Spatial Patterns Of Precipitation Research Articles

Improvement of summer precipitation simulation with indirect assimilation of spring soil moisture over the Tibetan Plateau

AbstractSoil moisture can be an important preceding signal in seasonal precipitation prediction because of its persistence and influence on the energy and water balance between land and atmosphere. The thawing process of frozen ground on the Tibetan Plateau (TP) during spring makes it difficult to accurately simulate soil variables (e.g., soil moisture and temperature) due to defective parameterizations in numerical models during spring. In this study, we investigate the effectiveness of soil moisture correction using an indirect nudging scheme to simulate the coupling between spring soil moisture and the subsequent precipitation with an advanced research version of the weather research and forecasting (WRF) model. The results without assimilation show that extreme cold and dry land surface states during spring cause a large bias in the spatial pattern of summer precipitation over almost the entire TP. However, the experiments with indirect assimilation in spring show that the spatial pattern of summer precipitation improved significantly. This comparison emphasizes the importance of correcting the spring soil moisture during the freeze–thaw period to significantly adjust the heat and water vapour exchange between the land and atmosphere. Additionally, changes in the circulation of the subtropical–tropical jet, vertical ascent, and region‐related moisture recycling can support an active convection environment over the eastern TP, thereby increasing summer precipitation. Furthermore, the pattern shift in water vapour convergence in summer highlights the regional horizontal transportation of water vapour. The differences between experiments with and without deep‐soil temperature nudging illustrate the significance of soil temperature in soil moisture simulation in the freeze–thaw process. More so, the deep‐soil temperature nudging scheme requires in‐depth studies to correct its variation and quantify its degree of impact on soil moisture.

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

AbstractThis study systematically assessed the performance of the Integrated Multi‐satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near‐real‐time “Late Run” (IMERG‐L) and the post‐real‐time “Final Run” (IMERG‐F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG‐F outperformed IMERG‐L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG‐F and IMERG‐L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG‐F were between 5.65 and 8.63%; however, for IMERG‐L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root‐mean‐square‐error [RMSE], correlation coefficient [CC], and Kling–Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region‐average perspective, IMERG‐F outperformed IMERG‐L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.

Sensitivity of simulated mesoscale convective systems over East Asia to the treatment of convection in a high-resolution GCM

Mesoscale convective systems (MCSs) downstream of the Tibetan Plateau (TP) exhibit unique precipitation features. These MCSs can have damaging impacts and there is a critical need for improving the representation of MCSs in numerical models. However, most global climate models are typically run at resolutions that are too coarse to reasonably resolve MCSs, and it is still unclear how well higher-resolution global models can reproduce the precipitation characteristics of MCSs. In this study, the sensitivity of MCSs simulated by a global high resolution (~ 10 km), atmosphere-only climate model to different treatments of convection (with and without parametrized convection, and a hybrid representation of convection) have been investigated. The results show that explicit convection (i.e., non-parameterized) can better reproduce the observed pattern of MCS precipitation over the East Asian Summer Monsoon region. In general, explicit convection better simulates the diurnal variability of MCSs over the eastern China, and is able to represent the distinctive diurnal variations of MCS precipitation over complex terrain particularly well, such as the eastern TP and the complex terrain of central-northern China. It is shown that explicit convection is better at simulating the timing of initiation and subsequent propagating features of the MCS, resulting in better diurnal variations and further a better spatial pattern of summer mean MCS precipitation. All three experiments simulate MCS rainfall areas which are notably smaller than those in observations, but with much stronger rainfall intensities, implying that these biases in simulated MCS morphological characteristics are not sensitive to the different treatment of convection.

