We describe the physical model, numerical algorithms, and software structure
of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the
level-set method, coupled with the Weather Research and Forecasting model. In
every time step, the fire model inputs the surface wind, which drives the fire,
and outputs the heat flux from the fire into the atmosphere, which in turn
influences the atmosphere. The level-set method allows submesh representation
of the burning region and flexible implementation of various ignition modes.
WRF-Fire is distributed as a part of WRF and it uses the WRF parallel
infrastructure for parallel computing.
We present version 3.0 of the atmospheric chemistry box model CAABA/MECCA. In addition to a complete update of the rate coefficients to the most recent recommendations, a number of new features have been added: chemistry in multiple aerosol size bins; automatic multiple simulations reaching steady-state conditions; Monte-Carlo simulations with randomly varied rate coefficients within their experimental uncertainties; calculations along Lagrangian trajectories; mercury chemistry; more detailed isoprene chemistry; tagging of isotopically labeled species. Further changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-3.0 is a community model published under the GNU General Public License.
We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.
Black carbon (BC) and mineral dust are among the most abundant insoluble aerosol components in the atmosphere. When released, most BC and dust particles are externally mixed with other aerosol species. Through coagulation with particles containing soluble material and condensation of gases, the externally mixed particles may obtain a liquid coating and be transferred into an internal mixture. The mixing state of BC and dust aerosol particles influences their radiative and hygroscopic properties, as well as their ability of forming ice crystals.
We introduce the new aerosol microphysics submodel MADE-in, implemented within the ECHAM/MESSy Atmospheric Chemistry global model (EMAC). MADE-in is able to track mass and number concentrations of BC and dust particles in their different mixing states, as well as particles free of BC and dust. MADE-in describes these three classes of particles through a superposition of seven log-normally distributed modes, and predicts the evolution of their size distribution and chemical composition. Six out of the seven modes are mutually interacting, allowing for the transfer of mass and number among them. Separate modes for the different mixing states of BC and dust particles in EMAC/MADE-in allow for explicit simulations of the relevant aging processes, i.e. condensation, coagulation and cloud processing. EMAC/MADE-in has been evaluated with surface and airborne measurements and mostly performs well both in the planetary boundary layer and in the upper troposphere and lowermost stratosphere.
Three detailed meteorological case studies are conducted with the global and regional atmospheric chemistry model system ECHAM5/MESSy(→COSMO/MESSy)n, shortly named MECO(n). The aim of this article is to assess the general performance of the on-line coupling of the regional model COSMO to the global model ECHAM5. The cases are characterised by intense weather systems in Central Europe: a cold front passage in March 2010, a convective frontal event in July 2007, and the high impact winter storm "Kyrill" in January 2007. Simulations are performed with the new on-line-coupled model system and compared to classical, off-line COSMO hindcast simulations driven by ECMWF analyses. Precipitation observations from rain gauges and ECMWF analysis fields are used as reference, and both qualitative and quantitative measures are used to characterise the quality of the various simulations. It is shown that, not surprisingly, simulations with a shorter lead time generally produce more accurate simulations. Irrespective of lead time, the accuracy of the on-line and off-line COSMO simulations are comparable for the three cases. This result indicates that the new global and regional model system MECO(n) is able to simulate key mid-latitude weather systems, including cyclones, fronts, and convective precipitation, as accurately as present-day state-of-the-art regional weather prediction models in standard off-line configuration. Therefore, MECO(n) will be applied to simulate atmospheric chemistry exploring the model's full capabilities during meteorologically challenging conditions.
A new, highly flexible model system for the seamless dynamical down-scaling of meteorological and chemical processes from the global to the meso-γ scale is presented. A global model and a cascade of an arbitrary number of limited-area model instances run concurrently in the same parallel environment, in which the coarser grained instances provide the boundary data for the finer grained instances. Thus, disk-space intensive and time consuming intermediate and pre-processing steps are entirely avoided and the time interpolation errors of common off-line nesting approaches are minimised. More specifically, the regional model COSMO of the German Weather Service (DWD) is nested on-line into the atmospheric general circulation model ECHAM5 within the Modular Earth Submodel System (MESSy) framework. ECHAM5 and COSMO have previously been equipped with the MESSy infrastructure, implying that the same process formulations (MESSy submodels) are available for both models. This guarantees the highest degree of achievable consistency, between both, the meteorological and chemical conditions at the domain boundaries of the nested limited-area model, and between the process formulations on all scales.
The on-line nesting of the different models is established by a client-server approach with the newly developed Multi-Model-Driver (MMD), an additional component of the MESSy infrastructure. With MMD an arbitrary number of model instances can be run concurrently within the same message passing interface (MPI) environment, the respective coarser model (either global or regional) is the server for the nested finer (regional) client model, i.e. it provides the data required to calculate the initial and boundary fields to the client model. On-line nesting means that the coupled (client-server) models exchange their data via the computer memory, in contrast to the data exchange via files on disk in common off-line nesting approaches. MMD consists of a library (Fortran95 and some parts in C) which is based on the MPI standard and two new MESSy submodels, MMDSERV and MMDCLNT (both Fortran95) for the server and client models, respectively.
MMDCLNT contains a further sub-submodel, INT2COSMO, for the interpolation of the coarse grid data provided by the server models (either ECHAM5/MESSy or COSMO/MESSy) to the grid of the respective client model (COSMO/MESSy). INT2COSMO is based on the off-line pre-processing tool INT2LM provided by the DWD.
We present an improved tagging method, which describes the combined effect of emissions of various species from individual emission categories, e.g. the impact of both, nitrogen oxides and non-methane hydrocarbon emissions on ozone. This method is applied to two simplified chemistry schemes, which represent the main characteristics of atmospheric ozone chemistry. Analytical solutions are presented for this tagging approach. In the past, besides tagging approaches, sensitivity methods were used, which estimate the contributions from individual sources based on differences in two simulations, a base case and a simulation with a perturbation in the respective emission category. We apply both methods to our simplified chemical systems and demonstrate that potentially large errors (factor of 2) occur with the sensitivity method, which depend on the degree of linearity of the chemical system. This error depends on two factors, the ability to linearise the chemical system around a base case, and second the completeness of the contributions, which means that all contributions should principally add up to 100%. For some chemical regimes the first error can be minimised by employing only small perturbations of the respective emission, e.g. 5%. The second factor depends on the chemical regime and cannot be minimized by a specific experimental set-up. It is inherent to the sensitivity method. Since a complete tagging algorithm for global chemistry models is difficult to achieve, we present two error metrics, which can be applied for sensitivity methods in order to estimate the potential error of this approach for a specific application.
