Surface Temperature Variability Research Articles

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.

Drivers of future extratropical sea surface temperature variability changes in the North Pacific

Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change.

Variations in the Central Mode Water in the North Pacific as a manifestation of the Pacific Decadal Oscillation

AbstractThe North Pacific Central Mode Water (CMW) is examined from the viewpoint of its volume variations. The volume of the CMW layers thicker than $$150 m$$ 150 m is used as an index of CMW variations, which successfully represents the year-to-year and decadal variations in the CMW volume. The CMW index shows the variation close to that of the Pacific Decadal Oscillation (PDO). The CMW variation is strongly tied with large-scale, dominant variations in sea surface temperature (SST), surface dynamic height (SDH), and sea surface height (SSH) anomalies in the North Pacific, with a significant correlation with the PDO. Year-to-year and decadal variations of CMW volume in May to July are significantly correlated with the wintertime Aleutian Low and 500$$hPa$$ hPa geopotential height variations, which indicates that the Aleutian Low induces the eastward extension/retreat of the strong winter westerlies and resultant net surface heat flux anomaly over the CMW distribution region. Thus, the CMW volume variation can be regarded as a significant manifestation of the PDO. The SST, SDH, and SSH anomalies are associated with the surface cooling in the northern sector of the CMW distribution region. On the other hand, the SDH and SSH anomalies throughout a year and the SST anomaly in the cold season in the southeastern sector of the CMW region are formed due to the heaving of isopycnal surfaces in the subsurface layer above the CMW in response to its volume variations.

Ocean Complexity Shapes Sea Surface Temperature Variability in a CESM2 Coupled Model Hierarchy

Abstract To improve understanding of ocean processes impacting monthly sea surface temperature (SST) variability, we analyze a Community Earth System Model version 2 hierarchy in which models vary only in their degree of ocean complexity. The most realistic ocean is a dynamical ocean model, as part of a fully coupled model (FCM). The next most realistic ocean, from a mechanically decoupled model (MDM), is like the FCM but excludes anomalous wind stress-driven ocean variability. The simplest ocean is a slab ocean model (SOM). Inclusion of a buoyancy coupled dynamic ocean as in the MDM, which includes temperature advection and vertical mixing absent in the SOM, leads to dampening of SST variance everywhere and reduced persistence of SST anomalies in the high latitudes and equatorial Pacific compared to the SOM. Inclusion of anomalous wind stress-driven ocean dynamics as in the FCM leads to higher SST variance and longer persistence timescales in most regions compared to the MDM. The net role of the dynamic ocean, as an overall dampener or amplifier of anomalous SST variance and persistence is regionally dependent. Notably, we find that efforts to reduce the complexity of the ocean models in the SOM and MDM configurations result in changes in the magnitude of the thermodynamic forcing of SST variability compared to the FCM. These changes, in part, stem from differences in the seasonally varying mixed layer depth and should be considered when attempting to quantify the relative contribution of certain ocean mechanisms to differences in SST variability between the models.

Analysis of spatiotemporal variation in land surface temperature on the Tibetan Plateau from 2001 to 2020

ABSTRACT With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP’s LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years.

Stability analysis of magnetohydrodynamic Casson fluid flow and heat transfer past an exponentially shrinking surface by spectral approach

The present analysis examines the magnetohydrodynamic (MHD) flow of non-Newtonian Casson fluid on an exponentially shrinking surface under constant and exponentially varying wall temperature with suction. The main objective is to investigate multiple solutions of self-similar coupled nonlinear ordinary differential equations derived semi-numerically via the shifted Chebyshev collocation approach. The occurrence of a dual solution is found and variations in the velocity and temperature profiles are analyzed under different physical flow governing factors. The stability analysis is performed and it confirms that the first solution is stable and the second solution is unstable. The obtained results are confirmed by comparing them with earlier published results and show good agreement. The skin friction coefficient and wall temperature gradient increase for the first solution and decline for the second solution with enhancement in Hartmann number and suction parameter in case of exponentially varying surface temperature. Whereas, for constant surface temperature, increasing the Hartmann number and suction parameter results in the intensification of the wall temperature gradient. The velocity and temperature profiles decrease with improving the Casson parameter. As the Prandtl number strengthens, the heat transfer rate in a constant surface temperature situation is comparatively higher than exponentially varying surface temperature situation.

Sea Surface Temperature Variability in NHA Trang Bay, Viet Nam: Patterns and Mechanisms

Sea Surface Temperature Variability in NHA Trang Bay, Viet Nam: Patterns and Mechanisms

Assessing satellite-derived wind speeds: insights from buoy measurements and analysis of deviation factors

