Single-column Model Research Articles

Introduction of prognostic rain in ECHAM5: design and single column model simulations

Abstract. Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG). To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoconversion rate than the standard ECHAM5 but shifts the emphasis towards the accretion rates in accordance with observations. ECHAM5-PROG is tested and evaluated with Single Column Model (SCM) simulations for two cases: the marine stratocumulus study EPIC (October 2001) and the continental mid-latitude ARM Cloud IOP (shallow frontal cloud case – March 2000). In case of heavy precipitation events, the prognostic equations for rain hardly affect the amount and timing of precipitation at the surface in different SCM simulations because heavy rain depends mainly on the large-scale forcing. In case of thin, drizzling clouds (i.e., stratocumulus), surface precipitation is sensitive to the number of sub-time steps used in the prognostic rain scheme. Cloud microphysical quantities, such as cloud liquid and rain water within the atmosphere, are sensitive to the number of sub-time steps in both considered cases. This results from the decreasing autoconversion rate and increasing accretion rate.

Comparison of stochastic parametrization approaches in a single-column model

We discuss and test the potential usefulness of single-column models (SCMs) for the testing of stochastic physics schemes that have been proposed for use in general circulation models (GCMs). We argue that although single-column tests cannot be definitive in exposing the full behaviour of a stochastic method in the full GCM, and although there are differences between SCM testing of deterministic and stochastic methods, SCM testing remains a useful tool. It is necessary to consider an ensemble of SCM runs produced by the stochastic method. These can be usefully compared with deterministic ensembles describing initial condition uncertainty and also with combinations of these (with structural model changes) into poor man's ensembles. The proposed methodology is demonstrated using an SCM experiment recently developed by the GCSS (GEWEX Cloud System Study) community, simulating transitions between active and suppressed periods of tropical convection.

Modeling the Interaction between Cumulus Convection and Linear Gravity Waves Using a Limited-Domain Cloud System–Resolving Model

Abstract A limited-domain cloud system–resolving model (CSRM) is used to simulate the interaction between cumulus convection and two-dimensional linear gravity waves, a single horizontal wavenumber at a time. With a single horizontal wavenumber, soundings obtained from horizontal averages of the CSRM domain allow the large-scale wave equation to be evolved, and thereby its interaction with cumulus convection is modeled. It is shown that convectively coupled waves with phase speeds of 8–13 m s−1 can develop spontaneously in such simulations. The wave development is weaker at long wavelengths (>∼10 000 km). Waves at short wavelengths (∼2000 km) also appear weaker, but the evidence is less clear because of stronger influences from random perturbations. The simulated wave structures are found to change systematically with horizontal wavelength, and at horizontal wavelengths of 2000–3000 km they exhibit many of the basic features of the observed 2-day waves. The simulated convectively coupled waves develop without feedback from radiative processes, surface fluxes, or wave radiation into the stratosphere, but vanish when moisture advection by the large-scale waves is disabled. A similar degree of vertical tilt is found in the simulated convective heating at all wavelengths considered, consistent with observational results. Implications of these results to conceptual models of convectively coupled waves are discussed. In addition to being a useful tool for studying wave–convection interaction, the present approach also represents a useful framework for testing the ability of coarse-resolution CSRMs and single-column models in simulating convectively coupled waves.

A Simple Parameterization for Detrainment in Shallow Cumulus

Abstract For a wide range of shallow cumulus convection cases, large-eddy simulation (LES) model results have been used to investigate lateral mixing as expressed by the fractional entrainment and fractional detrainment rates. It appears that the fractional entrainment rates show much less variation from hour to hour and case to case than the fractional detrainment rates. Therefore, in the parameterization proposed here, the fractional entrainment rates are assumed to be described as a fixed function of height, roughly following the LES results. Based on the LES results a new, more flexible parameterization for the detrainment process is developed that contains two important dependencies. First, based on cloud ensemble principles it can be understood that deeper cloud layers call for smaller detrainment rates. All current mass flux schemes ignore this cloud-height dependence, which evidently leads to large discrepancies with observed mass flux profiles. The new detrainment formulation deals with this dependence by considering the mass flux profile in a nondimensionalized way. Second, both relative humidity of the environmental air and the buoyancy excess of the updraft influence the detrainment rates and, therefore, the mass flux profiles. This influence can be taken into account by borrowing a parameter from the buoyancy-sorting concept and using it in a bulk sense. LES results show that with this bulk parameter, the effect of environmental conditions on the fractional detrainment rate can be accurately described. A simple, practical but flexible parameterization for the fractional detrainment rate is derived and evaluated in a single-column model (SCM) for three different shallow cumulus cases, which shows the clear potential of this parameterization. The proposed parameterization is an attractive and more robust alternative for existing, more complex, buoyancy-sorting-based mixing schemes, and can be easily incorporated in current mass flux schemes.

