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Chapter 4 El Niño and Year-to-Year Climate Prediction 4.1 Recap of El Niño basics 4.2 Tropical Pacific climatology 4.3 ENSO mechanisms I: Extreme phases.

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Presentation on theme: "Chapter 4 El Niño and Year-to-Year Climate Prediction 4.1 Recap of El Niño basics 4.2 Tropical Pacific climatology 4.3 ENSO mechanisms I: Extreme phases."— Presentation transcript:

1 Chapter 4 El Niño and Year-to-Year Climate Prediction 4.1 Recap of El Niño basics 4.2 Tropical Pacific climatology 4.3 ENSO mechanisms I: Extreme phases 4.4 Pressure gradients in an idealized upper layer 4.5 Transitions into the El Niño 4.6 El Niño mechanisms II: Dynamics of transition phases 4.7 El Niño prediction 4.8 El Niño remote impacts: teleconnections 4.9 Other interannual climate phenomena and prospects … Neelin, Climate Change and Climate Modeling, Cambridge UP

2 4.1 Recap of El Niño basics Climatology (C) Sea Surface Temp. Dec Anomaly (Dec.97 SST-Clim.) Supplementary Fig.: Reynolds SST data set [From chapter 1] Neelin, Climate Change and Climate Modeling, Cambridge UP

3 December 1997 Anomalies of precipitation during the fully developed warm phase of ENSO Recap Figure 1.8 Neelin, Climate Change and Climate Modeling, Cambridge UP

4 DJF Low-level wind anomalies during the El Niño relative to the climatology Recap Figure 1.9 Neelin, Climate Change and Climate Modeling, Cambridge UP

5 December 1997 Anomalies of sea level height during the fully developed warm phase of ENSO Recap Figure 1.10 Neelin, Climate Change and Climate Modeling, Cambridge UP

6 Recap Figure Tropical Pacific climatology Sea surface temperature climatology - January Sea surface temperature climatology - July Neelin, Climate Change and Climate Modeling, Cambridge UP

7 Recap Figure 2.13 Precipitation climatology - January Precipitation climatology - July Neelin, Climate Change and Climate Modeling, Cambridge UP

8 Equatorial Walker circulation Recap Figure 2.14 Adapted from Madden and Julian, 1972, J. Atmos. Sci., and Webster, 1983, Large-Scale Dynamical Processes in the Atmosphere Neelin, Climate Change and Climate Modeling, Cambridge UP

9 Pacific in three-dimensions under "Normal" conditions Atmosphere: Trade winds blow across Pacific air rises in convergence zone over the warm SSTs in the west. Ocean: Thermocline ~100m deeper in west [sea level 40cm higher; see 4.4 ] Pressure gradient in ocn. (eastward) balances wind stress in vert avg km ~28 C ~24 C ~20 C 15 C or colder 10 km Figure 4.1 Neelin, Climate Change and Climate Modeling, Cambridge UP

10 Recall equatorial upwelling: wind stress & Coriolis force either side of equator give surface divergence Shallow thermocline in east upwelling brings up colder water [ Equatorial undercurrent above thermocline flows eastward] Pacific in three-dimensions under "Normal" conditions Figure 4.1 Neelin, Climate Change and Climate Modeling, Cambridge UP

11 Pacific basin under El Niño conditions Figure 4.2a 4.3 ENSO mechanisms I: Extreme phases Warmer SST in east; rainfall tends to spread east Trade winds weaken Unbalanced eastward PGF in ocean anomalous currents (in vert avg through layer above thermocline) thermocline deepens in east Upwelling brings up water less cold than normal Neelin, Climate Change and Climate Modeling, Cambridge UP

12 Pacific basin under La Niña conditions Cooler SST in east; rainfall concentrated in west Trade winds strengthen Westward wind stress exceeds eastward PGF in ocean anomalous currents along Eq. thermocline shallows in east Upwelling brings up water colder than normal Figure 4.2b Neelin, Climate Change and Climate Modeling, Cambridge UP