The numerical weather prediction model of the Consortium for Small Scale Modelling (COSMO), maintained by the German weather service (DWD), is connected with the Modular Earth Submodel System (MESSy). This effort is undertaken in preparation of a new, limited-area atmospheric chemistry model. Limited-area models require lateral boundary conditions for all prognostic variables. Therefore the quality of a regional chemistry model is expected to improve, if boundary conditions for the chemical constituents are provided by the driving model in consistence with the meteorological boundary conditions. The new developed model is as consistent as possible, with respect to atmospheric chemistry and related processes, with a previously developed global atmospheric chemistry general circulation model: the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. The combined system constitutes a new research tool, bridging the global to the meso-γ scale for atmospheric chemistry research. MESSy provides the infrastructure and includes, among others, the process and diagnostic submodels for atmospheric chemistry simulations. Furthermore, MESSy is highly flexible allowing model setups with tailor made complexity, depending on the scientific question. Here, the connection of the MESSy infrastructure to the COSMO model is documented and also the code changes required for the generalisation of regular MESSy submodels. Moreover, previously published prototype submodels for simplified tracer studies are generalised to be plugged-in and used in the global and the limited-area model. They are used to evaluate the TRACER interface implementation in the new COSMO/MESSy model system and the tracer transport characteristics, an important prerequisite for future atmospheric chemistry applications. A supplementary document with further details on the technical implementation of the MESSy interface into COSMO with a complete list of modifications to the COSMO code is provided.
Chemistry-climate models (CCMs) are commonly
used to simulate the past and future development of Earth’s ozone layer. The fully coupled chemistry schemes calculate the chemical production and destruction of ozone interactively and ozone is transported by the simulated atmospheric
flow. Due to the complexity of the processes acting on ozone it is not straightforward to disentangle the influence of individual processes on the temporal development of ozone concentrations. A method is introduced here that quantifies the influence of chemistry and transport on ozone concentration changes and that is easily implemented in CCMs and chemistry-transport models (CTMs). In this method, ozone tendencies (i.e. the time rate of change of ozone) are partitioned into a contribution from ozone production and destruction (chemistry) and a contribution from transport of ozone (dynamics). The influence of transport on ozone in a specific region is further divided into export of ozone out of that region and import of ozone from elsewhere into that region. For this purpose, a diagnostic is used that disaggregates the ozone mixing ratio field into 9 separate fields according to in which of 9 predefined regions of the atmosphere the ozone originated. With this diagnostic the ozone mass fluxes between these regions are obtained. Furthermore, this method is used here to attribute long-term changes
in ozone to chemistry and transport. The relative change in ozone from one period to another that is due to changes in production or destruction rates, or due to changes in import or export of ozone, are quantified. As such, the diagnostics introduced here can be used to attribute changes in ozone on monthly, interannual and long-term time-scales to the responsible
mechanisms. Results from a CCM simulation are
shown here as examples, with the main focus of the paper being on introducing the method.
Variations in the mixing ratio of trace gases of tropospheric origin entering the stratosphere in the tropics are of interest for assessing both troposphere to stratosphere transport fluxes in the tropics and the impact of these transport fluxes on the composition of the tropical lower stratosphere. Anomaly patterns of carbon monoxide (CO) and long-lived tracers in the lower tropical stratosphere allow conclusions about the rate and the variability of tropical upwelling to be drawn. Here, we present a simplified chemistry scheme for the Chemical Lagrangian Model of the Stratosphere (CLaMS) for the simulation, at comparatively low numerical cost, of CO, ozone, and long-lived trace substances (CH4, N2O, CCl3F (CFC-11), CCl2F2 (CFC-12), and CO2) in the lower tropical stratosphere.
A quasi chemistry-transport model mode (QCTM) is presented for the numerical chemistry-climate simulation system ECHAM/MESSy Atmospheric Chemistry (EMAC). It allows for a quantification of chemical signals through suppression of any feedback between chemistry and dynamics. Noise would otherwise interfere too strongly. The signal follows from the difference of two QCTM simulations, reference and sensitivity. These are fed with offline chemical fields as a substitute of the feedbacks between chemistry and dynamics: offline mixing ratios of radiatively active substances enter the radiation scheme (a), offline mixing ratios of nitric acid enter the scheme for re-partitioning and sedimentation from polar stratospheric clouds (b). Offline methane oxidation is the exclusive source of chemical water-vapor tendencies (c). Any set of offline fields suffices to suppress the feedbacks, though may be inconsistent with the simulation setup. An adequate set of offline climatologies can be produced from a non-QCTM simulation of the reference setup. Test simulations reveal the particular importance of adequate offline fields associated with (a). Inconsistencies from (b) are negligible when using adequate fields of nitric acid. Acceptably small inconsistencies come from (c), but should vanish for an adequate prescription of water vapor tendencies. Toggling between QCTM and non-QCTM is done via namelist switches and does not require a source code re-compilation.
A new model to simulate and predict the properties of a large ensemble of contrails as a function of given air traffic and meteorology is described. The model is designed for approximate prediction of contrail cirrus cover and analysis of contrail climate impact, e.g. within aviation system optimization processes. The model simulates the full contrail life-cycle. Contrail segments form between waypoints of individual aircraft tracks in sufficiently cold and humid air masses. The initial contrail properties depend on the aircraft. The advection and evolution of the contrails is followed with a Lagrangian Gaussian plume model. Mixing and bulk cloud processes are treated quasi analytically or with an effective numerical scheme.
Contrails disappear when the bulk ice content is sublimating or precipitating. The model has been implemented in a "Contrail Cirrus Prediction Tool" (CoCiP). This paper describes the model assumptions, the equations for individual contrails, and the analysis-method for contrail-cirrus cover derived from the optical depth of the ensemble of contrails and background cirrus.
The model has been applied for a case study and compared to the results of other models and in-situ contrail measurements. The simple model reproduces a considerable part of observed contrail properties. Mid-aged contrails provide the largest contributions to the product of optical depth and contrail width, important for climate impact.
