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An array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure. Plain Language Summary: The atmospheric profile can be visualized as a three‐dimensional curve representing pressure, temperature, and humidity. By utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphere. In this paper, we confirmed the accuracy of atmospheric profile representation using signatures and introduced the characteristics of signatures revealed through machine‐learning models. The description of profiles by the signature was confirmed with adequate accuracy. Moreover, the model demonstrated robust annual accuracy, with minimal temperature and water vapor discrepancies. It effectively captured the vertical structure and instability of the atmosphere, even during heavy rainfall, characterized by significant temperature and water vapor content fluctuations. Key Points: The utilization possibility of the Signature method for atmospheric profiles was confirmedBy utilizing the Signature method, we can measure and quantify the profile's curve, allowing for comprehensive modeling of the atmosphereThe machine‐learning model developed with the signature can predict the profiles with high annual accuracy [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

This study addresses a critical gap in understanding anthropogenic influences on tropical climate dynamics by investigating the impact of aerosol‐cloud interactions on large‐scale circulation. Despite extensive research on greenhouse gas‐induced warming and its effects on tropical circulation, the impact of aerosols, particularly their interactions with clouds, on large‐scale circulation remains understudied. Utilizing large‐domain radiative convective equilibrium cloud‐resolving simulations, this research demonstrates that increasing aerosol concentration intensifies tropical overturning circulation, evaluated at the mid‐troposphere (I) $(\mathcal{I})$, strongly correlating with domain mean cloud and radiative properties. Employing a weak temperature gradient approximation, I attribute variations in I $\mathcal{I}$ to changes in clear‐sky radiative cooling rather than stability. These radiative cooling changes are linked to humidity changes driven by warm rain suppression by aerosols. This study's findings underscore the need to take into account microphysical changes, particularly aerosol concentrations, when studying anthropogenic effects on tropical circulation. Plain Language Summary: Clouds intricately couple with the large‐scale overturning circulation in the tropics. Aerosols, small particles suspended in the atmosphere, interact with clouds. Thus, changes in aerosols concentration (Na) have the potential to affect cloud properties and by that the large‐scale circulation, with important implications for tropical climate and climate change. Despite this crucial relationship, the subject has been insufficiently explored. In this paper, I use a simplified, yet realistic, representation of the tropical atmosphere—radiative convective equilibrium large‐domain cloud‐resolving simulations—to investigate the effect of aerosol‐cloud interaction on overturning tropical circulation. By varying Na under diverse sea surface temperatures, I demonstrate that the strength of the overturning circulation intensifies with increasing Na. Utilizing a semi‐analytical expression for subsidence strength in clear‐sky tropical regions, I show that the intensified overturning circulation with increased Na can be attributed to heightened humidity in the mid‐troposphere, leading to intense radiative cooling. This mid‐tropospheric humidification can be explained by warm rain suppression by aerosols, which fosters stronger humidity mixing between clouds and their surroundings. These findings underscore the need to consider aerosol concentration changes when examining anthropogenic impacts on tropical circulation. Key Points: The overturning tropical circulation intensifies with aerosol concentration in a radiative convective equilibrium modelChanges in circulation strength can be explained by changes in diabatic subsidence driven by radiative changesRadiative changes can be explained by increased humidity at the mid‐troposphere due to warm rain suppression [ABSTRACT FROM AUTHOR]

