Rainy Season Precipitation Research Articles

Triple oxygen isotope variability of precipitation in a tropical mountainous region

We present one year of δD, δ18O, d-excess, and Δʹ17O data from monthly precipitation at a Caribbean coastal site in Panama and from tap waters across the country to constrain geographic, climate, and moisture source controls on isotopic variability and better understand the sources and mechanisms of precipitation in Central America, a region facing significant modifications to the annual rainfall cycle due to climate change. Monthly precipitation δD ranged from –52.2 to +14.3 ‰, δ18O from –7.6 to +0.4 ‰, d-excess from +7.1 to +11.6 ‰, and Δ′17O from +11 to +29 per meg. Rainy season precipitation samples were found to have lower δD, δ18O, and d-excess due to Rayleigh distillation during the condensation and rainout of Pacific moisture over the central cordilleras, which results in decoupling between d-excess and Δ′17O. Outlier Δ′17O values during peak dry and rainy months may reflect seasonal changes in water vapor sourcing, from Caribbean to Pacific and/or locally recycled moisture, or may be a result of organic contamination. Tap water δD ranged from –82.3 to –14.3 ‰, δ18O from –11.6 to –2.4 ‰, d-excess from +4.3 to +12.2 ‰ and Δ′17O from –2 to +84 per meg. Tap water δD and δ18O values increase eastward due to lower orographic effects and Pacific and locally recycled moisture contributions to rainfall and greater secondary evaporation. Tap water d-excess and Δ′17O values are also de-coupled but lack clear spatial trends and controls. The results of this study indicate the promise of adding Δ′17O to the isotopic toolkit in tropical mountainous regions with complicated water cycling dynamics and provide a baseline for future triple oxygen isotope investigations.

Shifts of the pond area ratio for lowland polders: Implication for nutrient control

Shifts for natural ecosystems were increasingly concerned due to its profound impacts on ecosystem services. Ponds within lowland artificial watersheds (polders) play a critical role in nitrogen (N) and phosphorus (P) cycling. From the perspective of N & P control in management practices, it is needed to determine an optimal pond area ratio for polders. For this purpose, our study proposed a process-based modelling framework to investigate the response of polder N & P loss to pond area, and thus to determine the threshold value of pond area ratio to achieve maximum N & P reduction for polders. The proposed framework included two process-based models (NDP and PDP) specially developed to describe N & P dynamics in lowland polders. To evaluate the proposed performance of the framework, it was applied to 171 polders in Zhong River Watershed in Lake Taihu Basin, eastern China. Our investigation results revealed that the correlation between polder N & P reduction rate and pond area ratio had an abrupt shift of 13.6 %, 14.7 % for N & P, respectively. Therefore, polders with a pond area ratio of 13.6–14.7 % had the largest N & P reduction (5.27 and 0.19 kg/ha). Polder size affected P reduction rate, with smaller polders (<200 ha) showing a higher P reduction rate, while it did not affect N reduction rate. Compared with annual precipitation, rainy-season precipitation more significantly (P<0.01) determined polder N & P reduction. This study demonstrated the use of our process-based framework in characterizing the shifts for the pond area ratio for polders, and thus provided technical support for N & P control of lowland areas in water management practices.

The contrasting interannual variation and mechanism of the first rainy season precipitation before and after the South China Sea summer monsoon onset

The first rainy season (FRS) is usually considered as a whole when its precipitation variability and the underlying mechanism are discussed. This strategy does not consider the South China Sea summer monsoon (SCSSM) onset occurring within the FRS, which implies an abrupt change in nature and, thereby, the possible characteristics and mechanism of precipitation. To overcome this deficiency, this study divides the FRS into two stages before and after the SCSSM onset, revisits their interannual precipitation variability, and finds remarkable differences in the two FRS stages. During the FRS stage before the SCSSM onset, enhanced precipitation is accompanied by an anticyclone centered on the Indian Peninsula and extending further eastward to the Philippines, active synoptic-scale disturbances in the upstream regions of South China and an anomalous anticyclone in the mid-latitudes of East Asia. This configuration reinforces the warm-moisture air transportation from the Bay of Bengal and the South China Sea and cold air invasion from the mid-latitude regions to South China. It shapes the pattern of initial ascending motions over South China by both dynamical and thermal forcing. During the FRS stage after the SCSSM onset, in contrast, enhanced precipitation is accompanied by an anomalous anticyclone in the western North Pacific and the mid-latitude Silk Road pattern. This configuration reinforces warm-moisture air transportation from the tropical Western Pacific and cold air from the northwest direction to South China and shapes the initial ascending motion mainly by thermal forcing. The interannual precipitation variability is tied to a tropical Pacific sea surface temperature (SST) dipole for the FRS period before the SCSSM onset but not after. Such a difference is possibly due to the changes in the basic thermal-dynamical state of the atmosphere near the Maritime Continent and the tropical Northwestern Pacific and their different responses to the SST forcing before and after the SCSSM onset.

