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Yongliang Jiao
,
Ren Li
,
Tonghua Wu
,
Lin Zhao
,
Xiaodong Wu
,
Junjie Ma
,
Jimin Yao
,
Guojie Hu
,
Yao Xiao
,
Shuhua Yang
,
Wenhao Liu
,
Yongping Qiao
,
Jianzong Shi
,
Erji Du
,
Xiaofan Zhu
, and
Shenning Wang

Abstract

Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C.

Significance Statement

This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature.

Free access
Ruxuan Ma
and
Xing Yuan

Abstract

Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting.

Significance Statement

Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

Free access
Anatolii Anisimov
,
Vladimir Efimov
,
Margarita Lvova
,
Suleiman Mostamandi
, and
Georgiy Stenchikov

Abstract

In the present study, the convective event over the Black Sea area in September 2018 is analyzed using the Weather Research and Forecasting (WRF) Model configured with a fully convective-resolving setup. We test the WRF sensitivity to the choice of sea surface temperature (SST) dataset and microphysics scheme. The simulation is verified using weather radar measurements and ground observations. Both the choice of the microphysical scheme and SST dataset have a significant impact on the dynamic properties of the maritime convective system and associated rainfall. The best results are achieved with the WDM6 microphysical scheme and a more detailed and slightly warmer (compared to the default OSTIA SST) G1SST dataset. The optimally configured WRF simulations add value to coarser driving operational analysis, with more accurate amount and pattern of rainfall and the earlier arrival of the convective system, which is in better agreement with radar and weather station measurements. The vertical structure of the reflectivity profiles in the WDM6 scheme that simulates 15%–20% larger rainwater loading compared to other schemes agrees best with the observed data. Other schemes reproduce excessive reflectivity above the freezing level. Enhanced rainfall estimates and faster convective system propagation in the G1SST WDM6 simulations are linked to stronger cold pools caused by enhanced evaporation due to the higher rainwater content and droplet number concentrations. Stronger cold pools result in the 15%–20% enhancement of latent and sensible heat fluxes, reflecting the strong sensitivity of ocean–atmosphere heat and moisture exchange to the choice of microphysics scheme and SST dataset.

Free access
Maxim Lamare
,
Florent Domine
,
Jesús Revuelto
,
Maude Pelletier
,
Laurent Arnaud
, and
Ghislain Picard

Abstract

Expanding shrubs in the Arctic trap blowing snow, increasing snow height and accelerating permafrost warming. Topography also affects snow height as snow accumulates in hollows. The respective roles of topography and erect vegetation in snow accumulation were investigated using a UAV-borne lidar at two nearby contrasted sites in northern Quebec, Canada. The North site featured tall vegetation up to 2.5 m high, moderate snow height, and smooth topography. The South site featured lower vegetation, greater snow height, and rougher topography. There was little correlation between topography and vegetation height at both sites. Vegetation lower than snow height had very little effect on snow height. When vegetation protruded above the snow, snow height was well correlated with vegetation height. The topographic position index (TPI) was well correlated with snow height when it was not masked by the effect of protruding vegetation. The North site with taller vegetation therefore showed a good correlation between vegetation height and snow height, R 2 = 0.37, versus R 2 = 0.04 at the South site. Regarding topography, the reverse was observed between TPI and snow height, with R 2 = 0.29 at the North site and R 2 = 0.67 at the South site. The combination of vegetation height and TPI improved the prediction of snow height at the North site (R 2 = 0.59) but not at the South site because vegetation height has little influence there. Vegetation was therefore the main factor determining snow height when it protruded above the snow. When it did not protrude, snow height was mostly determined by topography.