An important issue in the evaluation of the environmental impact of emissions from concentrated sources such as transport modes, is to understand how processes occurring at the scales of exhaust plumes can influence the physical and chemical state of the atmosphere at regional and global scales. Indeed, three-dimensional global circulation models or chemistry transport models generally assume that emissions are instantaneously diluted into large-scale grid boxes, which may lead, for example, to overpredict the efficiency of NOx to produce ozone. In recent times, various methods have been developed to incorporate parameterizations of plume processes into global models that are based e.g. on correcting the original emission indexes or on introducing "subgrid" reaction rates in the models. This paper provides a review of the techniques proposed so far in the literature to account for local conversion of emissions in the plume, as well as the implementation of these techniques into atmospheric codes.
This study uses in-situ measurements collected during the FireFlux field
experiment to evaluate and improve the performance of coupled atmosphere-fire
model WRF-Sfire. The simulation by WRF-Sfire of the experimental burn shows
that WRF-Sfire is capable of providing realistic head fire rate-of-spread and
the vertical temperature structure of the fire plume, and, up to 10 m above
ground level, fire-induced surface flow and vertical velocities within the
plume. The model captured the changes in wind speed and direction before,
during, and after fire front passage, along with arrival times of wind speed,
temperature, and updraft maximae, at the two instrumented flux towers used in
FireFlux. The model overestimated vertical velocities and underestimated
horizontal wind speeds measured at tower heights above the 10 m, and it is
hypothesized that the limited model resolution over estimated the fire front
depth, leading to too high a heat release and, subsequently, too strong an
updraft. However, on the whole, WRF-Sfire fire plume behavior is consistent
with the interpretation of FireFlux observations. The study suggests optimal
experimental pre-planning, design, and execution of future field campaigns that
are needed for further coupled atmosphere-fire model development and
We present a new version of the coupled Earth system model GEOCLIM. The new release, GEOCLIM reloaded, links the existing atmosphere and weathering modules to a novel, temporally and spatially resolved model of the global ocean circulation, which provides a physical framework for a mechanistic description of the marine biogeochemical dynamics of carbon, nitrogen, phosphorus and oxygen. The ocean model is also coupled to a fully formulated, vertically resolved diagenetic model. GEOCLIM reloaded is thus a unique tool to investigate the short- and long-term feedbacks between climatic conditions, continental inputs, ocean biogeochemical dynamics and diagenesis. A complete and detailed description of the resulting Earth system model and its new features is first provided. The performance of GEOCLIM reloaded is then evaluated by comparing steady-state simulation under present-day conditions with a comprehensive set of oceanic data and existing global estimates of bio-element cycling in the pelagic and benthic compartments.
Soil organic matter decomposition is a very important process within the
Earth system because it controls the rates of mineralization of carbon
and other biogeochemical elements, determining their flux to the
atmosphere and the hydrosphere. SoilR is a modeling framework that
contains a library of functions and tools for modeling soil organic
matter decomposition under the R environment for computing. It
implements a variety of model structures and tools to represent carbon
storage and release from soil organic matter. In SoilR, organic matter
decomposition is represented as a linear system of ordinary differential
equations that generalizes the structure of most compartment-based
decomposition models. A variety of functions is also available to
represent environmental effects on decomposition rates. This document
presents the conceptual basis for the functions implemented in the
package. It is complementary to the help pages released with the
The interfaces by which users specify the scenarios to be simulated by scientific computer models are frequently primitive, under-documented and ad-hoc text files which make using the model in question difficult and error-prone and significantly increase the development cost of the model. In this paper, we present a model-independent system, Spud, which formalises the specification of model input formats in terms of formal grammars. This is combined with an automated graphical user interface which guides users to create valid model inputs based on the grammar provided, and a generic options reading module which minimises the development cost of adding model options.
Together, this provides a user friendly, well documented, self validating user interface which is applicable to a wide range of scientific models and which minimises the developer input required to maintain and extend the model interface.
An algorithm for the sequential analysis of the atmospheric oxidation of chemical species using output from a photochemical model is presented. Starting at a "root species", the algorithm traverses all possible reaction sequences which consume this species, and lead, via intermediate products, to final products. The algorithm keeps track of the effects of all of these reactions on their respective reactants and products. Upon completion, the algorithm has built a detailed picture of the effects of the oxidation of the root species on its chemical surroundings. The output of the algorithm can be used to determine product yields, radical recycling fractions, and ozone production potentials of arbitrary chemical species.
Here we describe the first version of the Minnesota Earth System Model for Ocean biogeochemistry (MESMO 1.0), an intermediate complexity model based on the Grid ENabled Integrated Earth system model (GENIE-1). As with GENIE-1, MESMO has a 3D dynamical ocean, energy-moisture balance atmosphere, dynamic and thermodynamic sea ice, and marine biogeochemistry. Main development goals of MESMO were to: (1) bring oceanic uptake of anthropogenic transient tracers within data constraints; (2) increase vertical resolution in the upper ocean to better represent near-surface biogeochemical processes; (3) calibrate the deep ocean ventilation with observed abundance of radiocarbon. We achieved all these goals through a combination of objective model optimization and subjective targeted tuning. An important new feature in MESMO that dramatically improved the uptake of CFC-11 and anthropogenic carbon is the depth dependent vertical diffusivity in the ocean, which is spatially uniform in GENIE-1. In MESMO, biological production occurs in the top two layers above the compensation depth of 100 m and is modified by additional parameters, for example, diagnosed mixed layer depth. In contrast, production in GENIE-1 occurs in a single layer with thickness of 175 m. These improvements make MESMO a well-calibrated model of intermediate complexity suitable for investigations of the global marine carbon cycle requiring long integration time.
