Diurnal and seasonal patterns of global urban dry islands

This study uses sub-daily meteorological observations of air and dew point temperature (T_air, T_dew) from HadISD version 3.1.1.2020f (Smith et al 2011, Dunn et al 2012, 2016, Dunn 2019). The HadISD data set is a subset of the Integrated Surface Database of the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (Smith et al 2011, Moustakis et al 2020, 2021) and is synthesized by the Met Office Hadley Centre through the application of additional data quality processes and criteria on data availability. Relative humidity (RH), specific humidity (q), vapor pressure (e), and vapor pressure deficit (VPD) are calculated as a function of T_air and T_dew (SI table 1 available online at stacks.iop.org/ERL/17/054044/mmedia).

The raw data set contains 8258 stations with at least partial data coverage during the analyzed time period of 1 January 1990 to 31 December 2018 (29 years). The raw meteorological time series of the HadISD data set are not formally homogenized. However, a homogeneity assessment, including the time and magnitude of break points for affected stations, is available and provided by Dunn et al (2014). The final set of stations used in this study is selected based on low inhomogeneity magnitudes of breakpoints as well as continuous data availability to compute the diurnal and seasonal cycles. The station selection criteria and data processing are outlined in detail in the supplementary information together with a methodological workflow figure (SI section 1, SI figure 1). The final data set comprises a total of 1089 stations with most of the stations located in the northern mid-latitudes (figure 1). For the further analysis, average diurnal cycles of T_air and humidity variables (q, e, RH, VPD) are calculated for each month in each year for each station. In the following, we will refer to this as YMH_Si data average (SI figure 1).

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Urbanization and climatic trends have co-occurred in the last decades, and both are not evenly distributed over the globe. The correlation of urbanization rate with change in temperature and humidity as done in our study (see section 2.4) could lead to a result, which is not caused by the process of urbanization itself but purely by an artifact of certain climatic trends occurring (or not) in regions experiencing urbanization. Therefore, we use the ERA5 reanalysis product (Hersbach et al 2020), which was bias corrected and aggregated to 0.5° resolution by Cucchi et al (2020) to check for such a co-linearity between trends in background climate and urbanization rate. The land surface scheme used to develop the ERA5 product does not currently include urbanization effects (Balsamo et al 2009, Hogan et al 2017, Bassett et al 2021, Venter et al 2021) and it has been successfully used in previous studies to represent the rural baseline climate (e.g. Bassett et al 2021, Venter et al 2021). The hourly ERA5 data was aggregated identically to the YMH_Si data averages, which includes the calculation of the average diurnal cycle of the meteorological variables for each station for each month in each year.

We extract the percentage of artificial impervious area within a buffer radius of 7 km from each selected meteorological station from an annual data set of global artificial impervious areas (GAIAs) at 30 m resolution (Gong et al 2020, downloaded from http://data.ess.tsinghua.edu.cn, 22 March 2021). The GAIA data set is based on 30 m resolution Landsat images and spans the years 1985–2018. The early years of the data set contain larger uncertainties due to limited data availability (Gong et al 2020). Hence, only the years 1990–2018 (29 years) are used in this study. To determine the appropriate size of the extraction radius, the urbanization rate Δimp around each meteorological station was first calculated with different buffer radii ranging from 1 to 30 km and their correlation with the trends in air temperature and humidity was analyzed for day and nighttime separately during the summer months (June, July, August). The correlation coefficients tend to stabilize between an extraction radius of 6–9 km (SI figure 2) and any of these radii are an acceptable choice for this study. A radius of 7 km was finally chosen for the impervious landcover extraction, which also corresponds to the value used by Luo and Lau (2019). Note that the results presented here were also derived with other extraction radii to confirm that the diurnal and seasonal patterns shown in this study are independent of the exact extraction radius for radii ranging from 2 to 9 km. The magnitude of the calculated UHI and UDI is, however, dependent on the chosen extraction radius as urbanization rates Δimp tend to decrease with increasing analyzed area and thus radius (SI figure 3). Diurnal and seasonal patterns of UHI and UDI calculated for a 4 km and 9 km buffer radius are shown in the supplementary information for comparison (SI figures 4–7). The urbanization rate Δimp (fraction/decade) is calculated as the linear trend of impervious landcover within the chosen buffer radius from each station over the 29 analyzed years (1990–2018) (Luo and Lau 2019). The majority of the urbanizing stations show a fairly linear urbanization trend (SI figure 8). Of the 1089 stations, 590 experience an artificial impervious land conversion rate larger than 1% per decade within the 7 km buffer radius while 499 experience no or negligible urbanization. Spatial distribution of non-urbanizing and urbanizing stations, including their urbanization rates, is shown in figure 1. Note, that some stations with a small urbanization rate can still be surrounded by urban areas, which developed before 1990. However, there is only a small number of stations for which this is the case. There are seven stations with an artificial impervious land cover of more than 10% in 1990 and an urbanization rate of less than 1% per decade.

