The determination of the Surface Mass Balance (SMB) for the Antarctic ice sheet remains subject to significant uncertainty. Sentinel-1 Synthetic Aperture Radar (SAR) satellite sensors with their large spatial coverage and ability to penetrate the snowpack, represent a promising tool to more effectively assess the SMB. However, it is challenging to directly relate SMB to the SAR backscatter signal. The multitude of interactions between the snow microstructure and the backscatter signal complicate a direct translation from the backscatter signal to SMB using physical models. Additionally, the lack of reliable ground truth data limits the establishment of an empirical relationship with SMB across all of Antarctica. In this study we focus on establishing an empirical relationship between the SMB and dual polarisation SAR backscatter locally across three ice rises in Dronning Maud Land. The SMB of the ice rises was reconstructed using ground penetrating radar data and compared to the incidence angle corrected, four year average of the Sentinel-1 cross-polarization ratio σhh/σHV. We found a correlation between the SMB and the cross-polarization ratio with an R- value of 0.65 when using all available orbits. To understand this relationship we ran a radiative transfer model (SMRT) together with a physical snowmodel (SNOWPACK), which was forced by field measurements across the central ice rise. The results show generally lower density and optically equivalent grain diameter in accumulation zones but also higher specific surface area of the grains. Overall the results show the existence of a relationship between the SMB and the cross-polarization ratio for the study area. This promising proxy could be combined with physical models and extended to larger parts of Antarctica in future research.

While of critical importance for coastal communities, Antarctica’s future sea-level contribution remains highly uncertain. This uncertainty largely stems from the complex interaction between the ocean and the ice shelves, which is both difficult to observe and model. To better understand and constrain land-ice response to reduced buttressing exerted by ice shelves, efforts are needed to fully comprehend basal melt rates and their impact on ice shelf weakening and retreat. Here we present high-resolution basal melt maps (50 m) of vulnerable ice shelves based on a combination of stereo imagery and satellite altimetry, revealing pronounced channelized melting patterns whose melt rates were previously substantially underestimated (42–50%), which could result in faster channel breakthrough. Accurately simulating small-scale dynamics in ice-sheet models remains challenging but is essential for accurate sea-level rise projections.

The production of cocoa beans contributes to 7.5 % of European Union (EU) driven deforestation. As a result, the recent European Union Deforestation-free Regulation (EUDR) mandates producers to track cocoa farm extents comprehensively. While Remote Sensing has enormous capacity in dynamic crop monitoring, cocoa crop detection shows challenges due to cocoa complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous research on cocoa crop detection has mainly focused on pixel-based classification, disregarding spatial context. In this research we have performed a semantic segmentation approach to incorporate spatial configuration and enhance cocoa crop detection. We have applied Convolutional Neural Network (CNN) for the to semantic segmentation of cocoa parcels, considering both spectral and spatial characteristics. Additionally, we have evaluated the impact of combining Synthetic Aperture RADAR (SAR) and MSI (Multi-Spectral Imagery) data in the training of a CNN to demonstrate the importance of texture, moisture, and canopy characteristics in identifying cocoa canopies. The impact of MSI dataset stack with different SAR polarizations, seasons and temporality has been evaluated. The methodology is tested on Sentinel 1 and 2 data over an area of 100 × 100 km in Ghana for which an extensive ground truth data set of almost 90,000 polygons was available for training and validation. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62 %. This research demonstrates the influence of SAR measurements, seasons, polarization, and ground truth classes on the semantic segmentation of cocoa.

Damage features, such as rifts and crevasses, are the first signs of a weakened ice shelf and the precursor for retreat. Yet, damage changes are not widely quantified on Antarctic ice shelves, leaving future ice shelf weakening poorly understood. Here we use satellite imagery to detect both long-term (24-year) and short-term (annual, 2015–2021) Antarctic-wide damage changes, revealing a multiyear damage development cycle strongly correlated to ice shelf area changes, and a net decline in damaged area from 1997 to 2021. We establish a data-driven link between damage and ice flow characteristics, which shows that ice flow acceleration, strain rate increases and thinning lead to more damage development, in particular under high-emission climate scenarios. This sensitivity to warming suggests that without quantification of damage impacts by detailed physical models the (timing of) ice shelf retreat and Antarctic mass loss may currently be underestimated.

