Advanced Search
Article Contents

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023


doi:  10.1007/s00376-023-3371-4

  • In 2023, the majority of the Earth witnessed its warmest boreal summer and autumn since 1850. Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society. We analyzed the monthly varying global mean surface temperature (GMST) in 2023 and found that the globe, the land, and the oceans in 2023 all exhibit extraordinary warming, which is distinct from any previous year in recorded history. Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics, the GMST in 2023 is predicted to be 1.41°C ± 0.07°C, which will certainly surpass that in 2016 as the warmest year since 1850, and is approaching the 1.5°C global warming threshold. Compared to 2022, the GMST in 2023 will increase by 0.24°C, with 88% of the increment contributed by the annual variability as mostly affected by El Niño. Moreover, the multidecadal variability related to the Atlantic Multidecadal Oscillation (AMO) in 2023 also provided an important warming background for sparking the GMST rise. As a result, the GMST in 2023 is projected to be 1.15°C ± 0.07°C, with only a 0.02°C increment, if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.
  • 加载中
  • Figure 1.  The anomalies of (a) GMST, (b) GLAT, and (c) GSST above preindustrial levels during 1950–2023. The values of 2016 and 2023 are marked by the black line and red line, respectively. (d) The spatial distribution of mean surface temperature (ST) from January to October 2023. (e) The difference between the ST from January to October 2023 and the annual mean ST in 2016. (f) The difference between the ST from June to October 2023 and the ST from January to October 2023. The datasets used for the global surface temperature anomaly include HadCRUT5, NOAAGlobal-Temp5, and BEST (Rohde and Hausfather, 2020; Morice et al., 2021; Vose et al., 2021).

    Figure 2.  (a) 2023 GMST prediction (relative to 1850–1900) started from November 2023 made by the IAP GMST statistical ensemble prediction model. The red dot and error bars represent the ensemble mean and ensemble spread of the forecasted result, respectively. The green dot and error bars show the predicted 2023 GMST without the effect of natural variabilities (ANV and MDV components). The black dots and error bars are the historical observed GMST during 1850–2022 and their uncertainties between different datasets. The red, blue and green lines show the three EEMD components of GMST. (b) The annual increments of the three EEMD components for GMST since 2010. The red, blue and green bars represent the annual increments of the ANV, MDV and SCT components, respectively. The black line represents the annual Niño-3.4 index. The values of annual increments and Niño-3.4 for 2023 all include prediction results. (c) Scatterplot of the ANV component and Niño-3.4 index during January to October 2023. Different colors represent the intensity of the Niño-3.4 index. The black line is the linear regression of the ANV component and Niño-3.4 index. (d) Increment of the forecasted ST in 2023 relative to that in 2022. (e) As in (d) but for the ANV and MDV components. (f) As in (d) but for the SCT component.

    Table 1.  The ranking of monthly GMST in 2023 since 1850.

    Month of 2023 GMST Ranking
    January 1.18 7
    February 1.26 4
    March 1.52 2
    April 1.29 4
    May 1.21 3
    June 1.33 1
    July 1.39 1
    August 1.47 1
    September 1.70 1
    October 1.58 1
    DownLoad: CSV

    Table 2.  The GMST, ANV, MDV, and SCT components for 2022 and 2023, and their increment in 2023 relative to 2022 (units: °C).

