Model Evaluation Studies Research Articles

Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment.

In the framework of the Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern expedition, the Leibniz Institute for Tropospheric Research, Leipzig, Germany, operated the shipborne OCEANET-Atmosphere facility for cloud and aerosol observations throughout the whole year. OCEANET-Atmosphere comprises, amongst others, a multiwavelength Raman lidar, a microwave radiometer, and an optical disdrometer. A cloud radar was operated aboard Polarstern by the US Atmospheric Radiation Measurement program. These measurements were processed by applying the so-called Cloudnet methodology to derive cloud properties. To gain a comprehensive view of the clouds, lidar and cloud radar capabilities for low- and high-altitude observations were combined. Cloudnet offers a variety of products with a spatiotemporal resolution of 30 s and 30 m, such as the target classification, and liquid and ice microphysical properties. Additionally, a lidar-based low-level stratus retrieval was applied for cloud detection below the lowest range gate of the cloud radar. Based on the presented dataset, e.g., studies on cloud formation processes and their radiative impact, and model evaluation studies can be conducted.

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

AbstractSeven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

Valorization of Biowaste for Nanocomposite Synthesis and Its Application for the Environmental Remediation of Bisphenol A: Optimization, Model Evaluation and Docking Studies

Valorization of Biowaste for Nanocomposite Synthesis and Its Application for the Environmental Remediation of Bisphenol A: Optimization, Model Evaluation and Docking Studies

Accuracy Assessment of WRF Model in the Context of Air Quality Modeling in Complex Terrain

Output data from the Weather Research and Forecasting (WRF) model are frequently used in air quality modeling for scientific, practical and regulatory purposes. Therefore, it is crucial to determine whether the accuracy of WRF predictions is suitable for application in air quality models on a local scale (<50 km) and in complex terrain. The presented research is unique because, to assess the accuracy of the WRF model, data from experimental data sets for the assessment of air quality models were used, which contained information about the actual conditions of selected meteorological parameters along the vertical profile of the atmosphere. The aim of the study was to conduct an evaluation of the WRF model using data derived from three field experiments designated to conduct air quality model evaluation studies for models such as AERMOD, ADMS or CALPUFF. Accuracy evaluation was carried out in relation to the grid resolution, station location (on-site and weather airport) and vertical profile of the atmosphere. Obtained results of the evaluation for temperature, wind speed and direction were analyzed with regard to the possibilities of application in air quality modeling systems. It was stated that the use of a grid with a resolution of 1 km generally resulted in statistically significantly lower values of errors for wind speed compared to a 4 km resolution. The outcomes of simulations for temperature and wind speed were sensitive with regard to the location. In on-site locations (complex terrain) significantly higher values of prediction errors (MB, MGE, RMSE) were obtained compared to the standard weather station locations (airport). In addition, wind speed predictions in on-site locations were generally biased (overestimated). Along the vertical profile of the atmosphere, up to the altitude of 100 m a.g.l., statistically significantly different outcomes of accuracy evaluation were achieved for wind speed and direction. Considering the above, caution should be exercised when using data from meteorological simulations in air quality modeling.

Warm air intrusions reaching the MOSAiC expedition in April 2020—The YOPP targeted observing period (TOP)

In the spring period of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, an initiative was in place to increase the radiosounding frequency during warm air intrusions in the Atlantic Arctic sector. Two episodes with increased surface temperatures were captured during April 12–22, 2020, during a targeted observing period (TOP). The large-scale circulation efficiently guided the pulses of warm air into the Arctic and the observed surface temperature increased from −30°C to near melting conditions marking the transition to spring, as the temperatures did not return to values below −20°C. Back-trajectory analysis identifies 3 pathways for the transport. For the first temperature maximum, the circulation guided the airmass over the Atlantic to the northern Norwegian coast and then to the MOSAiC site. The second pathway was from the south, and it passed over the Greenland ice sheet and arrived at the observational site as a warm but dry airmass due to precipitation on the windward side. The third pathway was along the Greenland coast and the arriving airmass was both warm and moist. The back trajectories originating from pressure levels between 700 and 900 hPa line up vertically, which is somewhat surprising in this dynamically active environment. The processes acting along the trajectory originating from 800 hPa at the MOSAIC site are analyzed. Vertical profiles and surface energy exchange are presented to depict the airmass transformation based on ERA5 reanalysis fields. The TOP could be used for model evaluation and Lagrangian model studies to improve the representation of the small-scale physical processes that are important for airmass transformation. A comparison between MOSAiC observations and ERA5 reanalysis demonstrates challenges in the representation of small-scale processes, such as turbulence and the contributions to various terms of the surface energy budget, that are often misrepresented in numerical weather prediction and climate models.

Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route

To facilitate shipping in ice and to meet the increasing requirements of icebreaker services, convoy operations are the most effective alternative. However, convoy operations are among the most dangerous operations as they can result in ship-ship collisions and/or ship besetting in ice. To safeguard the assisted ships and improve the efficiency of convoy operations, predicting the besetment event is a paramount proactive measure. In this study, a Bayesian Network model is developed to predict the probability of ship besetting in ice in a convoy operation along the Northern Sea Route (NSR). The model focuses on the first-assisted ship and is based on expert elicitation. Correspondingly, four scenarios that may result in the first assisted ship besetting in ice have been identified. Further, the applicability of the model is evaluated through 12 scenarios derived from the real NSR voyage of ‘TIAN YOU’ assisted by the icebreaker ‘VAYGACH’ in August 2018. The results of the model evaluation and validity studies indicate that the developed model is feasible and can adequately predict the besetment event of the first assisted ship in convoy operations. The most important factors contributing to besetting in ice were found to be ice concentration, distance between icebreaker and ship, and navigation experience.

Turbulence in The Marine Boundary Layer and Air Motions Below Stratocumulus Clouds at the ARM Eastern North Atlantic Site

AbstractMarine stratocumulus clouds are intimately coupled to the turbulence in the boundary layer and drizzle is known to be ubiquitous within them. Six years of data collected at the Atmospheric Radiation Measurement (ARM)’s Eastern North Atlantic site are utilized to characterize turbulence in the marine boundary layer and air motions below stratocumulus clouds. Profiles of variance of vertical velocity binned by wind direction (wdir) yielded that the boundary layer measurements are affected by the island when the wdir is between 90° and 310° (measured clockwise from North where air is coming from). Data collected during the marine conditions (wdir<90 or wdir>310) showed that the variance of vertical velocity was higher during the winter months than during the summer months due to higher cloudiness, wind speeds, and surface fluxes. During marine conditions the variance of vertical velocity and cloud fraction exhibited a distinct diurnal cycle with higher values during the nighttime than during the daytime. Detailed analysis of 32 cases of drizzling marine stratocumulus clouds showed that for a similar amount of radiative cooling at the cloud top, within the sub-cloud layer 1) drizzle increasingly falls within downdrafts with increasing rain rates, 2) the strength of the downdrafts increases with increasing rain rates, and 3) the correlation between vertical air motion and rain rate is highest in the middle of the sub-cloud layer. The results presented herein have implications for climatological and model evaluation studies conducted at the ENA site, along with efforts of accurately representing drizzle-turbulence interactions in a range of atmospheric models.

Southern Ocean cloud and aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage

Abstract. Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol–cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations such as the well-known positive shortwave radiation bias at the surface, as well as biases in numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements and are often obscured by multiple overlying cloud layers. Satellite measurements also do not constrain the aerosol–cloud processes very well with imprecise estimation of cloud condensation nuclei. In this work, we present a comprehensive set of ship-based aerosol and meteorological observations collected on the 6-week Southern Ocean Ross Sea Marine Ecosystem and Environment voyage (TAN1802) voyage of RV Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March 2018. Many distinct, but contemporaneous, data sets were collected throughout the voyage. The compiled data sets include measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here, we describe the voyage, the instruments, and data processing, and provide a brief overview of some of the data products available. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol, and their interaction. The data sets presented in this study are publicly available at https://doi.org/10.5281/zenodo.4060237 (Kremser et al., 2020).

Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication

Abstract. Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.

