Atmospheric Temperature Research Articles

Simulation of wind-temperature-density measurement in near space by spaceborne Fe resonance fluorescence Doppler lidar

Spaceborne resonance fluorescence Doppler lidar uses metal atoms as tracers to detect atmospheric temperature, wind speed, and metal atom number density from the top of the mesosphere to the bottom of the thermosphere in the global atmosphere. This study proposes a concept of spaceborne Fe resonance fluorescence Doppler lidar (spaceborne Fe lidar). To theoretically analyze the feasibility of this technology, key parameters of the lidar were designed. Starting from the lidar equation, the echo signal intensity of spaceborne Fe lidar and the random error of atmospheric parameters were simulated and analyzed. Satellite orbit height is designed to be 400 km, monopulse energy is 300 mJ, and vertical and horizontal distance resolution is 2 km and 230 km, respectively. The simulation calculation shows that the random error of temperature detection of the spaceborne Fe lidar is 3.32 K, the line of sight wind speed is 1.67 m/s, and the horizontal wind speed is 3.34 m/s at 90 km. At the same time, the atmospheric density and temperature of 30-70 km can be detected at night, and the error is less than 10%. The conclusion of this paper has reference value for developing spaceborne resonance fluorescence Doppler lidar to detect atmospheric parameters in near space.

The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa

The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth's surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.

Temperature and Ozone Response to Different Forcing in the Lower Troposphere and Stratosphere

To evaluate the contributions of different forcings to the temperature and atmospheric composition changes between 1980 and 2020, we exploited the chemistry-climate model (CCM) SOCOLv3. The study examined ozone content and atmospheric temperature response to (1) ozone-depleting substances; (2) greenhouse gas concentrations, ocean surface temperature, and sea ice coverage; (3) solar irradiance; and (4) stratospheric aerosol loading and, separately, (5) greenhouse gas concentrations, (6) ocean surface temperature and sea ice coverage, and (7) NOx surface emissions. To evaluate the impacts of specific factors, we performed model runs driven by each factor (1–7) variability as well as a reference experiment that accounted for the influence of all factors simultaneously. We identified the relative contribution of different factors to the evolution of the temperature and ozone content of the lower troposphere and stratosphere from 1980 to 2020. The model results were in good agreement with the reanalyses (MERRA2 and ERA5). We showed that stratospheric ozone depletion before the Montreal Protocol introduction and partial recovery after that were chiefly driven by ODS. Stratospheric aerosol from major volcanic eruptions caused only short-term (up to 5 years) ozone decline. Increased greenhouse gas emissions dominate the ongoing long-term stratospheric cooling as well as tropospheric and surface warming. Solar irradiance contributed to short-term fluctuations but had a minimal long-term impact. Furthermore, our analysis of the solar signal in the tropical stratosphere underscores the complex interplay of solar radiation with volcanic, oceanic, and atmospheric factors, revealing significant altitudinal distributions of temperature and ozone responses to solar activity. Our findings advocate further innovative methodologies to take into account the nonlinearity of the atmospheric processes.

The Spatiotemporal Dynamics of Temperature Variability Across Mts. Qinling: A Comparative Study from 1971 to 2022

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Catalytic enhancement of water gas shift reaction with Cu/ZnO/ZSM-5: Overcoming challenges of CO2 and H2 rich feeds

Water gas shift (WGS) reaction commonly converts CO to CO2 using H2O and produces H2 and CO2 in a catalytic fixed bed reactor. This study aims to develop a Cu/ZnO/ZSM-5 catalyst loaded in water gas shift reactor for converting typical synthesis gas produced from dry reforming of methane (DRM) at medium temperature shift (MTS, 200–350 oC) conditions and for preventing reverse reaction due to high feed concentrations of CO2 and H2. The Cu/ZnO/ZSM-5 catalyst was prepared by impregnation method with lower Cu metal loading of 5, 10, and 15 wt%, respectively, compared to commercial catalysts. The synthesized catalyst was characterized using XRF, XRD, SEM, H2-TPR, NH3-TPD, and TGA. The catalyst activity and stability for the WGS reaction were tested in the fixed bed reactor at atmospheric pressure and temperature of 325 °C. The experimental results show that CO conversion increases with increasing Cu loading. The 15 wt% Cu/ZnO/ZSM-5 catalyst showed the best results with a CO conversion of 35% and a yield of H2 of 36% and showed good stability for 32 h. Based on the TGA, the 15 wt% Cu/ZnO/ZSM-5 catalyst forms a 0.04 g carbon/g catalyst, which is much lower when compared to commercial catalyst (0.105 g carbon/g catalyst). The relative activity of the synthesized catalyst indicates 2.4 times better when compared to commercial catalysts. High concentrations of H2 and CO2 in the feed may initiate side reactions, including reverse WGS, methanation, and carbon formation, which will decrease H2 yield.