Assessing the Regional Climate Response to Different Hengduan Mountains Geometries With a High‐Resolution Regional Climate Model

AbstractThe Hengduan Mountains (HM) are located on the southeastern edge of the Tibetan Plateau and feature high mountain ridges (&gt;6,000 m MSL) separated by deep valleys. The HM region also features an exceptionally high biodiversity, believed to have emerged from the topography interacting with the climate. To investigate the role of the HM topography on regional climate, we conduct simulations with the regional climate model COSMO at high horizontal resolutions (at ∼12 km and a convection‐permitting scale of ∼4.4 km) for the period 2001–2005. We conduct one control simulation with modern topography and two idealized experiments with modified topography, inspired by past geological processes that shaped the mountain range. In the first experiment, we reduce the HM's elevation by applying a spatially non‐uniform scaling to the topography. The results show that, following the uplift of the HM, the local rainy season precipitation increases by ∼25%. Precipitation in Indochina and the Bay of Bengal (BoB) also intensifies. Additionally, the cyclonic circulation in the BoB extends eastward, indicating an intensification of the East Asian summer monsoon. In the second experiment, we remove deep valleys by applying an envelope topography to quantify the effects of terrain undulation with high amplitude and frequency on climate. On the western flanks of the HM, precipitation slightly increases, while the remaining fraction of the mountain range experiences ∼20% less precipitation. Simulations suggest an overall positive feedback between precipitation, erosion, and valley deepening for this region, which could have influenced the diversification of local organisms.

Multiple-Time-Scale Variations of the Record-Breaking Pre-Summer Rainfall over South China in 2022

Abstract South China encountered an exceptionally heavy pre-summer rainy season in 2022 with the regional precipitation ranking first in the past 44 years. This study aims to analyze the multiple-time-scale variations of precipitation in this pre-summer rainy season to shed light on the complex dynamics influencing pre-summer precipitation over South China. The findings reveal that the variation of precipitation was dominated by the 10–20-day oscillation during April–May, while interannual variation and trend during May–June. The 10–20-day oscillation of precipitation in pre-summer rainy season in South China demonstrates a strong association with cold-air activity, which can be traced back to the propagation of disturbances along a teleconnection, which represents the dominant mode of intraseasonal atmospheric circulation over Eurasia in high latitudes during April–May. This teleconnection plays a crucial role in facilitating cold-air invasion and triggering precipitation over East China and South China. The interannual component of abnormal precipitation is strong during May–June of 2022. It is primarily attributed to the abnormal highs in the lower troposphere over the subtropical western North Pacific and Japan. These abnormal highs are likely stimulated by the combined influences of Eurasian teleconnection propagation and cooling sea surface temperature anomalies (SSTAs) over the tropical central and eastern Pacific in the third year of a consecutive La Niña event. However, the universality of the impact of Eurasian teleconnection propagation on the abnormal high over Japan on interannual scale necessitates further investigation. Furthermore, there is a significant upward trend in pre-summer rainfall over South China, accounting for 38% of the total anomaly observed in 2022.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Spatiotemporal variation of rainy season span and precipitation recorded by lacustrine laminated pollen in the Tibetan Plateau during the past two millennia

Spatiotemporal variation of rainy season span and precipitation recorded by lacustrine laminated pollen in the Tibetan Plateau during the past two millennia

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province.

