Skip to main content
PMC home page
Scientific Reports logoLink to Scientific Reports
. 2016 Sep 12;6:33315. doi: 10.1038/srep33315

A Hiatus of the Greenhouse Effect

Jinjie Song 1, Yuan Wang 1,a, Jianping Tang 1
PMCID: PMC5018860  PMID: 27616203

Abstract

The rate at which the global average surface temperature is increasing has slowed down since the end of the last century. This study investigates whether this warming hiatus results from a change in the well-known greenhouse effect. Using long-term, reliable, and consistent observational data from the Earth’s surface and the top of the atmosphere (TOA), two monthly gridded atmospheric and surface greenhouse effect parameters (Ga and Gs) are estimated to represent the radiative warming effects of the atmosphere and the surface in the infrared range from 1979 to 2014. The atmospheric and surface greenhouse effect over the tropical monsoon-prone regions is found to contribute substantially to the global total. Furthermore, the downward tendency of cloud activity leads to a greenhouse effect hiatus after the early 1990 s, prior to the warming pause. Additionally, this pause in the greenhouse effect is mostly caused by the high number of La Niña events between 1991 and 2014. A strong La Niña indicates suppressed convection in the tropical central Pacific that reduces atmospheric water vapor content and cloud volume. This significantly weakened regional greenhouse effect offsets the enhanced warming influence in other places and decelerates the rising global greenhouse effect. This work suggests that the greenhouse effect hiatus can be served as an additional factor to cause the recent global warming slowdown.

The rate at which the global average surface air temperature (Ts) increases has slowed down during the past few decades1. This so-called hiatus, pause, or slowdown of global warming has inspired investigations into its potential causes worldwide1,2. Although some researchers doubted the existence of a global warming hiatus because of coverage bias3,4, artificial inconsistency5, and a change point analysis of instrumental Ts records6, it is now accepted that a recent warming deceleration can be clearly observed1. There are two primary hypotheses to explain the recent slowdown of the upward trend in Ts7. Both hypotheses attempt to explain the contradiction between the trendless Ts variation and the intensifying anthropogenic greenhouse effect resulting from the steadily increasing emission of greenhouse gases (GHGs). The first attributes the warming hiatus to external radiative forcings, such as decreasing solar irradiance8, increasing tropospheric and stratospheric aerosols9, reduced stratospheric water vapor10, and several small volcanic eruptions11. The warming effect of increasing GHGs is largely cancelled out by the decreasing solar shortwave radiation received by the Earth’s surface. The second considers the warming pause to be a result of internal oceanic and/or atmospheric decadal variabilities against the centennial warming trend12, in which two leading theories are proposed. One asserts that the recent warming hiatus likely results from a La Niña-like state or a negative phase of Interdecadal Pacific Oscillation (IPO) associated with the cooling tropical Pacific sea surface temperature (SST) and the increasing Pacific trade winds12,13,14,15,16,17,18,19,20,21,22,23,24,25,26. This theory is supported by the successful simulation of the warming hiatus by nudging the tropical pacific SST or trade winds relative to observations14,17,19. The other suggests that the warming hiatus is accompanied by increasing heat uptake in global deep oceans27,28,29,30,31. This extra heat, which originates from a positive radiative imbalance at the top of the atmosphere (TOA), is reserved in the deep oceans instead of warming the Earth’s skin32,33,34,35,36. Note that both aforementioned hypotheses indeed include an enhancing greenhouse effect in which more heat is captured by the Earth–atmosphere system. The main difference between them is how this additional energy is prevented from warming the Earth’s surface.

The variation of Ts is commonly influenced by changes in the greenhouse effect. In theory, an enhanced (reduced) greenhouse effect will accelerate (decelerate) the upward tendency of Ts. However, less discussion has addressed whether the Earth’s greenhouse effect is intensified as GHGs increase from the observational perspective. A few studies have used satellite-based TOA radiation observations to detect changes related to the greenhouse effect37,38,39,40,41. Harries et al.38 found that more terrestrial heat is captured by several main GHGs (e.g., CO2, CH4, and O3) in clear skies because the spectral brightness temperatures in their absorption bands used to measure the upwelling thermal energy were significantly reduced. However, their experimental evidence of an enhancing greenhouse effect was largely biased because the influences of water vapor and clouds, which contribute approximately 75% of the total effect, were not included42. In contrast, Raval and Ramanathan37 employed a parameter (Ga) to quantify the magnitude of the atmospheric greenhouse effect including all potential contributors. Ga is the residual obtained by subtracting the TOA outgoing longwave radiation (OLR) from the surface upwelling longwave radiation (SULR). This parameter measures the vertically integrated greenhouse effect in the entire atmosphere and enters directly into the basic equations describing the climate. Furthermore, Cess and Udelhofen43 reported a significant decreasing tendency of normalized Ga (ΔGa = Ga/SULR) for the 40°S to 40°N domain between 1985 and 1999 based on measurements of the TOA energy budget and Earth’s surface temperature. They attributed this downward trend of the greenhouse effect to a notable reduction in cloud cover43.