To investigate the drivers of lake drainages in Antarctica, we analyzed optical remote sensing data from the Shackleton Ice Shelf in East Antarctica over seven melt seasons, 2016 to 2023. Our study identified seven drainage event in 2016-2017, one in 2018-2019, fifteen in 2019-2020, and two in 2020-2021. All identified drainages occurred in regions with relatively medium to high levels of satellite-derived ice shelf damage and, except one, all with active damage development. Additionally, 17 out of 25 drainages coincided with increases in tidal heights. These findings provide insights into the factors influencing current lake drainages in Antarctica in both timing and distribution.

Patagonian glaciers have been rapidly losing mass in the last two decades, but the driving processes remain poorly known. Here we use two state-of-the-art regional climate models to reconstruct long-term (1940-2023) glacier surface mass balance (SMB), i.e., the difference between precipitation accumulation, surface runoff and sublimation, at about 5 km spatial resolution, further statistically downscaled to 500 m. High-resolution SMB agrees well with in-situ observations and, combined with solid ice discharge estimates, captures recent GRACE/GRACE-FO satellite mass change. Glacier mass loss coincides with a long-term SMB decline (−0.35 Gt yr⁻²), primarily driven by enhanced surface runoff (+0.47 Gt yr⁻²) and steady precipitation. We link these trends to a poleward shift of the subtropical highs favouring warm northwesterly air advections towards Patagonia (+0.14°C dec⁻¹ at 850 hPa). Since the 1940s, Patagonian glaciers have lost 1350 ± 449 Gt of ice, equivalent to 3.7 ± 1.2 mm of global mean sea-level rise.

Climate change and human-induced land degradation threaten dryland ecosystems, vital to one-third of the global population and pivotal to inter-annual global carbon fluxes. Early warning systems are essential for guiding conservation, climate change mitigation and alleviating food insecurity in drylands. However, contemporary methods fail to provide large-scale early warnings effectively. Here we show that a machine learning-based approach can predict the probability of abrupt shifts in Sudano–Sahelian dryland vegetation functioning (75.1% accuracy; 76.6% precision) particularly where measures of resilience (temporal autocorrelation) are supplemented with proxies for vegetation and rainfall dynamics and other environmental factors. Regional-scale predictions for 2025 highlight a belt in the south of the study region with high probabilities of future shifts, largely linked to long-term rainfall trends. Our approach can provide valuable support for the conservation and sustainable use of dryland ecosystem services, particularly in the context of climate change projected drying trends.

Rivers and slush fields on the Greenland Ice Sheet increasingly develop in locations where the accumulation zone hosts near-impermeable ice slabs. However, the division between runoff versus retention in these areas remains unmeasured. We present field measurements of superimposed ice formation onto slabs around the visible runoff limit. The quantity of superimposed ice varies by proximity to visible surface water and the surface slope, highlighting that meltwater can flow laterally before refreezing. We use heat conduction modelling and radar observations of autumn wetness to show that in our field area in 2022, 65% of superimposed ice formed during summer and the rest during autumn in the relict supraglacial hydrological network. Overall, 84% of melt around the visible runoff limit refroze. Ice-sheet-wide we estimate that slabs refroze 56 gigatonnes of melt (26-69 gigatonnes according to slab extent) between 2017 and 2022. Slabs are thus both hotspots of refreezing and emerging zones of runoff.

