Spatial Patterns Of Precipitation Research Articles

Compound precipitation and wind extremes over Europe and their relationship to extratropical cyclones

Extratropical cyclones and their associated extreme precipitation and winds can have a severe impact on society and the co-occurrence between the two extremes is important when assessing risk. In this study the extremal dependency measure, χ, is used to quantify the co-occurrence of extreme precipitation and wind gusts, and is investigated at individual grid points and spatially over Europe. Results using three observational datasets and a higher spatial and temporal resolution version of ERA5 than previously used confirm previous studies. Over Europe high co-occurrence is found over western coasts and low co-occurrence is found over eastern coasts. All datasets have qualitatively similar spatial patterns over most regions of Europe excluding some regions of high topography where ERA5 χ values are much larger. ERA5 represents the timings of daily extreme co-occurring events well, compared to observations. The differences in precipitation accumulation timescales are also accounted for by considering hourly, 6, 24 and 48 hourly co-occurrence. In a few regions co-occurrence changes with longer accumulations, indicating the different speeds and sizes of weather systems affecting these regions. χ in most regions has little increase by allowing a 24 h lag and lead between the precipitation and wind, with a few exceptions where χ is increased by up to 24%. Regions with the larger of these increases are on or around elevated topography. Using an objective feature tracking method, insight into the spatial pattern of extreme precipitation and wind within cyclones over Europe is given. As well as suggesting how many hours apart the extremes occur from one another in a particular location. Extreme co-occurring events are associated with cyclones far more of the time than non extreme events. Given an extreme co-occurring event the chance of a cyclone being within 1110 km is more than 70% for much of Europe. Regions with low co-occurrence have extremes caused by different weather systems and regions with large co-occurrence have both extremes caused by the same weather system. Cyclones linked to extreme events, particularly co-occurring and extreme wind, have larger intensity than those not and for most of Europe these cyclones also have faster mean speed.

PERSIANN-CCS-CDR, a 3-hourly 0.04\xb0 global precipitation climate data record for heavy precipitation studies

Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

Variability of the precipitation over the Tianshan Mountains, Central Asia. Part I: Linear and nonlinear trends of the annual and seasonal precipitation

AbstractThe Tianshan Mountains, with their status as ‘water tower’, receive quantities of precipitation that are among the highest in Central Asia. There are considerable knowledge gaps regarding the understanding of spatial and temporal patterns of precipitation over this water‐scarce region. Based on the Global Precipitation Climatology Centre (GPCC) data set, this study evaluated the precipitation variations over Tianshan Mountains on different time scales by using Mann‐Kendall (M‐K) test approaches and the ensemble empirical mode decomposition (EEMD) method. The results show that (a) most parts of Tianshan experienced increasing annual precipitation during 1950–2016 while Western Tianshan, which is the wettest region, faced a downtrend of precipitation during the same 67 years; (b) the annual precipitation in the Tianshan Mountains has exhibited high‐frequency variations with 3‐ and 6‐year quasi‐periods and low‐frequency variations with 12‐, 27‐year quasi‐periods. On the decadal scale, Tianshan had two dry periods (1950–1962 and 1973–1984) and two wet periods (1962–1972 and 1985–2016) and has experienced a tendency of continuous humidification since 2004; (c) the precipitation over the Tianshan Mountains shows a strong seasonality. In total, 63.6% of all precipitation falls in spring and summer. Distinctive differences are found in seasonal precipitation variations among the sub‐Tianshan regions. Obvious upward trends of precipitation over Eastern Tianshan were found in all seasons, with Eastern Tianshan entering a humid period as early as 1986. Northern and Central Tianshan experienced a decreasing trend in summer and spring. However, in the other seasons, those two sub‐Tianshan regions have been in humid periods since the 1990s. The precipitation over Western Tianshan showed an upward trend in summer and autumn. The obvious downward trends in spring and winter have led to dry periods in these two seasons from 1997–2014 to 2008–2016, respectively.

