Icosahedral Model Research Articles

Implementation and tests of the NLT3D scheme in the ICON model

AbstractThe main goal of this article is to test the long‐term performance of the three‐dimensional non‐local turbulence (NLT) parameterization scheme at different grid sizes in the so‐called gray zone between classical mesoscale modeling ( several km) and large eddy simulations (LES: several 100 m). For this, NLT has been implemented in the numerical weather prediction Icosahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD). Results are compared with a one‐dimensional version of NLT (NLT) and with two operational turbulence schemes available in ICON. Comparisons with observations from radiosondes, the operational surface synoptic (SYNOP) station network, and RAdar‐OnLine‐ANeichung (RADOLAN) radar data of DWD indicate that all turbulence schemes investigated perform reasonably well. Nonetheless, a more detailed study of the model results reveals several interesting differences between the turbulence parameterizations to be discussed in detail. Median absolute errors (MAE) from point‐to‐point comparisons between numerical results and SYNOP observations tend to be smaller than those from comparisons with averaging simulated fields over an environment around each station location. This behavior indicates an information loss caused by the averaging process. For the 2‐m temperature () and the hourly precipitation sums (), MAEs decrease with decreasing grid sizes, thus suggesting an information gain for finer grids. The nighttime MAEs of and obtained with NLT and NLT are similar to or lower than those of the operational turbulence schemes of ICON. Moreover, during a shallow warm‐air intrusion, NLT and especially NLT yield a more realistic representation of the horizontal structures of and, during nighttime stable boundary‐layer situations, also . Radiosonde profiles of the potential temperature confirm a reasonable vertical mixing as obtained with NLT and NLT.

Lagrangian transport based on the winds of the icosahedral nonhydrostatic model (ICON)

Lagrangian transport based on the winds of the icosahedral nonhydrostatic model (ICON)

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Comparison of COSMO and ICON-LAM high-resolution numerical forecast for Romanian territory: Case studies and evaluation

This paper aims to offer the first detailed inter-comparison of the performance from the numerical weather prediction models Consortium for Small-scale Modeling (COSMO) and Icosahedral Nonhydrostatic General Circulation Model, Limited Area Mode (ICON-LAM), integrated for the Romanian territory at the horizontal resolution of 2.8 km. As the ICON numerical model is set to replace COSMO, a comparison is carried out between the operational configurations of the two models. First, two cases with severe weather (strong atmospheric instability, observed heavy precipitation, and strong wind) are analyzed: February 3-6, 2020 (heavy precipitation [snow] and very strong wind) and May 3-5, 2020 (heavy precipitation). This is followed by a statistical inter-comparison between the two models for the summer of 2020 (June, July, August). A statistical evaluation of the forecast quality from the two models is performed objectively, through statistical scores computed using surface observations from all available meteorological stations on the Romanian territory. Stratifications depending on station altitude and location are carried out, with emphasis on stations of particular interest, depending on the evolution of the synoptic situation. Following the statistical evaluation, results for the precipitation forecast do not show significant improvement in favor of either model. However, ICON-2.8 km mostly outperforms COSMO-2.8 km for surface parameters.

