Global Temperature Variations Research Articles

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Temperature prediction of submerged arc furnace in ironmaking industry based on residual spatial-temporal convolutional neural network

The submerged arc furnace is widely regarded as one of the most promising ore smelting technologies. However, the real-time monitoring of the multiple physical fields including electric, thermal, and mas, through computational fluid dynamics demands significant computational resources. This paper introduces the spatial-temporal convolutional neural network algorithm to address this challenge. Initially, the influences of various working conditions on these physical fields are analyzed. Subsequently, a prediction model is developed based on the coupling of these multiple physical fields model. The spatial-temporal convolutional neural network algorithm is then employed to elucidate the main parameter distributions, enabling the automatic real-time detection of temperature variation trends and providing a theoretical foundation for intelligent furnace operation. The findings indicate that the electric field is the predominant factor causing non-uniform heat distribution, with localized overheating primarily occurring at the electrode ends. The application of the proposed model facilitates dynamic prediction of the temperature distribution, establishing relationships between historical and future time steps as well as local and global temperature variations. The reliability of the temperature prediction model is confirmed, with the model achieving an accuracy of 99.76 %, surpassing the 98.18 % accuracy of the traditional multi-layer perceptron model.

Comparative study between observed and numerical downscaled data of surface air temperature

Surface Air temperature, being one of the key parameters of climate, plays an important role in occurrence severe environmental issues like droughts, floods, rate of evaporation etc. In this study, an endeavor has been made to investigate the experiments available in Educational Global Climate Model (EdGCM) at Sylhet Agricultural University for recognizing best suited experiment to predict the changing pattern of surface air temperature at Sylhet region. Downscaling is a cutting-edge procedure to produce high-goals atmosphere change expectation from coarse to fine goals of worldwide atmosphere model outcomes and a conspicuous device for estimating future atmosphere situation at the local scale for some information alarm zone like Bangladesh. Maps are generated from EdGCM simulation run to collect data and depict global temperature variations. Transform software have been connected to downscale the outcomes of temperature data by Doubled_CO2, Global_Warming_01, Ice_Age_21kya, IPCC_A1F1_CO2, Modern_PredictedSST and Modern_SpecifiedSST. The trend analysis was performed using the Mann-Kendall Trend test and Sen-slope estimator. Temperature changes are significant for observed results, Doubled_CO2 and IPCC_A1F1_CO2 results where p-value is less than alpha 0.05. Doubled_CO2 results are more suited for dry season and IPCC_A1F1_CO2 provides better results for monsoon. The investigation could have been unquestionably progressively broad in the event that more situations of EdGCM information were utilized, and local boundary conditions were used.

Variations of Global Compound Temperature and Precipitation Events and Associated Population Exposure Projected by the CMIP6 Multi-Model Ensemble

Variations of Global Compound Temperature and Precipitation Events and Associated Population Exposure Projected by the CMIP6 Multi-Model Ensemble

Thermal Tolerance of Larval Flannelmouth Sucker Catostomus latipinnis Acclimated to Three Temperatures

As water temperatures rise in streams due to global temperature variations, dams, and increased water usage, native fish species face uncertain futures. Our study defines the thermal limits of flannelmouth sucker larvae. By raising sucker eggs at three acclimation temperatures (11 °C, 16 °C, and 22 °C), we defined ideal conditions for larval survival and the temperature tolerance range using critical thermal maximum (CTMax) and minimum (CTMin) trials. Larvae survived best at 16 °C. Within our three acclimation temperatures, our data suggest that larvae can survive static temperatures between 6.9 °C and 26.4 °C. Beyond an upper temperature of 34.8 °C and a lower temperature of 6.3 °C, these fish may fail to adapt. While flannelmouth suckers withstand high temperatures, even small temperature decreases prove detrimental. By defining the temperature limits of the flannelmouth sucker, we can make informed management decisions to preserve the populations of this desert fish.