    GMST ANV MDV SCT
    2022 1.17 −0.10 0.14 1.13
    2023 1.41 0.11 0.15 1.15
    Increment 0.24 0.21 0.01 0.02
    DownLoad: CSV
  • Amaya, D. J., A. J. Miller, S. P. Xie, and Y. Kosaka, 2020: Physical drivers of the summer 2019 North Pacific marine heatwave. Nature Communication, 11, 1903, https://doi.org/10.1038/s41467-020-15820-w.
    Bethke, I., S. Outten, O. H. Otterå, E. Hawkins, S. Wagner, M. Sigl, and P. Thorne, 2017: Potential volcanic impacts on future climate variability. Nature Climate Change, 7, 799−805, https://doi.org/10.1038/nclimate3394.
    Chapman, W. L., and J. E. Walsh, 1993: Recent variations of sea ice and air temperature in high latitudes. Bull. Amer. Meteor. Soc., 74, 33−48, https://doi.org/10.1175/1520-0477(1993)074<0033:RVOSIA>2.0.CO;2.
    Chen, J. J., R. L. Li, S. Xie, J. Q. Wei, and J. Shi, 2023: Characteristics and mechanisms of long–lasting 2021-2022 summer Northeast Pacific warm blobs. Frontiers in Marine Science, 10, 1158932, https://doi.org/10.3389/fmars.2023.1158932.
    Clancy, R., C. Bitz, and E. Blanchard-Wrigglesworth, 2021: The influence of ENSO on arctic sea ice in large ensembles and observations. J. Climate, 34, 9585−9604, https://doi.org/10.1175/JCLI-D-20-0958.1.
    Dai, A. G., J. C. Fyfe, S. P. Xie, and X. G. Dai, 2015: Decadal modulation of global surface temperature by internal climate variability. Nature Climate Change, 5, 555−559, https://doi.org/10.1038/nclimate2605.
    Gong, D. Y., D. Guo, and C. H. Ho, 2006: Weekend effect in diurnal temperature range in China: Opposite signals between winter and summer. J. Geophys. Res., 111, D18113, https://doi.org/10.1029/2006JD007068.
    Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth’s energy imbalance and implications. Atmospheric Chemistry and Physics, 11 , 13 421−13 449, https://doi.org/10.5194/acp-11-13421-2011.
    Henley, B. J., J. Gergis, D. J. Karoly, S. Power, J. Kennedy, and C. K. Folland, 2015: A Tripole index for the interdecadal Pacific Oscillation. Climate Dyn., 45, 3077−3090, https://doi.org/10.1007/s00382-015-2525-1.
    Hsu, P. C., Y. T. Qian, Y. Liu, H. Murakami, and Y. X. Gao, 2020: Role of abnormally enhanced MJO over the western Pacific in the formation and subseasonal predictability of the record-breaking Northeast Asian Heatwave in the Summer of 2018. J. Climate, 33, 3333−3349, https://doi.org/10.1175/JCLI-D-19-0337.1.
    Huang, J., X. Guan, and F. Ji, 2012: Enhanced cold-season warming in semi-arid regions. Atmospheric Chemistry and Physics, 12, 5391−5398, https://doi.org/10.5194/acp-12-5391-2012.
    Kim, H., S. W. Son, H. Kim, K. H. Seo, and M. J. Kang, 2023: MJO influence on subseasonal-to-seasonal prediction in the northern Hemisphere Extratropics. J. Climate, 36, 7943−7956, https://doi.org/10.1175/JCLI-D-23-0139.1.
    Kosaka, Y., and S. P. Xie, 2016: The tropical Pacific as a key pacemaker of the variable rates of global warming. Nature Geoscience, 9, 669−673, https://doi.org/10.1038/ngeo2770.
    Li, K. X., F. Zheng, D. Y. Luo, C. Sun, and J. Zhu, 2022: Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020. Environmental Research Letters, 17, 094034, https://doi.org/10.1088/1748-9326/AC8DAB.
    Li, K. X., F. Zheng, L. J. Cheng, T. Y. Zhang, and J. Zhu, 2023: Record-breaking global temperature and crises with strong El Niño in 2023-2024. The Innovation Geoscience, 1, 100030, https://doi.org/10.59717/j.xinn-geo.2023.100030.
    Li, X. C., and Coauthors, 2021: Tropical teleconnection impacts on Antarctic climate changes. Nature Reviews Earth & Environment, 2, 680−698, https://doi.org/10.1038/s43017-021-00204-5.
    Li, Z. C., Q. X. Li, and T. Y. Chen, 2024: Record-breaking high-temperature outlook for 2023: An assessment based on the China global Merged Temperature (CMST) dataset. Adv. Atmos. Sci., 41, 369−376, https://doi.org/10.1007/s00376-023-3200-9.
    Liang, Y., J. Yu, E. S. Saltzman, and F. Wang, 2017: Linking the tropical northern Hemisphere Pattern to the Pacific warm blob and Atlantic cold blob. J. Climate, 30, 9041−9057, https://doi.org/10.1175/JCLI-D-17-0149.1.
    Luo, B. H., D. H. Luo, A. G. Dai, I. Simmonds, and L. X. Wu, 2021: A connection of winter Eurasian cold anomaly to the modulation of Ural blocking by ENSO. Geophys. Res. Lett., 48, e2021GL094304, https://doi.org/10.1029/2021GL094304.
