Surface Temperature Time Series Research Articles

Variations of Lake Ice Phenology Derived from MODIS LST Products and the Influencing Factors in Northeast China

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD.

A recent surge in global warming is not detectable yet

The global mean surface temperature is widely studied to monitor climate change. A current debate centers around whether there has been a recent (post-1970s) surge/acceleration in the warming rate. Here we investigate whether an acceleration in the warming rate is detectable from a statistical perspective. We use changepoint models, which are statistical techniques specifically designed for identifying structural changes in time series. Four global mean surface temperature records over 1850–2023 are scrutinized within. Our results show limited evidence for a warming surge; in most surface temperature time series, no change in the warming rate beyond the 1970s is detected despite the breaking record temperatures observed in 2023. As such, we estimate the minimum changes in the warming trend required for a surge to be detectable. Across all datasets, an increase of at least 55% is needed for a warming surge to be detectable at the present time.

The Causal Relation within Air–sea Interaction as Inferred from Observations

Abstract The intricate cause–effect relations in air–sea interaction are investigated using a quantitative causal inference formalism. The formalism is first validated with a classical stochastic coupled model, and then applied to the observational time series of sea surface temperature (SST) and air–sea turbulent surface heat flux (SHF). We identify an overall asymmetry of causality between the two variables, namely, the causality from SHF to SST is significantly larger than that from SST to SHF over most of the global oceans. Geographically, the coupling is strongest in the tropics and gets weaker substantially in the extratropics. In the midlatitude ocean, SST makes higher contributions to the SHF variability in frontal regions. We further show that the identified causality is space- and time-scale dependent. The dominance of SHF driving SST occurs at basin scales, whereas the dominance of SST driving SHF mostly occurs at scales smaller than 10°. The causalities in both directions get larger with increasing time scale, and are less asymmetric at longer time scales. Also discussed here is the seasonality of the causality.

Inverse algorithm for boundary heat flux density based on the NARX neural network

Abstract The inverse heat transfer problem is vital for scientific research and engineering applications. This paper introduces a method using the Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network to identify heat boundary conditions in nonlinear transient heat transfer processes in real time. This method has two notable advantages: (1) It relies solely on surface temperature time series to obtain inversion results; (2) Even in the absence of knowledge regarding the system’s state equations, it can estimate heat flux density. The NARX neural network is trained by using Bayesian regularization with surface temperature and heat flux data. (3) As per the inversion results, the NARX neural network’s accuracy in predicting the boundary heat flux density (BHFD) increases as the temperature measurement points approach the heat flux boundary. This neural network calculates the current heat flux density by incorporating both present and past surface temperature measurements as inputs. Through numerical simulation experiments, the efficacy of the NARX method is confirmed, showcasing its exceptional accuracy, robustness against noise, and broad suitability.

Twenty Years of Thermal Infrared Observations (2004–2024) at Campi Flegrei Caldera (Italy) by the Permanent Surveillance Ground Network of INGV-Osservatorio Vesuviano

Thermal infrared (TIR) time series images acquired by ground, proximal TIR stations provide valuable data to study evolution of surface temperature fields of diffuse degassing volcanic areas. This paper presents data processing results related to TIR images acquired since 2004 by six ground stations in the permanent thermal infrared surveillance network at Campi Flegrei (TIRNet) set up by INGV-Osservatorio Vesuviano. These results are reported as surface temperature and heat flux time series. The processing methodologies, also discussed in this paper, allow for presentation of the raw TIR image data in a more comprehensible form, suitable for comparisons with other geophysical parameters. A preliminary comparison between different trends in the surface temperature and heat flux values recorded by the TIRNet stations provides evidence of peculiar changes corresponding to periods of intense seismicity at the Campi Flegrei caldera. During periods characterized by modest seismicity, no remarkable evidence of common temperature variations was recorded by the different TIRNet stations. Conversely, almost all the TIRNet stations exhibited common temperature variations, even on a small scale, during periods of significant seismic activity. The comparison between the seismicity and the variations in the surface temperature and heat flux trends suggests an increase in efficiency of heat transfer between the magmatic system and the surface when an increase in seismic activity was registered. This evidence recommends a deeper, multidisciplinary study of this correlation to improve understanding of the volcanic processes affecting the Campi Flegrei caldera.

