Temperature Trajectory Research Articles

CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor

CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor

Abstract 4141460: Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements

Introduction: Neonatal hypothermia (< 36.5 o C) after CPB is considered benign despite lack of evidence on its prognostic significance. Question: Are group-based trajectory modeling (GBTM), k-means, and self-organizing map (SOM) clustering approaches clinically useful for evaluating their associations with important outcomes? Aims: Identify distinct postoperative temperature trajectories in neonates after CPB using novel machine learning (ML) clustering methods, corroborate findings, and evaluate prognostic value on outcomes. Methods: Secondary cohort analysis of prospectively collected data. Accessed data from a single pediatric referral center's CardioAccess registry consistent of postoperative neonates with congenital heart defects (CHD). 48-hourly postoperative temperature measurements were extracted from patient records. GBTM, SOM, and k-means clustering identified cluster membership and model fit was optimized for 3 clusters based on existing knowledge. The primary outcome was a complication composite of severe bleeding, illness severity, cardiac arrest, arrhythmia, delayed first successful extubation, prolonged ICU LOS, very poor weight gain, and 30-day mortality. 3 data driven techniques were compared and then associated with outcomes using adjusted multivariable binary logistic regression (LR). Results: 450 neonates ≥ 34 weeks gestation undergoing CPB between 2015 and 2019 met inclusion criteria. GBTM, SOM, and k-means clustering identified temperature group assignment for 3 clusters: 1) persistent hypothermia (n=38, 9% vs. n=49, 11% vs. n=40, 9%), 2) resolving hypothermia (n=233, 51% vs. n=227, 50% vs. n=147, 33%), and 3) normothermia (n=179, 40% vs. n=174, 39% vs. n=263, 58%). Concordance of techniques showed strong agreement between GBTM and SOM (kappa= 0.92). The kappa test comparing GBTM and k-means assignment was 0.41, indicating a weak to moderate agreement. After adjustment in the multivariable binary LR, persistently hypothermic neonates compared to normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (OR: 2.8 [1.0-7.3], p 0.041) and SOM (OR: 2.3 [1.0-5.4], p 0.045) models, but not the k-means model (OR: 1.4 [0.7-3.1], p 0.38). Conclusion: Exploring concordance between different unsupervised ML techniques, shows that neonatal temperature after CPB follows distinct postoperative trajectories, and those exhibiting a persistent hypothermia trend are at higher risk of adverse outcomes.

FROT: A Framework to comprehensively describe radiative contributions to temperature responses

Abstract Different human activities and associated emissions of CO2 and non-CO2 radiative forcing agents and feedbacks determine the final state of Earth’s climate. To understand and explain contributions to global temperature changes, many emission-based metrics have been employed, such as CO2-equivalent or -forcing equivalent. None of these metrics, however, include dynamic responses from Earth system feedbacks in terms of carbon and heat redistribution, known to play an increasingly important role in ambitious mitigation scenarios. Here we introduce a framework that allows for an assessment of such feedbacks in addition to CO2, non-CO2 anthropogenic forcing and natural external variability contributions. FROT (Framework for Radiative cOntributions to Temperature response) allows for an assessment of components of direct radiative impact to the system (climate forcing), as well as Earth system feedbacks concerning heat and carbon. The framework is versatile in terms of applications and allows for exploring individual components contributions to, for example, temperature stabilisation simulations, or comparisons in different models and scenarios, as it can reasonably explain their simulated temperature variability. Here, we apply FROT to both an intermediate complexity and a fully coupled Earth system model, as we simulate highly ambitious mitigation scenarios. Comparing temperature stabilisation scenarios, we can show that both net-zero CO2 emissions and small amounts of positive CO2 emissions could lead to a stable global temperature trajectory. Our assessment reveals that the effects of non-CO2 climate forcings, especially the development of sulphate aerosols in the atmosphere, and the dynamics of the carbon cycle, play a pivotal role in the final level of warming and in enabling a temperature stabilisation. Under highly ambitious climate mitigation scenarios it becomes crucial to include Earth system feedbacks, specifically ocean heat uptake, to understand interannual to decadal temperature development, since previously secondary processes now become increasingly dominant. Our framework offers the opportunity to do so.

Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories.

Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes). Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes. The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor). Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines.

The impact of air temperature and humidity on Children’s blood pressure mediated by Lipids: A prospective cohort study

ObjectivesFew studies illustrate the mechanism between air temperature and blood pressure (BP) in childhood. This study aims to investigate the associations between air temperature, humidity exposure, and BP trajectories in children and adolescents, and explore the potential mediating roles of lipid profiles in these relationships. MethodsThis prospective cohort study included 5,971 children with 10,800 person-times measurements at baseline from the Chongqing Health Cohort, with evaluations conducted in 2014–2015 (baseline) and follow-ups in 2016 (urban areas) and 2019 (urban and rural areas). Multilevel mixed-effects models were used to analyse the impacts of air temperature and humidity on BP levels and the incidence of elevated BP, while accounting for potential confounders. Mediation analyses were performed to evaluate the mediating effects of lipid profiles, including low-density lipoprotein (LDL), total cholesterol (TC), and specific lipid species. ResultsAfter adjusting for covariates, higher air temperature quartiles were associated with both decreased BP levels and elevated BP risk (RR: 0.83; 95 % CIs: 0.78, 0.89; P = 0.028). Conversely, higher humidity quartiles exhibited a U-shaped relationship with BP levels. Greater variability in air temperature was linked to increase BP levels. The cumulative effects of air temperature exposure on BP were significant from pregnancy to age 10, with females exhibiting larger effects (β:-3.291, 95 % CIs: −4.242,-2.340, P < 0.001). LDL and TC partially mediated the associations between air temperature and BP levels, particularly in males. Specific lipid species, including SM (d21:1), LPC (17:0), and PC (O-36:3), also exhibited significant mediating effects. ConclusionsThis study provides novel insights into the intricate interplay between air temperature, humidity, lipid metabolism, and blood pressure regulation in children. Lower average temperatures and extreme humidity levels were associated with increased risks of elevated BP, potentially mediated by lipid profiles. Early interventions targeting air temperature exposure and lipid metabolism could mitigate hypertension risk, promoting improved cardiovascular outcomes in children.

Al-based amorphous coatings by warm spraying: Numerical simulation and experimental validation

Al-based fully amorphous coatings with low porosity were successfully produced via warm spraying, combining numerical simulation and experimental validation. A warm spray gun model, employing computational fluid dynamics, was developed to simulate the warm spraying process. The study delved into the effects of reactant flow rate, oxygen/fuel (O/F) ratio, coolant flow rate, spray distance, particle size, particle shape, particle injection velocity, and particle injection angle on the gas flow field parameters (pressure, temperature, and velocity) and particle in-flight behaviors (temperature, velocity, and in-flight trajectory). The optimum spraying parameters (OSP) predicted by the simulation results were determined: 0.008740 kg/s for reactant flow rate, 2.7 for the O/F ratio, 0.007356 kg/s for coolant flow rate, 142 mm for spraying distance, 10–35 μm for particle size range, spherical for particle shape, 10 m/s for particle injection velocity, and 0° for particle injection angle. Subsequently, warm spraying experiments based on the OSP yielded Al-based fully amorphous coatings with a porosity of 0.08 %. This work provides valuable insights for the production of Al-based fully amorphous coatings with minimal porosity through warm spraying.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.

Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies. A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models. We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality. CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.

Association Between Persistent Hypothermia After Cardiopulmonary Bypass in Neonates and Odds of Serious Complications.