Nonequilibrium Fluctuations of Global Warming

Abstract While the warming trends of Earth’s mean temperature are evident at climatological scales, the local temperature at shorter time scales are highly fluctuating. Here we show that the probabilities of such fluctuations are characterized by a special symmetry typical of small systems out of equilibrium. Their nearly universal properties are linked to the fluctuation theorem and reveal that the progressive warming is accompanied by growing asymmetry of temperature distributions. These statistics allow us to project the global temperature variability in the near future, in line with predictions from climate models, providing original insight about future extremes.

A regime shift between soil moisture and temperature in the 1950s over the permafrost region of Northeast China

AbstractPermafrost is one of the essential carbon pools in the world. Due to limited studies on historical soil moisture changes and the coupling relationship between soil moisture and temperature in permafrost regions, significant uncertainty exists in the carbon loss in permafrost predicted by different models under global warming scenarios. Based on the tree‐ring width chronology of Pinus sylvestris var. mongholica Litv. growing in the southern edge of the Eurasian continuous permafrost zone, we reconstructed the summer (June–September) 0–1 m soil moisture variations from 1705 to 2009, which could explain 45.6% of the variance in the observed soil moisture. Overall, local precipitation and temperature exhibited statistically significant positive feedback (p < 0.001) to soil moisture before the 1950s, indicating that the warm/humid climate pattern was conducive to soil moisture conservation before the Anthropocene Epoch. However, the effect of temperature on soil moisture has shifted suddenly to negative since the 1950s, implying that the positive soil moisture‐temperature relationship during the past three centuries has been disrupted by the unprecedented warming in the Modern Warm Period. Furthermore, we found that the temporal relationship of the soil moisture‐temperature (15‐year sliding correlation) in the study area is negatively regulated by the global mean temperature variations (p < 0.01). The regime shift between soil moisture and temperature might be attributed to the superimposed influence of natural and anthropogenic factors since the 1950s. Although the warming leads to the melting of the permafrost layer, and thus the increase in soil moisture content, the enhanced evapotranspiration caused by warming up results in more water loss and drier soil. This study provides historical evidence of shifted soil moisture‐temperature coupling in the permafrost zone, warning that soil moisture in the permafrost region may further decline under global warming scenarios, thereby affecting vegetation growth and forest carbon sequestration potential.

Predicting future global temperature and greenhouse gas emissions via LSTM model

This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–Short-Term Memory (LSTM) model. Data sets of global temperature were collected from 800,000 BC to 1950 AD from the National Oceanic and Atmospheric Administration (NOAA). Furthermore, another data set was obtained from The National Aeronautics and Space Administration (NASA) climate website. This contained records from 1880 to 2019 of global temperature and carbon dioxide levels. Curve fitting techniques, employing Sin, Exponential, and Fourier Series functions, were utilized to reconstruct both NOAA and NASA data sets, unifying them on a consistent time scale and expanding data size by representing the same information over smaller periods. The fitting quality, assessed using the R-squared measure, ensured a thorough process enhancing the model's accuracy and providing a more precise representation of historical climate data. Subsequently, the time-series data were converted into a supervised format for effective use with the LSTM model for prediction purposes. Augmented by the Mean Squared Error (MSE) as the analyzed loss function, normalization techniques, and refined data representation from curve fitting the LSTM model revealed a sharp increase in global temperature, reaching a temperature rise of 4.8 °C by 2100. Moreover, carbon dioxide concentrations will continue to boom, attaining a value of 713 ppm in 2100. In addition, the findings indicated that the RNN algorithm (LSTM model) provided higher accuracy and reliable forecasting results as the prediction outputs were closer to the international climate models and were found to be in good agreement. This study contributes valuable insights into the trajectory of global temperature and GHG emissions, emphasizing the potential of LSTM models in climate prediction.

