scholarly journals An assessment of cloud top thermodynamic phase products obtained from A-Train passive and active sensors

2013 ◽  
Vol 6 (5) ◽  
pp. 8371-8411 ◽  
Author(s):  
S. Zeng ◽  
J. Riedi ◽  
F. Parol ◽  
C. Cornet ◽  
F. Thieuleux
Keyword(s):  
General Circulation ◽  
Supercooled Liquid ◽  
General Circulation Models ◽  
Global Scale ◽  
Orthogonal Polarization ◽  
Liquid Droplets ◽  
High Confidence ◽  
Cloud Properties ◽  
Fractional Cloud ◽  
Thermodynamic Phase

Abstract. The A-Train observations provide an unprecedented opportunity for the production of high quality dataset describing cloud properties. We illustrate in this study the use of one year of coincident POLDER (Polarization and Directionality of the Earth Reflectance), MODIS (MODerate Resolution Imaging Spectroradiometer) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations to establish a reference dataset for the description of cloud top thermodynamic phase at global scale. We present the results of an extensive comparison between POLDER and MODIS cloud top phase products and discuss those in view of cloud vertical structure and optical properties derived simultaneously from collocated CALIOP active measurements. These results allow to identify and quantify potential biases present in the 3 considered dataset. Among those, we discuss the impacts of observation geometry, thin cirrus in multilayered and single layered cloud systems, supercooled liquid droplets, aerosols, fractional cloud cover and snow/ice or bright surfaces on global statistics of cloud phase derived from POLDER and MODIS passive measurements. Based on these analysis we define criteria for the selection of high confidence cloud phase retrievals which in turn can serve for the establishment of a reference cloud phase product. This high confidence joint product derived from POLDER/PARASOL and MODIS/Aqua can be used in the future as a benchmark for the evaluation of other cloud climatologies, for the assessment of cloud phase representation in models and the development of better cloud phase parametrization in the general circulation models (GCMs).

scholarly journals Cloud thermodynamic phase inferred from merged POLDER and MODIS data

2010 ◽  
Vol 10 (23) ◽  
pp. 11851-11865 ◽  
Author(s):  
J. Riedi ◽  
B. Marchant ◽  
S. Platnick ◽  
B. A. Baum ◽  
F. Thieuleux ◽  
...  
Keyword(s):  
Satellite Data ◽  
General Circulation ◽  
Hydrological Cycle ◽  
General Circulation Models ◽  
Liquid Water Content ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Index Value ◽  
Thermodynamic Phase ◽  
Insight Into

Abstract. The global spatial and diurnal distribution of cloud properties is a key issue for understanding the hydrological cycle, and critical for advancing efforts to improve numerical weather models and general circulation models. Satellite data provides the best way of gaining insight into global cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first step in the process of inferring cloud optical and microphysical properties from satellite measurements. It is important that cloud phase be derived together with an estimate of the confidence of this determination, so that this information can be included with subsequent retrievals (optical thickness, effective particle radius, and ice/liquid water content). In this study, we combine three different and well documented approaches for inferring cloud phase into a single algorithm. The algorithm is applied to data obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3 (Polarization and Directionality of the Earth Reflectance) instruments. It is shown that this synergistic algorithm can be used routinely to derive cloud phase along with an index that helps to discriminate ambiguous phase from confident phase cases. The resulting product provides a semi-continuous index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid phase, ice phase, mixed phase (potential combination of ice and liquid particles), or simply unknown phase clouds. The index value provides simultaneously information on the phase and the associated confidence. This approach is expected to be useful for cloud assimilation and modeling efforts while providing more insight into the global cloud properties derived from satellite data.

scholarly journals Cloud thermodynamic phase inferred from merged POLDER and MODIS data

2007 ◽  
Vol 7 (5) ◽  
pp. 14103-14137 ◽  
Author(s):  
J. Riedi ◽  
B. Marchant ◽  
S. Platnick ◽  
B. Baum ◽  
F. Thieuleux ◽  
...  
Keyword(s):  
Satellite Data ◽  
General Circulation ◽  
Hydrological Cycle ◽  
General Circulation Models ◽  
Liquid Water Content ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Thermodynamic Phase ◽  
Insight Into

Abstract. The global spatial and diurnal distribution of cloud properties is a key issue for understanding the hydrological cycle, and critical for advancing efforts to improve numerical weather models and general circulation models. Satellite data provides the best way of gaining insight into global cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first step in the process of inferring cloud optical and microphysical properties from satellite measurements. It is important that cloud phase be derived together with an estimate of the confidence of this determination, so that this information can be included with subsequent retrievals (optical thickness, effective particle radius, and ice/liquid water content). In this study, we combine three different and well documented approaches for inferring cloud phase into a single algorithm. The algorithm is applied to data obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3 (Polarization and Directionality of the Earth Reflectance) instruments. It is shown that this synergistic algorithm can be used routinely to derive cloud phase along with an index that helps to discriminate ambiguous phase from confident phase cases. The resulting product provides a semi-continuous confidence index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid phase, ice phase, mixed phase (potential combination of ice and liquid particles), or simply unknown phase clouds. This approach is expected to be useful for cloud assimilation and modeling efforts while providing more insight into the global cloud properties derived from satellite data.