As part of a broader effort to develop next-generation models for
numerical weather prediction and climate applications, a hydrostatic
atmospheric dynamical core is developed as an intermediate step to
evaluate a finite-difference discretization of the primitive equations
on spherical icosahedral grids. Based on the need for mass-conserving
discretizations for multi-resolution modelling as well as scalability
and efficiency on massively parallel computing architectures, the
dynamical core is built on triangular C-grids using relatively small
discretization stencils. This paper presents the
formulation and performance of the baseline version of the new dynamical
core, focusing on properties of the numerical solutions in the setting
of globally uniform resolution. Theoretical analysis reveals that the
discrete divergence operator defined on a single triangular cell using
the Gauss theorem is only first-order accurate, and introduces
grid-scale noise to the discrete model. The noise can be suppressed by
fourth-order hyper-diffusion of the horizontal wind field using a
time-step and grid-size-dependent diffusion coefficient, at the expense
of stronger damping than in the reference spectral model. A
series of idealized tests of different complexity are performed. In the
deterministic baroclinic wave test, solutions from the new dynamical
core show the expected sensitivity to horizontal resolution, and
converge to the reference solution at R2B6 (35 km grid spacing). In a
dry climate test, the dynamical core correctly reproduces key features
of the meridional heat and momentum transport by baroclinic eddies. In
the aqua-planet simulations at 140 km resolution, the new model is able
to reproduce the same equatorial wave propagation characteristics as in
the reference spectral model, including the sensitivity of such
characteristics to the meridional sea surface temperature profile. These results suggest that the triangular-C discretization
provides a reasonable basis for further development. The main issues
that need to be addressed are the grid-scale noise from the divergence
operator which requires strong damping, and a phase error of the
baroclinic wave at medium and low resolutions.
ATLAS is a new global Lagrangian Chemistry and Transport Model (CTM), which includes a stratospheric chemistry scheme with 46 active species, 171 reactions, heterogeneous chemistry on polar stratospheric clouds and a Lagrangian denitrification module. Lagrangian (trajectory-based) models have several important advantages over conventional Eulerian models, including the absence of spurious numerical diffusion, efficient code parallelization and no limitation of the largest time step by the Courant-Friedrichs-Lewy criterion. This work describes and validates the stratospheric chemistry scheme of the model. Stratospheric chemistry is simulated with ATLAS for the Arctic winter 1999/2000, with a focus on polar ozone depletion and denitrification. The simulations are used to validate the chemistry module in comparison with measurements of the SOLVE/THESEO 2000 campaign. A Lagrangian denitrification module, which is based on the simulation of the nucleation, sedimentation and growth of a large number of polar stratospheric cloud particles, is used to model the substantial denitrification that occured in this winter.
To develop fine particulate matter (PM<sub>2.5</sub>) air quality forecasts for the US, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM<sub>2.5</sub> forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts.
The bias-adjustment technique is found to improve PM<sub>2.5</sub> forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM<sub>2.5</sub> during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the Pacific Coast. In the Rocky Mountain region, the NAQFC system overestimates PM<sub>2.5</sub> for the whole year. The bias-adjusted forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematic improvements in forecast outputs across all seasons suggest that this technique can be easily adapted to perform bias adjustment for real-time PM<sub>2.5</sub> air quality forecasts.
Glaciers and ice caps exhibit currently the largest cryospheric
contributions to sea level rise. Modelling the dynamics and mass balance
of the major ice sheets is therefore an important issue to investigate
the current state and the future response of the cryosphere in response
to changing environmental conditions, namely global warming. This
requires a powerful, easy-to-use, scalable multi-physics ice dynamics
model. Based on the well-known and established ice sheet model of Pattyn
(2003) we develop the modular multi-physics thermomechanic ice model
RIMBAY, in which we improve the original version in several aspects like
a shallow-ice-shallow-shelf coupler and a full 3-D-grounding-line
migration scheme based on Schoof's (2007) heuristic analytical approach.
We summarise the Full-Stokes equations and several approximations
implemented within this model and we describe the different numerical
discretisations. The results are cross-validated against previous
publications dealing with ice modelling, and some additional artificial
set-ups demonstrate the robustness of the different solvers and their
internal coupling. RIMBAY is designed for an easy adaption to new
scientific issues. Hence, we demonstrate in very different set-ups the
applicability and functionality of RIMBAY in Earth system science in
general and ice modelling in particular.
This paper describes the scientific and structural updates to the latest release of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7) and points the reader to additional resources for further details. The model updates were evaluated relative to observations and results from previous model versions in a series of simulations conducted to incrementally assess the effect of each change. The focus of this paper is on five major scientific upgrades: (a) updates to the heterogeneous N2O5 parameterization, (b) improvement in the treatment of secondary organic aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode aerosol, (d) revisions to the cloud model, and (e) new options for the calculation of photolysis rates. Incremental test simulations over the eastern United States during January and August 2006 are evaluated to assess the model response to each scientific improvement, providing explanations of differences in results between v4.7 and previously released CMAQ model versions. Particulate sulfate predictions are improved across all monitoring networks during both seasons due to cloud module updates. Numerous updates to the SOA module improve the simulation of seasonal variability and decrease the bias in organic carbon predictions at urban sites in the winter. Bias in the total mass of fine particulate matter (PM2.5) is dominated by overpredictions of unspeciated PM2.5 (PMother) in the winter and by underpredictions of carbon in the summer. The CMAQv4.7 model results show slightly worse performance for ozone predictions. However, changes to the meteorological inputs are found to have a much greater impact on ozone predictions compared to changes to the CMAQ modules described here. Model updates had little effect on existing biases in wet deposition predictions.
The spin-up of land models to steady state of coupled carbon-nitrogen
processes is computationally so costly that it becomes a bottleneck
issue for global analysis. In this study, we introduced a
semi-analytical solution (SAS) for the spin-up issue. SAS is
fundamentally based on the analytic solution to a set of equations that
describe carbon transfers within ecosystems over time. SAS is
implemented by three steps: (1) having an initial spin-up with prior
pool-size values until net primary productivity (NPP) reaches
stabilization, (2) calculating quasi-steady-state pool sizes by letting
fluxes of the equations equal zero, and (3) having a final spin-up to
meet the criterion of steady state. Step 2 is enabled by averaged
time-varying variables over one period of repeated driving forcings. SAS
was applied to both site-level and global scale spin-up of the
Australian Community Atmosphere Biosphere Land Exchange (CABLE) model.
For the carbon-cycle-only simulations, SAS saved 95.7% and 92.4% of
computational time for site-level and global spin-up, respectively, in
comparison with the traditional method (a long-term iterative simulation
to achieve the steady states of variables). For the carbon-nitrogen
coupled simulations, SAS reduced computational cost by 84.5% and 86.6%
for site-level and global spin-up, respectively. The estimated
steady-state pool sizes represent the ecosystem carbon storage capacity,
which was 12.1 kg C m-2 with the coupled carbon-nitrogen
global model, 14.6% lower than that with the carbon-only model. The
nitrogen down-regulation in modeled carbon storage is partly due to the
4.6% decrease in carbon influx (i.e., net primary productivity) and
partly due to the 10.5% reduction in residence times. This steady-state
analysis accelerated by the SAS method can facilitate comparative
studies of structural differences in determining the ecosystem carbon
storage capacity among biogeochemical models. Overall, the computational
efficiency of SAS potentially permits many global analyses that are
impossible with the traditional spin-up methods, such as ensemble
analysis of land models against parameter variations.