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

A statistical approach for sedimentation inside a microphysical precipitation scheme

A statistical approach for the sedimentation of prognostic precipitating species inside a microphysical scheme is proposed. The unique fall speed of precipitation used by a typical advective scheme is replaced by three probabilities of transfer associated with the following three types of precipitation: (i) precipitation present in the layer at the beginning of the time step; (ii) precipitation coming from the layer above which crosses the layer under consideration and (iii) precipitation locally produced (or destroyed) during the time step. The use of a decreasing exponential Probability Distribution Function (PDF) as basis for these probabilities ensures that the normalized probabilities do not depend on the origin of the precipitation. The main advantage of this characteristic is that the sedimentation process can now be computed in a single vertical loop. Other functions can also be used as basis for the probabilities and the case with a step function is discussed. The performance of the proposed PDF-based sedimentation scheme is validated in an academic single column model and in a full 3-D framework.

DEVELOPMENT OF AN EARTHQUAKE DAMAGE EVALUATION METHOD FOR BRIDGE STRUCTURES USING ACCELERATION SENSORS

After an extreme earthquake, evaluation of damage and operability of bridge structures is currently done based on visual inspection by bridge experts, and there is a need to develop sensing technology to enable speedy and accurate evaluation of the degree of damage and operability of bridge structures. This paper proposes an evaluation method of the degree of damage of bridge structures. A series of shaking table tests of reinforced concrete single-column models validate the effectiveness and accuracy of the proposed method. A series of dynamic analyses were carried out to evaluate the efficiency of the proposed method for multi-span bridge model. For preventing secondary disasters and securing the traffic network during a disaster, a system, which is designed to collect bridge damage information evaluated with the proposed method, is also developed.

Evaluation of a new mixed‐phase cloud microphysics parameterization with CAM3 single‐column model and M‐PACE observations

Most global climate models generally prescribe the partitioning of condensed water into cloud droplets and cloud ice in mixed‐phase clouds according to a temperature‐dependent function, which affects modeled cloud phase, cloud lifetime and radiative properties. This study evaluates a new mixed‐phase cloud microphysics parameterization (for ice nucleation and water vapor deposition) against the Atmospheric Radiation Measurement (ARM) Mixed‐phase Arctic Cloud Experiment (M‐PACE) observations using the NCAR Community Atmospheric Model Version 3 (CAM3) single column model (SCAM). It is shown that SCAM with the new scheme produces a more realistic simulation of the cloud phase structure and the partitioning of condensed water into liquid droplets against observations during the M‐PACE than the standard SCAM. A sensitivity test indicates that ice number concentration could play an important role in the simulated mixed‐phase cloud microphysics, and therefore needs to be realistically represented in global climate models.

Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework

AbstractEvery cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.

