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.
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References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
Huang B, Hu ZZ (2007) Cloud-SST feedback in southeastern tropical Atlantic anomalous Events. J Geophys Res Oceans. https://doi.org/10.1029/2006JC003626
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.
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
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
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
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
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
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
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.
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
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
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
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
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
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
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.
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
Lübbecke JF, Burls NJ, Reason CJC, McPhaden MJ (2014) Variability in the South Atlantic Anticyclone and the Atlantic Niño Mode. J Clim 27(21):8135–8150
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
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
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.
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
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
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.
Richman MB (1986) Rotation of principal components. J Climatol 6(3):293–335. https://doi.org/10.1002/joc.3370060305
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
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
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
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
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
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
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.
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
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
Vimont DJ, Kossin JP (2007) The Atlantic meridional mode and hurricane activity. Geophys Res Lett 34(7):L07709. https://doi.org/10.1029/2007GL029683
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
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.
Xie SP, Carton J (2004) Tropical Atlantic variability: patterns, mechanisms, and impacts. Earth Clim. https://doi.org/10.1029/147GM07
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
Xie SP, Tanimoto Y (1998) A pan-Atlantic decadal climate oscillation. Geophys Res Lett 25(12):2185–2188. https://doi.org/10.1029/98GL01525
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
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
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.
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
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
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
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.
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Fang, G., Huang, B. Seasonal predictability of the tropical Atlantic variability: northern tropical Atlantic pattern. Clim Dyn 52, 6909–6929 (2019). https://doi.org/10.1007/s00382-018-4556-x
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DOI: https://doi.org/10.1007/s00382-018-4556-x