Institute of Arctic and Alpine Research

Earth's rivers, especially in mountainous settings, carry sediment with a wide variety of properties, among which are size and lithology. Despite their critical influence on bedrock incision, transport/deposition patterns, and topographic forms, sediment properties are often overlooked in landscape evolution models. Here we present a new set of Landlab components centered on the Enhanced Gravel Bedrock Eroder (EGBE), which simultaneously describes the evolution of a gravel-sized alluvium layer and the underlying bedrock in a network of rivers. The component implements numerical solutions to fluvial sediment transport, deposition, attrition, and bedrock incision, taking into account sediment load heterogeneity in size or toughness. Additionally, EGBE allows the user to select between two assumptions regarding channel geometry: a fixed-width model (in which channel width scales with water discharge) and a dynamic-width model (in which channel width adjusts such that the bed shear stress is slightly above the transport threshold for the median-size sediment grain). EGBE relies on other Landlab components that handle flow routing and mass exchange among different sediment classes, and it can be coupled with hillslope sediment transport components. These components are integrated in a code called EGBE-LEM.A set of 1D EGBE numerical experiments highlights the importance of sediment size for channel steepness. These experiments illustrate how an upstream source of coarse, resistant gravel leads to a steeper overall profile. Conversely, a downstream source steepens only the lower portion of the profile, leading to a break-in-slope that coincides with the lithologic transition. Several additional EGM-LEM experiments explored the impact of sediment toughness heterogeneity on landscape evolution. These demonstrate how upstream variations in toughness can influence the form and dynamics of channel networks downstream. For example, upstream variations in the toughness of source material can lead to asymmetric drainage divides and contrasts in the steepness index across adjacent drainage basins. In addition, we demonstrate how lithologic heterogeneity can influence river network concavity, planform geometry and steepness.EGBE improves on previous modeling efforts by explicitly representing sediment attrition and size-dependent transport of heterogeneous sediment load: a useful addition given that size and lithology are two of the most commonly used and accessible measures of river systems. In addition, the EGBE-LEM code provides an advanced integrated modeling platform for understanding mechanisms and dynamics across a range of river types and geological settings.

Methane is the second most important anthropogenic greenhouse gas. Since 2007, atmospheric methane concentrations have resumed growth following a period of relative stabilization, with the growth rate accelerating in recent years. At the same time, a reversal in the isotopic trend of atmospheric methane suggests possible changes in sources driving the observed increase in atmospheric CH₄.In this study, we use a novel approach based on the Miller-Tans method, which uses short-term devations from a smooth background signal in observed time series of CH4 mole fraction and isotopic composition to infer δ¹³C-CH₄ source signatures of this short-term component. This allows us to focus on regional scale changes in methane sources .We use 20 years of long-term atmospheric methane and carbon isotope (δ¹³C-CH₄) observations from a global sampling network to conduct a comprehensive analysis of the spatial distribution, seasonal variability, and long-term trends of δ¹³C-CH₄ source signatures. In particular, we observe a pronounced decrease in δ¹³C-CH₄ source signatures at high northern latitudes after 2007, indicating a relative increase in isotopically depleted, biogenic methane sources in this region. By comparing our observation-based results with inverse modeling studies, we further discuss how this regional shift is reflected at the global scale and explore possible explanations underlying the substantial shifts in the source mix at high northern latitudes.

Scientific research underpins effective conservation policy, yet current approaches for assessing whether scientific outputs meaningfully support defined management objectives rely primarily on manual expert review. This limitation constrains scalability, is time intensive and introduces potential bias in identifying knowledge gaps. We present a framework combining AI-assisted multi-target classification with systematic coverage analysis for automated evaluation of research alignment with conservation objectives. We compare traditional machine learning (TF-IDF + logistic regression), a generic BERT baseline, and an enhanced SciBERT approach incorporating domain-specific adaptations including multi-target architecture, balanced loss functions, and target weighting optimized for conservation science. The framework classifies research topics and conservation objective alignment, two dimensions requiring comprehension of scientific content and policy implications. We demonstrate the approach using 295 expert-annotated peer-reviewed studies from the Ross Sea region Marine Protected Area in Antarctica. Our enhanced multi-target SciBERT model achieved 70.0% macro F1, outperforming TF-IDF (59.5%) and BERT (52.0%) baselines, with per-target improvements of 21% on research topics and 14.5% on conservation objectives. The framework achieved 78% agreement with expert annotations, with particularly strong performance on conservation objective alignment (87.7% F1, 94% agreement). The integrated system successfully identified and quantified descriptive patterns in research coverage across thematic and policy dimensions, enabling systematic assessment for research prioritization and automated coverage analysis. While demonstrated in the Antarctic context, the framework architecture is broadly transferable, though successful adaptation requires retraining with domain-specific expert annotations and fine-tuning to match local management frameworks. • Enhanced multi-target SciBERT achieves 78% agreement with expert annotations for automated research-policy alignment assessment. • Framework reveals research coverage patterns with 59% of conservation topics appearing in <15 papers. • Multi-target architecture simultaneously classifies research themes and conservation objectives with 17.6% improvement over TF-IDF baseline. • Demonstrates scalable deployment across diverse contexts from Marine Protected Areas to SDG assessment. • Open-source implementation enables immediate adoption for evidence-based conservation planning.

