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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Influence of covariance of aerosol and meteorology on co-located precipitating and non-precipitating clouds over the Indo-Gangetic Plain

Aerosol–cloud–precipitation interaction (ACPI) plays a pivotal role in the global and regional water cycle and the earth's energy budget; however, it remains highly uncertain due to the different underlying physical mechanisms. Therefore, this study aims to systematically analyze the effects of aerosols and meteorological factors on ACPI in the co-located precipitating clouds (PCs) and non-precipitating clouds (NPCs) in winter and summer seasons by employing the long-term (2001–2021) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) coupled with the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-II datasets over the Indo-Gangetic Plain (IGP). The results exhibit a decadal increase in aerosol optical depth (AOD) over Lahore (5.2 %), Delhi (9 %), Kanpur (10.7 %), and Gandhi College (22.7 %) and a decrease over Karachi (-1.9 %) and Jaipur (-0.5 %). The most stable meteorology with high values of lower-tropospheric stability (LTS) is found in both seasons over Karachi. In the summer season, the occurrence frequency of clouds is high (74 %) over Gandhi College, 60 % of which are PCs. Conversely, the lowest number of PCs is found over Karachi. Similarly, in the winter season, the frequency of cloud occurrence is low over Karachi and high over Lahore and Gandhi College. The analysis of cloud top pressure (CTP) and cloud optical thickness (COT) indicates high values of cloud fraction (CF) for thick and high-level clouds over all study areas except Karachi. The microphysical properties such as cloud effective radius (CER) and cloud droplet number concentration (CDNC) bear high values (CER ≳ 15 µm and CDNC ≳ 50 cm -3 ) for both NPCs and PCs in summer. The AOD–CER correlation is good (weak) for PCs (NPCs) in winter. Similarly, the sensitivity value of the first indirect effect (FIE) is high (ranging from 0.2 ± 0.13 to 0.3 ± 0.01 in winter and from 0.19 ± 0.03 to 0.32 ± 0.05 in summer) for PCs and low for NPCs. The sensitivity value for the second indirect effect (SIE) is relatively higher (such as 0.6 ± 0.14 in winter and 0.4 ± 0.04 in summer) than FIE. Sensitivity values of the aerosol-cloud interaction (ACI) are low (i.e., -0.06 ± 0.09) for PCs in summer. Furthermore, the precipitation rate (PR) exhibits high values in the summer season, primarily due to the significant contribution from optically thick clouds with lower CDNC (≲ 50 cm -3 ) and larger CER and the intermediate contribution from optically thick clouds with higher CDNC ( ≳ 50 cm -3 ).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to Using a Cloud-resolving Model to Study the Interactions Between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as l0,OOO km in two-dimensions, and 1,OOO x 1,OOO km2 in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRSDate not supplied

166 GHz Ice Scattering Signal in Cold-Season Precipitating Cloud Systems

Surface snowfall can be produced by a variety of forcing mechanisms that are accompanied by different in-cloud physical processes and microphysical composition. For instance, shallow cumuliform snowfall forced by marine cold air outbreaks shows distinct geographic and seasonal signatures and plays in important role in annual snowfall production in many regions worldwide. Coincident CloudSat-CALIPSO observations confirm that oceanic regions exhibiting frequent shallow cumuliform snow are typically composed of mixed-phase cloud and precipitation particles and display passive microwave brightness temperature behavior that is dominated by cloud liquid water emission, while combined Global Precipitation Measurement (GPM) radiometer and surface radar studies of intense shallow cumuliform events indicate significant ice particle scattering signals at the highest GPM radiometer frequencies. Passive microwave imager channels in the 37 to 89 GHz range have long been used to detect precipitation-sized ice particle scattering signatures in clouds. Dual polarization plays an important role in distinguishing between scattering signatures and other background variability. The GPM Microwave Imager (GMI) is the first satellite radiometer to possess dual-polarized channels at 166 GHz. These high-frequency channels are more susceptible to emission and attenuation by cloud liquid water and water vapor than lower-frequency channels, but are also more sensitive to ice scattering. This research studies the ice scattering information contained in the 166 GHz channels compared to the historically used 85/89 GHz channels on previous sensors. A 166 GHz scattering index (SI) will be developed to understand how its correlation with non-precipitating ice scattering at higher altitudes also affects the 166 GHz scattering signature in cold-season precipitating cloud systems. The 166 GHz SI will be used to possibly isolate precipitation scattering signature for different snowfall modes (shallow convective vs. deep stratiform). The supercooled liquid water affecting shallow convective systems and drastically reducing the scattering signal could be detected and quantified, providing additional information for convective snow detection and quantification in solid precipitation retrieval algorithms.

