We explore the skill of the fully initialized decadal forecasting system EC-Earth3 in re-forecasting annual frequencies of recurrent, regional-scale atmospheric circulation types in the extra-tropics. To this end, 6-hourly sea-level pressure data from 10 model integrations run within the dcppA-hindcast experiment are transformed into discrete time series of categorical circulation types following the Jenkinson-Collison approach. The annual occurrence frequencies of these types are verified against those obtained from a reference reanalysis (ERA5) to estimate the hindcast skill, which is then compared with the skill of a 10-member ensemble run under historical conditions to estimate the added value of initialization. While the results for the Northern Hemisphere are heterogeneous, the initialized experiments provide systematic skill and added value in the Southern Hemisphere during austral summer, up to the second forecast year. Skill correlates with the Ratio of Predictable Components and the westerly wind conditions are particularly prone to the signal-to-noise paradox.

Machine learning methods provide a promising approach for exploiting relationships between raw forecasts and observations for forecast calibration. This paper highlights the role of data transformation in rainfall forecast calibration with machine learning. We develop a novel architecture that accounts for the positive skewness and zero bound of rainfall by incorporating a normalizing transformation (log-sinh) into distributional regression neural networks (NN). A unified loss function is formulated based on the negative log-likelihood function for parameter optimization. To test the importance of data transformation, we conduct five calibration experiments: one that does not use transformation at all (the baseline) while the others use the log-sinh transformation in different ways. All experiments are based on 10-day rainfall forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF) from 2011 to 2022. Overall, the calibration methods effectively correct spatiotemporally varying biases in raw forecasts and improve reliability, yielding mean skill improvements of approximately 2% to 11% and in the best case reducing forecast biases to less than 2%. Without transformation, the baseline method suffers from forecast biases ranging from −30% to 50%, due to its limited ability to characterize the uncertainty of rainfall forecasts. Of the four experiments that use the log-sinh transformation, the optimal performance is achieved by the combined use of transforming raw forecasts for the input layer and utilizing fixed transformation parameters for generating calibrated forecasts in the output layer. We show that this method marginally outperforms an advanced existing Bayesian Ensemble Model Output Statistics method in reducing forecast biases.

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery requires accurate atmospheric compensation to correctly disentangle its small contribution to the at-sensor radiance from other confounding factors. In spectral fitting SIF retrieval approaches this compensation is estimated in a joint optimization of free variables when fitting the measured at-sensor signal. Due to the computational complexity of Radiative Transfer Models (RTMs) that satisfy the level of precision required for accurate SIF retrieval, fully joint estimations are practically inachievable with exact physical simulation. We present in this contribution an emulator-based spectral fitting method neural network (EmSFMNN) approach integrating RTM emulation and self-supervised training for computationally efficient and accurate SIF retrieval in the O2-A absorption band of HyPlant imagery. In a validation study with in-situ top-of-canopy SIF measurements we find improved performance over traditional retrieval methods. Furthermore, we show that the model predicts plausible SIF emission in topographically variable terrain without scene-specific adaptations. Since EmSFMNN can be adapted to hyperspectral imaging sensors in a straightforward fashion, it may prove an interesting SIF retrieval method for other sensors on airborne and spaceborne platforms.

Tree height estimation is crucial for accurate biomass estimation and forest monitoring. Synthetic Aperture Radar (SAR) offers two products useful for tree height estimation: SLC (Single Look Complex) images and derived tomographic cubes. In this work, we present machine learning methods to predict forest height in two ways, directly from SLC images and from tomographic cubes, providing valuable context to the upcoming European Space Agency’s Biomass Satellite mission.

