Human influence on frequency of temperature extremes

This study focuses on area averages of the indices over land points of the globe (GLB) and six continents, including Asia (ASI), Europe (EUR), North America (NAM), South America (SAM), Australia (AUS) and Africa (AFR) as defined in Jones et al (2013, figure S1). Based on the ETCCDI definition, the frequency of temperature extremes are represented by percentage of days per year when daily maximum and minimum temperatures are greater than their respective 90th percentiles (TX90p and TN90p) or less than their respective 10th percentiles (TX10p and TN10p) in the base period 1961–1990.

The HadEX3 (available at www.metoffice.gov.uk/hadobs/hadex3) is used to investigate observed changes in the frequency of temperature extremes. This dataset is a global gridded land surface dataset of indices for temperature and precipitation extremes, available on 1.25° × 1.875° latitude-longitude grids and covering 1901–2018 (Dunn et al 2020). We select the grid boxes with sufficient data coverage, defined as grid boxes with at least 70% of data availability during 1951–2018 (i.e. at least 47 years of data) and additionally 3 years of data during 2016–2018 at the minimum. The detection and attribution analyses are conducted for the period 1951–2018 for all continents except South America and Africa. As shown in figure S2, the data coverage is generally good. There is a large data gap in the early period over South America, for this reason, we conduct the detection analyses for South America over the period 1961–2018 (figure S3). The data coverage for Africa is poor and only the northern portion of Africa has sufficient temporal coverage during 1951–2010. For this reason, the African continent is excluded in the detection and attribution analyses. But we will show time series and trends over 1951–2010 in the observations and model simulations as this period has better spatial coverage over the continent. For all the areas, we calculate the anomalies of temperature extremes relative to 1961–1990 at each grid box and then estimate area-weighted regional mean. The linear trends are calculated based on least square method.

We use outputs of daily temperatures simulated by the CMIP6 models. The extreme temperature indices are computed using the RClimDex/FClimdex software package (Zhang et al 2011). Historical simulations used in this study include those forced with the combined effect of all forcing (ALL), greenhouse gas forcing (GHG), anthropogenic aerosol forcing (AA) and natural forcing (NAT) over 1951–2018 (table 1). Seven models provide all experiments including historical ALL, GHG, AA, and NAT experiments. These simulations are used to estimate model responses during 1951–2014. The ALL simulations generally end in 2014, but simulations have been extended to 2018 with the SSP2-4.5 scenario by five models, including CanESM5, HadGEM3-GC31-LL, IPSL-CM6A-LR, MRI-ESM2-0 and NorESM2-LM. Simulations by these five models are used to conduct the detection analyses for the period 1951–2018. Unless stated otherwise, the results shown in figures 2–6 are based on these five model results while the seven model detection results for the period 1951–2014 are provided in the Supplementary Information (stacks.iop.org/ERL/15/064014/mmedia) for comparison. The preindustrial control (CTL) experiments by 16 models are used to estimate internal climate variability in the detection and attribution analyses. The CTL simulations are divided into 113 chunks of 68 years (the length of 1951–2018). Model indices are first computed at models' native grid and then bilinearly interpolated onto the 1.25° × 1.875° HadEX3 grids. The regridded data are masked for times and regions where observations are absent to mimic the availability of the observed indices during 1951–2018. The anomalies relative to 1961–1990 mean values at each grid box are calculated and then the regional averages are obtained. The response to anthropogenic forcing (ANT) is estimated as the differences between the model responses to ALL and NAT forcings.

We use the regularized optimal fingerprinting method (Ribes et al 2013, Ribes and Terray 2013) to regress the observations onto the signals, i.e. the modelled responses to external forcing. The method is a multivariate linear regression model Y= (X− ν) β + (Allen and Stott 2003, Ribes et al 2013). The regression coefficients or scaling factors β scale the signals to best match the observations. ν is the sampling noise in the multi-model ensemble mean, and is the regression residual representing internal variability of the climate. A residual consistency test is performed to test the consistency between model-simulated internal variability (estimated from the CTL) and the regression residual. A scaling factor whose 5th percentile is above zero means the corresponding signal is detected at the 5% level. If the 90% range for a $\beta$ is above zero and also includes unity, the observed changes are assessed to be consistent with the fingerprint of the external forcing (attribution). If the best estimate of the scaling factor is smaller/larger than unity, it means that the models overestimate/underestimate the response compared to the observations. The detection and attribution analyses are conducted on 5-year mean series of regional mean values. The last data point is the average from values over three year period (2016–2018). We use the regional mean series for the continental analyses. For the global analysis, we conduct space-time analysis by using 5-year mean series from 5 continents (Africa is not included due to lack of data) as 5-spacial dimension.

