130 research outputs found
Adebiyi etal: absorption of shortwave radiation by North African dust
No full textThe codes and datasets contained here are for the paper with the information below
Titled: "North African dust absorbs substantially less solar radiation than estimated by climate models and remote-sensing retrievals"
Author: Adeyemi A. Adebiyi, Yue Huang, Bjørn H. Samset and Jasper F. Kok
Please see the ReadMe.txt for additional details.
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Corresponding Authors:
Adeyemi Adebiyi
Email: [email protected];
Department of Life and Environmental Sciences,
University of California-Merced,
5200 North Lake Road Merced, CA 95343
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Chapter 6 - Future changes in precipitation distributions over Africa
No full textThe African continent is home to approximately 1.4 billion people, about 18% of the world's population. Monsoon precipitation is critical to African societies, contributing a large proportion of the average annual precipitation. Variations in monsoon precipitation have major impacts on, for example, agriculture, freshwater supply and sanitation, and hydropower generation. Unusually low precipitation can also lead to large and prolonged droughts across Africa, increasing mortality. Future changes in monsoon precipitation, therefore, have important societal implications. In this chapter, we summarize the impact of climate change on future changes in precipitation over Africa and for the end of the 21st century. Climate models show that precipitation is projected to increase over most of the African continent (the central Sahel, east and central Africa), while precipitation is projected to decrease over the western Sahel and south-eastern Africa. We document the impact of climate change on extreme precipitation events. We also explain the uncertainty in simulated changes in precipitation and discuss ways to improve the reliability of projections.</p
Dust Constraints from joint Observational-Modelling-experiMental analysis – DustCOMM Version 1
No full textDust Constraints from joint Observational-Modelling-experiMental analysis – DustCOMM Version 1
This is the version-1 of DustCOMM dataset (Adebiyi et al. Geoscientific Model Development), containing annual and seasonal climatologies of constrained dust aerosol properties, including spatially-varying dust size distribution, mass extinction efficiency, and atmospheric dust loading.
Please be aware that there will be future versions of DustCOMM that incorporates more observational, modeling and experimental constraints.
Please send an email to the corresponding authors if you have any question or would like to receive update of DustCOMM's future developments.
Below, we give a brief description of each data file. In each, we have included the mean, median, 1 and 2 sigma uncertainty estimates as a function of location. These uncertainty estimates are derived from probability distributions that describe the field over each location. Please contact the corresponding authors if you are interested in the full dataset.
The dimensions in these dataset are denoted as:
lon [144] -- Longitude. Unit of degrees_east. Global - 2.5 deg resolution.
lat [96] -- Latitude. Unit of degrees_north. Global - ~2 deg resolution
lev [35] -- Pressure levels. Unit of hPa. ~900 hPa to 100 hPa. Lower resolution in the boundary layer and higher resolution in the free troposphere.
D [200] -- Dust Geometric Diameter. Unit of microns. From 0.2-20 microns.
nseas [4] -- Seasons. They are DJF, MAM, JJA, and SON corresponding to December-January-February, March-April-May, June-July-August, and September-October-December respectively.