    Manabe, S., and R. T. Wetherald, 1975: The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmos. Sci., 32, 3−15, https://doi.org/10.1175/1520-0469(1975)032<0003:TEODTC>2.0.CO;2.
    McKay, D. I. A., and Coauthors, 2022: Exceeding 1.5°C global warming could trigger multiple climate tipping points. Science, 377, eabn7950, https://doi.org/10.1126/science.abn7950.
    Morice, C. P. and Coauthors, 2021: An updated assessment of near-surface temperature change from 1850: The HadCRUT5 data set. J. Geophys. Res., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361.
    Patterson M., A. Weisheimer, D. J. Befort, and C. H. O’reilly, 2022: The strong role of external forcing in seasonal forecasts of European summer temperature. Environmental Research Letters, 17, 104033, https://doi.org/10.1088/1748-9326/ac9243.
    Qian, C., and Coauthors, 2024: Rapid attribution of the record-breaking heatwave event in North China in June 2023 and future risks. Environmental Research Letters, 19, 014028, https://doi.org/10.1088/1748-9326/ad0dd9.
    Rohde, R. A., and Z. Hausfather, 2020: The Berkeley earth land/ocean temperature record. Earth System Science Data, 12, 3469−3479, https://doi.org/10.5194/essd-12-3469-2020.
    Rypdal, K., 2018: The life and death of the recent global surface warming hiatus parsimoniously explained. Climate, 6, 64, https://doi.org/10.3390/cli6030064.
    Shi, J., C. Tang, Q. Y. Liu, Y. Zhang, H. C. Yang, and C. Li, 2022: Role of mixed layer depth in the location and development of the Northeast Pacific warm blobs. Geophys. Res. Lett., 49, e2022GL098849, https://doi.org/10.1029/2022GL098849.
    Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J. Sanford, and G. K. Plattner, 2010: Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science, 327, 1219−1223, https://doi.org/10.1126/science.1182488.
    Tseng, Y. H., R. Q. Ding, and X. M. Huang, 2017: The warm Blob in the Northeast Pacific—the bridge leading to the 2015/16 El Niño. Environmental Research Letters, 12, 054019, https://doi.org/10.1088/1748-9326/aa67c3.
    Tung, K.-K., and X. Y. Chen, 2018: Understanding the recent global surface warming slowdown: A review. Climate, 6, 82. https://doi.org/10.3390/cli6040082.
    Vose, R. S., and Coauthors, 2021: Implementing full spatial coverage in NOAA’s global temperature analysis. Geophys. Res. Lett., 48, e2020GL090873, https://doi.org/10.1029/2020GL090873.
    Wang, Z., and Coauthors, 2024: Severe global environmental issues caused by Canada's record-breaking wildfires in 2023. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3241-0. (in press
    Wei, M., F. L. Qiao, Y. Q. Guo, J. Deng, Z. Y. Song, Q. Shu, and X. D. Yang, 2019: Quantifying the importance of interannual, interdecadal and multidecadal climate natural variabilities in the modulation of global warming rates. Climate Dyn., 53, 6715−6727, https://doi.org/10.1007/s00382-019-04955-2.
    Wu, B. L., X. P. Lin, and L. S. Yu, 2020: North Pacific subtropical mode water is controlled by the Atlantic Multidecadal Variability. Nature Climate Change, 10, 238−243, https://doi.org/10.1038/s41558-020-0692-5.
    Wu, Z. H., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1−41, https://doi.org/10.1142/S1793536909000047.
    Zhai, P. M., and Coauthors, 2016: The strong El Niño in 2015/2016 and its dominant impacts on global and China’s climate. Acta Meteorologica Sinica, 74, 309−321, https://doi.org/10.11676/qxxb2016.049.
    Zheng, F., and Coauthors, 2023: Will the globe encounter the warmest winter after the hottest summer in 2023? Adv. Atmos. Sci., https://doi.org/10.1007/s00376-023-3330-0. (in press
  • [1] Zichen LI, Qingxiang LI, Tianyi CHEN, 2024: Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature (CMST) Dataset, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 369-376.  doi: 10.1007/s00376-023-3200-9
    [2] Peng LIU, Chung-Hsiung SUI, 2014: An Observational Analysis of the Oceanic and Atmospheric Structure of Global-Scale Multi-decadal Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 316-330.  doi: 10.1007/s00376-013-2305-y
    [3] FANG Changfang*, WU Lixin, and ZHANG Xiang, 2014: The Impact of Global Warming on the Pacific Decadal Oscillation and the Possible Mechanism, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 118-130.  doi: 10.1007/s00376-013-2260-7
