Showing papers in "Geoscientific Model Development in 2011"
TL;DR: In this paper, the authors describe the development and evaluation of an Earth system model suitable for centennial-scale climate prediction, which includes terrestrial and ocean ecosystems and gas-phase tropospheric chemistry along with their coupled interactions.
Abstract: . We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.
1,290 citations
TL;DR: The Fire Inventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning as discussed by the authors.
Abstract: . The Fire INventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data, particularly for the non-methane organic compounds (NMOC). The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates. Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. Uncertainties in the emissions estimates arise from several of the method steps. The use of fire hot spots, assumed area burned, land cover maps, biomass consumption estimates, and emission factors all introduce error into the model estimates. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the global estimates agree reasonably well with other global inventories of biomass burning emissions for CO, CO2, and other species with less variable emission factors. FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.
1,264 citations
TL;DR: The Joint UK Land Environment Simulator (JULES) as discussed by the authors is developed from the Met Office Surface Exchange Scheme (MOSES) and can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model.
Abstract: . This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.
1,083 citations
TL;DR: In this article, an earth system model (MIROC-ESM 2010) is described in terms of each model component and their interactions, and results for the CMIP5 (Coupled Model Intercomparison Project phase 5) historical simulation are presented to demonstrate the model's performance from several perspectives: atmosphere, ocean, sea-ice, land-surface, ocean and terrestrial biogeochemistry, and atmospheric chemistry and aerosols.
Abstract: . An earth system model (MIROC-ESM 2010) is fully described in terms of each model component and their interactions. Results for the CMIP5 (Coupled Model Intercomparison Project phase 5) historical simulation are presented to demonstrate the model's performance from several perspectives: atmosphere, ocean, sea-ice, land-surface, ocean and terrestrial biogeochemistry, and atmospheric chemistry and aerosols. An atmospheric chemistry coupled version of MIROC-ESM (MIROC-ESM-CHEM 2010) reasonably reproduces transient variations in surface air temperatures for the period 1850–2005, as well as the present-day climatology for the zonal-mean zonal winds and temperatures from the surface to the mesosphere. The historical evolution and global distribution of column ozone and the amount of tropospheric aerosols are reasonably simulated in the model based on the Representative Concentration Pathways' (RCP) historical emissions of these precursors. The simulated distributions of the terrestrial and marine biogeochemistry parameters agree with recent observations, which is encouraging to use the model for future global change projections.
1,032 citations
Met Office1, Oak Ridge National Laboratory2, University of Reading3, June4, Korea Meteorological Administration5, University of Edinburgh6, Pierre-and-Marie-Curie University7, CSIRO Marine and Atmospheric Research8, Colorado State University9, University of Exeter10, University of Oxford11, University of Maryland, College Park12, Joint Global Change Research Institute13, Potsdam Institute for Climate Impact Research14, Max Planck Society15, University of Bristol16
TL;DR: In this paper, the authors outline the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments.
Abstract: . The scientific understanding of the Earth's climate system, including the central question of how the climate system is likely to respond to human-induced perturbations, is comprehensively captured in GCMs and Earth System Models (ESM). Diagnosing the simulated climate response, and comparing responses across different models, is crucially dependent on transparent assumptions of how the GCM/ESM has been driven – especially because the implementation can involve subjective decisions and may differ between modelling groups performing the same experiment. This paper outlines the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document the prescribed greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the HadGEM2-ES historical and future experiments following the Representative Concentration Pathways. In addition, we provide details of how HadGEM2-ES ensemble members were initialised from the control run and how the palaeoclimate and AMIP experiments, as well as the "emission-driven" RCP experiments were performed.
843 citations
TL;DR: The HadGEM2 family of configurations as discussed by the authors includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry.
Abstract: . We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.
837 citations
TL;DR: The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere as mentioned in this paper.
Abstract: . The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Many studies have demonstrated the important role of the land surface in the functioning of the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of climate change, increasing atmospheric carbon dioxide concentrations, changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. This paper describes the consolidation of these advances in the modelling of carbon fluxes and stores, in both the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.
