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Seasonal predictability of the tropical Atlantic variability: northern tropical Atlantic pattern

  • Guangyang FangEmail author
  • Bohua Huang
Article

Abstract

A series of ensemble predictability experiments in a setting of the “perfect” model scenario have been conducted using the global coupled community earth system model (CESM), Version 1.1, to evaluate the seasonal predictability of the northern tropical Atlantic (NTA) pattern in the tropical Atlantic variability (TAV). Our analysis of an 86-year control simulation shows that CESM reproduces the annual cycle and interannual variability realistically. In particular, the NTA patterns, extracted as the first modes of the rotated empirical orthogonal function (REOF) analyses, are consistent between the model and observations. A set of the extreme NTA events is selected from the control simulation based on the peaking values of the time series of the NTA mode. Ensemble predictability experiments are conducted to predict each of these events as the “truth” at seasonal lead times with small perturbations of the atmospheric initial states. The correlation and root mean square error (RMSE) of the ensemble mean, as well as the ensemble spread and reliability, are used to assess the prediction skill quantitatively. It demonstrates that the model can forecast the NTA events skillfully at monthly leads up to 9 months. Composite analysis of the predicted positive and negative events is conducted to explore the physical influences of the regional air–sea interaction and the remote forcing from outside the Atlantic basin, such as the El Niño-Southern Oscillation (ENSO). It is shown that the surface latent heat flux anomalies, generated by surface wind anomalies over the northern tropical Atlantic from boreal fall and winter, force the NTA SST anomalies that peak in spring. Most of these wind anomalies are in turn generated by the remote ENSO forcing. As a result, the NTA pattern can be predicted realistically as long as the ENSO events are predictable.

Keywords

Seasonal predictability TAV NTA ENSO CESM 

Notes

Acknowledgements

This study is supported by grants from NSF (AGS-1338427), NOAA (NA14OAR4310160), and NASA (NNX14AM19G). B. Huang is also supported by the NOAA MAP grant (NA17OAR4310144). We thank the editor and the three anonymous reviewers for the constructive comments and suggestions. The experiments are carried out on Yellowstone with computing resources provided by the project UGMU0006, UGMU0009 and P93300190 at NCAR’s Computational and Information Systems Laboratory (CISL). We thank the scientists and software engineers for developing CESM and providing technical support to our simulations and experiments.