    [4] LIU Chengyan* and WANG Zhaomin, , 2014: On the Response of the Global Subduction Rate to Global Warming in Coupled Climate Models, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 211-218.  doi: 10.1007/s00376-013-2323-9
    [5] Chibuike Chiedozie IBEBUCHI, Cameron C. LEE, 2024: Circulation Pattern Controls of Summer Temperature Anomalies in Southern Africa, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 341-354.  doi: 10.1007/s00376-023-2392-3
    [6] MA Xiaoyan, GUO Yufu, SHI Guangyu, YU Yongqiang, 2004: Numerical Simulation of Global Temperature Change during the 20th Century with the IAP/LASG GOALS Model, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 227-235.  doi: 10.1007/BF02915709
    [7] Ran LIU, Changlin CHEN, Guihua WANG, 2016: Change of Tropical Cyclone Heat Potential in Response to Global Warming, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 504-510.  doi: 10.1007/s00376-015-5112-9
    [8] Xiaofei GAO, Jiawen ZHU, Xiaodong ZENG, Minghua ZHANG, Yongjiu DAI, Duoying JI, He ZHANG, 2022: Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1285-1298.  doi: 10.1007/s00376-021-1138-3
    [9] Yiyong LUO, Jian LU, Fukai LIU, Xiuquan WAN, 2016: The Positive Indian Ocean Dipole-like Response in the Tropical Indian Ocean to Global Warming, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 476-488.  doi: 10.1007/s00376-015-5027-5
    [10] Ruosi ZHANG, Shang-Ping XIE, Lixiao XU, Qinyu LIU, 2016: Changes in Mixed Layer Depth and Spring Bloom in the Kuroshio Extension under Global Warming, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 452-461.  doi: 10.1007/s00376-015-5113-8
    [11] WEI Xiaolin, LIU Qian, Ka Se LAM, WANG Tijian, 2012: Impact of Precursor Levels and Global Warming on Peak Ozone Concentration in the Pearl River Delta Region of China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 635-645.  doi: 10.1007/s00376-011-1167-4
    [12] YANG Jing, BAO Qing, WANG Xiaocong, 2013: Intensified Eastward and Northward Propagation of Tropical Intraseasonal Oscillation over the Equatorial Indian Ocean in a Global Warming Scenario, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 167-174.  doi: 10.1007/s00376-012-1260-3
    [13] Jingchao LONG, Suping ZHANG, Yang CHEN, Jingwu LIU, Geng HAN, 2016: Impact of the Pacific-Japan Teleconnection Pattern on July Sea Fog over the Northwestern Pacific: Interannual Variations and Global Warming Effect, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 511-521.  doi: 10.1007/s00376-015-5097-4
    [14] Yawen DUAN, Peili WU, Xiaolong CHEN, Zhuguo MA, 2018: Assessing Global Warming Induced Changes in Summer Rainfall Variability over Eastern China Using the Latest Hadley Centre Climate Model HadGEM3-GC2, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1077-1093.  doi: 10.1007/s00376-018-7264-x
    [15] Tim LI, ZHANG Lei, Hiroyuki MURAKAMI, 2015: Strengthening of the Walker Circulation under Global Warming in an Aqua-Planet General Circulation Model Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1473-1480.  doi: 10.1007/s00376-015-5033-7
    [16] Yuan WANG, 2015: Air Pollution or Global Warming: Attribution of Extreme Precipitation Changes in Eastern China——Comments on "Trends of Extreme Precipitation in Eastern China and Their Possible Causes", ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1444-1446.  doi: 10.1007/s00376-015-5109-4
    [17] Chengyang GUAN, Xin WANG, Haijun YANG, 2023: Understanding the Development of the 2018/19 Central Pacific El Niño, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 177-185.  doi: 10.1007/s00376-022-1410-1
    [18] Shuangmei MA, Congwen ZHU, Juan LIU, 2020: Combined Impacts of Warm Central Equatorial Pacific Sea Surface Temperatures and Anthropogenic Warming on the 2019 Severe Drought in East China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1149-1163.  doi: 10.1007/s00376-020-0077-8
    [19] QIAN Cheng, YAN Zhongwei, Zhaohua WU, FU Congbin, TU Kai, 2011: Trends in Temperature Extremes in Association with Weather-Intraseasonal Fluctuations in Eastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 297-309.  doi: 10.1007/s00376-010-9242-9
    [20] Yueyue LI, Li DAN, Jing PENG, Junbang WANG, Fuqiang YANG, Dongdong GAO, Xiujing YANG, Qiang YU, 2021: Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1580-1595.  doi: 10.1007/s00376-021-0265-1

