Record High Warm 2021 February Temperature over East Asia

Jinbo Xie Lawrence Livermore National Laboratory, Livermore, California

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Human-induced warming is estimated to have increased the occurrence probability of events like the record-breaking warm February in East Asia by a factor of 4–20.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Jinbo Xie, xie7@llnl.gov

Supplemental material: 10.1175/BAMS-D-22-0139.2

Human-induced warming is estimated to have increased the occurrence probability of events like the record-breaking warm February in East Asia by a factor of 4–20.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Jinbo Xie, xie7@llnl.gov

Supplemental material: 10.1175/BAMS-D-22-0139.2

The record high warm 2021 of East Asia is the highest February since 1951; approximately 787 counties/cities in China have exceeded the winter high since the recording of meteorology data. Extreme warm events occurred in China from 18 to 21 February with daily maximum temperature records exceeded at 905 stations. Beijing and Henan hit 25.6° and 30°C on 21 February, among large parts of cities in northern China reached 25°–29°C (China Meteorology Administration 2022). It is also interesting that this event followed a record-breaking cold event in January where Beijing experienced –19.6°C, the third coldest day since 1951 (Zhou et al. 2022). This sharp transition of the temperature in 2 months has created an abnormal climate condition with potentially large climate impacts (Patel 2021; Ocko 2020; Uteuova 2022; Xie and Zhang 2017; Xie et al. 2019), and posed extreme challenge to the Beijing Olympic test matches (China Meteorology Administration 2022).

Studies have shown that the East Asian winter temperature were found to be associated with the wave activities in this region related to the Ural blocking high and Siberian high (Park et al. 2011; Cheung et al. 2016; Song and Wu 2017). During this period, the Ural high and Siberian high have shifted from a strong phase before 15 January (Fig. 2 in Yu et al. 2022) to a relatively weak phase after (Fig. 1b). This may have led to a weaker cold advection from the north and warm spells in the region.

Fig. 1.
Fig. 1.

(a) February 2021 monthly mean maximum temperature anomaly (relative to 1951–2014) using the GSOD interpolated onto the 1.0° x 1.0° grid. (b) The 500-hPa geopotential height anomaly relative to the 1951–2014 climatology taken from the NCEP reanalysis. (c) February 2-m temperature anomaly time series of the East Asia regional mean [averaged over the black box in (a)] from 1951 to 2021. (d) Return period (unit: years; red thick line) and 5%–95% confidence interval (red dashed lines) of the East Asia regional mean 2-m temperature, with the black horizontal line denoting the February 2021 warm event.

Citation: Bulletin of the American Meteorological Society 103, 12; 10.1175/BAMS-D-22-0139.1

Fig. 2.
Fig. 2.

(a) Return period (unit: years) of East Asia February monthly mean maximum temperature anomaly (average over the black dashed box in Fig. 1a) for generalized Pareto distribution (GPD) fit (best estimate) of the observation shifted to 1951 (upper black dashed line) and 2021 (lower black dashed line), ALL (red), and NAT (green) simulations from CMIP6. The black horizontal line denotes the February 2021 observation. (b) Best estimates of the scaling factors of regional February monthly mean maximum temperature anomaly using two-signal analysis of optimal fingerprint method, as well as the scaling factors′ 5%–95% confidence interval.

Citation: Bulletin of the American Meteorological Society 103, 12; 10.1175/BAMS-D-22-0139.1

Warmer winters are expected to be more frequent under global warming (Almazroui et al. 2020; Carvalho et al. 2021; Ren et al. 2021; Peng et al. 2022). Studies have also shown that regional forcing, such climate natural variability, and sea ice forcing, have also impacted the recent warm winter (Xie et al. 2019).

In this study, we will examine the 2021 record warm February temperature in a historical context and investigate the effects of anthropogenic climate change and natural climate variability on the likelihood of this record high warm event. This study focuses on two questions: 1) How extreme is this event in historical context? 2) What are the relative impacts of the natural variability and human-induced warming on the temperature extreme?