Whether the observational greenhouse effect is intensified during the warming hiatus period remains unclear. With the steady rise of anthropogenic GHG concentrations, does the heat trapped and then re-emitted to the surface by the atmosphere also increase? In addition, the change of the Earth’s surface temperature has been shown down to be non-uniform in different regions and different sub-periods during recent decades44. Does the greenhouse effect have some spatial or temporal characteristics similar to those in Ts? Thus, the primary goal of this study is to investigate the spatiotemporal evolution of the greenhouse effect to better evaluate its potential impact. In this work, the monthly gridded Ga between 1979 and 2014 is estimated from the High-resolution Infrared Radiation Sounder (HIRS) OLR climate dataset45 provided by the National Center for Environmental Information (NCEI) and the HadCRUT4 surface air temperature (Ts) dataset46 provided by the Climatic Research Unit (CRU). The SULR is calculated using the blackbody radiation law (SULR = σTs4, where σ is the Stefan–Boltzmann constant) of Raval and Ramanathan37. The monthly gridded surface greenhouse effect parameter (Gs), which is defined as the downwelling longwave radiation (F↓) at the Earth’s surface by Boer47, is also obtained using a radiative transfer model from the National Aeronautics and Space Administration (NASA) Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced And Filled (EBAF) product48.

Results

The radiative warming effects of the atmosphere and the surface in the infrared range can be described by Ga and Gs47, whose climatological means are 158 W m−2 and 345 W m−2, respectively, from 2003 to 2014. Ga represents the ability of the atmosphere to trap approximately 40% of the longwave radiation emitted by the Earth’s surface (399 W m−2). Gs indicates the energy sent by the atmosphere to the surface to heat the Earth. Nearly half of Gs comes from Ga, and the rest comprises the solar incidence, sensible and latent heat absorbed by the atmosphere49. Figure 1 represents the spatial patterns of the estimated mean Ga and Gs between 2003 and 2014.Although Ga and Gs are both spatially inhomogeneous, they share similar spatial distributions. First, on average, both Ga and Gs decrease with increasing latitude. The zonal means of Ga (Gs) are 189 W m−2 (394 W m−2) and 90 W m−2 (231 W m−2) in the tropics (30°S–30°N) and polar zones (90°S–60°S and 60°N–90°N), respectively. The latitudinal patterns of Ga and Gs are mostly caused by the zonal distribution of the atmospheric water vapor content, which is the most important contributor to the greenhouse effect42. The wetter atmosphere at low latitudes thus absorbs more terrestrial radiation than the drier atmosphere at high latitudes. The surface condition is another important contributor to the Gs distribution. The wetter and warmer surface in the tropics provides greater latent and sensible heat to the atmosphere, which is included in Gs47. Second, because more atmospheric and surface moisture is found at sea than on land, on average, the oceanic Ga and Gs (162 W m−2 and 358 W m−2) are slightly larger than the terrestrial values (148 W m−2 and 312 W m−2). When the large proportion of oceans covering the Earth’s surface is considered, the oceanic Ga (Gs) contributes more than three-quarters of the global total Ga (Gs). Moreover, nearly half of the global greenhouse effect is attributed to Ga (Gs) over the tropical oceans. Third, either Ga or Gs displays a meridional heterogeneity at the tropics. On the one hand, larger Ga and Gs (above 220 W m−2 and 420 W m−2, respectively) are found in the Indo-West Pacific, Amazon and East Africa. This pattern coincides with the location of the tropical monsoons50 that often deliver persistent convection. In the monsoon-prone areas, a stronger greenhouse effect is induced by the wetter and cloudier atmosphere and by the moist surface. On the other hand, the Ga over the East Pacific and East Atlantic is relatively low (below 180 W m−2) because these areas are generally controlled by persistent subsidence and have dry and cloudless atmospheres.

Figure 1. Spatial distributions of climatological averaged greenhouse effect parameter (G; unit: W m−2) on a 5° by 5° box between 2003 and 2014.

Figure 1

Open in a new tab

(a,b) refer to the atmospheric and surface greenhouse effect parameters (Ga and Gs), respectively. The maps were generated by the Grid Analysis and Display System (GrADS; http://www.opengrads.org/doc/wind32-v1/) version 1.90-rc1.

