Multi-physics Ensemble Research Articles

The crucial representation of deep convection for the cyclogenesis of Medicane Ianos

Abstract. This paper presents a model intercomparison study to improve the prediction and understanding of Mediterranean cyclone dynamics. It is based on a collective effort with five mesoscale models to look for a robust response among 10 numerical frameworks used in the community involved in the networking activity of the EU COST Action “MedCyclones”. The obtained multi-model, multi-physics ensemble is applied to the high-impact Medicane Ianos of September 2020 with a focus on the cyclogenesis phase, which was poorly forecast by numerical weather prediction systems. Models systematically perform better when initialised from operational IFS analysis data compared to the widely used ERA5 reanalysis. Reducing horizontal grid spacing from 10 km with parameterised convection to convection-permitting 2 km further improves the cyclone track and intensity. This highlights the critical role of deep convection during the early development stage. Higher resolution enhances convective activity, which improves the phasing of the cyclone with an upper-level jet and its subsequent intensification and evolution. This upscale impact of convection matches a conceptual model of upscale error growth in the midlatitudes, while it emphasises the crucial interplay between convective and baroclinic processes during medicane cyclogenesis. The 10 numerical frameworks show robust agreement but also reveal model specifics that should be taken into consideration, such as the need for a parameterisation of deep convection even at 2 km horizontal grid spacing in some models. While they require generalisation to other cases of Mediterranean cyclones, the results provide guidance for the next generation of global convection-permitting models in weather and climate.

Evaluation of Multi-Physics Ensemble Prediction of Monsoon Rainfall Over Odisha, the Eastern Coast of India

Evaluation of Multi-Physics Ensemble Prediction of Monsoon Rainfall Over Odisha, the Eastern Coast of India

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

A climate modeling study over Northern Africa and the Mediterranean basin with multi-physics ensemble and coupling to a regional ocean modeling system

We have developed a physics ensemble of Weather Research and Forecasting (WRF) model simulations for the Middle East, Mediterranean and North Africa (MEMNA) regions. These simulations use different configurations for the cumulus, microphysics, surface layer, planetary boundary layer, and land surface schemes and are forced by the Community Earth System Model (CESM) General Circulation Model for the historical period 1979–1993. We have also created a complementary ensemble in which the WRF model is fully-coupled to the Regional Ocean Modelling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. Analysis of our ensembles reveals that the simulated precipitation and near surface temperature (T2) fields in WRF are largely influenced by the cumulus and the land surface schemes during the summer and winter seasons, respectively. The coupling of Weather Research and Forecasting to Regional Ocean Modelling System yields Mediterranean sea surface temperatures that are directly correlated with T2 and have higher spatial resolution than the global model. Meanwhile no significant difference is found between the atmospheric fields from the coupled and uncoupled runs because the Community Earth System Model sea surface temperatures over the Mediterranean, that are used for surface forcing in the uncoupled runs, are already in close agreement with both Regional Ocean Modelling System and observations. We conclude that our high-resolution coupled atmosphere-ocean modelling system is capable of producing climate data of good quality, and we identify those combinations of physics schemes that result in an acceptable level of bias that facilitates their use in future studies.

Kilometer-Scale Multimodel and Multiphysics Ensemble Simulations of a Mesoscale Convective System in the Lee of the Tibetan Plateau: Implications for Climate Simulations

Abstract Kilometer-scale climate model simulations are useful tools to investigate past and future changes in extreme precipitation, particularly in mountain regions, where convection is influenced by complex topography and land–atmosphere interactions. In this study, we evaluate simulations of a flood-producing mesoscale convective system (MCS) downstream of the Tibetan Plateau (TP) in the Sichuan basin from a kilometer-scale multimodel and multiphysics ensemble. The aim is to better understand the physical processes that need to be correctly simulated for successfully capturing downstream MCS formation. We assess how the ensemble members simulate these processes and how sensitive the simulations are to different model configurations. The preceding vortex evolution over the TP, its interaction with the jet stream, and water vapor advection into the basin are identified as key processes for the MCS formation. Most modeling systems struggle to capture the interaction between the vortex and jet stream, and perturbing the model physics has little impact, while constraining the large-scale flow by spectral nudging improves the simulation. This suggests that an accurate representation of the large-scale forcing is crucial to correctly simulate the MCS and associated precipitation. To verify whether the identified shortcomings systematically affect the MCS climatology in longer-term simulations, we evaluate a 1-yr WRF simulation and find that the seasonal cycle and spatial distribution of MCSs are reasonably well captured and not improved by spectral nudging. While the simulations of the MCS case highlight challenges in extreme precipitation forecasting, we conclude that these challenges do not systematically affect simulated climatological MCS characteristics. Significance Statement Convective storm systems in mountain regions are not well understood, because the spatial resolution in conventional regional climate models is too coarse to resolve relevant processes. Here, we evaluate high-resolution climate model simulations of a storm system on the downwind side of the Tibetan Plateau. Understanding which models and model setups work well to represent this type of storm system is important because high-resolution models can help us understand mechanisms of storm formation in mountain regions and how climate change will affect these. A key finding is that most of the models struggle to capture the selected storm case, while a 1-yr simulation shows that the general statistics of storm systems around the Tibetan Plateau are still reasonably well captured.