Assessing Climate Sensitivity and Mechanisms in a One-Dimensional Climate Energy Balance Model

Addressing the pressing concerns surrounding climate sensitivity, particularly within the context of Earth's changing climate, remains a pivotal endeavor. In this paper, the earth global temperature variation modeled by a one-dimensional climate energy balance model (EBM) is studied using a variance-based sensitivity analysis. The outcomes of this study offer a comprehensive analysis on each parameter that govern Earth's climate dynamics. Specifically, the analyses highlight that land albedo possesses the most substantial First-Order Sobol indices; the solar multiplier and the constant coefficient in the linearized Stefan-Boltzmann Law emerge as the second and third-largest contributor to Sobol indices, while the sensitivity of albedo of ice, temperature dependent coefficient of linearized Stefan-Boltzmann Law, and Transfer Coefficient remain insignificance. The analysis also suggests the non-negligible role of ice-albedo feedback and supports the hypothesis of the "Snowball Earth". This research holds significance as it uniquely addresses Earth's climate sensitivity using a one-dimensional Energy Balance Model, offering fundamental yet essential insights into the dynamics of earth's climate system, particularly its temperature variations.

MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

Abstract. Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.

Regional but not global temperature variability underestimated by climate models at supradecadal timescales

Regional but not global temperature variability underestimated by climate models at supradecadal timescales

Revisiting a Constraint on Equilibrium Climate Sensitivity From a Last Millennium Perspective

AbstractDespite decades of effort to constrain equilibrium climate sensitivity (ECS), current best estimates still exhibit a large spread. Past studies have sought to reduce ECS uncertainty through a variety of methods including emergent constraints. One example uses global temperature variability over the past century to constrain ECS. While this method shows promise, it has been criticized for its susceptibility to the influence of anthropogenic forcing and the limited length of the instrumental record used to compute temperature variability. Here, we investigate the emergent relationship between ECS and two metrics of global temperature variability using model simulations and paleoclimate reconstructions over the last millennium (850–1999). We find empirical evidence in support of these emergent relationships. Observational constraints suggest a central ECS estimate of 2.5–2.7 K, consistent with the Intergovernmental Panel on Climate Change's consensus estimate of 3K. Moreover, they suggest ECS “likely” ranges of 1.7–3.3 K and 1.9–3.5 K.

Stage-specific genotype-by-environment interactions determine yield components in wheat.

In cereal crops, environmental fluctuations affect different physiological processes during various developmental phases associated with the formation of yield components. Because these effects are coupled with cultivar-specific phenology, studies investigating environmental responses in different cultivars can give contradictory results regarding key phases impacting yield performance. To dissect how genotype-by-environment interactions affect grain yield in winter wheat, we estimated the sensitivities of yield components to variation in global radiation, temperature and precipitation in 220 cultivars across 81 time-windows ranging from double ridge to seed desiccation. Environmental sensitivity responses were prominent in the short-term physiological subphases of spike and kernel development, causing phenologically dependent, stage-specific genotype-by-environment interactions. Here we reconcile contradicting findings from previous studies and show previously undetected effects; for example, the positive impact of global radiation on kernel weight during canopy senescence. This deep insight into the three-way interactions between phenology, yield formation and environmental fluctuations provides comprehensive new information for breeding and modelling cereal crops.

Orbitally-paced coastal sedimentary records and global sea-level changes in the early Permian

Orbital forcing affects climate system by regulating the amount of solar radiation received at the Earth's surface, which is vital for global hydrological cycles. However, a systematic understanding of the global water cycle with synergistic changes in ice sheets and sea levels under orbital forcing during the late Paleozoic ice age (LPIA) remains elusive. Here, we perform cyclostratigraphic analysis and sedimentary noise modeling for coastal sedimentary records (Well Greer #2 in the Permian Basin) from the early Permian and two sets of high-resolution paleoclimate simulation experiments using the Community Earth System Model. We provide the first high-resolution absolute astronomical time scale for the Permian Basin, which suggests that the timing of the early Permian Lower Leonard and Upper Wolfcamp Formations range from 286.29 ± 0.55 Ma to 289.05 ± 0.55 Ma, and the Lower Wolfcamp Formation ranges from 292.97 ± 0.16 Ma to 298.9 ± 0.15 Ma, respectively. In addition, we find a coupled phase relationship between high sea levels and eccentricity maxima, suggesting that eccentricity may have played a critical role in the early Permian sea-level oscillations. Paleoclimate simulations show that eccentricity and obliquity maxima lead to an overall increase in global mean temperature. Significant changes in surface temperature at middle to high latitudes are manifested as the polar amplification effect during the LPIA. Compared with obliquity, eccentricity leads to higher annual and summer variability in global surface temperature, and enhanced polar amplification results in ice retreat and sea-level rise. Evidence from Earth system modeling elucidates the phase relationship between orbital cycles and sea levels. Therefore, eccentricity, as the leading driving force in low latitudes, may dominate climate changes in the Earth's climate system and regulates global hydrological cycles during the LPIA.

