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Jiaojiao Gou, Chiyuan Miao, Luis Samaniego, Mu Xiao, Jingwen Wu, and Xiaoying Guo

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A long-term spatiotemporally continuous naturalized runoff record, CNRD v1.0, is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model.

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Emily L. Pauline, John A. Knox, Lynne Seymour, and Andrew J. Grundstein

Abstract

The occurrence of extreme weather and climate events has increased in recent decades. This increasing frequency has adversely impacted economic and health outcomes, leading to an increasingly urgent need to study climate extremes. The National Centers for Environmental Information (NCEI) created the Climate Extremes Index (CEI) in 1996 to quantify climate extremes. In this article, we explore the potential for enhancing the CEI via the use of the Z-score statistic to calculate the CEI on a numerical scale, to increase usability at smaller spatial scales, and to allow the creation of a new climate Extremes Vulnerability Index (EVI). The EVI combines the results from the revised CEI with values from the Social Vulnerability Index from the Centers for Disease Control and Prevention (CDC). The EVI can be used by policy-makers, planners, and the public to understand a subregion’s vulnerability to climate extremes. This information from the EVI could then be used to implement policies and changes in infrastructure that mitigate risk in vulnerable climate divisions. In a trial application, it is found that the southeastern and portions of the central United States had the highest levels of vulnerability for the abnormal month of December 2015.

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Ibrahim Hoteit, Yasser Abualnaja, Shehzad Afzal, Boujemaa Ait-El-Fquih, Triantaphyllos Akylas, Charls Antony, Clint Dawson, Khaled Asfahani, Robert J. Brewin, Luigi Cavaleri, Ivana Cerovecki, Bruce Cornuelle, Srinivas Desamsetti, Raju Attada, Hari Dasari, Jose Sanchez-Garrido, Lily Genevier, Mohamad El Gharamti, John A. Gittings, Elamurugu Gokul, Ganesh Gopalakrishnan, Daquan Guo, Bilel Hadri, Markus Hadwiger, Mohammed Abed Hammoud, Myrl Hendershott, Mohamad Hittawe, Ashok Karumuri, Omar Knio, Armin Köhl, Samuel Kortas, George Krokos, Ravi Kunchala, Leila Issa, Issam Lakkis, Sabique Langodan, Pierre Lermusiaux, Thang Luong, Jingyi Ma, Olivier Le Maitre, Matthew Mazloff, Samah El Mohtar, Vassilis P. Papadopoulos, Trevor Platt, Larry Pratt, Naila Raboudi, Marie-Fanny Racault, Dionysios E. Raitsos, Shanas Razak, Sivareddy Sanikommu, Shubha Sathyendranath, Sarantis Sofianos, Aneesh Subramanian, Rui Sun, Edriss Titi, Habib Toye, George Triantafyllou, Kostas Tsiaras, Panagiotis Vasou, Yesubabu Viswanadhapalli, Yixin Wang, Fengchao Yao, Peng Zhan, and George Zodiatis

Abstract

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

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Lei Wang, Tandong Yao, Chenhao Chai, Lan Cuo, Fengge Su, Fan Zhang, Zhijun Yao, Yinsheng Zhang, Xiuping Li, Jia Qi, Zhidan Hu, Jingshi Liu, and Yuanwei Wang

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The TP-River project is constructing a monitoring network for 13 major rivers at the Third Pole to quantify the total river runoff and its response to monsoon and westerly winds.

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N. C. Privé
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Bianca Adler, Alexander Gohm, Norbert Kalthoff, Nevio Babić, Ulrich Corsmeier, Manuela Lehner, Mathias W. Rotach, Maren Haid, Piet Markmann, Eckhard Gast, George Tsaknakis, and George Georgoussis

Abstract

While the exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing, thermally and dynamically driven mesoscale flows substantially contribute to the Earth–atmosphere exchange in the atmospheric boundary layer over mountainous terrain (MoBL). The interaction of these processes acting on multiple scales leads to a large spatial variability in the MoBL, whose observational detection requires comprehensive instrumentation and a sophisticated measurement strategy. We designed a field campaign that targets the three-dimensional flow structure and its impact on the MoBL in a major Alpine valley. Taking advantage of an existing network of surface flux towers and remote sensing instrumentation in the Inn Valley, Austria, we added a set of ground-based remote sensing instruments, consisting of Doppler lidars, a ceilometer, a Raman lidar, and a microwave radiometer, and performed radio soundings and aircraft measurements. The objective of the Cross-Valley Flow in the Inn Valley Investigated by Dual-Doppler Lidar Measurements (CROSSINN) experiment is to determine the mean and turbulent characteristics of the flow in the MoBL under different synoptic conditions and to provide an intensive dataset for the future validation of mesoscale and large-eddy simulations. A particular challenge is capturing the two-dimensional kinematic flow in a vertical plane across the whole valley using coplanar synchronized Doppler lidar scans, which allows the detection of cross-valley circulation cells. This article outlines the scientific objectives, instrument setup, measurement strategy, and available data; summarizes the synoptic conditions during the measurement period of 2.5 months; and presents first results.

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Cecile B. Menard, Richard Essery, Gerhard Krinner, Gabriele Arduini, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Emanuel Dutra, Xing Fang, Charles Fierz, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Hyungjun Kim, Matthieu Lafaysse, Thomas Marke, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Gerd Schädler, Vladimir A. Semenov, Tatiana Smirnova, Ulrich Strasser, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan

Abstract

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

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