Temperature Dataset Research Articles

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Digitizing observations from the 1861–1875 Met Office Daily Weather Reports using citizen scientist volunteers

AbstractWe describe the transcription and quality control processes for rescuing around 570,000 sub‐daily and daily weather observations which were recorded in the UK Met Office Daily Weather Reports during the 1861–1875 period. These data are from the start of coordinated weather observations and were collected with the aim of making the first‐ever weather forecasts. The observations were rescued thanks to 3500 volunteers and include sub‐daily sea‐level pressure, dry and wet bulb temperatures, daily maximum and minimum temperatures, and daily rainfall amounts from 70 different locations across Western Europe, and one in Canada. We highlight how these observations will be used to fill gaps in existing pressure and temperature datasets and use two case studies to show how the pressure observations will likely better constrain the atmospheric circulation during two severe storms. We also compare a sub‐sample of the newly rescued observations with data that were previously digitized for a small number of locations for the same dates, finding good agreement in general, although some discrepancies remain.

High‐resolution dataset of nocturnal air temperatures in Bern, Switzerland (2007–2022)

AbstractTo prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.

Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model.

Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.

TemproNet: A transformer-based deep learning model for seawater temperature prediction

Accurate prediction of seawater temperature is crucial for meteorological model understanding and climate change assessment. This study proposes TempreNet, a deep learning model based on a transformer and convolutional neural network, to accurately predict subsurface seawater temperature using satellite observations in the South China Sea. TemproNet uses multivariate sea surface observations such as sea level anomaly (SLA), sea surface temperature (SST), and sea surface wind (SSW) as model inputs, which employs a hierarchical transformer encoder to extract the multi-scale feature, uses a lightweight convolutional decoder to predict seawater temperature. We train and validate the model using the CMEMS temperature dataset and compare its accuracy with Attention-Unet, LightGMB, and ANN. Experimental results show that TemproNet has significantly outperformed other models with RMSE and R2 of 0.52 °C and 0.83 in a 32-layer temperature profile prediction task over 200 m in the South China Sea. In addition, we fully demonstrate the error of our model in space, in time, and at different depths, showing the efficiency and stability of our model. The input sensitivity analysis showed that SST contributed more to predicting shallow water temperature, while SLA significantly impacted the prediction of mid-deep water temperature. The results of this study provide an innovative and reliable solution for seawater temperature prediction and have important implications for meteorological model understanding and climate change assessment.

Drought Atlas of India, 1901–2020

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

Rectal and axillary admission temperature in preterm infants less than 32 weeks' gestation, a prospective study.

The purpose of this research was to evaluate the differences between rectal and axillary temperature measurements in preterm infants who were born less than 32 weeks' gestation using digital thermometers upon their admission to the Neonatal Intensive Care Unit (NICU). Prospective, observational, single centre study. Rectal and axillary temperatures measurements were performed using a digital thermometer. The study examined various maternal and neonatal factors to describe the study group, including the use of prenatal corticosteroids, the occurrence of maternal diabetes and hypertension, a history of maternal prolonged rupture of membranes (PROM), maternal chorioamnionitis, the mode of delivery, along with the neonate's gender, birth weight, and gestational age. The Pearson correlation coefficient (R) was calculated to ascertain the linear relationship between the temperatures taken at the rectal and axillary sites. The concordance between the two sets of temperature data was analyzed using the Bland-Altman method. Eighty infants with a mean gestational age of 28.4 weeks (SD = 2.9) and a mean birth weight of 1,229 g (SD = 456) were included in the study. The mean axillary temperature was 36.4 °C (SD = 0.7), which was lower than the mean rectal temperature of 36.6 °C (SD = 0.6) (p = 0.012). Rectal temperatures surpassed axillary measurements in 59% of instances, while the reverse was observed in 21% of cases. Rectal and axillary temperatures had a strong correlation (Pearson correlation coefficient of 0.915, p < 0.001). Bland-Altman plot showed a small mean difference of 0.1C between the two temperatures measurements but the limits of agreement were wide (+0.7 to -0.6 °C). For hypothermic infants, the mean difference between rectal and axillary temperatures was 0.27 °C, with a wide limit of agreement ranging from -0.5 °C to +1 °C. Conversely, for normothermic infants, the mean difference was smaller at 0.1 °C, with a narrower limit of agreement from -0.4 °C to +0.6 °C. While there is a good correlation between axillary and rectal temperatures, the wider limits of agreement indicate variability, particularly in hypothermic infants. For a more accurate assessment of core body temperature in hypothermic infants, clinicians should consider using rectal measurements to ensure effective thermal regulation and better clinical outcomes.