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

20-year permafrost evolution documented through petrophysical joint inversion, thermal and soil moisture data

This study investigates the ground characteristics of the high altitude (3410 m a.s.l.) permafrost site Stockhorn in the Swiss Alps using a combination of surface and subsurface temperature, soil moisture, electrical resistivity and P-wave velocity time series data including a novel approach to explicitly quantify changes in ground ice content. This study was motivated by the clear signal of permafrost degradation visible in the full dataset at this long-term monitoring site within the PERMOS (Permafrost Monitoring Switzerland) network. Firstly, we assess the spatio-temporal evolution of the ground ice and water content by a combined analysis of all available in situ thermal (borehole and ground surface temperature), hydrological (soil moisture) and geophysical (geoelectric and seismic refraction) data over two decades (2002–2022) regarding the driving factors for the spatially different warming. Secondly, we explicitly quantify the volumetric water and ice content and their changes in the subsurface from 2015 to 2022 using a time-consistent petrophysical joint inversion scheme within the open-source library pyGIMLi. The petrophysical joint inversion scheme has been improved by constraining the rock content to be constant in time for six subsequent inversions to obtain consistent changes in ice and water content over the monitoring period based on jointly inverted resistivity and traveltime data. All different data show a warming trend of the permafrost. The ice content modeled from the petrophysical joint inversion has decreased by about 15 vol.% between 2015 and 2022. Changes in ice content are first observed in the lower, south-facing part of the profile. As a result, resistivity and P-wave velocity have been decreasing significantly. Permafrost temperatures measured in the boreholes have increased between 0.5 °C and 1 °C in 20 years. Our study shows the high value of joint and quantitative analysis of datasets comprising complementary subsurface variables for long-term permafrost monitoring.

An investigation of the relationship between surface temperature and total ozone over New Delhi, India, using a frequency domain technique and binary fuzzy relation

ABSTRACT This work has explored spectral analysis to view the relationship between the time series of surface temperature and total ozone concentration (TOC) over New Delhi from 1995 to 2005. The time series has a discernible spectral density when analysed using harmonic analysis. Between the surface temperature and TOC, a common cycle of period of 10 months has been noted for the years under investigation. In addition, a binary fuzzy relation method has been used to examine the relationship between surface temperature and TOC using membership matrices produced by max-min composition. Overall, a fuzzy set theoretic approach has established a connectivity between surface temperature and TOC, and a spectral analysis approach which falls under the category of frequency domain approach has established the existence of a common cycle between these two time series.

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Abstract. Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict sea surface temperature (SST) time series and marine heatwaves in 16 regions of the Mediterranean Sea. ML algorithms, including the random forest (RForest), long short-term memory (LSTM), and convolutional neural network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 d ahead. For each region, we performed 15 different experiments for ML techniques, progressively sliding the training and the testing period window of 4 years from 1981 to 2017. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. For each area, the results show that all the ML methods performed with minimum root mean square errors (RMSEs) of about 0.1 °C at a 1 d lead time and maximum values of about 0.8 °C at a 7 d lead time. In all regions, both the RForest and LSTM consistently outperformed the CNN model across all lead times. LSTM has the highest predictive skill in 11 regions at all lead times. Importantly, the ML techniques show results similar to the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods compare favorably with MedFS up to 3 d lead time in 14 regions, while MedFS shows superior skill at 5 d lead time in 9 out of 16 regions. All methods predict the occurrence of MHWs with a confidence level greater than 50 % in each region. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself.

The Changing Biogeography of the Ligurian Sea: Seawater Warming and Further Records of Southern Species

Global warming is causing poleward expansion of species ranges. Temperate seas, in particular, are undergoing a process known as ‘tropicalisation’, i.e., the combination of sea-water warming and establishment of southern species. The Ligurian Sea is one of the coldest sectors of the Mediterranean and has thus been characterized by a dearth of warm-temperate species and a comparative abundance of cold-temperate species. This paper uses a time series of sea surface temperature (SST) and new records of thermophilic fish species to reconsider the biogeography of the Ligurian Sea. SST has risen by about 0.7 °C on average between 1948 and 2023, but two phases may be distinguished: a cool one (ended in the mid-1980s) and a warm one (still ongoing); the latter phase shows alternating periods of rapid warming and comparatively stationary temperature. The arrival of thermophilic species coincided with the periods of rapid warming; some of these species were established in the subsequent stationary periods. Heatwaves and climate-related diseases associated with the periods of rapid warming have caused mass mortalities of autochthonous species. Our knowledge on the biogeography of the Ligurian Sea was established during the cool phase; the present situation, however, calls for re-defining the chorological spectrum of the Ligurian Sea biota.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Coupled thermo–geophysical inversion for permafrost monitoring