Impact of Higher Resolution on Precipitation over China in CMIP6 HighResMIP Models

Climate models participated in the High Resolution Model Intercomparison Project (HighResMIP) of Coupled Model Intercomparison Project 6 (CMIP6) are evaluated to reveal the impact of enhanced resolution in simulating the climatological distribution of precipitation over China. The multi-model mean (MME) of five models with 30–50 km horizontal resolution in the atmosphere (MME-50) had a better performance in reproducing the observed spatial patterns of precipitation over Northwest China and Southwest China than the MME of their lower-resolution (70–140 km) models (MME-100). Such an improvement is mainly attributed to the topographical rainfall reproduced by the higher-resolution models, which have the superiority of reproducing the local vertical circulation over the complex terrain. The MME-50 also improves the skill score of the 850-hPa southwesterly of the Indian-Burma trough relative to the MME-100, which may contribute to better simulation skill of precipitation over Southwest China. The MME-50 (0.92) has a close skill score to the MME-100 (0.91) in the simulation of East Asian summer monsoon, which explains why the MME-50 cannot improve the simulation skill of the precipitation over Southeast China and Northern China. The skill score of the precipitation over the Tibetan Plateau (TP) simulated by the MME-50 is even lower than the MME-100, revealing that simulating the climate over the high plateau is still a challenge for the high-resolution models.

Calcium Positively Mediates Blue Light-Induced Anthocyanin Accumulation in Hypocotyl of Soybean Sprouts.

Soybean sprouts are a flavorful microgreen that can be eaten all year round and are widely favored in Southeast Asia. In this study, the regulatory mechanism of calcium on anthocyanin biosynthesis in soybean sprouts under blue light was investigated. The results showed that blue light, with a short wavelength, effectively induced anthocyanin accumulation in the hypocotyl of soybean sprout cultivar “Dongnong 690.” Calcium supplementation further enhanced anthocyanin content, which was obviously inhibited by LaCl3 and neomycin treatment. Moreover, exogenous calcium changed the metabolism of anthocyanins, and seven anthocyanin compounds were detected. The trend of calcium fluorescence intensity in hypocotyl cells, as well as that of the inositol 1,4,5-trisphosphate and calmodulin content, was consistent with that of anthocyanins content. Specific spatial distribution patterns of calcium antimonate precipitation were observed in the ultrastructure of hypocotyl cells under different conditions. Furthermore, calcium application upregulated the expression of genes related to anthocyanin biosynthesis, and calcium inhibitors suppressed these genes. Finally, transcriptomics was performed to gain global insights into the molecular regulation mechanism of calcium-associated anthocyanin production. Genes from the flavonoid biosynthesis pathway were distinctly enriched among the differentially expressed genes, and weighted gene co-expression network analysis showed that two MYBs were related to the accumulation of anthocyanins. These results indicated that calcium released from apoplast and intracellular stores in specific spatial-temporal features promote blue light-induced anthocyanin accumulation by upregulation of the expression of genes related to anthocyanin synthesis of “Dongnong 690” hypocotyl. The findings deepen the understanding of the calcium regulation mechanism of blue light-induced anthocyanin accumulation in soybean sprouts, which will help growers produce high-quality foods beneficial for human health.

Seasonal precipitation pattern analysis for decision support of agricultural irrigation management in Louisiana, USA

Rainfed agriculture is often a challenge in many humid regions due to the irregular distribution of precipitation events. Precipitation irregularity during crop growing seasons causes soil water deficits that negatively impact crop yields. To alleviate water stress, farmers are expected to compensate for water deficits through irrigation. Because irrigation consumes valuable water resources, it is critical to envision water management strategies that enhance precipitation water use and reduce irrigation water withdrawals. However, precipitation patterns generally vary depending on locations and seasons. Hence, a thorough understanding of crop exposure to water deficit in time and space is essential to improve agricultural water use efficiency. This study investigated both spatial and seasonal patterns of precipitation to elucidate the exposure of crops to water deficits at a regional scale. Specifically, a spatial regionalization technique was applied to a 33-year gridded time series of seasonal precipitation totals and numbers of events in Louisiana to determine two precipitation regions with distinct characteristics. Within each region, kernel density estimators were employed to approximate the actual probability distribution of seasonal precipitations. Estimates of crop water requirements for corn, soybean, cotton, grain sorghum, and sugarcane under both early and late planting scenarios were employed to evaluate the probabilities of crop exposure to water deficits during the growing seasons. The outcomes of this study include a distinction of precipitation regions for Louisiana and a detailed probabilistic evaluation of crops exposure to water deficits. These outcomes are intended to support irrigation management recommendations for farmers across the state of Louisiana.

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

For climate adaptation and risk mitigation, decision makers in water management or agriculture increasingly demand for regionalized weather and climate information. To provide these, regional atmospheric models, such as the Weather Research and Forecasting (WRF) model, need to be optimized in their physical setup to the region of interest. The objective of this study is to evaluate four cumulus physics (CU), two microphysics (MP), two planetary boundary layer physics (PBL), and two radiation physics (RA) schemes in WRF according to their performance in dynamically downscaling the precipitation over two typical South American regions: one orographically complex area in Ecuador/Peru (horizontal resolution up to 9 and 3 km), and one area of rolling hills in Northeast Brazil (up to 9 km). For this, an extensive ensemble of 32 simulations over two continuous years was conducted. Including the reference uncertainty of three high-resolution global datasets (CHIRPS, MSWEP, ERA5-Land), we show that different parameterization setups can produce up to four times the monthly reference precipitation. This underscores the urgent need to conduct parameterization sensitivity studies before weather forecasts or input for impact modeling can be produced. Contrarily to usual studies, we focus on distributional, temporal and spatial precipitation patterns and evaluate these in an ensemble-tailored approach. These ensemble characteristics such as ensemble Structure-, Amplitude-, and Location-error, allow us to generalize the impacts of combining one parameterization scheme with others. We find that varying the CU and RA schemes stronger affects the WRF performance than varying the MP or PBL schemes. This effect is even present in the convection-resolving 3-km-domain over Ecuador/Peru where CU schemes are only used in the parent domain of the one-way nesting approach. The G3D CU physics ensemble best represents the CHIRPS probability distribution in the 9-km-domains. However, spatial and temporal patterns of CHIRPS are best captured by Tiedtke or BMJ CU schemes. Ecuadorian station data in the 3-km-domain is best simulated by the ensemble whose parent domains use the KF CU scheme. Accounting for all evaluation metrics, no general-purpose setup could be identified, but suited parameterizations can be narrowed down according to final application needs.

Extreme daily precipitation in southern South America: statistical characterization and circulation types using observational datasets and regional climate models

The main features of daily extreme precipitation and circulation types in southern South America (SSA) were evaluated and compared in both multiple observational datasets (rain gauges, CHIRPS, CPC and MSWEP) and simulations from four regional climate models (RCMs) driven by ERA-Interim during 1980–2010. The inter-comparison of extreme events, characterised in terms of their intensity, frequency and spatial coverage, varied across SSA showing large differences among observational datasets and RCMs and reflecting the current observational uncertainty when evaluating precipitation extremes at a daily scale. The spread between observational datasets was smaller than for the RCMs. Most of the RCMs successfully captured the spatial pattern of extreme precipitation across SSA, although RCA4 (REMO) usually underestimated (overestimated) precipitation intensities, particularly the maximum amounts in southeastern South America (SESA), where the extremes are remarkable. The synoptic circulation was described by a classification of circulation types (CTs) using Self-Organizing Maps (SOM). Specific CTs were found to significantly enhance the occurrence of extreme precipitation events in sectorized areas of SESA. The RCMs adequately reproduced the SOM node frequencies, although they tended to simplify the predominant CTs into a more reduced number of configurations. They appropriately represented the extreme precipitation frequencies conditioned by each CT, exhibiting some limitations in the location and intensity of the resulting precipitation systems. These sorts of evaluations contribute to a better understanding of the physical mechanisms responsible for extreme precipitation and of their future projections in a climate change scenario.

A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Assessing the Effects of Land Cover Land Use Change on Precipitation Dynamics in Guangdong–Hong Kong–Macao Greater Bay Area from 2001 to 2019

The change of spatial and temporal distribution of precipitation has an important impact on urban water security. The effect of land cover land use change (LCLUC) on the spatial and temporal distribution of precipitation needs to be further studied. In this study, transfer matrix, standard deviation ellipse and spatial autocorrelation analysis techniques were used. Based on the data of land cover land use and precipitation, this paper analyzed the land cover land use change and its influence on the spatial and temporal distribution pattern of precipitation in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The results showed that from 2001 to 2019, the area of cropland, water, barren, forest/grassland in the GBA decreased by 44.03%, 8.05%, 50.22%, 0.43%, respectively, and the area of construction land increased by 20.05%. The precipitation in the GBA was mainly concentrated in spring and summer, and the precipitation in spring tended to increase gradually, while the precipitation in summer tended to decrease gradually, while the precipitation in autumn and winter has no obvious change. It was found that with the change of land cover land use, the spatial distribution of precipitation also changed. Especially in the areas where the change of construction land was concentrated, the spatial distribution of precipitation changed most obviously.