Long-term responses of permafrost to the dual impacts of climate warming and engineering disturbance along the Qinghai-Tibet Highway

The sensitivity of near-surface permafrost to both climate warming and engineering disturbance is widely acknowledged. However, long-term responses of permafrost to the dual impacts of climate warming and engineering disturbances remain poorly understood. Using a 20-year dataset of ground temperature under roadway and natural ground surface, in this study not only we identify the thermal impacts of the Qinghai-Tibet Highway, in addition to those of climate warming, but also quantify the differences between these two impacts. The results show that variation rate of permafrost table under the highway was approximately 3.3 to 11.1 times greater than that under natural ground surface, along with a significant warming rate of permafrost under the highway. Under the dual thermal impacts of climate warming and engineering disturbances, the duration of ground frozen state at a depth of 0.5 m under roadway is 38.5 ± 6.36% shorter than that under natural ground surface. Meanwhile, rising rate of ground temperature at depth of 0.5 m beneath the asphaltic road surface is 51.7 ± 14.46% faster than that beneath the natural ground surface. This work underscores the importance for correctly understanding the response mechanisms and mitigative measures for hazards resulting from the dual impacts of climate warming and engineering disturbances, particularly by altering the heat absorption effect of asphalt pavement and by utilizing the roadbed cooling approaches.

Risk assessment and spatio-temporal characteristics analysis of water bloom in three large-scale eutrophic reservoirs in Fujian Province, China

The occurrence of water blooms in reservoirs poses a serious threat to water safety, so the assessment of water bloom risk is crucial. In this study, we constructed a reservoir water bloom risk index (RWBRI) using the trophic state index (TSI), floating algae index (FAI), temperature, precipitation, and wind speed datasets, and estimated and validated the water bloom risk levels of three large-scale eutrophication reservoirs in Fujian Province, China, from 2019 to 2023 based on the Sentinel-2 MSI dataset at the pixel scale. The results showed that there is no positive correlation between the TSI and FAI parameters of the three reservoirs (the significance test coefficients for Shanzai Reservoir, Dongzhen Reservoir, and Shanmei Reservoir were −0.455**, −0.371**, and −0.049, respectively, and ** indicates that the reservoir passed the statistical significance test at the 0.01 significance level). The TSI of Shanmei Reservoir was less fluctuating and more stable than that of Shanzai Reservoir and Dongzhen Reservoir, but the average value of FAI was higher. The average value of water bloom risk in Shanzai Reservoir and Dongzhen Reservoirs is higher than Shanmei Reservoir (0.47, 0.46, and 0.41, respectively), but the trend of annual average changes indicates that the overall risk of Dongzhen Reservoir is decreasing, while the risk of Shanzai Reservoir and Shanmei Reservoir is increasing. Due to the influence of weather, the water bloom risk in spring and summer is higher than that in fall and winter in the three reservoirs, and the highest risk value usually occurs in summer. The high values in Shanzai Reservoir were mainly distributed in the two inlet channels in the northern and southwestern parts of the reservoir, the bank risk was higher than in the other areas of Dongzhen Reservoir, and the high value of the risk in Shanmei Reservoir was mainly distributed in the center of the reservoir. The RWBRI method can be used for water bloom risk identification in reservoirs with eutrophication trends by comparing it with four-hourly and monthly water quality level data.

A Fast and Easy Way to Produce a 1-Km All-Weather Land Surface Temperature Dataset for China Utilizing More Ground-Based Data

A Fast and Easy Way to Produce a 1-Km All-Weather Land Surface Temperature Dataset for China Utilizing More Ground-Based Data

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Estimation and Investigation of Photosynthetically Active Radiation Using Meteorological Parameters over Ikeja, Nigeria

Photosynthetically active radiation (PAR; 0.4 - 0.7μm) is a key driver in ecosystem biochemical processes, and thus a critical factor in agriculture productivity, ecosystem-atmosphere energy, and CO2 fluxes. In this study, the measured monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature dataset was utilized to estimate and investigate the Photosynthetically Active Radiation (PAR) for Ikeja (latitude 6.58 N, longitude 3.33 E and 39.4 m above sea level) located in the coastal region ofNigeria. The meteorological parameters used in this work were obtained from the archives of National Aeronautics and space Administration (NASA) for a period of thirty-eight years (1984-2021).The newly developed temperature PAR based models were statistically tested using the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test and index of Agreement (IA) to ascertain the accuracy and applicability of the models. The results show that, the highest and lowest values of PAR were found during the rainy season with 8.3426 MJ and 6.4936 MJ in the months of April and July respectively. Based on the developed models, the model equation (16g) is the most suitable for estimating PAR for Ikeja with R2, MBE, RMSE, MPE, t-test and IA values of 80.9 , 0.0062 MJ , 0.3415 MJ -0.1974 0.0601 and 92.8712 respectively.

Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms

Weather stations can provide accurate and high temporal resolution air temperature (Ta) measurements, but their limited spatial coverage due to sparse distribution poses an issue and challenge. However, satellite data can offer land surface temperature (LST) observations with high spatial coverage, which have a strong relationship with Ta, making them ideal for enhancing Ta estimation. This study uses satellite-derived and auxiliary data to create a monthly mean Ta dataset with a 1 km resolution over Taiwan from 2003 to 2020. We employed three machine learning (ML) algorithms and seven different datasets comprising 12 explanatory variables with LST obtained from the MODIS to find the optimal combination of algorithm and dataset for Ta estimation in Taiwan. We applied recursive feature elimination (RFE) to reduce the model complexity and overfitting issues. For model assessment, we used five-fold cross-validation to evaluate the ML models, and indicators such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square of error (RMSE) were employed. The results show that the XGB regressor performed the best among the three models with the highest accuracy. The RFE using the XGB model suggested eight selected variables, including nighttime LST, daytime LST, elevation, longitude, latitude, distance to the sea, month, and year. Based on the variance importance analysis, nighttime LST was the most crucial variable, followed by daytime LST and month. We found that the final monthly Ta dataset using the XGB model had an excellent five-fold cross-validated performance (R2 = 0.986, MAE = 0.477 °C, and RMSE = 0.639 °C). Furthermore, the XGB model not only performed well throughout all four seasons but also had high and consistent accuracy across months, years, and subsets, indicating its potential for accurately estimating Ta in Taiwan's complex topographic features with varying climate conditions. The resulting monthly Ta dataset created by our model can be an essential input for environmental studies.

Bayesian Spatial Models for Projecting Corn Yields

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.

Data-driven propagation prediction of subsonic and supersonic turbulent jets by combining self-similarity analysis model and artificial neural network

Previous self-similarity analysis models for turbulent jet flames (TJF) have inherent limitations in the flame tip location and velocity prediction based on experimental data. A novel model (BP-TJF) to predict the propagation process of subsonic and supersonic TJF is proposed by combining developed self-similarity analysis modeling and back propagation neural network (BPNN). The BP-TJF model is trained from three datasets of initial temperature, initial pressure, and oxygen content. The results show that the pressure difference prediction error was only 0.46 % for subsonic jets and 5 % for supersonic jets. The overall correlation coefficients (R) and mean squared errors (MSE) range from 0.95–0.97 and 0.01–0.1, respectively. The model optimized by genetic algorithm (GA) significantly improved the prediction stability. However, there is scope for improvement in the overpressure peak prediction. Due to the small-scale datasets and parameter errors of self-similar model for jet propagation, the model cannot provide real-time feedback on the interaction between the shock wave and the flame front. Jet tip locations and velocities obtained from the BP-TJF model and experiments are generally consistent in magnitude and overall trends. Without considering the flame structure, the prediction framework developed in this paper can calculate the jet tip propagation characteristics with little difference from experiments and CFD, which is a great advantage, especially in the calculation of subsonic jets.

Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements

The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D (x,y,z) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D (x,y) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0.97°C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.

Bayesian Semiparametric Local Clustering of Multiple Time Series Data

In multiple time series data, clustering the component profiles can identify meaningful latent groups while also detecting interesting change points in their trajectories. Conventional time series clustering methods, however, suffer the drawback of requiring the co-clustered units to have the same cluster membership throughout the entire time domain. In contrast to these “global” clustering methods, we develop a Bayesian “local” clustering method that allows the functions to flexibly change their cluster memberships over time. We design a Markov chain Monte Carlo algorithm to implement our method. We illustrate the method in several real-world datasets, where time-varying cluster memberships provide meaningful inferences about the underlying processes. These include a public health dataset to showcase the more detailed inference our method can provide over global clustering alternatives, and a temperature dataset to demonstrate our method’s utility as a flexible change point detection method. Supplemental materials for this article, including R codes implementing the method, are available online.

Unbiased Proxy Calibration

The linear calibration model is a powerful statistical tool that can be utilized to predict an unknown response variable, Y, through observations of a proxy or predictor variable, X. Since calibration involves estimation of regression model parameters on the basis of a limited amount of noisy data, an unbiased calibration slope estimation is of utmost importance. This can be achieved by means of state-of-the-art, data-driven statistical techniques. The present paper shows that weighted least-squares for both variables estimation (WLSXY) is able to deliver unbiased slope estimations under heteroscedasticity. In the case of homoscedasticity, besides WLSXY, ordinary least-squares (OLS) estimation with bias correction (OLSBC) also performs well. For achieving unbiasedness, it is further necessary to take the correct regression direction (i.e., of Y on X) into account. The present paper introduces a pairwise moving block bootstrap resampling approach for obtaining accurate estimation confidence intervals (CIs) under real-world climate conditions (i.e., non-Gaussian distributional shapes and autocorrelations in the noise components). A Monte Carlo simulation experiment confirms the feasibility and validity of this approach. The parameter estimates and bootstrap replications serve to predict the response with CIs. The methodological approach to unbiased calibration is illustrated for a paired time series dataset of sea-surface temperature and coral oxygen isotopic composition. Fortran software with implementation of OLSBC and WLSXY accompanies this paper.

Spatiotemporal Characteristics of Actual Evapotranspiration Changes and Their Climatic Causes in China

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ETa) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ETa in China from 2000 to 2018. The analysis covered future trends in ETa changes and the contribution of meteorological factors. Our results showed that the ETa in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ETa was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ETa in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ETa exists. According to the contribution analysis, precipitation contributed more to the change in ETa than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ETa changes, providing scientific proof for the optimization apportion of future water resources.

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.