Data and methods

The datasets used in the study include daily maximum 2-m temperature meteorology station data for the period 1951–2021, collected from global summary of the day (GSOD) (www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day). These are station record data that dates to 1921. The data period in this study is chosen due to the data availability in the East Asia region. The data are converted into monthly mean and interpolated onto the 1° × 1° grid using inverse distance weighting (Cressman 1959; Chen et al. 2008). Geopotential height data from 1951 to 2021 are taken from NCEP reanalysis data (Kalnay et al. 1996).

For model data, we retrieved models from the Coupled Model Intercomparison phase 6 (CMIP6) (Eyring et al. 2016), Detection and Attribution Model Intercomparison Phase (DAMIP) data (Gillet et al. 2016), including historical runs (ALL), natural forcing run (NAT), listed in Table 1. Due to data availability, an overall of 9 models with 36 pairs of simulations are selected, along with their preindustrial control run (piControl). A Kolmogorov–Smirnov two-sample test is used to evaluate if the simulations and the observation come from the same distribution (p > 0.05; failure to reject the null hypothesis that the modeled temperature has the same distribution as the observed). To be consistent with our observational and simulation analysis, all data are area averaged over the East Asia area (EA; black dashed box shown in Fig. 1a); both the observation and simulation anomalies are obtained relative to the period of 1951–2014. The ALL runs are extended using SSP585 runs taken from Scenario-MIPs (O’Neill et al. 2016).

Table 1.

List of CMIP6 models used in this study. Numbers represent the ALL and NAT simulation ensemble sizes or the number of 64-yr chunks for the piControl simulations.

Table 1.

The event is defined using monthly mean of the maximum daily temperature, since the warmth lasted for the whole February (China Meteorology Administration 2022) and the warm winters tend to affect activities such as agriculture or water supply on durations longer than daily time scale (Gammon 2021; Patel 2021). The generalized Pareto distribution (GPD) (Philips et al. 2020) is used to fit the tailed distribution of observation and simulations and estimate the return period in this study. We use 30% warmest events (defined as the events higher than the 70th percentile of the whole events) of the separate observation/simulations to fit their GPD distribution, respectively. This is to include more points considering the small sample size. We assume the location parameter shift proportionally to the smoothed global mean temperature, μ = μ0 + αT′, where μ0 is the location parameter corresponding to when the global temperature is set to 14°C and T′ is 0°C, α is the trend (2.24°C °C−1) computed as the regression of the February monthly mean of the EA daily maximum temperature onto the GISTEMP global mean temperature (Lenssen et al. 2019; GISTEMP Team 2022) smoothed with a 4-yr running average, following Van Oldenborgh. (2015).

To quantitatively assess the contributions of anthropogenic influence on the 2021 warm February temperature, we calculate the fraction of attributable risk (FAR; Stott et al. 2004), with definition of FAR = 1 – PNAT/PALL. Here PNAT denotes the probability of exceeding the 2021 warm February temperature (above 5.28°C) in the natural-forcing scenarios and PALL denotes the equivalent for the all-forcings scenarios. Bootstrapping resampling is performed 1,000 times to estimate the 5%–95% uncertainty range (Xie and Zhang 2017; Xie et al. 2019).

To further evaluate the human influence, we use optimal fingerprint total least squares (TLS) method to analyze the effect anthropogenic forcing (Hegerl et al. 1997; Allen and Stott 2003; Ribes et al. 2013). For this method, the ALL and NAT simulation time series of the area-averaged mean monthly maximum temperature for the years 1951–2014 are derived for the EA region (see online supplement; https://doi.org/10.1175/BAMS-D-22-0139.2). They are first evaluated separately with one-signal test to detect their impact on the local climate, and then the combined two-signal test to attribute their contribution to the historical climate. A total of 238 nonoverlapping chunks, each 64 years long, were obtained from preindustrial control simulations to estimate internal variability. Uncertainty ranges (5%–95%) for the scaling factors using the fingerprint method were evaluated, and the signal of human-induced warming is considered detected if the scaling factor is significantly greater than zero (Xie et al. 2016; Min et al. 2011).

