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Carbon monoxide (CO) is primarily the result of incomplete combustion, which has important impacts on the atmospheric chemical cycle and climate. Improved quantitative characterization of long-term CO trends is important for both atmospheric modeling and the design and implementation of policies to efficiently control multiple pollutants. Due to the limitations of high time-resolution and high-quality long-term observational data, studies on long-term trends in the CO concentration in China are quite limited. In this study, the observational data of the concentration of CO in a rural site of Beijing during 2006–2018 was used to analyze the long-term trend in CO concentration in Beijing. The Theil-Sen method and the generalized additive model (GAM)-based method, were used to conduct the trend estimation analysis. We found that the concentration of CO at the Shangdianzi site showed a significant downward trend during 2006–2018, with a decline rate of 22.8 ± 5.1 ppbV per year. The declining trend in CO also showed phasic characteristics, with a fast decreasing rate during the period of 2006–2008, stable variations during the period of 2009–2013 and a continuous downward trend after 2013. The declining trend in the CO concentration in the south to west (S-W) sectors where the polluted air masses come from is more rapid than that in the sectors where the clean air masses come from. The declining trend in the CO concentration implies the improved combustion efficiency and the successful air pollution control policies in Beijing and the surrounding area.

Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of 6-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+, and NH4+ wet deposition) in situ observations. Over East Asia, Europe and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200 %–300 % higher than in the inventories, and the available in situ observations from western and central Africa, as well as NH3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories cannot capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the US, resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the US may be related to unresolved temporal variability in the NH3 emissions.

Detailed meteorological analyses based on observations extending through the middle atmosphere (∼ 15 to 100 km altitude) can provide key information to whole atmosphere modeling systems regarding the physical mechanisms linking day-to-day changes in ionospheric electron density to meteorological variability near the Earth's surface. However, the extent to which independent middle atmosphere analyses differ in their representation of wave-induced coupling to the ionosphere is unclear. To begin to address this issue, we present the first intercomparison among four such analyses, JAGUAR-DAS, MERRA-2, NAVGEM-HA, and WACCMX+DART, focusing on the Northern Hemisphere (NH) 2009–2010 winter, which includes a major sudden stratospheric warming (SSW). This intercomparison examines the altitude, latitude, and time dependences of zonal mean zonal winds and temperatures among these four analyses over the 1 December 2009 to 31 March 2010 period, as well as latitude and altitude dependences of monthly mean amplitudes of the diurnal and semidiurnal migrating solar tides, the eastward-propagating diurnal zonal wave number 3 nonmigrating tide, and traveling planetary waves associated with the quasi-5 d and quasi-2 d Rossby modes. Our results show generally good agreement among the four analyses up to the stratopause (∼ 50 km altitude). Large discrepancies begin to emerge in the mesosphere and lower thermosphere owing to (1) differences in the types of satellite data assimilated by each system and (2) differences in the details of the global atmospheric models used by each analysis system. The results of this intercomparison provide initial estimates of uncertainty in analyses commonly used to constrain middle atmospheric meteorological variability in whole atmosphere model simulations.

The past four decades have seen an increase of terrestrial hot extremes during summer in the northern extratropics, accompanied by the Northern Hemisphere (NH) sea surface temperature (SST) warming (mainly over 10°–70°N, 0°–360°) and CO2 concentration rising. This study aims to understand possible causes for the increasing hot extremes, which are defined on a daily basis. We conduct a series of numerical experiments using the Community Atmosphere Model version 5 model for two periods, 1979–1995 and 2002–2018. The experiment by changing the CO2 concentration only with the climatological SST shows less increase of hot extremes days than that observed, whereas that by changing the NH SST (over 10°–70°N, 0°–360°) with constant CO2 concentration strengthens the hot extremes change over mid-latitudes. The experiment with both SST and CO2 concentration changes shows hot extremes change closer to the observation compared to the single-change experiments, as well as more similar simulations of atmospheric circulations and feedbacks from cloud and radiative processes. Also discussed are roles of natural variability (e.g., Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation) and other factors (e.g., Arctic sea ice and tropical SST).

Purpose. The aim of the work is to assess the role of the basic forces in formation of the dynamic structures of the upper layer in the Marmara Sea in different hydrological seasons. Methods and Results. The numerical model developed in Marine Hydrophysical Institute, Russian Academy of Sciences, was used to calculate the hydrodynamic and energy characteristics of the sea circulation. The horizontal spatial resolution was 1.22 × 0.83 km, 18 horizons were used vertically, and the time step was 0.5 min. The characteristics of the waters inflowing through the Bosporus and Dardanelles straits corresponded to the available observational data. On the sea surface, the daily average fields of tangential wind stress, heat fluxes, precipitation and evaporation for 2008 were preset; these parameters were calculated using the regional atmospheric model MM5. Spatial distributions of the vortex structures and the corresponding energy flows in the upper sea layer for different seasons were considered. Having been analyzed, the average seasonal spatial distributions of the current velocity fields and the components of the kinetic energy budget showed that during the year, the vortices in the upper sea layer were formed mainly by two mechanisms. In the central part of the sea, generation of a large-scale anticyclone and its seasonal variability are basically conditioned by the wind forcing, whereas formation and evolution of coastal cyclonic eddies are caused by the buoyancy force. In the fields where the buoyancy, pressure and friction forcing takes place, the zones of local extremes are distinguished. Two of them are the areas of water inflow through the Bosphorus and Dardanelles straits. The strongest variability is observed in the Bosporus region that is certainly conditioned by the fact that the inflowing Black Sea waters have a decisive influence on seasonal nature of the Marmara Sea circulation. The extreme values in the other zones are the result of the coastline structure heterogeneities, that leads to formation of the coastal eddies, the energy source for which is the available potential energy. Conclusions. Analysis of the results of the performed numerical experiment makes it possible to conclude that in the upper layer of the Marmara Sea, formation and variability of the central anticyclone are conditioned by the wind forcing, while those of the coastal cyclones – by the buoyancy effect.

