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As an important parameter of the Earth’s energy budget, the planetary albedo of Earth varies with the dynamics of atmospheric and surface variables. In this study, we investigated the spatiotemporal characteristics and driving factors of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) dataset and the Geodetector method. The results revealed that the planetary albedo can be decomposed into atmospheric and surface contributions, and the planetary albedo in the middle and low latitudes was predominantly affected by the atmospheric contribution. The global planetary albedo and the atmospheric and surface contributions exhibited decreasing trends of − 0.0020, − 0.0015, and − 0.0004/decade from 2001 to 2018, respectively, which were closely related to the variations of atmospheric and surface variables. The cloud fraction was the driving factor of the atmospheric contribution in the middle and low latitudes, and its influence was further enhanced by the aerosol optical thickness (AOT), ice water path (IWP), and liquid water path (LWP). The snow/ice coverage and normalized difference vegetation index (NDVI) were the driving factors of the surface contribution in the snow/ice-covered and vegetated areas, respectively. The interaction relationships between the surface variables were mainly bi-enhanced and nonlinearly enhanced. These results provide useful information about the driving factors of the planetary albedo and are benefit for improving the parametrization of the planetary albedo.

We evaluate the efficacy of microphysics and planetary-boundary-layer (PBL) parametrizations of the Weather Research and Forecasting (WRF) model for simulation of the coverage and intensity (visibility) of fog during the Coastal Fog (C-FOG) Research Program. The C-FOG observations are used for model validation, particularly focusing on offshore and onshore fog events during 13–14 and 28–29 September 2018. Sensitivity experiments with high horizontal (1 km) and vertical (99 levels) resolution are conducted to elicit possible physical processes underlying the fog life-cycle. Various microphysical and PBL parametrizations, as well as empirical algorithms available for visibility calculations, are evaluated. The model coastal fog formation and characteristics strongly depend on the simulated local meteorology (e.g., temperature, relative humidity, mixing ratio, and wind field) and the microphysical parametrization employed. High space–time resolution simulations for fog coverage and visibility based on the Mansell et al. (J Atmos Sci 67(1):171–194, 2010) microphysical parametrization compare better with data vis-a-vis other microphysical parametrizations, although spatial coverage and visibility are still overestimated. The disparities are likely related to uncertainties of model fog microphysical parameters, including the liquid water content (LWC), droplet number concentration (Nd), and aerosol particle size. It is found that (i) visibility algorithms that use both the variables LWC and Nd, instead of only LWC, provide improved fog estimates; (ii) different PBL parametrizations mainly affect the fog onset and dissipation; (iii) the WRF model has improved performance over the ocean than over land, possibly due to homogeneity of the ocean-surface cover.

Earth system and climate models are fundamental to understanding and projecting climate change. Although they have improved significantly over the last decades, considerable biases and uncertainties in their projections still remain. A large contribution to this uncertainty stems from differences in the representation of clouds and convection (i.e., deep clouds) occurring at scales smaller than the resolved model grid resolution that is typically in the order of 100 km in the horizontal. These long-standing deficiencies in cloud parametrizations have motivated developments of high-resolution cloud- and turbulence-resolving models that can explicitly resolve clouds and convection, yet are computationally extremely expensive and can therefore only be run for a short time and/or only over a small region. However, together with the recent developments in machine learning, and especially deep learning, these simulations can be used and harvested to develop new ML-based parametrizations for clouds and convection that have the potential to eliminate some of the long-standing systematic errors in climate models. Recent research demonstrated that deep convection explicitly simulated by a cloud-resolving climate model could be correctly emulated by a deep neural network that then replaced the original parametrization in the climate model. In this chapter we describe the principal approaches for ML-based cloud parametrizations with deep neural networks and advocate that these ML algorithms need to be guided both by data and by physical knowledge. Many challenges in this new interdisciplinary field of research remain that are also discussed.

The influence of drifting and blowing snow on surface mass and energy exchange is difficult to quantify due to limitations in both measurements and models, but is still potentially very important over large areas with seasonal or perennial snow cover. We present a unique set of measurements that make possible the calculation of turbulent moisture, heat, and momentum fluxes during conditions of drifting and blowing snow. From the data, Monin–Obukhov estimation of bulk fluxes is compared to eddy-covariance-derived fluxes. In addition, large-eddy simulations with sublimating particles are used to more completely understand the vertical profiles of the fluxes. For a storm period at the Syowa S17 station in East Antarctica, the bulk parametrization severely underestimates near-surface heat and moisture fluxes. The large-eddy simulations agree with the eddy-covariance fluxes when the measurements are minimally disturbed by the snow particles. We conclude that overall exchange over snow surfaces is much more intense than current models suggest, which has implications for the total mass balance of the Antarctic ice sheet and the cryosphere. Supplementary Information The online version supplementary material available at 10.1007/s10546-021-00653-x.

A crucial step in the application of the Weather Research and Forecasting (WRF) model to regional climate research is the selection of the proper combinations of physical parameterizations. In this study, we examined the performance of various parametrization schemes in the WRF model in terms of precipitation and temperature over the Haihe river basin in northern China. The WRF experiments were integrated with 13-km horizontal resolution and driven by ERA-INTERIM reanalysis data over the period from 1st June to 31st August, 2016. Fifty-eight members of physics combinations derived from five types of physics options were assessed against the available observational temperature and precipitation data by utilizing the multivariable integrated evaluation (MVIE) method. Our results indicated that the best combination of physical schemes consisted of CAM5.1 microphysics, MRF PBL, BMJ cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. The optimal setup’s differences with the observational data, temporally and spatially, were much smaller than other setups in terms of surface air temperature and precipitation, which proves that the optimal setup showed better performance than the other setups. Further analysis of the sensitivities of model outputs to different types of physics options suggests that the microphysics, planetary boundary layer (PBL), and cumulus schemes have a more significant impact on the model performances than the radiation scheme and Land Surface schemes.

The authors propose a mathematical model of salt particulate dynamics in a roadway, including convection and diffusion of particles with air flow, coagulation of particles, condensation of water on them, and gravity deposition of particles on the roadway floor. Using the finite difference method, the problem on flow of salt particulates in a roadway is solved, and the particulate concentrations and the average size of particles are determined. The calculations are compared with the analytical model and full-scale test data. The found correlation between the particulate deposition velocity and the aerosol parameters can be used for the parametrization of mathematical models of air–dust mixture flow in ventilation networks of potash mines.