Results

The anomalously warm February is shown in Figs. 1a and 1b. From the anomalous GSOD February mean maximum temperature (relative to the 1951–2014 climatology), we can see that the February warmth spanned a large part of the northern China and Mongolian region (Fig. 1a). This extends from 52°N southward to the southeast coast of China and covers large parts of the East China, creating unusually warm condition across these regions. The area average (dashed black box in Fig. 1a) of the temperature anomaly has shown that the February monthly anomaly is about 5.28°C (Fig. 1c), ranking the warmest on record since 1951, with an observation-based return period of over 100 years and a best estimate of around 750 years (Fig. 1d). The shifted return period (derived from GPD distribution with the location parameter shifted to 1951 and 2021 condition mentioned in the “Data and methods” section), on the other hand, shows an even lower probability of return period. The return period for the 2021 climate is around 24,300 (Fig. 1d), making this winter extremely unlikely under the 1951 climate condition.

To attribute the warm extremes in February 2021, CMIP6 simulations with ALL and NAT were used. Following the observational estimate (mentioned in the previous section), the simulation GPD is fitted against the warmest 30% of events for the simulated February mean maximum temperature (1951–2021) averaged over the northeast, excluding the 2021 warm event. While the return period for ALL is around 500 years (within the bound of the observational estimate), the return period for NAT is about 5,000 years. The FAR for the events higher than the 2021 February extreme (5.28°C) is 0.91, with an uncertainty estimated by bootstrap from 0.95 (+0.04) to 0.76 (−0.15) under the influence of anthropogenic climate change (Fig. 2a). This means that the human influence made the 2021 warm February temperature approximately 4–20 times more likely than the climate condition without human influence.

Furthermore, the best estimate scaling factors of the ALL in the two-signal test using TLS method is 1.93, with a confidence interval of 0.63–3.28 (Fig. 2b), suggesting the robustness of detectable human influence. The signal of NAT, on the other hand, was not detected in the one-signal or two-signal test (Fig. 2b). The results suggest the robustness of human influence detection on an abnormal event such as the 2021 warm February temperature.

Conclusions

Anomalous warm winters can have potential impact on society, including rain-on-snow events, decreasing snowpack water supply, and detrimental among other effects to the plant season, etc. (Xie and Zhang 2017; Xie et al. 2019; Ocko 2020; Gammon 2021; Patel 2021; Uteuova 2022). In February 2021, a record high warm winter struck across 787 counties and cities across the East Asia, forming the warmest February on record of meteorological data since 1951. Due to the timing, the anomalous warm winter posed an extreme challenge to the Beijing Olympic test matches that were organized in the North China pan-Beijing region at that time.

Attribution analysis based on the CMIP6 simulations with and without anthropogenic forcings indicates that the likelihood of extremely high temperature in North China like February 2021 increased by about 4–20 times due to anthropogenic climate change. Robustness of the human influence is also suggested by a detection and attribution analysis using the optimal fingerprint two-signal attribution process that utilizes the CMIP6 ALL, NAT, and using piControl run chunks as noise estimate.

Despite the results, however, there are certain caveats in the study. Due to inhomogeneity of the data in the study region, we limited our study to the period after 1951. This inhomogeneity caused exclusion of the anthropogenic influence before that time and may be the reason for the underestimate suggestion in the ALL compared to the 2021 shift fit (Fig. 2a). This also be the case for the best-estimate scaling factor (1.93). Another uncertainty lies in the model’s ability to simulate factors such as irrigation and land use, which may complicate the temperature attribution issue on the regional scale (van Oldenborg et al. 2022)—further evaluation may be enhanced following Otto et al. (2020). With these limitations in mind, we could potentially underestimate the effect of anthropogenic forcing on local climate. Consequently, the factor of 4–20 is a conservative estimate of the anthropogenic influence on the EA region. As warm winters are expected to be more frequent globally under climate change, this would pose serious challenges in future environmental protection and water supplies (Gammon 2021; Patel 2021)

Acknowledgments.

The GSOD data can be retrieved from ww.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day. The NOAA data can be retrieved from Research Data Archive at NOAA/PSL: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. GISTEMP data are provided by the NOAA/OAR/ESRL/PSL, Boulder, Colorado, from their website at https://psl.noaa.gov/data/gridded/data.gistemp.html. The CMIP6 simulation data can be retrieved from https://pcmdi.llnl.gov/CMIP6/. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344; the IM release number is LLNL-JRNL-836101. J. X. was supported by LLNL Institutional Postdoctoral Program. We thank the two anonymous reviewers for their constructive comment on improving this manuscript.

