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The precipitation over the Hengduan Mountains (HMs) during rainy seasons is investigated based on observations, reanalysis datasets, and 28 atmospheric general circulation models (AGCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5). Most CMIP5 AGCMs generally capture two observed precipitation centers over the southwestern HMs and on the west side of Sichuan basin (WSSB), but their location, range, and magnitude vary with models. As the horizontal resolution increases, the details of simulated precipitation pattern are improved and closer to observation and reanalysis, as well as the increasing magnitude of precipitation over the southwestern HMs. However, the simulated precipitation on the WSSB is overestimated regardless of resolution. Mechanisms involved in resolution affecting precipitation pattern and biases of precipitation on the WSSB are explored. Representation of topography in AGCMs influences orographic effect, which contributes to simulations of both horizontal and vertical moisture flux convergence and further precipitation over the HMs. The biases of WSSB precipitation between reanalysis and AGCMs are attributed to the discrepancy in the vertical distribution of upward motions. The simulated upward motions can reach a higher level than reanalysis, and a spurious center of upward motions develops at 400 hPa due to the overestimation of circulation-precipitation feedback in AGCMs.

Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35   10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

In this work we investigate the spatial variability of sub-daily rainfall extremes over Italy considering the influence of local orographic effects. We consider the average annual maxima computed from the recently-released Improved Italian - Rainfall Extreme Dataset (I 2 -RED) in about 3800 time series with at least 10 years of data (1916-2020 period) and we analyze the orographic effects through a local regression approach which gathers stations in a grid cell-centered area of 1 km resolution. Several constraints are considered to tackle problems determined by the low data density of some areas and by the extrapolation at low/high elevations. Different criteria for selecting the local sample are examined. This work confirms with increased detail previous findings, such as a generally positive gradient of the 24 h average annual maxima and the evidence of negative gradients in large mountainous areas for the 1 h maxima. The use of a local regression approach allows to identify the areas showing the reverse orographic effect, providing material for future investigations on the physical explanation of this evidence. Moreover, the reconstructed maps will allow to apply more accurate approaches in works related to the spatial variability of other rainfall statistics, such as the quantiles required for hydrologic design.

Remotely sensed data, including rainfall estimates and digital elevation models (DEMs), are increasingly available at various temporal and spatial scales, offering new opportunities for rainfall regionalization in regions with limited ground-based observations. We evaluate the efficacy of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimates and SRTM-derived elevation data as alternative spatial covariates for regionalizing average and extreme rainfall patterns across Greece. Using the Bilinear Surface Smoothing (BSS) framework, we assess and compare the regionalization of average daily rainfall and average annual maximum rainfall across multiple timescales (0.5 h to 48 h) by leveraging both IMERG-derived estimates and the elevation data as covariates. Additionally, the BSS framework is herein extended to provide Bayesian credible intervals for the final estimates, using the posterior variance estimate and the equivalent degrees of freedom determined through the Generalized Cross Validation error minimization procedure. Elevation-based models outperformed IMERG, particularly for indices of extreme rainfall, capturing the differential effects of orography. The exploration of the orographic effect based on the BSS framework revealed that the average annual rainfall maxima at small timescales exhibit a negative relation to elevation, which becomes positive and more significant with increasing timescale. However, IMERG proved valuable for regionalizing average daily rainfall, demonstrating its utility as a complementary tool. The results also underscore the role of temporal scale in regionalization efficiency of extreme rainfall, with higher accuracy observed at longer timescales (24 h and 48 h) and greater uncertainty at finer scales.

The characteristics and dynamics associated with the distribution, intensity, and triggering factors of local severe precipitation in Zhejiang Province induced by Super Typhoon Soudelor (2015) were investigated using mesoscale surface observations, radar reflectivity, satellite nephograms, and the final (FNL) analyses of the Global Forecasting System (GFS) of the National Center for Environmental Prediction (NCEP). The rainfall processes during Soudelor’s landfall and translation over East China could be separated into four stages based on rainfall characteristics such as distribution, intensity, and corresponding dynamics. The relatively less precipitation in the first stage resulted from interaction between the easterly wind to the north flank of this tropical cyclone (TC) and the coastal topography along the southeast of Zhejiang Province, China. With landfall of the TC in East China during the second stage, precipitation maxima occurred because of interaction between the TC’s principal rainbands and the local topography from northeastern Fujian Province to southwestern Zhejiang Province. The distribution of precipitation presented significant asymmetric features in the third stage with maximal rainfall bands in the northeast quadrant of the TC when Soudelor’s track turned from westward to northward as the TC decayed rapidly. Finally, during the northward to northeastward translation of the TC in the fourth stage, the interaction between a mid-latitude weather system and the northern part of the TC resulted in transfer of the maximum rainfall from the north of Zhejiang Province to the north of Jiangsu Province, which represented the end of rainfall in Zhejiang Province. Further quantitative calculations of the rainfall rate induced by the interaction between local topography and TC circulation (defined as “orographic effects”) in the context of a one-dimensional simplified model showed that orographic effects were the primary factor determining the intensity of precipitation in this case, and accounted for over 50% of the total precipitation. The asymmetric distribution of the TC’s rainbands was closely related to the asymmetric distribution of moisture resulted from changes of the TC’s structure, and led to asymmetric distribution of local intense precipitation induced by Soudelor. Based on analysis of this TC, it could be concluded that local severe rainfall in the coastal regions of East China is closely related to changes of TC structure and intensity, as well as the outer rainbands. In addition, precipitation intensity and duration will increase correspondingly because of the complex interactions between the TC and local topography, and the particular TC track along large-scale steering flow. The results of this study may be useful for the understanding, prediction, and warning of disasters induced by local extreme rainfall caused by TCs, especially for facilitating forecasting and warning of flooding and mudslides associated with torrential rain caused by interactions between landfalling TCs and coastal topography.