References

  1. Adam O, Schneider T, Brient F (2018) Regional and seasonal variations of the double-ITCZ bias in CMIP5 models. Clim Dyn 51(1–2):101–117.  https://doi.org/10.1007/s00382-017-3909-1 CrossRefGoogle Scholar
  2. Amaya DJ, Foltz GR (2014) Impacts of canonical and Modoki El Niño on tropical Atlantic SST. J Geophys Res Oceans 119(2):777–789.  https://doi.org/10.1002/2013JC009476 CrossRefGoogle Scholar
  3. Bates SC, Fox-Kemper B, Jayne SR et al (2012) Mean biases, variability, and trends in air–sea fluxes and sea surface temperature in the CCSM4. J Clim 25(22):7781–7801.  https://doi.org/10.1175/JCLI-D-11-00442.1 CrossRefGoogle Scholar
  4. Carton JA, Huang B (1994) Warm events in the tropical Atlantic. J Phys Oceanogr 24 (5): 888–903.  https://doi.org/10.1175/1520-0485(1994)024%3C0888:WEITTA%3E2.0.CO;2 CrossRefGoogle Scholar
  5. Chang P, Saravanan R, Ji L (2003) Tropical Atlantic seasonal predictability: the roles of El Niño remote influence and thermodynamic air–sea feedback. Geophys Res Lett 30(10):1501.  https://doi.org/10.1029/2002GL016119 CrossRefGoogle Scholar
  6. Chang CY, Chiang JCH, Wehner MF et al (2010) Sulfate aerosol control of tropical Atlantic climate over the twentieth century. J Clim 24(10):2540–2555.  https://doi.org/10.1175/2010JCLI4065.1 CrossRefGoogle Scholar
  7. Chiang JCH, Vimont DJ (2004) Analogous Pacific and Atlantic meridional modes of tropical atmosphere–ocean variability. J Clim 17(21):4143–4158.  https://doi.org/10.1175/JCLI4953.1 CrossRefGoogle Scholar
  8. Chiang JCH, Chang CY, Wehner MF (2013) Long-term behavior of the Atlantic interhemispheric SST gradient in the CMIP5 historical simulations. J Clim 26(21):8628–8640.  https://doi.org/10.1175/JCLI-D-12-00487.1 CrossRefGoogle Scholar
  9. Chikamoto Y, Timmermann A, Luo JJ et al (2015) Skilful multi-year predictions of tropical trans-basin climate variability. Nat Commun 6:ncomms7869.  https://doi.org/10.1038/ncomms7869 CrossRefGoogle Scholar
  10. Czaja, A, Vaart P, Marshall J (2002) A diagnostic study of the role of remote forcing in tropical Atlantic variability. J Clim 15(22):3280–3290.  https://doi.org/10.1175/1520-0442(2002)015%3C3280:ADSOTR%3E2.0.CO;2 CrossRefGoogle Scholar
  11. Danabasoglu G, Bates S, Briegleb BP et al (2011) The CCSM4 ocean component. J Clim 25(5):1361–1389.  https://doi.org/10.1175/JCLI-D-11-00091.1 CrossRefGoogle Scholar
  12. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  13. Ding H, Greatbatch RJ, Latif M et al (2015a) The impact of sea surface temperature bias on equatorial Atlantic interannual variability in partially coupled model experiments. Geophys Res Lett 42(13):5540–5546.  https://doi.org/10.1002/2015GL064799 CrossRefGoogle Scholar
  14. Ding H, Keenlyside N, Latif M et al (2015b) The impact of mean state errors on equatorial Atlantic interannual variability in a climate model. J Geophys Res Oceans 120(2):1133–1151.  https://doi.org/10.1002/2014JC010384 CrossRefGoogle Scholar
  15. Dippe T, Greatbatch RJ, Ding H (2018) On the relationship between Atlantic Niño variability and ocean dynamics. Clim Dyn 51(1–2):597–612.  https://doi.org/10.1007/s00382-017-3943-z CrossRefGoogle Scholar
  16. Dukowicz JK, Smith RD (1994) Implicit free-surface method for the Bryan-Cox-Semtner ocean model. ResearchGate 99(C4):7991–8014.  https://doi.org/10.1029/93JC03455 CrossRefGoogle Scholar
  17. Enfield DB, Mayer DA (1997) Tropical Atlantic sea surface temperature variability and its relation to El Niño-Southern Oscillation. J Geophys Res Oceans 102(C1):929–945.  https://doi.org/10.1029/96JC03296 CrossRefGoogle Scholar
  18. Evan AT, Vimont DJ, Heidinger AK et al (2009) The role of aerosols in the evolution of tropical north Atlantic Ocean temperature anomalies. Science 324(5928):778–781.  https://doi.org/10.1126/science.1167404 CrossRefGoogle Scholar
  19. Evan AT, Allen RJ, Bennartz R et al (2012) The modification of sea surface temperature anomaly linear damping time scales by stratocumulus clouds. J Clim 26(11):3619–3630.  https://doi.org/10.1175/JCLI-D-12-00370.1 CrossRefGoogle Scholar
