An Integrated Assessment of Thermodynamic and Dynamic Linkages Across Land, Atmosphere, and Ocean Systems

1. Introduction

The increasing severity of extreme weather events has been widely documented in many recent climatological studies. Observational studies indicates that both the frequency and magnitude of these events are rising under ongoing climate change (Zwiers et al., 2013; Nayak, 2018; Tyagi et al., 2022; Clarke et al., 2022; Sahu et al., 2023; Sayat et al., 2024; Nayak and Sahu, 2025). The weather phenomena such as heatwaves, cold surges, heavy rainfall, tropical cyclones, thunderstorms, and snowfall continue to cause significant risks to the human life and properties (Chaudhary and Piracha, 2021; Rajkovich et al., 2024; Koteshwaramma et al., 2024; Maity et al., 2025). Thus, understanding the physical interactions that precede such extremes remains essential for improving forecasting skill and mitigating societal impacts.

Several research has shown that extreme events are rarely driven by a single factor; rather, they emerge from coupled land–atmosphere–ocean processes. Many studies have examined these couplings from multiple perspectives. For instance, Walsh et al. (2020), Meinander et al. (2021), and Nayak et al. (2023) describe how synoptic-scale circulation reorganizes moisture and temperature fields. Crisp et al. (2022) highlight feedbacks among soil moisture, radiation balance, and boundary-layer thermodynamics, while Bindhajam et al. (2020) demonstrate how land-surface temperature and vegetation modulate atmospheric moisture supply. At the oceanic interface, Sahu et al. (2022) illustrate how sea-surface temperature and currents influence large-scale circulation patterns such as ENSO and tropical cyclone formation. Land-use change such as deforestation, agricultural intensification, and urbanization also modify the local climate extremes (Nayak and Mandal, 2012; Sahu et al., 2023; Mishra et al., 2025).

These findings emphasize that extreme weather arises from the co-evolution of temperature, humidity, pressure gradients, wind speed, and radiative fluxes. Heatwaves, for example, are closely linked to land-surface warming and humidity dynamics (Maity et al., 2021; Nayak et al., 2022, 2023). Tropical cyclones depend strongly on pressure anomalies and sea-surface temperatures (Singh et al., 2023; Nayak and Takemi, 2023), while flooding is driven by intense precipitation and interactions with sea-level rise and storm surge (Nayak and Takemi, 2020; Singh et al., 2022; Nayak, 2024). Drought events, in contrast, are formed by soil moisture depletion and atmospheric circulation anomalies (Lhotka et al., 2020; Qing et al., 2023). Despite this progress, the thermodynamic and dynamic relationships that support the development of extremes remain underexplored, especially where land–atmosphere coupling dominates (Dabral et al., 2023; Sahu et al., 2025).

The present study aims to explore the interactions among temperature, humidity, vapor pressure, dewpoint, wind speed, solar radiation, and pressure fields to discuss (1) how thermodynamic variables (e.g., temperature, RH, dewpoint) and dynamic indicators (e.g., wind speed, local pressure, sea-level pressure) evolve simultaneously and (2) how the integrated assessment of the physical drivers influence weather variability.

2. Methodology

This study uses hourly surface meteorological observations from the Radar-Automated Meteorological Data Acquisition System (Radar-AMeDAS) to investigate the thermodynamic and dynamic relationships relevant to the development of extreme weather conditions. Three months of hourly data (January–March) were analyzed, covering a comprehensive set of surface variables including air temperature, precipitation, wind speed, solar radiation, local pressure, sea-level pressure, relative humidity, vapor pressure, dew-point temperature, cloud cover, and visibility (Japan Meteorological Agency; https://www.jma.go.jp/jma/index.html).

Temperature, humidity, and vapor-pressure fields were used to compute dew-point temperature. The thermodynamic relationship was analyzed to understand the near-surface coupling to highlighting the saturation behavior of the atmosphere and its sensitivity to temperature fluctuations. The local pressure and sea-level pressure were examined alongside wind speed to identify signatures of dynamic forcing and the pressure drop’s association with cyclonic activity or windy conditions (Nayak et al., 2018; Saini et al., 2020; Nayak and Takemi, 2020, 2021; Sahu et al., 2022). The relationship between radiation and thermodynamic variables was analyzed to assess how surface heating influences moisture content, drying potential, and diurnal variations in relative humidity and vapor pressure. A correlation matrix was also discussed to capture the dominant connections between all variables.

3. Results

3.1. Thermodynamic Variability

Figure 1 illustrates the temporal evolution of three thermodynamic variables such as air temperature, vapor pressure, and relative humidity over the entire observation period. Temperature fluctuates between approximately 1°C and 22°C, with repeated short-duration warming and cooling cycles indicating active day–night variability and synoptic-scale transitions. Vapor pressure ranges from roughly 2 to 12 hPa, which follow the broad pattern of temperature due to its exponential dependence on thermal state. Relative humidity shows a much larger dynamic range (18% to nearly 100%), with sharp oscillations that reflect rapid moisture adjustments through changes in temperature, mixing processes, and varying cloud conditions. Periods of high temperature and moderately high vapor pressure correspond to RH depressions below 40%, indicating conditions conducive to drying and enhanced evaporative demand. On the other hand, RH frequently peaks above 85–95% when temperature drops or when moist air masses influence the station behaviors consistent with previous findings for winter and early-spring boundary-layer conditions in Japan (Nayak and Dairaku, 2016; Nayak et al., 2018; 2025).

3.2. Dynamical Coupling

Figure 2 examines the relationship between surface pressure and wind speed. Pressure values extend from approximately 992 to 1022 hPa, while wind speeds vary from near 0 to about 7.5 m/s. A broad but evident pattern shows higher wind speeds occurring specially at lower pressure values, consistent with the expected response to stronger horizontal pressure gradients during cyclonic passages or frontal activity. Sea-level pressure anomaly ranges from ~9.5 to 10.6 (normalized units).

oints with more negative or lower anomalies are located toward the lower-pressure side and often correspond to wind speeds above 4–6 m/s, strengthening the link between anomalous pressure deficits and enhanced low-level flow. This dynamical structure agrees with the typical winter and early-spring synoptic environment of Japan, where transient low-pressure systems and marginal baroclinic activity frequently modulate near-surface winds (Nayak et al., 2018; Nayak & Takemi, 2020).

3.3. Radiative–Moisture Interaction

Figure 3 shows the surface solar radiation clearly influences relative humidity. Solar radiation values range from 0 to ~3.2 MJ/m², with a substantial proportion of data occurring at or near zero, reflecting night-time and cloudy conditions. Relative humidity ranges 20–100%, and the air temperature ranges 1–22°C. At higher solar radiation (>1.5 MJ/m²), RH predominantly falls between 35–60%, indicating drying due to solar-driven warming and boundary-layer mixing.

At low or zero solar radiation, RH frequently clusters above 80–95%, representing nocturnal cooling, radiative saturation processes, and cloud-related moisture retention. Solar radiation and lower RH are consistent with enhanced evaporative demand under daytime heating. This pattern reproduces the established radiative–thermodynamic coupling in midlatitude surface environments, where shortwave heating drives boundary-layer deepening and humidity reduction (Nayak et al., 2021; Crisp et al., 2022).

3.4. Multivariate Atmospheric Relationships

Figure 4 presents the correlation matrix constructed from all the seven variables such as temperature, relative humidity, solar radiation, wind speed, surface pressure, vapor pressure, and sea-level pressure. The strongest positive correlations is noticed between temperature and vapor pressure (~0.70–0.80), indicating the exponential dependence of vapor pressure on temperature through the Clausius–Clapeyron relation. Temperature also shows a moderate positive correlation with solar radiation (~0.30–0.40), representing the contribution of daytime radiative forcing to surface warming. Relative humidity exhibits a robust negative correlation with temperature (around –0.45 to –0.55), consistent with expected thermodynamic behavior that warmer conditions reduce RH unless accompanied by substantial moisture increases.

RH is also strongly negatively correlated with solar radiation (~–0.50), highlighting the radiant–mixing driven humidity suppression described earlier. Wind speed shows generally weak correlations with most thermodynamic variables but a slight positive relationship with solar radiation and a modest negative relationship with SLP. The weak-to-moderate negative correlation between wind and pressure (~–0.20 to –0.30) agrees with link between enhanced winds and pressure that deficits during synoptic disturbances. Pressure and SLP correlate strongly with each other, as expected from their shared physical basis and diurnal pressure cycle. Both pressure fields exhibit weak negative correlations with temperature and vapor pressure, consistent with occasional warm-sector passages during low-pressure events.

4. Discussion

The analysis reveals that relative humidity increases evidently when temperatures fall, representing a strong inverse sensitivity between these two variables (Figure 1). This behavior is consistent with cold-season processes, where lower temperatures reduce the air’s moisture-holding capacity, causing RH to rise and intensifying the likelihood of cold waves, fog, or snowfall. Such conditions can exacerbate freezing events or enhance dew formation, similar to the cold-region responses noted in previous climatological assessments (Nayak, 2023). The pressure–wind relationship shows that lower local pressure corresponds with higher wind speeds (Figure 2). This pattern is of particular importance during storm development, in which strong pressure gradients drive intense winds and can lead to storm surge development. The clustering of low-pressure, high-wind observations agrees with established storm dynamics reported in regional studies (Nayak and Takemi, 2020, 2021; Morimoto et al., 2021; Nayak and Kanda, 2023; Koteshwaramma et al., 2025). High temperature and high dew point combinations correspond to extreme heat conditions, which increase thermal stress and magnify heat-wave intensity (Figure 3). This is consistent with previous work demonstrating that moist heat extremes are becoming more prominent under climate warming (Nayak, 2021; Nayak, 2023). The correlation suggests that moisture-rich conditions are conducive to heavy rainfall events (Figure 4). This is also consistent with previous findings on flood-inducing systems (Nayak et al., 2013; Nayak and Mandal, 2019). The structural coherence of these clusters suggests that thermodynamic processes primarily drive humidity- and heat-related extremes, while dynamic processes shape storm-induced wind and pressure anomalies.

Overall, the results reproduce a system in which climate change is likely to intensify the underlying relationships. Rising temperatures, increased atmospheric moisture, and changing sea-level and land-surface conditions can intensify the severity of tropical cyclones, heat waves, and heavy rainfall events (Nayak et al., 2013; Nayak, 2021; Nayak, 2023). Moreover, anthropogenic activities such as land-use change, urbanization, and industrial development may further alter surface–atmosphere interactions and may amplify the expression of extreme weather phenomena (Nayak and Behera, 2008, 2009; Nayak et al., 2019, 2021).

5. Conclusion

This study provides an integrated assessment of thermodynamic and dynamic atmospheric variables, and highlights how their interactions evolve. The results indicate that lower temperatures are associated with higher relative humidity, whereas higher temperatures coincide with elevated dew point values, suggesting enhanced heat–moisture interactions during warmer periods. Wind speed rises as pressure drops, highlighting the dynamic instability present during unsettled weather conditions. Thus, the climate change may amplify these interactions, with rising temperatures, increased humidity, and modified surface conditions potentially strengthening the intensity of extreme weather events such as TCs, heat waves, and heavy rainfall episodes. This study can be extended with data collected over a longer temporal window to better capture interannual variations and long-term trends in extreme weather behavior, thereby improving our understanding of climate change impacts (Nayak and Takemi, 2022, 2024, 2025). Moreover, the relationships between surface variables have strong potential applications in climate modelling and forecasting frameworks (Nayak et al., 2017, 2018, 2019; Nayak 2021; Maity et al., 2017, 2022), which support the improved risk assessment and early-warning capabilities for high-impact weather events.