Understanding day-night differences in dust aerosols over the dust belt of North Africa, the Middle East, and Asia

The diurnal variability of dust aerosols is not fully understood due to lack of high-spatiotemporal-resolution data. Remote sensing instruments that sample aerosols in visible bands often miss important information about dust aerosols at nighttime.  This challenge is partially addressed by the Infrared Atmospheric Sounder Interferometer (IASI).  IASI is an infrared sensor that provides measurement of dust aerosols at fine spectral and spatial resolutions twice daily (9:30 a.m. and 9:30 p.m. local solar equator-crossing time; ECT) at a global scale. It’s equal-quality performance of daytime and nighttime products allow a direct comparison between daytime and nighttime dust variables. Using dust optical depth (DOD) and dust layer height retrieved by the Laboratoire de Météorologie Dynamique (LMD) from IASI aboard MetOp-A satellite along with ground observations, we examined the day-night differences in dust aerosols over the dust belt and meteorological factors that contribute to these differences. Whether reanalysis products may capture the day-night differences in DOD shown by IASI is also examined.

Key findings:

• IASI DOD shows significant day-night differences over the dust belt in the 2008–2020 climatology, with higher DOD over dust source regions in the central to northern Sahara, the central to eastern Arabian Peninsula and the Taklamakan Desert at daytime (9:30 am ECT) and over the southern Sahel to the Guinea Coast and the western to central Indian subcontinent at night (9:30 pm ECT).
• The day-night differences in DOD are larger in boreal winter and spring than other seasons and are likely associated with greater increases in surface wind speeds and dust uplift potential in dust source regions at daytime.
• IASI dust layer height also shows significant day-night differences in the dust belt, with higher dust layer height over the southern Sahel to the Guinea Coast, southern parts of the Arabian Peninsula, and large parts of the Indian subcontinent during daytime, associated with a deeper planetary boundary layer and greater convective instability during daytime than nighttime, which promotes vertical transport and mixing of dust aerosols.
• DOD from reanalysis products such as MERRA-2 and EAC4 failed to capture the day-night differences revealed by IASI DOD as nighttime observations are not used to constrain aerosol optical depth.

(Tindan et al. 2023, Atmos. Chem. Phys.)

Compound heat wave, drought, and dust events in California

California is one of the nation’s top agricultural producers and is vulnerable to extreme events such as droughts and heat waves. Simultaneously occurring climate extremes, referred to as compound events, may intensify the impacts of individual events, further stressing energy and water resources and having adverse impacts on environment and human health. Concurrent heat waves and droughts are reported in California, while enhanced dust pollution during dry periods is also noticed. However, little is known about the features and mechanisms of concurrent drought, heat wave, and dust pollution events in California. Here we examine the characteristics, formation, and air quality impacts of the compounding drought, heat wave, and dust events in California. The method developed here may be applied to other regions to understand compound events.

Key findings:

• California compound events are characterized by enhanced surface temperatures up to 4.5 ℃, reduced soil moisture and vegetation density, and an increase in DOD by about 70%.
• Surface fine dust and PM_2.5 concentrations also increase associated with both enhanced dust emissions and a relatively stable atmosphere that traps pollutants.
• The development of the compound events is related to an anomalous high over the west coast embedded in a wave train over the North Pacific that develops up to 7 days before the events.

(Pu et al. 2022, Journal of Climate)

A record-breaking trans-Atlantic African dust plume associated with atmospheric circulation extremes in June 2020

A massive African dust plume traveled across the tropical North Atlantic and reached the U.S. during June 14–28, 2020, greatly degrading air quality over large areas in the Caribbean and the southeastern to central U.S. Daily PM_2.5 concentrations exceeded 50 μg m⁻³ in several Gulf States, and the air quality index reached unhealthy levels for sensitive groups in more than 11 States. It is not clear what caused the extreme trans-Atlantic dust event and whether similar events will occur more frequently in the future. In this study, we characterize this extreme trans-Atlantic dust event and examine the underlying mechanisms of this event.

Key findings:

• The magnitude and duration of aerosol optical depth (AOD) over the tropical North Atlantic Ocean was the greatest during summer over the past 18 years based on MODIS retrievals.
• The extreme trans-Atlantic dust event is associated with both enhanced dust emissions over western North Africa and atmospheric circulation extremes that favor long-range dust transport.
• While the atmospheric circulation anomalies in this event are similar to the typical circulation patterns that support the transport of African dust to the U.S., the magnitudes of the African easterly jet and Caribbean low-level jet and surface wind anomalies over western North Africa are exceptional, highlighting a substantial contribution of atmospheric circulation to this extreme trans-Atlantic dust event.
• While there are large uncertainties associated with assessing future trends in African dust emissions, model-projected atmospheric circulation changes in a warmer future generally favor increased long-range transport of African dust to the Caribbean Basin and the U.S.

(Pu and Jin, 2021, Bulletin of the American Meteorological Society)

Seasonal prediction potential for springtime dustiness in the U.S.

Severe dust storms reduce visibility and cause breathing problems and lung diseases, affecting public transportation and safety. Reliable forecasts for dust storms and overall dustiness are therefore important for hazard prevention and resource planning. Most dust forecast models focus on a short, sub-seasonal lead times, i.e., three to six days, and the skill of seasonal prediction is not clear. This work for the first time examined the potential of seasonal dust prediction in the U.S. using a hybrid method that combines a statistical model and results from a dynamic seasonal prediction model developed at NOAA Geophysical Fluid Dynamics Laboratory, the Forecast-Oriented Low Ocean Resolution (FLOR) Model. Our method shows skillful predictions of spring dustiness three to six months in advance. Findings here will help the development of a seasonal dust prediction system and hazard prevention.

Key findings:

• A regression model and ensembles from a seasonal prediction model (GFDL FLOR) initialized on December 1^st are used to predict springtime dustiness.
• About 71% of the variances of dustiness over the Great Plains and 63% over the southwestern U.S. from 2004 to 2016 are captured.
• Interannual variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors.

(Pu et al. 2019, Geophysical Research Letters; KU news; GFDL research highlight)

Retrieving the global distribution of threshold of wind erosion from satellite data and implementing it into the GFDL AM4.0/LM4.0 model

While dust aerosols play important roles in the Earth’s climate system, large uncertainties exist in modeling its lifecycle. Dust particles are lifted from dry and bare soils into the atmosphere by saltation and sandblasting. This process is initiated when surface winds reach a threshold velocity of wind erosion. For simplicity, constant thresholds of wind erosion are widely used in dust and climate models. In reality, the emission process is influenced by many land surface properties, such as soil moisture, soil texture, and vegetation residue, and thus varies spatially and temporally. In this study, we retrieved a climatological monthly global distribution of wind erosion threshold (V_threshold) and then implemented it to the GFDL AM4.0/LM4.0 to examine the benefit of using it.

Key findings:

• High resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue dust optical depth (DOD) and surface wind speeds from the NCEP1 reanalysis, along with other land surface factors that affect wind erosion, were used to develop a time-varying two-dimensional climatological threshold of wind erosion, V_threshold, based on the seasonal variations of DOD and surface wind distribution frequencies.
• This climatologically monthly V_threshold is then incorporated into the GFDL AM4.0/LM4.0 model. The climatology, seasonal cycle, and distribution of DOD are better captured over the “dust belt” (i.e. North Africa and the Middle East) than those with the default globally constant threshold.
• Annual mean V_threshold is also tested in the model and is found to overestimate DOD over dusty seasons, indicating the importance of including the seasonal cycle of V_threshold in the model.  Using time-varying V_threshold, the model was also able to capture a strong dust storm in the U.S. Great Plains in October 2012, which created deadly accidents, while some dust forecasting models failed to reproduce it. 
• This method to retrieve global V_threshold can be conducted under different resolutions or surface wind reanalyses to match the resolution of dust models and help improve their simulations and forecasting of dust distribution.

(Pu et al. 2020, Atmos. Chem. Phys.)

How reliable are CMIP5 models in simulating dust optical depth?

Dust aerosol plays an important role in the climate system by affecting the radiative and energy balances. Biases in dust modeling may result in biases in simulating global energy budget and regional climate. It is thus very important to understand how well dust is simulated in the CMIP5 models. While many features and variables are systematically examined in the CMIP5 multi-model output, we found that to our best knowledge evaluation of global dust modeling in CMIP5 models is still in blank. In this study we examined a key variable associated with dust radiative effect, DOD, using seven CMIP5 models with interactive dust emission schemes and DOD retrieved from MODIS Deep Blue products.

Key findings:
• We found that the global spatial pattern and magnitude of dust optical depth (DOD) are largely captured by Coupled Model Intercomparison Project Phase 5 (CMIP5) models in the 2004-2016 climatology, with an underestimation of DOD overland by -25.2% in MAM to -6.4% in DJF. The spatial pattern is better captured in boreal dusty seasons during MAM and JJA.
• While seasonal cycle, magnitude, and spatial patterns are generally captured by multi-model mean over major dust source regions such as North Africa and the Middle East, these variables are not so well represented by most of the models in South Africa and Australia.
• Models also do not capture the observed connections between DOD and local controlling factors such as surface wind speed, bareness, and precipitation. The constraints from surface bareness are largely underestimated while the influences of surface wind and precipitation are overestimated.
• Projections of DOD change in the late half of the 21st century under the Representative Concentration Pathways 8.5 (RCP 8.5) scenario by multi-model mean and by a regression model show some common features: similar DOD pattern over North Africa in DJF and JJA, an increase of DOD in the Arabian Peninsula in all seasons, and a decrease over northern China from MAM to SON.

(Pu and Ginoux, Atmos. Chem. Phys., 2018)

Climatic factors contributing to long-term variations in surface fine dust concentration in the United States

Studies found fine dust (with aerodynamic diameter less than 2.5 microns) is an important component of the total PM2.5 mass in the western and central U.S. in spring and summer and has positive trends over the southwestern and central U.S. However, the possible causes of the fine dust trends, especially the increase of fine dust over the central U.S., have not been thoroughly discussed by previous studies. This work explores the local climatic factors driving the long-term variations of fine dust from 1990 to 2015.

Key findings:
• The variations of the fine dust concentration can be largely explained by the variations of precipitation, surface bareness, and 10 m wind speed. Moreover, including convective parameters such as convective inhibition (CIN) and convective available potential energy (CAPE) better explains the variations and trends over the Great Plains from spring to fall.
• While the positive trend of fine dust concentration in the Southwest in spring is associated with precipitation deficit, the increasing of fine dust over the central Great Plains (CGP) in summer is largely associated with an enhancing of CIN and a weakening of CAPE, which are related to increased atmospheric stability due to surface drying and lower troposphere warming.
• The positive trend of the Great Plains low-level jet also contributes to the increase of fine dust concentration in the CGP in summer via its connections with surface winds and CIN.
• Dusty days in the CGP are associated with a westward extension of the North Atlantic subtropical high and an intensified jet.

(Pu and Ginoux, Atmos. Chem. Phys., 2018)

Projection of American dustiness in the late 21st century due to climate change

Climate models projected an “unprecedented” dry condition in the late 21st century over the southwestern and central U.S., regions co-located with the major dust sources. However, whether dust events in the U.S. will increase in the future is not clear, as most of the current climate models are not able to capture the spatial pattern and magnitude of the dust loading in the U.S. This work provided a projection of future dust activity in the Sates by using a regression model and output from CMIP5 simulations.

Key findings:
• Dust event frequency in the Great Plains and southwestern U.S. peak in the recent severe drought years.
• The variations of the frequency of dust events in the U.S. are largely associated with three key factors, precipitation, land surface bareness, and near-surface wind speed. Together these factors can explain about 71% to 88% of the variances of dust event frequency over the western U.S. and the Great Plains during 2003-2015.
• Using the output of 16 CMIP5 models and a regression model, we project that under the RCP8.5 scenario more dust events will occur in the southern Great Plains from spring to autumn (up to ~5 more dusty days compared to the historical condition) and part of the southwestern U.S. in summer and fall in the late half of the 21st century, largely due to reduced precipitation, enhanced bareness, and increased surface wind speed.

(Pu and Ginoux, Scientific Resports, 2017; GFDL Research highlight; Princeton blog)

The impact of Pacific Decadal Oscillation on springtime dust activity in Syria

Dust aerosols play an important role in the climate system by modulating energy budget and hydrological cycle as well as ocean biological cycle. Strong dust storms also have severe social and health impacts. The 2015 severe dust storm in Syria raised concerns as whether dust activities will increase in the region. First step toward answering this question is to understand the dust activities driven by the natural climate variability. This work clarifies the influence of the PDO on Syrian dust activities in springtime.

Key findings:
• The springtime dust optical depth (DOD) in Syria is significantly negatively correlated with the Pacific decadal oscillation (PDO) from 2003-2015.
• A negative PDO not only supports circulation patterns favorable to high DOD in Syria but also reduces precipitation over dust source regions over the southern Arabian Peninsula and northeastern Africa, facilitating dust transport from these regions to Syria.
• The development of strong dust storms in Syria is associated with both favorable large-scale condition related to a negative PDO and strong convection over Turkey and Syria.
• Currently GFDL atmospheric model (AM3) underestimates such a negative correlation between Syrian DOD and the PDO, likely due to the dust scheme that does not explicitly include the influence of soil moisture.

(Pu and Ginoux, Atmos. Chem. Phys., 2016)