http://www.geosci-model-dev.net/ Geoscientific Model Development Latest Articlesenhttp://www.geosci-model-dev.net/6/643/2013/<b>A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0)</b><br /><br />Geoscientific Model Development, 6, 643-685, 2013<br /><br />Author(s): M. Pfeiffer, A. Spessa, and J. O. Kaplan<br /><br />Fire is the primary disturbance factor in many terrestrial ecosystems. Wildfire alters vegetation structure and composition, affects carbon storage and biogeochemical cycling, and results in the release of climatically relevant trace gases including CO₂, CO, CH₄, NO_x, and aerosols. One way of assessing the impacts of global wildfire on centennial to multi-millennial timescales is to use process-based fire models linked to dynamic global vegetation models (DGVMs). Here we present an update to the LPJ-DGVM and a new fire module based on SPITFIRE that includes several improvements to the way in which fire occurrence, behaviour, and the effects of fire on vegetation are simulated. The new LPJ-LMfire model includes explicit calculation of natural ignitions, the representation of multi-day burning and coalescence of fires, and the calculation of rates of spread in different vegetation types. We describe a new representation of anthropogenic biomass burning under preindustrial conditions that distinguishes the different relationships between humans and fire among hunter-gatherers, pastoralists, and farmers. We evaluate our model simulations against remote-sensing-based estimates of burned area at regional and global scale. While wildfire in much of the modern world is largely influenced by anthropogenic suppression and ignitions, in those parts of the world where natural fire is still the dominant process (e.g. in remote areas of the boreal forest and subarctic), our results demonstrate a significant improvement in simulated burned area over the original SPITFIRE. The new fire model we present here is particularly suited for the investigation of climate–human–fire relationships on multi-millennial timescales prior to the Industrial Revolution.Fri, 17 May 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/617/2013/<b>Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)</b><br /><br />Geoscientific Model Development, 6, 617-641, 2013<br /><br />Author(s): R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan<br /><br />The Wetland and Wetland CH₄ Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH₄) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO₂) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO₂ concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH₄ fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH₄ fluxes from wetland systems. Even though modelling of wetland extent and CH₄ emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH₄ emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.Wed, 15 May 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/591/2013/<b>Evaluation of a near-global eddy-resolving ocean model</b><br /><br />Geoscientific Model Development, 6, 591-615, 2013<br /><br />Author(s): P. R. Oke, D. A. Griffin, A. Schiller, R. J. Matear, R. Fiedler, J. Mansbridge, A. Lenton, M. Cahill, M. A. Chamberlain, and K. Ridgway<br /><br />Analysis of the variability of the last 18 yr (1993–2012) of a 32 yr run of a new near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed layer depth (MLD), sea level anomaly (SLA), sea surface temperature (SST), and {\chla} indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, which results in errors in the {\chla}, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally averaged seasonal cycle of SST, meridional overturning, volume transports through key straits and passages, zonally averaged temperature and salinity, and El Niño-related SST indices. We find that the modelled seasonal cycle in SST is 0.5–1.5 °C weaker than observed; volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates; and the correlation between the modelled and observed NINO SST indices exceeds 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand upper ocean dynamics and ocean variability at mid- and low latitudes. The new model is intended to underpin a future version of Australia's operational short-range ocean forecasting system.Fri, 03 May 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/583/2013/<b>Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation</b><br /><br />Geoscientific Model Development, 6, 583-590, 2013<br /><br />Author(s): V. Yadav and A. M. Michalak<br /><br />Addressing a variety of questions within Earth science disciplines entails the inference of the spatiotemporal distribution of parameters of interest based on observations of related quantities. Such estimation problems often represent inverse problems that are formulated as linear