Based on the climatological (2003–2014) means of Ga and Gs, the long-term variations of their anomalies (Gaa and Gsa) can be obtained (Fig. 2). Because of the shorter period of the CERES EBAF product, the areal averaged Gsa is represented only between 2003 and 2014 in Fig. 2 but shows no notable trend over the globe, sea or land. Thus, the surface greenhouse effect has not been strengthened in the last decade. The temporal variations of Gsa and Gaa are highly correlated over the globe, sea and land in 2003–2014, with all correlation coefficients above 0.40 and significant at the 0.01 level based on Student’s t-test. By contrast, Gaa can be obtained from 1979 to 2014 because of the longer instrumental observations of Ts and OLR. The most obvious feature is that the decadal trends of the global averaged Gaa are not uniform throughout the period (Fig. 2a). In the 1980 s, a significant increasing Gaa tendency exists with a linear estimate of 0.19 W m−2 yr−1. However, this uprising trend pauses starting in circa 1992, when Gaa begins to slightly decrease at a rate of −0.01 W m−2 yr−1. This statistically non-significant trend indicates that the enhancing global atmospheric greenhouse effect is slowed down. Moreover, the atmospheric greenhouse effect hiatus can be found over both sea and land (Fig. 2b–c). Because the global total atmospheric greenhouse effect is largely controlled by the atmosphere over the oceans, the temporal variation of the averaged Gaa at sea is similar to the global value (Fig. 2b). The tendency of the averaged Gaa over the oceans also abruptly changes circa 1992. The oceanic Gaa exhibits a notable increasing trend with a rate of 0.21 W m−2 yr−1 in 1979–1991, whereas its rate of change (−0.04 W m−2 yr−1) during 1992–2014 is not statistically significant. By contrast, although a sudden change in the Gaa tendency is observed overland, the breakpoint is approximately 5 years later than that of the oceanic Gaa (Fig. 2c). The terrestrial Gaa trends are 0.12 W m−2 yr−1 and 0.05 W m−2 yr−1 before and after 1997, respectively.

Figure 2.

Figure 2

Open in a new tab

Monthly variations of the areal averaged atmospheric and surface greenhouse effect parameter anomalies (Gaa and Gsa) from 1979 to 2014 for the (a) globe, (b) sea and (c) land. Gaa and Gsa are represented by blue and red lines, respectively. Thin and thick solid lines indicate the monthly and 12-month moving averaged series, respectively. Vertical, thick, gray lines represent the break points of trends using the break function regression (see Methods). Green and yellow dashed lines refer to linear trend lines before and after the break points, respectively. The figure was plotted using MATLAB software.

Because Ga is jointly determined by the longwave radiation at the surface and the TOA, the Ts and OLR evolutions are employed to discuss the formation of the global atmospheric greenhouse effect hiatus (Fig. S1). Here, the time period is divided into three 12-year subperiods (1979–1990, 1991–2002 and 2003–2014). The first break point is used to separate the varying long-term global Gaa behavior in Fig. 2a. The second break point represents the beginning of the global warming pause because the increasing global averaged Ts tendency slowed down in the early 21st century15. In the first subperiod (1979–1990), the increasing Ts leads to a remarkable uprising trend in the global averaged SULR anomaly of 0.07 W m−2 yr−1, whereas the global averaged OLR anomaly exhibits a significant decreasing trend of −0.10 W m−2 yr−1. Both of these behaviors enhance the atmospheric greenhouse effect, as indicated by an increase in Gaa. However, in the following subperiod, the rates of change of the SULR and OLR anomalies are both significantly positive. The former (0.15 W m−2 yr−1) is comparable to the latter (0.14 W m−2 yr−1). Therefore, their contributions to the atmospheric greenhouse effect nearly cancel each other out. As a result, an unchanged global averaged Gaa is shown during 1991–2002. In the last subperiod, the global averaged SULR anomaly remains trendless (0.02 W m−2 yr−1) because Ts stops rising. Meanwhile, the long-term change of the global averaged OLR anomaly (−0.01 W m−2 yr−1) is also not statistically significant. Thus, these two phenomena result in a trendless Gaa.