  20. Foltz GR, McPhaden MJ, Lumpkin R (2011) A strong Atlantic meridional mode event in 2009: the role of mixed layer dynamics. J Clim 25(1):363–380.  https://doi.org/10.1175/JCLI-D-11-00150.1 CrossRefGoogle Scholar
  21. Giannini A, Saravanan R, Chang P (2004) The preconditioning role of tropical Atlantic variability in the development of the ENSO teleconnection: implications for the prediction of dordeste rainfall. Clim Dyn 22(8):839–855.  https://doi.org/10.1007/s00382-004-0420-2 CrossRefGoogle Scholar
  22. Goldenberg SB, Landsea CW, Mestas-Nuñez AM et al (2001) The recent increase in Atlantic hurricane activity: causes and implications. Science 293(5529):474–479.  https://doi.org/10.1126/science.1060040 CrossRefGoogle Scholar
  23. Häkkinen S, Mo KC (2002) The low-frequency variability of the tropical Atlantic Ocean. J Clim 15(3):237–250.  https://doi.org/10.1175/1520-0442(2002)015%3C0237:TLFVOT%3E2.0.CO;2 CrossRefGoogle Scholar
  24. Hastenrath S (1984) Interannual variability and annual cycle: mechanisms of circulation and climate in the tropical Atlantic sector. Mon Weather Rev 112(6):1097–1107.  https://doi.org/10.1175/1520-0493(1984)112%3C1097:IVAACM%3E2.0.CO;2.CrossRefGoogle Scholar
  25. Hastenrath S (2012) Exploring the climate problems of Brazil’s nordeste: a review. Clim Chan 112(2):243–251.  https://doi.org/10.1007/s10584-011-0227-1 CrossRefGoogle Scholar
  26. Hu ZZ, Huang B (2007) The predictive skill and the most predictable pattern in the tropical Atlantic: the effect of ENSO. Mon Weather Rev 135(5):1786–1806.  https://doi.org/10.1175/MWR3393.1 CrossRefGoogle Scholar
  27. Hu ZZ, Huang B, Pegion K (2008) Leading patterns of the tropical Atlantic variability in a coupled general circulation model. Clim Dyn 30:703–726.  https://doi.org/10.1007/s00382-007-0318-x CrossRefGoogle Scholar
  28. Hu ZZ, Kumar A, Huang B et al (2011) Persistent atmospheric and oceanic anomalies in the North Atlantic from summer 2009 to summer 2010. J Clim 24(22):5812–5830.  https://doi.org/10.1175/2011JCLI4213.1 CrossRefGoogle Scholar
  29. Huang B (2004) Remotely forced variability in the tropical Atlantic Ocean. Clim Dyn 23(2):133–152.  https://doi.org/10.1007/s00382-004-0443-8 CrossRefGoogle Scholar
  30. Huang B, Hu ZZ (2007) Cloud-SST feedback in southeastern tropical Atlantic anomalous Events. J Geophys Res Oceans.  https://doi.org/10.1029/2006JC003626 CrossRefGoogle Scholar
  31. Huang B, and Shukla J (1997) Characteristics of the interannual and decadal variability in a general circulation model of the tropical Atlantic Ocean. J Phys Oceanogr 27(8):1693–1712.  https://doi.org/10.1175/1520-0485(1997)027%3C1693:COTIAD%3E2.0.CO;2.CrossRefGoogle Scholar
  32. Huang B, Shukla J (2005) Ocean–atmosphere interactions in the tropical and subtropical Atlantic Ocean. J Clim 18(11):1652–1672.  https://doi.org/10.1175/JCLI3368.1 CrossRefGoogle Scholar
  33. Huang B, Schopf PS, Pan Z (2002) The ENSO effect on the tropical Atlantic variability: a regionally coupled model study. Geophys Res Lett 29(21):2044.  https://doi.org/10.1029/2002GL014872 CrossRefGoogle Scholar
  34. Huang B, Schopf PS, Shukla J (2004) Intrinsic ocean–atmosphere variability of the tropical Atlantic Ocean. J Clim 17(11):2058–2077.  https://doi.org/10.1175/1520-0442(2004)017%3C2058:IOVOTT%3E2.0.CO;2 CrossRefGoogle Scholar
  35. Huang B, Hu ZZ, Jha B (2007) Evolution of model systematic errors in the tropical Atlantic basin from coupled climate hindcasts. Clim Dyn 28(7–8):661–682.  https://doi.org/10.1007/s00382-006-0223-8 CrossRefGoogle Scholar
  36. Hurrell JW, Holland MM, Gent PR et al (2013) The community earth system model: a framework for collaborative research. Bull Am Meteorol Soc 94(9):1339–1360.  https://doi.org/10.1175/BAMS-D-12-00121.1 CrossRefGoogle Scholar
  37. Jouanno J, Hernandez O, Sanchez-Gomez E (2017) Equatorial Atlantic interannual variability and its relation to dynamic and thermodynamic processes. Earth Syst Dyn 8(4):1061–1069.  https://doi.org/10.5194/esd-8-1061-2017 CrossRefGoogle Scholar
  38. Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471.  https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2.CrossRefGoogle Scholar