A low-level-jet (LLJ) event that occurred over a coastal area in complex terrain is analyzed to investigate its effect on the dispersion of potential air pollutants released in the area. The atmospheric model RAMS (Regional Atmospheric Modeling System) is employed with a high vertical resolution close to the surface, adopting a rarely used nesting approach, in order to allow a detailed analysis of the flow and to characterize the specific features of the LLJ. After a comparison with meteorological variables measured by radiosondes, numerical experiments are performed adding a scalar tracer in the simulation. As a first test, the tracer is distributed uniformly throughout the domain to follow the dynamics of the LLJ and its effect on the tracer dispersion. Then, continuous releases from virtual point sources are simulated to address their possible impact in the area under LLJ conditions. This allows the identification of “hotspots” of pollutant accumulation due to very local circulations and convective cells that develop from the combined effect of terrain-induced flow and the interaction of the LLJ flow with complex topography. An original mass analysis is applied on the dispersion results for an advanced exploration of the LLJ impact on the tracer. The RAMS model provides reliable results demonstrating that with which atmospheric numerical models are useful tools with which to study LLJ dynamics and their effect on local circulation and pollutant dispersion.

Ethane ( C2H6 ) is an important greenhouse gas and plays a significant role in tropospheric chemistry and climate change. This study first presents and then quantifies the variability, sources, and transport of C2H6 over densely populated and highly industrialized eastern China using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing along with atmospheric modeling techniques. We obtained a retrieval error of 6.21 ± 1.2 (1 σ )% and degrees of freedom (DOFS) of 1.47 ± 0.2 (1 σ ) in the retrieval of C2H6 tropospheric column-averaged dry-air mole fraction (troDMF) over Hefei, eastern China (32 ∘ N, 117 ∘ E; 30 m a . s . l . ). The observed C2H6 troDMF reached a minimum monthly mean value of 0.36 ± 0.26 ppbv in July and a maximum monthly mean value of 1.76 ± 0.35 ppbv in December, and showed a negative change rate of − 2.60 ± 1.34 % yr−1 from 2015 to 2020. The dependencies of C2H6 troDMF on meteorological and emission factors were analyzed using generalized additive models (GAMs). Generally, both meteorological and emission factors have positive influences on C2H6 troDMF in the cold season (December–January–February/March–April–May, DJF/MAM) and negative influences on C2H6 troDMF in the warm season (June–July–August/September–October–November, JJA/SON). GEOS-Chem chemical model simulation captured the observed C2H6 troDMF variability and was, thus, used for source attribution. GEOS-Chem model sensitivity simulations concluded that the anthropogenic emissions (fossil fuel plus biofuel emissions) and the natural emissions (biomass burning plus biogenic emissions) accounted for 48.1 % and 39.7 % of C2H6 troDMF variability over Hefei, respectively. The observed C2H6 troDMF variability mainly results from the emissions within China (74.1 %), where central, eastern, and northern China dominated the contribution (57.6 %). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the midlatitude westerlies and the Asian monsoon system. Reduction in C2H6 abundance from 2015 to 2020 mainly results from the decrease in local and transported C2H6 emissions, which points to air quality improvement in China in recent years.

This work analyzes and discusses the influence of human activities on the meteorological conditions related to winter haze events in Beijing, Tianjin, and Hebei (i.e., the Jing–Jin–Ji region) during 1961–2016, using the results of two numerical simulation experiments based on the Community Atmosphere Model version 5.1.1 (http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1_1/book1.html) used in the international Climate Variability and Predictability Programme (CLIVAR) Climate of the 20th Century. Detection and Attribution Project (C20C+ D&A). The results show that, under the influence of human activities, the changes in dynamical and thermal meteorological conditions related to winter haze events in the Jing–Jin–Ji region are conducive to the formation and accumulation of haze, and prevent the diffusion of pollutants. The dynamical conditions mainly include the obvious weakening of the East Asian winter monsoon (EAWM) and the enhancement of the near-surface anomalous southerly wind. The thermal conditions include the obvious increase in surface temperature, and the enhancement of water vapor transport and near-surface inversion. The relative contribution of dynamical and thermal conditions to the variation of haze days in the Jing–Jin–Ji region is analyzed using statistical methods. The results show that the contribution of human activities to the increase of haze days in the Jing–Jin–Ji region is greater than that of natural forcing for the study period. To be specific, the dynamical meteorological factors contribute more to the haze days than the thermal meteorological factors. The contribution of thermal meteorological factors is basically the same in both scenarios.

Dust is the cause of generation reduction of solar photovoltaic (SPV) modules from the day it is installed. Its deposition depends on geographical location of a module where it is installed, due to difference in meteorological events, causing variation in generation efficiency. In an earlier article, the dry deposition processes are modeled in which effects of relative humidity (RH) and precipitation are not considered. In this article, a more generalized mathematical model for dust deposition under varying humidity and precipitation condition is developed. The increase in deposition with different RH has been investigated using the developed model demonstrating an appreciable effect. The contribution of different types of precipitation is also included. The model based observations are utilized to calculate the energy generation under different meteorological events over a specific period and compared with the measured ones over the same time span from a 10 kWp SPV power plant at IIEST, Shibpur. The deviations are found to be within ±6% establishing the efficacy of the model.

Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH 4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO 2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg −1 dry biomass burned. The CO / CO 2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH 4 / CO 2 emission ratio was 0.010 ± 0.004, and the CH 4 emission factor was 5.3 ± 1.8 g kg −1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH 4 emission ratios varied in synchrony, with higher CH 4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

In cold region, conceptual models assigned precipitation phase, liquid (rain) or solid (snow), cause vastly different atmospheric, hydrological, and ecological responses, along with significant differences in evaporation, runoff, and infiltration fates for measured precipitation mass. A set air temperature threshold (ATT) applied to the over 30% annual precipitation events occurring with surface air temperatures between −3 and 5 °C resulted in 11.0 and 9.8% misclassified precipitation in Norway and Sweden, respectively. Surface air temperatures do not account for atmospheric properties causing precipitation phase changes as snow falls toward the ground. However, cloud base height and relative humidity (RH) measured from the surface can adjust ATT for expected hydrometeor-atmosphere interactions. Applying calibrated cloud base height ATTs or a linear RH function for Norway (Sweden) reduced misclassified precipitation by 4.3% (2.8%) and 14.6% (8.9%) misclassified precipitation, respectively. Cloud base height ATTs had lower miss-rates with low cloud bases, 100 m in Norway and 300 m in Sweden. Combining the RH method with cloud base ATT in low cloud conditions resulted in 16.1 and 10.8% reduction in misclassified precipitation in Norway and Sweden, respectively. Therefore, the conceptual model output should improve through the addition of available surface data without coupling to an atmospheric model. HIGHLIGHTS This paper lays out two new methods to decrease misclassified precipitation in conceptual surface-based models using air temperature thresholds (ATTs) to distinguish between rain and snow, common in hydrological modeling.; The cloud base height method has not been written about in scientific publications and gives another simple solution to help reduce misclassified precipitation phase in surface-based models.; The linear relative humidity ATT formula suggested in this paper simplifies earlier attempts to include relative humidity in surface-based precipitation phase determination.; The information needed for both methods is widely available in surface meteorological reports and improves surface-based models without coupling to an atmospheric model for precipitation phase.; In this paper, misclassified precipitation between −3 and 5 °C was reduced by 4.3% (2.8%) using cloud base height ATTs, 14.6% (8.9%) using a linear relative humidity formula, and 16.1% (10.8%) using a combination of both methods in Norway (Sweden), showing great potential for reducing model uncertainties when applying this work.