Furthermore, the trends of Gaa are spatially inhomogeneous during individual subperiods (Fig. 3). Gaa increases the most over the central North Pacific with a tendency of approximately 0.12 W m−2 yr−1 in 1979–1990 (Fig. 3a). Significant upward Gaa trends are also found at the tropical Atlantic and the high latitudes of Eurasia. By contrast, almost no regions exhibit a significant downward Gaa trend. This finding explains why the global averaged Gaa increases during this period. Similar to the previous period, an uprising Gaa trend is found over the central North Pacific from 1991 to 2002 with a reduced rate (Fig. 3b). Meanwhile, Gaa increases by substantially more in the western tropical Pacific, where the largest tendency (0.18 W m−2 yr−1) is found, and in the central South Pacific. However, a remarkably decreasing Gaa trend (−0.27 W m−2 yr−1) exists over the central tropical Pacific, indicating a weakened atmospheric greenhouse effect in this area, which largely offsets the warming effect in the aforementioned surrounding regions. As a result, a trendless global averaged Gaa is displayed between 1991 and 2002 (Fig. 2). During the latest subperiod (2003–2014), the spatial pattern of the change in Gaa is quite similar to that in 1991–2002, but the proportion of regions with significant Gaa tendencies is significantly reduced (Fig. 3c). Although the maximum upward and downward Gaa tendencies also appear over the western tropical Pacific and the central tropical Pacific, respectively, the increasing trend is nearly absent in the extratropics. Again, no significant trend of the global averaged Gaa is found from 2003 to 2014 (Fig. 2) because the enhanced warming effect over the western tropical Pacific is largely counteracted by the weakened warming influence on the central tropical Pacific.

Figure 3.

Figure 3

Open in a new tab

Spatial structures of the atmospheric greenhouse effect parameter anomaly (Gaa) trend on a 5° by 5° box using the least-squares approach during three subperiods: (a) 1979–1990, (b) 1991–2002, and (c) 2003–2004. Regions with a significant tendency (at the 0.05 confidence level based on the F-test) are crossed. Maps were generated by GrADS (http://www.opengrads.org/doc/wind32-v1/) version 1.90-rc1.

The results above indicate that the notably downward Gaa tendency over the central tropical Pacific indeed plays an important role in inducing the greenhouse effect hiatus since the 1990 s. What causes this decreasing Gaa? The variation of the greenhouse effect is substantially influenced by its contributors, including water vapor, clouds, and GHGs42. GHG concentrations have risen steadily during recent decades1. The variations of metrics related to the other two contributors are given in Fig. 4a and are based on the CERES-EBAF products between 2003 and 2014. The total column precipitable water (TCPW) anomaly significantly increases at a rate of 0.44 cm yr−1. However, the cloud area fraction (CAF) anomaly is reduced by −0.60% yr−1, which is consistent with the decreasing cloud activity described in previous publications51. Therefore, although the greenhouse effect can be enhanced by increasing GHGs and water vapor in the atmosphere, it can be weakened by decreasing clouds. If these two actions offset each other, a hiatus of the global greenhouse effect will result. To confirm this, the variations of Gaa and Gsa in all-sky conditions are compared with those in clear-sky conditions in Fig. 4b,c. The clear-sky atmospheric and surface greenhouse effect parameters increase significantly at rates of 0.22 W m−2 yr−1 and 0.19 W m−2 yr−1, respectively. However, the atmospheric and surface greenhouse effect parameters both become trendless when clouds are considered. Moreover, the spatial pattern of the CAF anomaly trend (Fig. S2) is very similar to that of the Gaa trend (Fig. 3c) during 2003–2014. Cloud activity becomes less active over the central tropical Pacific, whereas it is enhanced over the western and eastern tropical Pacific. Overall, the downward tendency of clouds is the dominant contributor to the greenhouse effect hiatus.

Figure 4.

Figure 4

Open in a new tab

(a) Monthly variations of the global averaged TCPW (unit: cm) and CAF (unit: %) anomalies between 2003 and 2014. Dashed lines are the linear trend lines obtained by the least squares method. (b) Monthly variations of the atmospheric greenhouse effect parameter anomaly (Gaa; unit: W m−2) from 2003 to 2014 for all-sky (red lines) and clear-sky (blue lines) conditions. Dashed lines are the linear trend lines obtained by the least squares method. (c) Same as (b) but for the surface greenhouse effect parameter anomaly (Gsa; unit: W m−2). The figure was plotted using MATLAB software.

Interestingly, the spatial structure exhibits a seesaw pattern between the central tropical Pacific and the western/eastern tropical Pacific in both the Gaa tendency and CAF anomaly trend from 2003 to 2014 (Fig. 3c, Fig. S2). This pattern is similar to that of the composited SST anomaly in strong La Niña events51,52. Further, the decreasing Gaa trend can result from La Niña events occurring more frequently in the last two decades. The Niño 3.4 SST anomaly shows no significant tendency between 1979 and 1990, whereas it decreases remarkably after 1990 at a rate of −0.028 °C yr−1 (Fig. S3). Notably, the first half of the downward Niño 3.4 SST anomaly trend is much larger than the second half. This finding is consistent with a stronger decreasing Gaa trend over the central tropical Pacific during 1991–2002 (Fig. 3). Strong La Niña events are associated with strong anomalous cooling and suppressed convection in the central tropical Pacific52,53. Therefore, this La Niña-related phenomenon can reduce atmospheric water vapor content and cloud volume and further weaken the greenhouse effect over the central tropical Pacific.

