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Yumeng Liu, Xianhong Meng, Lin Zhao, Zhaoguo Li, Hao Chen, Lunyu Shang, Shaoying Wang, Lele Shu, and Guangwei Li

Abstract

Under the intensification of global warming, the characteristics of the Three Rivers source region (TRSR; i.e., headwaters of the Yellow River, the Yangtze River, and the Lancang River) in China were diagnosed in the summer season from 1979 to 2015 using observations and reanalysis data. The diagnoses indicate that summer precipitation decreased from 1979 to 2002 [by 9.01 mm day−1 (10 yr)−1; p < 0.05 by Student’s t test] and increased significantly after 2002 [by 5.52 mm day−1 (10 yr)−1]. This abrupt change year (2002) was further confirmed by the cumulative anomaly method, the moving t-test method, and the Yamamoto method. By compositing the thermodynamics before and after the abrupt change year (2002), the results reveal that increased water vapor and more substantial lower-level convergence were present over the TRSR during 2003–15. This marked interdecadal variability in the TRSR summer precipitation responded to the interdecadal position and intensity of the large-scale forcing East Asian westerly jet (EAWJ), which is significantly modulated by the low-frequency variability associated with Southern Oscillation index. The connection between the interannual TRSR precipitation and the location and intensity of EAWJ was also explored. The position index of the EAWJ is negatively (with correlation coefficient R of −0.446; p < 0.05 by Student’s t test) correlated with the precipitation over the TRSR, implying that southward and northward years of EAWJ are respectively associated with intensifying and weakening the TRSR summer precipitation, whereas the intensity of EAWJ is insignificantly correlated with the TRSR summer precipitation.

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Seung-Hee Ham, Seiji Kato, Fred G. Rose, Sunny Sun-Mack, Yan Chen, Walter F. Miller, and Ryan C. Scott

Abstract

Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.

Significance Statement

The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.

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Stephen Jewson

Abstract

A 2020 metastudy by Knutson et al. gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. The results form a great resource for those who model the impacts of tropical cyclones. However, a number of steps of processing may be required to use the results in practice. These include interpolation in time, distribution fitting, and reverse engineering of correlations. In this paper we study another processing step that may be required, which is adjusting the frequency change results so that they apply to landfalling frequencies. An adjustment is required because the metastudy results give frequency adjustments as a function of storm lifetime maximum intensity rather than landfall intensity. Increases in the frequency of category-4 and category-5 storms, by lifetime maximum intensity, then contribute to increases in the frequencies of storms of all intensities at landfall. We consider North Atlantic Ocean storms and use historical storm information to quantify this effect as a function of landfall intensity and region. Whereas the original metastudy results suggest that the mean frequency of category-3 storms will decrease, our analysis suggests that the mean frequency of landfalling category-3 storms will increase. Our results are highly uncertain, particularly because we assume that tracks and genesis locations of storms will not change, even though some recent climate model results suggest otherwise. However, making the adjustments we describe is likely to be a better way to model future landfall risk than applying the original metastudy frequency changes directly at landfall.

Significance Statement

A recent metastudy gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. For the North Atlantic Ocean, we show how to convert these results to changes at landfall. This conversion increases the changes in the frequencies of storms in intensity categories 0–3, and, in particular, the mean frequency change of storms in category 3 flips from decreasing to increasing in most regions.

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Yi-Chuan Lu and David M. Romps

Abstract

The heat index is a widely used measure of apparent temperature that accounts for the effects of humidity using Steadman’s model of human thermoregulation. Steadman’s model, however, gives unphysical results when the air is too hot and humid or too cold and dry, leading to an undefined heat index. For example, at a relative humidity of 80%, the heat index is only defined for temperatures in the range of 288–304 K (59°–88°F). Here, Steadman’s thermoregulation model is extended to define the heat index for all combinations of temperature and humidity, allowing for an assessment of Earth’s future habitability. The extended heat index can be mapped onto physiological responses of an idealized human, such as heat exhaustion, heat stroke, and even heat death, providing an indication of regional health outcomes for different degrees of global warming.

Significance Statement

The existing heat index is well-defined for most combinations of high temperature and humidity experienced on Earth in the preindustrial climate, but global warming is increasingly generating conditions for which the heat index is undefined. Therefore, an extension of the original heat index is needed. When extending the heat index, we use the same physiological model as in the original work of Steadman to ensure backward compatibility. Following Steadman, each value of the heat index is mapped onto a measurable physiological variable, which can be useful for assessing the health impacts of various combinations of temperature and humidity, especially for outdoor workers.

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Kip F. Nielsen and David A. Rahn

Abstract

Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city.

Significance Statement

Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.