Significance Statement

Wind-induced snow drifting is a major snow redistribution process in the Arctic. Shrubs trap drifting snow, and drifting snow accumulates in hollows. Determining the respective roles of both these processes in snow accumulation is required to predict permafrost temperature and its emission of greenhouse gases, because thicker snow limits permafrost winter cooling. Using a UAV-borne lidar, we have determined snow height distribution over two contrasted sites in the Canadian low Arctic, with varied vegetation height and topography. When snow height exceeds vegetation height, topography is a good predictor of snow height, with negligible effect of buried vegetation. When vegetation protrudes above the snow, combining both topography and vegetation height is required for a good prediction of snow height.

Open access
Aulia Febianda Anwar Tinumbang
,
Kazuaki Yorozu
,
Yasuto Tachikawa
, and
Yutaka Ichikawa

Abstract

Runoff generated by land surface models (LSMs) is extensively used to predict future river discharge under global warming. However, the structural bias of LSMs, the precipitation bias of the climate model, and other factors could cause the runoff to be biased. A model intercomparison study can help understand LSM behavior. Traditional model intercomparison can discover output variation and evaluate performance, but explaining the reason for the difference is challenging. This study developed a novel method to identify the reasons for disparities and suggest improvements. Consequently, we investigated the impacts of model settings by adopting the settings of another model in one model until it can mimic similar features in its output. Hence, we developed a process called the “emulation model.” We employed two LSMs [Simple Biosphere with Urban Canopy (SiBUC) and Meteorological Research Institute Simple Biosphere model (MRI-SiB)] in the Thai River basin. SiBUC produced a higher surface runoff than MRI-SiB, and the development of the MRI-SiB emulation revealed the cause of this variation. The differences in runoff characteristics affected streamflow estimation. For instance, the SiBUC peak discharge was faster and higher than observed in the dry year. Conversely, there was a tendency to underestimate the flow estimated by MRI-SiB runoff during the transition from dry to wet seasons. Incorporating other model settings can alleviate the shortcomings of each model. Overall, the proposed method can identify the strengths and weaknesses of a model and enhance the reproducibility of the hydrological characteristics of the observed discharge in the basin.

Significance Statement

This study aims to develop a new methodology for model intercomparison to identify the reasons for model output variation. Understanding why models behave differently is important to enhancing the reliability of model prediction. Our findings guide what affects disparities in land surface model runoff-based streamflow estimation, which will help reduce the uncertainty of future flood and drought predictions.

Open access
Jiaying Zhang
,
Kaiyu Guan
,
Rong Fu
,
Bin Peng
,
Siyu Zhao
, and
Yizhou Zhuang

Abstract

Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management.

Significance Statement

Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.

Free access
Giuseppe Torri
,
Alison D. Nugent
, and
Brian N. Popp

Abstract

Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. The data show considerable differences in isotopic ratios produced by different weather systems, with Kona lows and upper-level lows having the lowest δ 2H and δ 18O values, and trade-wind showers the highest. The data also show significant spatial variability, with some sites being characterized by higher isotope ratios than others. The amount effect is not observed consistently at all sites. Deuterium excess shows a marked seasonal cycle, which is attributed to the different origin and history of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line and a comparison of this dataset with a long-term historical record illustrate strong interannual variability and the need to establish a long-term precipitation isotope monitoring network for Hawai‘i.

Significance Statement

The isotopic composition of water is often used in the study of island water resources, but the scarcity of high-frequency datasets makes the interpretation of data difficult. The purpose of this study is to investigate the isotopic composition of rainfall on a mountainous island in the subtropics. Based on weekly data collection on O‘ahu, Hawai‘i, the results improve our understanding of the isotopic composition of rainfall due to different weather systems, like trade-wind showers or cold fronts, as well as its spatial and temporal variability. These results could inform the interpretation of data from other mountainous islands in similar climate zones.