optimization problems. Computational limitations arise when the number of observations and/or the size of the discretized state space becomes large, especially if the inverse problem is formulated in a probabilistic framework and therefore aims to assess the uncertainty associated with the estimates. This work proposes two approaches to lower the computational costs and memory requirements for large linear space–time inverse problems, taking the Bayesian approach for estimating carbon dioxide (CO₂) emissions and uptake (a.k.a. fluxes) as a prototypical example. The first algorithm can be used to efficiently multiply two matrices, as long as one can be expressed as a Kronecker product of two smaller matrices, a condition that is typical when multiplying a sensitivity matrix by a covariance matrix in the solution of inverse problems. The second algorithm can be used to compute a posteriori uncertainties directly at aggregated spatiotemporal scales, which are the scales of most interest in many inverse problems. Both algorithms have significantly lower memory requirements and computational complexity relative to direct computation of the same quantities (O(<i>n</i>^2.5) vs. O(<i>n</i>³)). For an examined benchmark problem, the two algorithms yielded massive savings in floating point operations relative to direct computation of the same quantities. Sample computer codes are provided for assessing the computational and memory efficiency of the proposed algorithms for matrices of different dimensions.Fri, 03 May 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/563/2013/<b>ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models</b><br /><br />Geoscientific Model Development, 6, 563-582, 2013<br /><br />Author(s): S. Faroux, A. T. Kaptué Tchuenté, J.-L. Roujean, V. Masson, E. Martin, and P. Le Moigne<br /><br />The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.Tue, 30 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/549/2013/<b>Simulating the mid-Pliocene Warm Period with the CCSM4 model</b><br /><br />Geoscientific Model Development, 6, 549-561, 2013<br /><br />Author(s): N. A. Rosenbloom, B. L. Otto-Bliesner, E. C. Brady, and P. J. Lawrence<br /><br />This paper describes the experimental design and model results from a 500 yr fully coupled Community Climate System, version 4, simulation of the mid-Pliocene Warm Period (mPWP) (ca. 3.3–3.0 Ma). We simulate the mPWP using the "alternate" protocol prescribed by the Pliocene Model Intercomparison Project (PlioMIP) for the AOGCM simulation (Experiment 2). Results from the CCSM4 mPWP simulation show a 1.9 °C increase in global mean annual temperature compared to the 1850 preindustrial control, with a polar amplification of ~3 times the global warming. Global precipitation increases slightly by 0.09 mm day⁻¹ and the monsoon rainfall is enhanced, particularly in the Northern Hemisphere (NH). Areal sea ice extent decreases in both hemispheres but persists through the summers. The model simulates a relaxation of the zonal sea surface temperature (SST) gradient in the tropical Pacific, with the El Niño–Southern Oscillation (Niño3.4) ~20% weaker than the preindustrial and exhibiting extended periods of quiescence of up to 150 yr. The maximum Atlantic meridional overturning circulation and northward Atlantic oceanic heat transport are indistinguishable from the control. As compared to PRISM3, CCSM4 overestimates Southern Hemisphere (SH) sea surface temperatures, but underestimates NH warming, particularly in the North Atlantic, suggesting that an increase in northward ocean heat transport would bring CCSM4 SSTs into better alignment with proxy data.Fri, 26 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/533/2013/<b>How should sparse marine in situ measurements be compared to a continuous model: an example</b><br /><br />Geoscientific Model Development, 6, 533-548, 2013<br /><br />Author(s): L. de Mora, M. Butenschön, and J. I. Allen<br /><br />This work demonstrates an example of the importance of an adequate method to sub-sample model results when comparing with in situ measurements. A test of model skill was performed by employing a point-to-point method to compare a multi-decadal hindcast against a sparse, unevenly distributed historic in situ dataset. The point-to-point method masked out all hindcast cells that did not have a corresponding in situ measurement in order to match each in situ measurement against its most similar cell from the model. The application of the point-to-point method showed that the model was successful at reproducing the inter-annual variability of the in situ datasets. Furthermore, this success was not immediately apparent when the measurements were aggregated to regional averages. Time series, data density and target diagrams were employed to illustrate the impact