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 14 December 2023
Manuscript revised: 25 December 2023
Manuscript accepted: 28 December 2023
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

    Corresponding author: Fei ZHENG, zhengfei@mail.iap.ac.cn
  • 1. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: In 2023, the majority of the Earth witnessed its warmest boreal summer and autumn since 1850. Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society. We analyzed the monthly varying global mean surface temperature (GMST) in 2023 and found that the globe, the land, and the oceans in 2023 all exhibit extraordinary warming, which is distinct from any previous year in recorded history. Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics, the GMST in 2023 is predicted to be 1.41°C ± 0.07°C, which will certainly surpass that in 2016 as the warmest year since 1850, and is approaching the 1.5°C global warming threshold. Compared to 2022, the GMST in 2023 will increase by 0.24°C, with 88% of the increment contributed by the annual variability as mostly affected by El Niño. Moreover, the multidecadal variability related to the Atlantic Multidecadal Oscillation (AMO) in 2023 also provided an important warming background for sparking the GMST rise. As a result, the GMST in 2023 is projected to be 1.15°C ± 0.07°C, with only a 0.02°C increment, if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

    • In 2023, the globe experienced its warmest boreal summer and autumn, with extraordinary temperature anomalies ceaselessly occurring over both land and sea (https://berkeleyearth.org/opinion-i-study-climate-change-the-data-is-telling-us-something-new/). Shockingly high temperatures (https://news.cctv.cn/2023/12/02/ARTIQHydetRQQYsal2We09P5231202.shtml) and several climatic anomalies (https://wmo.int/media/news/it-was-hottest-june-record-unprecedented-north-atlantic-warmth-record-low-antarctic-sea-ice) were detected in lots of regions around the world. Based on these facts, the World Meteorological Organization emphasized that the global mean surface temperature (GMST) in 2023 is set to be the warmest year, surpassing the previously warmest year of 2016 (https://wmo.int/media/news/2023-shatters-climate-records-major-impacts). This draws the attention of the scientific community to explore what happened in 2023 and determine the cause of the record-breaking high GMST.