Over the past few years large-eddy simulation (LES) has demonstrated success in modelling continental radiation fog, and several recent studies have used LES to investigate the sensitivity of fog formation to physical processes such as turbulent mixing and surface heat and moisture exchange, as well as to the parametrization of microphysical processes such as cloud droplet activation. Here we extend these sensitivity studies to marine fog. There are several important differences in the formation of marine and continental fog, however moisture availability is no longer a decisive factor, and surface temperature changes over a much longer time scale. Here LES is used to examine the sensitivity of simulated marine-fog formation and maintenance to the cloud-droplet number concentration, turbulent mixing, and air–sea temperature difference. The strength of the fog (in terms of liquid water content) is found to be highly sensitive to all three factors. Varying only the cloud-droplet number concentration, even within a range of physically realistic values for marine regions, can mean the difference between fog halving or doubling in liquid water content. The sensitivities demonstrated herein indicate the great need and challenge for constraining these parameters in numerical weather prediction. Similarities and differences to the findings for continental radiation fog are examined, and important considerations for future improvements in marine-fog forecasting are discussed.

A non‐eddy‐resolving microscale model is applied to simulate convection over three different leads (elongated channels in sea ice), which were observed by aircraft over the Arctic Marginal Ice Zone in 2013. The study aims to evaluate the quality of a local and a non‐local turbulence parametrization. The latter represents a lead‐width‐dependent approach for the turbulent fluxes designed for idealised conditions of a lead‐perpendicular, near‐neutral inflow in an atmospheric boundary layer (ABL) capped by a strong inversion at around 250 to 350 m height. The observed cases considered here are also characterised by an almost lead‐perpendicular flow but, in comparison to the idealised conditions, our analysis covers effects in stable inflow conditions and a much shallower ABL. The model simulations are initialised with observed surface parameters and upwind profiles, and the results are compared with measurements obtained above and downwind of the leads. The basic observed features related to the lead‐generated convection can be reproduced with both closures, but the observed plume inclination and vertical entrainment near the inversion layer by the penetrating plume are underestimated. The advantage of the non‐local closure becomes obvious by the more realistic representation of regions with observed vertical entrainment or where the observations hint at counter‐gradient transport. It is shown by comparison with the observations that results obtained with the non‐local closure can be further improved by including the determination of a fetch‐dependent inversion height and by specifying a parameter determining the plume inclination as a function of the upwind ABL stratification. Both effects improve the representation of fluxes, boundary‐layer warming, and vertical entrainment. The model is also able to reproduce the observed vanishing of a weak low‐level jet over the lead, but its downwind regeneration and related momentum transport are not always well captured, irrespective of the closure used., In typical springtime conditions of a cold atmospheric flow over the warm surfaces of leads, which are open‐water channels in sea ice, strong convective plumes are generated which have a large impact on atmospheric boundary‐layer characteristics. Here, a small‐scale model is applied to simulate such situations and model results obtained with different turbulence parametrizations are evaluated using airborne measurements. Based on the observations, a non‐local parametrization developed for the small‐scale modelling of the inhomogeneous convection over leads is further improved., Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via the Transregional Collaborative Research Center ArctiC Amplification (AC)3 (project number 268020496 TRR 172) and via the priority program SPP 1158 (grant LU 818/5‐1)

Rainfall over Rwanda is highly variable both in space and time. This variability leads to chronic food insecurity due to the overdependence of the economy on rain-fed agriculture systems. This study aims to evaluate the skills of Rossby Centre Regional Climate Model (RCA4) simulations driven by 10 GCMs for the period 1951-2005 using the Global Precipitation Climatology Centre (GPCC v8) as a reference. Different statistical and geospatial metrics were used to deduce the model’s skills in simulating seasonal and annual rainfall. Results show that the country received bimodal rainfall pattern; March-May (MAM) and September-December (SOND). The RCA4 models are inconsistent in simulating the MAM rainy peak. However, the models are coherent in simulating SOND seasonal peak despite exhibiting wet bias. The models show reasonable skills in simulating mean annual cycle than interannual variability as depicted by insignificant correlation and different signs of rainfall trend. Conclusively, the performance of RCA4 models in simulating observed rainfall characteristics over Rwanda is relatively weak. The performance of the models differs at various time scales. Nevertheless, the models can be ranked from the best performing to the least as; CSIRO, CanESM2, CNRM, GFDL, MIROC5, ENS, EC-Earth, HadGEM2, IPSL, MPI, and NorESM1. Ranking the performance of RCA4 historical models acts as a basis for future climate model’s selection depending on the purpose of the study. The findings of this study may help in devising appropriate climate adaptation measures to respond to the ongoing global warming for sustainable economic and livelihood development. Additionally, modelers may improve the model’s parametrization schemes and lessen the inherent chronic biases for a better presentation of the future.

Turbulent mixing is a vital component of vertical particulate transport, but ocean global circulation models (OGCMs) generally have low-resolution representations of near-surface mixing. Furthermore, turbulence data are often not provided in OGCM model output. We present 1D parametrizations of wind-driven turbulent mixing in the ocean surface mixed layer that are designed to be easily included in 3D Lagrangian model experiments. Stochastic transport is computed by Markov-0 or Markov-1 models, and we discuss the advantages and disadvantages of two vertical profiles for the vertical diffusion coefficient Kz. All vertical diffusion profiles and stochastic transport models lead to stable concentration profiles for buoyant particles, which for particles with rise velocities of 0.03 and 0.003 m s−1 agree relatively well with concentration profiles from field measurements of microplastics when Langmuir-circulation-driven turbulence is accounted for. Markov-0 models provide good model performance for integration time steps of Δt≈30 s and can be readily applied when studying the behavior of buoyant particulates in the ocean. Markov-1 models do not consistently improve model performance relative to Markov-0 models and require an additional parameter that is poorly constrained.

One of the main scientific and practical problems in the Arctic is the study of the dynamic state of the sea ice cover. The main parameters in the general model of drifting ice are the drift velocity vector, friction stress at the air-ice and ice-water interfaces, and the forces of dynamic interaction of ice fields. Establishing the connection between the large-scale processes in the atmosphere-ice-ocean system is necessary for developing methods of forecasting ice compression and ridging and the formation of local and extended fractures and leads, which help improve the existing climate models. The main aim is to obtain results of full-scale instrumental measurements of parameters of ice large-scale mechanics and dynamics, which provide a physical basis for explaining the nature of observed large-scale ice processes and allow one to perform physical parametrization. To accomplish this aim and evaluate the physicomechanical condition of the drifting ice cover of the Arctic Ocean, the “Transarktika-2019” expedition performed a real-time ice monitoring in April 2019. The investigation was conducted using seismometers and tiltmeters installed on the ice such that they formed a triangle with the sides measuring up to two kilometers. Data has been obtained on the wave and oscillation processes of crack formation, compression and ridging of ice. The possibilities of deciphering the initial data on the physics of wave and oscillatory processes in the icewater system considerably increase when using the known methods of processing seismic signals. With use of spectral Fourier analysis wavelet-transformation of oscillations significanlty extending possibilities of the seismic method at revelation of prognostic signs of crack formation and compression was applied. It is shown that the dynamics of ice processes can be connected with oceanic swell and tidal events. A possibility is created for obtaining new results in the investigation of large-scale mechanics of sea ice.

