Precipitation Variability Research Articles

Spatio-temporal analysis of American Tegumentary Leishmaniasis incidences in the Brazilian state of Amazonas: 2011 to 2022

This study aimed to conduct a spatio-temporal analysis of tegumentary leishmaniasis occurrences in the Amazonas state, Brazil. An ecological study encompassing time series and spatial analysis was performed, exploring the geographic distribution and temporal trends of American Tegumentary Leishmaniasis (ATL) in Amazonas between 2011 and 2022. Secondary data extracted from the Department of Informatics of the Unified Health System (DATASUS) were utilized for this analysis. The study evaluated the relationship between disease cases and environmental/climatic variables (deforestation, temperature, precipitation, and relative humidity). Over the study period, 19,730 cases of tegumentary leishmaniasis were recorded, averaging an incidence of 41.4/100,000 inhabitants across the 62 municipalities of Amazonas state. Disease intensity varied with seasons. Generally, Amazonas state displayed a declining trend in ATL cases. However, certain municipalities, notably Rio Preto da Eva and Presidente Figueiredo, exhibited high incidence rates, while Canutama, Envira, Eirunepé, and Pauini municipalities demand closer attention due to their demonstrated increasing temporal trend of ATL cases. The analysis indicated a correlation between the number of ATL cases reported and relative humidity as well as precipitation. These findings underscore the significance of tegumentary leishmaniasis as a public health issue in the region and emphasize the necessity for public initiatives aimed at preventing this endemic illness.

Assessment of Rainfall and Temperature Trends in the Yellow River Basin, China from 2023 to 2100

China’s Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB’s ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and the Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations in precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5° × 0.5° (i.e., about 55 km × 55 km), which is improved to 1 km × 1 km by the downscaling of Delta and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 °C/10a, 0.3 °C/10a, 0.5 °C/10a, and 0.7 °C/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast–northwest.

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

This 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.

Contribution of Recycled and External Advected Moisture to Precipitation and Its Inter‐Annual Variation Over the Tibetan Plateau

AbstractIn this study, we performed a high‐resolution simulation using the Weather Research and Forecasting model, integrated with water vapor tracers, covering the years 2005–2019. Our objective was to obtain deeper insights into the spatiotemporal dynamics of external advected and local evaporative water vapor, and to elucidate their impact on precipitation patterns across the Tibetan Plateau (TP). Our findings underscore that a significant proportion of TP's precipitation originates from external advected water vapor, primarily entering through the western and southern boundaries. During summer, stronger zonal and meridional water vapor transport, driven by prevailing westerly winds and the Asian monsoon, significantly influences seasonal and spatial precipitation variations. Additionally, we observed that the inter‐annual variation of precipitation is intricately linked to changes in the net water vapor influx, modulated by alterations in atmospheric circulation. We also analyze the Precipitation Recycling Ratio (PRR) which refers to the proportion of precipitation originated from local evaporative water vapor to the total precipitation, revealing distinctive elevation‐dependent variations aligned with grassland distribution. Notably, PRR exhibits asynchronous shifts with precipitation at different timescales, potentially linked to soil moisture‐precipitation feedback at intra‐annual scales. Moreover, the investigation highlights that inter‐annual variations in PRR are primarily linked to the inflow and outflow of water vapor as well as wind strength at 500 hPa, particularly prominent during colder seasons, while thermal factors carry comparable weight to dynamical factors in warmer seasons.

June insolation gradient and ice sheet forcing on Qaidam precipitation during the middle Piacenzian warm period

Whether the East Asian summer monsoon (EASM) migrated westward into the Asian interior during past warm periods is still unclear but is important for predicting precipitation variability in the Asian interior under future warming. Recent precipitation records from the Qaidam Basin in the Asian interior suggest that the EASM further migrated westward into the western Qaidam Basin during the middle Piacenzian warm period (mPWP: 3.264–3.025 Ma), indicating that precipitation in this ecologically fragile region was greater than it is currently. However, these precipitation records reveal weak sensitivity to insolation forcing, which is inconsistent with records from monsoon-influenced regions, hindering our understanding of orbital timescale precipitation variations in the Qaidam Basin during this Pliocene warmth. In this study, we report high-resolution (~3 kyr) magnetic parameter-based records and medium grain size records from the GS section of the western Qaidam Basin during 3.25–2.95 Ma. We interpret magnetic parameters and grain size as precipitation proxies based on their sensitivity to precipitation variations. Our precipitation records suggest that the EASM precipitation was likely the precipitation source for the Qaidam Basin during the mPWP. At the orbital timescale, precipitation records reveal dominant 20-kyr precessional cycles, consistent with records from the Chinese Loess Plateau and the South China Sea, suggesting that the June insolation gradient controlled precipitation variations in the Qaidam Basin during the mPWP. At the 40-kyr band, precipitation records show in-phase relationships with ice sheets, and we infer that Antarctic ice sheet forcing seems to also influence Qaidam precipitation via atmospheric or marine processes. These results provide the first evidence demonstrating that Qaidam precipitation was extremely sensitive to the June insolation gradient and ice sheets during the mPWP, improving our understanding of orbital timescale precipitation variations in the Asian interior during past warm periods.

Global Flash Droughts Characteristics: Onset, Duration, and Extent at Watershed Scales

AbstractAddressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

CROP IMPROVEMENT THROUGH DIFFERENT MEANS TO ADDRESS CLIMATE CHANGE AND FOOD SECURITY

This study evaluated several crop development techniques in addition to the effects of climate change on productivity in agriculture. Climate affects agricultural productivity and the distribution of crops worldwide. Given the current and projected variations in temperature, precipitation, and carbon dioxide concentrations, it is unclear whether agroecosystems will be able to produce enough food to meet the world's needs. Crop output, water productivity, and soil water balance are frequently altered by climate change. Soil moisture content and groundwater levels will be affected by the immediate effects of global warming-induced changes in temperature and precipitation patterns. Agricultural output is affected by several factors, such as crop variety, growing circumstances, soil deterioration, and water availability. Modern agricultural systems use genetics, biotechnology, plant breeding, environmental monitoring, and adjustments to management techniques to adapt to climate change. On the other hand, without a more thorough approach to agricultural systems, development cannot proceed. A strategy like this would mean growing amounts of a wider variety of superior crops and advancing equity, sustainability, nutrition, and food security in the future.

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

A new chronology of Cedrela fissilis (Meliaceae) for Southern Brazil: Combining classical dendrochronology and radiocarbon dating

Cedrela fissilis is a tree species widely distributed in the tropical biomes of South America. This species has visible annual growth rings and can live for several centuries. The present study aims to (i) develop a chronology of C. fissilis to the Southern Brazilian Plateau using traditional dendrochronological methods, (ii) validate the dendrochronological dating using the radiocarbon (14C) bomb pulse method, and (iii) determine the influence of temperature and precipitation variations and their teleconnections with the tropical Pacific Ocean temperatures on the annual radial growth of this species. The ring width chronology was developed using 24 C. fissilis trees. The Schulman years of 1957, 1962, 1963, 1966, 1969 and 1974 were independently dated using the 14C bomb pulse methodology by accelerator mass spectrometry (AMS). Tree-ring indices were compared with temperature and precipitation records from stations around the study forest. The chronology covers the period 1907–2018 (111 years) and is well replicated (> 10 trees) from 1941 onwards. Statistics commonly used in dendrochronology indicate that the chronology is highly reliable and of good quality (mean series intercorrelation r = 0.49; Rbar = 0.30; EPS = 0.86; MSI = 0.40). The 14C bomb pulse of selected calendar years showed that the trees were accurately dated using the classical cross-dating approach. Precipitation from November to December (wet period) is positively correlated with tree growth (r = 0.36, n = 49; p<0.05). In addition, variations in temperature from May to July are positively correlated with ring width (r = 0.39, n = 49; p<0.05), suggesting that C. fissilis growth is favored by abundant rainfall during the growing season and above-average winter temperatures. Interannual variation in the chronology is partially modulated by El Niño 3.4 (East Central Tropical Pacific Sea Surface Temperature) during Oct-Dec (r = 0.27, n = 68, p<0.05). The growth of C. fissilis trees is directly dependent on climate variability, suggesting that more abundant precipitation and higher winter temperatures, as projected for the future climate of southern Brazil, will have a positive effect on tree growth. However, prolonged droughts and high temperatures during the growing season will have a negative impact on tree growth, even in humid forests with high soil moisture content.

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769kgha-1) and water productivity (1,050 to 1,445kgm-3) with minimal application depths (< 150mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Fusarium mycotoxins: The major food contaminants.

Mycotoxins, which are secondary metabolites produced by toxicogenic fungi, are natural food toxins that cause acute and chronic adverse reactions in humans and animals. The genus Fusarium is one of three major genera of mycotoxin-producing fungi. Trichothecenes, fumonisins, and zearalenone are the major Fusarium mycotoxins that occur worldwide. Fusarium mycotoxins have the potential to infiltrate the human food chain via contamination during crop production and food processing, eventually threatening human health. The occurrence and development of Fusarium mycotoxin contamination will change with climate change, especially with variations in temperature, precipitation, and carbon dioxide concentration. To address these challenges, researchers have built a series of effective models to forecast the occurrence of Fusarium mycotoxins and provide guidance for crop production. Fusarium mycotoxins frequently exist in food products at extremely low levels, thus necessitating the development of highly sensitive and reliable detection techniques. Numerous successful detection methods have been developed to meet the requirements of various situations, and an increasing number of methods are moving toward high-throughput features. Although Fusarium mycotoxins cannot be completely eliminated, numerous agronomic, chemical, physical, and biological methods can lower Fusarium mycotoxin contamination to safe levels during the preharvest and postharvest stages. These theoretical innovations and technological advances have the potential to facilitate the development of comprehensive strategies for effectively managing Fusarium mycotoxin contamination in the future.

Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors

Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, shiny.io). The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Genetic Divergence and Evolutionary Adaption of Four Wild Almond Species (Prunus spp. L.)

Parallel evolution usually occurs among related species with similar morphological characters in adaptation to particular environments. Four wild almond species (Prunus) sharing the character of dry mesocarp splitting are distributed in China, most of which occur in arid Northwestern China. In the present study, we aimed to clarify the phylogenetic relationship, evolutionary history, and environmental adaptation of these wild almond species based on genome-wide SNP data and chloroplast genomes. Chloroplast phylogeny showed P. pedunculata and P. tenella were clustered with wild cherry species (Prunus), while P. mongolica and P. tangutica were clustered with wild peach species (Prunus). Genomic phylogeny suggested P. tenella formed an independent clade. An ABC-RF approach showed P. pedunculata was merged with P. tenella and, then, diverged from the ancestor of P. mongolica and P. tangutica. P. tenella was split from other wild almond species at ca. 7.81 to 17.77 Ma. Genetic environment association analysis showed precipitation variables contributed the most to genetic variations between P. mongolica from an arid environment and P. tangutica from a humid environment. Finally, a total of 29 adaptive loci were successfully annotated, which were related to physiological processes in response to abiotic stresses. Inconsistent genomic and chloroplast phylogenetic positions of P. tenella suggested this species could have originated from historical hybridization among different clades of Prunus. Physiological mechanisms promoted P. mongolica in adapting to the arid environment in Northwestern China.

Analysis of extreme precipitation variation characteristics in mountain grasslands of arid and semi-arid regions in China

Introduction: With global warming, the disaster losses caused by extreme precipitation events are increasing. The poor natural conditions and climate change make the arid and semi-arid mountainous grassland area a sensitive region of climate change. The study on the spatio-temporal variation characteristics of extreme precipitation events in this region is helpful to improve the ability of climate prediction and disaster prevention and reduction in grassland.Methods: Based on the daily precipitation data of four meteorological monitoring stations in the Yinshanbeilu from 1970 to 2020, the trend analysis, M-K test and wavelet analysis were used to select seven typical extreme precipitation indicators to analyze the temporal and spatial characteristics of extreme precipitation.Results and Discussion: The results showed that the precipitation in the Yinshanbeilu increased in the past 51a, and the number of heavy rain days increased significantly. The significance test of CDD and CWD showed that the number of continuous dry days and continuous wet days decreased abruptly. The spatial analysis showed that the high value areas of R95p, R95d and PRCRTOT were all located in Siziwang Banner, and it could be concluded that the extreme precipitation risk was the highest in Siziwang Banner, while the low value areas of SDII, Rx1day, R95p and PRCRTOT were all located in Sonid Right Banner, which could be inferred that the Sonid Right Banner was relatively dry. The first main cycle of the seven indexes of extreme precipitation almost runs through the whole time series, and the starting point of the minimum main cycle changes inconsistent. In addition to the number of consecutive dry days, the other indices have a good correlation with annual precipitation, flood season and monthly precipitation from June to September, and July is the peak period of extreme precipitation events.