  39. Karspeck AR, Kaplan A, Cane MA (2006) Predictability loss in an intermediate ENSO model due to initial error and atmospheric noise. J Clim 19(15):3572–3588.  https://doi.org/10.1175/JCLI3818.1 CrossRefGoogle Scholar
  40. Kay JE, Deser C, Phillips A, Mai A et al (2014) The community earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull Am Meteorol Soc 96(8):1333–1349.  https://doi.org/10.1175/BAMS-D-13-00255.1 CrossRefGoogle Scholar
  41. Krishnamurti TN, Kishtawal C, LaRow TE et al (1999) Improved weather and seasonal climate forecasts from multi-model superensemble. Science 285:1548–1550.  https://doi.org/10.1126/science.285.5433.1548 CrossRefGoogle Scholar
  42. Kushnir Y, Robinson WA, Chang P et al (2006) The physical basis for predicting Atlantic sector seasonal-to-interannual climate variability. J Clim 19(23):5949–5970.  https://doi.org/10.1175/JCLI3943.1 CrossRefGoogle Scholar
  43. Li X, Xie SP, Gille ST et al (2016) Atlantic-induced pan-tropical climate change over the past three decades. Nat Clim Change 6(3):275–279.  https://doi.org/10.1038/nclimate2840 CrossRefGoogle Scholar
  44. Lin JL (2007) The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean–atmosphere feedback analysis. J Clim 20(18):4497–4525.  https://doi.org/10.1175/JCLI4272.1 CrossRefGoogle Scholar
  45. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmo Sci 20(2):130–141.  https://doi.org/10.1175/1520-0469(1963)020%3C0130:DNF%3E2.0.CO;2.CrossRefGoogle Scholar
  46. Lorenz EN (1965) A Study of the predictability of a 28-variable atmospheric model. Tellus 17(3):321–333.  https://doi.org/10.1111/j.2153-3490.1965.tb01424.x CrossRefGoogle Scholar
  47. Lübbecke JF, Burls NJ, Reason CJC, McPhaden MJ (2014) Variability in the South Atlantic Anticyclone and the Atlantic Niño Mode. J Clim 27(21):8135–8150CrossRefGoogle Scholar
  48. Martín-Rey M, Polo I, Rodríguez-Fonseca B et al (2017) Is there evidence of changes in tropical Atlantic variability modes under AMO phases in the observational record? J Clim 31(2):515–536.  https://doi.org/10.1175/JCLI-D-16-0459.1 CrossRefGoogle Scholar
  49. McGregor S, Timmermann A, Stuecker MF et al (2014) Recent walker circulation strengthening and pacific cooling amplified by Atlantic warming. Nat Clim Change 4(10):888–892.  https://doi.org/10.1038/nclimate2330 CrossRefGoogle Scholar
  50. Moura AD, Shukla J (1981) On the dynamics of droughts in northeast Brazil: observations, theory and numerical experiments with a general circulation model. J Atmos Sci 38(12):2653–2675.  https://doi.org/10.1175/1520-0469(1981)038%3C2653:OTDODI%3E2.0.CO;2.CrossRefGoogle Scholar
  51. Palmer TN, Branković Č, Richardson DS (2000) A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Q J R Meteorol Soc 126(567):2013–2033.  https://doi.org/10.1002/qj.49712656703 CrossRefGoogle Scholar
  52. Penland C, Hartten LM (2014) Stochastic forcing of north tropical Atlantic sea surface temperatures by the north Atlantic oscillation. Geophys Res Lett 41(6):2126–2132.  https://doi.org/10.1002/2014GL059252 CrossRefGoogle Scholar
  53. Reynolds RW, Rayner NA, Smith TM et al (2002) An improved in situ and satellite SST analysis for climate. J Clim 15(13):1609–1625.  https://doi.org/10.1175/1520-0442(2002)015%3C1609:AIISAS%3E2.0.CO;2.CrossRefGoogle Scholar
  54. Richman MB (1986) Rotation of principal components. J Climatol 6(3):293–335.  https://doi.org/10.1002/joc.3370060305 CrossRefGoogle Scholar
  55. Richter I, Xie SP, Behera SK et al (2012) Equatorial Atlantic variability and its relation to mean state biases in CMIP5. Clim Dyn 42(1–2):171–188.  https://doi.org/10.1007/s00382-012-1624-5 CrossRefGoogle Scholar
  56. Richter I, Behera SK, Doi T et al (2014) What controls equatorial Atlantic winds in boreal spring? Clim Dyn 43(11):3091–3104.  https://doi.org/10.1007/s00382-014-2170-0 CrossRefGoogle Scholar
  57. Sasaki W, Doi T, Richards KJ (2015) The influence of ENSO on the equatorial Atlantic precipitation through the Walker circulation in a CGCM. Clim Dyn 44(1–2):191–202.  https://doi.org/10.1007/s00382-014-2133-5 CrossRefGoogle Scholar
