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Here we present for the first time a proof of concept for an emulation-based method that uses a large-eddy simulations (LESs) to present sub-grid cloud processes in a general circulation model (GCM). We focus on two key variables affecting the properties of shallow marine clouds: updraft velocity and precipitation formation. The LES is able to describe these processes with high resolution accounting for the realistic variability in cloud properties. We show that the selected emulation method is able to represent the LES outcome with relatively good accuracy and that the updraft velocity and precipitation emulators can be coupled with the GCM practically without increasing the computational costs. We also show that the emulators influence the climate simulated by the GCM but do not consistently improve or worsen the agreement with observations on cloud-related properties, although especially the updraft velocity at cloud base is better captured. A more quantitative evaluation of the emulator impacts against observations would, however, have required model re-tuning, which is a significant task and thus could not be included in this proof-of-concept study. All in all, the approach introduced here is a promising candidate for representing detailed cloud- and aerosol-related sub-grid processes in GCMs. Further development work together with increasing computing capacity can be expected to improve the accuracy and the applicability of the approach in climate simulations.

For short-lived climate forcers such as black carbon (BC), the atmospheric concentrations, radiative forcing (RF), and, ultimately, the subsequent effects on climate, depend on the location and timing of the emissions. Here, we employ the NorESM1-Happi version of the Norwegian Earth System Model to systematically study how the RF associated with BC emissions depends on the latitude, longitude, and seasonality of the emissions. The model aerosol scheme is run in an offline mode to allow for an essentially noise-free evaluation of the RF associated with even minor changes in emissions. A total of 960 experiments were run to evaluate the BC direct RF (dirRF) and the RF associated with BC in snow/ice (snowRF) for emissions in 192 latitude–longitude boxes covering the globe, both for seasonally uniform emissions and for emissions in each of the four seasons separately. We also calculate a rough estimate of the global temperature response to regional emissions and provide a Fortran-based tool to facilitate the further use of our results. Overall, the results demonstrate that the BC RFs strongly depend on the latitude, longitude, and season of the emissions. In particular, the global mean dirRF normalized by emissions (direct specific forcing; dirSF) depends much more strongly on the emission location than suggested by previous studies that have considered emissions from continental-/subcontinental-scale regions. Even for seasonally uniform emissions, dirSF varies by more than a factor of 10, depending on the emission location. These variations correlate strongly with BC lifetime, which varies from less than 2 to 11 d. BC dirSF is largest for emissions in tropical convective regions and in subtropical and midlatitude continents in summer, both due to the abundant solar radiation and strong convective transport, which increases BC lifetime and the amount of BC above clouds. The dirSF is also relatively large for emissions in high-albedo, high-latitude regions such as Antarctica and Greenland. The dependence of snow specific forcing (snowSF) on the emission location is even larger. While BC emissions originating from most low-latitude regions result in negligible snowSF, the maxima of snowSF for emissions in polar regions greatly exceed the largest values of dirSF for low-latitude emissions. The large magnitude of snowSF for high-latitude BC emissions suggests that, for a given mass of BC emitted, the climate impacts are also largest for high-latitude emissions. The additivity of the RFs resulting from BC emissions in different regions and seasons is also investigated. It is found that dirRF is almost additive for current-day emissions, so that summing the RFs computed for individual regions/seasons without considering BC emissions from elsewhere overestimates dirRF by less than 10 %. For snowRF, the overestimate is somewhat larger, at ∼ 20 %.

The number of cloud droplets formed at the cloud base depends on both the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large-eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parameterisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produces on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Previously, substantial uncertainties have been reported in NH snow water equivalent (SWE) estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared NH SWE estimates between CMIP6 (Coupled Model Intercomparison Project Phase 6) models and observation-based SWE reference data north of 40∘ N for the period 1982–2014 and analyzed with a regression approach whether model biases in temperature (T) and precipitation (P) could explain the model biases in SWE. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 data for the mountainous regions. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ snow depth data. The analysis shows that CMIP6 models tend to overestimate SWE; however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0∘ C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

South and East Asian anthropogenic aerosols mostly reside in an air mass extending from the Indian Ocean to the North Pacific. Yet the surface temperature effects of Asian aerosols spread across the whole globe. Here, we remove Asian anthropogenic aerosols from two independent climate models (ECHAM6.1 and NorESM1) using the same representation of aerosols via MACv2-SP (a simple plume implementation of the second version of the Max Planck Institute Aerosol Climatology). We then robustly decompose the global distribution of surface temperature responses into contributions from atmospheric energy flux changes. We find that the horizontal atmospheric energy transport strongly moderates the surface temperature response over the regions where Asian aerosols reside. Atmospheric energy transport and changes in clear-sky longwave radiation redistribute the temperature effects efficiently across the Northern Hemisphere and to a lesser extent also over the Southern Hemisphere. The model-mean global surface temperature response to Asian anthropogenic aerosol removal is 0.26±0.04 ∘C (0.22±0.03 for ECHAM6.1 and 0.30±0.03 ∘C for NorESM1) of warming. Model-to-model differences in global surface temperature response mainly arise from differences in longwave cloud (0.01±0.01 for ECHAM6.1 and 0.05±0.01 ∘C for NorESM1) and shortwave cloud (0.03±0.03 for ECHAM6.1 and 0.07±0.02 ∘C for NorESM1) responses. The differences in cloud responses between the models also dominate the differences in regional temperature responses. In both models, the northern-hemispheric surface warming amplifies towards the Arctic, where the total temperature response is highly seasonal and weakest during the Arctic summer. We estimate that under a strong Asian aerosol mitigation policy tied with strong climate mitigation (Shared Socioeconomic Pathway 1-1.9) the Asian aerosol reductions can add around 8 years' worth of current-day global warming during the next few decades.