826 citations
Goddard Institute for Space Studies1, Max Planck Society2, National Center for Atmospheric Research3, Aix-Marseille University4, University of Edinburgh5, Joseph Fourier University6, University of Bern7, Oeschger Centre for Climate Change Research8, Lund University9, Swiss Federal Institute of Aquatic Science and Technology10, University of Orléans11
TL;DR: In this article, the authors describe the approach taken in defining the scenarios used in the PMIP3, document the forcing reconstructions and discuss likely implications and discuss the likely implications.
Abstract: Simulations of climate over the Last Millennium (850-1850 CE) have been incorporated into the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). The drivers of climate over this period are chiefly orbital, solar, volcanic, changes in land use/land cover and some variation in greenhouse gas levels. While some of these effects can be easily defined, the reconstructions of solar, volcanic and land use-related forcing are more uncertain. We describe here the approach taken in defining the scenarios used in PMIP3, document the forcing reconstructions and discuss likely implications.
452 citations
TL;DR: Documentation of the HadGEM3-AO system provides a detailed reference for developers of HadGem3-based climate configurations and demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components.
Abstract: . This paper describes the development of a technically robust climate modelling system, HadGEM3, which couples the Met Office Unified Model atmosphere component, the NEMO ocean model and the Los Alamos sea ice model (CICE) using the OASIS coupler. Details of the coupling and technical solutions of the physical model (HadGEM3-AO) are documented, in addition to a description of the configurations of the individual submodels. The paper demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components. The model performance in early versions of this climate model is briefly described to demonstrate that the results are scientifically credible. HadGEM3-AO is the basis for a number of modelling efforts outside of the Met Office, both within the UK and internationally. This documentation of the HadGEM3-AO system provides a detailed reference for developers of HadGEM3-based climate configurations.
364 citations
TL;DR: In this paper, the authors describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module.
Abstract: . We describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module. 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. SFIRE is implemented by the level set method, which allows a submesh representation of the burning region and a flexible implementation of various kinds of ignition. The coupled model is capable of running on a cluster faster than real time even with fine resolution in dekameters. It is available as a part of the Open Wildland Fire Modeling (OpenWFM) environment at http://openwfm.org , which contains also utilities for visualization, diagnostics, and data processing, including an extended version of the WRF Preprocessing System (WPS). The SFIRE code with a subset of the features is distributed with WRF 3.3 as WRF-Fire.
210 citations
TL;DR: The Pliocene Model Intercomparison Project (PlioMIP) as mentioned in this paper is the first phase of the Palaeoclimate Modelling IntercomPARISON Project (POMIP).
Abstract: . The Palaeoclimate Modelling Intercomparison Project has expanded to include a model intercomparison for the mid-Pliocene warm period (3.29 to 2.97 million yr ago). This project is referred to as PlioMIP (the Pliocene Model Intercomparison Project). Two experiments have been agreed upon and together compose the initial phase of PlioMIP. The first (Experiment 1) is being performed with atmosphere-only climate models. The second (Experiment 2) utilises fully coupled ocean-atmosphere climate models. Following on from the publication of the experimental design and boundary conditions for Experiment 1 in Geoscientific Model Development, this paper provides the necessary description of differences and/or additions to the experimental design for Experiment 2.
TL;DR: 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.
Abstract: . 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.
TL;DR: In this paper, the authors developed four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially consistent Koppen-Geiger climate zones.
Abstract: . The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10–20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Koppen-Geiger climate zones. Using a beta (s) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.
TL;DR: The preprocessor PREP-CHEM-SRC as discussed by the authors is a comprehensive tool aiming at preparing emission fields of trace gases and aerosols for use in atmospheric-chemistry transport models.
Abstract: . The preprocessor PREP-CHEM-SRC presented in the paper is a comprehensive tool aiming at preparing emission fields of trace gases and aerosols for use in atmospheric-chemistry transport models. The considered emissions are from the most recent databases of urban/industrial, biogenic, biomass burning, volcanic, biofuel use and burning from agricultural waste sources. For biomass burning, emissions can be also estimated directly from satellite fire detections using a fire emission model included in the tool. The preprocessor provides emission fields interpolated onto the transport model grid. Several map projections can be chosen. The inclusion of these emissions in transport models is also presented. The preprocessor is coded using Fortran90 and C and is driven by a namelist allowing the user to choose the type of emissions and the databases.