13 "It is the gradient of SST along the equator which is the cause of [...] the Walker circulation. An increase in equatorial easterly winds [is associated with] an increase in upwelling and an increase in the east-west temperature contrast that is the cause of the Walker circulation in the first place. [...] On the other hand, a case can also be made for a trend of decreasing speed [...] There is thus ample reason for a never-ending succession of alternating trends by air-sea interaction in the equatorial belt, but just how the turnabout between trends takes place is not yet clear.” 1969 Jakob Bjerknes Neelin, Climate Change and Climate Modeling, Cambridge UP

14 The Bjerknes feedbacks (warm phase) Figure 4.3 Positive feedback loop reinforces initial anomaly Neelin, Climate Change and Climate Modeling, Cambridge UP

15 The Bjerknes feedbacks (cold phase) Positive feedback loop reinforces initial anomaly Figure 4.3 [Supplemental] Neelin, Climate Change and Climate Modeling, Cambridge UP

16 The El Niño Pumpkin Neelin, 2011.

17 Idealized upper ocean layer 4.4 Pressure gradients in an idealized upper layer Figure 4.4 Pressure  mass above At A ocean surface high so PGF from A toward B in upper ocean Deeper thermocline balances higher sea surface PGF small below thermocline Sea surface height  thermocline depth [cm vs. m] Neelin, Climate Change and Climate Modeling, Cambridge UP

18 Two positions of the thermocline, indicating region of thermocline anomalies Figure 4.5 Neelin, Climate Change and Climate Modeling, Cambridge UP

19 Buoy from the TAO array Courtesy of the Pacific Marine Environmental Laboratory Figure Transitions into the El Niño Neelin, Climate Change and Climate Modeling, Cambridge UP

20 Global tropical moored buoy array Figure 4.7 (the original TAO array in the Pacific augmented by subsequent programs) Neelin, Climate Change and Climate Modeling, Cambridge UP

21 The transition into the El Niño warm phase (Jan. 1997) Figure 4.8 After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

22 The transition into the El Niño warm phase (Apr. 1997) Figure 4.8 cont. After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

23 The transition into the El Niño warm phase (Sep. 1997) Figure 4.8 cont. After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

24 The transition into the El Niño warm phase (Jan. 1998) Figure 4.8 cont. After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

25 The transition into the La Niña cold phase (May 1998) Figure 4.9 After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

26 The transition into the La Niña warm phase (Sep. 1998) Figure 4.9 cont. After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

27 The transition into the La Niña phase (Jan. 1999) Figure 4.9 cont. After figures courtesy of David Pierce, Scripps Institute of Oceanography. Neelin, Climate Change and Climate Modeling, Cambridge UP

28 Schematic of an equatorial jet Figure El Niño mechanisms II: Dynamics of transition phases deep thermocline = high pressure in upper ocean, H current can flow along Eq. (Coriolis=0) equatorial jet: balance of deep thermocline and current anomalies near equator (with PGF= Coriolis; note change in CF with latitude crucial) Neelin, Climate Change and Climate Modeling, Cambridge UP

29 Schematic of an equatorial jet showing that it can extend itself eastward but not westward Figure 4.11 Neelin, Climate Change and Climate Modeling, Cambridge UP

30 Currents carry mass affects pressure Deep thermocline extends eastward where mass added (edge moving eastward = “Kelvin wave”) NB: something has to continually supply mass in the west for the jet to persist Shallow thermocline and westward currents also give equatorial jet ( just switch sign of anoms) Low is extended by removing water ( in the ocean upper layer), so also extends eastward Neelin, Climate Change and Climate Modeling, Cambridge UP

31 Kelvin wave front at the eastern edge of an equatorial jet Figure 4.12 Neelin, Climate Change and Climate Modeling, Cambridge UP

