logo

SCIENCE CHINA Earth Sciences, Volume 60 , Issue 2 : 397-408(2017) https://doi.org/10.1007/s11430-015-5465-y

Attribution analysis for the failure of CMIP5 climate models to simulate the recent global warming hiatus

Meng WEI 1,2,3, FangLi QIAO 1,2,3,*
More info
  • ReceivedMay 12, 2016
  • AcceptedAug 10, 2016
  • PublishedOct 19, 2016

Abstract

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) contains a group of state-of-the-art climate models and represents the highest level of climate simulation thus far. However, these models significantly overestimated global mean surface temperature (GMST) during 2006–2014. Based on the ensemble empirical mode decomposition (EEMD) method, the long term change of the observed GMST time series of HadCRUT4 records during 1850–2014 was analyzed, then the simulated GMST by 33 CMIP5 climate models was assessed. The possible reason that climate models failed to project the recent global warming hiatus was revealed. Results show that during 1850–2014 the GMST on a centennial timescale rose with fluctuation, dominated by the secular trend and the multi-decadal variability (MDV). The secular trend was relatively steady beginning in the early 20th century, with an average warming rate of 0.0883°C/decade over the last 50 years. While the MDV (with a ~65-year cycle) showed 2.5 multi-decadal waves during 1850–2014, which deepened and steepened with time, the alarming warming over the last quarter of the 20th century was a result of the concurrence of the secular warming trend and the warming phase of the MDV, both of which accounted one third of the temperature increase during 1975–1998. Recently the slowdown of global warming emerged as the MDV approached its third peak, leading to a reduction in the warming rate. A comparative analysis between the GMST time series derived from HadCRUT4 records and 33 CMIP5 model outputs reveals that the GMSTs during the historical simulation period of 1850–2005 can be reproduced well by models, especially on the accelerated global warming over the last quarter of 20th century. However, the projected GMSTs and their linear trends during 2006–2014 under the RCP4.5 scenario were significantly higher than observed. This is because the CMIP5 models confused the MDV with secular trend underlying the GMST time series, which results in a fast secular trend and an improper MDV with irregular phases and small amplitudes. This implies that the role of atmospheric CO2 in global warming may be overestimated, while the MDV which is an interior oscillation of the climate system may be underestimated, which should be related to insufficient understanding of key climatic internal dynamic processes. Our study puts forward an important criterion for the new generation of climate models: they should be able to simulate both the secular trend and the MDV of GMST.


Funded by

National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers(U1406404,Transparent Ocean Project (Grant No. 2015ASKJ01)


Acknowledgment

This work was supported by the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404) and the Transparent Ocean Project (Grant No. 2015ASKJ01), the corresponding author is also supported by Ao-Shan Talent Program.


References

[1] Balmaseda M A, Trenberth K E, Källén E. Distinctive climate signals in reanalysis of global ocean heat content. Geophys Res Lett, 2013, 40: 1754-1759 CrossRef ADS Google Scholar

[2] Banholzer S, Donner S. The influence of different El Niño types on global average temperature. Geophys Res Lett, 2014, 41: 2093-2099 CrossRef ADS Google Scholar

[3] Brown P T, Li W, Li L, Ming Y. Top-of-atmosphere radiative contribution to unforced decadal global temperature variability in climate models. Geophys Res Lett, 2014, 41: 5175-5183 CrossRef ADS Google Scholar

[4] Cavalieri D J, Parkinson C L. Antarctic sea ice variability and trends, 1979–2006. J Geophys Res, 2008, 113: C07004 CrossRef ADS Google Scholar

[5] Cazenave A, Dieng H B, Meyssignac B, von Schuckmann K, Decharme B, Berthier E. The rate of sea-level rise. Nat Clim Change, 2014, 4: 358-361 CrossRef ADS Google Scholar

[6] Chen X, Tung K K. Varying planetary heat sink led to global-warming slowdown and acceleration. Science, 2014, 345: 897-903 CrossRef ADS Google Scholar

[7] Easterling D R, Wehner M F. Is the climate warming or cooling?. Geophys Res Lett, 2009, 36: L08706 CrossRef ADS Google Scholar