The mesosphere and lower thermosphere (MLT) is a critical region that must be accurately reproduced in general circulation models (GCMs) that aim to include the coupling between the lower and middle atmosphere and the thermosphere. An accurate representation of the MLT is thus important for improved climate modelling and the development of a whole atmosphere model. This is because the atmospheric waves at these heights are particularly large, and so the energy and momentum they carry is an important driver of climatological phenomena through the whole atmosphere, affecting terrestrial and space weather. The Extended Unified Model (ExUM) is the recently developed version of the Met Office's Unified Model which has been extended to model the MLT. The capability of the ExUM to model atmospheric winds and tides in the MLT is currently unknown. Here, we present the first study of winds and tides from the ExUM. We make a comparison against meteor radar observations of winds and tides from 2006 between 80 and 100 km over two radar stations – Rothera (68∘ S, 68∘ W) and Ascension Island (8∘ S, 14∘ W). These locations are chosen to study tides in two very different tidal regimes – the equatorial regime, where the diurnal (24 h) tide dominates, and the polar regime, where the semi-diurnal (12 h) tide dominates. The results of this study illustrate that the ExUM is capable of reproducing atmospheric winds and tides that capture many of the key characteristics seen in meteor radar observations, such as zonal and meridional wind maxima and minima, the increase in tidal amplitude with increasing height, and the decrease in tidal phase with increasing height. In particular, in the equatorial regime some essential characteristics of the background winds, tidal amplitudes and tidal phases are well captured but with significant differences in detail. In the polar regime, the difference is more pronounced. The ExUM zonal background winds in austral winter are primarily westward rather than eastward, and in austral summer they are larger than observed above 90 km. The ExUM tidal amplitudes here are in general consistent with observed values, but they are also larger than observed values above 90 km in austral summer. The tidal phases are generally well replicated in this regime. We propose that the bias in background winds in the polar regime is a consequence of the lack of in situ gravity wave generation to generate eastward fluxes in the MLT. The results of this study indicate that the ExUM has a good natural capability for modelling atmospheric winds and tides in the MLT but that there is room for improvement in the model physics in this region. This highlights the need for modifications to the physical parameterization schemes used in the model in this region – such as the non-orographic spectral gravity wave scheme – to improve aspects such as polar circulation. To this end, we make specific recommendations of changes that can be implemented to improve the accuracy of the ExUM in the MLT.

Methane is one of the main greenhouse trace gases and seriously affects the radiation balance of Earth systems due to its strong heat absorption capacity and long atmospheric retention time. Based on the methane stratification data simulated by the community atmospheric model with chemistry (CAM-chem), near-surface methane concentrations were estimated by utilizing the Gaussian function, and the spatiotemporal variation in the near-surface methane concentration in China from 2001 to 2019 was discussed in this research. The results show that (1) based on the methane stratification concentration data simulated by the atmospheric chemical model, the near-surface CH4 concentration estimated by Gaussian function model is reliable, which provides a new method to estimate the near-surface CH4 concentration over China; (2) from 2001 to 2019, the near-surface methane concentration in China showed an increasing trend with an annual growth rate of 7.20±0.23 ppb·a-1. The annual maximum near-surface methane concentration was measured in winter, and the minimum was measured in summer; (3) the spatial distribution differences are obvious: the methane concentration in the east was higher than that in the west, and the methane concentration in the north was higher than that in the south. Moreover, the distributions of methane in the east and west are consistent with the division of Hu Huanyong population line.

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

In this study, we designed a sensitivity test using the half number concentration of sulfate in the nucleation calculation process to study the aerosol-cloud interaction (ACI) of sulfate on clouds, precipitation, and monsoon intensity in the summer over the eastern China monsoon region (ECMR) with the National Center for Atmospheric Research Community Atmosphere Model version 5. Numerical experiments show that the ACI of sulfate led to an approximately 30% and 34% increase in the cloud condensation nuclei and cloud droplet number concentrations, respectively. Cloud droplet effective radius below 850 hPa decreased by approximately 4% in the southern ECMR, while the total liquid water path increased by 11%. The change in the indirect radiative forcing due to sulfate at the top of the atmosphere in the ECMR during summer was − 3.74 W·m−2. The decreased radiative forcing caused a surface cooling of 0.32 K and atmospheric cooling of approximately 0.3 K, as well as a 0.17 hPa increase in sea level pressure. These changes decreased the thermal difference between the land and sea and the gradient of the sea-land pressure, leading to a weakening in the East Asian summer monsoon (EASM) and a decrease in the total precipitation rate in the southern ECMR. The cloud lifetime effect has a relatively weaker contribution to summer precipitation, which is dominated by convection. The results show that the ACI of sulfate was one possible reason for the weakening of the EASM in the late 1970s.

Estimates of the contribution of direct radiative forcing (DRF) to the surface air temperature trends over the Northern Hemisphere (NH) continents for the period 1979–2012 have been obtained based on an ensemble of numerical experiments with an atmospheric general circulation model using the prescribed historical sea surface temperature and sea ice concentration data with and without time-varying concentrations of greenhouse gases and aerosols in the atmosphere. The contribution of DRF to the simulated ensemble mean temperature variations over land in the NH in winter and summer is approximately 30% or 0.3°С and 0.2°С, respectively. The maximum warming associated with DRF was found, in particular, in winter and summer over the eastern part of the European territory of Russia. The results indicate that the response of atmosphere circulation to DRF may lead to a nonlinear DRF contribution to the temperature changes in the NH, in particular mitigating the temperature increase in the winter season.

