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Xiaomin Chen, Ming Xue, Bowen Zhou, Juan Fang, Jun A. Zhang, and Frank D. Marks

Abstract

Horizontal grid spacings of numerical weather prediction models are rapidly approaching O(1) km and have become comparable with the dominant length scales of flows in the boundary layer; within such “gray-zones,” conventional planetary boundary layer (PBL) parameterization schemes start to violate basic design assumptions. Scale-aware PBL schemes have been developed recently to address the gray-zone issue. By performing WRF simulations of Hurricane Earl (2010) at subkilometer grid spacings, this study investigates the effect of the scale-aware Shin–Hong (SH) scheme on the tropical cyclone (TC) intensification and structural changes in comparison to the non-scale-aware YSU scheme it is built upon. Results indicate that SH tends to produce a stronger TC with a more compact inner core than YSU. At early stages, scale-aware coefficients in SH gradually decrease as the diagnosed boundary layer height exceeds the horizontal grid spacing. This scale-aware effect is most prominent for nonlocal subgrid-scale vertical turbulent fluxes, in the nonprecipitation regions radially outside of a vortex-tilt-related convective rainband, and from the early stage through the middle of the rapid intensification (RI) phase. Both the scale awareness and different parameterization of the nonlocal turbulent heat flux in SH reduce the parameterized vertical turbulent mixing, which further induces stronger radial inflows and helps retain more water vapor in the boundary layer. The resulting stronger moisture convergence and diabatic heating near the TC center account for a faster inner-core contraction before RI onset and higher intensification rates during the RI period. Potential issues of applying these two PBL schemes in TC simulations and suggestions for improvements are discussed.

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Eviatar Bach, Safa Mote, V. Krishnamurthy, A. Surjalal Sharma, Michael Ghil, and Eugenia Kalnay

Abstract

Oscillatory modes of the climate system are among its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean—or the ensemble probability distribution—with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method that uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOC-DA). The oscillatory modes are extracted with a time series analysis method called multichannel singular spectrum analysis (M-SSA), and forecast using an analog method. We test these two methods using chaotic toy models with significant oscillatory components and show that they robustly reduce error compared to the uncorrected ensemble. We discuss the applications of this method to improve prediction of monsoons as well as other parts of the climate system. We also discuss possible extensions of the method to other data-driven forecasts, including machine learning.

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Marcos Samuel Matias Ribeiro, Lara de Melo Barbosa Andrade, Maria Helena Constantino Spyrides, Kellen Carla Lima, Pollyane Evangelista da Silva, Douglas Toledo Batista, and Idemauro Antônio Rodrigues de Lara

Abstract

The occurrence of environmental disasters affects different social segments, impacting health, education, housing, economy, and the provision of basic services. Thus, the objective of this study was to estimate the relationship between the occurrence of disasters and extreme climate, sociosanitary, and demographic conditions in the Northeast region of Brazil (NEB) during the period from 1993 to 2013. Initially, we analyzed the spatial pattern of the incidence of events; subsequently, generalized additive models for location, scale, and shape were used to identify and estimate the magnitude of associations between factors. Results showed that droughts are the predominant disasters in NEB representing 81.1% of the cases, followed by events triggered by excessive rainfall such as flash floods (11.1%) and floods (7.8%). Climate conditions presented statistically significant associations with the analyzed disasters, in which indicators of excess rainfall positively contributed to the occurrence of flash floods and floods but negatively contributed to the occurrence of drought. Sociosanitary factors, such as percentage of households with inadequate sewage, waste collection, and water supply, were also positively associated with the model’s estimations, that is, contributing to an increase in the occurrence of events, with the exception of floods, which were not significantly influenced by sociosanitary parameters. A decrease of 19% in the risk of drought occurrence was estimated, on average. On the other hand, events caused by excessive rainfall increased by 40% and 57%, in the cases of flash floods and floods, respectively.

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Zoe Schroder and James B. Elsner

Abstract

Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger-scale environmental factors. Here the authors demonstrate a method to quantify “outbreak”-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least 10 tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least 10 tornadoes and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least 10 tornadoes and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.

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Robert Ricker, Frank Kauker, Axel Schweiger, Stefan Hendricks, Jinlun Zhang, and Stephan Paul

Abstract

We investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and are partitioned into thermodynamic growth and dynamic volume change. Both components are compared with validated sea ice–ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of −2 km3 month−1 yr−1 on average over 2002–19 as derived from satellite observations.

Open access
Brooke Fisher Liu and Anita Atwell Seate

Abstract

Since the tragic tornado outbreaks in central Alabama and Joplin, Missouri, in 2011, the National Weather Service (NWS) has increasingly emphasized the importance of supporting community partners who help to protect public safety. Through impact-based decision support services (IDSS), NWS forecasters develop relationships with their core partners to meet their partners’ decision-making needs. IDSS presents a fundamental shift in NWS forecasting through highlighting the importance of connecting with partners instead of simply providing partners with forecasts. A critical challenge to the effective implementation of IDSS is a lack of social science research evaluating the success of IDSS. This paper addresses this gap through a cross-sectional survey with 119 NWS forecasters and managers in the central and southern regions of the United States. Findings uncover how NWS forecasters and management team members evaluate the importance of IDSS. Findings also provide a new instrument for NWS field offices to assess and improve their relationships with core partners.

