Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

Roberts, Alexander J.; Marsham, John H.; Knippertz, Peter

doi:10.1175/mwr-d-14-00218.1

Cited by 31 publications

(34 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evan et al, 2015). Most models currently struggle in regard to shortterm variability in water vapour (Birch et al, 2014;GarciaCarreras et al, 2013;Marsham et al, 2013a;Roberts et al, 2015), clouds (Roehrig et al, 2013;Stein et al, 2015) and dust (Evan et al, 2014), with many dust errors coming from moist convection (Heinold et al, 2013;. Forecast models typically have mean biases of up to 2 kg m −2 in column-integrated water vapour (equivalent to change in 2.6 W m −2 TOA net flux) and lack variability in dust, and thus are expected to poorly represent these couplings.…”

Section: Discussionmentioning

confidence: 99%

“…Note that the error in reanalysis at BBM is relatively small because the Fennec radiosonde data were assimilated. In the subsequent absence of such observational data, we expect reanalysis errors to be greater given the known problems of reanalysis model representation of mesoscale convective processes in the region Roberts et al, 2015;Todd et al, 2013). Such mesoscale convective "cold-pool" outflow haboobs are known to make a significant contribution to moisture advection in addition to being the dominant dust emission process Trzeciak et al, 2017).…”

Section: Atmospheric Profile and Surface Characteristicsmentioning

confidence: 99%

“…Addressing these research gaps is hindered by the acute shortage of routine observations in the region and large discrepancies between models and reanalyses (Evan et al, 2015a;Roberts et al, 2015). This paper seeks to address these gaps in our understanding of radiative processes within the SHL during early summer.…”

mentioning

confidence: 99%

See 2 more Smart Citations

The early summertime Saharan heat low: sensitivity of the radiation budget and atmospheric heating to water vapour and dust aerosol

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. The Saharan heat low (SHL) is a key component of the west African climate system and an important driver of the west African monsoon across a range of timescales of variability. The physical mechanisms driving the variability in the SHL remain uncertain, although water vapour has been implicated as of primary importance. Here, we quantify the independent effects of variability in dust and water vapour on the radiation budget and atmospheric heating of the region using a radiative transfer model configured with observational input data from the Fennec field campaign at the location of Bordj Badji Mokhtar (BBM) in southern Algeria (21.4 • N, 0.9 • E), close to the SHL core for June 2011. Overall, we find dust aerosol and water vapour to be of similar importance in driving variability in the top-of-atmosphere (TOA) radiation budget and therefore the column-integrated heating over the SHL (∼ 7 W m −2 per standard deviation of dust aerosol optical depth -AOD). As such, we infer that SHL intensity is likely to be similarly enhanced by the effects of dust and water vapour surge events. However, the details of the processes differ. Dust generates substantial radiative cooling at the surface (∼ 11 W m −2 per standard deviation of dust AOD), presumably leading to reduced sensible heat flux in the boundary layer, which is more than compensated by direct radiative heating from shortwave (SW) absorption by dust in the dusty boundary layer. In contrast, water vapour invokes a radiative warming at the surface of ∼ 6 W m −2 per standard deviation of column-integrated water vapour in kg m −2 . Net effects involve a pronounced net atmospheric radiative convergence with heating rates on average of 0.5 K day −1 and up to 6 K day −1 during synoptic/mesoscale dust events from monsoon surges and convective cold-pool outflows ("haboobs"). On this basis, we make inferences on the processes driving variability in the SHL associated with radiative and advective heating/cooling. Depending on the synoptic context over the region, processes driving variability involve both independent effects of water vapour and dust and compensating events in which dust and water vapour are co-varying. Forecast models typically have biases of up to 2 kg m −2 in column-integrated water vapour (equivalent to a change in 2.6 W m −2 TOA net flux) and typically lack variability in dust and thus are expected to poorly represent these couplings. An improved representation of dust and water vapour and quantification of associated radiative impact in models is thus imperative to further understand the SHL and related climate processes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Atmospheric Profile and Surface Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

The early summertime Saharan heat low: sensitivity of the radiation budget and atmospheric heating to water vapour and dust aerosol

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…The lack of observations in combination with the difficultto-represent meteorology also leads to substantial deviations among different analysis products, even on continental scales (Roberts et al, 2015), creating substantial differences in dust emissions (e.g., Menut, 2008). However, the fine-scale nature of dust emissions prevents large scale observations from providing constraint on what a "correct" dust source function is; rather available observations provide only a gross tuning parameter (Khade et al, 2013).…”

Section: User Requirements For Desert Mineral Dust Emissionsmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

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 ...

show abstract

“…The lack of observations in combination with the difficult-to-represent meteorology also leads to substantial deviations between different analysis products, even on continental scales (Roberts et al, 2015), creating substantial differences in dust emission (e.g. Menut (2008) of the Saharan heat low, which is crucial for the large-scale circulation over northern Africa and thus a dominating factor for dust generation, can vary substantially between different analyses or model simulations with different resolution (Marsham et al, 2011).…”

mentioning

confidence: 99%

Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements

Benedetti¹,

Reid²,

Baklanov³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centres due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, providers of climate services, and health professionals. The prediction of aerosol particle properties in Numerical Weather Prediction (NWP) models faces a number of challenges owing to 5 the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions. Errors in aerosol prediction concern all processes involved in the aerosol life cycle.These include errors on the source terms (for both anthropogenic and natural emissions), errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), as well as errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transfor-10 mation and growth, hygroscopicity). The main goal of current research on aerosol forecast consists in prioritizing these errors and trying to reduce the most important ones through model development and data assimilation. Aerosol particle observations from satellite and ground-based 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 parti-15 cle 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 white paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centres. Some of the requirements are equally applicable to aerosol-climate research. However, the focus here is on the global operational prediction 20 of aerosol properties such as mass concentrations and optical parameters. 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 to simulate the size information.However the next generation of aerosol operational models will have the capability to predict both mass and number density which will provide a more complete description of the aerosols properties. 25A brief overview of the state-of-the-art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

show abstract

Disagreements in Low-Level Moisture between (Re)Analyses over Summertime West Africa

Cited by 31 publications

References 63 publications

The early summertime Saharan heat low: sensitivity of the radiation budget and atmospheric heating to water vapour and dust aerosol

The early summertime Saharan heat low: sensitivity of the radiation budget and atmospheric heating to water vapour and dust aerosol

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

Status and future of Numerical Atmospheric Aerosol Prediction with a focus on data requirements

Contact Info

Product

Resources

About