Communications Earth & Environment

Communications Earth & Environment

Published by Springer Nature

Online ISSN: 2662-4435

Disciplines: Earth and Environmental Science

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Geological setting of the NCC and its surroundings
a Middle Triassic block reconstructions of NCC, citied after ref.⁹b Proposed paleogeographic models for the Middle Triassic, modified from refs. 23, 24–25; (c, d) Location and geological map of the Ordos Basin, western NCC⁷⁷ and sampling sites. Sample information is given in Supplementary Data 2. Characteristics of magmatic rocks are referred from the literatures33,43,46,78,79. NCC North China Craton, SCB South China Block, IMPU Inner Mongolia Paleo-uplift, northern NCC, ALS Alxa Block, QLS Qilian Shan, QL Qinling, SPGZ Songpan-Ganzi.
Detrital zircon U-Pb age distributions
a–c Detrital zircon U-Pb age kernel density estimation plots of Middle Triassic samples from the west Ordos Basin, southeast Ordos Basin, and Qinling and SPGZ, respectively. d Zircon U-Pb age distributions of the potential provenance area. e Non-metric MDS plot for Middle Triassic strata and its potential provenance (the closest and second closest neighbors are represented by solid and dashed lines, respectively), shaded areas indicating data affinity to the E-NCC (blue), W-NCC (light blue) and Qinling (light yellow). All original plots were generated using IsoplotR program (http://www.isoplotr.com/isoplotr/). Data and their references are available in Supplementary Data 1–3. W-NCC western NCC, E-NCC eastern NCC, IMPU Inner Mongolia Paleo-uplift, northern NCC, ALS Alxa Block, QLS Qilian Shan, S-NCC southern NCC, N-QL North Qinling, S-QL South Qinling.
Middle Triassic paleogeographic reconstructions of the NCC and eastern Paleo-Tethys Ocean, eastern Pangaea
a Middle Triassic block pattern, after ref. ⁹. b Q-F-L (quartz-feldspar-lithics) ternary diagrams of Middle Triassic sandstones of the NCC, sandstone classification and provenance fields according to refs. 39,76, data are available in Supplementary Data 4. c, d Sketch model showing the source-to-sink relations between the NCC and the eastern Paleo-Tethys Ocean (modified after refs. 24,41,80). The red dashed line in Fig. 3c represents the boundary between different source fingerprinting characteristics, and cycles are the detrital samples. W-NCC western NCC, E-NCC eastern NCC, QLS Qilian Shan, S-NCC southern NCC, N-QL North Qinling, S-QL South Qinling, SCB South China Block.
Fig.3c
Middle Triassic transcontinental connection between the North China Craton and the Paleo-Tethys Ocean

December 2024

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558 Reads

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1 Citation

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Chiyang Liu

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419 reads in the past 30 days

Land-to-sea indicators of the Zanclean megaflood

December 2024

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419 Reads

Aims and scope


Communications Earth & Environment is an open access journal from Nature Portfolio that publishes high-quality research in Earth, environmental, and planetary sciences. It covers a wide range of topics including climate science, atmospheric science, geology, hydrology, and environmental change. The journal aims to facilitate the rapid dissemination of significant research findings and encourages interdisciplinary studies.

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Observational-based analyses of reduced Arctic sea ice underneath an Arctic cyclone and tropopause polar vortex
a Potential temperature on the dynamic tropopause (colors; color interval 5 K) and sea level pressure (black contours; contour interval 4 hPa; only values at or below 1004 hPa are shown) at 00 UTC 21 August 2006 and b sea ice concentrations (colors) from passive microwave satellite radiometry composited over 21 August 2006. The red rectangle highlights a region of locally low sea ice concentration (b) under an observationally-based reanalysis of a TPV and Arctic cyclone (a). The dynamic tropopause is the 2 PVU surface, where 1 PVU = 10⁶ K m² kg⁻¹ s⁻¹. The sea ice concentration points near the pole (latitudes ≥ 87.5°N) are masked out.
There is significant sea ice variability at high frequencies (weather timescales) that is not represented in climate models
a SIE and b ΔSIE power spectral density (PSD) for a random CESM-LE ensemble member projection of the near present day equivalent period (green) compared to observations from the NSIDC sea ice index (1989–2023; blue). The 95% confidence interval from the theoretical red noise spectrum (dashed red) is also shown, and thus PSD values above this dashed red line indicate statistically significant a SIE or b ΔSIE variability. The vertical dashed black line denotes the 18-day cutoff period used in the high bandpass filter to retain the weather timescales. Units of PSD here are (variance Hz⁻¹).
Very rapid sea ice loss events contribute to an acceleration of Arctic sea ice reduction
a The 30-yr climatological range of ΔSIE (gray) compared with the bottom 5th percentile mean-removed VRILEs (ΔSIEmean removed; red) and range of filtered VRILEs (ΔSIEbwfilt; blue). Distribution of the (b) number of VRILEs for decadal periods with 1991 through 2001 (black), 2002 through 2012 (blue), and 2013 through 2023 (red) by month and (c) total number of filtered VRILEs (blue) plus mean-removed VRILEs (red) in June-August (JJA) by year. d 1 September SIE (black) and 1 September SIE minus the accumulated sea ice reductions from ΔSIEtotal (gray). The fractional reduction in SIE from ΔSIEtotal (red) is shown for comparison using Equation (1) and expressed as a percentage. The number of VRILEs for decadal periods 1991 through 2001 (black), 2002 through 2012 (blue), and 2013 through 2023 (red) by ΔSIE for (e) JJA and (f) all months except JJA.
Very rapid sea ice loss events occur in regions of strong pressure differences between cyclones and the Beaufort High
a–d Locations of filtered VRILEs, computed with (ΔSIEbwfilt), and e, f June-August 1989–2023 composite tropopause potential temperature (colors) and sea level pressure (contours) standardized anomalies. Filtered VRILE locations in a, c are for June–August while b, d are for non June–August months for the years a, b 1989–1998 and c, d 2014–2023. VRILE locations are determined by the number of sea ice concentration loss objects at any 0.5° grid point. In (a–d), mean sea level pressure is shown by gray contours with a 1 hPa interval and thick (thin) cyan contours are the monthly median (maximum) of a, c June–August and b, d non June–August 1-year ice age extent from the a, b 1989–1998 median (maximum) and c, d 2014–2023 median (maximum) ice age extent. Composites in e, f are averaged relative to the e VRILE location and f closest Arctic cyclone with dashed (solid) contours indicating negative (positive) anomalies.
Sea ice loss in association with Arctic cyclones
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January 2025

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2 Reads

Steven M. Cavallo

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Madeline C. Frank

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Cecilia M. Bitz

Arctic sea-ice extent has reduced by over 40% during late summer since 1979, and the day-to-day changes in sea ice extent have shifted to more negative values. Drivers of Arctic weather that cause large short-term changes are rarely predicted more than a week in advance. Here we investigate variability in changes in sea ice extent for periods of less than 18 days and their association to Arctic cyclones and tropopause polar vortices. We find that these very rapid sea ice loss events are substantial year-round and have increased over the last 30 years in June-August due to thinning sea ice that is more susceptible to forcings from ocean waves and low-level atmospheric wind. These events occur in regions of enhanced near-surface level pressure gradients between synoptic-scale high and low pressure systems over regions of relatively thin sea ice, and are preceded by tropopause polar vortices.


Two-stage oxidation of petrogenic organic carbon in a rapidly exhuming small mountainous catchment

January 2025

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6 Reads

Wan-Yin Lien

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Chih-Tung Chen

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Yun-Hsuan Lee

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Li-Hung Lin

Globally, the oxidative flux of petrogenic organic carbon rivals the drawdown by silicate weathering and burial of biospheric carbon. Where and how petrogenic organic carbon is susceptible to degradation along the short-path river-marine continuum in active orogens remains elusive. Here, we demonstrate the transformation of petrogenic organic carbon from a mountainous catchment in eastern Taiwan and its connecting submarine canyon. Our Raman analyses indicate that while highly graphitized carbon in slate/schist transformed into disordered form during soil development, the preferential elimination of disordered form was found along submarine transit. Additionally, quartz/rutile outperformed mica in protecting petrogenic organic carbon from transport abrasion and microbial degradation. Such an oxidative flux was estimated to be 20–35 metric tons of carbon per square kilometer per year, ranking among the greatest levels around the world and highlighting tectonically active islands and the surrounding marine systems as a hotspot of carbon emission.



