peter Brandt)• NSF Proposal (PI Ping Chang)
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Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Closing knowledge gaps – enhanced observationsGulf of Guinea and Eastern Boundary Upwelling regions
Glider campaigns and cruises in 2014, 2015, and 2016, various seasons
Enhanced ARGO floats in Eastern Atlantic
8E6S, PIRATA mooring
Current meter at 0E,eq
Mooring 20S
Introduction Data & Methods EUC Transport EUC-TAV Relation Equatorial Deep Jets Summary Outlook
Current meter mooring array was deployed at 11°S off Angola during Meteor cruise in July 2013
AcknowledgementsThis study was supported by the German
Federal Ministry of Education and Research as part of the co-operative projects “NORDATLANTIK” and “RACE” and by the German Science Foundation (DFG) as part of the Sonderforschungsbereich 754 “Climate-Biogeochemistry Interactions in the Tropical Ocean”.
Moored velocity observations were acquired in cooperation with the PIRATA project.
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