Spatial Variability Research Articles

Unraveling spatial heterogeneity of soil legacy phosphorus in subtropical grasslands.

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P-fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long-term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine-scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150-m interval grid) and analysis of 1438 surface soils (0-15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi-natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich-1, and Mehlich-3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture- or farm-level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Low-level Atmospheric Fields Over the Incheon International Airport

Abstract This study investigates the impact of initial/boundary conditions (IC/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) model is used for regional mesoscale model simulations and large-eddy simulations (LES). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as IC/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011-2020). Comparison of model simulations with observations obtained around the Yeong-Jong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Low-level Atmospheric Fields Over the Incheon International Airport

Abstract This study investigates the impact of initial/boundary conditions (IC/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) model is used for regional mesoscale model simulations and large-eddy simulations (LES). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as IC/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011-2020). Comparison of model simulations with observations obtained around the Yeong-Jong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields.

Effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure

In this study, the effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure (CRS) were investigated. The numerical model of the CRS embankment was established and validated with the field data. An application programming interface (API) was developed to deal with the data exchanging issue between the numerical model and the spatial variability characterization model. Based on the verified numerical model and the API, the probabilistic analysis with 500 Monte Carlo simulations was automatically computed. Three influencing factors of the retained soil (the mean of the friction angle, the variation of the friction angle and the vertical correlation length of the random field) are analysed by parametric analysis. The results show that the vertical correlation length of the random field is most important in the earth pressure calculation process, while the mean of the friction angle is the factor with least impact. On the whole, the spatial variability of soil properties has minimal impact on the distribution and magnitude of earth pressure behind the retaining structure.

Slope displacement reliability analysis considering rock parameters spatial variability subjected to stochastic mainshock-aftershock earthquake

A new and efficient framework for analyzing the stochastic dynamic response and reliability of the rock slope was constructed based on the generalized probability density evolution method (GPDEM). The left bank slope of Jinping dam was taken as an illustrative example and the effects of mainshock-aftershock sequence randomness, spatial variability of geotechnical materials and coupled uncertainties on the slope dynamic response and reliability were compared and analyzed for the first time. Firstly, a series of mainshock-aftershock sequences and parameter random fields were generated as stochastic factors inputs. Then, the effectiveness of GPDEM was validated by comparing with Monte Carlo Simulation (MCS). The Newmark slip displacement method was combined with GPDEM to analyze the slope dynamic response and reliability subjected to single and coupled uncertainties from a probabilistic viewpoint. Finally, the fragility curves of three risk levels were established based on the reliabilities with different seismic intensities. The result suggests that parameter spatial variability affects the dynamic response and reliability of the slope to some extent, but this effect will be overshadowed by the uncertainty of mainshock-aftershock sequences when it is considered simultaneously. Besides, aftershocks will cause the decrease of slope reliability and the impact of aftershock cannot be overlooked.

Uncovering the Triple Synergy of New-Type Urbanization, Greening and Digitalization in China

The in-depth discussion and analysis of the synergistic effect of new-type urbanization, greening and digitalization (NUGD) is important for the achievement of sustainable social, ecological and economic development. Therefore, in this study, an evaluation index system composed of these three subsystems was constructed for Chinese cities from 2011 to 2021. The comprehensive and collaborative development levels of each subsystem were measured by means including the entropy weight method and the coupling coordination model, respectively. Then, methods such as ESDA and the Dagum Gini coefficient were applied to investigate the spatiotemporal evolution and spatial differences in the triple synergy effect of the NUGD system in Chinese cities. Finally, the constraining factors of the triple synergy effect were revealed using the obstacle degree model. The findings demonstrated the following: (1) Overall, the NUGD subsystems and their comprehensive levels were increasing, with moderate overall development levels. (2) The synergistic development of the NUGD system exhibited an upward trend. Spatially, the synergistic development level showed distinct differentiation, being higher in the east and lower in the west. The multidimensional dynamic variation characteristics obtained through kernel density estimation revealed that the triple synergy level exhibits high stability. (3) The differences within the east and between the eastern and western areas were the largest, with the intensity of transvariation as the main source. (4) The five criterion layers, including social and spatial urbanization variables, were the key constraints that affected the triple synergy of the NUGD in Chinese cities, and the restrictive role of factors such as the proportion of urban construction land and the per capita postal business volume should not be ignored. This study provides a valuable reference and decision-making guidance to promote China’s acceleration toward a new urbanization path supported by both digitalization and green transformation.