Lisa Milani↗
NASA NTRS2016

Cloud Regimes as a Tool for Systematic Study of Various Aerosol-Cloud-Precipitation Interactions

Systematic changes of clouds and precipitation are notoriously difficult to ascribe to aerosols. This presentation will showcase yet one more attempt to at least credibly detect the signal of aerosol-cloud-precipitation interactions. We surmise that the concept of cloud regimes (CRs) is appropriate to conduct such an investigation. Previous studies focused on what we call here dynamical CRs, and while we continue to adopt those too for our analysis, we have found that a different way of organizing cloud systems, namely via microphysical regimes is also promising. Our analysis relies on MODIS Collection 6 Level-3 data for clouds and aerosols, and TRMM-TMPA data for precipitation. The regimes are derived by applying clustering analysis on MODIS joint histograms, and once each grid cell is assigned a regime, aerosol and precipitation data can be spatiotemporally matched and composited by regime. The composites of various cloud and precipitation variables for high (upper quartile of distribution) and low (lower quartile) aerosol loadings can then be contrasted. We seek evidence of aerosol effects both in regimes with large fractions of deep ice-rich clouds, as well as regimes where low liquid phase clouds dominate. Signals can be seen, especially when the analysis is broken by land-ocean and when additional filters are applied, but there are of course caveats which will be discussed.

cloud regimes↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard will be presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial-lkm scales are resolved in horizontal domains as large as 10,000 kilometers in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard wlll be is presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS2006

Using Multi-scale Modeling System to Study the Interactions between Clouds, Precipitation, Aerosols, Radiation and Land Surface

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. A review of developments, improvements and applications of cloud models (GCE and WRF) at Goddard will be presented in this talk. In particular, a new approach to using multi-scale modeling system to study the interactions between clouds, precipitation, aerosols and land will be presented.

Tao, Wei-Kuo↗
NASA NTRS2022

Satellite Precipitation Measurements: What Have We Learnt About Cloud-Precipitation Processes From Space?

Precipitation is one of the fundamental elements that define global and regional climatology. Precipitation systems consist of a broad spectrum of three-dimensional structures in which microphysical processes interact with macro-scale processes in the cloud system and the ambient environment that prescribe the evolution of the system. Since 1970s, satellite observations of precipitation have been a fundamental tool in quantifying this complex interaction. They have first quantified the frequency and intensity of global precipitation, including remote areas over open oceans and polar regions, thus providing today’s precipitation climatology. More recently, satellite observations of cloud and precipitation have been exploited for understanding the physical mechanisms governing precipitation systems. A subset of these studies also provided observation-based metrics to probe physical processes operating in cloud-precipitation systems and to apply them as diagnostic measures for evaluating the representation of the processes in numerical models for better projections of future climate. In this chapter, we first review the theoretical basis of precipitation remote sensing from space and describe how it is practically applied in satellite missions. In the first part of the chapter, an historical overview of the satellite missions is described, summarising the instruments and retrieval algorithms developed in the missions. In the second part, we introduce a set of studies discussing the fundamental mechanisms behind precipitation formation, highlighting what we have learnt to date on cloud-precipitation processes from satellite observations.

