Changes in regional wet heatwave in Eurasia during summer (1979–2017)

There is no universal definition of a heatwave, but extreme events associated with particularly hot sustained temperature have significant impacts on human health, infrastructure and biophysical systems (Easterling et al 2000, Welbergen et al 2008, McMichael and Lindgren 2011, Xu et al 2016, Campbell et al 2018). The effects of the most lethal heatwaves are not only due to high temperature, but also to the effects of humidity (Steadman 1979a, 1979b). Hot and humid conditions can be more dangerous than equivalently hot but dry conditions, as the ability of humans to cool themselves by sweating is diminished as relative humidity (RH) increase (Wehner et al 2017).

Eurasia is a region with large population densities, which is vulnerable to environmental extreme events such as heatwaves. Recent examples of heatwaves include the European heatwave of 2003 (Christoph and Gerd 2004) and the Russia heatwave of 2010 (Russo et al 2014), which caused more than 70 000 heat-related death rolls; the 2013 heatwave in eastern China (Hou et al 2014, Xia et al 2016) and the 2017 Shanghai heatwave (Chen et al 2019), causing widespread drought and cascading economic losses (e.g. Xia et al 2018). Many studies have found an increase in dry heatwave occurrences during the last few decades over most of the Eurasia (Karl et al 1995, Meehl and Tebaldi 2004, Alexander et al 2006), whereas others studies point out the regional differences in these trends (Zhai et al 2003, Perkins-Kirkpatrick et al 2016).

Previous research on heatwaves has mainly emphasized changes in temperature with limited studies of recent changes in hot and wet heatwave events in Eurasia. Most previous studies on wet heatwaves used observations from local ground stations that can provide accurate temperature and humidity datasets. For example, using station observation temperature and RH datasets, Ding (2011) classified the wet and dry heatwave by mean RH and investigated the geographical patterns and temporal variation of heatwave events in China. However, there are some issues when using observations directly, such as missing records, inconsistencies through time, especially the RH datasets, which has serious quality issues (Zhu et al 2015, Li et al 2020). Using reanalysis and CMIP5 dataset, Russo (2017) explored global humid heat wave hazards at different levels of global warming and found the magnitude and apparent temperature peak of heatwaves have been amplified by humidity, such as Chicago heatwave in 1995 and China heatwave in 2003. Based on a reanalysis dataset, Kang (2018) calculated the wet bulb temperature (T_w) in the North China Plain, which can be used to measure high temperature and humidity climatic events, finding that heatwave risk had increased due to the anthropogenic effects of irrigation in this region. However, this kind of wet heatwave analysis has not been previously studied in Eurasia.

This study focuses on the spatial-temporal variation of hot and wet extreme events in Eurasia, and the reason for its changes since 1979. We use T_w to identify the wet heatwave. This is a suitable index to investigate the impact of climate change on heat stress (Pal et al 2016, Tong et al 2017). While the core temperature of human body is around 37 °C, the skin temperature is slightly cooler at 35 °C. If the environmental T_w exceeds 35 °C, then human body is unable to dissipate heat by sweating, leading to hyperthermia (Sherwood et al 2010, Tong et al 2017). In the current climate, T_w rarely exceeds 31 °C at daily timescale (Sherwood and Huber 2010). Other combined empirical temperature and humidity indices have been used to investigate the impacts of climate change on heat stress such as wet bulb globe temperature (WBGT) (e.g. ISO 1989) which is the basis for time limitations of work in different heat exposure standards. The black globe temperature is not observed in many places (Luo and Lau 2019), which makes WBGT more complex and difficult to measure. In contrast, T_w can be measured using standard methods and it also provides a physically based relationship to the human body's core temperature (Stull 2011, Pal 2016). Hence, T_w is feasible for daily use and has been used more often in climate studies (e.g. Pal and Eltahir 2016, Raymond et al 2017).