Effect of orography on tropical rain drop size distribution (DSD), which was not well known, is evidenced through the present study. DSD is the number of raindrops/unit volume/diameter interval, which tells about the underlying physics of rainfall process. Rain DSD was studied, using a Joss–Waldvogel disdrometer, at three coastal and a hill station in the Tropics. The variation in the characteristics of three physically significant parameters derived from the DSD with rain rate clearly unraveled the effect of orography on rain physics. The orographic rain appears to have larger drops compared with nonorographic rains when rain rate is high.

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

The global atmospheric models based on the Multi‐scale Modeling Framework (MMF) are able to explicitly resolve subgrid‐scale processes by using embedded 2‐D Cloud‐Resolving Models (CRMs). Up to now, however, those models do not include the orographic effects on the CRM grid scale. This study shows that the effects of CRM grid‐scale orography can be simulated reasonably well by the Quasi‐3‐D MMF (Q3D MMF), which has been developed as a second‐generation MMF. In the Q3D framework, the surface topography can be included in the CRM component by using a block representation of the mountains, so that no smoothing of the topographic height is necessary. To demonstrate the performance of such a model, the orographic effects over a steep mountain are simulated in an idealized experimental setup with each of the Q3D MMF and the full 3‐D CRM. The latter is used as a benchmark. Comparison of the results shows that the Q3D MMF is able to reproduce the horizontal distribution of orographic precipitation and the flow changes around mountains as simulated by the 3‐D CRM, even though the embedded CRMs of the Q3D MMF recognize only some aspects of the complex 3‐D topography. It is also shown that the use of 3‐D CRMs in the Q3D framework, rather than 2‐D CRMs, has positive impacts on the simulation of wind fields but does not substantially change the simulated precipitation. Key Points: A limited‐area version of the Q3D MMF, which is a second‐generation MMF, is used to simulate the orographic effects over a steep mountain The results show that the distribution of orographic precipitation and the flow changes can be simulated reasonably well by the Q3D MMF Its ability to explicitly simulate the subgrid‐scale processes in the presence of topography can be useful for global modeling

During the Late Triassic, tropical Pangea drifted northward into subtropical latitudes and became progressively drier. In contrast, South China, despite experiencing a similar latitudinal shift, transitioned from an arid to humid climate. Based on the sedimentary record of the Zigui Basin, this study constrains the arid to humid climatic shift to the period of ca. 228−207 Ma in northern South China. Detrital zircons of Triassic ages were derived from the Qinling Orogen. They show an increase in Eu/Eu* ratios and a marked decrease in εHf(t) values. These geochemical trends suggest an increase in crustal thickness from ca. 40−50 km to >60 km in the Late Triassic in the Qinling Orogen and reveal a strong mountain building event with surface elevations of up to 5,000 m. Using these geological records, climate modeling indicates a significant orographic effect on regional precipitation during the Late Triassic in the Eastern Tethys.

The California Undercurrent (CUC) flows poleward mostly along the continental slope. It develops a narrow strip of large negative vertical vorticity through the turbulent boundary layer and bottom stress. In several downstream locations, the current separates, aided by topographic curvature and flow inertia, in particular near Point Sur Ridge, south of Monterey Bay. When this happens the high-vorticity strip undergoes rapid instability that appears to be mesoscale in “eddy-resolving” simulations but is substantially submesoscale with a finer computational grid. The negative relative vorticity in the CUC is larger than the background rotation f , and Ertel potential vorticity is negative. This instigates ageostrophic centrifugal instability. The submesoscale turbulence is partly unbalanced, has elevated local dissipation and mixing, and leads to dilution of the extreme vorticity values. Farther downstream, the submesoscale activity abates, and the remaining eddy motions exhibit an upscale organization into the mesoscale, resulting in long-lived coherent anticyclones in the depth range of 100–500 m (previously called Cuddies) that move into the gyre interior in a generally southwestward direction. In addition to the energy and mixing effects of the postseparation instability, there is are significant local topographic form stress and bottom torque that retard the CUC and steer the mean current pathway.