Persistent hypothermia after cardiopulmonary bypass (CPB) in neonates with congenital heart defects (CHD) has been historically considered benign despite lack of evidence on its prognostic significance. Examine associations between the magnitude and pattern of unintentional postoperative hypothermia and odds of complications in neonates with CHD undergoing CPB. Retrospective cohort study. Single northeastern U.S., urban pediatric quaternary care center with an established cardiac surgery program. Population-based sample of neonates greater than or equal to 34 weeks gestation undergoing their first CPB between 2015 and 2019. None. Hourly temperature measurements for the first 48 postoperative hours were extracted from inpatient medical records, and clinical characteristics and outcomes were accessed through the local patient registry. Group-based trajectory modeling (GBTM) identified latent temporal temperature trajectories. Associations of trajectories with outcomes were assessed using multivariable binary logistic regression. Outcomes (postoperative complications) were manually adjudicated by experts or were predefined by the patient registry. Four hundred fifty neonates met inclusion criteria. Their mean (sd) gestational age was 38 weeks (1.3), mean (sd) birth weight was 3.19 kilograms (0.55), median (interquartile range) surgical age was 4.7 days (3.3-7.0), 284 of 450 (63%) were male, and 272 of 450 (60%) were White. GBTM identified three distinct curvilinear temperature trajectories: persistent hypothermia (n = 38, 9%), resolving hypothermia (n = 233, 52%), and normothermia (n = 179, 40%). Compared with the normothermic group, those with persistent hypothermia had significantly higher odds of cardiac arrest, actionable arrhythmia, delayed first successful extubation, prolonged cardiac ICU length of stay, very poor weight gain, and 30-day hospital mortality. The persistent hypothermia group was characterized by greater odds of having a lower gestational age, more prevalent neurologic abnormalities, more unplanned reoperations, and a low surgical mortality risk assessment. Persistent postoperative hypothermia in neonates after CPB is independently associated with having greater odds of complications. Recovery patterns from postoperative hypothermia may be a clinically useful marker to identify patient instability in neonates. Additional research is needed for causal modeling and prospective validation before clinical adoption.

Temperature trajectories and mortality in hypothermic sepsis patients

ObjectivesHypothermia is associated with poor outcomes in sepsis patients, and hypothermic sepsis patients exhibit temperature alterations during initial treatment. The objective of this study was to classify hypothermic sepsis patients based on body temperature trajectories and investigate the associations of these patients with 28-day mortality. MethodsThis was a retrospective analysis of prospectively collected data from adult sepsis or septic shock patients who visited three emergency departments between August 2014 and December 2019. Hypothermic sepsis was defined as an initial body temperature <36 °C. delta temperature was calculated by subtracting the 0 h body temperature from the 6 h body temperature. We divided the patients into three groups according to delta temperature: Group A (delta temperature ≤ 0), Group B (0 < delta temperature ≤ 1) and Group C (delta temperature > 1). The primary outcome was 28-day mortality, and a multivariable Cox proportional hazards regression model was generated. ResultsAmong 7344 patients with sepsis or septic shock, 325 hypothermic patients were included in the analysis, and the overall mortality rate was 36%. While initial body temperature was not different between survivors and nonsurvivors, survivors exhibited a higher body temperature at 6 h. The 28-day mortality rates for Groups A, B and C were 53.1%, 36.0%, and 30.0%, respectively, and Group A had significantly higher mortality than Group C did (p < 0.05). Group C demonstrated a 44.2% decrease in 28-day mortality compared to Group A (adjusted hazard ratio of 0.558; 95% confidence interval of 0.330–0.941). ConclusionsIn hypothermic sepsis patients, an increase of 1 °C or more in body temperature after the initial 6 h is associated with a reduced risk of 28-day mortality.

How does extreme heat affect carbon emission intensity? Evidence from county-level data in China

This study investigates the impact of increasingly frequent extreme heat events due to climate change on carbon emission intensities in China. Using spline regressions on county-level data from 2000 to 2019, we identify an asymmetric U-shaped relationship between daily mean temperatures and carbon intensities. Each additional day with temperatures above 33 °C in each location results in a 0.9% rise in the average annual carbon intensity, driven by higher energy consumption for cooling. We also explore the mitigating effects of China's low-carbon city initiatives and emissions trading scheme. By combining our estimation results with future temperature trajectories, we simulate that, in the long run, China's carbon intensity can increase by 8–13% under the SSP1-2.6 scenario and 24–51% under the SSP5-8.5 scenario. Our findings underscore the urgency for policymakers to thoroughly assess the socioeconomic impacts of heat waves and incorporate climate resilience into overall sustainable development plans.

Out of the frying pan into the fire: Predicted warming in alpine streams suggests hidden consequences for aquatic ectotherms.