The paper describes a block for calculating the Earth’s natural atmospheric radiation in the IR range developed for the general circulation model simulating the lower and middle atmosphere. This block uses the new parametrization of molecular absorption in the frequency range from 10 to 2000 cm−1 at the altitude ranging from the Earth’s surface to 76 km. The algorithm for constructing this parametrization takes into account the change in the gas composition of the atmosphere with altitude and has some other significant advantages. In addition, for the numerical solution of the radiation transfer equation, the discrete ordinate method and the computational zenith angle grid with the step of about nine degrees are used. The results of the line-by-line calculations of the Earth’s internal atmospheric radiation field are compared with the results of the calculations performed using parametrization, and it is shown that the presented parametrization is accurate in the lower and middle atmosphere both in the absence of clouds and in the presence of cloud layers with a significant optical thickness.

The assessment of the ecological impact of different powertrain concepts is of increasing relevance considering the enormous efforts necessary to limit the global warming effect due to the man-made climate change. Within this contribution, we adopt existing methods for the optimization of electric and hybrid electric powertrains using a vehicle simulation environment and derive a method to identify the ecological potential of different powertrain concepts for a set of technological parameters in the reference year 2030. By optimizing the parametrization for each powertrain concept and by adapting the respective operating behaviour specifically to minimize the ecological impact, a reliable and unbiased comparison is enabled. We use our optimization environment with the Real Ecological Impact as objective function to compare different powertrain concepts on driving profiles that are based on real driving data recorded in Germany. Despite the fact that all of the considered driving profiles contain trips of similar length, their respective optimized powertrain concepts are different. Plug-In Hybrid vehicles achieve the greatest potential for long-range capable vehicles and are least sensitive to different driving profiles.

Inland sources of particulate chloride for atmospheric nitryl chloride (ClNO2) formation remain unknown and unquantified, hindering air quality assessments. Globally each winter, tens of millions of tons of road salt are spread on roadways for deicing. Here, we identify road salt aerosol as the primary chloride aerosol source, accounting for 80–100% of ClNO2 formation, at an inland urban area in the wintertime. This study provides experimental evidence of the connection between road salt and air quality through the production of this important reservoir for nitrogen oxides and chlorine radicals, which significantly impact atmospheric composition and pollutant fates. A numerical model was employed to quantify the contributions of chloride sources to ClNO2 production. The traditional method for simulating ClNO2 considers chloride to be homogeneously distributed across the atmospheric particle population; yet, we show that only a fraction of the particulate surface area contains chloride. Our new single-particle parametrization considers this heterogeneity, dramatically lowering overestimations of ClNO2 levels that have been routinely reported using the prevailing methods. The identification of road salt as a ClNO2 source links this common deicing practice to atmospheric composition and air quality in the urban wintertime environment., Road salt was identified as the primary source of chloride-containing aerosol in the wintertime urban atmosphere. This links deicing practices to air quality via the production of the trace gas ClNO2.

The Tibetan Plateau (TP) is one of the research hot spots in the climate change research due to its unique geographical location and high altitude. Downward longwave radiation (DLR), as a key component in the surface energy budget, has practical implications for radiation budget and climate change. A couple of attempts have been made to parametrize DLR over the TP based on hourly or daily measurements and crude clear-sky discrimination methods. This study uses 1 min shortwave and longwave radiation measurements at three stations over the TP to parametrize DLR during summer months. Three independent methods are used to discriminate clear sky from clouds based on 1 min radiation and lidar measurements. This guarantees the strict selection of clear-sky samples that is fundamental for the parametrization of clear-sky DLR. A total of 11 clear-sky and 4 cloudy DLR parametrizations are examined and locally calibrated. Compared to previous studies, DLR parametrizations here are shown to be characterized by smaller root-mean-square errors (RMSEs) and higher coefficients of determination (R2). Clear-sky DLR can be estimated from the best parametrization with a RMSE of 3.8 W m−2 and R2>0.98. Systematic overestimation of clear-sky DLR by the locally calibrated parametrization in one previous study is found to be approximately 25 W m−2 (10 %), which is very likely due to potential residual cloud contamination on previous clear-sky DLR parametrization. The cloud base height under overcast conditions is shown to play an important role in cloudy DLR parametrization, which is considered in the locally calibrated parametrization over the TP for the first time. Further studies on DLR parametrization during nighttime and in seasons except summer are required for our better understanding of the role of DLR in climate change.

The genesis and evolution of a sea-breeze front associated with a shallow convection episode at Fuerteventura Island (Canaries archipelago) are investigated using the Weather Research and Forecasting (WRF) numerical model. Three local and two non-local planetary boundary layer (PBL) parametrization schemes are used, and results of the numerical simulations are compared with observations from the local meteorological surface station network. Statistical analysis shows a good agreement between simulations and measurements, in particular for the 2-m temperature. These results are used in choosing the final PBL scheme and its coupled surface-layer scheme, which is used to simulate the daytime period of the sea-breeze development and weakening. During this episode, a sea-breeze front moved inland against a prevailing offshore flow 3 h after sunrise, colliding with a downslope flow near the centre of the island. The convergence of both flows from opposing directions produced a strong updraft, forming shallow convection a few hours later.

International audience; Cold-air outbreaks (CAO) lead to intense air–sea interactions, the appropriate representation of which are fundamental for climate modelling and numerical weather forecasting. We analyze a CAO event with low-level wind speeds of approximately 25 m s−1 observed in the north-western Mediterranean Sea. The marine atmospheric boundary layer (MABL) was sampled with an aircraft equipped for turbulence measurements, revealing the organization of the MABL flow in coherent structures oriented along the mean wind direction, which was then simulated in two steps. First, a one-dimensional simulation enabled the determination of the forcing terms (particularly horizontal advection) required to adequately reproduce the vertical structure of the MABL flow. These terms were computed from a limited-area forecast model in operation during the entire field campaign. Then, a large-eddy simulation (LES) was performed during the well-established phase of the CAO event. The LES output is validated with respect to airborne data, not only with respect to the mean wind-speed and thermodynamic profiles, but also the turbulence statistics and coherent structures. The validated LES results enable description of the turbulent field as well as the coherent structures. The main discrepancy is a considerable underestimation of the simulated evaporation (computed with a parametrization of the turbulent surface fluxes), and hence of the moisture fluctuations throughout the boundary layer. Several possible explanations may explain this underestimation. The structure of the boundary layer is nonetheless well reproduced by the LES model, including the organized structures and their characteristic scales, such as the structure wavelength, orientation, and aspect ratio, which closely agree with observations. A conditional-sampling analysis enables determination of the contribution of the coherent structures to the vertical exchange. Although they occupy a limited fractional area, organized structures are the primary contributors to the turbulent exchange.

