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The formation of organic nitrates (ONs) in the gas phase and their impact on mass formation of secondary organic aerosol (SOA) was investigated in a laboratory study for a-pinene and β-pinene photooxidation. Focus was the elucidation of those mechanisms that cause the often observed suppression of SOA mass formation by NOx, and therein the role of highly oxygenated multifunctional molecules (HOMs). We observed that with increasing NOx concentration (a) the portion of HOM organic nitrates (HOM-ONs) increased, (b) the fraction of accretion products (HOM-ACCs) decreased, and (c) HOM-ACCs contained on average smaller carbon numbers. Specifically, we investigated HOM organic nitrates (HOM-ONs), arising from the termination reactions of HOM peroxy radicals with NOx, and HOM permutation products (HOM-PPs), such as ketones, alcohols, or hydroperoxides, formed by other termination reactions. Effective uptake coefficients γeff of HOMs on particles were determined. HOMs with more than six O atoms efficiently condensed on particles (γeff > 0:5 on average), and for HOMs containing more than eight O atoms, every collision led to loss. There was no systematic difference in γeff for HOM-ONs and HOM-PPs arising from the same HOM peroxy radicals. This similarity is attributed to the multifunctional character of the HOMs: as functional groups in HOMs arising from the same precursor HOM peroxy radical are identical, vapor pressures should not strongly depend on the character of the final termination group. As a consequence, the suppressing effect of NOx on SOA formation cannot be simply explained by replacement of terminal functional groups by organic nitrate groups. According to their γeff all HOM-ONs with more than six O atoms will contribute to organic bound nitrate (OrgNO3) in the particulate phase. However, the fraction of OrgNO3 stored in condensable HOMs with molecular masses > 230 Da appeared to be substantially higher than the fraction of particulate OrgNO3 observed by aerosol mass spectrometry. This result suggests losses of OrgNO3 for organic nitrates in particles, probably due to hydrolysis of OrgNO3 that releases HNO3 into the gas phase but leaves behind the organic rest in the particulate phase. However, the loss of HNO3 alone could not explain the observed suppress ing effect of NOx on particle mass formation from a-pinene and β-pinene. Instead we can attribute most of the reduction in SOA mass yields with increasing NOx to the significant suppression of gas phase HOM-ACCs, which have high molecular mass and are potentially important for SOA mass formation at lowNOx conditions.

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.

Global Climate is one chapter from the State of the Climate in 2019 annual report and is avail- able from https://doi.org/10.1175/BAMS-D-20-0104.1 Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2019 is based on contributions from scien- tists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instru- ments located on land, water, ice, and in space.The full report is available from https://doi.org/10.1175/2020BAMSStateoftheClimate.1.

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

Since 1750, land-use change and fossil fuel combustion has led to a 46% increase in the atmospheric carbon dioxide (CO₂) concentrations, causing global warming with substantial societal consequences. The Paris Agreement aims to limit global temperature increases to well below 2°C above preindustrial levels. Increasing levels of CO₂ and other greenhouse gases (GHGs), such as methane (CH₄) and nitrous oxide (N₂O), in the atmosphere are the primary cause of climate change. Approximately half of the carbon emissions to the atmosphere are sequestered by ocean and land sinks, leading to ocean acidification but also slowing the rate of global warming. However, there are significant uncertainties in the future global warming scenarios due to uncertainties in the size, nature, and stability of these sinks. Quantifying and monitoring the size and timing of natural sinks and the impact of climate change on ecosystems are important information to guide policy-makers’ decisions and strategies on reductions in emissions. Continuous, long-term observations are required to quantify GHG emissions, sinks, and their impacts on Earth systems. The Integrated Carbon Observation System (ICOS) was designed as the European in situ observation and information system to support science and society in their efforts to mitigate climate change. It provides standardized and open data currently from over 140 measurement stations across 12 European countries. The stations observe GHG concentrations in the atmosphere and carbon and GHG fluxes between the atmosphere, land surface, and the oceans. This article describes how ICOS fulfills its mission to harmonize these observations, ensure the related long-term financial commitments, provide easy access to well-documented and reproducible high-quality data and related protocols and tools for scientific studies, and deliver information and GHG-related products to stakeholders in society and policy.

We present a novel high-resolution inverse modelling system (\"FLEXVAR\") based on FLEXPARTCOSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (\"baselines\") consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (\"FLExKF\") system and with TM5-4DVAR inversions at 1° × 1° resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km × 7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1° × 1°. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research. This paper introduces FuXi-S2S, a machine-learning model that outperforms conventional numerical weather prediction models at subseasonal timescales globally, extending the skillful Madden–Julian Oscillation prediction form 30 days to 36 days.

The time required for an ecosystem to recover from severe drought is a key component of ecological resilience. The phenology effects on drought recovery are, however, poorly understood. These effects centre on how phenology variations impact biophysical feedbacks, vegetation growth and, ultimately, recovery itself. Using multiple remotely sensed datasets, we found that more than half of ecosystems in mid- and high-latitudinal Northern Hemisphere failed to recover from extreme droughts within a single growing season. Earlier spring phenology in the drought year slowed drought recovery when extreme droughts occurred in mid-growing season. Delayed spring phenology in the subsequent year slowed drought recovery for all vegetation types (with importance of spring phenology ranging from 46% to 58%). The phenology effects on drought recovery were comparable to or larger than other well-known postdrought climatic factors. These results strongly suggest that the interactions between vegetation phenology and drought must be incorporated into Earth system models to accurately quantify ecosystem resilience.The authors reveal complex drought recovery responses to phenology shifts, in that early spring can shorten or lengthen recovery, while delayed spring following drought events delays it. These effects suggest a need to incorporate phenology aspects into resilience models.

Secondary organic aerosol contributes to the atmospheric particle burden with implications for air quality and climate. Biogenic volatile organic compounds such as terpenoids emitted from plants are important secondary organic aerosol precursors with isoprene dominating the emissions of biogenic volatile organic compounds globally. However, the particle mass from isoprene oxidation is generally modest compared to that of other terpenoids. Here we show that isoprene, carbon monoxide and methane can each suppress the instantaneous mass and the overall mass yield derived from monoterpenes in mixtures of atmospheric vapours. We find that isoprene ‘scavenges’ hydroxyl radicals, preventing their reaction with monoterpenes, and the resulting isoprene peroxy radicals scavenge highly oxygenated monoterpene products. These effects reduce the yield of low-volatility products that would otherwise form secondary organic aerosol. Global model calculations indicate that oxidant and product scavenging can operate effectively in the real atmosphere. Thus highly reactive compounds (such as isoprene) that produce a modest amount of aerosol are not necessarily net producers of secondary organic particle mass and their oxidation in mixtures of atmospheric vapours can suppress both particle number and mass of secondary organic aerosol. We suggest that formation mechanisms of secondary organic aerosol in the atmosphere need to be considered more realistically, accounting for mechanistic interactions between the products of oxidizing precursor molecules (as is recognized to be necessary when modelling ozone production). Adding reactive gases such as isoprene to mixtures lowers the production of secondary organic aerosol in the atmosphere, thus reducing the atmospheric particulate burden, with implications for human health and climate.

After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (10³ km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.