Applications of information theory in ensemble data assimilation

AbstractWe apply information theory within an ensemble‐based data assimilation approach and define information matrix in ensemble subspace. The information matrix in ensemble subspace employs a flow‐dependent forecast error covariance and it is of relatively small dimensions (equal to the ensemble size). The information matrix in ensemble subspace can be directly linked to the information matrix typically used in non‐ensemble‐based data assimilation methods, such as the Kalman Filter (KF) and the three‐dimensional variational (3D‐Var) methods, which provides a framework for consistent comparisons of information measures between different data assimilation methods.We evaluate information measures, such as degrees of freedom for signal, within the Maximum Likelihood Ensemble Filter (MLEF) data assimilation approach and compare them with those obtained using the KF approach and the 3D‐Var approach. We assimilate model‐simulated observations and use the Goddard Earth Observing System Single Column Model (GEOS‐5 SCM) as a dynamical forecast model.The experimental results demonstrate that the proposed framework is useful for comparing information measures obtained in different data assimilation approaches. These comparisons indicate that using a flow‐dependent forecast error covariance matrix (e.g. as in the KF and the MLEF experiments) is fundamentally important for adequately describing prior knowledge about the true model state when calculating information measures of assimilated observations. We also demonstrate that data assimilation results obtained using the KF and the MLEF approach (when ensemble size is larger than 10 ensemble members) are superior to the results of the 3D‐Var approach. Copyright © 2007 Royal Meteorological Society

Improvements in the Subgrid-Scale Representation of Moist Convection in a Cumulus Parameterization Scheme: The Single-Column Test and Its Impact on Seasonal Prediction

Abstract This study describes a revised approach for the subgrid-scale convective properties of a moist convection scheme in a global model and evaluates its effects on a simulated model climate. The subgrid-scale convective processes tested in this study comprise three components: 1) the random selection of cloud top, 2) the inclusion of convective momentum transport, and 3) a revised large-scale destabilization effect considering synoptic-scale forcing in the cumulus convection scheme of the National Centers for Environmental Prediction medium-range forecast model. Each component in the scheme has been evaluated within a single-column model (SCM) framework forced by the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment data. The impact of the changes in the scheme on seasonal predictions has been examined for the boreal summers of 1996, 1997, and 1999. In the SCM simulations, an experiment that includes all the modifications reproduces the typical convective heating and drying feature. The simulated surface rainfall is in good agreement with the observed precipitation. Random selection of the cloud top effectively moistens and cools the upper troposphere, and it induces drying and warming below the cloud-top level due to the cloud–radiation feedback. However, the two other components in the revised scheme do not play a significant role in the SCM simulations. On the other hand, the role of each modification component in the scheme is significant in the ensemble seasonal simulations. The random selection process of the cloud top preferentially plays an important role in the adjustment of the thermodynamic profile in a manner similar to that in the SCM framework. The inclusion of convective momentum transport in the scheme weakens the meridional circulation. The revised large-scale destabilization process plays an important role in the modulation of the meridional circulation when this process is combined with other processes; on the other hand, this process does not induce significant changes in large-scale fields by itself. Consequently, the experiment that involves all the modifications shows a significant improvement in the seasonal precipitation, thereby highlighting the importance of nonlinear interaction between the physical processes in the model and the simulated climate.

Ensemble single column model validation in the tropical western Pacific

Single column models (SCMs) are useful tools for the evaluation of parameterizations of radiative and moist processes used in general circulation models (GCMs). SCM applications have usually been limited to regions where high‐quality observations are available to derive the necessary boundary condition or forcing data. Recently, researchers have developed techniques for deriving SCM forcing data from other data sets, such as NWP (numerical weather prediction) analyses. The uncertainties inherent in these forcing data products have an unknown and possibly significant effect on SCM runs. This paper shows how an ensemble SCM (ESCM) approach can be used to minimize the uncertainty in SCM simulations resulting from uncertainties in the forcing data. Some innovative evaluation techniques have been applied to ESCM runs at the tropical western Pacific Atmospheric Radiation Measurement (ARM) program sites at Manus Island and Nauru. These techniques, making use of traditional ensemble verification methods and objectively determined cloud regimes, are shown to be able to highlight parameterization deficiencies and provide a useful tool for testing new or improved model parameterizations.