The primary goal of this paper is to present a model of snow surface albedo accounting for small-scale surface roughness effects. The model is based on photon recollision probability, and it can be combined with existing bulk volume albedo models, such as Two-streAm Radiative TransfEr in Snow (TARTES). The model is fed with in situ measurements of surface roughness from plate profile and laser scanner data, and it is evaluated by comparing the computed albedos with observations. It provides closer results to empirical values than volume-scattering-based albedo simulations alone. The impact of surface roughness on albedo increases with the progress of the melting season and is larger for larger solar zenith angles. In absolute terms, small-scale surface roughness can decrease the total albedo by up to about 0.1. As regards the bidirectional reflectance factor (BRF), it is found that surface roughness increases backward scattering especially for large solar zenith angle values.

A comprehensive comparison of more than 70 000 synchronous 1 min aerosol optical depth (AOD) data from three Global Atmosphere Watch precision-filter radiometers (GAW-PFR), traceable to the World AOD reference, and 15 Aerosol Robotic Network Cimel radiometers (AERONET-Cimel), calibrated individually with the Langley plot technique, was performed for four common or “near” wavelengths, 380, 440, 500 and 870 nm, in the period 2005–2015. The goal of this study is to assess whether, despite the marked technical differences between both networks (AERONET, GAW-PFR) and the number of instruments used, their long-term AOD data are comparable and consistent. The percentage of data meeting the World Meteorological Organization (WMO) traceability requirements (95 % of the AOD differences of an instrument compared to the WMO standards lie within specific limits) is >92 % at 380 nm, >95 % at 440 nm and 500 nm, and 98 % at 870 nm, with the results being quite similar for both AERONET version 2 (V2) and version 3 (V3). For the data outside these limits, the contribution of calibration and differences in the calculation of the optical depth contribution due to Rayleigh scattering and O3 and NO2 absorption have a negligible impact. For AOD >0.1, a small but non-negligible percentage (∼1.9 %) of the AOD data outside the WMO limits at 380 nm can be partly assigned to the impact of dust aerosol forward scattering on the AOD calculation due to the different field of view of the instruments. Due to this effect the GAW-PFR provides AOD values, which are ∼3 % lower at 380 nm and ∼2 % lower at 500 nm compared with AERONET-Cimel. The comparison of the Ångström exponent (AE) shows that under non-pristine conditions (AOD >0.03 and AE ) the AE differences remain . This long-term comparison shows an excellent traceability of AERONET-Cimel AOD with the World AOD reference at 440, 500 and 870 nm channels and a fairly good agreement at 380 nm, although AOD should be improved in the UV range.

Significant discrepancies remain in estimates of climate impacts of anthropogenic aerosols between different general circulation models (GCMs). Here, we demonstrate that eliminating differences in model aerosol or radiative forcing fields results in close agreement in simulated globally averaged temperature and precipitation responses in the studied GCMs. However, it does not erase the differences in regional responses. We carry out experiments of equilibrium climate response to modern-day anthropogenic aerosols using an identical representation of anthropogenic aerosol optical properties and the first indirect effect of aerosols, MACv2-SP (a simple plume implementation of the second version of the Max Planck Institute Aerosol CLimatology), in two independent climate models (NorESM, Norwegian Earth System Model, and ECHAM6). We find consistent global average temperature responses of −0.48 (±0.02) and −0.50 (±0.03) K and precipitation responses of −1.69 (±0.04) % and −1.79 (±0.05) % in NorESM1 and ECHAM6, respectively, compared to modern-day equilibrium climate without anthropogenic aerosols. However, significant differences remain between the two GCMs' regional temperature responses around the Arctic circle and the Equator and precipitation responses in the tropics. The scatter in the simulated globally averaged responses is small in magnitude when compared against literature data from modern GCMs using model intrinsic aerosols but same aerosol emissions −(0.5–1.1) K and −(1.5–3.1) % for temperature and precipitation, respectively). The Pearson correlation of regional temperature (precipitation) response in these literature model experiments with intrinsic aerosols is 0.79 (0.34). The corresponding correlation coefficient for NorESM1 and ECHAM6 runs with identical aerosols is 0.78 (0.41). The lack of improvement in correlation coefficients between models with identical aerosols and models with intrinsic aerosols implies that the spatial distribution of regional climate responses is not improved via homogenizing the aerosol descriptions in the models. Rather, differences in the atmospheric dynamic and snow/sea ice cover responses dominate the differences in regional climate responses. Hence, even if we would have perfect aerosol descriptions inside the global climate models, uncertainty arising from the differences in circulation responses between the models would likely still result in a significant uncertainty in regional climate responses.

