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Jingyi Niu
,
Ping Xie
,
Yan-Fang Sang
,
Liping Zhang
,
Linqian Wu
,
Yanxin Zhu
,
Bellie Sivakumar
,
Jingqun Huo
, and
Deliang Chen

Abstract

Accurate evaluation of the long-range dependence in hydroclimatic time series is important for understanding its inherent characteristics. However, the reliability of its evaluation may be questioned, since different methods may yield various outcomes. In this study, we evaluate the performances of seven widely used methods for estimating long-range dependence: absolute moment estimation, difference variance estimation, residuals variance estimation, rescaled range estimation, periodogram estimation, wavelet estimation (WLE), and discrete second derivative estimation (DSDE). We examine the influences of six major factors: data length, mean value, three nonstationary components (trend, jump, and periodicity), and one stationary component (short-range dependence). Results from the Monte Carlo experiments show that WLE and DSDE have greater credibility than the other five methods. They also reveal that data length, as well as stationary and nonstationary components, have notable influences on the evaluation of long-range dependence. Following it, we use the WLE and DSDE methods to evaluate the long-range dependence of precipitation during 1961–2015 on the Tibetan Plateau. The results indicate that the precipitation variability mirrors the long-range dependence of the Indian summer monsoon but with obvious spatial difference. This result is consistent with the observations made by previous studies, further confirming the superiority of the WLE and DSDE methods. The outcomes from this study have important implications for modeling and prediction of hydroclimatic time series.

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Shanchuan Xiao
,
Di Cheng
,
Ning Hu
,
Yongwei Wang
,
Huilin Zhang
,
Yuwang Gou
,
Xiang Li
, and
Zhenglin Lv

Abstract

The use of high-albedo roof materials is a simple and effective way to reduce roof temperature, conserve electricity required for air conditioning, and ease power shortages. In this study, three common cooling roof materials, namely, white elastomeric acrylic (AC) paint, a white thermoplastic polyolefin (TPO) membrane, and an aluminum foil composite film–covered styrene–butadiene–styrene bituminous (SBS) membranes, were chosen to conduct a nearly 4-yr experiment in Nanjing, China, to study the difference in surface temperatures (ΔTs ) between the cooling roof materials and concrete. The results showed that even during heatwaves, ΔTs was only 2.1°C (AC), 3.8°C (TPO), and 7.0°C (SBS) on average and 6.9°–18.2°C to the greatest extent, which was far less than those reported by many studies. The intensity of solar radiation where the cooling roof material is used and the roof material’s albedo contribute to the difference in ΔTs . The initial albedo of the AC was 0.53 and dropped to 0.16 due to rapid aging, which is close to that of concrete, in less than 3 months. The albedo of TPO and SBS dropped to 0.16 after 9 and 4.7 years, respectively. Further, SBS is the optimal choice in terms of cost and performance, costing only USD 0.67 m−2 yr−1. However, its albedo exhibits seasonal fluctuations and is significantly affected by air pollution. In particular, particulate matter settles on the surface, thereby decreasing the albedo. Nevertheless, manual cleaning can recover the albedo, extend service life, and further reduce costs.

Open access
Rouyi Jiang
,
Xiaopeng Cui
,
Jian Lin
, and
Jia Tian

Abstract

Southwest China (SWC) possesses complicated topography with frequent geological activities, where heavy precipitation occurs frequently in warm seasons. Few previous studies on extreme precipitation were carried out at hourly scales. In this study, spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms are investigated. Results show that the threshold and intensity of EHP present similar spatial distribution—lower (higher) in the west (east) part of SWC, while the EHP frequency is opposite. The long-term trend of EHP amount shows a more significant positive tendency than that of hourly precipitation (HP) amount due to synchronous increases in intensity and frequency. The significant increasing trend of EHP occurs in areas above 500-m terrain height, with a weak increasing trend below 500 m (e.g., Chongqing and eastern Sichuan). EHP appears mainly from June to August and exhibits a bimodal distribution in diurnal variation. The mechanism analysis demonstrates that occurrences of EHP are generally accompanied by positive anomalies of temperature, humidity, and geopotential height. Anomalous cyclonic circulation can also be found in the low-level wind field. The westward and northward extension of the western North Pacific subtropical high (WNPSH) as well as temperature rise may be the primary reason for the increase of EHP. For Chongqing and eastern Sichuan, the anticyclone circulation in low-level and the significantly weakened water vapor flux convergence cause poor moisture and dynamic conditions, inhibiting the growth of EHP.

Significance Statement

Heavy precipitation occurs frequently during the warm season in Southwest China (SWC), often causing severe impacts on human safety and economic property. This study analyses spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms. The increasing trend of EHP far exceeds that of hourly precipitation (HP), especially in areas above 500 m. The westward and northward extension of the western North Pacific subtropical high (WNPSH) and temperature rise may be the main reason for the increase of EHP. For areas below 500 m (e.g., Chongqing and eastern Sichuan), poor moisture and dynamic conditions inhibited the growth of EHP.