Implementation of a Single-Column Model for Fog and Low Cloud Forecasting at Central-Spanish Airports

Operations at Central-Spanish airports are often, especially in winter, affected by visibility reduction. The Instituto Nacional de Meteorologia (INM), the Spanish Weather Service, has developed a single-column model (SCM) in order to improve short-term forecasts of fog, visibility and low-clouds. The SCM, called H1D, is a one-dimensional version of the HIRLAM limited-area model. It is operationally run for three airports in the region: Madrid-Barajas, Almagro and Albacete-Los Llanos. Since SCMs cannot deal with horizontal heterogeneity, the terms that depend on the horizontal structure of the atmosphere are estimated from the outputs of the three-dimensional (3-D) model and introduced into the SCM as external forcings. The systematic analysis of the meteorological situations has evidenced the existence of a close relationship between fog formation and the presence of drainage winds in the region. Since the 3-D model docs not have the necessary resolution to correctly simulate the main features of the drainage flow caused by the complex topography in the proximity of Madrid-Barajas, it cannot provide the SCM with the correct forcings. This problem has been partially overcome through the introduction of a module that, under certain conditions, substitutes the values computed from the 3-D model outputs by others that are based on a conceptual model of the phenomenon and have been empirically derived from climatological knowledge. This module improves the H1D verification scores for the basic meteorological variables—wind, temperature and humidity—and reduces the false alarm rate in fog forecast.

Parameterization of aerosol indirect effect to complement McRAS cloud scheme and its evaluation with the 3-year ARM-SGP analyzed data for single column models

Microphysics of clouds with the Relaxed Arakawa–Schubert Scheme (McRAS) was upgraded for simulating the Aerosol Indirect Effects (AIE) for water clouds. The AIE comprises of i) Fountoukis and Nenes aerosol activation module for obtaining cloud condensation nuclei; ii) Seifert and Beheng algorithms for precipitation microphysics but with modified accretion constant for the coarse vertical-resolution typical of a global general circulation model (GCM); and iii) Khvorostyanov and Curry parameterization for computing the effective radius ( r e ) of cloud drops. The upgraded package, named McRAS-AC, was evaluated using the 3-year ARM-SGP Single Column Model (SCM) data. Invoking only the most dominant sulfate aerosols over the region, McRAS-AC simulated realistic annual mean and annual cycles of cloud water, cloud optical thicknesses, cloud drop number concentration, and r e . The follow-on SCM-sensitivity simulations showed that accretion of cloud water is sensitive to i) the terminal velocity of hydrometeors produced by autoconversion and ii) cloud height increases due to in-cloud condensation heating. The impact of aerosol mass concentration on the resultant column cloud water, and bulk optical properties of clouds were assessed by using 1/8 to 8 times the average monthly aerosol mass concentration estimates of GOCART aerosol climatology. A log-linear relation between cloud-radiative forcing and aerosol-mass concentration emerged in the simulated data.

Evaluation of the NCEP Global Forecast System at the ARM SGP Site

AbstractThis study evaluates the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) against observations made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program at the southern Great Plains site for the years 2001–04. The spatial and temporal scales of the observations are examined to search for an optimum approach for comparing grid-mean model forecasts with single-point observations. A single-column model (SCM) based upon the GFS was also used to aid in understanding certain forecast errors. The investigation is focused on the surface energy fluxes and clouds. Results show that the overall performance of the GFS model has been improving, although certain forecast errors remain. The model overestimated the daily maximum latent heat flux by 76 W m−2 and the daily maximum surface downward solar flux by 44 W m−2, and underestimated the daily maximum sensible heat flux by 44 W m−2. The model’s surface energy balance was reached by a cancellation of errors. For clouds, the GFS was able to capture the observed evolutions of cloud systems during major synoptic events. However, on average, the model largely underestimated cloud fraction in the lower and midtroposphere, especially for daytime nonprecipitating low clouds because shallow convection in the GFS does not produce clouds. Analyses of surface radiative fluxes revealed that the diurnal cycle of the model’s surface downward longwave flux (SDLW) was not in phase with that of the ARM-observed SDLW. SCM experiments showed that this error was caused by an inaccurate scaling factor, which was a function of ground skin temperature and was used to adjust the SDLW at each model time step to that computed by the model’s longwave radiative transfer routine once every 3 h. A method has been proposed to correct this error in the operational forecast model. It was also noticed that the SDLW biases changed from mostly negative in 2003 to slightly positive in 2004. This change was traced back to errors in the near-surface air temperature. In addition, the SDLW simulated with the newly implemented Rapid Radiative Transfer Model longwave routine in the GFS is usually 5–10 W m−2 larger than that simulated with the previous routine. The forecasts of surface downward shortwave flux (SDSW) were relatively accurate under clear-sky conditions. The errors in SDSW were primarily caused by inaccurate forecasts of cloud properties. Results from this study can be used as guidance for the further development of the GFS.

