ALBERT

Description: We present the first incorporation of the Common Representative Intermediates version 2.2 tropospheric chemistry mechanism, CRI v2.2, combined with stratospheric chemistry, into the global chemistry–climate United Kingdom Chemistry and Aerosols (UKCA) model to give the CRI-Strat 2 mechanism. A rigorous comparison of CRI-Strat 2 with the earlier version, CRI-Strat, is performed in UKCA in addition to an evaluation of three mechanisms, CRI-Strat 2, CRI-Strat and the standard UKCA chemical mechanism, StratTrop v1.0, against a wide array of surface and airborne chemical data. CRI-Strat 2 comprises a state-of-the-art isoprene scheme, optimized against the Master Chemical Mechanism v3.3.1, which includes isoprene peroxy radical isomerization, HOx recycling through the addition of photolabile hydroperoxy aldehydes (HPALDs), and isoprene epoxy diol (IEPOX) formation. CRI-Strat 2 also features updates to several rate constants for the inorganic chemistry, including the reactions of inorganic nitrogen and O(1D). The update to the isoprene chemistry in CRI-Strat 2 increases OH over the lowest 500 m in tropical forested regions by 30 %–50 % relative to CRI-Strat, leading to an improvement in model–observation comparisons for surface OH and isoprene relative to CRI-Strat and StratTrop. Enhanced oxidants also cause a 25 % reduction in isoprene burden and an increase in oxidation fluxes of isoprene and other biogenic volatile organic compounds (BVOCs) at low altitudes with likely impacts on subsequent aerosol formation, atmospheric lifetime, and climate. By contrast, updates to the rate constants of O(1D) with its main reactants relative to CRI-Strat reduces OH in much of the free troposphere, producing a 2 % increase in the methane lifetime, and increases the tropospheric ozone burden by 8 %, primarily from reduced loss via O(1D)+H2O. The changes to inorganic nitrogen reaction rate constants increase the NOx burden by 4 % and shift the distribution of nitrated species closer to that simulated by StratTrop. CRI-Strat 2 is suitable for multi-decadal model integrations and the improved representation of isoprene chemistry provides an opportunity to explore the consequences of HOx recycling in the United Kingdom Earth System Model (UKESM1). This new mechanism will enable a re-evaluation of the impact of BVOCs on the chemical composition of the atmosphere and further probe the feedback between the biosphere and the climate.

Description: Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.

Description: Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

Description: Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

Description: How global gridded crop models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Intercomparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for simple mechanistic model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer–Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed-in-time fraction of net primary productivity allocated to the grains (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow for the capture of the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in a few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.

Description: Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models that use parameters obtained from chamber experiments. In this study, a box model with a volatility basis set (VBS) scheme was developed, and the secondary organic aerosol (SOA) yields with vapor wall loss correction were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9 and leads to better agreement with measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The model results from the VBS schemes with standard (VBS_BASE) and vapor-wall-loss-corrected parameters (VBS_WLS), as well as the traditional two-product approach, were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM) or aerosol mass spectrometer (AMS) stations in the winter of 2011. An additional reference scenario, VBS_noWLS, was also developed using the same parameterization as VBS_WLS except for the SOA yields, which were optimized by assuming there is no vapor wall loss. The VBS_WLS generally shows the best performance for predicting OA among all OA schemes and reduces the mean fractional bias from −72.9 % (VBS_BASE) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than VBS_BASE and VBS_noWLS, respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ∼80 % (in the Balkans). VBS_WLS also leads to better agreement between the modeled SOA fraction in OA (fSOA) and the estimated values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in parameterizations based on laboratory studies for a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

Description: The evaluation of models in general is a nontrivial task and can, due to epistemological and practical reasons, never be considered complete. Due to this incompleteness, a model may yield correct results for the wrong reasons, i.e., via a different chain of processes than found in observations. While guidelines and strategies exist in the atmospheric sciences to maximize the chances that models are correct for the right reasons, these are mostly applicable to full physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is an atmospheric model employing linear mountain wave theory to represent the wind field. In this wind field, atmospheric quantities such as temperature and moisture are advected and a microphysics scheme is applied to represent the formation of clouds and precipitation. This study conducts an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. To contrast the obtained results from the linear-theory-based ICAR model to a full physics model, idealized simulations with the Weather Research and Forecasting (WRF) model are conducted. The impact of the developed recommendations is then demonstrated with a case study for the South Island of New Zealand. The results of this investigation suggest three modifications to improve different aspects of ICAR simulations. The representation of the wind field within the domain improves when the dry and the moist Brunt–Väisälä frequencies are calculated in accordance with linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Imposing boundary conditions at the upper boundary that are different to the standard zero-gradient boundary condition is shown to reduce errors in the potential temperature and water vapor fields. Furthermore, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. The method to determine the lowest model top elevation is applied to both the idealized simulations and the real terrain case study. Notable differences between the ICAR and WRF simulations are observed across all investigated quantities such as the wind field, water vapor and hydrometeor distributions, and the distribution of precipitation. The case study indicates that the precipitation maximum calculated by the ICAR simulation employing the developed recommendations is spatially shifted upwind in comparison to an unmodified version of ICAR. The cause for the shift is found in influences of the model top on cloud formation and precipitation processes in the ICAR simulations. Furthermore, the results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such an analysis may not reflect the skill of the model in capturing atmospheric processes like gravity waves and cloud formation.

Description: A new integrated mass-flux adjustment filter is introduced, which uses the analyzed integrated mass-flux divergence field to correct the analyzed wind field. The filter has been examined in twin experiments with rapid update cycling using an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it considerably diminishes spurious mass-flux divergence and the high surface pressure tendency, and it thus results in more dynamically balanced analysis states. For the ensuing 3 h forecasts, the experiment that employs the filter becomes more skillful after 1 h. These preliminary results show that the filter is a promising tool to alleviate the imbalance problem caused by data assimilation, especially for convective-scale applications.

Description: The present work analyzes the quality and reliability of an important class of general-purpose, second-order accurate finite-volume (FV) solvers for the large-eddy simulation of a neutrally stratified atmospheric boundary layer (ABL) flow. The analysis is carried out within the OpenFOAM® framework, which is based on a colocated grid arrangement. A series of open-channel flow simulations are carried out using a static Smagorinsky model for subgrid scale momentum fluxes in combination with an algebraic equilibrium wall-layer model. The sensitivity of the solution to variations in numerical parameters such as grid resolution (up to 1603 control volumes), numerical solvers, and interpolation schemes for the discretization of nonlinear terms is evaluated and results are contrasted against those from a well-established mixed pseudospectral–finite-difference code. Considered flow statistics include mean streamwise velocity, resolved Reynolds stresses, velocity skewness and kurtosis, velocity spectra, and two-point autocorrelations. A quadrant analysis along with the examination of the conditionally averaged flow field are performed to investigate the mechanisms responsible for momentum transfer in the flow. It is found that at the selected grid resolutions, the considered class of FV-based solvers yields a poorly correlated flow field and is not able to accurately capture the dominant mechanisms responsible for momentum transport in the ABL. Specifically, the predicted flow field lacks the well-known sweep and ejection pairs organized side by side along the cross-stream direction, which are representative of a streamwise roll mode. This is especially true when using linear interpolation schemes for the discretization of nonlinear terms. This shortcoming leads to a misprediction of flow statistics that are relevant for ABL flow applications and to an enhanced sensitivity of the solution to variations in grid resolution, thus calling for future research aimed at reducing the impact of modeling and discretization errors.

Description: Machine learning (ML) and data-driven approaches are increasingly used in many research areas. Extreme gradient boosting (XGBoost) is a tree boosting method that has evolved into a state-of-the-art approach for many ML challenges. However, it has rarely been used in simulations of land use change so far. Xilingol, a typical region for research on serious grassland degradation and its drivers, was selected as a case study to test whether XGBoost can provide alternative insights that conventional land-use models are unable to generate. A set of 20 drivers was analysed using XGBoost, involving four alternative sampling strategies, and SHAP (Shapley additive explanations) to interpret the results of the purely data-driven approach. The results indicated that, with three of the sampling strategies (over-balanced, balanced, and imbalanced), XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation. Four drivers accounted for 99 % of the grassland degradation dynamics in Xilingol. These four drivers were spatially allocated, and a risk map of further degradation was produced. The limitations of using XGBoost to predict future land-use change are discussed.

Description: Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.

Description: Aerosol forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System aerosol module (IFS-AER) for the years 2016–2019 (cycles 41r1–46r1) are compared to vertical profiles of particle backscatter from the Deutscher Wetterdienst (DWD) ceilometer network. The system has been developed in the Copernicus Atmosphere Monitoring Service (CAMS) and its precursors. The focus of this article is to evaluate the realism of the vertical aerosol distribution from 0.4 to 8 km above ground, coded in the shape, bias and temporal variation of the profiles. The common physical quantity, the attenuated backscatter β∗(z), is directly measured and calculated from the model mass mixing ratios of the different particle types using the model's inherent aerosol microphysical properties. Pearson correlation coefficients of daily average simulated and observed vertical profiles between r=0.6–0.8 in summer and 0.7–0.95 in winter indicate that most of the vertical structure is captured. It is governed by larger β∗(z) in the mixing layer and comparably well captured with the successive model versions. The aerosol load tends to be biased high near the surface, underestimated in the mixing layer and realistic at small background values in the undisturbed free troposphere. A seasonal cycle of the bias below 1 km height indicates that aerosol sources and/or lifetimes are overestimated in summer and pollution episodes are not fully resolved in winter. Long-range transport of Saharan dust or fire smoke is captured and timely, only the dispersion to smaller scales is not resolved in detail. Over Germany, β∗(z) values from Saharan dust and sea salt are considerably overestimated. Differences between model and ceilometer profiles are investigated using observed in situ mass concentrations of organic matter (OM), black carbon, SO4, NO3, NH4 and proxies for mineral dust and sea salt near the surface. Accordingly, SO4 and OM sources as well as gas-to-particle partitioning of the NO3–NH4 system are too strong. The top of the mixing layer on average appears too smooth and several hundred meters too low in the model. Finally, a discussion is included of the considerable uncertainties in the observations as well as the conversion from modeled to observed physical quantities and from necessary adaptions of varying resolutions and definitions.

Description: Transient processes play a major role in geophysical applications. In this paper, we quantify the significant influence arising from transient processes for conductive heat transfer problems for sedimentary basin systems. We demonstrate how the thermal properties are affected when changing the system from a stationary to a non-stationary (transient) state and what impact time-dependent boundary conditions (as derived from paleoclimate information) have on the system's overall response. Furthermore, we emphasize the importance of the time-stepping approach adopted to numerically solve for the transient case and the overall simulation duration since both factors exert a direct influence on the sensitivities of the thermal properties. We employ global sensitivity analyses to quantify not only the impact arising from the thermal properties but also their parameter correlations. Furthermore, we showcase how the results of such sensitivity analysis can be used to gain further insights into the complex Central European Basin System in central and northern Europe. This computationally very demanding workflow becomes feasible through the construction of high-precision surrogate models based on the reduced basis (RB) method.

Description: Effective numerical weather forecasting is vital in arid regions like the United Arab Emirates (UAE) where extreme events like heat waves, flash floods, and dust storms are severe. Hence, accurate forecasting of quantities like surface temperatures and humidity is very important. To date, there have been few seasonal-to-annual scale verification studies with WRF at high spatial and temporal resolution. This study employs a convection-permitting scale (2.7 km grid scale) simulation with WRF with Noah-MP, in daily forecast mode, from 1 January to 30 November 2015. WRF was verified using measurements of 2 m air temperature (T2 m), 2 m dew point (TD2 m), and 10 m wind speed (UV10 m) from 48 UAE WMO-compliant surface weather stations. Analysis was made of seasonal and diurnal performance within the desert, marine, and mountain regions of the UAE. Results show that WRF represents temperature (T2 m) quite adequately during the daytime with biases ≤+1 ∘C. There is, however, a nocturnal cold bias (−1 to −4 ∘C), which increases during hotter months in the desert and mountain regions. The marine region has the smallest T2 m biases (≤-0.75 ∘C). WRF performs well regarding TD2 m, with mean biases mostly ≤ 1 ∘C. TD2 m over the marine region is overestimated, though (0.75–1 ∘C), and nocturnal mountain TD2 m is underestimated (∼-2 ∘C). UV10 m performance on land still needs improvement, and biases can occasionally be large (1–2 m s−1). This performance tends to worsen during the hot months, particularly inland with peak biases reaching ∼ 3 m s−1. UV10 m is better simulated in the marine region (bias ≤ 1 m s−1). There is an apparent relationship between T2 m bias and UV10 m bias, which may indicate issues in simulation of the daytime sea breeze. TD2 m biases tend to be more independent. Studies such as these are vital for accurate assessment of WRF nowcasting performance and to identify model deficiencies. By combining sensitivity tests, process, and observational studies with seasonal verification, we can further improve forecasting systems for the UAE.