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Zhouliang Sun, Yanli Liu, Jianyun Zhang, Hua Chen, Zhangkang Shu, Xin Chen, Junliang Jin, Tiesheng Guan, Cuishan Liu, Ruimin He, and Guoqing Wang

Abstract

Water resources severely constrain high-quality development in the Yellow River basin (YRB). Predicting the trend of precipitation on the basis of satisfying precision has important guiding significance for future regional development. Using the projected precipitation in 12 CMIP6 models, this study applied the most appropriate correction method for each model from four quantile-mapping methods and projected future changes of annual precipitation in the YRB and three key regions. The projection uncertainty was quantitatively assessed by addressing model spread (MS) and range. The precipitation anomaly under all four scenarios would increase for the YRB and key regions. The increasing rates (the linear coefficient) from Shared Socioeconomic Pathway 126 (SSP126) to SSP585 were 30–62, 60–103, 84–122, and 134–204 mm (100 yr)−1, respectively. The largest increase was the sediment-yielding region, which reached about 40–60 mm in 2031–60 and 70–125 mm in 2061–90. The 400-mm isohyet was projected to move continuously to the northwest in the future. The uncertainty quantified by MS was reduced by 85.9%–94.6%, and projection ranges were less than 50 mm (about 10% of climatology) in most parts of YRB. From the increasing trend of future precipitation in the YRB, it can be inferred that the arid region will shrink. It may be a good opportunity to implement ecological conservation and high-quality development of the YRB successfully.

Significance Statement

We want to understand the spatial–temporal evolution pattern of future precipitation in the Yellow River basin (YRB) under climate change scenarios. In the future, the precipitation in the YRB and the three key regions will increase, with the sediment-yielding region increasing the most, and the arid region will shrink. Our findings confirm that the spatial–temporal patterns of precipitation in the YRB will change significantly under climate change scenarios. These findings will guide ecological protection and regional social and economic development in the YRB. Future research should focus on adaptation strategies of agricultural production patterns to climate change.

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Robert H. Nazarian, James V. Vizzard, Carissa P. Agostino, and Nicholas J. Lutsko

Abstract

The northeast United States is a densely-populated region with a number of major cities along the climatological storm track. Despite its economic and social importance, as well as the area’s vulnerability to flooding, there is significant uncertainty regarding future trends in extreme precipitation over the region. Here, we undertake a regional study of the projected changes in extreme precipitation over the NEUS through the end of the 21st century using an ensemble of high-resolution, dynamically-downscaled simulations from the NA-CORDEX project. We find that extreme precipitation increases throughout the region, with the largest changes in coastal regions and smaller changes inland. These increases are seen throughout the year, though the smallest changes in extreme precipitation are seen in the summer, in contrast to earlier studies. The frequency of heavy precipitation also increases, such that there are relatively fewer days with moderate precipitation and relatively more days with either no or strong precipitation. Averaged over the region, extreme precipitation increases by +3-5%/°C of local warming, with the largest fractional increases in southern and inland regions, and occurring during the winter and spring seasons. This is lower than the +7%/°C rate expected from thermodynamic considerations alone, and suggests that dynamical changes damp the increases in extreme precipitation. These changes are qualitatively robust across ensemble members, though there is notable intermodel spread associated with models’ climate sensitivity and with changes in mean precipitation. Together, the NA-CORDEX simulations suggest that this densely populated region may require significant adaptation strategies to cope with the increase in extreme precipitation expected at the end of the next century.

Open access
Patrick Hawbecker and Jason C. Knievel

Abstract

Simulations of Chesapeake Bay breezes are performed with varying water surface temperature (WST) datasets and formulations for the diurnal cycle of WST in order to determine if more accurate depictions of water surface temperature improve prediction of bay breezes. The accuracy of simulations is measured against observed WST, inland wind speed and temperature, and in simulations’ ability to detect bay breezes via a detection algorithm developed for numerical model output.

Missing WST data are found to be problematic within the Weather Research and Forecasting (WRF) model framework, especially when activating the prognostic equation for skin temperature, sst_skin. This is alleviated when filling all missing WST values with skin temperature values within the initial and boundary conditions. Performance of bay breeze prediction is shown to be somewhat associated with the resolution of the WST dataset. Further, model performance in simulating WST as well as in simulating the Chesapeake Bay breeze is improved when diurnal fluctuations of WST are considered via the sst_skin option. Prior to running simulations, model performance in simulating the bay breeze can be accurately predicted through the use of a simple formulation.

Open access
Émilie Bernard, Cécile de Munck, and Aude Lemonsu

Abstract

Cities develop a specific climate related to their morphology and the materials that constitute them. The addition of vegetation in urban areas induces cooling and shading effects that can modify local climate and thermal comfort conditions. The Town Energy Balance (TEB) urban canopy model offers several configurations for a more or less fine-tuned consideration of natural covers and associated physical processes in the urban environment. This study aims to evaluate the sensitivity of TEB to the representation of vegetation and the resolution of the chosen databases in the simulation of microclimatic variables, at the scale of a heterogeneous urban neighborhood located in Toulouse, France. First, the effect of the improved description of the vegetation input to the model is highlighted by comparing the results obtained with a readily available national database and then with a very-high-resolution satellite-derived vegetation database. Second, the two vegetation parameterizations, with or without explicit tree stratum, that are available in the TEB model are evaluated and compared. Measurements carried out on specific routes and stop points in a neighborhood of Toulouse allowed microclimatic variables to be evaluated. Results show that refining the vegetation database can somehow improve the modeling of air temperature. As a result of enhancing the vegetation description in the model, that is, physical processes associated with the presence of trees in urban canyons, the air temperature, but also the wind and the thermal comfort index, are better simulated. These results are encouraging for the use of TEB as a decision support tool for urban planning purposes.

Open access
Randy J. Chase, Stephen W. Nesbitt, Greg M. McFarquhar, Norman B. Wood, and Gerald M. Heymsfield

Abstract

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

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