Fifty Years of Atmospheric Boundary-Layer Research at Cabauw Serving Weather, Air Quality and Climate

An overview is given of 50-year Cabauw observations and research on the structure and dynamics of the atmospheric boundary layer. It is shown that over time this research site with its 200-m meteorological tower has grown into an atmospheric observatory with a comprehensive observational program encompassing almost all aspects of the atmospheric column including its boundary conditions. This is accomplished by the Cabauw Experimental Site for Atmospheric Research (CESAR) a consortium of research institutes. CESAR plays an important role in the educational programs of the CESAR universities. The current boundary-layer observational program is described in detail, and other parts of the CESAR observational program discussed more briefly. Due to an open data policy the CESAR datasets are used by researchers all over the world. Examples are given of the use of the long time series for model evaluation, satellite validation, and process studies. The role of tall towers is discussed in relation to the development of more and better ground-based remote sensing techniques. CESAR is now incorporated into the Ruisdael observatory, the large-scale atmospheric research infrastructure in the Netherlands. With Ruisdael the embedding of the Dutch atmospheric community in national policy landscape, and in the European atmospheric research infrastructures is assured for the coming decade.

Characterization of shallow oceanic precipitation using profiling and scanning radar observations at the Eastern North Atlantic ARM observatory

Abstract. Shallow oceanic precipitation variability is documented using three second-generation radar systems located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radar systems and measurement post-processing techniques, including sea-clutter removal and calibration against colocated disdrometer and Global Precipitation Mission (GPM) observations are described. Then, we present how a combination of profiling radar and lidar observations can be used to estimate adaptive (in both time and height) parameters that relate radar reflectivity (Z) to precipitation rate (R) in the form Z=αRβ, which we use to estimate precipitation rate over the domain observed by XSAPR2. Furthermore, constant altitude plan position indicator (CAPPI) gridded XSAPR2 precipitation rate maps are also constructed. Hourly precipitation rate statistics estimated from the three radar systems differ because KAZR2 is more sensitive to shallow virga and XSAPR2 suffers from less attenuation than KaSACR2 and as such is best suited for characterizing intermittent and mesoscale-organized precipitation. Further analysis reveals that precipitation rate statistics obtained by averaging 12 h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar time periods. However, it was determined that KAZR2 is unsuitable for characterizing domain-averaged precipitation rate over shorter periods. But even more fundamentally, these results suggest that these observations cannot produce an objective domain precipitation estimate and that the simultaneous use of forward simulators is desirable to guide model evaluation studies.

Discovery, synthesis and molecular substantiation of N-(benzo[d]thiazol-2-yl)-2-hydroxyquinoline-4-carboxamides as anticancer agents

The effort was taken to develop a series of benzothiazole and quinoline fused bioactive compounds obtained through a four-step synthetic route using a range of substituted acetoacetanilides. Achieved N-(benzo[d]thiazol-2-yl)-2-hydroxyquinoline-4-carboxamides (6a-l) were produced up to 96% of yield while the eco-friendly p-TSA used as a catalyst. Further, the anticancer activity of these compounds was determined using a range of cancer cell lines starting from MCF-7 (Breast cancer), HCT-116 (Colon cancer), PC-3 & LNCaP (Prostate) and SK-HEP-1 (Liver cancer). Present study compounds were also testified for antioxidant properties prior to anticancer studies since the Reactive Oxygen Species (ROS) being vital in cancer development. To determine the cell membrane stability effects of the compounds, human red blood cells (HRBC) based membrane protection assay was determined. In the results, compounds 6a-l were able to produce a dominated result values over PC3 cell lines (Prostate cancer) than the other cell lines used in this study. Since the connectivity of human germ cell alkaline phosphatase (hGC-ALP) in the development of prostate cancer is known, the most active compounds were evaluated for the hGC-ALP inhibition in order to ensure a mechanism of anticancer action of these compounds. The mode of interaction and binding affinity of these compounds was also investigated by a molecular docking study. In the results, 6d, 6i, 6k, and 6l were found with least IC50 values <0.075 µM and highest relative activity of 92%, 90%, and 96% respectively. The need for further animal model evaluation and pre-clinical studies recognized.