Description: The amount of solar radiation reaching the Earth's surface can be highly determined by atmospheric aerosols, which have been pointed to as the most uncertain climate forcing agents through their direct (scattering and absorption), semi-direct (absorption implying a thermodynamic effect on clouds) and indirect (modification of cloud properties when aerosols act as cloud condensation nuclei) effects. Nonetheless, regional climate models hardly ever dynamically model the atmospheric concentration of aerosols and their interactions with radiation (ARIs) and clouds (ACIs). The objective of this work is to evince the role of modeling ARIs and ACIs in Weather Research and Forecast (WRF) model simulations with fully interactive aerosols (online resolved concentrations) with a focus on summer mean surface downward solar radiation (RSDS) over Europe. Under historical conditions (1991–2010), both ARIs and ACIs reduce RSDS by a few percentage points over central and northern regions. This reduction is larger when only ARIs are resolved, while ACIs counteract the effect of the former by up to half. The response of RSDS to the activation of ARIs and ACIs is mainly led by the aerosol effect on cloud coverage, while the aerosol effect on atmospheric optical depth plays a very minor role, which evinces the importance of semi-direct and indirect aerosol effects. In fact, differences in RSDS among experiments with and without aerosols are smaller under clear-sky conditions. In terms of future projections (2031–2050 vs. 1991–2010), the baseline pattern (from an experiment without aerosols) shows positive signals southward and negative signals northward. While ARIs enhance the former and reduce the latter, ACIs work in the opposite direction and provide a flatter RSDS change pattern, further evincing the opposite impact from semi-direct and indirect effects and the nontrivial influence of the latter.

Description: With MLAir (Machine Learning on Air data) we created a software environment that simplifies and accelerates the exploration of new machine learning (ML) models, specifically shallow and deep neural networks, for the analysis and forecasting of meteorological and air quality time series. Thereby MLAir is not developed as an abstract workflow, but hand in hand with actual scientific questions. It thus addresses scientists with either a meteorological or an ML background. Due to their relative ease of use and spectacular results in other application areas, neural networks and other ML methods are also gaining enormous momentum in the weather and air quality research communities. Even though there are already many books and tutorials describing how to conduct an ML experiment, there are many stumbling blocks for a newcomer. In contrast, people familiar with ML concepts and technology often have difficulties understanding the nature of atmospheric data. With MLAir we have addressed a number of these pitfalls so that it becomes easier for scientists of both domains to rapidly start off their ML application. MLAir has been developed in such a way that it is easy to use and is designed from the very beginning as a stand-alone, fully functional experiment. Due to its flexible, modular code base, code modifications are easy and personal experiment schedules can be quickly derived. The package also includes a set of validation tools to facilitate the evaluation of ML results using standard meteorological statistics. MLAir can easily be ported onto different computing environments from desktop workstations to high-end supercomputers with or without graphics processing units (GPUs).

Description: More detailed observational capabilities in the microwave (MW) range and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in scattering databases. The majority of existing databases rely on the discrete dipole approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, and particle size. The snowScatt tool is innovative in that it provides consistent microphysical and scattering properties of an ensemble of 50 000 snowflake aggregates generated with different physical particle models. Many diverse snowflake types, including rimed particles and aggregates of different monomer composition, are accounted for. The scattering formulation adopted by snowScatt is based on the self-similar Rayleigh–Gans approximation (SSRGA), which is capable of modeling the scattering properties of large ensembles of particles. Previous comparisons of SSRGA and DDA are extended in this study by including unrimed and rimed aggregates up to centimeter sizes and frequencies up to the sub-millimeter spectrum. The results generally reveal the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA parameters can be used to infer scattering properties at any frequency and refractive index; snowScatt also provides tools to derive the SSRGA parameters for new sets of particle structures, which can be easily included in the library. The flexibility of the snowScatt tool with respect to applications that require continuously changing definitions of snow properties is demonstrated in a forward simulation example based on the output of the predicted particle properties (P3) scheme. The snowScatt tool provides the same level of flexibility as commonly used T-matrix solutions, while the computed scattering properties reach the level of accuracy of detailed discrete dipole approximation calculations.

Description: To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

Description: Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

Description: Crop growth in land surface models normally requires high-temporal-resolution climate data (3-hourly or 6-hourly), but such high-temporal-resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choices if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 ∘C warming, 2.0 ∘C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, crop yields were underestimated by 5 %–8 % across the three scenarios (1.5, 2.0 ∘C, and RCP4.5) for the global average, and 28 %–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

Description: In this paper, we present a new version of PLUME-MoM, a 1-D integral volcanic plume model based on the method of moments for the description of the polydispersity in solid particles. The model describes the steady-state dynamics of a plume in a 3-D coordinate system, and a modification of the two-size moment (TSM) method is adopted to describe changes in grain size distribution along the plume, associated with particle loss from plume margins and with particle aggregation. For this reason, the new version is named PLUME-MoM-TSM. For the first time in a plume model, the full Smoluchowski coagulation equation is solved, allowing us to quantify the formation of aggregates during the rise of the plume. In addition, PLUME-MOM-TSM allows us to model the phase change of water, which can be either magmatic, added at the vent as liquid from external sources, or incorporated through ingestion of moist atmospheric air. Finally, the code includes the possibility to simulate the initial spreading of the umbrella cloud intruding from the volcanic column into the atmosphere. A transient shallow-water system of equations models the intrusive gravity current, allowing computation of the upwind spreading. The new model is applied first to the eruption of the Calbuco volcano in southern Chile in April 2015 and then to a sensitivity analysis of the upwind spreading of the umbrella cloud to mass flow rate and meteorological conditions (wind speed and humidity). This analysis provides an analytical relationship between the upwind spreading and some observable characteristic of the volcanic column (height of the neutral buoyancy level and plume bending), which can be used to better link plume models and volcanic-ash transport and dispersion models.

Description: The ability to achieve high spatial resolutions is an important feature for numerical models to accurately represent the large spatial variability of urban air pollution. On the one hand, the well-established mesoscale chemistry transport models have their obvious shortcomings due to the extensive use of physical parameterizations. On the other hand, obstacle-resolving computational fluid dynamics (CFD) models, although accurate, are still often too computationally intensive to be applied regularly for entire cities. The major reason for the inflated computational costs is the required horizontal resolution to meaningfully apply obstacle discretization, which is mostly based on boundary-fitted grids, e.g., the marker-and-cell method. In this paper, we present the new City-scale AIR dispersion model with DIffuse Obstacles (CAIRDIO v1.0), in which the diffuse interface method, simplified for non-moving obstacles, is incorporated into the governing equations for incompressible large-eddy simulations. While the diffuse interface method is widely used in two-phase modeling, this method has not been used in urban boundary-layer modeling so far. It allows us to consistently represent buildings over a wide range of spatial resolutions, including grid spacings equal to or larger than typical building sizes. This way, the gray zone between obstacle-resolving microscale simulations and mesoscale simulations can be addressed. Orographic effects can be included by using terrain-following coordinates. The dynamic core is compared against a standard quality-assured wind-tunnel dataset for dispersion-model evaluation. It is shown that the model successfully reproduces dispersion patterns within a complex city morphology across a wide range of spatial resolutions tested. As a result of the diffuse obstacle approach, the accuracy test is also passed at a horizontal grid spacing of 40 m. Although individual flow features within individual street canyons are not resolved at the coarse-grid spacing, the building effect on the dispersion of the air pollution plume is preserved at a larger scale. Therefore, a very promising application of the CAIRDIO model lies in the realization of computationally feasible yet accurate air-quality simulations for entire cities.

Description: Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited spatio-temporal coverage of bulk or surface variables only. In particular, visible–near-infrared (Vis–NIR) reflectance observations can provide information about the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables the estimation of any physical variable virtually anywhere, but it is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information and to propagate information from observed areas to non-observed areas. Here, we present CrocO (Crocus-Observations), an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and Vis–NIR reflectances) in a spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties and a particle filter (PF) to reduce them. The PF is prone to collapse when assimilating too many observations. Two variants of the PF were specifically implemented to ensure that observational information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an iterative inflation of observation errors, while the klocal algorithm is a localised approach performing a selection of the observations to assimilate based on background correlation patterns. Feasibility testing experiments are carried out in an identical twin experiment setup, with synthetic observations of HS and Vis–NIR reflectances available in only one-sixth of the simulation domain. Results show that compared against runs without assimilation, analyses exhibit an average improvement of the snow water equivalent continuous rank probability score (CRPS) of 60 % when assimilating HS with a 40-member ensemble and an average 20 % CRPS improvement when assimilating reflectance with a 160-member ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a possibility for the assimilation of real observations of reflectance or of any snowpack observations in a spatialised context.

Description: A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Description: Canada has the longest coastline in the world and includes diverse ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the east coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas in support of marine activities including emergency response, search and rescue, and safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26∘ N in the Atlantic Ocean through the Arctic Ocean to 44∘ N in the Pacific Ocean, with a model grid resolution that varies between 3 and 8 km. RIOPSv2 includes a multivariate data assimilation system based on a reduced-order extended Kalman filter together with a 3D-Var bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration, allowing inhomogeneous anisotropic multivariate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and allow for the time-varying harmonic constants necessary in seasonally ice-infested waters. Compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature (SST). In addition to the tidal harmonic analysis, the observation operator for sea level anomaly (SLA) is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found near the Gulf Stream for all model fields due to the higher model grid resolution, with smaller root mean squared (rms) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 ∘C for SST. Verification against along-track satellite observations demonstrates the improved representation of mesoscale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced rms differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–2019) provide a rare glimpse at errors in Arctic water mass properties and show average salinity biases over the upper 500 m of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.

Description: The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation of the Himalaya in the models. However, the accuracy of atmospheric models remains limited in this region due to highly complex mountainous topography. This article delineates the effects of spatial resolution on the modeled meteorology and dynamics over the CH by utilizing the Weather Research and Forecasting (WRF) model extensively evaluated against the Ganges Valley Aerosol Experiment (GVAX) observations during the summer monsoon. The WRF simulation is performed over a domain (d01) encompassing northern India at 15 km × 15 km resolution and two nests (d02 at 5 km × 5 km and d03 at 1 km × 1 km) centered over the CH, with boundary conditions from the respective parent domains. WRF simulations reveal higher variability in meteorology, e.g., relative humidity (RH = 70.3 %–96.1 %) and wind speed (WS = 1.1–4.2 m s−1), compared to the ERA-Interim reanalysis (RH = 80.0 %–85.0 %, WS = 1.2–2.3 m s−1) over northern India owing to the higher resolution. WRF-simulated temporal evolution of meteorological variables is found to agree with balloon-borne measurements, with stronger correlations aloft (r = 0.44–0.92) than those in the lower troposphere (r = 0.18–0.48). The model overestimates temperature (warm bias by 2.8 ∘C) and underestimates RH (dry bias by 6.4 %) at the surface in d01. Model results show a significant improvement in d03 (P = 827.6 hPa, T = 19.8 ∘C, RH = 92.3 %), closer to the GVAX observations (P = 801.4 hPa, T = 19.5 ∘C, RH = 94.7 %). Interpolating the output from the coarser domains (d01, d02) to the altitude of the station reduces the biases in pressure and temperature; however, it suppresses the diurnal variations, highlighting the importance of well-resolved terrain (d03). Temporal variations in near-surface P, T, and RH are also reproduced by WRF in d03 to an extent (r〉0.5). A sensitivity simulation incorporating the feedback from the nested domain demonstrates the improvement in simulated P, T, and RH over the CH. Our study shows that the WRF model setup at finer spatial resolution can significantly reduce the biases in simulated meteorology, and such an improved representation of the CH can be adopted through domain feedback into regional-scale simulations. Interestingly, WRF simulates a dominant easterly wind component at 1 km × 1 km resolution (d03), which is missing in the coarse simulations; however, the frequency of southeasterlies remains underestimated. The model simulation implementing a high-resolution (3 s) topography input (SRTM) improved the prediction of wind directions; nevertheless, further improvements are required to better reproduce the observed local-scale dynamics over the CH.

Description: Here, we developed a zero-dimensional (0-D) modeling framework (LEVCHEM_v1) to provide insights into the atmospheric degradation of a key tracer emitted during biomass burning – levoglucosan (LEV), while additionally exploring its effects on the dynamics of secondary organic aerosols (SOA) and other gases. For this, we updated existing chemical mechanisms (homogeneous gas-phase chemistry and heterogeneous chemistry) in the BOXMOXv1.7 model to include the chemical degradation of LEV and its intermediary degradation products in both phases (gas and aerosol). In addition, we added a gas-particle partitioning mechanism to the model to account for the effect of evaporation and condensation on the phase-specific concentrations of LEV and its degradation products. Comparison of simulation results with measurements from various chamber experiments (spanning summer and winter conditions) show that the degradation timescale of LEV varied by phase, with gas-phase degradation occurring over ∼1.5–5 d and aerosol-phase degradation occurring over ∼8–36 h. These relatively short timescales suggest that most of the initial LEV concentration can be lost chemically or deposited locally before being transported regionally. We varied the heterogeneous reaction rate constant in a sensitivity analysis (for summer conditions only) and found that longer degradation timescales of LEV are possible, particularly in the aerosol phase (7 d), implying that some LEV may be transported regionally. The multiphase chemical degradation of LEV has effects on SOA and other gases. Several first- or second-generation products resulted from its degradation; most of the products include one or two carbonyl groups, one product contains a nitrate group, and a few products show the cleavage of C−C bonds. The relative importance of the products varies depending on the phase and the timing of the maximum concentration achieved during the simulation. Our estimated secondary organic aerosol SOA yields (4 %–32 %) reveal that conversion of LEV to secondary products is significant and occurs rapidly in the studied scenarios. LEV degradation affected other gases by increasing the concentrations of radicals and decreasing those of reactive nitrogen species. Decreases of the mixing ratios of nitrogen oxides appear to drive a more rapid increase in ozone compared with changes in volatile organic compounds levels. An important next step to confirm longer degradation timescales will be to extend the evaluation of the modeled LEV degradation beyond 3–6 h by using more extensive data from chambers and, possibly, from fire plumes. The mechanism developed here can be used in chemical transport models applied to fire plumes to trace LEV and its degradation products from source to deposition, to assess their atmospheric implications and to answer questions relevant to fire tracing, carbon and nitrogen cycling, and climate.