Precipitation↗
NASA NTRSDate not supplied

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

We extend and enhance the concept of the Cloud Regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), by adding precipitation information in order to better understand cloud-precipitation relationships. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, cloud-precipitation “hybrid” regimes are derived by implementing the k-means clustering algorithm with advanced initialization and objective measures to determine the most optimal clusters. By expressing precipitation rates within 1-degree grid cell as histograms and making choices on the relative weight of cloud and precipitation, we could obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part of the hybrid entroids resembles the centroid of cloud-only regimes, but still tightens the cloud-precipitation relationship by decreasing the precipitation variability of each regime. As precipitation weight progressively increases, the shape of the cloudy part of the hybrid centroids becomes blunter, while the precipitation part of the centroids sharpens. In the case where cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct features in the precipitation parts of the centroids, which allows us to project them onto the 30-minly IMERG domain. Such a projection can be used to overcome the temporal sparseness of MODIS cloud observations, which leads to great application potential for various convection-focused studies, including diurnal cycle analysis.

cloud-precipitation↗
NASA NTRS1989

Radiative transfer to space through a precipitating cloud at multiple microwave frequencies. III - Influence of large ice particles

The time-dependent influence of large ice particles on the brightness temperatures, T(B)s, that would be observed from space above severe thunderstorms is investigated using a vertically and angularly detailed plane-parallel microwave radiative transfer model. The impact of large ice particles on passive microwave T(B)s over an evolving model rain cloud was examined at ten separate frequencies in the EHF/SHF spectrum. It is shown that the frequency-dependent vertically distributed generalized emission/scattering weighting functions, introduced in this simulation to vertically resolve the contributions by individual cloud and precipitation layers to the T(B)s, can be used to identify the specific layers responsible for regulating the magnitude of top-of-atmoshere T(B)s.

Smith, Eric A.↗
NASA NTRS1984

Microwave radiances from precipitating clouds containing aspherical ice, combined phase, and liquid hydrometeors

A numerical algorithm based on Eddington's second approximation to the equation of radiative transfer has been developed in order to compute the radiances with horizontal and vertical polarization that emerge from precipitating clouds. This algorithm yields a rapid solution to problems pertaining to clouds with vertically inhomogeneous structure. Precipitating clouds containing liquid, mixtures of phases, and ice hydrometeors are modelled. It is shown that the lower-frequency radiances are sensitive to liquid precipitation at low altitudes while the higher-frequency radiances are more sensitive to the ice hydrometeors at the cloud tops. The extinction coefficients of aspherical hydrometeors are presented as a function of rainfall rates. The fact that vertically polarized radiances are warmer than horizontally polarized ones at high rainfall rates, with the difference diminishing at lower frequencies, is due to aspherical ice hydrometeors in the upper regions of precipitating clouds.

Wu, R.↗
NASA NTRS1986

Microwave radiances from horizontally finite precipitating clouds containing ice and liquid hydrometeors

Microwave radiances that would be measured from satellite borne radiometers were computed as a function of rainfall rates from horizontally finite precipitating clouds containing ice and liquid hydrometeors capped by a layer of nonprecipitating ice that covers the remainder of the footprint. Ice at the top of the precipitating clouds depresses the brightness temperatures which depend on rainfall rates because the ice hydrometeor concentrations are assumed to be related to the rainfall rates at the cloud base. It is also found that the brightness temperatures of footprints partially covered by precipitation cells are a nonlinear function of the rainfall rate averaged over the footprint. Thus the average brightness temperatures depend on the peak rainfall rates and the size of the precipitating cloud.

Kummerow, C.↗
DOE OSTI2023

Changing Characteristics of Tropical Extreme Precipitation–Cloud Regimes in Warmer Climates