Spatio-Temporal Variation of Precipitation and Evaporation on the Tibetan Plateau and Their Influence on Regional Drought

The Tibetan Plateau (TP) is an important water source in Asia, and precipitation and evaporation patterns at different geographical and temporal scales play a significant role in managing water resource distribution. Based on quality control data from 87 meteorological stations, this study analyzed the spatial and temporal evolution patterns of precipitation and pan evaporation (Epan) on the TP in 1966–2016 using the Mann–Kendall test, the moving t-test, wavelet analysis, Sen’s slope method, and correlation analysis. The results revealed that the average mean temperature in the TP area increased by about 2.1 °C during the study period, and precipitation steadily increased at an average rate of 8.2 mm/10a, with summer and autumn precipitation making up about 80% of the year. In contrast, Epan showed an overall decreasing trend at a decline rate of 20.8 mm/10a, with spring and summer Epan values making up about 67% of the year. The time series of the precipitation and Epan within the TP region clearly exhibit nonstationary features. Precipitation is more concentrated in the southeast than in the northwest, while Epan is mostly concentrated in the southwest and northeast of the plateau around the Qaidam Basin. The “evaporation paradox” phenomenon was common in the TP region for about 40 years (1960s–1990s) and gradually faded in the 21st century. In addition, we introduced a standardized precipitation evaporation index (SPEI) to investigate the differences and relationships between precipitation and Epan time series over the past 50 years. The findings indicate that the southern Qinghai was dominated by an arid trend, while the central and southeast TP remained wet. Droughts and floods coexist in the eastern Qinghai and southern Tibet areas with high population concentrations, and the risk of both is rising as the inhomogeneity of precipitation distribution in the TP region will increase in the future. This study can be used as a reference for managing water resources and predicting regional drought and flood risk.

Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05°, correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of 0.25° or 0.1°, correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations.

Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product’s algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement.

The capability of CMIP6 models on seasonal precipitation extremes over Central Asia

The accurate extreme precipitation simulations of the GCMs during the historical period are more likely to offer reliable forecasts in the future. In this study, we evaluated the abilities of 22 GCMs of CMIP6 in simulating extreme precipitation over Central Asia in the early spring, summer, and late winter, when extreme precipitation can be very threatening. Based on seven extreme precipitation indices, robust skill score techniques such as Taylor diagrams and DISO (Distance between Indices of Simulation and Observation) are employed to rank the overall model performance. The results show that all models have difficulties in simulating the overall spatial patterns of all extreme precipitation in early spring and late winter. Differently, in summer, most of them can fairly well capture the spatial pattern of PRCPTOT and the CDD. Most models show consistent bias of extreme precipitation indices in early spring and late winter, with overall overestimated CDD and CWD and underestimated SDII, Rx5day, R95PTOT, and R10mm. However, there is high inter-model variability in the regional bias of summer, and the areas of inconsistency that emerge always contain most of Xinjiang or its surrounding mountain regions, possibly due to a lack of topographic correction in these areas with very complex topography. The model ensemble simulations generally outperform the individual models in terms of CC (Correlation coefficient) and DISO, while the improvement is limited regarding RMS (Centered mean square error) and RSD (Relative standard deviations). This study may serve as a reference to further investigate the sources of persistent systematic biases in current GCMs over Central Asia, which is expected to assist policymakers in adopting strategies reflecting the state of scientific understanding of the likelihood.

The Impact of Climate Change as Well as Land-Use and Land-Cover Changes on Water Yield Services in Haraz Basin

Water-yield assessment is crucial to effectively achieve water supply management at the basin scale. It is affected by climate change, and Land Use/Land Cover Change (LULCC). This paper quantifies the combined impact of LULCC and climate change on water yield over short and long-term periods in the Haraz basin, Iran. This basin has experienced severe forest degradation, agricultural expansion, and urbanization. Most agricultural lands are within the plains and coastal areas, and 25% of the basin is covered by forest, which includes part of the Hyrcanian conservation forests. Four scenarios to distinguish the relative portion of Land Use/Land Cover (LULC) and climate for water supply variations are considered, including real conditions at the beginning of the period (Sn1), real conditions without changes in LULC (Sn2), real conditions without climate change (Sn3), and real conditions at the end of the period (Sn4). Four study periods include 1992–2007, 2007–2016, 2016–2026 as a short term, and 1992–2026 as a long-term period. The spatial patterns of annual precipitation showed decreasing amounts in the three studied years from the northwest to the south. Water yield also reduced during the study period 1992–2007 and 2007–2016, and climate change plays an essential role in this reduction. Forecasts show that rainfall will recover in 2026 and water yield will increase in the northern sub-basins. In the long run, from 1992 to 2026, the contribution of LULCC and climate change factors to water yield are equal. The results can provide references for land use management to enhance water yield considering global climate change.

Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America

This study aims to validate the accuracy of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset at representing climate variability over various time scales. The study is carried out in Antioquia, in northwestern Colombia, and uses statistically independent information provided by 75 rain gauges located at different sites from 1981 to 2018. In this study, statistical metrics are used to analyze the error structure of the CHIRPS data. Resilient methods for validation are included based on Gauss-Markov diagnostic problems and influence points, leverage, and outliers in the form of the trimmed least squares boot method and principal component analysis. To represent the performance of CHIRPS more accurately at estimating precipitation at different time scales, a comparison is made using 12 specific rain gauge located in different subregions of Antioquia, which had well-differentiated climatic characteristics. The results show that the accuracy of CHIRPS estimates is conditioned by geographic and climatic characteristics of the region in which precipitation events occur. CHIRPS works well in most weather conditions in Antioquia, even during the most intense periods of ENSO. However, it does better in the Andean subregions and underperforms in warmer regions. This study finds that CHIRPS is a rainfall data source that can be considered for analyzing seasonal and interannual variability and spatial precipitation patterns. It provides good spatio-temporal coverage and is particularly suitable for areas with few rainfall gauges. However, its accuracy is minimal for daily analysis.

Correction to: Spatial patterns of extreme precipitation and their changes under ~ 2 °C global warming: a large-ensemble study of the western USA

Correction to: Spatial patterns of extreme precipitation and their changes under ~ 2 °C global warming: a large-ensemble study of the western USA

The spatial pattern of extreme precipitation from 40 years of gauge data in the central Himalaya

The topography of the Himalaya exerts a substantial control on the spatial distribution of monsoonal rainfall, which is a vital water source for the regional economy and population. But the occurrence of short-lived and high-intensity precipitation results in socio-economic losses. This study relies on 40 years of daily data from 204 ground stations in Nepal to derive extreme precipitation thresholds, amounts, and days at the 95th percentile. We additionally determine the precipitation magnitude-frequency relation. We observe that extreme precipitation amounts follow an almost uniform band parallel to topographic contour lines in the southern Himalaya mountains in central and eastern Nepal but not in western Nepal. The relationship of extreme precipitation indices with topographic relief shows that extreme precipitation thresholds decrease with increasing elevation, but extreme precipitation days increase in higher elevation areas. Furthermore, stations above 1 km elevation exhibit a power-law relation in the rainfall magnitude-frequency framework. Stations at higher elevations generally have lower values of power-law exponents than low elevation areas. This suggests a fundamentally different behaviour of the rainfall distribution and an increased occurrence of extreme rainfall storms in the high elevation areas of Nepal.

Summer regional climate simulations over Tibetan Plateau: from gray zone to convection permitting scale

The Tibetan Plateau (TP) is often referred to as ‘the Third Pole’ and plays an essential role in the global climate. However, it remains challenging for most global and regional models to realistically simulate the characteristics of climate over the TP. In this study, two Weather Research and Forecasting model (WRF) experiments using spectral nudging with gray-zone (GZ9) and convection-permitting (CP3) resolution are conducted for summers from 2009 to 2018. The surface air temperature (T2m) and precipitation from the two simulations and the global reanalysis ERA5 are evaluated against in-situ observations. The results show that ERA5 has a general cold bias over southern TP, especially in maximum T2m (Tmax), and wet bias over whole TP. Both experiments can successfully capture the spatial pattern and daily variation of T2m and precipitation, though cold bias for temperature and dry bias for precipitation exist especially over the regions south of 35° N. Compared with ERA5, the added value of the two WRF experiments is mainly reflected in the reduced cold bias especially for Tmax with more improvement found in CP3 and the reduced wet bias. However, the ability of the convection-permitting WRF experiment in improving the simulation of precipitation seems limited when compared to the gray-zone WRF experiment, which may be related to the biases in physical parameterization and lack of representativeness of station observation. Further investigation into surface radiation budget reveals that the underestimation of net shortwave radiation contributes a lot to the cold bias of T2m over the southeastern TP in GZ9 which is improved in CP3. Compared with GZ9, CP3 shows that larger specific humidity at low-level (mid-high level) coexists with more precipitation (clouds) over the southern TP. This improvement is achieved by better depiction of topographic details, underlying surface and atmospheric processes, land–atmosphere interactions and so on, leading to stronger northward water vapor transport (WVT) in CP3, providing more water vapor for precipitation at surface and much wetter condition in the mid-high level.

Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR’s severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC’s precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (<25 mm/day) and underestimate heavy precipitation events (>42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.

Wet deposition of atmospheric selenium and sensitivity to emission and precipitation patterns

Wet deposition has been well recognized to be affected by species concentration and precipitation; nevertheless, the regimes in the controlling factor of concentration or precipitation have not yet been clarified. Using a trace element, selenium (Se), with dual effects on human health as a testbed, we first reproduce the spatial distribution of atmospheric Se concentrations and wet deposition fluxes through GEOS-Chem on a global scale, and examine the spatial patterns and relative importance of anthropogenic emissions vs. natural emissions over various regions around the world. We find that over most Northern Hemisphere continental regions, anthropogenic emissions are the dominant source for atmospheric Se concentration and deposition, while it is dominated by natural sources in the other areas. Nested grid simulations covering China and the continental United States are further conducted. The factors (i.e., Se concentration and precipitation) controlling the wet deposition flux of atmospheric Se are analyzed in detail, through the construction of wet deposition-concentration-precipitation (WETD-C-P) diagram for two regions (mainland China and the continental United States) based on the monthly results. The two regions show distinctive features, reflecting the different spatial patterns of Se emissions and precipitation. Both Se emissions and precipitation are higher in the eastern United States than that in the western United States. In contrast, the emissions and precipitation in northern and southern China show dipole features with stronger emissions over the northern side and higher precipitation on the southern side. We further investigate the impacts of future emission changes in China on atmospheric Se deposition and its sensitivity to emissions and precipitation, revealing a modulation of regime shifts, i.e., from the precipitation dominant regime to the concurrent governance of both precipitation and emissions. The proposed WETD-C-P relationship is useful in elucidating the regime and factors governing the spatial and temporal variations in wet deposition.

Analysis of precipitation characteristics and changes of drought and flood disasters on Anhui Province between 1961 and 2020, based on time series

Abstract The analysis of annual precipitation evolution characteristics is of great value and significance for revealing the spatial and temporal variation patterns of regional precipitation, water resources development and utilization, short-term climate, drought, flood disaster prediction, etc. The Mann-Kendall (MK) mutation test, cumulative distance level method, and Morlet wavelet analysis were used to analyze the precipitation evolution in Anhui Province from 1961 to 2020. The results showed that the average annual temperature and precipitation in Anhui Province showed a significant increasing trend during 1961–2020, with warming and humidification. 1994 was the year of abrupt climate change in Anhui Province, and the temperature after the abrupt change was 2.10 times that before the abrupt change. El Niño-Southern Oscillation (ENSO) has a synchronized resonance cycle with droughts and floods in Anhui Province at 5.8 a. The annual scale of ENSO events is an important theoretical support for regional drought and flood warnings. The chance of drought and flooding in Anhui Province is greater than 50% in the year of ENSO event or two years after the event, and the year of ENSO event or the year after is prone to drought and flooding, so we should strengthen the flood and drought warning, disaster prevention and mitigation.

Review of Rain or Shine: An Introduction to Soil Physical Properties and Processes. Tyson Ochsner. Published by Oklahoma State University Libraries under the Creative Commons Attribution 4.0 International License. Version 6. Last updated 19 Jan. 2022. DOI: https://doi.org/10.17605/OSF.IO/Z4RBT

Review of <i>Rain or Shine: An Introduction to Soil Physical Properties and Processes</i>. Tyson Ochsner. Published by Oklahoma State University Libraries under the Creative Commons Attribution 4.0 International License. Version 6. Last updated 19 Jan. 2022. DOI: https://doi.org/10.17605/OSF.IO/Z4RBT

A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts

Abstract An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.

