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Shiori Sugimoto, Kenichi Ueno, Hatsuki Fujinami, Tomoe Nasuno, Tomonori Sato, and Hiroshi G. Takahashi

Abstract

A numerical experiment with a 2-km resolution was conducted using the Weather Research and Forecasting (WRF) Model to investigate physical processes driving nocturnal precipitation over the Himalayas during the mature monsoon seasons between 2003 and 2010. The WRF Model simulations of increases in precipitation twice a day, one in the afternoon and another around midnight, over the Himalayan slopes, and of the single nocturnal peak over the Himalayan foothills were reasonably accurate. To understand the synoptic-scale moisture transport and its local-scale convergence generating the nocturnal precipitation, composite analyses were conducted using the reanalysis dataset and model outputs. In the synoptic scale, moisture transport associated with the westward propagation of low pressure systems was found when nocturnal precipitation dominated over the Himalayan slopes. In contrast, moisture was directly provided from the synoptic-scale monsoon westerlies for nocturnal precipitation over the foothills. The model outputs suggested that precipitation occurred on the mountain ridges in the Himalayas during the afternoon and expanded horizontally toward lower-elevation areas through the night. During the nighttime, the downslope wind was caused by radiative cooling at the surface and was intensified by evaporative cooling by hydrometeors in the near-surface layer. As a result, convergence between the downslope wind and the synoptic-scale flow promoted nocturnal precipitation over the Himalayas and to the south, as well as the moisture convergence by orography and/or synoptic-scale circulation patterns. The nocturnal precipitation over the Himalayas was not simulated well when we used the coarse topographic resolution and the smaller number of vertical layers.

Open access
Peter E. Goble, Rebecca A. Bolinger, and Russ S. Schumacher

Abstract

Agricultural droughts afflicting the contiguous United States (CONUS) are serious and costly natural hazards. Widespread damage to a single cash crop may be crippling to rural communities that produce it. While drought is insidious in nature, drought indices derived from meteorological data and drought impact reports both provide essential guidance to decision-makers about the location and intensity of developing and ongoing droughts. However, response to dry meteorological conditions is not consistent from one crop type to the next, making crop-specific drought appraisal difficult using weather data alone. Additionally, drought impact reports are often subjective, latent, or both. To rectify this, we developed drought indices using meteorological data, and phenological information for the row crops most commonly grown over CONUS: corn, soybeans, and winter wheat. These are referred to as crop-specific standardized precipitation–evapotranspiration indices (CSPEIs). CSPEIs correlate more closely with end-of-season yields than traditional meteorological indicators for the eastern two thirds of CONUS for corn, and offer an advantage in predicting winter wheat yields for the High Plains. CSPEIs do not always explain a higher fraction of variance than traditional meteorological indicators. In such cases, results provide insight on which meteorological indicators to use to most effectively supplement impacts information.

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Kaihao Long, Dagang Wang, Guiling Wang, Jinxin Zhu, Shuo Wang, and Shuishi Xie

Abstract

The relationship between extreme precipitation intensity and temperature has been comprehensively studied over different regions worldwide. However, the effect of temperature on the spatiotemporal organization of precipitation, which can have a significant impact on precipitation intensity, has not been adequately studied or understood. In this study, we propose a novel approach to quantifying the spatial and temporal concentration of precipitation at the event level and study how the concentration varies with temperature. The results based on rain gauge data from 843 stations in the Ganzhou county, a humid region in south China, show that rain events tend to be more concentrated both temporally and spatially at higher temperature, and this increase in concentration qualitatively holds for events of different precipitation amounts and durations. The effects of temperature on precipitation organization in space and in time differ at high temperatures. The temporal concentration increases with temperature up to a threshold (approximately 24°C) beyond which it plateaus, whereas the spatial concentration keeps rising with temperature. More concentrated precipitation, in addition to a projected increase of extreme precipitation, would intensify flooding in a warming world, causing more detrimental effects.

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J. F. González-Rouco, N. J. Steinert, E. García-Bustamante, S. Hagemann, P. de Vrese, J. H. Jungclaus, S. J. Lorenz, C. Melo-Aguilar, F. García-Pereira, and J. Navarro

Abstract

The representation of the thermal and hydrological states in land surface models is important for a realistic simulation of land–atmosphere coupling processes. The available evidence indicates that the simulation of subsurface thermodynamics in Earth system models is inaccurate due to a zero-heat-flux bottom boundary condition being imposed too close to the surface. To assess the influence of soil model depth on the simulated terrestrial energy and subsurface thermal state, sensitivity experiments have been carried out in piControl, historical, and RCP scenarios. A deeper bottom boundary condition placement has been introduced into the JSBACH land surface model by enlarging the vertical stratification from 5 to 12 layers, thereby expanding its depth from 9.83 to 1416.84 m. The model takes several hundred years to reach an equilibrium state in stand-alone piControl simulations. A depth of 100 m is necessary, and 300 m recommendable, to handle the warming trends in historical and scenario simulations. Using a deep bottom boundary, warming of the soil column is reduced by 0.5 to 1.5 K in scenario simulations over most land areas, with the largest changes occurring in northern high latitudes, consistent with polar amplification. Energy storage is 3–5 times larger in the deep than in the shallow model and increases progressively with additional soil layers until the model depth reaches about 200 m. While the contents of Part I focus on the sensitivity of subsurface thermodynamics to enlarging the space for energy, Part II addresses the sensitivity to changing the space for water and improving hydrological and phase-change interactions.