Sea salt aerosol (SSA) significantly affects the atmospheric albedo, the radiation balance of the Earth, and has consequences for global and regional climate. There is currently no reliable radiative large-scale aerosol model of the marine and coastal atmosphere. All these lead to uncertainties in radiation boxes, short-term weather forecasts, and climate models and to uncertainties in interpretation of the lidar return data obtained and at the development of new lidar systems. The MaexPro model is shown to calculate adequately the aerosol extinction coefficients $\varepsilon (\lambda)$ and the concentration of SSA particles for different wind regimes, fetches, and humidity. The calculated $\varepsilon (\lambda)$ profiles well agree with the experimental data and with calculations using Advanced Navy Aerosol Model (ANAM) and MODTRAN codes. A new approach in studying the influence of the meteorological parameters on spectral $\varepsilon (\lambda)$ gives a new explanation for previously obtained conflicting experimental data, namely, to the effect of decreasing transparency, manifested in the coastal zone, and makes it possible indirectly to account for the type of air mass (marine or coastal). It is shown that the particle size distribution functions and spectral $\varepsilon (\lambda)$ , calculated using the model with the input parameters corresponding most closely to the conditions of experiment, agree well with observations, using OLAF transmissometer, and calculations using MODTRAN code and the experimental results receiving the same conditions of observations (numerical or field). The results obtained are of interest both in the calculations of radiative characteristics of the atmosphere and in interpretation of the lidar return data and calibration signal when developing new lidar systems.

A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.

The Sun was dimmer earlier in Earth’s history, but glaciation was rare in the Precambrian: this is the ‘faint young Sun problem’. Most solutions rely on changes to the chemical composition of the atmosphere to compensate via a stronger greenhouse effect, whereas physical feedbacks have received less attention. We perform global climate model experiments, using two versions of the Community Atmosphere Model, in which a reduced solar constant is offset by higher CO2. Model runs corresponding to past climate show a substantial decrease in low clouds and hence planetary albedo compared with present, which contributes 40% of the required forcing to offset the faint Sun. Through time, the climatically important stratocumulus decks have grown in response to a brightening Sun and decreasing greenhouse effect, driven by stronger cloud-top radiative cooling (which drives low cloud formation) and a stronger inversion (which sustains clouds against dry air entrainment from above). We find that systematic changes to low clouds have had a major role in stabilizing climate through Earth’s history, which demonstrates the importance of physical feedbacks on long-term climate stabilization, and a smaller role for geochemical feedbacks. Reduced planetary albedo due to fewer low clouds on early Earth could explain some 40% of the required forcing to offset the faint young Sun, according to global climate model experiments.

Cirrus cloud radiative effects are largely affected by ice microphysical properties, including ice water content (IWC), ice crystal number concentration (Ni) and mean diameter (Di). These characteristics vary significantly due to thermodynamic, dynamical and aerosol conditions. In this work, a global-scale observation dataset is used to examine regional variations of cirrus cloud microphysical properties, as well as several key controlling factors, i.e., temperature, relative humidity with respect to ice (RHi), vertical velocity (w) and aerosol number concentrations (Na). Results are compared with simulations from the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 6 (CAM6). Observed and simulated ice mass and number concentrations are constrained to ≥62.5 µm to reduce potential uncertainty from shattered ice in data collection. The differences between simulations and observations are found to vary with latitude and temperature. Comparing with averaged observations at ∼100 km horizontal scale, simulations are found to underestimate (overestimate) IWC by a factor of 3–10 in the Northern (Southern) Hemisphere. Simulated Ni is overestimated in most regions except the Northern Hemisphere midlatitudes. Simulated Di is underestimated by a factor of 2, especially for warmer conditions (−50 to −40 ∘C), possibly due to misrepresentation of ice particle growth/sedimentation. For RHi effects, the frequency and magnitude of ice supersaturation are underestimated in simulations for clear-sky conditions. The simulated IWC and Ni show bimodal distributions with maximum values at 100 % and 80 % RHi, differing from the unimodal distributions that peak at 100 % in the observations. For w effects, both observations and simulations show variances of w (σw) decreasing from the tropics to polar regions, but simulations show much higher σw for the in-cloud condition than the clear-sky condition. Compared with observations, simulations show weaker aerosol indirect effects with a smaller increase of IWC and Di at higher Na. These findings provide an observation-based guideline for improving simulated ice microphysical properties and their relationships with key controlling factors at various geographical locations.

This paper discusses the interannual variation of winter cold surges (CSs) over East Asia (EA) from the perspective of the cold air path and its relationship with autumn Barents Sea ice concentration (BSIC). Flexible particle dispersion model for large-scale atmospheric transport process simulation is used to track the cold air path of 301 CSs in EA in winters from 1979 to 2017, and the probability density distribution of cold air path is calculated by kernel density estimation. The cold air mainly originates from the Ural Mountains, and is continuously cooled by the impact of cold land, then accumulates and strengthens near the Lake Baikal, and finally invades EA under the guidance of the northwest airflow. The autumn BSIC has a significant negative correlation with CSs over EA. The decreased autumn BSIC hinders the spread of zonal warm advection over Eurasia and makes the soil temperature near Lake Baikal drop, and the negative anomaly of soil temperature persists from autumn to winter, strengthening cold source therein, which provides suitable thermal conditions for occurring more CSs over EA. Moreover, the decreased autumn BSIC can also persist to winter, which leads to the positive anomaly of the geopotential height over the polar region. Through the propagation of Rossby wave, the negative geopotential height anomaly over EA is excited and provides the appropriate dynamic conditions for forming more CSs in EA. The simulations from the Community Atmosphere Model version 5.3 (CAM5.3) further confirm the proposed possible mechanism.

The Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model coupled to Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. The Community Atmosphere Model with Chemistry (CAM-chem) and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points to fill in missing fire emissions in the Fire INventory from NCAR (FINN) and scaled by a factor of 7 for an 8 d period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on 7, 8, and 10 November. Modeled aerosol composition in Delhi showed secondary inorganic aerosols (SIAs) and secondary organic aerosols (SOAs) comprised 30 % and 27 % of total PM2.5 concentration, respectively, during November, with a modeled OC/BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high or the model did not mix the smoke down fast enough. Results also showed long-range-transported dust did not affect Delhi's air quality during the episode. Spatial plots showed averaged aerosol optical depth (AOD) of 0.58 (±0.4) over November. The model AODs were biased high over central India and low over the eastern IGP, indicating improving emissions in the eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matter.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based ( T- based) and radiance-based ( R -based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T- based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R -based validation from 2012 to 2018. The T- based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T- based sites during the daytime, with a mean bias of −2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of −0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T- based sites, with a mean bias of −1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R -based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T- based and three of the R -based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

Long-range transport of biomass burning (BB) aerosol from regions affected by wildfires is known to have a significant impact on the radiative balance and air quality in receptor regions. However, the changes that occur in the optical properties of BB aerosol during long-range transport events are insufficiently understood, limiting the adequacy of representations of the aerosol processes in chemistry transport and climate models. Here we introduce a framework to infer and interpret changes in the optical properties of BB aerosol from satellite observations of multiple BB plumes. Our framework includes (1) a procedure for analysis of available satellite retrievals of the absorption and extinction aerosol optical depths (AAOD and AOD) and single-scattering albedo (SSA) as a function of the BB aerosol photochemical age and (2) a representation of the AAOD and AOD evolution with a chemistry transport model (CTM) involving a simplified volatility basis set (VBS) scheme with a few adjustable parameters. We apply this framework to analyze a large-scale outflow of BB smoke plumes from Siberia toward Europe that occurred in July 2016. We use AAOD and SSA data derived from OMI (Ozone Monitoring Instrument) satellite measurements in the near-UV range along with 550 nm AOD and carbon monoxide (CO) columns retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) and IASI (Infrared Atmospheric Sounding Interferometer) satellite observations, respectively, to infer changes in the optical properties of Siberian BB aerosol due to its atmospheric aging and to get insights into the processes underlying these changes. Using the satellite data in combination with simulated data from the CHIMERE CTM, we evaluate the enhancement ratios (EnRs) that allow isolating AAOD and AOD changes due to oxidation and gas–particle partitioning processes from those due to other processes, including transport, deposition, and wet scavenging. The behavior of EnRs for AAOD and AOD is then characterized using nonlinear trend analysis. It is found that the EnR for AOD strongly increases (by about a factor of 2) during the first 20–30 h of the analyzed evolution period, whereas the EnR for AAOD does not exhibit a statistically significant increase during this period. The increase in AOD is accompanied by a statistically significant enhancement of SSA. Further BB aerosol aging (up to several days) is associated with a strong decrease in EnRs for both AAOD and AOD. Our VBS simulations constrained by the observations are found to be more consistent with satellite observations of strongly aged BB plumes than “tracer” simulations in which atmospheric transformations of BB organic aerosol were disregarded. The simulation results indicate that the upward trends in EnR for AOD and in SSA are mainly due to atmospheric processing of secondary organic aerosol (SOA), leading to an increase in the mass scattering efficiency of BB aerosol. Evaporation and chemical fragmentation of the SOA species, part of which is assumed to be absorptive (to contain brown carbon), are identified as likely reasons for the subsequent decrease in the EnR for both AAOD and AOD. Hence, our analysis reveals that the long-range transport of smoke plumes from Siberian fires is associated with major changes in BB aerosol optical properties and chemical composition. Overall, this study demonstrates the feasibility of using available satellite observations for evaluating and improving representations in atmospheric models of the BB aerosol aging processes in different regions of the world at much larger temporal scales than those typically addressed in aerosol chamber experiments.

Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m−2, and regional changes of −2 W m−2 (Africa) and −0.5 W m−2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state., Wildfires produce aerosols known to impact the climate, but the wider-reaching effects of this biomass burning are poorly constrained in models. Here the authors use a suite of observations from 12 campaigns around the globe to determine that the values used by most climate models overestimate the contribution of biomass burning aerosols.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998–2018, we reconstructed the Southern Ocean's pCO2 grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 μatm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO2 mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010–2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50–70°S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35–50°S) continues to serve as a carbon sink.

For a pulsed laser transmitted in the raised dust from battlefield vehicle movement, the premise of obtaining its transmission properties and anti-jamming method is to acquire the spatial distribution rules of dust mass concentration. In this study, the emission mechanism for vehicle-raised dust in the battlefield environment is analyzed, as well as various influencing factors of the dust emission velocity. A finite volume method (FVM)-based model for simulating the spatial dispersion of dust raised by a certain moving tank is built, and the effects of mass flow rate, traveling speed, wind speed and wind direction on the spatial distribution of dust mass concentration are explored in the battlefield environment. The results show that the raised dust is plume-shaped within the computational domain, and the spatial mass concentration of plume is positively correlated with the mass flow rate. Besides, it is necessary to determine the impact of tank travelling speed on the spatial mass concentration of dust based on the relationship between the travelling speed and the dust mass flow rate. Although front wind reduces the spatial mass concentration of dust plumes, the reduction rate decelerates when the wind speed exceeds 4 m/s. Crosswind leads to the deflection and decreased mass concentration of dust plumes. Under varying wind directions, the variation of crosswind component exerts a more pronounced influence on the spatial mass concentration of dust than that of the front wind component. This study provides a technical support for the further study of near-ground laser weapon system against vehicle dust interference in the battle field.

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration ( R s) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R 2 of 0.72 and RMSE of 0.96 g·C·m−2·day−1. The performance was also good when predicting across-site ( R 2 = 0.75) and seasonal patterns ( R 2 = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and R s. This study indicates the importance of considering the influence of water-related variables and R s in the estimation of NEE at the global scale.

