Main features of the tropical oceans

Transcription

1 Main features of the tropical 1 oceans Atlantic Warm Pool Pacific Warm Pool Cold Tongues

2 Normal SST: Extremes in the 2 Annual Cycle Equatorial SSTs are warmest in April Equatorial cold tongues are strongest in Jul.-Oct.

3 3

4 Sistema acoplado Mar-Atmósfera 4 Vientos Circulación atmosférica Temperatura superficial del mar Precipitación Calentamiento

5 EL NIÑO 5

6 LA NIÑA 6

7 Animation of Subsurface Temperatures:

8 8 EL CICLO DEL ENSO (EL NIÑO-SOUTHERN OUTHERN OSCILLATION)

9 EL CICLO DEL ENSO La Oscilación del Sur 9

10 Evolución del ENSO 10

11 Evolución del ENSO 11

12 SST Animation:

13 13 The ENSO Cycle Naturally occurring phenomenon Equatorial Pacific fluctuates between warmer-than-average (El Niño ) and colder-than-average (La Niña) conditions The changes in SSTs affect the distribution of tropical rainfall and atmospheric circulation features (Southern Oscillation) Changes in intensity and position of jet streams and storm activity occur at higher latitudes. ENSO is associated with global impacts as a result of these adjustments in the tropical and extratropical circulation.

14 Typical Evolution of the 14 ENSO Cycle Irregular cycle with alternating periods of warm (El Niño) and cold (La Niña) conditions El Niño tends to occur every 3-4 years and generally lasts months Strongest El Niño episodes occur every years La Niña episodes may last from 1 to 3 years Transitions from El Niño to La Niña are more rapid

15 Normal Precipitation: 15 Major Features Storm Tracks ITCZ SPCZ SACZ

16 Normal Precipitation: Extremes in 16 the Annual Cycle

17 Precipitation: 17 El Niño vs. La Niña Enhanced rainfall occurs over warmer-thanaverage waters during El Niño. Reduced rainfall occurs over colder-thanaverage waters during La Niña.

18 18 EL NIÑO LA NIÑA

19 Model simulations (from Alexander et al J. Climate) 19 GFDL R30 Atmospheric GCM Observed SST were specified in the eastern tropical Pacific ( ) MLM simulation: Grid of column ocean models (ML model atop a multilayer system) is coupled to atmosphere outside of the eastern tropical Pacific. Local atmosphere-ocean fluxes, penetrating solar radiation and turbulent entrainment of water into ML. No sea-ice model. Ocean is not active beneath the ice No vertical motions or horizontal processes 14 simulations

20 20 Composites of observed (NCEP reanalysis) and simulated (ensemble average of 16 MLM integrations) values Warm and cold ENSO events 1997, BAMS) (from Trenberth, Which is the atmospheric response to tropical SST anomalies (as represented by these simulations)?

21 El Niño 21

22 El Niño 22

23 La Niña 23

24 La Niña 24

25 Forecasting ENSO 25 ENSO Forecasters rely on: (1) Real-time data from the equatorial Pacific Ocean (collected from buoys, satellites, etc) and their knowledge of previous ENSO episodes (2) Dynamical models: mathematical equations combined with current observations and run on a computer - NCEP Climate Forecast System (CFS): a coupled computer model (ocean and atmosphere interact) (3) Statistical models: use observations of the past to make predictions of the future - Consolidated Forecast Tool ( CON ): statistically combines different models to take advantage of independent information provided by each model

26 How well do models predict ENSO? 26 Statistical and Dynamical models have comparable forecast skill (note: this assessment may be changed soon pending peer review) Models have trouble with transition timing and predicting amplitude of ENSO events. Stronger ENSO events tend to be better predicted than weaker ones. Spring barrier: historically, forecasts before the Northern Hemisphere Spring have low skill. Intraseasonal variability (i.e. MJO) is not captured in most of these models and these phenomenon can have considerable impact on ENSO evolution.

27 Can other climate patterns exert a stronger influence than ENSO? 27 Short answer: YES, especially in the extratropics. There is uncertainty associated with seasonal forecasts even during strong ENSO episodes. This is why it is important to emphasize that the existence of ENSO means a tilt in the odds toward particular temperature/precipitation anomalies. These anomalies are never guaranteed, which is why climate outlooks are always probabilistic.

28 Interannual Variability in the La Plata Basin (LPB) 28 -ENSO warm events -SAM negative phase ( ) Positive OND precipitation anomalies in LPB LPB Correlations between precipitation anomalies in LPB and (left) SST anomalies and (right) 500-hPa geopotential height anomalies. Significant values at 90, 95 and 99% are shaded. NCEP reanalysis data. (Vera and Silvestri 2009)