of switching from the regional average method to the point-to-point method. The comparison based on regional averages gave significantly different and sometimes contradicting results that could lead to erroneous conclusions on the model performance. Furthermore, the point-to-point technique is a more correct method to exploit sparse uneven in situ data while compensating for the variability of its sampling. We therefore recommend that researchers take into account for the limitations of the in situ datasets and process the model to resemble the data as much as possible.Thu, 25 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/517/2013/<b>Simulations of the mid-Pliocene Warm Period using two versions of the NASA/GISS ModelE2-R Coupled Model</b><br /><br />Geoscientific Model Development, 6, 517-531, 2013<br /><br />Author(s): M. A. Chandler, L. E. Sohl, J. A. Jonas, H. J. Dowsett, and M. Kelley<br /><br />The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASA/GISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates. <br><br> Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasise features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.Wed, 24 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/495/2013/<b>Modeling agriculture in the Community Land Model</b><br /><br />Geoscientific Model Development, 6, 495-515, 2013<br /><br />Author(s): B. Drewniak, J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob<br /><br />The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon–nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements for soybean, but not as well for maize. CLM-Crop yields were comparable with observations in countries such as the United States, Argentina, and China, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation, in agreement with other modeling studies. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model – simulating agriculture in a realistic way, complete with fertilizer and residue management practices. Results are encouraging, with improved representation of human influences on the land surface and the potentially resulting climate impacts.Fri, 19 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/477/2013/<b>MESMO 2: a mechanistic marine silica cycle and coupling to a simple terrestrial scheme</b><br /><br />Geoscientific Model Development, 6, 477-494, 2013<br /><br />Author(s): K. Matsumoto, K. Tokos, A. Huston, and H. Joy-Warren<br /><br />Here we describe the second version of Minnesota Earth System Model for Ocean biogeochemistry (MESMO 2), an earth system model of intermediate complexity, which consists of a dynamical ocean, dynamic-thermodynamic sea ice, and energy moisture balanced atmosphere. The new version has more realistic land ice masks and is driven by seasonal winds. A major aim in version 2 is representing the marine silica cycle mechanistically in order to investigate climate-carbon feedbacks involving diatoms, a critically important class of phytoplankton in terms of carbon export production. This is achieved in part by including iron, on which phytoplankton uptake of silicic acid depends. Also, MESMO 2 is coupled to an existing terrestrial model, which allows for the exchange of carbon, water and energy between land and the atmosphere. The coupled model, called MESMO 2E, is appropriate for more complete earth system simulations. The new version was calibrated, with the goal of preserving reasonable interior ocean ventilation and various biological production rates in the ocean and land, while simulating key features of the marine silica cycle.Fri, 12 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/469/2013/<b>PORT, a CESM tool for the diagnosis of radiative forcing</b><br /><br />Geoscientific Model Development, 6, 469-476, 2013<br /><br />Author(s): A. J. Conley, J.-F. Lamarque, F. Vitt, W. D. Collins, and J. Kiehl<br /><br />The Parallel Offline Radiative Transfer (PORT) model is a stand-alone tool, driven by model-generated datasets, that can be used for any radiation calculation that the underlying radiative transfer schemes can perform, such as diagnosing radiative forcing. In its present distribution, PORT isolates the radiation code from the Community Atmosphere Model (CAM4) in the Community Earth System Model (CESM1). The current configuration focuses on CAM4 radiation with the constituents as represented in present-day conditions in CESM1, along with their optical properties. PORT includes an implementation of stratospheric temperature adjustment under the assumption of fixed dynamical heating, which is necessary to compute radiative forcing in addition to the more straightforward instantaneous radiative forcing. PORT can be extended to use radiative constituent distributions from other models or model simulations. Ultimately, PORT can be used with