Performance gains in an ESM using parallel ad-hoc file systems

Earth System Models (ESM) got much more demanding over the last years. Modelled processes got more complex and more and more processes are considered in models. In addition resolutions of the models got higher to improve weather and climate forecasts. This requires faster high performance computers (HPC) and better I/O performance. Within our Pilot Lab Exascale Earth System Modelling (PL-EESM) we do performance analysis of the ESM EMAC using a standard Lustre file system for output and compare it to the performance using a parallel ad-hoc overlay file system. We will show the impact for two scenarios: one for todays standard amount of output and one with artificial heavy output simulating future ESMs. An ad-hoc file system is a private parallel file system which is created on-demand for an HPC job using the node-local storage devices, in our case solid-state-disks (SSD). It only exists during the runtime of the job. Therefore output data have to be moved to a permanent file system before the job has finished. Quasi in-situ data analysis and post-processing allows to gain performance as it might result in a decreased amount of data which you have to store - saving disk space and time during the transfer of data to permanent storage. We will show first tests for quasi in-situ post-processing

Computational Methods in Science and Engineering : Proceedings of the Workshop SimLabs@KIT, November 29 - 30, 2010, Karlsruhe, Germany

In this proceedings volume we provide a compilation of article contributions equally covering applications from different research fields and ranging from capacity up to capability computing. Besides classical computing aspects such as parallelization, the focus of these proceedings is on multi-scale approaches and methods for tackling algorithm and data complexity. Also practical aspects regarding the usage of the HPC infrastructure and available tools and software at the SCC are presented

Mountain-wave-induced polar stratospheric clouds and their representation in the global chemistry model ICON-ART

Polar stratospheric clouds (PSCs) are a driver for ozone depletion in the lower polar stratosphere. They provide surface for heterogeneous reactions activating chlorine and bromine reservoir species during the polar night. The large-scale effects of PSCs are represented by means of parameterisations in current global chemistry–climate models, but one process is still a challenge: the representation of PSCs formed locally in conjunction with unresolved mountain waves. In this study, we investigate direct simulations of PSCs formed by mountain waves with the ICOsahedral Nonhydrostatic modelling framework (ICON) with its extension for Aerosols and Reactive Trace gases (ART) including local grid refinements (nesting) with two-way interaction. Here, the nesting is set up around the Antarctic Peninsula, which is a well-known hot spot for the generation of mountain waves in the Southern Hemisphere. We compare our model results with satellite measurements of PSCs from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and gravity wave observations of the Atmospheric Infrared Sounder (AIRS). For a mountain wave event from 19 to 29 July 2008 we find similar structures of PSCs as well as a fairly realistic development of the mountain wave between the satellite data and the ICON-ART simulations in the Antarctic Peninsula nest. We compare a global simulation without nesting with the nested configuration to show the benefits of adding the nesting. Although the mountain waves cannot be resolved explicitly at the global resolution used (about 160 km), their effect from the nested regions (about 80 and 40 km) on the global domain is represented. Thus, we show in this study that the ICON-ART model has the potential to bridge the gap between directly resolved mountain-wave-induced PSCs and their representation and effect on chemistry at coarse global resolutions

Case study of ozone anomalies over northern Russia in the 2015/2016 winter: measurements and numerical modelling

Episodes of extremely low ozone columns were observed over the territory of Russia in the Arctic winter of 2015/2016 and the beginning of spring 2016. We compare total ozone columns (TOCs) from different remote sensing techniques (satellite and ground-based observations) with results of numerical modelling over the territory of the Urals and Siberia for this period. We demonstrate that the provided monitoring systems (including the new Russian Infrared Fourier Spectrometer IKFS-2) and modern three-dimensional atmospheric models can capture the observed TOC anomalies. However, the results of observations and modelling show differences of up to 20&thinsp;%–30&thinsp;% in TOC measurements. Analysis of the role of chemical and dynamical processes demonstrates that the observed short-term TOC variability is not a result of local photochemical loss initiated by heterogeneous halogen activation on particles of polar stratospheric clouds that formed under low temperatures in the mid-winter.</p

Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period

The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8:7*10^5 and 12:8*10^5 molec cm-3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and volatile organic compound (VOC) chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0:3*10^5 molec cm-3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in the CH4 mixing ratio in 2010, which represents 7%–20% of the model-simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of > 30 ppbv. Over the full 2000–2016 time period, using a common stateof- the-art but nonoptimized emission scenario, the impact of [OH] changes tested here can explain up to 54% of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions

An assessment of the climatological representativeness of IAGOS-CARIBIC trace gas measurements using EMAC model simulations

Measurement data from the long-term passenger aircraft project IAGOS-CARIBIC are often used to derive climatologies of trace gases in the upper troposphere and lower stratosphere (UTLS). We investigate to what extent such climatologies are representative of the true state of the atmosphere. Climatologies are considered relative to the tropopause in mid-latitudes (35 to 75° N) for trace gases with different atmospheric lifetimes. Using the chemistry–climate model EMAC, we sample the modeled trace gases along CARIBIC flight tracks. Representativeness is then assessed by comparing the CARIBIC sampled model data to the full climatological model state. Three statistical methods are applied for the investigation of representativeness: the Kolmogorov–Smirnov test and two scores based on the variability and relative differences. Two requirements for any score describing representativeness are essential: representativeness is expected to increase (i) with the number of samples and (ii) with decreasing variability of the species considered. Based on these two requirements, we investigate the suitability of the different statistical measures for investigating representativeness. The Kolmogorov–Smirnov test is very strict and does not identify any trace-gas climatology as representative – not even of long-lived trace gases. In contrast, the two scores based on either variability or relative differences show the expected behavior and thus appear applicable for investigating representativeness. For the final analysis of climatological representativeness, we use the relative difference score and calculate a representativeness uncertainty for each trace gas in percent. In order to justify the transfer of conclusions about representativeness of individual trace gases from the model to measurements, we compare the trace gas variability between model and measurements. We find that the model reaches 50–100 % of the measurement variability. The tendency of the model to underestimate the variability is caused by the relatively coarse spatial and temporal model resolution. In conclusion, we provide representativeness uncertainties for several species for tropopause-referenced climatologies. Long-lived species like CO2 have low uncertainties ( ≤ 0.4 %), while shorter-lived species like O3 have larger uncertainties (10–15 %). Finally, we translate the representativeness score into a number of flights that are necessary to achieve a certain degree of representativeness. For example, increasing the number of flights from 334 to 1000 would reduce the uncertainty in CO to a mere 1 %, while the uncertainty for shorter-lived species like NO would drop from 80 to 10 %

Mountain-wave induced polar stratospheric clouds and their representation in the global chemistry model ICON-ART112

Abstract. Polar stratospheric clouds (PSCs) are a driver for ozone depletion in the lower polar stratosphere. They provide surfaces for heterogeneous reactions activating chlorine and bromine reservoir species during the polar night. PSCs are represented in current global chemistry-climate models, but one process is still a challenge: the representation of PSCs formed locally in conjunction with unresolved mountain waves. In this study, we present simulations with the ICOsahedral Nonhydrostatic modelling framework (ICON) with its extension for Aerosols and Reactive Trace gases (ART) that include local grid refinements (nesting) with two-way interaction. Here, the nesting is set up around the Antarctic Peninsula which is a well-known hot spot for the generation of mountain waves in the southern hemisphere. We compare our model results with satellite measurements from the Cloud-Aerosol LIdar with Orthogonal Polarisation (CALIOP) and the Atmospheric InfraRed Sounder (AIRS). We study a mountain wave event that took place from 19 to 29 July 2008 and find similar structures of PSCs as well as a fairly realistic development of the mountain wave in the Antarctic Peninsula nest. We compare a global simulation without nesting with the nested configuration to show the benefit. Although the mountain waves cannot be resolved adequately in the used global resolution (about 160 km), their effect from the nested regions (about 80 and 40 km) on the global domain is represented. Thus, we show in this study that by using the two-way nesting technique the gap between directly resolved mountain-wave induced PSCs and their representation and effect on chemistry in coarse global resolutions can be bridged by the ICON-ART model