Assessing Potential Bias in PFAS Concentrations in Groundwater and Surface Water Samples

AbstractPer‐ and polyfluoroalkyl substances (PFAS) encompass a large group of recalcitrant anthropogenic compounds that have been used in a variety of consumer and industrial products and processes over many decades. Drinking water standards were recently finalized for two PFAS at low nanogram per liter concentrations. Due to the widespread use of PFAS and low health‐based thresholds, a literature review and laboratory and field studies were conducted to assess several potential sources of bias in PFAS concentrations. Fluoropolymers are commonly present in equipment, products, and materials used during field sampling, potentially leading to false positive results. Laboratory results confirmed the presence of PFAS in multiple commonly used field materials. However, best practices can be used to limit pathways for cross contamination. Laboratory‐specified hold times and sample storage temperatures are scientifically founded and adequately prevent bias due to PFAS sorption to sampling containers or partial degradation of some PFAS to form others. PFAS are also known to accumulate at the air‐water interface and other interfaces. Results from laboratory and field studies indicate that PFAS enrichment in foam and in the surface microlayer does occur. However, the effect of this phenomenon is within the range of analytical and spatial sampling variability and therefore a change in sampling procedures is not needed. Findings were distilled into science‐based and practical recommendations to minimize bias during PFAS sampling without incurring unnecessary cost and effort.

Uncovering the Triple Synergy of New-Type Urbanization, Greening and Digitalization in China

The in-depth discussion and analysis of the synergistic effect of new-type urbanization, greening and digitalization (NUGD) is important for the achievement of sustainable social, ecological and economic development. Therefore, in this study, an evaluation index system composed of these three subsystems was constructed for Chinese cities from 2011 to 2021. The comprehensive and collaborative development levels of each subsystem were measured by means including the entropy weight method and the coupling coordination model, respectively. Then, methods such as ESDA and the Dagum Gini coefficient were applied to investigate the spatiotemporal evolution and spatial differences in the triple synergy effect of the NUGD system in Chinese cities. Finally, the constraining factors of the triple synergy effect were revealed using the obstacle degree model. The findings demonstrated the following: (1) Overall, the NUGD subsystems and their comprehensive levels were increasing, with moderate overall development levels. (2) The synergistic development of the NUGD system exhibited an upward trend. Spatially, the synergistic development level showed distinct differentiation, being higher in the east and lower in the west. The multidimensional dynamic variation characteristics obtained through kernel density estimation revealed that the triple synergy level exhibits high stability. (3) The differences within the east and between the eastern and western areas were the largest, with the intensity of transvariation as the main source. (4) The five criterion layers, including social and spatial urbanization variables, were the key constraints that affected the triple synergy of the NUGD in Chinese cities, and the restrictive role of factors such as the proportion of urban construction land and the per capita postal business volume should not be ignored. This study provides a valuable reference and decision-making guidance to promote China’s acceleration toward a new urbanization path supported by both digitalization and green transformation.

