Surface Temperature Time Series Research Articles

Temperature distribution in a permafrost-affected rock ridge from conductivity and induced polarization tomography

SUMMARY Knowledge of the thermal state of steep alpine rock faces is crucial to assess potential geohazards associated with the degradation of permafrost. Temperature measurements at the rock surface or in boreholes are however expensive, invasive, and provide spatially limited information. Electrical conductivity and induced polarization tomography can detect permafrost. We test here a recently developed petrophysical model based on the use of an exponential freezing curve applied to both electrical conductivity and normalized chargeability to infer the distribution of temperature below the freezing temperature. We then apply this approach to obtain the temperature distribution from electrical conductivity and normalized chargeability field data obtained across a profile extending from the SE to NW faces of the lower Cosmiques ridge (Mont Blanc massif, Western European Alps, 3613 m a.s.l., France). The geophysical data sets were acquired both in 2016 and 2019. The results indicate that only the NW face of the rock ridge is frozen. To evaluate our results, we model the bedrock temperature across this rock ridge using CryoGRID2, a 1-D MATLAB diffusive transient thermal model and surface temperature time-series. The modelled temperature profile confirms the presence of permafrost in a way that is consistent with that obtained from the geophysical data. Our study offers a promising low-cost approach to monitor temperature distribution in Alpine rock walls and ridges in response to climate change.

Mixed-Precision Kernel Recursive Least Squares.

Kernel recursive least squares (KRLS) is a widely used online machine learning algorithm for time series predictions. In this article, we present the mixed-precision KRLS, producing equivalent prediction accuracy to double-precision KRLS with a higher training throughput and a lower memory footprint. The mixed-precision KRLS applies single-precision arithmetic to the computation components being not only numerically resilient but also computationally intensive. Our mixed-precision KRLS demonstrates the 1.32, 1.15, 1.29, 1.09, and 1.08× training throughput improvements using 24.95%, 24.74%, 24.89%, 24.48%, and 24.20% less memory footprint without losing any prediction accuracy compared to double-precision KRLS for a 3-D nonlinear regression, a Lorenz chaotic time series, a Mackey-Glass chaotic time series, a sunspot number time series, and a sea surface temperature time series, respectively.

A Markov chain approach to the predictability of surface temperature over the northeastern part of India

The present study reports a two-state Markov chain approach as well as an autoregressive approach to study the behavior of the surface temperature time series over northeast India. Considering the minimum requirement of the chi-square test, 1998–2007 (> 100 months) have been considered for testing the Markovian and autoregressive behavior. The monthly surface temperature time series involves monthly data that corresponds to a continuous random variable. This random variable has been discretized to binary data, and the transition probabilities have been computed up to the fourth-order Markov chain model. The best order of Markov chain has been derived through the minimization of the Bayesian information criterion (BIC). By analyzing the time series autocorrelation function and the Akaike information criteria (AIC), the autoregressive model of order two has been found to be a representative and the best autoregressive method for the average monthly time series of surface temperatures over northeast India.

Spatial seated occupancy detection in offices with a chair-based temperature sensor array

Sensors used to monitor indoor environmental conditions and utility consumption can result in well-defined working and living spaces. Real-time spatiotemporal occupancy detection techniques used to track humans indoors paired with these data can be used to better understand the role of occupants on indoor air quality and building energy consumption. This study introduces a novel occupancy sensing technique whereby a chair-based temperature sensor array is used to detect the presence of occupants seated in a living laboratory open-plan office. Seat surface temperatures were tracked for twenty individuals over seven months using K-type thermocouples with battery-powered dataloggers secured to office chairs in known locations. The temperature differential between the occupant and seat surface enables for rapid conduction and thus detection of one's seated presence. Seat surface temperature time-series were converted to binary seated occupancy for each chair and totaled to achieve a spatial map of room seated occupancy with a time resolution of 15 s. Trends in spatiotemporal seated occupancy profiles in the office were evaluated for seven months using the novel technique. This highly localized form of occupancy detection offers several advantages compared to delocalized sensing techniques, including visualization of spatial occupancy patterns over time; determination of individual seated occupancy histories visualized in the form of “occupancy barcodes; ” quantification of total seated hours per occupant in different spatial zones across varying time-scales; and characterization of diurnal and weekly trends in seated occupancy probabilities categorized by an occupant's relative level of presence.

