Diurnal Time Scales Research Articles

How Do Schumann Resonance Frequency Changes in the Vertical Electric Field Component Reflect Global Lightning Dynamics at Different Time Scales?

AbstractThe electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth‐ionosphere cavity confined between the Earth's surface and the lower ionosphere. The peak frequencies of SR are known to vary with source‐observer distance (SOD), while the daily frequency range (DFR: fmax − fmin) is also indicative of the average size of thunderstorm regions. This paper provides observational evidence for these relationships based on SR frequency observations of the vertical electric (EZ) field component at Nagycenk (NCK), Hungary in Central Europe from the period 1994–2015. Variations of the peak frequencies are considered on the annual, seasonal and diurnal time scales as well as during a specific event when squall‐line formation of lightning activity in South America moves toward NCK. DFR is studied in relation to the El Niño Southern Oscillation (ENSO). Increasing area of lightning activity in mid‐high Northern hemisphere latitudes has been identified by DFR variations during the transition from warm to cold episodes of the ENSO in 1998 and 2010. The extension of the lightning area is considered as a consequence of energy released in the tropics and exported to higher latitudes with some months of delay from the end of the El Niño episodes. The frequency variations are interpreted via model calculations and supported with satellite‐based optical lightning observations (Optical Transient Detector, Geostationary Lightning Mapper). The described variations of SR peak frequencies and DFR yield information on the global/regional lightning dynamics and on this basis they have important application to climate issues as well.

Multi-scale variability of southeastern Australian wind resources

Abstract. There is growing need to understand wind variability in various regions throughout the world, including in relation to wind energy resources. Here we examine wind variability in southeastern Australia in relation to the El Niño–Southern Oscillation (ENSO) as a dominant mode of atmospheric and oceanic variability for this region. The analysis covers variability from seasonal to diurnal timescales for both land and maritime regions of relevance to wind energy generation. Wind speeds were obtained from the 12 km grid length Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) reanalysis, with a focus on wind at a typical hub-height of 100 m above the surface. Results show spatiotemporal variations in how ENSO influences wind speeds, including consistency in these variations over the wind speed distribution. For example, ENSO-related variations in mean winds were mostly similar in sign to ENSO-related variations in weak winds, noting uncertainties for strong winds given available data. Diurnal variability in wind speed was larger for summer than winter and for land than ocean regions, with the diurnal cycle maxima typically occurring in the afternoon and evening rather than morning, plausibly associated with sensible heating of air above land following solar radiation. Localised variations in the diurnal cycle were identified around mountains and coastal regions. The results show some indication of ENSO influences on the diurnal variability. These findings are intended to help enhance scientific understanding on wind variability, including in relation to ENSO, and to contribute information towards practical guidance in planning such as for use in energy sector applications.

Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector

AbstractThe Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

Multi-scale variations of subglacial hydro-mechanical conditions at Kongsvegen glacier, Svalbard

Abstract. The flow of glaciers is largely controlled by changes at the ice–bed interface, where basal slip and sediment deformation drive basal glacier motion. Determining subglacial conditions and their responses to hydraulic forcing remains challenging due to the difficulty of accessing the glacier bed. Here, we monitor the interplay between surface runoff and hydro-mechanical conditions at the base of the Kongsvegen glacier in Svalbard. From July 2021 to August 2022, we measured both subglacial water pressure and till strength. Additionally, we derived median values of subglacial hydraulic gradient and radius of channelized subglacial drainage system from seismic power, recorded at the glacier surface. To characterize the variations in the subglacial conditions caused by changes in surface runoff, we investigate the variations of the following hydro-mechanical properties: measured water pressure, measured sediment ploughing forces, and derived hydraulic gradient and radius, over seasonal, multi-day, and diurnal timescales. We discuss our results in light of existing theories of subglacial hydrology and till mechanics to describe subglacial conditions. We find that during the short, low-melt-rate season in 2021, the subglacial drainage system evolved at equilibrium with runoff, increasing its capacity as the melt season progressed. In contrast, during the long and high-melt-rate season in 2022, the subglacial drainage system evolved transiently to respond to the abrupt and large water supply. We suggest that in the latter configuration, the drainage capacity of the preferential drainage axis was exceeded, promoting the expansion of hydraulically connected regions and local weakening of ice–bed coupling and, hence, enhanced sliding.

Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China

Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.

Data-based investigation of the effects of canopy structure and shadows on chlorophyll fluorescence in a deciduous oak forest

Abstract. Data from satellite, aircraft, drone, and ground-based measurements have already shown that canopy-scale sun-induced chlorophyll fluorescence (SIF) is tightly related to photosynthesis, which is linked to vegetation carbon assimilation. However, our ability to effectively use those findings are hindered by confounding factors, including canopy structure, fluctuations in solar radiation, and sun–canopy geometry that highly affect the SIF signal. Thus, disentangling these factors has become paramount in order to use SIF for monitoring vegetation functioning at the canopy scale and beyond. Active chlorophyll fluorescence measurements (FyieldLIF), which directly measures the apparent fluorescence yield, have been widely used to detect physiological variation of the vegetation at the leaf scale. Recently, the measurement of FyieldLIF has become feasible at the canopy scale, opening up new opportunities to decouple structural, biophysical, and physiological components of SIF at the canopy scale. In this study, based on top-of-canopy measurements above a mature deciduous forest, reflectance (R), SIF, SIF normalized by incoming photosynthetically active radiation (SIFy), FyieldLIF, and the ratio between SIFy and FyieldLIF (named Φk) were used to investigate the effects of canopy structure and shadows on the diurnal and seasonal dynamics of SIF. Further, random forest (RF) models were also used to not only predict FyieldLIF and Φk, but also provide an interpretation framework by considering additional variables, including the R in the blue, red, green, red-edge, and near-infrared bands; SIF; SIFy; and solar zenith angle (SZA) and solar azimuth angle (SAA). Results revealed that the SIF signal is highly affected by the canopy structure and sun–canopy geometry effects compared to FyieldLIF. This was evidenced by the weak correlations obtained between SIFy and FyieldLIF at the diurnal timescale. Furthermore, the daily mean SIF‾y captured the seasonal dynamics of daily mean F‾yieldLIF and explained 58 % of its variability. The findings also revealed that reflectance in the near-infrared (R-NIR) and the NIRv (the product of R-NIR and normalized difference vegetation index (NDVI)) are good proxies of Φk at the diurnal timescale, while their correlations with Φk decrease at the seasonal timescale. With FyieldLIF and Φk as outputs and the abovementioned variables as predictors, this study also showed that the RF models can explain between 86 % and 90 % of FyieldLIF, as well as 60 % and 70 % of Φk variations under clear-sky conditions. In addition, the predictor importance estimates for FyieldLIF RF models revealed that R at 410, 665, 740, and 830 nm; SIF; SIFy; SZA; and SAA emerged as the most useful and influential factors for predicting FyieldLIF, while R at 410, 665, 705, and 740 nm; SZA; and SAA are crucial for predicting Φk. This study highlighted the complexity of interpreting diurnal and seasonal dynamics of SIF in forest canopies. These dynamics are highly dependent on the complex interactions between the structure of the canopy, the vegetation biochemical properties, the illumination angles (SZA and SAA), and the light conditions (ratio of diffuse to direct solar radiation). However, such measurements are necessary to better separate the variability in SIF attributable to radiation and measurement conditions from the subtler variability attributable to plant physiological processes.

Systematically tracking the hourly progression of large wildfires using GOES satellite observations

Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).

AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales.

Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.

An Analytical Model of Active Layer Depth Under Changing Ground Heat Flux

AbstractImproved modeling of permafrost active layer freeze‐thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze‐thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non‐gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications.

Diurnal variability of atmospheric water vapour, precipitation and cloud top temperature across the global tropics derived from satellite observations and GNSS technique

The diurnal cycle of convection plays an important role in clouds and water vapour distribution across the global tropics. In this study, we utilize integrated moisture derived from the global navigation satellite system (GNSS), satellite precipitation estimates from TRMM and merged infrared dataset to investigate links between variability in tropospheric moisture, clouds development and precipitation at a diurnal time scale. Over 16 years of observations were analysed to estimate the diurnal cycles for 42 sites that span across the global tropics and assess the GNSS precipitable water vapour suitability in tropical moisture variability studies. Results show that the GNSS technique can be successfully used for studies related to the high-resolution temporal variability of integrated atmospheric moisture and, thus, in the analysis of multi-scale interactions and clouds development. For most of the analysed cases, GNSS integrated humidity shows one daily maximum (regardless of the season), which is related to the afternoon precipitation and cloud top temperature daily minimum. Although diurnal integrated moisture variations typically do not exceed 2 mm (usually below 5% of the daily mean value), in favourable large-scale conditions such relatively small changes can lead to the development of convective clouds and can affect precipitation distribution. Thus, GNSS technology allows reliable monitoring of tropospheric moisture variability at a diurnal time scale, which can be leveraged for improved monitoring and prediction of high-impact weather.

Design and Assessment of a Novel Approach for Ecosystem Warming Experiments in High‐Energy Tidal Wetlands

AbstractCoastal salt marshes have an important role in climate change adaptation and mitigation. Direct and indirect responses to warming are expected to vary along the marsh elevation gradient, making ecosystem responses to warming at this marine‐terrestrial ecotone uncertain. The Marsh Ecosystem Response to Increased Temperatures (MERIT) experiment was established in 2018 on the North Sea coast of Germany. Experimental plots are evenly distributed over three elevational marsh zones (pioneer, low marsh, and high marsh) and include three temperature treatments (ambient, +1.5°C, +3.0°C). MERIT's novel design combines active warming (horizontal surface warming cables and vertical soil warming pins) with passive, partially covered domes. For performance assessment, temperature deltas between ambient and warmed plots were calculated and evaluated at seasonal, daily, and diurnal timescales. We used Linear Mixed Models with Residual Maximum Likelihood for evaluating warming treatment effects and constraining environmental factors. MERIT was effective at ecosystem warming in this high‐energy environment both above‐ and belowground. Mixed models show that warming treatment dominates temperature differences belowground and at the soil surface, along with factors such as wind speed, flooding duration, and solar radiation. Aboveground warming was lower than belowground warming, but the dome design minimized issues seen in other open‐top chamber experiments. The combination of passive aboveground warming with feedback‐controlled active surface and belowground heating provides a setup for understanding warming effects on tidal ecosystems without altering the natural impacts of wind, radiation, and tidal inundations at high‐energy coastlines. Our design creates opportunities to expand future warming experiments to remote locations and technically challenging environments.

Impacts of tidal mixing on diurnal and intraseasonal air-sea interactions in the Maritime Continent

The Maritime Continent (MC) is a region with enhanced tidal mixing and ocean cooling, which influences regional-scale sea surface temperatures (SSTs). We examine the coupled impacts of tidal mixing on near-surface stratification, SST, and deep convection on diurnal and intraseasonal time-scales, using ensembles of high-resolution, coupled ocean-atmosphere regional model simulations, with and without tidal forcing. Results show that the area-averaged SST in the eastern MC is reduced by 0.20 °C due to tidal forcing, with cooling exceeding 1 °C in the nearshore zones of shallow and complex bathymetry. The reduced SSTs decrease surface heat fluxes, leading to tropospheric drying and reduced precipitation, which are most pronounced in the nearshore zones. The results show that the magnitude of tidally-induced SST cooling is phase-dependent during the passage of the Madden Julian Oscillation (MJO). Strong westerly winds enhance entrainment cooling through wind-driven mixing and upwelling during the active phase. Conversely, the upper-ocean stratification is enhanced during the suppressed phase, and SSTs are less sensitive to subsurface cooling. Such spatio-temporal variability in the SST response to tides is accompanied by consistent changes to deep convection and atmospheric circulation. On the diurnal time-scale, nearshore cooling weakens the early-morning convection when the land-based convection propagates offshore and interacts with the cooler SST. On intraseasonal time-scales, the coupling between SST and precipitation is strengthened because of the asymmetric impacts of tide-induced mixing on SST and MJO-induced winds. The robust SST and precipitation responses demonstrated in this study suggest the need for an accurate representation of tidal forcing and vertical mixing processes in local MJO prediction models for the MC.

High-frequency dissolved oxygen dynamics in an urban estuary, the Long Island Sound.

The seasonal occurrence of deep-water hypoxia in western Long Island Sound (LIS) has been documented for decades by water quality cruise surveys and fixed mooring buoys. While previous studies have focused on factors modulating bottom dissolved oxygen (DO) at subtidal timescales, here we analyze continuous timeseries data from a moored buoy during summers 2021 and 2022 to examine factors controlling high-frequency fluctuations in surface and bottom DO at diurnal and semidiurnal timescales. Fluctuations in surface DO at diurnal timescales are associated with biological production, while fluctuations in bottom DO near semidiurnal timescales are associated with horizontal advection of DO by tides from the upper East River tidal strait into western LIS. Results from timeseries analysis are supported by weekly cruise surveys that resolve horizontal and vertical DO gradients in the western narrows. However, inferences regarding the duration of hypoxia during a given summer vary across datasets in part because weekly survey data do not resolve dominant timescales of variability within a particular summer. While prior studies have illustrated the importance of nutrient loading, stratification, and wind in controlling the development of hypoxia, the results presented here demonstrate the role of tidal advection in modulating hypoxia in far western LIS. Despite stronger stratification in 2021, the duration of hypoxia was 11.1 days shorter compared to 2022 in part due to greater advection of DO by tidal currents that intermittently increased bottom DO near the buoy. Furthermore, five-year averaged hypoxic area in the western narrows has increased since 2017, which highlights the spatially variable response of DO to nutrient load reductions. Future analysis of hypoxia in LIS should focus on leveraging high-frequency information contained in continuous datasets to improve estimates of hypoxia based on less temporally resolved water quality surveys.

Chemical kinetics of near-surface ozone at a suburban location in India

The spatial gradient in near-surface ozone (O3) is controlled by its production, sink, and net transport (advection/convection and diffusive) in the atmosphere. In this work, we used continuous long-term measurements of O3, oxides of nitrogen (NOx = NO + NO2), and meteorological data in the suburban location of Shadnagar, India. Data analyses were performed to investigate the governing processes that control O3 variability on diurnal and seasonal time scales. The role of chemistry in O3 variability, including both formation and destruction processes, was investigated using known chemical kinetics and a radiative transfer model. The residual between observations and chemical estimation was further analyzed to examine the role of transport and unresolved processes/uncertainty in the dataset. The O3 residual was duly validated using model reanalysis data of O3 and meteorological parameters to further estimate the O3 transport. Our analyses show that the average net production and net transport of near-surface O3 are 3.18 and 0.87 ppbv/h, respectively, while horizontal advection is 0.01 ppbv/h in the daytime. The production of ozone was found to be dominant, indicating the influx of ozone at the site. Overall, our results highlight that spatio-temporal variability in near-surface ozone is strongly controlled by net production in Shadnagar and may be applicable in similar environments globally.

Stratification and temporal evolution of mixing regimes in diurnally heated river flows

Direct numerical simulations of stratified open channel flows subject to a varying surface heat flux are performed. The influence of the diurnal heating time on the spatial and temporal variation of mixing in the flow and the characteristics of the mean flow state are examined. The control parameters are the bulk stability parameter lambda_{B}, defined through the ratio of the channel height delta and a bulk Obukhov length scale mathscr{L}_{B}, and the diurnal time scale hat{t}, defined as the ratio of the heating time to an eddy turnover time. The Prandtl number Pr and Reynolds number Re_{tau} have values of 1 and 400. Simulations are performed over hat{t} = 1 to 24 and lambda_{B} = 0.6 to 26. Two key flow features are used to classify the flow regimes observed, namely the laminar layer depth (LLD) and stratified layer depth (SLD) where the LLD is defined as the depth from the free surface when the buoyancy Reynolds number Re_{B} approx 7 and the SLD is the depth from the free surface when the turbulent Froude number Fr approx 1. This study attempts to characterise how these length scales vary across the diel cycle. The LLD is a viscous length scale and a regime map of a viscous parameter, the bulk Obhukov Reynolds number Re_mathscr{L}, and hat{t} is presented to classify the LLD behaviour. A regime map of lambda _{B} and hat{t} is presented to classify the behaviour of the SLD. Three classifications for each layer depth behaviour within a diel cycle form the basis of the regime maps for this paper: a neutral flow where the LLD or SLD does not exist (denoted by NL and NS), a stratified flow where the LLD or SLD are diurnally varying (denoted as DL and DS) and a persistent layer of the LLD or SLD (denoted as PL and PS). The transition between the NL to DL is hat{t} propto Re_{mathscr{L}}^{4.5}, DL to PL is hat{t} propto Re_{mathscr{L}}^{- 0.5}, NS to DS is hat{t} propto lambda _{B}^{0} and DS to PS is hat{t} propto lambda _{B}^{1}. The regime maps may be used as a predictive tool to determine when suppressed mixing regimes occur in rivers. At each flow depth, the flow sweeps though a range of mixing states across the diel cycle. The local mixing efficiency are briefly assessed and found to scale well with the instantaneous Fr number according to the regimes proposed by Garanaik and Venayagamoorthy (J. Fluid Mech., vol. 867, 2019, pp. 323-333).

The Role of Anthropogenic Forcings on the Regional Climate of Summertime Diurnal Variations over North China

Abstract Anthropogenic greenhouse gases (GHG) and anthropogenic aerosols (AA) have changed radiation balance and regulated the regional climate at seasonal or longer time scales. Based on station observations, reanalyses, and satellite observations, this study examines how anthropogenic forcings affect surface temperature and strongly regulate the regional climate at a diurnal time scale over the North China plains (NCP) in July and August. As AA cooling is dominant in the daytime over low-lying plains, it leads to a cooler day–warmer night temperature trend that decreases the diurnal temperature range over NCP but increases the thermal contrast between NCP and its west highlands. In response to the daytime cooling, the weakened vertical thermal contrast decreases the boundary layer turbulent mixing in the daytime and reduces friction to low-level winds, which leads to anomalous southerlies at 2000 LST over NCP. In contrast, nighttime warming results in anomalous northerlies at 0200 LST. On the other hand, in response to the enlarged horizontal thermal contrast, the stronger mountain–plain circulation helps to intensify low-level ascent over the plains at 0200 LST. These human-induced changes in the diurnal variation of regional circulations are conducive to the increased moisture convergence at 2000 and 0200 LST over NCP. The nighttime proportion of precipitation accordingly exhibits an increasing trend over NCP, though the seasonal precipitation decreases because of the weakened monsoon background. These findings highlight that the diurnal cycle of regional circulations can express a strong dynamic response to the radiation effect of anthropogenic forcings and thus affect the long-term change in regional climate.

Plant canopies exhibit stronger thermoregulation capability at the seasonal than diurnal timescales

Plant canopy temperature (Tc) plays a crucial role in regulating plant growth and metabolism. Although dominant controls on Tc are observed to differ across timescales, whether this would cause differences in plant thermoregulation capability (PTC) remains unclear, raising concerns about extrapolating findings on plant thermoregulation from one timescale to another. Here we constructed diurnal and seasonal datasets of Tc, air temperature (Ta), and other biotic and abiotic factors from global hourly flux data, and explored diurnal and seasonal variations in PTC (indicated by Tcvs. Ta regression slope, with lower slopes indicating higher Tc stability and stronger thermoregulation). Our result revealed significantly lower Tcvs. Ta slopes (i.e. stronger PTC) at seasonal than diurnal timescales, primarily due to different transpiration cooling at high Ta between the two timescales. At the diurnal timescale, transpiration rates initially increase before decreasing with Ta after reaching a specific temperature threshold (∼85th percentile of Ta; related to midday depression of stomatal activities); Conversely, at the seasonal timescale, transpiration rates consistently increase with Ta (related to the coincidence among high water availability and the peak annual Ta). PTC also displays considerable spatial variability, with latent heat vs. net radiation relationship and water availability being the dominant regulators. Collectively, we recommend caution when extrapolating thermoregulation-relevant conclusions drawn from short-term observations to longer-term predictions, and vice versa, since they have different patterns and underlying mechanisms.

Uncertainty of CYGNSS-Derived Heat Flux Variations at Diurnal to Seasonal Time Scales over the Tropical Oceans

Air–sea heat flux is one of the most important factors that affects ocean circulation, weather, and climate. Satellite remote sensing could serve as an important supplement to the sparse in situ observations for heat flux estimations. In this study, we analyze the uncertainty of the turbulent heat fluxes derived from wind speed measured by the Cyclone Global Navigation Satellite System (CYGNSS) over the global tropical oceans at different time scales. In terms of spatial distribution, there is large uncertainty (approximately 50 to 85 W·m−2 in the RMSE) near the equator in the western Pacific Ocean, the Arabian Sea, the Bay of Bengal, and near the Gulf of Guinea. The turbulent heat fluxes are in agreement with the buoys in representing the intraseasonal and seasonal variability, but more specific regional validations are needed for revealing the synoptic and sub-synoptic phenomena and the diurnal cycle. The uncertainty of the CYGNSS wind speed contributes approximately 50–57% to the uncertainty of the estimation of turbulent heat fluxes at the frequency band with a typical period of 3–7 days. In addition, the input sea surface temperature, rather than the wind speed, results in differences in the estimation of the monthly mean turbulent heat fluxes in the tropical Atlantic Ocean based on the COARE 3.5 algorithm. In conclusion, although the CYGNSS-derived turbulent heat fluxes are basically in good agreement with the in situ observations, our analysis highlights the importance of considering the limitations of these datasets, particularly in high wind speed conditions and for higher-frequency variations, including at synoptic, sub-synoptic, and diurnal time scales.

Evaporation induced convection enhances mixing in the upper ocean

The upper ocean surface layer is directly affected by the air-sea fluxes. The diurnal variations in these fluxes also cause the upper ocean mixed layer turbulence and mixing to diurnally vary. The underlying thermohaline structure also varies accordingly throughout the day. Here we use large-eddy simulation to quantify the role of surface evaporation in modulating the diurnal mixed layer turbulence and mixing in the presence of wind forcing. During daytime, the upper ocean boundary layer becomes thermally stratified, and a salinity inversion layer is formed in the upper 10m, leading to double diffusive salt-fingering instability. During nighttime, the mixed layer undergoes convective deepening due to surface buoyancy loss redfrom both surface cooling and evaporation. We find that salinity makes a major contribution to the convective instability during both transitions between day and night. Overall surface evaporation increases the mixed layer depth and irreversible mixing through convection, both during nighttime and daytime, and leads to better prediction of the dynamical variables as sea surface salinity (SSS) and sea surface temperature (SST). Our findings can help improve the ocean parameterizations to improve the forecasts on a diurnal timescale.

Analysis of Himalayan summer monsoon rainfall characteristics using Indian High‐Resolution Regional Reanalysis

AbstractThis study assesses the high‐resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset in representing the Indian summer monsoon (ISM) precipitation over the Himalayan region against ground‐based gridded (IMD), satellite‐based (TRMM, GPM‐IMERG and CHIRPS) and ERA5 reanalysis datasets at seasonal mean, trends, interannual and diurnal timescales. We first evaluate the relative performance of IMDAA rainfall using various statistical performance measures against the aforementioned datasets. Analysis suggests that IMDAA successfully captures the spatial distribution of ISM mean precipitation and seasonal cycle of daily rainfall over the Himalayas as in gridded, satellite and ERA5 reanalysis datasets, albeit with some overestimation in magnitude. Furthermore, statistical results show that IMDAA exhibits a wetter tendency, more RMSE, and higher precipitation variations over the foothills of the Himalayas. The results of the skill score determine that IMDAA diligently captured moderate and extreme precipitation events but was unable to detect heavy and low‐precipitation events. Our analysis further reveals that IMDAA shows a positive trend in mean ISM precipitation over the western Himalayan region, in line with CHIRPS, IMERG, IMD, ERA5 and TRMM datasets. Investigation of rainfall trends from station observations over the foothills of the Himalayas revealed mixed trends, with some stations (Tehri, Uttarkashi and Mukhim) showing an increasing trend and others (Dunta and Bhatwari) showing a decreasing trend. However, IMDAA reanalysis agrees well with all gauge station‐based trends. Additionally, the interannual variability of Himalayan precipitation is well represented in IMDAA as its principal component correlates well with its seasonal precipitation anomalies. It is found that the leading empirical orthogonal function mode of IMDAA accounts 24% of the total variance, demonstrating a single mode of variability. Furthermore, it is noticeable that strong convective activity during wet ISM years than in dry years, suggesting that the IMDAA can capture these variations successfully compared to ERA5. The interannual variability of ISM precipitation over the Himalayas is strongly associated with the El Niño–Southern Oscillation, which is well represented by IMDAA. In addition, IMDAA successfully replicated the diurnal cycle of precipitation over the central Himalayas, showing the presence of bimodal precipitation maxima: one in the afternoon and another one in the early morning, which is consistent with satellite observations. Thus, our results suggest that the IMDAA is a valuable dataset to understand the spatial–temporal pattern of summer precipitation over such a complex high mountain Asian region.