Free access
Sean A. Matus
,
Francina Dominguez
, and
Trent W. Ford

Abstract

The warm season in the United States Great Plains (GP) is characterized by frequent nocturnal low-level jets (LLJs). The GPLLJ serves as a major mechanism of atmospheric moisture transport, contributing to severe weather and precipitation in the region. A combination of synoptic and regional forcing modulates GPLLJ frequency and intensity. The GPLLJ has primarily been studied at the diurnal scale. We hypothesize that, due to the memory of the land surface, longer time scale variability associated with surface moisture also modulates GPLLJ intensity. This work identifies GPLLJ days from ECMWF Reanalysis v5 (ERA5) wind data and isolates extremes using a peaks-over-threshold approach. Extreme GPLLJs are classified by geographic region and synoptic state. Composites of daily soil moisture anomalies show a preference for extreme GPLLJs to occur over anomalously dry soil. Critically, antecedent soil moisture anomalies emerge weeks before the extreme jet occurrence. The dry soil moisture signal coexists with clear skies and drying of the surface at the synoptic time scale. A diurnal PBL heat accumulation, which intensifies the buoyancy oscillation, is also present. The identification of a subseasonal dry anomaly suggests that, although the GPLLJ is generated by diurnally varying oscillations and intensified by synoptic-scale processes, the memory of the land surface can modulate the GPLLJ far beyond the diurnal and synoptic scale. Additionally, the location of the antecedent soil moisture anomalies corresponds with the eventual GPLLJ. The spatiotemporal characteristic of these antecedent anomalies suggests the potential for improved prediction of the GPLLJ activity.

Free access
Lu Yi
,
Zhangyang Gao
,
Zhehui Shen
,
Haitao Lin
,
Zicheng Liu
,
Siqi Ma
,
Cunguang Wang
,
Stan Z. Li
, and
Ling Li

Abstract

Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness–rain rate relationship. To resolve these problems, we construct a deep learning model using a spherical convolutional neural network to properly represent Earth’s spherical surface. With data input directly from IR bands 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), our new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained and tested with a 3-month-long dataset, and then validated in a 2-yr period. Compared to the commonly used IR-based precipitation product PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE, and CC, especially in the dry region and for extreme rainfall events. Decomposed with the four-component error decomposition (4CED) method, the overestimation of PEISCNN was averaged 47.66% lower than the CCS at the hourly scale. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.

Significance Statement

An IR-based precipitation algorithm is irreplaceable in satellite precipitation inversion, since an IR sensor can provide observations of high frequency, fine temporal resolution, and wide coverage. Considering the spherical nature of Earth’s surface which has been overlooked in previous IR-based precipitation retrieval algorithms, we proposed a new deep learning model PEISCNN, which can address the problems that exist in IR-based precipitation estimations such as overestimation in dry regions, deficiency in extreme rainfall events, and reliance on the empirical cloud-top brightness–rain rate relationship. PEISCNN provides a new insight to improve the accuracy of the satellite IR-based or multisensor-based precipitation estimation, and it has great potential to benefit a range of related hydrological research, applications in water resource management, and flood predictions.

Free access
Dominique Brunet
and
Jason A. Milbrandt

Abstract

The surface precipitation network in Canada suffers from large data gaps due to the challenge of covering a large country with a low population density. A proof-of-concept for an optimal network design is proposed to more efficiently estimate precipitation in Canada with the design goal of minimizing the interpolation uncertainty. The network design is based on a statistical model of precipitation that accounts for intermittency and non-Gaussianity of precipitation. Our results indicate that the greatest needs for new stations are in British Columbia, where coastal and mountain climate leads to more uncertainty in precipitation amounts, while the Prairie Provinces (Alberta, Saskatchewan, and Manitoba) could gain efficiencies by reducing their network size. Despite the current low density of stations in the territories north of Canada, these drier and colder regions only have a moderate need for more stations, mostly in the mountainous regions of Yukon. However, from a spatially varying wind undercatch measurement error model, it is shown that these northern regions have greatest need for higher-accuracy measurements.

Significance Statement

The proposed methodology can guide in the optimal placement of precipitation gauges across a large country such as Canada, which will provide value for money in how rain and snow are monitored.

Open access