Cloud Properties Simulated by a Single-Column Model. Part II: Evaluation of Cumulus Detrainment and Ice-Phase Microphysics Using a Cloud-Resolving Model

Abstract This paper is the second in a series in which kilometer-scale-resolving observations from the Atmospheric Radiation Measurement Program and output from the University of California, Los Angeles/Colorado State University cloud-resolving model (CRM) are used to evaluate the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model. Part I demonstrated that kilometer-scale cirrus properties analyzed by applying the SCM’s assumptions about cloud vertical overlap and horizontal homogeneity to its profiles of cloud water/ice mixing ratio, cloud fraction, and snow flux differed from the cloud radar observations while the CRM simulation reproduced most of the observed cirrus properties. The present study evaluates, through a comparison with the CRM, the SCM’s representation of detrainment from deep cumulus and ice-phase microphysics in an effort to better understand the findings of Part I. This study finds that, although the SCM’s detrainment rate profile averaged over the entire simulation period is comparable to the CRM’s, detrainment in the SCM is comparatively sporadic and vertically localized. Too much detrained ice is sublimated when first detrained. Snow formed from detrained cloud ice falls through too deep of a layer. These aspects of the SCM’s parameterizations may explain many of the differences in the cirrus properties between the SCM and the observations (or between the SCM and the CRM), and suggest several possible improvements for the SCM: 1) allowing multiple coexisting cumulus cloud types as in the original Arakawa–Schubert scheme, 2) prognostically determining the stratiform cloud fraction, and 3) explicitly predicting the snow mixing ratio. These would allow better representation of the detrainment from deep convection, better coupling of the volume of detrained air with cloud fraction, and better representation of snow flux.

Determination of 3D Cloud Ice Water Contents by Combining Multiple Data Sources from Satellite, Ground Radar, and a Numerical Model

Abstract This study aims at determining the three-dimensional distribution of ice water content over a broad area near the Atmospheric Radiation Measurement Program Southern Great Plains site, where cloud radar and meteorological observations have been routinely conducted. Together with wind fields from other measurements, the ice water content retrievals can be used to derive the cloud ice water advective tendency terms needed for single-column model simulations. In this study, a Bayesian retrieval algorithm has been developed that combines multiple data sources from satellite high-frequency microwave radiometry, ground cloud radar observations, and mesoscale numerical model analysis. The cloud radar observations allow the characteristics of vertical ice water content structures to be inferred. The numerical model data are used to locate the cloud height. The satellite data provide information on the integrated ice water path, its horizontal distribution over a broad area, and, to a lesser extent, the vertical structure of ice water content. The approach herein is to retrieve the three-dimensional cloud ice water content in a 10° × 10° area surrounding the cloud radar site by combining all the information contained in the above datasets through a Bayesian framework. Validation of the algorithm has been done by comparing the retrievals with measurements from two ground radars. The comparison shows that the mean ice water content profiles and the two-dimensional (height–ice water content) probability density functions retrieved for 19 coincident cases agree fairly well with the validation data. However, the retrieved ice water contents generally lack detailed vertical structures because of the low sensitivity of satellite data to the vertical variation of cloud ice.