The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R2 = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R2 = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R2 = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =-0.19, p< 0.01) and the EOS advanced 2.8 days (r =-0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

Combination patterns of precipitation and its concentration degree determining the risk of dengue outbreaks in China

The amount and distribution of precipitation can determine dengue risk by affecting mosquito breeding; however, previous studies failed to incorporate this bivariate characteristic to examine dengue fever transmission. In the present research, nationwide data on daily dengue cases in China between January 2005 and December 2020 were obtained, and the top 12 cities accounting for 78% of total cases were selected for analysis. Precipitation patterns were quantified by weekly precipitation and precipitation concentration degree (PCD). On the basis of the combinations of both parameters, the exposure-response relationships of precipitation with dengue risk were established using generalised additive models, and the high-dengue-risk thresholds of precipitation patterns were further identified. Dengue burden was assessed by calculating attributable dengue cases. For the same amount of precipitation, the dispersed precipitation in the pre-summer rainy season leads to a higher dengue risk in autumn. The weekly precipitation of 100–150 mm and PCD of 0.2–0.4 constitute the highest risk scenario, and the average frequency of precipitation associated with dengue risk in 2013–2020 is 1.6 times higher than that in 2005–2012. A total of 3093 attributable dengue cases are identified. From 2005 to 2020, the amount of dispersed precipitation increased in southern and southwestern China and posed high dengue risks in central China. This study has improved the understanding of the health impacts of irregular rainfall under climate change. Our approach to identifying thresholds provides information for early warning systems and helps reduce the risk of dengue transmission in the long run.

Lowland artificial watersheds with unique nutrient transport: Response to natural and anthropogenic drivers

Excess nitrogen (N) and phosphorus (P) from watersheds is a key factor causing eutrophication and water quality deterioration in downstream lakes/rivers. However, tracking N & P loss and transport at a watershed scale is challenging especially in lowland artificial watersheds (polders) with complex hydrological processes. To address this challenge, this study selected 171 polders in lower reach of Yangtze River in China as the study area, and investigated the response of N & P loss to both natural and anthropogenic drivers using two process-based models (PDP & NDP) specifically developed for polders. Our modelling practices revealed that these polders had a high N & P loss intensity of 38.37 and 1.75 kg/ha/yr, respectively. Rainy-season precipitation and TN & TP concentrations in connected-rivers were characterized as sensitive factors determining polder N & P loss with a threshold value of 950 mm/yr, 0.2 and 0.02 mg/L, respectively. Compared with annual precipitation, rainy-season precipitation more highly (P < 0.001, R2 >0.8) determined polder N & P loss, with a loss-intensity increase of 6.05 and 0.28 kg/ha/yr for 100 mm increase of rainy-season precipitation. Polder N & P loss was highly related (P < 0.001) to the area of farmland and surface water within these polders. This study can potentially improve our understanding of N & P cycling for lowland polders, and can thus support N & P control in water management practices.

Characteristics of tropical–extratropical cloud bands over tropical and subtropical South America simulated by BAM‐1.2 and HadGEM3‐GC3.1

AbstractTropical–extratropical cloud bands are common in South America (SAm), contributing significantly to the total rainy season precipitation. Thus, it is fundamental that climate and weather forecast models correctly represent them and their associated dynamic aspects. Adopting an event‐based framework, we evaluate the performance of two global models in simulating the observed cloud bands over SAm: the Brazilian Global Atmospheric Model version 1.2 (BAM‐1.2) and the Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1). Both models reproduce the main characteristics of cloud bands and the dynamical aspects leading to their development and persistence. Nonetheless, the biases in precipitation during simulated cloud bands contribute more than 50% of the bias in total precipitation in some regions. BAM‐1.2 simulates fewer but more persistent cloud bands than observed; HadGEM3‐GC3.1 simulates weaker cloud band activity during early summer and more persistent events after January than observed. In all models, the biases in cloud band events arise from the interaction between biases in the basic state and the synoptic‐scale regional circulation. In the basic state, stronger upper level westerlies over the midlatitude South Pacific support the propagation of longer and slower Rossby waves towards subtropical SAm, increasing the duration of the cloud band events. This bias interacts with negative biases in the upper level westerlies over subtropical SAm, increasing the wind shear, hindering the propagation of synoptic‐scale Rossby waves into lower latitudes, and resulting in biases in the cloud band location, intensity, and seasonality. The application in this study of an event‐based framework robust to differences in model resolution and complexity enables the identification of small but critical biases in circulation. These biases are linked to synoptic‐scale rainfall system biases and help to explain the season total rainfall model biases.