Observations of planetary heating since the 1980s from multiple independent datasets

Time series of global mean surface temperature are widely used to measure the rate of climate change that results from Earth’s energy imbalance. However, studies based on climate model simulations suggest that on annual-to-decadal timescales global ocean heat content is a more reliable indicator. Here we examine the observational evidence for this, drawing together multiple datasets that span the past ∼30 years. This observational analysis strongly supports the model-based finding that global ocean heat content and sea level are more reliable than surface temperature for monitoring Earth’s energy accumulation on these timescales. Global ocean temperature anomalies in the 0–100 m and 100–250 m layers are negatively correlated (r = −0.36), primarily explained by the influence of the Tropical Pacific, and a clearer heating signal is revealed by integrating over deeper ocean layers. The striking agreement between multiple independent datasets represents unequivocal evidence of ongoing planetary heating.

A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series

Abstract. Reliable estimates of historical effective radiative forcing (ERF) are important for understanding the causes of past climate change and for constraining predictions of future warming. This study proposes a new linear-filtering method for estimating historical radiative forcing from time series of global mean surface temperature (GMST), using energy-balance models (EBMs) fitted to GMST from CO2-quadrupling general circulation model (GCM) experiments. We show that the response of any k-box EBM can be represented as an ARMA(k, k−1) (autoregressive moving-average) filter. We show how, by inverting an EBM's ARMA filter representation, time series of surface temperature may be converted into radiative forcing. The method is illustrated using three-box EBM fits to two recent Earth system models from CMIP5 and CMIP6 (Coupled Model Intercomparison Project). A comparison with published results obtained using the established ERF_trans method, a purely GCM-based approach, shows that our new method gives an ERF time series that closely matches the GCM-based series (correlation of 0.83). Time series of estimated historical ERF are obtained by applying the method to a dataset of historical temperature observations. The results show that there is clear evidence of a significant increase over the historical period with an estimated forcing in 2018 of 1.45±0.504 W m−2 when derived using the two Earth system models. This method could be used in the future to attribute past climate changes to anthropogenic and natural factors and to help constrain estimates of climate sensitivity.

MODIS land surface temperature time series decomposition for detecting and characterizing temporal intensity variations of coal fire induced thermal anomalies in Jharia coalfield, India

Long-term MODIS time series Land Surface Temperature (LST) observations over coal fire affected areas were utilized for detection and characterization of coal fire as a function of its temporal intensity variation (e.g. growing, temporally consistent or diminishing) on annual basis in Jharia coalfield. LST pixel time series (LPTS) vectors were generated for selected coal fire and non-coal fire locations using 782 LST maps of the duration 2001 − 2017. LPTS vectors were decomposed to extract the nonlinear trend component using Seasonal Trend decomposition based on Loess model. Background-referenced trends were generated for coal fire pixels. Slope, p-value of Mann-Kendall test and average annual deviation parameters were calculated for annually segmented background-referenced coal fire trends to characterize coal fire on annual basis and to separate coal fire induced anomalous trend and non-coal fire trends. The results were validated with that of published literature.

A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to study its seasonal cycle and look for a possible warming trend in the basin. From the analysis of the Mediterranean mean SST time series, a new temporal seasonal division is derived that differs from the one used in atmospheric climatology. Then, the SST time series were decomposed into their seasonal and trend components, and a consistent warming trend of 0.035 °C/year was obtained. The nature of this trend has been investigated, indicating a higher warming trend for both maximum and high/summer SST values than for the winter/colder ones. This reinforces the consistency of the SST increase since it is not only based on the presence of extreme values, but on a homogeneous basin global increase of high SST records as well. Although warming is found throughout the Mediterranean basin, the spatial variability found leads to the division of the basin into three distinct subareas regarding warming.