Snow cover is a crucial driver for plant species distributions in cold environments. The primary source of snow cover data used in distribution models is remotely sensed satellite imagery, which is characterized by coarser spatial resolutions than plot-scale observations of plant distributions. This scale-mismatch was hypothesized to limit model accuracy. Here, we used a common modeling framework to assess the contribution of snow melt-out dates derived from four data sources (satellite imagery, numerical snowpack modeling, webcam imagery and in-situ soil temperature measurements) at 1 m and 20 m spatial resolution to the predictive power of distribution models of 74 plant species in an alpine landscape of the Austrian Alps. We found that >80 % of the distribution models of all species were significantly improved by at least one snow melt-out data set when considering Area Under the Curve (AUC). Satellite-based melt-out led to significantly improved models for the highest number of species (>50 % for AUC) and increased True-Skill-Statistic and AUC on average by 16 % and 5 %, respectively. Surprisingly, fine-scale and in-situ measured melt-out data did not improve models more than the coarser scale (20 m) satellite-based melt-out data. Moreover, numerical snowpack modeling delivered results comparable to the other sources, which supports its use for projecting future species distributions. We conclude that the additional effort needed for producing high resolution, in-situ datasets as compared to commonly used satellite imagery might hence be worthwhile for some species but not for plant distribution modeling in cold ecosystems in general.

The Normalized Difference Snow Index (NDSI) is essential for accurate snow monitoring, but the widely used MODIS NDSI products generally have significant data gaps mainly due to cloud cover. Existing gap-filling methods often introduce artifact issue in regions with extensive and persistent cloud cover, where gap areas produce inaccurate results influenced by cloud shapes. To address NDSI gap-filling issue, we developed a mask-aware Transformer integrating multi-source data (MAT-MS) to effectively fill these gaps in MODIS NDSI data. The MAT-MS model leverages spatiotemporal information related to meteorology, topography, and geographic location. By incorporating a mask-aware technique, the MAT-MS can learn cloud shapes and patterns, helping to mitigate the common artifact issue. Validation using data from the Tibetan Plateau demonstrated the superior performance of the MAT-MS model, with averaged MAE, RMSE, and R² of 1.585, 5.531, and 0.868, respectively. The model reduced RMSE by over 30 % compared to traditional spatiotemporal interpolation methods, and by 9 % compared to mainstream deep learning models. Using MAT-MS, we generated a daily gap-free NDSI dataset for the Tibetan Plateau spanning from 2003 to 2020. This spatiotemporally continuous dataset is critical for detailed snow identification, enabling enhanced estimates of snow cover area, fractional snow cover, and snow depth. The flexibility of the MAT-MS model also makes it applicable to a wide range of continuous remote sensing datasets affected by data gaps.

Firn density plays a crucial role in assessing the surface mass balance of the Antarctic ice sheet. However, our understanding of the spatial and temporal variations in firn density is limited due to (i) spatial and temporal limitations of in situ measurements, (ii) potential modelling uncertainties, and (iii) lack of firn density products driven by satellite remote-sensing data. To address this gap, this paper explores the potential of satellite microwave radiometer (Special Sensor Microwave Imager/Sounder (SSMIS)) and scatterometer (Advanced Scatterometer (ASCAT)) observations for assessing spatial and temporal dynamics of dry-firn density over the Antarctic ice sheet. Our analysis demonstrates a clear relation between density anomalies at a depth of 40 cm and fluctuations in satellite observations. However, a linear relationship with individual satellite observations is insufficient to explain the spatial and temporal variation in snow density. Hence, we investigate the potential of a non-linear random forest (RF) machine learning approach trained on radiometer and scatterometer data to derive the spatial and temporal variations in dry-firn density. In the estimation process, 10 years of SSMIS observations (brightness temperature) and ASCAT observations (backscatter intensity) is used as input features to a random forest (RF) regressor. The regressor is first trained on time series of modelled density and satellite observations at randomly sampled pixels and then applied to estimate densities in dry-firn areas across Antarctica. The RF results reveal a strong agreement between the spatial patterns estimated by the RF regressor and the modelled densities. The estimated densities exhibit an error of ±10 kg m−3 in the interior of the ice sheet and ±35 kg m−3 towards the ocean. However, the temporal patterns show some discrepancies, as the RF regressor tends to overestimate summer densities, except for high-elevation regions in East Antarctica and specific areas in West Antarctica. These errors may be attributed to underestimations of short-term or seasonal variations in the modelled density and the limitations of RF in extrapolating values outside the training data. Overall, our study presents a potential method for estimating unknown Antarctic firn densities using known densities and satellite parameters.