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Dust_Size_Distr_dVdD_annual.nc - Dimension: [lon, lat, lev, D]
Dust_Size_Distr_dVdD_seasonal.nc - Dimension: [nseas, lon, lat, lev, D]
-- DustCOMM annual and seasonal climatologies of 3-D normalized dust (volume) size distribution. Fields include:
-> dVdD_mean -- Mean Normalized Dust Volume Size Distribution
-> dVdD_median -- Median Normalized Dust Volume Size Distribution
-> dVdD_Pos1sig -- +1 Sigma Normalized Dust Volume Size Distribution
-> dVdD_Neg1sig -- -1 Sigma Normalized Dust Volume Size Distribution
-> dVdD_Pos2sig -- +2 Sigma Normalized Dust Volume Size Distribution
-> dVdD_Neg2sig -- -2 Sigma Normalized Dust Volume Size Distribution
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Dust_3D_MEE_annual.nc - Dimension: [lon, lat, lev]
Dust_3D_MEE_seasonal.nc - Dimension: [nseas, lon, lat, lev]
-- DustCOMM annual and seasonal climatologies of 3-D dust mass extinction efficiency (m2/g). Fields include:
-> MEE_mean -- Mean Dust 3D Mass Extinction Efficiency
-> MEE_median -- Median Dust 3D Mass Extinction Efficiency
-> MEE_Pos1sig -- +1 Sigma Dust 3D Mass Extinction Efficiency
-> MEE_Neg1sig -- -1 Sigma Dust 3D Mass Extinction Efficiency
-> MEE_Pos2sig -- +2 Sigma Dust 3D Mass Extinction Efficiency
-> MEE_Neg2sig -- -2 Sigma Dust 3D Mass Extinction Efficiency
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Dust_2D_MEE_annual.nc - Dimension: [lon, lat]
Dust_2D_MEE_seasonal.nc - Dimension: [nseas, lon, lat]
-- DustCOMM annual and seasonal climatologies of 2-D dust mass extinction efficiency (m2/g). Fields include:
-> MEE_mean -- Mean Dust 2D Mass Extinction Efficiency
-> MEE_median -- Median Dust 2D Mass Extinction Efficiency
-> MEE_Pos1sig -- +1 Sigma Dust 2D Mass Extinction Efficiency
-> MEE_Neg1sig -- -1 Sigma Dust 2D Mass Extinction Efficiency
-> MEE_Pos2sig -- +2 Sigma Dust 2D Mass Extinction Efficiency
-> MEE_Neg2sig -- -2 Sigma Dust 2D Mass Extinction Efficiency
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Dust_Load_annual.nc - Dimension: [lon, lat]
Dust_Load_seasonal.nc - Dimension: [nseas, lon, lat]
-- DustCOMM annual and seasonal climatologies of 2-D atmospheric dust loading (g/m2). Fields include:
-> Load_mean -- Mean Atmospheric Dust Loading
-> Load_median -- Median Atmospheric Dust Loading
-> Load_Pos1sig -- +1 Sigma Atmospheric Dust Loading
-> Load_Neg1sig -- -1 Sigma Atmospheric Dust Loading
-> Load_Pos2sig -- +2 Sigma Atmospheric Dust Loading
-> Load_Neg2sig -- -2 Sigma Atmospheric Dust Loading
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Correspondng Authors:
Adeyemi Adebiyi and Jasper Kok
Department of Atmospheric and Oceanic Sciences,
University of California Los Angeles, CA, USA
Email: [email protected] and [email protected]</p
DustCOMM
No full text<p>Dust Constraints from joint Observational-Modelling-experiMental analysis – DustCOMM Version 1</p>
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The Impact of Meteorology on Smoke and Low-level Clouds over the Southeast Atlantic
No full textIn this dissertation, we use radiosondes and satellite observation, reanalysis datasets, as well as radiative and trajectory models to document the relationship between the low-level clouds, smoke and meteorology over the southeast Atlantic. The southeast Atlantic presents a natural environment with one of the world’s largest marine low-level clouds, occurring along with the largest consumption of biomass fire over the adjacent southern African continent. This combination results in an extensive region of above-cloud biomass burning aerosols (predominantly smoke) over the marine low-level clouds, whereby the elevated smoke could lead to the stabilization of the lower troposphere, reduction of the cloud-top entrainment, and the build-up of water vapor within the boundary layer, which may eventually lead to increases in cloud fraction and decreases in cloud-top heights, in a process called semi-direct aerosol effect. The smokes are transported at a preferred altitude (∼750hPa – 550hPa) by a background easterly winds between July and October. During the same period, strong surface winds and ocean-influenced cold surface temperature characterize the meteorology within the boundary layer. The marine low-level cloud region is also associated with strong climatological subsidence above it, and cloud-top temperature inversion layer. The meteorological variations occurring above and below the low-level clouds are capable of influencing the cloud properties, and therefore may confound with the aerosol effects, making the separation of the aerosol and meteorological influences, on the low-level cloud, a very difficult challenge. We address this problem by identifying the dynamical and thermodynamical variations above the low-level clouds during the the peak aerosol months (July–October). Specifically, three areas are explored in this dissertation: the convolution of the dynamical and moisture effects with shortwave-absorbing aerosols over the low-level clouds; the role of the mid-tropospheric easterly-transporting system on both the elevated smoke and the low-level cloud environments; and the synoptic-scale sensitivity of the low-level clouds to a range of interacting meteorological conditions, with and without the presence of the elevated smoke. First, the analysis of the radiosondes at St. Helena Island, a small island located approximately 1800 km offshore of continental Africa (15.9◦S, 5.6◦W), shows the presence of mid-tropospheric moisture within the smoke layer, and above the low-level clouds. The smoke layer has previously been associated with hot and dry layer, perhaps in analogy to the Saharan air layer. The mid-tropospheric moisture over the south-east Atlantic has not been previously documented, and it occurs more than 70% of the time between September and October. During the same months, as the aerosol loading increases, the amount of the mid-tropospheric moisture also increases. The result of the radiative transfer calculations shows that the mid-tropospheric moisture-induced longwave cooling accounts for about 30% of the aerosol-induced shortwave warming, within the smoke layer. At the cloud top, increased mid-tropospheric moisture could increase cloud-top heights by enhancing the downwelling longwave radiation at the cloud top, and potentially reducing the turbulent mixing within the boundary layer, which may ultimately reduce the cloud fraction. This process essentially opposes the cloud-thickening semi-direct aerosol effect due to the elevated smoke alone.
Dynamical changes occurring during the polluted condition over the southeast Atlantic include enhanced above-cloud warm horizontal temperature advection, and reduced subsidence. The flow is from the warmer adjacent continent air, and the warm advection directly above the marine low-level cloud is also capable of the increases in cloudiness through its influence on stability, that is separate from the stability associated with semi-direct aerosol effect. In addition, though a previous modeling study focussing on the southeast Atlantic has similarly identified the association between the elevated shortwave-absorbing aerosols (smoke) and the reduced subsidence, it remains unclear if the reduced subsidence is a response of the elevated aerosols alone, or an associated change in the above-cloud meteorological features, or both. Here, we show that during the September–October period, the southeast Atlantic is characterized by the Southern African Easterly Jet (AEJ-S), a region of maximum mid-tropospheric easterly winds responsible for the westward transport of both the aerosols and the moisture above the low-level clouds. The AEJ-S can be thought of as a southern counterpart of a better-known northern African easterly jet, and using the wealth of information available for the northern African easterly jet, we show that the AEJ-S is associated with induced upward motion directly below the jet, between 5◦S-15◦S. The AEJ-S-induced upward motion strengthens the large-scale ascent over the land, and therefore enhances the efficiency of trajectory-derived aerosol lifting emissions into the free-troposphere. Over ocean, the induced upward motion also reduces the large-scale subsidence over the marine low-level cloud. All else equal, the reduction of the offshore subsidence can be associated with reduction in nearby cloud fraction and increases in cloud-top heights, between 5◦S-15◦S. Between the effects of AEJ-S and the elevated aerosols on subsidence, the AEJ-S can be associated with 16% reduction in subsidence compared to 7% reduction by the elevated smoke-aerosols, between 5◦S-15◦S. By isolating the independent effects of selected above-cloud and surface-based meteorological influences during July–October period, it is shown that increases in lower tropospheric stability (LTS, defined as θ800hP a − θ1000hP a ), cold surface advection and warm 800 hPa horizontal temperature advection, all lead to increases in marine low-level cloudiness over the southeast Atlantic. In addition, the mid-tropospheric moisture is shown to independently result in reduction of cloud fraction, especially in the near- coastal region. Enhanced subsidence independently leads to increased cloudiness north of 20◦S, but decreased cloudiness south of 20◦S. The subsidence-induced reduction in cloudiness, south of 20◦S, contributes significantly to counteracting the stability-induced increases in cloudiness over the same region. When aerosol is present north of 20◦S, the effect of LTS on the low-level cloud is increased by about 13% over the region where the elevated aerosols are more likely separated from the underlying clouds, and by about 32% when the aerosols are more likely in contact with the underlying cloud layer. The results of this dissertation provide a detailed, useful meteorological context, highlighting new information, for the several multi-national upcoming field campaigns planned over the southeast Atlantic region. It also provides a useful understanding of the background dynamics and basic physical connections between the aerosols, the low-level clouds and the meteorology over the southeast Atlantic, that can be used in future studies involving aerosol-cloud-radiation interactions, and ultimately in numerical climate models to improve the estimation of climate sensitivity to subtropical low-level clouds.</p
Low Cloud Cover Sensitivity to Biomass-Burning Aerosols and Meteorology over the Southeast Atlantic
No full textAbstract
Shortwave-absorbing aerosols seasonally cover and interact with an expansive low-level cloud deck over the southeast Atlantic. Daily anomalies of the MODIS low cloud fraction, fine-mode aerosol optical depth (AODf), and six ERA-Interim meteorological parameters (lower-tropospheric stability, 800-hPa subsidence, 600-hPa specific humidity, 1000- and 800-hPa horizontal temperature advection, and 1000-hPa geopotential height) are constructed spanning July–October (2001–12). A standardized multiple linear regression, whereby the change in the low cloud fraction to each component’s variability is normalized by one standard deviation, facilitates comparison between the different variables. Most cloud–meteorology relationships follow expected behavior for stratocumulus clouds. Of interest is the low cloud–subsidence relationship, whereby increasing subsidence increases low cloud cover between 10° and 20°S but decreases it elsewhere. Increases in AODf increase cloudiness everywhere, independent of other meteorological predictors. The cloud–AODf effect is partially compensated by accompanying increases in the midtropospheric moisture, which is associated with decreases in low cloud cover. This suggests that the free-tropospheric moisture affects the low cloud deck primarily through longwave radiation rather than mixing. The low cloud cover is also more sensitive to aerosol when the vertical distance between the cloud and aerosol layer is relatively small, which is more likely to occur early in the biomass burning season and farther offshore. A parallel statistical analysis that does not include AODf finds altered relationships between the low cloud cover changes and meteorology that can be understood through the aerosol cross-correlations with the meteorological predictors. For example, the low cloud–stability relationship appears stronger if aerosols are not explicitly included.</jats:p
The role of the southern African easterly jet in modifying the southeast Atlantic aerosol and cloud environments
No full textThe westward transport of biomass‐burning (BB) aerosols by mid‐tropospheric winds over the southeast Atlantic stratocumulus deck has long been recognized, but the coupling to the large‐scale circulation has yet to be investigated fully. This goal is furthered here using satellite observations and reanalysis datasets spanning 2001–2012, as well as 10 day forward trajectory calculations of satellite‐detected smoke emissions. The results highlight the important role of a mid‐tropospheric zonal wind maximum, the Southern African Easterly Jet (AEJ‐S), in transporting BB aerosol west off the African continent. The AEJ‐S, defined through daily‐mean 600 hPa easterly wind speeds exceeding 6 m s−1 between 5°S and 15°S and centred zonally on the coastline, is most pronounced during September–October. The AEJ‐S is part of a meridional circulation that is diabatically forced by the temperature–moisture gradient between the southern hot–dry and northern cool–moist convective structures over land. 45% of 24 264 total identified smoke trajectories exit the continent to its west between 5°S and 15°S. These thereafter follow three major pathways: northwestward (8%), directly westward (55%) and anticyclonically recirculated (37%). The AEJ‐S induces an upward motion directly below the jet that enhances prevailing updraughts over land, lifting emissions and transporting aerosols more efficiently over the southeast Atlantic. Offshore, the prevailing large‐scale mean subsidence is reduced, with an associated increase in the nearby cloud‐top heights and reduction in the nearby marine low‐level cloud fraction. Further from the jet, increased warm continental temperature advection at 800 hPa associated with the strengthened land‐based anticyclone decreases mean low‐level cloud heights.
Westward‐moving 600 hPa winds at the northern edge of a land‐based anticyclone become the southern African easterly jet (AEJ‐S, blue contours, 6‐10 m/s) in September‐October. 10‐day smoke trajectories (red to yellow indicating age), for September of 2007, visualize shortwave‐absorbing aerosol transport from satellite‐detected fire emissions (fire‐counts in maroon) far offshore, over the southeast Atlantic stratocumulus deck (greyscale, cloud fractions of 0.5 to 1.0). We further examine the impact of the AEJ‐S's secondary circulation on the stratocumulus clouds and aerosol distribution
Correction to: Adebiyi, Sulaimon Olanrewaju, Oyatoye, Emmanuel Olateju, Amole, Bilqis Bolanle “Improved Customer Churn and Retention Decision Management Using Operations Research Approach” Emering Markets Journal 6 (2): 12-21. 10.5195/emaj.2016.101
Get PDFUniversity affiliations for first author (Adebiyi, Sulaimon Olanrewaju) and third author (Amole, Bilqis Bolanle) were changed. The numbering for Literature Review section was changed from 1 to 2. Accordingly, numbering of all future (next) sections was adjusted. Corrections to figures and tables were made. Table 2.1 is now numbered 1, Figure 2.1 is numbered Figure 1, Figure 1 is numbered Figure 2, Table 4.1 is numbered Table 2, Figure 4.1 is numbered Figure 3, Table 4.2 is numbered Table 3 and Figure 4.2 is numbered Figure 4. A duplicated reference to Adeleke, A and Aminu S.A. (2012) on page 19 was removed. The original article can be found via the DOI http://dx.doi.org/10.5195/emaj.2016.10
DustCOMM_v1 Input Dataset
No full text<p>This repository contains the input data for Dust Constraints from joint Observational-Modelling-experiMental analysis – DustCOMM Version 1.</p>
<p>The DustCOMM code is publicly available at http://doi.org/10.5281/zenodo.2620556, while the DustCOMM output dataset is similarly available at http://doi.org/10.5281/zenodo.2620475</p>
<p>Below, we give a brief description of each file:</p>
<p>1. model_dust_concentration -- Simulated 3-D dust concentration (ug/m3) from 6 model simulations. This folder contains both annually-averaged and seasonally-averaged model simulations.<br>
2. model_dust_Vweight -- 3-D model dust vertical weight used in DustCOME. This is calculated from the modeled dust concentration. This folder contains both annually-averaged and seasonally-averaged model simulations.<br>
3. DAOD_clim_reanalysis_all.nc -- Constrained annually-averaged dust aerosol optical depth. See Adebiyi et al. Geoscientific Model Development for details<br>
4. DAOD_seas_reanalysis_all.nc -- Constrained seasonally-averaged dust aerosol optical depth. See Adebiyi et al. Geoscientific Model Development for details<br>
5. DustCOMM_lat.nc -- Latitude array used in DustCOMM. Dimension = 96.<br>
6. DustCOMM_lon.nc -- Longitude array used in DustCOMM. Dimension = 144.<br>
7. DustCOMM_lev.nc -- Altitude (hPa) array used in DustCOMM. Dimension = 48.<br>
8. Globally_averaged_dust_PSD.mat -- Constrained Globally-averaged dust size distribution from Kok et al, Nature Geoscience, 2017.<br>
9. Size_resolved_ext_eff.mat -- Constrained Globally-averaged dust extinction efficiency from Kok et al, Nature Geoscience, 2017.</p>
<p>If you have any question, please email the [email protected].</p>
Improved representation of the global dust cycle using observational constraints on dust properties and abundance
Get PDFEven though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2 relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.This research has been supported by the National Science Foundation (NSF) (grant nos. 1552519 and 1856389) and the Army Research Office (cooperative agreement number W911NF-20-2-0150). This research was further supported by the University of California President's Postdoctoral Fellowship awarded to Adeyemi A. Adebiyi and the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 708119 awarded to Samuel Albani and no. 789630 awarded to Martina Klose. Ramiro Checa-Garcia received funding from the European Union Horizon 2020 research and innovation grant 641816 (CRESCENDO). Akinori Ito received support from JSPS KAKENHI grant number 20H04329 and Integrated Research Program for Advancing Climate Models (TOUGOU) grant number JPMXD0717935715 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Peter R. Colarco and Adriana Rocha-Lima were supported by the NASA Atmospheric Composition: Modeling and Analysis Program (Richard Eckman, program manager) and the NASA Center for Climate Simulation (NCCS) for computational resources. Yue Huang was supported by NASA grant 80NSSC19K1346 awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. Ron L. Miller and Vincenzo Obiso received support from the NASA Modeling, Analysis and Prediction Program (NNG14HH42I) along with the NASA EMIT project and the Earth Venture Instrument program with computational resources from the NASA Center for Climate Simulation (NCCS). Samuel Albani received funding from MIUR (Progetto Dipartimenti di Eccellenza 2018-2022). Carlos Pérez García-Pando received support from the European Research Council (grant no. 773051, FRAGMENT), the EU H2020 project FORCES (grant no. 821205), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (RYC-2015-18690 and CGL2017-88911-R). Longlei Li received support from the NASA EMIT project and the Earth Venture – Instrument program (grant no. E678605). Yves Balkanski and Ramiro Checa-Garcia received funding from the PolEASIA ANR project under allocation ANR-15-CE04-0005.Peer Reviewed"Article signat per 20 autors/es: Jasper F. Kok, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Mian Chin, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Danny M. Leung, Longlei Li, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Carlos Pérez García-Pando, Adriana Rocha-Lima, Jessica S. Wan, and Chloe A. Whicker"Postprint (published version
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