Due to the complexity and heterogeneity mechanism of aerosol scattering and absorption, there is no systematic operational net surface shortwave radiation (NSSR) retrieval algorithm for considering the forces of different aerosols. This study is to estimate NSSR under dust aerosol conditions. A parameterized model is proposed to quantify the net shortwave radiative forcing (ASRF) of aerosol. Considering the particularity of dust aerosol on shortwave radiation and its mechanisms, aerosol optical properties and atmospheric water vapor content (WVC) are used to quantify the ASRF, and the previous Tang’s parameterization model for estimating NSSR is refined to estimate NSSR under dust aerosol conditions. On this basis, a more accurate radiative transfer model MODTRAN 5 is primitively utilized to simulate and fit the complex relationship. Moreover, the coefficients for parameterization are recalculated by an improved algorithm under diverse types of aerosols. Finally, the NSSR measurements in the study areas and the NSSR products from the MODIS satellites are utilized to verify the retrieved NSSR. The root-mean-square error (RMSE) is below 11 W/m2 under various land cover types and the research findings can significantly increase the estimation accuracy of NSSR around 40 W/m2 under dust aerosol conditions.

The study has been attempted to investigate the relationship between the soil-water and the Indian summer monsoon (ISM) rainfall through the simulation of a global climate model named Community Atmosphere Model (CAM3). Two sets of simulation have been done during monsoon season for the years 2009 to 2012 using the pre-monsoon (May) and the previous winter season (December of previous year) state of soil-water as the model initial conditions. The control simulation and four sensitivity cases assuming 25% and 50% dryer and wetter soil-water respectively have been considered for all the aforesaid four years and for both the set of experiments. It has been observed that the impact of upper level soil-water persist for 15 to 20 days of simulation during the summer monsoon; the middle and lower layer soil state persist for a longer period of time due to its slow-varying nature with time. The daily surface temperature shows strong coupling with the upper layer of soil-water. When taken into comparison with the wet soil conditions, the dry soil state in most of the circumstances causes less rainfall. The Pearson correlation coefficient (PCC) and partial correlation technique have been implied to demonstrate the relationship between the daily soil-water columns, subsequent 30-days accumulated rainfall and past 21-days accumulated rainfall. Strong negative correlation has been reported between the soil-water and subsequent 30-days accumulated (All-India Rainfall) AIR for different simulation cases with dry soil conditions; however, the relation weakened and turned positive over some parts of the region for the simulations with wet soil conditions.

It is an essential component of the operational weather forecast agencies to capture the diurnal behavior of the rainfall on the spatiotemporal scale by numerical models for its accurate prediction. In this study, the mean diurnal cycle of precipitation is investigated with (i) atmospheric model-based predictions, (ii) space-based estimates, and (iii) ground-based rainfall observations during the active phase of Indian monsoon. Further, high-resolution precipitation data are used to identify the characteristics features of the model forecasts. The variability of mean diurnal variations in rain rate and frequency of rain is analyzed to determine the characteristics feature of precipitation. It is found that precipitation activity over Western Ghats, Bay of Bengal regions, is maximum during late afternoon. The time of peak rain rate is advanced during day time. The active phase of monsoon has the highest amplitude of diurnal variation in precipitation. The variability in the specific humidity is increased during this active period. The numbers of rainy hours are highly varying in nature for the space-based rainfall estimation than the model.

Each year, considerable levels of dust particles coming from arid regions of the earth contribute to the atmosphere. Because dust has serious environmental and human health effects, predictions of dust concentrations and their contribution to PM surface concentrations are essential for atmospheric research and the implementation of air quality programs and rules. This study aims to assess dust contributions to PM2.5 in Tehran in order to provide guidance for air quality management systems as well as validate the updated Dust Regional Atmospheric Model version 2 (BSC-DREAM8b) and the NMMB/BSC-Dust model using chemical speciation of ground-based measurements of PM2.5. Accurate and reliable measurements are necessary to determine the sources of pollutants as well as to confirm, validate, and improve models. For this purpose, dust concentration and contribution were calculated from chemical speciation of PM2.5 samples that were obtained on a 6 day basis for a whole year from February 2014 to February 2015. The comparison of the dust observations to the model products revealed that model outputs were in good agreement for the monthly averaged values. However, for the daily averaged values, models and ground observation showed a considerable difference. In order to understand possible reasons for the discrepancy, differences between neighboring grid cells were investigated, as well as the effect of sampling frequency. It is found that agreement between models and observations was sensitive to sampling duration and frequency but not to spatial variability between grid cells. This study is the first validation of model outputs with the calculated dust concentration from ground-based chemical speciation of PM2.5 rather than using total PM or aerosol optical depth measurements as a proxy for dust concentrations.

The response of the Black Sea upper layer to the quasitropical cyclone on September 25–29, 2005 is studied using the coupled model consisting of the WRF atmosphere model, NEMO ocean model, and OASIS coupler. The circulation arising in the sea under the influence of the quasitropical cyclone is considered. It is demonstrated how sea surface temperature varied under the cyclone during its evolution and movement. The possible mechanisms of the significant (by >10°С) temperature drop under the cyclone are analyzed. It is shown that the main reason for the sea surface cooling is upwelling, i.e., the cold water lifting from the thermocline to the sea surface, and heat exchange with the atmosphere has an insignificant effect on the temperature variation. It is also demonstrated that the value of the sea surface temperature anomaly simulated by the coupled modeling is consistent with observational data much better than the results of atmospheric reanalysis and marine reprocessing.

Large volcanic eruptions reaching the stratosphere have caused marked perturbations to the global climate including cooling at the Earth's surface, changes in large-scale circulation and precipitation patterns and marked temporary reductions in global ocean heat content. Many studies have investigated these effects using climate models; however, uncertainties remain in the modelled response to these eruptions. This is due in part to the diversity of forcing datasets that are used to prescribe the distribution of stratospheric aerosols resulting from these volcanic eruptions, as well as uncertainties in optical property derivations from these datasets. To improve this situation for the sixth phase of the Coupled Model Intercomparison Project (CMIP6), a two-step process was undertaken. First, a combined stratospheric aerosol dataset, the Global Space-based Stratospheric Aerosol Climatology (GloSSAC; 1979–2016), was constructed. Next, GloSSAC, along with information from ice cores and Sun photometers, was used to generate aerosol distributions, characteristics and optical properties to construct a more consistent stratospheric aerosol forcing dataset for models participating in CMIP6. This “version 3” of the stratospheric aerosol forcing has been endorsed for use in all contributing CMIP6 simulations. Recent updates to the underlying GloSSAC from version 1 to version 1.1 affected the 1991–1994 period and necessitated an update to the stratospheric aerosol forcing from version 3 to version 4. As version 3 remains the official CMIP6 input, quantification of the impact on radiative forcing and climate is both relevant and timely for interpreting results from experiments such as the CMIP6 historical simulations. This study uses two models, the Canadian Earth System Model version 5 (CanESM5) and the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1), to estimate the difference in instantaneous radiative forcing in simulated post-Pinatubo climate response when using version 4 instead of version 3. Differences in temperature, precipitation and radiative forcings are generally found to be small compared to internal variability. An exception to this is differences in monthly temperature anomalies near 24 km altitude in the tropics, which can be as large as 3 ∘C following the eruption of Mt. Pinatubo.