Reliable Flood Inundation Mapping (FIM) is a key component in the flood forecasting and analysis framework. The selection of FIM prediction solvers is a trade-off between computational and data costs and accuracy. Physically based (hydraulic) models are considered more accurate but computationally and data expensive, while low complexity terrain-based solvers offer efficiency and scalability at the expense of accuracy. The NOAA Office of Water Predictions (OWP) operational FIM framework is based on the Height Above Nearest Drainage (HAND) approach. In this study, a deep Convolutional neural network (CNN) technique is explored to enhance the OWP HAND-FIM using a surrogate modeling approach. Among various architectures, an encoder-decoder U-Net architecture performs well to classify the flooded area precisely. U-Net's skip connections help to maintain high-resolution spatial information by combining characteristics from multiple Convolutional layers, which improves prediction accuracy, whereas skip connections help reduce vanishing gradient concerns, which improves the model's generalization capabilities. The model is trained using physics-based HEC-RAS simulations and a suite of other input features (digital elevation model, topographical wetness index, land use, land cover, flow accumulations, etc.) over three HUC8-scale watersheds. The results show a considerable improvement in FIM accuracy when using a surrogate CNN model compared to the original OWP HAND-FIM. The transferability of the model is tested in a fourth HUC8 region, showing considerable enhancement in the OWP HAND-FIM after the trained surrogate model is applied. The enhanced FIM was also evaluated using building footprints, resulting in a significant improvement in the surrogate model impact detection accuracy. This demonstrates that CNN holds a generalizing capability by capturing the spatial correlation between pixels. Introducing physical laws to attain behaviors during modeling and increasing the size and diversity of its training data is expected to further enhance the model's transferability and predicting capability.

In the context of urban green infrastructure planning, which is increasingly important due to global warming and the rise in extreme weather events, solar panel plants have been widely adopted as a practical solution for climate change adaptation. This study harnesses advanced remote sensing techniques, specifically Sentinel satellite data and texture analysis using the Gray Level Co-occurrence Matrix (GLCM), to explore the relationship between green infrastructure and solar power potential in urban environments using open-source satellite images. Lijiang, a city renowned for its environmental and cultural diversity in southwestern China, serves as the case study due to its significant role in constructing a green city towards net zero beyond China and its long-standing industrial-free urban development model.Our method combines the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) to quantify vegetation health and coverage, while applying GLCM texture metrics to enhance the characterization of green infrastructure. Utilising Sentinel-2 multi-spectral imagery and digital surface models from Sentinel-2 data for 2023, we develop an innovative approach to simultaneously evaluate green infrastructure and identify optimal locations for solar panel installations at a city scale. We calculate solar radiation and solar potentials across various counties, along with derivatives from Digital Terrain Models (DTMs), such as slope, aspect, and shadowing effects of the terrain, evaluating their impact on solar energy harvesting. Through spatial analytics and suitability analyses, we identify areas where existing green infrastructure in Lijiang may support or conflict with optimal sites for solar panel installations and harvesting.The findings aim to inform the most recent urban green infrastructure planning strategies and ongoing solar energy projects in Lijiang, highlighting the trade-offs and synergies between maintaining green infrastructure and advancing solar energy solutions. This research provides valuable insights for balancing ecological benefits and renewable energy needs in urban planning through the application of multi-sourced remote sensing images and technologies. Keywords: Sustainability, Climate change, Carbon emission reduction, Resilience, Urban planning

Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational datasets. Variability exists not just among the data products, but also within the creation of individual ones. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual datasets and their various groupings. Our methodology, utilizing both spatial and temporal data, reveals impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduces a discernible biases. Furthermore, these results highlight that the variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

Observations of strong internal waves (IWs) off the Amazon Shelf by the Surface Water and Ocean Topography (SWOT) mission are analyzed. Distinct IWs signatures with wavelengths ranging from 3 to 50 km in coincident sea surface height anomalies (SSHA) and near-nadir normalized radar cross section (NRCS) are clearly identified. Using a three-layer approximation to describe the upper ocean stratification, SWOT SSHAs are converted to IW-induced thermocline displacements, reaching up to 80 m amplitude. This confirms SWOT’s unique ability to quantitatively inform about the state of the ocean interior, the energy and depth distribution of IWs. Moreover, joint SWOT measurements of SSHAs and NRCS further provide new means to precisely study the mechanisms leading to identify IWs from radar intensity measurements. SWOT data can indeed be analyzed in terms of a modulation transfer function (MTF), relating the SWOT NRCS contrasts to divergence of IW surface currents derived from SWOT SSHA measurements. Thanks to these new observations, SWOT-based MTF estimates are derived to quantify relationships between the NRCS contrasts, the amplitude and wavenumber of IWs, and the local wind conditions. In particular, it is shown that the maximum SWOT NRCS contrasts occur when IWs propagate in the wind direction, corresponding to resonant conditions between short wind waves and internal waves.