Discussion

The Earth’s environment is suitable for life because of the greenhouse effect. Our planet has become increasingly warm since the Industrial Revolution because of the increased GHG emissions, which greatly enhance the greenhouse effect. However, the uprising rate of the Earth’s Ts has slowed down in recent years. Whether this global warming pause is accompanied by a hiatus of the greenhouse effect is investigated in this study. The regional and global greenhouse effects are quantitatively estimated from reliable Ts observations and consistent OLR satellite products in 1979–2014. Although the change of OLR is theoretically in accordance with that of the tropospheric temperature according to the Stefan–Boltzmann Law, in reality, their relationship is more complex54. Different tendencies of OLR and Ts can be seen in different periods, leading to an atmospheric and surface greenhouse effect hiatus since the early 1990 s. This pause exists not only over the oceans but also over the continents. Further analysis indicates that this hiatus is very likely a result of the occurrence of more La Niña events after 1992. In the strong La Niña phase, both the atmospheric water vapor and the cloud volume are greatly reduced over the central tropical Pacific, amplifying the regional weakened greenhouse effect. Therefore, Gaa decreases significantly during 1992–2014 over the central tropical Pacific, which offsets the upward Gaa tendencies occurring elsewhere.

Interestingly, the atmospheric greenhouse effect hiatus occurs ahead of the global warming slowdown. Does the former lead to the latter? To answer this question, the cross-correlation coefficients between the Gaa and the Ts anomaly (Ta) on different timescales are given in Fig. 5. The simultaneous correlations are largest on the whole timescales, which likely indicates a positive feedback between Ts and the greenhouse effect. By contrast, the secondary maximum consistently appears with a lag of approximately 5 years. Moreover, larger and more significant correlations are found when Gaa leads Ta than when Gaa trails Ta. Thus, the variability of Ta may depend on the foregoing change of Gaa. In conclusion, the pause of the greenhouse effect since the 1990 s may be one of the reasons for the global warming hiatus starting in the early 2000 s.

Figure 5.

Figure 5

Open in a new tab

Cross-correlation coefficients between the global averaged atmospheric greenhouse effect parameter anomaly (Gaa) and the global mean surface temperature anomaly (Ta) on three time scales: (a) monthly, (b) seasonal and (c) annual. The horizontal dashed line refers to the significance at the 0.05 level. Positive (negative) lags indicate that Ta leads (trails) Gaa. The figure was plotted using MATLAB software.

It is well accepted that the recent global warming slowdown is attributable to the joint effect of internal natural variability and external forcing12. In general, the warming hiatus is mainly driven by internal variability such as a negative phase of the IPO as well as a more La Niña-dominated state, with a minor external contribution8. However, a recent study found that the phase of the IPO could be modulated by anthropogenic aerosols, in which case external forcing was attributed to be the primary factor decelerating global warming55. By contrast, this study is not focused on the potential causes of different IPO or ENSO phases. Instead, we represent an alternative pathway of internal variability driving the warming slowdown. A La Niña-like state suppresses convection in the tropical central Pacific and concomitantly reduces cloud coverage. Consequently, a zero-trend greenhouse effect is achieved under the balance of its primary contributors (e.g. water vapor, clouds, and GHGs). Finally, the hiatus of the greenhouse effect-driven warming leads to the recent global warming slowdown, in which the atmosphere traps (emits) near constant heat from (to) the surface.

Methods

Surface temperature (T s )

The global monthly absolute Ts records are derived by combining the temperature anomaly and the base temperature with a spatial resolution of 5°long × 5°lat from 1979 to 2014. The combined land and marine Ts anomalies relative to the base period 1961–1990 are provided by the joint work of the CRU of the University of East Anglia and the Hadley Centre of the UK Met Office56. The corresponding absolute Ts for the aforementioned base period is given by Jones et al.57.

HIRS OLR

The global monthly product of OLR at the TOA is obtained from the Climate Data Record (CDR) program organized by the National Oceanic and Atmospheric Administration (NOAA) in 1979–201445. These consistent, long-term OLR records are derived using the radiance observations from the HIRS onboard the NOAA Television Infrared Observation Satellite (TIROS)-N series and the Eumetsat MetOp-A satellites. The original gridded 2.5° × 2.5° OLR data are interpolated on a 5° by 5° box.