various radiative transfer models. As illustrations of the use of PORT, we perform the computation of radiative forcing from doubling of carbon dioxide, from the change of tropospheric ozone concentration from the year 1850 to 2000, and from present-day aerosols. The radiative forcing from tropospheric ozone (with respect to 1850) generated by a collection of model simulations under the Atmospheric Chemistry and Climate Model Intercomparison Project is found to be 0.34 (with an intermodel standard deviation of 0.07) W m⁻². Present-day aerosol direct forcing (relative to no aerosols) is found to be −1.3 W m⁻².Wed, 10 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/457/2013/<b>Inclusion of ash and SO₂ emissions from volcanic eruptions in WRF-Chem: development and some applications</b><br /><br />Geoscientific Model Development, 6, 457-468, 2013<br /><br />Author(s): M. Stuefer, S. R. Freitas, G. Grell, P. Webley, S. Peckham, S. A. McKeen, and S. D. Egan<br /><br />We describe a new functionality within the Weather Research and Forecasting (WRF) model with coupled Chemistry (WRF-Chem) that allows simulating emission, transport, dispersion, transformation and sedimentation of pollutants released during volcanic activities. Emissions from both an explosive eruption case and a relatively calm degassing situation are considered using the most recent volcanic emission databases. A preprocessor tool provides emission fields and additional information needed to establish the initial three-dimensional cloud umbrella/vertical distribution within the transport model grid, as well as the timing and duration of an eruption. From this source condition, the transport, dispersion and sedimentation of the ash cloud can be realistically simulated by WRF-Chem using its own dynamics and physical parameterization as well as data assimilation. Examples of model applications include a comparison of tephra fall deposits from the 1989 eruption of Mount Redoubt (Alaska) and the dispersion of ash from the 2010 Eyjafjallajökull eruption in Iceland. Both model applications show good coincidence between WRF-Chem and observations.Tue, 09 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/445/2013/<b>Evaluation of roadway Gaussian plume models with large-scale measurement campaigns</b><br /><br />Geoscientific Model Development, 6, 445-456, 2013<br /><br />Author(s): R. Briant, C. Seigneur, M. Gadrat, and C. Bugajny<br /><br />Gaussian models are commonly used to simulate atmospheric pollutant dispersion near sources because they provide an efficient compromise between reasonable accuracy and manageable computational time. The Gaussian dispersion formula provides an exact solution to the atmospheric diffusion equation for the dispersion of a pollutant emitted from a point source. However, the Gaussian dispersion formula for a line source, which is convenient to model emissions from on-road traffic, is exact only when the wind is perpendicular to the line source. A novel approach that reduces the error in the line source formula when the wind direction is not perpendicular to the road was recently developed. This model is used to simulate NO_x concentrations in a large case study (1371 road sections representing about 831 km). NO₂, NO and O₃ concentrations are then computed using the photostationary-state approximation. NO₂ concentrations are compared with measurements made at 242 locations in the domain area. Model performance is satisfactory with mean normalised errors of 22% (winter month) to 31% (summer month). Results obtained here are also compared with those obtained with a previous formulation and with a standard model used for regulatory applications, ADMS-Urban. Discrepancies among the results obtained with those models are discussed.Wed, 03 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/429/2013/<b>Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations</b><br /><br />Geoscientific Model Development, 6, 429-443, 2013<br /><br />Author(s): J. Wong, M. C. Barth, and D. Noone<br /><br />The Price and Rind lightning parameterization based on cloud-top height is a commonly used method for predicting flash rate in global chemistry models. As mesoscale simulations begin to implement flash rate predictions at resolutions that partially resolve convection, it is necessary to validate and understand the behavior of this method within such a regime. In this study, we tested the flash rate parameterization, intra-cloud/cloud-to-ground (IC:CG) partitioning parameterization, and the associated resolution dependency "calibration factor" by Price and Rind using the Weather Research and Forecasting (WRF) model running at 36 km, 12 km, and 4 km grid spacings within the continental United States. Our results show that while the integrated flash count is consistent with