We present a new global Chemical Transport Model (CTM) with full stratospheric chemistry and Lagrangian transport and mixing called ATLAS (Alfred Wegener InsTitute LAgrangian Chemistry/Transport System). Lagrangian (trajectory-based) models have several important advantages over conventional Eulerian (grid-based) models, including the absence of spurious numerical diffusion, efficient code parallelization and no limitation of the largest time step by the Courant-Friedrichs-Lewy criterion. The basic concept of transport and mixing is similar to the approach in the commonly used CLaMS model. Several aspects of the model are different from CLaMS and are introduced and validated here, including a different mixing algorithm for lower resolutions which is less diffusive and agrees better with observations with the same mixing parameters. In addition, values for the vertical and horizontal stratospheric bulk diffusion coefficients are inferred and compared to other studies. This work focusses on the description of the dynamical part of the model and the validation of the mixing algorithm. The chemistry module, which contains 49 species, 170 reactions and a detailed treatment of heterogeneous chemistry, will be presented in a separate paper.
Calculating the equilibrium composition of atmospheric aerosol particles, using all variations of Köhler theory, has largely assumed that the total solute concentrations define both the water activity and surface tension. Recently however, bulk to surface phase partitioning has been postulated as a process which significantly alters the predicted point of activation. In this paper, an analytical solution to calculate the removal of material from a bulk to a surface layer in aerosol particles has been derived using a well established and validated surface tension framework. The applicability to an unlimited number of components is possible via reliance on data from each binary system. Whilst assumptions regarding behaviour at the surface layer have been made to facilitate derivation, it is proposed that the framework presented can capture the overall impact of bulk-surface partitioning. Demonstrations of the equations for two and five component mixtures are given while comparisons are made with more detailed frameworks capable at modelling ternary systems at higher levels of complexity. Predictions made by the model across a range of surface active properties should be tested against measurements. Indeed, reccomendations are given for experimental validation and to assess sensitivities to accuracy and required level of complexity within large scale frameworks. Importantly, the computational efficiency of using the solution presented in this paper is roughly a factor of 20 less than a similar iterative approach, a comparison with highly coupled approaches not available beyond a 3 component system.
REMO-HAM is a new regional aerosol-climate model. It is based on the
REMO regional climate model and includes most of the major aerosol
processes. The structure for aerosol is similar to the global
aerosol-climate model ECHAM5-HAM, for example the aerosol module HAM is
coupled with a two-moment stratiform cloud scheme. On the other hand,
REMO-HAM does not include an online coupled aerosol-radiation nor a
secondary organic aerosol module. In this work, we evaluate the model
and compare the results against ECHAM5-HAM and measurements. Four
different measurement sites were chosen for the comparison of total
number concentrations, size distributions and gas phase sulfur dioxide
concentrations: Hyytiälä in Finland, Melpitz in Germany, Mace
Head in Ireland and Jungfraujoch in Switzerland. REMO-HAM is run with
two different resolutions: 50 × 50 km2 and 10 ×
10 km2. Based on our simulations, REMO-HAM is in reasonable
agreement with the measured values. The differences in the total number
concentrations between REMO-HAM and ECHAM5-HAM can be mainly explained
by the difference in the nucleation mode. Since we did not use
activation nor kinetic nucleation for the boundary layer, the total
number concentrations are somewhat underestimated. From the
meteorological point of view, REMO-HAM represents the precipitation
fields and 2 m temperature profile very well compared to measurement.
Overall, we show that REMO-HAM is a functional aerosol-climate model,
which will be used in further studies.
We present a new aerosol microphysics and gas aerosol partitioning submodel (Global Modal-aerosol eXtension, GMXe) implemented within the ECHAM/MESSy Atmospheric Chemistry model (EMAC, version 1.8). The submodel is computationally efficient and is suitable for medium to long term simulations with global and regional models. The aerosol size distribution is treated using 7 log-normal modes and has the same microphysical core as the M7 submodel (Vignati et al., 2004). The main developments in this work are: (i) the extension of the aerosol emission routines and the M7 microphysics, so that an increased (and variable) number of aerosol species can be treated (new species include sodium and chloride, and potentially magnesium, calcium, and potassium), (ii) the coupling of the aerosol microphysics to a choice of treatments of gas/aerosol partitioning to allow the treatment of semi-volatile aerosol, and, (iii) the implementation and evaluation of the developed submodel within the EMAC model of atmospheric chemistry. Simulated concentrations of black carbon, particulate organic matter, dust, sea spray, sulfate and ammonium aerosol are shown to be in good agreement with observations (for all species at least 40% of modeled values are within a factor of 2 of the observations). The distribution of nitrate aerosol is compared to observations in both clean and polluted regions. Concentrations in polluted continental regions are simulated quite well, but there is a general tendency to overestimate nitrate, particularly in coastal regions (geometric mean of modelled values/geometric mean of observed data ≈2). In all regions considered more than 40% of nitrate concentrations are within a factor of two of the observations. Marine nitrate concentrations are well captured with 96% of modeled values within a factor of 2 of the observations.
A size-resolved particle dry deposition scheme is developed for inclusion in large-scale air quality and climate models where the size distribution and fate of atmospheric aerosols is of concern. The "resistance" structure is similar to what is proposed by Zhang et al. (2001), while a new "surface" deposition velocity (or surface resistance) is derived by simplification of a one-dimensional aerosol transport model (Petroff et al., 2008b, 2009). Compared to Zhang et al.'s model, the present model accounts for the leaf size, shape and area index as well as the height of the vegetation canopy. Consequently, it is more sensitive to the change of land covers, particularly in the accumulation mode (0.1–1 micron). A drift velocity is included to account for the phoretic effects related to temperature and humidity gradients close to liquid and solid water surfaces. An extended comparison of this model with experimental evidence is performed over typical land covers such as bare ground, grass, coniferous forest, liquid and solid water surfaces and highlights its adequate prediction. The predictions of the present model differ from Zhang et al.'s model in the fine mode, where the latter tends to over-estimate in a significant way the particle deposition, as measured by various investigators or predicted by the present model. The present development is thought to be useful to modellers of the atmospheric aerosol who need an adequate parameterization of aerosol dry removal to the earth surface, described here by 26 land covers. An open source code is available in Fortran90.