This study assesses the change in anthropogenic aerosol forcing from the mid-1970s to the mid-2000s. Both decades had similar global-mean anthropogenic aerosol optical depths but substantially different global distributions. For both years, we quantify (i) the forcing spread due to model-internal variability and (ii) the forcing spread among models. Our assessment is based on new ensembles of atmosphere-only simulations with five state-of-the-art Earth system models. Four of these models will be used in the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016). Here, the complexity of the anthropogenic aerosol has been reduced in the participating models. In all our simulations, we prescribe the same patterns of the anthropogenic aerosol optical properties and associated effects on the cloud droplet number concentration. We calculate the instantaneous radiative forcing (RF) and the effective radiative forcing (ERF). Their difference defines the net contribution from rapid adjustments. Our simulations show a model spread in ERF from −0.4 to −0.9 W m−2. The standard deviation in annual ERF is 0.3 W m−2, based on 180 individual estimates from each participating model. This result implies that identifying the model spread in ERF due to systematic differences requires averaging over a sufficiently large number of years. Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for individual models, although there are major model differences in natural aerosols and clouds. The model-ensemble mean ERF is −0.54 W m−2 for the pre-industrial era to the mid-1970s and −0.59 W m−2 for the pre-industrial era to the mid-2000s. Our result suggests that comparing ERF changes between two observable periods rather than absolute magnitudes relative to a poorly constrained pre-industrial state might provide a better test for a model's ability to represent transient climate changes.

The regional climate model REMO was coupled with the FLake lake model to include an interactive treatment of lakes. Using this new version, the Fenno-Scandinavian climate and lake characteristics were studied in a set of 35-year hindcast simulations. Additionally, sensitivity tests related to the parameterization of snow albedo were conducted. Our results show that overall the new model version improves the representation of the Fenno-Scandinavian climate in terms of 2 m temperature and precipitation, but the downside is that an existing wintertime cold bias in the model is enhanced. The lake surface water temperature, ice depth and ice season length were analyzed in detail for 10 Finnish, 4 Swedish and 2 Russian lakes and 1 Estonian lake. The results show that the model can reproduce these characteristics with reasonably high accuracy. The cold bias during winter causes overestimation of ice layer thickness, for example, at several of the studied lakes, but overall the values from the model are realistic and represent the lake physics well in a long-term simulation. We also analyzed the snow depth on ice from 10 Finnish lakes and vertical temperature profiles from 5 Finnish lakes and the model results are realistic.

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.

This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

The impact of ice clouds on solar disk and circumsolar radiances is investigated using a Monte Carlo radiative transfer model. The monochromatic direct and diffuse radiances are simulated at angles of 0 to 8° from the center of the sun. Input data for the model are derived from measurements conducted during the 2010 Small Particles in Cirrus (SPARTICUS) campaign together with state-of-the-art databases of optical properties of ice crystals and aerosols. For selected cases, the simulated radiances are compared with ground-based radiance measurements obtained by the Sun and Aureole Measurements (SAM) instrument. First, the sensitivity of the radiances to the ice cloud properties and aerosol optical thickness is addressed. The angular dependence of the disk and circumsolar radiances is found to be most sensitive to assumptions about ice crystal roughness (or, more generally, non-ideal features of ice crystals) and size distribution, with ice crystal habit playing a somewhat smaller role. Second, in comparisons with SAM data, the ice cloud optical thickness is adjusted for each case so that the simulated radiances agree closely (i.e., within 3 %) with the measured disk radiances. Circumsolar radiances at angles larger than ≈ 3° are systematically underestimated when assuming smooth ice crystals, whereas the agreement with the measurements is better when rough ice crystals are assumed. Our results suggest that it may well be possible to infer the particle roughness directly from ground-based SAM measurements. In addition, the results show the necessity of correcting the ground-based measurements of direct radiation for the presence of diffuse radiation in the instrument's field of view, in particular in the presence of ice clouds.