Map of study sites
The locations of 65 multi-annual (≥5 years) eddy-covariance flux sites within the northern forest region (above 30° N latitude), defined by areas with forest cover exceeding 15%.
Sensitivities (regression slopes) between ΔNEE and environmental drivers in different seasons
a–c, Slopes of ΔNEE versus air-temperature anomalies (ΔTa) a, ΔNEE versus Enhanced Vegetation Index (EVI) anomalies (ΔEVI) b, and ΔNEE versus α anomalies (Δα) c as derived from a linear mixed-effects regression model for seasonal periods. Error bars show 95% CIs of estimated slope parameters, all statistical analysis were significant at 0.05 except for winter seasons (p > 0.1). Mean NEE values are represented by the color scale. The size of data points corresponds to the sensitivity values.
Sensitivities (regression slopes) between ΔNEE and environmental drivers in different seasons characterized by stand age groups (i.e., stand age <40 years, stand age between 40 and 90 years and stand age >90 years)
a–c, Slopes of ΔNEE versus air-temperature anomalies (ΔTa) a, ΔNEE versus EVI anomalies (ΔEVI) b, and ΔNEE versus α anomalies (Δα) c as derived from a linear mixed-effects regression model for seasonal periods and stand age groups. Error bars show 95% CIs of estimated slope parameters, all statistical analysis were significant at 0.05 except for winter seasons (p > 0.1). The red star inside circles indicate statistical significance at 0.05. Mean NEE values are represented by the color scale.
Estimated ΔNEE across northern forests (>30° N with forest coverage > 15%)
Maps of ΔNEE (between the periods 1951–1970 and 2001–2020) for seasonally uniform warming (same warming rate across all seasons) (a), for seasonally varying warming (b), for seasonally varying warming using young forests temperature sensitivities (c), and for seasonally varying warming considering stand age impacts on NEE temperature (d).
Estimated ΔNEE during different seasons
Box plot of ΔNEE (between the periods 1951–1970 and 2001–2020) during different seasons for seasonally uniform warming (Annual warming), for seasonally varying warming (Seasonal warming1), for seasonally varying warming using young forests temperature sensitivities (Seasonal warming2), and for seasonally varying warming considering stand age (Seasonal warming3) (a); Stacked plot of ΔNEE during different seasons for seasonally varying warming considering stand age (b).
Seasonal warming responses of the carbon dioxide sink from northern forests are sensitive to stand age

January 2025

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8 Reads

Peng Liu

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Tianshan Zha

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T. Andrew Black

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Xinhao Li

Northern forests (forests north of 30°N) are major terrestrial carbon dioxide (CO2) sinks, while rapid warming can disturb their CO2 sink function. Here we use multi-year net CO2 exchange observations from 65 northern forest sites to show that the increased net CO2 uptake during warmer springs was more pronounced in old forests (>90 years old) compared to young (<40 years old) and mid-aged (40–90 years old) forests. In addition, the decreased net CO2 uptake during warmer summers and autumns was more pronounced in young forests compared to mid- and old-aged forests. Annually, this resulted in an increase in net CO2 uptake due to seasonal warming for old forests (4.8 g C m⁻² yr⁻¹) and a decrease in young- and mid-aged forests (3.2 and 0.8 g C m⁻² yr⁻¹, respectively). In future projections, increasingly uneven seasonal warming may amplify the impacts of stand age on CO2 sinks of northern forests.


Map of the seafloor bathymetry of the eastern offshore of the UAE
The map covers the continental shelf, shelf break, slope base, and locating the newly discovered gas seeps (cyan circles). The yellow circles locate the water column physical parameters profiles with their numbers, whereas the gray lines refer to the seismic reflection profiles. Inset: the eastern Arabian continental margin; the light green area refers to the location of the acquired datasets. BONF Batinah Onshore Normal Fault, BOFNF Batinah Offshore Normal Fault, MuP Musandam Peninsula.
Slope gradient maps of the eastern offshore of the UAE
a A slope gradient map derived from the multibeam bathymetry, showing the locations of b–d. The light blue circles represent the discovered gas seeps, the red dots refer to the circular pockmarks (CiPM), and the solid green and blue polygons refer to the crescent-shaped pockmarks (CrPM) and complex pockmark strings (PM strings), respectively. The dashed blue and red polygons represent areas containing most of the complex-shaped pockmarks and the simple circular pockmarks, respectively. b–d highlight the different pockmark geometries with inset 1–3 as bathymetric profiles across these pockmarks.
Seismic reflection profiles show how the pockmarks relate geometrically to the faults and the effect of erosion on the pockmark base
a A seismic profile illustrates the seismic disturbances (SD) or acoustic turbidity (that indicate fracture conduits), seafloor pockmarks, and fault conduits (black dashed line). b, c seismic profiles show several bright spots (BS), with insets 1 and 2 representing bathymetric profiles that show seafloor pockmarks across the same profiles. d Seismic profile shows the same features as b, c, in addition to inset 3, which illustrates the vertical displacement of the seafloor. e Inset 4 shows polarity reversal (PR). f, g Show several faults and fracture conduits below seafloor pockmarks, with inset 5 illustrating the erosional truncation of the seismic reflections by the seafloor.
Water column backscatter data (echograms) with the discovered gas seeps in the Gulf of Oman
a An echogram from the wideband echosounder data (at 18 kHz) showing evidence of fluid escaping from the seafloor, gas bubbles, over almost flat bathymetry. b An echogram showing several gas seeps over rugged bathymetry.
Sections of the water column physical parameters of the eastern offshore of the UAE
a An E-W section of the water column physical parameters showing the bottom Arabian Gulf Outflow (AGO), exhibiting mainly a uniform thickness over flat shelf bathymetry. The AGO is characterized by high salinity, temperature, and beam attenuation. b, c represent mainly N-S and E-W water column physical parameters sections, respectively, where the AGO has an almost uniform thickness over flat shelf bathymetry and exhibits reworking or generation of secondary currents, hence increased thickness, within the seafloor irregularities, i.e., pockmarks.
Evidence of pockmarks and seafloor gas venting in the northwestern Arabian Sea

January 2025

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82 Reads

Marine gas seeps are common along tectonically active margins, but they have not been previously observed along the Arabian continental margin. Here we present evidence of gas escape structures, pockmarks, and active gas seeps in the Gulf of Oman. Multibeam bathymetry, water column backscatter and physical parameters, and two-dimensional seismic reflection data were used to map active seafloor seeps and pockmarks. Circular and crescent-shaped pockmarks and complex pockmark strings were identified. These features are confined to regions shoreward of the shelf break. Thirty-five active gas bubble trains were observed, mostly not coincident with the mapped pockmarks. With progressive gas release, the gas seeps are anticipated to lead to development of pockmarks over time. Bright spots on the seismic data indicate shallow subsurface gas accumulation alongside normal fault and fracture conduits, strongly correlated to the presence of pockmarks. These findings suggest an important carbon flux into the Arabian Sea and atmosphere.


Dominant inflation of the Arctic Ocean’s Beaufort Gyre in a warming climate

January 2025

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50 Reads

The Arctic Ocean’s Beaufort Gyre, the largest Arctic freshwater reservoir, plays a crucial role for climate and marine ecosystems. Understanding how it changes in a warming climate is therefore essential. Here, using high-resolution simulations and Coupled Model Intercomparison Project phase 6 data, we find that the Beaufort Gyre will increasingly accumulate freshwater, elevate sea level, and spin up its circulation as the climate warms. These changes, collectively referred to as inflation, are more pronounced in the Beaufort Gyre region than in other Arctic areas, amplifying the spatial asymmetry of the Arctic Ocean. The inflation is driven by increased surface freshwater fluxes and intensified surface stress from wind strengthening and sea ice decline. Current climate models tend to underestimate this inflation, which could be alleviated by high-resolution ocean models and improved atmospheric circulation simulations. The inflation of the Beaufort Gyre underscores its growing importance in a warming climate.


Multi-platform observations and constraints reveal overlooked urban sources of black carbon in Xuzhou and Dhaka

January 2025

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22 Reads

Here we use multi-waveband single scattering albedo observations from ground-based instruments and satellite to constrain black carbon aerosol's physical properties and loading over Xuzhou, China, and Dhaka, Bangladesh. Our daily high-resolution findings reveal smaller black carbon cores and spatially variable morphology dominate both regions. Column loadings reveal higher black carbon mass in Dhaka, while higher total aerosol mass and number are observed in Xuzhou. These findings reflect differences in emission sources, atmospheric conditions, and regulatory policies. Spatial analysis reveals notable enhanced black carbon along Dhaka’s urban riverbanks (8–9 mg m⁻²), and over rapidly changing, small industrial sites in China, indicating overlooked sources. Complex daily interactions between wind, accumulation, and dispersion challenge traditional seasonal dynamics. These findings demonstrate high-resolution data can be tailored from available remote sensing platforms, providing nuanced insights into regional air quality, enhancing assessment capabilities and informing targeted mitigation strategies.


