Climatology Research Articles

Promoting transdisciplinary collaboration in academia: uniting for climate-resilient health

In a world where some regions are directly experiencing the effects of the climate change, while others are more vulnerable; this article dives into the deep and frequently catastrophic impact of the climate change on vulnerable countries. This review emphasizes the need for transdisciplinary academic collaboration, including social scientists, healthcare professionals, engineers, policy experts, and climate scientists, and underscores academia’s potential role in advancing climate-resilient health systems in these areas. The study promotes inclusive research that prioritizes at-risk groups, involves communities, and supports culturally sensible methodologies. Beyond their traditional tasks, academia has an obligation for action, learning, innovation, and change. To address the complex health concerns brought on by the planetary crisis, collaboration across academic fields and in tandem with communities, governments, and international organizations is crucial. Ongoing collaboration between academia, governments, and stakeholders is crucial to developing healthcare systems and technologies that are climate resilient. The complex role of academia involves developing pioneering solutions in healthcare for a sustainable future, advocating for policies based on evidence, and educating the next generation of professionals.

Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.

The Politicization of Climate Science: Media Consumption, Perceptions of Science and Scientists, and Support for Policy

Differential media treatment of climate change, including conservative media’s tendency to reject the anthropogenic climate change scientific consensus, has reinforced polarized perceptions of climate change. Studies have found differences in coverage patterns and in perceptions among those relying more heavily on conservative rather than liberal or moderate media. This scholarship has been limited by narrow measurements of media exposure, climate-related outcomes, and the mechanism of effects. We analyzed nationally representative US data (N = 1,181) using measures that included not only reported use of mainstream print, cable, and social media captured in past research, but also science programming, as well as far-right, alternative-health, and Christian media. On average, participants relied more heavily on centrist and liberal media, followed by Fox News and social media. The results corroborate findings associating exposure to centrist media with pro-climate attitudes, and conservative media, including Fox News with the opposite views. Use of far-right outlets was associated with the lowest levels of belief in anthropogenic climate change, perceptions of personal threat, and support for climate-friendly policy. Reliance on science media was associated with pro-climate views. Most associations were mediated via perceptions of science and scientists (using the Factors Associated with Self-Presentation of Science, FASS scale).

Ecological effects of land use and land cover changes on lakes in urban environments

AbstractUrban lakes confront significant threats due to changes in land use and land cover (LULC) resulting from urbanization and subsequent climate change. This review discusses the intricate effects of the urban heat island phenomenon on lakes, specifically attributed to LULC changes. Utilizing the Scopus and Web of Science databases, this study gathers the most pertinent earlier research in the LULC and water bodies. This study systematically categorizes variables into five distinct groups and scrutinizes the drivers, parameters, tools, and management strategies influencing the LULC dynamics on lakes. A research gap is identified in understanding the conjoined impacts of LULC and urban heat island effects within urban lake environments. The review further investigates diverse ways in which LULC impacts lakes, intersecting with multiple United Nations sustainable development goals (SDGs), notably SDGs 6, 11, 13, and 15. Consequently, this review serves as a valuable contribution to the understanding of LULC to provide substantial benefits to the urban climate research toward meeting SDGs.

Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface.

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.

Trust in climate science and climate scientists: A narrative review

Trust in climate science provides the foundation for evidence-based policymaking on climate change mitigation and adaptation and public perceptions of the urgency of climate change. Here we consider the possibility that lack of public trust in climate science and climate scientists may undermine the effectiveness of climate science communication. To this end, we narratively review three topics of relevance to climate science and climate scientists: 1) The current state of trust; 2) Reasons for distrust; 3) How political engagement affects trust. We then draw on insights from communication and behavioral science to recommend how climate change communicators can become more trustworthy.