Snowfall events in the Cantabrian Mountains of northwestern Spain: WRF multiphysics ensemble assessment based on ground and multi-satellite observations

Snowfall in elevated areas of the mid-latitudes has a strong impact on infrastructure, freshwater availability, and the climate system. The Cantabrian Mountains of the northwestern Iberian Peninsula are very vulnerable to climate change because of their moderate altitudes, which limits their snowfall. Monitoring snow events is essential for the evaluation of weather and climate prediction models. However, measurement networks are scarce in mountainous areas and have great uncertainties because of blizzards. In this study, a multiphysics ensemble of the Weather Research and Forecasting (WRF) model was designed using three microphysics and two planetary boundary layer (PBL) schemes to simulate nine snowfall events in the Cantabrian Mountains during autumn and winter 2021–2022. The WRF was validated using several snow characteristics, such as liquid water equivalent, snow cover, and snow depth. Liquid water equivalent was evaluated using snow-gauge networks and satellite products in an assessment of snow cover. In addition, a monitoring network of webcams and snow poles was implemented, improving the low density of snow observations in the mountains. The results showed good model performance for detection of snow cover and slight overestimation of liquid water equivalent and snow thickness, which may have been caused by under-catchment that is generally an effect of wind on the measurement systems and by snow compaction, respectively. Morrison microphysics and Mellor-Yamada-Nakanishi-Niino (MYNN PBL) yielded better results for liquid water equivalent at higher altitudes and output greater snow cover. The results help determine the best configurations for snow modelling in the study area to develop future studies of the spatiotemporal patterns of snow distribution.

The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.

Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region

The Tibetan Plateau and its surrounding mountains have an average elevation of 4,400 m and a glaciated area of $$\sim $$ 100,000 $$\hbox {km}^{2}$$ giving it the name “Third Pole (TP) region”. The TP is the headwater of many major rivers in Asia that provide fresh water to hundreds of millions of people. Climate change is altering the energy and water cycle of the TP at a record pace but the future of this region is highly uncertain due to major challenges in simulating weather and climate processes in this complex area. The Convection-Permitting Third Pole (CPTP) project is a Coordinated Regional Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) that aims to revolutionize our understanding of climate change impacts on the TP through ensemble-based, kilometer-scale climate modeling. Here we present the experimental design and first results from multi-model, multi-physics ensemble simulations of three case studies. The five participating modeling systems show high performance across a range of meteorological situations and are close to having ”observational quality” in simulating precipitation and near-surface temperature. This is partly due to the large differences between observational datasets in this region, which are the leading source of uncertainty in model evaluations. However, a systematic cold bias above 2000 m exists in most modeling systems. Model physics sensitivity tests performed with the Weather Research and Forecasting (WRF) model show that planetary boundary layer (PBL) physics and microphysics contribute equally to model uncertainties. Additionally, larger domains result in better model performance. We conclude by describing high-priority research needs and the next steps in the CPTP project.