Stability of potential ice road index from 1979 to 2017
‘Decrease’ or ‘Increase’ indicates that the temporal trend analysis has detected a significant (p < 0.05) decrease or increase in the potential ice road index, ‘Stable’ signifies that the potential ice road index is consistent and nonzero annually, ‘Unsuitable’ denotes the potential ice road index remains zero every year, and ‘Fluctuant’ indicates the others. The bar chart for each subfigure indicates the percentage of area with decreased regions in Eurasia and North America. The bar on the right side indicates the area statistics for the decreased regions for each month.
Variations of the potential ice road indicators from 1979 to 2017
Temporal trends of PIRDs a, PIROD b, and PIRED c with potential ice road index = 12. The crossed lines indicate the region that has passed the test of statistical significance (p < 0.05).
Potential ice road index from December to April during 1979 − 2017, 2020 − 2050, and 2050 − 2100
For 1979 − 2017, it is obtained by calculating the mode based on annual results. For 2020 − 2050 and 2050 − 2100, we firstly calculated the mean values of the climate variables for different SSPs, and then obtained the mode for the annual potential ice road index extracted from these data. Higher index indicates more suitable natural conditions for paving ice roads.
Increased vulnerability of Arctic potential ice roads under climate change

January 2025

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25 Reads

Temporary ice roads built by the process of snow compaction, watering, and icing during cold winters are lifelines for land access in remote Arctic. In the context of the Arctic amplified warming, the vulnerability of potential ice roads under the influence of complex climate system remains unclear. Here, we construct a potential ice road assessment model that allows quantization of the climatic suitability of potential ice roads in the Arctic. Using satellite remote sensing and meteorological data, we find changes in surface air temperature and snow cover reduced the climatic suitability of potential ice roads during 1979–2017. Spatially, potential ice roads in North America face more immediate threats due to decreased snow depth compared to Eurasia. Before the end of the 21st century, we project a further decline in the climatic suitability of potential ice roads, primarily due to increasing surface air temperatures and decreasing permafrost stability. Taking precious metal/diamond exploration as an example, we conclude that mining activities associated with ice roads will face access difficulties by 2050–2100 due to the decreased potential ice roads. These results give new insights into the challenges and opportunities of Arctic overland travel.


Global changes in soil moisture by the end of the 21st century and its uncertainty
a Spatial distribution of the multi-model ensemble mean of future soil moisture changes (ΔSM, %) by 2070–2099 relative to 1980–2014 and its uncertainty (inter-model standard deviation, σ) based on the Coupled Model Intercomparison Project phase 6 (CMIP6) outputs under the SSP5-8.5 scenario (Supplementary Table 1). b Latitudinal mean of ΔSM (black) and the associated uncertainty (gray). c, d Histogram (normalized scale of relative frequency) of grid-level σ and ΔSM across global land areas, respectively.
Physical mechanisms behind global ΔSM
a Scatter plot of CMIP6 ΔSM (%) against predicted ΔSM under the SSP5-8.5 scenario during 2070–2099 relative to 1980–2014. Each circle corresponds to the global mean ΔSM of a CMIP6 model (see Supplementary Table 1). The red, blue, yellow, and purple colors indicate the predictions by the regression models based on CMIP6 outputs of temperature change (ΔT) only (LT), precipitation change (ΔP) only (LP), both ΔT and ΔP (LTP) and their interactions (NTP), respectively. For each ESM, the simulated ΔSM from CMIP6 outputs is plotted on the x-axis, while the predicted ΔSM by four regression models are plotted on the y-axis. The corresponding fitting line and coefficient of determination (R²) is shown, with * indicating statistically significant at 95% confidence level. b Scatterplot of future ΔT against historical T trend during 1980–2014. The dashed black line indicates the linear fitting line, with R² shown on the top left and 95% confidence interval shown by shaded gray areas. The probability density functions (PDFs) of ΔT and T trends across CMIP6 models are shown on the left and at the bottom, respectively. c Same as (b) but for precipitation.
Emergent constraints on global ΔSM
a Each circle corresponds to a CMIP6 model (Supplementary Table 1), which shows the simulated historical (1980–2014) T trend on the x-axis, P trend on y-axis, and ΔSM (2070–2099 relative to 1980–2014). The observational mean of T and P trends during the historical period are shown by the vertical and horizontal dotted lines, respectively, with the associated uncertainties indicated by the shading (±1 standard deviation, Supplementary Table 3). b The probability density functions (PDFs) of future ΔSM before (black line) and after (colored lines) constraints. The red, blue, yellow, and purple lines are the PDFs of ΔSM after applying the emergent constraints (ECs) of LT, LP, LTP and NTP, respectively. The corresponding error bars (mean ±1 standard deviation) are illustrated at the top. The star in the legend indicates that the PDF curves after constraint are statistically significantly different (p < 0.05) from that before constraints according to the Kolmogorov-Smirnov (K-S) test. The coefficients of determination (R²) of the four ECs (* indicates p < 0.05), the differences in the central estimate of ΔSM after constraint relative to that before constraint (Δμ = μafter-μbefore, %) and the differences in the associated uncertainties (Δσ = (σafter-σbefore)/σbefore, %) are given by the table inset.
Emergent constraints on regional ΔSM
a The coefficients of determination (R²) of the emergent relationships based on LT (red), LP (blue), LTP (yellow) and NTP (purple) constraint for Hyper arid, Arid, Semi-arid, Dry sub-humid and Humid regions. Hyper arid g and Arid g represent the results for Hyper arid and Arid regions using global precipitation trend as the predictor instead of regional precipitation. A dot indicates a good fit of the emergent relationship is statistically significant (p < 0.05). b The differences in the central estimate of ΔSM after constraint relative to that before constraint (Δμ = μafter-μbefore, %). c The differences in the uncertainties of ΔSM after constraint relative to that before constraint (Δσ = (σafter-σbefore)/σbefore, %). Definitions of five climate regions are provided in Supplementary Fig. 11.
Emergent constraints on global soil moisture projections under climate change

January 2025

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8 Reads

Lei Yao

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Guoyong Leng

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[...]

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Jian Peng

Surface soil moisture is projected to decrease under global warming. Such projections are mostly based on climate models, which show large uncertainty (i.e., inter-model spread) partly due to inadequate observational constraint. Here we identify strong physically-based emergent relationships between soil moisture change (2070–2099 minus 1980–2014) and recent air temperature and precipitation trends across an ensemble of climate models. We extend the commonly used univariate Emergent Constraints to a bivariate method and use observed temperature and precipitation trends to constrain global soil moisture changes. Our results show that the bivariate emergent constraints can reduce soil moisture change uncertainty by 7.87%, which is four times more effective than traditional temperature-based univariate constraints. The bivariate emergent constraints change the sign of soil moisture change from negative to positive for semi-arid, dry sub-humid and humid regions and global land as a whole, but exacerbates the drying trend in arid and hyper-arid regions.


