Improving the stomatal resistance, photosynthesis and two big leaf algorithms for grass in the regional climate model COSMO-CLM

Climatic changes towards warmer temperatures require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes which depend on temperature, water availability, and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in the regional climate model COSMO-CLM (CCLM v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” approach by a “two-big leaf” one. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 2010 to 2015 (Parc, Linden and Lindenberg domain). Hereby, we tested three alternative formulations of the new algorithms against a reference simulation (CCLMref) with no changes. The first formulation (CCLM3.5) adapts the algorithms for stomatal resistance from the Community Land Model (CLM v3.5), which depend on leaf photosynthesis, CO2 partial and vapor pressure and maximum stomatal resistance. The second one (CCLM4.5) includes a soil water stress function as in CLM v4.5. The third one (CCLM4.5e) is similar to CCLM4.5, but with adapted equations for dry leaf calculations. The results revealed major differences in the annual cycle of stomatal resistance compared to the original algorithm (CCLMref) of the reference simulation. The largest changes in stomatal resistance are observed from October to April when stomata are closed while summer values are generally less than control values that come closer to measured values. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model (e.g.: transpiration rate or total evapotranspiration). These results were received by comparing COSMO-CLM parameters with FLUXNET data, meteorological observations at the sites, and GLEAM and HYRAS datasets.

Accurate Inlet Boundary Conditions to Capture Combustion Chamber and Turbine Coupling with Large-Eddy Simulation

The coupling between different components of a turbomachinery is becoming more widely studied especially by use of Computational Fluid Dynamics. Such simulations are of particular interest especially at the interface between a combustion chamber and a turbine, for which the prediction of the migration of hotspots generated in the chamber is of paramount importance for performance and life-duration issues. The objective of the present study is to investigate available solutions to perform isolated simulations while taking into account the effect of multi-component coupling. Investigations presented in the paper focus on the FACTOR configuration. The fist step of the proposed method is to record conservative variables solved by the LES code at the interface plane between the chamber and the turbine of a reference simulation. Then, using the Spectral Proper Orthogonal Decomposition method, the recorded data is analysed and can be partially reconstructed using different numbers of frequencies. Using the partial reconstructions, it is then possible to replicate a realistic inlet boundary condition for isolated turbine simulations with both velocity and temperature fluctuations, while reducing the storage cost compared to the initial database. The integrated simulation is then compared to the isolated simulations as well as against simulations making use of averaged quantities with or without synthetic turbulence injection at their inlet. The isolated simulations for which the inlet condition is reconstructed with a large number of frequencies show very good agreement with the fully integrated simulation compared to the typical isolated simulation using average quantities at the inlet.

Quantifying how model assumptions affect aerosol-cloud interactions in large-eddy simulations of warm stratocumulus clouds

<p>Subtropical low-level marine stratocumulus clouds effectively reflect downwelling shortwave radiation while having a small effect on outgoing longwave radiation. Hence, they impose a strong negative net radiative effect on the Earth’s radiation balance. The optical and microphysical properties of these clouds are susceptible to anthropogenic changes in aerosol abundance. Although these aerosol-cloud-climate interactions (ACI) are generally explicitly treated in state-of-the-art Earth System Models (ESMs), they are accountable for large uncertainties in current climate projections.</p><p>Here, we present preliminary work where we exploit Large-Eddy-Simulations (LES) of warm stratocumulus clouds to identify and constrain processes and model assumptions that affect the response of cloud droplet number concentration (N<sub>d</sub>) to changes in aerosol number concentration (N<sub>a</sub>). Our results are based on simulations with the MISU-MIT Cloud-Aerosol (MIMICA, Savre et al., 2014) LES, which has two-moment bulk microphysics (Seifert and Beheng, 2001) and a two-moment aerosol scheme (Ekman et al., 2006). The reference simulation is based on observations made during the Dynamics and Chemistry of Marine Stratocumulus Field Study (DYCOMS-II, Stevens et al., 2003) which were used extensively during previous LES studies (e.g., Ackerman et al., 2009).</p><p>Starting from the reference simulation, we conduct sensitivity experiments to examine how the susceptibility (β=dln(N<sub>d</sub>)/dln(N<sub>a</sub>)) changes depending on different model setups. We run the model with fixed and interactive aerosol concentrations, with and without saturation adjustment, with different aerosol populations, and with different model parameter choices. Our early results suggest that β is sensitive to these choices and can vary roughly between 0.6 to 0.9 depending on the setup. The overall purpose of our study is to guide future model developments and evaluations concerning aerosol-cloud-climate interactions.  </p><p> </p><p><strong>References</strong></p><p>Ackerman, A. S., vanZanten, M. C., Stevens, B., Savic-Jovcic, V., Bretherton, C. S., Chlond, A., et al. (2009). Large-Eddy Simulations of a Drizzling, Stratocumulus-Topped Marine Boundary Layer. Monthly Weather Review, 137(3), 1083–1110. https://doi.org/10.1175/2008MWR2582.1</p><p>Ekman, A. M. L., Wang, C., Ström, J., & Krejci, R. (2006). Explicit Simulation of Aerosol Physics in a Cloud-Resolving Model: Aerosol Transport and Processing in the Free Troposphere. Journal of the Atmospheric Sciences, 63(2), 682–696. https://doi.org/10.1175/JAS3645.1</p><p>Savre, J., Ekman, A. M. L., & Svensson, G. (2014). Technical note: Introduction to MIMICA, a large-eddy simulation solver for cloudy planetary boundary layers. Journal of Advances in Modeling Earth Systems, 6(3), 630–649. https://doi.org/10.1002/2013MS000292</p><p>Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., et al. (2003). Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bulletin of the American Meteorological Society, 84(5), 579–594. https://doi.org/10.1175/BAMS-84-5-579</p>

