npj Climate and Atmospheric Science最新文献

Mid-latitude weather systems play a significant role in causing floods, wind damage, and related societal impacts. Advances in numerical modeling and observational methods have led to the development of numerous conceptual models in mid-latitude synoptic and dynamical research. As these models proliferate, integrating new insights into a cohesive understanding can be challenging. This paper uses a kinematic perspective to interpret mid-latitude research in a way that synthesises various concepts and create a schematic diagram of an atmospheric river lifecycle. Our analysis demonstrates that, despite varying methods, definitions, and terminology used to describe extratropical cyclones, warm conveyor belt airflows, and atmospheric rivers, the underlying mechanisms driving their formation and development are consistent. Thus, while studying these features independently is valuable, it is important to recognise that they are all part of a larger atmospheric flow pattern. We hope this kinematic approach will serve as a bridge to link research on these phenomena.

Artificial Intelligence (AI) weather models are explored for initial condition sensitivity studies to analyze the physicality of the relationships learned. Gradients (or sensitivities) of the target metric of interest are computed with respect to the variable fields at initial time by means of the backpropagation algorithm, which does not assume linear perturbation growth. Here, sensitivities from an AI model at 36-h lead time were compared to those produced by an adjoint of a dynamical model for an extreme weather event, cyclone Xynthia, presenting very similar structures and with the evolved perturbations leading to similar impacts. This demonstrates the ability of the AI weather model to learn physically meaningful spatio-temporal links between atmospheric processes. These findings should enable researchers to conduct initial condition studies in minutes, potentially at lead times into the non-linear regime (typically >5 days), with important applications in observing network design and the study of atmospheric dynamics.

Controlling ammonia (NH₃) emissions through agricultural management has been recognized as effectively mitigating fine particulate matter (PM_2.5) pollution in eastern China. However, agricultural nitrogen oxide (NO_x) emissions are often overlooked. Here we estimate agricultural NO_x emissions and design a set of atmospheric chemistry model experiments to assess their role in present and future PM_2.5 pollution mitigation in eastern China. The results show that when fossil fuel emissions decrease to 2060 levels, the contribution of agricultural NO_x emissions to secondary inorganic aerosol (SIA) concentrations during the crop-growing season will reach 40% over intensive agricultural areas such as North China Plain, and the efficiency of reducing agricultural NO_x emissions in mitigating SIAs will become comparable to reducing NH₃ emissions. By estimating the optimal reactive nitrogen (Nr) emission control pathway, we find that when including agricultural NO_x emissions, the strategies will shift in favor of controlling agricultural Nr emissions to achieve more efficient PM_2.5 mitigation. Such additional benefits of agricultural nitrogen management should be considered when designing future air quality strategies for agricultural-intensive regions.

The superimposed fluctuations of temperature, precipitation, and CO₂ concentration are crucial for the Alpine Vegetation Carbon Flux on the Qinghai-Tibet Plateau. This study updates the Lund-Potsdam-Jena Model (LPJ) with plant functional types native to alpine regions and assimilates the daily LAI remote sensing datasets. And, the influence of climate factors and CO₂ concentration on Alpine Vegetation carbon fluxes was simulated. Validation against field data shows the model accurately simulates daily GPP with R² of 0.8332 and 0.8608, RMSE of 1.96 and 1.485 for 2013–2014, respectively. For NEP, the RMSE are 1.15 and 1.19 for the same years. The research reveals the pronounced spatiotemporal variations of carbon fluxes were highly responsive to temperature changes. Precipitation shows a more consistent interannual variation relationship with carbon fluxes than temperature does. Notably, NPP/GPP increase only with concurrent rises in CO₂ and precipitation, highlighting the superimposed implications of climate-induced carbon flux changes in Alpine vegetation.

El Niño is responsible for the largest part of the seasonal-to-interannual climate variability, so forecasting El Niño events correctly is important. However, forecasting El Niño events during boreal spring remains challenging. The dynamical seasonal forecast models of the North American Multi-Model Ensemble are over-confident for high confidence (>75% ensemble member agreement) El Niño forecasts. In general, confident El Niño forecasts have a warming tendency in equatorial SSTs in the month prior to the forecast initialization and positive equatorial heat content anomalies during the first month of the forecast. However, confident forecasts often fail when negative SST anomalies were present in the subtropical north eastern Pacific. We find that the models’ equatorial SST anomalies persist too long and that the precipitation response along the warm pool edge to these anomalies is too deterministic. Therefore, the forecast models are too reliant on coupled equatorial processes resulting in excessively deterministic forecasts.