Microstructure evolution and nitriding mechanism of Ti‐6Al‐4 V alloy in alumina‐based refractories

AbstractThe aims of this study were to explore the nitriding mechanism of Ti‐6Al‐4 V alloy in Al2O3‐based refractories. Al2O3‐based composite refractories were fabricated using Ti‐6Al‐4 V and sintered alumina as main materials, followed by nitriding at different temperatures in a nitrogen atmosphere. Phase and microstructure evolution of the products was analyzed by X‐ray diffraction and field‐emission scanning electron microscopy equipped with energy‐dispersive X‐ray spectroscopy. The results reveal a multi‐stage nitridation process of Ti‐6Al‐4 V at different temperatures. At 900°C, nitridation of the Ti‐6Al‐4 V surface results in the formation of a (Ti,V)N solid solution, while Al and V migrate inward, forming intermetallic compounds Ti3Al and a‐Ti (Ti1‐x‐yVxAly) with rich Al and V. At 1100°C, nitridation of Ti3Al produces intermediate products like Ti2AlN or Ti3AlN, with continued inward migration of Al or V to form the intermetallic α2 phase of (Ti1‐xVx)3yAly in the center of the alloy particles. At 1300°C, decomposition of Ti2AlN or Ti3AlN results in the outward migration of Al atoms and the presence of AlN on the outer layer of Ti‐6Al‐4 V particles, thus forming a hollow structure. Finally, at 1500°C, the (Ti,V)N solid solution grains grow as a result of further diffusion and dissolution of nitrogen.

DFT investigation of thermodynamic, electronic, optical, and mechanical properties of XLiH3 (X= Mg, Ca, Sr, and Ba) hydrides for hydrogen storage and energy harvesting

The present study investigates the hydrogen storage capacity and physical properties of the perovskite hydrides XLiH3 (X = Mg, Ca, Sr, and Ba). The studied compounds MgLiH3, CaLiH3, SrLiH3, and BaLiH3 have lattice constants 3.76, 4.29, 4.64, and 5.04 Å, respectively. These compounds are also observed to be stable in cubic phase under atmospheric pressure and temperature conditions. They have hydrogen storage capacities 8.76 wt%, 5.99 wt%, 3.07 wt%, and 2.03 %, respectively. The SrLiH3 compound has the greatest Debye temperature (θD) of 344.41 K. The analysis of the electronic band structure and density of states reveals that perovskite hydrides have semiconducting characteristics with indirect band gap values of 2.66, 2.34, 1.94, and 1.37 eV, respectively. The materials are semiconductors and have suitable band gaps to be utilized in optical devices. Therefore, the optoelectronic properties of dielectric constants, absorption, and energy loss have been determined to predict the potential of materials for optoelectronic applications. Furthermore, the elastic constants, moduli, and anisotropy are also calculated for the observed materials. The Possion and Pugh ratios indicate these compounds exhibit ductile behaviour and significant anisotropy. Therefore, large values of hydrogen capacities, stabilities, and extraordinary physical behaviour make them important for hydrogen storage systems.

Thermodynamic analysis for definition of low-potential heat sources

This study presents a method for defining low-potential energy sources based on thermodynamic analysis. It defines low potential energy sources and presents the results of calculating temperature levels corresponding to the concept of a low potential energy source. A classification of energy sources by thermal level was carried out, including sources of cold, low-potential heat and cold at reduced temperatures, low-potential heat and cold at elevated temperatures, and high-temperature sources. The analysis demonstrates the correspondence between thermodynamic assessment and practical views of many researchers on low-potential energy sources. Conclusions about the influence of external factors on the temperature range for these sources are drawn. It is shown that factors affecting the range of low-potential temperatures include: the temperature potential of consumers, as a reference point for temperature, and the efficiency of local stations for converting heat into work or electricity. Narrowed assessment is presented based on optimal conditions for humans (22 °C), from −90 °C to 203 °C, and extended assessment based on limits of liquid water existence at atmospheric pressure and temperatures (0–100 °C) from −104 °C to 329 °C with an efficiency of heat conversion stations of 38 %.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

Limb Temperature Observations in the Stratosphere and Mesosphere Derived from the OMPS Sensor

Molecular scattering (Rayleigh scattering) has been extensively used from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we investigate how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. The OMPS consists of three instruments whose main purpose is to study the composition of the stratosphere. One of these, the Limb Profiler (LP), measures the radiance of the limb of the middle atmosphere (stratosphere and mesosphere, 12 to 90 km altitude) at wavelengths from 290 to 1020 nm. This new data set has been used with a New Simplified Radiative Transfer Model (NSRTM) to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars and the ERA5 and MSIS models. The results show that OMPS and the lidars are in agreement within a range of about 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. We investigated various sources of bias, such as different attenuation sources, which can produce errors of up to 120 K in the UV range, instrumental errors around 0.8 K and noise problems of up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturised instrument that could provide real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats with our NSRTM.

