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Jefferson S. Wong, Xuebin Zhang, Shervan Gharari, Rajesh R. Shrestha, Howard S. Wheater, and James S. Famiglietti

Abstract

Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km2, for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month-1, respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

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Mengye Chen, Zhi Li, Shang Gao, Xiangyu Luo, Oliver L. Wing, Xinyi Shen, Jonathan J. Gourley, Randall L. Kolar, and Yang Hong

Abstract

As climate change will increase the frequency and intensity of precipitation extremes and coastal flooding, there is a clear need for an integrated hydrology and hydraulic system that has the ability to model the hydrologic conditions over a long period and the flow dynamic representations of when and where the extreme hydrometeorological events occur. This system coupling provides comprehensive information (flood wave, inundation extents and depths) about coastal flood events for emergency management and risk minimization. This study provides an integrated hydrologic and hydraulic coupled modeling system that is based on the Coupled Routing and Excessive Storage (CREST) model and the Australia National University- Geophysics Australia (ANUGA) model to simulate flood. Forced by the near-real-time Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimates (QPEs), this integrated modeling system was applied during the 2017 Hurricane Harvey event to simulate the streamflow, the flood extent, and the inundation depth. The results were compared with post-event Water High Mark (WHM) survey data and its interpolated flood extent by the United States Geological Survey (USGS) and the Federal Emergency Management Agency (FEMA) flood insurance claims, as well as a satellite-based flood map, the National Water Model (NWM) and the Fathom (LISFLOOD-FP) model simulated flood map. The proposing hydrologic and hydraulic model simulation indicated that it could capture 87% of all flood insurance claims within the study area, and the overall error of water depth was 0.91 meters, which is comparable to the mainstream operational flood models (NWM and Fathom).

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Astrid Pacini, Robert S. Pickart, Isabela A. Le Bras, Fiammetta Straneo, N.P. Holliday, and M.A. Spall

Abstract

The boundary current system in the Labrador Sea plays an integral role in modulating convection in the interior basin. Four years of mooring data from the eastern Labrador Sea reveal persistent mesoscale variability in the West Greenland boundary current. Between 2014 and 2018, 197 mid-depth intensified cyclones were identified that passed the array near the 2000 m isobath. In this study, we quantify these features and show that they are the downstream manifestation of Denmark Strait Overflow Water (DSOW) cyclones. A composite cyclone is constructed revealing an average radius of 9 km, maximum azimuthal speed of 24 cm/s, and a core propagation velocity of 27 cm/s. The core propagation velocity is significantly smaller than upstream near Denmark Strait, allowing them to trap more water. The cyclones transport a 200-m thick lens of dense water at the bottom of the water column, and increase the transport of DSOW in the West Greenland boundary current by 17% relative to the background flow. Only a portion of the features generated at Denmark Strait make it to the Labrador Sea, implying that the remainder are shed into the interior Irminger Sea, are retroflected at Cape Farewell, or dissipate. A synoptic shipboard survey east of Cape Farewell, conducted in summer 2020, captured two of these features which shed further light on their structure and timing. This is the first time DSOW cyclones have been observed in the Labrador Sea—a discovery that could have important implications for interior stratification.

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Sarah Tessendorf, Allyson Rugg, Alexei Korolev, Ivan Heckman, Courtney Weeks, Gregory Thompson, Darcy Jacobson, Dan Adriaansen, and Julie Haggerty

Abstract

Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (> 500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in-situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

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Dana M. Tobin and Matthew R. Kumjian

Abstract

A unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in co-polar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle, and the orientation of this liquid with respect to the horizontal.

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Inez Z. Ponce de Leon

Abstract

Supertyphoon Haiyan hit the Philippines in 2013, causing massive damage and loss of lives. The media blamed the government for faulty warnings, including using the term "storm surge", which people reportedly did not understand. As a result, the national agency tasked with disaster risk management recommended translating the term for better response in future storms. Such an approach shortchanges the complexity of risk construction, and dismisses the possibility that different communities also have different understandings of risk. In this study, the researcher examined the special case of Coron, Palawan: a major tourist destination that is hardly hit by storms, but which became the site of Haiyan's last landfall. Guided by Encoding-Decoding Theory, the researcher interviewed local government officials, and carried out focus group discussions with representatives of two communities (whose names have been hidden under pseudonyms for this study): Central, close to the municipal center; and Island, a coastal village far away from potential aid and rescue. The researcher found a portrait of contrasts that split Coron: a mayor who surrendered all control and a risk management officer who planned for long- term hazard response; Island waiting for government instructions despite knowing about storm behavior and Central taking the initiative to create long term solutions. Island also knew what storm surges were, and did not need translation of the term. These findings show that risk constructions can differ even at the municipal level, which should prompt further research into the role of local knowledge in understanding risk and hazard warnings.

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Clay S. Tucker, Jill C. Trepanier, Pamela B. Blanchard, Ed Bush, James W. Jordan, Mark J. Shafer, and John Andrew Nyman

Abstract

Environmental education is key in solving environmental problems and for producing a future workforce capable of solving issues of climate change. Over the last two decades, the Coastal Roots Program at Louisiana State University (LSU) has reached more than 26,676 K-12 students in Louisiana to teach them environmental science and has brought them to restoration sites to plant 194,336 school-grown trees and grasses. The co-directors of Coastal Roots are continually searching for opportunities to enrich the experience of teachers and students in connecting school subjects, Coastal Roots, and stewardship. In school year 2018–2019, students in five local schools entered a pilot program to learn how tree-ring science informs environmental science broadly. During their scheduled restoration planting trips, students were asked to collect the following tree data: tree cores, tree height, tree diameter, tree species, and global positioning system location points. Datawere given to scientists atLSUfor preliminary analysis, and graphical representation of the data were shown to the students for their interpretation. Results from this program indicate that bringing students into the field and teaching them a newscientific skill improved their understanding of environmental science and their role in coastal restoration, and tree-ring data showed significant correlations to various climate parameters in Louisiana. Additionally, we find that bringing this knowledge to teachers allows the knowledge to spread for multiple generations of students. Here we present tree-ring data from this project, lessons learned during the pilot program, advantages to student-based citizen science, and recommendations for similar programs.

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Jing-Yi Zhuo and Zhe-Min Tan

Abstract

A deep learning-based method augmented by prior knowledge of tropical cyclones (TCs), called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) imagery over the North Atlantic Ocean. While standard deep learning practices have many advantages over conventional analysis approaches and can produce reliable estimates of TCs, the data-driven models informed by machine-readable physical knowledge of TCs could achieve higher performance. To this end, two approaches are explored to develop the physics-augmented DeepTCNet: (i) infusing the auxiliary physical information of TCs into models for single-task learning; (ii) learning auxiliary physical tasks for multi-task learning. More specifically, augmented by auxiliary information of TC fullness (a measure of the radial decay of the TC wind field), the DeepTCNet yielded a 12% improvement in estimating TC intensity over the non-augmented one. By learning TC wind radii and auxiliary TC intensity task simultaneously, the model’s wind radii estimation skill is improved by 6% over only learning four wind radii tasks, and by 9% over separately learning a single wind radii task. The evaluation results showed that the DeepTCNet is in-line with the Satellite Consensus technique (SATCON) but systematically outperforms the Advanced Dvorak Technique (ADT) at all intensity scales with an averaged 39% enhancement in TC intensity estimation. The DeepTCNet also surpasses the Multi-platform Tropical Cyclone Surface Wind Analysis technique (MTCSWA) with an average improvement of 32% in wind radii estimation.

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Benjamin Pohl, Thomas Saucède, Vincent Favier, Julien Pergaud, Deborah Verfaillie, Jean-Pierre Féral, Ylber Krasniqi, and Yves Richard

Abstract

Daily weather regimes are defined around the Kerguelen Islands (Southern Ocean) based on daily 500 hPa geopotential height anomalies derived from the ERA5 ensemble reanalysis over the period 1979-2018. Ten regimes are retained as significant. Their occurrences are highly consistent across reanalysis ensemble members. Regimes show weak seasonality and non-significant long-term trends in their occurrences. Their sequences are usually short (1-3 days), with extreme persistence values above 10 days. Seasonal regime frequency is mostly driven by the phase of the Southern Annular Mode over Antarctica, mid-latitude dynamics over the Southern Ocean like the Pacific South American mode, and to a lesser extent, tropical variability, with significant but weaker relationships with El Niño Southern Oscillation. At the local scale over the Kerguelen Islands, regimes have a strong influence on measured atmospheric and oceanic variables, including minimum and maximum air temperature, mostly driven by horizontal advections, sea water temperature recorded 5 m below the surface, wind speed and sea level pressure. Relationships are weaker for precipitation amounts. Regimes also modify regional contrasts between observational sites in Kerguelen, highlighting strong exposure contrasts. The regimes allow improving our understanding of weather and climate variability and interactions in this region; they will be used in future work to assess past and projected long-term circulation changes in the southern mid-latitudes.

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Camille Hankel and Eli Tziperman

Abstract

Winter Arctic sea-ice loss has been simulated with varying degrees of abruptness across Global Climate Models (GCMs) run in the Coupled Model Intercomparison Project 5 (CMIP5) under the high-emissions extended RCP8.5 scenario. Previous studies have proposed various mechanisms to explain modeled abrupt winter sea-ice loss, such as the existence of a wintertime convective cloud feedback or the role of the freezing point as a natural threshold, but none have sought to explain the variability of the abruptness of winter sea-ice loss across GCMs. Here we propose a year-to-year local positive feedback cycle, in which warm, open oceans at the start of winter allow for the moistening and warming of the lower atmosphere, which in turn increases the downwards clear-sky longwave radiation at the surface and suppresses ocean freezing. This leads to delayed and diminished winter sea-ice growth, and allows for increased shortwave absorption due to lowered surface albedo during springtime. Finally, the ocean stores this additional heat throughout the summer and fall seasons, setting up even warmer ocean conditions that lead to further sea-ice reduction. We show that the strength of this feedback, as measured by the partial temperature contributions of the different surface heat fluxes, correlates strongly with the abruptness of winter sea-ice loss across models. Thus, we suggest that this feedback mechanism may explain inter-model spread in the abruptness of winter sea-ice loss. In models where the feedback mechanism is strong, this may indicate the possibility of hysteresis and thus irreversibility of sea-ice loss.

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