ICON-ART 2.1 – A flexible tracer framework and its application for
composition studies in numerical weather forecasting and climate
simulations

Schröter, Jennifer; Rieger, Daniel; Stassen, Christian; Vogel, H.; Weimer, Michael; Werchner, Sven; Förstner, Jochen; Prill, Florian; Reinert, Daniel; Zängl, Günther; Giorgetta, Marco; Ruhnke, Roland; Vogel, Bernhard; Braesicke, Peter

doi:10.5194/gmd-2017-286

Cited by 4 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These temperature fluctuations would otherwise be missing as current global climate models have a spatial resolution of some hundreds of kilometers and are therefore only able to explicitly resolve waves with relatively long horizontal wavelengths, resulting in insufficient PSC formation and a systematic over‐prediction of springtime stratospheric ozone at high‐latitudes (Eyring et al., 2006). Efforts are underway to develop variable‐resolution global CCMs, such as ICON‐ART (ICOsahedral Nonhydrostatic model with Aerosols and Reactive Trace gases; Schröter et al., 2018), which are able to use local grid refinement over mountainous regions in order that the mountain‐wave‐induced temperature fluctuations are explicitly resolved, obviating the need for their parameterization. Progress toward this goal was limited until detailed observations of PSCs and mountain waves became available, which are essential for a complete evaluation of model results (Hoffmann et al., 2017; Höpfner, Larsen et al., 2006; Spang et al., 2018).…”

Section: Dynamical Forcing Of Pscsmentioning

confidence: 99%

Polar Stratospheric Clouds: Satellite Observations, Processes, and Role in Ozone Depletion

Tritscher

Pitts

Poole

et al. 2021

Reviews of Geophysics

View full text Add to dashboard Cite

Polar stratospheric clouds (PSCs) play important roles in stratospheric ozone depletion during winter and spring at high latitudes (e.g., the Antarctic ozone hole). PSC particles provide sites for heterogeneous reactions that convert stable chlorine reservoir species to radicals that destroy ozone catalytically. PSCs also prolong ozone depletion by delaying chlorine deactivation through the removal of gas-phase HNO 3 and H 2 O by sedimentation of large nitric acid trihydrate (NAT) and ice particles. Contemporary observations by the spaceborne instruments Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Microwave Limb Sounder (MLS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) have provided an unprecedented polar vortex-wide climatological view of PSC occurrence and composition in both hemispheres. These data have spurred advances in our understanding of PSC formation and related dynamical processes, especially the firm evidence of widespread heterogeneous nucleation of both NAT and ice PSC particles, perhaps on nuclei of meteoritic origin. Heterogeneous chlorine activation appears to be well understood. Reaction coefficients on/in liquid droplets have been measured accurately, and while uncertainties remain for reactions on solid NAT and ice particles, they are considered relatively unimportant since under most conditions chlorine activation occurs on/in liquid droplets. There have been notable advances in the ability of chemical transport and chemistry-climate models to reproduce PSC temporal/spatial distributions and composition observed from space. Continued spaceborne PSC observations will facilitate further improvements in the representation of PSC processes in global models and enable more accurate projections of the evolution of polar ozone and the global ozone layer as climate changes.Plain Language Summary Polar stratospheric clouds (PSCs) occur during winter and early spring in the polar stratosphere, when temperatures are low enough to enable cloud formation despite the extremely dry conditions. Ground-based PSC sightings date back to the late 19th century, but they were little more than a scientific curiosity until the discovery of the Antarctic ozone hole in 1985. Soon thereafter, it was shown that PSCs play a crucial role in converting stable halogen (mainly chlorine) species of anthropogenic origin into reactive gases that rapidly destroy ozone. Considerable progress was made over the next two decades in quantifying these processes through laboratory studies, field campaigns, and limited spaceborne observations. We are now reaping the benefits of new PSC observations over the entire polar regions from three complementary 21st century spaceborne instruments. This study reviews these instruments and highlights new findings on PSC occurrence and composition. These datasets have also triggered advances in understanding how PSCs form and the influence of atmospheric dynamics, as well as improvements in how detailed cloud processes are approximated in global models. Thi...

show abstract

Section: Dynamical Forcing Of Pscsmentioning

confidence: 99%

Polar Stratospheric Clouds: Satellite Observations, Processes, and Role in Ozone Depletion

Tritscher

Pitts

Poole

et al. 2021

Reviews of Geophysics

View full text Add to dashboard Cite

show abstract

“…A detailed description of the model can be found in Zängl et al (2015) and Giorgetta et al (2018). With the extension Aerosols and Reactive Trace gases (ICON-ART) developed at the Karlsruhe Institute of Technology (KIT), the model is able to simulate aerosols, trace gases, and related feedbacks (Rieger et al, 2015;Schröter et al, 2018). The limited area mode, applied here, enables the model to simulate a confined region at high resolution with prescribed lateral boundary conditions.…”

Section: Weather and Climate Modelmentioning

confidence: 99%

Comparison of optical-equivalent snow grain size estimates under Arctic low Sun conditions during PAMARCMiP 2018

Jäkel

Carlsen

Ehrlich

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Non-hydrostatic – Aerosols and Reactive Trace gases) simulations. The study is focused on low Sun and partly rough surface conditions encountered during a three-week campaign conducted North of Greenland in March/April 2018 within the framework of the PAMARCMiP (Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project) project. Further, we propose an adjusted airborne retrieval method by using the albedo at 1700 nm wavelength. This reduced the effect of atmospheric masking and improved the sensitivity on ropt. From this approach we achieved a significantly improved uncertainty (

show abstract

“…The choice of input variables was also made in such a way that it is possible to calculate them for the grid points. Some input parameters of our model (e.g., Cl y , Br y , NO y , HO y ) are generally not available in climate models like ICON (Rieger et al, 2015;Schröter et al, 2018). To produce the training data of Neural-SWIFT for any grid point, these variables were calculated from the chemical species of the full chemistry The variables of the photolysis frequencies can be taken from a photolysis table when implemented in a climate model (see Section 4).…”

Section: Implementation Into Climate Modelsmentioning

confidence: 99%

Neural representation of the stratospheric ozone chemistry

Mohn,

Kreyling,

Wohltmann

et al. 2023

Environ. Data Science

View full text Add to dashboard Cite

In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone layer in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of the ATLAS Chemistry and Transport Model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-h ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by replacing the full chemistry module of ATLAS and comparing computation time, accuracy, and stability. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a 2-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation.

show abstract

ICON-ART 2.1 – A flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations

Cited by 4 publications

References 32 publications

Polar Stratospheric Clouds: Satellite Observations, Processes, and Role in Ozone Depletion

Polar Stratospheric Clouds: Satellite Observations, Processes, and Role in Ozone Depletion

Comparison of optical-equivalent snow grain size estimates under Arctic low Sun conditions during PAMARCMiP 2018

Neural representation of the stratospheric ozone chemistry

Contact Info

Product

Resources

About