Measurement report: Optical characterization, seasonality, and sources of brown carbon in fine aerosols from Tianjin, North China: year-round observations

Abstract. To investigate the optical characteristics and sources of brown carbon (BrC) in North China, where the atmospheric aerosol loadings are high and have severe impacts on Earth's climate system, we collected fine aerosols (PM2.5) at an urban site in Tianjin over a 1-year period. We measured the ultraviolet (UV) light absorption and excitation emission matrix (EEM) fluorescence of the water-soluble BrC (WSBrC) and the water-insoluble but methanol-soluble BrC (WI-MSBrC) in the PM2.5 using a three-dimensional fluorescence spectrometer. The average light absorption efficiency of both WSBrC (Abs365, WSBrC) and WI-MSBrC (Abs365, WI-MSBrC) at 365 nm was found to be highest in winter (10.4 ± 6.76 and 10.0 ± 5.13 Mm−1, respectively) and distinct from season to season. The averages of the fluorescence index (FI) and the biological index (BIX) of WSBrC were lower in summer than in the other seasons and opposite to that of the humification index (HIX), which implied that the secondary formation and further chemical processing of aerosols were more intensive during the summer period than in the other seasons. However, in winter, the higher HIX together with the higher FI and BIX of WI-MSBrC suggested that the BrC loading was mainly influenced by primary emissions from biomass burning and coal combustion. Based on the EEM, the types of fluorophores in WSBrC were divided into humic-like substances (HULIS), including low-oxygenated and high-oxygenated species and protein-like compounds (PLOM), though mostly PLOM in the WI-MSBrC. The direct radiation absorption by both WSBrC and WI-MSBrC in the range of 300–400 nm accounted for ∼ 40 % of that (SFEAbs, 4.97 ± 2.71 and 7.58 ± 5.75 W g−1, respectively) in the range 300–700 nm.

Climate concepts for supporting political goals of mitigation and adaptation: The case for “climate crisis”

AbstractClimate concepts are crucial to understand the effects of human activity on the climate system scientifically, and to formulate and pursue policies to mitigate and adapt to these effects. Yet, scientists, policymakers, and activists often use different terms such as “global warming,” “climate change,” “climate crisis,” or “climate emergency.” This advanced review investigates which climate concept is most suitable when we pursue mitigation and adaptation goals in a scientifically informed manner. It first discusses how survey experiments and social science reviews on climate frames draw normative recommendations about which terms to use for public climate communication. It is suggested that such normative claims can be refined by including the scientific alongside lay uses of a climate concept, and by using explicit assessment conditions to evaluate how suitable a concept is for formulating mitigation and adaptation goals. Drawing on philosophical theories of conceptual change in science and conceptual engineering, a novel framework with two assessment conditions is introduced and then applied to “global warming,” “climate change,” “climate emergency,” and “climate crisis.” The assessment suggests that currently, “climate crisis” is most suitable to formulate and pursue climate mitigation and adaptation goals. Using this concept promotes the epistemic goals of climate science to a high degree, bridges scientific, political, and activist discourse, and fosters for democratic participation when articulating climate policies.This article is categorized under: The Social Status of Climate Change Knowledge > Climate Science and Decision Making The Social Status of Climate Change Knowledge > Knowledge and Practice Perceptions, Behavior, and Communication of Climate Change > Communication

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Abstract. Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

The effect of local climatic conditions on household consumption: a case study of South Africa

The article explores the causal effect of local climate conditions on household consumption in South Africa. The climatic conditions are represented by monthly average temperature and precipitation. The study utilises the nationally representative 2017 National Income Dynamics Study (NIDS), wave 5 data and 2017 Climate Research Unit (CRU) climate data. The parsimonious quantile regression shows that climatic conditions (precipitation, temperature, wet days, and cloud cover) impact household per capita consumption. The quadratic quantile regression model analysis shows that household per capita consumption is convex in precipitation. Below the turning point, increased precipitation is associated with decreased household per capita consumption. Above the turning point, increased precipitation is related to increased household per capita consumption. Regions that receive very low precipitation or experience extreme temperatures (very cold or very hot) require tailor-made interventions to alleviate consumption. When we control for household characteristics, the impact of climatic conditions on household per capita consumption is weak. Providing inclusive development policies and programmes can mitigate the impact of climatic conditions on household per capita consumption.

Evaluation of five gridded precipitation products for estimating precipitation and drought over Yobe, Nigeria