Forest canopy species’ crown architecture traits, and hypotheses related to Tcan and Tdiff
a Images represent typical crowns (octagons) of each species, sampled from UAV-collected RGB imagery: ‘Ōhi’a lehua (Metrosideros polymorpha) and Koa (Acacia koa) dominate the canopy at Laupāhoehoe forest plot (see distribution in forest crown map in Fig. 1 and field location in fig. S1). ʻŌlapa (Cheirodendron trigynum) and Pilo (Coprosma rhynchocarpa) are subdominants that also reach canopy heights. b Boxplots show species’ (represented by color; ōh = ‘ōhi’a, k = koa, ōl = ‘ōlapa, p = pilo) differences in crown architectural traits derived from Lidar (~20 cm), from left to right: mean crown height (m), mean crown density (pts m⁻³), crown rugosity (rumple index), and leaf clumping (CV). These are the same traits used in analyses of biotic and abiotic explainers of Tdiff. The center line represents the median, the box edges represent the interquartile range, and the whiskers represent the variability in the upper and lower quartiles. c Hypotheses related to species’ traits and crown temperatures (Tcan), and crown temperature deviations from ambient air (Tdiff).
Data collection and filtering steps for observing canopy temperature at two levels of observation
a Map of canopy temperature from one flight (of five) over the 4-hectare Laupāhoehoe forest plot on the Island of Hawaiʻi. Five UAV flights collected thermal data across two days at 9:02, 10:18, and 10:48 am, and 1:01, 2:56 pm. Explanation of filtering steps to separate the effects of forest and crown physical structure on sunlit leaf temperatures. Analyses were repeated after each filter (1 and 2) at 2 levels of analysis¹: crown-level: pixels with dense vegetation identified using LiDAR intensity values. Analysis at the crown-level includes pixels with high density photosynthetic and non-photosynthetic vegetation in the sun and shade and excludes pixels with sparse vegetation and understory; and² leaf-level: sunlit vegetation identified using sun sensor geometry and green photosynthetic vegetation (leaves) identified using spectral filters. Analysis at the leaf-level includes pixels with high density sunlit green leaves, excluding non-photosynthetic vegetation and shaded areas of the canopy. By isolating sunlit pixels with high green leaf density, cumulative filtering steps remove the effects of canopy and crown structure on crown temperatures to just represent the effects of sunlit leaves on observed temperatures. The scatterplot shows the relationship between LiDAR point intensity and the Normalized Difference Vegetation Index (NDVI) with each black point representing a single 20 cm² pixel. The purple box represents the structural LiDAR filter (filter 1) which identifies and removes pixels below the 90th percentile of LiDAR intensity values – pixels in which vegetation density is low. The horizontal green line represents the non-linear relationship between NDVI and canopy structure, where NDVI saturates around 0.65 (filter 2b), above which green standing biomass is captured. The green box highlights pixels retained after all filtering steps. b Top shows a LiDAR-derived canopy height model (CHM, scale in meters) used in combination with RGB imagery and tree census maps to outline tree crowns; Below shows each crown, as in the CHM, identified to species, represented by color. To analyze data, pixels remaining at each step of the filtering process were aggregated by crown and averaged to calculate Tcan.
Species canopy temperature comparisons
Boxplots represent species crown temperatures in degrees Celsius (°C) across all five UAV flights for (a) sunlit and shaded dense biomass, “crown” and, (b) for sunlit green leaves, “leaf.” In (a, b) each data point is an individual crown measurement. The center line represents the median, the box edges represent the interquartile range, and the whiskers represent the variability in the upper and lower quartiles. Letters denote statistically significant differences between species at p < 0.05. In (c, d) Points represent mean crown temperature differences in °C between species pairs across all five UAV flights (plus or minus 1 standard deviation) for “crown” (c) and “leaf” (d). In (c, d) statistically significant differences at p < 0.05 between species are denoted with asterisks.
Accounting for crown architectural traits reveals different drivers of Tdiff for sunlit green leaves
Linear mixed-effects models describe canopy variation in Tdiff across all five UAV flights. All variables were standardized (z-transformed) prior to running the models. Species identity was included in the model as a random effect. a Bar plot represents the percentage (%) of residual variation in Tdiff explained by species identity at the crown (sunlit and shaded dense vegetation; represented by darker grey bars) and leaf (sunlit green leaves; represented by lighter grey bars) level of analysis, and when only abiotic factors are included in models (leftmost two bars) vs. when abiotic and biotic factors are included in models (rightmost two bars). b Relative strength of biotic (leaf clumping, density, rugosity, height) and abiotic (wind speed, vapor pressure deficit, net radiation) predictors explaining forest Tdiff at two levels of data analysis (crown and leaf, represented by darker grey and lighter grey, respectively, as in (a). Error bars denote the standard error around the estimates. The abiotic factors in the analysis vary across time while the biotic factors in the analysis vary across space (different tree crowns).
Bar plots represent the results of linear models split by species
Strength of biotic (leaf clumping, crown density, crown rugosity, crown height) and abiotic (wind speed (WS), vapor pressure deficit (VPD), net radiation (Rn)) predictors explaining Tdiff at two levels of data analysis (crown and leaf, represented by darker and lighter shades of color, respectively). “Crown” refers to sunlit and shaded dense vegetation, while “leaf” refers to only sunlit green leaves. All predictors were standardized (z-transformed). Error bars denote the standard error around the estimates. The abiotic factors in the analysis vary across time while the biotic factors in the analysis vary across space (different tree crowns).
Scale-dependent responses to environmental fluctuations in tropical tree species’ crown temperatures

January 2025

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22 Reads

Tropical forests may be nearing critical temperatures, yet tree species may respond differently. Using high-resolution thermal, hyperspectral, and LiDAR imagery, we mapped 652 crowns of four Hawaiian tree species to study the effects of crown traits and abiotic conditions on species’ temperatures at two scales (whole crown vs. sunlit leaves). We show scale-dependent, species-specific relationships with environmental fluctuations. Net radiation was consistently the dominant determinant of crown temperature deviations from air temperature (Tdiff), while vapor pressure deficit, wind speed, and crown traits (e.g., roughness) varied in importance by species and scale. Species explained 17% and 44% of Tdiff variation at the crown and leaf scales, respectively, after controlling for climatic factors. Findings suggest that leaf temperatures overestimate larger-scale temperature differences, while canopy-scale observations underestimate leaf heat stress. Because leaf and crown traits can have opposing effects on Tdiff, disentangling these can advance our understanding of species’ thermoregulation under climate change.


Spatiotemporal trends of water availability (WA) seasonality during the past century and sub-periods
a Time series of WA seasonality anomalies based on CMIP6 simulations and reanalysis. The black line indicates the ensemble mean of 12 CMIP6 models with ±SD (gray shaded area). Green, orange, pink and blue lines represent WA seasonality calculated using GLDAS2.0, JRA55, NCEP, and TerraClimate reanalysis, respectively. b Trends in WA during wet and dry seasons across different periods. ** and * denote significance levels of p < 0.01 and p < 0.05, respectively. c–e Spatial patterns of wet season WA trends during different periods. f–h Spatial patterns of dry season WA trends during different periods. i–k Spatial patterns of annual range (wet season minus dry season, WA seasonality) trends during different periods. Stippling indicates significant trends at p < 0.05.
Spatiotemporal trends in water availability (WA) seasonality under various forcings
a Time series of WA seasonality anomalies under different forcings (NAT, GHG, and AER). The shaded area represents mean values ± SD. The orange, red, and blue lines indicate the changes in WA seasonality under NAT, GHG, and AER forcings, respectively. b Trends in WA seasonality across different periods and forcings. ** denotes a significance level of p < 0.01. c–e Spatial patterns of WA seasonality trends during different periods under NAT forcing. f–h Spatial patterns of WA seasonality trends during different periods under GHG forcing. i–k Spatial patterns of WA seasonality trends during different periods under AER forcing. Stippling indicates significant trends at p < 0.05.
Temporal trends of regional WA seasonality under various forcings and different periods
ALL, NAT, GHG, and AER represent different forcings: all experiments, nature forcing-only experiment, greenhouse forcing-only experiment, and aerosols forcing-only experiment, respectively. GLDAS, JRA55, and TerraClimate denote the three reanalysis datasets. The seven subregions are Amazon (AM), Eurasian (EA), North America (NA), North Europe (NE), West Africa (WAF), Southeast Africa (SEA), and South Asia (SA).
Counteracting greenhouse gas and aerosol influences intensify global water seasonality over the past century

January 2025

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52 Reads

Variations in water availability seasonality significantly impact society and ecosystems. While many studies have focused on mean or extreme precipitation, the response of water availability seasonality, influencing yearly water distribution beyond individual extremes, to human-induced climate change remains underexplored. Here we examine global and regional water availability seasonality changes from 1915 to 2014, quantifying how anthropogenic greenhouse gases and aerosols have influenced these variations using reanalysis and simulations from Coupled Model Intercomparison Project Phase 6. Despite large spatiotemporal uncertainties due to regional variability and model assumptions, we find that greenhouse gases significantly amplify the seasonality, while aerosols reduce it. Given that the positive effects of greenhouse gases surpass the aerosols’ negative effects, the counterbalancing influences have led to an overall enhancement in seasonality of water availability over the past century. This trend is expected to continue in the future as greenhouse gases-induced warming continues to rise and aerosol levels decline.


Influence of winter Saharan dust on equatorial Atlantic variability

January 2025

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49 Reads

While Saharan dust’s influence on sea surface temperature variability in the tropical North Atlantic is well-documented, its effects on the equatorial region remain underexplored. This relationship is particularly important due to the strong influence of equatorial Atlantic variability on both local and remote climates. Here, we use observational and reanalysis data to investigate Saharan dust’s role in boreal winter, a period when dust transport is typically near the equator. A unique footprint of Saharan dust forcing is revealed, as well as a complex, non-monotonic response. Specifically, in contrast to the expected cooling due to shortwave blocking by Saharan dust, lower tropospheric warming, and stabilization lead to a strong sea surface warming off the coast of northwestern Africa and to the development of an off-equatorial warm front. The front drives cross-equatorial winds that induce a northward shift of the Atlantic rain belt, equatorial cooling, and equatorial wave activity leading to delayed equatorial warming. Winter Saharan dust is therefore an important contributor to equatorial Atlantic variability, with cross-regional implications.