[8] England M H, McGregor S, Spence P, Meehl G A, Timmermann A, Cai W, Gupta A S, McPhaden M J, Purich A, Santoso A. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat Clim Change, 2014, 4: 222-227 CrossRef ADS Google Scholar

[9] Fu C B, Qian C, Wu Z H. Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach. Sci China Earth Sci, 2011, 54: 1400-1406 CrossRef Google Scholar

[10] Fyfe J C, Gillett N P, Zwiers F W. Overestimated global warming over the past 20 years. Nat Clim Change, 2013, 3: 767-769 CrossRef ADS Google Scholar

[11] Guemas V, Doblas-Reyes F J, Andreu-Burillo I, Asif M. Retrospective prediction of the global warming slowdown in the past decade. Nat Clim Change, 2013, 3: 649-653 CrossRef ADS Google Scholar

[12] Hansen J, Sato M, Kharecha P, von Schuckmann K. Earth’s energy imbalance and implications. Atmos Chem Phys, 2011, 11: 13421-13449 CrossRef ADS Google Scholar

[13] Haywood J M, Jones A, Jones G S. The impact of volcanic eruptions in the period 2000–2013 on global mean temperature trends evaluated in the HadGEM2-ES climate model. Atmos Sci Lett, 2014, 15: 92-96 CrossRef Google Scholar

[14] Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A-Math Phys Eng Sci, 1998, 454: 903-995 CrossRef PubMed ADS Google Scholar

[15] IPCC. 2007. Climate change 2007. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M, Miller H L, eds. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. 996. Google Scholar

[16] IPCC. 2013. Climate change 2013. In: Stocker T F, Qin D, Plattner G K, Tignor M, Allen S K, Boschung J, Nauels A, Xia Y, Bex V, Midgley P M, eds. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. 1535. Google Scholar

[17] Kamae Y, Shiogama H, Watanabe M, Ishii M, Ueda H, Kimoto M. Recent slowdown of tropical upper tropospheric warming associated with Pacific climate variability. Geophys Res Lett, 2015, 42: 2995-3003 CrossRef ADS Google Scholar

[18] Katsman C A, van Oldenborgh G J. Tracing the upper ocean’s “missing heat”. Geophys Res Lett, 2011, 38: L14610 CrossRef ADS Google Scholar

[19] Kaufmann R K, Kauppi H, Mann M L, Stock J H. Reconciling anthropogenic climate change with observed temperature 1998–2008. Proc Natl Acad Sci USA, 2011, 108: 11790-11793 CrossRef PubMed ADS Google Scholar

[20] Kavvada A, Ruiz-Barradas A, Nigam S. AMO’s structure and climate footprint in observations and IPCC AR5 climate simulations. Clim Dyn, 2013, 41: 1345-1364 CrossRef ADS Google Scholar

[21] Kim H M, Webster P J, Curry J A. Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys Res Lett, 2012, 39: L10701 CrossRef ADS Google Scholar

[22] Knight J R, Kennedy J J, Folland C, Harris G, Jones G S, Palmer M, Parker D, Scaife A, Stott P. 2009. Do global temperature trends over the last decade falsify climate predictions? Bull Amer Meteorol Soc, 90: 22–23. Google Scholar

[23] Kosaka Y, Xie S P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 2013, 501: 403-407 CrossRef PubMed ADS Google Scholar

[24] Kumar S, Kinter J, Dirmeyer P A, Pan Z, Adams J. 2013. Multi-decadal climate variability and the “Warming Hole” in North America-results from CMIP5 20th and 21st Century climate simulations. J Clim, 26: 3511–3527. Google Scholar

[25] Latif M, Martin T, Park W. Southern ocean sector centennial climate variability and recent decadal trends. J Clim, 2013, 26: 7767-7782 CrossRef Google Scholar

[26] Lean J L, Rind D H. How will Earth’s surface temperature change in future decades?. Geophys Res Lett, 2009, 36: L15708 CrossRef ADS Google Scholar

[27] Lee S K, Park W, Baringer M O, Gordon A L, Huber B, Liu Y. Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nat Geosci, 2015, 8: 445-449 CrossRef ADS Google Scholar