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N. J. Steinert, J. F. González-Rouco, P. de Vrese, E. García-Bustamante, S. Hagemann, C. Melo-Aguilar, J. H. Jungclaus, and S. J. Lorenz

Abstract

The impact of various modifications of the JSBACH land surface model to represent soil temperature and cold-region hydro-thermodynamic processes in climate projections of the twenty-first century is examined. We explore the sensitivity of JSBACH to changes in the soil thermodynamics, energy balance and storage, and the effect of including freezing and thawing processes. The changes involve 1) the net effect of an improved soil physical representation and 2) the sensitivity of our results to changed soil parameter values and their contribution to the simulation of soil temperatures and soil moisture, both aspects being presented in the frame of an increased bottom boundary depth from 9.83 to 1418.84 m. The implementation of water phase changes and supercooled water in the ground creates a coupling between the soil thermal and hydrological regimes through latent heat exchange. Momentous effects on subsurface temperature of up to ±3 K, together with soil drying in the high northern latitudes, can be found at regional scales when applying improved hydro-thermodynamic soil physics. The sensitivity of the model to different soil parameter datasets is relatively low but shows important implications for the root zone soil moisture content. The evolution of permafrost under preindustrial forcing conditions emerges in simulated trajectories of stable states that differ by 4–6 × 106 km2 and shows large differences in the spatial extent of 105–106 km2 by 2100, depending on the model configuration.

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Daniel Regenass, Linda Schlemmer, Oliver Fuhrer, Jean-Marie Bettems, Marco Arpagaus, and Christoph Schär

Abstract

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

Open access
Pengfei Shi, Jiangyuan Zeng, Kun-Shan Chen, Hongliang Ma, Haiyun Bi, and Chenyang Cui

Abstract

The Tibetan Plateau (TP), known as the “Third Pole,” is a climate-sensitive and ecology-fragile region. In this study, the spatiotemporal trends of soil moisture (SM) and vegetation were analyzed using satellite-based ESA CCI SM and MODIS LAI data, respectively, in the growing season during the last 20 years (2000–19) over the TP covering diverse climate zones. The climatic drivers (precipitation and air temperature) of SM and LAI variations were fully investigated by using both ERA5 reanalysis and observation-based gridded data. The results reveal the TP is generally wetting and significantly greening in the last 20 years. The SM with significant increasing trend accounts for 21.80% (fraction of grid cells) of the TP, and is about twice of the SM with significant decreasing trend (10.19%), while more than half of the TP (58.21%) exhibits significant increasing trend of LAI. Though the responses of SM and LAI to climatic factors are spatially heterogeneous, precipitation is the dominant driver of SM variation with 48.36% (ERA5) and 32.51% (observation-based) precipitation data showing the strongest significant positive partial correlation with SM. Temperature rise largely explains the vegetation greening, though precipitation also plays an important role in vegetation growth in arid and semiarid zones. The combined trend of SM and LAI indicates the TP is mainly composed of wetting and greening areas, followed by drying and greening regions. The change rate of SM is negative at low altitudes and becomes positive as altitude increases, while the LAI value and its change rate decrease as altitude increases.

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Andrew Hoell, Trent W. Ford, Molly Woloszyn, Jason A. Otkin, and Jon Eischeid

Abstract

Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought.

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Zheng Duan, Edward Duggan, Cheng Chen, Hongkai Gao, Jianzhi Dong, and Junzhi Liu

Abstract

Evaluating the accuracy of precipitation products is essential for many applications. The traditional method for evaluation is to calculate error metrics of products with gauge measurements that are considered as ground truth. The multiplicative triple collocation (MTC) method has been demonstrated powerful in error quantification of precipitation products when ground truth is not known. This study applied MTC to evaluate five precipitation products in Germany: two raw satellite-based products (CMORPH and PERSIANN), one reanalysis product (ERA-Interim), one soil moisture–based product (SM2RAIN-ASCAT), and one gauge-based product (REGNIE). Evaluation was performed at the 0.5° daily spatial–temporal scales. MTC involves a log transformation of data, necessitating dealing with zero values in daily precipitation. Effects of 12 different strategies for dealing with zero values on MTC results were investigated. Seven different triplet combinations were tested to evaluate the stability of MTC. Results showed that different strategies for replacing zero values had considerable effects on MTC-derived error metrics, particularly for root-mean-square error (RMSE). MTC with different triplet combinations generated different error metrics for individual products. The MTC-derived correlation coefficient (CC) was more reliable than RMSE. It is more appropriate to use MTC to compare the relative accuracy of different precipitation products. Based on CC with unknown truth, MTC with different triplet combinations produced the same ranking of products as the traditional method. A comparison of results from MTC and the classic TC with additive error model showed the potential limitation of MTC in arid areas or dry time periods with a large ratio of zero daily precipitation.

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Weijiao Wang, Yuqing Zhang, Bin Guo, Min Ji, and Ying Xu

Abstract

Compound droughts and heat waves have garnered increasing attentions due to their disastrous impacts on the structure and function of ecosystems and societies. A drought is generally characterized by a precipitation deficit, and its negative impact can be amplified by the simultaneous occurrence of a heat wave. More recent studies have highlighted the multicharacteristics of compound droughts and heat waves, which may call for improved efforts on assessing the impact of compound extremes. In this study, a compound drought and heat wave magnitude index (CDHMI) is built to characterize the severity of compound extremes in the Huai River basin (HRB) during 1961–2017. The CDHMI considers the impact of both drought/extreme heat conditions and the duration of drought/extreme heat. In addition, the magnitude index has been graded according to the degree of severity to detect the most drastic extreme compound events. The results show that from 1961 to 2017, mild and moderate events occurred more often than severe events. A significant increase in all compound events was observed from 2003 to 2017. Compound drought and heat wave events, especially in drought status, have increased significantly with the global climate warming in recent decades. The assessment of the impact for compound drought and heat wave events over the HRB needs to be improved in the context of global climate changing. Therefore, the CDHMI can be used to accurately assess the risk of compound droughts and heat waves.

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