Optimal Estimation of Stochastic Energy Balance Model Parameters

AbstractThis study has developed a rigorous and efficient maximum likelihood method for estimating the parameters in stochastic energy balance models (with any k > 0 number of boxes) given time series of surface temperature and top-of-the-atmosphere net downward radiative flux. The method works by finding a state-space representation of the linear dynamic system and evaluating the likelihood recursively via the Kalman filter. Confidence intervals for estimated parameters are straightforward to construct in the maximum likelihood framework, and information criteria may be used to choose an optimal number of boxes for parsimonious k-box emulation of atmosphere–ocean general circulation models (AOGCMs). In addition to estimating model parameters the method enables hidden state estimation for the unobservable boxes corresponding to the deep ocean, and also enables noise filtering for observations of surface temperature. The feasibility, reliability, and performance of the proposed method are demonstrated in a simulation study. To obtain a set of optimal k-box emulators, models are fitted to the 4 × CO2 step responses of 16 AOGCMs in CMIP5. It is found that for all 16 AOGCMs three boxes are required for optimal k-box emulation. The number of boxes k is found to influence, sometimes strongly, the impulse responses of the fitted models.

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

Dynamic Satellite Seascapes as a Biogeographic Framework for Understanding Phytoplankton Assemblages in the Florida Keys National Marine Sanctuary, United States

Physical, chemical, geological, and biological factors interact in marine environments to shape complex but recurrent patterns of organization of life on multiple spatial and temporal scales. These factors define biogeographic regions in surface waters that we refer to as seascapes. We characterize seascapes for the Florida Keys National Marine Sanctuary (FKNMS) and southwest Florida shelf nearshore environment using multivariate satellite and in situ measurements of Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs). The study focuses on three periods that cover separate oceanographic expeditions (March 11-18, May 9-13, and September 12-19, 2016). We collected observations on bio-optical parameters (particulate and dissolved spectral absorption coefficients), phytoplankton community composition, and hydrography from a ship. Phytoplankton community composition was evaluated using 1) chemotaxonomic analysis (CHEMTAX) based on high-performance liquid chromatography (HPLC) pigment measurements, and 2) analysis of spectral phytoplankton absorption coefficients (aphy). Dynamic seascapes were derived by combining satellite time series of sea surface temperature, chlorophyll-a concentration, and normalized fluorescent line height (nFLH) using a supervised thematic classification. The seascapes identified areas of different salinity and nutrient concentrations where different phytoplankton communities were present as determined by hierarchical cluster analyses of HPLC pigments and aphy spectra. Oligotrophic, Mesotrophic and Transition seascape classes of deeper offshore waters were dominated by small phytoplankton ( 60 %). Spectral analysis of aphy indicated higher absorption levels at 492 and 550 nm wavelengths in seascapes carrying predominantly small phytoplankton than in classes dominated by larger taxa. Seascapes carrying large phytoplankton showed absorption peaks at the 673 nm wavelength. The seascape framework promises to be a tool to detect different biogeographic domains quickly, providing information about the changing environmental conditions experienced by coral reef organisms including coral, sponges, fish and higher trophic levels. The effort illustrates best practices developed under the Marine Biodiversity Observation Network (MBON) demonstration project, in collaboration with the South Florida Ecosystem Restoration Research (SFER) project managed by the Atlantic Oceanographic and Meteorological Laboratory of NOAA (AOML-NOAA).

Multiscale and multitemporal surface temperature monitoring by satellite thermal infrared imagery at Mayon Volcano, Philippines

Mayon Volcano on eastern Luzon Island is the most active volcano in the Philippines. The high level of vulnerability to volcanic hazards is apparent since Mayon is surrounded by eight cities and municipalities with a total population of approximately one million inhabitants. Thus there is an urgent need to develop and expand reliable and affordable long-term methods in volcano monitoring. Despite abundant open access satellite dataset archives, satellite remote sensing has been underutilized in Mayon's hazard mapping and monitoring system. Here, we perform monitoring analysis on a nineteen-year time series of Land Surface Temperature (LST) obtained from satellite thermal infrared images. Both ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) thermal imagery (with 90 m spatial resolution) and MODIS (Moderate Resolution Imaging Spectroradiometer) LST products (1 km spatial resolution) are used for the analysis. The Ensemble Empirical Mode Decomposition (EEMD) is applied as the decomposition tool to extract components of different timescales from the LST time series. Physical interpretation of decomposed LST components at various periods are explored and compared with Mayon's eruption records. Specifically, the LST annual period component is compared to regular annual cycle air temperature. Results show that LST annual period component tends to lose its regularity following an eruption. The dissimilarity of the two time series has been taken as an indicator of volcanic restlessness. Trends of different timescales based on LST components have been constructed, which all indicate the rising trend. This means that Mayon volcano will remain restless in the future decades. This study presents the advantages and effectiveness of satellite remote sensing on volcanic monitoring.

Spatial shifts in productivity of the coastal ocean over the past two decades induced by migration of the Pacific Anticyclone and Bakun's effect in the Humboldt Upwelling Ecosystem

Intensification and poleward expansion of upwelling favourable winds have been predicted as a response to anthropogenic global climate change and have recently been documented in most Eastern Boundary Upwelling Ecosystems of the world. To identify how these processes are impacting nearshore oceanographic habitats and, especially, long term trends of primary productivity in the Humboldt Upwelling Ecosystem (HUE), we analysed time series of sea level pressure, wind stress, sea surface and atmospheric surface temperatures, and Chlorophyll-a, as a proxy for primary productivity, along 26{\deg} - 36{\deg} S. We show that climate induced trends in primary productivity are highly heterogeneous across the region. On the one hand, the well documented poleward migration of the South Pacific Anticyclone (SPA) has led to decreased spring upwelling winds in the region between ca. 30{\deg} and 34{\deg} S, and to their intensification to the south. Decreased winds have produced slight increases in sea surface temperature and a pronounced and meridionally extensive decrease in surface Chlorophyll-a in this region of central Chile. To the north of 30{\deg} S, significant increases in upwelling winds, decreased SST, and enhanced Chlorophyll-a concentration are observed in the nearshore. We show that this increased in upwelling driven coastal productivity is probably produced by the increased land-sea pressure gradients (Bakun's effect) that have occurred over the past two decades north of 30{\deg} S. Thus, climate drivers along the HUE are inducing contrasting trends in oceanographic conditions and primary productivity, which can have far-reaching consequences for coastal pelagic and benthic ecosystems and lead to geographic displacements of the major fisheries.

Spectral analysis approach to study the association between total ozone concentration and surface temperature

The present study endeavors to explore the association between total column ozone (TCO) and surface temperature (ST) over Kolkata, India, during September–November through time series approach. A spectral analysis approach is adopted to investigate the existence of any common cycle in TCO and ST time series. The spectra have been computed, and it has been observed that there exist common spectra of period 24 in both the time series explored in daily scale. The existence of common spectra among TOC and ST during the transition period from monsoon to post-monsoon has been interpreted as a proof of the similarity in fluctuation pattern of TCO and ST. Details of the dependence of the variability of TCO on ST have been discussed.

Variability and change in the hydro-climate and water resources of Iran over a recent 30-year period

Comprehensive assessment of hydro-climatic variations and change trends is essential for understanding, mitigating, and adapting to key water resource changes in different parts of the world. We performed such an assessment on Iran, as representative of an arid/semi-arid and geopolitically important world region. We acquired and calculated data time series of surface temperature (T), precipitation (P), runoff (R), evapotranspiration (ET), and water storage change (DS), to determine their status and changes in and among the 30 main hydrological basins in Iran over the period 1986–2016. From 1986–2000 to 2001–2016, the country warmed, P mostly decreased and R even more so, while water storage was depleted (DS < 0) and ET increased in some basins. Overall, the extra water provided from primarily groundwater depletion has fed and kept ET at levels beyond those sustained by the annually renewable water input from P. This indicates unsustainable use of water for maintaining and expanding human activities, such as irrigated agriculture, in this part of the world.

