Skip to main content
Springer Nature Link
Log in

Critical influence of the pattern of Tropical Ocean warming on remote climate trends

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Evidence is presented that the recent trend patterns of surface air temperature and precipitation over the land masses surrounding the North Atlantic Ocean (North America, Greenland, Europe, and North Africa) have been strongly influenced by the warming pattern of the tropical oceans. The current generation of atmosphere–ocean coupled climate models with prescribed radiative forcing changes generally do not capture these regional trend patterns. On the other hand, even uncoupled atmospheric models without the prescribed radiative forcing changes, but with the observed oceanic warming specified only in the tropics, are more successful in this regard. The tropical oceanic warming pattern is poorly represented in the coupled simulations. Our analysis points to model error rather than unpredictable climate noise as a major cause of this discrepancy with respect to the observed trends. This tropical error needs to be reduced to increase confidence in regional climate change projections around the globe, and to formulate better societal responses to projected changes in high-impact phenomena such as droughts and wet spells.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from €39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

Notes

  1. We also reconstructed the trends using a \( {\mathbf{G}}\left( {{\mathbf{X}}_{\text{obs}} } \right) \) derived from the CCM3 patch experiment (Barsugli et al. 2006), and compared them over the Atlantic Rim land masses with the ensemble-mean trends from the CCM3 GLB simulations (Table 2). The pattern correlations (r.m.s. magnitude ratios) were 0.70 (0.85) and 0.92 (1.11) for surface temperature and precipitation. With the land-averaged trends removed, the pattern correlations (r.m.s. magnitude ratios) were 0.68 (0.88) and 0.92 (1.10) for surface and temperature and precipitation.

  2. Note that although the observed tropical SST trends are identical in the reconstruction and the GLB simulations, they produce slightly different trend responses around the globe because the reconstruction is linear and the GLB simulations are nonlinear.

  3. Dai et al. (2004) show that the global PDSI trends over 1950–2002 were mostly associated with changes of precipitation (see their Fig. 7). The poor representation of PDSI trends in the coupled simulations in Fig. 9 is also mostly associated with the poor representation of regional precipitation trends in those simulations, which is itself strongly associated with the poor representation of the spatial variation of the tropical SST trends in those simulations.

References

  • Anderson JL et al (2004) The new GFDL global atmosphere and land model AM2-LM2: evaluation with prescribed SST simulations. J Clim 17:4641–4673

    Article  Google Scholar 

  • Barnston AG, Kumar A, Goddard L, Hoerling MP (2005) Improving seasonal prediction practices through attribution of climate variability. Bull Am Meteorol Soc 86:59–72

    Article  Google Scholar 

  • Barsugli JJ, Sardeshmukh PD (2002) Global atmospheric sensitivity to tropical SST anomalies throughout Indo-Pacific basin. J Clim 15:2205–2231

    Article  Google Scholar 

  • Barsugli JJ, Shin S-I, Sardeshmukh PD (2006) Sensitivity of global warming to the pattern of Tropical Ocean warming. Clim Dyn 27:483–492

    Article  Google Scholar 

  • Bracco A, Kucharski F, Kallummal R, Molteni F (2004) Internal variability, external forcing and climate trends in multi-decadal AGCM ensembles. Clim Dyn 23:659–678

    Article  Google Scholar 

  • Chen M, Xie P, Janowiak JE, Arkin PA (2002) Global land precipitation: a 50-yr monthly analysis based on gauge observations. J Hydrometeorol 3:249–266

    Article  Google Scholar 

  • Collins WD et al (2006) The community climate system model version 3 (CCSM3). J Clim 19:2122–2143

    Article  Google Scholar 

  • Compo GP, Sardeshmukh PD (2009) Oceanic influences on recent continental warming. Clim Dyn 32:333–342

    Article  Google Scholar 

  • Dai AG, Trenberth KE, Karl TR (1998) Global variations in droughts and wet spells: 1900–1995. Geophys Res Lett 25:3367–3370

    Article  Google Scholar 

  • Dai AG, Trenberth KE, Qian T (2004) A global dataset of Palmer Drought Severity Index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol 5:1117–1130

    Article  Google Scholar 

  • Delworth TL et al (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:643–674

    Article  Google Scholar 

  • Deser C, Phillips AS (2009) Atmospheric circulation trends, 1950–2000: the relative roles of sea surface temperature forcing and direct atmospheric radiative forcing. J Clim 22:396–413

    Article  Google Scholar 

  • Deser C, Phillips AS, Hurrell JW (2004) Pacific interdecadal climate variability: linkages between the tropics and the North Pacific during boreal winter since 1900. J Clim 17:3109–3124

    Article  Google Scholar 

  • Folland CK, Sexton DMH, Karoly D, Johnson C, Rowell D, Parker D (1998) Influences of anthropogenic and oceanic forcing on recent climate change. Geophys Res Lett 25:353–356

    Article  Google Scholar 

  • Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamtsø NG, Sorteberg A (2003) Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21:27–51