observations when model biases in convection are taken into account, an erroneous frequency distribution is simulated. When the spectral characteristics of lightning flash rate are a concern, we recommend the use of prescribed IC:CG values. In addition, using cloud-top from convective parameterization, the "calibration factor" is also shown to be insufficient in reconciling the resolution dependency at the tested grid spacing used in this study. We recommend scaling by areal ratio relative to a base-case grid spacing determined by convective core density.Wed, 03 Apr 2013 00:00:00 +0200http://www.geosci-model-dev.net/6/417/2013/<b>A new method to diagnose the contribution of anthropogenic activities to temperature: temperature tagging</b><br /><br />Geoscientific Model Development, 6, 417-427, 2013<br /><br />Author(s): V. Grewe<br /><br />This study presents a new methodology, called temperature tagging. It keeps track of the contributions of individual processes to temperature within a climate model simulation. As a first step and as a test bed, a simple box climate model is regarded. The model consists of an atmosphere, which absorbs and emits radiation, and of a surface, which reflects, absorbs and emits radiation. The tagging methodology is used to investigate the impact of the atmosphere on surface temperature. Four processes are investigated in more detail and their contribution to the surface temperature quantified: (i) shortwave influx and shortwave atmospheric absorption ("sw"), (ii) longwave atmospheric absorption due to non-CO₂ greenhouse gases ("nC"), (iii) due to a base case CO₂ concentration ("bC"), and (iv) due to an enhanced CO₂ concentration ("eC"). The differential equation for the temperature in the box climate model is decomposed into four equations for the tagged temperatures. This method is applied to investigate the contribution of longwave absorption to the surface temperature (greenhouse effect), which is calculated to be 68 K. This estimate contrasts an alternative calculation of the greenhouse effect of slightly more than 30 K based on the difference of the surface temperature with and without an atmosphere. The difference of the two estimates is due to a shortwave cooling effect and a reduced contribution of the shortwave to the total downward flux: the shortwave absorption of the atmosphere results in a reduced net shortwave flux at the surface of 192 W m⁻², leading to a cooling of the surface by 14 K. Introducing an atmosphere results in a downward longwave flux at the surface due to atmospheric absorption of 189 W m⁻², which roughly equals the net shortwave flux of 192 W m⁻². This longwave flux is a result of both the radiation due to atmospheric temperatures and its longwave absorption. Hence the longwave absorption roughly accounts for 91 W m⁻² out of a total of 381 W m⁻² (roughly 25%) and therefore accounts for a temperature change of 68 K. In a second experiment, the CO₂ concentration is doubled, which leads to an increase in surface temperature of 1.2 K, resulting from a temperature increase due to CO₂ of 1.9 K, due to non-CO₂ greenhouse gases of 0.6 K and a cooling of 1.3 K due to a reduced importance of the solar heating for the surface and atmospheric temperatures. These two experiments show the feasibility of temperature tagging and its potential as a diagnostic for climate simulations.Tue, 26 Mar 2013 00:00:00 +0100http://www.geosci-model-dev.net/6/389/2013/<b>The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections</b><br /><br />Geoscientific Model Development, 6, 389-415, 2013<br /><br />Author(s): T. Iversen, M. Bentsen, I. Bethke, J. B. Debernard, A. Kirkevåg, Ø. Seland, H. Drange, J. E. Kristjansson, I. Medhaug, M. Sand, and I. A. Seierstad<br /><br />NorESM is a generic name of the Norwegian earth system model. The first version is named NorESM1, and has been applied with medium spatial resolution to provide results for CMIP5 (<a href="http://cmip-pcmdi.llnl.gov/cmip5/index.html" target=_blank>http://cmip-pcmdi.llnl.gov/cmip5/index.html</a>) without (NorESM1-M) and with (NorESM1-ME) interactive carbon-cycling. Together with the accompanying paper by Bentsen et al. (2012), this paper documents that the core version NorESM1-M is a valuable global climate model for research and for providing complementary results to the evaluation of possible anthropogenic climate change. NorESM1-M is based on the model CCSM4 operated at NCAR, but the ocean model is replaced by a modified version of MICOM and the atmospheric model is extended with online calculations of aerosols, their direct effect and their indirect effect on warm clouds. Model validation is presented in the companion paper (Bentsen et al., 2012). NorESM1-M is estimated to have equilibrium climate sensitivity of ca. 2.9 K and a transient climate