The active layer soils of Greenlandic permafrost areas can function as important sinks for volatile organic compounds

January 2025

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41 Reads

Permafrost is a considerable carbon reservoir harboring up to 1700 petagrams of carbon accumulated over millennia, which can be mobilized as permafrost thaws under global warming. Recent studies have highlighted that a fraction of this carbon can be transformed to atmospheric volatile organic compounds, which can affect the atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols. In this study, active layer soils from the seasonally unfrozen layer above the permafrost were collected from two distinct locations of the Greenlandic permafrost and incubated to explore their roles in the soil-atmosphere exchange of volatile organic compounds. Results show that these soils can actively function as sinks of these compounds, despite their different physiochemical properties. Upper active layer possessed relatively higher uptake capacities; factors including soil moisture, organic matter, and microbial biomass carbon were identified as the main factors correlating with the uptake rates. Additionally, uptake coefficients for several compounds were calculated for their potential use in future model development. Correlation analysis and the varying coefficients indicate that the sink was likely biotic. The development of a deeper active layer under climate change may enhance the sink capacity and reduce the net emissions of volatile organic compounds from permafrost thaw.


Catch projections for different fishing pressures
A Overfished stocks, medium term. B Overfished stocks, long term. C All stocks, medium term. D All stocks, long term. Note: For all panels. The catch projections are relative to the initial year (t0 = 1). FMSY (turquoise lines) denotes the catches resulting from applying MSY fishing pressure to the overfished stocks. Fcurrent (magenta lines) denotes the catches resulting from applying the most recent estimated fishing pressure to all overfished stocks. The blue horizontal lines represent the MSY catches relative to the initial ones.
Domestic food price impacts. Percentage difference from the baseline. Distribution across countries
A All food products except fish. Vegetable products (purple fill) include grains, oilseeds, pulses, and roots and tubers. Other processed products ((green fill)) include sugar, high fructose corn syrup, and vegetable oil. Meat (turquoise fill) includes beef, pork, poultry, and sheep and goat. Dairy (orange fill) includes fresh dairy products, butter, cheese, skim milk powder, whole milk powder, casein, and whey. B Fish. Note: For both panels. FMSY_OF denotes the impacts of MSY management of overfished stocks. FMSY refers to MSY managements of all fish stocks Fcurrent refers to the scenario where current fishing pressures are maintained for all stocks. All impacts refer to the catch equilibrium (MSY or the 200th stock projection year for the Fcurrent scenario) compared to the baseline in 2030. The boxplot displays the median, two hinges and two whiskers of a continuous variable distribution. The lower and upper hinges (colored bars) correspond to the first and third quartiles (the 25th and 75th percentiles). The upper whisker extends from the hinge to the largest value no further than 1.5 than the inter-quartile range (IQR), or distance between the first and third quartiles. The lower whisker extends from the hinge to the smallest value at most 1.5 ∗ IQR of the hinge. Data beyond the end of the whiskers are ‘outliers’ points and are plotted individually as dots.
Kobe plot of the global fish stocks
Note: Based on Mangin et al. ¹⁵. Table S8 contains information about the species included in each of the categories.
Harvest and stock projections for different fishing pressures
Note: For all panels. ‘FMSY’ (turquoise lines) denotes the catches and stock sizes resulting from the applying the MSY fishing pressure to the stocks. ‘Fcurrent’ (magenta lines) denotes the catches and stock sizes resulting from applying the most recent estimated fishing pressure to the stock. Catches are in Kt and in differences from the initial catches. Stock sizes are relative to their MSY levels. Representative stocks from each quadrant in the Kobe plot.
Introducing maximum sustainable yield targets in fisheries could enhance global food security

January 2025

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26 Reads

Aquatic foods are crucial for global food and nutrition security, but overfishing has led to depleted fish stocks, threatening both food security and the environment. Here, we combine a fish stock model with a global agriculture and food market model in order to analyze scenarios involving a continuation of current fishing trends versus optimal management through maximum sustainable yield targets. Maximum sustainable yield management of overfished stocks could increase yields by 10.6 Megatons, equivalent to 12% of total catches and 6% of aquatic animal production in 2022. This would alleviate the need for aquaculture expansion by an equivalent of 3 years of growth in the aquaculture sector at its current level, and reduce meat and feed demand. Lower food prices and additional supply could enhance global food security. Conversely, continued overfishing will likely lead to lower catches over time, adding pressure to the agricultural and aquaculture sectors. Although maximum sustainable yield management is not a panacea, it represents a positive step towards achieving sustainable food production.


Temporal dynamics of the response ratio of flash droughts at different onset times
a Trends of the percentage of 1-, 2-, 3-, 4-, and 5-pentad onset flash droughts resulting in negative saGPP relative to all flash droughts with saGPP responses across five different datasets. b Temporal trends of the percentage of different onset flash droughts resulting in negative saGPP relative to all flash droughts with saGPP responses across 21 regions, based on the mean results from GLEAM, MERRA2, Noah, CLSM, and VIC, evaluated by the magnitude of Sen’s slope. Bars with slashes indicate trends that are statistically significant at the 0.05 level. The linear annual trends are estimated using Sen’s slope estimator, and statistical significance is determined by the Mann-Kendall test for the study period (2001–2018).
Relationship between the saGPP reduction and the SM decline
a Variation in saGPP reduction rates across different timescales of flash droughts onset phases. The Mann–Whitney U test was employed to determine the significant difference (P < 0.01) between flash drought onset timescales. The red lines above each bar indicate the range of uncertainty across five different datasets. b A cubic relationship between saGPP reduction rates and intensification rates of flash drought onset phase. The navy dots represent the mean value of saGPP rates in the corresponding SM depletion rate category from the five datasets. The blue ribbon represents the 95% confidence interval, reflecting the structural uncertainty of the cubic spline models.
Detection of uWUE and potential meteorological influences on the sensitivity of GPP to faster-onset flash droughts
a Comparison of uWUE anomalies during the onset stage between flash droughts with and without GPP responses at different onset times. The gray lines above each bar represent the range of uncertainty across five datasets. b Significant causal relationships between SM and meteorological factors. c Significant causal relationships between meteorological factors and GPP reduction. An asterisk denotes a significant causal connection (P < 0.05) identified through convergent cross mapping. The x axis represents the time-series length (L, years). The y axis represents the cross-map skill measured by correlation coefficient (ρ). The shaded regions indicate the 90% confidence interval. The results are based on the mean from five datasets for ET and VPD, while other factors are from four datasets except GLEAM.
The sensitivity of saGPP reduction rate to flash droughts onset timescales for different plant functional types (PFTs)
a–i Variation in saGPP reduction rates across different timescales of flash drought onset phases for the nine selected PFTs. The red lines above each bar indicate the range of uncertainty across the Noah and VIC datasets. The significance testing follows the same criteria as in Fig. 2a. SAV, CRO, GRA, SHB, EBF, ENF, DBF, DNF, and MF represent savannas, croplands, grasslands, shrublands classes (closed shrublands and open shrublands), evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest, respectively.
Schematic representation of the method used to identify ecosystem response during a flash drought event
As for flash droughts, SM decreases from above the 40th percentile (S0) to below the 20th percentile (S1) with an average decline rate of no less than the 5th percentile for each pentad, and SM below the 20th percentile should last for no less than 3 pentads. Ecosystem response to flash droughts is determined as the first occurrence of saGPP less than zero (G1) during the whole duration of flash droughts from S0 to S2, and saGPP before (one pentad prior to flash drought onset point (S0)) should be greater than zero (G0). saGPP decreases from the first negative response (G1) to its minimum value (G2). It should be noted that saGPP may reach the peak (G2) later than flash drought termination (S2). The black solid line represents the 5-day mean SM percentile for a grid point, and the black shading line indicates the onset phase of a flash drought event. The green solid line represents the saGPP at one pentad for a grid point, and the green shading line indicates the development of saGPP response.
Gross primary productivity is more sensitive to accelerated flash droughts

January 2025

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38 Reads

Flash droughts, characterized by their rapid onset, substantially affect terrestrial ecosystems. However, the sensitivity of ecosystem productivity to the rapid development of flash droughts under varying vegetation conditions remains poorly understood. Here we investigate the ecosystem response to the speed of flash drought onset for different plant functional types, considering the decline rate of root-zone soil moisture and standardized gross primary productivity anomaly. Our findings reveal a significant increase of approximately 10% in the proportion of 1- and 2-pentad (5 and 10 days) onset flash droughts leading to negative standardized gross primary productivity anomalies during 2001–2018. Furthermore, while standardized gross primary productivity anomalies decline at higher rates, they do not promptly respond on a shorter timescale to faster-onset flash droughts compared to slower-onset flash droughts. Vegetation types with shallower root systems exhibit higher sensitivities to faster-onset flash droughts, suggesting an escalating threat to terrestrial ecosystems in a changing climate.