    Article  Google Scholar 

  • Gates WL (1992) AMIP: the atmospheric model intercomparison project. Bull Am Meteorol Soc 73:1962–1970

    Article  Google Scholar 

  • Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA (2001) Current approaches to seasonal to interannual climate predictions. Int J Climatol 21:1111–1152

    Article  Google Scholar 

  • Gordon C et al (2000) The simulation of SST, sea ice extents and ocean heat transport in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168

    Article  Google Scholar 

  • Gordon HB et al (2002) The CSIRO Mk3 climate system model. Tech. Rep. 60, CSIRO Atmospheric Research, Aspendale, Victoria, Australia, p 134

  • Graham NE (1994) Decadal-scale climate variability in the tropical and North Pacific during the 1970s and 1980s: observations and model results. Clim Dyn 10:135–162

    Article  Google Scholar 

  • Gualdi S, Scoccimarro E, Navarra A (2006) Changes in tropical cyclone activity due to global warming: results from a high-resolution coupled general circulation model. J Clim 21:5204–5228

    Article  Google Scholar 

  • Hansen JE et al (2001) A closer look at United States and global surface temperature change. J Geophys Res 106:23947–23963

    Article  Google Scholar 

  • Hansen JE et al (2007) Climate simulations for 1880–2003 with GISS modelE. Clim Dyn 29:661–696

    Article  Google Scholar 

  • Herweijer C, Seager R (2008) The global footprint of persistent extra-tropical drought in the instrumental era. Int J Climatol. doi:10.1002/joc.1950

  • Hoerling MP, Kumar A (2003) The perfect ocean for drought. Science 299:691–699

    Article  Google Scholar 

  • Hoerling MP, Hurrell JW, Xu T (2001) Tropical origins for recent North Atlantic climate change. Science 292:90–92

    Article  Google Scholar 

  • Hoerling MP, Kumar A, Eischeid J, Jha B (2008) What is causing the variability in global mean land temperature? Geophys Res Lett 35:L23712. doi:10.1029/2008GL035984

  • Hurrell JW, Hoerling MP, Phillips AS, Xu T (2004) Twentieth century North Atlantic climate change. Part I: assessing determinism. Clim Dyn 23:371–389

    Article  Google Scholar 

  • Hurrell JW, Hack JJ, Phillips AS, Caron J, Yin J (2006) The dynamical simulation of the community atmosphere model version 3 (CAM3). J Clim 19:2162–2183

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor MMB, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental panel on climate change, Cambridge University Press, Cambridge, UK and New York, p 996

  • Johns T et al (2006) The new Hadley Centre climate model HadGEM1: evaluation of coupled simulations. J Clim 19:1327–1353

    Article  Google Scholar 

  • Jungclaus J et al (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19:3952–3972

    Article  Google Scholar 

  • K-1 model developers (2004) K-1 coupled model (MIROC) description. In: Hasumi H, Emori S (eds) K-1 Tech. Rep. 1, Center for Climate System Research, University of Tokyo, p 34

  • Kaplan A, Cane M, Kushnir Y, Clement A, Blumenthal M, Rajagopalan B (1998) Analyses of global sea surface temperature 1856–1991. J Geophys Res 103:18567–18589

    Article  Google Scholar 

  • Kim SJ, Flato GM, de Boer GJ, McFarlane NA (2002) A coupled climate model simulation of the Last Glacial Maximum. Part I: transient multi-decadal response. Clim Dyn 19:515–537

    Article  Google Scholar 

  • King MP, Kucharski F (2006) Observed decadal connections between the tropical oceans and the North Atlantic Oscillation in the 20th century. J Clim 19:1032–1041

    Article  Google Scholar 

  • Kucharski F, Molteni F, Bracco A (2006) Decadal interactions between the Western Tropical Pacific and the North Atlantic Oscillation. Clim Dyn 26:79–91

    Article  Google Scholar 

  • Lau N-C (1997) Interactions between global SST anomalies and the midlatitude atmospheric circulation. Bull Am Meteorol Soc 78:21–33

    Article  Google Scholar 

  • Lau N-C, Nath MJ (1994) A modeling study of the relative roles of tropical and extratropical SST anomalies in the variability of the global atmosphere–ocean system. J Clim 7:1184–1207

    Article  Google Scholar 

  • Lin JL (2007) The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean–atmosphere feedback analysis. J Clim 20:4497–4525

    Article  Google Scholar 

  • Lucarini L, Russell GL (2002) Comparison of mean climate trends in the northern hemisphere between National Centers for environmental prediction and two atmosphere-ocean model forced runs. J Geophys Res 107:4269. doi:10.1029/2001JD001247

    Article  Google Scholar 

  • Marti O et al (2005) The new IPSL climate system model: IPSL-CM4. Tech. Rep., Institut Pierre Simon Laplace des Sciences de l’Environment Global, IPSL, Case 101, Paris, France, p 86

  • Min S-K, Legutke S, Hense A, Kwon W-T (2005) Internal variability in a 1000-yr control simulation with the coupled model ECHO-G-I: near-surface temperature, precipitation and mean sea level pressure. Tellus 57A:605–621