ABSTRACT: The objective of this study was to evaluate the GreenFeed (GF) system and respiration chambers (RC) for daily and intraday measurements of the enteric gaseous exchange, as well as the metabolic heat production, lying behavior, and feed intake (FI) rate of dairy cows at these 2 respective housing conditions (tiestall barn [TSB] vs. RC) during the summer periods. Sixteen multiparous lactating dairy cows were recruited and arranged in a randomized complete block design with a baseline period established for each cow. Cows were given a basal diet (CON) for a baseline period of 7 d and were then fed a 3-nitrooxypropanol (3-NOP)-containing feed for the subsequent 26 d as experimental period. During both the baseline and the last 7 d of treatment period, gaseous exchanges of each animal were measured in the TSB using GF for 8 staggered measurements over 3 d, immediately followed by the measurement in RC for 2 d. Corresponding DMI, milk yield, and behavior parameters (e.g., lying behavior and FI rate) in TSB and RC were recorded. The correlation coefficients of CH4 and H2 using raw data were 0.84 and 0.85, respectively. For all gases, correlation coefficients between GF and RC on individual cow level decreased when the marginal fixed effects (e.g., inhibitor and breed) were corrected by a mixed model. There were no differences in daily CH4 production or intensity between GF and RC (442 vs. 443 g CH4/d or 16.6 vs. 16.2 g CH4 /kg MY). However, greater CH4 yield was measured by GF than RC (19.0 vs. 17.8 g CH4/kg DMI), driven by a lower DMI (23.3 vs. 24.6 kg/d) when cows were housed in TSB sampled by GF compared with cows being housed and sampled in RC. The correlations for CO2 production and O2 consumption were moderate and expected due to the variation associated with the mild heat stress condition during GF measurements in the TSB (temperature-humidity index [THI] 56 vs. 68), as indicated by the reduced lying time (−2.1 h/d). At the intraday level, there was an interaction between techniques and hour-of-day for CH4 production, as indicated by the discrepancies in postprandial CH4 emissions between techniques. In summary, this set of results showed that there were strong positive correlations for CH4 and H2 emissions between GF and RC based on individual cow data. However, such relationship should be interpreted with caution, given the data clustering resulting from the use of inhibitor 3-NOP. On treatment level, these 2 techniques detected similar inhibitor effect on the estimated daily CH4 emissions. The intraday patterns of CH4 and H2 production captured by GF provided a close approximation for those measured by RC. Nevertheless, potential underestimation may occur, especially following fresh feed delivery. For measuring CO2 production and O2 consumption, the GF captured similar intraday variations to those in the RC. However, the estimated daily production and consumption were not directly comparable, which was expected due to the variable thermal conditions during the summer. Further evaluations under the same weather conditions are warranted.

Higher education students in Finland and all over the world are often engaged in a paid job alongside their studies. The purpose of the present study is to explore how humanities students' work experience is related to their career engagement and metacognitive awareness. More precisely, the aim is to investigate how the nature and amount of work are associated with career engagement and metacognitive awareness. A total of 302 master's students filled out the questionnaire. The data were analysed by exploratory and confirmatory factor analyses, Pearson correlations, independent samples t-test and One-Way ANOVA. The results showed that working students had higher scores on networking than non-working students. In addition, the study revealed that the amount of work and work related to the study field were positively related to career engagement (career planning and networking). Regarding metacognitive awareness, the amount of work matters. Full-time working students had higher scores on both dimensions of metacognitive awareness, namely knowledge of cognition and regulation of cognition. In addition, work related to one's own study field was connected with the perceived relevance of work. The present study indicates that students' working can enhance students' career engagement and their metacognitive awareness.

The albedo of a snowpack depends on the single-scattering properties of individual snow crystals, which have a variety of shapes and sizes, and are often bounded in clusters. From the point of view of optical modelling, it is essential to identify the geometric dimensions of the population of snow particles that synthesize the scattering properties of the snowpack surface. This involves challenges related to the complexity of modelling the radiative transfer in such an irregular medium, and to the difficulty of measuring microphysical snow properties. In this paper, we illustrate a method to measure the size distribution of a snow particle parameter, which roughly corresponds to the smallest snow particle dimension, from two-dimensional macro photos of snow particles taken in Antarctica at the surface layer of a melting ice sheet. We demonstrate that this snow particle metric corresponds well to the optically equivalent effective radius utilized in radiative transfer modelling, in particular when snow particles are modelled with the droxtal shape. The surface albedo modelled on the basis of the measured snow particle metric showed an excellent match with the observed albedo when there was fresh or drifted snow at the surface. In the other cases, a good match was present only for wavelengths longer than 1.4 μm. For shorter wavelengths, our modelled albedo generally overestimated the observations, in particular when surface hoar and faceted polycrystals were present at the surface and surface roughness was increased by millimetre-scale cavities generated during melting. Our results indicate that more than just one particle metric distribution is needed to characterize the snow scattering properties at all optical wavelengths, and suggest an impact of millimetre-scale surface roughness on the shortwave infrared albedo.