Rainfall Potential and Consequences on Structural Soil Degradation of the Most Important Agricultural Region of Mexico

This study investigates the historical variability in annual average precipitation in the northwest region of Mexico, aiming to evaluate the cumulative impact of precipitation on soil degradation and associated risks posed by rainfall. Despite being known as “The Agricultural Heart of Mexico”, the region’s soil has experienced significant damage to its granulometric structure due to unpredictable rainfall patterns attributed to climate change. Sixteen historical series of average annual rainfall were analyzed as stationary stochastic processes for spectral analysis. The results revealed exponential decay curves in each radial spectrum, indicating a linear relationship between frequency and amplitude. These curves identified initial impulses correlated with moments of severity for structural damages caused by rainfall-induced degradation. The degradation process, exacerbated by water stress, accelerates, as evidenced by maps illustrating approximately 75% soil damage. In the context of climate change and the uncertainty surrounding soil responses to extreme meteorological events, understanding this phenomenon becomes crucial. Recognizing the dynamic nature of soil responses to environmental stressors is essential for effective soil management. Emphasizing the need to employ numerical processes tailored to new environmental considerations related to observed soil damages is crucial for sustainable soil management practices in any region.

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

Variation in the strength and stationarity of southern longleaf pine seasonwood climate-growth relationships

Climate change is predicted to cause asymmetric warming and increased precipitation variability across different seasons. These changes and their effects may be captured in subannual seasonwood measures of tree growth (i.e., earlywood, latewood). Longleaf pine is a foundational tree species throughout the broad southeastern U.S. Its latewood growth may be more variable and sensitive to climate change than earlywood, but prior studies have geographical and ecological sampling limitations. Here, we use dendroecological methods to develop ring-width and seasonwood (earlywood, latewood, and adjusted latewood) chronologies and analyze climate-growth relationships for longleaf pine from eight sites in the Southern Coastal Plain of Florida, USA. Sites spanned a range of environmental conditions to generate an ecologically representative dataset. We evaluated chronology strength and variability and determined monthly and seasonal correlations with precipitation and temperature. We furthermore evaluated climate-growth stationarity using a moving response function and bootstrapped transfer function stability tests. We found adjusted latewood chronologies were most climatically sensitive although totalwood chronologies had higher interseries correlations. Totalwood measurements were more variable than early- or latewood year to year. Tree growth was positively correlated with summer precipitation at six out of eight sites and negatively correlated with late summer maximum temperatures at six out of eight sites. Negative relationships between growth and maximum temperature occurred earlier in the year at southern sites and the positive effect of June precipitation on tree growth was notable, found at six out of eight sites. However, the strength and significance of climate-growth relationships were non-stationary through space and time, with shifts largely occurring post-1950s. Longleaf pine at its southern range extent was less sensitive to climate than more northern populations, which may indicate non-linear responses to warming captured at the species’ range margin. Our results highlight dynamic climate-growth relationships related to the broad range of environmental conditions where longleaf pine is found.

Southward shift of the westerly jet intensified late Holocene dust storms on the Tibetan Plateau

The climate change on the Tibetan Plateau (TP) exhibits a north-south dipole trend during the Holocene. However, it is unclear whether regional differences exist in the variations and driving mechanisms of dust storm activities across the entire TP, and whether these mechanisms align with those found in monsoonal northern China. Here, we present a high-resolution Holocene record of dust storms from an undisturbed lake in the research gap area of the TP, and compile previously published records from other TP regions. Our findings indicate that the dust storm activity increased from the early Holocene to the late Holocene, peaking after 5 cal ka BP. Importantly, our comparison with other dust storm records reveals a unified intensification of dust storm activity across the entire TP during the late Holocene, a pattern distinctly different from the north-south dipole trend of precipitation variation. Moreover, this unified pattern of dust storm intensification across the TP cannot be explained by the decreasing intensity of the East Asian winter monsoon (EAWM) during the Holocene. The observations suggested a limited role for precipitation and EAWM in modulating TP dust storms on the millennial timescale during this period. By contrast, the increases in dust storms on the TP after 5 cal ka BP correlates well to the southward shift of the westerly jet driven by the North Atlantic Oscillation (NAO). As the NAO transitioned to a negative phase during the late Holocene, a meandering westerly jet shifted southward to the interior of the TP, intensifying wind intensity and leading to dust storm outbreaks.