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Large-eddy simulation (LES) is used to explore the role of various processes in regulating the stratocumulus to cumulus transition (SCT). Simulations are based on a composite case derived from a Lagrangian analysis of 2 yr of data from the northeastern Pacific. The simulations reproduce well the observed transition from a compact stratocumulus layer to more broken fields of cumulus, simply as a response to increasing sea surface temperatures (SSTs) along the transition. In so doing they support earlier theoretical work that argued that the SCT was a response of boundary layer circulations to increased forcing by surface latent heat fluxes. Although the basic features of the SCT imposed by the increase in SST are robust, a variety of other factors affect the detailed character of the SCT. For example, enhanced precipitation or increased downwelling longwave radiative fluxes can accelerate the reduction in cloud cover that accompanies the SCT, while a gradual decrease in the large-scale divergence can make changes in cloud cover that accompany the SCT relatively more modest. The simulations also demonstrate that the pace of the SCT is mainly set by the strength of the temperature inversion capping the initial stratocumulus-topped boundary layer.

The characterization of systematic forecast errors in lower-tropospheric winds is an essential component of model improvement. This paper is motivated by a global, long-standing surface bias in the operational medium-range weather forecasts produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Over the tropical oceans, excessive easterly flow is found. A similar bias is found in the western North Atlantic trades, where the EUREC4A field campaign provides an unprecedented wealth of measurements. We analyze the wind bias in the IFS and ERA5 reanalysis throughout the entire lower troposphere during EUREC4A. The wind bias varies greatly from day to day, resulting in root mean square errors (RMSEs) up to 2.5 m s-1, with a mean wind speed bias up to -1 m s-1 near and above the trade inversion in the forecasts and up to -0.5 m s-1 in reanalyses. These biases are insensitive to the assimilation of sondes. The modeled zonal and meridional winds exhibit a diurnal cycle that is too strong, leading to a weak wind speed bias everywhere up to 5 km during daytime but a wind speed bias below 2 km at nighttime that is too strong. Removing momentum transport by shallow convection reduces the wind bias near the surface but leads to stronger easterly near cloud base. The update in moist physics in the newest IFS cycle (cycle 47r3) reduces the meridional wind bias, especially during daytime. Below 1 km, modeled friction due to unresolved physical processes appears to be too strong but is (partially) compensated for by the dynamics, making this a challenging coupled problem.

In the 1990s, scientists at European Centre for Medium‐Range Weather Forecasts (ECMWF) suggested that artificially enhancing turbulent diffusion in stable conditions improves the representation of two important aspects of weather forecasts, i.e., near‐surface temperatures and synoptic cyclones. Since then, this practice has often been used for tuning the large‐scale performance of operational numerical weather prediction (NWP) models, although it is widely recognized to be detrimental for an accurate representation of stable boundary layers. Here we investigate why, 20 years on, such a compromise is still needed in the ECMWF model. We find that reduced turbulent diffusion in stable conditions improves the representation of winds in stable boundary layers, but it deteriorates the large‐scale flow and the near‐surface temperatures. This suggests that enhanced diffusion is still needed to compensate for errors caused by other poorly represented processes. Among these, we identify the orographic drag, which influences the large‐scale flow in a similar way to the turbulence closure for stable conditions, and the strength of the land‐atmosphere coupling, which partially controls the near‐surface temperatures. We also take a closer look at the relationship between the turbulence closure in stable conditions and the large‐scale flow, which was not investigated in detail with a global NWP model. We demonstrate that the turbulent diffusion in stable conditions affects the large‐scale flow by modulating not only the strength of synoptic cyclones and anticyclones, but also the amplitude of the planetary‐scale standing waves. Key Points some NWP models use excessive diffusion in stable conditions reducing turbulent diffusion improves stable boundary layers enhanced diffusion helps offsetting errors due to other processes

Some of the largest and most persistent circulation errors in global numerical weather prediction and climate models are attributable to the inadequate representation of the impacts of orography on the atmospheric flow. Existing parametrization approaches attempting to account for unresolved orographic processes, such as turbulent form drag, low-level flow blocking or mountain waves, have been successful to some extent. They capture the basic impacts of the unresolved orography on atmospheric circulation in a qualitatively correct way and have led to significant progress in both numerical weather prediction and climate modelling. These approaches, however, have apparent limitations and inadequacies due to poor observational evidence, insufficient fundamental knowledge and an ambiguous separation between resolved and unresolved orographic scales and between different orographic processes. Numerical weather prediction and climate modelling has advanced to a stage where these inadequacies have become critical and hamper progress by limiting predictive skill on a wide range of spatial and temporal scales. More physically based approaches are needed to quantify the relative importance of apparently disparate orographic processes and to account for their combined effects in a rational and accurate way in numerical models. We argue that, thanks to recent advances, significant progress can be made by combining theoretical approaches with observations, inverse modelling techniques and high-resolution and idealized numerical simulations.

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four‐month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well‐tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large‐scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden‐Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations. Plain Language Summary We present the world's first global simulation of an entire season of the Earth's atmosphere with 1.4 km average grid spacing and the top of the modeled atmosphere as high as 80 km. Albeit only a single realization due to its considerable computational cost, the resulting model output provides a reference and guidance for future simulations. For illustration we compare to simulations at 9 km grid spacing that represent the state of the art in numerical weather prediction and are still considerably finer when compared to models that are used for climate projections today. Thanks to its unprecedented detail, the simulation output will support future model development and satellite mission planning and may be seen as a prototype contribution to a future digital twin of our Earth. Key Points A unique simulation with 1.4 km average grid spacing is presented for model development and process evaluation The 1.4 km simulation shows remarkable fidelity with respect to the well‐calibrated simulation at 9 km with parametrized deep convection Switching off deep convection at a too coarse resolution (9 km) generates too strong convective gravity waves

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization.