A transdisciplinary, comparative analysis reveals key risks from Arctic permafrost thaw

January 2025

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114 Reads

Permafrost thaw poses diverse risks to Arctic environments and livelihoods. Understanding the effects of permafrost thaw is vital for informed policymaking and adaptation efforts. Here, we present the consolidated findings of a risk analysis spanning four study regions: Longyearbyen (Svalbard, Norway), the Avannaata municipality (Greenland), the Beaufort Sea region and the Mackenzie River Delta (Canada) and the Bulunskiy District of the Sakha Republic (Russia). Local stakeholders’ and scientists’ perceptions shaped our understanding of the risks as dynamic, socionatural phenomena involving physical processes, key hazards, and societal consequences. Through an inter- and transdisciplinary risk analysis based on multidirectional knowledge exchanges and thematic network analysis, we identified five key hazards of permafrost thaw. These include infrastructure failure, disruption of mobility and supplies, decreased water quality, challenges for food security, and exposure to diseases and contaminants. The study’s novelty resides in the comparative approach spanning different disciplines, environmental and societal contexts, and the transdisciplinary synthesis considering various risk perceptions.


Homo erectus adapted to steppe-desert climate extremes one million years ago

January 2025

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185 Reads

Questions about when early members of the genus Homo adapted to extreme environments like deserts and rainforests have traditionally focused on Homo sapiens. Here, we present multidisciplinary evidence from Engaji Nanyori in Tanzania’s Oldupai Gorge, revealing that Homo erectus thrived in hyperarid landscapes one million years ago. Using biogeochemical analyses, precise chronometric dating, palaeoclimate simulations, biome modeling, fire history reconstructions, palaeobotanical studies, faunal assemblages, and archeological evidence, we reconstruct an environment dominated by semidesert shrubland. Despite these challenges, Homo erectus repeatedly occupied fluvial landscapes, leveraging water sources and ecological focal points to mitigate risk. These findings suggest archaic humans possessed an ecological flexibility previously attributed only to later hominins. This adaptability likely facilitated the expansion of Homo erectus into the arid regions of Africa and Eurasia, redefining their role as ecological generalists thriving in some of the most challenging landscapes of the Middle Pleistocene.


Accounting for differences between crops and regions reduces estimates of nitrate leaching from nitrogen-fertilized soils

January 2025

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115 Reads

Nitrate (NO3⁻) leaching from nitrogen (N) fertilized soils is a significant global concern, affecting both the environment and public health. However, substantial uncertainties and variabilities in NO3⁻ leaching factors (LFs) among regions or crops impede accurate assessments of NO3⁻ leaching. Here we synthesize 2500 field observations worldwide and show that LFs vary by an order of magnitude across regions and crops, primarily driven by hydroclimatic and edaphic conditions rather than N fertilizer management. Global cropland NO3⁻ leaching from synthetic N fertilization, calculated through spatially explicit (15.4, 14.8–16.1 Tg N yr–1) and crop-specific (12.9, 11.0–14.8 Tg N yr–1) LFs, is 41% lower than the Intergovernmental Panel on Climate Change Tier 1 global inventory. Over 47% of this leaching is concentrated in China, India, and the United States, with maize, wheat, rice and vegetables accounting for nearly half of it. Improved regional and crop-specific LFs will provide a benchmark for NO3⁻ leaching abatement by pinpointing potential global hotspots.


Map of the study area with modern sea ice extent and general surface circulation around Svalbard Archipelago
a Dashed lines show the median sea ice extent from 1981 to 2010 for March (maximum sea ice extents) and September (minimum sea ice extents) National Snow and Ice Data Center (NSIDC), https://nsidc.org/. Red and blue arrows display the Atlantic-sourced West Spitsbergen Current (WSC) and the Arctic Ocean-sourced Spitsbergen Polar Current (SPC), respectively. The Arctic Ocean base map is based on IBCAOv4¹⁵³. b Location of the sediment core NYA 17–154 at the edge of the Kongsfjorden. Satellite image from TopoSvalbard (Norwegian Polar Institute, https://toposvalbard.npolar.no/).
Climate variability in the study region with respect to bulk carbon and biomarkers from NYA 17–154 core, which covers the last 780 years
The lines show a the PAGES Arctic 2k annual temperature reconstruction (averaged to decadal values)³⁶, b the δ¹⁸O of Austfonna ice core (Svalbard)³⁷, and c the reconstructed North Atlantic Oscillation index (NAO)³⁸. It further shows NYA 17–154 data for d total organic carbon (TOC), e Δ¹⁴C of bulk OC (corrected for year of deposition), f δ¹³C of bulk OC. The solid curves correspond to the generalized additive mode (GAM) trend, with light bands representing 95% across-the-function confidence intervals. The gray columns highlight the Little Ice Age (LIA) and its coldest phase (dark gray)¹⁵⁴.
Source contribution of n-alkanoic acids and n-alkanes to the NYA 17–154 OC pool
The figure shows the relative influence of five distinct end members for n–alkanoic acids and n–alkanes in the Kongsfjorden: marine sediments42,43, soil42,43, coal42,43, ice-rafted detritus (IRD)⁴², and sediments affected by proglacial river discharge (Bayelva sediment)⁴³. Carbon Preference Index (CPI) and Average Chain Length (ACL) of an–alkanoic acids and bn–alkanes for the different end members are compared with NYA 17–154 sediment core. Symbols and bars display mean, and s.d. of NYA 17–154 results and each source is derived from literature data.
Plant biomarkers from NYA 17–154 core compared with Arctic sea ice extent and glaciers retreat over the last 780 years
The lines show a the 40-year smoothed late-summer Arctic sea ice extent reconstructed from high-resolution terrestrial proxies²⁰, b sum of cutin acids concentration, c ratio of C16 ω-hydroxy alkanoic acids to the sum of C16 ω-hydroxy alkanoic acids, DAs, and mid-chain hydroxy and epoxy acids (ω-C16/∑C16). d ratio of p-coumaric acid (pCd) to ferulic acid (Fd), e Lignin Phenol Vegetation Index (LPVI), and f proximal glacier foraminifera (C. reniforme and E. excavatum f. clavatum)³¹. The solid curves correspond to the generalized additive mode (GAM) trend, with light bands representing 95% across-the-function confidence intervals. The gray columns highlight the Little Ice Age (LIA) and its coldest phase (dark gray)¹⁵⁴. The yellow areas display periods with low sea ice extent²⁰.
Plant biomarker fingerprint and growth-ring chronology of S. polaris from Svalbard¹¹⁹ compared with cutin trend from NYA 17–154 core
a Biomarker fingerprint of pCd/Fd against ω-C16/∑C16 for different species of vascular plants and mosses. Symbols and bars display mean and s.d., respectively. b Evidence of a near-simultaneous increase of both proxies, S. polaris growth-ring chronology¹¹⁹ and cutin trend (NYA 17–154), provides a clear greening signal for the Svalbard region.
Greening of Svalbard in the twentieth century driven by sea ice loss and glaciers retreat

January 2025

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65 Reads

The greening of previously barren landscapes in the Arctic is one of the most relevant responses of terrestrial ecosystem to climate change. Analyses of satellite data (available since ~1980) have revealed a widespread tundra advance consistent with recent global warming, but the length is insufficient to resolve the long-term variability and the precise timing of the greening onset. Here, we measured plant-derived biomarkers from an Arctic fjord sediment core as proxies for reconstructing past changes in tundra vegetation during the transition from the Little Ice Age to modern warming. Our findings revealed a rapid expansion of the tundra since the beginning of the twentieth century, largely coinciding with the decline of summer sea ice extent and glacier retreat. The greening trend inferred from biomarker analysis peaked significantly in the late 1990s, along with a shift in the tundra community towards a more mature successional stage. Most of these signals were consistent with the biomolecular fingerprints of vascular plant species that are more adapted to warmer conditions and have widely expanded in proglacial areas during recent decades. Our results suggest that the greening of Arctic fjords may have occurred earlier than previously thought, improving our mechanistic understanding of vegetation-climate-cryosphere interactions that will shape tundra vegetation under future warming projections.