NASA CERES EBAF satellite products

The monthly OLR and surface downwelling longwave radiation in all-sky and clear-sky conditions between 2003 and 2014 are provided by the NASA CERES EBAF product48. The monthly CAF and TCPW are from the same product. The original gridded 1° × 1° data are all interpolated on a 5° by 5° box.

Atmospheric greenhouse effect parameter (G a )

The monthly Ga values are calculated by Inline graphicon each 5° by 5° grid42, where σ is the Stenfan–Boltzmann constant (5.67 × 10−8 W m−2 K−4). Ga represents the difference between the heat emitted by the Earth’s surface and the longwave radiation escaping from the TOA. The former energy is estimated based on the assumption of blackbody radiation; thus, the emissivity (ε) is close to one for every underlying surface. This assumption is confirmed by the global surface emissivity map (Fig. S4) derived using the NASA CERES EBAF products48.

Surface greenhouse effect parameter (G s )

The monthly Gs values are defined by Inline graphic on individual 5° by 5° grids, where F↓ refers to the downwelling longwave radiation at the Earth’s surface.

Break function regression

This procedure objectively estimates the change point of different tendencies in time series by combining a weighted least-squares criterion with a brute-force search and the linear trends before and after the discontinuity58. It is similar to the piecewise linear regression method with one change point reported in other publications6,59,60. The break function model for time series X(T), written as

graphic file with name srep33315-m3.jpg

has four parameters: x1, x2, x3 and t2. The former three can be estimated using the least-squares approach when t2 is fixed. The break point t2 is determined by maximizing the explained variance. The trends before and after the break point are determined as (x2-x1)/(t2-t1) and (x3-x2)/(t3-t2), respectively.

Trend analysis

The time series trend is estimated by the least squares method. In this study, the tendency is considered significant when it passes the F-test at the 0.05 level.

Additional Information

How to cite this article: Song, J. et al. A Hiatus of the Greenhouse Effect. Sci. Rep. 6, 33315; doi: 10.1038/srep33315 (2016).

Supplementary Material

Supplementary Information
srep33315-s1.pdf (476.8KB, pdf)

Acknowledgments

This work was jointly funded by the National Grand Fundamental Research 973 Program of China (2015CB452800) and the National Science Foundation of China (grant 41375075). We would like to thank Dr. Hai-Tien Lee for suggestions related to the HIRS OLR data set. We would like to thank Dr. Phil Klotzbach at Colorado State University for improving the language. We wish to express our sincere thanks to the anonymous reviewers for their helpful comments on an earlier manuscript.

Footnotes

Author Contributions J.S. performed data analysis and wrote the manuscript. Y.W. proposed the original idea and set up the structure of this article. J.T. prepared the data and computed other parameters. All the authors reviewed the manuscript.

