Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m⁻², and regional changes of −2 W m⁻² (Africa) and −0.5 W m⁻² (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state.

Primary and secondary aerosol particles originating from biomass burning contribute significantly to the atmospheric aerosol budget and thereby to both direct and indirect radiative forcing. Based on detailed measurements of a large number of biomass burning plumes of variable age in southern Africa, we show that the size distribution, chemical composition, single-scattering albedo, and hygroscopicity of biomass burning particles change considerably during the first 2–4 h of their atmospheric transport. These changes, driven by atmospheric oxidation and subsequent secondary aerosol formation, may reach a factor of 6 for the aerosol scattering coefficient and a factor >10 for the cloud condensation nuclei concentration. Since the observed changes take place over the spatial and temporal scales that are neither covered by emission inventories nor captured by large-scale model simulations, the findings reported here point out a significant gap in our understanding on the climatic effects of biomass burning aerosols.

As Artificial Intelligence (AI) systems become increasingly widespread, we have begun to witness various failures highlighting issues in these systems. These incidents have sparked public discussion related to AI ethics and further accelerated the on-going academic discussion in the area. High-level guidelines and tools for managing AI ethics have been introduced to help industry organizations make more ethical AI systems, but we currently know little about the state of industrial practice. Have these guidelines been adopted by the software industry for developing AI solutions? Are these failures that make the news just the tip of the iceberg? We provide insights into the current state of practice by presenting the results of a survey of 211 software companies.

Commercially available Doppler lidars have now been proven to be efficient tools for studying winds and turbulence in the planetary boundary layer. However, in many cases low signal-to-noise ratio is still a limiting factor for utilising measurements by these devices. Here, we present a novel post-processing algorithm for Halo Stream Line Doppler lidars, which enables an improvement in sensitivity of a factor of 5 or more. This algorithm is based on improving the accuracy of the instrumental noise floor and it enables longer integration times or averaging of high temporal resolution data to be used to obtain signals down to −32 dB. While this algorithm does not affect the measured radial velocity, it improves the accuracy of radial velocity uncertainty estimates and consequently the accuracy of retrieved turbulent properties. Field measurements using three different Halo Doppler lidars deployed in Finland, Greece and South Africa demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the planetary boundary layer, improves detection of high-altitude cirrus clouds and enables the observation of elevated aerosol layers.

Mixing layer height (MLH) is one of the key parameters in describing lower tropospheric dynamics and capturing its diurnal variability is crucial, especially for interpreting surface observations. In this paper we introduce a method for identifying MLH below the minimum range of a scanning Doppler lidar when operated at vertical. The method we propose is based on velocity variance in low-elevation-angle conical scanning and is applied to measurements in two very different coastal environments: Limassol, Cyprus, during summer and Loviisa, Finland, during winter. At both locations, the new method agrees well with MLH derived from turbulent kinetic energy dissipation rate profiles obtained from vertically pointing measurements. The low-level scanning routine frequently indicated non-zero MLH less than 100 m above the surface. Such low MLHs were more common in wintertime Loviisa on the Baltic Sea coast than during summertime in Mediterranean Limassol.