The atmospheric methane (CH4) growth rate surged after 2019, peaking at 16.2 parts per billion per year (ppb year-1) in 2020 before declining to 8.6 ppb year-1 in 2023. Using multiple atmospheric inversions constrained by observation- and model-based prescribed hydroxyl radical (OH) fields and CH4 atmospheric data, we show that a drop of OH radicals in 2020-2021, followed by recovery in 2022-2023, accounted for 83% of year-on-year variations in the CH4 growth rate, the rest being explained by wetland and inland water emissions, which increased between 2019 and 2020-2022 [+8.6 ± 2.6 teragrams of CH4 per year (TgCH4 year-1)] and then decreased between 2022 and 2023 (-9.9 ± 3.3 TgCH4 year-1). Most emission changes from 2019 to 2023 occurred in northern tropical wetlands in Africa and Asia, whereas South American wetlands emissions declined and Arctic emissions increased after 2019.

Abstract Ice cores from Mt. Logan, the second highest peak in North America located in the St. Elias mountains in southwest Yukon, Canada, have provided conflicting accumulation records, thus the hydroclimate response to changing atmospheric conditions in the highest elevation regions is not well constrained. Here, we present the accumulation record from the new 325 m Mt. Logan ice core drilled at 5,334 m asl on the summit plateau in May 2022. Multi‐parameter annual layer counting, confirmed with radionuclide and volcanic tephra measurements, extends to 1911 CE, associated with a depth of 257 m. The thinning‐corrected annual accumulation record reveals an average rate of 3.0 m water equivalent per year (m weq a −1 ) from 1912 to 2020 CE, greater than six times higher than the previous estimate of ∼0.41 m weq a −1 from the 2002 Mt. Logan Prospector Russell Col core. Correlation analysis between the annual accumulation record and regional climate data sets (e.g., Japanese 55‐year Reanalysis, weather stations) reveal a strong positive relationship with warm‐season total precipitable water and temperature. Thus, we suggest interdecadal precipitation variability on Mt. Logan is at least partially driven by warm‐season atmospheric water vapor loading, potentially related to atmospheric temperature responses associated with the warm‐season Alaska Blocking Index. Further, the record reveals a statistically significant increase in accumulation of 0.13 m weq per decade since 1970. These results reveal a drastically different Mt. Logan ice core record and provide a new warm‐season perspective on drivers of high‐elevation accumulation variability in the North Pacific.

Abstract. Methane (CH4) is a potent greenhouse gas; however, the causes of its growth since 2006 are a subject of debate. While measurements of CH4 mole fraction and carbon isotopic composition (δ13C-CH4) have been extensively used to investigate the global CH4 budget, the hydrogen isotopic composition (δD-CH4) remains underutilised despite its unique sensitivity to source types and oxidation processes. Here, we assimilate a newly harmonised 35-year dataset of dual isotope measurements from high-latitude monitoring stations in both hemispheres within a two-box Bayesian inversion to quantify global CH4 sources and sinks. The model integrates prior emissions from five source categories based on global bottom-up inventories. Methane removal processes are represented by sink-specific kinetic isotope effects as tropospheric and stratospheric loss, and soil uptake. We find that the inclusion of δD-CH4 improves the model's ability to constrain emission apportionment between biogenic and thermogenic sources, particularly for fossil fuel emissions during the late 1990s and early 2000s, which affects CH4 lifetime estimate. CH4 increase post-2006 is driven mainly by rising wetland emissions, while fossil-fuel growth is modest, biomass burning declines, and agriculture and waste make smaller, regionalised contributions. The optimised inversion results favour a strong 13C kinetic isotope effect in the total CH4 removal in the troposphere and a net shortening of NH lifetime of CH4 by 0.2 years. This study demonstrates the added value of incorporating δD-CH4 into inverse modelling frameworks and underscores the importance of long-term δD-CH4 measurements for advancing our understanding of CH4 biogeochemistry and its role in the global carbon cycle.