Snow cover properties have a large impact on the partitioning of surface energy fluxes and thereby on near‐surface weather parameters. Snow schemes of intermediate complexity have been widely used for hydrological and climate studies, whereas their impact on typical weather forecast time scales has received less attention. A new multilayer snow scheme is implemented in the European Centre for Medium‐range Weather Forecasts Integrated Forecasting System and its impact on snow and 2‐m temperature forecasts is evaluated. The new snow scheme is evaluated offline at well‐instrumented field sites and compared to the current single‐layer scheme. The new scheme largely improves the representation of snow depth for most of the sites considered, reducing the root‐mean‐square error averaged over all sites by more than 30%. The improvements are due to a better description of snow density in thick and cold snowpacks, but also due to an improved representation of sporadic melting episodes because of the inclusion of a thin top snow layer with a low thermal inertia. The evaluation of coupled 10‐day weather forecasts shows an improved representation of snow depth at all lead times, demonstrating a positive impact at the global scale. Regarding the impact on weather parameters, the multilayer snow scheme improves the simulated minimum 2‐m temperature, by decreasing the positive bias and improving the amplitude of the diurnal cycle over snow‐covered regions. However, the increased variability of the 2‐m temperature can have a detrimental impact in regions characterized by preexisting errors in the daily mean temperature, associated with errors in cloud processes or surface albedo. Key Points A multilayer snow scheme is implemented in the ECMWF Integrated Forecasting System (IFS) and evaluated in offline and coupled simulations The new multilayer snow scheme improves the snowpack representation on a wide range of spatial and temporal scales The new scheme improves the diurnal cycle of 2‐m temperature and the mean error of minimum 2‐m temperature over snow‐covered regions

Among the artists’ materials of the nineteenth century, pastel crayons merit scientific interest since their early commercial formulations are mostly unknown and, until now, have been considerably less studied with respect to other contemporary painting materials. In this framework, research herein reports the results of a comprehensive multi-analytical study of 44 pastel crayons of two recognized brands (LeFranc and Dr. F. Schoenfeld) from the Munch museum collection of original materials belonging to Edvard Munch. The integrated use of complementary spectroscopic and hyphenated mass-spectrometry techniques allowed the compositional profiles of the crayons to be traced providing the identification of the inorganic and organic pigments, the fillers/extenders and the binders. All crayons resulted to be oil- based and the binder was identified to be a mixture of a drying oil (safflower or linseed oil), palm oil or Japan wax and beeswax. Among others, pigments such as ultramarine, chrome yellows, Prussian blue, manganese violet, viridian and madder lake have been identified. A significant alignment in formulations of the brands was observed with the only exception of the greens which showed distinctive pigment and filler compositions. The analytical information provided for these commercial artists’ materials will be of great interest for academia, museum and other institutions hosting art collections dating from the same period and it will be used by the Munch museum to draw proper conservation strategies of its own artwork collections.

Biocolonization and biodeterioration phenomena in Cultural Heritage is presently considered a relevant issue when planning conservation strategies and preservation measures in museum collections. Artworks such as easel paintings are source of various ecological niches for microbial communities’ growth due to the presence of several organic resources. Therefore, the identification of proteinaceous materials may play an important role in the evaluation of their conservation status, in the characterisation of the artistic technique, and in the definition of compatible conservation/restoration processes. Another challenge is to understand the microbiota associated to the degradative processes when developing conservation strategies in CH artworks. For this study Edvard Munch paintings belonging to Munch Museum in Oslo presenting surface alterations were analysed to increase the knowledge about the materials used by the painter and try to understand the source and the dynamics of the associated colonising microbiota, helping in devising a conservation intervention plan. Immunoenzymatic assays was carried out in microsamples allowing the detection of casein as the binder used by the artist. The high throughput sequencing approaches allowed us to explore and characterise the microbial communities that colonise these artworks. Bacterial communities found in these artworks were mainly composed by species characterised by proteolytic capacity, an important biodeteriogenic characteristic for these paintings. Simulation assays performed in paint models prepared with casein as binder display signs of degradative action promoted by the proteolytic strains isolated from the damaged areas. This approach can be useful to promote effective intervention processes in E. Munch’s paintings with the same pathologies. Graphical abstract

The Year Of Polar Prediction (YOPP) dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) contains initial condition and forecast model output from the operational global, coupled numerical weather prediction system. The dataset has been created to support model forecast evaluation, predictability studies and model error analyses over polar areas, which are strongly affected by climate change with yet unknown feedbacks on global circulation. The dataset complements YOPP observation and modeling research activities that represent a key deliverable of the World Meteorological Organization’s Polar Prediction Program. The dataset covers the period from mid-2017 until the end of the MOSAiC field campaign, expected for autumn 2020. Initial conditions and forecasts up to day-15 are included for the atmosphere and land surface for the entire period, and for ocean and sea-ice model components after June 2019. In addition, tendencies from model dynamics and individual physical processes are included for the first two forecast days. These are essential for characterizing the contribution of individual processes to model state evolution and, hence, for diagnosing sources of model error. Measurement(s) temperature of air • air moisture • temperature of sea surface • water-based rainfall • ozone • pressure • atmospheric wind speed Technology Type(s) satellite imaging of a planet • weather station • computational modeling technique Factor Type(s) spectral space • year of data collection • hourly data collection Sample Characteristic - Environment atmosphere • ocean • sea • ice Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13013528