Evaluation of clear sky models to estimate the surface direct aerosol radiative effect over Germany

<p>The radiation budget of the earth and its climate system is driven by the solar radiation, which interacts with gases, aerosol particles and clouds. Focusing on aerosol, a fundamental measure is the radiative forcing resulting from aerosol-radiation interactions (RFari) which is also known as the aerosol direct radiative effect. Quantifying the surface RFari on regional scales aids the understanding of the role of aerosol in the climate system and is important for the planning of solar energy systems.</p><p>This study is based on a one year dataset (2015) of shortwave broadband global and diffuse horizontal irradiance measured with shaded and unshaded pyranometers at 26 station across Germany within the German Weather Service (DWD) observational network. A variety of clear-sky models are utilized to quantify RFari with a clear sky fitting technique. Clear sky models used are MMAC, MRM v.6.1, METSTAT, ESRA, Heliosat-1, CEM and the simplified Solis model. As these models have not been designed to estimate the clear sky irradiance without the presence of aerosol, we evaluated the accuracy of RFari with an reference simulation.</p><p>The reference RFari is simulated using the TROPOS (Leibniz Institute of Tropospheric Research) Cloud and Aerosol Radiative Simulator (T-CARS) utilizing the offline version of the ECMWF radiation scheme (ecRad) with input data of meteorological state of the atmosphere, trace-gases and aerosol from CAMS reanalysis.</p><p>The clear sky fitting approach for this set of clear sky models agrees well with T-CARS, showing an RMSE of 6.7 Wm<sup>-2</sup> and an correlation of 0.75. The annual mean of surface RFari over the observation stations in Germany shows a value of -13.2 Wm<sup>-2</sup> as an average over all clear sky models, compared to -13.4 Wm<sup>-2</sup> from T-CARS. Out of this set of clear sky models, best performance is shown by the ESRA and MRM v6.1 models. Although, the accuracy of the annual mean RFari from the clear sky fitting approach is strongly depended on the number available clear-sky irradiance measurements and its distribution over the year. Therefore, this approach is not recommended for climatological studies, but may serve as valuable information for e.g. the evaluation of power generation and the influence by aerosol of photo-voltaic power plants.</p>

The response of mesospheric H₂O and CO to solar irradiance variability in models and observations

Water vapor (H2O) is the source of reactive hydrogen radicals in the middle atmosphere, whereas carbon monoxide (CO), being formed by CO2 photolysis, is suitable as a dynamical tracer. In the mesosphere, both H2O and CO are sensitive to solar irradiance (SI) variability because of their destruction/production by solar radiation. This enables us to analyze the solar signal in both models and observed data. Here, we evaluate the mesospheric H2O and CO response to solar irradiance variability using the Chemistry-Climate Model Initiative (CCMI-1) simulations and satellite observations. We analyzed the results of four CCMI models (CMAM, EMAC-L90MA, SOCOLv3, and CESM1-WACCM 3.5) operated in CCMI reference simulation REF-C1SD in specified dynamics mode, covering the period from 1984–2017. Multiple linear regression analyses show a pronounced and statistically robust response of H2O and CO to solar irradiance variability and to the annual and semiannual cycles. For periods with available satellite data, we compared the simulated solar signal against satellite observations, namely the GOZCARDS composite for 1992–2017 for H2O and Aura/MLS measurements for 2005–2017 for CO. The model results generally agree with observations and reproduce an expected negative and positive correlation for H2O and CO, respectively, with solar irradiance. However, the magnitude of the response and patterns of the solar signal varies among the considered models, indicating differences in the applied chemical reaction and dynamical schemes, including the representation of photolyzes. We suggest that there is no dominating thermospheric influence of solar irradiance in CO, as reported in previous studies, because the response to solar variability is comparable with observations in both low-top and high-top models. We stress the importance of this work for improving our understanding of the current ability and limitations of state-of-the-art models to simulate a solar signal in the chemistry and dynamics of the middle atmosphere.

