Near-surface Air Research Articles

Evolution of land surface feedbacks on extreme heat: Adapting existing coupling metrics to a changing climate

Episodes of extreme heat are increasing globally, and dry land surface states have been implicated as an amplifying factor in several recent heat waves. Metrics used to quantify land-heat coupling in the current climate, relating sensible heat fluxes to near-surface air temperature, are applied to multimodel simulations of the past, present, and future climate to investigate the evolving role of land–atmosphere feedbacks in cases of extreme heat. Two related metrics are used: one that describes the climatological state of land-heat coupling and one that gives an episodic estimate of land feedbacks, here defined as the metric’s value at the 90th percentile of monthly mean temperatures. To provide robust statistics, seasonal multimodel medians are calculated, with the significance of changes determined by the degree of model consensus on the sign of the change. The climatological land-heat coupling mirrors other metrics of land–atmosphere interaction, peaking in transition regions between arid and humid climates. Changes from preindustrial to recent historical conditions are dominated by decreased land surface controls on extreme heat, mainly over the broad areas that have experienced expanded or intensified agriculture over the last 150 years. Future projections for increased atmospheric CO2 concentrations show a waning of areas of weakened land-heat feedbacks, while areas of increasing feedbacks expand over monsoon regions and much of the midlatitudes. The episodic land-heat metric is based on anomalies, which creates a quandary: how should anomalies be defined in a nonstationary climate? When the episodic coupling is defined relative to the means and variances for each period, a broadly similar evolution to the climatological metric is found, with historically dominant decreases giving way to widespread moderate increases in future climate scenarios. Basing all statistics on preindustrial norms results in huge increases in the coupling metric, showing its sensitivity to the definition of anomalies. When the metric is reformulated to isolate the impact of changing land and temperature variability, the tropics and Western Europe emerge as regions with enhanced land feedbacks on heatwaves, while desert areas and much of the remainder of the midlatitudes show reduced land-heat coupling.

Using a process-based dendroclimatic proxy system model in a data assimilation framework: a test case in the Southern Hemisphere over the past centuries

Abstract. Currently available data-assimilation-based reconstructions of past climate variations have only used statistical proxy system models to make the link between climate model outputs and indirect observations from tree rings. However, the linearity and stationarity assumptions of the statistical approach may have limitations. In this study, we incorporate the process-based dendroclimatic model MAIDEN into a data assimilation procedure using the reconstruction of near-surface air temperature, precipitation and winds in the midlatitudes of the Southern Hemisphere over the past 400 years as a test case. We compare our results with a data assimilation approach including a linear regression as a proxy system model for tree-ring width proxies. Overall, when compared to instrumental data, the reconstructions using MAIDEN as a proxy system model offer a skill equivalent to the experiment using the regression model. However, knowing the advantages that a process-based model can bring and the improvements that can still be made with MAIDEN, those results are promising.

An Analysis of Spatio-Temporal Relationship between Satellite-Based Land Surface Temperature and Station-Based Near-Surface Air Temperature over Brazil

A better understanding of the relationship between land surface temperature (Ts) and near-surface air temperature (Ta) is crucial for improving the simulation accuracy of climate models, developing retrieval schemes for soil and vegetation moisture, and estimating large-scale Ta from satellite-based Ts observations. In this study, we investigated the relationship between multiple satellite-based Ts products, derived from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite, and Ta from 204 meteorological stations over Brazil during 2003–2016. Monthly satellite-based Ts products used in this study include: (1) AIRS Version 6 with 1° spatial resolution, (2) AIRS Version 7 with 1° spatial resolution, (3) MODIS Collection 6 with 0.05° spatial resolution, and (4) MODIS Collection 6 with 1° spatial resolution re-sampled from (3) for a direct comparison with AIRS products. We found that satellite-based Ts is lower than Ta over the forest area, but higher than Ta over the non-forest area. Nevertheless, the correlation coefficients (R) between monthly Ta and four Ts products during 2003–2016 are greater than 0.8 over most stations. The long-term trend analysis shows a general warming trend in temperatures, particularly over the central and eastern parts of Brazil. The satellite products could also observe the increasing Ts over the deforestation region. Furthermore, we examined the temperature anomalies during three drought events in the dry season of 2005, 2010, and 2015. All products show similar spatio-temporal patterns, with positive temperature anomalies expanding in areal coverage and magnitude from the 2005 to 2015 event. The above results show that satellite-based Ts is sensitive in reflecting environmental changes such as deforestation and extreme climatic events, and can be used as an alternative to Ta for climatological studies. Moreover, the observed differences between Ts and Ta may inform how thermal assumptions can be improved in satellite-based retrievals of soil and vegetation moisture or evapotranspiration.