A new spatial modeling and interpolation approach for high-resolution temperature maps combining reanalysis data and ground measurements

Despite of their increasing importance as inputs to models for a wide range of scientific fields, high-resolution meteorological variables are not recorded very often on spatially regular grids. This problem is usually overcome by using data from reanalysis models, although they are less accurate. This paper discusses the development of a new spatial downscaling methodology to provide high-resolution maps of daily maximum and minimum air temperature. The application of this approach provides thorough observations in sparsely sampled areas by combining the accuracy of measurements from ground-based stations with the high availability and uniformity of model-based data. The dataset includes more than a decade (2003–2013) of data collected at 113 stations, about 30% of which constituted an independent set for the validation procedure. The efficacy of this approach is evaluated using statistical scores that are regularly employed in model evaluation studies and the improvements over the classical approach are remarkable. The results show that overall the our "hybrid" method provides fair estimates of temperature values. Particularly, MBE is less than 0.29 °C and 0.60 °C for the daily maximum and minimum air temperature respectively; RMSE is less than 1.24 °C for the maximum temperature and 1.86 °C for the minimum temperature, the analysis on MAE assures that there is not contribution of the errors in the spatial variability (MAE ≈ RMSE). The correlation coefficient, close to 1 (ρ ≈ 0.97), indicates a strong positive linear relationship.

Approaching the potential of model-data comparisons of global land carbon storage

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.

The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation

AbstractThe increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

Abstract. The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue.Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.

A comparison of metrics for assessing state-of-the-art climate models and implications for probabilistic projections of climate change

A major task of climate science are reliable projections of climate change for the future. To enable more solid statements and to decrease the range of uncertainty, global general circulation models and regional climate models are evaluated based on a 2 × 2 contingency table approach to generate model weights. These weights are compared among different methodologies and their impact on probabilistic projections of temperature and precipitation changes is investigated. Simulated seasonal precipitation and temperature for both 50-year trends and climatological means are assessed at two spatial scales: in seven study regions around the globe and in eight sub-regions of the Mediterranean area. Overall, 24 models of phase 3 and 38 models of phase 5 of the Coupled Model Intercomparison Project altogether 159 transient simulations of precipitation and 119 of temperature from four emissions scenarios are evaluated against the ERA-20C reanalysis over the 20th century. The results show high conformity with previous model evaluation studies. The metrics reveal that mean of precipitation and both temperature mean and trend agree well with the reference dataset and indicate improvement for the more recent ensemble mean, especially for temperature. The method is highly transferrable to a variety of further applications in climate science. Overall, there are regional differences of simulation quality, however, these are less pronounced than those between the results for 50-year mean and trend. The trend results are suitable for assigning weighting factors to climate models. Yet, the implications for probabilistic climate projections is strictly dependent on the region and season.

MUSICA MetOp/IASI {H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O,&amp;lt;i&amp;gt;δ&amp;lt;/i&amp;gt;D} pair retrieval simulations for validating tropospheric moisture pathways in atmospheric models

Abstract. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.

A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals

Abstract. Systematic measurements of dust concentration profiles at a continental scale were recently made possible by the development of synergistic retrieval algorithms using combined lidar and sun photometer data and the establishment of robust remote-sensing networks in the framework of Aerosols, Clouds, and Trace gases Research InfraStructure Network (ACTRIS)/European Aerosol Research Lidar Network (EARLINET). We present a methodology for using these capabilities as a tool for examining the performance of dust transport models. The methodology includes considerations for the selection of a suitable data set and appropriate metrics for the exploration of the results. The approach is demonstrated for four regional dust transport models (BSC-DREAM8b v2, NMMB/BSC-DUST, DREAMABOL, DREAM8-NMME-MACC) using dust observations performed at 10 ACTRIS/EARLINET stations. The observations, which include coincident multi-wavelength lidar and sun photometer measurements, were processed with the Lidar-Radiometer Inversion Code (LIRIC) to retrieve aerosol concentration profiles. The methodology proposed here shows advantages when compared to traditional evaluation techniques that utilize separately the available measurements such as separating the contribution of dust from other aerosol types on the lidar profiles and avoiding model assumptions related to the conversion of concentration fields to aerosol extinction values. When compared to LIRIC retrievals, the simulated dust vertical structures were found to be in good agreement for all models with correlation values between 0.5 and 0.7 in the 1–6 km range, where most dust is typically observed. The absolute dust concentration was typically underestimated with mean bias values of -40 to -20 μg m−3 at 2 km, the altitude of maximum mean concentration. The reported differences among the models found in this comparison indicate the benefit of the systematic use of the proposed approach in future dust model evaluation studies.