Modeling of solid-particle effects on bubble breakage and coalescence in slurry bubble columns

Solid particles heavily affect the hydrodynamics in slurry bubble columns. The effects arise through varying breakup and coalescence behavior of the bubbles with the presence of solid particles where particles in the micrometer range lead to a promotion of coalescence in particular. To simulate the gas-liquid-solid flow in a slurry bubble column, the Eulerian multifluid approach can be employed to couple computational fluid dynamics (CFD) with the population balance equation (PBE) and thus to account for breakup and coalescence of bubbles.In this work, three approaches are presented to modify the breakup and coalescence models to account for enhanced coalescence in the coupled CFD-PBE framework. The approaches are applied to a reference simulation case with available experimental data. In addition, the impacts of the modifications on the simulated bubble size distribution (BSD) and the applicability of the approaches are evaluated. The capabilities as well as the differences and limits of the approaches are demonstrated and explained.

Stress testing as complement to climate scenarios: recharge scenarios to quantify streamflow drought sensitivity

Precipitation deficits and temperature anomalies are often the main cause for low flows and summer streamflow droughts. However, where groundwater is the main contribution to sustain water availability and ecological integrity during dry spells, the role of recharge and catchment storage is crucial to understand streamflow drought sensitivity. Here we introduce recharge stress tests as complement to climate scenarios to characterize and quantify the streamflow drought sensitivities of catchments. The stress tests are presented by applying them to six headwater catchments in Switzerland with various catchment and streamflow characteristics. The stress tests drive the bucket-type hydrological model HBV in a framework, in which pre-drought recharge conditions can be decreased to test how catchments respond to and recover from drought. We identified an upper limit of stress test durations around 12 months as indicator of maximum recharge- and storage-memory for the study catchments. Varying response on stress testing across the catchments suggests different storage properties and thus different recovery times from drought. From the stress test simulations, we found up to 200 d longer summer streamflow droughts with additional streamflow deficits which account for up to 40 d of median flow. Using a worst-case pre-drought recharge in stress test simulation leads to minimum flow reductions of 50 %–80 % compared with the reference simulation. Based on the results we conclude with recommendations for further stress test research in drought hydrology.

The response of mesospheric H₂O and CO to solar irradiance variability in the models and observations

Water vapor (H2O) is the source of reactive hydrogen radicals in the middle atmosphere, whereas carbon monoxide (CO), being formed by CO2 photolysis, is suitable as a dynamical tracer. In the mesosphere, both H2O and CO are sensitive to solar irradiance variability because of their destruction/production by solar radiation. This enables to analyze the solar signal in both, models and observed data. Here, we evaluate the mesospheric H2O and CO response to solar irradiance variability using the Chemistry-Climate Model Initiative (CCMI-1) simulations and satellite data. We analyzed the results of four CCMI models (CMAM, EMAC-L90MA, SOCOLv3, CESM1-WACCM 3.5) operated in CCMI reference simulation REF-C1SD in specified dynamics mode, covering the period from 1984 to 2017. Multiple linear regression analysis shows a pronounced and statistically robust response of H2O and CO to solar irradiance variability, and to the annual and semiannual cycles. For periods with available satellite data, we compared the simulated solar signal against satellite observations, namely during 1992–2017 for H2O and 2005–2017 for CO. The model results generally agree with observations and reproduce an expected negative and positive correlation for H2O and CO, respectively, with solar irradiance. However, the magnitude of the response and patterns of the solar signal varies among the considered models, indicating differences in the applied chemical reaction and dynamical schemes including the representation of photolyses. We suggest that there is no dominating thermospheric influence of solar irradiance in CO, as reported in previous studies because the response to solar variability is comparable with observations in both, low-top and high-top models. We stress the importance of this work for improving our understanding of the current ability and limitations of state-of-the-art models to simulate a solar signal in the chemistry and dynamic of the middle atmosphere.

Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections.