Increased nighttime land surface temperatures in a seasonally muggy climate

Increased nighttime land surface temperatures in a seasonally muggy climate | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Increased nighttime land surface temperatures in a seasonally muggy climate Nkosi Muse, Brian McNoldy, Amy Clement, Katharine Mach This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5380761/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Climatic Change → Version 1 posted 5 You are reading this latest preprint version Abstract Globally, increasing air moisture limits not only the human body’s ability to cool down, but also the Earth’s surface. A gap remains, however, in understanding how limited surface cooling can quantitatively impact metrics of thermal comfort. Here, we examine how air moisture influences nighttime land surface temperatures (LSTs) and thermal comfort during summer months across the Florida peninsula, during which time air moisture reaches the highest values of any place in the United States. For June–August during 2018–2022, we ask: 1) How do urbanicity, measured as impervious surface, and geography, measured as latitude and distance from the coast, modulate air moisture, measured as specific humidity? 2) How does specific humidity limit LST cooling at night? And 3) what are the resulting consequences for thermal comfort? Based on data from 30 weather stations along the peninsula, we find that specific humidity increases closer to the coast and at lower latitudes. In regions with higher air moisture levels, LSTs cool off less at night (as measured at 1 AM), resulting in lower differences between daytime and nighttime (diurnal) LSTs. Elevated nighttime LSTs have pronounced implications for measures of thermal comfort—for every 1.0 °C increase in nighttime LST, nighttime surface air temperatures increase, on average, by 1.1 °C, heat indices by 2.0 °C, and wet bulb temperatures by 0.5 °C. This analysis therefore underscores the importance of heat mitigation and adaptation strategies that reduce elevated nighttime LSTs in seasonally muggy climates, increasing thermal comfort. land surface temperature specific humidity thermal comfort water vapor Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Across subtropical-to-tropical regions, heat hazards are exacerbated by the presence of high amounts of air moisture (i.e., water vapor), especially during the warm season (Raymond et al., 2020). Increased air moisture can limit cooling of the human body, reducing thermal comfort and increasing heat stress (Baldwin et al., 2023; Di Napoli et al., 2023; Hanna & Tait, 2015; Matthews, 2018; K. Zhang et al., 2023). These thermal environments are typically characterized by “muggy” dew point temperatures, frequently used as meteorological proxies for how humid the air is—that is, how much water vapor the air holds. For example, in “seasonally muggy” climates, where average dew point temperatures remain above 65 °F (~18 °C) during the warm season, the air is considered to contain what the U.S. National Weather Service has labeled “oppressive” amounts of air moisture, associated with dangers to human health (National Oceanic and Atmospheric Administration, n.d.-a). As compared to more arid climates, regions with seasonally muggy climates can have additional consequences for heat hazards overnight through more limited nighttime cooling (Feng et al., 2020; Hwang, 2024; M. Li et al., 2019; Yin et al., 2020). Effective radiative cooling of land surfaces can be inhibited by an intensified greenhouse effect, where higher concentrations of water vapor absorb and reemit more longwave radiation back to the surface (Chang & Zhang, 2019; Dai, 2006; Dong et al., 2019; Held & Soden, 2000; Hossain & Gu, 2016; Liu et al., 2019; Sherwood et al., 2018). Such a phenomenon is especially important in areas that exhibit elevated land surface temperatures (LSTs) (e.g., surface urban heat islands) (Athukorala & Murayama, 2020; Haashemi et al., 2016; Lemoine-Rodríguez et al., 2022; Manoli et al., 2020; Muse et al., 2022, 2024; Peng et al., 2012; Wu et al., 2019; Zhou et al., 2019). In the absence of solar radiation, the surface is a primary source of energy to heat air of the lower atmosphere (Bechtel, 2015; Hulley et al., 2019; Tomlinson et al., 2011). With increased air moisture, elevated nighttime LSTs may, in turn, increase nighttime surface air temperatures (SATs; the ambient temperature that humans experience) and other measures of thermal comfort (e.g., heat index, wet bulb temperature) (Chung et al., 2020; Hulley et al., 2019; Shandas et al., 2019; Yao et al., 2011; Zeng et al., 2015). These factors could also potentially exacerbate daytime heat hazards into the following day (Muse et al., 2024). Such a thermal environment in seasonally muggy climates can be dangerous to human health, as stress on the body is exacerbated by a lack of respite from heat hazards across the diurnal cycle (Di Napoli et al., 2023; He et al., 2022; Raymond et al., 2021; Rogers et al., 2021). These relationships among air moisture, LST, and thermal comfort, however, remain understudied across regions with seasonally muggy climates. Understanding the relationships among air moisture, LST, and thermal comfort can inform heat response priorities in seasonally muggy climates. If thermal comfort is decreased as a result of higher nighttime LSTs in seasonally muggy climates, reducing surface absorption of daytime solar radiation in ways that ensure that air moisture is not further increased becomes an important consideration for local heat mitigation. Such a consideration would be of increased importance in muggy climates as opposed to drier climates, where a dry atmosphere more freely allows for the reemission of longwave radiation through the lower atmosphere. Potential response options include the expansion of tree canopy cover focused on species with lower evapotranspiration rates (Chàfer et al., 2020; Qiu et al., 2013; Yang et al., 2023; Y. Zhang & Dai, 2022), measures to increase albedo or surface reflectance (Akbari et al., 2001; Priyadarsini et al., 2008; Taha et al., 1988), or cool roofs (Takebayashi & Moriyama, 2007; Wang et al., 2022; Zonato et al., 2021). These response options can also improve thermal comfort during the day for urban outdoor workers and commuters using public transit (e.g., at bus and train stops). In addition, these responses can reduce the need for energy-dependent cooling methods such as air conditioning and thereby decrease energy burdens, emissions, and heat inputs into urban heat islands (Akbari et al., 2001; X. ‘Cathy’ Li et al., 2024; Mohammed et al., 2024; Taha et al., 1988; Zhao & Zhang, 2023). Further, ensuring that buildings and homes are weatherized in ways that equitably maintain and insulate cooler and drier air indoors, as well as limit contributions of excess heat to the lower atmosphere, is also of high priority in seasonally muggy climates, where nighttime outdoor temperatures are a greater hazard (Litardo et al., 2022; National Oceanic and Atmospheric Administration & Center for Disease Control and Prevention, n.d.; Stone & Rodgers, 2001; Vijayan et al., 2022; Yurchenko et al., 2019). Using the case of the seasonally muggy Florida peninsula, this study seeks to examine the relationships among air moisture, LST, and thermal comfort across summer months (June–August, 2018–2022), where dew points remain well above 18 °C. Using data from 30 quality-controlled weather stations, we ask: 1) How do urbanicity and geography modulate air moisture as measured as specific humidity? As opposed to a dew point temperature (°C), the amount of moisture in the air can be quantified as specific humidity (g/kg)—the ratio of the mass of water vapor to the mass of moist air. Urbanicity is measured as impervious surface and geography as latitude and proximity to the coast. 2) How does air moisture limit LST cooling at night? And 3) what are the consequences of elevated LSTs for thermal comfort at night in seasonally muggy climates? Thermal comfort is assessed through three commonly used measures: SAT, heat index, and wet bulb temperature. The results of this study provide insight into mechanisms behind exacerbated heat exposures in regions that experience seasonally muggy climates, which can inform appropriate heat mitigation and risk reduction strategies. In addition, this study has important applications for future climate adaptation planning as water vapor concentrations increase under the implications of global warming. Seasonally muggy, subtropical-to-tropical regions with increased air moisture may require unique heat responses as compared to more arid or temperate regions. 2. Materials and Methods 2.1. Study Area The Southeastern United States is, on average, the warmest and most humid region of the country (Carter et al., 2018). With an annual mean temperature of 21.5 °C (70.7 °F), Florida is not only the warmest state in the Southeast region but the warmest across the continental United States (National Oceanic and Atmospheric Administration, 2023). The state also experiences the country’s highest nighttime, or minimum, temperatures (US Department of Agriculture, 2023). While most of the state falls under a humid subtropical classification (Cfa), South Florida experiences the most tropical climate across the continental United States (Koppen-Geiger climate classifications, Aw: tropical savanna, Am: tropical monsoon, and Af: tropical rainforest) (Beck et al., 2023; Kottek et al., 2006). Florida’s unique geography along the peninsula greatly influences the state’s climate and weather conditions (Cloutier-Bisbee et al., 2019; Winsberg, 2011). The Gulf of Mexico to the west and the Atlantic Ocean to the east maintain the state’s highest levels of air moisture across the U.S., especially during summer months (Fig. 1) (Zierden & Griffin, 2014). 2.2. Data LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument were retrieved from the NASA Terra satellite. The MODIS instrument orbits Earth at 1-to-2-day cycles, capturing LST data at 1-kilometer resolution within 36 spectral bands (National Aeronautics and Space Administration, 2023). More specifically, MOD11A2 Version 6 data were used, where each 1-kilometer pixel represents an average 8-day LST value in Kelvin (Wan et al., 2015). Although other satellites such as Landsat and ECOSTRESS capture LST data at higher resolution, MODIS captures both daytime and nighttime LST most consistently. MODIS data are cloud masked before download from the USGS EarthExplorer website (USGS, 2023), and 8-day average LST provides the best surface coverage with reduced cloud interference. The Florida peninsula falls within horizontal tile 10 and vertical tile 6 of the Terra satellite’s record database. 112 scenes were downloaded and utilized for this study. Observational data from 30 peninsular Florida weather stations were used for this study (Fig. 1; Table S1). Geographic data, SAT, and parameters for the calculation of specific humidity and other thermal comfort indices were downloaded from Iowa State University’s IEM ASOS Network daily across the study period (June–August, 2018–2022) at hourly intervals for each weather station (Iowa State University, 2023). Weather stations were classified by latitudinal region based on approximate USDA plant hardiness zones for minimum temperatures (US Department of Agriculture, 2023). North Florida stations ( n = 10) are located at a latitude above 28.5 °N, Central Florida ( n = 10) at 28.5 °N to 27 °N, and South Florida ( n = 10) less than 27 °N. Available impervious surface data for 2021 and 2019 were downloaded from the National Land Cover Database (NLCD) at 30-meter resolution (National Land Cover Database, 2023). Each 30x30m pixel value represents a percentage of impervious surface (0–100%) for that area. 2.3. Methodology This study used airport weather station data and MODIS satellite imagery to examine the role of air moisture for nighttime cooling across the seasonally muggy Florida peninsula during summer months (June–August, 2018–2022). LST data, processed in ArcGIS Pro software (Environmental Systems Research Institute (Esri), 2021), were converted to Celsius (°C) with a scale factor of 0.02. Statistical analyses were performed to explore the degree to which urbanicity and geography modulate specific humidity across the study region. Nighttime LST across the peninsula was then examined to understand the role that air moisture plays in limiting surface cooling overnight. The relationships between elevated nighttime LST and thermal comfort indices (SAT, heat index, wet bulb temperature) were also explored. All statistical analyses were performed in R (Posit team, 2024). 2.3.1. Specific Humidity Specific humidity ( q ) is the mass of water vapor per mass of moist air (American Meteorological Society, 2022). Specific humidity in Fig. 1 was estimated from Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group dew point data at 800 meter resolution using elevation and barometric pressure (PRISM Climate Group, 2023). For airport station data analysis, specific humidity was calculated utilizing equations from Bolton (Bolton, 1980): 2.3.3. Statistical Analysis Multiple linear regression was first performed to understand the relationships between specific humidity and relevant predictor variables. Predictor variables included proximity to the coast (kilometers), impervious surface (%), and latitude. Average specific humidity was calculated at each station (2018–2022) from daily observations. Impervious surface represents the mean value of pixels (% impervious surface) within a census tract that a weather station is located in. Second, correlations between nighttime LST and nighttime specific humidity were determined. Average nighttime (7 PM to 7 AM local time) specific humidity values (2018–2022) were compared to mean nighttime LST values measured at each station. Mean nighttime LST pixels were calculated for 2018–2022 across the peninsula. Mean nighttime LST pixels were then aggregated to their mean value within a 1-kilometer buffer around each weather station for comparison to specific humidity. The relationship between diurnal LST (difference between daytime and nighttime LST) and specific humidity was also explored. Diurnal LST was calculated by subtracting mean nighttime LST pixels from daytime LST pixels across the peninsula. Pixels that contain diurnal LST values were then aggregated to their mean value with a 1-km buffer for comparison to specific humidity. Lastly, to understand the role of nighttime LST in nighttime thermal comfort, nighttime LST was compared to nighttime SAT, heat index, and wet bulb temperature at each station. Since MODIS nighttime imagery is captured at 1 AM, nighttime LST was compared to 1 AM SAT, heat index, and wet bulb temperature observations. 3. Results 3.1. Modulating Factors for Specific Humidity Specific humidity (2018-2022) increases as latitude and distance to the coast decrease (Fig. S1). Decreasing latitude results in the largest increase in specific humidity, as represented by the coefficient of the largest magnitude (-0.14) among predictor variables (Table 1). More specifically, a unit decrease in latitude southwards produces approximately a 0.14 g/kg increase in average specific humidity. Along the latitudinal range of peninsular Florida’s weather stations (24.57N to 29.95N), average specific humidity has a range of only 1.68 g/kg (18.77 g/kg to 17.09 g/kg)—highlighting the elevated amounts of water vapor the entire region experiences during summer months. Proximity or distance to the coast exhibits a similar relationship, with a smaller coefficient (-0.01). Each kilometer increase in distance from the coast produces approximately a 0.01 g/kg decrease in specific humidity. Across stations, air near the station furthest from the coast (94.9 km) contains 0.9 g/kg less air moisture as compared to air near the station closest to the coast (0.26 km), without accounting for latitudinal impacts. Impervious surface was found to be statistically insignificant in the multiple regression model (Table 1). Table 1 Florida peninsula average daily specific humidity vs predictor variables. The relationships for specific humidity and latitude, impervious surface, and coastal proximity are provided for 30 weather stations across the peninsula. Each coefficient can be interpreted a unit change in specific humidity per a unit increase in a variable, while other variables are held constant (e.g., a kilometer increase in distance from the coast equals a -0.006 decrease in g/kg of average specific humidity) Independent Variable Coefficient Std. Error p-value Latitude* -0.140 0.050 p = 0.009 Impervious Surface (%) -0.001 0.003 p = 0.881 Proximity to coast (km)* -0.006 0.003 p = 0.059 *Statistically significant (p < 0.1) Constant Adjusted R-Squared Model p-value 21.85* 0.38 p = 0.00135 3.2. Nighttime LST and Specific Humidity Nighttime LST is higher where specific humidity is higher (Fig. 2). Nighttime LST increases by 1.30 °C per unit increase in specific humidity (g/kg) (Fig. 2b). The highest nighttime LSTs, upwards of 25.0 °C, coupled with the highest levels of air moisture (above 18 g/kg), are located at stations of lower latitude and along peninsular coasts (Table 2). Nighttime LST decreases by 0.02 °C per kilometer increase in distance from peninsular Florida coasts (Fig. 2c). As expected, nighttime LSTs also remain higher in more urbanized areas with increased imperviousness (Table 2). Urban development is most extensive and continuous along the southeastern coast, where the state’s largest metropolitan region by population is located (West Palm Beach–Fort Lauderdale–Miami). As such, this region particularly stands out as warmest, with nighttime LSTs near 25.0 °C, on average. Developed regions with increased impervious surface across the peninsula are also closer to the coast, on average, where specific humidity is increased (Table 2). Stations situated in census tracts with greater than or equal to 10 percent impervious surface are on average 17 kilometers away from the coast, while stations situated in tracts with less than or equal to 10 percent impervious surface are on average 42 kilometers away from the coast. Table 2 Average specific and relative humidity (daily average), mean nighttime (1 AM) and diurnal LST (daytime, 3 pm – nighttime) and average nighttime (7 PM–7 AM) thermal comfort measures by state region (North, Central, or South Florida), distance to coast, and urbanicity (June–August, 2018-2022) as measured at Florida peninsula weather stations ( n = 30) Region Average Daily Specific Humidity (g/kg) Average Daily Relative Humidity (%) Mean Nighttime LST (°C) Mean Diurnal LST (°C) Average Nighttime SAT (°C) Average Nighttime Heat Index (°C) Average Nighttime Wet Bulb Temperature (°C) North Florida 17.60 82.11 23.46 9.22 24.67 25.55 23.51 Central Florida 17.67 78.88 23.64 10.77 25.59 27.11 23.83 South Florida 18.22 77.14 24.76 9.31 26.76 29.24 24.53 All Regions (Peninsula) 17.83 79.38 23.96 9.77 25.67 27.3 23.96 < 10 km from coast 18.09 77.96 24.56 8.95 26.44 28.67 24.31 ≥ 10 km from coast 17.57 80.80 23.34 10.59 24.91 25.92 23.6 < 10 % Impervious Surface 17.64 81.99 23.06 9.84 24.62 26.53 23.54 ≥ 10 % Impervious Surface 17.93 78.07 24.40 9.73 26.20 27.57 24.16 Higher nighttime LSTs also correspond with decreased diurnal LSTs, or lower differences between daytime and nighttime LSTs (Fig. 3b). For every 1.0 °C increase in nighttime LST, diurnal LST decreases by 0.61 °C. Less urban areas, surprisingly, exhibit slightly smaller diurnal differences in LST than urban areas (Table 2). Because increased impervious surface leads to higher daytime LSTs, in many cases it can be assumed that the potential range between daytime and nighttime LSTs could be larger, as less urban areas have less heat to release during evening hours. However, stations in tracts with increased impervious surface are closer to peninsular coasts where specific humidity is also increased (Table 2), potentially offsetting the role of increased urbanicity increasing diurnal LSTs. Despite this finding, pockets of higher diurnal LSTs (greater than 10 °C) are still most prevalent in the peninsula’s most urbanized areas (Fig. 3a). Regardless of urbanicity, North Florida exhibits the smallest mean diurnal LST measured across regional stations, but also exhibits the lowest mean daytime LST across regional stations (32.68 °C; 34.41 °C and 34.10 °C in Central and South Florida, respectively). 3.3. Nighttime and Diurnal LST vs Nighttime and Diurnal Thermal Comfort Indices Nighttime thermal comfort decreases where nighttime LST is increased (Table 2). Nighttime LST is significantly and positively correlated with nighttime SAT, heat index, and wet bulb temperature (Fig. 4). Across the peninsula, nighttime SAT increases 1.1 °C for every 1.0 °C increase in nighttime LST, and nighttime LST explains 85% of the variance in nighttime SAT (Fig. 4d), which is expected since LST represents a primary source of energy to heat the lower atmosphere. SAT is also a key component of both the heat index and wet bulb temperature. Compared to nighttime LST, nighttime heat index values increase at an even greater rate than nighttime SAT (2.0 °C for every 1.0 °C increase) (Fig. 4e). Nighttime wet bulb temperature is least impacted and explained by nighttime LST (slope = 0.45, R = 0.78), but still with a positive relationship (Fig. 4f). Wet bulb temperature thresholds for human health concerns also occur at lower values. Although average nighttime wet bulb temperature does not reach the “dangerous threshold” across weather stations, this can become of concern in the near future as temperatures and air moisture concentrations increase globally. Elevated nighttime LSTs decrease thermal comfort to a greater extent at lower latitudes and closer to the coast. The warmest heat index values fall above the “extreme caution” threshold (NOAA, n.d.) at mean nighttime LSTs above 25 °C at lower latitudes (Fig. 4b, 4e). Wet bulb temperatures near “dangerous” thresholds can also be associated with higher nighttime LSTs at lower latitudes (Fig. 4c, 4f). In addition, stations with the lowest levels of thermal comfort are closer to the coast (Fig. 4d, 4e, 4f). The largest regression coefficients or slopes across all thermal comfort measures can be found in Central and South Florida. Nighttime LST has a similar impact on SAT in both Central and South Florida, increasing SAT by over 1.0 °C per degree increase in LST (Fig. 4a). Nighttime LST in has a slightly stronger impact on the nighttime heat index in South Florida, increasing the heat index by over 2.0 °C per degree increase in LST (Fig. 4b). Wet bulb temperature, however, is most impacted by nighttime LST in Central Florida (Fig. 4c). Diurnal values in thermal comfort measures are also smaller in regions where nighttime LSTs are increased (e.g., South Florida and areas closer to the coast) (Fig. S2, Table S2). However, diurnal wet bulb temperature displays less sensitivity to areas of increased nighttime LST compared to diurnal SAT and heat indices. This is consistent with results shown in Fig. 4f, where wet bulb temperatures show less of a response to changes in nighttime LSTs. Despite higher urbanicity increasing diurnal LST across the peninsula, thermal comfort measures exhibit smaller differences between daytime and nighttime observations with increased impervious surface. 4. Discussion Higher levels of air moisture exacerbate heat hazards, especially in regions that experience “seasonally muggy” climates. In this study, we demonstrate the role that air moisture, as represented by specific humidity, plays in limiting surface cooling and decreasing thermal comfort across the very muggy, summertime Florida peninsula. The results of this research show lower latitude regions and areas closer to the coast exhibiting increased nighttime LSTs and decreased diurnal differences in LST that correspond to higher levels of observed specific humidity. As expected, increased impervious surface leads to increased nighttime LSTs, and subsequently higher nighttime SATs, heat indices, and wet bulb temperatures. Therefore, increasing levels of air moisture with global warming could continue to maintain and amplify heat hazards overnight, especially in urban areas where surface energy balances are increased. The results of this study highlight water vapor’s localized greenhouse effect in the lower atmosphere (Chang & Zhang, 2019; Dai, 2006; Dong et al., 2019; Held & Soden, 2000; Hossain & Gu, 2016; Liu et al., 2019; Sherwood et al., 2018). In a peninsular region at lower latitudes, intense solar radiation that heats the surface inducing high rates of evaporation (Becker & Boyd, 1957; Bhatia, 2014; Muse et al., 2024; NASA, n.d.; Roca et al., 2010) from surrounding waters can be linked to the region’s unique heat hazards. Land-sea interactions, like the ones along peninsular Florida’s coasts, are defined by an advection of abundant moisture inland due to the onshore-offshore gradient in temperature (Estoque, 1962; Misra et al., 2011; Pielke, 1973). Such a phenomenon, similar to much of the Caribbean (Cloutier-Bisbee et al., 2019; Di Napoli et al., 2023), results in an intensified local greenhouse effect that limits cooling of the surface (Bhardwaj & Misra, 2019; Misra & Mishra, 2016; Raghavendra et al., 2019). Because of these interactions, nighttime LSTs remain considerably elevated with respect to their daytime highs (Fig. 2, Table 2), leading to decreased thermal comfort across the diurnal cycle (Fig. 4, Fig. S2), especially in urban regions (Behrens et al., 2019; Raymond et al., 2021). Such an interaction is even more prominent on the Florida peninsula’s west coast that borders the warmer and shallower Gulf of Mexico, as compared to the Atlantic Ocean that borders Florida’s east coast (Misra & Mishra, 2016). Stations along the peninsula’s Gulf coast report an average nighttime specific humidity of 18.2 g/kg, as compared to an average of 17.9 g/kg along the Atlantic coast. We hypothesize that similar patterns that can threaten human health across the diurnal cycle also exist along much of the United States’ Gulf Coast that exhibits muggy conditions for much of the year (Greene et al., 2011; Läderach & Raible, 2013; Petkova et al., 2015). These interactions contrast the effects found near cooler waters that increase thermal comfort such as Chicago’s lake effect (Chakraborty et al., 2023) and along California’s Pacific Coast (Gershunov & Guirguis, 2012). Nighttime LST is strongly correlated with decreased thermal comfort. For example, nighttime SAT exhibits a strong, positive relationship with nighttime LST ( R = 0.85), contrasting relationships found during the day in tropical climates (Muse et al., in press). The positive relationship between LST and SAT is especially important for multiple reasons: 1) at nighttime, LST is one of the main sources of energy to increase SAT; 2) SAT is a key component of commonly used measures of thermal comfort; and 3) as SAT increases, the capacity of the air to hold additional moisture also increases (Trenberth et al., 2003; Vecellio et al., 2022). Thus, the heat index and wet bulb temperature, two measures of thermal comfort dependent on SAT and air moisture, also increase with higher nighttime LSTs (Fig. 4). These thermal comfort measures increase further in urban regions where nighttime LST is highest due to increased surface energy balances (Table 2). Such urban conditions in seasonally muggy regions can produce a compound heat hazard, where increased air moisture and increased nighttime LSTs are both present (Table 2). As air moisture concentrations increase under the implications of global warming, the resulting localized greenhouse effects can intensify hazardous conditions and direct heat-health impacts without proper heat adaptations (Coffel et al., 2018; Dai, 2006; Raymond et al., 2017). Interventions that reduce inputs to surface energy balances, especially in coastal or low latitude urban areas, are of immediate priority for preventing exacerbated heat health impacts as a result of intensified, lower atmosphere greenhouse effects. Also a key component in measures of thermal comfort (e.g., heat index, wet bulb temperature), is relative humidity. Such a measure is widely used alone as a measure of moisture or “dryness”, especially in urban areas (Chakraborty et al., 2022; Lokoshchenko, 2017; K. Zhang et al., 2023). However, relative humidity is not a measure of the actual amount of moisture in the air, due to its dependence on SAT and specific humidity (i.e., at constant specific humidity, relative humidity decreases with increasing SAT). Large disparities in temperature can exist between urban and rural areas as a result of the surface urban heat island phenomenon (Athukorala & Murayama, 2020; Haashemi et al., 2016; Lemoine-Rodríguez et al., 2022; Manoli et al., 2020; Muse et al., 2022, 2024; Peng et al., 2012; Wu et al., 2019; Zhou et al., 2019). Thus, although similar levels of specific humidity may exist across varying levels of urbanicity, relative humidity will be lower in warmer, urban areas (Table 2). In comparing the quantity of air moisture content across urban and rural gradients, specific or absolute humidity are more accurate. However, at lower latitudes, relative humidity remains an important measure for human health and thermal comfort, especially in regions that have high daytime and nighttime temperatures as a result of increased air moisture (Ivanovich et al., 2024; National Oceanic and Atmospheric Administration, n.d.-b; National Oceanic and Atmospheric Administration & Center for Disease Control and Prevention, n.d.; Raymond et al., 2021). This research was not conducted without limitations. First, specific humidity, as well as other parameters and thermal comfort measures observed at weather stations remain highly hyperlocal (Clement et al., 2023). As such, conditions close to and in-between weather stations may vary. Second, this study focused only on peninsular Florida via MODIS imagery, within horizontal tile 10 and vertical tile 6 of the satellite’s global image acquisition technology, a region that exhibits high to extreme seasonally muggy conditions. As shown in Fig. 1, however, seasonally muggy regions extend to higher latitudes (upwards of 40° N). In future work, analysis could continue to analyze the impact of increased summertime specific humidity in seasonally muggy climates on maintaining heat hazards throughout the diurnal cycle. Such work could be especially important for heat adaptation responses along the Gulf Coast, a region that experiences heightened levels of seasonal mugginess much like peninsular Florida (Fig. 1). Third, satellite data was also collected at 1-km resolution, at 3 PM and 1 AM local time. Higher resolution data at different times of night (or day), as well as additional local weather stations could reveal more heterogenous results of local microclimates. Future work could also address the role of other greenhouse gases of various potency (e.g., carbon dioxide, CO 2 ; methane, CH 4 ; nitrous oxide, N 2 O, etc.) in limiting surface cooling, with or without the impact of increased water vapor. Such analysis could be especially relevant in urban areas, where anthropogenic heating is at its peak. In addition, many thresholds for thermal comfort are defined by daytime highs in temperature, yet thresholds for thermal comfort during the evening may be different, as physiological impacts of heat hazards and decreased thermoregulation can be different at night. Establishing thresholds for nighttime thermal comfort, both indoors and outdoors, could highlight the differences between daytime and nighttime heat risks. 5. Conclusions The results of this study establish key findings that concern air moisture concentrations across a seasonally muggy region, as well as what increased air moisture means for lower atmospheric temperature and thermal comfort in these regions. LST remains higher during evening hours in places that exhibit higher specific humidity, highlighting the localized role of water vapor’s greenhouse effect. In seasonally muggy regions, equitable adaption to reduce risk associated with intensified nighttime and diurnal heat hazards is important, especially in highly populous urban regions. Anthropogenic activities such as urbanization have resulted in excess energy being contributed to local energy balances through higher rates of solar radiation absorption, increasing local temperatures. In coastal regions at low latitudes, heat responses that reduce localized energy inputs (e.g., increasing albedo and shading canopy), especially in urban heat islands, would in turn reduce the amount of reemitted radiation during evenings. With such responses, there is less heat energy available to be trapped by higher amounts of water vapor in hot, muggier climates, ultimately decreasing the potential for exacerbated heat hazard risks linked to surface heating. In addition to these adaptation measures, weatherizing buildings and homes is also of high priority for heat health implications and energy security, especially during evening hours, as air moisture continues to increase alongside average temperature for the foreseeable future. Statements and Declarations Funding This research was funded by the McKnight Doctoral Fellowship Program of the Florida Education Fund; the Rosenstiel School of Marine, Atmospheric, and Earth Science at the University of Miami; and the University of Miami Laboratory for Integrative Knowledge (U-LINK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflict of Interest The authors have no competing interests to declare that are relevant to the content of this article. Data Availability MODIS land surface temperature data can be downloaded through NASA/USGS EarthExplorer (USGS, 2023). Weather station data can be downloaded through the ISU Iowa Environmental Mesonet (Iowa State University, 2023). Data processing and analysis and figure production were performed in ArcGIS Pro Software and R (Environmental Systems Research Institute (Esri), 2021; Posit team, 2024). Plots in R were produced using ggplot (Wickham, 2016). Author Contribution Statement Conceptualization: Nkosi Muse, Brian D. McNoldy, Amy Clement, Katharine J. Mach; Methodology: Nkosi Muse, Brian D. McNoldy, Amy Clement, Katharine J. Mach; Formal analysis and investigation: Nkosi Muse; Writing - original draft preparation: Nkosi Muse; Writing - review and editing: Brian D. McNoldy, Amy Clement, Katharine J. Mach; Supervision: Katharine J. Mach References Akbari, H., Pomerantz, M., & Taha, H. (2001). Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy , 70 (3), 295–310. https://doi.org/10.1016/S0038-092X(00)00089-X American Meteorological Society. (2022). 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Journal of Geophysical Research: Atmospheres , 126 (21), e2021JD035002. https://doi.org/10.1029/2021JD035002 Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Climatic Change → Version 1 posted Editorial decision: Revise 04 Feb, 2025 Reviewers agreed at journal 06 Jan, 2025 Reviewers invited by journal 05 Dec, 2024 Editor assigned by journal 05 Nov, 2024 First submitted to journal 02 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5380761","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":386846335,"identity":"781351ad-2950-48e9-9d90-a499d69c52e6","order_by":0,"name":"Nkosi Muse","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHAC5sc/Khh4GBgYGxgY2IjTwmbMcIZELQzSjG1w7UQoNxc7nWBcOG+bDP+0ww0MH8oOE9ZiOTt3w+OZ227zSNxObGCccY4ILQa3czcY8AK1MAC1MPO2EalFgnfObR55kJa/xGqR5m24zWMA0sJIpJZthjOO3eYxBGo52HMunSgtmx98qLltL3c7/eGDH2XWhLWggAMkqh8Fo2AUjIJRgAsAAP4sP/gwFg6nAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3453-0862","institution":"University of Miami Rosenstiel School of Marine and Atmospheric Science: University of Miami Rosenstiel School of Marine Atmospheric and Earth Science","correspondingAuthor":true,"prefix":"","firstName":"Nkosi","middleName":"","lastName":"Muse","suffix":""},{"id":386846336,"identity":"6179b0b4-5129-4ba9-9b99-94cf8fbfbbbf","order_by":1,"name":"Brian McNoldy","email":"","orcid":"","institution":"University of Miami Rosenstiel School of Marine and Atmospheric Science: University of Miami Rosenstiel School of Marine Atmospheric and Earth Science","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"McNoldy","suffix":""},{"id":386846337,"identity":"fa9deb0b-0ced-4323-818e-5f4bd959a399","order_by":2,"name":"Amy Clement","email":"","orcid":"","institution":"University of Miami Rosenstiel School of Marine and Atmospheric Science: University of Miami Rosenstiel School of Marine Atmospheric and Earth Science","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Clement","suffix":""},{"id":386846338,"identity":"95cc8e14-9c03-4af2-8cbc-af3d3e9ddcab","order_by":3,"name":"Katharine Mach","email":"","orcid":"","institution":"University of Miami Rosenstiel School of Marine and Atmospheric Science: University of Miami Rosenstiel School of Marine Atmospheric and Earth Science","correspondingAuthor":false,"prefix":"","firstName":"Katharine","middleName":"","lastName":"Mach","suffix":""}],"badges":[],"createdAt":"2024-11-03 06:24:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5380761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5380761/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10584-025-04030-2","type":"published","date":"2025-10-08T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71986032,"identity":"26c70670-c5a5-4571-935c-be47ed938247","added_by":"auto","created_at":"2024-12-20 10:53:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312663,"visible":true,"origin":"","legend":"\u003cp\u003eMean summer (June–August, 1991-2020) specific humidity (g/kg) across the eastern United States (a) and the Florida peninsula, the area of study (b). (a) The northern contour line highlights the 13 g/kg threshold for mean summer specific humidity, while the southern contour line highlights the 16.5 g/kg threshold; these are approximately equivalent to dew point temperatures of 18 and 22 °C (65 and 72 °F) (European Centre for Medium-Range Weather Forecasts (ECMWF), n.d.; National Science Foundation \u0026amp; MetPy, n.d.). The black box highlights the area of study. (b) Stars indicate the 30 weather stations used for analysis across the Florida peninsula. Each station falls within a regional classification of Florida (North, Central, and South), based on minimum temperatures associated with approximate USDA plant hardiness zones (US Department of Agriculture, 2023)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/4c3151f21ad5e92c3e4fbbe6.png"},{"id":71986030,"identity":"be380a65-8e6e-494d-9e02-a3ded8c11265","added_by":"auto","created_at":"2024-12-20 10:53:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267366,"visible":true,"origin":"","legend":"\u003cp\u003eNighttime LST across peninsular Florida and its relationships with specific humidity and proximity to coast. (a) Each circle represents the average summer (June–August, 2018–2022) nighttime (7 AM to 7 PM) specific humidity observed at each station. Each 1-km LST pixel represents the mean nighttime (1 AM) LST value for that location over the study period. (b) Each data point represents an average summer nighttime specific humidity value observed at each weather station versus the mean of mean summer nighttime LST values within a 1-km buffer around the corresponding weather station, colored by latitude. (c) Each data point represents a station’s distance from the coast in kilometers versus the mean of mean summer nighttime LST values within a 1-km buffer around the corresponding weather station, colored by latitude\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/ad3d5e07af817996080ab19b.png"},{"id":71986034,"identity":"979de99e-628d-4171-bbf9-4d2cc2b7f8c6","added_by":"auto","created_at":"2024-12-20 10:53:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219378,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal LST across peninsular Florida, its relationship with nighttime LST, and distribution by state region. (a) Each circle represents the average summer (June–August, 2018–2022) nighttime (7 AM to 7 PM) specific humidity observed at each station. Each 1-km LST pixel represents the difference in mean daytime (3 PM) and mean nighttime (1 AM) LST value for that location over the study period. (b) Each data point represents the mean of mean summer nighttime LST within a 1-km buffer around a weather station versus the mean of mean summer diurnal LST values within a 1-km buffer around the corresponding weather station, colored by latitude. (c) Each boxplot represents mean summer diurnal LST values within a 1-km buffer around the corresponding weather station, colored by Florida peninsula region. Box and whiskers display the 1\u003csup\u003est\u003c/sup\u003e, 25\u003csup\u003eth\u003c/sup\u003e, 75\u003csup\u003eth\u003c/sup\u003e, and 99\u003csup\u003eth\u003c/sup\u003e percentiles of mean diurnal LST, the box line represents the median value, and colored dots represent mean diurnal LST values by region. Each black triangle represents the mean of diurnal LST for each region\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/03920655bc4504d3eb1444e4.png"},{"id":71987222,"identity":"e449b5c4-1906-4535-8438-e67df6729c9d","added_by":"auto","created_at":"2024-12-20 11:01:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202198,"visible":true,"origin":"","legend":"\u003cp\u003eNighttime SAT (a), heat index (b), and wet bulb temperature (c) vs nighttime LST (°C) across Florida peninsula weather stations (\u003cem\u003en\u003c/em\u003e = 30). Each triangle represents a mean of mean summer (June–August, 2018–2022) nighttime (1 AM) LST values within a 1-kilometer buffer around each weather station versus the average nighttime thermal comfort measure (1 AM) measured at each weather station. Background colors represent “caution” and “extreme caution” heat index thresholds (b) as defined by the National Weather Service (NOAA, n.d.)and “critical” wet bulb temperature thresholds (c) as defined across the literature (Raymond et al., 2020; Sherwood \u0026amp; Huber, 2010; Vecellio et al., 2022)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/6db20d63f73a9f3329182c2c.png"},{"id":93419829,"identity":"be3d83ca-b3ed-49a5-aa51-3b67bb6d25b4","added_by":"auto","created_at":"2025-10-13 16:08:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1798537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/a649a574-2086-4982-8776-3827e8fcbc3c.pdf"},{"id":71986043,"identity":"5d20c4ae-1304-4da3-a196-ca48fab415c6","added_by":"auto","created_at":"2024-12-20 10:53:06","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":3027598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5380761/v1/98359d9dada6ab4b9fd1a5db.docx"}],"financialInterests":"","formattedTitle":"Increased nighttime land surface temperatures in a seasonally muggy climate","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eAcross subtropical-to-tropical regions, heat hazards are exacerbated by the presence of high amounts of air moisture (i.e., water vapor), especially during the warm season (Raymond et al., 2020). Increased air moisture can limit cooling of the human body, reducing thermal comfort and increasing heat stress (Baldwin et al., 2023; Di Napoli et al., 2023; Hanna \u0026amp; Tait, 2015; Matthews, 2018; K. Zhang et al., 2023). These thermal environments are typically characterized by \u0026ldquo;muggy\u0026rdquo; dew point temperatures, frequently used as meteorological proxies for how humid the air is\u0026mdash;that is, how much water vapor the air holds. For example, in \u0026ldquo;seasonally muggy\u0026rdquo; climates, where average dew point temperatures remain above 65 \u0026deg;F (~18 \u0026deg;C) during the warm season, the air is considered to contain what the U.S. National Weather Service has labeled \u0026ldquo;oppressive\u0026rdquo; amounts of air moisture, associated with dangers to human health (National Oceanic and Atmospheric Administration, n.d.-a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs compared to more arid climates, regions with seasonally muggy climates can have additional consequences for heat hazards overnight through more limited nighttime cooling (Feng et al., 2020; Hwang, 2024; M. Li et al., 2019; Yin et al., 2020). Effective radiative cooling of land surfaces can be inhibited by an intensified greenhouse effect, where higher concentrations of water vapor absorb and reemit more longwave radiation back to the surface (Chang \u0026amp; Zhang, 2019; Dai, 2006; Dong et al., 2019; Held \u0026amp; Soden, 2000; Hossain \u0026amp; Gu, 2016; Liu et al., 2019; Sherwood et al., 2018). Such a phenomenon is especially important in areas that exhibit elevated land surface temperatures (LSTs) (e.g., surface urban heat islands) (Athukorala \u0026amp; Murayama, 2020; Haashemi et al., 2016; Lemoine-Rodr\u0026iacute;guez et al., 2022; Manoli et al., 2020; Muse et al., 2022, 2024; Peng et al., 2012; Wu et al., 2019; Zhou et al., 2019). In the absence of solar radiation, the surface is a primary source of energy to heat air of the lower atmosphere (Bechtel, 2015; Hulley et al., 2019; Tomlinson et al., 2011). With increased air moisture, elevated nighttime LSTs may, in turn, increase nighttime surface air temperatures (SATs; the ambient temperature that humans experience) and other measures of thermal comfort (e.g., heat index, wet bulb temperature)\u0026nbsp;(Chung et al., 2020; Hulley et al., 2019; Shandas et al., 2019; Yao et al., 2011; Zeng et al., 2015). These factors could also potentially exacerbate daytime heat hazards into the following day\u0026nbsp;(Muse et al., 2024). Such a thermal environment in seasonally muggy climates can be dangerous to human health, as stress on the body is exacerbated by a lack of respite from heat hazards across the diurnal cycle\u0026nbsp;(Di Napoli et al., 2023; He et al., 2022; Raymond et al., 2021; Rogers et al., 2021). These relationships among air moisture, LST, and thermal comfort, however, remain understudied across regions with seasonally muggy climates.\u003c/p\u003e\n\u003cp\u003eUnderstanding the relationships among air moisture, LST, and thermal comfort can inform heat response priorities in seasonally muggy climates. If thermal comfort is decreased as a result of higher nighttime LSTs in seasonally muggy climates, reducing surface absorption of daytime solar radiation in ways that ensure that air moisture is not further increased becomes an important consideration for local heat mitigation. Such a consideration would be of increased importance in muggy climates as opposed to drier climates, where a dry atmosphere more freely allows for the reemission of longwave radiation through the lower atmosphere. Potential response options include the expansion of tree canopy cover focused on species with lower evapotranspiration rates (Ch\u0026agrave;fer et al., 2020; Qiu et al., 2013; Yang et al., 2023; Y. Zhang \u0026amp; Dai, 2022), measures to increase albedo or surface reflectance (Akbari et al., 2001; Priyadarsini et al., 2008; Taha et al., 1988), or cool roofs (Takebayashi \u0026amp; Moriyama, 2007; Wang et al., 2022; Zonato et al., 2021). These response options can also improve thermal comfort during the day for urban outdoor workers and commuters using public transit (e.g., at bus and train stops). In addition, these responses can reduce the need for energy-dependent cooling methods such as air conditioning and thereby decrease energy burdens, emissions, and heat inputs into urban heat islands (Akbari et al., 2001; X. \u0026lsquo;Cathy\u0026rsquo; Li et al., 2024; Mohammed et al., 2024; Taha et al., 1988; Zhao \u0026amp; Zhang, 2023). Further, ensuring that buildings and homes are weatherized in ways that equitably maintain and insulate cooler and drier air indoors, as well as limit contributions of excess heat to the lower atmosphere, is also of high priority in seasonally muggy climates, where nighttime outdoor temperatures are a greater hazard\u0026nbsp;(Litardo et al., 2022; National Oceanic and Atmospheric Administration \u0026amp; Center for Disease Control and Prevention, n.d.; Stone \u0026amp; Rodgers, 2001; Vijayan et al., 2022; Yurchenko et al., 2019).\u003c/p\u003e\n\u003cp\u003eUsing the case of the seasonally muggy Florida peninsula, this study seeks to examine the relationships among air moisture, LST, and thermal comfort across summer months (June\u0026ndash;August, 2018\u0026ndash;2022), where dew points remain well above 18 \u0026deg;C. Using data from 30 quality-controlled weather stations, we ask: 1) How do urbanicity and geography modulate air moisture as measured as specific humidity? As opposed to a dew point temperature (\u0026deg;C), the amount of moisture in the air can be quantified as specific humidity (g/kg)\u0026mdash;the ratio of the mass of water vapor to the mass of moist air. Urbanicity is measured as impervious surface and geography as latitude and proximity to the coast. 2) How does air moisture limit LST cooling at night? And 3) what are the consequences of elevated LSTs for thermal comfort at night in seasonally muggy climates? Thermal comfort is assessed through three commonly used measures: SAT, heat index, and wet bulb temperature. The results of this study provide insight into mechanisms behind exacerbated heat exposures in regions that experience seasonally muggy climates, which can inform appropriate heat mitigation and risk reduction strategies. In addition, this study has important applications for future climate adaptation planning as water vapor concentrations increase under the implications of global warming. Seasonally muggy, subtropical-to-tropical regions with increased air moisture may require unique heat responses as compared to more arid or temperate regions.\u003c/p\u003e"},{"header":"2.\tMaterials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp;Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Southeastern United States is, on average, the warmest and most humid region of the country (Carter et al., 2018). With an annual mean temperature of 21.5 \u0026deg;C (70.7 \u0026deg;F), Florida is not only the warmest state in the Southeast region but the warmest across the continental United States (National Oceanic and Atmospheric Administration, 2023). The state also experiences the country\u0026rsquo;s highest nighttime, or minimum, temperatures (US Department of Agriculture, 2023). While most of the state falls under a humid subtropical classification (Cfa), South Florida experiences the most tropical climate across the continental United States (Koppen-Geiger climate classifications, Aw: tropical savanna, Am: tropical monsoon, and Af: tropical rainforest) (Beck et al., 2023; Kottek et al., 2006). Florida\u0026rsquo;s unique geography along the peninsula greatly influences the state\u0026rsquo;s climate and weather conditions (Cloutier-Bisbee et al., 2019; Winsberg, 2011). The Gulf of Mexico to the west and the Atlantic Ocean to the east maintain the state\u0026rsquo;s highest levels of air moisture across the U.S., especially during summer months (Fig. 1) (Zierden \u0026amp; Griffin, 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp;Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument were retrieved from the NASA Terra satellite. The MODIS instrument orbits Earth at 1-to-2-day cycles, capturing LST data at 1-kilometer resolution within 36 spectral bands (National Aeronautics and Space Administration, 2023). More specifically, MOD11A2 Version 6 data were used, where each 1-kilometer pixel represents an average 8-day LST value in Kelvin (Wan et al., 2015). Although other satellites such as Landsat and ECOSTRESS capture LST data at higher resolution, MODIS captures both daytime and nighttime LST most consistently. MODIS data are cloud masked before download from the USGS EarthExplorer website (USGS, 2023), and 8-day average LST provides the best surface coverage with reduced cloud interference. The Florida peninsula falls within horizontal tile 10 and vertical tile 6 of the Terra satellite\u0026rsquo;s record database. 112 scenes were downloaded and utilized for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObservational data from 30 peninsular Florida weather stations were used for this study (Fig. 1; Table S1). Geographic data, SAT, and parameters for the calculation of specific humidity and other thermal comfort indices were downloaded from Iowa State University\u0026rsquo;s IEM ASOS Network daily across the study period (June\u0026ndash;August, 2018\u0026ndash;2022) at hourly intervals for each weather station (Iowa State University, 2023). Weather stations were classified by latitudinal region based on approximate USDA plant hardiness zones for minimum temperatures (US Department of Agriculture, 2023). North Florida stations (\u003cem\u003en\u003c/em\u003e = 10) are located at a latitude above 28.5 \u0026deg;N, Central Florida (\u003cem\u003en\u003c/em\u003e = 10) at 28.5 \u0026deg;N to 27 \u0026deg;N, and South Florida (\u003cem\u003en\u003c/em\u003e = 10) less than 27 \u0026deg;N.\u003c/p\u003e\n\u003cp\u003eAvailable impervious surface data for 2021 and 2019 were downloaded from the National Land Cover Database (NLCD) at 30-meter resolution (National Land Cover Database, 2023). Each 30x30m pixel value represents a percentage of impervious surface (0\u0026ndash;100%) for that area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp;Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used airport weather station data and MODIS satellite imagery to examine the role of air moisture for nighttime cooling across the seasonally muggy Florida peninsula during summer months (June\u0026ndash;August, 2018\u0026ndash;2022). LST data, processed in ArcGIS Pro software (Environmental Systems Research Institute (Esri), 2021), were converted to Celsius (\u0026deg;C) with a scale factor of 0.02. Statistical analyses were performed to explore the degree to which urbanicity and geography modulate specific humidity across the study region. Nighttime LST across the peninsula was then examined to understand the role that air moisture plays in limiting surface cooling overnight. The relationships between elevated nighttime LST and thermal comfort indices (SAT, heat index, wet bulb temperature) were also explored. All statistical analyses were performed in R (Posit team, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSpecific Humidity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecific humidity (\u003cem\u003eq\u003c/em\u003e) is the mass of water vapor per mass of moist air (American Meteorological Society, 2022). Specific humidity in Fig. 1 was estimated from Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group dew point data at 800 meter resolution using elevation and barometric pressure (PRISM Climate Group, 2023). For airport station data analysis, \u0026nbsp;specific humidity was calculated utilizing equations from Bolton (Bolton, 1980):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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YUidTodWV1f1pQx872S5u91uGuZ5Hj1+/FgnORHkWLq4uEjNZjNzHRydOI4Lbeis6v0o9xZm0HEkjmPqdDp08eJFokRnxhgqlUo66kDI+es46gE+5sw6ZnEcU6PR0MFDEYYhzc7Okuu6qYOmGbScUqlExpjcgSGOY1pdXaWVlZU0rNlsZspttVrkeR7VajWiZHLoum76qnl3d5couaFWKhUiItrZ2UnTh2FIW1tbx+aUjcKg+gN2Wq3WsQx2QRAUPkQ4rRyXPo6LMAz7TjbPMue9fqeBcY2hJ9VWW1tb5Pt+OtkbZMJ279699N6Zt7Qq797KzM/P66AT4ayOpWeNer2ug8CQjPvBxdzcnA4C40C/QusHL/FxXTddXiFfYXKWcsme53nGqKUT/OpULhnSr2F1vrZ8+DPKUgzP80y3201f5+o8RimH89I0m83c+rEuXNftWa7SbDbTerOuGFJLCm3lFsHpWWZ+/az1rl9z63I5jD9czyL9+b6fxpNLEGR5vCyU66/jynAbsgxbGg7jV/HSFllOrkO3283YtF7epmWR6SR6KSvnxXiel+Yl41KiV9d1e+SWcujybGh9U2I3Uh7WPV+TSLmKlpbIPHipjyxT2j1ZbJk/0mZkuO5PjNS91Je2K04v9Sdl0O3D8drtdqZueTqX9qRlKOpjcgxh+VlWmafugzx+yPbVS0p0G0i4v7BseeOtrb/Ia/y/bl/ZR/LKMspudB01uo2MsBPf91O7K6JfebJ+3DeMskcdV/aLPP0xrAsbOi0pe5PhegyS2NoqiqLC8U0yzLjL+tR61/VmoihK689jXD+k3KOix0HdVzicdWND50GJLRT1YcmgY6mxjBm6HWSZRumI20S3q0TmrXUh+4jOR9pvnmyU9C1ph7oMbeuyL8pxnAp0xTqQujqq3k3OfFYi5bOVV1Q3o+4tkn5jkxxbi8YRY2lDTsf/DzNe6/Zg2WU9ZN/Q/VTrQ6LLsun7vNM76ypADqCsNN155Hfb4Ck7QVM4HSZJL7/L/LlD65uSzVgHQd5guS55HXSYcvTgyPi+32NgujPK/xmZH8fvdrs9uvUSJ3NQuEN4yX4013XTSYyUnwcknVa3O+uO08t66PicpxxEbO3LceX/8jsVtJkRHZzl1zozSnYdRw4erB9O43leKr/OV6aTZWkb4Dg8IPIgKeOwXuSNUPcTjqd1V4SW2aiHLhxOqu3YXkzOGKBh25LYdM665Ty5vmz//N2mD43UP8eTdmByJsO6Lroslk3qjdssTx7dx42Qj5F9jOtL4uEX5+GJPTh6nOE0sg4ch2Vtt9uZOksbln2S29jzvB75dXvo68aiN5PzwEGXZZJ24rxYTzJvCZcjy2q326mO+HpRv+hXnu277Hu6HYyyb60frT+TY4sasvQzm2xF+WhZjKiz1IPun8OOu7L/FcnDNJMJO//fr81Mzvg1DNpGZV/g77KPF9XDJgvXQ/dhqfthx1J5Xdu2HDeMagMv2VMk6yvhsnVeHFfbJ4n2l31Zl835sRzSrnSenIbL5O/aDovawSi9G1E32WeH1bscd23xXTXvlbowA9bNZh+yT3IafZ3L5ev99KPbxlj6ghlyvLbVQ8om8zIj3oeeNXo9iAJY6Uw3edvE+GriyfFlw2kDlsagBwRpiDbDpT4dqgg/eZoqv8u6SYYph+ug0boxljrp+hlLftyJpEzN5Cm5UZNHXZ7GFkfrxTYgaH3ogUlf199NziCr8+kmzhGj9VrUZibnuq6LHlj0wGWrv5Zdt6MpyEfae78BMS+OztskMg2DLQ8uS4bpm5JuMzmo2rDV21MH5Mg2l7ZsLDdCmz40trrpNtJtaCx2qsvSeTA6ncSWpl8f4z4v66jtVMtmcuQgMf566uGNbm/uLzJPll/bMX+31c8mm7ZrW1nd5Mm+hG/Qeeiy2snbTKbZbGbqLOlXnm3M1vXgtpLItuqnP5Njixrdtk3LAw/WhbZPxtZW2vaMpd66frZx1QaJhwVaVonOi/o4fiZHj8PAumJ0O3rigAT+nodNlrw+zG0YjTCWynaytaWuA8ulxwSNbm9ts/q67gM2e9Cy6TbV+vcsB7/oMWKQfqJ1YNQYMazeWYd6LGNZdT2MRe+D1E23Z7+xydb/B9GPzVb1GGoK2ljGMZb7EtejaKzyRrgPPWsMtcesVCqR67rpMZuVSiWzn+nmzZvU6XTS46XX19fJ9/10XXsYhnTp0iWiZN18FEWZ/HgdPa+DNcZQrVajVqtFruum5YyDzc3NdC8XJbJTn0NATgO1Wo1Mstl5cXGxZ1/Z7OwsNZtNMsZQp9PJXa/PXL9+PfN9c3OT5ubm0jaZmZkhIqKDg4NMPMne3h4tLi6mx4uPysLCQuZgh52dnXQvHR9ywnI5jpPaFdubDZvdFNUlD94sOyo7Ozvkuu7Im2wllUqFXNdNN/LHcUzT09M62si8+OKLme+s393dXYqiKNMGnU6ncP8Iy8qHF8RxnOqA+9r+/n4af2trixqNRpo/t5+MMwpc5iht3w/XdYeSb9A+Ng5b8Twvbb9Op0MzMzNpubw/4NGjR2l8baO8d7ZSqVC9Xk9lHQe6rJ2dHep0Ohn7iqKI9vb2MumKuHLlSppHtVqlxcXFdAzR9Ctvc3OzZ3wcluPS39bWVs/R2Hw/03Y0CHp8Yx2MOu5Scv/msYHHDU0QBD17x+Sc4bjg+ygRUblc7tnDd/369bSPBkFAGxsbmeuDovswjxOjjKWjzoeuXr2qgzLIMZmUzVJysMTGxgbFBQec2eTRdmi7R8mxSV/nOdlR90WxbT9+/HhovfPYKPWzsbGRnhWwvb3dU3e+53Fbj1K3fmPT1tYWzc7O6mTHih6vi+B5PiP1O8p96FljKMeMkgGbB1JH/QaCnoTt7++nBhiGIe3s7NCVK1fS+JQ0GDsa/OEBQf4mRV7HGQU+QEAaPd8w8w4BOSrT09M9EwzulKwT13V7BrP9/X3yPC8TJpmZmUlvGuzs8kDseR5tbW2pFP3hUyHlp+hAEXbItre305vdKNy4cYOiKOo5aVKi5TJHOFHoLLO8vJxOXtbX1/vefMeF67o9+u/X5tLhvnPnDq2srJDrurS9vW11KvnBgvzIQ3POA8P2sXEhT7Hjj3xAZaNer5PjOHTt2rVjOcVP4nlej3x63CyCJ5We56UTm6Lx5KjlDcJJ6u+40DoyBeOuPKSB24NPONUPCpeWljIPYhzh+Om446ZcLpOTnBapncPFxcV0zjE3N9fjBI+DYcfS45oP9YMdstnZ2bRvnWWG1fukOImxaVKMch96lhjKMYvjmFqtVnrEZrPZ7HnSNDs7S1tbWxQEAd28eTPzdH9/fz8dzPkvnzTIBEFAYRhSGIbpkbvjnrA0Go0eo+D69HMMRoWdDvmUUb9FWVhY6HGkip7aVqvVsd/oy+UybW9vZ8K43W3E4gj/o06eS6USeZ5Hy8vLFAQB3bhxI73Gb3F027Rarb5Pbos4jhuujUuXLvW0/1HgQazVatHW1lbuW4FxcvHiRWsd+p2WdfXq1Z50CwsLtLq6Suvr65l2LpVKPX2ABiijH1w2l6WdwaMQRVH6AGoQhu1jR2Fvby8dP1zXzZziSkl/KlolEAQBra6uDnXj1G9fBuXSpUvWp/HDtD3fPzY2Nsgkk8jl5WUdjWjA8my2KNFPhjWj6G8QSqUSdcTqFBI2Ps6HNKOMu3Ec98TnE4YlQRCkk339cV238Gcxjgr/GK/JcTDr9XrqVEZRRFEUFfaTYRl2LD3O+VCpVLIeQ8/jEa/COU6nwNbe/AblqPe2R48ekeu6VKlUhtY7j2Xanlk309PT1jljFEV07do1ohHr1m9symuzURh1vB6VUe5DzxpDOWaUODVshJcuXeoZbHk549raWmp0PAnTk6Fms0lzc3OZTsLp2HD5rZJ24EgsIRpmUlOv13uejjE84Oljd0cpR1Mqlcj3/cwA0Gg0en7/pNPppAbKf20DcSv5vTLZsUvJUlM2+k6nk+vU5bG0tESrq6uZTlL0Robbh9tLD1A0pP7m5+ep0+nQ9vZ25obJ+tNLgba2tqw31mGQAxy3/czMjLUuo8KTMrn8gNvedd3csuQyDD0Z8n2fGo3G0G1MYsL16NGjdDLbj0qlQp7n9Syh0Dc5DT+cmZ2dTR0YdpDu3r2bab9bt25RRy3BDYIgvckV6UMjf7NpdnY2c4Q3JTdPTs/9stFo9LVR2Wer1Sp5npd7g+XyDg4O0pvPsH1sGOSDslayMoDHj+Xl5cz4TYmO9CoGCb/BZz3pG6qtfgyP2UFynHin06FqtZpe1/DkXcYpat885BhRKpVyx4d+5S0vL2fG4yJYp6zz1dVVqtfrffU3KHoM5Ydgsi/euXNnaFvsx6jjrh4/gyCgKIoyD2Hm5ubo1q1b6XfJ8vIyRVHUty+OShzHGUdDO+BxHGcmwVQwgT2JsXSQ+dCo3Lp1i6IoysxNgiBIx6MoijJLtW0Ow1FZW1vrkWFpaSkzV2MniAocKYZtO04eHPPDmWH1zvG1/bM+FhcXyXXdzPVWstSU7/mD1E3Tb2y6efNmT56rq6sU5SwZHoRhx2tKHjLqe2g/RrkPPXPoTWdFRMnx+J7liE2J3MRoLJtJJbxZkz8yjquOBpf/y7S8sZA3EeqNlozMT2+SZBnlh+ugy8kjL71E6s6mDylHXnncBjZk+kHikEVfvEGUP3LDsi2drBNvpJXla/3J+GQxQcrRnREbQ/ljsz9jKSNPdh0uN6Ly//KaztemK1s6W1l6k2uefXK43sTN+WkdyHKK4Lr4vm/th3nyaB0Mgp9zkIK2PZOjJ0mePiQ6D5s9yfpxm3GeNn0wMrxIBobbWdbfZjcyLn90PbzkpCqbbJyPvKbJs82iNpXhUj4ts6yfHq89sZm8qCxT0A80uq7cZ6Vui9IzReXp9iDLuCrr00w2ukublWm1/vrpgtFjKKPz7odsK5st5Mmj9aDHHAnLofPiNHm2z+h2lXIY1V5k0YkResmTU8sg69dNjseX+tEyariufnKCpczb1od1Ov4UIeut+5cu8+///u8z323jn8SmD0a3hyxLy6/r+rWvfa0nrc6Pxy8tg5ZZ5m2bOzHarm02oOXuh7Y5jbyuxwfTp276mrS1orFJ19Pvc/iHrjPl9G9/iPFaymCrh7ZLbgst+yD3oWcJx3w8iAEAziDVajV3Y3oYhvTo0aOxLqECpxfHcajZbFrfsIPxwE+Q8/ocOD3EcUzr6+voDwCAM8XQSxkBAKeDMAwLlzFieQAA4FllXMuDAQDgJIFjBsAZZXl5ObNng4mTU7SuXbtWuA8EAADOI7zPJm+/HQAAnFawlBGAM0Sr1UoPeOh2u5h4AIrjOHMIk+/7Rz4hFfRSLpfTwwdc1z3WU+oAAAA8m8AxAwAAAAAAAIAJg6WMAAAAAAAAADBh4JgBAAAAAAAAwISBYwYAAAAAAAAAEwaOGQAAAAAAAABMmGNxzIIgIMdxKI5jfQnkUK/XqVwu6+BjB211MkyqfU8DYRiS4zgUhqG+NBTaVvlnAYIg0FHBKaBcLo91XAmCgOr1ug5+JnAch1qtlg4+dVSr1fRHuI+Ls6KLs8IkxlHHcdLPUe8Lpx3bvX+YfhLHcU/648ImV7lcfmbH3UlxLI5ZrVYjYwx+QymHIAh6BqOVlZUTP345CAKam5vTwcfCpDu2TefHha2sSbRvq9XK3ACr1Wp6E7bdGPU1XYc8Wq1W7gQ8DEOamZnRwUOjbVUfET8McRyf64ldGIYnOsnSsB2tra2l9wB2qh3HyR0LbPZKyWSBkvvK9PT0iU1SJoVNP8YYWlxc1MFjx1b2oFSrVep0Ojp47JyULs4ruo1LpRIZY6hWq2XCjwvHcSiKIjLGkOd5ND8/r6OcOLZ7n+0+rnXXj3q9Tqurq5mwYfpJEAQ9P83BDzr5w/czfQ8eVlabXPInQsDJcSyOGShmbW1NB02EWq1G7XZbBx8LenA6aU5S5ydZVhGLi4vEv4bh+z5tbGykN2HP84iSSQ7/Fhpfc12Xoiga+DfS7t69q4NSKpUKdbtdHTw02lZLpdLIN4z19XUddK64d++eDjpRZmdnM7+xF4Yhra2tkTGGjDE0PT3d4xg7jkONRiONY4yhpaUlchwnMylZXFykhYWFnqe654U4jmlzc1MHnwhhGPZM7oZhY2MjHVfA6eSobXxU+IERP7DZ2Ng48QeWGn7I12w207FneXm556H1KH1zZWWFfN/PhA3aT+I4prm5ufQeTuJBZ7vdTmWdnZ1Nf9tUxhu2nW1y7e3tjfwAFIwOHLMTJgiCnqcS551hn9yMm5PU+UmWNSjNZrPnhrK0tEQkbpRMEAQ0Ozs78NvuVqs1soM0CeI47rmJnSfCMJzoQ5BWq0Wzs7MZp35nZye1N0qcq62trfR7uVwm13UzExBKnPooinrsa3Fxkfb29nps9zwwybHyNLy5AMfLpNv44OBAB02c3d1domRcYWxOykn3zXq93vPgfGdnh1zXzbzdtDlPk25ncETMELiua4jIEJHxPM8YY0wURWlYu902xhjj+75xXTeT1vf9NB4RmW63m7mukfkSkYmiyBhLPgzL5vu+McaYbrebicfhDMsr89My6TyazWbmuud5xvO8NB6X0W63remazWYmnJT8WkYdX8vHaaScWkYbss4saxRFGbm5LM/zDBH1tKeMq69JOD1/2G7MAPXTyHqapB62ayTaQpdBiY74f7Yrth8pX177DlMWY2tfbSfcf5hR21fD6aV+uX/J+ppEp7Z2kHLydd0X8+xAli/HENa9tjHZ92V9pa3KeO12O2NnNvmNpd1k3Lzxxoh2NcJOWFbZhrptXNc1zWYzYxe6jbVMEj8ZR3Vb6TR54w5f4/+LbD2vLKP6sK6jhiz673a7Pf2e82GZi/LV/cYkeWjbZaS+Oa3UibxPybrm6ZVhPbD80kZsSFvX7c7h3W43LUfGl+Xb+i/LIducy5D5aHQZjAzjvJl+7c/XfN9Px0wbUr+6DaQssi9GUZRpB5suBhkndf8eBD2+yTKLxoQi+t03te02m03T7XYH1p2x3HPZTmUYCR3pcZWRcXWbyjSyvCJ0+Rx/0LFH5y/Hf6k3bif+btOzhOui+3u73U7z0v2GZZby2trI5MyHi/qJEXlpuJ7avuV4JOXkuFJuo+xQ2rVNLu5f4OTobfk+eJ7XY2TdbrdnsJBx5CBmkobWjS/RRsl5MpyfHkjYeGSH1fH1AK3l5jxZBv2dy+ABgw3Z9/3MzYGNXcuurxvR6aXxN5tNqw71zVfqUuvZhp/cPBnWg66nlE8PLE01KerXnja5+tXPhsxD6quovU1OnWw3Izko5bXvKGXZ2pfL1995wB21ffPQg2u73U7LkDrQfZvry3F0fW161LBOZDwum9E2ZsTkk9Flyb7M+uZ2K5JH51s03nB9KWmLSIwhbBcyHsNpZFkch2XlNmA8MbayDNz+3Of62Z++bix6M8rW88oySTtxXqynvD7K1226ZxlI9QMuW/aXQSgqy+Rct9nYoGNIUziT+poNWSeuu7RR+b+UScso+w6n4e+yT3M6tlGOJ+1ctjnXN+8606/95Xeup85DovVsLH1H1oFt0uTowh1gnNRlyj4tbVGi87Dlz+VGYkzI6xsmSSd1I/Pk67LtuRyuq60MLae2H11H3cayX0p71uVwXXUa13UzdintyYZNXs5Hjz2eGF/5O8vJuuC0XC7Xj8uw6cwG2xEVjEVat8aiT1t5g6TTaFuQsJysCxu2/F01B+D+JOs7SDpw/Aw9y7Pd8HRn1IaojUxf1/i+nzHsfjcEkwwWLBN3DjY4bYC2zsNhsoNrY+RBhMuRAwCj9cODGH/XsjDa+LV8HEfqjQczRpetsV0fRD7dXq64cZoBJip6MDYD1k8jy4jEk9R+7a2/G0u9jWVQsrXvKGUZS/u64mbC6PKGbd8idBt44k0gt0O32+2RqSkmpMbST2x61Nh0wvlw2drGjOVGr8vSeTA6nUZf7zfe2Oxbt6eWzVjK4TBOx23AcB4cpscbM4D92XRik03buq2srnrTZSw2KdE2Nghs80X2Y0PrwYbWA8sndaf1lqfXZrOZaW8/eUhjo52sxJDodpdy6XbQ/UDLYlR+JqeNtY166p5mu67HG93Wsv199ZDPWPLQ2OxT240rJvtG6cemC22THId1wbqRaBk0zT5zlrwxQfd3iVtw39T2Zyx1HUR3uk10e+jvxmI7uu4yDpetxypjsS8bWl5TMPboMK4/l8Hfpc5t+es4ebAc/JF14+taL1qftjYaJJ1mkOtSVj0W2dLr/q7tywyYDhw/Q+8xq1Qq5LruUBvoeU8AJZu8++2B2NzcpLm5ufTUGT7VTa5P9n0/c8jCwcFBui+GDzGoVCpUr9cHOhWuVCqR67q0v79P9P9v7/5f3Eju/PE/9QckPnn807JnjFr5YUnAIZa9wTdjsGGnlU3IL3FuNA6EgRzed+uWwN05nr1ZLyGsfWfpnAsHOcmzxDA/JJYcO9wRbuzRBLxgaYes7RwjkpCDTIthWfYnyZtN/oD6/LD96k91dbfU0misGfv5AGFPdam/VFdXV3VXlQA0Gg0cOXIkEOfcuXMAgI8++sgPM/shT09PQ3kzUubz+dAA0iRkJqAXX3wxEH7+/PnQeIuosUD6/umkf3LUd5LqdDpwXReWZfnnR8bsxG3XNMzx6Wzb9rcLb2AtRjzfSZnndxzbkjQ8fPhwIFz6heuDdqPOVdJ01p08eRIwBl/LtSzX0c2bN3H27NnA9+7fv4/FxUX/XEtfdrlORiXHtRtjDvTrOIkk5Q1izsWwbNv2z2+j0cDMzIy/XSkrPvzwQz++eb2OI//FMbe1sbGBRqPh71/Km01t0oP1kzLvEdvb27Asy58c5ebNm6GJb+LS9eTJk6hWq0h5s0pWKpXY/PDgwQM/rnygXddnzpzx81u9Xsfa2pqxhmTM+9Mga2trqFQq/qxu/cpaJDj/6+vrOHPmjPm1HZudnfWvi1arlSh9os6FWU7q5aplWaHrW5e0zmJuN67cGXTflHFOSSdcilOpVLC2toaON8vgKOOd79+/H5r5VMY0mWn2wgsvBP4edsIJEVX2wEjfTCYTKD+FeR8dVaVSgdImxpqZmdmz41jX1tagvIm64KWfOYMk7V9DN8zgVaBlJrZ6vR6qzEWRwkgpFZqlJoo+64x89MGZ586dQ6PRQKfTQafTCV2cxWIRqVQKp06dGsuscMPI5/NIpVK4dOlSaPDms0Kmu9U/O72pDLK2tuanp2VZgcG4T/N8P81tjYveCKvX6zh16hTgVYLk5t3pdEIVDXiTh5jnWhrFz4pB5c1uaTaboe0Omrb6aeY/27ZD+zfOhplU7KViOk76PaJer+PcuXOYnZ0NTYQj+qWrPHCzLMtvdJkVRJ3jOKF0kwbGxYsX/UbR/Px8qBK8W6RBdvnyZf9YBtnt8x+lUqn46T8zM7PjGTgLhQIs7WFyq9WKfDBmGrbOksRu3zelQTY7OwulNTIonjktvjR6oE2StVeY0+JvbW35Zcnly5e1mLSfjdQwO3v2LFzXRb1eD7ypipPP5/0bVRLZbBYPHjwIhHWM3x6Siua1a9dw586dQGWmXq+jWq1CJajk6FzX9SuslmWFpgGXJ9n9CtJyuYytra0dFbjyFMqc9np7e3tHBe3hw4fhum7fCoX5BMwk59qsSJmFWz+jHl+xWPR/I69Wq6FarfqVrmHPt/m2LqlRtmWSNDSn1f/ggw9CTw7H6fz582g0GlhZWfH3Xd4CF4vFyKffmUwmMIOe2OkMVZIH5aHOsE//+3Fd1z+uJJKUN+OytbXlp7NlWf6TYdEa8Btko+S/UfP64cOHI5+4x517eSvbr3wxSeO3XwWoXC6HyhZ5G9KvvJLy9+HDh3jw4AGmp6dx7tw5uK6Lcrnsl/VIkK5SIZKyXe49UY4cORLZ+JN0KxaL/hs615t1st85HxeZZjvJGygkOP/ZbDaybOgnSdlWLBb9hnCz2USj0Qid/2FdvnzZf/M/MzODUqkUeZ7FsHWWQZLeN/sdZ5K0m52dRalU2lHjOZPJ+A80hPxfrvHdJo1m87rQy89xMusi8B5IDpLknIzizJkzsefQ7K2WyWQSPTh4Wg+AaOdGapjJK+VLly4NfOoE72LSL/Kom5bu0qVLqFargYvyzp07oULh/PnzkV0M5HW7bNOs/Ai9MlAsFmFp05CurKzAdd1AJeTSpUsDL9bt7e1AFxGzwicViQ8//DBUKItMJoNSqRRIg06ng2q12rcCM4gc2+zsrB8mjQPzVbikWcubftt1Xf/CLpVKmJ+fD5zTlZWV2Iao5JFOp+NXSkY5Pj2+VDYzmczA890vzeVGWfemuW80Gn2f0O5kW7parYZGoxGo/C8uLu7qUy/z+oH2gKNarUa++b5w4UJoP+vaGzc5Dx999FHoaZ5Jv/nNzs7CcZzAjU1/aCDX3eLi4sAGkt7AzefzsG07Ni9C6+ooja+k5c0o9Kn55acFpDEilUU9j9y8eRMnTpzw/zYNyn+Snh988EGokTdsXpc3DXqcfudXbwgNo9lswnVdpLwufzrZtnk+NzY2YNv2wIqR4zi4dOmS3/CX/L64uBhI50HpCqPczGazsQ8T5I2Yfu9otVp+fCkHoZ0vuY6OHDni3z/iGsCjkOPSj1O/T8HbF6kMykOwfud/YWEhUDZ0Oh0/Xw2qBMp9saP9fIWc//X1dX+dUp72a4AP0jJ+R08leBs+bJ0liX73TUnrJNOc90s713UD3SnNhrV5jqNIbwg9v1+7dm1guTpOhUIBtm0H0qterwfKz3GqVquh9FhcXMT58+f9v+OuTT2NJXx+fj7UqBzGyZMnA/dDnXlP7Hh1Jz3vxJ1nPR9L/JmZmdh6Ck2IOegsqagBtSpiUKIeVz76IEtz0KKQgYnyiRq8GTVgVsRtr+RNKyr/l3BzcKaK2Ad9QKelzeKjf1fWHbVt+b6kkQyo1OPrAy/1/UPExAL6d8w0jhvYHLd/+rr17cqAXjN9zH2LOge6qP0y1xGXF4TjzYwYtc2oY4IxmYss09enf0cf+Bp3ftUI29Lj6+fXPGdxxxN3fiVsmIG5ljbLmpBjjxOVZ3SSVlHXqDDXYe6DMtJcjk3WaeaVuLTqtw9C1qWfV/Nal/Xo5xdeHtX/tr2ZxKL2TdajLzNFnVcVU44KPTwq/0mYfk775fV+21IDrgVTyZgkYxjmfqBPuRKVj6NI+upKEZMbqAHpWvKmLZewfteL0Ndnngv9vOt5Vs9fTW+2Y309//Ef/xH42/GmVNfDzLws29aPyfImzJD/K+Ma0Mvifudf37asc1Da6MdkeRNmwJjAQl+vnGczLcy/48pJ85qVj3ksOnM9etp961vfCiwz19/v+KPOlTDXExVnUNqZ69f3W0WcYzN+XLmqX9NR3+mXR4T+HWgzHuthJn35oPLaLKvNOHHlUs2bFt885/p1qSKuTaF/R9bRrxxPklbK+65ZxpW8+qt5Dsx45nlWEflL9lWOJWq/9Pjm/cLcJo1PSn2a+M+VjvdL77VarW93BiKiUaVSKZRKpV15wruXZbPZvm/Qd6per2NlZSVxlzyicrkcug5brRY2NjZC4XtFq9XCzMwMms3mrl1LtHdJPXWvVtGL3iRINH4jdWUkIiKKsrW1tWszmknXUzbKKKl8Ph/ZTfuFF15INBSDaBIymQyazebAiYYmQe+aTePHhhkREY2VUgorKytjrVDU63Vsb2/HDoonitJoNELjh+CNoWKPGdrLZBIcfbzfpOXzeVy+fHnPvml+Fjx3XRmle4B4HrsaEdHukS4ownEcdvkgmqBsNhua7GQvV33q9XrgN1A57ILo+fHcNcyIiIiIiIj2GnZlJCIiIiIimjA2zIiIiIiIiCaMDTMiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimjA2zIiIiIiIiCZs3zbMstnsWH+8lIiIiIiIaFJGapilUin/k8/nzcUAgGKx6Mep1+vmYsD7sedUKoVyuWwu8sl6zEbY1tYWLMuKXTcREREREdF+MXTDLJvNQikFpRRc10Wj0Qg1zorFIjqdjh9vfn4+1IAqFouYmZkJhJlSqRSq1aoZ7JN1t1otcxEREREREdG+MVTDrNVqYX193f87k8mgVCqh0Wj4YZ1OB9VqFZVKxQ8rlUq4dOmS/zcAVCoVKKUCYSalFGq1mhkcUKvVsLCwYAYTERERERHtG0M1zKanp5HJZAJhhw8fhmVZ/t937tyBZVmBeGfPnoXruqHuiONQKBTgum7grZl0oSQiIiIiItoPhmqYRXnw4AHOnz/v/729vY1sNhuIIx4+fGgGjYVt27h586b/t3ShJCIiIiIi2g923DBbX1/HxYsXzeCnKpPJ7MrbOCIiIiIioqdhRw2zYrEYGHNGREREREREwxu5YVYul3Hq1KnQmLMjR45ga2srEPbRRx8BAE6cOBEIJyIiIiIiohEbZjL1faFQMBdFTvSxsbERmhBknDqdzq6tm4iIiIiIaLcN3TCr1+t48OBBYFxZp9NBsVgEvPFejuP4fwPA4uIiVlZW/L/HrdFo4Ny5c2YwERERERHRvjBUw6xcLmN+fh7VatWfkj6VSsGyLFy4cMGPJ79hJsubzSamp6e1NX26LpnSfnFxMXImx2w2i/n5eQCAZVkol8tmFNTrdViWFVg/p8snIiIiIqL9JKX2+bzy2WwWKysroYYfERERERHRfrGvG2apVAq1Wi1yrBsREREREdF+sW8bZtlsFuvr65z0g4iIiIiI9r192zAjIiIiIiJ6Vgw1+QcRERERERGNHxtmREREREREE8aGGRERERER0YSxYUZERERERDRhbJgRERERERFNGBtmREREREREE8aGGRERERER0YSxYUZERERERDRhbJgRERERERFNGBtmREREREREE8aGGRERERER0YSxYUZERERERDRhbJgRERERERFNGBtmREREREREE8aGGRERERER0YSxYUZERERERDRhbJgRERERERFNGBtmREREREREE8aGGRERERER0YSllFLKDCQiGkan04FlWWYw7XO8PRARET09fGNG+1an00GxWES5XDYX0VP28OFDlEolKKX4eYY+9Kler4dyuYxCoWAuIvIxnxDRTrFhRvtSvV7HjRs30Ol0zEU0AQ8ePMDJkyfNYKJ9r91u46233sL29jb+9Kc/mYuJgCHziTxUfPPNN81FtM8tLy+jWCyybkIjY8OM9qVCoYArV67gzJkz5qKQvXQTbLVaz+QbvvX1dUxPT5vBtIeMkvdYyQCOHj2KSqWCc+fOmYtC5I1JPp83Fz3TmE+S55N2u43Z2VmcO3cOV65cMRfTPvfaa6/h3LlzmJ2dRbvdNhcTDcSG2S4pFApIpVJ9P5PWbrdD+2R+hq3I7TVJboLtdhv5fB6pVAoHDx4cqgF39+5dZLNZMxgAUC6XQ+k5MzNjRvPJfrRaLXMR4FV+stmsv5/FYhG9Xi8Q5/jx46FtykcqTXfv3g0tk0+xWAysL4l2u41cLhcKM9edSqVw/Pjx0M3KjCvH32q1AuHmsY5buVz20zeVSqFQKIT21VSv1/28E3d8un7dnFqtFgqFAg4ePIhUKoVsNot6vW5G8+nxl5eXA8uS5r1WqxXIM+Yxs5KRXK/XQz6fx4EDB7C2tgZ416x5HsxP3PW+G6LuS3Flnh5Xb2jq+V2+x3ySTK/Xw+nTp7GyshL5IEuuff0TVw7ocfX7tP7du3fvBr6zWwbdu6LIQ4zjx4/j4MGDgWWj3KP0stjMg0nKVn1/ZFuFQiH2vtPr9WKHUkxPT+MXv/gFTp8+/Vw/rKARKdoVlmUpx3GUUko1m00FQDWbTaWUUqVSSeVyOeMbT1+tVlPpdFptbm4qpZSybVvZtq2UUqrb7ap0Oq1WV1eNb31KP55xs21bAYj8mEqlkiqVSmawUtox9NtPiWOeq6WlJTNqgOu6am5uLna/lLdvcct03W7XjxuXrqVSKXAszWYzsN8inU6rbrcbCMvlcoF4UWm2urqqACjXdQPhSZRKJVWr1cxg/5gkf7muq2zbVul02ozqx71+/XogfGlpSQGIzYdx4tIxjpm++r6a6Slkn+XYXddVlmVFHl+z2VS5XE4B8K8xczkA/zx1u10/f0n66RzHUZZlqVqtFrl/SfKebFPS3HVdlcvllGVZZlS1ubmp0un00PlDtrETpVIpMs3GQfYv6mNeI81ms+9+2LYd+o7jOCqXy/nnSF+vbDvq/EUZR1oqbx/m5ub8v2u1WiAf6AAoy7IC+9jtdhW8fGzu+6j5ZBz2Qz5xHCd2mYq4Jrvdrl8GmuWAxNXPpdLK8kH3sHFIcu+KIvnEcZzQcakh71Hdblflcjll23bkfSJp2SrlvYTFpa/S6k5R51/nOE7oHp3EMGlJz56dl/IU0u12A5Ubs5LUbDafSqE5yNLSUqBCbRYyUTde5RWqe6XgiCrAxaCboFJKXb9+XcGoHDmOEzhfJinYNzc3Q+dW12+ZznEctbq66t8IotIVWgNAyA076hwJWWfUzU9n23bkDSgJ27ZDN0vlHb9ZyZc0MY8xLq2WlpYiGzqDRG2jn3Q6HcpH/c6H8vbZTLOo42g2m8pxHNXtdpWtPfww45jHGbf9paWlQGU/StR+mGzbDlUapPITVcEZpZIhx7ATc3NzkWn2tDX7VLilomaek1wu518bUedzmAd040hLFVOWIOaBASIqntIwi2rIqRHzyTjsh3ySTqdDaa+Tc6yXpxJmnoeo/KS0a3hQmT8OSe5dJtd1B6ZDlLh7lPng0ZS0bLVtO5Sno8rr69ev++ci6rzo5FyY5cIg5r7R82XnpTwNFHVx7zVRBVWUWq2mLMtSiHmTVavV/DcDZmVXnszatq02NzeV7b0ZsywrsmKfRFxhrRLcBFXMuZH9HJQWakAFuN+yKHHnYNhwXZLKyk4a2uZDCJ0dUfF3HCd0k1Qx50EluOnGGfZ4LMsKPSyR9B2mgmPbdt/KdtxxRil5b/H0ayPpuUqS9xBTqYgLH6WSIWk4Kikj9I/sW7PZDLy11ssBeSIv+ysPW9J9egEMUvIeNEQd/9zc3MB8muSc9LPTtFQxFX/XdRUiGlpx5UvcmwsxSj7Zqf2QT6LS3iTf1cn9yCyH4vLT0tJSaB27LS6vRElyTzLFlXvXr1+PfCAySFTZaup6vWnM+4JOz2dR5CHGsHkp6ljp+RG+qmnsBl28o4i6EUV9km43rpCPElcIz83N+U/yu1p3Ab3QlJvG0tKS6na7gS4Nw5B90D/6/iS5CSqv8WbeJOKOL0q/dJNlkg5mQ9UUt12pOJmFe1x8Efc9k+N1t+qn6T0FNre1uroae+OC9mS+q3V5iWosIyKvyk0tKv4g/dIlityo5TuSf5Pmy6ZX+XO8N2NxkjTM5E2s7T3A0DleF0apwMF7sGGe4yR5L6oxqmLOhRqxkiF5dCei0kwqZJI+crz6vkkDwXEc5bqu6npdnkaptGJAuZokn0YdxzDGkZaSb0TT62Ib9QY2rmwbVPEfJZ+MQ1T67qV8Ir0z+rGNh1nyJjaqHIo6XrWDh1k7MeheJOSetLS0FGgsDyo34+5RlmX55a40sKPKTdGvbBXdbletrq4qO6Jrssk8x1GSxDElSUt6dvUvJWjHkhZYkxZXyEdJekxyw9PjmRUDNeS2k0pyE1ReAWhuO+nxqT6VF+Udv61185N9iiuk+21XKk+yrs3NTf/GFhVfaZX4fuRG2a9SKV03oioIjteVxSTHon+cmPEEUXH1z6DGdZR+6RJHzqV8oiqrUSS+pE+/7wzK67b2wGXO6y6ry+VyKq29Ce5qT/n1dEqS96SbqKST67p+GsTl0X7Losi53YlBaaa0xoC+b7JtPV36Xa+jinuabzL3b1jjSkvJX/Aa9eabMmHG1T9mOWDa6bGOYq/nkyTrNNPZjhk3pSLi6p9+5flu6Hfv0kmdYG5uzk/vZsx4aRF3j5JzmcvlAmVYXKN6UNmqjHuRPADrBwnyeZI4piRpSc8uzsq4yzY2NgBvlp69rNFoJJp6fhif/exnzSAAiJ3FcJw++eQTM+ipe/XVV7G2toZMJgN4M5flcjncvn3bjDrQT37yE0xNTcGyLH8Wvi996UsAgBdeeMGMjl6vh2q1iu9973vmooAbN24gnU7HzhQIb78vXryIN954A9VqNTDL1Pr6Ol599dVAfHj5Pp1OQymFbrcLy7Lw5MkTHD161IzqXyPdbhfewyIopVAqlWBZlp9+caJmIASAmZmZQFi/Kczr9TquXr2KZrMJpRRc18XU1BRefvnl2Fm5hOzvT3/6U9Trdbz88stmlMTW1taglMLm5iZc18UXv/jFQHo/fvwYJ06c8M/X1NQU3n77bQDAr371Kz9ekrz3j//4jygUCn46LS0t4fDhwwDg/zssfca+lDYTpHl+omYy24mpqSkzyDco/+zUX/7yFzMoRGarG+a3/nYjLRuNBq5fvw6lFK5fvw7XdfHlL3/ZjAZ4cZeWlgLX5Kd1RuDUqVNm9KGYxxb3GebYkphkPhlE8sjm5iaUUpibm8PW1lZkeSJxV1dXA+em2WwCAE6cOGF8I8ic8bbfZ5x+97vfAQBef/11P72np6fhOE5olkQRd4/6wx/+AAD45je/6devMpkMfvCDH8B13dDMjIPKVnj7orx71ve+9z0sLi7GzgI5LuO4f9GzhQ2zXXb//n3Ytm0G79g4b2yjVBqi9Ho9LC8vI5/PI5/PR07PvddEnZsPP/wQAPCZz3zGXLRjU1NTePz4sRk80NGjR/0by5MnT1CpVLC9vY1cLhdZoZCb2dmzZ81FPmm8vfHGG+aiSLKuO3fuADHT5Ivbt29jdnYW8I75/PnzuHXrVmQj5/bt27BtO1Rp0tfRz8WLFyMrj9LIko9MYR6lWCzCcZzADb5SqcB1Xf94B3n11Vf97wwzbXSUo0eP4tatW4CW3gCQTqe1WJ+SdBv0MMLMe1NTU6hUKn766BWjV155xf//MCSPmhVF8/xcvHjR/OrQ6vU6CoWCP637XjbKA7pxp6VMnS4Nsddeew0A8POf/zwQD1rcr3zlK5Hhgyr+g5jHFvdJemz97JV8cuDAATMo4N69e0in0/7Dq9dffx2u6+L99983o+LevXuAV+aY4UkeZkkDJMlnnOLS4MCBA/j444/N4L73qLj7szwQjntgEle26qampvDaa6/BcRxUq1Vz8ViN4/5FzxY2zHbZbryJwphvbKNUGqLk83m0222sra1hbW3Nr0hMQtwNwHTs2DE0Go1Ag+G3v/1t4AY5qpT222Gi1+tFNgaH1W63Ua1W8aMf/chchF6vh6tXr8JxnFBjR3fjxg18/PHH+M53vmMuipTJZOA4Dq5evYper4df//rXOH36tBkNvV4Pjx8/DiyTRr885RQS17xGJHynT+aTiqoUiLgGT7lcDj1NjXtLPEir1Qq9SY7Kw7Ozs9ja2gqESd79whe+4IeNkvd6vR4uXbqEUqnUN9/sBW+++SZ++MMf4j//8z9Rr9f9yswkxFUQdbv1gG4Y7733Xqhcs20bv/nNbwLx4MVFxD3hvffeS1Tx3yv2Uj75/Oc/D3hld5T19fXAgyhJe3nLpFtfX4/MT+Y69hp5KCAPP8Unn3wS+ZCv3z3q6NGjSKfT2N7eDoT/+c9/BgC89NJLwBBlaz6fD721i3oQNoyosploEDbMdpH5o69CfjxR3mb1ej3/SZ7833xtv1t6vR7eeecdM7gvKfDkqZ38yOjjx4/9gqzT6eDmzZvatz61vb2Nhw8fBsJ6vV6gYTQOg26CQgr8t956C9AaPPoTOun2EfUWRCrtcfs/NzfnL1teXsbjx4/x3e9+14wGaDcU+TdKr9dDvV7H6dOnUavVQhUneF3a4m5munfeeWdg48104cIFfPzxx/jVr36Fd999N7IblDyF/Ou//ms/TPZT8oyQuOaNS8JHbegMy7ZtVKtV/xz3ej1cu3Yt8NYxKh/o3+l0Ovj+97+PXC4XeV4wIK+7rhsoE956663QW89vf/vbkfEsywo9PR8m7929exf5fB6zs7OxD3MmVck4duwYHj58iE6ng3a7jXq9jt/85jd+vu31enjzzTdhWVbge1L508uAQdfrKKSh8/vf/95cBHjbbzQaZvBT1ev1cOvWrVAanTlzJvRgSuKaFWUJH1RZZT6JNj09jXQ6HXo4BW/bjx8/Du2bbduhru8S99ixY5Hhg87Pbuh379LrOkePHkUul8OlS5f8B0etVgvVahX/9E//ZHxz8D1K3miZZbD5nagy0yxbAUTu19LSUiCOkLwR9+AOgP+2M6o7KlEsc9AZjYc5oYE5kDOXy/kDQmU6XBkM6zhOKP5u0QfEDho4rZOBzJZl+YNoZQYseIPDZaCvzIolxwltymKZlEC+M076JAn9rK6u+jPcRc1eFzWwWX5LSvY97c3uqA8ovn79eiBOLpeLHMhdq9VCA+1t2w7t++rqqkqn02pubq5v/rAjpqk3RQ14T8q2bWVZVuQA63753py4wowrk2aY4aMwtz1I1/shV8kHiBggbuYD13X9CVYwYPIPSTNZt2VZgeut681aqecXc/uiZvxkRdQ2k+Y91/tNoaj8ZpLr2dxWP5JmO+F6P9wN7Udzm82mHybXnRxrrVZTm9o06Gnv5xn0OFF5dyf6zeAp28QIkwDodpKWceV81GQMelx91k49vN9xjJJPxmE/5BMn5rc1ZXtm2i55v1Wpnwc9rn5N6+HDlH07keTeZR5TV5uxWdI4qmxKeo+SSYwQc/9OWrY2vZl1ZV1WzOQfm95P/Ug8eOVr1Ay3zoi/6fc0zyHtPSk1yXf7z7FyuYzt7W1UKhUUi0V0Oh1cunQJ09PTKBaLqFQq5ldoSJKu7Js9Xq1WCzMzM3AcZ8/m01QqhWazGfvmioYn3TaHOeeSV57120y9XkexWMQf//jH2Kf7O7Vf0nKUfPK86PV6+NznPodf/vKXLJueca1WC1//+tfx+PHjobv+8v71fGNXxgk5cOAAOp0O6vV6YBxNuVz2B2XTzrz99tt4+PBhZBdEGt309DQsy8LXvvY1c9GeoZTiTW2MWq0W6vU6Lly4YC7qa9qbZOBZVygUcOLECdy4ccNcNDb7IS1HzSfPi6mpKbz77rtYWFjgfekZ1mq1sLCwgHfffXfoRhl4/3rusWE2IZ///OfR6/Xw3//93ygUCjhz5gz+4R/+AdDGLNDO8Ca4e7a2tkJjmujZtNNKxvPiZz/7GW7fvh07tvhZx3ySzNGjR7G+vo6bN2/647Pp2bG8vIybN29ifX2ddTkaCbsyTki73cbp06f919zlchn3799nt7td0Ol0/Ikcrly5Yi4mohjLy8tot9u4cOECK9sJ9Ho93Lhx47kry5lPiIjGgw0zIiIiIiKiCWNXRiIiIiIiogljw4yIiIiIiGjC2DAjIiIiIiKaMDbMiIjGrNPpoFAo4ODBg0ilUsjn8+j1emY0IiIiIh8bZkREY/bRRx/BdV388Y9/hOu62Nrawp07d8xoRERERD42zIiIxmx6ehqPHj3C1NQUMpkM0uk0Dhw4YEYjIiIi8nG6fCKiXSK/a7W9vY1KpWIuJiIiIvLxjRkR0S5otVo4dOgQrl69CniNNCIiIqI4bJgR0XOh0+kglUo9lQ+87oxKKTx+/BiPHj3CjRs3zF0iIiIi8rFhRkTPhYcPH6JUKkEpteufVquFQqGAXq+HAwcOYGpqytydPafT6aBYLKJcLpuLiIiI6Clgw4yIngsPHjzAyZMnzeBd8dJLL+HgwYM4dOgQDh06hEwmg+985ztmtD2jXq/jxo0b6HQ65qIQacC9+eab5iLax5aXl1EsFhPlASIi2h2c/IOIngvZbBZbW1tmMGnkbdnFixfNRQCAdruNb3zjG1hZWcH09LS5mPa5VquFhYUF/OIXv8DRo0fNxbSLyuUyTp48yeuK6DnHN2ZET0G73UY+n0cqlcLBgwcTvW3o9XooFov+uCXpGme6e/custksUqkUstks6vW6GcXvWic/eBwXb3l52V/XwYMHUSwWI7cJbf/iur7dvXsXx48f9/f/+PHjuHv3biBOr9dDuVwOxIs6zna7jUKh4MfJZrOx243SbreRy+XMYD899E9c2uhx9W3r3zWP71nS6/Vw+vTpyEZZ0rxqKpfLfroeP34c7XY7sDwqf0TlyaT5W8g1EyfJNSXk2m61WuaiXdFut0N5Ni79zLiyj61WKxAu6Tk9PY1f/OIXOH369ETenL355ptIeWM0k9Dzx8GDB83FKJfL/nlMefnSTCNdvV73y2kzXpI8HpVfo+LJNvTP4uJiIA4RPacUEe2qbrer0um0chxHKaVUs9lUANTS0pIZNcC2bZXL5VS321Wu6yrLslQulwvEkXXVajWllFKlUkkBUKurq6E4sv1ut6vm5uYUALW5uenHK5VKKp1Oq2az6X9P329drVZT6XRaAVClUslc7G/z+vXrSnnbXFpaUgD89SvvGNPptL8f8r25uTk/jp5+3W5XKW/7cduOUiqV/DTS9dtPPW30uPq+KaXU6upqovPZj5ku4yR5IupjbrNUKsWmqeM4yrZtM1iphHnVJPltc3PTz5PpdFq5ruvHkfwh+ynnXc+TSfO3Ukq5rusvQ8ztL8k1pbzt6GlrpuVuku3K8bmu66eVSeJKHheSz83jUt65jrrud5Oke9x5MW1ubvrlgnmeVUR5pqeRlCOi2+2qXC6nbNuOTA+VMI8nKc8kXty1RETPt2QlIBGN7Pr16wpAoDLgOE7fCsjm5mao0iSVUr0CODc3F6ocWJYVuOk3vQaWTioM+rr0yqiQypu+79evX/cr74hpHM3NzUVWPMz4tm2HKoxmpUXSz2TG68e27UCFX0g66MskzDyuqDRTWsMsqnKYhJxrc72T0K9hlk6nQ/lDDZFXTel0OtCYdV03lO62bYf2RyrQImn+lgbb5uam31iJkuSaUt41vLq6GrmtOJubm8q27VDDQMg6BymVSsqyrFBY1H7EHevS0lIo3YTk6bj9HDdpGA0qF4XrurH5UaTT6VDeiTtXsu04SfN4kvIsLoyISCml2JWRaJf913/9F2zbDszMd+rUKcDrUhSl0WgAAF599VU/7MSJEwCAjY0NP+zWrVv45je/6f8NALOzs/734XVPevLkSSDOxsYG0uk0XnjhBUDbjxdffDEQ7ytf+QoA4A9/+IMf9tprr8WOQRIHDx4Mdd8RBw4c8P+/traG1157zf+71+vh4cOHOHbsmB8m8aPW91d/9VdmUEiv18PW1hYymYy5CBsbG7AsK7Dsww8/BADYtq3F/P/T3ezG995778GyrJHG5NTrdXzjG98AAMzMzPjdmvTl0i3q4MGDgS6U9XodqVQK+Xw+0FU2m82O3A3t/v37+N///V8zGK1WCx9//LGfB3VJ86pO1if5CwAymQwsy8L9+/f9sLW1tVBeM2e4TJK/4X2vXq8PPE9JrikAqFQqgWNO4sUXX0Sv10M+nw/l52KxiEePHuHll18OhEe5f/8+ZmdnA2Hb29tIp9Oh/Hn//v1QXgaA9fV1FAoFMxgA/H14//33zUW74t///d/xd3/3dzhy5Ii5KNLS0hJOnDgRu//wyqBPPvnEDAYAfOYzn/H/v7y8DNd18fbbbwfi6JLm8STlGRFRP2yYEe2yhw8fmkGhBpApqnJsNizMMRCiX+Wm3W6jXC7j/v37ePfdd/11SgX2z3/+s/GN0Zw7dw6PHz/G8vKyH1Yul5HL5XD27NlAXHgVmLt37+Jb3/oW3njjDVy5csVf9sorryCdTuOtt97yK7OtVgsPHz7EP//zP/vxWq1W5Fif999/H3Nzc4EwYVZw6/U6isUiHMcJVeD7VXDNSnJShUIBKysrAIBms+lPty/LfvjDH2JtbQ3dbhezs7NYXFz006BQKGBpaQlbW1v4+c9/jp/97GfY3NzEkydPcO3atcB2BpExR41GA7du3UJKG48EAL///e+BiDyIhHnVJOsz9Rv3BW9GyEajge9+97vmIqBP/k5qlGtqGFNTU1hbWwO8cUZyLqVRtra2Fmp4Rmk0Gv7DHRnXVK1WUalUzKhoNBo4c+ZMIKzX6+Hx48f+OkyyD7/73e/MRWPXarWwvr4eaND00+l0cOvWLRw7diww7tQce3j+/HlUq1U/H/d6Pfz4xz8OXdv/9m//htnZWbz11lv+GEV52CGGzeP9yjOxtbXlP3TJZrPP9PhUIkqODTOiXfbxxx+bQQP96U9/MoNC/vKXv5hBfeXzeXzxi1/E4uJi6E1TJpNBLpfD97//ff9tS7vdxo9//ONAvKSmp6fRbDbx//7f//MrTlevXsVPfvKTUMWz1Wrh0KFD+OpXvxo5a+LU1BT++Mc/ol6v49ChQ0ilUpiZmUGlUvErWMvLy9jY2MDDhw9x8+bNwPf/53/+B3/zN38TCBONRgPVatXfx5WVFfz0pz+NreA2Gg0/rnz6VXB3ol6v49GjR5iamsLU1BS+/e1vA8bbS3mbeOXKFUxNTeHo0aM4ceLE0G/M5Mew9Y/+5iXuzQMS5lVTv/X1Mzc3B8dxIt9U9cvfSQ17TY1iamoKjx49wvHjx5HP54dulElDY35+HqlUCocOHcL29jY2NzdDb5Ak7uLiYiDPHjp0CNDe+kxKr9fDwsICfvKTn5iLYv3f//0fAMB1XfzLv/wLlFJoNpuo1+t46623/HgXL17EG2+84b+JPnToUOjNWK/Xg+u6cF0X586dw5MnT+C6Lnq9nv8mG0Pm8UHlGQCcOXMGs7OzgYcuX/3qV0MPlYjo+cOGGdFzYm1tDUopbG5uwnVdfPGLXwxU4KXRZFkWDh48iOXlZXzpS18CtDdqSbXbbXz961/H9evXoZRCt9uF4zg4ffp0qPIhjYJut4vvfe97WFxcRLFY9JdL169CoYButwulFGq1Gubn5/03ctK98o033kC1Wg0c1/r6emRFXvZjc3MTSinMzc1ha2srsiuZxF1dXQ00XprNJvCUKrif/exnzSAgwVumZ0WxWMTx48cjG81IkL/3mkqlgo8//hjVahW3bt1K1CiD1k1TrhnLsvDkyZPQG16JC8C/buRTKpVCXXiHJW9Zk3zi/P3f/z3Onz8fue/5fN4MArS3eK+//rq//9PT03AcJzB7Zr1ex9WrV/030a7rYmpqCi+//LL/Zk0ecnzzm9/0H0RkMhn84Ac/gOu6sW9Q+xlUnsFrNFYqFf+hizQW7927F4hHRM8fNsyIdllU9zcZx6SPddCZXY/gdeGB9pbEHEsiZKxJnKNHj+LWrVsAgDt37gTCpXL75MkTVCoVbG9vI5fLDV2B+9d//VdYluV3T5qamsKVK1dw8ODB2LdwU1NTeO211+A4DqrVqh9+584dPH78GG+//bZfeS0UCnAcJ9CVEYDfTVKOqx0zTT68SlA6nfYrha+//jpc140cVyMVJrOBd+/evR1XcOP0ej0sLy8jn88jn89jZmbGjPLU6OMCTUnyqinuh763trYi33ZJxTauUaaLy99JjHpNDUseNqTTaTiOg7m5uUA3vH5u377td52dmprC+fPncevWrcjv3759OzS+VcJH7X4rot6yxn2itNtt3Lp1K/A2T6aMT3ndaqPE5akDBw4EeidIl2S9wVWpVOC6rp8v4spfeQgib1BHyeNx5VkUOT+/+c1vzEVE9Jxhw4xolx07dgyNRiNQcfrtb38baBSYvvCFLwDe7ykJ6cLz5S9/2Q/L5XK4ffu2/zcAPHr0KFDparVaobcqcZUJXbvdRrVaxY9+9CNz0UD9uv7oy/L5fOg3oswKcL9ub2Y30UwmA8dxcPXqVfR6Pfz617/G6dOnA3GEOTZMKnBR42rW19cjG9jmOsZJxrmsra1hbW3Nfzs3CZ///OeBmDFYSfOq7qWXXgKMNwTSrcw8X8vLy3j06FGgUaa/TRk1f8dJck3thDTKer0e1tbWUKlU/G6NUY0rXc8bG6ankTRy9S6u0OKajQoJ79f9VvZDzu1uOHr0aKgBVyqVgE+nZYxt0Emekodb4pNPPgk8hDHLBp2UKUePHkU6ncb29nZguYy1lXyaNI8nKc+i8qukt3muiOg5ZE7TSETjJdOAy3TM8vs7+lTOUdM4y+/kdLvdwO/s6GTKZpk2OmoKZ1m3bK/b7SrHcUK/GSW63a7/O2X9pqPudrsKMb/fJVN061NHy7T3+pTTtm0ry7L8/ZCpz/V1yv7rv2MWFU9IetdqNX96dJNMf21+3/Z+q0gXFzcufFhmOsq/epjruv5U4vq5lfOoy+VyoWMYh375YVBejcrfsu9yfuRvfYp2uVb0MJkqXQybv1XMz0CIJNeUTqaWTzLNvaSNpJXOcZzIcF3UNaSMvCLi4saF6572dPlCzotJP7/Ky+NR5YaeP23jN/Di8oVsU+K5rhs5ff6gPK5GKM9Un/0ioudTuAQkorFbXV1VlmUpACqd8Pd15DeP4P3oqt4w0ZW8H1IFoCzLClW4ut4P4eZyOX9dcQ2W1dVVlU6n1dzcXGxFVPZLtglA5XK5yIqhvs1cLhfat2az6f+wsOy/mTYST0+LuHhCKkjmbz0JWQ+MCp9U0vRj0ePq+6+Hx6VVUtKQtSzLPy/Xr1/308Xxft8KXv7Z3Nz0GwzQfsBWKt3ynXFy+vzA9KC8GpW/pUIq37FtO5Qn9XWaH309SfP30tJSIF46nY7c7qBrSnkNNnP/bNuObbwqL53MtNHJeY4iaSgfPS1zuVygYWLGle2Z4XGcCfzAtJ6fzesWxnXa1X5EXOKb6db1fixeyl0MyBdyvqPKZ5Ugj6uE5VnS/ZLlOy1biGh/Sam4/gJERPtUq9XCzMwMHMdJNC6JBuv1evjc5z6HX/7yl7FjsWj/a7Va+PrXv47Hjx/vythJGk6xWGQZRvQc4RgzInrmTE9Pw7IsfO1rXzMX0Yimpqbw7rvvYmFhITSzJj0bWq0WFhYWRvoNOBq/er3edywgET17+MaMiIgS63Q6uHbtGtLpdOQP59L+tLy8jHa7jQsXLrBRtgdks1nMzs7ybRnRc4YNMyIiIiIiogljV0YiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimjA2zIiIiIiIiCaMDTMiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimjA2zIiIiIiIiCaMDTMiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimjA2zIiIiIiIiCaMDTMiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimjA2zIiIiIiIiCaMDTMiIiIiIqIJY8OMiIiIiIhowtgwIyIiIiIimrBdb5i1Wi2kUim0Wi1zEdFT1+l0kM1mzeAdKRaLqNfrZvBTk8/nkc/nzWB6xqVSKZTLZTN4KPV6HalUCp1Ox1w0NsVicezX3DAGpVNUGpTLZaRSqUC8cSuXy333a5BWq8XrnojoGbOrDbNWq4WZmRkzeCLa7TZSqVTfz05uks+SVqu1o4ZGp9MJpKusr1/jvFwu9628FYvF0PkyP4POX71eh2VZ2Nra8sP09cYdcz6fD22n0+mgWCwCACqVClZWVvy/n6Z8Po9Go2EGRzp48GAozbLZbORx63H1dNW/e/fu3cB3nmdP+9ynRmg0dDqd0DVSKBSglEImkwmEj0uxWES1WjWDnyqlFC5evGgGA16ZMD8/bwbj4sWLUEoFwuS6H4d8Po/t7W1/v6RxaF5vunw+H1g2PT2NhYWFkfICERHtTbvaMJuenkaz2TSDJ+IPf/gD0uk0Njc3oZSCbduwbRtKKXS7XaTTaXzhC18wv/ZcunnzphmUWKfTgWVZKJVKUEpBKYXLly9HVn50i4uLcF23b+OtVqv567QsC47j+H/XajVsb2+bX/F1Oh3Mz88HKlvlchlHjhzx17GyshLYvrzthVe5kw8AWJblxwOAtbW1yIrvbltbW4Nt22ZwpF/+8pcAgOvXr/v5fm5uDvPz82i325Fx5+bmApXa1dVVAMDS0hJeffVVP/x5FtWw3W1moyGJO3fumEG7rlKpwHEcM3jPKBQKqNVqZnCkd955xwwaSblcRiaTQaVSAbSyqdlsotlsYnFxMVQOlstlnDlzJtTAlP3v91CLiIj2j11tmO0lv/3tb1GpVHD06FEAQKPRwJkzZwAAU1NTOHHiBF5++WXjW8+fVqu1oyfcDx8+BLwnzmJQ46Fer6NUKgEALl++bC4GABw5cgSFQsEM9vVbBu/JvVkBu3//fmA/L126hI2NDcCrLM3MzMBxHKytrWnf+vTYarVa6Ol5pVLB4uJiKHyveeWVVwAv33/lK18BvOshyuuvv24GAQD+9m//1gx6bg166LAXdDodLC4umsGUULlchuu6ZvDQ5DxIowxamTk9PY3p6WkA8MsheGWyWVbpCoUCstnsU38oRERE4zdUwyybzfrdLaR7mt61Scjf5pNkvduYWXnV12U+/ctms/44HrOrh75P5vZ0V65c8Svv8jTy5MmT/vK1tTVMTU35f5tku2Vv7IG+LT0N9Cedsr96HPPYZHyQvJ3Ru0T1OzZZVq/XQ92okqSlbE+OS74nXU/n5+eR0s6Tfnzm9qKYcRYWFgJ/6y5duoSzZ8/CcRw0Go1Q3oDR0IujV3Z0rVYLjUYj1Hg7c+ZMIC9dvnzZzxPXrl0DAJw7d85frisUCqHuX5lMBrZtD3wzIWkZdX7kfOjn0Dz3Ei9l5JdBNjY2YFlWYL8//PBDAAg1nKViKBVF8d5778GyLP8BR5x++VrvGqqnv3SBbXljZySOnh/k+oOXBnr66eWLmS6yTMoR/TqV7UZtr6iNj5LlMq5HvgcA1WoVKe1cyf5LmRGVp4UeT19Hv/2Ko8eX/W61Wv4b3sXFRaS0cko/Ptm+uT05Fn08kx53mHFOUd+RMDlnet6X/ZR8JOWPvo96Opn7on9Pp68jiuRfeHGlUWtZFrLZbKBslv3W98Pcnrh27drQbxBnZmZCD4dMCwsLY3ujR0REE6SG5DiOsm07EAZAlUol/+9araaazaZSSqlms6kAKMuyVK1WU0opZVmWchzHj18qlQLrtCzL/9uyLAVA2bbtb1u2BcDfTq1WC/zdT6lUUsMcOgB/H0qlkn8srusqAMp1XaW09bqu6/9fPvq65Nhs2/b/luOTdOl3bI7j+GnpOE5g/UnSUg8z00KOSdavvP2Ubdu2rSzL8pdFke3o+xyn2Wz6xyx5Rc9Lccw81I+csyiSfuYxyzEMq9+2lLHfcryyXdkPPY7jOKH16d+RvGFek1Ekj4larabS6XRkOkqeNOVyucj4un75Wq4dZRy//N/MN7IupeVVWXetVvPTxtbKBcnD8netVvO3b15Lsl2hX0/yfz0vSHw9X5t5sVQq+dvWy4Qo+vHoZUu//dK/q18r/fJWVHz9+ISZPnpc/f/63wD65gnHy8PDpKEeR88bev6xvHJM9t9cr/49fVuOcQ+TOHKO9PwrJF308yjb18m5i2NZVqh8k/yqH6usw9bK3n70dRAR0f41dM1TblA6y7ICN3f9xiM3Gr2CYN4YLcsK3PDMyoy5fmVUtgQGVBBEXKWzH/NGrYwKmIqoEMpx6Mxj0yueYtCxOY4T2O6waanHl/Mjy+MaZubfg0ilTz5xFQaz4qFXtPoxK3L9jHq+k+yHKer60JkVMz2/yN/6cZnrM68dNcTx6ecDXn5eXV01oykVEVf/6HkhTlS+bjabof3U86NZKdXDZJuSn/U4ruuGzpWej2q1WmC7pVLJ/76jPeRQEduTfKwz08DMi6VSKfC34zixDTMVU7YM2i8VkXeS5C39b6U1mnTmeppeg0GY6RGVRrqo5eaxmGlo5oWo4zf3ParsiluPfj7kGtPDzGsqKo65bmU0YE1R+ydk/fo5Kmn3F/kuIvKKiFs3ERHtH0N1ZYQ2lke627RaLczOzgYmboiahOHFF18M/C0z43U6HbiuC8uy/G4g0m3ko48+8uPPzs76/weABw8e+N2H9C4pSbr76OPLhmF+5/79+37XoFQq5XcXijp+IV3l9GMzu5ENOrZTp0752y2Xy343l6RpaXbBg7HcdObMGb9rY71eH9itBl63QuVNsgKvO47ZJU+OR+8ut7CwMHASkP1sa2sLFy9eDHRzNR05csQM8tNqfX09lA+TkPSUyW/m5uawtbUVOa5S4q6urkJpk57IRD4nTpwwvhHNzNcbGxtoNBqBfO26bmCWTJPkjQ8++CAyHN4YHdd1A+ttNBr+mKATJ074283n87h48aL//fX1dT9vp1Ipvyuvvj1zohdE7I/u5MmT/vVbLBZRqVQirzmdeU6T7JcpSd5K4vz584FucRsbG356Sd7Q01rGpPYre4dNwzjmfWRYUd15RzU9PQ3LsvzJkjqdTuS1m4TMjqm8GSRbxriy2dlZf0KlRqPB8WRERM+ooRtmAOA4DlZWVgBvBj8Z27OxsYFWq4VTp04Z3xjMdd1AJVApFRrfYtJn5ZPPoEaDVCz08WU7oc8+KJ+4sU7D6HdschOHNmZEN0pa9nPx4kW/kjs/Px8aF6Uzx++sra35+3rp0iUt5qfjLcyKukykEDcJyNNi2zZc1+1b2RyFVJofPHjgp8vTcO/ePaTTaX9s2Ouvvw7XdfH++++bUXHv3j0ACM26eO/evR1XamUmVP3Tr2GWlGVZofVK+mYyGSjvIYHkNz2P6rN9yifJmMY409PTUN7ModJAGyUfDbtf48pbZ8+eHfhwxNwvtYtT7u9lly9f9humd+7cGdt9RR9XJg/cZN22beP+/fvGN4iI6FkwUsPs1KlT/iQN8oTQcRy88847uHnzZmiShX7kZi4zUwmzgm86cuQI1tfXzeDQoH9T3KQGo8hkMpE3yH77IJMt9Nv+oGOTf6VCBG0KZoyQloMUi0W/cuu6LlzXDb390kVNty+zLopOp4P19fVQ5U4p1XcSkFGcOXNm6Mq/NCJlEpAo5kQD8N4CRL0dgHfMMi32qI33bDYbmecGWV9fD7x1lvz3u9/9Tov1qfX19dDbLgk331wP4/Dhw5GzP/a7XiQP9Kvwvvjii5GNaH0yiVar5T8ksG3bb/hns1k8ePAg8L3ODn/2QCb72NragvIaaP3yUZRh92sceUvIJDaXL19GvV7H2bNn/WUvvPACoD3gEoMmOBmk38OecTp8+HBkXhmV3OvK5TLu37/ft1zPZDKwLGvgm8J8Pj/0z8zI8ez0jSIREU3WSA0zuRnNzs76N+1z586NfMMrlUqYn58PfHdlZaXvTU7e4uiVular1bcrSa/XG+vMVRcuXAh1K6nX66E3hvry+fn5UCPFlOTY9EaBZVk4fPgwMGJa6qRx98EHH6DlzbzZ0X5MWZb3qwBUq9VQZXtxcRHnz5/3/y4Wi7FvxS5cuAAMqLAP4+TJk0PnzenpaZRKJVSr1cgGWCqVCr0BhNe9VT9OnXQXlca5WblNYmFhIZDnOp0OGo0GGo1GbOW23W7j8ePHoQajbdu4fft2IEziHjt2LDI8nU4HwodRKBRgWVYgPaPOiZ4visUibNvum3+np6dh23ao0aivW/+h+0wm4+fjS5cuoVqtBh40DPvmI5vN+tuSPKvvSzab7VsuRRl2v5LkLcuysL293beBJySfPXjwIPAmLJPJwHGcQHrCy/c7fWOmP4ySWVxnZmYij2VU+r1LSO8Py7JityXl3UcffRRqhDqOg8XFxVB31Cizs7N9H6yUvd8r0/O7NOjkoWJUV/yHDx/Csqy+1wkREe0D5qCzpGxjhjcVMWhcH9AsA5NlUgD5CBnULx8ZUK2HRQ16HrRcp297UFyhD7pGxOQi/ZbLMenHZg7Wl3BzAL7qc2yO4/gDz81tqiHSMur8yPr1bcpEBBLPnEBAV/NmJTPXbU5CoO+HTj8u+eiD681jM5fHkeMdhbk9xFw2khf6TfRg5kHJA3pegHdOzWOV49TDZfIMMx11+jr087C0tKQAqKWlpci4+sQgUfsRZ1C+jlsu514/PjPf6/thpnNc2SJ5MmqbKiLPSRqZ64u71vW8rrTJRSQs7tzErU/E7Vfc9+Lylmxf0lWO3zw+E/pMJjHoXAhzG3H7boZLmkaVJVH3kai0ivpe1LbkWOQY4vKnhJvlX5LrXkjcKCVjRl2dvs9RcWxtVlKlnZ+480dERHtTSn16A6ZdUC6Xsbi46Hc3pMnpdDr+OKTdUiwWceTIkb7jgCheq9XCzMwMms0mn/zTvpLP5weObxblchnb29s77nIqWq0WFhYWhu6uTUREe89IXRmJ9ptMJoNmsznyRAyDSPc8NsqIni+tVivUtbAfKSOiukgPq16vY2Zmho0yIqJnBBtm9NyQ2fLMsUg7VSwWsbCwMLYn4ES0f1y+fDkwQUoSlUoFCwsLA8f69dNqtbCysrKrvQCIiOjpYlfGXVIsFv1plOFNYb/TwfFEz6p6ve7/VAK8qeKHmd2V6GmSbuoA2PWWiIjGhg0zIiIiIiKiCWNXRiIiIiIiogljw4yIiIiIiGjC2DAjIiIiIiKaMDbMiIiIiIiIJowNMyIiIiIiogljw4yIiIiIiGjC2DAjIiIiIiKaMDbMiIiIiIiIJowNMyIiIiIiogn7/wA/RQlJU1UN7wAAAABJRU5ErkJggg==\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3.\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple linear regression was first performed to understand the relationships between specific humidity and relevant predictor variables. Predictor variables included proximity to the coast (kilometers), impervious surface (%), and latitude. Average specific humidity was calculated at each station (2018\u0026ndash;2022) from daily observations. Impervious surface represents the mean value of pixels (% impervious surface) within a census tract that a weather station is located in.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, correlations between nighttime LST and nighttime specific humidity were determined. Average nighttime (7 PM to 7 AM local time) specific humidity values (2018\u0026ndash;2022) were compared to mean nighttime LST values measured at each station. Mean nighttime LST pixels were calculated for 2018\u0026ndash;2022 across the peninsula. Mean nighttime LST pixels were then aggregated to their mean value within a 1-kilometer buffer around each weather station for comparison to specific humidity. The relationship between diurnal LST (difference between daytime and nighttime LST) and specific humidity was also explored. Diurnal LST was calculated by subtracting mean nighttime LST pixels from daytime LST pixels across the peninsula. Pixels that contain diurnal LST values were then aggregated to their mean value with a 1-km buffer for comparison to specific humidity.\u003c/p\u003e\n\u003cp\u003eLastly, to understand the role of nighttime LST in nighttime thermal comfort, nighttime LST was compared to nighttime SAT, heat index, and wet bulb temperature at each station. Since MODIS nighttime imagery is captured at 1 AM, nighttime LST was compared to 1 AM SAT, heat index, and wet bulb temperature observations.\u0026nbsp;\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp;Modulating Factors for Specific Humidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecific humidity (2018-2022) increases as latitude and distance to the coast decrease (Fig. S1). Decreasing latitude results in the largest increase in specific humidity, as represented by the coefficient of the largest magnitude (-0.14) among predictor variables (Table 1). More specifically, a unit decrease in latitude southwards produces approximately a 0.14 g/kg increase in average specific humidity. Along the latitudinal range of peninsular Florida\u0026rsquo;s weather stations (24.57N to 29.95N), average specific humidity has a range of only 1.68 g/kg (18.77 g/kg to 17.09 g/kg)\u0026mdash;highlighting the elevated amounts of water vapor the entire region experiences during summer months. Proximity or distance to the coast exhibits a similar relationship, with a smaller coefficient (-0.01). Each kilometer increase in distance from the coast produces approximately a 0.01 g/kg decrease in specific humidity. Across stations, air near the station furthest from the coast (94.9 km) contains 0.9 g/kg less air moisture as compared to air near the station closest to the coast (0.26 km), without accounting for latitudinal impacts. Impervious surface was found to be statistically insignificant in the multiple regression model (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Florida peninsula average daily specific humidity vs predictor variables. The relationships for specific humidity and latitude, impervious surface, and coastal proximity are provided for 30 weather stations across the peninsula. Each coefficient can be interpreted a unit change in specific humidity per a unit increase in a variable, while other variables are held constant (e.g., a kilometer increase in distance from the coast equals a -0.006 decrease in g/kg of average specific humidity)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\" style=\"margin-right: calc(52%); width: 48%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.3076%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.3076%;\"\u003e\n \u003cp\u003eLatitude*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.3076%;\"\u003e\n \u003cp\u003eImpervious Surface (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.3076%;\"\u003e\n \u003cp\u003eProximity to coast (km)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.3076%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e*Statistically significant (p \u0026nbsp;\u0026lt; 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.3076%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R-Squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.3076%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.934%;\"\u003e\n \u003cp\u003e21.85*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8068%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.9517%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.00135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp;Nighttime LST and Specific Humidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNighttime LST is higher where specific humidity is higher (Fig. 2). Nighttime LST increases by 1.30 \u0026deg;C per unit increase in specific humidity (g/kg) (Fig. 2b). The highest nighttime LSTs, upwards of 25.0 \u0026deg;C, coupled with the highest levels of air moisture (above 18 g/kg), are located at stations of lower latitude and along peninsular coasts (Table 2). Nighttime LST decreases by 0.02 \u0026deg;C per kilometer increase in distance from peninsular Florida coasts (Fig. 2c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs expected, nighttime LSTs also remain higher in more urbanized areas with increased imperviousness (Table 2). Urban development is most extensive and continuous along the southeastern coast, where the state\u0026rsquo;s largest metropolitan region by population is located (West Palm Beach\u0026ndash;Fort Lauderdale\u0026ndash;Miami). As such, this region particularly stands out as warmest, with nighttime LSTs near 25.0 \u0026deg;C, on average. Developed regions with increased impervious surface across the peninsula are also closer to the coast, on average, where specific humidity is increased (Table 2). Stations situated in census tracts with greater than or equal to 10 percent impervious surface are on average 17 kilometers away from the coast, while stations situated in tracts with less than or equal to 10 percent impervious surface are on average 42 kilometers away from the coast.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Average specific and relative humidity (daily average), mean nighttime (1 AM) and diurnal LST (daytime, 3 pm \u0026ndash; nighttime) and average nighttime (7 PM\u0026ndash;7 AM) thermal comfort measures by state region (North, Central, or South Florida), distance to coast, and urbanicity (June\u0026ndash;August, 2018-2022) as measured at Florida peninsula weather stations (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 30)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Daily Specific Humidity (g/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Daily Relative Humidity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Nighttime\u0026nbsp;\u003cbr\u003e\u0026nbsp;LST (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Diurnal\u0026nbsp;\u003cbr\u003e\u0026nbsp;LST (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Nighttime SAT (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Nighttime Heat\u0026nbsp;\u003cbr\u003e\u0026nbsp;Index (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Nighttime Wet Bulb\u0026nbsp;\u003cbr\u003e\u0026nbsp;Temperature (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNorth Florida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e82.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e24.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e25.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e23.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eCentral Florida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e78.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e27.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e23.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eSouth Florida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e18.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e77.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e24.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e29.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e24.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eAll Regions (Peninsula)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e79.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e25.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e23.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt; 10 km from coast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e18.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e77.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e24.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e28.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026ge; 10 km from coast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e80.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e10.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e24.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e25.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt; 10 % Impervious Surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e81.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e23.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e24.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e26.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e23.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026ge; 10 % Impervious Surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e78.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e24.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e27.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e24.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHigher nighttime LSTs also correspond with decreased diurnal LSTs, or lower differences between daytime and nighttime LSTs (Fig. 3b). For every 1.0 \u0026deg;C increase in nighttime LST, diurnal LST decreases by 0.61 \u0026deg;C. Less urban areas, surprisingly, exhibit slightly smaller diurnal differences in LST than urban areas (Table 2). Because increased impervious surface leads to higher daytime LSTs, in many cases it can be assumed that the potential range between daytime and nighttime LSTs could be larger, as less urban areas have less heat to release during evening hours. However, stations in tracts with increased impervious surface are closer to peninsular coasts where specific humidity is also increased (Table 2), potentially offsetting the role of increased urbanicity increasing diurnal LSTs. Despite this finding, pockets of higher diurnal LSTs (greater than 10 \u0026deg;C) are still most prevalent in the peninsula\u0026rsquo;s most urbanized areas (Fig. 3a). Regardless of urbanicity, North Florida exhibits the smallest mean diurnal LST measured across regional stations, but also exhibits the lowest mean daytime LST across regional stations (32.68 \u0026deg;C; 34.41 \u0026deg;C and 34.10 \u0026deg;C in Central and South Florida, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp;Nighttime and Diurnal LST vs Nighttime and Diurnal Thermal Comfort Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNighttime thermal comfort decreases where nighttime LST is increased (Table 2). Nighttime LST is significantly and positively correlated with nighttime SAT, heat index, and wet bulb temperature (Fig. 4). Across the peninsula, nighttime SAT increases 1.1 \u0026deg;C for every 1.0 \u0026deg;C increase in nighttime LST, and nighttime LST explains 85% of the variance in nighttime SAT (Fig. 4d), which is expected since LST represents a primary source of energy to heat the lower atmosphere. SAT is also a key component of both the heat index and wet bulb temperature. Compared to nighttime LST, nighttime heat index values increase at an even greater rate than nighttime SAT (2.0 \u0026deg;C for every 1.0 \u0026deg;C increase) (Fig. 4e). Nighttime wet bulb temperature is least impacted and explained by nighttime LST (slope = 0.45, \u003cem\u003eR\u003c/em\u003e = 0.78), but still with a positive relationship (Fig. 4f). Wet bulb temperature thresholds for human health concerns also occur at lower values. Although average nighttime wet bulb temperature does not reach the \u0026ldquo;dangerous threshold\u0026rdquo; across weather stations, this can become of concern in the near future as temperatures and air moisture concentrations increase globally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eElevated nighttime LSTs decrease thermal comfort to a greater extent at lower latitudes and closer to the coast. The warmest heat index values fall above the \u0026ldquo;extreme caution\u0026rdquo; threshold (NOAA, n.d.) at mean nighttime LSTs above 25 \u0026deg;C at lower latitudes (Fig. 4b, 4e). Wet bulb temperatures near \u0026ldquo;dangerous\u0026rdquo; thresholds can also be associated with higher nighttime LSTs at lower latitudes (Fig. 4c, 4f). In addition, stations with the lowest levels of thermal comfort are closer to the coast (Fig. 4d, 4e, 4f). The largest regression coefficients or slopes across all thermal comfort measures can be found in Central and South Florida. Nighttime LST has a similar impact on SAT in both Central and South Florida, increasing SAT by over 1.0 \u0026deg;C per degree increase in LST (Fig. 4a). Nighttime LST in has a slightly stronger impact on the nighttime heat index in South Florida, increasing the heat index by over 2.0 \u0026deg;C per degree increase in LST (Fig. 4b). Wet bulb temperature, however, is most impacted by nighttime LST in Central Florida (Fig. 4c).\u003c/p\u003e\n\u003cp\u003eDiurnal values in thermal comfort measures are also smaller in regions where nighttime LSTs are increased (e.g., South Florida and areas closer to the coast) (Fig. S2, Table S2). However, diurnal wet bulb temperature displays less sensitivity to areas of increased nighttime LST compared to diurnal SAT and heat indices. This is consistent with results shown in Fig. 4f, where wet bulb temperatures show less of a response to changes in nighttime LSTs. Despite higher urbanicity increasing diurnal LST across the peninsula, thermal comfort measures exhibit smaller differences between daytime and nighttime observations with increased impervious surface.\u003c/p\u003e"},{"header":"4.\tDiscussion ","content":"\u003cp\u003eHigher levels of air moisture exacerbate heat hazards, especially in regions that experience “seasonally muggy” climates. In this study, we demonstrate the role that air moisture, as represented by specific humidity, plays in limiting surface cooling and decreasing thermal comfort across the very muggy, summertime Florida peninsula. The results of this research show lower latitude regions and areas closer to the coast exhibiting increased nighttime LSTs and decreased diurnal differences in LST that correspond to higher levels of observed specific humidity. As expected, increased impervious surface leads to increased nighttime LSTs, and subsequently higher nighttime SATs, heat indices, and wet bulb temperatures. Therefore, increasing levels of air moisture with global warming could continue to maintain and amplify heat hazards overnight, especially in urban areas where surface energy balances are increased.\u003c/p\u003e\n\u003cp\u003eThe results of this study highlight water vapor’s localized greenhouse effect in the lower atmosphere\u0026nbsp;(Chang \u0026amp; Zhang, 2019; Dai, 2006; Dong et al., 2019; Held \u0026amp; Soden, 2000; Hossain \u0026amp; Gu, 2016; Liu et al., 2019; Sherwood et al., 2018). In a peninsular region at lower latitudes, intense solar radiation that heats the surface inducing high rates of evaporation (Becker \u0026amp; Boyd, 1957; Bhatia, 2014; Muse et al., 2024; NASA, n.d.; Roca et al., 2010) from surrounding waters can be linked to the region’s unique heat hazards. Land-sea interactions, like the ones along peninsular Florida’s coasts, are defined by an advection of abundant moisture inland due to the onshore-offshore gradient in temperature (Estoque, 1962; Misra et al., 2011; Pielke, 1973). Such a phenomenon, similar to much of the Caribbean (Cloutier-Bisbee et al., 2019; Di Napoli et al., 2023), results in an intensified local greenhouse effect that limits cooling of the surface (Bhardwaj \u0026amp; Misra, 2019; Misra \u0026amp; Mishra, 2016; Raghavendra et al., 2019). Because of these interactions, nighttime LSTs remain considerably elevated with respect to their daytime highs (Fig. 2, Table 2), leading to decreased thermal comfort across the diurnal cycle (Fig. 4, Fig. S2), especially in urban regions (Behrens et al., 2019; Raymond et al., 2021). Such an interaction is even more prominent on the Florida peninsula’s west coast that borders the warmer and shallower Gulf of Mexico, as compared to the Atlantic Ocean that borders Florida’s east coast (Misra \u0026amp; Mishra, 2016). Stations along the peninsula’s Gulf coast report an average nighttime specific humidity of 18.2 g/kg, as compared to an average of 17.9 g/kg along the Atlantic coast. We hypothesize that similar patterns that can threaten human health across the diurnal cycle also exist along much of the United States’ Gulf Coast that exhibits muggy conditions for much of the year (Greene et al., 2011; Läderach \u0026amp; Raible, 2013; Petkova et al., 2015). These interactions contrast the effects found near cooler waters that increase thermal comfort such as Chicago’s lake effect (Chakraborty et al., 2023) and along California’s Pacific Coast (Gershunov \u0026amp; Guirguis, 2012).\u003c/p\u003e\n\u003cp\u003eNighttime LST is strongly correlated with decreased thermal comfort. For example, nighttime SAT exhibits a strong, positive relationship with nighttime LST (\u003cem\u003eR\u003c/em\u003e = 0.85), contrasting relationships found during the day in tropical climates (Muse et al., in press). The positive relationship between LST and SAT is especially important for multiple reasons: 1) at nighttime, LST is one of the main sources of energy to increase SAT; 2) SAT is a key component of commonly used measures of thermal comfort; and 3) as SAT increases, the capacity of the air to hold additional moisture also increases (Trenberth et al., 2003; Vecellio et al., 2022). Thus, the heat index and wet bulb temperature, two measures of thermal comfort dependent on SAT and air moisture, also increase with higher nighttime LSTs (Fig. 4). These thermal comfort measures increase further in urban regions where nighttime LST is highest due to increased surface energy balances (Table 2). Such urban conditions in seasonally muggy regions can produce a compound heat hazard, where increased air moisture and increased nighttime LSTs are both present (Table 2). As air moisture concentrations increase under the implications of global warming, the resulting localized greenhouse effects can intensify hazardous conditions and direct heat-health impacts without proper heat adaptations (Coffel et al., 2018; Dai, 2006; Raymond et al., 2017). Interventions that reduce inputs to surface energy balances, especially in coastal or low latitude urban areas, are of immediate priority for preventing exacerbated heat health impacts as a result of intensified, lower atmosphere greenhouse effects.\u003c/p\u003e\n\u003cp\u003eAlso a key component in measures of thermal comfort (e.g., heat index, wet bulb temperature), is relative humidity. Such a measure is widely used alone as a measure of moisture or “dryness”, especially in urban areas (Chakraborty et al., 2022; Lokoshchenko, 2017; K. Zhang et al., 2023). However, relative humidity is not a measure of the actual amount of moisture in the air, due to its dependence on SAT and specific humidity (i.e., at constant specific humidity, relative humidity decreases with increasing SAT). Large disparities in temperature can exist between urban and rural areas as a result of the surface urban heat island phenomenon (Athukorala \u0026amp; Murayama, 2020; Haashemi et al., 2016; Lemoine-Rodríguez et al., 2022; Manoli et al., 2020; Muse et al., 2022, 2024; Peng et al., 2012; Wu et al., 2019; Zhou et al., 2019). Thus, although similar levels of specific humidity may exist across varying levels of urbanicity, relative humidity will be lower in warmer, urban areas (Table 2). In comparing the quantity of air moisture content across urban and rural gradients, specific or absolute humidity are more accurate. However, at lower latitudes, relative humidity remains an important measure for human health and thermal comfort, especially in regions that have high daytime and nighttime temperatures as a result of increased air moisture (Ivanovich et al., 2024; National Oceanic and Atmospheric Administration, n.d.-b; National Oceanic and Atmospheric Administration \u0026amp; Center for Disease Control and Prevention, n.d.; Raymond et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was not conducted without limitations. First, specific humidity, as well as other parameters and thermal comfort measures observed at weather stations remain highly hyperlocal (Clement et al., 2023). As such, conditions close to and in-between weather stations may vary. Second, this study focused only on peninsular Florida via MODIS imagery, within horizontal tile 10 and vertical tile 6 of the satellite’s global image acquisition technology, a region that exhibits high to extreme seasonally muggy conditions. As shown in Fig. 1, however, seasonally muggy regions extend to higher latitudes (upwards of 40° N). In future work, analysis could continue to analyze the impact of increased summertime specific humidity in seasonally muggy climates on maintaining heat hazards throughout the diurnal cycle. Such work could be especially important for heat adaptation responses along the Gulf Coast, a region that experiences heightened levels of seasonal mugginess much like peninsular Florida (Fig. 1). Third, satellite data was also collected at 1-km resolution, at 3 PM and 1 AM local time. Higher resolution data at different times of night (or day), as well as additional local weather stations could reveal more heterogenous results of local microclimates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture work could also address the role of other greenhouse gases of various potency (e.g., carbon dioxide, CO\u003csub\u003e2\u003c/sub\u003e; methane, CH\u003csub\u003e4\u003c/sub\u003e; nitrous oxide, N\u003csub\u003e2\u003c/sub\u003eO, etc.) in limiting surface cooling, with or without the impact of increased water vapor. Such analysis could be especially relevant in urban areas, where anthropogenic heating is at its peak. In addition, many thresholds for thermal comfort are defined by daytime highs in temperature, yet thresholds for thermal comfort during the evening may be different, as physiological impacts of heat hazards and decreased thermoregulation can be different at night. Establishing thresholds for nighttime thermal comfort, both indoors and outdoors, could highlight the differences between daytime and nighttime heat risks.\u003c/p\u003e"},{"header":"5.\tConclusions","content":"\u003cp\u003eThe results of this study establish key findings that concern air moisture concentrations across a seasonally muggy region, as well as what increased air moisture means for lower atmospheric temperature and thermal comfort in these regions. LST remains higher during evening hours in places that exhibit higher specific humidity, highlighting the localized role of water vapor’s greenhouse effect. In seasonally muggy regions, equitable adaption to reduce risk associated with intensified nighttime and diurnal heat hazards is important, especially in highly populous urban regions. Anthropogenic activities such as urbanization have resulted in excess energy being contributed to local energy balances through higher rates of solar radiation absorption, increasing local temperatures. In coastal regions at low latitudes, heat responses that reduce localized energy inputs (e.g., increasing albedo and shading canopy), especially in urban heat islands, would in turn reduce the amount of reemitted radiation during evenings. With such responses, there is less heat energy available to be trapped by higher amounts of water vapor in hot, muggier climates, ultimately decreasing the potential for exacerbated heat hazard risks linked to surface heating. In addition to these adaptation measures, weatherizing buildings and homes is also of high priority for heat health implications and energy security, especially during evening hours, as air moisture continues to increase alongside average temperature for the foreseeable future.\u003c/p\u003e"},{"header":"Statements and Declarations ","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the McKnight Doctoral Fellowship Program of the Florida Education Fund; the Rosenstiel School of Marine, Atmospheric, and Earth Science at the University of Miami; and the University of Miami Laboratory for Integrative Knowledge (U-LINK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMODIS land surface temperature data can be downloaded through NASA/USGS EarthExplorer (USGS, 2023). Weather station data can be downloaded through the ISU Iowa Environmental Mesonet (Iowa State University, 2023). Data processing and analysis and figure production were performed in ArcGIS Pro Software and R (Environmental Systems Research Institute (Esri), 2021; Posit team, 2024). Plots in R were produced using ggplot (Wickham, 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e Nkosi Muse, Brian D. McNoldy, Amy Clement, Katharine J. Mach; \u003cstrong\u003eMethodology:\u003c/strong\u003e Nkosi Muse, Brian D. McNoldy, Amy Clement, Katharine J. Mach; \u003cstrong\u003eFormal analysis and investigation:\u003c/strong\u003e Nkosi Muse; \u003cstrong\u003eWriting - original draft preparation:\u003c/strong\u003e Nkosi Muse; \u003cstrong\u003eWriting - review and editing:\u003c/strong\u003e Brian D. McNoldy, Amy Clement, Katharine J. Mach; \u003cstrong\u003eSupervision:\u003c/strong\u003e Katharine J. Mach\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAkbari, H., Pomerantz, M., \u0026amp; Taha, H. (2001). Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. \u003cem\u003eSolar Energy\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(3), 295\u0026ndash;310. https://doi.org/10.1016/S0038-092X(00)00089-X\u003c/li\u003e\n \u003cli\u003eAmerican Meteorological Society. (2022). \u003cem\u003eGlossary of Meteorology\u003c/em\u003e. Specific Humidity. https://glossary.ametsoc.org/wiki/Specific_humidity\u003c/li\u003e\n \u003cli\u003eAthukorala, D., \u0026amp; Murayama, Y. (2020). Spatial variation of land use/cover composition and impact on surface urban heat Island in a tropical sub-saharan city of Accra, Ghana. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(19), Article 19. https://doi.org/10.3390/su12197953\u003c/li\u003e\n \u003cli\u003eBaldwin, J. W., Benmarhnia, T., Ebi, K. L., Jay, O., Lutsko, N. J., \u0026amp; Vanos, J. K. (2023). 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Florida State University. https://climatecenter.fsu.edu/images/docs/Humidity-in-Florida.pdf\u003c/li\u003e\n \u003cli\u003eZonato, A., Martilli, A., Gutierrez, E., Chen, F., He, C., Barlage, M., Zardi, D., \u0026amp; Giovannini, L. (2021). Exploring the Effects of Rooftop Mitigation Strategies on Urban Temperatures and Energy Consumption. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e(21), e2021JD035002. https://doi.org/10.1029/2021JD035002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"land surface temperature, specific humidity, thermal comfort, water vapor","lastPublishedDoi":"10.21203/rs.3.rs-5380761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5380761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, increasing air moisture limits not only the human body’s ability to cool down, but also the Earth’s surface. A gap remains, however, in understanding how limited surface cooling can quantitatively impact metrics of thermal comfort. Here, we examine how air moisture influences nighttime land surface temperatures (LSTs) and thermal comfort during summer months across the Florida peninsula, during which time air moisture reaches the highest values of any place in the United States. For June–August during 2018–2022, we ask: 1) How do urbanicity, measured as impervious surface, and geography, measured as latitude and distance from the coast, modulate air moisture, measured as specific humidity? 2) How does specific humidity limit LST cooling at night? And 3) what are the resulting consequences for thermal comfort? Based on data from 30 weather stations along the peninsula, we find that specific humidity increases closer to the coast and at lower latitudes. In regions with higher air moisture levels, LSTs cool off less at night (as measured at 1 AM), resulting in lower differences between daytime and nighttime (diurnal) LSTs. Elevated nighttime LSTs have pronounced implications for measures of thermal comfort—for every 1.0 °C increase in nighttime LST, nighttime surface air temperatures increase, on average, by 1.1 °C, heat indices by 2.0 °C, and wet bulb temperatures by 0.5 °C. This analysis therefore underscores the importance of heat mitigation and adaptation strategies that reduce elevated nighttime LSTs in seasonally muggy climates, increasing thermal comfort.\u003c/p\u003e","manuscriptTitle":"Increased nighttime land surface temperatures in a seasonally muggy climate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-20 10:53:00","doi":"10.21203/rs.3.rs-5380761/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revise","date":"2025-02-04T12:14:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-01-06T14:29:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-05T21:24:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-06T03:27:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2024-11-03T01:24:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8c4ba0be-a1eb-4526-bf39-2e0ebfa9edf5","owner":[],"postedDate":"December 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:03:39+00:00","versionOfRecord":{"articleIdentity":"rs-5380761","link":"https://doi.org/10.1007/s10584-025-04030-2","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2025-10-08 15:56:51","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2024-12-20 10:53:00","video":"","vorDoi":"10.1007/s10584-025-04030-2","vorDoiUrl":"https://doi.org/10.1007/s10584-025-04030-2","workflowStages":[]},"version":"v1","identity":"rs-5380761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5380761","identity":"rs-5380761","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}