Description: A number of important questions concern processes at the margins of ice sheets where multiple components of the Earth system, most crucially ice sheets and oceans, interact. Such processes include thermodynamic interaction at the ice–ocean interface, the impact of meltwater on ice shelf cavity circulation, the impact of basal melting of ice shelves on grounded ice dynamics and ocean controls on iceberg calving. These include fundamentally coupled processes in which feedback mechanisms between ice and ocean play an important role. Some of these mechanisms have major implications for humanity, most notably the impact of retreating marine ice sheets on the global sea level. In order to better quantify these mechanisms using computer models, feedbacks need to be incorporated into the modelling system. To achieve this, ocean and ice dynamic models must be coupled, allowing runtime information sharing between components. We have developed a flexible coupling framework based on existing Earth system coupling technologies. The open-source Framework for Ice Sheet–Ocean Coupling (FISOC) provides a modular approach to coupling, facilitating switching between different ice dynamic and ocean components. FISOC allows fully synchronous coupling, in which both ice and ocean run on the same time step, or semi-synchronous coupling in which the ice dynamic model uses a longer time step. Multiple regridding options are available, and there are multiple methods for coupling the sub-ice-shelf cavity geometry. Thermodynamic coupling may also be activated. We present idealized simulations using FISOC with a Stokes flow ice dynamic model coupled to a regional ocean model. We demonstrate the modularity of FISOC by switching between two different regional ocean models and presenting outputs for both. We demonstrate conservation of mass and other verification steps during evolution of an idealized coupled ice–ocean system, both with and without grounding line movement.

Description: Polar stratospheric clouds (PSCs) contribute to catalytic ozone destruction by providing surfaces for the conversion of inert chlorine species into active forms and by denitrification. The latter describes the removal of HNO3 from the stratosphere by sedimenting PSC particles, which hinders chlorine deactivation by the formation of reservoir species. Therefore, an accurate representation of PSCs in chemistry–climate models (CCMs) is of great importance to correctly simulate polar ozone concentrations. Here, we evaluate PSCs as simulated by the CCM SOCOLv3.1 for the Antarctic winters 2006, 2007 and 2010 by comparison with backscatter measurements by CALIOP on board the CALIPSO satellite. The year 2007 represents a typical Antarctic winter, while 2006 and 2010 are characterized by above- and below-average PSC occurrence. The model considers supercooled ternary solution (STS) droplets, nitric acid trihydrate (NAT) particles, water ice particles and mixtures thereof. PSCs are parameterized in terms of temperature and partial pressures of HNO3 and H2O, assuming equilibrium between the gas and particulate phase. The PSC scheme involves a set of prescribed microphysical parameters, namely ice number density, NAT particle radius and maximum NAT number density. In this study, we test and optimize the parameter settings through several sensitivity simulations. The choice of the value for the ice number density affects simulated optical properties and dehydration, while modifying the NAT parameters impacts stratospheric composition via HNO3 uptake and denitrification. Depending on the NAT parameters, reasonable denitrification can be modeled. However, its impact on ozone loss is minor. The best agreement with the CALIOP optical properties and observed denitrification was for this case study found with the ice number density increased from the hitherto used value of 0.01 to 0.05 cm−3 and the maximum NAT number density from 5×10-4 to 1×10-3 cm−3. The NAT radius was kept at the original value of 5 µm. The new parameterization reflects the higher importance attributed to heterogeneous nucleation of ice and NAT particles following recent new data evaluations of the state-of-the-art CALIOP measurements. A cold temperature bias in the polar lower stratosphere results in an overestimated PSC areal coverage in SOCOLv3.1 by up to 40 %. Offsetting this cold bias by +3 K delays the onset of ozone depletion by about 2 weeks, which improves the agreement with observations. Furthermore, the occurrence of mountain-wave-induced ice, as observed mainly over the Antarctic Peninsula, is continuously underestimated in the model due to the coarse model resolution (T42L39) and the fixed ice number density. Nevertheless, we find overall good temporal and spatial agreement between modeled and observed PSC occurrence and composition. This work confirms previous studies indicating that simplified PSC schemes, which avoid nucleation and growth calculations in sophisticated but time-consuming microphysical process models, may also achieve good approximations of the fundamental properties of PSCs needed in CCMs.

Description: Lossy compression has been applied to the data compression of large-scale Earth system model data (ESMD) due to its advantages of a high compression ratio. However, few lossy compression methods consider both global and local multidimensional coupling correlations, which could lead to information loss in data approximation of lossy compression. Here, an adaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on the foundation of a stream compression method for geospatial data called blocked hierarchical geospatial field data representation (Blocked-HGFDR). In addition, the original Blocked-HGFDR method is also improved from the following perspectives. Firstly, the original data are divided into a series of data blocks of a more balanced size to reduce the effect of the dimensional unbalance of ESMD. Following this, based on the mathematical relationship between the compression parameter and compression error in Blocked-HGFDR, the control mechanism is developed to determine the optimal compression parameter for the given compression error. By assigning each data block an independent compression parameter, Adaptive-HGFDR can capture the local variation of multidimensional coupling correlations to improve the approximation accuracy. Experiments are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions compared with ZFP and Blocked-HGFDR. For the compression results among 22 climate variables, Adaptive-HGFDR can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity and fast changing rate. This study provides a new potential method for the lossy compression of the large-scale Earth system model data.

Description: As our knowledge and understanding of atmospheric aerosol particle evolution and impact grows, designing community mechanistic models requires an ability to capture increasing chemical, physical and therefore numerical complexity. As the landscape of computing software and hardware evolves, it is important to profile the usefulness of emerging platforms in tackling this complexity. Julia is a relatively new programming language that promises computational performance close to that of Fortran, for example, without sacrificing the flexibility offered by languages such as Python. With this in mind, in this paper we present and demonstrate the initial development of a high-performance community mixed-phase atmospheric 0D box model, JlBox, written in Julia. In JlBox v1.1 we provide the option to simulate the chemical kinetics of a gas phase whilst also providing a fully coupled gas-particle model with dynamic partitioning to a fully moving sectional size distribution, in the first instance. JlBox is built around chemical mechanism files, using existing informatics software to parse chemical structures and relationships from these files and then provide parameters required for mixed-phase simulations. In this study we use mechanisms from a subset and the complete Master Chemical Mechanism (MCM). Exploiting the ability to perform automatic differentiation of Jacobian matrices within Julia, we profile the use of sparse linear solvers and pre-conditioners, whilst also using a range of stiff solvers included within the expanding ODE solver suite the Julia environment provides, including the development of an adjoint model. Case studies range from a single volatile organic compound (VOC) with 305 equations to a “full” complexity MCM mixed-phase simulation with 47 544 variables. Comparison with an existing mixed-phase model shows significant improvements in performance for multi-phase and mixed VOC simulations and potential for developments in a number of areas.

Description: Understanding future changes in the terrestrial carbon cycle is important for reliable projections of climate change and impacts on ecosystems. It is well known that nitrogen (N) could limit plants' response to increased atmospheric carbon dioxide and it is therefore important to include a representation of the N cycle in Earth system models. Here we present the implementation of the terrestrial nitrogen cycle in the Joint UK Land Environment Simulator (JULES) – the land surface scheme of the UK Earth System Model (UKESM). Two configurations are discussed – the first one (JULES-CN) has a bulk soil biogeochemical model and the second one is a development configuration that resolves the soil biogeochemistry with depth (JULES-CNlayer). In JULES the nitrogen (N) cycle is based on the existing carbon (C) cycle and represents all the key terrestrial N processes in a parsimonious way. Biological N fixation is dependent on net primary productivity, and N deposition is specified as an external input. Nitrogen leaves the vegetation and soil system via leaching and a bulk gas loss term. Nutrient limitation reduces carbon-use efficiency (CUE – ratio of net to gross primary productivity) and can slow soil decomposition. We show that ecosystem level N limitation of net primary productivity (quantified in the model by the ratio of the potential amount of C that can be allocated to growth and spreading of the vegetation compared with the actual amount achieved in its natural state) falls at the lower end of the observational estimates in forests (approximately 1.0 in the model compared with 1.01 to 1.38 in the observations). The model shows more N limitation in the tropical savanna and tundra biomes, consistent with the available observations. Simulated C and N pools and fluxes are comparable to the limited available observations and model-derived estimates. The introduction of an N cycle improves the representation of interannual variability of global net ecosystem exchange, which was more pronounced in the C-cycle-only versions of JULES (JULES-C) than shown in estimates from the Global Carbon Project. It also reduces the present-day CUE from a global mean value of 0.45 for JULES-C to 0.41 for JULES-CN and 0.40 for JULES-CNlayer, all of which fall within the observational range. The N cycle also alters the response of the C fluxes over the 20th century and limits the CO2 fertilisation effect, such that the simulated current-day land C sink is reduced by about 0.5 Pg C yr−1 compared to the version with no N limitation. JULES-CNlayer additionally improves the representation of soil biogeochemistry, including turnover times in the northern high latitudes. The inclusion of a prognostic land N scheme marks a step forward in functionality and realism for the JULES and UKESM models.

Description: Trajectory models are intuitive tools for airflow studies. But in general, they are limited to non-turbulent, i.e. laminar flow, conditions. Therefore, trajectory models are not particularly suitable for investigating airflow within the turbulent atmospheric boundary layer. To overcome this, a common approach is handling the turbulent uncertainty as a random deviation from a mean path in order to create a statistic of possible solutions which envelops the mean path. This is well known as the Lagrangian particle dispersion model (LPDM). However, the decisive factor is the representation of turbulence in the model, for which widely used models such as FLEXPART and HYSPLIT use an approximation. A conceivable improvement could be the use of a turbulence parameterisation approach based on the turbulent kinetic energy (TKE) at high temporal resolution. Here, we elaborated this approach and developed the LPDM Itpas, which is coupled online to the German Weather Service's mesoscale weather forecast model COSMO. It benefits from the prognostically calculated TKE as well as from the high-frequency wind information. We demonstrate the model's applicability for a case study on agricultural particle emission in eastern Germany. The results obtained are discussed with regard to the model's ability to describe particle transport within a turbulent boundary layer. Ultimately, the simulations performed suggest that the newly introduced method based on prognostic TKE sufficiently represents the particle transport.

Description: In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence-resolving PALM model system 6.0 (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name). The new chemistry model is implemented in the PALM model as part of the PALM-4U (PALM for urban applications) components, which are designed for application of the PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3) has been utilized for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state (PHSTAT) to mechanisms with a strongly simplified volatile organic compound (VOC) chemistry (e.g. the SMOG mechanism from KPP) and the Carbon Bond Mechanism 4 (CBM4; Gery et al., 1989), which includes a more comprehensive, but still simplified VOC chemistry. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure and input requirements along with its various features and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study comprises part of Berlin, Germany. Emissions are considered using street-type-dependent emission factors from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied, and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. Even though the feedback of the model's aerosol concentrations on meteorology is not yet considered in the current version of the model, the results show the importance of online photochemistry and dispersion of air pollutants in the urban boundary layer for high spatial and temporal resolutions. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. The CBM4 and SMOG mechanisms show better agreement with observations than the steady-state PHSTAT mechanism.

Description: Following the protocol of the fourth phase of the Paleoclimate Modelling Intercomparison Project (PMIP4), we performed numerical experiments targeting distinctive past time periods using the Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations (MIROC-ES2L), which is an Earth system model. Setup and basic performance of the experiments are presented. The Last Glacial Maximum was one of the most extreme climate states during the Quaternary and conducting numerical modeling experiments of this period has long been a challenge for the paleoclimate community. We conducted a Last Glacial Maximum experiment with a long spin-up of nearly 9000 years. Globally, there was reasonable agreement between the anomalies relative to the present day derived from model climatology and those derived from proxy data archives, while some regional discrepancies remained. By changing orbital and greenhouse gas forcings, we conducted experiments for two interglacial periods: 6000 and 127 000 years before present. Model anomalies relative to the present day were qualitatively consistent with variations in solar forcing. However, anomalies in the model were smaller than those derived from proxy data archives, suggesting that processes that play a role in past interglacial climates remain lacking in this state-of-the-art model. We conducted transient simulations from 850 to 1850 CE and from 1850 to 2014 CE. Cooling in the model indicated a clear response to huge volcanic eruptions, consistent with paleo-proxy data. The contrast between cooling during the Little Ice Age and warming during the 20th to 21st centuries was represented well at the multidecadal timescale.