      In this report, the monthly variations of GMST in 2023 are examined and compared with those in other years, particularly in 2016, which was the once-warmest year prior to 2023 (Fig. 1a). According to historical observations, anomalously warm GMSTs typically occur in boreal autumn and winter (October to March in the following year) (gray lines in Fig. 1a), because the warming trend of surface temperature in boreal winter is more pronounced than that in boreal summer (Chapman and Walsh, 1993; Gong et al., 2006; Huang et al., 2012). The monthly changes of GMST in 2016 followed this characteristic (black line in Fig. 1a), whereas the changes in 2023 show a different trait (red line in Fig. 1a). From January to October 2023, the GMST peaked in March; then, following a small decline, it started to rise again in June and consistently set records dating back to 1850 (Table 1). The GMST in March, September, and October even exceeded 1.5°C above the pre-industrial 1850–1900 baseline.

      Figure 1.  The anomalies of (a) GMST, (b) GLAT, and (c) GSST above preindustrial levels during 1950–2023. The values of 2016 and 2023 are marked by the black line and red line, respectively. (d) The spatial distribution of mean surface temperature (ST) from January to October 2023. (e) The difference between the ST from January to October 2023 and the annual mean ST in 2016. (f) The difference between the ST from June to October 2023 and the ST from January to October 2023. The datasets used for the global surface temperature anomaly include HadCRUT5, NOAAGlobal-Temp5, and BEST (Rohde and Hausfather, 2020; Morice et al., 2021; Vose et al., 2021).

      Month of 2023 GMST Ranking
      January 1.18 7
      February 1.26 4
      March 1.52 2
      April 1.29 4
      May 1.21 3
      June 1.33 1
      July 1.39 1
      August 1.47 1
      September 1.70 1
      October 1.58 1

      Table 1.  The ranking of monthly GMST in 2023 since 1850.

      Unique monthly variations of warming in 2023 were witnessed over land. Even if the global land surface air temperature (GLAT) from January to May 2023 did not reach the same level of warmth as in 2016, it soared and surpassed the 2016 GMST for five successive months from June 2023 (Fig. 1b). Nearly every continent experienced positive anomalies of global surface temperature from January to October 2023, with the mid-to-high latitudes in the Northern Hemisphere being the most affected (Fig. 1d). The unprecedent warm temperatures in summer and autumn 2023 boosted the occurrence of heat waves (Qian et al., 2024) that heated these areas and triggered severe climatic crises, which further induced severe wildfires and human deaths (Wang et al., 2024; https://bnn.network/world/the-year-of-the-heatwave-2023s-global-climate-crisis/).

      Over the oceans, the monthly changes of global sea surface temperature (GSST) in 2023 also show unprecedented warming. In contrast to the cooling tendency of the GSST in 2016, the GSST in 2023 rose steadily from the beginning of the year, exceeded the 2016 GSST in April, and reached more than 1.2°C in July (Fig. 1c). The GSST directly increased by 0.3°C within the first eight months of this year, compared to only an approximate 0.9°C growth for the 173 years during 1850–2022. Anomalously warm waters were mainly located in the tropical Pacific, northern Pacific, and northern Atlantic. In the tropical Pacific, an eastern Pacific–type El Niño formed in June and quickly developed into a medium–strong event around October (Fig. 1f; Li et al., 2023; Zheng et al., 2023), making the eastern equatorial Pacific warmer than normal. The warm blob over the northern Pacific, formed in May 2023, gradually spread westward and became stronger, possibly because of the local heat budget or teleconnections associated with the North Pacific Oscillation and tropical Northern Hemisphere pattern (Liang et al., 2017; Tseng et al., 2017; Shi et al., 2022; Chen et al., 2023). At the same time, the positive phase of the Atlantic Multidecadal Oscillation (AMO) was also making the Northern Atlantic surface warmer than in 2016 (Li et al., 2024), and contributed significantly to the high GSST in 2023 relative to the anomalously cold SST over the same region in 2016.

      Therefore, the unusual warmings of GMST, GLAT, and GSST in 2023 are crucial because these phenomena have rarely happened since 1850 (https://www.nature.com/articles/d41586-023-03775-z). Whether the GMST in 2023 will turn out to be the warmest, and what caused the year’s unique warming signals, need to be determined.

