Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0

Klose, Martina; Jorba, Oriol; Gonçalves, M.; Escribano, Jerónimo; Dawson, Matthew L.; Obiso, Vincenzo; Tomaso, Enza Di; Basart, Sara; Pinto, Gilbert Montané; Macchia, Francesca; Ginoux, Paul; Guerschman, Juan Pablo; Prigent, Catherine; Huang, Yongjun; Kok, Jasper F.; Miller, Ron; García‐Pando, Carlos Pérez

doi:10.5194/gmd-2021-32

Cited by 16 publications

(34 citation statements)

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“…To simulate the dust cycle, we used the Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model (Pérez et al, 2011;Haustein et al, 2012;Jorba et al, 2012;Badia et al, 2017;Di Tomaso et al, 2017;Klose et al, 2021). MONARCH is a fully online integrated system for meso-to global-scale applications developed at the Barcelona Supercomputing Center (BSC).…”

Section: Monarch Modelmentioning

confidence: 99%

“…Dust is described using eight particle-size bins within 0.2-20 µm in diameter. The MONARCH dust module is described in detail in Pérez et al (2011) and Klose et al (2021). MONARCH offers a diversity of mineral dust emission schemes along with multiple configurations.…”

Section: Monarch Modelmentioning

confidence: 99%

“…For this study we computed dust emissions by averaging the emission produced by the four configurations listed in Table 1. All configurations used a modified version of the dust emission scheme of Ginoux et al (2001) with modifications described in Klose et al (2021) that include the use of friction velocity instead of 10 m wind speed, a dust-particle size independent threshold friction velocity for particle entrainment taken as the minimum value of the threshold-function from Shao and Lu (2000), and an emitted size distribution following Kok (2011). The entrainment threshold accounts for soil moisture using the correction from Belly (1964) as described in Ginoux et al (2001).…”

Section: Monarch Modelmentioning

confidence: 99%

“…Areas where dust emission is allowed are constrained by satellite observations, specifically by the frequency of occurrence (FoO) of the Moderate Resolution Imaging Spectroradiometer (MODIS) DB DOD exceeding 0.2 (Hsu et al, 2004;Ginoux et al, 2012). Dust can be emitted for areas in which FoO > 0.025 (Klose et al, 2021). The four configurations differ with regard to the description of (a) the preferential dust sources used for scaling of the dust emission flux and (b) vegetation and surface roughness effects.…”

Section: Monarch Modelmentioning

confidence: 99%

“…Configurations I and II used the MODIS FoO of DOD > 0.2 to scale the dust emission flux obtained with the modified Ginoux et al (2001) Marticorena and Bergametti (1995) with corrections from King et al (2005) in combination with a monthly climatology of MODIS-derived leaf-area index (Myeni et al, 2015) and aerodynamic roughness length data for arid regions from Prigent et al (2012). In contrast, configurations II and IV utilized the drag partition parameterization from Raupach et al (1993) together with a monthly climatology of photosynthetic and non-photosynthetic vegetation cover data from Guerschman et al (2015) (Klose et al, 2021). The drag partition corrections were applied to the threshold friction velocity for particle entrainment.…”

Section: Monarch Modelmentioning

confidence: 99%

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Assimilating spaceborne lidar dust extinction improves dust forecasts

Escribano

Tomaso

Jorba

et al. 2021

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Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from space-borne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating space-borne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the space-borne lidar profiles upon the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of two months over Northern Africa, the Middle East and Europe. We assimilate DOD derived from VIIRS/SUOMI-NPP Deep Blue, and for the first time CALIOP-based LIVAS pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AERONET Sun photometers and ground-based lidar dust extinction profiles from field campaigns (CyCARE and Pre-TECT). Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discriminating desert dust from other aerosol types.

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Section: Monarch Modelmentioning

confidence: 99%

Section: Monarch Modelmentioning

confidence: 99%

Section: Monarch Modelmentioning

confidence: 99%

Section: Monarch Modelmentioning

confidence: 99%

Section: Monarch Modelmentioning

confidence: 99%

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Assimilating spaceborne lidar dust extinction improves dust forecasts

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Tomaso

Jorba

et al. 2021

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Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

O’Neill

Xian²,

Flemming

et al. 2022

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Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

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MONARCH Regional Reanalysis of Desert Dust Aerosols: An Initial Assessment

Tomaso

Escribano

Basart

et al. 2022

Air Pollution Modeling and Its Application XXVIII

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Aerosol reanalyses are a well-established tool for monitoring aerosol trends, for validation and calibration of weather chemical models, as well as for the enhancement of strategies for environmental monitoring and hazard mitigation. By providing a consistent and complete data set over a sufficiently long period, they address the shortcomings of aerosol observational records in terms of temporal and spatial coverage and aerosol speciation. These shortcomings are particularly severe for dust aerosols. A 10-year dust aerosol regional reanalysis has been recently produced on the Barcelona Supercomputing Center HPC facilities at the high spatial resolution of 0.1 • . Here we present a brief description and an initial assessment of this data set. An innovative dust optical depth data set, derived from the MODIS Deep Blue products, has been ingested in the dust module of the MONARCH model by means of a LETKF with a four-dimensional extension. MONARCH ensemble

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Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0

Cited by 16 publications

References 0 publications

Assimilating spaceborne lidar dust extinction improves dust forecasts

Assimilating spaceborne lidar dust extinction improves dust forecasts

Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

MONARCH Regional Reanalysis of Desert Dust Aerosols: An Initial Assessment

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