[28] Levitus S, Antonov J I, Boyer T P, Locarnini R A, Garcia H E, Mishonov A V. Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys Res Lett, 2009, 36: L07608 CrossRef ADS Google Scholar

[29] Loeb N G, Lyman J M, Johnson G C, Allan R P, Doelling D R, Wong T, Soden B J, Stephens G L. Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat Geosci, 2012, 5: 110-113 CrossRef ADS Google Scholar

[30] McGregor S, Timmermann A, Stuecker M F, England M H, Merrifield M, Jin F F, Chikamoto Y. Recent walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat Clim Change, 2014, 4: 888-892 CrossRef ADS Google Scholar

[31] Meehl G A, Arblaster J M, Fasullo J T, Hu A, Trenberth K E. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat Clim Change, 2011, 1: 360-364 CrossRef ADS Google Scholar

[32] Meehl G A, Hu A, Arblaster J M, Fasullo J, Trenberth K E. Externally forced and internally generated decadal climate variability associated with the interdecadal Pacific Oscillation. J Clim, 2013, 26: 7298-7310 CrossRef Google Scholar

[33] Morice C P, Kennedy J J, Rayner N A, Jones P D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J Geophys Res, 2012, 117: D08101 CrossRef ADS Google Scholar

[34] Neely R R, Toon O B, Solomon S, Vernier J P, Alvarez C, English J M, Rosenlof K H, Mills M J, Bardeen C G, Daniel J S, Thayer J P. Recent anthropogenic increases in SO2 from Asia have minimal impact on stratospheric aerosol. Geophys Res Lett, 2013, 40: 999-1004 CrossRef ADS Google Scholar

[35] Parkinson C L, Cavalieri D J. Antarctic sea ice variability and trends, 1979‒2010. Cryosphere Discuss, 2012, 6: 931-956 CrossRef ADS Google Scholar

[36] Roemmich D, Church J, Gilson J, Monselesan D, Sutton P, Wijffels S. Unabated planetary warming and its ocean structure since 2006. Nat Clim Change, 2015, 5: 240-245 CrossRef ADS Google Scholar

[37] Ruiz-Barradas A, Nigam S, Kavvada A. The Atlantic Multidecadal Oscillation in twentieth century climate simulations: Uneven progress from CMIP3 to CMIP5. Clim Dyn, 2013, 41: 3301-3315 CrossRef ADS Google Scholar

[38] Santer B D, Bonfils C, Painter J F, Zelinka M D, Mears C, Solomon S, Schmidt G A, Fyfe J C, Cole J N S, Nazarenko L, Taylor K E, Wentz F J. Volcanic contribution to decadal changes in tropospheric temperature. Nat Geosci, 2014, 7: 185-189 CrossRef ADS Google Scholar

[39] Scafetta N. Discussion on climate oscillations: CMIP5 general circulation models versus a semi-empirical harmonic model based on astronomical cycles. Earth-Sci Rev, 2013, 126: 321-357 CrossRef ADS arXiv Google Scholar

[40] Schlesinger M E, Ramankutty N. An oscillation in the global climate system of period 65–70 years. Nature, 1994, 367: 723-726 CrossRef ADS Google Scholar

[41] Schmidt G A, Shindell D T, Tsigaridis K. Reconciling warming trends. Nat Geosci, 2014, 7: 158-160 CrossRef ADS Google Scholar

[42] Sheffield J, Camargo S J, Fu R, Hu Q, Jiang X, Johnson N, Karnauskas K B, Kinter J, Kumar S, Langenbrunner B, Maloney E, Mariotti A, Meyerson J E, Neelin J D, Pan Z, Ruiz-Barradas A, Seager R, Serra Y L, Sun D, Wang C, Xie S, Yu J, Zhang T, Zhao M. 2013. North American climate in CMIP5 experiments. Part II: Evaluation of 20th Century intra-seasonal to decadal variability. J Clim, 23: 9247–9290. Google Scholar

[43] Solomon S, Daniel J S, Neely R R, Vernier J P, Dutton E G, Thomason L W. The persistently variable “Background” stratospheric aerosol layer and global climate change. Science, 2011, 333: 866-870 CrossRef PubMed ADS Google Scholar

