Modeling Of Atmospheric Chemistry.pdf
Marcia Swindell
The Journal of Atmospheric Chemistry is devoted to the study of the chemistry of the Earth's atmosphere, with particular emphasis on the region below about 100 km. The strongly interdisciplinary nature of atmospheric chemistry means that it embraces a great variety of sciences, but the journal concentrates on such topics as:
Observational, interpretative and modeling studies of the composition of air and precipitation and the physiochemical processes in the Earth's atmosphere, excluding air pollution problems of local importance only;
the role of the atmosphere in biogeochemical cycles; the chemical interaction of the oceans, land surface and biosphere with the atmosphere;
laboratory studies of the mechanics in homogeneous and heterogeneous transformation processes in the atmosphere;
descriptions of major advances in instrumentation developed for the measurement of atmospheric composition and chemical properties.
One class of parameterization used for boundary layer clouds includes higher-order turbulence closure models. As of yet, no single turbulence closure model has succeeded in modeling both cumulus and stratocumulus regimes without case-specific adjustments. For instance, Bougeault (1981a,b) developed a closure model with prognostic equations for all the turbulent moments up to the third order. This higher-order closure scheme was coupled with a statistical representation of the subgrid-scale cloudiness. He used the scheme to simulate a trade wind cumulus layer observed during the Puerto Rico Experiment. However, in order to model a different regime like a marine stratocumulus layer, Bougeault (1985) changed the statistical cloudiness scheme and the representation of the mixing length from the ones in his original model.
Our parameterization can be regarded as a traditional higher-order closure model that uses a new closure based on a double Gaussian family of PDFs. Alternatively, our parameterization can be regarded as an extension of Lappen and Randall's model, in which the double delta function PDF they used for closure is generalized to a double Gaussian PDF. The generalization is inspired by the fact that Larson et al. (2002) evaluated the performance of several families of joint PDFs and found that atmospheric PDFs resemble double Gaussians more than double delta functions.
The Global Modeling Initiative (GMI) Chemical Transport Model (CTM) is part of the NASA Modeling Analysis and Prediction (MAP) program. The GMI CTM is used to assess the impacts of atmospheric circulation and composition change due to anthropogenic emissions, such as those from aircraft, biomass burning, fossil fuel combustion, and use of ozone depleting substances (ODS). GMI studies investigate changes in stratospheric ozone and the roles of long-range transport and changing emissions on air quality.
Grey bodies, like black bodies, absorb electromagnetic radiation. The absorptivity of a grey body is the ratio of the amount of energy absorbed by the body to the amount of energy absorbed by a black body at the same temperature. Note that the definition of the absorptivity is parallel to the definition of emissivity. If the components of a grey body, the particles and molecules in a sample of an atmospheric aerosol, for example, are in thermal equilibrium, then the emissivity and absorptivity for thermal radiation must be equal. If the emissivity and absorptivity were not the same, the sample could spontaneously develop cooler and warmer regions, which violates the second law of thermodynamics. Since the emissivity and absorptivity are the same for a grey body in thermal equilibrium, we will use the same symbol, ε, for both emissivity and absorptivity in the single-layer model for the atmospheric warming mechanism illustrated in this figure.
If the emissivity and absorptivity are zero, no radiation from the surface will be absorbed. This is identical to the energy balance for Earth acting as a black body in the absence of an atmosphere, for which the planetary temperature is calculated to be 255 K, just as in this graph. If the emissivity and absorptivity are unity, the atmosphere is a black body and all radiation from the surface is absorbed. For this model, the graph shows that the surface temperature for ε = 1 is 303 K. In this case, the atmospheric temperature is:
The past 20 years of research using Earth system models (ESMs) is reviewed with an emphasis on results from the ESM based on MIROC (Model for Interdisciplinary Research on Climate) developed in Japan. Earth system models are climate models incorporating biogeochemical processes such as the carbon cycle. The development of ESM was triggered by studies of the feedback between climate change and the carbon cycle. State-of-the-art ESMs are much more realistic than the first ESMs. They now include various biogeochemical processes other than carbon, such as atmospheric chemistry and the nitrogen and iron cycles as well as nutrient transport by atmospheric dust and rivers. They are used to address many practical issues, such as evaluating the amount of carbon dioxide emissions that is consistent with climate change mitigation targets, and are indispensable tools for the development of climate change mitigation policies. Novel, ambitious attempts to use ESMs include coupling socioeconomics with Earth systems, and projecting the carbon cycle on decadal timescales. Development of ESMs requires ongoing integration of multiple aspects of climate science. Emerging applications of ESMs can bring forth meaningful insights, and should be directed toward expanding connections with fields outside climate science, e.g., socioeconomics.