Assessment and intercomparison of ozone dry deposition schemes over two ecosystems based on Noah-MP in China

Dry deposition of ozone (O3) is a major sink of O3 in near-surface air. The uncertainties in any existing dry deposition schemes are large. In this study, we incorporate four dry deposition schemes into the Noah with multi-parameterization options model (Noah-MP) to assess the importance of choice of different dry deposition schemes on simulated dry deposition velocity of O3 (Vd(O3)) over forest and agricultural ecosystems. Our simulated Vd(O3) values from different schemes were compared with observational data. All schemes performed similarly for agricultural ecosystems (correlation coefficient of approximately 0.4). For the forest ecosystem, Ball–Berry type schemes performed better than Jarvis-type schemes, with correlation coefficients for Vd(O3) between simulations and observations being approximately 0.55 and 0.38, respectively. To further improve the efficacy of the simulation of Vd(O3), we modified two canopy stomatal resistance mechanisms. For the Jarvis stomatal resistance mechanism, the minimum stomatal resistance was modified, with a mean bias of daytime reduction of by 39.3% for the forest ecosystem and 60.9% for the agricultural ecosystem. For Ball–Berry stomatal resistance mechanism, the stomatal resistance mechanism and nitrogen-limiting scheme for photosynthesis were modified. However, the results showed no significant improvement in the Vd(O3) simulation. The results demonstrate the importance of canopy resistance parameterization over agricultural and forest ecosystems, thus making a strong case for further evaluation and model improvement of O3 dry deposition in different ecosystems.

Building the cooling roads with high thermal conductivity pavements to relieve urban heat island effect

This study aims to propose a new highly thermally conductive pavement (HTCP) structure and evaluate the cooling effect of the structural layers and near-surface air. The HTCP consisted of coarse quartzite aggregates with high thermal conductivity instead of limestone. The HTCP and conventional asphalt pavement layers were placed in the environmental chamber. The incandescent lamp was used to simulate solar radiation to analyze the air temperature changes of the structural layers and near-surface air. The test results show that quartzite improves the thermal inertia of asphalt pavement layers. And the HTCP effectively transfers the heat accumulated in the upper layer and reduces the daily temperature of structure layers and near-surface air. However, HTCP releases more sensible heat at night, negatively impacting the urban heat island effect. This research contributes to the improvement of HTCP from an aggregate perspective and provides new ideas for mitigating the UHI effect.

Spatiotemporal Reconstruction of Antarctic Near-Surface Air Temperature from MODIS Observations

Abstract MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05° × 0.05°, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09°C, a mean absolute error of 2.23°C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001–18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.

Global-Scale Interpretable Drought Reconstruction Utilizing Anomalies of Atmospheric Dynamics

Abstract Droughts and associated near-surface temperature anomalies can be attributed to amplified vertical subsidence and anomalous anticyclonic circulations from dynamic perspectives. However, two open and interesting issues remain unknown: 1) whether hydrometeorological situations under droughts can be reproduced directly utilizing variability of atmospheric dynamics and 2) what specific roles atmospheric dynamics play in drought reconstruction. To explore these questions, this study employs three kinds of dynamic features (i.e., vertical velocity, relative vorticity, and horizontal divergence) for hydrometeorological reconstruction (e.g., precipitation and near-surface air temperature) under drought situations through a so-called XGBoost (extreme gradient boosting) ensemble learning method. The study adopts two different reconstruction schemes (i.e., statistically preexisting dynamic–hydrometeorological relationships and interannual variability) and finds dynamically based reconstruction feasible. The three main achievements are as follows. 1) Regarding different hydrometeorological situations reconstructed with preexisting dynamic–hydrometeorological relationships, good reconstruction performance can be captured with the same or different lead times, depending on whether the evolution of dynamic anomalies (e.g., vertical motion and relative vorticity) is temporally asynchronous. 2) Reconstruction on the interannual scale performs relatively well, seemingly regardless of seasonality and drought-inducing mechanisms. 3) More importantly, from interpretable perspectives, global-scale analysis of dynamic contributions helps discover unexpected dynamic drought-inducing roles and associated latitudinal modulation. That is, low-level cyclonic/anticyclonic anomalies contribute to drought development in the northern middle and high latitudes, while upper-level vertical subsidence contributes significantly to tropical near-surface temperature anomalies concurrent with droughts. These achievements could provide guidance for dynamically based drought monitoring and prediction in different geographic regions. Significance Statement It is common sense that severe drought events are physically attributable to amplified vertical subsidence and anomalous anticyclonic circulations. However, the specific contributions of atmospheric dynamics, together with the feasibility of dynamically based drought reconstruction, are crucial components that are seldom investigated. To our knowledge, this manuscript is the first to reconstruct drought utilizing atmospheric dynamics and to interpret quantified dynamic contributions; it also represents a new interdisciplinary attempt to reproduce hydrological variability based on routine atmospheric dynamic variables.

Integrating Meteorological Forcing from Ground Observations and MSWX Dataset for Streamflow Prediction under Multiple Parameterization Scenarios

Precipitation and near-surface air temperatures are significant meteorological forcing for streamflow prediction where most basins are partially or fully data-scarce in many parts of the world. This study aims to evaluate the consistency of MSWXv100-based precipitation, temperatures, and estimated potential evapotranspiration (PET) by direct comparison with observed measurements and by utilizing an independent combination of MSWXv100 dataset and observed data for streamflow prediction under four distinct scenarios considering model parameter and output uncertainties. Initially, the model is calibrated/validated entirely based on observed data (Scenario 1), where for the second calibration/validation, the observed precipitation is replaced by MSWXv100 precipitation and the daily observed temperature and PET remained unchanged (Scenario 2). Furthermore, the model calibration/validation is done by considering observed precipitation and MSWXv100-based temperature and PET (Scenario 3), and finally, the model is calibrated/validated entirely based on the MSWXv100 dataset (Scenario 4). The Kling–Gupta Efficiency (KGE) and its components (correlation, ratio of bias, and variability ratio) are utilized for direct comparison, and the Hanssen–Kuiper (HK) skill score is employed to evaluate the detectability strength of MSWXv100 precipitation for different precipitation intensities. Moreover, the hydrologic utility of MSWXv100 dataset under four distinct scenarios is tested by exploiting a conceptual rainfall-runoff model under KGE and Nash–Sutcliffe Efficiency (NSE) metrics. The results indicate that each scenario depicts high streamflow reproducibility where, regardless of other meteorological forcing, utilizing observed precipitation (Scenario 1 and 3) as one of the model inputs, shows better model performance (KGE = 0.85) than MSWXv100-based precipitation, such as Scenario 2 and 4 (KGE = 0.78–0.80).

Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Thermal infrared imagery from NOAA's Geostationary Operational Environmental Satellites (GOES) R-series presents an opportunity to observe mountain surface temperatures at high temporal resolution. These observations are needed to better understand the transient surface energy balance, influenced by near-surface air temperatures and water fluxes ranging from evapotranspiration to snowmelt. From geostationary orbit, the GOES Advanced Baseline Imager (ABI) instrument views the Earth from a fixed longitude above the equator. ABI images therefore have increasingly off-nadir view angles and increasingly larger pixel dimensions the further a point on the Earth's surface is from the sub-satellite point. In thermal infrared imagery from ABI, these 2+ km pixels blur together the different surface temperatures of heterogeneous mountain landscapes. We compared GOES-16 ABI band 14 (11.2 μm) brightness temperatures to 90 m spatial resolution ASTER band 14 (11.3 μm) and 1 km spatial resolution MODIS band 31 (11.03 μm) brightness temperatures and investigated how differences change over space and time for a study region in the central Sierra Nevada of California for the 2017–2020 snow seasons. We demonstrated the necessity of orthorectifying ABI imagery of mountain terrain to correct for the parallax effect in off-nadir imagery. This reduced the mean difference between ABI and ASTER ~11 μm brightness temperatures from 1.6 °C to 1.0 °C and increased the r-squared value between ABI and ASTER thermal infrared brightness temperatures from 0.43 to 0.93. The ABI brightness temperatures were found to more closely match those of forest canopy temperatures than snow surface (with RMS differences of 1.2 °C and 3.0 °C respectively), and those of sunlit slopes as opposed to slopes facing away from the sun (with RMS differences of 1.6 °C and 3.4 °C respectively), in coincident ASTER imagery. This work demonstrates the use of GOES ABI, and associated challenges, for observing surface temperatures of forested mountain environments with seasonal snow.

Cloud forcing of surface energy balance from in situ measurements in diverse mountain glacier environments

Abstract. Clouds are an important component of the climate system, yet our understanding of how they directly and indirectly affect glacier melt in different climates is incomplete. Here we analyse high-quality datasets from 16 mountain glaciers in diverse climates around the globe to better understand how relationships between clouds and near-surface meteorology, radiation and surface energy balance vary. The seasonal cycle of cloud frequency varies markedly between mountain glacier sites. During the main melt season at each site, an increase in cloud cover is associated with increased vapour pressure and relative humidity, but relationships to wind speed are site specific. At colder sites (average near-surface air temperature in the melt season <0 ∘C), air temperature generally increases with increasing cloudiness, while for warmer sites (average near-surface air temperature in the melt season ≫0 ∘C), air temperature decreases with increasing cloudiness. At all sites, surface melt is more frequent in cloudy compared to clear-sky conditions. The proportion of melt from temperature-dependent energy fluxes (incoming longwave radiation, turbulent sensible heat and latent heat) also universally increases in cloudy conditions. However, cloud cover does not affect daily total melt in a universal way, with some sites showing increased melt energy during cloudy conditions and others decreased melt energy. The complex association of clouds with melt energy is not amenable to simple relationships due to many interacting physical processes (direct radiative forcing; surface albedo; and co-variance with temperature, humidity and wind) but is most closely related to the effect of clouds on net radiation. These results motivate the use of physics-based surface energy balance models for representing glacier–climate relationships in regional- and global-scale assessments of glacier response to climate change.

The Consistent Variations of Precipitable Water and Surface Water Vapor Pressure at Interannual and Long-Term Scales: An Examination Using Reanalysis

Water vapor (WV) is a vital basis of water and energy cycles and varies with space and time. When researching the variations of moisture in the atmosphere, it is intuitive to think about the total WV of the atmosphere column, precipitable water (PW). It is an element that needs high-altitude observations. A surface quantity, surface WV pressure (SVP), has a close relationship to PW because of the internal physical linkage between them. The stability of their linkage at climatic scales is verified using monthly mean data from 1979 to 2021, while studies before mainly focused on daily and annual cycles in local areas. The consistency of their variations is checked with three reanalysis datasets from three angles, the interannual variations, the long-term trends, and the empirical orthogonal function (EOF) modes. Results show that the interannual correlation of SVP and PW can reach a level that is quite high and are significant in most areas, and the weak correlation mainly exists over low-latitude oceans. The long-term trends, as well as the first EOF modes of these two quantities, also show that their variations are consistent, with spatial correlation coefficients between the long-term trends of two variables that are generally over 0.6, but specific differences appearing in some regions including the Tropical Indian Ocean and Middle Africa. With the correspondence of PW and SVP, the variations of total column WV can be indicated by surface elements. The correspondence is also meaningful for the analysis of the co-variation in total column vapor and temperature. For example, we could research the relations between SVP and air temperature, and they can reflect the co-variance of total column vapor and near-surface air temperature, which can avoid analyzing the relation between column-integrated moisture content and surface air temperature directly.

A Model between Near-Surface Air Temperature Change and Dynamic Influencing Factors in the Eastern Tibetan Plateau, China

Climate change, characterized by global warming, is profoundly affecting the global environment, politics, economy, and social security. Finding the main causes of climate change and determining their quantitative contributions are key points to making climate decisions on responses to climate change. The Tibetan Plateau (TP) is sensitive to global climate change. Taking the 100 km buffer zones of 45 meteorological stations in the eastern TP as research objects, we conducted an experimental study on temperature change and its influencing factors. Using the least squares multivariate statistical analysis method, a model between the annual and seasonal standardized temperature change and its dynamic influencing factors in the past 20 years was established. The results showed that, in the eastern TP, temperature change was affected by different factors in different periods. Vegetation cover and snow cover were the most correlated factors to temperature change. The influence of carbon dioxide, vegetation cover, and water cover was subject to seasonal changes. Urban cover and bare land cover did not pass the t-test. This research not only provides a theoretical basis for the analysis of temperature change over the TP, but also points out the direction for the analysis of temperature change causes in three polar regions.

Study on air temperature estimation and its influencing factors in a complex mountainous area

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Prediction of near-surface air temperature in glacier regions using ERA5 data and the random forest regression method

Near-surface air temperature is an essential physical parameter for estimating the glacier ablation model that is significant to understanding the dynamics of the changes and constant monitoring of glaciers. There is a significant difference between the temperatures in glacier and non-glacier areas at the same altitude, and the estimation model that uses low-altitude automatic weather station (AWS) data cannot be applied to glaciers far away from the stations. In this study, a scheme for air temperature prediction using the random forest regression (RFR) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data was analyzed. The results show that the average mean absolute error (MAE) and root mean square error (RMSE) of the ERA5 raw temperature product for three glaciers were 4.32 °C and 5.72 °C, respectively. After the RFR model revision, the MAE and RMSE significantly decreased to 1.01 °C and 1.41 °C, respectively. In addition, when the Linzhi weather station was used to calculate the temperature in the Parlung No. 4 Glacier area, the MAE of the Greuell and Böhm (GB) model decreased by 0.18 °C compared to that obtained using the lapse rate method. With our method, long time series of glacier surface air temperature data with high accuracy can be obtained by using short term in-situ temperature measurements.

Location-specific verification of near-surface air temperature from IMDAA regional reanalysis

Location-specific verification of near-surface air temperature from IMDAA regional reanalysis

Remote effect of model systematic bias in tropical SST on the cold bias over the Tibetan Plateau

Most state-of-the-art climate models substantially underestimate the near-surface air temperature (SAT) over the Tibetan Plateau (TP), especially for the cold season. While previous studies have attributed this cold bias to local factors such as the elevation difference, coarse resolution, and excessive snow cover, this investigation addresses the potential contributions of the systematic bias of tropical sea surface temperature (SST) to the TP cold bias. Experiments with the NCAR Community Atmosphere Model demonstrate that tropical SST bias results in an apparent cold bias over the TP, especially in boreal winter, and explains about 40% of the deviation in multi-model mean SAT over the TP relative to station observations. Forced by the tropical SST bias, heat flux exhibits an anomalous divergence over the plateau, causing a cooling center in the mid- and lower-troposphere over the TP. This atmospheric cooling in turn leads to a reduction of the downward longwave radiative fluxes reaching the surface, less energy supply, and thus a cold bias over the TP.

General Overview of Climate Change and Global Warming: Their Effect on Microorganisms

Global warming is the rise in average global temperatures brought on by the greenhouse effect. Sunlight heats the earth's surface, but certain gases in the atmosphere act like the glass of a greenhouse, trapping the heat as it radiates back into space. Greenhouse gases build up in the atmosphere, causing the Earth to warm. This process leads to climate change, which is also known as rapid climate change. Numerous factors that contribute to global warming have negative effects on people, plants, and animals. Both natural and human-caused factors may contribute to these issues. The modern world faces significant issues due to global warming and climate change. In this regard, microbes have a significant impact on the environment as users or producers of greenhouse gases. Due to its capacity to thrive in a variety of climatic situations and to its particular metabolic characteristics, microbial diversity in diverse ecosystems has a significant impact on climate change by reducing its negative effects. There are a variety of potential roles for mitigation that microorganisms and their biological constituents can play by supplying a forward response. They offer a great deal of potential, particularly when used to treat and lower greenhouse gas emissions through nutrient recycling. This review article gives a general overview of the argument over public policy and the science behind global warming. It takes into account the elements contribute to rising near-surface air temperatures, greenhouse gases potential ecological and societal impacts of temperature increases and the influence of climatic and environmental change on microorganisms.

Temperature lapse rate estimation and snowmelt runoff simulation in a high-altitude basin

As a key parameter of hydrological process modeling, the near-surface air temperature lapse rate reflects the vertical changes in air temperature characteristics in alpine basins but often lacks the support of sufficient ground observation data. This study estimated the lapse rate of the Lhasa River Basin (LRB) from the monthly air temperature dataset (2001–2015), which was derived based on good relationships between the observed air temperature at eight gauged stations and the corresponding gridded land surface temperature of MODIS. The estimated annual average air temperature lapse rate was approximately 0.62 °C/100 m. The monthly lapse rate in different years varied seasonally in the range of 0.45–0.8 °C/100 m; the maximum was in May, and the relatively low value occurred from September to January. The snow cover in the zones with relatively low altitudes showed seasonal variation, which was consistent with the air temperature variation. Permanent snow cover appeared in the area above 5000 m and expanded with increasing elevation.

A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018

Abstract. The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis which is very difficult to obtain in areas where there are no meteorological observation stations. This study proposes an estimation framework for obtaining high-precision Tmax. Firstly, we build a near-surface air temperature diurnal variation model to estimate Tmax with a spatial resolution of 0.1∘ for China from 1979 to 2018 based on multi-source data. Then, in order to further improve the estimation accuracy, we divided China into six regions according to climate conditions and topography and established calibration models for different regions. The analysis shows that the mean absolute error (MAE) of the dataset (https://doi.org/10.5281/zenodo.6322881, Wang et al., 2021) after correction with the calibration models is about 1.07 ∘C and the root mean square error (RMSE) is about 1.52 ∘C, which is higher than that before correction to nearly 1 ∘C. The spatial–temporal variations analysis of Tmax in China indicated that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. In summer and autumn, the Tmax in northeast China increased the fastest among the six regions, which was 0.4∘C per 10 years and 0.39∘C per 10 years, respectively. The number of summer days and warm days showed an increasing trend in all regions while the number of icing days and cold days showed a decreasing trend. The abnormal temperature changes mainly occurred in El Niño years or La Niña years. We found that the influence of the Indian Ocean basin warming (IOBW) on air temperature in China was generally greater than those of the North Atlantic Oscillation and the NINO3.4 area sea surface temperature after making analysis of ocean climate modal indices with air temperature. In general, this Tmax dataset and analysis are of great significance to the study of climate change in China, especially for environmental protection.

Nearsurface Air Layer Pollution with Micronic Dust Particles in Large-Scale Blasting in Open Pit Mining

Nearsurface Air Layer Pollution with Micronic Dust Particles in Large-Scale Blasting in Open Pit Mining