scholarly journals Investigation of Regional and Seasonal Variations in Marine Boundary Layer Cloud Properties from MODIS Observations

2008 ◽  
Vol 21 (19) ◽  
pp. 4955-4973 ◽  
Author(s):  
Michael P. Jensen ◽  
Andrew M. Vogelmann ◽  
William D. Collins ◽  
Guang J. Zhang ◽  
Edward P. Luke
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Climate Models ◽  
Marine Boundary Layer ◽  
General Circulation Models ◽  
Test Bed ◽  
Liquid Water Path ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Peak Versus

Abstract To aid in understanding the role that marine boundary layer (MBL) clouds play in climate and assist in improving their representations in general circulation models (GCMs), their long-term microphysical and macroscale characteristics are quantified using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the National Aeronautics and Space Administration’s (NASA’s) Terra satellite. Six years of MODIS pixel-level cloud products are used from oceanic study regions off the west coasts of California, Peru, the Canary Islands, Angola, and Australia where these cloud types are common. Characterizations are given for their organization (macroscale structure), the associated microphysical properties, and the seasonal dependencies of their variations for scales consistent with the size of a GCM grid box (300 km × 300 km). MBL mesoscale structure is quantified using effective cloud diameter CD, which is introduced here as a simplified measure of bulk cloud organization; it is straightforward to compute and provides descriptive information beyond that offered by cloud fraction. The interrelationships of these characteristics are explored while considering the influences of the MBL state, such as the occurrence of drizzle. Several commonalities emerge for the five study regions. MBL clouds contain the best natural examples of plane-parallel clouds, but overcast clouds occur in only about 25% of the scenes, which emphasizes the importance of representing broken MBL cloud fields in climate models (that are subgrid scale). During the peak months of cloud occurrence, mesoscale organization (larger CD) increases such that the fractions of scenes characterized as “overcast” and “clumped” increase at the expense of the “scattered” scenes. Cloud liquid water path and visible optical depth usually trend strongly with CD, with the largest values occurring for scenes that are drizzling. However, considerable interregional differences exist in these trends, suggesting that different regression functionalities exist for each region. For peak versus off-peak months, the fraction of drizzling scenes (as a function of CD) are similar for California and Angola, which suggests that a single probability distribution function might be used for their drizzle occurrence in climate models. The patterns are strikingly opposite for Peru and Australia; thus, the contrasts among regions may offer a test bed for model simulations of MBL drizzle occurrence.

Ecosystem modelling of the cycling of marine dimethylsulfide: a review of current approaches and of the potential for extrapolation to global scales

2004 ◽  
Vol 61 (5) ◽  
pp. 845-856 ◽  
Author(s):  
Alain F Vézina
Keyword(s):  
General Circulation ◽  
Global Climate ◽  
Turbulence Models ◽  
Ecosystem Model ◽  
General Circulation Models ◽  
Global Scale ◽  
Ecosystem Modelling ◽  
Ecosystem Models ◽  
Surface Ocean ◽  
Wide Range

There is rising interest from oceanic and atmospheric scientists in the potential role of dimethylsulphide (DMS) in regulating global climate. The increased availability of field observations of DMS and related compounds (DMS(P)) and of their transformation rates in the ocean has stimulated the development of ecosystem models of marine sulfur cycling. The models cover a wide range of complexity levels and spatial/temporal scales, from zero-dimensional local simulations spanning a few days to regional/global simulations driven by ocean general circulation models. The degree of complexity required to model DMS(P) dynamics, particularly the differentiation into phyto plankton species or groups, remains an important open question. First attempts to drive these models with vertically resolved turbulence models suggest interesting interactions between DMS(P) dynamics and fine-scale ocean mixing that can modify fluxes of DMS to the atmosphere. Recent models also bring into focus the strong affinities between the cycling of DMS(P) and that of dissolved organic carbon in the surface ocean. Formal parameter estimation techniques, which are increasingly used in ecosystem modelling of carbon and nitrogen dynamics, should play a stronger role in the development of DMS sulfur modelling. Extrapolation of DMS cycling and fluxes to the global scale presently relies largely on empirical approach. A semiempirical approach, based on a simple ecosystem model, is shown to reproduce gross features of the global distribution of DMS in the surface ocean. This shows promise for the continuing development of ecosystem models for global modelling of marine sulfur fluxes to the atmosphere.