Earth Observation Satellite (EOS)‐04 launched on 14 February 2022, carries a C‐band Synthetic Aperture Radar (SAR) for Agriculture, Forestry, Hydrology, and Flood mapping applications. In this paper, we have used C‐band SAR images and near‐simultaneous observations from the global precipitation measurements (GPM) to study the signatures of multiple convective rain cells. The bright patches are found on C‐band SAR imagery, which depicts the information of hydrometeors such as graupels or hails in the melting layer. For the first time, unambiguously estimated the diameter of the convective core rain cells from the C‐band SAR backscattered signal and compared with near‐simultaneous observations from GPM‐GMI and Ku‐band radar to confirm our findings. Thus, the present study demonstrates the potential of C‐band SAR for identifying the signatures of convective rain cells. Plain Language Summary: Earth Observation Satellite‐04 launched on 14 February 2022, carries a C‐band Synthetic Aperture Radar (SAR) for Agriculture, Forestry, Hydrology, and Flood mapping applications. The Convective clouds produce a significant amount of precipitation, and the signature of convective rain cells appeared as bright oceanic patches on the SAR imagery. The present study demonstrates the potential of C‐band SAR for the signatures of convective rain cells and near‐simultaneous observations from global precipitation measurements to confirm our findings. Key Points: The present study brought out the signatures of convective rain cells using C‐band Synthetic Aperture Radar (SAR) onboard Earth Observation Satellite ‐04 satelliteFor the first time, unambiguously estimated the diameter of the convective core rain cells from the C‐band SAR backscattered signalWe compared near‐simultaneous observations from GPM‐GMI and Ku‐band precipitation radar to confirm the present findings [ABSTRACT FROM AUTHOR]

The North American Monsoon (NAM) is a critical component of the hydrologic cycle in southwestern North America. However, our understanding of the long‐term evolution of this monsoon system is incomplete due to a paucity of long‐term paleoclimatic records from the core and periphery of the NAM domain. C. Routson et al. (2022, https://doi.org/10.1029/2022gl099772) leverage a new compilation of Holocene proxy records to generate a new composite index of NAM evolution over the Holocene. They identify a relationship between NAM strength and the latitudinal temperature gradient over the Holocene. The authors suggest that shifts in the strength of the westerlies altered NAM convection via ventilation, a process whereby the large‐scale circulation imports low moist entropy air into the monsoon domain. These results highlight the importance of paleoclimatic records, especially from past "warm" intervals, for generating new insights about the evolution of regional monsoons. Plain Language Summary: The North American Monsoon is a circulation that brings summer rain to regions of the US southwest and western Mexico. Despite the fact that this circulation provides a majority of the precipitation in many parts of the arid southwest, we do not fully understand the factors that govern long‐term changes in this monsoon. A new paper by C. Routson et al. (2022, https://doi.org/10.1029/2022gl099772) uses proxy indicators of past summer rainfall, stretching from Central America to Arizona, to reconstruct changes in the monsoon over the geologic epoch known as the Holocene, or the last 11,000 years of Earth history. The results of this study suggest that Holocene changes in the temperature difference between the equator to pole, known as the "latitudinal temperature gradient," exerted an important influence over monsoon strength in the southwest. This has implications for the future of the summer monsoon, especially as the high latitudes of the northern hemisphere are expected to warm in the future. Key Point: The strength North American Monsoon tracks changes in the position of the midlatitude westerlies over the Holocene [ABSTRACT FROM AUTHOR]

The North American monsoon (NAM) is an important source of rainfall to much of Mexico and southwestern United States. Westerly winds (westerlies) can suppress monsoon circulation and impact monsoon timing, intensity, and extent. Recent Arctic warming is reducing the temperature gradient between the equator and the pole, which could weaken the westerlies; however, the implications of these changes on the NAM are uncertain. Here we present a new composite index of the Holocene NAM. We find monsoon strength reached a maximum circa 7,000 years ago and has weakened since then. Proxy observations of temperature, hydroclimate and upwelling, along with model simulations, show that the NAM was modulated by the westerlies over the Holocene. If the observed Holocene pattern holds for current warming, a weaker meridional temperature gradient and weaker westerlies could lead to a stronger future NAM. Plain Language Summary: The North American monsoon (NAM) is a main source of summertime rain in Mexico and southwestern North America. Here we explore how the NAM rainfall has changed over the past 11,000 years using information from natural archives of past climate (called "proxies") as well as climate models. We find that monsoon was strongest around 7,000 years ago and has weakened since then. This weakening of the monsoon appears to be caused by a strengthening of the mid‐latitude jet stream, which was influenced by changes in high‐latitude versus low‐latitude temperatures over that period. This paper explores the relationship between temperatures, the jet stream, and the NAM in data and models, which is relevant to future climate in the region. Key Points: A new multi‐proxy‐based estimate of the Holocene North American monsoon (NAM) is presentedProxy and modeled evidence show westerly winds were a dominant control on the Holocene NAMHigh‐latitude temperature drove changes in the Holocene westerly winds and thus the NAM [ABSTRACT FROM AUTHOR]