Ten-year return levels of sub-daily extreme precipitation over Europe

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments, and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolutions, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe, and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy-precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme-rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability in the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24 h durations with a spatial resolution of 0.11∘ (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with Spearman's rank correlation coefficients >0.76 for the area covered by observations. Also, the rainfall intensity of the observational data set is in the range of the climate-model-generated intensities in 60 % (77 %, 78 %, 83 %, 78 %) of the area for hourly (3, 6, 12, 24 h) durations. This results in biases between −16.3 % (hourly) to +8.2 % (24 h) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3 to 24 h durations in large parts of the study area. However, for an hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

An evaluation of Global Satellite Mapping of Precipitation (GSMaP) datasets over Iran

This study aimed to quantitatively evaluate three GSMaP products including near-real-time (GSMaP-NRT), microwave-infrared reanalyzed (GSMaP-MVK), and gauge-adjusted (GSMaP-Gauge) data with a spatial resolution of 0.1° × 0.1° versus gauge-observed data (observation) at daily and monthly time scales over Iran. Different statistical metrics including correlation coefficient (R), percent bias (PBias), root mean square error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to evaluate the capability of the GSMaP products. The results indicated that all three products were generally capable of capturing the spatial pattern of precipitation. However, they all overestimated precipitation in general, and there was a considerable difference between the two satellite-only products GSMaP-MVK and GSMaP-NRT and the gauge-corrected product GSMaP-Gauge. GSMaP-Gauge performed better than the other products at both daily and monthly time scales. The evaluations demonstrated that all the GSMaP products provided better estimations of precipitation over Western than Eastern Iran. The strongest agreement between the products and the observed data was observed for the rainy months, while performance was poor for the dry months. The findings could help GSMaP product developers to better understand the characteristics of the involved errors.

A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.

Patterns of Extreme Precipitation and Characteristics of Related Systems in the Northern Xinjiang Region

Based on 40 years of daily precipitation, 272 extreme precipitation days in the Northern Xinjiang region are defined. Using the daily precipitation data on these days, four precipitation spatial patterns were obtained through principal component analysis. Then, daily-averaged reanalysis data were used to analyze the variations of synoptic systems on extreme precipitation days and the two days before and after. The rainfall centers shifted with the influential systems at 500 hPa. Water vapor of the western Tianshan type (Type WT) and the north of Northern Xinjiang type (Type NN) comes from the west, while vapor of the Central Tianshan type (Type CT) mainly comes from the east. In the east of Northern Xinjiang type (Type EN), water vapor converges from both sides. The centers of the upper-level jets are located west of 80° E in Type WT and CT. However, they are to the east of 80° E in the other types. This article summarizes the variations of the systems at 500 hPa, the South Asia High, the westerly jet, and the water vapor transport between the surface and 500 hPa in four types of patterns, and builds the conceptual model for each type. The models built can be applied to the heavy rainfall forecast of Northern Xinjiang.

Technical note: Characterising and comparing different palaeoclimates with dynamical systems theory

Abstract. Numerical climate simulations produce vast amounts of high-resolution data. This poses new challenges to the palaeoclimate community – and indeed to the broader climate community – in how to efficiently process and interpret model output. The palaeoclimate community also faces the additional challenge of having to characterise and compare a much broader range of climates than encountered in other subfields of climate science. Here we propose an analysis framework, grounded in dynamical systems theory, which may contribute to overcoming these challenges. The framework enables the characterisation of the dynamics of a given climate through a small number of metrics. These may be applied to individual climate variables or to several variables at once, and they can diagnose properties such as persistence, active number of degrees of freedom and coupling. Crucially, the metrics provide information on instantaneous states of the chosen variable(s). To illustrate the framework's applicability, we analyse three numerical simulations of mid-Holocene climates over North Africa under different boundary conditions. We find that the three simulations produce climate systems with different dynamical properties, such as persistence of the spatial precipitation patterns and coupling between precipitation and large-scale sea level pressure patterns, which are reflected in the dynamical systems metrics. We conclude that the dynamical systems framework holds significant potential for analysing palaeoclimate simulations. At the same time, an appraisal of the framework's limitations suggests that it should be viewed as a complement to more conventional analyses, rather than as a wholesale substitute.

Intercomparisons of multiproxy-based gridded precipitation datasets in Monsoon Asia: cross-validation and spatial patterns with different phase combinations of multidecadal oscillations