European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell

Abstract. We present the first application of the ICOsahedral Nonhydrostatic model with Aerosols and Reactive Trace gases (ICON-ART) in inverse modeling in inverse modeling of greenhouse gas fluxes with an ensemble Kalman smoother. For this purpose, we extended ICON-ART to efficiently handle gridded emissions, generate an ensemble of perturbed emissions during runtime and use nudging on selected variables to keep the simulations close to analyzed meteorology. We show that the system can optimize total and anthropogenic European CH4 fluxes on a national scale in an idealized setup using pseudo-observations from a realistic network of measurement stations. However, we were unable to constrain the sum of the natural emission sources of comparatively low magnitude. Also regions with low emissions and regions with low observational coverage could not be optimized individually for lack of observational constraints. Furthermore, we investigated the sensitivities towards different inversion parameters and design choices with 15 sensitivity runs using the same idealized setup, demonstrating the robustness of the approach when regarding some minimal requirements of the setup (e.g., number of ensemble members). Subsequently, we applied the system to real in situ observations from 28 European stations for three years, 2008, 2013 and 2018. We used a priori anthropogenic fluxes from the EDGARv6 inventory and a priori natural fluxes from peatlands and mineral soils, inland waters, the ocean, biofuels and biomass burning, and geology. Our results for the year 2018 indicate that anthropogenic emissions may be underestimated in EDGARv6 by ca. 25 % in the Benelux countries and, to a smaller degree, in northwestern France and southern England. In the rest of the domain, anthropogenic fluxes are corrected downwards by the inversion, suggesting an overestimation in the a priori. For most countries, this means that the a posteriori country-total anthropogenic emissions are closer to the values reported to the United Nations Framework Convention on Climate Change (UNFCCC) than the a priori emissions from EDGARv6. Aggregating the a posteriori emissions across the EU27 + UK results in a total of 17.4 Tg yr−1, while the a priori emissions were 19.9 Tg yr−1. Our a posteriori is close to the total reported to the UNFCCC of 17.8 Tg yr−1. Natural emissions are reduced from their a priori magnitude almost everywhere, especially over Italy and Romania–Moldova, where a priori geological emissions are high, and over the United Kingdom and Scandinavia, where emissions from peatlands and wetlands were possibly unusually low during the hot and dry summer of 2018. Our a posteriori anthropogenic emissions for the EU27 + UK fall within the range estimated by global top-down studies but are lower than most other regional inversions. However, many of these studies have used observations from different measurement stations or satellite observations. The spatial pattern of the emission increments in our results, especially the increase in the Benelux countries, also agrees well with other regional inversions.

How do different pathways connect the stratospheric polar vortex to its tropospheric precursors?

Abstract. Processes involving troposphere–stratosphere coupling have been identified as important contributors to an improved subseasonal to seasonal prediction in the mid-latitudes. However, atmosphere models still struggle to accurately predict stratospheric extreme events. Based on a novel approach in this study, we use ERA5 reanalysis data and ensemble simulations with the ICOsahedral Non-hydrostatic atmospheric model (ICON) to investigate tropospheric precursor patterns, localised troposphere–stratosphere coupling mechanisms, and the involved timescales of these processes in the Northern Hemisphere extended winter. We identify two precursor regions: mean sea level pressure in the Ural region is negatively correlated with the strength of the stratospheric polar vortex for the following 5–55 d with a maximum at 25–45 d, and the pressure in the extended Aleutian region is positively correlated with the strength of the stratospheric polar vortex the following 10–50 d with a maximum at 20–30 d. A simple precursor index based on the mean pressure difference of these two regions is very strongly linked to the strength of the stratospheric polar vortex in the following month. The pathways connecting these two regions to the strength of the stratospheric polar vortex, however, differ from one another. Whereas a vortex weakening can be connected to prior increased vertical planetary wave forcing due to high-pressure anomalies in the Ural region, the pathway for the extended Aleutian region is less straightforward. A low-pressure anomaly in this region can trigger a Pacific–North American-related (PNA-related) pattern, leading to geopotential anomalies of the opposite sign in the mid-troposphere over central North America. This positive geopotential anomaly travels upward and westward in time, directly penetrating into the stratosphere and thereby strengthening the stratospheric Aleutian High, a pattern linked to the displacement towards Eurasia and subsequent weakening of the stratospheric polar vortex. Overall, this study emphasises the importance of the time-resolved and zonally resolved picture for an in-depth understanding of troposphere–stratosphere coupling mechanisms. Additionally, it demonstrates that these coupling mechanisms are realistically reproduced by the global atmosphere model ICON.

The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.

Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle

Abstract. Dusty cirrus clouds are extended optically thick cirrocumulus decks that occur during strong mineral dust events. So far they have mostly been documented over Europe associated with dust-infused baroclinic storms. Since today's global numerical weather prediction models neither predict mineral dust distributions nor consider the interaction of dust with cloud microphysics, they cannot simulate this phenomenon. We postulate that the dusty cirrus forms through a mixing instability of moist clean air with drier dusty air. A corresponding sub-grid parameterization is suggested and tested in the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART). Only with the help of this parameterization is ICON-ART able to simulate the formation of the dusty cirrus, which leads to substantial improvements in cloud cover and radiative fluxes compared to simulations without this parameterization. A statistical evaluation over six Saharan dust events with and without observed dusty cirrus shows robust improvements in cloud and radiation scores. The ability to simulate dusty cirrus formation removes the linear dependency on mineral dust aerosol optical depth from the bias of the radiative fluxes. For the six Saharan dust episodes investigated in this study, the formation of dusty cirrus clouds is the dominant aerosol–cloud–radiation effect of mineral dust over Europe.

Learning by Doing: Seasonal and Diurnal Features of Tropical Precipitation in a Global‐Coupled Storm‐Resolving Model

AbstractUsing the global and coupled ICOsahedral Nonhydrostatic model with the Sapphire configuration (ICON‐S) and a grid spacing of 5 km, we describe seasonal and diurnal features of the tropical rainbelt and assess the limits of ICON‐S in representing tropical precipitation. ICON‐S shows that, by resolving meso‐beta scale process, the rainbelt structure and its seasonality (zonal and meridional migration and enlargement) is reproduced, with better performance over land than over ocean and with a very high degree of agreement to observations. ICON‐S especially struggles in capturing the seasonal features of the tropical rainbelt over the oceans of the Eastern Hemisphere, an issue associated with a cold sea surface temperature (SST) bias at the equator. ICON‐S also shows that a perfect representation of the diurnal cycle of precipitation over land is not a requirement to capture the seasonal features of the rainbelt over land, while over the ocean, 5 km is sufficient to adequately represent the diurnal cycle of precipitation.

Process Studies of the Impact of Land-Surface Resolution on Convective Precipitation Based on High-Resolution ICON Simulations

This study investigated the relevant processes responsible for differences of convective precipitation caused by land-surface resolution. The simulations were performed with the ICOsahedral Nonhydrostatic model (ICON) with grid spacing of 156 m and Large Eddy Simulation physics. Regions of different orographic complexity, days with weak synoptic forcing and favourable convective conditions were selected. The resolution of land-surface properties (soil type, vegetation) and/or the orography was reduced from 156 to 5000 m. Analyses are based on backward trajectories (Lagrangian Analysis Tool (LAGRANTO)), heat budget and convective organisation potential (COP) calculations. On average, the relative difference of areal mean daily precipitation at 1250 and 5000 m land-surface resolutions compared to 156 m were 6% and 15%, respectively. No consistent dependency of precipitation on orography or land-surface properties was found. Both factors impact convective initiation over areas with embedded mesoscale-sized land-surface heterogeneities. The position of convective precipitation was often influenced by the resolution of orography. Coarsening from 156 to 5000 m considerably changed the location of wind convergence and associated convection initiation. It also affects the onset times of clouds (<20 min) and precipitation (≈1 h). Cloud aggregation and microphysical processes proved to be important for further development towards convective precipitation.

Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation

Abstract. The predictability of deep moist convection is subject to large uncertainties resulting from inaccurate initial and boundary data, the incomplete description of physical processes, or microphysical uncertainties. In this study, we investigate the response of convective clouds and precipitation over central Europe to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD), both of which are not well constrained by observations. We systematically evaluate the relative impact of these uncertainties in realistic convection-resolving simulations for multiple cases with different synoptic controls using the new icosahedral non-hydrostatic ICON model. The results show a large systematic increase in total cloud water content with increasing CCN concentrations and narrower CDSDs, together with a reduction in the total rain water content. This is related to a suppressed warm-rain formation due to a less efficient collision–coalescence process. It is shown that the evaporation at lower levels is responsible for diminishing these impacts on surface precipitation, which lies between +13 % and −16 % compared to a reference run with continental aerosol assumption. In general, the precipitation response was larger for weakly forced cases. We also find that the overall timing of convection is not sensitive to the microphysical uncertainties applied, indicating that different rain intensities are responsible for changing precipitation totals at the ground. Furthermore, weaker rain intensities in the developing phase of convective clouds can allow for a higher convective instability at later times, which can lead to a turning point with larger rain intensities later on. The existence of such a turning point and its location in time can have a major impact on precipitation totals. In general, we find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. The narrowing of the CDSD not only decreases the absolute values of autoconversion and accretion but also decreases the relative role of the warm-rain formation in general, independent of the prevailing weather regime. We further find that increasing CCN concentrations reduce the effective radius of cloud droplets in a stronger manner than larger shape parameters. The cloud optical depth, however, reveals a similarly large increase with larger shape parameters when changing the aerosol load from maritime to polluted. By the frequency of updrafts as a function of height, we show a negative aerosol effect on updraft strength, leading to an enervation of deep convection. These findings demonstrate that both the CCN assumptions and the CDSD shape parameter are important for quantitative precipitation forecasting and should be carefully chosen if double-moment schemes are used for modeling aerosol–cloud interactions.

Inline coupling of simple and complex chemistry modules within the global weather forecast model FIM (FIM-Chem v1)

Abstract. The global Flow-following finite-volume Icosahedral Model (FIM), which was developed in the Global Systems Laboratory (GSL) of NOAA, has been coupled inline with aerosol and gas-phase chemistry schemes of different complexity using the chemistry and aerosol packages from WRF-Chem v3.7, named FIM-Chem v1. The three chemistry schemes include (1) the simple aerosol modules from the Goddard Chemistry Aerosol Radiation and Transport model that includes only simplified sulfur chemistry, black carbon (BC), organic carbon (OC), and sectional dust and sea salt modules (GOCART); (2) the photochemical gas phase of the Regional Atmospheric Chemistry Mechanism (RACM) coupled to GOCART to determine the impact of more realistic gas-phase chemistry on the GOCART aerosol simulations (RACM_GOCART); and (3) a further sophistication within the aerosol modules by replacing GOCART with a modal aerosol scheme that includes secondary organic aerosols (SOAs) based on the volatility basis set (VBS) approach (RACM_SOA_VBS). FIM-Chem is able to simulate aerosol, gas-phase chemical species, and SOA at various spatial resolutions with different levels of complexity and quantify the impact of aerosol on numerical weather prediction (NWP). We compare the results of RACM_GOCART and GOCART schemes which use the default climatological model fields for OH, H2O2, and NO3. We find significant reductions of sulfate that are on the order of 40 % to 80 % over the eastern US and are up to 40 % near the Beijing region over China when using the RACM_GOCART scheme. We also evaluate the model performance by comparing it with the Atmospheric Tomography Mission (ATom-1) aircraft measurements in the summer of 2016. FIM-Chem shows good performance in capturing the aerosol and gas-phase tracers. The model-predicted vertical profiles of biomass burning plumes and dust plumes off western Africa are also reproduced reasonably well.