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Megan S. Mallard
,
Kevin D. Talgo
,
Tanya L. Spero
,
Jared H. Bowden
, and
Christopher G. Nolte

Abstract

Phenological indicators (PI) are used to study changes to animal and plant behavior in response to seasonal cycles, and they can be useful to quantify the potential impacts of climate change on ecosystems. Here, multiple global climate models and emission scenarios are used to drive dynamically downscaled simulations using the WRF Model over the contiguous United States (CONUS). The wintertime dormancy of plants [chilling units (CU)], timing of spring onset [extended spring indices (SI)], and frequency of proceeding false springs are calculated from regional climate simulations covering historical (1995–2005) and future periods (2025–2100). Southern parts of the CONUS show projected CU decreases (inhibiting some plants from flowering or fruiting), while the northern CONUS experiences an increase (possibly causing plants to break dormancy too early, becoming vulnerable to disease or freezing). Spring advancement (earlier SI dates) is projected, with decadal trends ranging from approximately 1–4 days per decade over the CONUS, comparable to or exceeding those found in observational studies. Projected changes in risk of false spring (hard freezes following spring onset) vary across members of the ensemble and regions of the CONUS, but generally western parts of the CONUS are projected to experience increased risk of false springs. These projected changes to PI connote significant effects on cycles of plants, animals, and ecosystems, highlighting the importance of examining temperature changes during transitional seasons.

Significance Statement

This study examines how phenological indicators, which track the life cycles of plants and animals, could change from 2025 to 2100 as simulated in a regional climate model over the contiguous United States. Chilling units quantify the presence of cooler weather that can benefit plants prior to their growing season. They are projected to decrease in the southern United States, possibly inhibiting agricultural production. Spring onset is projected to occur earlier in the year, advancing by 1–4 days on average over each future decade. Risk of false springs (damaging hard freezes after spring onset) increases in the western United States. Our findings highlight the need to understand effects of climate change during transitional seasons, which can impact agriculture and ecosystems.

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Darryn W. Waugh
,
Benjamin Zaitchik
,
Anna A. Scott
,
Peter C. Ibsen
,
G. Darrel Jenerette
,
Jason Schatz
, and
Christopher J. Kucharik

Abstract

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Significance Statement

Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

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S. Jamaer
,
D. Allaerts
,
J. Meyers
, and
N. P. M. van Lipzig

Abstract

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean.

Significance Statement

Numerical tools used to simulate the effect of large, offshore wind farms on neighboring farms and the atmosphere are very expensive. Therefore, they can only be computed for a limited number of cases. As temperature is one of the most important parameters in these kinds of simulations, this work provides a new vertical temperature model and an analysis framework that allows for a smart selection of these cases such that they ideally represent the full variation of the atmosphere’s temperature profiles.

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Andrew J. Heymsfield
,
Micael A. Cecchini
,
Andrew Detwiler
,
Ryan Honeyager
, and
Paul Field

Abstract

Measurements from the South Dakota School of Mines and Technology T-28 hail-penetrating aircraft are analyzed using recently developed data processing techniques with the goals of identifying where the large hail is found relative to vertical motion and improving the detection of hail microphysical properties from radar. Hail particle size distributions (PSD) and environmental conditions (temperature, relative humidity, liquid water content, air vertical velocity) were digitally collected by the T28 between 1995 and 2003 and synthesized by Detwiler et al. The PSD were forward modeled by Cecchini et al. to simulate the radar reflectivity of the PSD at multiple radar wavelengths. The T-28 penetrated temperatures primarily between 0° and −10°C. The largest hailstones were sampled near the updraft/downdraft interface. Liquid water contents were highest in the updraft cores, whereas total (liquid + frozen) water contents were highest near the updraft/downdraft interface. The fitted properties of the PSD (intercept and slope) are directly related to each other but do not show any dependence on the region of the hailstorm where sampled. The PSD measurements and the radar reflectivity calculations at multiple radar wavelengths facilitated the development of relationships between the PSD bulk properties—hail kinetic energy and kinetic energy flux—and the radar reflectivity. Rather than using the oft-assumed sphericity and solid ice physical properties, actual measurements of hail properties are used in the analysis. Results from the maximum estimated size of hail (MESH) and vertical integrated liquid water (VIL) algorithms are evaluated based on this analysis.

Significance Statement

Hailstorms in the United States have caused over $10 billion in damage for each of the last 14 years, according to insurance industry estimates (). Algorithms have been developed to identify the presence and size of hail from radar. Numerical simulations of hailstorms have improved significantly since the 1970s, and further improvements will provide better resolution and more accurate estimates of the sizes of hailstones falling to the ground. Measurements of the properties of hailstones—their mass and terminal velocities—have been improved in recent years but in general are not incorporated in the algorithms developed for radar estimates of hail sizes or for the properties of hail used in the model simulations. This study synthesizes in situ aircraft data, computed radar backscatter cross sections, together with recent estimates of the physical characteristics of hailstones to improve the representation of hail in numerical models and quantitative assessment hail properties in storms using weather radar.

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Stephen Jewson

Abstract

We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage, we find that changes in the frequency of category-4 storms dominate. For distributions of annual damage, we find that changes in the frequency of category-4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties, and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward speed, sea level, rainfall, and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change.

Significance Statement

We investigate how changes in the frequencies of hurricanes of different intensities as a result of climate change may contribute to changes in U.S. economic damage due to wind and surge. We find that economic damage will likely increase as a result of projected increases in the frequency of landfalling hurricanes. Analysis of our results shows that increases in the frequency of category-4 storms are the main driver of the changes. Our best estimate results, based on a multimodel ensemble, give modest increases in damage, but within the ensemble there are individual scenarios that give much larger increases in damage. The large range of individual damage estimates is a motivation for continuing efforts to reduce the uncertainty around hurricane projections under climate change.

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Takuto Sato
and
Hiroyuki Kusaka

Abstract

This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.

Significance Statement

Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.

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