    2.   GMST in 2023 is predicted to be approaching the 1.5°C global warming threshold
    • In order to obtain complete information for 2023 in advance and answer the question of whether the GMST in 2023 will set a new record, we used the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics (IAP) to predict the 2023 GMST (Zheng et al., 2023). As illustrated by the forecasted result started from November, the annual GMST in 2023 is predicted to be 1.41°C ± 0.07°C higher than that during 1850–1900 (red dot and error bars in Fig. 2a), indicating that 2023 will undoubtedly be the warmest year (100% chance) since 1850. The predicted GMST in 2023 surpasses that in 2022 by 0.24°C (Table 2)—an astonishingly large margin of warming—and will beat the once-warmest year of 2016 by about 0.12°C. Significant warming of nearly 4°C is expected to be located in Eurasia, America, and the Arctic in 2023 (not shown). In the tropical Pacific and northern Pacific, the SST anomaly is about 1.5°C to 2.5°C, while in the areas around the Amundsen Sea and Ross Sea, the sea surface is going to be colder than the baseline temperature during 1850–1900.

      Figure 2.  (a) 2023 GMST prediction (relative to 1850–1900) started from November 2023 made by the IAP GMST statistical ensemble prediction model. The red dot and error bars represent the ensemble mean and ensemble spread of the forecasted result, respectively. The green dot and error bars show the predicted 2023 GMST without the effect of natural variabilities (ANV and MDV components). The black dots and error bars are the historical observed GMST during 1850–2022 and their uncertainties between different datasets. The red, blue and green lines show the three EEMD components of GMST. (b) The annual increments of the three EEMD components for GMST since 2010. The red, blue and green bars represent the annual increments of the ANV, MDV and SCT components, respectively. The black line represents the annual Niño-3.4 index. The values of annual increments and Niño-3.4 for 2023 all include prediction results. (c) Scatterplot of the ANV component and Niño-3.4 index during January to October 2023. Different colors represent the intensity of the Niño-3.4 index. The black line is the linear regression of the ANV component and Niño-3.4 index. (d) Increment of the forecasted ST in 2023 relative to that in 2022. (e) As in (d) but for the ANV and MDV components. (f) As in (d) but for the SCT component.

      GMST ANV MDV SCT
      2022 1.17 −0.10 0.14 1.13
      2023 1.41 0.11 0.15 1.15
      Increment 0.24 0.21 0.01 0.02

      Table 2.  The GMST, ANV, MDV, and SCT components for 2022 and 2023, and their increment in 2023 relative to 2022 (units: °C).

      Taking into account the uncertainties resulting from data measurement, climate noises, and prediction methods, the upper limit of the forecasted GMST in 2023 is 1.48°C, which is approaching the global warming threshold of 1.5°C. This raises extensive concern about whether some particular climate tipping points are approaching, which may trigger serious climatic disasters such as ice sheet collapse, dieback of biodiverse biomes, and permafrost thawing (McKay et al., 2022). Consequently, examining the sources of the phenomenal warming in 2023 and whether such sharp warming may continue in the future, is extremely urgent.

    3.   Strong El Niño and positive-phase AMO sparked the phenomenal rise in GMST in 2023
    • The variabilities of GMST are modulated by natural variabilities, such as the Madden–Julian Oscillation (Hsu et al., 2020; Kim et al., 2023), El Niño–Southern Oscillation (ENSO; Tung and Chen, 2018; Luo et al., 2021), Interdecadal Pacific Oscillation (Kosaka and Xie, 2016), and AMO (Dai et al., 2015). Another important factor that affects GMST changes is external forcings, which include greenhouse gas forcings, volcanic eruptions, and aerosols (Manabe and Wetherald, 1975; Solomon, 2010; Hansen et al., 2011; Bethke et al., 2017; Patterson et al., 2022). For the annual GMST, the effects of natural variabilities and external forcings often overlap with each other. Therefore, to detect the cause of abrupt warming in the 2023 GMST, the ensemble empirical mode decomposition (EEMD) algorithm (Wu and Huang, 2009) was used to decompose the series of GMST anomalies into three components with different periods (Fig. 2a): the annual variability (ANV) component, the multidecadal variability (MDV) component, and the secular trend (SCT) component. The ANV and MDV components can be attributed to natural variabilities, while the SCT component is indicative of external forcing (Li et al., 2022).