Sources of Subseasonal Skill and Predictability in Wintertime California Precipitation Forecasts

AbstractThe relation between the El Niño–Southern Oscillation (ENSO) and California precipitation has been studied extensively and plays a prominent role in seasonal forecasting. However, a wide range of precipitation outcomes on seasonal time scales are possible, even during extreme ENSO states. Here, we investigate prediction skill and its origins on subseasonal time scales. Model predictions of California precipitation are examined using Subseasonal Experiment (SubX) reforecasts for the period 1999–2016, focusing on those from the Flow-Following Icosahedral Model (FIM). Two potential sources of subseasonal predictability are examined: the tropical Pacific Ocean and upper-level zonal winds near California. In both observations and forecasts, the Niño-3.4 index exhibits a weak and insignificant relationship with daily to monthly averages of California precipitation. Likewise, model tropical sea surface temperature and outgoing longwave radiation show only minimal relations with California precipitation forecasts, providing no evidence that flavors of El Niño or tropical modes substantially contribute to the success or failure of subseasonal forecasts. On the other hand, an index for upper-level zonal winds is strongly correlated with precipitation in observations and forecasts, across averaging windows and lead times. The wind index is related to ENSO, but the correlation between the wind index and precipitation remains even after accounting for ENSO phase. Intriguingly, the Niño-3.4 index and California precipitation show a slight but robust negative statistical relation after accounting for the wind index.

Improved Circulation in the Northern Hemisphere by Adjusting Gravity Wave Drag Parameterizations in Seasonal Experiments With ICON‐NWP

AbstractThe stratosphere is one of the main potential sources for subseasonal to seasonal predictability in midlatitudes in winter. The ability of an atmospheric model to realistically simulate the stratospheric dynamics is essential in order to move forward in the field of seasonal predictions in midlatitudes. Earlier studies with the ICOsahedral Nonhydrostatic atmospheric model (ICON) point out that stratospheric westerlies in ICON are underestimated. This is the first extensive study on the evaluation of Northern Hemisphere stratospheric winter circulation with ICON in numerical weather prediction (NWP) mode. Seasonal experiments with the default setup are able to reproduce the basic climatology of the stratospheric polar vortex. However, westerlies are too weak and major stratospheric warmings too frequent in ICON. Both a reduction of the nonorographic, and a reduction of the orographic gravity wave and wake drag lead to a strengthening of the stratospheric vortex and a bias reduction, in particular in January. However, the effect of the nonorographic gravity wave drag scheme on the stratosphere is stronger. Stratosphere‐troposphere coupling is intensified and more realistic due to a reduced gravity wave drag. Furthermore, an adjustment of the subgrid‐scale orographic drag parameterization leads to a significant error reduction in the mean sea level pressure. As a result of these findings, we present our current suggested improved setup for seasonal experiments with ICON‐NWP.

Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

Extreme Wind and Wave Predictability From Operational Forecasts at the Drake Passage

Abstract The Drake Passage is an ocean area between South America and Antarctica with very extreme wind and wave climates. The quality of forecasts of surface winds and significant wave heights is analyzed in the present paper, in order to study the error and distribution of model uncertainties as a function of forecast range, severity, location, and numerical model. The operational metocean forecasts selected are run twice a day by the Brazilian Navy and the forecast horizon analyzed is five days. A period of 1 year (2017) is considered for the assessment. Observations consist of four satellite missions: JASON2, JASON3, CRYOSAT, and SARAL. The atmospheric models with 10-m winds are Global Forecast System (GFS) and Icosahedral Nonhydrostatic Model (icon). They are used as inputs to force the wave model WAVEWATCH III with a mosaic of two grids. Results show that under calm to moderate conditions, within the first 2 days of forecast, the wind and wave model skill is very high. However, above the 90th percentile and beyond the third forecast day, the predictability drops significantly. It highlights specific contours of forecast range versus percentiles where the wind and wave modelers should focus, in order to anticipate and to improve the predictability of extreme events at the Drake Passage.