In recent decades, satellite radar altimetry has been widely used to assess volume changes over the Greenland Ice Sheet. In particular, melt events result in drastic changes in the volume scattering of firn, which induces a pronounced change in the parameters derived from radar altimeter data. Due to the recent and increasingly frequent melt events over Greenland, the impacts of these events on the firn condition, i.e. the formation of ice lenses and reduction in firn air content, need to be better understood. This study therefore exploits the ability of long-term CryoSat-2 data to indicate changes in firn volume scattering in order to assess the spatiotemporal firn condition variations in Greenland. More specifically, this study utilises the leading edge width (LeW) parameter derived from CryoSat-2 Low Resolution Mode data, which has been proven to be a parameter strongly sensitive to changes in volume scattering, and assesses its variation between September 2010 and September 2024. With a combined analysis of remote sensing observations, in situ observations, and outputs from regional climate models, our study demonstrates that the LeW drop induced by extreme melt events in the interior of Greenland experiences a gradual recovery, which can potentially be explained by new-snow deposition. However, in many high-elevation regions of Greenland where firn layers were originally dry, the recent recurrence of extensive melt has prevented a full recovery of the firn volume scattering to pre-2012 conditions, indicating a persistent increase in firn density under a changing climate. Finally, our study also confirms the utility of radar altimeter data for long-term monitoring of the impact of melt and refreezing events on the properties of the upper firn layer

Snowfall is an important climate change indicator affecting surface albedo, glaciers, sea ice, freshwater storage, cloud lifetime, and ecosystems. Precise snowfall measurements at high latitudes are particularly important for the estimation of the mass balance of ice sheets; however, the snowfall is difficult to quantify with in situ measurements in those locations. In this context, spaceborne radar and radiometer atmospheric missions can help in the assessment of snowfall at high latitudes. The decommissioned NASA CloudSat mission provided invaluable information about global snowfall climatology from 2006 to 2023. The CloudSat-based estimates of global snowfall are considered the reference for global snowfall estimates, but these data suffer from poor sampling and the inability to see shallow or retrieve heavy precipitation, which limits their use, for example, as input to surface mass balance models of the major ice sheets. WIVERN (WInd VElocity Radar Nephoscope), one of the ESA Earth Explorer 11 selected missions, is equipped with a conical scanning 94 GHz Doppler radar and a passive 94 GHz radiometer, with the main objective of measuring global in-cloud horizontal winds, but also quantifying cloud ice water content and precipitation rate. Its conically scanning system, with a 42° incidence angle, is expected to reduce the radar blind zone near the surface (especially over the ocean) and allows the mission to have a swath width of 800 km and 70 times more sampled points than a fixed-looking instrument. The proposed radar measurements tackle the current uncertainties in snowfall estimates, highly improving the sampling frequency and accuracy of snowfall measurements. The uncertainty in snowfall measurements arises from various factors, including the diurnal cycle, uncertainty in the <Z-<S relationship, and the sampling error. This study quantifies each of these contributors individually and demonstrates the improved sampling capabilities of the WIVERN conically scanning geometry for some specific regions (Antarctica, Greenland) by computing the sampling error at different spatial and temporal scales via simulations of WIVERN vs. CloudSat orbits and scanning geometry, based on the snowfall rates produced by ERA5 reanalysis. Results show that a WIVERN-like conically scanning system significantly reduces the uncertainty in polar snowfall estimates if compared to a CloudSat-like near-nadir fixed viewing geometry. While CloudSat generates acceptable errors at the annual zonal scales, WIVERN can produce estimates within the climatological variability for latitude-longitude domain larger than 0.5° × 0.5° already at the monthly timescale, making it a valuable product for regional climate model evaluation and as an input to surface mass balance models of the major ice sheets and glaciers.