We present an adaptable tool, the OPTSIM (OPTical properties SIMulation)
software, for the simulation of optical properties and lidar attenuated
backscattered profiles (β') from aerosol concentrations calculated
by chemistry transport models (CTM). It was developed to model both
Level 1 observations and Level 2 aerosol lidar retrievals in order to
compare model results to measurements: the level 2 enables to estimate
the main properties of aerosols plume structures, but may be limited due
to specific assumptions. The level 1, originally developed for this
tool, gives access to more information about aerosols properties
(β') requiring, at the same time, less hypothesis on aerosols
types. In addition to an evaluation of the aerosol loading and optical
properties, active remote sensing allows the analysis of aerosols'
vertical structures. An academic case study for two different species
(black carbon and dust) is presented and shows the consistency of the
simulator. Illustrations are then given through the analysis of dust
events in the Mediterranean region during the summer 2007. These are
based on simulations by the CHIMERE regional CTM and observations from
the CALIOP space-based lidar, and highlight the potential of this
approach to evaluate the concentration, size and vertical structure of
the aerosol plumes.
A modal aerosol module (MAM) has been developed for the Community Atmosphere Model version 5 (CAM5), the atmospheric component of the Community Earth System Model version 1 (CESM1). MAM is capable of simulating the aerosol size distribution and both internal and external mixing between aerosol components, treating numerous complicated aerosol processes and aerosol physical, chemical and optical properties in a physically-based manner. Two MAM versions were developed: a more complete version with seven lognormal modes (MAM7), and a version with three lognormal modes (MAM3) for the purpose of long-term (decades to centuries) simulations. In this paper a description and evaluation of the aerosol module and its two representations are provided. Sensitivity of the aerosol lifecycle to simplifications in the representation of aerosol is discussed.
Simulated sulfate and secondary organic aerosol (SOA) mass concentrations are remarkably similar between MAM3 and MAM7. Differences in primary organic matter (POM) and black carbon (BC) concentrations between MAM3 and MAM7 are also small (mostly within 10%). The mineral dust global burden differs by 10% and sea salt burden by 30-40% between MAM3 and MAM7, mainly due to the different size ranges for dust and sea salt modes and different standard deviations of the log-normal size distribution for sea salt modes between MAM3 and MAM7. The model is able to qualitatively capture the observed geographical and temporal variations of aerosol mass and number concentrations, size distributions, and aerosol optical properties. However, there are noticeable biases; e.g., simulated BC concentrations are significantly lower than measurements in the Arctic. There is a low bias in modeled aerosol optical depth on the global scale, especially in the developing countries. These biases in aerosol simulations clearly indicate the need for improvements of aerosol processes (e.g., emission fluxes of anthropogenic aerosols and precursor gases in developing countries, boundary layer nucleation) and properties (e.g., primary aerosol emission size, POM hygroscopicity). In addition, the critical role of cloud properties (e.g., liquid water content, cloud fraction) responsible for the wet scavenging of aerosol is highlighted.
A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone mixing ratios. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. Sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While changes in total fine particulate mass are small, predictions of in-cloud SOA increase substantially.
This paper describes a method to automatically generate a large ensemble of air quality simulations. This is achieved using the Polyphemus system, which is flexible enough to build various different models. The system offers a wide range of options in the construction of a model: many physical parameterizations, several numerical schemes and different input data can be combined. In addition, input data can be perturbed. In this paper, some 30 alternatives are available for the generation of a model. For each alternative, the options are given a probability, based on how reliable they are supposed to be. Each model of the ensemble is defined by randomly selecting one option per alternative. In order to decrease the computational load, as many computations as possible are shared by the models of the ensemble. As an example, an ensemble of 101 photochemical models is generated and run for the year 2001 over Europe. The models' performance is quickly reviewed, and the ensemble structure is analyzed. We found a strong diversity in the results of the models and a wide spread of the ensemble. It is noteworthy that many models turn out to be the best model in some regions and some dates.
Explicit time integration methods are characterised by a small numerical
effort per time step. In the application to multiscale problems in
atmospheric modelling, this benefit is often more than compensated by
stability problems and step size restrictions resulting from stiff
chemical reaction terms and from a locally varying
Courant-Friedrichs-Lewy (CFL) condition for the advection terms.
Splitting methods may be applied to efficiently combine implicit and
explicit methods (IMEX splitting). Complementarily multirate time
integration schemes allow for a local adaptation of the time step size
to the grid size. In combination, these approaches lead to schemes which
are efficient in terms of evaluations of the right-hand side. Special
challenges arise when these methods are to be implemented. For an
efficient implementation, it is crucial to locate and exploit
redundancies. Furthermore, the more complex programme flow may lead to
computational overhead which, in the worst case, more than compensates
the theoretical gain in efficiency. We present a general splitting
approach which allows both for IMEX splittings and for local time step
adaptation. The main focus is on an efficient implementation of this
approach for parallel computation on computer clusters.
The formulation of a 3-D ice sheet-shelf model is described. The model
is designed for long-term continental-scale applications, and has been
used mostly in paleoclimatic studies. It uses a hybrid combination of
the scaled shallow ice and shallow shelf approximations for ice flow.
Floating ice shelves and grounding-line migration are included, with
parameterized ice fluxes at grounding lines that allows relatively
coarse resolutions to be used. All significant components and
parameterizations of the model are described in some detail. Basic
results for modern Antarctica are compared with observations, and
simulations over the last 5 million years are compared with previously
published results. The sensitivity of ice volumes during the last
deglaciation to basal sliding coefficients is discussed.