Monte Carlo study of a generalized icosahedral model on the simple cubic lattice

We study the critical behavior of a generalized icosahedral model on the simple cubic lattice. The field variable of the icosahedral model might take one of twelve vectors of unit length, which are given by the normalized vertices of the icosahedron, as value. Similar to the Blume-Capel model, where in addition to $-1$ and $1$, as in the Ising model, the spin might take the value $0$, we add in the generalized model $(0,0,0)$ as allowed value. There is a parameter $D$ that controls the density of these voids. For a certain range of $D$, the model undergoes a second-order phase transition. On the critical line, $O(3)$ symmetry emerges. Furthermore, we demonstrate that within this range, similar to the Blume-Capel model on the simple cubic lattice, there is a value of $D$, where leading corrections to scaling vanish. We perform Monte Carlo simulations for lattices of a linear size up to $L=400$ by using a hybrid of local Metropolis and cluster updates. The motivation to study this particular model is mainly of technical nature. Less memory and CPU time are needed than for a model with $O(3)$ symmetry at the microscopic level. As the result of a finite-size scaling analysis we obtain $\nu=0.71164(10)$, $\eta=0.03784(5)$, and $\omega=0.759(2)$ for the critical exponents of the three-dimensional Heisenberg universality class. The estimate of the irrelevant renormalization group eigenvalue that is related with the breaking the $O(3)$ symmetry is $y_{ico}=-2.19(2)$.

Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m−2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m−2.

Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM

Abstract. Natural and anthropogenic disturbances, in particular forest management, affect forest age structures all around the globe. Forest age structures in turn influence key land surface processes, such as photosynthesis and thus the carbon cycle. Yet, many dynamic global vegetation models (DGVMs), including those used as land surface models (LSMs) in Earth system models (ESMs), do not account for subgrid forest age structures, despite being used to investigate land-use effects on the global carbon budget or simulating biogeochemical responses to climate change. In this paper we present a new scheme to introduce forest age classes in hierarchical tile-based DGVMs combining benefits of recently applied approaches the first being a computationally efficient age-dependent simulation of all relevant processes, such as photosynthesis and respiration, using a restricted number of age classes and the second being the tracking of the exact forest age, which is a prerequisite for any implementation of age-based forest management. This combination is achieved by using the tile hierarchy to track the area fraction for each age on an aggregated plant functional type level, whilst simulating the relevant processes for a set of age classes. We describe how we implemented this scheme in JSBACH4, the LSM of the ICOsahedral Non-hydrostatic Earth system model (ICON-ESM). Subsequently, we compare simulation output to global observation-based products for gross primary production, leaf area index, and above-ground biomass to assess the ability of simulations with and without age classes to reproduce the annual cycle and large-scale spatial patterns of these variables. The comparisons show decreasing differences and increasing computation costs with an increasing number of distinguished age classes. The results demonstrate the benefit of the introduction of age classes, with the optimal number of age classes being a compromise between computation costs and error reduction.

Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund

Abstract. Low-level mixed-phase clouds have a substantial impact on the redistribution of radiative energy in the Arctic and are a potential driving factor in Arctic amplification. To better understand the complex processes around mixed-phase clouds, a combination of long-term measurements and high-resolution modeling able to resolve the relevant processes is essential. In this study, we show the general feasibility of the new high-resolution icosahedral nonhydrostatic large-eddy model (ICON-LEM) to capture the general structure, type and timing of mixed-phase clouds at the Arctic site Ny-Ålesund and its potential and limitations for further detailed research. To serve as a basic evaluation, the model is confronted with data streams of single instruments including a microwave radiometer and cloud radar and also with value-added products like the CloudNet classification. The analysis is based on a 11 d long time period with selected periods studied in more detail focusing on the representation of particular cloud processes, such as mixed-phase microphysics. In addition, targeted statistical evaluations against observational data sets are performed to assess (i) how well the vertical structure of the clouds is represented and (ii) how much information is added by higher horizontal resolutions. The results clearly demonstrate the advantage of high resolutions. In particular, with the highest horizontal model resolution of 75 m, the variability of the liquid water path can be well captured. By comparing neighboring grid cells for different subdomains, we also show the potential of the model to provide information on the representativity of single sites (such as Ny-Ålesund) for a larger domain.