Analysis and Evaluation of Global Temperature Changes and the Prediction of Future Global Temperature

Temperatures have continued to rise in recent years, many areas have shown extreme temperatures, and global warming has been a perennial topic. We use various optimization prediction and optimization evaluation methods in this paper to determine the trends and causes of global temperature variation and make effective suggestions for them. We first constructed a first-order differential temperature model and found that the temperature rise in March 2022 was larger than that observed in the past 10 years. By constructing a prediction model (BP) based on BP neural network and a grey temperature prediction model (GAGM) optimized based on adaptive genetic algorithm, and by importing the data, we find that the average temperature in 2116 and 2145 will reach 20℃. In addition, if the correlation factor evaluation model (FAEM) is constructed in the country, and we conclude that there is a correlation between location, time and temperature. Next, we analyzed key environmental factors, such as volcanic eruptions, forest fires, and COVID-19. We imported the CO 2 concentration and temperature data into the FAEM evaluation model to obtain the results that natural disasters can lead to the temperature increase. Then nine environmental factors, including COND, NYKC, NYXH, GDP, PJRK, LJL, LZMJ, KJSP, and GJJY. Taking these factors as independent variables and temperature as the dependent variables, COND, NYXH, PJRK, LJMJ, JSL are the main causes of global temperature change and KJSP. This study provides an assessment of future global temperature changes, making recommendations for policymakers to optimize the energy mix and plant trees to effectively mitigate the global temperature rise.

Physiology and Yield of Chickpea (Cicer arietinum L.) Genotypes in Response to Different Sowing Dates in Semi Arid Regions of North India

Background: Climate change causes variation in global temperature in last two decades which causes detrimental effects on agricultural crops being grown in arid and semi-arid regions. Chickpea crop sensitive to variation in temperature as the late-sown crop is exposed to high temperatures ( greater than 35°C) at its reproductive stage in the months of February and March and early sown crops is exposed to low temperature ( less than 5°C) at its vegetative stage in December and January so optimum sowing time plays a vital role for overall growth and development of the plants. Methods: The experiment comprising of three sowing dates that is 15th October (early sown condition in Haryana), 15th November (Normal sown condition in Haryana) and 15th December (late sown condition in Haryana) with ten chickpea (Cicer arietinum L.) genotypes namely ICCV 88503, ICCV 92944, HC-1, HC-3, HC-5, H12-64, H13-01, H13-02, H14-01 and H14-04 in randomized block design during Rabi season of 2017-18 and 2018-19 at Pulses Section, Chaudhary Charan Singh Haryana Agricultural University, Hisar Result: Statistically no significant differences in seed yield was recorded between 15th October and 15th November sowing, however, further delay in sowing to 15th December showed significant reduction in seed yield while maximum values for recorded physiological parameters in chickpea genotypes were found in 15th November sowing and minimum in 15th December sowing. Among genotypes maximum physiological parameters and seed yield were observed in H12-64 and H13-01 while minimum were found in H14-04.