Brazilian Annual Precipitation Analysis Simulated by the Brazilian Atmospheric Global Model

The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the country’s precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0∘× 1.0∘ and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year−1 in the extreme south to 1750 mm year−1 in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year−1, within the expected average, compared to observed values of 750–1500 mm year−1 (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved.

Effects of Soil Thermal Conductivity on Rainy Season Precipitation in Northern China

Abstract Parameterization schemes such as soil thermal conductivity (STC) have an important impact on precipitation simulation. The precipitation in the rainy season (April–September) is the main factor affecting aridification in northern China. However, it is unclear how STC affects precipitation simulation during the rainy season. In this study, comparative experiments were conducted using the regional climate model RegCM4.6 coupled with the third-generation land surface model NCAR CLM4.5 to assess the effect of the Johansen and Lu–Ren STC schemes on precipitation. The results show that the STC had a significant effect on the simulation of rainy season precipitation and its variation in northern China. The precipitation variation characteristics simulated by the Lu–Ren scheme were closer than that of the Johansen scheme to the observation. The difference in land surface temperatures (LSTs) simulated by the two STC schemes could be a major cause of the sensitivity in the simulated precipitation. When the local LST increases by 1 K, precipitation decreases by 5–30 mm in most areas of northern China. The numerical experiments revealed that the rise of LST increases the longwave radiation, reduces the surface net radiation, and causes the redistribution of sensible and latent heat flux, forming local water vapor and thermal conditions that are not conducive to precipitation. Moreover, the difference of LST significantly changes the 500-hPa large-scale circulation field, the 700-hPa vapor transportation, and its divergence. The combined action of local heat, water vapor, and large-scale circulation factors reduces the precipitation in the rainy season. On the other side, the variation of the East Asia summer monsoon (EASM) affects the soil water content. In addition, a new STC scheme was added to NCAR CLM4.5, promoting the development of this land surface model.

Divergent litterfall nutrient responses to rainfall seasonality revealed through long-term observations in a tropical dry forest

Abstract Long-term climate and vegetation data were used to determine the role of rainfall variability and its seasonal distribution on litterfall nutrients. Based on a 20-year data set on rainfall (range 334–1,506 mm per year) and litterfall nutrients from old-growth tropical dry forest (TDF) in Mexico, we examined litterfall N and P concentrations from the rainy and dry seasons in response to rainfall in the rainy (June–October) and the dry (November–May) seasons, the latter referred to as out-of-season precipitation (OSP). Rainy-season litterfall N concentrations, but not P concentrations nor N:P ratios, changed positively (p &lt; 0.001) in response to rainy-season precipitation. Dry-season litterfall N concentrations and N:P ratios, but not litterfall P, increased (p ≤ 0.02) in response to rainfall from the preceding rainy season. N:P ratios of dry-season litterfall in years with OSP were higher only during dry years and N concentrations decreased in wet years (p &lt; 0.05). The narrow range in dry-season litterfall P concentrations (1.00–1.15 mg g-1), irrespective of rainfall amount and OSP, suggests P conservation. The variation in litterfall N, but not litterfall P, in response to rainfall variability reveals a divergent nutrient response along steep changes in water availability in this TDF.

Climate change has limited effect on the growth of Afzelia africana Sm. and Anogeissus leiocarpus (DC.) Guill. and Perr. in riparian forests in the savannas of Ghana

Abstract The growth of trees in riparian forests in semi-arid savannas is resilient to the natural variations in temperature and precipitation due to the availability of riverine water. Climate change can nevertheless, intensify the evapotranspiration of tree species, altering biodiversity, plant productivity and ecosystem services. Understanding the growth response of riparian tree species to climate change is, therefore, critical for their management and conservation. Here, we used 23 cross-dated stem discs of Anogeissus leiocarpus (DC.) Guill. and Perr. and Afzelia africana Sm. randomly sampled from riparian forests in the humid and dry savanna regions of Ghana to assess their growth response to climate change. A generalized additive mixed model (GAMM) was used to integrate species-specific basal area increments to an array of explanatory variables that may affect growth, including tree size and seasonal temperature and precipitation between 1982 and 2013. We observed significant association between tree size, rainy and dry season temperatures and precipitation variables, and changes in tree growth. Despite the strong fluctuations in tree growth over time, the estimated growth rates of the species from the residuals of the GAMMs showed no significant change in growth. Our findings suggest that these riparian forests are highly resistant to weather extremes and therefore, might persist (up to a certain point) even if climate change continues to intensify.

Climatic conditions and household food security: Evidence from Tanzania

Food security and adequate nutrition are critical for achieving progress toward sustainable development. Two billion people worldwide experience moderate to severe food insecurity, and rates of hunger have increased over the past several years after declining steadily for decades. The FAO attributes this increase in large part to climate change, though empirical evidence on the relationship between climatic conditions and food security remains limited. We examine this question by linking nationally representative longitudinal data from four rounds of the Tanzania National Panel Survey to high-resolution gridded climate data. We then estimate a set of fixed effects regression models to understand the linkages between recent rainy season precipitation and temperature and two indicators of household food security: Food Consumption Score (FCS) and reduced Coping Strategies Index (rCSI). We find that low rainfall—particularly dry and cool conditions—is negatively associated with food security. Moving from a typical rainfall year to a particularly dry one increases the risk of being food insecure on both measures simultaneously by 13 percentage points. This suggests that a lack of rainfall impedes households’ ability to access food, likely through reduced agricultural production and increased food prices, leading to lower dietary diversity and food shortages. Vulnerability is higher among households with fewer working age members, suggesting that households with a greater supply of labor can better withstand droughts. As climate change alters precipitation and temperature patterns over the coming decades, policies to increase resilience will be critical for improving food security, particularly among populations heavily reliant on agriculture.

Large‐Scale Afforestation Enhances Precipitation by Intensifying the Atmospheric Water Cycle Over the Chinese Loess Plateau

AbstractAfforestation is an appropriate approach to control soil erosion, but consumes extra water resources. It also exerts feedbacks on local precipitation, affecting the water availability for water‐limited areas. The Chinese Loess Plateau (LP) has implemented the world's largest afforestation program to mitigate severe soil erosion. However, it remains unclear how the atmospheric water cycle responds to large‐scale afforestation under climate change. Here we show that afforestation elevates precipitation in rainy season by intensifying the atmospheric water cycle over the LP. Results show the upward trend of vegetation coverage fraction increased by 3.15% decade−1 and climatological precipitation increased by 54.62 mm after launching the large‐scale afforestation in 1999. Such afforestation also enhanced evapotranspiration by 22.56 mm. We found the moisture inflow and outflow continuously decreased, but more atmospheric moisture was retained since the rate of decline of inflow was less than that of outflow. Using a dynamic precipitation recycling method, we explored changes in precipitation recycling and components of the atmospheric water budget. The precipitation recycling ratio increased from 9.16 ± 0.35 to 10.18 ± 0.28% after the afforestation. Both external and internal branches of the atmospheric water cycle have been strengthened, and their contributions to the increased precipitation are 84.93% and 15.07%, respectively. The afforestation caused a more humid atmosphere, heightened soil moisture‐vegetation‐precipitation feedback, stronger moisture convergence, and more clouds; these changes favored precipitation generation. This study advances the understanding of how afforestation affects regional climate from a precipitation recycling perspective, further assisting decision‐makers to establish sustainable afforestation strategies.