In this study, both an atmospheric model [Weather Research and Forecasting (WRF) model] and an atmosphere (WRF)-ocean (Princeton Ocean Model; POM) coupled model are used to simulate the tropical cyclone (TC) Kaemi (2006). By comparing the simulation results of the models, effects of oceanic elements, especially the TC-induced sea surface temperature (SST) cooling, on the simulated TC size and destructiveness are identified and analyzed. The results show that there are no notable differences in the simulated TC track and its intensity between the uncoupled and coupled experiments; however, there are large differences in the TC size (i.e., the radius of gale-force wind) between the two experiments, and it is the TC-induced SST cooling that decreases the TC size. The SST cooling contributes to the decrease of air-sea moisture difference (ASMD) outside the TC eyewall, which subsequently leads to the decreases in surface enthalpy flux (SEF), radial sea-level pressure gradient, absolute vorticity advection, and wind speed outside the TC eyewall. As a result, the TC size and size-dependent TC destructive potential all decrease remarkably.

Previous observational evidence and numerical simulations have revealed that the surface sensible heating in spring (March–April–May, MAM) over the Tibetan Plateau (TPSH) can affect the Asian regional hydrological cycle, surface energy balance, and climate through altering atmospheric heat source of the Tibetan Plateau (TP). This study aims to investigate the impacts of MAM TPSH on the interannual variability of East Asian dust cycle by using CAM4-BAM (version 4 of the Community Atmosphere Model coupled to a bulk aerosol model), MERRA-2 (version 2 of the Modern-Era Retrospective analysis for Research and Applications) surface dust concentration, and TPSH measurements. Our simulations show that the surface dust concentrations over the East Asian (EA) dust source region and over the northwestern Pacific (NP) in MAM are significantly positively correlated with TPSH, with regionally averaged correlation coefficients of 0.49 for EA and 0.44 for NP. Similar positive correlations are also shown between the MAM TPSH measurements averaged over the 73 observation sites and the surface dust concentration from MERRA-2. Simulation-based comparisons between strongest and weakest TPSH years reveal that, the MAM surface dust concentration in the strongest TPSH years increases with relative differences of 13.1 % over EA and 36.9 % over NP. These corresponding differences are found in MERRA-2 with 22.9 % and 13.3 % over EA and NP, respectively. Further simulated results show that the processes of whole dust cycles (e.g., dust loading, emission, and transport, as well as dust deposition) are also significantly enhanced during the strongest TPSH years over EA and NP. Through enhancing the TP heat source, stronger TPSH in MAM generates an anticyclonic anomaly in middle and upper troposphere over the TP and over the downstream Pacific region, respectively. These atmospheric circulation anomalies induced by the increased TPSH result in increasing the westerly winds over both EA and NP, which in turn increases dust emissions over the dust source, and dust transport over these two regions, as well as the regional dust cycles. These results suggest that addressing the East Asian dust changes in the future climates requires considering not only increasing greenhouse gas emissions but also the variations of the TP's heat source under global warming.

An ensemble-based data assimilation framework for a coupled ocean–atmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average.

We present top-of-atmosphere and surface radiative kernels based on the atmospheric component (GA7.1) of the HadGEM3 general circulation model developed by the UK Met Office. We show that the utility of radiative kernels for forcing adjustments in idealised CO2 perturbation experiments is greatest where there is sufficiently high resolution in the stratosphere in both the target climate model and the radiative kernel. This is because stratospheric cooling to a CO2 perturbation continues to increase with height, and low-resolution or low-top kernels or climate model output are unable to fully resolve the full stratospheric temperature adjustment. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), standard atmospheric model data are available up to 1 hPa on 19 pressure levels, which is a substantial advantage compared to CMIP5. We show in the IPSL-CM6A-LR model where a full set of climate diagnostics are available that the HadGEM3-GA7.1 kernel exhibits linear behaviour and the residual error term is small, as well as from a survey of kernels available in the literature that in general low-top radiative kernels underestimate the stratospheric temperature response. The HadGEM3-GA7.1 radiative kernels are available at https://doi.org/10.5281/zenodo.3594673 (Smith, 2019).

The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km2 spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m3/m3. The RMSE remains around 0.06 m3/m3 even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.