Description: Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services, such as freshwater supply. Streamflow variability and temporal evolution are impacted by the presence of lakes in the river network; therefore, any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global-scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river-routing model CTRIP to resolve the specific mass balance of open-water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on global-scale river-routing performance. In the current study, an offline evaluation was performed for four river networks using a set of state-of-the-art quality atmospheric forcings and a combination of in situ and satellite measurements for river discharge and lake level observations. The results reveal a general improvement in CTRIP-simulated discharge and its variability, while also generating realistic lake level variations. MLake produces more realistic streamflows both in terms of daily and seasonal correlation. Excluding the specific case of Lake Victoria having low performances, the mean skill score of Kling–Gupta efficiency (KGE) is 0.41 while the normalized information contribution (NIC) shows a mean improvement of 0.56 (ranging from 0.15 to 0.94). Streamflow results are spatially scale-dependent, with better scores associated with larger lakes and increased sensitivity to the width of the lake outlet. Regarding lake level variations, results indicate a good agreement between observations and simulations with a mean correlation of 0.56 (ranging from 0.07 to 0.92) which is linked to the capability of the model to retrieve seasonal variations. Discrepancies in the results are mainly explained by the anthropization of the selected lakes, which introduces high-frequency variations in both streamflows and lake levels that degraded the scores. Anthropization effects are prevalent in most of the lakes studied, but they are predominant for Lake Victoria and are the main cause for relatively low statistical scores for the Nile River However, results on the Angara and the Neva rivers also depend on the inherent gap of ISBA-CTRIP process representation, which relies on further development such as the partitioned energy budget between the snow and the canopy over a boreal zone. The study is a first step towards a global coupled land system that will help to qualitatively assess the evolution of future global water resources, leading to improvements in flood risk and drought forecasting.

Description: Stratospheric ozone affects climate directly as the predominant heat source in the stratosphere and indirectly through chemical reactions controlling other greenhouse gases. The U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1) implemented a new ozone chemistry module that improves the simulation of the sharp tropopause gradients, replacing a version based partly on long-term average climatologies that poorly represented heating rates in the lowermost stratosphere. The new O3v2 module extends seamlessly into the troposphere and preserves the naturally sharp cross-tropopause gradient, with 20 %–40 % less ozone in this region. Additionally, O3v2 enables the diagnosis of stratosphere–troposphere exchange flux of ozone, a key budget term lacking in E3SMv1. Here, we evaluate key features in ozone abundance and other closely related quantities in atmosphere-only E3SMv1 simulations driven by observed sea surface temperatures (SSTs, years 1990–2014), comparing them with satellite observations of ozone and also with the University of California, Irvine chemistry transport model (UCI CTM) using the same stratospheric chemistry scheme but driven by European Centre forecast fields for the same period. In terms of stratospheric column ozone, O3v2 shows reduced mean bias and improved northern midlatitude variability, but it is not quite as good as the UCI CTM. As expected, SST-forced E3SMv1 simulations cannot synchronize with observed quasi-biennial oscillations (QBOs), but they do show the typical QBO pattern seen in column ozone. This new O3v2 E3SMv1 model mostly retains the same climate state and climate sensitivity as the previous version, and we recommend its use for other climate models that still use ozone climatologies.

Description: When solving hydrodynamic equations in spherical or cylindrical geometry using explicit finite-difference schemes, a major difficulty is that the time step is greatly restricted by the clustering of azimuthal cells near the pole due to the Courant–Friedrichs–Lewy condition. This paper adapts the azimuthal averaging–reconstruction (ring average) technique to finite-difference schemes in order to mitigate the time step constraint in spherical and cylindrical coordinates. The finite-difference ring average technique averages physical quantities based on an effective grid and then reconstructs the solution back to the original grid in a piecewise, monotonic way. The algorithm is implemented in a community upper-atmospheric model, the Thermosphere–Ionosphere Electrodynamics General Circulation Model (TIEGCM), with a horizontal resolution up to 0.625∘×0.625∘ in geographic longitude–latitude coordinates, which enables the capability of resolving critical mesoscale structures within the TIEGCM. Numerical experiments have shown that the ring average technique introduces minimal artifacts in the polar region of general circulation model (GCM) solutions, which is a significant improvement compared to commonly used low-pass filtering techniques such as the fast Fourier transform method. Since the finite-difference adaption of the ring average technique is a post-solver type of algorithm, which requires no changes to the original computational grid and numerical algorithms, it has also been implemented in much more complicated models with extended physical–chemical modules such as the Coupled Magnetosphere–Ionosphere–Thermosphere (CMIT) model and the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (WACCM-X). The implementation of ring average techniques in both models enables CMIT and WACCM-X to perform global simulations with a much higher resolution than that used in the community versions. The new technique is not only a significant improvement in space weather modeling capability, but it can also be adapted to more general finite-difference solvers for hyperbolic equations in spherical and polar geometries. Highlights. The ring average technique is adapted to solve the issue of clustered grid cells in polar and spherical coordinates with a finite-difference method. The ring average technique is applied to develop a 0.625∘×0.625∘ high-resolution TIEGCM and more complicated geoscientific models with polar and spherical coordinates as well as finite-difference numerical schemes. The high-resolution TIEGCM shows good capability in resolving mesoscale structures in the ionosphere–thermosphere (I–T) system.

Description: Over the past few years, increasing attention has been focused on the need to publish computer code as an integral part of the research process. This has been reflected in improved policies on publication in scientific journals, including key related issues such as repositories and licensing. We explore the state of the art of code availability and the sharing of climate models using the Fifth Coupled Model Intercomparison Project (CMIP5) models as a test bed, and we include some particular reflections on this case. Our results show that there are many limitations in terms of access to the code for these climate models and that the climate modelling community needs to improve its code-sharing practice to comply with best practice in this regard and the most recent editorial publishing policies.

Description: Monitoring networks aims at capturing the spatial and temporal variability of one or several environmental variables in a specific environment. The optimal placement of sensors in an ocean or coastal observatory should maximize the amount of collected information and minimize the development and operational costs for the whole monitoring network. In this study, the problem of the design and optimization of ocean monitoring networks is tackled throughout the implementation of data assimilation techniques in the Shallow water HYdrodynamic Finite Element Model (SHYFEM). Two data assimilation methods – nudging and ensemble square root filter – have been applied and tested in the Lagoon of Venice (Italy), where an extensive water level monitoring network exists. A total of 29 tide gauge stations were available, and the assimilation of the observations results in an improvement of the performance of the SHYFEM model, which went from an initial root mean square error (RMSE) on the water level of 5.8 cm to a final value of about 2.1 and 3.2 cm for each of the two data assimilation methods. In the monitoring network optimization procedure, by excluding just one tide gauge at a time and always the station that contributes less to the improvement of the RMSE, a minimum number of tide gauges can be found that still allow for a successful description of the water level variability. Both data assimilation methods allow identifying the number of stations and their distribution that correctly represent the state variable in the investigated system. However, the more advanced ensemble square root filter has the benefit of keeping a physically and mass-conservative solution of the governing equations, which results in a better reproduction of the hydrodynamics over the whole system. In the case of the Lagoon of Venice, we found that, with the help of a process-based and observation-driven numerical model, two-thirds of the monitoring network can be dismissed. In this way, if some of the stations must be decommissioned due to a lack of funding, an a priori choice can be made, and the importance of a single monitoring site can be evaluated. The developed procedure may also be applied to the continuous monitoring of other ocean variables, like sea temperature and salinity.

Description: A multiscale modeling approach for studying the ocean surface turbulent mixing is explored by coupling an ocean general circulation model (GCM) MPAS-Ocean with the Parallelized Large Eddy Simulation Model (PALM). The coupling approach is similar to the superparameterization approach that has been used to represent the effects of deep convection in atmospheric GCMs. However, the focus of this multiscale modeling approach is on the small-scale turbulent mixing and their interactions with the larger-scale processes in the ocean, so that a more flexible coupling strategy is used. To reduce the computational cost, a customized version of PALM is ported on the general-purpose graphics processing unit (GPU) with OpenACC, achieving 10–16 times overall speedup as compared to running on a single CPU. Even with the GPU-acceleration technique, a superparameterization-like approach to represent the ocean surface turbulent mixing in GCMs using embedded high fidelity and three-dimensional large eddy simulations (LESs) over the global ocean is still computationally intensive and infeasible for long simulations. However, running PALM regionally on selected MPAS-Ocean grid cells is shown to be a promising approach moving forward. The flexible coupling between MPAS-Ocean and PALM allows further exploration of the interactions between the ocean surface turbulent mixing and larger-scale processes, as well as future development and improvement of ocean surface turbulent mixing parameterizations for GCMs.

Description: In this study, we improve the representation of global river runoff in the Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) framework, allowing for a more realistic treatment of coastal plume dynamics. We use a suite of experiments to explore the sensitivity of coastal plume regions to runoff forcing, model grid resolution, and grid type. The results show that simulated sea surface salinity (SSS) is reduced as the model grid resolution increases. Compared to Soil Moisture Active Passive (SMAP) observations, simulated SSS is closest to SMAP when using daily, point-source runoff (DPR) and the intermediate-resolution LLC270 grid. The Willmott skill score, which quantifies agreement between models and SMAP, yields up to 0.92 for large rivers such as the Amazon. There was no major difference in SSS for tropical and temperate coastal rivers when the model grid type was changed from the ECCO v4 latitude–longitude–polar-cap grid to the ECCO2 cube–sphere grid. We also found that using DPR forcing and increasing model resolution from the coarse-resolution LLC90 grid to the intermediate-resolution LLC270 grid elevated the river plume area, volume, stabilized the stratification and shoal the mixed layer depth (MLD). Additionally, we find that the impacts of increasing model resolution from the intermediate-resolution LLC270 grid to the high-resolution LLC540 grid are regionally dependent. The Mississippi River Plume is more sensitive than other regions, possibly because the wider and shallower Texas–Louisiana shelf drives a stronger baroclinic effect, as well as relatively weak sub-grid vertical mixing and adjustment in this region. Since rivers deliver large amounts of freshwater and anthropogenic materials to coastal regions, improving the representation of river runoff in global, high-resolution models will advance studies of coastal hypoxia, carbon cycling, and regional weather and climate and will ultimately help to predict land–ocean–atmospheric feedbacks seamlessly in the next generation of Earth system models.

Description: Diversity plays a key role in the adaptive capacity of marine ecosystems to environmental changes. However, modelling the adaptive dynamics of phytoplankton traits remains challenging due to the competitive exclusion of sub-optimal phenotypes and the complexity of evolutionary processes leading to optimal phenotypes. Trait diffusion (TD) is a recently developed approach to sustain diversity in plankton models by introducing mutations, therefore allowing the adaptive evolution of functional traits to occur at ecological timescales. In this study, we present a model called Simulating Plankton Evolution with Adaptive Dynamics (SPEAD) that resolves the eco-evolutionary processes of a multi-trait plankton community. The SPEAD model can be used to evaluate plankton adaptation to environmental changes at different timescales or address ecological issues affected by adaptive evolution. Phytoplankton phenotypes in SPEAD are characterized by two traits, the nitrogen half-saturation constant and optimal temperature, which can mutate at each generation using the TD mechanism. SPEAD does not resolve the different phenotypes as discrete entities, instead computing six aggregate properties: total phytoplankton biomass, the mean value of each trait, trait variances, and the inter-trait covariance of a single population in a continuous trait space. Therefore, SPEAD resolves the dynamics of the population's continuous trait distribution by solving its statistical moments, wherein the variances of trait values represent the diversity of ecotypes. The ecological model is coupled to a vertically resolved (1D) physical environment, and therefore the adaptive dynamics of the simulated phytoplankton population are driven by seasonal variations in vertical mixing, nutrient concentration, water temperature, and solar irradiance. The simulated bulk properties are validated by observations from Bermuda Atlantic Time-series Studies (BATS) in the Sargasso Sea. We find that moderate mutation rates sustain trait diversity at decadal timescales and soften the almost total inter-trait correlation induced by the environment alone, without reducing the annual primary production or promoting permanently maladapted phenotypes, as occur with high mutation rates. As a way to evaluate the performance of the continuous trait approximation, we also compare the solutions of SPEAD to the solutions of a classical discrete entities approach, with both approaches including TD as a mechanism to sustain trait variance. We only find minor discrepancies between the continuous model SPEAD and the discrete model, with the computational cost of SPEAD being lower by 2 orders of magnitude. Therefore, SPEAD should be an ideal eco-evolutionary plankton model to be coupled to a general circulation model (GCM) of the global ocean.