This study investigates the mechanisms acting at the air–sea interface during a heavy precipitation event that occurred between 12 and 14 October 2016 over the north-western Mediterranean area and led to large amounts of rainfall (up to 300 mm in 24 h) over the Hérault region (southern France). The study case was characterized by a very strong (>20 m s−1) easterly to south-easterly wind at low level that generated very rough seas (significant wave height of up to 6 m) along the French Riviera and the Gulf of Lion. In order to investigate the role of the waves on air–sea exchanges during such extreme events, a set of numerical experiments was designed using the Météo-France kilometre-scale AROME-France numerical weather prediction model – including the WASP (Wave-Age-dependant Stress Parametrization) sea surface turbulent flux parametrization – and the WaveWatch III wave model. Results from these sensitivity experiments in the forced or coupled modes showed that taking the waves generated by the model into account increases the surface roughness. Thus, the increase in the momentum flux induces a slowdown of the easterly low-level atmospheric flow and a displacement of the convergence line at sea. Despite strong winds and a young sea below the easterly flow, the turbulent heat fluxes upstream of the precipitating system are not significantly modified. The forecast of the heaviest precipitation is finally modified when the sea state is taken into account; notably, in terms of location, this modification is slightly larger in the forced mode than in the coupled mode, as the coupling interactively balances the wind sea, the stress and the wind.

Individual-level simulation models are used to assess the effects of health interventions in complex settings. However, estimating valid causal effects using these models requires correct parametrization of the relationships between time-varying treatments, outcomes, and other variables in the causal structure. To parameterize these relationships, individual-level simulation models typically need estimates of the direct effects of treatment. However, direct effects of treatment are often not well- defined and therefore cannot be validly estimated from any data. In this paper, we explain the causal meaning of the parameters of individual-level simulation models as direct effects, describe why direct effects may be difficult to define unambiguously in some settings, and conclude with some suggestions for the design of individual-level simulation models in those settings.

With the ongoing expansion of wind energy onshore and offshore, large-scale wind-farm-flow effects in a temporally- and spatially-heterogeneous atmosphere become increasingly relevant. Mesoscale models equipped with a wind-farm parametrization (WFP) can be used to study these effects. Here, we conduct a systematic literature review on the existing WFPs for mesoscale models, their applications and findings. In total, 10 different explicit WFPs have been identified. They differ in their description of the turbine-induced forces, and turbulence-kinetic-energy production. The WFPs have been validated for different target parameters through measurements and large-eddy simulations. The performance of the WFP depends considerably on the ability of the mesoscale model to simulate the background meteorological conditions correctly as well as on the model set-up. The different WFPs have been applied to both onshore and offshore environments around the world. Here, we summarize their findings regarding (1) the characterizations of wind-farm-flow effects, (2) the environmental impact of wind farms, and (3) the implication for wind-energy planning. Since wind-farm wakes can last for several tens of kilometres downstream depending on stability, surface roughness and terrain, neighbouring wind farms need to be taken into account for regional planning of wind energy. Their environmental impact is mostly confined to areas close to the farm. The review suggests future work should include benchmark-type validation studies with long-term measurements, further developments of mesoscale model physics and WFPs, and more interactions between the mesoscale and microscale community.

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

International audience; Abstract• Key messageThe best options to parametrize a radiative transfer model change according to the response variable used for fitting. To predict transmitted radiation, the turbid medium approach performs much better than the porous envelop, especially when accounting for the intra-specific variations in leaf area density but crown shape has limited effects. When fitting with tree growth data, the porous envelop approach combined with the more complex crown shape provides better results. When using a joint optimization with both variables, the better options are the turbid medium and the more detailed approach for describing crown shape and leaf area density.• ContextSolar radiation transfer is a key process of tree growth dynamics in forest.• AimsDetermining the best options to parametrize a forest radiative transfer model in heterogeneous oak and beech stands from Belgium.• MethodsCalibration and evaluation of a forest radiative transfer module coupled to a spatially explicit tree growth model were repeated for different configuration options (i.e., turbid medium vs porous envelope to calculate light interception by trees, crown shapes of contrasting complexity to account for their asymmetry) and response variables used for fitting (transmitted radiation and/or tree growth data).• ResultsThe turbid medium outperformed the porous envelope approach. The more complex crown shapes enabling to account for crown asymmetry improved performances when including growth data in the calibration.• ConclusionOur results provide insights on the options to select when parametrizing a forest radiative 3D-crown transfer model depending on the research or application objectives.

The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time., J.M.G.V. acknowledges support from Ministerio de Ciencia e Innovación under grant PGC2018-101282-B-I00 (MCI/AEI/FEDER, UE). L.S. acknowledges support from the University of California, Davis.

The exchange of momentum across the air–sea boundary is an integral component of the earth system and its parametrization is essential for climate and weather models. This study focuses on the impact of gustiness on the momentum flux using three months of direct flux observations from a moored surface buoy. Gustiness, which quantifies the fluctuations of wind speed and direction, is shown to impact air–sea momentum fluxes. First, we put forward a new gustiness formula that simultaneously evaluates the impact of fluctuations in wind direction and speed. A critical threshold is established using a cumulative density function to classify runs as either gusty or non-gusty. We find that, during runs classified as gusty, the aerodynamic drag coefficient is increased up to 57% when compared to their non-gusty counterparts. This is caused by a correlated increase in vertical fluctuations during gusty conditions and explains variability in the drag coefficient for wind speeds up to 20 m/s. This increase in energy is connected with horizontal fluctuations through turbulent interactions between peaks in the turbulent spectra coincident with peaks in the wave spectra. We discus two potential mechanistic explanations. The results of this study will help improve the representation of gustiness in momentum flux parameterizations leading to more accurate ocean models.

Weather prediction and climate simulations need reliable parameterizations of turbulent fluxes in the stable surface layer. Especially in these conditions, the uncertainties of such parametrizations are still large. Most of them rely on the Monin‐Obukhov similarity theory (MOST), for which universal stability functions (SFs) represent important ingredients. The SFs are nonlinear, if so, a numerical iteration of the MOST equations is required. Moreover, presently available SFs are significantly different at large stability. To simplify the calculations, a non‐iterative parametrization of fluxes is derived and corresponding bulk transfer coefficients for momentum and heat for a package of five pairs of state‐of‐the‐art SFs are proposed. For the first time, a parametrization of the related transfer coefficients is derived in a universal framework for all package members. The new parametrizations provide a basis for a cheap systematic study of the impact of surface layer turbulent fluxes in weather prediction and climate models.

The influence of the initial vertical moisture profile on precipitating shallow cumulus cloud organization in terms of the column‐averaged moisture variance is investigated using large‐eddy simulations. Five idealized simulations based on the Rain in Cumulus over the Ocean field experiment with different initial moisture profiles are investigated. All cases simulate precipitating shallow cumulus convection in a marine sub‐tropical region under large‐scale subsidence. The results show that the moisture variance is mainly generated through the interaction of the moisture flux and the moisture gradient in the gradient production term at the top of the boundary layer. The development is characterized by three regimes: initial, transition, and quasi‐steady regime. During the initial regime, the moisture gradient is built up by moisture accumulation until precipitating convection starts. Within the transition regime, precipitation enables mesoscale cloud organization with enhanced convective activity and moisture fluxes. The moisture variance increases from the moist to the dry initial moisture profiles. In a following quasi‐steady regime, the moisture variance is approximately preserved. Thereby, the initial moisture gradient between the average sub‐cloud layer and the free atmosphere is found to be an important factor for the generation of the quasi‐steady column‐averaged moisture variance. The result suggests that a resolved‐scale variable like the moisture gradient can be used to estimate the quasi‐steady state conditions resulting from cloud organization. This finding may serve as a starting point for the parametrization of the subgrid scale cloud organization caused by precipitating shallow convection., Key Points: Mesoscale organization of precipitating shallow cumulus changes the bulk properties of the atmospheric boundary layer (e.g., cloud cover). The increase of moisture variance during cloud organization is sensitive to the initial vertical moisture profiles. The average gradient between the average sub‐cloud layer and the free atmosphere is a good predictor of the moisture variance., Hans Ertel Centre for Weather Research

Weather simulations are sensitive to subgrid processes that are parameterized in numerical weather prediction (NWP) models. In this study, we investigated the response of tropical cyclone Idai simulations to different cumulus parameterization schemes using the Weather Research and Forecasting (WRF) model with a 6 km grid length. Seventy-two-hour (00 UTC 13 March to 00 UTC 16 March) simulations were conducted with the New Tiedtke (Tiedtke), New Simplified Arakawa–Schubert (NewSAS), Multi-Scale Kain–Fritsch (MSKF), Grell–Freitas, and the Betts–Miller–Janjic (BMJ) schemes. A simulation for the same event was also conducted with the convection scheme switched off. The twenty-four-hour accumulated rainfall during all three simulated days was generally similar across all six experiments. Larger differences in simulations were found for rainfall events away from the tropical cyclone. When the resolved and convective rainfall are partitioned, it is found that the scale-aware schemes (i.e., Grell–Freitas and MSKF) allow the model to resolve most of the rainfall, while they are less active. Regarding the maximum wind speed, and minimum sea level pressure (MSLP), the scale aware schemes simulate a higher intensity that is similar to the Joint Typhoon Warning Center (JTWC) dataset, however, the timing is more aligned with the Global Forecast System (GFS), which is the model providing initial conditions and time-dependent lateral boundary conditions. Simulations with the convection scheme off were found to be similar to those with the scale-aware schemes. It was found that Tiedtke simulates the location to be farther southwest compared to other schemes, while BMJ simulates the path to be more to the north after landfall. All of the schemes as well as GFS failed to simulate the movement of Idai into Zimbabwe, showing the potential impact of shortcomings on the forcing model. Our study shows that the use of scale aware schemes allows the model to resolve most of the dynamics, resulting in higher weather system intensity in the grey zone. The wrong timing of the peak shows a need to use better performing global models to provide lateral boundary conditions for downscalers.

Cosmic rays are messengers from highly energetic events in the Universe. These rare ultra-high-energy particles can be detected efficiently and in an affordable way using large arrays of radio antennas. Linearly polarized geomagnetic emission is the dominant emission mechanism produced when charged particles in air showers get deflected in the Earth's magnetic field. The sub-dominant Askaryan emission is radially polarized and produced due to the time-varying negative-charge excess in the shower front. The relative amplitude of these two emission components depends on various air shower parameters, such as the arrival direction and the depth of the shower maximum. We studied these dependencies using CoREAS simulations of the radio emission from air showers at the South Pole using a star-shaped antenna layout. On the one hand, the parametrization of the Askaryan-to-geomagnetic ratio can be used as input for a more accurate reconstruction of the shower energy. On the other hand, if measured precisely enough, this ratio may provide a new method to reconstruct the atmospheric depth of the shower maximum.

Ensemble forecasts are run operationally to determine forecast uncertainty arising from initial condition, model physics and boundary condition uncertainty. However, global configuration ensembles, which use a convection parametrization scheme, may miss uncertainty because of the misrepresentation of intense convection by such schemes. Here the impacts of the misrepresentation of Mesoscale Convective Systems (MCSs) on downstream ensemble forecast skill and evolution are determined for a case study. MCS perturbations (calculated from the difference between output from convection-parametrizing and convection-permitting Met Office model configurations) are added to six members of a global configuration ensemble created by downscaling forecasts from the global version of the Met Office Global and Regional Ensemble Prediction System. For the first 36 h differences grow on the convective scale related to the MCSs leading to systematic deepening of a developing UK cyclone, although there is damping of the perturbations found in root mean square difference calculations between the forecasts with and without the perturbations (particularly in mean sea level pressure). Subsequently, differences grow rapidly onto the synoptic scale and by five days impact the entire northern hemisphere. The MCS perturbations can have systematic effects on the ensemble forecasts (e.g. a systematic displacement of a downstream cyclone is found), but for this case, there is no discernible change in forecast skill as measured by root mean square error of the ensemble means and the effects of the MCS perturbations are smaller than those generated by the initial condition perturbations. The spread of the combined ensemble (the two ensembles with and without the MCS perturbations) is larger than that of the individual ensembles. Thus, perturbing convection-parametrizing models to include PV anomalies associated with MCSs represented in convection-permitting forecasts, or idealised representations of them, produces alternative realisations to those generated by initial condition perturbations and has the potential to be useful operationally.

Spectral reflectance of natural snow samples representing various stratigraphies was investigated in a controlled dark laboratory environment. Mean and Std dev. of band specific reflectance values were determined for several satellite sensor bands utilized in remote sensing of snow. The reflectance values for dry, moist, wet and wet and littered snow for different instruments varied between 0.63–0.97 in the visible and near-infrared bands at an incoming light zenith angle of θ = 55°. The results indicate that in MODIS band 4 (545–565 nm), essential to snow mapping, the reflectance of snow drops by 9% when dry snow changes to wet snow and by a further 10% when typical forest litter inclusions exist on the wet snow surface. A separate investigation of individual snow types revealed that they can be grouped either as dry or wet snow based on their spectral behavior. However, some snow types were located between these two distinct groups, such as snow with near-surface melt-freeze crusts, and could not be clearly distinguished. The reflectance statistics collected and analyzed here can be directly used to refine accuracy characterization and parametrization of snow mapping algorithms, such as the SCAmod method, used for the mapping of snow cover area.