Structure of the original version of the Earth system model (ESM) used for the Coupled Model Intercomparison Project (CMIP) Phase 3, developed by Team MIROC. From CMIP Phase 5 onwards, the full atmospheric chemistry component model CHASER was added. The land biogeochemical model was later replaced by SEIB-DGVM for CMIP Phase 5 and then by VISIT for CMIP Phase 6. All model acronyms are listed in the Abbreviations
River transport is incorporated into MIROC-ES2L; a certain portion of active nitrogen on land is carried by rivers into the ocean; this terrestrial component becomes part of the marine inorganic nitrogen, which is consumed in oceanic phytoplankton photosynthesis (Hajima et al. 2020). For CMIP5, MIROC-ESM only had a closed nitrogen cycle in the ocean. For CMIP6, nitrogen cycles on land and ocean are linked in MIROC-ES2L; the modeled ocean has an additional source of nitrogen, necessitating explicit treatment of nitrogen sinks and other sources within the ocean. Like many other ESMs in CMIP6, MIROC-ES2L takes nitrogen fixation and denitrification into consideration. Dust deposition of nitrogen from the atmosphere is externally provided as input data. The impacts on oceanic net primary production of river transport and atmospheric deposition have been examined by Hajima et al. (2020). The effect of anthropogenic nitrogen river loading is becoming detectable in some coastal regions, albeit not at basin or larger scales (Gruber and Galloway 2008; Rabalais 2002). However, it would be desirable for future model development to include physical transport processes near coasts, thereby enabling evaluation of impacts of nitrogen loading, which can undergo explosive increases related to population growth (Bodirsky et al. 2012).
Iron supply via atmospheric dust is a vital part of the oceanic iron cycle. Influxes of iron to the ocean have conventionally been evaluated by multiplying dust deposition (e.g., Duce and Tindale 1991; Jickells et al. 2005) by a constant parameter (Duce et al. 1991). This simple approach was adopted because of a lack of adequate observation data, although clearly this is insufficient to reproduce the geographical distribution of iron inputs. Currently, process-based models are being developed that may be able to capture the geographical distribution. One example is the model by Ito et al. (2019), which takes into account the key process associated with pyrogenic iron changes. In MIROC-ES2L, pyrogenic and lithogenic iron are differentiated, although emission of pyrogenic iron is prescribed as input data. Treating pyrogenic and lithogenic iron separately is a characteristic feature of the MIROC series of ESMs, and enables special variation in solubility of iron supplied to the ocean to be reproduced (Hajima et al. 2020).
Besides nitrogen and iron cycling, another important biogeochemical process yet to be incorporated into many (including the MIROC series) of the existing ESMs is CO2 or CH4 emissions related to permafrost thawing, as pointed out by the IPCC (2018). Despite this issue being known for many years, model development has been hampered by the scarcity of data. Some modeling groups have started to embed detailed treatment of permafrost, as observation data become available (Xia et al. 2017).
These forcing agents are termed as short-lived climate forcers (SLCFs) since they tend to have shorter residence times than CO2 because of their high chemical reactivity in or rapid deposition out of the atmosphere. At its 49th plenary in 2019, the IPCC decided to revise SLCF inventory methodology to improve the emissions dataset (IPCC 2019). This development can serve as a timely stimulus for the development of ESMs that explicitly incorporate full atmospheric chemistry.
Coupling of component models in a MIROC-ESM-CHEM used for CMIP5 and b MIROC-ES2H used for CMIP. T42 and T85 indicate horizontal resolutions that correspond to approximately 280 and 140 km, respectively. L80 and L81 indicate 80 and 81 vertical layers in the atmospheric model.
The MIROC-ES2H is the latest version of the MIROC series for CMIP6 with full chemistry (Sudo et al. 2002). Except for the treatment of atmospheric chemistry and horizontal resolution, it has inherited most of the features of MIROC-ES2L. It is at T85 (140 km) resolution, which is relatively high for such a complex model. Because computational load would be unrealistically high if the chemical component were to be directly coupled with the main body of the ESM, the atmosphere-only model with full chemistry is run at T42 in parallel with the main coupled model at T85 (Fig. 3b). The model coupling software Jcup (Arakawa et al. 2020) exchanges data between the T42 and T85 models, and computational requirements are maintained at a level that can be feasibly met.
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