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Ryan L. Fogt and Charlotte J. Connolly

Abstract

Because continuous meteorological observations across Antarctica did not start until the middle of the twentieth century, little is known about the full spatial pattern of pressure variability across the extratropical Southern Hemisphere (SH) in the early twentieth century, defined here as the period from 1905 to 1956. To fill this gap, this study analyzes pressure observations across the SH in conjunction with seasonal pressure reconstructions across Antarctica, which are based on observed station-to-station statistical relationships between pressure over Antarctica and the southern midlatitudes. Using this newly generated dataset, it is found that the early twentieth century is characterized by synchronous but opposite-signed pressure relationships between Antarctica and the SH midlatitudes, especially in austral summer and autumn. The synchronous pressure relationships are consistent with the southern annular mode, extending its well-known influence on SH extratropical pressure since 1957 into the early twentieth century. Apart from connections with the southern annular mode, regional and shorter-duration pressure trends are found to be associated with influences from tropical variability and potentially the zonal wavenumber 3 pattern. Although the reduced network of SH observations and Antarctic reconstruction captures the southern annular mode in the early twentieth century, reanalysis products show varying skill in reproducing trends and variability, especially over the oceans and high southern latitudes prior to 1957, which stresses the importance of continual efforts of historical data rescue in data-sparse regions to improve their quality.

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Nandini Ramesh, Quentin Nicolas, and William R. Boos

Abstract

Over most tropical land areas, the annual peak in precipitation occurs during summer, associated with the local monsoon circulation. However, in some coastal regions in the tropics the bulk of annual precipitation occurs in autumn, after the low-level summer monsoon westerlies have abated. Examples include the Nordeste region of Brazil, southeastern India and Sri Lanka, and coastal Tanzania. Unlike equatorial regions, they receive little rainfall during local spring. Such regions are present along the eastern coasts of nearly all continents, suggesting that they comprise a coherent yet previously unrecognized global phenomenon. In this study, we identify eight tropical locations that experience an “autumn monsoon” and show that this unusual seasonal cycle is generated by similar mechanisms in all of these. When these regions receive their peak rainfall, they lie poleward of the ITCZ in easterly low-level winds. The spatial structure of precipitation in these regions can be explained by their placement to the east of mountain ranges that organize moist convection on their windward sides. However, orographic forcing alone cannot explain their unique seasonal cycle: despite similarities in wind direction, surface humidity, and sea surface temperatures (SSTs) between autumn and spring, these regions receive significantly more rainfall in autumn than in spring. We show that this is due to differences in the large-scale atmospheric stability between the equinoctial seasons, which can be captured by a relative SST metric and is influenced by SSTs in the remote eastern upwelling zones of the Pacific and Atlantic Oceans.

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Oscar Guzman and Haiyan Jiang

Abstract

Based on 19 years of precipitation data collected by the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission, a comparison of the rainfall produced by tropical cyclones (TCs) in different global basins is presented. A total of 1789 TCs were examined in the period from 1998 to 2016 by taking advantage of more than 47 737 observations of TRMM and GPM 3B42 multisatellite-derived rainfall amounts. The axisymmetric component of the TC rainfall is analyzed in all TC-prone basins. The resulting radial profiles show that major hurricanes in the Atlantic basin exhibit significantly heavier inner-core rainfall rates than those in any other basins. To explain the possible causes of this difference, rainfall distributions for major hurricanes are stratified according to different TC intensity and environmental variables. Based on the examination of these parameters, we found that the stronger rainfall rates in the Atlantic major hurricanes are associated with higher values of convective available potential energy, drier relative humidity in the low to middle troposphere, colder air temperature at 250 hPa, and stronger vertical wind shear than other basins. These results have important implications in the refining of our understanding of the mechanisms of TC rainfall.

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Adrian Brügger, Christina Demski, and Stuart Capstick

Abstract

The proportion of the world’s population exposed to above-average monthly temperatures has been rising consistently in recent decades and will continue to grow. This and similar trends make it more likely that people will personally experience extreme weather events and seasonal changes related to climate change. A question that follows from this is to what extent experiences may influence climate-related beliefs, attitudes, and the willingness to act. Although research is being done to examine the effects of such experiences, many of these studies have two important shortcomings. First, they propose effects of experiences but remain unclear on the psychological processes that underlie those effects. Second, if they do make assumptions about psychological processes, they do not typically corroborate them with empirical evidence. In other words, a considerable body of research in this field rests on relatively unfounded intuitions. To advance the theoretical understanding of how experiences of climate change could affect the motivation to act on climate change, we introduce a conceptual framework that organizes insights from psychology along three clusters of processes: 1) noticing and remembering, 2) mental representations, and 3) risk processing and decision-making. Within each of these steps, we identify and explicate psychological processes that could occur when people personally experience climate change, and we formulate theory-based, testable hypotheses. By making assumptions explicit and tying them to findings from basic and applied research from psychology, this paper provides a solid basis for future research and for advancing theory.

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