Distributions of ellipsoids are often used to simulate the optical properties of non-ellipsoidal atmospheric particles, such as dust. In this work, the applicability of ellipsoids for retrieving the refractive index of dust-like target model particles from scattering data is investigated. This is a pure modeling study, in which stereogrammetrically retrieved model dust shapes are used as targets. The primary objective is to study whether the refractive index of these target particles can be inverted from their scattering matrices using ellipsoidal model particles. To achieve this, first scattering matrices for the target model particles with known refractive indices are computed. First, a non-negative least squares fitting is performed, individually for each scattering matrix element, for 46 differently shaped ellipsoids by using different assumed refractive indices. Then, the fitting error is evaluated to establish whether the ellipsoid ensemble best matches the target scattering matrix elements when the correct refractive index is assumed. Second, we test whether the ellipsoids best match the target data with the correct refractive index, when a predefined (uniform) shape distribution for ellipsoids is assumed, instead of optimizing the shape distribution separately for each tested refractive index. The results show not only that for both of these approaches using ellipsoids with the true refractive index produces good results but also that for each scattering matrix element even better results are acquired by using wrong refractive indices. In addition, the best agreement is obtained for different scattering matrix elements using different refractive indices. The findings imply that retrieval of refractive index of non-ellipsoidal particles whose single-scattering properties have been modeled with ellipsoids may not be reliable. Furthermore, it is demonstrated that the differences in single-scattering albedo and asymmetry parameter between the best-match ellipsoid ensemble and the target particles may give rise to major differences in simulated aerosol radiative effects.

Snow consists of non-spherical grains of various shapes and sizes. Still, in many radiative transfer applications, single-scattering properties of snow have been based on the assumption of spherical grains. More recently, second-generation Koch fractals have been employed. While they produce a relatively flat phase function typical of deformed non-spherical particles, this is still a rather ad hoc choice. Here, angular scattering measurements for blowing snow conducted during the CLimate IMpacts of Short-Lived pollutants In the Polar region (CLIMSLIP) campaign at Ny Ålesund, Svalbard, are used to construct a reference phase function for snow. Based on this phase function, an optimized habit combination (OHC) consisting of severely rough (SR) droxtals, aggregates of SR plates and strongly distorted Koch fractals is selected. The single-scattering properties of snow are then computed for the OHC as a function of wavelength λ and snow grain volume-to-projected area equivalent radius rvp. Parameterization equations are developed for λ = 0.199–2.7 μm and rvp = 10–2000 μm, which express the single-scattering co-albedo β, the asymmetry parameter g and the phase function P11 as functions of the size parameter and the real and imaginary parts of the refractive index. The parameterizations are analytic and simple to use in radiative transfer models. Compared to the reference values computed for the OHC, the accuracy of the parameterization is very high for β and g. This is also true for the phase function parameterization, except for strongly absorbing cases (β > 0.3). Finally, we consider snow albedo and reflected radiances for the suggested snow optics parameterization, making comparisons to spheres and distorted Koch fractals.

The turbulent structure of a stratocumulus-topped marine boundary layer over a 2-day period is observed with a Doppler lidar at Mace Head in Ireland. Using profiles of vertical velocity statistics, the bulk of the mixing is identified as cloud driven. This is supported by the pertinent feature of negative vertical velocity skewness in the sub-cloud layer which extends, on occasion, almost to the surface. Both coupled and decoupled turbulence characteristics are observed. The length and timescales related to the cloud-driven mixing are investigated and shown to provide additional information about the structure and the source of the mixing inside the boundary layer. They are also shown to place constraints on the length of the sampling periods used to derive products, such as the turbulent dissipation rate, from lidar measurements. For this, the maximum wavelengths that belong to the inertial subrange are studied through spectral analysis of the vertical velocity. The maximum wavelength of the inertial subrange in the cloud-driven layer scales relatively well with the corresponding layer depth during pronounced decoupled structure identified from the vertical velocity skewness. However, on many occasions, combining the analysis of the inertial subrange and vertical velocity statistics suggests higher decoupling height than expected from the skewness profiles. Our results show that investigation of the length scales related to the inertial subrange significantly complements the analysis of the vertical velocity statistics and enables a more confident interpretation of complex boundary layer structures using measurements from a Doppler lidar.

The impacts of representing cloud microphysical processes in a stochastic subcolumn framework are investigated, with emphasis on estimating the aerosol indirect effect. It is shown that subgrid treatment of cloud activation and autoconversion of cloud water to rain reduce the impact of anthropogenic aerosols on cloud properties and thus reduce the global mean aerosol indirect effect by 19%, from −1.59 to −1.28 W m−2. This difference is partly related to differences in the model basic state; in particular, the liquid water path (LWP) is smaller and the shortwave cloud radiative forcing weaker when autoconversion is computed separately for each subcolumn. However, when the model is retuned so that the differences in the basic state LWP and radiation balance are largely eliminated, the global-mean aerosol indirect effect is still 14% smaller (i.e. −1.37 W m−2) than for the model version without subgrid treatment of cloud activation and autoconversion. The results show the importance of considering subgrid variability in the treatment of autoconversion. Representation of several processes in a self-consistent subgrid framework is emphasized. This paper provides evidence that omitting subgrid variability in cloud microphysics contributes to the apparently chronic overestimation of the aerosol indirect effect by climate models, as compared to satellite-based estimates.

