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We investigate the dust forecasts from two operational global atmospheric models in comparison with in situ and remote sensing measurements obtained during the AERosol properties – Dust (AER-D) field campaign. Airborne elastic backscatter lidar measurements were performed on board the Facility for Airborne Atmospheric Measurements during August 2015 over the eastern Atlantic, and they permitted us to characterise the dust vertical distribution in detail, offering insights on transport from the Sahara. They were complemented with airborne in situ measurements of dust size distribution and optical properties, as well as datasets from the Cloud–Aerosol Transport System (CATS) spaceborne lidar and the Moderate Resolution Imaging Spectroradiometer (MODIS). We compare the airborne and spaceborne datasets to operational predictions obtained from the Met Office Unified Model (MetUM) and the Copernicus Atmosphere Monitoring Service (CAMS). The dust aerosol optical depth predictions from the models are generally in agreement with the observations but display a low bias. However, the predicted vertical distribution places the dust lower in the atmosphere than highlighted in our observations. This is particularly noticeable for the MetUM, which does not transport coarse dust high enough in the atmosphere or far enough away from the source. We also found that both model forecasts underpredict coarse-mode dust and at times overpredict fine-mode dust, but as they are fine-tuned to represent the observed optical depth, the fine mode is set to compensate for the underestimation of the coarse mode. As aerosol–cloud interactions are dependent on particle numbers rather than on the optical properties, this behaviour is likely to affect their correct representation. This leads us to propose an augmentation of the set of aerosol observations available on a global scale for constraining models, with a better focus on the vertical distribution and on the particle size distribution. Mineral dust is a major component of the climate system; therefore, it is important to work towards improving how models reproduce its properties and transport mechanisms.

Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period., International Cooperative for Aerosol Predictions (ICAP) model 550 nm total AOD RMSE of the 72 h forecast versus corresponding mean AODs for AERONET sites listed in Table 2. Large black dots are ICAP multi‐model ensemble consensus means. Individual models are in small coloured dots. Validation of fine‐ and coarse‐modal AODs and dust AOD is also available in Figure 2.

A record-breaking dust episode took place in Crete on 22 March 2018. The event was characterized by surface concentrations exceeding 1 mg m&minus, 3 for a period of 4&ndash, 7 h, reaching record values higher than 6 mg m&minus, 3 at the background station of Finokalia. We present here a detailed analysis of the atmospheric dynamical processes during this period, to identify the main reasons for such extreme dust advection over Crete. At the synoptic scale, the weakening of the polar vortex and the meridional transport of polar air masses at upper tropospheric layers resulted in a strong jet streak over north Africa and Central Mediterranean and corresponding tropospheric folding that brought cold stratospheric air in mid and upper troposphere. Cyclogenesis occurred at the Gulf of Sirte in Libya, resulting in strong winds over the north-east parts of Libya, enhancing particle emissions. The dust plume traveled at low altitude (0.5&ndash, 3 km) along the warm conveyor belt preceding the depression cold front. This type of dusty southerly wind is commonly known as &ldquo, Khamsin&rdquo, As the flow approached Crete, Foehn winds at the lee side of the island favored the downward mixing of dust towards the surface, resulting in local maxima of PM10 in Heraklion and Finokalia. The analysis is based on the combination of high-resolution WRF-Chem simulations reaching up to 1 &times, 1 km grid space over Crete, ground-based and satellite remote sensing of the dust plumes (PollyXT LiDAR, MSG-SEVIRI, MODIS) and detailed surface aerosol in situ measurements at urban (Heraklion, Chania, Greece) and background (Finokalia) stations in Crete.

Colocated Spinning Enhanced Visible and Infrared Imager (SEVIRI) retrieved dust optical depths at 0.55 microns, τ055, and Geostationary Earth Radiation Budget (GERB) fluxes at the top of atmosphere are used to provide, for the first time, an observationally based estimate of the cloud-free net direct radiative effect (DRE) of mineral dust aerosol from geostationary satellite observations, providing new insights into the influence of time of day on the magnitude and sign of the shortwave, longwave, and overall net effect during sunlit hours. Focusing on the Geostationary Earth Radiation Budget Intercomparison of Longwave and Shortwave radiation (GERBILS) campaign over North Africa during June 2007, the presence of mineral dust aerosol reduces the outgoing longwave radiation at all times of day with the peak reduction clearly following the diurnal cycle of surface temperature. The instantaneous shortwave DRE shows strong dependencies on pristine sky albedo and solar zenith angle such that the same dust loading can induce a positive or negative value dependent on time of day. However, the area mean net DRE over the GERBILS period is dominated by the longwave component at all sampled times of day, with mineral dust inducing a reduction in outgoing net flux of the order of 10W m−2. Hence, in the mean sense, Saharan dust is found to warm the Earth-atmosphere system over northern Africa and the Middle East.

Before the onset of the south Asian summer monsoon, sea surface temperature (SST) of the north Indian Ocean warms to 30–32°C. Climatological mean mixed layer depth in spring (March–May) is 10–20 m, and net surface heat flux (Q net ) is 80–100 W m−2 into the ocean. Previous work suggests that observed spring SST warming is small mainly because of (1) penetrative flux of solar radiation through the base of the mixed layer (Q pen ) and (2) advective cooling by upper ocean currents. We estimate the role of these two processes in SST evolution from a two-week Arabian Sea Monsoon Experiment process experiment in April–May 2005 in the southeastern Arabian Sea. The upper ocean is stratified by salinity and temperature, and mixed layer depth is shallow (6 to 12 m). Current speed at 2 m depth is high even under light winds. Currents within the mixed layer are quite distinct from those at 25 m. On subseasonal scales, SST warming is followed by rapid cooling, although the ocean gains heat at the surface: Q net is about 105 W m−2 in the warming phase and 25 W m−2 in the cooling phase; penetrative loss Q pen is 80 W m−2 and 70 W m−2. In the warming phase, SST rises mainly because of heat absorbed within the mixed layer, i.e., Q net minus Q pen ; Q pen reduces the rate of SST warming by a factor of 3. In the second phase, SST cools rapidly because (1) Q pen is larger than Q net and (2) advective cooling is ∼85 W m−2. A calculation using time-averaged heat fluxes and mixed layer depth suggests that diurnal variability of fluxes and upper ocean stratification tends to warm SST on subseasonal timescale. Buoy and satellite data suggest that a typical premonsoon intraseasonal cooling event occurs under clear skies when the ocean is gaining heat through the surface. In this respect, premonsoon SST cooling in the north Indian Ocean is different from that due to the Madden-Julian oscillation or monsoon intraseasonal oscillation.

Over the past several years, there has been a rapid development in the number and quality of global aerosol models intended for operational forecasting use. Indeed, most centers with global numerical weather prediction (NWP) capabilities have some program for aerosol prediction. These aerosol models typically have differences in their underlying meteorology as well as aerosol sources, sinks, microphysics and transformations. However, like similar diversity in aerosol climate models, the aerosol forecast models have fairly similar overall bulk error statistics for aerosol optical thickness (AOT)-one of the few aerosol metrics that is globally available. Experience in climate and weather prediction has shown that in situations such as this where there are several independent models, a multi-model ensemble or consensus will be top performing in many key error metrics. Further, multi-model ensembles provide a highly valuable tool for forecasters attempting to predict severe aerosol events. Here we present the first steps in developing a global multi-model aerosol forecasting ensemble intended for eventual operational and basic research use. Drawing from members of the International Cooperative for Aerosol Prediction (ICAP) latest generation of quasi-operational aerosol models, five day AOT forecasts are analyzed for December 2011 through November 2012 from four institutions: ECMWF, JMA, NASA GSFC, and NRL/FNMOC. For dust, we also include the NOAA NGAC product in our analysis. The Barcelona Supercomputing Centre (NMMC) and UK Met office dust product have also recent become available with ICAP, but have insufficient data to be included in this analysis period. A simple consensus ensemble of member and mean AOT fields for modal species (e.g., fine and coarse mode, and a separate dust ensemble) is used to create the ICAP Multi-Model Ensemble (ICAP-MME). The ICAP-MME is run daily at 0Z for 6 hourly forecasts out to 120 h. Basing metrics on comparisons to 21 regionally representative Aerosol Robotic Network (AERONET) sites, all models generally captured the basic aerosol features of the globe. However, there is an overall AOT low bias among models, particularly for high AOT events. Biomass burning regions have the most diversity in seasonal average AOT. The southern oceans, though low in AOT, nevertheless also have high diversity. In regard to root mean square error, as expected the ICAP-MME placed first over all models worldwide, and was typically first or second in ranking against all models at individual sites. These results are encouraging; as more global operational aerosol models come on line, we expect their inclusion in a robust operational multi-model ensemble will provide valuable aerosol forecasting guidance.