The method of Dynamic–Stochastic parametrization of the nonconvective cloud amount in the general circulation model of the atmosphere is formulated. The proposed algorithm is evaluated on the basis of the general circulation model of the atmosphere with the specified temperature of the ocean surfaces. The results of calculations are compared to the satellite observation data and to the results of calculations of the cloud amounts performed using a coupled high-resolution atmosphere–ocean general circulation model. This approach looks highly promising in the results.

In order to separate global and local effects of atmospheric electricity, measurements of the fair-weather electric field were performed in Nizhny Novgorod in 2013-2018. As a result of processing 139 diurnal records from four observation points spaced 6–8 km apart, diurnal variations in the fair-weather atmospheric electric field for different seasons and weekdays (working days and weekends) were studied. The curve of the local diurnal variation is shown to always have two maxima. The evening maximum of the diurnal variation (19:00–20:00 UT) coincides in time with the maximum of the Carnegie curve, which is a characteristic of the global electrical circuit. The highest values of the field amplitude are reached in the winter period. The field-intensity maximum in the first half of the day (09:00–11:00 LT) is characteristic of the urban environment and shows that local effects associated with the presence of aerosol particles in the air significantly contribute to the formation of diurnal variation, especially in summer. According to the 2013–2018 measurements, the seasonal variation in the monthly-average values of the atmospheric electric field is revealed and analyzed compared with the results of measurements of seasonal variation in other regions of the globe. The obtained results allow one to reveal the role of local effects in the formation of diurnal variation in the mid-latitude areas with temperate continental climate and provide a basis for developing a theory which can explain the physical mechanisms of local effects and suggest appropriate parametrization for finding the surface electric field in the weather and climate models.

Evapotranspiration (ET) is considered a key variable in the understanding of the Amazonian tropical forests and their response to climate change. Remote-Sensing (RS) based evapotranspiration models are presented as a feasible means in order to provide accurate spatially-distributed ET estimates over this region. In this work, the performance of four commonly used ET RS models was evaluated over Amazonia using Moderate Resolution Imaging Spectroradiometer (MODIS) data. RS models included i) Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), ii) Penman-Monteith MODIS operative parametrization (PM-Mu), iii) Surface Energy Balance System (SEBS), and iv) Satellite Application Facility on Land Surface Analysis (LSASAF). These models were forced using two ancillary meteorological data sources: i) in-situ data extracted from Large-Scale Biosphere-Atmosphere Experiment (LBA) stations (scenario I), and ii) three reanalysis datasets (scenario II), including Modern-Era Retrospective analysis for Research and Application (MERRA-2), European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim), and Global Land Assimilation System (GLDAS-2). Performance of algorithms under the two scenarios was validated using in-situ eddy-covariance measurements. For scenario I, PT-JPL provided the best agreement with in-situ ET observations (RMSE = 0.55 mm/day, R = 0.88). Neglecting water canopy evaporation resulted in an underestimation of ET measurements for LSASAF. SEBS performance was similar to that of PT-JPL, nevertheless SEBS estimates were limited by the continuous cloud cover of the region. A physically-based ET gap-filling method was used in order to alleviate this issue. PM-Mu tended to overestimate in-situ ET observations. For scenario II, quality assessment of reanalysis input data demonstrated that MERRA-2, ERA-Interim and GLDAS-2 contain biases that impact model performance. In particular, biases in radiation inputs were found the main responsible of the observed biases in ET estimates. For the region, MERRA-2 tends to overestimate daily net radiation and incoming solar radiation. ERA-Interim tends to underestimate both variables, and GLDAS tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these reanalysis inputs generally explain the observed discrepancies in model spatial and temporal patterns.

The Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is used in ensemble forecast systems to represent uncertainties due to the atmospheric physics parametrizations. The scheme works by introducing random perturbations to the tendencies from the physics schemes. The size of the perturbations is modulated by the size of the net tendencies from the full physics suite. Recent work has identified an inconsistency in this approach to attributing model uncertainty: greater uncertainty is attributed to the night‐time sky than to the daytime sky in clear‐sky conditions, which is not deemed physically realistic. This paper documents the details of a revised configuration of SPPT, which became active in the recent operational version of the ensemble forecast system (“CY45R1”) at the European Centre for Medium‐Range Weather Forecasts (ECMWF) — released in June 2018. In this work, a modification to SPPT is outlined such that the clear‐sky contribution from the radiation scheme is subtracted from the physics tendencies that are acted on by SPPT. Inspection of individual vertical profiles of physics tendencies provides a greater understanding of the interactions between processes, and helps demonstrate that the revision gives a more realistic description of the associated uncertainties. Forecast experiments with the revised version of SPPT demonstrate neutral to positive impacts on the skill of the ensemble forecasts in the medium‐range; and in the extended‐range, a significantly improved match between the spread and error of the principal components describing the Madden–Julian Oscillation. In addition, it is shown that including the revisions in the ensemble of data assimilations leads to increases in spread in the boundary layer.

We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

Author(s): Freeman, SW; Igel, AL; van den Heever, SC | Abstract: Rainfall prediction by weather forecasting models is strongly dependent on the microphysical parametrization being utilized within the model. As forecasting models have become more advanced, they are more commonly using double-moment bulk microphysical parametrizations. While these double-moment schemes are more sophisticated and require fewer a priori parameters than single-moment parametrizations, a number of parameter values must still be fixed for quantities that are not Prognosed or diagnosed. Two such parameters, the width of the rain drop size distribution and the choice of collection efficiencies between liquid hydrometeors, are examined here. Simulations of deep convective storms were performed in which the collection efficiency dataset and the a priori width of the rain drop size distribution (RSD) were individually and simultaneously modified. Analysis of the results show that the a priori width of the RSD was a larger control on the total accumulated precipitation (a change of up to 75% over the typical values tested in this article) than the choice of collection efficiency dataset used (a change of up to 10%). Changing the collection efficiency dataset produces most of the impacts on precipitation rates through changes in the warm rain process rates. On the other hand, the decrease in precipitation with narrowing RSDs occurs in association with the following processes: (a) decreased rain production due to increased evaporation, (b) decreased rain production due to decreased ice melting, and (c) slower raindrop fall speed which leads to longer residency times and changes in rain self-collection. These results add to the growing body of work showing that the representation of hydrometeor size distributions is critically important, and suggests that more work should be done to better represent the width of the RSD in models, including further development of triple-moment and bin schemes.