High-performance computing resources allow us to conduct numerical simulations with a horizontal grid spacing that is sufficiently high to resolve cloud systems on a global scale, and high-resolution models (HRMs) generally provide better simulation performance than low-resolution models (LRMs). In this study, we execute a next-generation model that is capable of simulating global aerosols using version 16 of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM.16). The simulated aerosol distributions are obtained for 3 years with an HRM using a global 14 km grid spacing, an unprecedentedly high horizontal resolution and long integration period. For comparison, a NICAM with a 56 km grid spacing is also run as an LRM, although this horizontal resolution is still high among current global aerosol climate models. The comparison elucidated that the differences in the various variables of meteorological fields, including the wind speed, precipitation, clouds, radiation fluxes and total aerosols, are generally within 10 % of their annual averages, but most of the variables related to aerosols simulated by the HRM are slightly closer to the observations than are those simulated by the LRM. Upon investigating the aerosol components, the differences in the water-insoluble black carbon and sulfate concentrations between the HRM and LRM are large (up to 32 %), even in the annual averages. This finding is attributed to the differences in the aerosol wet deposition flux, which is determined by the conversion rate of cloud to precipitation, and the difference between the HRM and LRM is approximately 20 %. Additionally, the differences in the simulated aerosol concentrations at polluted sites during polluted months between the HRM and LRM are estimated with normalized mean biases of −19 % for black carbon (BC), −5 % for sulfate and −3 % for the aerosol optical thickness (AOT). These findings indicate that the impacts of higher horizontal grid spacings on model performance for secondary products such as sulfate, and complex products such as the AOT, are weaker than those for primary products, such as BC. On a global scale, the subgrid variabilities in the simulated AOT and cloud optical thickness (COT) in the 1∘×1∘ domain using 6-hourly data are estimated to be 28.5 % and 80.0 %, respectively, in the HRM, whereas the corresponding differences are 16.6 % and 22.9 % in the LRM. Over the Arctic, both the HRM and the LRM generally reproduce the observed aerosols, but the largest difference in the surface BC mass concentrations between the HRM and LRM reaches 30 % in spring (the HRM-simulated results are closer to the observations). The vertical distributions of the HRM- and LRM-simulated aerosols are generally close to the measurements, but the differences between the HRM and LRM results are large above a height of approximately 3 km, mainly due to differences in the wet deposition of aerosols. The global annual averages of the effective radiative forcings due to aerosol–radiation and aerosol–cloud interactions (ERFari and ERFaci) attributed to anthropogenic aerosols in the HRM are estimated to be -0.293±0.001 and -0.919±0.004 W m−2, respectively, whereas those in the LRM are -0.239±0.002 and -1.101±0.013 W m−2. The differences in the ERFari between the HRM and LRM are primarily caused by those in the aerosol burden, whereas the differences in the ERFaci are primarily caused by those in the cloud expression and performance, which are attributed to the grid spacing. The analysis of interannual variability revealed that the difference in reproducibility of both sulfate and carbonaceous aerosols at different horizontal resolution is greater than their interannual variability over 3 years, but those of dust and sea salt AOT and possibly clouds were the opposite. Because at least 10 times the computer resources are required for the HRM (14 km grid) compared to the LRM (56 km grid), these findings in this study help modelers decide whether the objectives can be achieved using such higher resolution or not under the limitation of available computational resources.

Tropical storms (TS) are highly sensitive to the behavior of nearby oceans, and the only way to improve their predictability is to include all the possible factors contributing to their genesis and motion. The present study evaluates the response of air–sea coupling on TS Mora, which occurred during the pre-monsoon season of 2017. Mora is simulated using a non-hydrostatic regional atmospheric modeling system coupled to a regional ocean modeling system model. The observations show a low-level Rossby wave in the atmosphere during the storm genesis. There was also evidence of increased sea surface height simultaneously in the form of a downwelling Kelvin wave at the west coast of the Bay of Bengal. The present study emphasizes the relationship between near-surface oceanic and atmospheric circulation patterns. We have tried to elucidate the process with the help of dynamical equations and model solutions. We also analyze the pivotal role of the atmospheric Rossby wave in the formation of the coastal Kelvin wave. The Kelvin wave seems to produce a warm pool required to form a low-pressure system, which further gave rise to the tropical storm Mora. The changes in the temperature and salinity profile within the ocean subsurface layers during storm passage are further used for inferences regarding the vertical structure of the upper ocean due to wind-generated mixing. The model analysis reveals that the ocean and atmospheric counterparts responsible for storm genesis are well recorded in the coupled model when compared to the observations. It was found that air–sea coupling in the model made it possible to realistically capture ocean and atmospheric responses.

Objective: To find the influence of aerosols on the sun rays arriving at the surface and to measure aerosol radiative forcing over Kanpur, Indo-gangetic basin. The study was performed during period 2001-2015. Methodology: The present work is credited to the availability of data through the network relying on the ground based optical observations obtained with the help of ground based instrument called Cimel sun photometer using AERONET (Aerosol Robotic Network) radiative transfer model with particular focus on Indo-Gangetic plain over Kanpur. The aerosol radiative forcing at the surface, atmosphere and top of atmosphere has been computed with the help of radiative transfer approach depending on AERONET aerosol retrievals. Findings: Our study investigated the seasonal and spectral variations of ARF along with aerosol radiative forcing with special reference to Kanpur region (IGP) by using AERONET data. The monthly average (±σ) ARF during the entire observational period (2001-2015), at three levels i.e., surface (ARFSFC), atmosphere (ARFATM) and the atmospheric top (ARFTOA) are recorded as – 89.6±18.6 Wm–2, +64.4±16.5 Wm–2 and –25.2±6.8 Wm–2 respectively. From the ARF measurements, it is found that there is a large reduction in surface arriving solar rays due to presence of absorbing and scattering types of aerosols. Novelty: Since this study provides an overview of the present state of prevailing aerosols and their climatic effects. So, findings may be used to create an aerosol climate map for the region under study. Keywords: Aerosol robotic network; aerosol radiative forcing; global atmospheric model; sun photometer

A mass based and volume based hygroscopicities models were applied to the data extracted from Optical Properties of Aerosols and Clouds (OPAC).The microphysical properties obtained were radii, density, refractive index, mass and volume of the atmospheric aerosols of continental average, continental clean, continental polluted, maritime tropical, maritime polluted, maritime clean, at eight different relative humidity of 0%, 50%, 70%, 80%, 90%, 95%, 98% and 99%.Using the microphysical properties, hygroscopic growth factors and effective radii of the mixtures were determined while the parameters Av and Bv for volume based and Am and Bm for mass based of the aerosols were determined using multiple regression analysis with SPSS 16.0 at each relative humidity. Although it was discovered that Bv is more dominant than Av, the R2 for all the models are greater than 90%. The significances are less than 0.05, Am>Av and Bm

Solar photovoltaic (SPV) array does not give constant power output due to the adverse effects of various stresses over its performance. Dust is a critical affecting factor, especially in tropical, arid, and semiarid regions, as it reduces power output by lowering the transmittance of the glass cover. Usually, total dust accumulation on an SPV module goes through three processes viz., deposition, rebound, and re-suspension. In this article, a detailed analysis of all these phenomena has been carried out to build up a comprehensive model for dry deposition of dust to assess power output reduction for this. Variations of all these processes for particles with different diameters and modules at various tilt angles have also been investigated. The model shows that transmittance becomes minimum at a tilt angle of about 15°, which is validated by laboratory experimentation. The deviation between measured and the model-based calculated dust deposition density is found to be 4.84%. The energy output of 10 kWP PV power plant has been calculated with this model with weather data collected from a weather monitoring station (at latitude 22.5713°N, longitude 88.3109°E) and dust data from a website of Government of India. Daily generated energy in this plant for 15 days is recorded by a digital meter and compared with the calculated ones. The deviation between the two is seen to be within ±6%.