Using 25 years of ERA5 reanalysis data, this study shows that wind divergence is partially driven by small‐scale sea surface temperature (SST) patterns via their effect on the boundary layer stability. Moreover, strong warm‐to‐cold fronts (the upper quartile) are associated with a mean increase of cloud cover of 10% ± 5% and a mean increase in the probability of a rain event of 15% ± 6%, with respect to the average values. The cloud and rainfall dependence on SST fronts is more pronounced in Fall, probably due to the stronger SST gradients present at the end of the summer season. Plain Language Summary: Oceanic thermal structures impact surface winds, low‐level clouds, and rainfall. Using reanalysis data over the Mediterranean Sea, this paper shows that the reduced stability over warm patches increases the coupling of surface winds with winds aloft. This process affects the entire marine atmospheric boundary layer. This results in a cloud cover and rainfall response: when the wind blows from warm to cold (from cold to warm) water, a converging (diverging) cell is enhanced, increasing (decreasing) low‐cloud cover and favoring by about 15% a rain event. Key Points: Oceanic thermal feedback acts on the marine atmospheric boundary layer dynamics at daily time‐scaleThe downward momentum mixing mechanism imprints cloud cover and rainSST gradients partially drive cloud structures and rainfall over the Mediterranean Sea [ABSTRACT FROM AUTHOR]

Efficient quantification of conditional errors in radar rainfall (RR) products requires a realistic model of random error distribution. Nonparametric estimate of the probability density function (pdf) of standardized RR errors is obtained using a large data sample. The standardization is based on a second‐order separation of systematic and random effects. The estimated empirical distribution is skewed and has exponential shapes of its tails with two different scales. It cannot be modeled with the Gaussian law that was used in several previous studies. A good representation of the tails of the empirical distribution of RR errors is obtained with a three‐parameter modified Laplace model with a shift and unequal slopes of its two sides. Plain Language Summary: Reliable measurement of rainfall is important for hydrology, water management, agriculture, and other users. It is a challenging task because rainfall is highly variable and networks of rain gauges are too sparse to capture this variability. Thus, rainfall estimates based on weather radars are becoming indispensable because they provide continuous coverage of the area up to about 100 km around the radars. However, they are ridden by many uncertainties that must be known in order to avoid dangerous mistakes in all applications of the estimates. In this paper, we contribute an important new piece to this knowledge—the empirically based information about the frequency of occurrence of different magnitudes of the errors that is called the error probability distribution. It allows to evaluate their impact on different applications of radar rainfall. It also enables more reliable comparisons of different rainfall estimation methods in order to monitor progress in this area. The new information was obtained by applying a new mathematical modeling method to a large sample of radar rainfall estimates combined with rain gauge measurements of true rainfall. Many previous applications assumed that radar rainfall errors were distributed according to the commonly used bell curve called normal distribution. Our results show that the actual error distribution differs considerably from normality and that using the normal model leads to misrepresentation of the uncertainties. We now have the strongly supported mathematical tool to treat them in a much more realistic way. Key Points: Empirical distribution of conditional random errors in radar rainfall is estimated using second‐order separation of systematic and random effectsThe error distribution cannot be described with Gaussian model used beforeAsymmetric shifted Laplace model fits well the tails of the empirical distribution [ABSTRACT FROM AUTHOR]