UDI and UHI are traditionally calculated as the difference of humidity and temperature measured within an urban area and at a rural reference station (Oke et al 2017) and measurements need to be obtained simultaneously in multiple locations within and around a city. However, such simultaneous meteorological measurements within cities and their rural surroundings are limited to a few sites with dense meteorological networks or dedicated measurement campaigns but are lacking at the global scale. In this study, we take an alternative approach to overcome this limitation by focusing on temporal differences in landcover as proposed by Luo and Lau (2019). During the process of urbanization, natural landcover is replaced by urban infrastructure leading to the development of UDI and UHI over time. Meteorological measurements at fixed locations experiencing urbanization in their surroundings are likely measuring trends in temperature and humidity, which differ from trends at rural and non-urbanizing stations nearby and are associated with this landcover change. Following this reasoning, we correlate trends in humidity and temperature measured at the 1089 HadISD stations with their urbanization rates (Δimp) over the analyzed 29 year time period to obtain magnitudes of UDI and UHI (figure 2). Trends in humidity (ΔRH, ΔVPD, Δe, Δq) and air temperature (ΔT_air) are calculated for each station, urbanizing and non-urbanizing, as the linear trend over the 29 year time period (1990–2018). Climatic trends derived for rural HadISD stations are likely associated to climate change, while climatic trends in urbanizing areas include both climate change as well as urbanization effects in the HadISD data set. Monthly ERA5 trends for each station are subtracted from the monthly HadISD trends. By means of this correction, we remove any climate change signal and any trends resulting from a co-linearity between background climate trends and urbanization rate, and we focus only on urbanization effects as further explained in the supplementary information (SI section 2, SI figures 1, 9–13).

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Magnitudes of UDI and UHI and climate change effects can be derived from the slope and intercept parameters of the linear regression between urbanization rate (Δimp) and climate trends (ΔRH, ΔVPD, Δe, Δq, ΔT_air) as shown here for the example of T_air: ΔT_air = S_urb × Δimp + CC. The slope S_urb is the UHI magnitude, which is expected to develop if a location experiences complete urbanization starting from rural conditions (i.e. an increase from 0% to 100% of artificial impervious areas within a radius of 7 km from the station). The linear regression in figure 2 shows an intercept (CC) close to 0 as the average climate change effects are removed with the ERA5 correction. Statistical significance of UDI and UHI magnitudes are determined by means of a two-sided t-test at the 5% significance level (i.e. p < 0.05 indicating a slope S_urb different than zero).

UDI and UHI magnitudes shown in figures 3–5 correspond to the slope of the linear regression for each season and hour of the day. Winter comprises the months December to February (June to August), spring the months March to May (September to November), summer the months June to August (December to February), and autumn the months September to November (March to May) for the northern (southern) hemisphere. Daytime includes the hours from 7 to 18 local time (LT) and nighttime the hours 19–6 LT. As most of the stations are located in the northern hemisphere, figures 3–5 display the northern hemisphere months of the seasonal UDI and UHI effects. However, the southern hemisphere stations are also included in the displayed results in figures 3–5 with the appropriate correction (i.e. the southern hemisphere month January is included in the results for the northern hemisphere month July and so forth).

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To analyze differences in UDI according to the aridity level of the background climate, we split the dataset into stations with wetness index WI < 1 and WI > 1, corresponding to climates, in which evapotranspiration is water or energy limited, respectively. The WI is calculated as the ratio between mean annual precipitation and mean annual potential evapotranspiration (WI = P/PET) and the corresponding data is extracted from the Climatic Research Unit gridded Time Series (CRUTS4.05) data set (Harris et al 2020).

Note that positive UHI values denote higher T_air in urban areas compared to natural land surfaces. Less humidity in cities is shown by negative UDI_q , negative UDI_e , negative UDI_RH, and positive UDI_VPD. For the following discussion, we introduce the terms 'absolute UDI' and 'relative UDI'. Absolute UDI includes UDI_q and UDI_e , which specify the change in actual water content in the air. Relative UDI comprises UDI_RH and UDI_VPD.

Observed changes in humidity in cities can be attributed to changes in T_air caused by UHI and changes in humidity supply because of the increase in artificial impervious area, leading to reduced water availability for evapotranspiration. To separate these two causes, we partition the measured UDI_VPD (δVPD/δimp) into changes associated to an increase in e_sat caused by the UHI (de_sat/dT_air × δT_air/δimp) and to a change in e (δe/δimp):

$$\begin{align*}\frac{{\delta {\text{VPD}}}}{{\delta {\text{imp}}}} = \frac{{{\text{d}}{e_{{\text{sat}}}}}}{{{\text{d}}{T_{{\text{air}}}}}}{ }\frac{{\delta {T}_{{\text{air}}}}}{{\delta {\text{imp}}}} - { }\frac{{\delta e}}{{\delta {\text{imp}}}}\end{align*}$$

where d and δ denote path-independent (exact) and path-dependent (inexact) differential operators. e_sat is calculated as a function of T_air according to SI table 1.

We find a statistically significant UDI at the annual scale for both absolute and relative humidity measures. Annual average RH decreases by 4.8%, VPD increases by 1.22 hPa, e decreases by 0.78 hPa, and q decreases by 0.5 g kg⁻¹ at a full replacement of natural landcover by artificial impervious areas within a radius of 7 km from the meteorological stations (figure 2, SI figure 14). The changes in annual average T_air due to the increase in impervious area from 0% to 100% is +0.23 °C and not statistically significant with a p-value of 0.094 (t-test for zero slope, figure 2), indicating that the UHI effect is not apparent on the annual average based on the current data set and methodology. Note, that while a UHI magnitude of +0.23 °C at a full replacement of natural landcover seems small, this number represents the annual average UHI, including all weather conditions, hours of the day and seasons. At the diurnal and seasonal scale, larger UHI intensities are found (see section 3.2) with magnitudes similar to the ones reported by other large scale studies (e.g. Venter et al 2021).

While there is no statistically significant UHI detected at the annual scale at a significance level of 0.05 (section 3.1), we recover the typical diurnal pattern of UHI, with higher T_air at night and negligible UHI during daytime (Oke et al 2017, Venter et al 2021). The nighttime UHI is largest during the warm period of the year with a magnitude of 0.5 °C to 1.4 °C from April to September (October to March) in the northern (southern) hemisphere and negligible during winter. The same seasonal pattern is also observed in large scale studies quantifying surface UHI throughout the year (Zhou et al 2013, Manoli et al 2020, Paschalis et al 2021). The UHI effect is not detected during daytime throughout the year (figures 3 and 4).

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The absolute UDI is largest during the warm period (from May to September and November to March in the northern and southern hemisphere, respectively) ranging from −0.9 to −1.1 g kg⁻¹ and −1.4 to −1.7 hPa during day and −0.4 to −0.75 g kg⁻¹ and −0.6 to −1.2 hPa during nighttime for UDI_q and UDI_e, respectively. Statistically significant UDI_q and UDI_e are also detected during some winter months but with a smaller magnitude (figures 3 and 4). Absolute UDI is largest during the daytime and in the early evening hours reaching a peak of −1.4 g kg⁻¹ and −2.1 to −2.2 hPa from 17 to 19 h LT in summer for UDI_q and UDI_e , respectively. Absolute UDI is minimal from midnight to the early morning hours or even noon, depending on the season (figures 3 and 4, SI figure 15).

In contrast, relative UDI magnitudes are larger during night compared to daytime. Peak values range from −10% to −11% and 2.9 to 3.6 hPa between 20–00 LT during summer for UDI_RH and UDI_VPD, respectively. Diurnal differences of relative UDI magnitudes are larger for UDI_RH than for UDI_VPD. UDI_VPD is largest during the warm season ranging from 1.4 to 2 hPa and 1.6 to 2.7 hPa during May to September for day and nighttime, respectively. Similarly, nighttime UDI_RH shows a distinct seasonal cycle with larger nighttime UDI_RH values during the warm period (−8% to −10% from May to September). Daytime UDI_RH is smaller and does not show a distinct seasonal pattern.

In summary, absolute UDI is largest during the warm season and daytime with a peak in the late afternoon and early evening hours. Relative UDI is largest during the warm season and at nighttime with a peak in the late evening hours. Diurnal differences are more distinct for UDI_RH than for UDI_VPD and daytime UDI_RH does not show a strong seasonal dependence.

The separation of stations using WI < 1 and WI > 1 showed a larger absolute and relative UDI for wet climates during summer and daytime (SI figure 16). However, the results are dependent on the selected WI cut-off point (section 2.4). Hence, the here reported differences of UDI with WI are deemed not robust and require larger datasets to be fully analyzed.

Relative UDI, here shown using UDI_VPD, are partly caused by UDI_e and UHI, which leads to an increase in e_sat. Partitioning of UDI_VPD shows that both, UHI and UDI_e , have a substantial contribution to the overall UDI_VPD during the warm season. Following the diurnal patterns of UHI and UDI_e , UDI_VPD is largely caused by UDI_e during daytime and by UHI from midnight to the early morning hours during summer (figure 5(a)). During the late evening hours in summer, UDI_VPD is caused by both UHI and UDI_e in roughly equal magnitude leading to peak values of UDI_VPD during this time. Due to the absence of a significant UHI, wintertime UDI_VPD is almost only caused by UDI_e and is much smaller in magnitude (figure 5(c)).

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Current UDI literature, based on the analysis of single cities (e.g. Cuadrat et al 2015, Lokoshchenko 2017, Yang et al 2017) or urbanized regions (e.g. Hao et al 2018, Luo and Lau 2019), reported diverging findings, ranging from a drier urban environment, i.e. UDI (e.g. Moriwaki et al 2013, Luo and Lau 2019), to air with higher moisture content in cities and thus a UMI (e.g. Lee 1991, Wang et al 2021). While parts of these differences are likely caused by site specific environmental conditions, such as anthropogenic water sources (e.g. Huang et al 2021, Wang et al 2021) or regional moisture transport (e.g. Du et al 2022), the distinction between relative and absolute UDI, which are assessed by relative humidity metrics (RH, VPD) and by metrics specifying the absolute water content in the air (e, q, absolute humidity), respectively, plays an important role in the reported differences. Furthermore, UDIs show a distinct seasonal and diurnal cycle (figures 3 and 4), which can also lead to the presence or absence of UDI, depending on the period of investigation. Our large-scale study, quantifying the diurnal and seasonal patterns of relative and absolute UDI, can generalize the UDI behavior for the first time at the global scale.

When analyzing urban–rural differences in relative humidity measures, such as RH and VPD, results agree amongst different studies. Relative UDI has been reported in cities around the globe with various background climates such as Beijing (Yang et al 2017) and the urban agglomeration of the Yangtze River Delta in China (Hao et al 2018, Luo and Lau 2019), Zaragoza in Spain (Cuadrat et al 2015), Moscow in Russia (Lokoshchenko 2017), Berlin in Germany (Langendijk et al 2019), Lodz in Poland (Fortuniak et al 2006), Chicago in the USA (Ackerman 1987), and Edmonton in Canada (Hage 1975). The results of our large-scale analysis confirm and generalize these results by showing higher relative UDI during night compared to daytime (e.g. Yang et al 2017) and during the warm compared to the cold season (e.g. Langendijk et al 2019, Luo and Lau 2019).

However, literature results start to diverge when assessing absolute UDI, with studies showing both the presence of UDI (e.g. Moriwaki et al 2013, Luo and Lau 2019) and UMI (e.g. Lee 1991, Wang et al 2021). The occurrence of either UDI or UMI is dependent on time of the day and season. Absolute UDI was found mostly during daytime and summer (Hage 1975, Tapper 1990, Lee 1991, Moriwaki et al 2013), while UMI is mostly reported at night and during winter (Hage 1975, Tapper 1990, Lee 1991) except for a study focusing on Hong Kong (Wang et al 2021), which has a strong UMI likely caused by substantial anthropogenic moisture sources in the city (Huang et al 2021) and regional moisture transport (Du et al 2022). While our large-scale analysis does not find a significant UMI, the same distinct diurnal pattern is shown with the largest absolute UDI during daytime and a small to non-existent absolute UDI at night. At the seasonal time-scale, our results also generalize previous results showing higher values of UDI during the warm season compared to winter (e.g. Moriwaki et al 2013, Luo and Lau 2019).

Warmer air, such as caused by the UHI, increases e_sat and can cause relative UDI with a minimal change in absolute moisture in the air as shown for nighttime conditions. However, warmer air is also capable to hold more moisture in the absence of water supply limitations (e.g. Shuttleworth 2012). Hence, UHI might cause absolute UMI as reported in some studies (Hage 1975, Tapper 1990, Lee 1991) if ample moisture is available due to local moisture sources and evapotranspiration (Huang et al 2021), or lateral advection of moisture (Du et al 2022). Higher temperatures in cities and different surface properties can also lead to the absence or a delayed dewfall causing an absolute UMI (Kuttler et al 2007). Hence, due to the higher water holding capacity of warmer air, a small absolute UDI in correspondence with considerable UHI does not necessarily indicate that impervious land cover does not influence local atmospheric moisture, but simply that UHI partially offsets absolute UDI. This is shown by the presence of a high relative UDI as seen in this study at night and also often reported in literature (e.g. Cuadrat et al 2015, Yang et al 2017, Hao et al 2018, Luo and Lau 2019) even in the absence/presence of absolute UDI/UMI. However, the influence of limited evapotranspiration on humidity in cities, caused by impervious landcover, is best shown during daytime due to the absence of UHI and higher evapotranspiration rates. Daytime absolute UDI as well as higher absolute UDI in summer than winter suggest that evapotranspiration differences between urban and rural areas are very likely causing such an absolute UDI (see also Hao et al 2018).

In contrast to UHIs (e.g. Manoli et al 2019, Venter et al 2021), large scale studies quantifying diurnal and seasonal patterns of UDI and UMI magnitudes have been absent even though altered humidity in cities can similarly impact human health, building energy consumption, and urban ecology.

Heat stress is influenced by both temperature and humidity (Mora et al 2017, Coffel et al 2018) and especially, in hot and humid regions, an increase in humidity can decrease human outdoor thermal comfort (OTC) (Chow et al 2016, Meili et al 2021a). While urbanization increases UHI (Huang et al 2019), the concurrent intensification of the UDI was found to offset some of the anticipated increase in hot and humid heat stress in summer in Beijing (Wang and Gong 2010). On the other hand, a large-scale increase in irrigation, albeit not in cities, has been shown to enhance moist heat stress extremes in India due to the increase in humidity (Mishra et al 2020). Studies like these show that not just high temperatures and UHI but also humidity and UDI can impact OTC, heat stress, and consequently human health. Hence, further studies are required to systematically quantify the effects of the observed magnitude of UDI reported here on heat stress, which could be especially relevant as UDI are largest during the warm season and daytime (figures 3 and 4), when heat mitigation is mostly needed.

Heat mitigation strategies, such as urban greenery, influence both UHI and UDI. Increasing humidity due to plant evapotranspiration could limit the improvement in OTC associated with urban vegetation (Hass et al 2016, Meili et al 2021a). At the same time, high VPD, likely exacerbated by UDI in cities, can impact plant stomatal response (Chen et al 2011, Novick et al 2016, Gillner et al 2017, Winbourne et al 2020) potentially limiting the evpotranspirative cooling potential of urban vegetation during periods of intense heat (Meili et al 2021b). These counteracting processes of reduced evapotranspiration due to stomatal closure at times of high VPD and the general increase in humidity by evapotranspiration in cities during normal VPD conditions are often neglected in studies predicting vegetation effects on urban climate. Only few urban microclimate models include detailed vegetation formulations (e.g. Nice et al 2018, Meili et al 2020) and further measurements and modelling studies are needed to understand the complex interaction between urban humidity, plant transpiration, and vegetation benefits during heatwaves.

Higher temperatures in cities can also lead to an increase in building energy demand for cooling (Santamouris 2014). Recent studies have shown that making predictions based on air temperature alone without the consideration of humidity can lead to an underprediction of up to 10%–15% of present and future building cooling demand (Maia-Silva et al 2020). In hot and humid regions, Fonseca and Schlueter (2020) found that the increase in energy usage for building cooling is largely driven by higher needs for dehumidification rather than cooling itself. This is of special importance as tropical and sub-tropical cities have a large potential for an increase in energy usage for cooling in the future (Waite et al 2017). Due to the impact of humidity on the quantification of building energy cooling demand, UDI and UMI magnitudes thus require careful evaluation, and their impacts need to be considered in future studies.

The UDI and UHI magnitudes calculated in this study are averages over many cities and hence, depend on the ensemble of stations used. As most of the rural and urban meteorological stations are located in the northern mid-latitudes, the trends observed here are representing predominantly UDI and UHI magnitudes of mid-latitude cities in North America, Europe, and Asia. As discussed, humidity can have a large impact on moist heat stress and on energy usage for dehumidification in hot and humid regions, which suggests studies characterizing the urban humidity environment and UDI in tropical cities are urgently needed. To analyze possible differences in UDI due to background aridity, we analyzed trends for stations with WI < 1 (water limited) and WI > 1 (energy limited), separately. However, the results (SI figure 16) are highly dependent on the chosen WI cut-off point and hence are deemed not very robust. Furthermore, urbanization is approximated in this study by the increase in artificial impervious areas without consideration of city structure and urban fabric, number of inhabitants or other site-specific characteristics, which could affect the magnitude of UHI and UDI. However, due to the very large number of stations used, our approach is expected to average out many of these city-level differences and provide an 'average' estimate of UDI magnitude and patterns.

While the land surface scheme used in the construction of the ERA5 reanalysis product does currently not simulate urban areas (Balsamo et al 2009, Hogan et al 2017, Bassett et al 2021, Venter et al 2021), the ERA5 product could still include implicitly urbanization due to air temperature assimilation from weather stations. However, such effects have been found to be negligible in a study quantifying UHI trends in London with the use of ERA5 (Bassett et al 2021).

As the increase in artificial impervious area is quantified within a radius of 7 km around each meteorological station, the reported results are representative of the large-scale urbanization effect on the atmospheric conditions and thus of mesoscale UDI and UHI changes. This means that the results do not represent the effects of small scale-land cover change (e.g. a local parking lot, increase in building heights, construction of a pond) on local microclimate, which might exist in some of the analyzed stations. We further would like to highlight, that while the diurnal and seasonal patterns presented in this study do not change with changing buffer radius in the range of 2–9 km, the absolute magnitude of UHI and UDI is dependent on the selected buffer radius as urbanization rates tend to decrease at higher buffer radii (SI figure 3). Magnitudes of diurnal and seasonal patterns of UHI and UDI calculated for a 4 km and a 9 km buffer radius are shown in the supplementary information (SI figures 4–7). Further note that the horizontal extent of urban temperature effects can extend beyond the city boundary (Fan et al 2017) and hence, some stations could also be influenced by adjacent regions with large land conversion rates. Last, in our analysis, a fully urbanized area not undergoing any landcover change during the study period has an urbanization rate of zero equal to a natural area not undergoing urbanization. We also assume that the relationship between urbanization and the formation of UHI and UDI is linear and that there is no interaction between the absolute fraction of impervious urban land cover and the climate change signal. However, rural and urban stations could respond differently to an overall changing climate as Li and Bou-Zeid (2013) have shown that there could be a synergistic interaction between UHI and heat waves increasing temperatures in cities beyond the simple sum of the UHI and the heat wave signal. While we expect some of these limitations to affect the magnitude of the computed UHI and UDI, the seasonal and diurnal patterns are much more robust as shown by the sensitivity analysis to the buffer radius.