In ice sheet modelling, the shallow-ice approximation (SIA) and second-order shallow-ice approximation (SOSIA) schemes are approaches to approximate the solution of the full Stokes equations governing ice sheet dynamics. This is done by writing the solution to the full Stokes equations as an asymptotic expansion in the aspect ratio , i.e. the quotient between a characteristic height and a characteristic length of the ice sheet. SIA retains the zeroth-order terms and SOSIA the zeroth-, first-, and second-order terms in the expansion. Here, we evaluate the order of accuracy of SIA and SOSIA by numerically solving a two-dimensional model problem for different values of , and comparing the solutions with a finite element solution to the full Stokes equations obtained from Elmer/Ice. The SIA and SOSIA solutions are also derived analytically for the model problem. For decreasing , the computed errors in SIA and SOSIA decrease , but not always in the expected way. Moreover, they depend critically on a parameter introduced to avoid singu-larities in Glen's flow law in the ice model. This is because the assumptions behind the SIA and SOSIA neglect a thick, high-viscosity boundary layer near the ice surface. The sensitivity to the parameter is explained by the analytical solutions. As a verification of the comparison technique, the SIA and SOSIA solutions for a fluid with Newtonian rheology are compared to the solutions by Elmer/Ice, with results agreeing very well with theory.
At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The downscaling model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air–sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. Also, the spatial differences become established rapidly (
This paper discusses the implementation and performance of an array of gas-phase chemistry solvers for the state-of-the-science GEOS-Chem global chemical transport model. The implementation is based on the Kinetic PreProcessor (KPP). Two perl parsers automatically generate the needed interfaces between GEOS-Chem and KPP, and allow access to the chemical simulation code without any additional programming effort. This work illustrates the potential of KPP to positively impact global chemical transport modeling by providing additional functionality as follows. (1) The user can select a highly efficient numerical integration method from an array of solvers available in the KPP library. (2) KPP offers a wide variety of user options for studies that involve changing the chemical mechanism (e.g., a set of additional reactions is automatically translated into efficient code and incorporated into a modified global model). (3) This work provides access to tangent linear, continuous adjoint, and discrete adjoint chemical models, with applications to sensitivity analysis and data assimilation.
We conducted a regional-scale simulation over Northeast Asia for the
year 2006 using an aerosol chemical transport model, with time-varying
lateral and upper boundary concentrations of gaseous species predicted
by a global stratospheric and tropospheric chemistry-climate model. The
present one-way nested global-through-regional-scale model is named the
Meteorological Research Institute-Passive-tracers Model system for
atmospheric Chemistry (MRI-PM/c). We evaluated the model's performance
with respect to the major anthropogenic and natural inorganic
components, SO42-, NH4+,
NO3-, Na+ and Ca2+ in the
air, rain and snow measured at the Acid Deposition Monitoring Network in
East Asia (EANET) stations. Statistical analysis showed that
approximately 40-50 % and 70-80 % of simulated concentration and wet
deposition of SO42-, NH4+,
NO3-and Ca2+ are within factors of 2
and 5 of the observations, respectively. The prediction of the sea-salt
originated component Na+ was not successful at near-coastal
stations (where the distance from the coast ranged from 150 to 700 m),
because the model grid resolution (Δx=60 km) is too coarse to
resolve it. The simulated Na+ in precipitation was
significantly underestimated by up to a factor of 30.
A climate model is an executable theory of the climate; the model
encapsulates climatological theories in software so that they can be
simulated and their implications investigated. Thus, in order to trust a
climate model, one must trust that the software it is built from is
built correctly. Our study explores the nature of software quality in
the context of climate modelling. We performed an analysis of defect
reports and defect fixes in several versions of leading global climate
models by collecting defect data from bug tracking systems and version
control repository comments. We found that the climate models all have
very low defect densities compared to well-known, similarly sized
open-source projects. We discuss the implications of our findings for
the assessment of climate model software trustworthiness.
A new version of the p-TOMCAT Chemical Transport Model (CTM) which includes an improved photolysis code, Fast-JX, is validated. Through offline testing we show that Fast-JX captures well the observed J(NO2) and J(O1D) values obtained at Weybourne and during a flight above the Atlantic, though with some overestimation of J(O1D) when comparing to the aircraft data. By comparing p-TOMCAT output of CO and ozone with measurements, we find that the inclusion of Fast-JX in the CTM strongly improves the latter's ability to capture the seasonality and levels of tracers' concentrations. A probability distribution analysis demonstrates that photolysis rates and oxidant (OH, ozone) concentrations cover a broader range of values when using Fast-JX instead of the standard photolysis scheme. This is not only driven by improvements in the seasonality of cloudiness but also even more by the better representation of cloud spatial variability. We use three different cloud treatments to study the radiative effect of clouds on the abundances of a range of tracers and find only modest effects on a global scale. This is consistent with the most relevant recent study. The new version of the validated CTM will be used for a variety of future studies examining the variability of tropospheric composition and its drivers.
An ensemble Kalman filter (EnKF) has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground-based measurements on a regional scale. The number of ensembles is reduced to 20, which allows for future operational use of the system for air quality analysis and forecast. Observation sites of the European ozone monitoring network have been classified using criteria on ozone temporal variability, based on previous work by Flemming et al. (2005). This leads to the choice of specific subsets of suburban, rural and remote sites for data assimilation and for evaluation of the reference run and the assimilation system. For a 10-day experiment during an ozone pollution event over Western Europe, data assimilation allows for a significant improvement in ozone fields: the RMSE is reduced by about a third with respect to the reference run, and the hourly correlation coefficient is increased from 0.75 to 0.87. Several sensitivity tests focus on an a posteriori diagnostic estimation of errors associated with the background estimate and with the spatial representativeness of observations. A strong diurnal cycle of both these errors with an amplitude up to a factor of 2 is made evident. Therefore, the hourly ozone background error and the observation error variances are corrected online in separate assimilation experiments. These adjusted background and observational error variances provide a better uncertainty estimate, as verified by using statistics based on the reduced centered random variable. Over the studied 10-day period the overall EnKF performance over evaluation stations is found relatively unaffected by different formulations of observation and simulation errors, probably due to the large density of observation sites. From these sensitivity tests, an optimal configuration was chosen for an assimilation experiment extended over a three-month summer period. It shows a similarly good performance as the 10-day experiment.
Ideally, a validation and assimilation scheme should maintain the physical principles embodied in the model and be able to evaluate and assimilate lower dimensional features (e.g., discontinuities) contained within a bulk simulation, even when these features are not directly observed or represented by model variables. We present such a scheme and suggest its potential to resolve or alleviate some outstanding problems that stem from making and applying required, yet often non-physical, assumptions and procedures in common operational data assimilation. As proof of concept, we use a sea-ice model with remotely sensed observations of leads in a one-step assimilation cycle. Using the new scheme in a sixteen day simulation experiment introduces model skill (against persistence) several days earlier than in the control run, improves the overall model skill and delays its drop off at later stages of the simulation. The potential and requirements to extend this scheme to different applications, and to both empirical and statistical multivariate and full cycle data assimilation schemes, are discussed.