[44] Solomon S, Rosenlof K H, Portmann R W, Daniel J S, Davis S M, Sanford T J, Plattner G K. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 2010, 327: 1219-1223 CrossRef PubMed ADS Google Scholar

[45] Stauning P. Reduced solar activity disguises global temperature rise. Atmos Clim Sci, 2014, 4: 60-63 CrossRef Google Scholar

[46] Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design. Bull Amer Meteorol Soc, 2012, 93: 485-498 CrossRef ADS Google Scholar

[47] Ting M, Kushnir Y, Seager R, Li C. Forced and internal Twentieth-Century SST trends in the North Atlantic. J Clim, 2009, 22: 1469-1481 CrossRef ADS Google Scholar

[48] Trenberth K E, Fasullo J T. An apparent hiatus in global warming?. Earth’s Future, 2013, 1: 19-32 CrossRef ADS Google Scholar

[49] Tung K K, Zhou J. Using data to attribute episodes of warming and cooling in instrumental records. Proc Natl Acad Sci USA, 2013, 110: 2058-2063 CrossRef PubMed ADS Google Scholar

[50] Watanabe M, Kamae Y, Yoshimori M, Oka A, Sato M, Ishii M, Mochizuki T, Kimoto M. Strengthening of ocean heat uptake efficiency associated with the recent climate hiatus. Geophys Res Lett, 2013, 40: 3175-3179 CrossRef ADS Google Scholar

[51] Wei M, Qiao F, Deng J. A quantitative definition of global warming hiatus and 50-year prediction of global-mean surface temperature. J Atmos Sci, 2015, 72: 3281-3289 CrossRef ADS Google Scholar

[52] Wu Z, Huang N E. Ensemble empirical mode decomposition: A Noise-Assisted data analysis method. Adv Adapt Data Anal, 2009, 1: 1-41 CrossRef Google Scholar

[53] Wu Z, Huang N E, Long S R, Peng C K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci USA, 2007, 104: 14889-14894 CrossRef PubMed ADS Google Scholar

[54] Wu Z, Huang N E, Wallace J M, Smoliak B V, Chen X. On the time-varying trend in global-mean surface temperature. Clim Dyn, 2011, 37: 759-773 CrossRef ADS Google Scholar

[55] Zhang R, Delworth T L. Anticorrelated multidecadal variations between surface and subsurface tropical North Atlantic. Geophys Res Lett, 2007, 34: L12713 CrossRef ADS Google Scholar

[56] Zhou T, Yu R. Twentieth-Century surface air temperature over China and the globe simulated by coupled climate models. J Clim, 2006, 19: 5843-5858 CrossRef ADS Google Scholar

  • Figure 1

    Global mean surface temperature annual anomalies (GMSTA, °C, relative to 1961–1990) time series derived from HadCRUT4 observations (solid black curve) and 33 CMIP5 models simulations. Gray vertical line denotes the location of 2005, which separates CMIP5 historical simulation (1850–2005) and future projection under the RCP4.5 scenario (since 2006). Gray dotted curve indicates multi-model ensemble mean during 1850–2005, and dark and light gray shading respectively denote 50% and 90% confidence intervals of 33 models. Red dotted curve indicates multi-model mean during 2006–2050, and dark and light red shading respectively denote 50% and 90% confidence intervals of 33 models. Light orange and blue histograms represent the warming and cooling episodes of 10-year running mean HadCRUT4 GMSTA time series. Yellow histogram shows the location and duration of recent warming hiatus, i.e. 1998–2014.

  • Figure 2

    Evaluation of CMIP5 models historical simulation results for GMSTA during 1850–2005. (a) Taylor diagram, (b) mean temperature during 1850–2005, (c) linear trends during1850–2005. (a), the radius in the polar coordinates denotes standard deviation of temperature time series observed or modeled (black dashed line), the azimuthal angle denotes correlation coefficient between modeled and observed results (gray solid line), and the distance from observation denotes the root mean square error (RMSE, gray dotted line). Both the stanard deviation and RMSE are normalized and the unit is one standard deviation of observed results. (b), two gray dashed lines denote 0.01°C higher or lower than the mean of observed results, respectivley. (c), two gray dashed lines denote 0.02°C/decade higher or lower than the trend of observed temperature, respectivley.

  • Figure 3

    Evaluation of future projection results under the RCP4.5 scenario for GMSTA from 2006 to 2014.

  • Figure 4

    The EEMD results and significance tests of GMSTA time series from HadCRUT4 ((a) and (b)) and from CMIP5 MMM ((c) and (d)).

  • Figure 5

    The MDV (red), secular trend (blue), superposition of secular trend and MDV (purple), and original GMSTA records (gray) of HadCRUT4.

  • Figure 6

    The secular trend and MDV from HadCRUT4 and 33 CMIP5 models. (a) Secular trend, (b) MDV, (c) 70-year moving correlation coefficient between observed and simulated MDV time series, (d) the amplitude of MDV. In (d), two gray dashed lines denote 0.1 times higher or lower than the observed ampitude, respectivley.

  • Figure 7

    The secular trends (blue and green), superposition of secular trend and MDV (purple and red), and original GMST time series (gray and brown) from HadCRUT4 and CMIP5 MMM.

  • Table 1   Information of 33 CMIP5 climate models

    No.

    Model

    Horizontal resolution

    Research institution

    01

    ACCESS1-0

    145×192

    CSIRO, BOM, Australia

    02

    ACCESS1-3

    145×192

    CSIRO, BOM, Australia

    03

    BCC-CSM1-1-m

    160×320

    BCC, China

    04

    BCC-CSM1-1

    64×128

    BCC, China

    05

    BNU-ESM

    64×128

    BNU, China

    06

    CanESM2

    64×128

    CCCMA, Canada

    07

    CCSM4

    192×288

    NCAR, USA

    08

    CESM1-BGC

    192×288

    NSF, DOE, NCAR, USA

    09

    CESM1-CAM5

    192×288

    NSF, DOE, NCAR, USA

    10

    CMCC-CM

    240×480

    CMCC, Italy

    11

    CMCC-CMS

    96×192

    CMCC, Italy

    12

    CNRM-CM5

    128×256

    CNRM, CERFACS, France

    13

    CSIRO-Mk3-6-0

    96×192

    CSIRO, QCCCE, Australia

    14

    EC-EARTH

    160×320

    ICHEC, Ireland

    15

    FGOALS-g2

    60×128

    IAP, THU, China

    16

    FGOALS-s2

    108×128

    IAP, China

    17

    FIO-ESM

    64×128

    FIO, China

    18

    GISS-E2-H-CC

    90×144

    NASA/GISS, USA

    19

    GISS-E2-H

    90×144

    NASA/GISS, USA

    20

    GISS-E2-R-CC

    90×144

    NASA/GISS, USA

    21

    GISS-E2-R

    90×144

    NASA/GISS, USA

    22

    INM-CM4

    120×180

    INM, Russia

    23

    IPSL-CM5A-LR

    96×96

    IPSL, France

    24

    IPSL-CM5A-MR

    143×144

    IPSL, France

    25

    IPSL-CM5B-LR

    96×96

    IPSL, France

    26

    MIROC-ESM-CHEM

    64×128

    JAMSTEC, AORI, NIES, Japan

    27

    MIROC-ESM

    64×128

    JAMSTEC, AORI, NIES, Japan

    28

    MIROC5

    128×256

    AORI, NIES, JAMSTEC, Japan

    29

    MPI-ESM-LR

    96×192

    MPI-M, Germany

    30

    MPI-ESM-MR

    96×192

    MPI-M, Germany

    31

    MRI-CGCM3

    160×320

    MRI, Japan

    32

    NorESM1-M

    96×144

    NCC, Norway

    33

    NorESM1-ME

    96×144

    NCC, Norway

  • Table 2   Linear trends during different periods of GMSTA timeseries derived from HadCRUT4 and CMIP5 MMM

    Trend (°C/decade)

    1850–2014

    1915–2014

    1965–2014

    1990–2014

    2005–2014

    HadCRUT4

    0.0479

    0.0739

    0.1530

    0.1459

    0.0112

    CMIP5 MMM

    0.0593

    0.0925

    0.2056

    0.2793

    0.2216

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号