Persistent warm and dry extremes over the eastern Mediterranean during winter: The role of North Atlantic blocking and central Mediterranean cyclones

AbstractPersistent dry winter events over the eastern Mediterranean (EM) disrupt the rainy (winter) season precipitation patterns and dramatically reduce water availability in the region. Here we objectively identify persistent dry, warm winter events over Israel, and apply a Lagrangian approach to three case‐studies, aiming to understand the relation between the synoptic setting, precursor Rossby waves, and how the dry, warm conditions emerge. Self‐organizing map classification of atmospheric profile data over Israel shows that the most persistent (at least 5 days) dry and warm winter events are induced by a stagnant upper tropospheric ridge over the EM, a pronounced trough or cut‐off low over the western/central Mediterranean and blocking over the North Atlantic. The leading mechanisms of the warm and dry conditions are: adiabatic heating during slantwise subsidence, heating by sensible heat fluxes from the surface, and advection of warm and dry continental air. The relative contributions of the mechanisms and geographical locations of the back trajectories vary greatly both within and among the events. In addition, the Atlantic blocking and EM ridge are supported by upstream diabatic heating in warm conveyor belts of North Atlantic cyclones and Mediterranean cyclones, respectively. A quantitative classification of the back trajectories above the Atlantic and the Mediterranean has shown transitions from adiabatic to diabatic contributions and vice versa along these paths. The sequential relation between Atlantic ridge (or block), trough over Europe and ridge over EM and/or west Russia places local persistent warm and dry extremes into a large‐scale context and thus provides new opportunities for understanding their predictability at weather and climate scales.

Rainy season precipitation forecasts in coastal Peru from the North American Multi‐Model Ensemble

AbstractWe assessed operational forecasts of rainy season (January–March [JFM]) precipitation over coastal Peru from seven global climate models (GCMs) in the North American Multi‐Model Ensemble (NMME) from 1982 to 2020. Similar to previous representations by GCMs, regional climate models, and atmospheric reanalyses, the NMME models produce excessive precipitation (more than twice the observed values) over all of coastal Peru. Based on a set of commonly used metrics (correlation, mean error, RMSE, and Heidke skill score), there is a wide range in accuracy among the NMME models. As expected, forecast accuracy degrades with increasing lead time from December to July, but the rate of decline varies greatly by region and among models. The cause(s) of differences in model performance remains unclear, but resolution does not appear to be a significant factor. Highest forecast skill is found for southwestern Peru (correlations with observations are significant at all lead times), probably because of its stronger relationship between rainfall and El Niño–Southern Oscillation (ENSO), and lowest skill occurs for west‐central Peru (insignificant correlations at all lead times). The distribution of interannual rainy season precipitation in northwestern Peru is extremely positively skewed (skewness &gt;2), making evaluation of forecasts difficult from only a few years that deviate strongly from the mean. Improved correlations with observations across regions are generally found by subsetting multi‐model mean forecasts using the better‐performing models. Over northwest Peru at short lead times, NMME rainy season precipitation forecasts outperform a simple statistical correlation between JFM rainfall and ENSO, but not at longer leads, while NMME's forecasts for west‐central Peru underperform statistical correlations at all lead times. Over southwest Peru, the NMME models are consistently more skilful than a statistical correlation, and these differences become larger with increasing lead time.

Moisture sources for the weather pattern classified extreme precipitation in the first rainy season over South China

AbstractMoisture transport, associated with moisture sources and synoptic‐scale weather conditions, is a key dynamic process of precipitation events. Using the K‐means clustering method and the FLEXPART Lagrangian particle dispersion model, this paper investigates moisture contributions from different source regions to extreme precipitation in the first rainy season (hereafter FRS) over South China. In average, land regions contribute more to the FRS extreme precipitation over South China than the ocean regions. The main source regions are Southeast Asia (22.7%), South China (17.2%), the South China Sea (14.3%), and the Bay of Bengal (8.3%). Extreme precipitation events are classified into three types by the K‐means clustering based on 850 hPa geopotential height, which are all characterized by an anomalous low‐pressure system over South China with varying intensity and locations. The distribution of geopotential height anomaly for Type I (30.3%) is characterized the low trough extending from Japan to South China, while Type II (42.5%) and Type III (27.2%) are characterized by “west negative–east positive” and “north positive–south negative” patterns over East Asia with anomalous cyclone over South China, respectively. The much larger contribution of land sources than ocean regions are mainly concentrated in Type I and Type III, of which the contribution from each source region is similar. Ocean sources play a more important role in Type II and are mainly from the Indian Ocean (16.2%) associated with the onset of South China Sea summer monsoon.