Developing large‐scale forcing data for single‐column and cloud‐resolving models from the Mixed‐Phase Arctic Cloud Experiment

This study represents an effort to develop Single‐Column Model (SCM) and Cloud‐Resolving Model large‐scale forcing data from a sounding array in the high latitudes. An objective variational analysis approach is used to process data collected from the Atmospheric Radiation Measurement Program (ARM) Mixed‐Phase Arctic Cloud Experiment (M‐PACE), which was conducted over the North Slope of Alaska in October 2004. In this method the observed surface and top of atmosphere measurements are used as constraints to adjust the sounding data from M‐PACE in order to conserve column‐integrated mass, heat, moisture, and momentum. Several important technical and scientific issues related to the data analysis are discussed. It is shown that the analyzed data reasonably describe the dynamic and thermodynamic features of the Arctic cloud systems observed during M‐PACE. Uncertainties in the analyzed forcing fields are roughly estimated by examining the sensitivity of those fields to uncertainties in the upper‐air data and surface constraints that are used in the analysis. Impacts of the uncertainties in the analyzed forcing data on SCM simulations are discussed. Results from the SCM tests indicate that the bulk features of the observed Arctic cloud systems can be captured qualitatively well using the forcing data derived in this study, and major model errors can be detected despite the uncertainties that exist in the forcing data as illustrated by the sensitivity tests. Finally, the possibility of using the European Center for Medium‐Range Weather Forecasts analysis data to derive the large‐scale forcing over the Arctic region is explored.

Simulating the coupling between atmosphere–ocean processes and the planktonic ecosystem during SERIES

Abstract We have developed a 1-D atmosphere–ocean-biogeochemical model to investigate the coupling between atmosphere–ocean exchanges and the planktonic ecosystem during the Subarctic Ecosystem Response to Iron Enrichment Study (SERIES) in 2002. The atmospheric Single Column Model (SCM) is based on the Canadian Centre for Climate Modelling and Analysis (CCCma) Atmospheric General Circulation Model (AGCM). The ocean component employs the General Ocean Turbulence Model (GOTM). A seven-component ecosystem model is embedded in GOTM, which includes nitrogen, organic and inorganic carbon, silica and oxygen cycling. We use observations from SERIES combined with atmospheric reanalysis data to initiate and force the coupled physical model. We found that atmospheric temperatures and humidities are higher and the stratification more stable if nudged to National Centre of Environmental Prediction (NCEP) rather than to European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis data. Doubling the vertical resolution in the atmosphere improved the representation of mixing and the thermal structure, affecting cloudiness and radiative fluxes at the ocean surface as well as planetary boundary layer heights and gas dispersion in the lower atmosphere. From observed ocean-surface dimethyl sulphide (DMS) concentrations (outside the patch) we simulated DMS dispersion in the atmospheric boundary layer by applying a first-order loss term, with turnover times ranging from 1 to 4 days. During SERIES, shallow boundary-layer heights that occurred when DMS production was highest prevented dispersion into the atmosphere beyond several 100 m. Finally, successive model runs with iron fertilization starting on June 25, July 10 and 25 showed that the general nature of the response to iron enrichment at OSP (SERIES) is robust, but the strength as well as length of the response depend strongly on short-term atmospheric conditions (wind and radiative fluxes).

Optimal design and operation of a certain class of asynchronous simulated moving bed processes

A compact representation of the cyclic operation of simulated moving-bed chromatography is established from the governing equations for the analogous single-column model that reproduces the cyclic steady-state (CSS) behavior of the multi-column process. A broad class of physically realizable asynchronous processes is then derived by dropping the integrality condition on the number of columns per zone, which now represents the average over a cycle. The steady periodic solution of the multi-column unit is computed by solving the analogous single-column model using a full-discretization method. The nonlinear algebraic system resulting from the simultaneous discretization of both spatial and temporal coordinates is solved using the gPROMS software. This solution strategy leads to shorter computational times than those previously reported in the literature. Process optimization is handled using single objective functions, to avoid competing effects, which are explicitly constrained by product quality and maximum allowable internal flow rates. The process is optimized for maximum feed throughput, with a possible upper bound on eluent consumption or flow rate, or minimum eluent consumption for a given feed flow rate. The nonlinear programming problem is solved by an external solver while still carrying out the CSS calculations in gPROMS. The feasibility of the approach is demonstrated on the chromatographic separation of an enantiomeric mixture with nonlinear competitive isotherm. Emphasis is given to the benefits that can be gained by upgrading an existing system to asynchronous operation. It is shown that eluent consumption for optimized asynchronous configurations in the higher feed-throughput region can be significantly reduced by modulation of eluent flow rate and selective product withdrawal.