ABSTRACT To evaluate the latest generation of satellite wind speed from AMSR2, ASCAT, GMI, SSMIS, SMAP, and WindSat, buoy measurements were obtained from buoys in coastal and tropical open ocean areas. Root mean square errors were calculated, resulting in values of 1.19 m/s for AMSR2, 1.25 m/s for ASCAT, 1.37 m/s for GMI, 1.38 m/s for SSMIS, 2.48 m/s for SMAP, and 1.18 m/s for WindSat. Numerous data pairs fall above the 45-degree line, mainly due to decreased buoy ac-curacy during high winds. Factors contributing to this include underestimation of buoy readings due to rough sea states and wind profile distortion. Satellite biases exhibit unimodal fluctuations over a one-year cycle, aligning with annual sea surface temperature variations. Statistical effects in extreme wind speed range cause abnormal fluctuations in statistics. Higher latitudes experi-ence larger deviations due to rough sea state. Ocean currents affect satellite wind speed observa-tions, particularly when aligned with wind direction. Coastal wind speed gradients primarily influenced from land contamination like rugged mountainous terrain along the West Coast of North America. Coastal upwelling also contributes to bias, with the west coast showing higher bias. A global comparison between SMAP and WindSat retrieval wind speeds demonstrates SMAP’s poor ability to monitor general wind speeds and validates the satellite-buoy experiment.

Spatial and Temporal Variations in Land Surface Temperature as an Indicator of Learning Comfort Level in Banten Province

Increasing population and rapid development of built-up land can cause an increase in land surface temperature. These changes are often associated with many factors, including changes in land use, and surface parameters, economic development, and vegetation. This research aims to analyze the temperature factor as an indicator of indoor thermal comfort to support the learning process in schools in the Banten Province area. This research uses a spatial temporal approach with descriptive statistical analysis. Based on the results, show that the land surface temperature at each school location in Banten province shows conditions that are not constant. The number of schools with uncomfortable conditions varies from 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), and 2023 (52.44%). This shows that there has been a decrease in uncomfortable conditions for studying by 36.63% in the last 5 years.

Analysing Spatiotemporal Variability of Chlorophyll-a Concentration and Water Surface Temperature in Coastal Lagoons of the Ebro Delta (NW Mediterranean Sea, Spain)

Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ data to (1) develop preliminary algorithms for retrieving the Chl-a concentration and water surface temperature of six lagoons located in the Ebro Delta (NE Mediterranean Sea, Spain), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms’ accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons’ trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms’ robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters.

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

AbstractWe use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

The examination of the relationship between land cover and land surface temperature in Karang Baru Sub-District, Aceh Province, Indonesia

Abstract Land cover change can lead to variations in land surface temperature, primarily because different materials have varying capacities to receive, absorb, and re-emit solar radiation. Karang Baru, situated as the capital of Aceh Tamiang Regency near North Sumatra, one of the most densely populated Regency, has been experiencing continuous development since 2002, following its separation from East Aceh Regency. The development in Karang Baru has resulted in a noticeable influence on land surface temperature. The primary objective of this study is to investigate the relationship between land cover and land surface temperature in Karang Baru from 2002 to 2022. This research classified land cover using the supervised classification method employing the Maximum Likelihood Algorithm, and land surface temperature was estimated using the Split Window Algorithm method. Subsequently, simple regression analysis in this context indicates a quantitative approach to understanding the relationship between land cover and land surface temperature in Karang Baru, providing valuable insights for environmental and urban planning considerations. The findings of this research indicate that the type of land cover indeed influences the land surface temperature in Karang Baru. In summary, this study establishes that land cover can substantially impact the regional surface temperature, with temperature increases associated with surfaces featuring buildings, roads, fields, or similar materials. In contrast, vegetated surfaces tend to exhibit lower land surface temperatures in the area.

The examination of the relationship between land cover and land surface temperature in Karang Baru Sub-District, Aceh Province, Indonesia

Abstract Land cover change can lead to variations in land surface temperature, primarily because different materials have varying capacities to receive, absorb, and re-emit solar radiation. Karang Baru, situated as the capital of Aceh Tamiang Regency near North Sumatra, one of the most densely populated Regency, has been experiencing continuous development since 2002, following its separation from East Aceh Regency. The development in Karang Baru has resulted in a noticeable influence on land surface temperature. The primary objective of this study is to investigate the relationship between land cover and land surface temperature in Karang Baru from 2002 to 2022. This research classified land cover using the supervised classification method employing the Maximum Likelihood Algorithm, and land surface temperature was estimated using the Split Window Algorithm method. Subsequently, simple regression analysis in this context indicates a quantitative approach to understanding the relationship between land cover and land surface temperature in Karang Baru, providing valuable insights for environmental and urban planning considerations. The findings of this research indicate that the type of land cover indeed influences the land surface temperature in Karang Baru. In summary, this study establishes that land cover can substantially impact the regional surface temperature, with temperature increases associated with surfaces featuring buildings, roads, fields, or similar materials. In contrast, vegetated surfaces tend to exhibit lower land surface temperatures in the area.

