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1826 items found

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Assessing Lagrangian coherence in atmospheric blocking

We identify spatially coherent air streams into atmospheric blockings, which are important weather phenomena. By adapting mathematical methods to the atmosphere, we confirm previous findings. Our work shows that spatially coherent air streams featuring cloud formation correlate with strengthening of the blocking. The developed framework also allows for statements about the spatial behavior of the air parcels as a whole and indicates that blockings reduce the dispersion of the air parcels.

Evaluating F10.7 and F30 radio fluxes as long-term solar proxies of energy deposition in the thermosphere

We study how well the F10.7 and F30 solar radio fluxes have represented solar energy input in the thermosphere in the last 60 years. We found that increased saturation of radio fluxes at recent solar minima leads to an overestimation of solar energy, which changes the relation between thermospheric parameters and F10.7, but this is not an issue for F30 because of a relative increase in F30 with respect to F10.7. This explains why F30 has been found to represent solar energy better than F10.7.

CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland

This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.

Sensitivity of tropical orographic precipitation to wind speed with implications for future projections

Rainfall in mountainous regions constitutes an important source of freshwater in the tropics. Yet how it will change with global warming remains an open question. Here, we reveal a strong sensitivity of this rainfall to the speed of prevailing winds. This relationship, validated by theory, simulations, and observational data, suggests that regional wind shifts will significantly influence future rainfall changes in the tropics.

Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective

In our article, we review uncertainties in global climate change projections and current methods using Earth observations as constraints, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss the challenges of applying machine learning to climate science.

Brief communication: Sea-level projections, adaptation planning, and actionable science

The Cryosphere
DOI 10.5194/tc-19-793-2025
25 February 2025

As communities try to adapt to climate change, they look for “actionable science” that can inform decision-making. There are risks in relying on novel results that are not yet accepted by the science community. We propose a practical criterion for determining which scientific claims are actionable. We show how premature acceptance of sea-level-rise predictions can lead to confusion and backtracking, and we suggest best practices for communication between scientists and adaptation planners.

Synoptic perspective on the conversion and maintenance of local available potential energy in extratropical cyclones

Although extratropical cyclones in the North Atlantic are among the most impactful midlatitude weather systems, their intensification is not entirely understood. Here, we explore how individual cyclones convert available potential energy (APE) into kinetic energy and relate these conversions to the synoptic development of the cyclones. By combining potential vorticity thinking with a local APE framework, we offer a novel perspective on established concepts in dynamic meteorology.

Toward more robust net primary production projections in the North Atlantic Ocean

Biogeosciences
DOI 10.5194/bg-22-841-2025
20 February 2025

The marine biogeochemistry components of Coupled Model Intercomparison Project phase 6 (CMIP6) models vary widely in their process representations. Using an innovative bioregionalization of the North Atlantic, we reveal that this model diversity largely drives the divergence in net primary production projections under a high-emission scenario. The identification of the most mechanistically realistic models allows for a substantial reduction in projection uncertainty.

Creating a national urban flood dataset for China from news texts (2000–2022) at the county level

We create China’s first open county-level urban flood dataset (2000–2022) using news media data with the help of deep learning. The dataset reflects both natural and societal influences and includes 7595 urban flood events across 2051 counties, covering 46 % of China’s land area. It reveals the predominance of summer floods, an upward trend since 2000, and a decline from southeast to northwest. Notably, some highly developed regions show a decrease, likely due to improved flood management.

What controls planktic foraminiferal calcification?

Biogeosciences
DOI 10.5194/bg-22-791-2025
17 February 2025

Planktic foraminifers are a plankton whose fossilised shell weight is used to reconstruct past environmental conditions such as seawater CO2. However, there is debate about whether other environmental drivers impact shell weight. Here we use a global data compilation and statistics to analyse what controls their weight. We find that the response varies between species and ocean basin, making it important to use regional calibrations and consider which species should be used to reconstruct CO2.

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.

Causes of the exceptionally high number of fatalities in the Ahr valley, Germany, during the 2021 flood

Natural Hazards and Earth System Sciences
DOI 10.5194/nhess-25-581-2025
12 February 2025

In July 2021, flooding killed 190 people in Germany, 134 of them in the Ahr valley, making it the deadliest flood in recent German history. The flash flood was extreme in terms of water levels, flow velocities and flood extent, and early warning and evacuation were inadequate. Many died on the ground floor or in the street, with older and impaired individuals especially vulnerable. Clear warnings should urge people to seek safety rather than save belongings, and timely evacuations are essential.

Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)

Few observations exist in the Amundsen Sea. Consequently, studies rely on reanalysis (e.g., ERA5) to investigate how the atmosphere affects ocean variability (e.g., sea-ice formation and melt). We use data collected along ice shelves to show that cold, dry air blowing from Antarctica triggers large ocean heat loss, which is underestimated by ERA5. We then use an ocean model to show that this bias has an important impact on the ocean, with implications for sea-ice forecasts.

Internal tides off the Amazon shelf in the western tropical Atlantic: analysis of SWOT Cal/Val mission data

This study focuses on the internal tides (ITs) off the Amazon shelf in the tropical Atlantic. It is based on 2 km horizontally gridded observations along the swaths of SWOT (Surface Water and Ocean Topography) track 20 during the calibration/validation phase (Cal/Val, 1 d orbit) from late March to early July 2023. We evaluate the amplitude of M2, N2, and S2 frequencies and use the M2 atlas as an internal tide correction model for SWOT observations. Internal tide amplitudes (models or atlases) are first derived by harmonic analysis of the SWOT sea level anomaly (SLA). The estimation is improved by performing a principal component analysis before the harmonic analysis. The results compare very well with the high-resolution empirical tide (HRET) internal tide model, the reference product for internal tide corrections in altimetry observations. The coherent mode 1 and mode 2 M2 can be distinguished in the internal tide model derived from SWOT, while the higher modes with their strong SLA signature are seen mostly in the incoherent part. In comparison to HRET, the correction of SWOT observations with SWOT-based atlases may be more relevant for this track.

The alongshore tilt of mean dynamic topography and its implications for model validation and ocean monitoring

Mean dynamic topography (MDT) describes variations in the mean sea surface height above a reference surface called a geoid. We show that MDT predicted by a regional ocean model, including a significant tilt of several centimeters along the coast of Nova Scotia, is in good agreement with estimates based on sea level observations. We demonstrate that this alongshore tilt of MDT can provide a direct estimate of the average alongshore current and also of the area-integrated nearshore circulation.

Assessment framework to predict sensitivity of marine calcifiers to ocean alkalinity enhancement – identification of biological thresholds and importance of precautionary principle

The environmental impacts of ocean alkalinity enhancement (OAE) are unknown. Our synthesis, based on 68 collected studies with 84 unique species, shows that 35 % of species respond positively, 26 % respond negatively, and 39 % show a neutral response to alkalinity addition. Biological thresholds were found from 50 to 500 µmol kg−1 NaOH addition. A precautionary approach is warranted to avoid potential risks, while current regulatory framework needs improvements to assure safe biological limits.

Blending 2D topography images from the Surface Water and Ocean Topography (SWOT) mission into the altimeter constellation with the Level-3 multi-mission Data Unification and Altimeter Combination System (DUACS)

The Surface Water and Ocean Topography (SWOT) mission delivers unprecedented swath-altimetry products. In this paper, we describe how we extended the Level-3 algorithms to handle SWOT’s unique swath-altimeter data. We also illustrate and discuss the benefits, relevance, and limitations of Level-3 swath-altimeter products for various research domains.

Modelling current and future forest fire susceptibility in north-eastern Germany

Natural Hazards and Earth System Sciences
DOI 10.5194/nhess-25-383-2025
30 January 2025

In this study we applied a random forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-eastern Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % and 71 %, showing moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters. This study will support regional forest fire prevention and management.

Present-day mass loss rates are a precursor for West Antarctic Ice Sheet collapse

In this study, we present an improved way of representing ice thickness change rates in an ice sheet model. We apply this method using two ice sheet models of the Antarctic Ice Sheet. We found that the two largest outlet glaciers on the Antarctic Ice Sheet, Thwaites Glacier and Pine Island Glacier, will collapse without further warming on a timescale of centuries. This would cause a sea level rise of about 1.2 m globally.

Opinion: Understanding the impacts of agriculture and food systems on atmospheric chemistry is instrumental to achieving multiple Sustainable Development Goals

We discuss our current understanding of and knowledge gaps in how agriculture and food systems affect air quality and how agricultural emissions can be mitigated. We argue that scientists need to address these gaps, especially as the importance of fossil fuel emissions is fading. This will help guide food-system transformation in economically viable, socially inclusive, and environmentally responsible ways and is essential to help society achieve sustainable development.