Despite the importance of the primary production in coastal Large Marine Ecosystems, the complex bio-optical properties in some coastal waters make the estimation of primary productivity using satellite data quite challenging. Here we critically reviewed the accuracy of parametrization of three core variables, i.e. chlorophyll-a, chlorophyll-specific photosynthetic rate, and euphotic depth, in the Yellow Sea, East China Sea, and Sea of Japan/East Sea Large Marine Ecosystems. We also compared the annual primary productivity estimates of the three Large Marine Ecosystems by a depth-integrated model with those from two other global assessments, which used different primary productivity models: an absorption-based model, and a time and depth-resolved model. Our assessment suggests that 1) accurate parametrization of the core variables is more important than choosing a primary productivity model, and 2) the previous global Large Marine Ecosystem assessments might have overestimated the annual primary productivity in the Yellow Sea by a factor of 2 or so.

Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.

Snowfall and the subsequent evolution of the snowpack play important roles in the cryospheric and hydrospheric processes that occur on the Tibetan Plateau (TP). Current literature provides scarce evidence covering the sensitivity of solid precipitation to land surface physics schemes and initial and boundary conditions on the TP. Six numerical experiments using the Weather Research and Forecasting (WRF) model were conducted to simulate a snow event over the TP in March 2017. Different land surface physics schemes, that is, Community Land Model (CLM), Noah, and Noah-MP, and initial and boundary conditions provided by atmospheric reanalysis data sets, that is, the National Centers for Environmental Prediction-FNL and ERA-Interim data sets, were applied in sensitivity analyses. The observed near-surface air temperature, snow depth, and snow water equivalent (SWE) values were used to evaluate each model's performance. The results demonstrate that (1) the sensitivity of the near-surface air temperature to land surface physics schemes is greater than it is to both the initial and boundary conditions; (2) the best performance is achieved when applying WRF + CLM with a root-mean-square error of 8.4 °C, a mean absolute deviation of 7.3 °C, a correlation coefficient of 0.75, and a spatial correlation coefficient of ~0.5 to air temperature estimates. A potentially important factor appears to be the advanced parametrization of albedo in the CLM scheme; (3) the advanced land surface schemes in the WRF model describes the physics of cryospheric and hydrospheric processes in detail, and the land surface response is determined by multiple variables and parameters in such schemes. The spatial patterns in such variables and parameters determined the detailed spatial variabilities observed in snow cover and amount and its temporal evolution. The WRF model overestimates, however, the intensity and extent of snow depth and SWE; (4) simulations of solid precipitation are more accurate when applying CLM or Noah-MP + ERA-Interim in WRF; and (5) WRF performance with regard to SWE estimates clearly depends upon the discrimination of lighter from heavier snowfall.

Turbulent mixing processes are important in determining the evolution of convective clouds,and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow “shower” case, and a “deep convection” case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models.

The polar ocean regions are characterised by a large variety of interactions between sea ice surfaces, open water, and the atmosphere. Especially between late autumn and spring, leads (open-water channels in sea ice) may play a crucial role within this system: Due to large temperature differences between the surface of leads and the near-surface atmosphere, strong turbulent convective plumes are generated with an enhanced turbulent transport of heat, moisture, and momentum. In consequence, lead-generated convection has a strong impact on the characteristics of the polar atmospheric boundary layer (ABL). We apply a plume- but non-eddy-resolving, microscale model to study the convection over three different leads, which had been observed during the aircraft campaign STABLE over the Arctic Marginal Sea Ice Zone in March 2013. Model simulations are performed using a local and a non-local turbulence closure. The latter represents a lead-width-dependent approach for the turbulent fluxes based on large eddy simulation and it is designed for an idealised, lead-perpendicular, and near-neutral inflow in an ABL of 300m thickness. The observed cases from STABLE are also characterised by lead-perpendicular inflow conditions, but the ABL is much shallower than in the idealised cases and the inflow stratification is partly (slightly) stable. Our main goal is to study the quality of both parametrizations and to evaluate, if the non-local parametrization shows advantages as compared to the local closure.We show that the basic observed features of the lead-generated convection are represented with both closures despite some minor differences that will be explained. However, the advantages of the non-local closure become clearly obvious by the physically more realistic representation of regions with observed vertical entrainment or where the observations hint at counter-gradient transport. Moreover, we also show that some weaknesses of the simulations can be almost overcome by introducing two further modifications of the non-local closure. We consider our results as another important step in the development of atmospheric turbulence parametrizations for non-eddy-resolving, microscale simulations of strongly inhomogeneous convective boundary layers.

Global Climate Models are a keystone of modern climate research. In many applications relevant for decision making, and particularly when deriving future projections with the delta-change method, they are assumed to be perfect. However, these models have not been originally developed to reproduce the regional-scale climate, which is where information is needed in practice. To overcome this dilemma, two general efforts have been made since their introduction in the late 1960ies. First, the models themselves have been steadily improved in terms of physical and chemical processes, parametrization schemes, resolution and complexity, giving rise to the term Earth System Model. Second, the global models' output has been refined at the regional scale using Limited Area Models or statistical methods in what is known as dynamical or statistical downscaling. Both approaches, however, are in principle unable to correct errors resulting from a wrong representation of the large-scale circulation in the global model. Also, dynamical downscaling has a high computational demand and thus cannot be applied to all available global models in practice. On this background, there is an ongoing debate in the downscaling community on whether to thrive away from the model democracy paradigm towards a careful selection strategy based on the global models' capacity to reproduce key aspects of the observed climate. The present study attempts to be useful for such a selection by providing a performance assessment of the historical global model experiments from CMIP5 and 6 based on recurring regional atmospheric circulation patterns (Lamb, 1972). The latest model generation is found to perform better on average, which can be partly explained by a moderately strong statistical relationship between performance and horizontal resolution in the atmosphere. A few models rank favourably over almost the entire northern hemisphere extratropics, but the better models tend to be less complex than others. Model selection should therefore not solely rely on model performance but also on model complexity and a discussion is needed on how to combine these two criteria. Internal model variability only has a small influence on the model ranks. Reanalysis uncertainty is an issue in Greenland and the surrounding seas, the southwestern United States and the Gobi desert, but is otherwise negligible.

Both ice sheets in Greenland and Antarctica are discharging ice into the ocean. In many regions along the coast of the ice sheets, the icebergs calve into a bay. If the addition of icebergs through calving is faster than their transport out of the embayment, the icebergs will be frozen into a mélange with surrounding sea ice in winter. In this case, the buttressing effect of the ice mélange can be considerably stronger than any buttressing by mere sea ice would be. This in turn stabilizes the glacier terminus and leads to a reduction in calving rates. Here we propose a simple parametrization of ice mélange buttressing which leads to an upper bound on calving rates and can be used in numerical and analytical modelling.