TL;DR: The CSIRO Mk3L climate system model as mentioned in this paper is a coupled general circulation model, designed primarily for millennial-scale climate simulations and palaeoclimate research, which includes components which describe the atmosphere, ocean, sea ice and land surface, and combines computational efficiency with a stable and realistic control climate.
Abstract: . The CSIRO Mk3L climate system model is a coupled general circulation model, designed primarily for millennial-scale climate simulations and palaeoclimate research. Mk3L includes components which describe the atmosphere, ocean, sea ice and land surface, and combines computational efficiency with a stable and realistic control climatology. This paper describes the model physics and software, analyses the control climatology, and evaluates the ability of the model to simulate the modern climate. Mk3L incorporates a spectral atmospheric general circulation model, a z-coordinate ocean general circulation model, a dynamic-thermodynamic sea ice model and a land surface scheme with static vegetation. The source code is highly portable, and has no dependence upon proprietary software. The model distribution is freely available to the research community. A 1000-yr climate simulation can be completed in around one-and-a-half months on a typical desktop computer, with greater throughput being possible on high-performance computing facilities. Mk3L produces realistic simulations of the larger-scale features of the modern climate, although with some biases on the regional scale. The model also produces reasonable representations of the leading modes of internal climate variability in both the tropics and extratropics. The control state of the model exhibits a high degree of stability, with only a weak cooling trend on millennial timescales. Ongoing development work aims to improve the model climatology and transform Mk3L into a comprehensive earth system model.
TL;DR: In this paper, a simplified parameterization for secondary organic aerosol (SOA) formation in polluted air and biomass burning smoke is tested and optimized in this work, towards the goal of a computationally inexpensive method to calculate pollution and biomass-burning SOA mass and hygroscopicity in global and climate models.
Abstract: . A simplified parameterization for secondary organic aerosol (SOA) formation in polluted air and biomass burning smoke is tested and optimized in this work, towards the goal of a computationally inexpensive method to calculate pollution and biomass burning SOA mass and hygroscopicity in global and climate models. A regional chemistry-transport model is used as the testbed for the parameterization, which is compared against observations from the Mexico City metropolitan area during the MILAGRO 2006 field experiment. The empirical parameterization is based on the observed proportionality of SOA concentrations to excess CO and photochemical age of the airmass. The approach consists in emitting an organic gas as lumped SOA precursor surrogate proportional to anthropogenic or biomass burning CO emissions according to the observed ratio between SOA and CO in aged air, and reacting this surrogate with OH into a single non-volatile species that condenses to form SOA. An emission factor of 0.08 g of the lumped SOA precursor per g of CO and a rate constant with OH of 1.25 × 10−11 cm3 molecule−1 s−1 reproduce the observed average SOA mass within 30 % in the urban area and downwind. When a 2.5 times slower rate is used (5 × 10−12 cm3 molecule−1 s−1) the predicted SOA amount and temporal evolution is nearly identical to the results obtained with SOA formation from semi-volatile and intermediate volatility primary organic vapors according to the Robinson et al. (2007) formulation. Our simplified method has the advantage of being much less computationally expensive than Robinson-type methods, and can be used in regions where the emissions of SOA precursors are not yet available. As the aged SOA/ΔCO ratios are rather consistent globally for anthropogenic pollution, this parameterization could be reasonably tested in and applied to other regions. The evolution of oxygen-to-carbon ratio was also empirically modeled and the predicted levels were found to be in reasonable agreement with observations. The potential enhancement of biogenic SOA by anthropogenic pollution, which has been suggested to play a major role in global SOA formation, is also tested using two simple parameterizations. Our results suggest that the pollution enhancement of biogenic SOA could provide additional SOA, but does not however explain the concentrations or the spatial and temporal variations of measured SOA mass in the vicinity of Mexico City, which appears to be controlled by anthropogenic sources. The contribution of the biomass burning to the predicted SOA is less than 10% during the studied period.
TL;DR: In this article, a set of benchmark tests are designed to quantify the performance of the land surface model that is used in the UK Hadley Centre General Circulation Model (JULES: Joint UK Land Environment Simulator).
