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Calibrating estimates of ionospheric long-term change

Long-term change in the ionosphere are expected due to increases in greenhouse gases in the lower atmosphere. Empirical formulae are used to estimate height. Through comparison with independent data we show that there are seasonal and long-term biases introduced by the empirical model. We conclude that estimates of long-term changes in ionospheric height need to account for these biases.

Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

In this study, for the first time, we combined aerosol data from six satellites using a unified algorithm. The global datasets are generated at a high spatial resolution of about 25 km with an interval of 30 min. The new datasets are compared against ground truth and verified. They will be useful for various applications such as air quality monitoring, climate research, pollution diurnal variability, long-range smoke and dust transport, and evaluation of regional and global models.

Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission

MethaneSAT is an upcoming satellite mission designed to monitor methane emissions from the oil and gas (O&G) industry globally. Here, we present observations from the first flight campaign of MethaneAIR, a MethaneSAT-like instrument mounted on an aircraft. MethaneAIR can map methane with high precision and accuracy over a typically sized oil and gas basin (~200 km2) in a single flight. This paper demonstrates the capability of the upcoming satellite to routinely track global O&G emissions.

The EarthCARE lidar cloud and aerosol profile processor (A-PRO): the A-AER, A-EBD, A-TC, and A-ICE products

ATLID (atmospheric lidar) is the lidar to be flown on the Earth Clouds and Radiation Explorer satellite (EarthCARE). EarthCARE is a joint European–Japanese satellite mission that was launched in May 2024. ATLID is an advanced lidar optimized for cloud and aerosol property profile measurements. This paper describes some of the key novel algorithms being applied to this lidar to retrieve cloud and aerosol properties. Example results based on simulated data are presented and discussed.

Biological and dust aerosols as sources of ice-nucleating particles in the eastern Mediterranean: source apportionment, atmospheric processing and parameterization

Ice nucleating particle (INP) concentrations are required for correct predictions of clouds and precipitation in a changing climate, but they are poorly constrained in climate models. We unravel source contributions to INPs in the eastern Mediterranean and find that biological particles are important, regardless of their origin. The parameterizations developed exhibit superior performance and enable models to consider biological-particle effects on INPs.

Representation learning with unconditional denoising diffusion models for dynamical systems

We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.

Optimizing the iodide-adduct chemical ionization mass spectrometry (CIMS) quantitative method for toluene oxidation intermediates: experimental insights into functional-group differences

We introduce detailed and improved quantitation and semi-quantitation methods of iodide-adduct time-of-flight chemical ionization mass spectrometry (I-CIMS) to measure toluene oxidation intermediates. We assess the experimental sensitivity of various functional group species and their binding energy with iodide ions in I-CIMS. A novel classification approach was introduced to significantly enhance the accuracy of semi-quantitative methods (improving R2 values from 0.52 to beyond 0.88).

The crucial representation of deep convection for the cyclogenesis of Medicane Ianos

Cyclone Ianos of September 2020 was a high-impact but poorly predicted medicane (Mediterranean hurricane). A community effort of numerical modelling provides robust results to improve prediction. It is found that the representation of local thunderstorms controlled the interaction of Ianos with a jet stream at larger scales and its subsequent evolution. The results help us understand the peculiar dynamics of medicanes and provide guidance for the next generation of weather and climate models.

Atmospheric H2 observations from the NOAA Cooperative Global Air Sampling Network

Hydrogen (H2) is a gas in trace amounts in the Earth’s atmosphere with indirect impacts on climate and air quality. Renewed interest in H2 as a low- or zero-carbon source of energy may lead to increased production, uses, and supply chain emissions. NOAA measurements of weekly air samples collected between 2009 and 2021 at over 50 sites in mostly remote locations are now available, and they complement other datasets to study the H2 global budget.

Using historical temperature to constrain the climate sensitivity, the transient climate response, and aerosol-induced cooling

Using errors in climate model simulations this paper derives correction factors for the impacts of greenhouse gases and particles that bring these simulated temperature fields into agreement with an observational reconstruction of the Earth’s temperature. On average across eight models, a reduction by about one-half of the particle-induced cooling would be required, causing only 0.24 K of cooling since 1850–1899. The greenhouse gas warming simulated by several highly sensitive models would also reduce.

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