References

  1. Fyfe J. et al. Making sense of the early 2000 s warming slowdown. Nature Climate Change 6, 224–228 (2016). [Google Scholar]
  2. Hawkins E., Edwards T. & McNeall D.. Pause for thought. Nature Climate Change, 4, 154–156 (2014). [Google Scholar]
  3. Cowtan K. & Way R.. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140, 1935–1944 (2014). [Google Scholar]
  4. Lewandowsky S., Risbey J. & Oreskes N.. On the definition and identifiability of the alleged “hiatus” in global warming. Sci. Rep. 5, 16784 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Karl T. et al. Possible artifacts of data biases in the recent global surface warming hiatus. Science 348, 1469–1472 (2015). [DOI] [PubMed] [Google Scholar]
  6. Cahill N., Rahmstorf S. & Parnell A.. Change points of global temperature. Environ. Res. Lett. 10, 084002 (2015). [Google Scholar]
  7. Trenberth K. Has there been a hiatus? Science 349, 691–692 (2015). [DOI] [PubMed] [Google Scholar]
  8. Huber M. & Knutti R.. Natural variability, radiative forcing and climate response in the recent hiatus reconciled. Nature Geoscience 7, 651–656 (2014). [Google Scholar]
  9. Kaufmann R., Kauppi H., Mann M. & Stock J.. Reconciling anthropogenic climate change with observed temperature 1998–2008. Proc. Natl. Acad. Sci. 108, 11790–11793 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Solomon S. et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 327, 1219–1223 (2010). [DOI] [PubMed] [Google Scholar]
  11. Santer B. et al. Volcanic contribution to decadal changes in tropospheric temperature. Nature Geoscience 7, 185–189 (2014). [Google Scholar]
  12. Liu W. et al. Tracking ocean heat uptake during the surface warming hiatus. Nature Communications 7, 10926 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Meehl G. et al. Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J. Climate 26, 7298–7310 (2013). [Google Scholar]
  14. Kosaka Y. & Xie S.. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013). [DOI] [PubMed] [Google Scholar]
  15. Watanabe M. et al. Strengthening of ocean heat uptake efficiency associated with the recent climate hiatus. Geophys. Res. Lett. 40, 3175–3179 (2013). [Google Scholar]
  16. Trenberth K. & Fasullo J.. An apparent hiatus in global warming? Earth’s Future 1, 19–32 (2013). [Google Scholar]
  17. England M. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change 4, 222–227 (2014). [Google Scholar]
  18. Trenberth K., Fasullo J., Branstator G. & Phillips A.. Seasonal aspects of the recent pause in surface warming. Nature Climate Change 4, 911–916 (2014). [Google Scholar]
  19. Watanabe M., Shiogama H., Tatebe H., Hayashi M., Ishii M. & Kimoto M.. Contribution of natural decadal variability to global warming acceleration and hiatus. Nature Climate Change, 4, 893–897 (2014). [Google Scholar]
  20. Meehl G., Teng H. & Arblaster J.. Climate model simulations of the observed early-2000s hiatus of global warming. Nature Climate Change 4, 898–902 (2014). [Google Scholar]
  21. Drijifhout S. et al. Surface warming hiatus caused by increased heat uptake across multiple ocean basins. Geophys. Res. Lett., 41, 7868–7874 (2014). [Google Scholar]
  22. Dai A., Fyfe J., Xie S. & Dai X.. Decadal modulation of global surface temperature by internal climate variability. Nature Climate Change 5, 555–559 (2015). [Google Scholar]
  23. Steinman B., Mann M. & Miller S.. Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures. Science 347, 988–991 (2015). [DOI] [PubMed] [Google Scholar]
  24. Nieves V., Willis J. & Patzert W.. Recent hiatus caused by decadal shift in Indo-Pacific heating. Science 349, 532–535 (2015). [DOI] [PubMed] [Google Scholar]
  25. Xie S., Kosaka Y. & Okumura Y.. Distinct energy budgets for anthropogenic and natural changes during global warming hiatus. Nature Geosciences, doi: 10.1038/ngeo2581 (2015). [DOI] [Google Scholar]
  26. Takahashi C. & Watanabe M.. Pacific trade winds accelerated by aerosol forcing over the past two decades. Nature Climate Change (2016). [Google Scholar]
  27. Barnett T. et al. Penetration of the human-induced warming into the world’s oceans. Science 209, 284–287 (2005). [DOI] [PubMed] [Google Scholar]
  28. Palmer M. et al. A new perspective on warming of the global oceans. Geophys. Res. Lett. 36, L20709 (2009). [Google Scholar]
  29. Gleckler P. et al. Human-induced global ocean warming on multidecadal timescales. Nature Climate Change 2, 524–529 (2012). [Google Scholar]
  30. Pierce D. et al. The fingerprint of human-induced changes in the ocean’s salinity and temperature fields. Geophys. Res. Lett. 39, L21704 (2012). [Google Scholar]
  31. Balmaseda M., Trenberth K. & Kallen E.. Distinctive climate signals in reanalysis of global ocean heat content. Geophys. Res. Lett. 40, 1754–1759 (2013). [Google Scholar]
  32. Purkey S. & Johnson G.. Warming of global abyssal and deep Southern Ocean waters between the 1990s and 2000s: contributions to global heat and sea level budgets. J. Climate 23, 6336–6351 (2010). [Google Scholar]