Abstract Satellite observations of sea surface temperature (SST) and ocean chlorophyll are critical for validating Earth system models (ESMs). However, missing satellite data due to cloud cover, sea ice, and low solar angle can introduce sampling bias that distorts model–observation comparisons. Here, we quantify satellite sampling bias in Moderate Resolution Imaging Spectroradiometer (MODIS) SST and chlorophyll and demonstrate how accounting for this bias changes our estimates of model performance. We apply realistic MODIS sampling to modeled SST and chlorophyll from an ocean‐only hindcast simulation (2003–2016) of the Community Earth System Model. These model outputs are compared to real‐world MODIS observations to examine how selective sampling affects the magnitude and spatial patterns of the apparent model bias. We find that model bias generally exceeds sampling bias, though the relative importance of the two depends on the spatial and temporal scale. Sampling bias is most pronounced at high‐latitudes and in persistently cloudy regions, where it can impact annual means and apparent long‐term trends. Accounting for sampling bias reduces model–observation differences in the multi‐year means: the root mean square error decreases from 0.976 to 0.792°C for SST and from 0.635 to 0.624 (log‐transformed units) for chlorophyll. However, in some regions, correcting for satellite sampling bias increases model bias. These results demonstrate that while sampling bias is generally a small uncertainty compared to model bias, it can meaningfully influence model evaluation and should be considered in assessments of ESM performance for SST and chlorophyll.

Abstract Atmospheric models generally exhibit cold biases in 2‐m air temperature (T2) across mountainous regions during snow seasons. This study evaluates whether snow cover errors are the primary driver for the persistent T2 cold bias in coupled WRF/Noah‐MP simulations across the western U.S (WUS) through analyses of T2, ground snow cover fraction (SCF), snow water equivalent (SWE), and surface albedo. Baseline simulations revealed widespread cold biases in maximum daily T2 (T2 max ), mainly during November–April when snowpack influences the terrestrial energy budget (mean bias = −1.05°C to −1.32°C), with higher elevation areas showing biases up to −5.31°C. Cold biases across the WUS are reduced, on average, by 17%–24% in the simulations using recently enhanced snow compaction and ground SCF parameterizations (hereinafter ARnew) that tend to reduce snow cover and surface albedo. However, notable cold biases still persist in the ARnew simulations, especially in high‐elevation areas, despite underestimates of ground SCF and SWE. Statistical and information theory analyses show very weak connections between T2 max biases and biases in ground SCF and SWE. Further, an experiment that imposes unrealistically low ground SCF (mean bias = −75%) still exhibits cold biases, particularly in high‐elevation areas (mean bias for Z &gt; 2,000 m = −1.46°C). These results indicate that snow cover errors can modestly contribute to cold biases but are not their primary cause. Therefore, resolving the cold bias issue will require addressing factors beyond snow cover, potentially including uncertainties in canopy radiative transfer, surface turbulence, and other atmospheric processes in complex terrain.

Abstract Phytoplankton net primary production in the Arctic has historically been constrained to a short, intense summer bloom that sustains fish, seabird, and marine mammal populations. However, climate change is altering Arctic phytoplankton bloom phenology. We use an ensemble of Earth system model simulations to isolate the impact of climate change on the timing, duration, and importance (relative contribution to total net primary production) of the bloom. Earlier blooms emerge across 71% of the Arctic Ocean by 2100, when blooms begin 34 days earlier and last 15 days longer than in 1970. Productivity is less concentrated in a single bloom in sub-Arctic seas and on Arctic inflow shelves by 2100, indicating that the bloom declines in importance. In contrast, bloom phenology and productivity exhibit only small changes by 2020. Our study demonstrates that anthropogenic climate change will greatly alter the timing and importance of the Arctic Ocean phytoplankton bloom by 2100.