We conduct single-, two- and three-signal analyses. We only consider ALL forcing in the single-signal analysis because ALL contains all known forcings. For the two-signal analyses, the observations are regressed onto two signals including ANT and NAT, here ANT is computed as the difference between ALL and NAT (i.e. ANT = ALL—NAT). For the three-signal analyses, predictors in the regression include GHG, AA and NAT. In the two- and three-signal detections, if the 90% ranges of $\beta$ for these predictors are above zero, we claim that they are detected and separated.

Most previous global and continental analyses of extreme temperature indices are based on HadEX2, and the last year with data in that dataset is 2010. To provide some comparison with earlier results, figure 1 shows trends in the four temperature extreme indices in HadEX3 for two periods 1951–2010 and 1951–2018. There have been increases in the warm days and nights (TX90p and TN90p) and decreases in the cold days and nights (TX10p and TN10p) since 1951 in most land areas, consistent with warming in global mean temperature. For TX90p and TN90p, prominent increases are found in western Europe, most parts of Asia and central part of South America while the decrease in TX90p is seen in south-eastern North America. For TX10p and TN10p, a largest decrease is seen mainly in eastern Russia, China, Mongolia and the central part of South America. For all the warm and cold extremes, the changes in the nighttime extremes (TN90p and TN10p) are larger than corresponding daytime extremes, though they have similar spatial patterns. The trend patterns for the two periods are largely similar, but with some notable differences. The area with cooling trend in southern parts of North America and South America becomes smaller in the longer period, especially for daytime extremes. These indicate that the warming observed in 1951–2010 has persisted and expanded after 2010.

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Figure 2 shows trends in the observation and the CMIP6 simulations during 1951–2018. The multi-model trends for different forcings are computed as the following: we first compute the medium values of trends of individual model runs for a particular model, 2) the median of these medium values for all models are then taken. The multi-model trends under ALL forcing agree well with the observations in almost all the regions, indicating a good performance of the new generation of climate models in simulating the observed changes in temperature extremes. In the southern part of North America, the ALL results do not reproduce the observed decrease in warm days and the observed increase in cold days. As the cooling area has reduced with additional recent data, that cooling may be more a reflection of natural multi-decadal variability, which is consistent with the study by Kumar et al (2013). The GHG trend patterns are similar to those of ALL, but with larger magnitude of changes in general, indicating stronger warming in the GHG simulations. The AA results show opposite trend but with smaller magnitudes than those of ALL and GHG, indicating cooling effects from anthropogenic aerosols. As for the NAT, it shows quite small trends in the range of −0.2% to 0.2% per decade in most regions, with slight decreases in the warm days and nights and small increases in the cold days and nights, suggesting that the combined effect of the solar irradiance changes and volcanic activities is cooling with very small magnitude. All these indicate that the warming effect of greenhouse gas forcing dominates on the changes in temperature extremes, offset by the cooling effect of anthropogenic aerosols.

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The regional mean time series of the temperature indices and trends in the observations and simulations by the 5 models during 1951–2018 are shown in figure 3. For warm days and nights, there is a clear upward increase over global land and the continents since the 1970s. The largest increase is observed in South America, while the smallest increase is seen in North America. The large linear trends in South America and Africa are related to a large increase since the 1980s. For the cold days and nights, all the regions show decreases after the 1970s. Large decreases in cold nights are seen in South America and Africa while the smallest changes are seen in North America.

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For the model results, the ALL simulations are consistent with the observations in all regions. The GHG results show the largest warming trends while trends from the AA simulations indicate cooling. The absolute magnitude of trend in AA is smaller than that in GHG. There are no clear trends in NAT simulations. Among all the individual forcing simulations, only the GHG simulations are consistent with the observations but both AA and NAT simulations are of opposite sign. This suggests that the observed changes cannot be explained without GHG forcing.

The best estimates and their 5%–95% confidence intervals of the scaling factors from the single- and two-signal detection analyses are summarized in figure 4. A general impression is that the ALL and ANT signals are robustly detected over global land and almost all the continents. The lower bounds of the 90% confidence intervals of the scaling factors are above zero and most of the residual consistency tests are passed. The confidence intervals for warm extremes are smaller than those for cold extremes, indicating a smaller uncertainty in the estimates associated with warm extremes than cold extremes. For single-signal detection, the best estimates of scaling factors are smaller than unity for most indices in different continents, indicating that models generally have overestimated the observed warming. These values are noticeably smaller than relevant values in Asia from CMIP5 simulations (Dong et al 2018). We note that the CMIP6 models generally have higher climate sensitivity than previous model generations (table 1, Zelinka et al 2020; Flynn and Mauritsen 2020). This suggests that the high sensitivity of the CMIP6 models may also be reflected in their simulated changes in temperature extremes and that the high sensitivity is not realistic. Additionally, changes in aerosol forcing, in particular over Asia in CMIP6 simulations could also play a role. For the two-signal detections, ANT signal can be detected in every region while NAT signal is generally not detected except in NAM. This indicates that the ANT signal plays the dominant role in the changes of warm and cold days and nights. Note that the scaling factor for ANT signal is of similar magnitude to that of ALL, and is smaller than one in almost all cases, indicating once again the models simulate too much warming. These results are consistent with previous results overall, that changes in warm and cold extremes appear to be almost impossible without anthropogenic effect (Morak et al 2011, 2013, Christidis and Stott 2016). We note that NAT signal is generally not detectable in our analysis though it was detected in some of previous studies (Christidis and Stott 2016, Dong et al 2018). The small difference in the results may be due to multiple reasons including differences in datasets, time periods, the details of processing the data, and the fact that the small natural signal is hard to detect because it would need a huge ensemble to provide a robust and clear fingerprint and robust estimate of natural variability (DelSole et al 2019).

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Figure 5 summarizes results from the three-signal analyses. Generally speaking, the GHG signal is detected in almost all the indices over global land and the five continents, with the cold nights (TN10p) over AUS being the exception. The AA signal can be robustly detected over GLB and most continents for warm extremes. The NAT signal is not detected for almost all the regions. For the warm days (TX90p), the GHG and AA can both be detected and separated from each other over GLB and most continents except AUS, but confidence interval for AA in SAM is large. Only GHG signal can be detected over AUS and the NAT signal can be detected only over ASI. For the warm nights (TN90p), the GHG and AA can both be detected over GLB, ASI, EUR and NAM. In other continents, only GHG signals can be detected. These results indicate that when these three signals are taken into account together, the GHG signal is dominant over global land and all continents while AA signal can be robustly detected against noise only over GLB and three of the five studied continents.

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The detection of the signals in the cold extremes is less often than that in warm extremes. The GHG signal is detected over global land and almost all the continents, except for the cold nights (TN10p) over AUS. However, the residual consistency test failed in this case, indicating the detection is not robust. The best estimate of scaling factor of AA signal is greater than zero for TX10p at 5% significance level over GLB but the residual consistency test did not pass. These do not necessarily indicate that AA signal is weak, rather, they may be a reflection of the difficulties in separating highly co-linear signals in detection and attribution analyses as discussed in previous studies (Jones et al 2013, Schurer et al 2018, DelSole et al 2019). The weak NAT signal cannot be distinguished from internal climate variability in any regions. For the cold nights (TN10p), the models overestimate the observed variability over EUR, NAM and AUS, thus indicating a reduced robustness for the detection results.

Together, the results from three-signal detection and two-signal detection analyses show clearly that GHG is the dominant factor in the observed changes in the frequency of temperature extremes and that the models generally overestimate the response. The AA signal is less detectable, which perhaps is a result of co-linearity with GHG signal. Climate responses to external forcings are easier to detect in warm extremes than in cold extremes. The scaling factors for the globe is very similar to those over Asia and the largest difference between the scaling factors are seen for cold extremes. We also performed the detection analyses based on seven CMIP6 models over the period 1951–2014 (figures S4–S6). The results based on 7 models and the shorter period are quite similar to those based on 5 models with longer period 1951–2018, though it seems that the detectability for some indices in a longer period is slightly higher.

The changes in the indices of temperature extremes attributable to GHG, AA and NAT forcings are obtained by multiplying the linear trends in the multi-model mean responses to the corresponding scaling factors estimated in the three-signal analyses (figure 6). Attributable changes are not computed if a scaling factor is negative as that is physically not meaningful. In most cases, the best estimate of the increase in warm extremes attributable to GHG signal is larger than the observed changes, which is offset to a certain extent by the decrease due to AA influence. The NAT signal has also offset slightly the GHG signal over global land. It is estimated that the GHG forcing alone has increased TX90p by 11.2% on global scale (90% confidence interval: 9%–13%), and that this has been offset by 2.5% (1%–4%) by AA's cooling effect and again by 0.2% (0%–0.3%) by NAT forcing. The resulting changes due to the external forcings are very close to the observed change of approximately 8.5% (6%–11%) over 1951–2018.

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For the cold extremes, the decrease attributable to GHG forcing is significant but those from AA and NAT forcings are not significant because the relevant scaling factor is not significantly greater than zero or not meaningful to compute as the corresponding scaling factor is negative. For global TX10p, the estimated decrease attributable to GHG signal is about 7% (4%–10%), the observed decrease is 5% (4%–6%), and the estimated increase attributable to AA signal is 2% (0%–4%). However, as the residual consistency test failed in the detection analyses for global TX10p because model simulated variability is large, the corresponding confidence interval of the scaling factor may be conservative. Generally, except the observed change in TN10p over AUS, the GHG induced decrease is slightly more than the observed change in TX10p and TN10p, which has been offset by the increase induced by AA forcing.