  58. Servain J (1991) Simple climatic indices for the tropical Atlantic Ocean and some applications. J Geophys Res Oceans 96(C8):15137–15146.  https://doi.org/10.1029/91JC01046 CrossRefGoogle Scholar
  59. Shukla J (1985) Predictability. In: Saltzman B (eds) Advances in geophysics. Issues in Atmospheric and Oceanic Modeling, vol 28. Elsevier, Amsterdam, pp 87–122.  https://doi.org/10.1016/S0065-2687(08)60186-7 CrossRefGoogle Scholar
  60. Stockdale TN, Balmaseda MA, Vidard A (2006) Tropical Atlantic SST prediction with coupled ocean–atmosphere GCMs. J Clim 19(23):6047–6061.  https://doi.org/10.1175/JCLI3947.1 CrossRefGoogle Scholar
  61. Straus DM, Shukla J (2002) Does ENSO force the PNA? J Clim 15(17):2340–2358.  https://doi.org/10.1175/1520-0442(2002)015%3C2340:DEFTP%3E2.0.CO;2.CrossRefGoogle Scholar
  62. Sutton RT, Hodson DLR (2007) Climate response to basin-scale warming and cooling of the north Atlantic ocean. J Clim 20(5):891–907.  https://doi.org/10.1175/JCLI4038.1 CrossRefGoogle Scholar
  63. Vimont DJ (2011) Analysis of the Atlantic meridional mode using linear inverse modeling: seasonality and regional influences. J Clim 25(4):1194–1212.  https://doi.org/10.1175/JCLI-D-11-00012.1 CrossRefGoogle Scholar
  64. Vimont DJ, Kossin JP (2007) The Atlantic meridional mode and hurricane activity. Geophys Res Lett 34(7):L07709.  https://doi.org/10.1029/2007GL029683 CrossRefGoogle Scholar
  65. Xiang B, Zhao M, Held IM et al (2017) Predicting the severity of spurious ‘double ITCZ’ problem in CMIP5 coupled models from AMIP simulations. Geophys Res Lett 44(3):1520–1527.  https://doi.org/10.1002/2016GL071992 CrossRefGoogle Scholar
  66. Xie P, Arkin PA (1997) Global Precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78(11):2539–2558.  https://doi.org/10.1175/1520-0477(1997)078%3C2539:GPAYMA%3E2.0.CO;2.CrossRefGoogle Scholar
  67. Xie SP, Carton J (2004) Tropical Atlantic variability: patterns, mechanisms, and impacts. Earth Clim.  https://doi.org/10.1029/147GM07 CrossRefGoogle Scholar
  68. Xie SP, Philander SGH (1994) A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus A 46(4):340–350.  https://doi.org/10.1034/j.1600-0870.1994.t01-1-00001.x CrossRefGoogle Scholar
  69. Xie SP, Tanimoto Y (1998) A pan-Atlantic decadal climate oscillation. Geophys Res Lett 25(12):2185–2188.  https://doi.org/10.1029/98GL01525 CrossRefGoogle Scholar
  70. Yang Y, Xie SP, Wu L et al (2017) ENSO forced and local variability of north tropical Atlantic SST: model simulations and biases. Clim Dyn.  https://doi.org/10.1007/s00382-017-3679-9 CrossRefGoogle Scholar
  71. Yu L, Jin X, Weller RA (2008) Multidecade global flux datasets from the objectively analyzed air-sea fluxes (OAFlux) project: latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. ​WHOI, OAFlux project technical report. http://oaflux.whoi.edu/pdfs/OAFlux_TechReport_3rd_release.pdf
  72. Zebiak SE (1993) Air–sea interaction in the equatorial Atlantic region. J Clim 6(8):1567–1586.  https://doi.org/10.1175/1520-0442(1993)006%3C1567:AIITEA%3E2.0.CO;2.CrossRefGoogle Scholar
  73. Zhang T, Huang B, Yang S et al (2018) Predictable patterns of the atmospheric low-level circulation over the Indo-Pacific region in project minerva: seasonal dependence and intraensemble variability. J Clim 31(20):8351–8379.  https://doi.org/10.1175/JCLI-D-17-0577.1 CrossRefGoogle Scholar
  74. Zhu Y (2005) Ensemble forecast: a new approach to uncertainty and predictability. Adv Atmos Sci 22(6):781–788.  https://doi.org/10.1007/BF02918678 CrossRefGoogle Scholar
  75. Zhu J, Huang B, Balmaseda BA (2012) An ensemble estimation of the variability of upper-ocean heat content over the tropical Atlantic Ocean with multi-ocean reanalysis products. Clim Dyn 39(3–4):1001–1020.  https://doi.org/10.1007/s00382-011-1189-8 CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Atmospheric, Oceanic, and Earth Sciences, College of ScienceGeorge Mason UniversityFairfaxUSA
  2. 2.Center for Ocean-Land-Atmosphere StudiesFairfaxUSA

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