Thermal regimes of aquatic ecosystems are predicted to change as climate warming progresses over the next century, with high-latitude and high-elevation regions predicted to be particularly impacted. Here, we have modelled alpine stream water temperatures from air temperature data and used future predicted air temperature trajectories (representative concentration pathway [rcp] 4.5 and 8.5) to predict future water temperatures. Modelled stream water temperatures have been used to calculate cumulative degree days (CDDs) under current and future climate conditions. These calculations show that degree days will accumulate more rapidly under the future climate scenarios, and with a stronger effect for higher CDD values (e.g., rcp 4.5: 18-28 days earlier [CDD = 500]; 42-55 days earlier [CDD = 2000]). Changes to the time to achieve specific CDDs may have profound and unexpected consequences for alpine ecosystems. Our calculations show that while the effect of increased CDDs may be relatively small for organisms that emerge in spring-summer, the effects for organisms emerging in late summer-autumn may be substantial. For these organisms, the air temperatures experienced upon emergence could reach 9°C (rcp 4.5) or 12°C (rcp 8.5) higher than under current climate conditions, likely impacting on the metabolism of adults, the availability of resources, including food and suitable oviposition habitat, and reproductive success. Given that the movement of aquatic fauna to the terrestrial environment represents an important flux of energy and nutrients, differential changes in the time periods to achieve CDDs for aquatic and terrestrial fauna may de-couple existing predator-prey interactions.

Effect of Ethanol and Iso-Octane Blends on Isolated Low-Temperature Heat Release in a Spark Ignition Engine

&lt;div&gt;Low-temperature heat release (LTHR) is of interest for its potential to help control autoignition in advanced compression ignition (ACI) engines and mitigate knock in spark ignition (SI) engines. Previous studies have identified and investigated LTHR in both ACI and SI engines before the main high-temperature heat release (HTHR) event and, more recently, LTHR in isolation has been demonstrated in SI engines by appropriately curating the in-cylinder thermal state during compression and disabling the spark discharge. Ethanol is an increasingly common component of market fuel blends, owing to its renewable sources. In this work, the effect of adding ethanol to iso-octane (2,2,4-trimethylpentane) blends on their LTHR behavior is demonstrated. Tests were run on a motored single-cylinder engine elevated inlet air temperatures and pressures were adjusted to realize LTHR from blends of iso-octane and ethanol without entering the HTHR regime. The blends were tested with inlet temperatures of 40°C–140°C at equivalence ratios of 0.5, 0.67, and 1.0 with boosted (1.5 barA) conditions. The measured LTHR decreased with increasing ethanol content for all conditions tested; iso-octane–ethanol blends with above 20% ethanol content (by volume) showed minimal LTHR under engine conditions. These net effects resulted from the combination of thermal effects (charge cooling) and chemical effects (reactivity changes at low temperatures). The effect of temperature, pressure, fuel composition, and equivalence ratio on ignition delay times calculated from chemical kinetic modeling are presented alongside pressure–temperature trajectories of the in-cylinder gases to explain the trends. The underlying cause of the trends is explained by using a sensitivity analysis to determine the contribution of each reaction within the chemical kinetic mechanism to first-stage ignition, revealing the effect of introducing ethanol on the OH radical pool and resulting LTHR intensity.&lt;/div&gt;

Comparative dynamic optimization study of batch hydrothermal liquefaction of two microalgal strains for economic bio-oil production

This work presents dynamic optimization strategies of batch hydrothermal liquefaction of two microalgal species, Aurantiochytrium sp. KRS101 and Nannochloropsis sp. to optimize the reactor temperature profiles. Three dynamic optimization problems are solved to maximize the endpoint biocrude yield, minimize the final time, and minimize the reactor thermal energy. The biocrude maximization and time minimization problems demonstrated 11% and 6.18% increment in the optimal biocrude yields and reduction of 78.2% and 61.66% in batch times compared to the base cases for the microalgae with higher lipid and protein fractions, respectively. The energy minimization problem revealed a significant reduction in the reactor thermal energies to generate the targeted biocrude yields compared to the biocrude maximization. Therefore, the identified optimal temperature trajectories outperformed the conventional fixed temperature profiles and could improve the overall economics of the batch bio-oil production from the algal-based biorefineries by significantly enhancing the reactor performance.