      The changes of ANV are greatly affected by ENSO, and its annual increment keeps pace with the Niño-3.4 index (Fig. 2b). During 2010–2022, the correlation between the annual increment of ANV and the Niño-3.4 index was 0.61 (Rypdal, 2018). As a result, enhanced by the medium–strong El Niño, the ANV component in 2023 is predicted to be 0.11°C (Fig. 2c), while it was −0.1°C in 2022, when successive La Niña events occurred at both the beginning and end of the year. The ANV component in 2023 is 0.21°C greater than that in 2022, accounting for 88% of the GMST increment (Table 2). Furthermore, the changes of MDV are significantly affected by the AMO, which has a correlation of 0.62 during the period of 1856–2022 (Wei et al., 2019). Motivated by the shift from the negative to positive phase of the AMO around 2000, the MDV component also changed to a state greater than zero, and has still been rising with an increasing AMO index in recent years (https://psl.noaa.gov/data/timeseries/AMO/). As a result, although the increment of the MDV component is relatively small, the 2023 MDV is forecasted to be 0.15°C, providing an important natural warming background for the abrupt increase in GMST in 2023. Besides, the negative phase of the Pacific Decadal Oscillation (https://www.ncei.noaa.gov/access/monitoring/pdo/) and positive phase of the AMO may have benefited the development of the warm blob over the northern Pacific (Henley et al., 2015; Amaya et al., 2020; Wu et al., 2020), contributing to the warming of GMST in 2023. Meanwhile, the sea-ice extent and snow-cover extent also decreased in 2023 compared to that in 2022, especially for the sea ice in the Antarctic (https://www.ncei.noaa.gov/access/monitoring/snow-and-ice-extent/sea-ice/G/0). The interannual sea-ice loss may be partly ascribed to the effect of El Niño and can favor ANV warming over the poles (Clancy et al., 2021; Li et al., 2021).

      When the effects of natural variabilities, ANV and MDV, are eliminated and only the influence of SCT is considered in the prediction, the GMST in 2023 is expected to be 1.15°C ± 0.07°C (green dot and error bars in Fig. 2a). This is just 0.02°C higher than the SCT component in 2022 and not enough to beat the hottest record set in 2016. The forecasted spatial distribution of the annual increment for the global surface temperature in 2023 relative to that in 2022 is depicted in Fig. 2d. Obvious warming is situated in the eastern tropical Pacific, the north of North America, and the Arctic. Some cooling signals exist in Greenland and the mid-to-high latitudes over the Southern Hemisphere. The natural variabilities related to El Niño and the AMO contribute the vast majority of the increment (Fig. 2e), while the SCT component accounts for very little (Fig. 2f).

      In conclusion, with the vital decadal-scale background warming mainly provided by the AMO, the natural variabilities that are mainly stimulated by the current El Niño event have made a prominent contribution to the astonishingly large margin of annual warming in the 2023 GMST. Global warming alone is still not enough to have caused the GMST to spike to the record high in 2023. Furthermore, because of the substantial heat released by strong El Niño events that spreads to the atmosphere in the extratropics (Zhai et al., 2016; Li et al., 2023), the GMST may climb to a higher level during years when such strong El Niños decay, as compared with the years in which they develop. Therefore, similar to the once-warmest year of 2016, the 2024 GMST may be much higher than the 2023 GMST, thereby setting another new record. However, even though natural variability is the cause of the abrupt increase in the exceptionally high GMSTs in 2023–2024, the abnormally warm background that the SCT component provides cannot be ignored. It might be easier for the GMST to hit record highs in the future as the share of the SCT component increases with future global warming.

      Acknowledgements. This work was supported by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No. ZDBS-LY-DQC010), and the National Natural Science Foundation of China (Grant No. 42175045).

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return