Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 ∘C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume

The photolysis module in Environment and Climate Change Canada's online chemical transport model GEM-MACH (GEM: Global Environmental Multi-scale – MACH: Modelling Air quality and Chemistry) was improved to make use of the online size and composition-resolved representation of atmospheric aerosols and relative humidity in GEM-MACH, to account for aerosol attenuation of radiation in the photolysis calculation. We coupled both the GEM-MACH aerosol module and the MESSy-JVAL (Modular Earth Submodel System) photolysis module, through the use of the online aerosol modeled data and a new Mie lookup table for the model-generated extinction efficiency, absorption and scattering cross sections of each aerosol type. The new algorithm applies a lensing correction factor to the black carbon absorption efficiency (core-shell parameterization) and calculates the scattering and absorption optical depth and asymmetry factor of black carbon, sea salt, dust and other internally mixed components. We carried out a series of simulations with the improved version of MESSy-JVAL and wildfire emission inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) for 2 months, compared the model aerosol optical depth (AOD) output to the previous version of MESSy-JVAL, satellite data, ground-based measurements and reanalysis products, and evaluated the effects of AOD calculations and the interactive aerosol feedback on the performance of the GEM-MACH model. The comparison of the improved version of MESSy-JVAL with the previous version showed significant improvements in the model performance with the implementation of the new photolysis module and with adopting the online interactive aerosol concentrations in GEM-MACH. Incorporating these changes to the model resulted in an increase in the correlation coefficient from 0.17 to 0.37 between the GEM-MACH model AOD 1-month hourly output and AERONET (Aerosol Robotic Network) measurements across all the North American sites. Comparisons of the updated model AOD with AERONET measurements for selected Canadian urban and industrial sites, specifically, showed better correlation coefficients for urban AERONET sites and for stations located further south in the domain for both simulation periods (June and January 2018). The predicted monthly averaged AOD using the improved photolysis module followed the spatial patterns of MERRA-2 reanalysis (Modern-Era Retrospective analysis for Research and Applications – version 2), with an overall underprediction of AOD over the common domain for both seasons. Our study also suggests that the domain-wide impacts of direct and indirect effect aerosol feedbacks on the photolysis rates from meteorological changes are considerably greater (3 to 4 times) than the direct aerosol optical effect on the photolysis rate calculations.

The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)

Domain size can have significant impact on regional modeling results, but few studies examined the sensitivities of simulated aerosol impact to regional domain size. This study investigates the regional modeling sensitivities of aerosol impact on the East Asian summer monsoon (EASM) to domain size. The simulations with two different domain sizes demonstrate consistently that aerosols induce the cooling of the lower troposphere that leads to the anticyclone circulation anomalies and thus the weakening of EASM moisture transport. The aerosol-induced adjustment of monsoonal circulation results in an alternate increase and decrease pattern of precipitation over China. Domain size has a great influence on the simulated meteorological fields. For example, the simulation with larger domain size produces weaker EASM circulation, which also affects aerosol distributions significantly. This leads to the difference of simulated strength and area extent of aerosol-induced changes of lower-tropospheric temperature and pressure, which further results in different distributions of circulation and precipitation anomalies over China. For example, over southeastern China, aerosols induce the increase (decrease) of precipitation from the smaller-domain (larger-domain) simulation. Different domain sizes consistently simulate an aerosol-induced increase in precipitation around 30∘ N over eastern China. This study highlights the important influence of domain size on regional modeling results of aerosol impact on circulation and precipitation, which may not be limited to East Asia. More generally, this study also implies that proper modeling of meteorological fields with appropriate domain size is one of the keys to simulating robust aerosol climatic impact.

Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ∼ 100 d, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

Importance of radiative transfer processes in urban climate models: a study based on the PALM 6.0 model system

Including radiative transfer processes within the urban canopy layer into microscale urban climate models (UCMs) is essential to obtain realistic model results. These processes include the interaction of buildings and vegetation with shortwave and longwave radiation, thermal emission, and radiation reflections. They contribute differently to the radiation budget of urban surfaces. Each process requires different computational resources and physical data for the urban elements. This study investigates how much detail modellers should include to parameterize radiative transfer in microscale building-resolving UCMs. To that end, we introduce a stepwise parameterization method to the Parallelized Large-eddy Simulation Model (PALM) system 6.0 to quantify individually the effects of the main radiative transfer processes on the radiation budget and on the flow field. We quantify numerical simulations of both simple and realistic urban configurations to identify the major and the minor effects of radiative transfer processes on the radiation budget. The study shows that processes such as surface and vegetation interaction with shortwave and longwave radiation will have major effects, while a process such as multiple reflections will have minor effects. The study also shows that radiative transfer processes within the canopy layer implicitly affect the incoming radiation since the radiative transfer model is coupled to the radiation model. The flow field changes considerably in response to the radiative transfer processes included in the model. The study identified those processes which are essentially needed to assure acceptable quality of the flow field. These processes are receiving radiation from atmosphere based on the sky-view factors, interaction of urban vegetation with radiation, radiative transfer among urban surfaces, and considering at least single reflection of radiation. Omitting any of these processes may lead to high uncertainties in the model results.

Irrigation quality and management determine salinization in Israeli olive orchards

Olive (Olea europaea L.) orchard brackish water irrigation with incorrect irrigation management reduces soil fertility and degrades soil health through soil salinization. This study was conducted in the Beit She'an Valley, one of the main agricultural regions in Israel, in an olive orchard in which a combination of soil salinization and poor drainage conditions impedes plant development and causes severe economic damage. By combining various research methods, including soil salinity monitoring, field experiments, remote sensing (frequency domain electromagnetic – FDEM), and unsaturated soil profile saline water movement modeling, the salinization processes were quantified. Irrigation water conductance of 3.13 dS m−1 points to salinization within the tree upper root zone, whereas the modeling results suggest that salinization danger is greater with brackish treated wastewater rather than with lower-salinity brackish irrigation groundwater and that irrigation with potable water can help reduce salt accumulation and recover damaged plots.

Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment

This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. The purpose of this contribution is twofold. First, we discuss the mathematical and numerical issues involved in solving the Richardson–Richards equation, conventionally known as the Richards equation, and the heat equation in heterogeneous soils. In particular, for the Richardson–Richards equation (R2) we take advantage of the nested Newton–Casulli–Zanolli (NCZ) algorithm that ensures the convergence of the numerical solution in any condition. Second, starting from numerical and modelling needs, we present the design of software that is intended to be the first building block of a new customizable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code, adopting the object-oriented paradigm and a generic programming approach in order to improve its usability and expandability. WHETGEO is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides. Finally, the paper presents the 1D deployment of WHETGEO, WHETGEO-1D, which has been tested against the available analytical solutions presented in the Appendix.

ISWFoam: a numerical model for internal solitary wave simulation in continuously stratified fluids

A numerical model, ISWFoam, for simulating internal solitary waves (ISWs) in continuously stratified, incompressible, viscous fluids is developed based on a fully three-dimensional (3D) Navier–Stokes equation using the open-source code OpenFOAM®. This model combines the density transport equation with the Reynolds-averaged Navier–Stokes equation with the Coriolis force, and the model discrete equation adopts the finite-volume method. The k–ω SST turbulence model has also been modified according to the variable density field. ISWFoam provides two initial wave generation methods to generate an ISW in continuously stratified fluids, including solving the weakly nonlinear models of the extended Korteweg–de Vries (eKdV) equation and the fully nonlinear models of the Dubreil–Jacotin–Long (DJL) equation. Grid independence tests for ISWFoam are performed, and considering the accuracy and computing efficiency, the appropriate grid size of the ISW simulation is recommended to be 1/150th of the characteristic length and 1/25th of the ISW amplitude. Model verifications are conducted through comparisons between the simulated and experimental data for ISW propagation examples over a flat bottom section, including laboratory scale and actual ocean scale, a submerged triangular ridge, a Gaussian ridge, and slope. The laboratory test results, including the ISW profile, wave breaking location, ISW arrival time, and the spatial and temporal changes in the mixture region, are well reproduced by ISWFoam. The ISWFoam model with unstructured grids and local mesh refinement can effectively simulate the evolution of ISWs, the ISW breaking phenomenon, waveform inversion of ISWs, and the interaction between ISWs and complex topography.