Modeling changes trend of time series of land surface temperature (LST) using satellite remote sensing productions (case study: Sistan plain in east of Iran)

Modeling changes trend of time series of land surface temperature (LST) using satellite remote sensing productions (case study: Sistan plain in east of Iran)

Identifying Spatially Correlated Patterns between Surface Water and Frost Risk Using EO Data and Geospatial Indices

Frost is one the most significant hazards affecting various aspects of human lives including infrastructure, agriculture, economy and biodiversity. Water bodies are one of the key factors controlling temperature fluctuations and frost. This study introduces a contemporary method for identifying and spatially analyzing frost risk and also explores its spatial correlation with water bodies. The proposed technique is based on coupling freely distributed geospatial data with a time series of land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. A region located in NW Greece, which annually suffers from very low temperatures and frost conditions and hosts important infrastructure and other human activities, was selected as a case study. In total, 5944 images were processed covering a 14-year long period. A frost frequency map of the study area was compiled along with two geospatial indices associated to distance from rivers/lakes (Hydro Distance Index—HDI) and from the seashore (Sea Distance Index—SDI). Their combined statistical and spatial correlation analysis indicated a protective buffer zone from frost at distances up to 20 km from sea and up to 5 km from lakes and rivers respectively, suggesting that the protective buffer zone depends on the water volume. Statistically, frost frequency was found to be positively correlated with both SDI (rs = 0.527) and HDI (rs = 0.145). Also, the effect of topography was examined in our analysis. Results showed that altitude and slope were moderately correlated to frost frequency; yet, the significance of the correlation was reported to be lower to SDI. Furthermore, the spatial autocorrelation analysis revealed a correspondence in the clustering of frost frequency maps with the HDI and SDI. Our findings demonstrate that water bodies are a major controlling factor for frost occurrence, by lowering frost frequency in water surrounding areas. Furthermore, it highlights the promising potential of our proposed methodology in quantifying frost effects, which can form a potentially useful tool assisting effective planning of protection measures and frost hazard mitigation in general.

Impacts of spatial heterogeneity patterns on long-term trends of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature time series

Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies.

Statistical Modeling of Urban Heat Island Intensity in Warsaw, Poland Using Simultaneous Air and Surface Temperature Observations

Urban heat island (UHI) is one of the most distinctive characteristics of urban climate. The objective of this study is to apply a statistical modeling of the nocturnal atmospheric UHI based on the relationship between observed air temperature from ground stations and remotely sensed temperature of the urban surface. The goal of the approach is to limit input data for the developed modeling method in order to assure transferability of the methodology in different cities. Time series of surface temperature and normalized difference vegetation index are obtained from the MODIS instrument for a 10-year period (2008–2017). The air temperature is collected from the in-situ observational network of 21 stations. The studies are conducted for different locations with gradual changes in urbanization in order to assess the impact of urbanization on the relationship between simultaneous air and surface UHI. The urbanization is described by commonly available land cover metrics. Results showed that the proposed approach provides satisfactory AUHI modeling results for the locations with the least degree of urbanization. The best results are obtained with a simple linear regression model with the iterative procedure to minimize the mean absolute gross error (MAGE). The lowest MAGE for modeled UHI is 1.18 °C with 69% of the variance explained. The strongest linear relationship between simultaneous SUHI and AUHI is noted for those station pairs whose surroundings have the highest differences in urbanization, and the highest UHI intensities are observed. The strength of the SUHI/AUHI linear relationship decreases gradually with the increasing urbanization of the stations' surroundings.

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler.

Surface temperature variation in a broiler’s head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler’s head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.