32 Response of the ocean to a westerly wind anomaly Figure 4.13 To east of the wind anomalies, equatorial jet (Kelvin wave) extends east, deepening thermocline (H) (recall: warms SST…) To west, inflow of water to jet (in oc. upper layer) comes from off the equator (but little effect on SST) shallow thermocline in west extends westward (Rossby wave), as mass transferred to east by jet when reaches western boundary, can no longer supply mass by extending shallow region Weakening of jet extends eastward, ending warm phase As wind anomalies weaken, shallow thermocline extends eastward: transition to cold phase Wind anoms currents Deep Thermocline anoms Re: onset and demise of El Niño warm phase Neelin, Climate Change and Climate Modeling, Cambridge UP

33 Response of the ocean to a easterly wind anomaly Re: Onset and demise of La Niña cold phase (supplementary Fig.) Same as Figure 4.13 but anomalies of opposite sign Neelin, Climate Change and Climate Modeling, Cambridge UP

34 ENSO transitions Figure 4.14 Recall: feedbacks that strengthen El Niño Meantime, in the West Pacific (subsurface) Delay: no surface effect until… And vice versa… Onset of La Niña cold phase Neelin, Climate Change and Climate Modeling, Cambridge UP

35 Forecast of SST anomalies (as three month averages) 4.7 El Niño prediction Forecast of the onset of the El Niño From National Center for Environmental Prediction climate model Data through March 1997 (previous wind stress anomalies, ocean subsurface temperatures, SSTs,…) [“Data assimilation” process includes interpolation of sparse observations to all model grid points, balancing terms in model equations,…] climate model runs forward in prediction mode (from April) Courtesy of the National Center for Environmental Prediction Figure 4.15 Neelin, Climate Change and Climate Modeling, Cambridge UP

36 Supplementary Figure: NCEP Forecast vs. Observation Courtesy of the National Center for Environmental Prediction Neelin, Climate Change and Climate Modeling, Cambridge UP

37 Commonly used index regions for ENSO SST anomalies Recall: Figure 1.5 Neelin, Climate Change and Climate Modeling, Cambridge UP

38 Series of forecasts of SST anomalies averaged over the Niño-3 region of the equatorial Pacific Track record of forecasts: From forecasts made each month, collect all the forecast SST anomalies at 3- month lead (i.e. after each forecast had gone three months into the future), 6-month lead, 9-month lead, … E.g. March 1997 forecast shown Compare each forecast to the SST anomaly that was later observed (solid line) skill decreases with longer lead; still useful at 9 months Figure 4.16 Courtesy of the National Center for Environmental Prediction Neelin, Climate Change and Climate Modeling, Cambridge UP

39 Loss of skill in ENSO forecasts: (i)Imperfections in the forecast system -e.g., model errors, scarcity of input data (can be improved, if $) (ii) Fundamental limits to predictability -weather unpredictable beyond two weeks (chaos theory): slightly different initial conditions lead to later weather patterns as dissimilar as weather maps chosen at random (except for aspects determined by sea surface temperature…) - “weather noise”: acts like a random forcing on slow ocean- atmosphere interaction e.g. in the Bjerknes hypothesis, SST gradient determines average strength of Tradewinds. But in a particular month, storms or other transient weather events can cause equatorial Easterlies to differ from this, causing a greater or lesser change of currents than you would expect from the SST anomalies Limits to skill in ENSO forecasts Neelin, Climate Change and Climate Modeling, Cambridge UP

40 Schematically, random weather events cause cycle to depart from the evolution it would otherwise have had Cumulative effects cause departure from prediction Effects of weather noise on the ENSO cycle Figure 4.17 Neelin, Climate Change and Climate Modeling, Cambridge UP

41 Start coupled model from different ocean initial conditions (leading also to changes in atm. ) Initial differences grow  ensemble of prediction runs Ensemble spread gives estimate of uncertainty Spread tends to grow with time (due to weather noise & coupled feedbacks) Ensemble mean gives best estimate An ensemble of forecasts during the onset of the La Niña Figure 4.18 Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP

42 Supplementary Figure ECMWF forecast of the 09/10 El Nino from May 2009 with overlaid observations Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP

43 Supplementary Figure Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP ECMWF forecast from March 2010 predicting transition to La Niña of 2010

44 Supplementary Figure Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP ECMWF forecast from March 2010: with overlaid observations for verification

45 Supplementary Figure Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP ECMWF forecast from Sept. 2010: with overlaid observations for verification

46 Supplementary Figure Courtesy of the European Centre for Medium-range Weather Forecasting. Neelin, Climate Change and Climate Modeling, Cambridge UP ECMWF forecast from March 2011

47 Regions with statistically reliable relation of precipitation and surface air temperature to El Niño and La Niña Figure El Niño remote impacts: teleconnections La Niña similar but opposite sign Impact regions change with seasonal climatology Neelin, Climate Change and Climate Modeling, Cambridge UP

48 Patterns of typical response to El Niño observed for northern hemisphere winter Figure 4.20 Neelin, Climate Change and Climate Modeling, Cambridge UP

49 Jet stream and storm track changes associated with El Niño or La Niña Figure 4.21 Neelin, Climate Change and Climate Modeling, Cambridge UP

50 Schematic of shift of probability distribution of precipitation, e.g. in Southern California, during El Niño E.g., find value of precip which only 1/3 of winters exceed, and ask what fraction of El Niño winters exceed it Probability of rainy winter enhanced (but far from certain) Figure 4.22 Neelin, Climate Change and Climate Modeling, Cambridge UP

51 Figure 4.23 Factors that affect tropical cyclone development Hurricane season forecasts NOAA GOES-9 satellite photo of hurricane Linda. NASA Goddard Space Flight Center. Initial rendering by Marit Jentoft-Nilsen. Cross-section follows Emanuel, K. A., 1988, Am. Sci. Neelin, Climate Change and Climate Modeling, Cambridge UP

52 Effect of ENSO on number of Atlantic “named storms” (tropical storms and hurricanes) in July-Oct. each year Tropical storm: sustained winds > 18 m/s; hurricane: winds > 33m/s (74 mph); Category 5 > 69 m/s avg 8-9 Regression: La Niña ~10 El Niño ~6 But large scatter (& increases w earlier SST) Figure 4.24 Tang and Neelin, Geophys. Res. Lett., Neelin, Climate Change and Climate Modeling, Cambridge UP

53 Sahel: region at margin of African monsoon (seasonal movement of convection zones) On border with arid regions, just south of Sahara desert Receives all its rainfall in June- Sept. when convection zone moves north Recap Figure 2.13, zoomed on Africa: Precipitation climatology – January & July Box shows averaging region for Fig (next slide) Sahel drought Neelin, Climate Change and Climate Modeling, Cambridge UP

54 Sahel drought: annual rainfall anomalies (13-20N, 15W-20E) Sahel region has experienced decades of drought from 1970s to present, compared to 1950s and 1960s Also has year to year variation Three hypotheses: (i) Land surface change increases albedo. More sunlight reflected gives less energy transferred from surface to atmosphere to drive convection (ii) (Most likely) SST anomalies in Atlantic and Indian oceans cause the drought by teleconnections (iii) Possible anthropogenic contribution by aerosols/warming Figure 4.25 Data from Hulme, Global Precipitation and Climate Change, Neelin, Climate Change and Climate Modeling, Cambridge UP

55 Supplementary Fig.: Lake Chad (Africa) shrinking due to drought LandSat images (US Geological Survey) a.) 12/08/72 b.) 12/14/87 c.) 12/18/2002

56 Northern and Southern Annular Modes: low pressure near pole, high at mid-latitudes (positive phase) or vice versa NAO roughly the Atlantic sector of N. Annular Mode surface pressure shown; extends to stratosphere Winds enhance/reduce jet (in pos./neg. phase), shifting end of storm track north/south impacts European precipitation atmospheric origin but includes decadal vbty/trend The North Atlantic Oscillation (NAO) and annular modes Neelin, Climate Change and Climate Modeling, Cambridge UP


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