Description: This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

Description: For the first time, the Limited-Area Mode of the new ICON (Icosahedral Nonhydrostatic) weather and climate model has been used for a continuous long-term regional climate simulation over Europe. Built upon the Limited-Area Mode of ICON (ICON-LAM), ICON-CLM (ICON in Climate Limited-area Mode, hereafter ICON-CLM, available in ICON release version 2.6.1) is an adaptation for climate applications. A first version of ICON-CLM is now available and has already been integrated into a starter package (ICON-CLM_SP_beta1). The starter package provides users with a technical infrastructure that facilitates long-term simulations as well as model evaluation and test routines. ICON-CLM and ICON-CLM_SP were successfully installed and tested on two different computing systems. Tests with different domain decompositions showed bit-identical results, and no systematic outstanding differences were found in the results with different model time steps. ICON-CLM was also able to reproduce the large-scale atmospheric information from the global driving model. Comparison was done between ICON-CLM and the COnsortium for Small-scale MOdeling (COSMO)-CLM (the recommended model configuration by the CLM-Community) performance. For that, an evaluation run of ICON-CLM with ERA-Interim boundary conditions was carried out with the setup similar to the COSMO-CLM recommended optimal setup. ICON-CLM results showed biases in the same range as those of COSMO-CLM for all evaluated surface variables. While this COSMO-CLM simulation was carried out with the latest model version which has been developed and was carefully tuned for climate simulations on the European domain, ICON-CLM was not tuned yet. Nevertheless, ICON-CLM showed a better performance for air temperature and its daily extremes, and slightly better performance for total cloud cover. For precipitation and mean sea level pressure, COSMO-CLM was closer to observations than ICON-CLM. However, as ICON-CLM is still in the early stage of development, there is still much room for improvement.

Description: The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, specific humidity, and turbulent kinetic energy. It is composed of various sub-models: a rural model, an urban vertical diffusion model, a radiation model, and a building energy model. Forced with weather data from a nearby rural site, the rural model is used to solve for the vertical profiles of potential temperature, specific humidity, and friction velocity at 10 m a.g.l. The rural model also calculates a horizontal pressure gradient. The rural model outputs are applied to a vertical diffusion urban microclimate model that solves vertical transport equations for potential temperature, momentum, specific humidity, and turbulent kinetic energy. The urban vertical diffusion model is also coupled to the radiation and building energy models using two-way interaction. The aerodynamic and thermal effects of urban elements, surface vegetation, and trees are considered. The predictions of the VCWG model are compared to observations of the Basel UrBan Boundary Layer Experiment (BUBBLE) microclimate field campaign for 8 months from December 2001 to July 2002. The model evaluation indicates that the VCWG predicts vertical profiles of meteorological variables in reasonable agreement with the field measurements. The average bias, root mean square error (RMSE), and R2 for potential temperature are 0.25 K, 1.41 K, and 0.82, respectively. The average bias, RMSE, and R2 for wind speed are 0.67 m s−1, 1.06 m s−1, and 0.41, respectively. The average bias, RMSE, and R2 for specific humidity are 0.00057 kg kg−1, 0.0010 kg kg−1, and 0.85, respectively. In addition, the average bias, RMSE, and R2 for the urban heat island (UHI) are 0.36 K, 1.2 K, and 0.35, respectively. Based on the evaluation, the model performance is comparable to the performance of similar models. The performance of the model is further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, building energy configuration, radiation configuration, seasonal variations, and different climate zones on the model predictions. The results obtained from the explorations are reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

Description: The Arctic regional coupled sea-ice–ocean–atmosphere model (ArcIOAM) has been developed to provide reliable Arctic sea ice prediction on seasonal timescales. The description and implementation of ArcIOAM and its preliminary results for the year of 2012 are presented in this paper. In the ArcIOAM configuration, the Community Coupler 2 (C-Coupler2) is used to couple the Arctic sea-ice–oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. A scalability test is performed to investigate the parallelization of the coupled model. As the first step toward reliable Arctic seasonal sea ice prediction, ArcIOAM, implemented with two-way coupling strategy along with one-way coupling strategy, is evaluated with respect to available observational data and reanalysis products for the year of 2012. A stand-alone MITgcm run with prescribed atmospheric forcing is performed for reference. From the comparison, all the experiments simulate reasonable evolution of sea ice and ocean states in the Arctic region over a 1-year simulation period. The two-way coupling has better performance in terms of sea ice extent, concentration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.

Description: This paper details and tests numerical improvements to the ADvanced CIRCulation (ADCIRC) model, a widely used finite-element method shallow-water equation solver, to more accurately and efficiently model global storm tides with seamless local mesh refinement in storm landfall locations. The sensitivity to global unstructured mesh design was investigated using automatically generated triangular meshes with a global minimum element size (MinEle) that ranged from 1.5 to 6 km. We demonstrate that refining resolution based on topographic seabed gradients and employing a MinEle less than 3 km are important for the global accuracy of the simulated astronomical tide. Our recommended global mesh design (MinEle = 1.5 km) based on these results was locally refined down to two separate MinEle values (500 and 150 m) at the coastal landfall locations of two intense storms (Hurricane Katrina and Super Typhoon Haiyan) to demonstrate the model's capability for coastal storm tide simulations and to test the sensitivity to local mesh refinement. Simulated maximum storm tide elevations closely follow the lower envelope of observed high-water marks (HWMs) measured near the coast. In general, peak storm tide elevations along the open coast are decreased, and the timing of the peak occurs later with local coastal mesh refinement. However, this mesh refinement only has a significant positive impact on HWM errors in straits and inlets narrower than the MinEle and in bays and lakes separated from the ocean by these passages. Lastly, we demonstrate that the computational performance of the new numerical treatment is 1 to 2 orders of magnitude faster than studies using previous ADCIRC versions because gravity-wave-based stability constraints are removed, allowing for larger computational time steps.

Description: The forward stratigraphic simulation approach is applied to predict porosity and permeability distribution. Synthetic well logs from the forward stratigraphic model served as secondary data to control porosity and permeability representation in the reservoir model. Building a reservoir model that fits data at different locations comes with high levels of uncertainty. Therefore, it is critical to generate an appropriate stratigraphic framework to guide lithofacies and associated porosity–permeability simulation. The workflow adopted for this task consists of three parts: first, there is simulation of 20 scenarios of sediment transportation and deposition using the geological process modelling (GPM™) software developed by Schlumberger. Secondly, there is an estimation of the extent and proportion of lithofacies units in the stratigraphic model using the property calculator tool in Petrel™. Finally, porosity and permeability values are assigned to corresponding lithofacies units in the forward stratigraphic model to produce a forward stratigraphic-based porosity and permeability model. Results show a forward stratigraphic-based lithofacies model, which depends on sediment diffusion rate, sea-level variation, sediment movement, wave processes, and tectonic events. This observation is consistent with the natural occurrence, where variations in sea level, sediment supply, and accommodation control stratigraphic sequences and therefore facies distribution in a geological basin. Validation wells VP1 and VP2 showed a notable match after a comparing the original and forward stratigraphic-based porosity models. However, a significant discrepancy is recorded in the permeability estimates. These results suggest that the forward stratigraphic modelling approach can be a practical addition to geostatistical-based workflows for realistic prediction of porosity and permeability.

Description: The potential of the antidiffusive transport scheme proposed by Després and Lagoutière (1999) for resolving vertical transport in chemistry-transport models is investigated in an idealized framework with very encouraging results. We show that, compared to classical higher-order schemes, the Després and Lagoutière (1999) scheme reduces numerical diffusion and improves accuracy in idealized cases that are typical of atmospheric transport of tracers in chemistry-transport models. The increase in accuracy and the reduction in diffusion are substantial when, and only when, vertical resolution is insufficient to properly resolve vertical gradients, which is very frequent in chemistry-transport models. Therefore, we think that this scheme is an extremely promising solution for reducing numerical diffusion in chemistry-transport models.

Description: Mass movements such as debris flows and landslides differ in behaviour due to their material properties and internal forces. Models employ generalized multi-phase flow equations to adaptively describe these complex flow types. Such models commonly assume unstructured and fragmented flow, where internal cohesive strength is insignificant. In this work, existing work on two-phase mass movement equations are extended to include a full stress–strain relationship that allows for runout of (semi-)structured fluid–solid masses. The work provides both the three-dimensional equations and depth-averaged simplifications. The equations are implemented in a hybrid material point method (MPM), which allows for efficient simulation of stress–strain relationships on discrete smooth particles. Using this framework, the developed model is compared to several flume experiments of clay blocks impacting fixed obstacles. Here, both final deposit patterns and fractures compare well to simulations. Additionally, numerical tests are performed to showcase the range of dynamical behaviour produced by the model. Important processes such as fracturing, fragmentation and fluid release are captured by the model. While this provides an important step towards complete mass movement models, several new opportunities arise, such as application to fragmenting mass movements and block slides.

Description: Detailed reconstruction of deep crustal targets by seismic methods remains a long-standing challenge. One key to address this challenge is the joint development of new seismic acquisition systems and leading-edge processing techniques. In marine environments, controlled-source seismic surveys at a regional scale are typically carried out with sparse arrays of ocean bottom seismometers (OBSs), which provide incomplete and down-sampled subsurface illumination. To assess and minimize the acquisition footprint in high-resolution imaging process such as full waveform inversion, realistic crustal-scale benchmark models are clearly required. The deficiency of such models prompts us to build one and release it freely to the geophysical community. Here, we introduce GO_3D_OBS – a 3D high-resolution geomodel representing a subduction zone, inspired by the geology of the Nankai Trough. The 175km×100km×30km model integrates complex geological structures with a viscoelastic isotropic parameterization. It is defined in the form of a uniform Cartesian grid containing ∼33.6e9 degrees of freedom for a grid interval of 25 m. The size of the model raises significant high-performance computing challenges to tackle large-scale forward propagation simulations and related inverse problems. We describe the workflow designed to implement all the model ingredients including 2D structural segments, their projection into the third dimension, stochastic components, and physical parameterization. Various wavefield simulations that we present clearly reflect in the seismograms the structural complexity of the model and the footprint of different physical approximations. This benchmark model is intended to help to optimize the design of next-generation 3D academic surveys – in particular, but not only, long-offset OBS experiments – to mitigate the acquisition footprint during high-resolution imaging of the deep crust.

Description: Computational models of mantle convection must accurately represent curved boundaries and the associated boundary conditions of a 3-D spherical shell, bounded by Earth's surface and the core–mantle boundary. This is also true for comparable models in a simplified 2-D cylindrical geometry. It is of fundamental importance that the codes underlying these models are carefully verified prior to their application in a geodynamical context, for which comparisons against analytical solutions are an indispensable tool. However, analytical solutions for the Stokes equations in these geometries, based upon simple source terms that adhere to physically realistic boundary conditions, are often complex and difficult to derive. In this paper, we present the analytical solutions for a smooth polynomial source and a delta-function forcing, in combination with free-slip and zero-slip boundary conditions, for both 2-D cylindrical- and 3-D spherical-shell domains. We study the convergence of the Taylor–Hood (P2–P1) discretisation with respect to these solutions, within the finite element computational modelling framework Fluidity, and discuss an issue of suboptimal convergence in the presence of discontinuities. To facilitate the verification of numerical codes across the wider community, we provide a Python package, Assess, that evaluates the analytical solutions at arbitrary points of the domain.

Description: The availability of phosphorus (P) and nitrogen (N) constrains the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions in both space and time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2), which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the Northern Hemisphere (NH) during recent decades despite an a priori realistic gross primary productivity (GPP) response to rising CO2. This result suggests either that processes other than CO2 fertilization, which are omitted in ORCHIDEE-CNP such as changes in biomass turnover, are predominant drivers of the northern land sink and/or that the model parameterizations produce emerging nutrient limitations on biomass growth that are too strict in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry as well as plant P use efficiency, pointing towards P limitations that are too severe towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

Description: Periglacial features, such as various kinds of patterned ground, cryoturbations, frost wedges, solifluction structures, and blockfields, are among the most common relics of cold climate periods, which repetitively occurred throughout the Quaternary. As such, they are widespread archives of past environmental conditions. Climate controls on the development of most periglacial features, however, remain poorly known, and thus empirical palaeo-climate reconstructions based on them have limited validity. This study presents and evaluates a simple new inverse modelling scheme called PERICLIMv1.0 (PERIglacial CLIMate) that derives palaeo-air temperature characteristics related to the palaeo-active-layer thickness, which can be recognized using many relict periglacial features found in past permafrost regions. The evaluation against modern temperature records showed that the model reproduces air temperature characteristics with average errors ≤1.3 ∘C. The past mean annual air temperature modelled experimentally for two sites in the Czech Republic hosting relict cryoturbation structures was between -7.0±1.9 and -3.2±1.5 ∘C, which is well in line with earlier reconstructions utilizing various palaeo-archives. These initial results are promising and suggest that the model could become a useful tool for reconstructing Quaternary palaeo-environments across vast areas of mid-latitudes and low latitudes where relict periglacial assemblages frequently occur, but their full potential remains to be exploited.