This study characterizes changes to the ocean structure and ocean thermal energy budget produced by tropical cyclones (TCs) over the North West Shelf of Australia using a 20-year composite of cyclone data (1996-2016) created from Bluelink ReANalysis data (BRAN). Part 1 focuses on the general effects of the passage of a TC over the region and the effects of the location in respect to the shelf edge. Cold temperature anomalies develop at the ocean surface beneath the TC core in response to strong mixing and deep upwelling driven by surface divergence and Ekman pumping. In the TC outer circulation, surface cold anomalies also develop. However, warm temperature anomalies appear in the subsurface below the mixed layer due to wind-driven mixing across the mixed layer. In the month following the passage of the TC, the surface cold temperature anomalies recover relatively quickly due to air-sea fluxes, while the subsurface warm anomalies take longer to recover. Over the shelf, stronger cold anomalies develop at the surface, and the TC direction of motion can further impact the development of currents. TCs that move perpendicular to the shelf edge drive stronger vertical currents in a wide region around the track and stronger SST anomalies and TCs that move parallel to the coast drive colder temperature anomalies at depth and stronger upward currents beneath the track. The presence of the shelf can affect the development of downwelling currents along the coast for TCs that are further offshore but move parallel to the coast.

The ocean is an important sink for anthropogenic carbon, its size providing a vital constraint on the global carbon budget. Climate projections also depend on accurate modeling of the ocean carbon sink. However, estimates of the sink from data products based on surface ocean observations differ significantly from the results of global biogeochemical models (GOBMs), with the former suggesting more decadal variability over the observational period and a steeper uptake trend since around the year 2002. We present an alternative method for diagnosing the ocean carbon sink and characterizing transport and mixing of carbon in the ocean interior, using observational data. Our method combines a machine learning reconstruction of ocean interior carbon with an optimal transformation method (OTM) that uses a water mass framework to simultaneously close budgets of heat, freshwater and carbon. Focusing on two decades, we find a global sink of 2.03±0.22PgCyr-1 for 1993-2002 and 2.86±0.25PgCyr-1 for 2003-2012. The trend of 0.83±0.5PgCyr-1 is 75% larger compared to the data products (although within uncertainties), and nearly quadruples that suggested by the GOBMs, and is driven mainly by the North Pacific (>10N) and Southern Ocean (>35S) basins. The transport and mixing diagnosed by the OTM suggests an intensification of carbon uptake during Southern Ocean mode and intermediate water formation and a reduction in uptake during North Atlantic Deep Water formation have contributed to a southwards redistribution of carbon in the Atlantic. Over the same period, ocean transport and mixing acted to redistribute carbon from south to north in the Indo-Pacific.

Arctic infrastructure is challenged by ice-rich permafrost thaw that causes differential ground subsidence. Economic impact estimates of permafrost thaw damages require accurate infrastructure inventories. We developed a deep learning-based mapping pipeline, HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), to automatically detect infrastructure from Maxar satellite imagery in 285 Alaskan communities. Combining HABITAT with OpenStreetMap, we mapped a building footprint of 53M m2 and a road network of 50,477 km across Alaska. HABITAT adds 17M m2 to the statewide building footprint not accounted for by OpenStreetMap and 6M m2 within discontinuous and continuous permafrost. We identified infrastructure at risk of damage from bearing capacity loss and ground subsidence between the decades 2015–2024 and 2055–2064 projected by a permafrost geotechnical model. Estimated damages to buildings and roads could cost Alaska $37B to $51B under the SSP245 and SSP585 scenarios, respectively. This is $20 to $48B more than estimates from previous literature due to the additional building footprint mapped by HABITAT.

This study analyzes the climatology of Large Scale Traveling Ionospheric Disturbances (LSTIDs) over Europe from 2014 to 2023 using the HF-Interferometry method (HF-INT), which provides LSTID's activity detected in near-real-time by a network of European ionosondes. For this purpose, this LSTID activity has been analyzed in depth and a Catalogue of observed LSTID events has been obtained providing, among others, the onset time, duration, dominant period, and propagation velocity vector of each LSTID event. The results, derived from this Catalogue of LSTIDs, reveal that LSTID occurrence is significantly dependent on local time, seasonal variations, and geomagnetic conditions, with activity peaks observed during equinoxes, particularly at night and in the early morning hours. Key propagation characteristics include velocities ranging from 500 to 700 m/s and azimuths, suggesting a southward direction, closely associated with auroral activity. Additionally, a distinct westward propagation in the morning hours is attributed to the solar terminator effect. These findings highlight the complex interactions between ionospheric dynamics and geomagnetic conditions, offering new insights into the mechanisms governing TID generation and propagation.