response of ca. 1.4 K. This sensitivity is in the lower range amongst the models contributing to CMIP5. Cloud feedbacks dampen the response, and a strong AMOC reduces the heat fraction available for increasing near-surface temperatures, for evaporation and for melting ice. The future projections based on RCP scenarios yield a global surface air temperature increase of almost one standard deviation lower than a 15-model average. Summer sea-ice is projected to decrease considerably by 2100 and disappear completely for RCP8.5. The AMOC is projected to decrease by 12%, 15–17%, and 32% for the RCP2.6, 4.5, 6.0, and 8.5, respectively. Precipitation is projected to increase in the tropics, decrease in the subtropics and in southern parts of the northern extra-tropics during summer, and otherwise increase in most of the extra-tropics. Changes in the atmospheric water cycle indicate that precipitation events over continents will become more intense and dry spells more frequent. Extra-tropical storminess in the Northern Hemisphere is projected to shift northwards. There are indications of more frequent occurrence of spring and summer blocking in the Euro-Atlantic sector, while the amplitude of ENSO events weakens although they tend to appear more frequently. These indications are uncertain because of biases in the model's representation of present-day conditions. Positive phase PNA and negative phase NAO both appear less frequently under the RCP8.5 scenario, but also this result is considered uncertain. Single-forcing experiments indicate that aerosols and greenhouse gases produce similar geographical patterns of response for near-surface temperature and precipitation. These patterns tend to have opposite signs, although with important exceptions for precipitation at low latitudes. The asymmetric aerosol effects between the two hemispheres lead to a southward displacement of ITCZ. Both forcing agents, thus, tend to reduce Northern Hemispheric subtropical precipitation.Fri, 22 Mar 2013 00:00:00 +0100http://www.geosci-model-dev.net/6/373/2013/<b>The OASIS3 coupler: a European climate modelling community software</b><br /><br />Geoscientific Model Development, 6, 373-388, 2013<br /><br />Author(s): S. Valcke<br /><br />This paper presents the OASIS3 coupling software used in five of the seven European Earth System Models (ESMs) participating to the Fifth Coupled Model Intercomparison Project (CMIP5). A short history of the coupler development is followed by a technical description of OASIS3. The performances of a few relatively high resolution models coupled with OASIS3 are then described. It is shown that, although its limited field-per-field parallelism will eventually become a bottleneck in the simulation, OASIS3 can still be considered an appropriate tool for most of these relatively demanding coupled configurations. Its successful use in different CMIP5 ESMs is then detailed. A discussion of the benefits and drawbacks of the OASIS3's approach and a presentation of planned developments conclude the paper.Thu, 14 Mar 2013 00:00:00 +0100http://www.geosci-model-dev.net/6/353/2013/<b>Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation</b><br /><br />Geoscientific Model Development, 6, 353-372, 2013<br /><br />Author(s): N. H. Savage, P. Agnew, L. S. Davis, C. Ordóñez, R. Thorpe, C. E. Johnson, F. M. O'Connor, and M. Dalvi<br /><br />The on-line air quality model AQUM (Air Quality in the Unified Model) is a limited-area forecast configuration of the Met Office Unified Model which uses the UKCA (UK Chemistry and Aerosols) sub-model. AQUM has been developed with two aims: as an operational system to deliver regional air quality forecasts and as a modelling system to conduct air quality studies to inform policy decisions on emissions controls. This paper presents a description of the model and the methods used to evaluate the performance of the forecast system against the automated UK surface network of air quality monitors. Results are presented of evaluation studies conducted for a year-long period of operational forecast trials and several past cases of poor air quality episodes. The results demonstrate that AQUM tends to over-predict ozone (~8 μg m⁻³ mean bias for the year-long forecast), but has a good level of responsiveness to elevated ozone episode conditions – a characteristic which is essential for forecasting poor air quality episodes. AQUM is shown to have a negative bias for PM₁₀, while for PM_2.5 the negative bias is much smaller in magnitude. An analysis of speciated PM_2.5 data during an episode of elevated particulate matter (PM) suggests that the PM bias occurs mainly in the coarse component. The sensitivity of model predictions to lateral boundary conditions (LBCs) has been assessed by using LBCs from two different global reanalyses and