Abstract: . Evaluating the models we use in prediction is important as it allows us to identify uncertainties in prediction as well as guiding the priorities for model development. This paper describes a set of benchmark tests that is designed to quantify the performance of the land surface model that is used in the UK Hadley Centre General Circulation Model (JULES: Joint UK Land Environment Simulator). The tests are designed to assess the ability of the model to reproduce the observed fluxes of water and carbon at the global and regional spatial scale, and on a seasonal basis. Five datasets are used to test the model: water and carbon dioxide fluxes from ten FLUXNET sites covering the major global biomes, atmospheric carbon dioxide concentrations at four representative stations from the global network, river flow from seven catchments, the seasonal mean NDVI over the seven catchments and the potential land cover of the globe (after the estimated anthropogenic changes have been removed). The model is run in various configurations and results are compared with the data. A few examples are chosen to demonstrate the importance of using combined use of observations of carbon and water fluxes in essential in order to understand the causes of model errors. The benchmarking approach is suitable for application to other global models.
TL;DR: In this article, the authors examined the performance of the Community Multiscale Air Quality (CMAQ) model in predicting wet deposition of sulfate (SO4=), ammonium (NH4+) and nitrate (NO3−).
Abstract: . This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002–2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO4=), ammonium (NH4+) and nitrate (NO3−). Performance of the wet deposition estimates from the model is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP), specifically the National Trends Network (NTN). For SO4= wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO4= wet deposition than the 36-km simulation on average. The result is a slightly larger normalized mean bias (NMB) for the 12-km simulation; however both simulations had annual biases that were less than ±15 % for each of the five years. The model estimated SO4= wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH4+ wet deposition over the eastern US, with a slightly larger underestimation in the 36-km simulation. The largest underestimations occur in the winter and spring periods, while the summer and fall have slightly smaller underestimations of NH4+ wet deposition. The underestimation in NH4+ wet deposition is likely due in part to the poor temporal and spatial representation of ammonia (NH3) emissions, particularly those emissions associated with fertilizer applications and NH3 bi-directional exchange. The model performance for estimates of NO3− wet deposition are mixed throughout the year, with the model largely underestimating NO3− wet deposition in the spring and summer in the eastern US, while the model has a relatively small bias in the fall and winter. Model estimates of NO3− wet deposition tend to be slightly lower for the 36-km simulation as compared to the 12-km simulation, particularly in the spring. The underestimation of NO3− wet deposition in the spring and summer is due in part to a lack of lightning generated NO emissions in the upper troposphere, which can be a large source of NO in the spring and summer when lightning activity is the high. CMAQ model simulations that include production of NO from lightning show a significant improvement in the NO3− wet deposition estimates in the eastern US in the summer. Overall, performance for the 36-km and 12-km CMAQ model simulations is similar for the eastern US, while for the western US the performance of the 36-km simulation is generally not as good as either eastern US simulation, which is not entire unexpected given the complex topography in the western US.
TL;DR: In this paper, the US EPA regional emission model SMOKE was adopted and modified to create temporally and spatially distributed emission for Europe and surrounding countries based on official reports and public domain data only.
Abstract: . The US EPA regional emission model SMOKE was adopted and modified to create temporally and spatially distributed emission for Europe and surrounding countries based on official reports and public domain data only. The aim is to develop a flexible model capable of creating consistent high resolution emission data for long-term runs of Chemical Transport Models (CTMs). This modified version of SMOKE, called SMOKE for EUROPE (SMOKE-EU) was successfully used to create hourly gridded emissions for the timespan 1970–2010. In this paper the SMOKE-EU model and the underlying European datasets are introduced. Emission data created by SMOKE-EU for the year 2000 are evaluated by comparison to data of three different state-of-the-art emission models. SMOKE-EU produced a range of values comparable to the other three datasets. Further, concentrations of criteria pollutants calculated by the CTM CMAQ using the four different emission datasets were compared against EMEP measurements with hourly and daily resolution. Using SMOKE-EU gave the most reliable modelling of O3, NO2 and SO42−. The amount of simulated concentrations within a factor of 2 (F2) of the observations for these species are: O3 (F2 = 0.79, N = 329 197), NO2 (F2 = 0.55, N = 11 465) and SO42− (F2 = 0.62, N = 17 536). The lowest values were found for NH4+ (F2 = 0.34, N = 7400) and NO3− (F2 = 0.25, N = 6184). NH4+ concentrations were generally overestimated, leading to a fractional bias (FB) averaged over 22 measurement stations of (FB = 0.83 ± 0.41) while better agreements with observations were found for SO42− (FB = 0.06 ± 0.38, 51 stations) and NO3− (FB = 0.13 ± 0.75, 18 stations). CMAQ simulations using the three other emission datasets were similar to those modelled using SMOKE-EU emissions. Highest differences where found for NH4+ while O3 concentrations were almost identical.