Two-level optimization strategy for vehicle speed and battery thermal management in connected and automated EVs

The performance of the battery is affected by temperature, and the battery thermal management (BTM) system consumes considerable energy to maintain the temperature in the suitable range. The unnecessary acceleration and deceleration of electric vehicles (EVs) during driving causes higher energy consumption in the powertrain. The emergence of connected and automated vehicle (CAV) technology provides an opportunity for predictive control of thermal and energy management. To explore the coordination optimization between battery thermal and vehicle energy management, this article proposes a two-level optimization framework for the speed and BTM of EVs, which improves energy efficiency and battery safety. Each level consists of a sequential optimization of speed and battery thermal. In the upper layer, speed planning based on iterative dynamic programming (IDP) is first proposed to reduce powertrain energy consumption using intelligent traffic information. Then, based on the BTM system and the battery thermodynamics features, the long-term optimal trajectory of the battery temperature is derived according to optimized speed. In the lower layer, the model predictive controllers (MPC) are designed to track reference speed and temperature trajectories in real-time and enforce energy saving. Meanwhile, to improve the prediction accuracy of the system model, we integrate the Gaussian process (GP) model in the MPC and build the learning-based MPC strategy. Simulation results verify the performance of the proposed method which reduces the powertrain energy consumption by 20.95%. In the high and low temperature environment, compared with normal MPC, PID-based and Rule-based, it reduces BTM energy consumption by up to 15.69%, 29.68% and 38.73%.

Safe and efficient drilling and completion technology for deep shale gas in Sichuan and Chongqing

The world is rich in coalbed methane (CBM) resources and has broad exploration and development prospects. In this paper, first, the technical difficulties of drilling and completion in deep shale formations in the Sichuan and Chongqing region is systematically summarized. Second, a systematic evaluation was conducted on the optimization of temperature resistance performance and trajectory control technology of the rotary guidance system. Third, evaluation and economic analysis were conducted on the experimental effects of different technologies. Results show that: (a) the optimization technology for temperature resistance performance of integrated high-temperature resistant guide head and surface cooling equipment has been formed, which can effectively reduce the circulation cooling time and improve drilling efficiency. (b) Real time measurement of underground engineering parameters and monitoring and evaluation of wellbore cleanliness can reduce drilling risks, while achieving quantitative evaluation of drilling fluid gas content. (c) Assisted by real-time optimization technology of mechanical drilling speed based on underground surface engineering parameters, the acceleration effect has been significantly improved.

Health Management for PEMFC System Long-Term Operation Based on Optimal Temperature Trajectory Real-Time Optimization

Health Management for PEMFC System Long-Term Operation Based on Optimal Temperature Trajectory Real-Time Optimization

Interdecadal variabilities in growth and temperature trajectories of Trachurus japonicus juveniles: 1960s–1970s vs 2000s–2010s

Interdecadal variabilities in growth and temperature trajectories of Trachurus japonicus juveniles: 1960s–1970s vs 2000s–2010s

Ceramic Firing Temperature Trajectory Monitoring System on IoT-Based Gas Furnace

This study introduces a novel system where a conventional ceramic furnace is upgraded with IoT capabilities, specifically utilizing NodeMCU and a thermocouple sensor. The integrated system enables real-time temperature monitoring, facilitating the correction of firing trajectory errors in the ceramic firing process. The sensors reading minimizes errors by detecting temperature changes with a maximum deviation of -2.5 °C and a minimum deviation of -1 °C. The average error ranges from 1.81% to 3.79%. The collected data is seamlessly transmitted to Google Spreadsheet for online monitoring. This comprehensive solution not only minimizes recording errors but also ensures the quality of final ceramic products by preventing issues such as cracks, breaks, black cores, and non-uniform sizes. The incorporation of NodeMCU, thermocouple sensors, and online monitoring represents a significant technological leap in optimizing ceramic firing processes for heightened precision and efficiency.

Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture

Abstract. Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new “MESMER-X” version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts.

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.