ABSTRACT Ground observations are often considered as the most reliable and precise source of precipitation data. However, long-term precipitation data from ground observations are lacking in many parts of the world. Gridded precipitation products (GPPs) therefore have emerged as crucial alternatives to ground observations, but it is essential to assess their capability to accurately replicate precipitation patterns. This study aims to evaluate the performance of five GPPs, NASA POWER, TerraClimate, Climate Hazards Group Infrared Precipitation with Climate Data (CHIRPS), GPCC, and Climate Research Unit (CRU), in capturing precipitation and drought patterns from 1981 to 2021 in Yobe, Nigeria. The results indicate that GPCC had good performance at both monthly and annual scales, with high correlation coefficients and low error values. However, it tends to underestimate precipitation amounts in certain areas. Other products also exhibit satisfactory performance with moderate correlations with ground observations. Drought analysis indicates that GPCC outperforms other products in standardised precipitation index-6 calculations, while NASA POWER demonstrates inconsistencies with ground observations, particularly during the early 1980s and mid-2000s. In conclusion, GPCC is the most preferable GPP for precipitation and drought analysis in the Yobe State in Nigeria.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Projecting marine fish distributions during early life stages under future climate scenarios

AbstractChanges to Earth's climate affect organisms globally; in marine systems, these impacts are seen through warming water temperatures, ocean acidification, hypoxia and frequent marine heatwaves. These effects may lead to the movement of species to more favourable conditions. While climate‐driven movement is well studied at the adult stage, how the early life stages of marine fish will respond to future variability is less clear. Many fish species are constrained by specific spawning locations or phenology. Spawning in certain locations allows for local retention of offspring, while precise timing can facilitate transport of offspring to nursery locations through seasonal circulation patterns. Our research investigates how changing oceans impact the location and timing of spawning of Bering Sea groundfishes over the next century. We used ROMS SST and SSS model output and NOAA survey data in species distribution models to hindcast and project distributions and centre of gravity for eggs and larvae of six groundfish species. Our analyses found that most of our study species exhibit flexible geography. However, the speed and direction of egg and larval movement did not track the speed and direction of their respective thermal niches. Hence, the projected distributional patterns of adult stages may be limited by their early life stages. This response is likely to be mirrored globally by other species with planktonic eggs and larvae. These results indicate that life history considerations are critical for the management of commercially important species, as effects on early life stages are strongly connected to the success or failure of adult populations.

Investigating the impact of tropical deforestation on Indian monsoon hydro-climate: a novel study using a regional climate model

AbstractThis study uses a state-of-the-art regional climate model (RCM) to examine how tropical deforestation affects the meteorology of the Indian Summer Monsoon (ISM). Incorporating insights from existing research on deforestation by climate scientists, alongside evidence of environmental deterioration in semi-arid, hilly and tropical regions of Southeast Asia, this research seeks to elucidate the critical influence of anthropogenic reasons of climate change on the hydroclimate of ISM. Employing “tropical deforestation” design experiments with the ICTP-RegCMv4.4.5.10 RCM the study evaluates the effects on meteorological parameters including precipitation, circulation patterns and surface parameters. This experimental design entails substituting vegetation type in the land use map of RegCMv4.4.5.10 model, such as deciduous and evergreen trees in Southeast Asia with “short grass” to mimic tropical deforestation. Findings reveal that deforestation induces abnormal anti-cyclonic circulation over eastern India curtails moisture advection, diminishing latent heat flux and moisture transport, leads to a decrease in precipitation compared to control experiment scenario. Alterations in albedo and vegetation roughness length attributable to deforestation impact temperature, humidity, precipitation, consequently exacerbating drought and heatwave occurrences. Additionally, the study also explores deforestation-induced feedback on ISM precipitation variability. The study concludes that deforestation substantially alters land-surface characteristics, water and energy cycle, and atmospheric circulation, thereby influencing regional climate dynamics. These findings offer foundational insights into comprehending land-use and land-cover changes and their implications for climate change adaptation strategies.

Dealing with the Quiet Opposition? News Coverage of Climate Skepticism in Two Finnish Newspapers 1990–2021

ABSTRACT This article examines news coverage of climate skepticism in two Finnish newspapers between 1990 and 2021. It approaches climate skepticism as a professional problem that involves a tension between conventional professional ideals that underscore balanced coverage of conflicts and emerging ideals that stress communicating climate change responsibly. Content analysis focused on two core forms of climate skepticism that have remained relatively stable throughout the 31-year research period: evidence and process skepticism. The presence of all kinds of skeptical contents (skeptical arguments, arguments against skepticism, and descriptions of skepticism) was examined to evalutate the overall media visibility of climate skepticism. The analysis then focused specifically on the coverage of skeptical arguments. The role of sources as well as the use of journalistic voice in the presentation of skeptical contents was examined. This study elaborates earlier anecdotal accounts concerning the limited public role of climate skepticism in Finland. Four per cent of news articles included sources who were quoted presenting their own skeptical views. Skeptical arguments were mainly presented by quoting foreign sources whereas arguments against skepticism were presented by quoting domestic climate scientists. “Climategate” caused the most notable increase in the volume of skeptical contents during the research period.

Burden of Stroke Attributable to Nonoptimal Temperature in 204 Countries and Territories: A Population-Based Study, 1990-2019.

Stroke attributable to nonoptimal temperature needs more attention with dramatic climate change. The aim of this study was to estimate the global burden and distribution characteristics of the burden. In this ecological study, we collected data from the Climate Research Unit Gridded Time Series, the World Bank databases, and the Global Burden of Diseases study to estimate the distribution of burden. We used the joinpoint model, decomposition analysis, age-period-cohort model, panel data analysis, and health inequality analysis to assess the different types of stroke burden attributable to different climatic conditions. The burden of stroke attributable to nonoptimal temperature continued to grow, and aging was a key factor in this increase. In 2019, 521,031 (95% uncertainty interval [UI] 402,433-663,996) deaths and 9,423,649 (95% UI 7,207,660-12,055,172) disability-adjusted life years [DALYs] attributable to stroke due to nonoptimal temperature were recorded globally. Globally, men (age-standardized mortality rate [ASMR] 7.70, 95% UI 5.80-9.73; age-standardized DALY rate [ASDR] 139.69, 95% UI 102.96-178.54 in 2019) had a heavier burden than women (ASMR 5.89, 95% UI 4.50-7.60; ASDR 96.02, 95% UI 72.62-123.85 in 2019). Central Asia (ASMR 18.12, 95% UI 13.40-24.53; ASDR 327.35, 95% UI 240.24-440.61 in 2019) had the heaviest burden at the regional level. In the national level, North Macedonia (ASMR 32.97, 95% UI 20.57-47.44 in 2019) and Mongolia (ASDR 568.54, 95% UI 242.03-1,031.14 in 2019) had the highest ASMR/ASDR, respectively. Low temperature currently contributes to the main burden (deaths 474,002, 95% UI 355,077-606,537; DALYs 8,357,198, 95% UI 6,186,217-10,801,911 attributable to low temperature vs deaths 48,030, 95% UI 5,630-104,370; DALYs 1,089,329, 95% UI 112,690-2,375,345 attributable to high temperature in 2019). However, the burden due to high temperature has increased rapidly, especially among people aged older than 10 years, and was disproportionately concentrated in low sociodemographic index (SDI) regions such as Africa. In addition, the rapid increase in the stroke burden due to high temperature in Central Asia also requires special attention. This is the first study to assess the global stroke burden attributed to nonoptimal temperature. The dramatic increase in the burden due to high temperature requires special attention, especially in low-SDI countries.

Global Atlas of Methane Metabolism Marker Genes in Soil.

Methane, a greenhouse gas, plays a pivotal role in the global carbon cycle, influencing the Earth's climate. Only a limited number of microorganisms control the flux of biologically produced methane in nature, including methane-oxidizing bacteria, anaerobic methanotrophic archaea, and methanogenic archaea. Although previous studies have revealed the spatial and temporal distribution characteristics of methane-metabolizing microorganisms in local regions by using the marker genes pmoA or mcrA, their biogeographical patterns and environmental drivers remain largely unknown at a global scale. Here, we used 3419 metagenomes generated from georeferenced soil samples to examine the global patterns of methane metabolism marker gene abundances in soil, which generally represent the global distribution of methane-metabolizing microorganisms. The resulting maps revealed notable latitudinal trends in the abundances of methane-metabolizing microorganisms across global soils, with higher abundances in the sub-Arctic, sub-Antarctic, and tropical rainforest regions than in temperate regions. The variations in global abundances of methane-metabolizing microorganisms were primarily governed by vegetation cover. Our high-resolution global maps of methane-metabolizing microorganisms will provide valuable information for the prediction of biogenic methane emissions under current and future climate scenarios.

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Climate model selection via conformal clustering of spatial functional data

AbstractClimate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.

Gauge-Optimal Approximate Learning for Small Data Classification.

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space and that it can be approximated through a monotonically convergent algorithm that presents-under the assumption of a discrete segmentation of the feature space-a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Niño Southern Oscillation and inference of epigenetically induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems in both learning performance and computational cost.