Description: This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

Description: The impact of diffuse radiation on photosynthesis has been widely documented in field measurements. This impact may have evolved over time during the last century due to changes in cloudiness, increased anthropogenic aerosol loads over polluted regions, and to sporadic volcanic eruptions curtaining the stratosphere with sulfate aerosols. The effects of those changes in diffuse light on large-scale photosynthesis (GPP) are difficult to quantify, and land surface models have been designed to simulate them. Investigating how anthropogenic aerosols have impacted GPP through diffuse light in those models requires carefully designed factorial simulations and a reconstruction of background diffuse light levels during the preindustrial period. Currently, it remains poorly understood how diffuse radiation reconstruction methods can affect GPP estimation and what fraction of GPP changes can be attributed to aerosols. In this study, we investigate different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) under preindustrial aerosol emission conditions using a land surface model with a two-stream canopy light transmission scheme that resolves diffuse light effects on photosynthesis in a multi-layered canopy, ORCHIDEE_DF. We show that using a climatologically averaged monthly Fdf, as has been done by earlier studies, can bias the global GPP by up to 13 PgC yr−1 because this reconstruction method dampens the variability of Fdf and produces Fdf that is inconsistent with shortwave incoming surface radiation. In order to correctly simulate preindustrial GPP modulated by diffuse light, we thus recommend that the Fdf forcing field should be calculated consistently with synoptic, monthly, and inter-annual aerosol and cloud variability for preindustrial years. In the absence of aerosol and cloud data, alternative reconstructions need to retain the full variability in Fdf. Our results highlight the importance of keeping consistent Fdf and radiation for land surface models in future experimental designs that seek to investigate the impacts of diffuse radiation on GPP and other carbon fluxes.

Description: A source-resolved three-dimensional chemical transport model, PMCAMx-SR (Particulate Matter Comprehensive Air-quality Model with extensions – Source Resolved), was applied in the continental US to investigate the contribution of the various components (primary and secondary) of biomass burning organic aerosol (bbOA) to organic aerosol levels. Two different schemes based on the volatility basis set were used for the simulation of the bbOA during different seasons. The first is the default scheme of PMCAMx-SR, and the second is a recently developed scheme based on laboratory experiments of the bbOA evolution. The simulations with the alternative bbOA scheme predict much higher total bbOA concentrations when compared with the base case ones. This is mainly due to the high emissions of intermediate-volatility organic compounds (IVOCs) assumed in the alternative scheme. The oxidation of these compounds is predicted to be a significant source of secondary organic aerosol. The impact of the other parameters that differ in the two schemes is low to negligible. The monthly average maximum predicted concentrations of the alternative bbOA scheme were approximately an order of magnitude higher than those of the default scheme during all seasons. The performance of the two schemes was evaluated against observed total organic aerosol concentrations from several measurement sites across the US. The results were different for the different seasons examined. The default scheme performed better during July and September, while the alternative scheme performed a little better during April. These results illustrate the uncertainty of the corresponding predictions and the need to quantify the emissions and reactions of IVOCs from specific biomass sources and to better constrain the total (primary and secondary) bbOA levels.

Description: Interactions between atmospheric forcing, topographic constraints to air and water flow, and resonant character of the basin make sea level modelling in the Adriatic a challenging problem. In this study we present an ensemble deep-neural-network-based sea level forecasting method HIDRA, which outperforms our set-up of the general ocean circulation model ensemble (NEMO v3.6) for all forecast lead times and at a minuscule fraction of the numerical cost (order of 2×10-6). HIDRA exhibits larger bias but lower RMSE than our set-up of NEMO over most of the residual sea level bins. It introduces a trainable atmospheric spatial encoder and employs fusion of atmospheric and sea level features into a self-contained network which enables discriminative feature learning. HIDRA architecture building blocks are experimentally analysed in detail and compared to alternative approaches. Results show the importance of sea level input for forecast lead times below 24 h and the importance of atmospheric input for longer lead times. The best performance is achieved by considering the input as the total sea level, split into disjoint sets of tidal and residual signals. This enables HIDRA to optimize the prediction fidelity with respect to atmospheric forcing while compensating for the errors in the tidal model. HIDRA is trained and analysed on a 10-year (2006–2016) time series of atmospheric surface fields from a single member of ECMWF atmospheric ensemble. In the testing phase, both HIDRA and NEMO ensemble systems are forced by the ECMWF atmospheric ensemble. Their performance is evaluated on a 1-year (2019) hourly time series from a tide gauge in Koper (Slovenia). Spectral and continuous wavelet analysis of the forecasts at the semi-diurnal frequency (12 h)−1 and at the ground-state basin seiche frequency (21.5 h)−1 is performed. The energy at the basin seiche in the HIDRA forecast is close to that observed, while our set-up of NEMO underestimates it. Analyses of the January 2015 and November 2019 storm surges indicate that HIDRA has learned to mimic the timing and amplitude of basin seiches.

Description: This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1∘ horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in the long-term mean climate. Examples of changes in 10-year mean zonal averages include the following: up to 0.5 K of warming in the lower troposphere and cooling in the tropical and subtropical upper troposphere, 1 %–10 % decreases in relative humidity throughout the troposphere, and 10 %–20 % decreases in cloud fraction in the upper troposphere and decreases exceeding 20 % in the subtropical lower troposphere. In terms of the 10-year mean geographical distribution, systematic decreases of 20 %–50 % are seen in total cloud cover and cloud radiative effects in the subtropics. These changes imply that the reduction of temporal truncation errors leads to a notable although unsurprising degradation of agreement between the simulated and observed present-day climate; to regain optimal climate fidelity in the absence of those truncation errors, the model would require retuning. A coarse-grained attribution of the time step sensitivities is carried out by shortening time steps used in various components of EAM or by revising the numerical coupling between some processes. Our analysis leads to the finding that the marked decreases in the subtropical low-cloud fraction and total cloud radiative effect are caused not by the step size used for the collectively subcycled turbulence, shallow convection, and stratiform cloud macrophysics and microphysics parameterizations but rather by the step sizes used outside those subcycles. Further analysis suggests that the coupling frequency between the subcycles and the rest of EAM significantly affects the subtropical marine stratocumulus decks, while deep convection has significant impacts on trade cumulus. The step size of the cloud macrophysics and microphysics subcycle itself appears to have a primary impact on cloud fraction in the upper troposphere and also in the midlatitude near-surface layers. Impacts of step sizes used by the dynamical core and the radiation parameterization appear to be relatively small. These results provide useful clues for future studies aiming at understanding and addressing the root causes of sensitivities to time step sizes and process coupling frequencies in EAM. While this study focuses on EAMv1 and the conclusions are likely model-specific, the presented experimentation strategy has general value for weather and climate model development, as the methodology can help researchers identify and understand sources of time integration error in sophisticated multi-component models.

Description: The Mobius model building system is a new open-source framework for building fast and flexible environmental models. Mobius makes it possible for researchers with limited programming experience to build performant models with potentially complicated structures. Mobius models can be easily interacted with through the MobiView graphical user interface and through the Python programming language. Mobius was initially developed to support catchment-scale hydrology and water-quality modelling but can be used to represent any system of hierarchically structured ordinary differential equations, such as population dynamics or toxicological models. Here, we demonstrate how Mobius can be used to quickly prototype several different model structures for a dissolved organic carbon catchment model and use built-in auto-calibration and statistical uncertainty analysis tools to help decide on the best model structures. Overall, we hope the modular model building platform offered by Mobius will provide a step forward for environmental modelling, providing an alternative to the “one size fits all” modelling paradigm. By making it easier to explore a broader range of model structures and parameterisations, users are encouraged to build more appropriate models, and in turn this improves process understanding and allows for more robust modelling in support of decision making.

Description: Climate projections including chemical feedbacks rely on state-of-the-art chemistry–climate models (CCMs). Of particular importance is the role of methane (CH4) for the budget of stratospheric water vapour (SWV), which has an important climate impact. However, simulations with CCMs are, due to the large number of involved chemical species, computationally demanding, which limits the simulation of sensitivity studies. To allow for sensitivity studies and ensemble simulations with a reduced demand for computational resources, we introduce a simplified approach to simulate the core of methane chemistry in form of the new Modular Earth Submodel System (MESSy) submodel CH4. It involves an atmospheric chemistry mechanism reduced to the sink reactions of CH4 with predefined fields of the hydroxyl radical (OH), excited oxygen (O(1D)), and chlorine (Cl), as well as photolysis and the reaction products limited to water vapour (H2O). This chemical production of H2O is optionally fed back onto the specific humidity (q) of the connected general circulation model (GCM), to account for the impact onto SWV and its effect on radiation and stratospheric dynamics. The submodel CH4 is further capable of simulating the four most prevalent CH4 isotopologues for carbon and hydrogen (CH4 and CH3D, as well as 12CH4 and 13CH4). Furthermore, the production of deuterated water vapour (HDO) is, similar to the production of H2O in the CH4 oxidation, optionally passed back to the isotopological hydrological cycle simulated by the submodel H2OISO, using the newly developed auxiliary submodel TRSYNC. Moreover, the simulation of a user-defined number of diagnostic CH4 age and emission classes is possible, the output of which can be used for offline inverse optimization techniques. The presented approach combines the most important chemical hydrological feedback including the isotopic signatures with the advantages concerning the computational simplicity of a GCM, in comparison to a full-featured CCM.

Description: This paper presents Shyft, a novel hydrologic modeling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables rapid development and implementation in operational settings and the capability to perform distributed hydrologic modeling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well-known routines or through the creation of new routines, each defined for processes such as evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an application programming interface (API) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The API allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide enterprise-level software, strong focus is given to computational efficiency, code quality, documentation, and test coverage. Shyft is released open-source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os (last access: 22 November 2020), facilitating effective cooperation between core developers, industry, and research institutions.

Description: The development of chemical transport models with advanced physics and chemical schemes could improve air-quality forecasts. In this study, the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, a comprehensive chemistry module incorporating gaseous chemistry and a size-segregated multicomponent aerosol algorithm, was coupled to the Weather Research and Forecasting (WRF) framework with chemistry (WRF-Chem) using an interface procedure to build the WRF/CUACE v1.0 model. The latest version of CUACE includes an updated aerosol dry deposition scheme and the introduction of heterogeneous chemical reactions on aerosol surfaces. We evaluated the WRF/CUACE v1.0 model by simulating PM2.5, O3, NO2, and SO2 concentrations for January, April, July, and October (representing winter, spring, summer and autumn, respectively) in 2013, 2015, and 2017 and comparing them with ground-based observations. Secondary inorganic aerosol simulations for the North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB) were also evaluated. The model captured well the variations of PM2.5, O3, and NO2 concentrations in all seasons in eastern China. However, it is difficult to accurately reproduce the variations of air pollutants over SCB, due to its deep basin terrain. The simulations of SO2 were generally reasonable in the NCP and YRD with the bias at −15.5 % and 24.55 %, respectively, while they were poor in the Pearl River Delta (PRD) and SCB. The sulfate and nitrate simulations were substantially improved by introducing heterogeneous chemical reactions into the CUACE model (e.g., change in bias from −95.0 % to 4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate in the NCP). Additionally, The WRF/CUACE v1.0 model was revealed with better performance in simulating chemical species relative to the coupled Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and CUACE model. The development of the WRF/CUACE v1.0 model represents an important step towards improving air-quality modeling and forecasts in China.

Description: Eastern boundary upwelling systems (EBUSs) are physically and biologically active regions of the ocean with substantial impacts on ocean biogeochemistry, ecology, and global fish catch. Previous studies have used models of varying complexity to study EBUS dynamics, ranging from minimal two-dimensional (2-D) models to comprehensive regional and global models. An advantage of 2-D models is that they are more computationally efficient and easier to interpret than comprehensive regional models, but their key drawback is the lack of explicit representations of important three-dimensional processes that control biology in upwelling systems. These processes include eddy quenching of nutrients and meridional transport of nutrients and heat. The authors present the Meridionally Averaged Model of Eastern Boundary Upwelling Systems (MAMEBUS) that aims at combining the benefits of 2-D and 3-D approaches to modeling EBUSs by parameterizing the key 3-D processes in a 2-D framework. MAMEBUS couples the primitive equations for the physical state of the ocean with a nutrient–phytoplankton–zooplankton–detritus model of the ecosystem, solved in terrain-following coordinates. This article defines the equations that describe the tracer, momentum, and biological evolution, along with physical parameterizations of eddy advection, isopycnal mixing, and boundary layer mixing. It describes the details of the numerical schemes and their implementation in the model code, and provides a reference solution validated against observations from the California Current. The goal of MAMEBUS is to facilitate future studies to efficiently explore the wide space of physical and biogeochemical parameters that control the zonal variations in EBUSs.

Description: This study presents the integration of the phosphorus (P) cycle into CoupModel (v6.0, referred to as Coup-CNP). The extended Coup-CNP, which explicitly considers the symbiosis between soil microbes and plant roots, enables simulations of coupled carbon (C), nitrogen (N), and P dynamics for terrestrial ecosystems. The model was evaluated against observed forest growth and measured leaf C∕P, C∕N, and N∕P ratios in four managed forest regions in Sweden. The four regions form a climatic and fertility gradient from 64∘ N (northern Sweden) to 56∘ N (southern Sweden), with mean annual temperature varying from 0.7–7.1 ∘C and soil C∕N and C∕P ratios varying between 19.8–31.5 and 425–633, respectively. The growth of the southern forests was found to be P-limited, with harvested biomass representing the largest P losses over the studied rotation period. The simulated P budgets revealed that southern forests are losing P, while northern forests have balanced P budgets. Symbiotic fungi accounted for half of total plant P uptake across all four regions, which highlights the importance of fungal-tree interactions in Swedish forests. The results of a sensitivity analysis demonstrated that optimal forest growth occurs at a soil N∕P ratio between 15–20. A soil N∕P ratio above 15–20 will result in decreased soil C sequestration and P leaching, along with a significant increase in N leaching. The simulations showed that Coup-CNP could describe shifting from being mostly N-limited to mostly P-limited and vice versa. The potential P-limitation of terrestrial ecosystems highlights the need for biogeochemical ecosystem models to consider the P cycle. We conclude that the inclusion of the P cycle enabled the Coup-CNP to account for various feedback mechanisms that have a significant impact on ecosystem C sequestration and N leaching under climate change and/or elevated N deposition.