Abstract. This study explores an alternative way of deriving soil thermal properties from surface geophysical measurements. We combined ground surface temperature time series with time lapse geoelectrical acquisitions measured from the ground surface in a fully coupled inversion scheme to calibrate a heat conduction model. The quantitative link between the thermal and geoelectrical parts of the modeling framework is the temperature-dependent unfrozen water content, which is also the main factor influencing electrical response of the ground. The apparent resistivity data were incorporated into the coupled framework without being inverted separately, thus reducing the uncertainty inevitably associated with inverted resistivity models. We show that geoelectrical time lapse data are useful as alternative calibration data and can provide as good results as borehole temperature measurements. The fully coupled modeling framework using field data achieved performance comparable to calibration on borehole temperature records in terms of model fit within 0.6 ∘C, inversion convergence metrics, as well as the predictive performance of the calibrated model.

Wavelets in the analysis of local time series of the Earth's surface air

The practical application of local smoothing and wavelet analysis methods for studying the spectral composition and coherent relationships of local average annual surface air temperatures with solar activity and the displacement of the Earth's North Pole is presented. A preliminary analysis of local time series of surface temperatures revealed the presence of emissions and their localization. It is shown that to eliminate the influence of outliers (short-term events) on the reliability of identifying a long-term nonlinear trend, the wavelet decomposition method, which filters high frequencies, is most suitable. Functional approximation models are constructed and compared at different levels of wavelet decomposition of the data. Time or scale smoothing is used to improve the reliability of the wavelet spectrum. Based on data on average annual surface air temperatures in Yalta (44.48⁰, 34.17⁰, = 72.0 m) for the time interval from 1869 to 2022, functional models of long-term trends were built and used to obtain short-term forecasts. Information about the linear relationship of events in the compared time series is obtained and discussed in the analysis of wavelet cross-correlation, wavelet coherence and phase coherence. Local similarities were discovered between data on surface air temperature and solar activity data (Wolf numbers) for a period of ∼(30–70) years, as well as oscillations with period of 11 years, manifested in the constancy of the phase difference and an increase in the modulus of wavelet coherence power over time. Localized similarities were also found in data on surface air temperature in Yalta and in data on displacements of the Earth's mean pole relative to the conventional beginning of EOP (IERS) CO1 in the interval of periods ∼ (30–70) years.

Climate attribution time series track the evolution of human influence on North Pacific sea surface temperature

We apply climate attribution techniques to sea surface temperature time series from five regional North Pacific ecosystems to track the growth in human influence on ocean temperatures over the past seven decades (1950–2022). Using Bayesian estimates of the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) derived from 23 global climate models, we show that human influence on regional ocean temperatures could first be detected in the 1970s and grew until 2014–2020 temperatures showed overwhelming evidence of human contribution. For the entire North Pacific, FAR and RR values show that temperatures have reached levels that were likely impossible in the preindustrial climate, indicating that the question of attribution is already obsolete at the basin scale. Regional results indicate the strongest evidence for human influence in the northernmost ecosystems (Eastern Bering Sea and Gulf of Alaska), though all regions showed FAR values > 0.98 for at least one year. Extreme regional SST values that were expected every 1000–10 000 years in the preindustrial climate are expected every 5–40 years in the current climate. We use the Gulf of Alaska sockeye salmon fishery to show how attribution time series may be used to contextualize the impacts of human-induced ocean warming on ecosystem services. We link negative warming effects on sockeye fishery catches to increasing human influence on regional temperatures (increasing FAR values), and we find that sockeye salmon migrating to sea in years with the strongest evidence for human effects on temperature (FAR ⩾ 0.98) produce catches 1.4 standard deviations below the long-term log mean. Attribution time series may be helpful indicators for better defining the human role in observed climate change impacts, and may thus help researchers, managers, and stakeholders to better understand and plan for the effects of climate change.

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.

Drought Monitoring from Fengyun Satellite Series: A Comparative Analysis with Meteorological-Drought Composite Index (MCI)

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices.

Upwelling characteristics in the Gulf of Riga (Baltic Sea): multiple data source approach

Upwellings are characteristic for the Baltic Sea region including the Gulf of Riga, although the current knowledge is rather limited with only few research conducted in the Gulf itself. Upwelling events in the Gulf of Riga in 2010–2022 were studied by analyzing sea surface temperature time series from coastal stations and SmartBuoy, together with satellite data, model data, and CTD (conductivity, temperature, and depth) surveys. The starting/end point, active, and relaxation phases were defined in each event to describe the characteristics and length of each phase. Upwellings were less frequent (41%) on the eastern coast but lasted longer and had higher temperature drops than on the western coast. On the western coast, a variety of upwelling characteristics between stations only 30 km apart were found with the likely reason being the different orientations of the coastline with respect to the wind direction. Satellite data revealed that on the western coast of the Gulf, rather small upwelling events form along specific sections of the coastline. Of all upwelling events, 30% were characterized by an immediate temperature increase after reaching the minimum temperature, and we suggest that this is related to a distinct change in wind direction. The results from the simulations indicated smaller lateral density and salinity gradients in the sea surface than in larger Baltic Sea gulfs. It signals that conditions for the occurrence of baroclinic instabilities are rather small; thus, we suggest that weaker gradients could explain quite fast upwelling relaxation in the basin if compared to, e.g., the Gulf of Finland.

Climate adaptive urban measures in Mediterranean areas: Thermal effectiveness of an advanced multilayer green roof installed in Palermo (Italy)

Several nature based and climate adaptive solutions have been proposed to improve cities resilience to the effects of global warming and restore natural processes in strongly anthropized areas. Green roofs are among the most efficient nature based solutions to address recurrent urban challenges, such as pluvial floods and urban heat islands. Various benefits offered by green roofs are rather known, such as their capacity to enhance buildings thermal insulation; green roofs also favor urban biodiversity, improving buildings aesthetic value and human well being. Multilayer green roofs (MGRs) are green roofs with an additional layer that increases their water storage capacity. Deep analyses on MGRs are still lacking due to their recent development, and the few works in literature are prevalently focused on their stormwater retention primary function. This work explores the thermal function of an experimental MGR prototype installed in Palermo (Italy), comparing its response to local climate with that of an unaltered portion of the rooftop through the analysis of surface temperature time series collected over a two years monitoring period. Performances are evaluated thought various daily thermal indices, also analyzing the role of the water stored into the system. Results contribute to raise awareness about the benefits arising from the use of MGRs in semi-arid Mediterranean urban areas, confirming, as main thermal advantage, their cooling effect, with mean daily surface temperature reduced by 8.4% outdoor and 5.8% indoor; performances increases with water storage and are particularly evident during the hot and dry summers that typically characterize such regions.

A Stable Isotope Sclerochronology‐Based Forensic Method for Reconstructing Debris Drift Paths With Application to the MH370 Crash

AbstractA flaperon belonging to Malaysian Airlines flight MH370 washed ashore on Réunion Island covered with the barnacle Lepas anatifera in July 2015, more than a year after the plane's disappearance. Here, we report the first high‐precision δ18Ocalcite versus temperature relationship for L. anatifera reared under laboratory conditions to unlock clues to the flaperon's drift path and origin. Using this experimental relationship and known growth rates for L. anatifera, we also demonstrate a new method for (a) converting δ18O data for one of the MH370 barnacles into a dated time series of sea surface temperatures (SSTs) experienced during the last part of the flaperon's drift and (b) identifying best fits between the observed flaperon SST time series and 50,000 SST histories generated from a particle‐tracking simulation. Our new method identifies a flaperon drift path far south of a previous isotope‐based reconstruction. We conclude with specific recommendations for using our method to continue the search for MH370 and other applications.

Thermal pattern at kueishantao (KST) volcano of Taiwan from satellite-observed temperatures and its implication

Abstract Kueishantao (KST) is an active volcanic island off the northeastern coast of Taiwan. Tectonically, it lies in the south of the Okinawa Trough and opposite to the Ilan plain, in which is the southwestern end of the trough. KST provides a convenient observation site for the subsurface geological and geothermal activity and mechanism at its proximity. Land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. LST thus is a key parameter to the understanding of the volcanic process and geothermal resources. This research utilizes the satellite-observed multi-temporal land surface temperature imagery in 1999–2022 on the Kueishantao volcano of Taiwan to explore its geothermal state. The U.S. NASA Earth-observing satellites onboard three thermal sensors (i.e., Landsat ETM+, Terra ASTER, and Aqua/Terra MODIS) derived time series of land surface temperature from 1999 are employed to define the past and current pattern of geothermal activity plus the future trend of the KST. The spatiotemporal LST distribution of KST volcano is explored and analyzed. The spatial LST distribution of the KST volcano indicates that LST anomaly areas are mainly located on the southeast island, which is well correlated with the possible magma reservoir location from previous geophysical and geological surveys. An increasing trend of two-decade LST time series is revealed from all three thermal sensors. The retrieved surface thermal pattern shows non-linear temperature variations that imply the non-steady-state nature of the subsurface thermal sources at this volcano. In summary, satellite LST observations facilitate the understanding on the subsurface magmatic processes of active volcanoes for further management of geothermal resources.