We developed an ecosystem/biogeochemical model system, which includes
multiple phytoplankton functional groups and carbon cycle dynamics, and
applied it to investigate physical-biological interactions in Icelandic
waters. Satellite and in situ data were used to evaluate the model.
Surface seasonal cycle amplitudes and biases of key parameters (DIC, TA,
pCO2, air-sea CO2 flux, and nutrients) are
significantly improved when compared to surface observations by
prescribing deep water values and trends, based on available data. The
seasonality of the coccolithophore and "other phytoplankton" (diatoms
and dinoflagellates) blooms is in general agreement with satellite ocean
color products. Nutrient supply, biomass and calcite concentrations are
modulated by light and mixed layer depth seasonal cycles. Diatoms are
the most abundant phytoplankton, with a large bloom in early spring and
a secondary bloom in fall. The diatom bloom is followed by blooms of
dinoflagellates and coccolithophores. The effect of biological changes
on the seasonal variability of the surface ocean pCO2 is
nearly twice the temperature effect, in agreement with previous studies.
The inclusion of multiple phytoplankton functional groups in the model
played a major role in the accurate representation of CO2
uptake by biology. For instance, at the peak of the bloom, the exclusion
of coccolithophores causes an increase in alkalinity of up to 4 μmol
kg-1 with a corresponding increase in DIC of up to 16 μmol
kg-1. During the peak of the bloom in summer, the net effect
of the absence of the coccolithophores bloom is an increase in
pCO2 of more than 20 μatm and a reduction of atmospheric
CO2 uptake of more than 6 mmol m-2 d-1.
On average, the impact of coccolithophores is an increase of air-sea
CO2 flux of about 27%. Considering the areal extent of the
bloom from satellite images within the Irminger and Icelandic Basins,
this reduction translates into an annual mean of nearly 1500 tonnes C
We present a new, non-flux corrected AOGCM, GENMOM, that combines the GENESIS version 3 atmospheric GCM (Global ENvironmental and Ecological Simulation of Interactive Systems) and MOM2 (Modular Ocean Model version 2). We evaluate GENMOM by comparison with reanalysis products (e.g., NCEP2) and eight models used in the IPCC AR4 assessment. The overall present-day climate simulated by GENMOM is on par with the models used in IPCC AR4. The model produces a global temperature bias of 0.6 °C. Atmospheric features such as the jet stream structure and major semi-permanent sea level pressure centers are well simulated as is the mean planetary-scale wind structure that is needed to produce the correct position of stormtracks. The gradients and spatial distributions of annual surface temperature compare well both to observations and to the IPCC AR4 models. A warm bias of ~2 °C is simulated by MOM between 200–1000 m in the ocean. Most ocean surface currents are reproduced except where they are not resolved well by the T31 resolution. The two main weaknesses in the simulations is the development of a split ITCZ and weaker-than-observed overturning circulation.
This paper describes the coupling of the Community Atmosphere Model
(CAM) version 5 with a unified multi-variate probability density
function (PDF) parameterization, Cloud Layers Unified by Binormals
(CLUBB). CLUBB replaces the planetary boundary layer (PBL), shallow
convection, and cloud macrophysics schemes in CAM5 with a higher-order
turbulence closure based on an assumed PDF. Comparisons of single-column
versions of CAM5 and CAM-CLUBB are provided in this paper for several
boundary layer regimes. As compared to large eddy simulations (LESs),
CAM-CLUBB and CAM5 simulate marine stratocumulus regimes with similar
accuracy. For shallow convective regimes, CAM-CLUBB improves the
representation of cloud cover and liquid water path (LWP). In addition,
for shallow convection CAM-CLUBB offers better fidelity for
subgrid-scale vertical velocity, which is an important input for aerosol
activation. Finally, CAM-CLUBB results are more robust to changes in
vertical and temporal resolution when compared to CAM5.
The accurate modeling of cascades to unresolved scales is an important part of the tracer transport component of dynamical cores of weather and climate models. This paper aims to investigate the ability of the advection schemes in the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5) to model this cascade. In order to quantify the effects of the different advection schemes in CAM5, four two-dimensional tracer transport test cases are presented. Three of the tests stretch the tracer below the scale of coarse resolution grids to ensure the downscale cascade of tracer variance. These results are compared with a high resolution reference solution, which is simulated on a resolution fine enough to resolve the tracer during the test. The fourth test has two separate flow cells, and is designed so that any tracer in the western hemisphere should not pass into the eastern hemisphere. This is to test whether the diffusion in transport schemes, often in the form of explicit hyper-diffusion terms or implicit through monotonic limiters, contains unphysical mixing.
An intercomparison of three of the dynamical cores of the National Center for Atmospheric Research's Community Atmosphere Model version 5 is performed. The results show that the finite-volume (CAM-FV) and spectral element (CAM-SE) dynamical cores model the downscale cascade of tracer variance better than the semi-Lagrangian transport scheme of the Eulerian spectral transform core (CAM-EUL). Each scheme tested produces unphysical mass in the eastern hemisphere of the separate cells test.
The variational formulation of Bayes' theorem allows inferring
CO2 sources and sinks from atmospheric concentrations at much
higher time-space resolution than the ensemble or analytical approaches.
However, it usually exhibits limited scalable parallelism. This
limitation hinders global atmospheric inversions operated on decadal
time scales and regional ones with kilometric spatial scales because of
the computational cost of the underlying transport model that has to be
run at each iteration of the variational minimization. Here, we
introduce a physical parallelization (PP) of variational atmospheric
inversions. In the PP, the inversion still manages a single physically
and statistically consistent window, but the transport model is run in
parallel overlapping sub-segments in order to massively reduce the
computation wall-clock time of the inversion. For global inversions, a
simplification of transport modelling is described to connect the output
of all segments. We demonstrate the performance of the approach on a
global inversion for CO2 with a 32 yr inversion window
(1979-2010) with atmospheric measurements from 81 sites of the NOAA
global cooperative air sampling network. In this case, we show that the
duration of the inversion is reduced by a seven-fold factor (from months
to days), while still processing the three decades consistently and with
improved numerical stability.