This paper investigates changes in wet heatwave events in Eurasia using T_w, calculated from a reanalysis dataset. In this study, we compare the variation of the temperature and humidity during wet heatwaves in Eurasia, focusing on the large scale pattern of change in wet heatwaves and analyzing the reason for differential regional changes. The data and methods are described in section 2; the results are presented in section 3, and main conclusions and discussion are given in section 4.

2.1. Study region and datasets

2.2. Quantification of wet heatwaves

T_w was calculated using equation (1) of Stull (2011) giving T_w (°C) as a function of temperature (T_mean) and RH (%)

$$\begin{align}{T_{\text{w}}} &= {T_{{\text{mean}}}}{\text{atan}}\left[ {A{{\left( {{\text{RH}} + B} \right)}^{\frac{1}{2}}}} \right]\nonumber\\ &\quad+ {\text{atan}}\left( {{T_{{\text{mean}}}} + {\text{RH}}} \right) - {\text{atan}}\left( {{\text{RH}} - C} \right)\nonumber\\ &\quad + { }D{\left( {{\text{RH}}} \right)^{\frac{3}{2}}}{\text{atan}}\left( {E \times {\text{RH}}} \right){ } - { }F\end{align} \tag{ 1 }$$

where A, B, C, D, E, F are constants (A = 0.152, B = 8.314, C = 1.676, D = 0.004, E = 0.023, F = 4.686), obtained by fitting functions (Stull 2011).

Extreme T_w days were defined as days when T_w was above the 90th percentile summer (June–August) days. A wet heatwave event was defined as three or more consecutive extreme T_w days during the summer period (Russo et al 2015, Freychet et al 2020).

Several climate indices have been applied in order to quantify the dry heatwave duration (HWD) and intensity based on daily maximum and minimum temperature (Meehl and Tebaldi 2004, Alexander et al 2006, Fischer and Schär 2010, Perkins et al 2012) and there is no one universal index. Based on the previous research, we defined three new indices to represent the frequency, duration, and intensity of wet heatwaves: heatwave day frequency (HWF) which is the total number of days in heatwave events per summer; HWD which is the duration of the longest heatwave in a summer; heatwave amplitude (HWA) which is the maximum T_w value in all heatwave events in a summer.

2.3.  T_mean and RH contribution to T_w

Based on equation (1), T_w is affected by T_mean and RH. In order to quantify the contributions of changes in T_mean and RH to changes in T_w, we calculated ∂T_w/∂T_mean (^oC/^oC) and ∂T_w/∂RH (^oC/%) which are:

$$\begin{align}\frac{{\partial {T_{\text{w}}}}}{{\partial {\text{RH}}}} &= \frac{{A \times {T_{{\text{mean}}}}}}{{2{{\left( {{\text{RH}} + B} \right)}^{\frac{1}{2}}}\left[ {1 + {{\left( A \right)}^2}\left( {{\text{RH}} + B} \right)} \right]}}\nonumber\\ &\quad - \frac{1}{{1 + {{\left( {{\text{RH}} - C} \right)}^2}}} + D\left[ \frac{3}{2}{{\left( {{\text{RH}}} \right)}^{\frac{1}{2}}}{\text{atan}}\right.\nonumber\\ &\quad\times\left.\left( {E \times {\text{RH}}} \right) + {{\left( {{\text{RH}}} \right)}^{\frac{3}{2}}}\frac{E}{{1 + {{\left( {E \times {\text{RH}}} \right)}^2}}} \right]{ }\end{align} \tag{ 2 }$$

$$\begin{equation}\frac{{\partial {T_{\text{w}}}}}{{\partial {T_{{\text{mean}}}}}} = {\text{atan}}\left[ {A{{\left( {{\text{RH}} + B} \right)}^{\frac{1}{2}}}} \right] + \frac{1}{{1 + {{\left( {{T_{{\text{mean}}}} + {\text{RH}}} \right)}^2}}}.\end{equation} \tag{ 3 }$$

A, B, C, D, E, F are the same as equation (1). For small changes in mean temperature (${{\Delta }}{T_{{\text{mean}}}}$) and RH (${{\Delta RH}}$) then the change in wet bulb temperature is (Taylor 1717):

$$\begin{equation}{{\Delta }}{T_{\text{w}}} = {{\Delta }}{T_{{\text{mean}}}}\frac{{\partial {T_{\text{w}}}}}{{\partial {T_{{\text{mean}}}}}} + {{\Delta \text{RH}}}\frac{{\partial {T_{\text{w}}}}}{{\partial {\text{RH}}}} + R\end{equation} \tag{ 4 }$$

where $R$ is a residual representing 2nd, and higher, order terms. We use this to partition changes in wet bulb temperature due to changes in mean temperature and RH.

2.4. Statistical methods

3.1. Climatological characteristics of wet heatwaves in Eurasia

Figures 1(a) and (b) show the annual mean HWF and HWA during the summer (June, July and August) in Eurasia. Wet heatwave frequency is largest (more than five days/summer) in Saudi Arabia, parts of Southern and Eastern Asia (figure 1(a)). For HWA (figure 1(b)), two areas of wet heatwaves are located in SAS and EAS with largest values in the Indus–Ganges plain and in eastern China. Regional mean HWA is 24.4 °C and 22.1 °C for SAS and EAS, respectively. These monsoonal regions have high temperature and humidity, which leads to higher T_w heatwave intensity (figure 1(b)). High values of HWA also occur around the Persian Gulf with HWA values of 30.3 °C. This is the largest value of HWA in Eurasia and is close to the dangerous T_w of 31 °C (Sherwood and Huber 2010), which is rarely exceeded in the current climate. One possible reason for this is the high humidity in coastal areas, which contributes to the high T_w value. Therefore, it is meaningful to investigate the regional differences of the contribution of humidity and temperature on T_w.

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SAS (figure 1(c)) is the region with the highest significant (p < 0.1) increasing trends in regional mean HWF and HWD, with 87% and 80% of the grids, respectively, showing an increasing trend indicating that heatwaves have become more frequent and last longer here. In most regions, HWA shows significant (p < 0.1) positive trends with largest values in NEU and MED, at 0.38 °C and 0.32 °C·decade⁻¹, respectively. However, the CAS region shows little change in wet heatwave indicators.

3.2. Changes of humidity and temperature during heatwave days and summer mean

T_w is affected by both temperature and humidity and so the contributions of temperature and humidity to wet heatwave changes might have strong regional differences in Eurasia. To explore this we first compare the mean value and the linear trend of temperature and humidity during 1979–2017, then the quantitative contributions of T_mean and RH to T_w changes are discussed in section 3.3.

When a heatwave occurs, largest T_w values occur in the monsoon regions including SAS and EAS (figure 2(a)). Highest T_mean values, during wet heatwaves, occur mostly in part of SAH, especially Saudi Arabia, CAS, SAS and EAS. High RH centers are located in SAS, EAS, and part of NAS. Lower RH in Saudi Arabia, CAS and Mongolia was due to high temperatures and low humidity in these regions. In contrast, in northeast NAS, low values of temperature lead to high RH in this region.

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To examine how different heatwave conditions are from summer-mean conditions, we calculated the differences of mean T_w, T_mean, and RH on heatwave days from the 1979 to 2017 summer mean (figures 2(b), (d), (f)). Where these differences are large, this is where heatwave T_w is larger than the summer mean. T_w differences between wet heatwave and summer days increased smoothly from South to North (figure 2(b)) with largest differences in Arctic Ocean coast. This suggests that wet heatwaves are more extreme in these regions, which might lead to more serious impact. T_mean (figure 2(d)) differences are similar to T_w differences and are also smooth, possibly indicating the dominant contribution (see below) of dry-bulb temperature to T_w. This implies that wet heatwaves are different from conventionally defined dry heatwaves, because dry heatwaves often happens under high pressure centers (anticyclone) hence with dry air (Baldi et al 2006, Gershunov et al 2009). For RH differences (figure 2(f)), high values are found at SAS, TIB and north EAS. In most regions, heatwave RH is about 20% higher than the climatological mean, though with significant regional heterogeneity, suggesting a large, though heterogeneous, increase in RH when wet heatwaves occur.

To understand the regional trends in T_mean and RH when wet heatwave happens, we analyze trends in T_w, T_mean and RH during heatwaves (figures 3(a), (c), (e)). In NEU, northwest EAS, and parts of Arabia and TIB, increasing T_w is accompanied by an increase in temperature and RH. This implies that increasing T_w in these regions is due to increases in both temperature and humidity. In eastern parts of CAS and SAS, as T_w increases, a significant positive trend in RH is observed, with no significant positive T_mean trends in these areas. An extreme case is in parts of SAS, where T_mean declined significantly in heatwave days in part of the region. Thus, the increases in T_w in these regions are likely mainly due to the increase in RH. In western Russia, southeastern EAS and the northern Mediterranean, where T_w increased significantly, T_mean has significant positive trends and changes in RH are not significant. Thus, T_w increases are likely being driven by T_mean increases. Quantitative analysis of the contributions of changes in T_mean and RH to changes in T_w is shown in section 3.3.

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Over most of Eurasia, differences between the trends of heatwave day and summer mean for T_w, T_mean and RH are not significant during 1979–2017 (figures 3(b), (d), (e)). This suggests that significant changes of T_w, T_mean and RH in heatwave days are largely due to changes in the summer mean.

3.3. Quantitative contribution from changing T_mean and RH to T_w

To measure quantitatively the sensitivity of T_w to T_mean and RH, respectively, we calculated ∂T_w/∂T_mean (^oC/^oC) and ∂T_w/∂RH (^oC/%) from mean conditions during wet heatwaves (equations (2) and (3)). For most of Eurasia, an increase of 1 °C in T_mean during wet heatwaves increases T_w by 0.6 °C–1 °C (figure 4(a)) with lowest sensitivity in arid regions and highest sensitivity in Scotland, Ireland, Southeast Asia, parts of India and the Pacific coast of Russia. A 1% increase in RH causes a T_w increase of 0.05 °C–0.2 °C (figure 4(b)) with higher RH sensitivity in the Arabian Peninsula, eastern China, South EAS and SAS and lower sensitivity in Scandinavia, Siberia and TIB.

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To measure quantitatively the contributions of changes in T_mean and RH to changes in T_w (see equation (4)), both the sensitivity of T_w and the changes of T_mean and RH were considered (figures 4(c)–(e)). T_mean is the largest driver of changes in T_w though the quantitative contribution depends on both the sensitivity of T_w to temperature and the change in temperature (figures 4(c)–(e)). Considering Eurasia as a whole, changes in T_w are largely driven by changes in temperature, but the contributions of temperature and humidity to the HWA changes exhibit significant regional differences (figure 4(c)). Over the eight regions, the dominant driver of increasing median T_w on heatwave days is dry-bulb temperature with only small contributions from RH trends. In CAS drying offsets the warming effect leading to only small changes in T_w. The Mediterranean region also shows a drying effect which reduces the temperature driven increase in T_w. In contrast, the TIB region shows a modest increase in T_w over the dominant temperature driven increase from changes in RH.