Description: In this paper, we present a parallel version of the finite-element model of the Arctic Ocean (FEMAO) configured for the White Sea and based on MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the number of computations because the complexity of the ocean part depends on the bottom depth, while that of the sea-ice component does not. In the first step, we decided to locate both submodels on the same CPU cores with a common horizontal partition of the computational domain. The model domain is divided into small blocks, which are distributed over the CPU cores using Hilbert-curve balancing. Partitioning of the model domain is static (i.e., computed during the initialization stage). There are three baseline options: a single block per core, balancing of 2D computations, and balancing of 3D computations. After showing parallel acceleration for particular ocean and ice procedures, we construct the common partition, which minimizes joint imbalance in both submodels. Our novelty is using arrays shared by all blocks that belong to a CPU core instead of allocating separate arrays for each block, as is usually done. Computations on a CPU core are restricted by the masks of non-land grid nodes and block–core correspondence. This approach allows us to implement parallel computations into the model that are as simple as when the usual decomposition to squares is used, though with advances in load balancing. We provide parallel acceleration of up to 996 cores for the model with a resolution of 500×500×39 in the ocean component and 43 sea-ice scalars, and we carry out a detailed analysis of different partitions on the model runtime.

Description: In this paper the CHemistry with Aerosol Microphysics in Python (PyCHAM) box model software for aerosol chambers is described and assessed against benchmark simulations for accuracy. The model solves the coupled system of ordinary differential equations for gas-phase chemistry, gas–particle partitioning and gas–wall partitioning. Additionally, it can solve for coagulation, nucleation and particle loss to walls. PyCHAM is open-source, whilst the graphical user interface, modular structure, manual, example plotting scripts, and suite of tests for troubleshooting and tracking the effect of modifications to individual modules have been designed for optimal usability. In this paper, the modelled processes are individually assessed against benchmark simulations, and key parameters are described. Examples of output when processes are coupled are also provided. Sensitivity of individual processes to relevant parameters is illustrated along with convergence of model output with increasing temporal resolution and number of size bins. The latter sensitivity analysis informs our recommendations for model setup. Where appropriate, parameterisations for specific processes have been chosen for their general applicability, with their rationale detailed here. It is intended for PyCHAM to aid the design and analysis of aerosol chamber experiments, with comparison of simulations against observations allowing improvement of process understanding that can be transferred to ambient atmosphere simulations.

Description: The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

Description: As global warming is proceeding due to rising greenhouse gas concentrations, the Earth system moves towards climate states that challenge adaptation. Past Earth system states are offering possible modelling systems for the global warming of the coming decades. These include the climate of the mid-Pliocene (∼ 3 Ma), the last interglacial (∼ 129–116 ka) and the mid-Holocene (∼ 6 ka). The simulations for these past warm periods are the key experiments in the Paleoclimate Model Intercomparison Project (PMIP) phase 4, contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). Paleoclimate modelling has long been regarded as a robust out-of-sample test bed of the climate models used to project future climate changes. Here, we document the model setup for PMIP4 experiments with EC-Earth3-LR and present the large-scale features from the simulations for the mid-Holocene, the last interglacial and the mid-Pliocene. Using the pre-industrial climate as a reference state, we show global temperature changes, large-scale Hadley circulation and Walker circulation, polar warming, global monsoons and the climate variability modes – El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). EC-Earth3-LR simulates reasonable climate responses during past warm periods, as shown in the other PMIP4-CMIP6 model ensemble. The systematic comparison of these climate changes in past three warm periods in an individual model demonstrates the model's ability to capture the climate response under different climate forcings, providing potential implications for confidence in future projections with the EC-Earth model.

Description: TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

Description: Colored dissolved organic matter (CDOM) in marine environments impacts primary production due to its absorption effect on the photosynthetically active radiation. In coastal seas, CDOM originates from terrestrial sources predominantly and causes spatial and temporal changing patterns of light absorption which should be considered in marine biogeochemical models. We propose a model approach in which Earth Observation (EO) products are used to define boundary conditions of CDOM concentrations in an ecosystem model of the Baltic Sea. CDOM concentrations in riverine water derived from EO products serve as forcing for the ecosystem model. For this reason, we introduced an explicit CDOM state variable in the model. We show that the light absorption by CDOM in the model can be improved considerably in comparison to approaches where CDOM is estimated from salinity. The model performance increases especially with respect to spatial CDOM patterns due to the consideration of single river properties. A prerequisite is high-quality CDOM data with sufficiently high spatial resolution which can be provided by the new generation of ESA satellite sensor systems (Sentinel 2 MSI and Sentinel 3 OLCI). Such data are essential, especially when local differences in riverine CDOM concentrations exist.

Description: The dry deposition process refers to flux loss of an atmospheric pollutant due to uptake of the pollutant by the Earth's surfaces, including vegetation, underlying soil, and any other surface types. In chemistry transport models (CTMs), the dry deposition flux of a chemical species is typically calculated as the product of its surface layer concentration and its dry deposition velocity (Vd); the latter is a variable that needs to be highly empirically parameterized due to too many meteorological, biological, and chemical factors affecting this process. The gaseous dry deposition scheme of Zhang et al. (2003) parameterizes Vd for 31 inorganic and organic gaseous species. The present study extends the scheme of Zhang et al. (2003) to include an additional 12 oxidized volatile organic compounds (oVOCs) and hydrogen cyanide (HCN), while keeping the original model structure and formulas, to meet the demand of CTMs with increasing complexity. Model parameters for these additional chemical species are empirically chosen based on their physicochemical properties, namely the effective Henry's law constants and oxidizing capacities. Modeled Vd values are compared against field flux measurements over a mixed forest in the southeastern US during June 2013. The model captures the basic features of the diel cycles of the observed Vd. Modeled Vd values are comparable to the measurements for most of the oVOCs at night. However, modeled Vd values are mostly around 1 cm s−1 during daytime, which is much smaller than the observed daytime maxima of 2–5 cm s−1. Analysis of the individual resistance terms and uptake pathways suggests that flux divergence due to fast atmospheric chemical reactions near the canopy was likely the main cause of the large model–measurement discrepancies during daytime. The extended dry deposition scheme likely provides conservative Vd values for many oVOCs. While higher Vd values and bidirectional fluxes can be simulated by coupling key atmospheric chemical processes into the dry deposition scheme, we suggest that more experimental evidence of high oVOC Vd values at additional sites is required to confirm the broader applicability of the high values studied here. The underlying processes leading to high measured oVOC Vd values require further investigation.

Description: Nitrogen oxides, classified as NOx (nitric oxide (NO) + nitrogen dioxide (NO2)) and NOy (NOx+ NO3, N2O5 HNO3, + HNO4+ HONO + Peroxyacetyl nitrate (PAN) + organic nitrates + any oxidized N compound), are important trace gases in the troposphere, which play an important role in the formation of ozone, particulate matter (PM), and secondary organic aerosols (SOA). There remain many uncertainties in the origin and fate of atmospheric N compounds including the understanding of NOy cycling, NOx emission budgets, unresolved issues within the heterogeneous uptake coefficients of N2O5, and the formation of organic nitrates in urban forests, to name a few. A potential tool to resolve some of these uncertainties are using natural abundance N isotopes in NOy compounds. Here we have developed a photochemical mechanism used to simulate tropospheric photochemistry to include 15N compounds and reactions as a means to simulate δ15N values in NOy compounds. The 16 N compounds and 96 reactions involving N used in the Regional Atmospheric Chemistry Mechanism (RACM) were replicated using 15N in a new mechanism called iNRACM. The 192 N reactions in iNRACM were tested to see if isotope effects were relevant with respect to significantly changing the δ15N values (±1 ‰) of NOx, HONO, and/or HNO3. The isotope fractionation factors (α) for relevant reactions were assigned based on recent experimental or calculated values. Each relevant reaction in the iNRACM mechanism was tested individually and in concert in order to assess the controlling reactions. The controlling reactions and their diurnal importance are discussed. A comparison between iNRACM predictions and observed δ15N NO3- in particulate matter from Tucson, Arizona, suggests the model, and isotope fractionation factors incorporated into it, are accurately capturing the isotope effects occurring during the photochemistry of NOy. The implication is that measurements of δ15N in NOy compounds may be a new way of tracing in situ N chemistry and a means of assessing NOx emission budgets.

Description: A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. Building on variational autoencoders, we introduce a novel statistical model – the Latent Linear Adjustment Autoencoder (LLAAE) – that enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the Latent Linear Adjustment Autoencoder combines a linear component, which models the relationship between circulation and the latent space of an autoencoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12 km resolution over Europe, capturing, for instance, key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remain in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. Future applications could include, for instance, weather generators emulating climate model simulations of regional precipitation, detection and attribution at subcontinental scales, or statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations.

Description: The formation of stratigraphy in shallow marine environments has long been an important topic within the geologic community. Although many advances have been made in the field of forward stratigraphic modeling (FSM), there are still some areas that can be improved in the existing models. In this work, the authors present our recent development and application of Sedapp, which is a new nonlinear open-source R code for FSM. This code uses an integrated depth–distance related function as the expression of the transport coefficient to underpin the FSM with more alongshore details. In addition to conventional parameters, a negative-feedback sediment supply rate and a differentiated deposition–erosion ratio were also introduced. All parameters were implemented in a nonlinear manner. Sedapp is a 2DH tool that is also capable of running 1DH scenarios. Two simplified case studies were conducted. The results showed that Sedapp not only assists in geologic interpretation but is also an efficient tool for internal architecture predictions.

Description: At a regional scale, the best predictor for the 3D geology of the near-subsurface is often the information contained in a geological map. One challenge we face is the difficulty in reproducibly preparing input data for 3D geological models. We present two libraries (map2loop and map2model) that automatically combine the information available in digital geological maps with conceptual information, including assumptions regarding the subsurface extent of faults and plutons to provide sufficient constraints to build a prototype 3D geological model. The information stored in a map falls into three categories of geometric data: positional data, such as the position of faults, intrusive, and stratigraphic contacts; gradient data, such as the dips of contacts or faults; and topological data, such as the age relationships of faults and stratigraphic units or their spatial adjacency relationships. This automation provides significant advantages: it reduces the time to first prototype models; it clearly separates the data, concepts, and interpretations; and provides a homogenous pathway to sensitivity analysis, uncertainty quantification, and value of information studies that require stochastic simulations, and thus the automation of the 3D modelling workflow from data extraction through to model construction. We use the example of the folded and faulted Hamersley Basin in Western Australia to demonstrate a complete workflow from data extraction to 3D modelling using two different open-source 3D modelling engines: GemPy and LoopStructural.

Description: The West Antarctic Peninsula (WAP) is a rapidly warming region, with substantial ecological and biogeochemical responses to the observed change and variability for the past decades, revealed by multi-decadal observations from the Palmer Antarctica Long-Term Ecological Research (LTER) program. The wealth of these long-term observations provides an important resource for ecosystem modeling, but there has been a lack of focus on the development of numerical models that simulate time-evolving plankton dynamics over the austral growth season along the coastal WAP. Here, we introduce a one-dimensional variational data assimilation planktonic ecosystem model (i.e., the WAP-1D-VAR v1.0 model) equipped with a model parameter optimization scheme. We first demonstrate the modified and newly added model schemes to the pre-existing food web and biogeochemical components of the other ecosystem models that WAP-1D-VAR model was adapted from, including diagnostic sea-ice forcing and trophic interactions specific to the WAP region. We then present the results from model experiments where we assimilate 11 different data types from an example Palmer LTER growth season (October 2002–March 2003) directly related to corresponding model state variables and flows between these variables. The iterative data assimilation procedure reduces the misfits between observations and model results by 58 %, compared to before optimization, via an optimized set of 12 parameters out of a total of 72 free parameters. The optimized model results capture key WAP ecological features, such as blooms during seasonal sea-ice retreat, the lack of macronutrient limitation, and modeled variables and flows comparable to other studies in the WAP region, as well as several important ecosystem metrics. One exception is that the model slightly underestimates particle export flux, for which we discuss potential underlying reasons. The data assimilation scheme of the WAP-1D-VAR model enables the available observational data to constrain previously poorly understood processes, including the partitioning of primary production by different phytoplankton groups, the optimal chlorophyll-to-carbon ratio of the WAP phytoplankton community, and the partitioning of dissolved organic carbon pools with different lability. The WAP-1D-VAR model can be successfully employed to link the snapshots collected by the available data sets together to explain and understand the observed dynamics along the coastal WAP.