    Google Scholar 

  • Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological drought. Research Paper 45, US Department of Commerce, p 58

  • Ray AJ et al (2008) Climate change in Colorado: a synthesis to support water resources management and adaptation. A Report for the Colorado Water Conservation Board, University of Colorado at Boulder, p 52

  • Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407–4443. doi:10.1029/2002JD002670

    Article  Google Scholar 

  • Rodwell MJ, Rowell DP, Folland CK (1999) Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature 398:320–323

    Article  Google Scholar 

  • Roeckner E et al (1996) The atmospheric general circulation model ECHAM4: model description and simulation of present-day climate. Max Planck Institute for Meteorology Rep 218, Hamburg, Germany, p 90

  • Roeckner E et al (2006) Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J Clim 19:3771–3791

    Article  Google Scholar 

  • Rudolf B, Beck C, Grieser J, Schneider U (2005) Global precipitation analysis products. Global Precipitation Climatology Centre (GPCC), DWD, Internet Publication, 1–8

  • Salas-Mélia D et al (2005) Description and validation of the CNRM-CM3 global coupled model, CNRM working note 103

  • Saravanan R (1998) Atmospheric low-frequency variability and its relationship to mid latitude SST variability: studies using the NCAR climate system model. J Clim 11:1386–1404

    Article  Google Scholar 

  • Sardeshmukh PD, Hoskins BI (1984) Spatial Smoothing on the Sphere. Mon Weather Rev 12:2524–2529

    Article  Google Scholar 

  • Schneider EK, Bengtsson L, Hu Z (2003) Forcing of northern hemisphere climate trends. J Atmos Sci 60:1504–1521

    Article  Google Scholar 

  • Schubert SD, Suarez MJ, Region PJ, Koster RD, Bacmeister JT (2004) On the cause of the 1930s dust bowl. Science 303:1855–1859

    Article  Google Scholar 

  • Shin S-I, Sardeshmukh PD, Webb RS, Oglesby RJ, Barsugli JJ (2006) Understanding the mid-Holocene climate. J Clim 19:2801–2817

    Article  Google Scholar 

  • Sidall M, Kaplan MR (2008) Climate science: a tale of two ice sheets. Nat Geosci 1:570–572

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2005) A global merged land-air-sea surface temperature reconstruction based on historical observations (1880–1997). J Clim 18:2021–2036

    Article  Google Scholar 

  • Sutton RT, Hodson DLR (2003) The influence of the ocean on North Atlantic climate variability 1871–1999. J Clim 16:3296–3313

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192

    Article  Google Scholar 

  • Volodin EM, Diansky NA (2004) El Niño reproduction in coupled general circulation model. Russ Meteorol Hydrol 12:5–14

    Google Scholar 

  • Washington WM et al (2000) Parallel climate model (PCM) control and transient simulations. Clim Dyn 16:755–774

    Article  Google Scholar 

  • Webb RS, Rosenzweig CE, Levine ER (1993) Specifying land surface characteristics in general circulation models: soil profile data set and derived water holding capacities. Global Biogeochem Cycles 7:97–108

    Article  Google Scholar 

  • Wilhite DA (2000) Drought as a natural hazard: concepts and definitions. In: Wilhite DA (ed) Drought: a global assessment, Routledge, pp 3–18

  • Yu Y, Zhang X, Guo Y (2004) Global coupled ocean- atmosphere general circulation models in LASG/IAP. Adv Atmos Sci 21:444–455

    Article  Google Scholar 

  • Yukimoto S, Noda A (2002) Improvements in the meteorological research institute global ocean-atmosphere coupled GCM (MRI-CGCM2) and its climate sensitivity. Tech. Rep. 10, NIES, Japan, p 8

Download references

Acknowledgments

We thank E. Schneider for his comments on an earlier version of manuscript that helped clarify the role of the tropical SST biases on remote trends in the coupled model simulations. The Max Planck Institute for Meteorology kindly provided the ECHAM5 code used in this study. Our own simulations were performed at the NOAA ESRL High Performance Computing Systems (HPCS) facility. This work was partly supported by NOAA’s Climate Program Office.

Author information

Author notes

  1. Sang-Ik Shin

    Present address: College of Marine Science, University of South Florida, St. Petersburg, FL, USA

Authors and Affiliations

  1. CIRES Climate Diagnostics Center, University of Colorado, and NOAA Earth System Research Laboratory, R/PSD1, 325 Broadway, Boulder, CO, 80305-3328, USA

    Sang-Ik Shin & Prashant D. Sardeshmukh

Authors
  1. Sang-Ik Shin
    View author publications

    Search author on:PubMed Google Scholar

  2. Prashant D. Sardeshmukh
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Sang-Ik Shin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shin, SI., Sardeshmukh, P.D. Critical influence of the pattern of Tropical Ocean warming on remote climate trends. Clim Dyn 36, 1577–1591 (2011). https://doi.org/10.1007/s00382-009-0732-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s00382-009-0732-3

Keywords