In summary, this work provides an overview of the changes in summer wet heatwave in Eurasia and investigates the reasons for its changes. Significant increasing trends in the frequency, duration and intensity were observed in most of Eurasia, especially the monsoon regions including SAS and EAS, which show the highest HWA during 1979–2017. In most of Eurasia, T_w shows a significant increasing trend during heatwave days. The main driver of this change is increasing dry-bulb temperatures with reductions in RH partly offsetting this increase in some regions. The decrease trend of RH in these regions suggests that the saturated water vapor pressure increases faster than actual water vapor pressure with rising temperature, consistent with Lou and Lau (2019). They use apparent temperature as a measure of heat stress, and, like us, find heat stress in China is mainly caused by high temperature, rather than high RH. Significant changes in T_w during wet heatwave days are largely due to the T_mean changes in summer mean. Compared with humid areas, arid areas are more sensitive to changes in RH and less sensitive to T_mean increases. This result is partly consistent with the conclusion of Wang et al (2019), who used Chinese observations. Wang et al (2019) also show that humidity has higher dominance in the arid and semiarid regions compared with in the Southern and Northeastern China. In our study, Eurasia is divided into eight sub-regions which highlights the regional difference. Over the eight regions, the dominant driver of increasing median T_w on heatwave days is dry-bulb temperature, whereas changes in RH play a relatively minor role. This is different in some sub-regions. In CAS drying offsets the warming effect leading to only small changes in T_w. The Mediterranean region also shows a drying effect which reduces the temperature driven increase in T_w. In contrast, the TIB region shows a modest increase in T_w over the dominant temperature driven increase from changes in RH.

These results are derived from a reanalysis gridded dataset which may introduce errors through interpolation of inhomogeneous station observations. This is especially so for RH observations (Willett et al 2008, 2014, Freychet et al 2020), which are prone to inhomogeneous biases due to non-climate changes in the local observing system. For example, Song (2012) found that RH in most of China showed a reducing trend. Some researchers have suggested that changes in the Chinese RH observing system from manual to automated is the main reason for the decline of RH in humid areas during the early 2000s (Yu and Mou 2008, Yuan et al 2010) likely due to use of Soviet practice and instruments for manual wet-bulb measurements in humid regions (Hu 2004). In this work, we used ERA5 RH data corrected in China as Freychet (2020), which partly reduced the impact of inhomogeneity of RH in this region, leading to an increasing trend of T_w in China. Therefore, it is important to explore the sensitivity of these results to use of other higher quality global reanalysis data. In addition, the main findings of this study are dependent on the definition of heat stress parameter. In this study, wet heatwave is measured by T_w. However, many other heat stress indicators, such as heat index (Rothfusz and Headquarters 1990, Lou and Lau 2019), humidex (Masterson and Richardson 1979), discomfort index (Epstein and Moran 2006), temperature exposure (Jones et al 2015), etc, have also been used in the previous research. Although many of them describe the joint effects of high temperature and humidity, a thoroughly and unified assessment is needed to reduce uncertainty in the future.

Overall, it is important to recognize regional differences in setting heatwave adaptation policies to reduce the impacts of wet heatwaves. Our main finding is that summer-mean temperature changes are the main driver for changes in wet HWA in Eurasia. Thus, one can hypothesize, with global warming, such increases in wet heatwave events will continue into future leading to more severe society impacts especially dealing with human health. Based on two greenhouse gas scenarios, Pal et al (2016) suggests that by the end of the century (2071–2100), annual T_w in parts of southwest Asia (e.g. Dhabi, Dubai) was projected to exceed 35 °C, the threshold which people cannot exposure in for more than 6 h, several times in the 30 years. However, current projections of T_w were mainly based on the variation in the mean T_w, and the research region was usually small and not suitable for identifying the regional differences across a large scale. More research is needed to understand the impacts of extreme wet heat events now and in the future, and different mitigation measures should be considered in different regions.

S Y and Z Y were supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20020201) and the Ministry of Science and Technology international collaboration (Grant No. 2017YFE0133600). S Y carried out this work as a visiting Student to the University of Edinburgh funded through the UK-China Joint Research and Innovation Partnership Fund PhD. Placement Programme 20180252701. S F B T and N F were supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund.

The data that support the findings of this study are available upon reasonable request from the authors.