Identifying errors in dust models from data assimilation

Pope, Richard J.; Marsham, John H.; Knippertz, Peter; Brooks, M. E.; Roberts, Alexander J.

doi:10.1002/2016gl070621

Cited by 37 publications

(32 citation statements)

References 80 publications

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“…The use of soil moisture in the model 10 is also implicated, since the model uses soil moisture over a 10cm layer, whereas in reality it is the skin soil moisture that is relevant and both the soil makeup (sandy soils) and the hot, dry conditions in the northern Sahel and Sahara mean that the actual time between rainfall and dust emission can be much shorter than that predicted by the SWAMMA simulations (Gillette et al, 2001, Bergametti et al, 2016. This role of the land-surface errors in the Sahel is consistent with recent analysis of operational global UM runs (Pope et al, 2016). Finally, clay fraction is a crucial soil texture parameter in several of the dust 15 emission and flux calculations and high clay fractions over the west coast in combination with strong northerly winds blowing off the Atlantic cause high AOD values there which are not seen in observations.…”

Section: Discussionsupporting

confidence: 72%

Can explicit convection improve modelled dust in summertime West Africa?

Roberts¹,

Woodage²,

Marsham³

et al. 2018

Preprint

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Abstract. Global and regional models have large systematic errors in their modelled dust fields over West Africa. It is well established that cold pool outflows from moist convection (haboobs) can raise over 50 % of the dust over the Sahara and Sahel in summer, but parameterised moist convection tends to give a very poor representation of this in models. Here, we test the hypothesis that an explicit representation of convection improves haboob winds and so may reduce errors in modelled dust fields. The results show that despite varying both grid-spacing and the representation of convection there are only minor 15 changes in dust aerosol optical depth (AOD) and dust mass loading fields between simulations. In all simulations there is an AOD deficit over the observed central Saharan dust maximum and a high bias in AOD along the west coast: both features consistent with many climate (CMIP5) models. Cold pool outflows are present in the explicit simulations and do raise dust. Consistent with this there is an improved diurnal cycle in dust-generating winds with a seasonal peak in evening winds at locations with moist convection that is absent in simulations with parameterised convection. However, the explicit convection 20 does not change the AOD field significantly for several reasons. Firstly, the increased windiness in the evening from haboobs is approximately balanced by a reduction in morning winds associated with the breakdown of the nocturnal low-level jet (LLJ).Secondly, although explicit convection increases the frequency of the strongest winds, these are still weaker than observed, especially close to the observed summertime Saharan dust maximum: this results from the fact that although large mesoscale convective systems (and resultant cold pools) are generated, they have a lower frequency than observed and haboob winds are 25 too weak. Finally, major impacts of the haboobs on winds occur over the Sahel, where, although dust uplift is known to occur in reality, uplift in the simulations is limited by a seasonally constant bare soil fraction in the model, together with soil moisture and clay fractions which are too restrictive of dust emission in seasonally-varying vegetated regions. For future studies, the results demonstrate 1) the improvements in behaviour produced by the explicit representation of convection, 2) the value of simultaneously evaluating both dust and winds and 3) the need to develop parameterisations of the land surface alongside those 30 of dust-generating winds.Atmos. Chem. Phys. Discuss., https://doi

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Section: Discussionsupporting

confidence: 72%

Can explicit convection improve modelled dust in summertime West Africa?

Roberts¹,

Woodage²,

Marsham³

et al. 2018

Preprint

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“…Measurements should include the following: mass concentrations of chemical components (soot, organics, ammonia, sulfate, nitrate, mineral dust, and sea salt), number concentrations (of PM 1 , PM 2.5 , and PM 10 ), and size distribution (if possible resolved by chemical species). Evaluating whether relevant emissions and feedback processes are treated accurately by a model is challenging, although data assimilation can provide valuable information (Pope et al, 2016). In addition to key meteorological parameters associated with aerosol emissions (e.g., surface winds and soil moisture), the effects of aerosols on radiation and clouds, for example, depend on the physical and chemical properties of the aerosols.…”

Section: User Requirements For Benchmark Testingmentioning

confidence: 99%

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

et al. 2018

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Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite-and groundbased platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of ...

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“…For example, through analysis of rain and lightning observations, Pope et al . [] concluded that “ haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme .” Heinold et al . [] made simulations over West Africa with 40, 12, and 4 km grid spacing.…”

Section: Introductionmentioning

confidence: 99%

Improving the simulation of convective dust storms in regional‐to‐global models

Foroutan

Pleim

2017

J Adv Model Earth Syst

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Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

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Identifying errors in dust models from data assimilation

Cited by 37 publications

References 80 publications

Can explicit convection improve modelled dust in summertime West Africa?

Can explicit convection improve modelled dust in summertime West Africa?

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

Improving the simulation of convective dust storms in regional‐to‐global models

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