The global potential for collecting usable water from dew on an artificial collector sheet was investigated by utilizing 34 years of meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were mostly below 0.1 mm. Nevertheless, some water-stressed areas such as parts of the coastal regions of northern Africa and the Arabian Peninsula show potential for large-scale dew harvesting, as the yearly yield may reach up to 100 L m−2 for a commonly used polyethylene foil. Statistically significant trends were found in the data, indicating overall changes in dew yields of between ±10% over the investigated time period.

The timing of springtime end of snowmelt (snow-off date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature Ts is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents Ts from rising above 0 °C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.

Climate–aerosol interactions in India are studied by employing the global climate–aerosol model ECHAM5-HAM and the GAINS inventory for anthropogenic aerosol emissions. Model validation is done for black carbon surface concentrations in Mukteshwar and for features of the monsoon circulation. Seasonal cycles and spatial distributions of radiative forcing and the temperature and rainfall responses are presented for different model setups. While total aerosol radiative forcing is strongest in the summer, anthropogenic forcing is considerably stronger in winter than in summer. Local seasonal temperature anomalies caused by aerosols are mostly negative with some exceptions, e.g., parts of northern India in March–May. Rainfall increases due to the elevated heat pump (EHP) mechanism and decreases due to solar dimming mechanisms (SDMs) and the relative strengths of these effects during different seasons and for different model setups are studied. Aerosol light absorption does increase rainfall in northern India, but effects due to solar dimming and circulation work to cancel the increase. The total aerosol effect on rainfall is negative for northern India in the months of June–August, but during March–May the effect is positive for most model setups. These differences between responses in different seasons might help converge the ongoing debate on the EHPs and SDMs. Due to the complexity of the problem and known or potential sources for error and bias, the results should be interpreted cautiously as they are completely dependent on how realistic the model is. Aerosol–rainfall correlations and anticorrelations are shown not to be a reliable sole argument for deducing causality.

The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data. The models tend to over-predict the occurrence of rain rates that are less than ≈ 3 mm h−1 compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase. Despite having a larger climatological mean upper-tropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location. Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

There has been growing interest in the vertical structure of the recent Arctic warming. We investigated temperatures at the surface, 925, 700, 500 and 300 hPa levels in the Arctic (north of 70° N) using observations and four reanalyses: ERA-Interim, CFSR, MERRA and NCEP II. For the period 1979–2011, the layers at 500 hPa and below show a warming trend in all seasons in all the chosen reanalyses and observations. Restricting the analysis to the 1998–2011 period, however, all the reanalyses show a cooling trend in the Arctic-mean 500 hPa temperature in autumn, and this also applies to both observations and the reanalyses when restricting the analysis to the locations with available IGRA radiosoundings. During this period, the surface observations mainly representing land areas surrounding the Arctic Ocean reveal no summertime trend, in contrast with the reanalyses whether restricted to the locations of the available surface observations or not. In evaluating the reanalyses with observations, we find that the reanalyses agree better with each other at the available IGRA sounding locations than for the Arctic average, perhaps because the sounding observations were assimilated into reanalyses. Conversely, using the reanalysis data only from locations matching available surface (air) temperature observations does not improve the agreement between the reanalyses. At 925 hPa, CFSR deviates from the other three reanalyses, especially in summer after 2000, and it also deviates more from the IGRA radiosoundings than the other reanalyses do. The CFSR error in summer T925 is due mainly to underestimations in the Canadian-Atlantic sector between 120° W and 0°. The other reanalyses also have negative biases in this longitude band.

A new method for parameterizing the subgrid variations of vertical velocity and cloud droplet number concentration (CDNC) is presented for general circulation models (GCMs). These parameterizations build on top of existing parameterizations that create stochastic subgrid cloud columns inside the GCM grid cells, which can be employed by the Monte Carlo independent column approximation approach for radiative transfer. The new model version adds a description for vertical velocity in individual subgrid columns, which can be used to compute cloud activation and the subgrid distribution of the number of cloud droplets explicitly. Autoconversion is also treated explicitly in the subcolumn space. This provides a consistent way of simulating the cloud radiative effects with two-moment cloud microphysical properties defined at subgrid scale. The primary impact of the new parameterizations is to decrease the CDNC over polluted continents, while over the oceans the impact is smaller. Moreover, the lower CDNC induces a stronger autoconversion of cloud water to rain. The strongest reduction in CDNC and cloud water content over the continental areas promotes weaker shortwave cloud radiative effects (SW CREs) even after retuning the model. However, compared to the reference simulation, a slightly stronger SW CRE is seen e.g. over mid-latitude oceans, where CDNC remains similar to the reference simulation, and the in-cloud liquid water content is slightly increased after retuning the model.

We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.