TL;DR: In this paper, the effects of clouds on aerosols are treated by using an explicit cloud parameterized-pollutant (ECPP) approach that links aerosol and chemical processes on the large-scale grid with statistics of cloud properties and processes resolved by the CRM.
Abstract: . Anthropogenic aerosol effects on climate produce one of the largest uncertainties in estimates of radiative forcing of past and future climate change. Much of this uncertainty arises from the multi-scale nature of the interactions between aerosols, clouds and large-scale dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we develop a multi-scale aerosol-climate model that treats aerosols and clouds across different scales, and evaluate the model performance, with a focus on aerosol treatment. This new model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each grid column of a GCM. In this extension, the effects of clouds on aerosols are treated by using an explicit-cloud parameterized-pollutant (ECPP) approach that links aerosol and chemical processes on the large-scale grid with statistics of cloud properties and processes resolved by the CRM. A two-moment cloud microphysics scheme replaces the simple bulk microphysics scheme in the CRM, and a modal aerosol treatment is included in the GCM. With these extensions, this multi-scale aerosol-climate model allows the explicit simulation of aerosol and chemical processes in both stratiform and convective clouds on a global scale. Simulated aerosol budgets in this new model are in the ranges of other model studies. Simulated gas and aerosol concentrations are in reasonable agreement with observations (within a factor of 2 in most cases), although the model underestimates black carbon concentrations at the surface by a factor of 2–4. Simulated aerosol size distributions are in reasonable agreement with observations in the marine boundary layer and in the free troposphere, while the model underestimates the accumulation mode number concentrations near the surface, and overestimates the accumulation mode number concentrations in the middle and upper free troposphere by a factor of about 2. The overestimation of accumulation model number concentrations in the middle and upper free troposphere is consistent with large aerosol mass fraction above 5 km in the MMF model compared with other models. Simulated cloud condensation nuclei (CCN) concentrations are within the observational variations. Simulated aerosol optical depths (AOD) are in reasonable agreement with observations (within a factor of 2), and the spatial distribution of AOD is consistent with observations, while the model underestimates AOD over regions with strong fossil fuel and biomass burning emissions. Overall, this multi-scale aerosol-climate model simulates aerosol fields as well as conventional aerosol models.
TL;DR: The MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient utilization, sequestration and acidification) model as mentioned in this paper is a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplanktondetritus (NPZD) models, and remains amenable to global-scale evaluation.
Abstract: . The ongoing, anthropogenically-driven changes to the global ocean are expected to have significant consequences for plankton ecosystems in the future. Because of the role that plankton play in the ocean's "biological pump", changes in abundance, distribution and productivity will likely have additional consequences for the wider carbon cycle. Just as in the terrestrial biosphere, marine ecosystems exhibit marked diversity in species and functional types of organisms. Predicting potential change in plankton ecosystems therefore requires the use of models that are suited to this diversity, but whose parameterisation also permits robust and realistic functional behaviour. In the past decade, advances in model sophistication have attempted to address diversity, but have been criticised for doing so inaccurately or ahead of a requisite understanding of underlying processes. Here we introduce MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification), a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplankton-detritus (NPZD) models, and remains amenable to global-scale evaluation. MEDUSA-1.0 includes the biogeochemical cycles of nitrogen, silicon and iron, broadly structured into "small" and "large" plankton size classes, of which the "large" phytoplankton class is representative of a key phytoplankton group, the diatoms. A full description of MEDUSA-1.0's state variables, differential equations, functional forms and parameter values is included, with particular attention focused on the submodel describing the export of organic carbon from the surface to the deep ocean. MEDUSA-1.0 is used here in a multi-decadal hindcast simulation, and its biogeochemical performance evaluated at the global scale.
ETH Zurich1, Swiss Federal Laboratories for Materials Science and Technology2, Karlsruhe Institute of Technology3, University of Manchester4, University of Helsinki5, Finnish Meteorological Institute6, University of Colorado Boulder7, Forschungszentrum Jülich8, Paul Scherrer Institute9, Leibniz Association10, Lund University11
TL;DR: In this article, the authors evaluated the performance of the online-coupled regional chemistry transport model COSMO-ART for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain.
Abstract: . The online-coupled, regional chemistry transport model COSMO-ART is evaluated for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain. Measurements used in the comparison include long-term station observations, satellite and ground-based remote sensing products, and complex datasets of aerosol chemical composition and number size distribution from recent field campaigns. This is the first time these comprehensive measurements of aerosol characteristics in Europe are used to evaluate a regional chemistry transport model. We show a detailed analysis of the simulated size-resolved chemical composition under different meteorological conditions. Mean, variability and spatial distribution of the concentrations of O3 and NOx are well reproduced. SO2 is found to be overestimated, simulated PM2.5 and PM10 levels are on average underestimated, as is AOD. We find indications of an overestimation of shipping emissions. Time evolution of aerosol chemical composition is captured, although some biases are found in relative composition. Nitrate aerosol components are on average overestimated, and sulfates underestimated. The accuracy of simulated organics depends strongly on season and location. While strongly underestimated during summer, organic mass is comparable in spring and autumn. We see indications for an overestimated fractional contribution of primary organic matter in urban areas and an underestimation of SOA at many locations. Aerosol number concentrations compare well with measurements for larger size ranges, but overestimations of particle number concentration with factors of 2–5 are found for particles smaller than 50 nm. Size distribution characteristics are often close to measurements, but show discrepancies at polluted sites. Suggestions for further improvement of the modeling system consist of the inclusion of a revised secondary organic aerosols scheme, aqueous-phase chemistry and improved aerosol boundary conditions. Our work sets the basis for subsequent studies of aerosol characteristics and climate impacts with COSMO-ART, and highlights areas where improvements are necessary for current regional modeling systems in general.
TL;DR: In this article, a coupled model that consists of the University of Victoria Earth System Climate Model (UVic ESCM) and the Pennsylvania State University Ice model (PSUI) is described.
Abstract: . The need to better understand long-term climate/ice sheet feedback loops is motivating efforts to couple ice sheet models into Earth System models which are capable of long-timescale simulations. In this paper we describe a coupled model that consists of the University of Victoria Earth System Climate Model (UVic ESCM) and the Pennsylvania State University Ice model (PSUI). The climate model generates a surface mass balance (SMB) field via a sub-gridded surface energy/moisture balance model that resolves narrow ice sheet ablation zones. The ice model returns revised elevation, surface albedo and ice area fields, plus coastal fluxes of heat and moisture. An arbitrary number of ice sheets can be simulated, each on their own high-resolution grid and each capable of synchronous or asynchronous coupling with the overlying climate model. The model is designed to conserve global heat and moisture. In the process of improving model performance we developed a procedure to account for modelled surface air temperature (SAT) biases within the energy/moisture balance surface model and improved the UVic ESCM snow surface scheme through addition of variable albedos and refreezing over the ice sheet. A number of simulations for late Holocene, Last Glacial Maximum (LGM), and Eemian climate boundary conditions were carried out to explore the sensitivity of the coupled model and identify model configurations that best represented these climate states. The modelled SAT bias was found to play a significant role in long-term ice sheet evolution, as was the effect of refreezing meltwater and surface albedo. The bias-corrected model was able to reasonably capture important aspects of the Antarctic and Greenland ice sheets, including modern SMB and ice distribution. The simulated northern Greenland ice sheet was found to be prone to ice margin retreat at radiative forcings corresponding closely to those of the Eemian or the present-day.
TL;DR: In this paper, the authors introduce the new aerosol microphysics submodel MADE-in, implemented within the ECHAM/MESSy Atmospheric Chemistry global model (EMAC).
Abstract: . 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.
TL;DR: In this article, the authors used MIROC4m, a fully coupled atmosphere-ocean general circulation model (AOGCM), and its atmospheric component alone to simulate the mid-Pliocene warm period (mPWP), 3.3-3.0 million years ago.