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Monthly variation and correlation analysis of global temperature and wind resources under climate change

Renewable energy is widely used as an alternative source of energy, and climate change has a significant impact on its variation and harnessing. This study conducted a monthly scale analysis of global temperature and wind-speed variations and their correlations using Theil–Sen’s median linear regression and Mann-Kendall test from 1989 to 2021. The results revealed that the global temperature averagely increased by 0.34 °C/10 a from 1989 to 2021, showing clearly increasing trends in most months but with significant differentiation in each month. The regions of East Europe Plain, Central Siberia, Central Asia, and Greenland showed the most significant increasing temperature trends ranging from 0.17 to 0.49 °C/10 a. Additionally, the regions in the East Europe Plain, Siberia, and North America showed distinctly decreasing temperature trends ranging from 0.27 to 0.51 °C/10 a. The global average wind speed changed slightly, with slopes of −0.026, −0.014, and 0.019 m/(s⋅10 a) for eastward, northward, and synthesis wind, respectively. However, the decreasing trends were relatively evident in regions across central Europe via the south of the Eastern European Plain to the north of Central Asia with slopes ranging from −0.13 to −0.3 m/(s⋅10 a). These monthly variations in eastward, northward, and synthesis wind speeds showed significant correlations with temperature, and the correlation coefficients (CCs) differed significantly for each month. For positive CCs higher than 0.34 and negative CCs lower than −0.35 (p < 0.05), the land coverage areas reached 72.3 million km2, accounting for 48% of the global land area. In addition, the synthesis wind speed showed significant positive and negative CCs with temperature, especially from November to March of the following year. The highly positive CCs of global temperature and synthesis wind speed are primarily distributed across land areas from Europe to Siberia, North America, and North Africa in the Northern Hemisphere. The areas with high negative CCs are distributed across South America, South Africa, and Australia in the Southern Hemisphere. Climate change has a substantial impact on wind-speed variation, which should be considered during wind source harnessing. Monthly variation analysis of global temperature and wind speed and their correlation could provide key scientific support for climate change adaptation and wind resource harnessing.

Global sea level changes or local tectonics? Pliocene, Miocene and Oligocene biomarkers in cored sedimentary rocks from IODP Expedition 317, Canterbury Basin, New Zealand

The various influences of global sea level (eustasy) and local tectonic changes on sedimentation processes in continental margin deposits are important topics in sedimentary research. International Ocean Discovery Program Expedition 317 to the Canterbury Basin, on the eastern margin of the South Island of New Zealand, provided an opportunity to study sediment geochemistry in contrasting depositional settings, from mid-shelf to upper continental slope sedimentary rocks from the Eocene to the Holocene. This paper provides the first examination of the organic geochemical record from Pliocene, Miocene, and Oligocene sediment samples recovered during the expedition from the U1351, U1352, and U1353 sites, using bulk geochemistry and hydrocarbon distributions, including biomarkers. The main aim of this research was to correlate changes in organic accumulation with local tectonic activity and global climate changes that occurred in the Miocene. There is good preservation of C11 to C35n-alkanes, with varying predominance of odd-over-even chain length n-alkanes, and much variation in the terrigenous/aquatic ratio over the cores. Based on biomarker and Rock Eval parameters, thermal maturity varies from low in the Pliocene, to early oil window in the deeper Miocene and Oligocene sedimentary rocks. The pristane/phytane ratios for the three cores indicate anoxic to oxic depositional environments. Marine organic matter input is indicated by the high C30 sterane index, whilst high relative amounts of oleanane, C24 tetracyclic terpane, and C29 steranes indicates major terrigenous organic matter input. Local tectonic activity is suggested to have had a significant influence on the accumulation of the organic matter in the Canterbury Basin. The influence of eustasy and global sea temperature variations are suggested for the periods < 14 Ma, 12–7 Ma, and ∼ 6 Ma. An increase in terrigenous organic matter input during the late Miocene correlates with the uplift of the Southern Alps and an increase in the continental slope angle for the South Island of New Zealand.

Temporal Upscaling of MODIS 1-km Instantaneous Land Surface Temperature to Monthly Mean Value: Method Evaluation and Product Generation

The monthly mean land surface temperature (MMLST) reflects more stable intra- and inter-annual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), a simple average model for two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is <20 and ABD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MWA</sub> when NOD is ≥20. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error of 1.6 K. The intercomparison with MMLSTs from geostationary satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with AIRS3STM product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.