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Supplementary Materials

Save
  • Allen, M., and P. Stott , 2003: Estimating signal amplitudes in optimal fingerprinting, part I: Theory. Climate Dyn., 21, 477491, https://doi.org/10.1007/s00382-003-0313-9.

    • Search Google Scholar
    • Export Citation
  • Almazroui, M., F. Saeed, S. Saeed, M. Nazrul Islam, M. H. Ismail, N. A. B. Klutse, and M. H. Siddiqui , 2020: Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst. Environ., 4, 455475, https://doi.org/10.1007/s41748-020-00161-x.

    • Search Google Scholar
    • Export Citation
  • Cappuci, M. , 2021: Abnormally warm weather to dominate Lower 48 through winter solstice. Washington Post, 7 December, www.washingtonpost.com/weather/2021/12/07/warm-winter-weather-united-states/.

    • Search Google Scholar
    • Export Citation
  • Carvalho, D., S. Cardoso Pereira, and A. Rocha , 2021: Future surface temperatures over Europe according to CMIP6 climate projections: An analysis with original and bias-corrected data. Climatic Change, 167, 10, https://doi.org/10.1007/s10584-021-03159-0.

    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak , 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Cheung, H. N., W. Zhou, Y. T. Leung, C. M. Shun, S. M. Lee, and H. W. Tong , 2016: A strong phase reversal of the Arctic Oscillation in midwinter 2015/16: Role of the stratospheric polar vortex and tropospheric blocking. J. Geophys. Res. Atmos., 121, 13 44313 457, https://doi.org/10.1002/2016JD025288.

    • Search Google Scholar
    • Export Citation
  • China Meteorology Administration, 2022: China Climate Bulletin (2021) (in Chinese). China Meteorology Administration, www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202203/t20220301592530.html.

  • Cressman, G. P. , 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367374, https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor , 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Gammon, K. , 2021: Warmer winters can wreak as much havoc as hotter summers, say scientists. Guardian, 17 December, www.theguardian.com/environment/2021/dec/17/warmer-winters-climate-crisis-scientists.

  • Gillett, N. P. , and Coauthors, 2016: The Detection and Attribution Model Intercomparison Project (DAMIP v1. 0) contribution to CMIP6. Geosci. Model Dev., 9, 36853697, https://doi.org/10.5194/gmd-9-3685-2016.

    • Search Google Scholar
    • Export Citation
  • GISTEMP Team, 2022: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies, accessed DD MMM YYYY, https://data.giss.nasa.gov/gistemp/.

  • Hegerl, G. C., K. Hasselmann, U. Cubasch, J. F. B. Mitchell, E. Roeckner, R. Voss, and J. Waszkewitz , 1997: Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change. Climate Dyn., 13, 613634, https://doi.org/10.1007/s003820050186.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss , 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, 63076326, https://doi.org/10.1029/2018JD029522.

    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl , 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378381, https://doi.org/10.1038/nature09763.

    • Search Google Scholar
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  • Fig. 1.

    (a) February 2021 monthly mean maximum temperature anomaly (relative to 1951–2014) using the GSOD interpolated onto the 1.0° x 1.0° grid. (b) The 500-hPa geopotential height anomaly relative to the 1951–2014 climatology taken from the NCEP reanalysis. (c) February 2-m temperature anomaly time series of the East Asia regional mean [averaged over the black box in (a)] from 1951 to 2021. (d) Return period (unit: years; red thick line) and 5%–95% confidence interval (red dashed lines) of the East Asia regional mean 2-m temperature, with the black horizontal line denoting the February 2021 warm event.

  • Fig. 2.

    (a) Return period (unit: years) of East Asia February monthly mean maximum temperature anomaly (average over the black dashed box in Fig. 1a) for generalized Pareto distribution (GPD) fit (best estimate) of the observation shifted to 1951 (upper black dashed line) and 2021 (lower black dashed line), ALL (red), and NAT (green) simulations from CMIP6. The black horizontal line denotes the February 2021 observation. (b) Best estimates of the scaling factors of regional February monthly mean maximum temperature anomaly using two-signal analysis of optimal fingerprint method, as well as the scaling factors′ 5%–95% confidence interval.

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