Abstract: . Recently, PlioMIP (Pliocene Model Intercomparison Project) was established to assess the ability of various climate models to simulate the mid-Pliocene warm period (mPWP), 3.3–3.0 million years ago. We use MIROC4m, a fully coupled atmosphere-ocean general circulation model (AOGCM), and its atmospheric component alone to simulate the mPWP, utilizing up-to-date data sets designated in PlioMIP as boundary conditions and adhering to the protocols outlined. In this paper, a brief description of the model is given, followed by an explanation of the experimental design and implementation of the boundary conditions, such as topography and sea surface temperature. Initial results show increases of approximately 10°C in the zonal mean surface air temperature at high latitudes accompanied by a decrease in the equator-to-pole temperature gradient. Temperatures in the tropical regions increase more in the AOGCM. However, warming of the AOGCM sea surface in parts of the northern North Atlantic Ocean and Nordic Seas is less than that suggested by proxy data. An investigation of the model-data discrepancies and further model intercomparison studies can lead to a better understanding of the mid-Pliocene climate and of its role in assessing future climate change.
TL;DR: In this article, an efficient computational procedure for constructing non-diagonal background error covariance matrices which account for the spatial correlations of errors is discussed. But, as models evolve toward finer resolutions, they capture less of the spatial error correlations.
Abstract: . Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere. It is widely accepted that a key ingredient for successful data assimilation is a realistic estimation of the background error distribution. Particularly important is the specification of the background error covariance matrix, which contains information about the magnitude of the background errors and about their correlations. As models evolve toward finer resolutions, the use of diagonal background covariance matrices is increasingly inaccurate, as they captures less of the spatial error correlations. This paper discusses an efficient computational procedure for constructing non-diagonal background error covariance matrices which account for the spatial correlations of errors. The correlation length scales are specified by the user; a correct choice of correlation lengths is important for a good performance of the data assimilation system. The benefits of using the non-diagonal covariance matrices for variational data assimilation with chemical transport models are illustrated.
TL;DR: In this article, a submodel of the ECHAM5/MESSy Atmospheric Chemistry model (EMAC) has been developed to simulate the main types of polar stratospheric clouds (PSC).
Abstract: . The submodel PSC of the ECHAM5/MESSy Atmospheric Chemistry model (EMAC) has been developed to simulate the main types of polar stratospheric clouds (PSC). The parameterisation of the supercooled ternary solutions (STS, type 1b PSC) in the submodel is based on Carslaw et al. (1995b), the thermodynamic approach to simulate ice particles (type 2 PSC) on Marti and Mauersberger (1993). For the formation of nitric acid trihydrate (NAT) particles (type 1a PSC) two different parameterisations exist. The first is based on an instantaneous thermodynamic approach from Hanson and Mauersberger (1988), the second is new implemented and considers the growth of the NAT particles with the aid of a surface growth factor based on Carslaw et al. (2002). It is possible to choose one of this NAT parameterisation in the submodel. This publication explains the background of the submodel PSC and the use of the submodel with the goal of simulating realistic PSC in EMAC.
TL;DR: In this paper, a new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system, which reduces model mean bias for ozone at elevated observed ozone concentrations.
Abstract: . 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 concentrations. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. A sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While it increases in-cloud secondary organic aerosol substantially, its impact on total fine particle mass concentration is small.
TL;DR: In this article, a methodological evaluation of the Dynamic Global Vegetation Models (DGVM) is performed by correlating satellite-derived Vegetation Index time series with those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR).
Abstract: . Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.
TL;DR: GenMOM as mentioned in this paper combines the GENESIS version 3 atmospheric GCM (Global Environmental and Ecological Simulation of Interactive Systems) and MOM2 (Modular Ocean Model version 2) nominally at T31 resolution.
Abstract: . 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) nominally at T31 resolution. We evaluate GENMOM by comparison with reanalysis products (e.g., NCEP2) and three models used in the IPCC AR4 assessment. GENMOM 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. Most ocean surface currents are reproduced except where they are not resolvable at T31 resolution. Overall, GENMOM captures reasonably well the observed gradients and spatial distributions of annual surface temperature and precipitation and the simulations are on par with other AOGCMs. Deficiencies in the GENMOM simulations include a warm bias in the surface temperature over the southern oceans, a split in the ITCZ and weaker-than-observed overturning circulation.