Temporal and Spatial Variability of Ground Frost Indices in Northeast China

Frost is one of the most frequent, intense, and influential agrometeorological disasters that occurs frequently in Northeast China. The study of the spatiotemporal changes of ground frost is of great significance for farmers and policymakers in Northeast China, as it can inform decisions related to crop selection, planting schedules, and the development of regional climate adaptation plans. In this study, the spatiotemporal changes of frost indices (last spring frost (LSF), first fall frost (FFF), and frost-free period (FFP)) in Northeast China were analyzed from 1961 to 2020. Then, we investigated the mutation characteristics of the frost indices and their correlation with geographical factors. The results revealed that (1) the LSF, FFF, and FFP in Northeast China were concentrated at 120–140 DOY, 260–280 DOY, and 110–170 days, respectively. The spatial distribution of frost indices exhibited significant spatial heterogeneity. (2) The LSF, FFF, and FFP showed significant trends of advancement, delay, and extension, with trends of −1.94 days/10 a, 1.72 days/10 a, and 4.21 days/10 a, respectively. (3) More than 80% of the LSF, FFF, and FFP of the sites showed trends of advancement, delay, and extension, with greater variability in the central part of Heilongjiang Province. (4) The FFF and FFP experienced an abrupt change in the late 1990s. (5) The correlation between latitude and LSF, FFF, and FFP was the strongest, with correlation coefficients of 0.77, −0.79, and −0.78, respectively. This study provides a comprehensive understanding of the changing characteristics of ground frost indices that impact agricultural production in Northeast China against the backdrop of climate change. The findings hold significant scientific value in guiding the adaptation of agricultural production layouts in Northeast China to the evolving climatic conditions.

Temporal and Spatial Variability of Ground Frost Indices in Northeast China

Frost is one of the most frequent, intense, and influential agrometeorological disasters that occurs frequently in Northeast China. The study of the spatiotemporal changes of ground frost is of great significance for farmers and policymakers in Northeast China, as it can inform decisions related to crop selection, planting schedules, and the development of regional climate adaptation plans. In this study, the spatiotemporal changes of frost indices (last spring frost (LSF), first fall frost (FFF), and frost-free period (FFP)) in Northeast China were analyzed from 1961 to 2020. Then, we investigated the mutation characteristics of the frost indices and their correlation with geographical factors. The results revealed that (1) the LSF, FFF, and FFP in Northeast China were concentrated at 120–140 DOY, 260–280 DOY, and 110–170 days, respectively. The spatial distribution of frost indices exhibited significant spatial heterogeneity. (2) The LSF, FFF, and FFP showed significant trends of advancement, delay, and extension, with trends of −1.94 days/10 a, 1.72 days/10 a, and 4.21 days/10 a, respectively. (3) More than 80% of the LSF, FFF, and FFP of the sites showed trends of advancement, delay, and extension, with greater variability in the central part of Heilongjiang Province. (4) The FFF and FFP experienced an abrupt change in the late 1990s. (5) The correlation between latitude and LSF, FFF, and FFP was the strongest, with correlation coefficients of 0.77, −0.79, and −0.78, respectively. This study provides a comprehensive understanding of the changing characteristics of ground frost indices that impact agricultural production in Northeast China against the backdrop of climate change. The findings hold significant scientific value in guiding the adaptation of agricultural production layouts in Northeast China to the evolving climatic conditions.

Spatial and Temporal Variations in the Coupled Relationship between Ecosystem Services and Human Well-Being in Gansu Province Counties and the Factors Affecting Them

Investigating the interplay between ecosystem services and human well-being is crucial for enhancing ecological conservation and achieving a superior quality of development. This research examined the spatial–temporal disparities in ecosystem services and human well-being across 87 counties in Gansu Province using the coupling coordination degree model and geographically weighted regression analysis. The key findings include the following: (1) Over two decades, from 2000 to 2020, ecosystem services in Gansu Province witnessed a financial appreciation of approximately CNY 29.32 billion. The distribution displayed a notable trend, with higher values in the southeastern counties, particularly in Gannan and Longnan, whereas lower values prevailed in the Hexi area. (2) The well-being of the populace experienced a substantial enhancement, registering a 75% increase during the same period, characterized by higher well-being indices in the north, especially in Hexi, and the lowest indices in Gannan and the Linxia sector of Longzhong. (3) The coupling coordination degree between ecosystem services and human well-being escalated from 0.412 to 0.587, transitioning from moderate dysfunction to moderate coordination over the two decades. Regions such as Hexi and Gannan recorded a moderate discordance in their coupling coordination, whereas regions including Longzhong, Longnan, and Longdong demonstrated a more stable, basic coordination. (4) Influences on the coupling coordination degree between county ecosystem services and human well-being displayed significant spatial variability, often aligned along distinct geographic bands. Economic and natural foundations were predominantly aligned southeast to northwest, while investment and urbanization levels were more prominent from west to east, and industrialization levels were distributed along an east–west axis.