Wind is a significant cause of power outages in overhead line networks. Present electricity distribution network management systems provide an extensive amount of data on network faults. These data combined with the openly available weather and soil information allow to analyze the impact of wind and soil frost on electricity system reliability more accurately than in the past. The objective is to determine the effect of soil frost on wind-induced faults in the electricity distribution system and identify variables that provide the best correlation between wind speed and the number of power supply interruptions. Five geographical areas in Finland are analyzed based on extensive electricity distribution interruption statistics collected in the country between 2008 and 2018. We introduce a methodology to analyze the effect of wind speed and soil frost and present numerical data analyzed for the areas under consideration. The methodology employs a multivariate linear regression model to show the dependence between wind, soil frost, and power supply interruptions. The results indicate that there is a strong positive correlation between the number of power supply interruptions and wind speed, and furthermore, in the multivariate model, adding the variable of soil frost improves correlation in all the areas under consideration. On average, the correlation with wind gusts and the number of power supply interruptions is approximately 0.67. However, with the soil frost variable, the correlation increases to 0.72. The results show that soil frost should be considered in studies on interruption modeling in Nordic conditions.

Clouds cool Earth's climate by reflecting 20% of the incoming solar energy, while also trapping part of the outgoing radiation. The effect of human activities on clouds is poorly understood, but the present-day anthropogenic cooling via changes of cloud albedo and lifetime could be of the same order as warming from anthropogenic addition in CO2. Soluble trace gases can increase water condensation to particles, possibly leading to activation of smaller aerosols and more numerous cloud droplets. We have studied the effect of nitric acid on the aerosol indirect effect with the global aerosol-climate model ECHAM5.5-HAM2. Including the nitric acid effect in the model increases cloud droplet number concentrations globally by 7%. The nitric acid contribution to the present-day cloud albedo effect was found to be −0.32 W m−2 and to the total indirect effect −0.46 W m−2. The contribution to the cloud albedo effect is shown to increase to −0.37 W m−2 by the year 2100, if considering only the reductions in available cloud condensation nuclei. Overall, the effect of nitric acid can play a large part in aerosol cooling during the following decades with decreasing SO2 emissions and increasing NOx and greenhouse gases.

The statistics of cloud base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that AROME significantly underestimates the variability of vertical velocity at cloud base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4–8 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70–80 % of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 10 km in the presented case. Adding a TKE-term on the resolved grid-point vertical velocity can compensate for the underestimation, but only for altitudes below approximately the boundary layer top height. The results illustrate the need for a careful consideration of the scales the model is able to accurately resolve, as well as for a special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

A climate-aerosol model is employed to study spatial and temporal variability of aerosol properties over India and China for recent (year 2006) and future conditions (year 2020) under different emission pathways. We present results for aerosol mass concentration in different size classes and optical properties for the five different aerosol species treated by the model. Aerosol mass concentration and optical depth have significant contributions from both anthropogenic and natural aerosols. Different species have maxima in different regions, with the highest anthropogenic aerosol concentrations found in Kolkata and elsewhere in the Ganges basin in India and on the northern part of the east coast and in the Sichuan basin in China. In India, natural aerosols have a maximum in the summer due to higher wind speeds, whereas anthropogenic aerosols have a maximum in the winter due to less efficient wet removal. Surface concentrations also tend to be higher in winter due to the additional reason of lower average boundary layer height. In China, seasonal cycles are weaker with natural aerosols having a maximum in the spring and sulfate contribution to the aerosol optical depth (AOD) being higher in the latter half of the year. MODIS AOD spatial distributions are reproduced well by the model, except for the Ganges valley with high absorption and for the Thar desert with high dust concentrations. Seasonal cycles compare qualitatively well with MODIS measurements.

The release of vast quantities of methane into the atmosphere as a result of clathrate destabilization is a potential mechanism for rapid amplification of global warming. Previous studies have calculated the enhanced warming based mainly on the radiative effect of the methane itself, with smaller contributions from the associated carbon dioxide or ozone increases. Here, we study the effect of strongly elevated methane (CH4) levels on oxidant and aerosol particle concentrations using a combination of chemistry-transport and general circulation models. A 10-fold increase in methane concentrations is predicted to significantly decrease hydroxyl radical (OH) concentrations, while moderately increasing ozone (O3). These changes lead to a 70 % increase in the atmospheric lifetime of methane, and an 18 % decrease in global mean cloud droplet number concentrations (CDNC). The CDNC change causes a radiative forcing that is comparable in magnitude to the longwave radiative forcing ("enhanced greenhouse effect") of the added methane. Together, the indirect CH4-O3 and CH4-OH-aerosol forcings could more than double the warming effect of large methane increases. Our findings may help explain the anomalously large temperature changes associated with historic methane releases.