Hourly assimilation of different sources of observations including satellite radiances in a mesoscale convective system case during RELAMPAGO campaign

This paper evaluates the impact of assimilating high-resolution surface networks and satellite observations using the WRF-GSI-LETKF over central and north eastern Argentina where the surface and upper air observing networks are relatively coarse. A case study corresponding to a huge mesoscale convective system (MCS) that developed during November 22, 2018 was used. The accumulated precipitation associated with this MCS was quite high, exceeding 200 mm over northern Argentina and Paraguay. The MCS developed during the Intense Observing Period (IOP) of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. The GSI-4DLETKF data assimilation package is used to produce analyses by assimilating observations every hour with 10-km horizontal grid spacing and a 60-member multiphysics ensemble. Four assimilation experiments are conducted using different sets of observations: CONV, consisting of conventional observations from NCEP’s prepBUFR files; AWS, combining CONV and dense automatic surface weather station networks (AWS), SATWND, combining AWS with satellite-derived winds, and RAD, including SATWND; and satellite radiances from different microwave and infrared sensors. The assimilation of observations with high temporal and spatial frequency generates an important impact on the PBL, primarily on the precipitable water content, that leads to the development of deep convection and heavy precipitation closer to the observed in this case study. The assimilation of radiance observations produces a better development of the convection mainly during the mature state of the MCS leading to an increase in the accumulated precipitation. Ensemble forecasts initialized from each experiment were also simulated to evaluate their skill to predict precipitation. The hourly assimilation of the observations in AWS, SATWND, and RAD helped to improve the precipitation forecast.

Probability forecasts of ice accretion on wind turbines derived from multiphysics and neighbourhood ensembles

AbstractThis study explores the potential of a multiphysics regional ensemble prediction system to improve forecasts of wind turbine icing, examining several error‐representation schemes to capture the forecasting uncertainties of the icing process. An 11‐member multiphysics ensemble based on the Weather Research and Forecasting (WRF) model is run for two winter periods over Europe. Regional verification of surface variables shows that parametrization diversity makes the multiphysics ensemble less underdispersive compared with the European Centre for Medium‐Range Weather Forecasts (ECMWF) global ensemble, without deteriorating the overall forecast accuracy significantly, in particular at forecast ranges below 36 hr. Probability forecasts of active ice growth in the day‐ahead time range (12–36 hr) are derived for two wind farms on hilly terrain in Central Europe and their skill is assessed in terms of relative operating characteristics, reliability, and potential economic value (PEV). Probability forecasts enhance the maximum PEV significantly, but the improvement of the multiphysics ensemble seems modest compared with a simple neighbourhood ensemble approach. Icing forecasts are affected by a considerable degree of overconfidence, meaning that forecast probabilities cannot be used at face value, but require calibration for users to draw benefit from them. The multiphysics ensemble fares slightly better in this regard; however, results point to persistent ensemble underdispersiveness and yet underrepresented forecast uncertainty. Overall, findings show that a large portion of the gain in skill through the use of probabilistic icing forecasts is obtained with a computationally cheap neighbourhood method, a technique easily accessible to forecast users without complex ensemble prediction systems.

Performance of HAILCAST and the Lightning Potential Index in simulating hailstorms in Croatia in a mesoscale model – Sensitivity to the PBL and microphysics parameterization schemes

Hailstorms, although extremely damaging severe weather hazards, remain a very challenging phenomenon to predict. To better understand dynamic processes and model performance, which can be helpful in forecasting hailstorms, three selected hailstorms in Croatia are simulated with the WRF model at convection-permitting (1 km) grid spacing using the HAILCAST module. In addition, the performance of the Lightning Potential Index (LPI) algorithm in representing the observed lightning activity during the selected hailstorms is analyzed. A multiphysics ensemble of 12 sensitivity simulations with the combinations of four different microphysics and three different planetary boundary layer parameterization (PBL) schemes is adopted to assess the forecasting ability of HAILCAST and LPI and their sensitivity to the choice of microphysics and PBL parameterization schemes. First, the model's ability to reproduce surface measurements of 2-m temperature, 2-m relative humidity, 2-m equivalent potential temperature and 10-m wind are examined using root mean square error (RMSE) decomposition. Then, the LPI is assessed against lightning observations via the object-based Structure-Amplitude-Location (SAL) method. Finally, an upscaled neighborhood verification method is proposed to assess HAILCAST against hail observations from the Croatian hailpad network. The results show that the observed hail and lightning activity is represented well by the model. There is a greater sensitivity to the choice of microphysics scheme than the PBL scheme, with National Severe Storms Laboratory double-moment scheme (NSSL2) microphysics scheme differing the most among the entire sensitivity ensemble. Nonetheless, both HAILCAST and LPI show promising performance in simulating observed hail and lightning activity, although HAILCAST tends to overestimate the area affected by hail. Nonetheless, the discrepancies between model configurations highlight the importance of simulating convection correctly to obtain a meaningful forecast of hail and lightning.