The concept that oceanic lithosphere mechanically limits upwelling and decompression melting of mantle plumes is known as the lid effect and is backed up by observations of ocean island basalt (OIB) geochemistry. Nevertheless, in a recent companion study on OIB geochemistry, we identified several additional factors that further influence OIB compositions including a temperature filter, whereby OIBs sample progressively hotter plumes in thicker lithosphere, and a memory effect, whereby the high-pressure signature associated with the deepest melting within the melt domain is preserved in basalts erupted at the surface. Here, we test the ability of coupled geochemical and geodynamical simulations of mantle plumes to match these observational inferences. In addition to confirming operation of the lid effect and temperature filter, our models suggest that it is possible to extract melts from across the melting domain without complete homogenisation, and that much of the spread in observed OIB geochemistry at single ocean islands can be achieved using a single source lithology. Nevertheless, we also find that an OIB source composed solely of primitive mantle contains insufficient rare earth element enrichment, necessitating a recycled crustal component of either 10% basalt or 20% gabbro in the source of OIBs.

The Gonnan superstorm significantly impacted the thermosphere-ionosphere system, causing unusual plasma density redistribution. Observations revealed enhanced electron density at the equator and a complete absence of Equatorial Ionization Anomaly (EIA) crests. These changes observed using data from Global‐scale Observations of the Limb and Disk (GOLD), ground-based vertical Total electron content (VTEC), and Swarm satellites are attributed to thermospheric winds, compositional changes, and a strong Counter Electrojet (CEJ) driven by the Disturbance Dynamo Electric Field (DDEF). Additionally, the equatorward movement of enhanced O density in the ΣO/N2 ratio over time is likely driven by thermospheric winds originating at high latitudes generated by the superstorm.

Wave-particle resonant interactions are a main driver of radiation belt dynamics. The electron motion in strong dipole field includes three types of periodicity and possesses three corresponding adiabatic invariants. Consequently, three different types of waves contribute to violation of these invariants and associated electron acceleration, scattering, and radial transport. For the two fastest types of periodic motion, gyromotion and bounce motion, theoretical models of adiabatic invariant violation have covered two main regimes – the quasi-linear regime of invariant diffusion and the nonlinear regime, characterized by large-amplitude jumps of invariants. For the slowest type of particle motion, the azimuthal drift, until very recently there were only quasi-linear models of electron diffusion by ultra-low-frequency (ULF) waves. However, the growing number of spacecraft observations of narrow-band intense ULF waves requires a formulation of a new theoretical framework accounting for the electron nonlinear resonance with such waves. In this commentary we review the recent progress in development of this framework largely reported in four recent papers (Li et al., 2018, 2020, 2021, and 2024). We also discuss the potential importance of nonlinear electron resonances with ULF waves for explaining observational features in electron flux dynamics.

A document by Tasfia Tasnim. Click on the document to view its contents.

Dipolarization events with inductive, radial electric fields are investigated with multi-spacecraft analysis techniques. Observations by Magnetospheric Multiscale (MMS) with separations around ion scales are used to study spatial and temporal variations of these events in the inner magnetosphere. JxB force, magnetic pressure force, and tension force are compared based on the curlometer technique and the Taylor expansion method. The magnetic pressure force, possibly related to earthward transport of energetic particles and magnetic flux, tends to be balanced by the JxB force for the background components near the equator, while the tension force, related to Alfvén waves, is balanced for the fluctuating components or outside the equator. The scale length is thousands of km for the background components, probably related to meso-scale structures, while that length is tens to a thousand km for fluctuating components. The small-scale fluctuations would be related to particle acceleration. The fluctuations are inferred to be Alfvén waves with quasi-perpendicular propagation directions and with finite temperature or kinetic effects. These fluctuations could be intermittent and not self-similar between different inter-spacecraft distances. Some energy transfer from fluctuations to particles would occur.

A document by Kavya Johny. Click on the document to view its contents.