In this study, we investigated the changing characteristics of climatic scale (monthly) tropical extreme precipitation in warming climates using the Energy Exascale Earth System Model (E3SM). The results are from Atmospheric Model Intercomparison Project (AMIP)-type simulations driven by (a) a control experiment with the present-day sea surface temperature (SST) and CO 2 concentration, (b) P4K, the same as in (a) but with a uniform increase of 4K in the SST globally, and (c) the same as in (a), but with an imposed SST and CO 2 concentration from the outputs of the coupled E3SM forced by a 4xCO 2 concentration. We found that as the surface warmed under P4K and 4xCO 2 , both convective and stratiform rain increased. Importantly, there was an increasing fractional contribution of stratiform rain as a function of the precipitation intensity, with the most extreme but rare events occurring preferentially over land more than the ocean, and more so under 4xCO 2 than P4K. Extreme precipitation was facilitated by increased precipitation efficiency, reflecting accelerated rates of recycling of precipitation cloud water (both liquid and ice phases) in regions with colder anvil cloud tops. Changes in the vertical profiles of clouds, condensation heating, and vertical motions indicate increasing precipitation–cloud–circulation organization from the control and P4K to 4xCO 2 . The results suggest that large-scale ocean warming, that is, P4K, was the primary cause contributing to an organization structure resembling the well-known mesoscale convective system (MCS), with increased extreme precipitation on shorter (hourly to daily) time scales. Additional 4xCO 2 atmospheric radiative heating and dynamically consistent anomalous SST further amplified the MCS organization under P4K. Analyses of the surface moist static energy distribution show that increases in the surface moisture (temperature) under P4K and 4xCO 2 was the key driver leading to enhanced convective instability over tropical ocean (land). However, a fast and large increase in the land surface temperature and lack of available local moisture resulted in a strong reduction in the land surface relative humidity, reflecting severe drying and enhanced convective inhibition (CIN). It is argued that very extreme and rare “record-breaking” precipitation events found over land under P4K, and more so under 4xCO 2 , are likely due to the delayed onset of deep convection, that is, the longer the suppression of deep convection by CIN, the more severe the extreme precipitation when it eventually occurs, due to the release of a large amount of stored surplus convective available potential energy in the lower troposphere during prolonged CIN.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Changing Characteristics of Tropical Extreme Precipitation-Cloud Regimes in Warmer Climates

In this paper, we have investigated the changing characteristics of climatic scale (monthly) tropical extreme precipitation in warming climates using the Energy Exascale Earth System Model (E3SM). Results are from Atmospheric Model Intercomparison Project (AMIP)-type simulations driven by a) control experiment with present-day sea surface temperature (SST) and CO 2 concentration, b) P4K, same as in a) but with uniform increase by 4K in SST globally, and c) same as in a), but with imposed SST and CO 2 concentration from outputs of coupled E3SM forced by 4xCO 2 concentration. We find that as the surface warms under P4K and 4xCO 2 , both convective and stratiform rain increase. Importantly, there is increasing fractional contribution of stratiform rain as a function of precipitation intensity, with the most extreme but rare events occurring preferentially over land more than ocean, more so under 4xCO 2 than P4K. Extreme precipitation is facilitated by increased precipitation efficiency, reflecting accelerated rates of recycling of precipitation-cloud water (both liquid and ice phases) in regions with colder anvil cloud tops. Changes in vertical profiles of clouds, condensation heating and vertical motions indicate increasing precipitation-cloud-circulation organization from control, P4K to 4xCO 2 . Results suggest that large-scale ocean warming, i.e., P4K, is the primary cause contributing to organization structure resembling the well-known Mesoscale Convective System (MCS) for increased extreme precipitation on shorter (hourly to daily) time scales. Additional 4xCO 2 atmospheric radiative heating, and dynamically consistent anomalous SST further amplifies the MCS organization under P4K. Analyses of the surface moist static energy distribution show that increase in surface moisture (temperature) under P4K and 4xCO 2 is the key driver leading to enhanced convective instability over tropical ocean (land). However, the fast and large increase in land surface temperature and lack of available local moisture result in strong reduction in land surface relative humidity, reflecting severe drying and enhanced convective inhibition (CIN). It is argued that very extreme and rare “record-breaking” precipitation events found over land under P4K, more so under 4xCO 2 , are likely due to the delayed onset of deep convection, i.e., the longer the suppression of deep convection by CIN, the more severe the extreme precipitation when eventually occur, due to the release of large amount of stored surplus convective available potential energy in the lower troposphere during prolonged CIN.

climate-scale extreme tropical precipitation↗
DOE OSTI2023

The Joint Ninth Aerosol, Clouds, Precipitation and Climate (ACPC) initiative and TRacking Aerosol Convection interactions ExpeRiment (TRACER) Workshop

The joint ninth Aerosols-Clouds-Precipitation-and-Climate (ACPC; http://www.acpcinitiative.org) initiative and TRacking Aerosol Convection interactions ExpeRiment (TRACER; Jensen et al., 2022) workshop was convened from 16–19 May 2023 at Texas Southern University (TSU), Houston, TX, and online to discuss progress towards understanding the interactions among aerosols, clouds, precipitation, and climate.

54 ENVIRONMENTAL SCIENCES↗