Quasi-experimental study design
North America was divided into blocks defined by the combination of climate (ecoregions) and space (3˚ latitude × 6˚ longitude grid). In total, the study design encompassed 298 blocks, with a total of 58,280 250×250 m landscape plots. These plots were composed of six different land-cover (LC) types (A: agriculture, F: forest, G: grassland, S: shrubland, U: urban, and W: wetland), resulting in 56 unique LC compositions. Thereby, gradients in land-cover type richness (LCR) were constructed within each block so that the different land covers were represented equally at all levels of LCR (e.g., if a block containing the LC types A, G, and U, the 2-LC mixtures AG, AU, GU were included, and the 3-LC mixture AGU; see Methods and Supplementary Table 1). Each LC composition was replicated 20 times per block.
Decorrelation of land-cover type diversity from other potential drivers of productivity
To avoid a statistical confounding of landscape diversity, here measured as land-cover type richness (LCR), with other potential drivers of productivity, we used a stochastic subsampling technique that minimized correlations between these (Methods). The panels on the left show, in the full set of landscape plots, and for each block (Fig. 1), the correlations of LCR with the altitude, the north gradient of the slope, and the fraction of a landscape plot covered with a particular land-cover (only forest shown here as an example). With such a quasi-experimental study design and by probabilistic subsampling we obtained a dataset where the correlations of the landscape’s properties with LCR were minimized. Histograms at the right of the panels show the distribution of each variable.
Higher productivity in mixed LC-type landscapes
Land-cover-type richness (LCR) effect on a normalized growing-season-integrated productivity (EVIgs’), b the net diversity effect (NE) calculated as the difference between observed and expected EVIgs’ in mixed landscapes, with (c) the distribution of the NE{EVIgs’} values of each LC type compositions shown as histogram, and (d) inverse coefficient of inter-annual variation of EVIgs’ (CV⁻¹{EVIgs’}). Black lines and areas shaded in blue: model-predicted mean ± s.e.m; dots: averages for each land-cover composition. See Table 1 for corresponding statistical significance tests.
Decadal trends in productivity and their dependence on landscape diversity
a 20-year trend (years 2000–2019) in growing-season-integrated EVI’ (EVIgs’) shown by ecoregion (see Fig. 1). b Net diversity effect on 20-year trend in EVIgs’. Data are shown as modeled linear trends relative to model-predicted values for the first year (2000). The gray areas represent the blocks that were dropped from the analysis.
Net diversity effects on productivity proxy in landscapes with different land-cover combinations
Ranked net diversity effects on growing-season-integrated EVI (NE{EVIgs’}) in mixed LC-type landscapes. Black dots and error bars: mean ± s.e.m. for each LC composition. Red: NE is significantly positive; Gray: NE is not significantly different from zero; Blue: NE is significantly negative. A: agriculture, F: forest, G: grassland, S: shrub, U: urban, and W: wetland.https://datadryad.org/stash/dataset/doi:10.5061/dryad.v41ns1s3p.
Landscape diversity promotes landscape functioning in North America

January 2025

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134 Reads

Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. Here, we show that landscape-level diversity, measured as number of land-cover types (different ecosystems) per 250×250 m, is positively related to landscape-wide remotely-sensed primary production across all of North America, covering 16 of 18 ecoregions of Earth. At higher landscape diversity, productivity was temporally more stable, and 20-year greening trends were accelerated. These effects occurred independent of local species diversity, suggesting emergent mechanisms at hitherto neglected levels of biological organization. Specifically, mechanisms related to interactions among land-cover types unfold at the scale of entire landscapes, similar to, but not necessarily resulting from, interactions between species within single ecosystems.


Reference map and eruptive history of Campi Flegrei
a Simplified scheme showing the volcanic history of Campi Flegrei. b Simplified geological map of Campi Flegrei, showing the distribution of pyroclastic deposits, the outer caldera boundary and crater rims (modified with permission after Natale et al.³, co-author). c Map showing the sampling location of the deposits ascribed to the Maddaloni/X-6 eruption (yellow circles and squares). The satellite imagery was provided by Bing Maps, licensed by Microsoft, accessed on the 5th of June 2024 via QGIS open-access software 3.36.0. Yellow squares indicate the samples for which thickess data is not available. Circles are sized by their thickness in cm (Supplementary Table 1).
Major element composition of the Maddaloni/X-6 eruption
a Major element composition of the samples ascribed to the Maddaloni/X-6 eruption, including (i) TAS (Total Alkali vs Silica) diagram; (ii) CaO vs K2O/Na2O diagram, showing the HAR (high alkali ratio) and LAR (low alkali ratio) components; (iii) CaO/FeO vs Cl; (iv) SiO2 vs MgO. b Comparison between the Maddaloni/X-6 and the CI major element composition. LGdM: Lago Grande di Monticchio, GdC: Grotta del Cavallo, TP: Tenaghi Philippon. For more detailed information on the data source, the reader is referred to Supplementary Table 1.
Modelled isopach maps of the Plinian and co-ignimbrite phases of the Maddaloni/X-6 eruption
Black values on the isopachs represent the thickness in cm. The results were obtained solving an inversion problem for (a) the Plinian phase and (b) the co-ignimbrite phase. For the choice of the optimal solutions, the statistical index has been minimized (Chi2 in Supplementary Table 2) and we have accounted for performance on single sites to have the best on each location; model extrapolations have been provided on areas where observations are not available. In b, the contour of the co-ignimbrite easternmost distribution is dashed, as it falls in areas not covered by observed data points. The main eruption source parameters obtained from the model are provided in the tables at the top of each figure. The rose diagrams (top-right corners) show the variation of the wind field pattern and speed (in m/s) with elevation above sea level. On the bottom-right corners, the Maddaloni/X-6 eruption dynamics is illustrated: the first phase produces a Plinian column, with a maximum height of ~30 km and transporting materials to E-NE; the second phase produces a co-ignimbrite, associated with a potential caldera collapse, reaching ~55 km in height and transporting volcanic products to E-SE. The topographic map was obtained from Global Multi-Resolution Topography Data Synthesis (GMRT MapTool). The isopach maps showing the combination of the two phases can be found in Supplementary Fig. 1.
The Maddaloni/X-6 eruption stands out as one of the major events during the Late Pleistocene at Campi Flegrei

January 2025

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78 Reads

The Campi Flegrei caldera (Italy) is among the most productive volcanoes of the Mediterranean area. However, the volcanic history preceding the VEI 7 Campanian Ignimbrite eruption (~40 ka) is still poorly constrained. Here, we use a tephra dispersal model to reconstruct the eruption source parameters of the Maddaloni/X-6 eruption (~109 ka), one of the most widespread Late Pleistocene Mediterranean marker tephra from Campi Flegrei. Our results suggest that the eruption was characterized by an early Plinian phase involving ~6 cubic kilometers (within the range of 3–21 cubic kilometers) of magma, followed by a co-ignimbrite phase erupting ~148 cubic kilometers (range of 60–300 cubic kilometers). This ranks the Maddaloni/X-6 as a high-magnitude (M7.6) eruption, resulting at least as the second largest known event from Campi Flegrei. This study provides insights into the capability of the Campi Flegrei magmatic system to repeatedly generate large explosive eruptions, which has broad implications for hazard assessment in the central Mediterranean area.


Niño 3.4 SST power spectra and aerosol and GHG effects on the spectrum peak
a Power spectra of the Niño 3.4 indices of CMIP6 piControl (gray), HIST-AER (blue), and HIST-GHG (red) simulations as well as their 95% confidence limits (dashed/dotted curves). b The averaged energy spectra over a 3-to-5-year period for individual CMIP6 models to construct (a) for their piControl (gray), HIST-AER (blue), and HIST-GHG (red) simulations. c Differences of the averaged energy spectra over the 3-to-5-period between HIST-AER and piControl (HIST-AER minus piControl, green) and between HIST-GHG and piControl (HIST-GHG minus piControl, pink). All panels show the ensemble and multi-model mean results.
Bjerknes index and coefficients changes due to aerosol and GHG forcing
a The BJ index and individual components of piControl (gray), HIST-AER (blue), and HIST-GHG (red) simulations. TD, MA, ZA, EK, and TH represent the mean advection, thermal damping, zonal advection, Ekman, and thermocline feedbacks, respectively. b Changes in the percentage of different regression coefficients and mean temperature gradients due to aerosol forcing (HIST-AER minus piControl, light blue) and GHG forcing (HIST-GHG minus piControl, pink). Both panels show the ensemble and multi-model mean results. Error bars denote one standard deviation among models.
SST and precipitation in the eastern equatorial Pacific
Scatterplots of monthly SST versus monthly precipitation in the Niño-3 region for a piControl, b HIST-AER, and c HIST-GHG simulations. Orange dots represent cases when precipitation is larger than 5 mm/day. The red vertical dashed line represents the critical temperature for convection. The black vertical and horizontal dashed lines denote the climatological mean values of SST and precipitation, respectively.
Peak phase histograms depicting ENSO phase-locking
Probabilities of peak months during ENSO of a HIST-AER (blue) and piControl (gray), and b HIST-GHG (red) and piControl (gray) simulations. Both panels illustrate the ensemble and multi-model mean results. Error bars in (a) and (b) denote one standard deviation among models for HIST-AER and HIST-GHG simulations, respectively.
SST patterns and statistics corresponding to EP and CP El Niño events
a,c,e Linear regression of SST anomalies (color shading, in °C) onto the E-index for a piControl, c HIST-GHG, and e HIST-AER, all for years 1850-2014. b,d,f As in a,c,e but for SST regression onto the C-index. g The fraction of EP and CP El Niño events in piControl (gray), HIST-GHG (red), and HIST-AER (blue) simulations. All panels show the ensemble and multi-model mean results. Error bars in g denote one standard deviation among models. The base map in a–f is from NCAR Command Language map outline databases.
Distinct anthropogenic aerosol and greenhouse gas effects on El Niño/Southern Oscillation variability

January 2025

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14 Reads

El Niño/Southern Oscillation variability has conspicuous impacts on ecosystems and severe weather. Here, we probe the effects of anthropogenic aerosols and greenhouse gases on El Niño/Southern Oscillation variability during the historical period using a broad set of climate models. Increased aerosols significantly amplify El Niño/Southern Oscillation variability primarily through weakening the mean advection feedback and strengthening the zonal advection and thermocline feedbacks, as linked to a weaker annual cycle of sea surface temperature in the eastern equatorial Pacific. They prevent extreme El Niño events, reduce interannual sea surface temperature skewness in the tropical Pacific, influence the likelihood of El Niño/Southern Oscillation events in April and June and allow for more El Niño transitions to Central Pacific events. While rising greenhouse gases significantly reduce El Niño/Southern Oscillation variability via a stronger sea surface temperature annual cycle and attenuated thermocline feedback. They promote extreme El Niño events, increase SST skewness, and enlarge the likelihood of El Niño/Southern Oscillation peaking in November while inhibiting Central Pacific El Niño/Southern Oscillation events.