Diagnosing Atmospheric Heating Rate Changes Using Radiative Kernels

AbstractAtmospheric radiative heating rate, which manifests radiative energy convergence in the atmosphere, is a fundamental factor shaping the Earth's climate and driving climate change. Compared to the radiative energy budget at the top of atmosphere or surface, the atmospheric energy budget and heating rate are less studied due to a lack of observational constraints and diagnostic tools. Motivated by growing interest in atmospheric energy budget and to facilitate the heating rate analysis, we innovate a set of radiative kernels, which quantitatively measure the sensitivity of atmospheric heating rate to different geophysical variables. When multiplied with the changes in these geophysical variables, these kernels quantify their contributions to the heating rate change. A climate change experiment of Global Climate Models (GCMs) is used to test the application of heating rate kernels. The results indicate the radiative heating rate change simulated by GCMs can be well reproduced by the kernels, validating the kernel method. The decomposition of the heating rate changes reveals the contributing mechanisms. For example, in the tropical upper troposphere, the negative heating anomaly in a warmer climate is dominated by atmospheric temperature and water vapor. Increases in both variables intensify atmospheric thermal radiation to space, partially offset by a positive heating anomaly caused by the lifting high‐cloud tops. Moreover, compared to the results corrected using the kernels, the cloud effect inferred from the radiative heating difference between clear‐ and all‐skies (“cloud radiative heating”) has a non‐negligible bias, necessitating the use of kernels to quantify the cloud‐induced heating rate changes.

Assessment of FY-3E GNOS II Radio Occultation Data Using an Improved Three-Cornered Hat Method

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

Impact of Average Atmospheric Temperature on Rainbow Trout Production in South Africa: A Time Series Analysis Approach

Climate change has various effects on the global food supply and the environment as a whole. Similarly, the production of fish like Rainbow Trout is affected by variation in the atmospheric temperature higher than 21oC, leading to fluctuations in yield. Therefore, this study sought to investigate the impact of average atmospheric temperature on Rainbow Trout production in South Africa using time series data spanning from 2000 to 2022. The unit root test, Johannsen cointegration test, and the Vector Error Correction model were employed to analyse the data. The results show that the change in average atmospheric temperature affects the production of Rainbow Trout. The correlation between average temperature and Rainbow Trout production is positive in the short-run and long-run. I Prolonged exposure to high temperature may lead to fish stress, migration and increased susceptibility to diseases which ultimately affects the economic viability of the sector in both the short-run and the long-run. In addition, the overall change in the atmospheric temperature has a negative effect on the production of the fish highlighting the need for climate change intervention to sustain fish production. The study recommends implementing strategies that reduce the impact of temperature changes on fish production to support both economic growth and food security.

Manufacturing and aging behavior of CuCr1Zr powder: Analysis and modelling of close-coupled gas atomization of CuCr1Zr and its impact on the melt beam disintegration and analysis powder oxidation kinetics

Powder bed-based additive manufacturing methods represent an increasingly vital role in industrial applications. Specifically, there has been considerable focus on highly reflective metals, such as CuCr1Zr, in recent years. However, current research primarily revolves around developing appropriate manufacturing parameters e.g. laser powder bed fusion (PBF/LB-M) and assessing the corresponding mechanical and electrical properties of CuCr1Zr. The impact of manufacturing routines and oxidation behavior of CuCr1Zr powder are seldom discussed in additive manufacturing. Therefore, the present study addresses the analysis of powder production and the oxidation behavior of CuCr1Zr powder via close-coupled gas atomization to analyze the disintegration of a CuCr1Zr melting jet during gas atomization of CuCr1Zr by using a high-speed camera. Critical conditions for secondary disintegration were calculated as a function of the initial gas pressure. Additionally, calorimetric measurements and isothermal oxidation experiments were conducted to quantify oxide formation and growth. According to the results, initial oxide formation occurs after 42 days with CuCr1Zr powder at room temperature in a nitrogen atmosphere. Subsequently, oxide growth follows a logarithmic law, associated with the formation and conversion of Cu2O to CuO.

