Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

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Abstract

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Latest Results

Abstract

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach

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Abstract

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km2 watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach

Latest Results

Abstract

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km2 watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Projections of precipitation extremes over the Volta Basin: insight from CanESM2 regional climate model under RCP 4.5 and 8.5 forcing scenarios

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Abstract

Perturbations in extreme precipitation characteristics are investigated over the Volta Basin (VB) and its three subdomains (Sahel, Soudano-Sahel and Guinea Coast) for the early-21st (2030–2053) and mid-twenty-first centuries (2057–2080) under representative concentration pathways (RCPs) 4.5 and 8.5. Seven climate indices from the Expert Team on Climate Change Detection and Indices were selected to examine future extreme precipitation features. Owing to its performance over the West African sub region, CanESM2 model results were used with Global Precipitation Climatology Centre (GPCC v7) dataset serving as reference data. Results generally show lowering trends in extreme precipitation events over the VB. The declines in extremes were dominant in the Sahel and Soudano-Sahel zones with some degree of upsurges observed in the Guinea Coast. Spatially over the basin, wet spells (CWD) were projected to shorten under RCP 8.5 (~ 7–27 days/year) relative to RCP 4.5 (~ 8–30 days/year). Similar pattern was observed for dry spells (CDD) with ranges of ~ 64–198 days/year and ~ 61–186 days/year respectively for RCPs 4.5 and 8.5. As revealed, future alterations in precipitation events have the propensity to cause alternating drought or flood events. In this line, sustainable adaptation measures and coping strategies need to be devised in time to minimize the consequences of these events, particularly those on water resources availability and ecosystem functions and services.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

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Abstract

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Equity as a priority in EAT–Lancet-aligned food system transformations

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Abstract

Food systems drive human and environmental change, reflect diverse cultural and ecological contexts, and, in their diversity, can bolster nutrition and planetary health. Ignoring structural inequities in food system transformations risks offsetting potential gains. We summarize current evidence on the context-dependent implications of EAT–Lancet goals and propose six priority areas to guide equitable food system transformations, targeting food and nutrition security, just sustainability and cultural diversity. Priority areas—namely, diverse and nutritious food access, food industry regulation, climate-resilient food production, localized, small-scale food systems, cultural diversity and social well-being—can be achieved through public, private and civil society action.

Traces of collisional and transtensional processes between the Carpathia and the European platform in the geoelectric image (NE Slovakia and SE Poland)

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Abstract

We present the latest magnetotelluric models on profiles in the northeastern part of Slovakia and the southeastern part of Poland. These models are focused on deciphering the tectonic structures at the contact of the Inner Carpathians with the European Platform in this area. For the Inner Carpathian block, we propose the term Carpathia. Profile SA-01 shows shallower structures and the parallel MT-05 profile shows deeper structures. These models are also correlated with the seismic profile CEL-05. All results are compatible and show an original subduction-collisional structure, which was later replaced by a transpressive-transtensional one. The most striking structures are thick highly conductive subhorizontal zones in the middle crust and a tectonically controlled deep vertical conductive structure—the Carpathian conductive zone. Other significant structures, which also appear in the seismic section, are back thrusting of Flysch Belt and the Klippen Belt basement (Penninic crust) uplift.

Application of the MHVSR method for determining the location of landslide areas before geotechnical project proposal: a case study of Tortum Lake, Turkey

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Abstract

Tortum Lake, in Turkey, which is a natural feature and a large landslide barrier lake, was formed due to a natural landslide disaster. In this context, areas with the potential to create landslides in Tortum Lake and the waterfall region are identified and displaced with zoning. Microtremor horizontal-to-vertical spectral ratio method and seismic refraction studies were used, and by considering the results, the areas with high landslide potential were determined. It aims to choose an effective study area by determining the areas with high landslide capability and revealing the boundaries of the geotechnical project. Areas with high landslide potential were compared with those where landslides have happened, and it showed that this method can be used for determining sliding risk before it happens. Results showed that regions with low VS30, combined with the slope, create the potential for landslides. Afterward, when the map of ground amplification values obtained from the microtremor data was reviewed, it was found that the physical properties of areas with high ground amplification values would be the same as those of areas with low VS30 values. When these two maps are reviewed, it is observed that areas with low VS30 values and high ground amplification values are in the same locations as those with high sliding potential. The correct determination of the geotechnical project work area is important both to draw attention to the right area to be designed and to avoid wasting unnecessary time and money on unnecessary areas.

Pre-Pandemic and Recent Oral and Medical Health Care Utilization among Young American Indian Children and Their Caregivers

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Abstract

Children from diverse ethnic groups are at significantly increased risk for dental caries. In particular, American Indian (AI) children have the highest incidence of detal caries of any ethnic group. The COVID-19 pandemic dramatically restricted health care access, including preventive oral health care. Given this context, it is unclear whether or not preventive oral health care for AI children has resumed since lockdown. To address this question, we surveyed adult AI caregivers (N = 152) of children aged 0–5 years, assessing recent (12-month) and pre-COVID (for caregivers of children aged 3–5 years) preventive oral and medical health services. We also examined medical health care access and utilization among caregivers. Among children aged 3–5 years old, both pre-pandemic and past year medical care utilization were generally high (80 and 90%, respectively) as was any oral health care utilization (64 & 78%, respectively). Oral health check-ups were more common over the last year (62%) compared to pre-COVID (44%). Recent health care utilization among children 1–5 years old in this sample were generally comparable to national estimates, except for higher reported preventive medical care (99% vs. 87.6%, respectively) and higher preventive oral care (96% vs. 59.6%, respectively). More caregivers reported delaying or foregoing needed health care due to COVID (28–38%) versus due to cost (8–17%). In this survey of AI caregivers, recent child preventive health care utilization was high, and changes in utilization following the lockdown phases of the pandemic were comparable for oral and medical health care.