Imaginary part of the self-energies
Imaginary part of the calculated self-energies on the real axis. In the top and middle panels, we show the individual self-energies (blue lines) and their average ⟨ImΣ⟩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\langle {{\rm{Im}}}\Sigma \rangle$$\end{document} (black thick). In the bottom panel, the four average self-energies are shown, at the ICB (full) and CMB (dashed) conditions.
Optical and thermal conductivity
Optical electrical (top) and thermal (bottom) conductivity for pure Fe and Fe0.91O0.09. The ICB and CMB cases are shown on the left and right, respectively. The dashed lines (that overlap closely with the filled ones) indicate a simplified calculation using fully (site, orbital, and spin) averaged self-energies.
Self-energies effect in κ(ω)
Thermal conductivity for pure Fe and Fe0.91O0.09 for the ICB (top) and CMB cases (bottom). We show the results obtained with orbitally and site-resolved self-energies (dots) as well as those calculated using the average self-energy (full line). The latter was first averaged over all sites and orbitals on the Matsubara grid and then analytically continued. The differences between the two are very small. With dash-dotted line, we show results calculated by exchanging the average self-energy between the Fe and Fe0.91O0.09.
Results of resistivity and thermal conductivity in the context of literature
Resistivity (top) and thermal conductivity (bottom) for Fe and Fe0.91O0.09 for the ICB and CMB cases calculated using DMFT (including both electron–electron and e.–ph scattering) and DFT (e.–ph. only) are shown with full symbols. The results from previous works6,23,25,27,34,36,69 are also shown for comparison (open symbols and crosses) as well as solid phases calculated at ICB conditions23,25,36.
Suppression of Lorenz ratio
(left) Lorenz number for Fe (bullets) and Fe0.91O0.09 (crosses) and for the ICB (full line) and CMB cases (dashed line). We use the parameter α to artificially change the magnitude of the EES Σ→Σα=ReΣ+αiImΣ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Sigma \to {\Sigma }_{\alpha }={{\rm{Re}}}\Sigma +\alpha i{{\rm{Im}}}\Sigma$$\end{document}, with α = 1 corresponding to the full DMFT calculation. One sees that the Lorenz number is reduced by the presence of oxygen. Results from literature6,34,69 are shown separately on the left with symbols and colors as in Fig. 4. These points correspond to a vanishing EES i.e., α = 0. (right) Transport distribution Γ(ω). Solid curves the obtained from the full DMFT self-energy, dashed lines from a constant scattering rate approximation.
Influence of oxygen on electronic correlation and transport in iron in the Earth’s outer core

January 2025

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17 Reads

Knowing the transport properties of iron at the Earth’s core conditions is essential for the geophysical modeling of Earth’s magnetic field generation. Besides by extreme pressures and temperatures (which cause scattering by thermal disorder to dominate), transport may be also influenced by the presence of light elements and electron-electron scattering. We used a combination of molecular dynamics, density functional theory, and dynamical mean-field theory methods to examine the impact of oxygen impurities on the electronic correlations and transport in the Earth’s liquid outer core. We find electronic correlations to be moderately enhanced by oxygen admixture. At realistic 10 atomic% of oxygen, the thermal conductivity suppression by electronic correlations (about 20%) is of the same magnitude as that due to oxygen inclusion. Hence, both play an equally important role in reducing the conductivity and stabilizing the geodynamo. We also explain the reduction of Lorenz ratio in core matter.


IMO2020 perturbations of cloud properties
3-year mean change in (a) cloud droplet number concentration (Nd) and (b) relative cloud radiative effect (rCRE) during 2020–2022 for the three stratocumulus decks. rCRE climatology (2003-2018 mean) is indicated by gray contours. Black dots denote grid points for which the IMO2020 aerosol-induced (all else equal) perturbation (i.e., OBSIMO versus NNEIMO) is statistically significant according to the Wilcoxon signed-rank test at 95% confidence interval. The fraction of significant (fsig) grids is indicated in white labels.
IMO2020 radiative forcings
a Global shortwave radiative forcings based on the 3 stratocumulus decks due to changes in low-cloud fraction (LCF), low-cloud albedo (Ac), and low-cloud rCRE (including LCF and Ac adjustments). Positive values indicate gain in incoming solar radiation, i.e., warming. b As in (a), but for changes in Nd. Box-whiskers indicate the 10th, 25th, 75th, 90th percentiles, and the means of the spatial distribution of the 3-year mean. ΔNd values outside the 10th and 90th percentiles are indicated by small dots. c Effective radiative forcing estimates of the IMO2020 event from literature are compared to this study which reports the forcing due to changes in the SW cloud radiative effect from the 3 stratocumulus decks. Mean values are shown in symbols with uncertainty ranges indicated by vertical bars.
Definition of IMO2020 detectability
A table showing how detectability of the IMO2020 event is characterized in this study. OBSIMO denotes the observational record post-IMO2020, OBSpre-IMO denotes the observational record pre-IMO2020, and NNEIMO denotes the NNE-predicted counterfactual cloud fields based on the 2020–2022 meteorological conditions, representing the business-as-usual scenario, with all else equal except for the aerosol. Color scheme mimics that in Fig. 4.
Maps of IMO2020 detectability
Detectability of (a) Nd and (b) rCRE for the IMO2020 event, when using the 2017–2019 observational record as OBSpre-IMO, based on the Wilcoxon signed-rank test at 95% confidence interval (see Methods and Fig. 3). Grids where observational records before (OBSpre-IMO) and after (OBSIMO) IMO2020 are statistically different are shaded in yellow, including true detection (magenta dots on yellow) and false detection (crossings on yellow). Grids where OBSIMO is indifferent from OBSpre-IMO are shaded in gray, including missed detection (blue dots on gray) and no detection (gray). Inserts indicate the areal fraction of the four detection-scenarios for each region. The fraction of significant IMO2020 (aerosol-induced) perturbation being detected (PTD) and the fraction of insignificant IMO2020 (aerosol-induced) perturbation being detected (PFD) are labeled in white. rCRE climatology (2003–2018 mean) are overlaid with white contours.
Detectability of each deck as ROC curves
Regional (Sc deck-level) detectability illustration for (a) Nd and (b) rCRE in terms of true and false detection rates. Shades of colors indicate the number of years that goes into the construction of OBSpre-IMO with light colors denoting more years (see Methods). Open circles highlight the results shown in Fig. 4 where the 3-year period immediately proceeding 2020 is used as OBSpre-IMO. Perfect detectability lies at the upper-left corner (0,1) of the diagram, and a random detectability lies on the 1-to-1 line. Detectability increases from bottom-right to upper-left.
Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect

January 2025

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32 Reads

Reduction in aerosol cooling unmasks greenhouse gas warming, exacerbating the rate of future warming. The strict sulfur regulation on shipping fuel implemented in 2020 (IMO2020) presents an opportunity to assess the potential impacts of such emission regulations and the detectability of deliberate aerosol perturbations for climate intervention. Here we employ machine learning to capture cloud natural variability and estimate a radiative forcing of +0.074 ±0.005 W m⁻² related to IMO2020 associated with changes in shortwave cloud radiative effect over three low-cloud regions where shipping routes prevail. We find low detectability of the cloud radiative effect of this event, attributed to strong natural variability in cloud albedo and cloud cover. Regionally, detectability is higher for the southeastern Atlantic stratocumulus deck. These results raise concerns that future reductions in aerosol emissions will accelerate warming and that proposed deliberate aerosol perturbations such as marine cloud brightening will need to be substantial in order to overcome the low detectability.



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8.1 (2023)

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31%

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8 days

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1.3 (2023)

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0.01616 (2023)

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3.304 (2023)

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