With several cities worldwide pursuing carbon neutrality in the upcoming decades, there is an increasing interest in quantifying cities’ anthropogenic carbon emissions using atmospheric observations. The challenge with both in-situ and remote sensing methods is, however, that the observations include both anthropogenic and biogenic signals. To reduce uncertainties in anthropogenic emission estimations, it is critical to partition biogenic fluxes of carbon dioxide (CO₂) from the observed data. In this study, we, for the first time, examine the suitability of carbonyl sulfide (COS), a proxy for photosynthesis, on partitioning biogenic CO₂ uptake from the ecosystem exchange measured with the eddy covariance (EC) technique over an urban area in Helsinki, Finland. The urban vegetation acts as a clear sink for COS whereas anthropogenic processes show minimal COS emissions within the source area of the measured net carbon flux. We show that two different COS flux-based methods are able to produce the dynamics of photosynthesis by an independent light-response curve-based estimation. Together with commonly used soil and vegetation respiration proxy, we removed biogenic signals from the urban net CO₂ exchange and demonstrated that together with CO₂ fluxes, COS flux can successfully be used to get realistic estimations of anthropogenic carbon emissions using the EC method.

This study investigated the link between long-term exposure to PM_2.5 components and the risk of developing chronic obstructive pulmonary disease (COPD) using UK Biobank data. The exposure dataset, derived from the European Monitoring and Evaluation Program (EMEP) model, included elemental carbon (EC), organic matter (OM), ammonium (NH₄⁺), nitrate (NO₃⁻), and sulfate (SO₄²⁻). The risk of COPD was assessed using the Cox proportional hazards model, and the contribution of each component was evaluated with quantile g-computation. A polygenic risk score for COPD was used to explore genetic interactions with PM_2.5 constituents. Adjusted hazard ratios showed an increased risk for each component and the mixed exposure, with SO₄²⁻ (40.8%) contributing the most. We observed synergistic effects between genetic risk and exposure to PM_2.5, EC, NH₄⁺, and SO₄²⁻, accounting for 10–18% of total COPD risk. Prolonged exposure to PM_2.5, especially SO₄²⁻, increased the risk of COPD, with genetic factors modifying the effect.

By using model simulations, we show that historical land use and land cover change since 1850 has impacted aridity index (AI) worldwide, causing divergent responses in different regions. Locally, AI tends to increase (getting humid) in reforestation regions and most humid regions. Owing to these changes, the area of the humid zone expanded insignificantly by 0.22% of the global land area at the expense of drylands.

Earth’s climate sensitivity quantifies the ultimate change in global mean surface air temperature in response to a doubling of atmospheric CO₂ concentrations. Recent assessments estimate that Earth’s climate sensitivity very likely lies between 2.3 °C and 4.7 °C, with the representation of clouds in climate models accounting for a large portion of its uncertainty. Here, we adjust the climate sensitivity of individual contemporary climate models after using satellite observations to alleviate biases in their representation of mixed-phase clouds. A resulting moderate average climate sensitivity of 3.63 ± 0.98(1σ) °C arises due to opposing responses of clouds. While increasing the proportion of liquid within cold clouds prior to CO₂ doubling increases climate sensitivity via transitions from solid to liquid hydrometeors, a strongly opposing increase in reflective cloud cover decreases climate sensitivity. This emphasizes the need to reconsider the role of mixed-phase cloud cover changes in climate sensitivity assessments.

Global high-resolution emission inventories of trace gases require refinement to align with ground-based observations, especially for extreme events and changing sources. This study utilizes two satellites to globally quantify NO₂ and CO concentrations on daily to weekly scales and estimate emissions with uncertainty bounds, grid-by-grid, for regions with significant variability in 2010. These emissions demonstrate overall increased emissions and identify missing sources compared with various inventories. The NO_x and CO emissions are 5.76 × 10⁵–6.25 × 10⁶ Mt/yr and 1.06 × 10⁷–2.78 × 10⁷ Mt/yr, representing a mean 200% and 130% increase. Significant emissions originate from typical and atypical sources, exhibiting short-to-medium-term variability, primarily driven by biomass burning and anthropogenic activities, with substantial redistribution and compression due to long-range transport. The extra CO emissions chemically decay into CO₂, resulting in an increase in CO₂ mass equivalent to 3.5% of CO₂ emissions from Central Africa and 6.1% from Amazon, reflecting the importance of addressing CO from biomass burning.