by comparing the standard, single-nested configuration with a configuration having an intermediate European nest. We conclude that, even with a much larger regional domain, the LBCs remain an important source of model error for relatively long-lived pollutants such as ozone. To place the model performance in context we compare AQUM ozone forecasts with those of another forecasting system, the MACC (Monitoring Atmospheric Composition and Climate) ensemble, for a 5-month period. An analysis of the variation of model skill with forecast lead time is presented and the insights this provides to the relative sources of error in air quality modelling are discussed.Tue, 12 Mar 2013 00:00:00 +0100http://www.geosci-model-dev.net/6/345/2013/<b>Using model reduction to predict the soil-surface C¹⁸OO flux: an example of representing complex biogeochemical dynamics in a computationally efficient manner</b><br /><br />Geoscientific Model Development, 6, 345-352, 2013<br /><br />Author(s): W. J. Riley<br /><br />Earth system models (ESMs) must calculate large-scale interactions between the land and atmosphere while accurately characterizing fine-scale spatial heterogeneity in water, carbon, and other nutrient dynamics. We present here a high-dimension model representation (HDMR) approach that allows detailed process representation of a coupled carbon and water tracer (the &delta;¹⁸O value of the soil-surface CO₂ flux (&delta; <i>F</i>_s)) in a computationally tractable manner. &delta; <i>F</i>_s depends on the &delta;¹⁸O value of soil water, soil moisture and temperature, and soil CO₂ production (all of which are depth dependent), and the &delta;¹⁸O value of above-surface CO₂. We tested the HDMR approach over a growing season in a C₄-dominated pasture using two vertical soil discretizations. The difference between the HDMR approach and the full model solution in the three-month integrated isoflux was less than 0.2% (0.5 mol m⁻² &permil;), and the approach is up to 100 times faster than the full numerical solution. This type of model reduction approach allows representation of complex coupled biogeochemical processes in regional and global climate models and can be extended to characterize subgrid-scale spatial heterogeneity.Tue, 12 Mar 2013 00:00:00 +0100http://www.geosci-model-dev.net/6/327/2013/<b>Modeling atmospheric ammonia and ammonium using a stochastic Lagrangian air quality model (STILT-Chem v0.7)</b><br /><br />Geoscientific Model Development, 6, 327-344, 2013<br /><br />Author(s): D. Wen, J. C. Lin, L. Zhang, R. Vet, and M. D. Moran<br /><br />A new chemistry module that simulates atmospheric ammonia (NH₃) and ammonium (NH⁺₄) was incorporated into a backward-in-time stochastic Lagrangian air quality model (STILT-Chem) that was originally developed to simulate the concentrations of a variety of gas-phase species at receptors. STILT-Chem simulates the transport of air parcels backward in time using ensembles of fictitious particles with stochastic motions, while accounting for emissions, deposition and chemical transformation forward in time along trajectories identified by the backward-in-time simulations. The incorporation of the new chemistry module allows the model to simulate not only gaseous species, but also multi-phase species involving NH₃ and NH⁺₄. The model was applied to simulate concentrations of NH₃ and particulate NH⁺₄ at six sites in the Canadian province of Ontario for a six-month period in 2006. The model-predicted concentrations of NH₃ and particulate NH⁺₄ were compared with observations, which show broad agreement between simulated concentrations and observations. Since the model is based on back trajectories, the influence of each major process such as emission, deposition and chemical conversion on the concentration of a modeled species at a receptor can be determined for every upstream location at each time step. This makes it possible to quantitatively investigate the upstream processes affecting receptor concentrations. The modeled results suggest that the concentrations of NH₃ at those sites were significantly and frequently affected by Ohio, Iowa, Minnesota, Michigan, Wisconsin, southwestern Ontario and nearby areas. NH₃ is mainly contributed by emission sources whereas particulate NH⁺₄ is mainly contributed by the gas-to-aerosol chemical conversion of NH₃. Dry deposition is the largest removal process for both NH₃ and particulate NH⁺₄. This study revealed the contrast between agricultural versus forest sites. Not only were emissions of NH₃ higher, but removal mechanisms (especially chemical loss for NH₃ and dry deposition for NH⁺₄) were less efficient for agricultural sites. This combination explains the significantly higher concentrations of NH₃ and particulate NH⁺₄ observed at agricultural sites.Mon, 11 Mar 2013 00:00:00 +0100