Global climate models have difficulties to simulate the northward extension of the monsoonal precipitation over north Africa during the mid-Holocene as revealed by proxy data. A common feature of these models is that they usually operate on too coarse grids to explicitly resolve convection, but convection is the most essential mechanism leading to precipitation in the west African monsoon region. Here, we investigate how the representation of tropical deep convection in the ICON climate model affects the meridional distribution of monsoonal precipitation during the mid-Holocene, by comparing regional simulations of the summer monsoon season (July to September, JAS) with parameterized (40km-P) and explicitly resolved convection (5km-E). The spatial distribution and intensity of precipitation, are more realistic in the explicitly resolved convection simulations than in the simulations with parameterized convection.However, in the JAS-mean the 40km-P simulation produces more precipitation and extents further north than the 5km-E simulation, especially between 12° N and 17° N. The higher precipitation rates in the 40km-P simulation are consistent with a stronger monsoonal circulation over land. Furthermore, the atmosphere in the 40km-P simulation is less stably stratified and notably moister. The differences in atmospheric water vapor are the result of substantial differences in the probability distribution function of precipitation and its resulting interactions with the land surface. The parametrization of convection produces light and large-scale precipitation, keeping the soils moist and supporting the development of convection. In contrast, less frequent but locally intense precipitation events lead to high amounts of runoff in explicitly resolved convection simulations. The stronger runoff inhibits the moistening of the soil during the monsoon season and limits the amount of water available to evaporation.

Mountain lee waves usually involve aircraft icing and turbulence events. These weather phenomena, in turn, are a threat to aviation safety. For this reason, mountain lee waves are an interesting subject of study for the scientific community. This paper analyses several mountain lee waves events in the south-east of the Guadarrama mountain range, near the Adolfo Suarez Madrid-Barajas airport (Spain), using the Weather Research and Forecasting (WRF) and the HARMONIE-AROME high-resolution numerical models. For this work, simulated brightness temperature from the optimum WRF parametrization schemes and from the HARMONIE are validated using satellite observations to evaluate the performance of the models in reproducing the lenticular clouds associated to mountain lee waves. The brightness temperature probability density shows interesting differences between both models. Following, a mountain wave characterization is performed simulating some atmospheric variables (wind direction, wind speed, atmospheric stability, liquid water content and temperature) in several grid points located in the leeward, windward and over the summit of the mountains. The characterization results are compared for both numerical models and a decision tree is developed for each to forecast and warn the mountain lee waves, lenticular clouds and icing events with a 24 to 48 h lead time. These warnings are validated using several skill scores, revealing similar results for both models.

Effective nutrient pollution mitigation measures require in-depth understanding of spatio-temporal controls on water quality which can be obtained by analyzing export regime and hysteresis patterns in concentration-discharge (urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0001) relationships. Such analyses require high-frequency data (hourly or higher resolution), hampering the assessment of hysteresis patterns in widely available low-frequency (monthly, biweekly) regulatory water quality data. We propose a reproducible classification of urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0002 relationships considering export regime (dilution, constancy, enrichment) and long-term average hysteresis pattern (clockwise, no hysteresis, anticlockwise) applicable to low-frequency water quality data. The classification is based on power-law urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0003 models with separate parametrization for low and high discharge and rising and falling hydrograph limb, enabling a better representation of urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0004 dynamics. The classification has been applied to a 30-years record of daily streamflow and monthly spot samples of solute concentrations in 45 Scottish catchments with contrasting characteristics in terms of topography, climate, soil and land cover. We found that urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0005 classification is solute- and catchment-specific and linked to upland versus lowland catchments and streamflow variability. However as the relationship between solute behavior and catchment characteristics is variable, we propose that future typologies should integrate both water quality response, that is, urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0006 classification, and catchment characteristics. The data-driven urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0007 classification allows us to increase the information content of low-frequency water quality data and thus inform mitigation measures, monitoring strategies, and modeling approaches. Such approaches open up an ability to characterize processes and best management for a wider number of catchments, subject to regulatory surveillance and outside of research catchments Rural and Environment Science and Analytical Services Division (RESAS) Spanish Ministry of Science, Innovation and Universities. Grant Numbers: RTI2018-095518-B-C21, 2019-2021

The assessment of thermal performance and efficiency of the Pavement Solar Collector (PSC) systems is essential in designing such systems. In this paper, a simplified simulation model is developed to predict PSC's performance at the design stage. Due to its simplicity, accuracy, and extremely quick simulation run-time, the model output can be linked with other thermal models and renewable heat sources to apply real-time changes in the PSC systems and used in the automation of a PID-controller. The output results show good compatibility between the predicted and simulated data, where the relative error values for model parametrization and validation are less than 0.21% and 0.5%. In terms of computational cost, a yearly long-term performance run time of the present model was simply 3 s (without parametrization), 300,000 times faster than a FEM model for only one month. A systematic sensitivity analysis on different design parameters shows that the efficiency index increases with an increase in pipe spacing, pipe embedment depth, and flow rate, while it decreases with an increase in the asphalt thermal conductivity in the wintertime. Also, the variation of the heat transfer coefficient UA* and correlation parameter k in different PSC configurations was established.

We focus on processes where the physics-dynamics interaction is strong and which remain challenging even at future O (1–5 km) resolutions for both the model and the data assimilation: the coupling of deep convection with the larger-scale circulation, gravity waves and their impact in the stratosphere, the diurnal cycle of convection and mesoscale convective systems, and the cloudy convective boundary-layer and its impact on the global shortwave radiation budget. We use the European Centre of Medium-range Weather Forecast Integrated Forecast System (IFS) with its 9 km horizontal resolution as a benchmark for global numerical weather prediction and also explore 4 km resolution reforecasts with and without the deep convection parametrization and ensemble forecasts with stochastically perturbed convection parameters. It is shown that the IFS realistically forecasts the tropical heating profiles and the large-scale convectively coupled waves, but there is potential to further improve the forecasts of the Madden Julian oscillation beyond the current predictability of around 30 days. There are still challenges in improving the analysis of convective structures as the assimilation of microwave satellite data critically depends on a successful simulation of the microphysics of these clouds. Large-amplitude gravity waves of various origins have a large impact on the stratosphere. Increasing the horizontal resolution to 4 km improves the stratospheric gravity wave spectrum in the mesoscales. Nighttime organized convection over land tends to be underestimated with parameterized convection. Convection-permitting simulations at 4 km may more realistically represent the occurrence and propagation of these systems but overestimate their intensity. Ensemble systems are needed to represent the stochastic nature of mesoscale convective systems. Finally, good progress has been made in addressing the cloud and radiation errors in coupled models in the cumulus and stratocumulus regions.