  33. Loeb N. et al. Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent with uncertainty. Nature Geoscience 5, 110–113 (2012). [Google Scholar]
  34. Meehl G., Arblaster J., Fasullo J., Hu A. & Trenberth K.. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nature Climate Change 1, 360–364 (2011). [Google Scholar]
  35. Chen X. & Tung K.. Varying planetary heat sink led to global-warming slowndown and acceleration. Science 345, 897–903 (2014). [DOI] [PubMed] [Google Scholar]
  36. Lee S., Park W., Baringer M., Gordon A., Huber B. & Liu Y.. Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nature Geoscience 8, 445–449 (2015). [Google Scholar]
  37. Raval A. & Ramanathan V.. Observational determination of the greenhouse effect. Nature, 342, 758–761 (1989). [Google Scholar]
  38. Harries J., Brindley H., Sagoo P. & Bantges R.. Increases in greenhouse forcing inferred from the outgoing longwave radiation spectra of the Earth in 1970 and 1997. Nature 410, 355–357 (2001). [DOI] [PubMed] [Google Scholar]
  39. Philipona R., Durr B., Marty C., Ohmura A. & Wild M.. Radiative forcing - measured at Earth’s surface - corroborate the increasing greenhouse effect. Geophys. Res. Lett. 31, L03202 (2004). [Google Scholar]
  40. Timofeev N. Greenhouse effect of the atmosphere and its influence on Earth’s climate (satellite data). Physical Oceanography 16, 322–336 (2006). [Google Scholar]
  41. Gastineau G., Soden B., Jackson D. & O’Dell C.. Satellite-Based Reconstruction of the Tropical Oceanic Clear-Sky Outgoing Longwave Radiation and Comparison with Climate Models. J. Climate 27, 941–957 (2014). [Google Scholar]
  42. Schmidt G., Ruedy R., Miller R. & Lacis A.. The attribution of the present-day total greenhouse effect. J. Geophys. Res. 115, D20106 (2010). [Google Scholar]
  43. Cess R. & Udelhofen P.. Climate change during 1985–1999: cloud interactions determined from satellite measurements. Geophys. Res. Lett. 30, 1019 (2003). [Google Scholar]
  44. Ji F., Wu Z., Huang J. & Chassignet E.. Evolution of land surface air temperature trend. Nature Climate Change 4, 462–466 (2014). [Google Scholar]
  45. Lee H., Gruber A., Ellingson R. & Laszlo I.. Development of the HIRS Outgoing Longwave Radiation climate data set. J. Atmos. Ocean. Tech. 24, 2029–2047 (2007). [Google Scholar]
  46. Kennedy J., Rayner N., Smith R., Saunby M. & Parker D.. Reassessing biases and other uncertainties in sea-surface temperature observations measured in situ since 1850 part 2: biases and homogenisation. J.Geophys. Res. 116, D14104 (2011). [Google Scholar]
  47. Boer G. Climate Change and the regulation of the surface moisture and energy budgets. Clim. Dyn. 8, 225–239 (1993). [Google Scholar]
  48. Wielicki B., Barkstrom B., Harrison E., Lee R. III, Smith G. & Cooper J.. Clouds and the Earth’s radiant energy system (CERES): an Earth observing system experiment. Bull. Amer. Meteor. Soc. 77, 853–868 (1996). [Google Scholar]
  49. Trenberth K., Fasullo J. & Kiehl J.. Earth’s global energy budget. Bull. Amer. Meteor. Soc. 90, 311–323 (2009). [Google Scholar]
  50. McKnight T. & Hess D.. “Climate Zones and Types”. Physical Geography: A Landscape Appreciation (Prentice Hall, 2000). [Google Scholar]
  51. Herman J. et al. A net decrease in the Earth’s cloud, aerosol, and surface 340 nm reflectivity during the past 33 yr (1979–2011). Atmos. Chem. Phys. 13, 8505–8524 (2013). [Google Scholar]
  52. Takahashi K. et al. ENSO regimes: reinterpreting the canonical and ModokiEl Niño. Geophys. Res. Lett. 38, L10704 (2011). [Google Scholar]
  53. Dommenget D., Bayr T. & Frauen C.. Analysis of the non-linearity in the pattern and time evolution of El Niño southern oscillation. Clim. Dyn. 40, 2825–2847. [Google Scholar]
  54. Von Schuckmann K. et al. An imperative to monitor Earth’s energy imbalance. Nature Climate Change 6, 138–144 (2016). [Google Scholar]
  55. Smith D. et al. Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nature Climate Change 6, 1–6 (2016). [Google Scholar]
  56. Morice C. et al. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J. Geophys. Res. 117, D08101 (2012). [Google Scholar]
  57. Jones P., New M., Parker D., Martin S. & Rigor I.. Surface air temperature and its variations over the last 150 years. Reviews of Geophysics 37, 173–199 (1999). [Google Scholar]
  58. Mudelsee M. Break function regression: a tool for quantifying trend changes in climate time series. Eur. Phys. J. Special Topics 174, 49–63 (2009). [Google Scholar]
  59. Ying L., Shen Z. & Piao. S. The recent hiatus in global warming of the land surface: scale-dependent breakpoint occurrences in space and time. Geophys. Res. Lett. 42, 6471–6478 (2015). [Google Scholar]
  60. Werner R. et al. Study of structural break points in global and hemispheric temperature series by piecewise regression. Advances in Space Research 56, 2323–2334 (2015). [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep33315-s1.pdf (476.8KB, pdf)

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES