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Seasonal regimes of stream temperatures are important for ecological health as well as for societal water use. Seasonal regimes can be captured in the annual temperature cycle (the mean temperature for each day of the year) or in summary statistics such as seasonal mean temperatures, the former of which is the focus of this work. The annual temperature cycle is often characterized as a sine function, which performs satisfactorily for most streams. However, the sine function is unable to capture major seasonal variations, particularly for colder, drier, and high‐elevation regions. Seasonal summary statistics are effective for classification but do not capture the full time series, preventing the use of lost time‐series information, and lack context for the comparison of trends, hindering distinction between different causes of similar seasonal trends. We propose an improved function called the "three‐sine model" to describe the stream annual temperature cycle with higher accuracy and demonstrate its use in two case studies. The three‐sine model uses a cosine function over the entire year coupled with two seasonal anomaly sine functions. The three‐sine model captures the stream annual temperature cycle with eight parameters, reveals distinct spatial trends, and outperforms the sinusoidal model for all elevations and 99% of streams. We conclude that this approach can support improved stream temperature analysis by capturing detailed seasonal trends in context. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness. [ABSTRACT FROM AUTHOR]

Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships between LSTs and LST-related descriptors. To address these issues, we propose an improved ATC model (termed the ATC_GL), which combines both the spatiotemporally global and local interpolations. Using the random forest (RF) algorithm, the ATC_GL model quantifies the complex relationships between LSTs and LST-related descriptors such as the surface air temperature, normalized difference vegetation index, and digital elevation model. The performances of the ATC_GL and several extensively used LST reconstruction methods were compared under both clear-sky and overcast conditions. In the scenario with randomly missing LSTs, the accuracy of the ATC_GL was 2.3 K and 3.1 K higher than that of the ATCE (the enhanced ATC model) and the ATCO (the original ATC model), respectively. In the scenario with LST gaps of various sizes, the ATC_GL maintained the highest accuracy and was less sensitive to gap size when compared with the ATCH (the hybrid ATC model), Kriging interpolation, RSDAST (Remotely Sensed Daily Land Surface Temperature), and HIT (Hybrid Interpolation Technique). In the scenario of overcast conditions, the accuracy of the ATC_GL was 1.0 K higher than that of other LST reconstruction methods. The ATC_GL enriches the ATC model family and provides enhanced performance for generating spatiotemporally seamless LST products with high accuracy. [ABSTRACT FROM AUTHOR]

Weakening of annual temperature cycle (ATC) on the Tibetan Plateau (TP) in the last decades has been identified, and there is a subsequent change in the frequency of the ATC magnitude extremes. These changes in the climate have a potential influence on biological and ecological systems. However, the frequency of occurrences of the ATC magnitude extremes in the recent decades compared to the preindustrial period, as well as the potential driving forces remains unclear. This can limit the robust predictions and risk assessments for extreme events of the ATC in the future. In this study, we analyzed the frequency of occurrence of the ATC magnitude extremes using both recent observations (1955-2011) and a tree-ring-based reconstruction covering the past three centuries over the TP. The results indicate that the ATC magnitude extremes in maximum obviously decrease and in minimum distinctly increase under climate warming, both for the instrumental period and the past three centuries. Moreover, the occurrence of the ATC magnitude extremes on the TP had a distinct regional characteristic and was mainly related to the winter temperature extremes triggered by the anomalous meridional gradient of geopotential height impelling cold air intrudes the TP. Although volcanic eruptions were expected to result in summer cooling and winter warming, and thus trigger the minimum ATC magnitude extremes, this effect only can be identified in event-specific eruptions (e.g., the 1835 Cosiguina eruption). Our study results provide an essential background for developing robust predictions and risk assessments for the ATC magnitude extremes on the TP. [ABSTRACT FROM AUTHOR]

The mountainous region affected by the 2008 Wenchuan earthquake was frequently analyzed to assess its vegetation recovery. However, the dynamic of surface thermal environment was rarely investigated but directly influenced local geophysical and biophysical processes. Under this background, this research aimed to analyze the trend of surface temperature based on Moderate Resolution Imaging Spectroradiometer daily land surface temperature product from 2000 to 2017 and understand its change mechanism. An annual temperature cycle model was applied to obtain the daytime and nighttime annual cycle parameters (ACPs), including mean annual surface temperature (MAST), yearly amplitude of surface temperature, and phase shift. The trend analysis with the Mann‐Kendall test illustrated the strong inverse relationship between the two ACPs (MAST and yearly amplitude of surface temperature) and elevation, and the nighttime terms showed a stronger connection than those of the daytime. The areas with significant decreases in vegetation coverage induced by the earthquake exhibited an obvious upward trend in daytime MAST and annual maximum temperature, while the vegetation improved areas presented a cooling effect. Comparatively, the nighttime ACPs was little sensitive to vegetation change and the change magnitude was relatively small. For the high mountain area, with the shrinkage of snow cover duration, the global warming effect resulted in a warming trend by increasing the annual minimum temperature at both the daytime and nighttime. Overall, this study provided insights to the impact of the earthquake to mountain thermal environment and its different responses to changes in vegetation cover and climatic environment. Key Points: The periodic information of LST mapping using annual temperature cycle model presents the general thermal pattern over mountainous regionEarthquake‐induced destruction in surface vegetation cover greatly influences the thermal environment, and its impact will last a long timeGlobal warming has a strong effect to high mountain region by reducing snow cover duration but shows small impact to lower surfaces [ABSTRACT FROM AUTHOR]

The century-long search for the precise mechanisms responsible for urban heat islands continues, while urban warming worsens in many megacities. Most studies have focused on mean temperature, daily and annual temperature ranges and urban heat island intensity. We hypothesize that an analysis of the changes in the characteristics of the complete daily and annual temperature cycles, including not only the mean temperature and temperature ranges (amplitudes) but also the maximum and minimum temperatures and the phases, can provide more information on urban warming phenomena. Through a detailed analysis of long-term observations in Hong Kong, we found that the difference in the daily cycle between urban and rural stations is very distinct, whereas the annual cycles are much more similar, suggesting that the urban environment has a greater effect on the daily cycle than on the annual cycle. The daily phase has shifted a total of 1.77 h later over the last 130 years (1.36 h per century) in the urban area of Hong Kong according to the Hong Kong Observatory (HKO) data. The annual phase change at HKO reflects the globally observed phenomenon that the annual phase advances or seasons onset earlier. [ABSTRACT FROM AUTHOR]

Satellite thermal remote sensing provides access to acquire large-scale Land surface temperature (LST) data, but also generates missing and abnormal values resulting from non-clear-sky conditions. Given this limitation, Annual Temperature Cycle (ATC) model was employed to reconstruct the continuous daily LST data over a year. The original model ATCO used harmonic functions, but the dramatic changes of the real LST caused by the weather changes remained unclear due to the smooth sine curve. Using Aqua/MODIS LST products, NDVI and meteorological data, we proposed enhanced model ATCE based on ATCO to describe the fluctuation and compared their performances for the Yangtze River Delta region of China. The results demonstrated that, the overall root mean square errors (RMSEs) of the ATCE was lower than ATCO, and the improved accuracy of daytime was better than that of night, with the errors decreased by 0.64 K and 0.36 K, respectively. The improvements of accuracies varied with different land cover types: the forest, grassland and built-up areas improved larger than water. And the spatial heterogeneity was observed for performance of ATC model: the RMSEs of built-up area, forest and grassland were around 3.0 K in the daytime, while the water attained 2.27 K; at night, the accuracies of all types significantly increased to similar RMSEs level about 2 K. By comparing the differences between LSTs simulated by two models in different seasons, it was found that the differences were smaller in the spring and autumn, while larger in the summer and winter. [ABSTRACT FROM AUTHOR]

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters. [ABSTRACT FROM AUTHOR]

A widely accepted standpoint contends that plant growth near the cold edge of the species range, such as treelines, does not depend on the annual temperature seasonality (i.e. difference between maximum and minimum temperature values) but rather on the warmth of summer season. In contrast to this expectation, we show that the growth of treeline Norway spruce ( Picea abies) is well explained by temperature seasonality as a single climatic determinant. To do so, the tree-ring data of spruce trees growing on alpine treeline in Lapland was compared with long climate records. Biennial time-series of temperature seasonality capture both the decadal and abrupt growth fluctuations with a correlation coefficient of r = 0.601. We also show that the archetypal association between summer temperature and treeline tree growth may in fact be by far a more complex relationship than previously thought. Spruce growth appears responsive to late- June (r = 0.494) and mid-July (r = 0.310) temperatures but unresponsive to temperatures during the early July, that is, during the grand period of the tracheid formation. Climatic warming may enhance the treeline spruce growth unless the warming is concentrated on unresponsive interval in the midst of the growing season. Water relations did not play significant role as agents of P. abies growth. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing. [ABSTRACT FROM AUTHOR]

The scientific community has widely reported the impacts of climate change on the Central Himalaya. To qualify and quantify these effects, long-term land surface temperature observations in both the daytime and nighttime, acquired by the Moderate Resolution Imaging Spectroradiometer from 2000 to 2017, were used in this study to investigate the spatiotemporal variations and their changing mechanism. Two periodic parameters, the mean annual surface temperature (MAST) and the annual maximum temperature (MAXT), were derived based on an annual temperature cycle model to reduce the influences from the cloud cover and were used to analyze their trend during the period. The general thermal environment represented by the average MAST indicated a significant spatial distribution pattern along with the elevation gradient. Behind the clear differences in the daytime and nighttime temperatures at different physiographical regions, the trend test conducted with the Mann-Kendall (MK) method showed that most of the areas with significant changes showed an increasing trend, and the nighttime temperatures exhibited a more significant increasing trend than the daytime temperatures, for both the MAST and MAXT, according to the changing areas. The nighttime changing areas were more widely distributed (more than 28%) than the daytime changing areas (around 10%). The average change rates of the MAST and MAXT in the daytime are 0.102 °C/yr and 0.190 °C/yr, and they are generally faster than those in the nighttime (0.048 °C/yr and 0.091 °C/yr, respectively). The driving force analysis suggested that urban expansion, shifts in the courses of lowland rivers, and the retreat of both the snow and glacier cover presented strong effects on the local thermal environment, in addition to the climatic warming effect. Moreover, the strong topographic gradient greatly influenced the change rate and evidenced a significant elevation-dependent warming effect, especially for the nighttime LST. Generally, this study suggested that the nighttime temperature was more sensitive to climate change than the daytime temperature, and this general warming trend clearly observed in the central Himalayan region could have important influences on local geophysical, hydrological, and ecological processes. [ABSTRACT FROM AUTHOR]

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications. [ABSTRACT FROM AUTHOR]