Proxy-based reconstructions are essential for investigating precipitation change in Asia on multidecadal to centennial timescales. Thus far, a total of four gridded precipitation/drought datasets for the past half millennium have been developed, including reconstructed Asian summer precipitation (RAP), May–September precipitation in China (IGGPRE), warm season precipitation for Asia (WSP), and Monsoon Asia Drought Atlas (MADA). This study uses some independent evidence to evaluate and compare the four reconstructions and then examine the spatial patterns of precipitation under different phase combinations of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO). The results suggest that RAP and IGGPRE capture most variance of observations in eastern China, WSP performs better in central Asia and India, while MADA shows skill over Mongolia and the eastern Tibet Plateau. When proxies are evenly distributed in space, increasing the sample size contributes more to the explained variance than increasing the signal-to-noise ratio. In terms of the responses to the multidecadal oscillations, the spatial correlation coefficients are all negative between pairs of patterns, with only one of the oscillations residing in opposite phase. This means that all four reconstructed datasets capture the reverse spatial pattern when the phase of multidecadal oscillation changes. The WSP is the only dataset that shows positively correlated spatial patterns when the AMO and the PDO reverse together. The RAP and IGGPRE are more sensitive to the AMO than to the PDO, because the negative correlations caused by the AMO exhibit higher confidence levels. The spatial pattern in history is similar to that in the twentieth century when the PDO and AMO are both in positive phase, which indicates that the mechanism in this situation is stable over the long-term period.

Impact of Soil Moisture Initializations on WRF‐Simulated North American Monsoon System

AbstractThis study investigates the influence of soil moisture initialization on the North American Monsoon System (NAMS) using the Weather Research and Forecasting (WRF) model coupled with Noah‐MP Land Surface Model. Five sets of experiments using different soil moisture initializations were conducted from May 1 to October 1 in 2015–2017. The model forecast for each set of experiments consists of five ensemble members. The simulation using North American Regional Reanalysis is the control run (WRF‐Ctrl). Four sets of sensitivity experiments were conducted with extremely wet (WRF‐Wet) and dry (WRF‐dry) initial conditions and using different soil moisture products from the Global Land Data Assimilation System (WRF‐GLDAS) and NASA Soil Moisture Active Passive L4 (WRF‐SMAP). Results show that the WRF model can capture the key features of the NAMS, but it exhibits a wet bias in the higher elevation regions and a dry bias in the lower elevation regions. Our analysis of extremely wet and dry cases reveals that initial soil moisture conditions can affect precipitation through both modulating surface energy partitioning and influencing large‐scale pressure and wind patterns. When different soil moisture products are used to initialize the model, it can lead to substantial changes in spatial pattern of the NAM precipitation. Initializing the model with SMAP and GLDAS can reduce the overestimation of precipitation over high terrain areas. The results indicate that high‐quality soil moisture products have the potential to improve model representation of the NAMS, especially with future improvements in model convective parameterization.

The Unstable Relationship Between the Precipitation Dipole Pattern in the Tibetan Plateau and Summer NAO

AbstractSubstantial progress has been made in understanding the relationship between summer precipitation in the Tibetan Plateau (TP) and North Atlantic Oscillation (NAO), but impacts of the summer NAO on precipitation spatial pattern remain controversial. Using the long‐term observational and reanalysis datasets, we have identified the intimate relationship between summer NAO and TP precipitation dipole pattern has been weakened since the late 1990s. Changes in regional atmospheric circulation induced by the asymmetrical wave propagation structure along the Eurasian continent are responsible for this weakened linkage. Further analyses revealed that the wave propagation anomalies are mainly induced by interdecadal change of summer NAO, which is attributed to the Atlantic multidecadal oscillation (AMO) variations. Moreover, the model simulations derived from the Geophysical Fluid Dynamics Laboratory (GFDL AM2.1) provide direct evidence to demonstrate that the AMO phase transition contributes to the unstable relationship between the summer NAO and precipitation dipole pattern in the eastern TP.

Evaluation of the GPM IMERG V06 products for light rain over Mainland China

Monitoring the spatiotemporal characteristics of light rain events is vital to water resources assessment and environmental protection. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide new-generation satellite precipitation measurements with improved light rain estimates. However, the spatial distribution of the quality and error structure of IMERG light precipitation detections and the climatic dependence of the error sources have not been studied sufficiently. In this study, we evaluated the GPM IMERG daily light precipitation estimates' performance from 2015 to 2018 over mainland China. We found that: (1) IMERG captured the spatial patterns of light precipitation over mainland China but has limited ability to detect light rainfall events below 5 mm/d; (2) the hit bias is the dominant error of IMERG under 5 mm/d; (3) IMERG exhibits a worse performance and has relatively higher miss bias for light rainfall in areas with a complex winter precipitation phase; (4) the evaluation of IMERG for light rain is affected by the mismatch between gauge data and IMERG data caused by air pollution. These findings can improve the IMERG algorithms for light rainfall and provide a reference for applications based on these datasets.

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High‐Resolution Global Nonhydrostatic Model

AbstractCurrent climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14‐km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30‐years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by ∼35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low‐level SSTP but increase over high‐level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ∼50%/°C and ∼8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.