Description: The Antarctic coastal ocean impacts sea level rise, deep-ocean circulation, marine ecosystems, and the global carbon cycle. To better describe and understand these processes and their variability, it is necessary to combine the sparse available observations with the best-possible numerical descriptions of ocean circulation. In particular, high ice shelf melting rates in the Amundsen Sea have attracted many observational campaigns, and we now have some limited oceanographic data that capture seasonal and interannual variability during the past decade. One method to combine observations with numerical models that can maximize the information extracted from the sparse observations is the adjoint method, a.k.a. 4D-Var (4-dimensional variational assimilation), as developed and implemented for global ocean state estimation by the Estimating the Circulation and Climate of the Ocean (ECCO) project. Here, for the first time, we apply the adjoint-model estimation method to a regional configuration of the Amundsen and Bellingshausen seas, Antarctica, including explicit representation of sub-ice-shelf cavities. We utilize observations available during 2010–2014, including ship-based and seal-tagged CTD measurements, moorings, and satellite sea-ice concentration estimates. After 20 iterations of the adjoint-method minimization algorithm, the cost function, here defined as a sum of the weighted model–data difference, is reduced by 65 % relative to the baseline simulation by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. The sea-ice and ocean components of the cost function are reduced by 59 % and 70 %, respectively. Major improvements include better representations of (1) Winter Water (WW) characteristics and (2) intrusions of modified Circumpolar Deep Water (mCDW) towards the Pine Island Glacier. Sensitivity experiments show that ∼40 % and ∼10 % of improvements in sea ice and ocean state, respectively, can be attributed to the adjustment of air temperature and wind. This study is a preliminary demonstration of adjoint-method optimization with explicit representation of ice shelf cavity circulation. Despite the 65 % cost reduction, substantial model–data discrepancies remain, in particular with annual and interannual variability observed by moorings in front of the Pine Island Ice Shelf. We list a series of possible causes for these residuals, including limitations of the model, the optimization methodology, and observational sampling. In particular, we hypothesize that residuals could be further reduced if the model could more accurately represent sea-ice concentration and coastal polynyas.

Description: The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Description: In this paper, we present a newly developed mesoscale nesting interface for the PALM model system 6.0, which enables PALM to simulate the atmospheric boundary layer under spatially heterogeneous and non-stationary synoptic conditions. The implemented nesting interface, which is currently tailored to the mesoscale model COSMO, consists of two major parts: (i) the preprocessor INIFOR (initialization and forcing), which provides initial and time-dependent boundary conditions from mesoscale model output, and (ii) PALM's internal routines for reading the provided forcing data and superimposing synthetic turbulence to accelerate the transition to a fully developed turbulent atmospheric boundary layer. We describe in detail the conversion between the sets of prognostic variables, transformations between model coordinate systems, as well as data interpolation onto PALM's grid, which are carried out by INIFOR. Furthermore, we describe PALM's internal usage of the provided forcing data, which, besides the temporal interpolation of boundary conditions and removal of any residual divergence, includes the generation of stability-dependent synthetic turbulence at the inflow boundaries in order to accelerate the transition from the turbulence-free mesoscale solution to a resolved turbulent flow. We demonstrate and evaluate the nesting interface by means of a semi-idealized benchmark case. We carried out a large-eddy simulation (LES) of an evolving convective boundary layer on a clear-sky spring day. Besides verifying that changes in the inflow conditions enter into and successively propagate through the PALM domain, we focus our analysis on the effectiveness of the synthetic turbulence generation. By analysing various turbulence statistics, we show that the inflow in the present case is fully adjusted after having propagated for about two to three eddy-turnover times downstream, which corresponds well to other state-of-the-art methods for turbulence generation. Furthermore, we observe that numerical artefacts in the form of grid-scale convective structures in the mesoscale model enter the PALM domain, biasing the location of the turbulent up- and downdrafts in the LES. With these findings presented, we aim to verify the mesoscale nesting approach implemented in PALM, point out specific shortcomings, and build a baseline for future improvements and developments.

Description: The assimilation of clear-sky radiance in the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric analysis relies on the clear-sky radiances observation operator. Some of these radiances have frequencies that make them sensitive to both the surface and atmosphere. Because the atmospheric and surface analyses are currently not strongly coupled, a specific treatment of the surface is required. The observation operator specifically expects a skin temperature value at the observation location and time as well as the profiles of the atmospheric variables along the viewing path. This skin temperature is added to the control variable and optimised simultaneously with all of the atmospheric variables to produce optimal simulated radiances. We present two approaches to add the skin temperature to the control variable. In the current TOVS Control Variable (TOVSCV) approach, a series of skin temperature values per observation location is added to the control variable. Effectively, in the optimisation process, the skin temperature acts as a sink variable in observation space and is uncoupled from the skin temperature at other locations. In the novel SKin Temperature in the Extended Control Vector (SKTECV) approach, two-dimensional skin temperature fields are added to the control variable. All clear-sky radiances then participate in the optimisation of these two-dimensional fields, and the analysis produces temporally and spatially consistent skin temperature fields. We compare the two approaches over two seasons of 3 months each. Overall, there is a neutral impact of the new approach on the analysis and forecast. Moreover, there is some evidence that the contribution of the subsurface layers should be represented in the new approach for the skin temperature associated with the microwave instruments.

Description: Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

Description: Coasts are among the most intensely used environments on the planet, but they also present dynamic and unique hazards, including flooding and erosion. Sea level rise and changing wave climates will alter patterns of erosion and deposition, but some existing coastline evolution models are unable to simulate these effects due to their one-dimensional representation of the systems or the sediment transport processes. In this paper, the development and application of the Coastline Evolution Model 2D (CEM2D) are presented, a model which incorporates these influences. The model has been developed from the established CEM and is capable of simulating fundamental cause–effect relationships in coastal systems. The two-dimensional storage and transport of sediment in CEM2D, which are only done in one-dimension in CEM, mean it is also capable of exploring the influence of a variable water level on sediment transport and the formation and evolution of morphological features and landforms at the mesoscale. The model sits between one-dimensional and three-dimensional models, with the advantage of increased complexity and detail in model outputs compared to the former but with more efficiency and less computational expense than the latter.

Description: Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be useful for augmenting training sets cheaply. Here we explore the use of copula-based models for generating synthetically augmented datasets in weather and climate by testing the method on a toy physical model of downwelling longwave radiation and corresponding neural network emulator. Results show that for copula-augmented datasets, predictions are improved by up to 62 % for the mean absolute error (from 1.17 to 0.44 W m−2).

Description: In this study, we implement a vertical grid refinement scheme in the radiation routine of the global aerosol–climate model ECHAM-HAM, aiming to improve the representation of stratocumulus clouds and address the underestimation of their cloud cover. The scheme is based on a reconstruction of the temperature inversion as a physical constraint for the cloud top. On the refined grid, the boundary layer and the free troposphere are separated and the cloud's layer is made thinner. The cloud cover is recalculated either by conserving the cloud volume (SC-VOLUME) or by using the Sundqvist cloud cover routine on the new grid representation (SC-SUND). In global climate simulations, we find that the SC-VOLUME approach is inadequate, as there is a mismatch, in most cases, between the layer of the inversion and the layer of the stratocumulus cloud, which prevents its application and is itself likely caused by an overly low vertical resolution. Additionally, we find that the occurrence frequency of stratocumulus clouds is underestimated in ECHAM-HAM, limiting a priori the potential benefits of a scheme like SC-VOLUME targeting only cloud amount when present. With the SC-SUND approach, the possibility for new clouds to be formed on the refined grid results in a large increase in mean total cloud cover in stratocumulus regions. In both cases, however, the changes exerted in the radiation routine are too weak to produce a significant improvement in the simulated stratocumulus cloud cover. We investigate and discuss the reasons behind this. The grid refinement scheme could be used more effectively for this purpose if implemented directly in the model's cloud microphysics and cloud cover routines, but other possible ways forward are also discussed.

Description: Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. To ensure that these models represent the atmosphere adequately, it is important to compare their outputs with measurements. However, ground based measurements of atmospheric composition are typically sparsely distributed and representative of much smaller spatial scales than those resolved in models; thus, direct comparison incurs uncertainty. In this study, we investigate the feasibility of using observations of one or more atmospheric constituents to estimate parameters in chemistry transport models and to explore how these estimates and their uncertainties depend upon representation errors and the level of spatial coverage of the measurements. We apply Gaussian process emulation to explore the model parameter space and use monthly averaged ground-level concentrations of ozone (O3) and carbon monoxide (CO) from across Europe and the US. Using synthetic observations, we find that the estimates of parameters with greatest influence on O3 and CO are unbiased, and the associated parameter uncertainties are low even at low spatial coverage or with high representation error. Using reanalysis data, we find that estimates of the most influential parameter – corresponding to the dry deposition process – are closer to its expected value using both O3 and CO data than using O3 alone. This is remarkable because it shows that while CO is largely unaffected by dry deposition, the additional constraints it provides are valuable for achieving unbiased estimates of the dry deposition parameter. In summary, these findings identify the level of spatial representation error and coverage needed to achieve good parameter estimates and highlight the benefits of using multiple constraints to calibrate atmospheric chemistry transport models.

Description: Recent developments and options in the GF (Grell and Freitas, 2014; Freitas et al., 2018) convection parameterization are presented. The parameterization has been expanded to a trimodal spectral size to simulate three convection modes: shallow, congestus, and deep. In contrast to usual entrainment and detrainment assumptions, we assume that beta functions (BFs), commonly applied to represent probability density functions (PDFs), can be used to characterize the vertical mass flux profiles for the three modes and use the BFs to derive entrainment and detrainment rates. We also added a new closure for nonequilibrium convection that improved the simulation of the diurnal cycle of convection, with a better representation of the transition from shallow to deep convection regimes over land. The transport of chemical constituents (including wet deposition) can be treated inside the GF scheme. The tracer transport is handled in flux form and is mass-conserving. Finally, the cloud microphysics have been extended to include the ice phase to simulate the conversion from liquid water to ice in updrafts with resulting additional heat release and the melting from snow to rain.

Description: When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.

Description: Atmospheric chemical forecasts heavily rely on various model parameters, which are often insufficiently known, such as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties with an ensemble of forecasts is impaired by the high dimensionality of the system. This study presents a novel approach, which substitutes the problem into a low-dimensional subspace spanned by the leading uncertainties. It is based on the idea that the forecast model acts as a dynamical system inducing multivariate correlations of model uncertainties. This enables an efficient perturbation of high-dimensional model parameters according to their leading coupled uncertainties. The specific algorithm presented in this study is designed for parameters that depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen–Loéve expansion. Due to their perceived simulation challenge, the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state that highly correlated uncertainties of regional biogenic emissions can be represented by a low number of dominant components. Depending on the required level of detail, leading parameter uncertainties with dimensions of ?(106) can be represented by a low number of about 10 ensemble members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.

Description: Prediction of spatiotemporal chaotic systems is important in various fields, such as numerical weather prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as reservoir computing (RC), have recently been recognized as promising tools to predict spatiotemporal chaotic systems. However, the sensitivity of the skill of the machine-learning-based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and local ensemble transform Kalman filter (LETKF) by applying them to the prediction of the Lorenz 96 system. In order to increase the scalability to larger systems, we applied a parallelized RC framework. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.

Description: This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average −0.09 W m−2 with a standard deviation due to interannual variability of 0.25 W m−2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘C with an interannual standard deviation of 0.17 ∘C, exhibiting a small drift of 0.015 ± 0.005 ∘C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘C, and its transient climate response is estimated at 2.1 ∘C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.05 ∘C with a standard deviation across ensemble members of 0.35 ∘C in the Northern Hemisphere and 1.29 ± 0.02 ∘C with a corresponding standard deviation of 0.05 ∘C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘C above the preindustrial mean. A 0.5 ∘C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘C.

Description: Terrestrial surface water temperature is a key variable affecting water quality and energy balance, and thermodynamics and fluid dynamics are tightly coupled in fluvial and lacustrine systems. Streamflow generally plays a role in the horizontal redistribution of heat, and thermal exchange in lakes predominantly occurs in a vertical direction. However, numerical models simulate the water temperature for uncoupled rivers and lakes, and the linkages between them on a global scale remain unclear. In this study, we proposed an integrated modeling framework: Tightly Coupled framework for Hydrology of Open water Interactions in River–lake network (TCHOIR, read as “tee quire”). The objective is to simulate terrestrial fluvial and thermodynamics as a continuum of mass and energy in solid and liquid phases redistributed among rivers and lakes. TCHOIR uses high-resolution geographical information harmonized over fluvial and lacustrine networks. The results have been validated through comparison with in situ observations and satellite-based data products, and the model sensitivity has been tested with multiple meteorological forcing datasets. It was observed that the “coupled” mode outperformed the “river-only” mode in terms of discharge and temperature downstream of lakes; moreover, it was observed that seasonal and interannual variation in lake water levels and temperature are also more reliable in the “coupled” mode. The inclusion of lakes in the coupled model resulted in an increase in river temperatures during winter at midlatitudes and a decrease in temperatures during summer at high latitudes, which reflects the role of lakes as a form of large heat storage. The river–lake coupling framework presented herein provides a basis for further elucidating the role of terrestrial surface water in Earth's energy cycle.