Climate models contain closure parameters to which the model climate is sensitive. These parameters appear in physical parameterization schemes where some unresolved variables are expressed by predefined parameters rather than being explicitly modeled. Currently, best expert knowledge is used to define the optimal closure parameter values, based on observations, process studies, large eddy simulations, etc. Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (n=4) of closure parameters appearing in the ECHAM5 climate model. The parameters considered are related to clouds and precipitation and they are sampled by an adaptive random walk process of the MCMC. The parameter probability densities are estimated simultaneously for all parameters, subject to an objective function. Five alternative formulations of the objective function are tested, all related to the net radiative flux at the top of the atmosphere. Conclusions of the closure parameter estimation tests with a low-resolution ECHAM5 climate model indicate that (i) adaptive MCMC is a viable option for parameter estimation in large-scale computational models, and (ii) choice of the objective function is crucial for the identifiability of the parameter distributions.

The global aerosol-climate model ECHAM5-HAM was modified to improve the representation of new particle formation in the boundary layer. Activation-type nucleation mechanism was introduced to produce observed nucleation rates in the lower troposphere. A simple and computationally efficient model for biogenic secondary organic aerosol (BSOA) formation was implemented. Here we study the sensitivity of the aerosol and cloud droplet number concentrations (CDNC) to these additions. Activation-type nucleation significantly increases aerosol number concentrations in the boundary layer. Increased particle number concentrations have a significant effect also on cloud droplet number concentrations and therefore on cloud properties. We performed calculations with activation nucleation coefficient values of 2×10−7s−1, 2×10−6s−1 and 2×10−5s−1 to evaluate the sensitivity to this parameter. For BSOA we have used yields of 0.025, 0.07 and 0.15 to estimate the amount of monoterpene oxidation products available for condensation. The hybrid BSOA formation scheme induces large regional changes to size distribution of organic carbon, and therefore affects particle optical properties and cloud droplet number concentrations locally. Although activation-type nucleation improves modeled aerosol number concentrations in the boundary layer, the use of a global activation coefficient generally leads to overestimation of aerosol number. Overestimation can also arise from underestimation of primary emissions.

Recent laboratory experiments showed that at conditions resembling those near the tropopause region, small ice particles can be coated by a liquid H2SO4/H2O over-layer formed after the freezing of diluted sulfuric acid/water aerosol drops. Here, idealized radiative transfer tests are conducted to evaluate the impact that such an over-layer would have on the radiative effects produced by sub-visible cirrus clouds (SVCs). Spherical particle shape is assumed to keep the problem tractable. The calculations show that the over-layer increases both the shortwave (SW) and longwave (LW) cloud radiative effects (CRE), but the impact is small: ~0.02 W m−2, or even less, for the total (LW+SW) CRE at the top of the atmosphere. For the smallest ice particles, for which the over-layer is thickest, the fractional change in CRE can, however, reach ~20% for the SW CRE and over 50% for the LW CRE. The dependence of LW and SW CRE on particle size is also studied in the paper. Calculations for spherical and spheroidal uncoated ice particles show that even for high, optically thin cirrus, the total CRE can be negative, if the diameter of the particles is smaller than about 3–4 μm. Apart from the SVCs, this result could be relevant for contrail cirrus clouds, which are believed to consist of large numbers of very small ice particles.

The present study aims to deepen our understanding of the relationship between metacognitive awareness and approaches to learning in a multidisciplinary context of higher education using a person-oriented approach. The participants in the present study were 462 third year students of humanities, social sciences and theology. The students filled in a HowULearn questionnaire which included 18 items related to metacognitive awareness and 12 to approaches to learning. Exploratory and confirmatory factor analyses were conducted to explore the factor structure of the instruments. The data were analyzed using Pearson's correlation, K-means cluster analysis and One-way ANOVA. The results showed that two dimensions of metacognitive awareness, namely knowledge about cognition and regulation of cognition, emerged from the data. Knowledge about cognition was evaluated higher than regulation of cognition. The results showed that both dimensions of metacognitive awareness were statistically significantly related to a deep approach to learning and organized studying and negatively to an unreflective approach. Furthermore, three learning profiles were identified and they differed in both of these dimensions of metacognitive awareness. More precisely, students representing "organized students" applying a deep approach had higher scores on knowledge about cognition as well as on regulation of cognition compared with the students representing "unorganized studentsapplying a deep approach" or those with a "dissonant profile." In addition, students representing the "dissonant profile" had statistically significantly lower scores on knowledge about cognition than those students representing "unorganized students applying a deep approach." The present study implies that students with different study profiles need different types of support for the metacognitive awareness of their own learning processes.

This article describes how Finnish health education teachers reflect on their views of multiliteracy and the instructional practices they used to implement it. Narrative interviews were conducted among eight junior high school and high school teachers. Nexus analysis was used to guide the analysis on the teachers' views and practices. The results indicate that the study participants considered the promotion of multiliteracy to be part of their work as a health education teacher and they implemented multiliteracy instruction in diverse ways, such as assigning information-seeking and production tasks, or by creating role-playing games. However, the study revealed tensions between need-based literacy teaching and curriculum-steered multiliteracy promotion as well as common and novel teaching practices. At its best, reflecting on these tensions can serve as a steppingstone toward professional change and development.