Channelized basal melting is a critical process influencing ice shelf weakening, as basal channels create zones of thinning and vulnerability that can potentially lead to ice shelf destabilization. In this study, we reveal and examine the rapid development of a channel within the George VI Ice Shelf's extensive channelized network, characterized by a 23 m surface lowering over a nine-year period. We study changes in ice flow, ocean circulation and heat potential as possible drivers behind the channel, under the hypotheses that it is either a fracture, a basal melt channel, or a combination of the two. Our findings show that the onset of this channel coincides with significant changes in ocean forcing, including increased ocean temperatures and salinity, that occurred during the 2015 El Niño Southern Oscillation event. Modelling of basal melting further suggests that channel re-routing has taken place over this nine-year period, with the new channel serving as a basal melt channel in the latest years. We further observe subtle shifts in ice flow indicative of fracturing. Our findings thus indicate that this channel likely contributes to the weakening of an already thin ice shelf through a combination of basal melting and fracturing. These findings offer insight into how similar potentially destabilizing processes could unfold on other Antarctic ice shelves. Monitoring the evolution of this channel and its impact on ice shelf integrity will be critical for understanding the mechanisms of ice shelf retreat, especially on heavily channelized ice shelves.

Digital twins of the Earth are digital representations of the Earth system, spanning scales and domains. Their purpose is to monitor, forecast and assess the Earth system and the consequences of human interventions on the Earth system. Providing users with the capability to interact with and interrogate the system, digital twins of the Earth are decision support systems for addressing environmental challenges. By informing humans of their impact on the Earth system, digital twins aspire to promote new pathways moving forward. By answering causal queries through intervention analysis, they can enhance evidence-based policy making. Existing digital twins of the Earth are primarily technological information systems that represent the physical world. However, as the social and physical worlds are intrinsically interconnected, we argue that humans must be accounted for both within and outside digital twins of the Earth: Within twins to represent human impacts and responses that are integral to the Earth system; and outside twins to govern access and development and to guide responsible use of information acquired from twins. Incorporating human interactions in digital twins of the Earth represents a transformative frontier, promising unparalleled insights into Earth system dynamics and empower humans for action.

Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high-resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small-scale melt processes. To address this limitation, we present SUPREME (SUPer-REsolution-based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically-informed super-resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing-derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr⁻¹ and 4.5 mm w.e. yr⁻¹, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super-resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super-resolution techniques with physical constraints for high-resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing.

Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar observations from Sentinel-1 provide the potential to monitor the variability of SMB processes through changes in the scattering response of near-surface and internal snow layers. However, the interplay between several factors, such as accumulation, wind erosion, deposition, and melt, complicates the interpretation of scattering changes of the microwave signal. Additionally, lack of reliable ground truth measurements of the snow surface limits our capability to associate the SMB processes with dominant scattering mechanism. In this study, we aim to quantify the dominant scattering in Sentinel-1 signal and evaluate the scattering changes in drifting snow-dominated regions of East Antarctica. We introduce a scattering indicator, alpha -{text{scat},varepsilon }, derived from scattering-type and entropy descriptors, providing a measure between volume and pure scattering. By relating the field measurements to alpha -{text{scat},varepsilon }, we establish that the evolution of dominant scattering in the presence of snowdrift is complex. First, alpha -{text{scat},varepsilon } strongly correlates with surface roughness (R^{2}=0.92, RMSE =2^circ). Spatially variable erosion patterns significantly increase the roughness and result in a strong affinity towards pure scattering despite net accumulation. Second, high surface densities also tend to influence pure scattering; however, the effect is dependent on the accumulation rate. With more accumulation, we observe an increasing dominance of volume scattering from internal snow layers. Long-term trends in alpha -{text{scat},varepsilon } (2017/2023) further suggest that it is challenging to address the causes behind the scattering source based on a single snow surface process. We thus demonstrate the potential and limitations of alpha -{text{scat},varepsilon } to infer the variability in dominant scattering from changes in surface processes.

Most of the Greenland and Antarctic ice sheets are covered with firn — the transitional material between snow and glacial ice. Firn is vital for understanding ice-sheet mass balance and hydrology, and palaeoclimate. In this Review, we synthesize knowledge of firn, including its formation, observation, modelling and relevance to ice sheets. The refreezing of meltwater in the pore space of firn currently prevents 50% of meltwater in Greenland from running off into the ocean and protects Antarctic ice shelves from catastrophic collapse. Continued atmospheric warming could inhibit future protection against mass loss. For example, warming in Greenland has already contributed to a 5% reduction in firn pore space since 1980. All projections of future firn change suggest that surface meltwater will have an increasing impact on firn, with melt occurring tens to hundreds of kilometres further inland in Greenland, and more extensively on Antarctic ice shelves. Although progress in observation and modelling techniques has led to a well-established understanding of firn, the large uncertainties associated with meltwater percolation processes (refreezing, ice-layer formation and storage) must be reduced further. A tighter integration of modelling components (firn, atmosphere and ice-sheet models) will also be needed to better simulate ice-sheet responses to anthropogenic warming and to quantify future sea-level rise.

This paper presents an overview of the Sub-daily Land Atmosphere INTEractions (SLAINTE) mission. SLAINTE comprises a constellation of identical synthetic aperture radars (SAR) with interferometric capability. It aims to bridge a critical observation gap, by providing sub-daily, ≤1 km scale observations related to ecosystem water status, including vegetation water content and surface soil moisture over key regions of scientific, ecological, societal and economic interest. These data will provide unprecedented insight into vegetation water, carbon and health improving our ability to study, understand and model the response of ecosystems to climate change and human impact. This mission concept has been submitted in response to ESA's call for proposals for Earth Explorer 12.

Over 60% of all meteorites found on Earth are found in Antarctica. Antarctic meteorites are collected from so-called
blue ice areas in the interior of the continent. In these blue ice areas, meteorological processes and a redirected ice flow lead to the removal of surface layers. All material that was once embedded in the removed layers of ice becomes exposed at the surface, including meteorites. Consequently, meteorite concentrations can be collected in the field, as the meteorites’ dark color visually contrasts with the light-blue colored ice. However, meteorites can disappear from the surface when they warm up in the sun and melt the underlying ice. The dark meteorites warm up more than the ice as they absorb more solar radiation. As such, most of the ice remains frozen, but very local melt takes place directly under the meteorite. This melt creates a depression in the ice which over time deepens to a hole, and in conjunction with refreezing meltwater the meteorite fully disappears from the surface, hidden from sight during recovery missions. This process can explain why no meteorites have been found in areas where temperatures are relatively high [1]. Moreover, in a warming climate, meteorites are more prone to become unrecoverable.
Using a data-driven approach, we estimated that with the currently increasing surface temperatures, meteorite loss rates exceed recovery rates by a factor of five [2]. To estimate this loss rate, we first calculated the total number of Antarctic meteorites using a machine learning algorithm that identifies meteorite-rich sites using over 12,000 known meteorite finding locations and their corresponding properties such as ice flow velocity and surface temperature. Next, we used the same algorithm, but altered the surface temperature input based on regional climate model simulations. Consequently, we obtained the number of meteorites until the end of the century under a low and a high emissions scenario. Until mid-century, the projected losses are identical for the emissions scenarios, after which losses are reduced for the low emissions scenario and nearly constant for the high emissions scenario. By correlating meteorite losses directly to global temperature increases, we estimated that for every tenth of a degree of global warming 1-2% of the current number of meteorites is lost.
These future projections demonstrate how climate change threatens Antarctic meteorites. With temperatures remaining well below zero, even with several degrees of warming, meteorites are affected even by very minor (decimal) increases of surface temperatures during exceptionally warm events, which are expected to occur more frequently. These results motivate an intensification of Antarctic meteorite recovery missions, prioritizing the most prone low-elevation areas.
References:
[1] Harvey, R. (2003) Geochemistry 63(2):93-147.
[2] Tollenaar, V., Zekollari, H., Kittel, C., Farinotti, D., Lhermitte, S., Debaille, V., Goderis, S., Claeys, P., Joy, K.H., and Pattyn, F. (2024) Nature Climate Change 14(4):340-343.