Online in-situ modification of biochar for the efficient removal of elemental mercury and co-benefit of SO2/NO removal

In this study, non-thermal plasma discharge was proposed for online in-situ modification of biochar injected into coal-biomass co-combustion flue gas, aiming to achieve the combined removal of elemental mercury (Hg0), NO, and SO2 from flue gas. After plasma discharge, the pore structure of biochar was slightly improved whereas the surface morphology of biochar was almost unchanged. After plasma discharge under O2, SO2, and HCl atmospheres, the oxygen, sulfur, and chlorine contents of biochar were obviously increased. More exactly, additional functional groups were formed on the modified biochar surface, such as CO, C(O)OC, CS, and CCl. Compared with raw biochar, the biochar modified under HCl, SO2, O2, H2O and CO2 atmospheres exhibited higher Hg0 removal efficiency, and longer modification time resulted in better Hg0 removal performance. The optimal modification atmosphere and reaction temperature were the simulated flue gas (SO2 + HCl + CO2 + O2 + H2O + NO + N2) and 20 ℃-100 ℃, respectively. The modified biochar exhibited excellent resistance to SO2 and H2O. The addition of HCl, CO2, NO, and O2 contributed to Hg0 removal. The removal efficiency of NO and SO2 attained a maximum of 100 %. The Hg0 removal mechanism was further revealed, where CO, C(O)OC, CS, and CCl served as active components.

Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−1 to 133.94 PgCyear−1 from 2000 to 2022, with an average of 125.93 PgCyear−1. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−1 during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.

On the рossibility of multichannel optical backscattering sondes for joint balloon and lidar studies of the aerosol composition of the middle atmosphere

Aerosol backscattering sondes in the practice of aerological sounding, along with lidar observations, are used at night to study and monitor polar stratospheric clouds, tropospheric and stratospheric aerosol, cirrus clouds, pyroconvection, volcanic aerosol, as well as to verify remote methods and means of ground-based and satellite-based aerosol observations. For aerosol sondes, a simple two-wave measurement technique is used, which makes it possible to diagnose changes in aerosol composition by color index. The possibilities of the two-wave technique have limitations, which are discussed in this article. Aerological sounding combined with lidar observations expands the wavelength range for multi-wavelength studies, and direct measurements of atmospheric temperature increase the accuracy of aerosol sensing. The paper considers the application of 3 or more wavelenght techniques. Data from probe measurements using wavelengths of 470, 528, 850 and 940 nm and lidar sensing at wavelengths of 355 and 532 nm are presented.

Characterization of hydrogeochemical elements in determining the ground water quality for irrigation potential and its correlation with climatological parameters of chennai basin aquifer system, southern india.

An attempt has been made to comprehend the ground water quality and climate impacts of the Chennai River basin, which is aimed at its main socio-economic growth of the state of Tamil Nadu. The ground water samples collected from the study area were analyzed for its hydrogeochemical elements. The ground water quality and irrigation suitability were determined using several water quality assessment metrics. Ground water is extensively utilized for irrigation in the entire basin area for the past two decades, especially in the 38 over-exploited Firkas out of the 109 Firkas of the basin. It is inferred that the phreatic aquifer ground water quality is fresh in about 20%, as indicated by the EC value (< 750µs/cm) at 25°C. In about 63% of the ground water indicating the moderately fresh showing the EC varies between 751 and 2250µs/cm at 25°C, 11% of ground water exerted an EC ranging between 2251 and 3000µs/cm at 25°C indicating that the ground water is slightly mineralized, and in about 6% of groundwater, the EC is > 3000µs/cm at 25°C indicating that the ground water is highly mineralized. There were no water samples that exceeded the permissible limit of chloride either in phreatic aquifer or in fracture aquifer. The changes in rainfall frequency and atmospheric temperature affect the ground water movement and storage directly and indirectly. Similarly, the temperature data shows a positive relationship with the concentration of fluoride and nitrate ions in the water.

Modelling the effect of free convection on permafrost melting rates in frozen rock clefts

Abstract. This research develops a conceptual model of a karst system subject to mountain permafrost. The transient thermal response of a frozen rock cleft after the rise in the atmospheric temperature above the melting temperature of water is investigated using numerical simulations. Free convection in liquid water (i.e. buoyancy-driven flow) is considered. The density increase in water from 0 to 4 °C causes warmer meltwater to flow downwards and colder upwards, resulting in significant enhancement of the heat transferred from the ground surface to the melting front. Free convection increases the melting rate by approximately an order of magnitude compared to a model based on thermal conduction in stagnant water. The model outcomes are compared qualitatively with field data from the Monlesi ice cave (Switzerland) and confirm the agreement between real-world observations and the proposed model when free convection is considered.