A Mathematical Modelling of A Multi-Physics Ensemble Approach for Exploring the Sensitivity of Climate

The amount of global surface warming that will effectively respond to twice of atmospheric CO2 concentrations compared with pre-industrial levels is referred to as climate sensitivity. The aim to explore the sensitivity of climate by using the mathematical model of the multi-physics ensemble approach. It’s considered as a multi-physics MM5 ensemble of 30 years hindcast simulations run through a complicated and climatically varied area. In this study, eight multi-physics ensembles (MPEs) models were used, MIROC5 physics systems were replaced with MIROC3 physics systems. The analysis is based on a seasonal time scale with an emphasis on average temperature and precipitation values as well as interannual variability. Multi-parameter MPE was made a set ensemble of perturbed-physics in which the parameter value for individual MPE model is swept. The previously evaluated MPE approach can be better understand and improve in the simulation of the multi-physics climate by using Bayesian inference. Bayesian inference allows actions often associated with a post-model flexible project to be incorporated into the model development process. As a result, an ensemble of model configurations has been created, which allows for a more thorough assessment of the remaining uncertainties. The value of model physics is shown by demonstrating that the dispersion between experiments is comparable.

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

Future Changes in Tropical Cyclone and Easterly Wave Characteristics over Tropical North America

Tropical Cyclones (TCs) and Easterly Waves (EWs) are the most important phenomena in Tropical North America. Thus, examining their future changes is crucial for adaptation and mitigation strategies. The Community Earth System Model drove a three-member regional model multi-physics ensemble under the Representative Concentration Pathways 8.5 emission scenario for creating four future scenarios (2020–2030, 2030–2040, 2050–2060, 2080–2090). These future climate runs were analyzed to determine changes in EW and TC features: rainfall, track density, contribution to seasonal rainfall, and tropical cyclogenesis. Our study reveals that a mean increase of at least 40% in the mean annual TC precipitation is projected over northern Mexico and southwestern USA. Slight positive changes in EW track density are projected southwards 10° N over the North Atlantic Ocean for the 2050–2060 and 2080–2090 periods. Over the Eastern Pacific Ocean, a mean increment in the EW activity is projected westwards across the future decades. Furthermore, a mean reduction by up to 60% of EW rainfall, mainly over the Caribbean region, Gulf of Mexico, and central-southern Mexico, is projected for the future decades. Tropical cyclogenesis over both basins slightly changes in future scenarios (not significant). We concluded that these variations could have significant impacts on regional precipitation.

Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India

The southern peninsular India is characterized by unique climatology with rainfall processes throughout the year from land–ocean contrasts. In addition, the complex terrain induces localized effects causing huge spatial and temporal variability in the observed precipitation. This study aims at evaluating the sensitivity of the high-resolution Weather Research and Forecasting (WRF) model (4 km) to multi-physics parameterizations, 3D variational data assimilation, and domain configuration, in the study domain covering southern peninsular India. Furthermore, the study focusses on the formulation of an ensemble method to improve the simulation of precipitation across seasons. A total of 120 experiments were set up across four crucial rainfall events, of varying spatial extent and duration, dominated by different rainfall generation mechanisms. The assessment of the experiments shows that the model’s cumulus and microphysics schemes have the highest impact on the location, intensity, and spread of the simulated 4-day long Quantitative Precipitation Forecasts (QPFs). Applying cumulus schemes at all domains represented the variability in the QPFs, across space and time, for the precipitation events dominated by convective activity. The cases without cumulus schemes at the convective scale domain (4 km), captured the higher intensity rains during organized cyclonic circulations in the north-east monsoon period. Hence, a 10-member multi-physics ensemble approach including members with and without cumulus parameterization at the fine resolution domain was adopted. The preliminary results demonstrate that the mean from the suggested ensemble approach (n-MPP) performed well in capturing the dynamics of QPFs across the rainfall events, as opposed to a single-member deterministic simulation and mean from larger member conventional multi-physics ensemble approach (c-MPP) without cumulus parameterization at the convective scale. The rank histogram, delta semi-variance plots, and outlier statistics at various lead times clearly showed that the suggested n-MPP was able to capture the high-intensity rainfall, increasing the spread of precipitation forecasts and consequently reducing the occurrence of outliers.