Quantitative evaluation on the effects of the spatial variability in concrete materials on seismic damage of concrete gravity dams

The influencing mechanism of the spatial variability in concrete materials on the seismic damage of concrete gravity dams is still unclear, and existing methods for evaluating the seismic damage are insufficient. In this work, the effects of concrete’s spatial variability on the seismic damage distribution, energy dissipation, and deformation in concrete gravity dams are performed based on the damaged plastic model of concrete. Prior to the seismic damage analysis, the method for seismic inputting and the correlation function for realizing random fields of concrete materials are carefully determined. Based on the seismic damage analysis of the Koyna gravity dam, the tensile strength has the greatest influence on the seismic damage, followed by the elastic modulus and fracture energy. Aiming at the parameter of tensile strength, the decrease of correlation distance and the increase of the coefficient of variation increase the damage degree and complicate the damage distribution. A convenient and comprehensive damage profiling indicator is proposed to avoid the one-sidedness and evaluation error caused by using a single scalar damage value. The triangular area enclosed by the three individual damage indexes represents the comprehensive damage degree, and the shape change of the damage triangle indicates the change in the damage pattern of the dam. This damage profiling indicator is specifically designed to quantitatively distinguish and evaluate the damage degrees between a series of damage cases.

Foam density mapping via THz imaging

Plastic foams, near-ubiquitous in everyday life and industry, show properties that depend primarily on density. Density measurement, although straightforward in principle, is not always easy. As such, while several methods are available, plastic foam industry is not yet supported with a standard technique that effectively enables to control density maps. To overcome this issue, this paper proposes Terahertz (THz) time-of-flight imaging using normal reflection measurements as a fast, relatively cheap, contactless, non-destructive and non-dangerous way to map plastic foam density, based on the expected relationship between density and refractive index. The approach is demonstrated in the case of polypropylene foams. First, the relationship between the estimated effective refractive index and the polypropylene foam density is derived by characterizing a set of carefully crafted samples having uniform density in the range 70–900 kg/m3. The obtained calibration curve subtends a linear relationship between the density and the refractive index in the range of interest. This relationship is validated against a set of test samples, whose estimated average densities are consistent with the nominal ones, with an absolute error lower than 10 kg/m3 and a percentage error on the estimate of 5%. Exploiting the calibration curve, it is possible to build quantitative images depicting the spatial distribution of the sample density. THz images are able to reveal the non-uniform density distribution of some samples, which cannot be appreciated from visual inspection. Finally, the complex spatial density pattern of a graded foam sample is characterized and quantitatively compared with the density map obtained via X-ray microscopy. The comparison confirms that the proposed THz approach successfully determines the density pattern with an accuracy and a spatial scale variability compliant with those commonly required for plastic foam density estimate.

Methodology for evaluating soil carbon stock changes based on regional geochemical survey data: A case study in Huangpi, China

Soil carbon pools play a crucial role in the Earth's ecosystem, acting as significant carbon sinks and sources. Through a detailed analysis of soil carbon content using data from the Huangpi District in Wuhan, China, this research employs geochemical survey data, field replicates, and spatial autocorrelation information to establish an assessment model for soil carbon stocks. The model addresses the sources of errors and their effects on carbon pool changes, using both traditional statistical theories and geostatistical models to detect changes in carbon density and estimate carbon sources and sinks with minimized error ranges. Key findings indicate that sampling errors, influenced by small-scale spatial variability, are the primary source of observational inaccuracies in assessing total soil carbon and organic carbon, accounting for over 90% of the variation. Meanwhile, analytical errors are more significant when quantifying soil inorganic carbon content due to its lower concentrations. From 2001 to 2022, no significant changes were observed in the soil organic carbon stock in Huangpi District, while a modest increase in inorganic carbon was noted. The study highlights that increasing sample density beyond a certain threshold does not significantly affect carbon stock estimates or their error ranges, emphasizing the stability of the block kriging method in estimating regional carbon stocks.

Annual Dynamics of Shortwave Radiation as Consequence of Smoothing Previously Plowed Bare Arable Land Surface in Europe

This paper quantifies the annual dynamics of the shortwave radiation reflected from bare arable land as a result of smoothing previously plowed land located in three different agricultural subregions of the European Union and associated countries. This estimate takes into account the annual variation of the bare arable land area, obtained from Sentinel 2 satellite imagery; the spatial variability of soil units within croplands, obtained from digital soil and land-cover maps; and the laboratory spectral reflectance characteristics of these units, obtained from soil samples stored in the LUCAS soil database. The properties of the soil units, which cover an area of at least 4% of each subregion, were characterized. The highest amounts of shortwave radiation reflected under clear-sky conditions from air-dried, bare arable land surfaces—approximately 850 PJ day−1 and 1.10 EJ day−1 for land shaped by a plow (Pd) and smoothing harrow (Hs), respectively—were found in the summer around 8 August in the western subregion. However, the lowest radiation occurred in the spring on 10 April at 340 PJ day−1 for Pd and 430 PJ day−1 for Hs in the central subregion. The largest and the smallest amounts of this radiation throughout the year—only as a result of smoothing, by Hs, land that was previously treated by Pd—was estimated at 42 EJ for the western and southern subregions and 19 EJ for the central subregion, respectively.

Detection of spatial, temporal and spatiotemporal distribution of diarrhea incidence among under-five children in Central Gondar zone, Northwest Ethiopia: a time-series study (2019–2022)

Under-five children’s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children’s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

Assessment of the Solar Energy Potential over South America Estimated by CMIP6 Models in the Present and Future Climate

Developing the renewable energy matrix of South America (SA) is fundamental for sustainable socioeconomic growth and mitigating climate change's adverse effects. Thus, this study estimates changes in SA's solar irradiance and solar power potential using data from eight global climate models (GCMs) belonging to the Coupled Model Intercomparison Project—Phase 6 (CMIP6). Applying statistical downscaling and bias correction with the Quantile Delta Mapping (QDM) technique, we evaluate projected changes in the Concentrated Solar Power (CSP) and Photovoltaic Power (PVP) outputs under different future climate scenarios (SSP2-4.5 and SSP5-8.5). Historical simulations (1995–2014) are validated using ERA5 reanalysis and CLARA-A3 satellite observations. The QDM method reduces the models' systematic biases, decreasing the ensemble's errors by 50% across SA throughout the year. Regarding future decades (2020–2099), the CMIP6 ensemble shows spatial and seasonal variability in solar generation. For CSP, estimates suggest that regions traditionally favorable to solar energy generation (such as the Brazilian Northeast and portions of Chile) will maintain their suitable conditions during the 21st century, projecting a potential 1–6% increase (particularly under the SSP5-8.5 scenario in southern Chile and most of Brazil). Concerning PVP generation, the CMIP6 ensemble projects a rise of 1–4% (mainly under the SSP5-8.5 scenario in the Amazonia, Midwest, and Southeast Brazilian sectors). Moreover, trend analyses projected individually by the CMIP6 GCMs converge on an increasing PVP, mainly in Brazil's Amazonia and Midwest regions. In contrast, for South Brazil, approximately 84% of the projections show a negative trend (or no trend), evidencing unfavorable or uncertain conditions for solar generation development in the region. Despite the data and processes' inherent limitations, this study yields a first analysis of statistically downscaled projections from CMIP6 for solar power generation in South America, providing valuable information for energy sector decision-makers.

Indian ocean warming, extreme positive Indian Ocean Dipole events, and their impact on monthly Indian Monsoon rainfall from June to November in NMME models

Indian Ocean (IO) warming, the frequency of extreme positive Indian Ocean Dipole (EPIOD) events, and its impact on Indian monsoon rainfall (IMR) are more prominent in the recent era. A proper understanding of the relationship between the EPIOD and IMR is very important because two thirds of the people in India are dependent on agriculture and related economy. The primary focus of this study is on three main aspects: (i) analysing the monthly trend of sea surface temperature (SST) in the IO; (ii) the spatial variability of SST anomalies (SSTA) linked to the EPIOD and its impact on the IMR; and (iii) the factor responsible for the intensification of the EPIOD. These aspects are thoroughly assessed using 12 NMME models, comparing them with observations and reanalysis datasets from 1990 to 2020. The analysis reveals a warming trend in the central IO during June and July, with significant increases observed from August to November. Over the past 31 years, the central IO has experienced an approximate rise of 0.9 to 1 °C from June to November. Among the 12 models scrutinised, GFDL_FLOR_A, FLOR_B, and RSMAS_CCSM4 closely align with observed spatial trends. In contrast, models such as GFDL_SPEAR and NASA_GEOSS tend to overestimate these spatial trends. An increase in easterlies over the equatorial eastern IO was identified as a possible driver for the rising SST in the central IO during October and November. In response to EPIOD on the monthly spatial rainfall distribution over India, all NMME models often underestimate rainfall over the west coast and exhibit wet biases over the leeward side of the Western Ghats. Grid-wise skill scores for accumulated monthly rainfall demonstrated variations among NMME models, with GFDL_SPEAR, RSMAS_CCSM4, and other IC3 models showing higher skill. The interhemispheric pressure gradient (IHPG) has been identified as a precursor to EPIOD events. A positive IHPG from March to June is correlated with EPIOD occurrences from October to December. It is observed that half of the NMME models are capable of simulating a positive relationship between IOD and IHPG, consistent with the observed data.

Regional differences in the impact paths of climate on aboveground biomass in alpine grasslands across the Qinghai-Tibet Plateau

Alpine grasslands on the Qinghai-Tibet Plateau (QTP) play an essential role in water conservation, biodiversity protection and climate feedback, with aboveground biomass (AGB) serving as a crucial indicator of grassland health and functionality. While previous studies have independently explored the phenological differences, cumulative effects, and spatial variability of climatic impacts on biomass/productivity in alpine grasslands, the cascading effects regarding climate and phenology on AGB still present knowledge gaps. Here, using peak AGB measurements, remote sensing and gridded climate data in the QTP alpine grasslands during 2002–2018, we systematically analyzed the impact paths of climatic variables (i.e., cumulative precipitation, CP; growing degree-days, GDD) and phenology-mediated paths (start and peak date of the growing season, SOS and POS) on AGB and their regional differences. During the preseason (pre60) or the growing season (sos-pos), climate primarily directly impacted variations in AGB across different climatic regions, although a phenology-mediated path by which climate indirectly affected AGB existed (i.e., GDDsos-pos → POS → AGB). Three general patterns were revealed: In the plateau temperate arid regions, an increase in CPpre60 significantly promoted AGB (path coefficients w = 0.61–0.71), whereas an increase in GDDpre60 inhibited AGB (w = −0.42 ~ −0.49); In the plateau sub-cold regions, increases in both CPsos-pos and GDDsos-pos significantly promoted AGB, respectively (w = 0.46–0.81 and w = 0.37–0.70); Similarly, in the plateau temperate arid or semi-arid regions, increases in CPsos-pos also significantly promoted the AGB (w = 0.56–0.73). This study highlights that the water and heat accumulation mainly exert direct impacts on alpine grassland AGB across various climatic regions and phenological stages, providing insights into the mechanism driving AGB by climate and phenology during spring and summer.