Multi‐Year Potential Predictability of the Wintertime Heavy Precipitation Potentials in East Asia

AbstractWe validate the multiyear potential predictability of wintertime heavy precipitation potential in East Asia by combining initialized decadal hindcasts of the global climate model and large ensemble simulations from a high‐resolution global atmospheric model. By analyzing a set of initialized hindcasts, the major predictive components of sea surface temperature (SST) variability beyond interannual timescales are identified as high‐latitudes multidecadal variability and the so‐called trans‐basin variability (TBV). A set of 100 ensemble simulations using a high‐resolution atmospheric model showed a significantly large signal‐to‐noise ratio for the wintertime heavy precipitation potential in East Asia, which is closely related to the TBV. When the SST around the maritime continent is higher, the anomalously low pressure in the northwestern Pacific enhances low‐level cold air transport due to the winter monsoon. Consequently, the resultant weaker baroclinicity in the lower atmosphere reduces storm activity and wintertime heavy precipitation potential in East Asia.

Study on temperature field distribution and leakage point localization of buried carbon dioxide pipeline leakage

Leakage from buried CO2 pipelines is a critical issue in the context of carbon capture and storage (CCS). Therefore, we developed a small-scale experimental system for buried CO2 pipeline leakage. to investigate the effects of leak pressure, leak aperture size, and soil type on the temperature distribution near the leak point. The results of the study showed that the lowest soil temperature appeared directly above the leak. The minimum temperature at the soil axial measurement point increased with increasing leak pressure, but the peak maximum temperature difference decreased. 3mm and 5mm leak pore sizes, the peak maximum temperature difference occurred at 10cm and 20cm, respectively. At all pressures, the maximum temperature difference peaked at 20cm. Soil surface temperature variations were mainly influenced by leak pressure. The maximum peak temperature difference at the soil surface measurement points also increased with increasing leak pressure. Smaller leakage apertures slowed down the decrease of soil surface temperature. In addition, based on the experimental results, we propose a new method to locate leaks based on the soil temperature gradient.

Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model

Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies.

Moderate Indian Ocean Dipole dominates spring fire weather conditions in southern Australia

Patterns of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian ocean (WTIO) to by the eastern tropical Indian ocean. How the different types of the IOD variability patterns affect Australian fires differently is unknown, nor is it certain how the impacts may change under greenhouse warming. Here, we find that the moderate IOD, dominated by WTIO sea surface temperature (SST) variability, plays a primary role in affecting southern Australian fire weather conditions during austral spring. During a positive moderate IOD, broad-scaled warm SST anomalies in WTIO force an atmospheric stationary Rossby wave with a high-pressure anomaly over southern Australia. This elevated pressure and associated anomalous atmospheric conditions provide suitable fire weather with hot, dry, and windy conditions, raising fire risks in southern Australia. Such impact is distinctively different from that strong IOD-induced. As predicted by climate models, decreased moderate IOD variability in the future will result in weakened Australian fire weather responses.

Analysis of scuffing through temperature measurement experiment and numerical simulation for spiral bevel gears

Tooth surface scuffing failure, as one of the primary failure modes of gears, frequently occurs during the operation of gear mechanisms. Scuffing properties of spiral bevel gears were investigated through a numerical analysis and experimental study. A novel temperature model for a single tooth was proposed to predict scuffing temperatures on the tooth surface. The model allows the analysis of variations in tooth surface temperature, considering both operating conditions and tooth contact scenarios. Experiments were carried out to explore tooth surface adhesion of spiral bevel gears under precisely defined operating conditions. Measurement of tooth surface temperatures was conducted by combining non-contact infrared temperature sensors and temperature-measuring cameras. This experiment verifies temperature changes on the tooth surface adhesion of spiral bevel gears. The combination of two temperature measurement methods ensures accurate temperature measurements, with errors below 8%, and facilitates the swift opening of the end cover. Experimental validation of the scuffing prediction model yields a maximum temperature difference of 5.93%. The method establishes a theoretical foundation for evaluating tooth surface adhesion of spiral bevel gears.

Numerical Modeling of Atmospheric Temperature and Stratospheric Ozone Sensitivity to Sea Surface Temperature Variability

The results of numerical experiments with a chemistry–climate model of the lower and middle atmosphere are presented to study the sensitivity of the polar stratosphere of the Northern and Southern Hemispheres to sea surface temperature (SST) variability, both as a result of interannual variability associated with the Southern Oscillation, and because of long-term increases in SST under global warming. An analysis of the results of model experiments showed that for both scenarios of SST changes, the response of the polar stratosphere for the Northern and Southern Hemispheres is very different. In the Arctic, during the El Niño phase, conditions are created for the polar vortex to become less stable, and in the Antarctic, on the contrary, for it to become more stable, which is expressed in a weakening of the zonal wind in the winter in the Arctic and its increase in the Antarctic, followed by a spring decrease in temperature and concentration of ozone in the Antarctic and their increase in the Arctic. Global warming creates a tendency for the polar vortex to weaken in winter in the Arctic and strengthen it in the Antarctic. As a result, in the Antarctic, the concentration of ozone in the polar stratosphere decreases both in winter (June–August) and, especially, in spring (September–November). Global warming may hinder ozone recovery which is expected as a result of the reduced emissions of ozone-depleting substances. The model results demonstrate the dominant influence of Brewer–Dobson circulation variability on temperature and ozone in the polar stratosphere compared with changes in wave activity, both with changes in SST in the Southern Oscillation and with increases in SST due to global warming.