Modelling a tropical-like cyclone in the Mediterranean Sea under present and warmer climate

Abstract. This study focuses on a single Mediterranean hurricane (hereafter medicane), to investigate its response to global warming during the middle of the 21st century and assesses the effects of a warmer ocean and a warmer atmosphere on its development. Our investigation uses the state-of-the-art regional climate model WRF to produce the six-member, multi-physics ensembles. Results show that our model setup simulates a realistic cyclone track and the transition from an initial disturbance to a tropical-like cyclone with a deep warm core. However, the simulated transition occurs earlier than for the observed medicane. The response of the medicane to future climate change is investigated with a pseudo global warming (PGW) approach. This is the first application of the PGW framework to medicanes. The PGW approach adds a climate change delta (defined as difference between future and present climate) to WRF's boundary conditions which is obtained for all prognostic variables using the mean change in an ensemble of CMIP5 simulations. A PGW simulation where the climate change delta is added to all prognostic variables (PGWALL) shows that most of the medicane characteristics moderately intensify, e.g. surface wind speed, uptake of water vapour, and precipitation. However, the minimum sea level pressure (SLP) is almost identical to that under present climate conditions. Two additional PGW simulations were undertaken; One simulation adds the projected change in sea surface and skin temperature only (PGWSST) while the second simulation adds the PGW changes to only atmospheric variables (PGWATMS); i.e. we use present-day sea surface temperatures. These simulations show opposing responses of the medicane. In PGWSST, the medicane is more intense than PGWALL as indicated by lower SLP values, the stronger surface wind, and the more intense evaporation and precipitation. In contrast, the medicane in PGWATMS still transitions into a tropical-like cyclone with a deep warm core, but the PGWATMS medicane weakens considerably (SLP, surface wind, and rainfall decrease). This difference can be explained by an increase in water vapour driven by the warmer ocean surface (favourable for cumulus convection). The warmer and drier atmosphere in PGWATMS tends to inhibit condensation (unfavourable for cumulus convection). The warmer ocean and warmer atmosphere have counteracting effects which leads to only a modest enhancement of the medicane by global warming. The novel approach in this study provides new insights into the different roles of warming of the ocean and atmosphere in medicane development.

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

Abstract. The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China – A multi-physics ensemble approach

Intense precipitation events frequently occur in the Hanjiang River Basin (HRB) in China, causing major economic losses and affecting human security. Accurate precipitation simulations are sensitive to the choice of physical schemes in numerical weather prediction models. Therefore, in this study the performance of the different combinations of physical schemes in the Weather Research and Forecasting (WRF) model was investigated for the HRB. Five intense precipitation events in summer during the period 2008–2011 were selected. For simulating each intense precipitation event, an 18-member multi-physics ensemble was designed by using three cumulus (CU) schemes and four microphysics (MP) schemes. The temporal dynamics and spatial patterns of the simulated precipitation were evaluated systematically by a comparison to the China precipitation analysis reference data. A quantitative analysis using the Euclidean distance metric to synthesize the evaluation results was performed. The performed uncertainty analysis suggests that the skill of precipitation simulation is more sensitive to the choice of the CU scheme and less sensitive to the choice of the MP scheme. Our analysis shows that a suitable WRF physical configuration consists of Kain-Fritsch and WRF Single-Moment 6-class, which reaches a performance similar to that of the ensemble mean method. To our knowledge, this multi-physics ensemble study is the first effort to comprehensively evaluate the applicability of the WRF model to intense precipitation simulations over the HRB. Therefore, this study potentially contributes to the improvement of climate simulations for central China.

An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting (WRF) model users. This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area. The ensemble is created by using statistical techniques and some heuristics. An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling (LHS). Then, after several rounds of screening, a final ensemble of 40 combinations were chosen. The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics, including threat score (TS), Brier score (BS), relative operating characteristics (ROC), and ranked probability score (RPS). The results show that TS of the final ensemble improved by 9%-33% over that of the initial ensemble. The reliability was improved for rain ≤ 10 mm day-1, but decreased slightly for rain > 10 mm day-1 due to insufficient samples. The resolution remained about the same. The final ensemble forecasts were better than that generated from randomly sampled scheme combinations. These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.