Tree growth sensitivity to climate varies across a seasonal precipitation gradient

Spatial patterns of precipitation in the southwestern United States result in a complex gradient from winter-to-summer moisture dominance that influences tree growth. In response, tree growth exhibits seasonal-to-annual variability that is evident in the growth of whole tree rings, and in sub-annual sections such as earlywood and latewood. We evaluated the influence of precipitation and temperature on the growth of Pinus ponderosa trees in 11 sites in the southwestern US. Precipitation during the year of growth and the prior year accounted for about half of the climate influence on annual growth, with the other half reflecting conditions 2-4years prior to growth, indicating that individual trees do indeed exhibit multi-year "memory" of climate. Trees in wetter sites exhibited weaker influence of past precipitation inputs, but longer memory of climatic variability. Conversely, trees in dry sites exhibited shorter memory of long-term climatic variability, but greater sensitivity to past precipitation effects. These results are consistent with the existence of complex interactions between endogenous (phenotype) effects and exogenous (climate) effects in controlling climate memory in trees. After accounting for climate, residual variability in latewood growth was negatively correlated with earlywood growth, indicating a potential tradeoff between latewood versus earlywood growth. This study provides new insights that will assist the accurate prediction of woody biomass growth and forest carbon sequestration across a southwestern US precipitation gradient.

Spatiotemporal Patterns of Hydrometeorological Extremes over 1986–2015 in Nepal

Abstract To better understand the hydrometeorological mean conditions and changes in Nepal, this study analyzes annual and monthly changes of mean and extreme precipitation indices for 34 stations (1986–2015) and streamflow indices for 19 stations (different available periods ranging from 1962 to 2014). The annual and monthly precipitation–streamflow relations in the major basins of Nepal are also analyzed. Nepal has heterogeneous precipitation spatial patterns in its mean and changes. The spatial pattern of extreme precipitation shows that the dry regions are prone to dry extremes and wet regions are prone to wet extremes during the past three decades. However, the annual precipitation trend is decreasing in general and the monsoon-dominated regions, namely, southern, eastern, and central Nepal, are becoming drier with less intensive precipitation while the western region is getting wetter. The trend of annual precipitation on average is decreasing at the rate of 4.2 mm yr−1. The risk of drought is increasing in Nepal as 100% of stations show a positive trend of consecutive dry days (CDD) and the all-Nepal trend is 0.8 days yr−1, which can put stress on the agriculture and health sector. The highest increase in CDD is at Ilam in the eastern hills with 2.9 days yr−1. The Southeast Asian monsoon index shows a statistically significant 1-month-lagged influence on country-wise precipitation in Nepal. The effect of decreasing precipitation is seen in the streamflow trend as the majority of stations show decreasing runoff in June and July, as opposed to an increase in the winter months. CDD and maximum 1-day (Rx1day) and 5-day (Rx5day) precipitation significantly decrease with elevation.

The relationship between precipitation and its spatial pattern in the trades observed during EUREC4A

AbstractTrade wind convection organises into a rich spectrum of spatial patterns, often in conjunction with precipitation development. Which role spatial organisation plays for precipitation and vice versa is not well understood. We analyse scenes of trade‐wind convection scanned by the C‐band radar Poldirad during the EURECA field campaign to investigate how trade‐wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We find that the mean rain rate (i.e., the amount of precipitation in a scene) and the intensity of precipitation (mean conditional rain rate) relate differently to the spatial pattern of precipitation. Whereas the amount of precipitation increases with mean cell size or number, as it scales well with the precipitation fraction, the intensity increases predominantly with mean cell size. In dry scenes, the increase of precipitation intensity with mean cell size is stronger than in moist scenes. Dry scenes usually contain fewer cells with a higher degree of clustering than moist scenes do. High precipitation intensities hence typically occur in dry scenes with rather large, few, and strongly clustered cells, whereas high precipitation amounts typically occur in moist scenes with rather large, numerous, and weakly clustered cells. As cell size influences both the intensity and amount of precipitation, its importance is highlighted. Our analyses suggest that the cells' spatial arrangement, correlating mainly weakly with precipitation characteristics, is of second‐order importance for precipitation across all regimes, but it could be important for high precipitation intensities and to maintain precipitation amounts in dry environments.