scholarly journals Impact of mixing state and hygroscopicity on CCN activity of biomass burning aerosol in Amazonia

2016 ◽  
Author(s):  
Madeleine Sánchez Gácita ◽  
Karla M. Longo ◽  
Julliana L. M. Freire ◽  
Saulo R. Freitas ◽  
Scot T. Martin
Keyword(s):  
Biomass Burning ◽  
General Circulation ◽  
Cloud Model ◽  
Cloud Condensation Nuclei ◽  
General Circulation Models ◽  
Mixing State ◽  
Activation Kinetics ◽  
Cloud Properties ◽  
Aerosol Mass ◽  
Biomass Burning Aerosol

Abstract. Smoke aerosols prevail throughout Amazonia because of widespread biomass burning during the dry season. External mixing, low variability in the particle size distribution and low particle hygroscopicity are typical. There can be profound effects on cloud properties. This study uses an adiabatic cloud model to simulate the activation of smoke particles as cloud condensation nuclei (CCN) and to assess the relative importance of variability in hygroscopicity, mixing state, and activation kinetics for the activated fraction and maximum supersaturation. The analysis shows that use of medium values of hygroscopicity representative of smoke aerosols for other biomass burning regions on Earth can lead to significant errors, compared to the use of low hygroscopicity reported for Amazonia. Kinetic limitations, which can be significant for medium and high hygroscopicity, did not play a strong role for CCN activation of particles representative of Amazonia smoke aerosols, even when taking into account the large aerosol mass and number concentrations typical of the region. Internal compared to external mixing of particle components of variable hygroscopicity resulted in a significant overestimation of the activated fraction. These findings on uncertainties and sensitivities provide guidance on appropriate simplifications that can be used for modeling of smoke aerosols within general circulation models.

A new method for assessing the performance of general circulation models based on their ability to simulate the response to observed forcing

2021 ◽  
pp. 1-52
Author(s):  
M.A. Altamirano del Carmen ◽  
F. Estrada ◽  
C. Gay-García
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
General Circulation ◽  
Statistical Tests ◽  
General Circulation Models ◽  
Warming Trend ◽  
Global Scale ◽  
Adequate Representation ◽  
Warming Rate

AbstractThe reliability of General Circulation Models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate and thus, the evaluation of GCMs performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCMs performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs ability to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and eight sub-continental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the southern hemisphere. However, GCMs tend to overestimate the warming rate for domains in the northern hemisphere, particularly since the mid-1990s.

scholarly journals Determining Cloud Thermodynamic Phase from the Polarized Micro Pulse Lidar

2020 ◽  
Author(s):  
Jasper R. Lewis ◽  
James R. Campbell ◽  
Simone Lolli ◽  
Sebastian A. Stewart ◽  
Ivy Tan ◽  
...  
Keyword(s):  
Liquid Water ◽  
Supercooled Liquid ◽  
Mixed Phase ◽  
Orthogonal Polarization ◽  
Water Ice ◽  
Earth Observing System ◽  
Micro Pulse Lidar ◽  
Mixed Phase Clouds ◽  
Ice Water ◽  
Thermodynamic Phase

Abstract. A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5) assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (−37 °C–0 °C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.

scholarly journals Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar

2020 ◽  
Vol 13 (12) ◽  
pp. 6901-6913
Author(s):  
Jasper R. Lewis ◽  
James R. Campbell ◽  
Sebastian A. Stewart ◽  
Ivy Tan ◽  
Ellsworth J. Welton ◽  
...  
Keyword(s):  
Liquid Water ◽  
Supercooled Liquid ◽  
Mixed Phase ◽  
Orthogonal Polarization ◽  
Water Ice ◽  
Earth Observing System ◽  
Micro Pulse Lidar ◽  
Mixed Phase Clouds ◽  
Ice Water ◽  
Thermodynamic Phase

Abstract. A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5), assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD, are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (−37–0 ∘C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.

scholarly journals Extraction, classification and visualization of 3-dimensional clouds simulated by cloud-resolving atmospheric model

2017 ◽  
Vol 08 (04) ◽  
pp. 1750051 ◽  
Author(s):  
Daisuke Matsuoka
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Vertical Direction ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Global Scale ◽  
3 Dimensional ◽  
Cloud Ice ◽  
The Tropics ◽  
Atmospheric Phenomena

Cloud-resolving atmospheric general circulation models using large-scale supercomputers reproduce realistic behavior of 3-dimensional atmospheric field on a global scale. To understand the simulation result for scientists, conventional visualization methods based on 2-dimensional cloud classification are not enough for understanding individual clouds and their physical characteristics. In this study, we propose a new 3-dimensional extraction and classification method of simulated clouds based on their 3-dimensional shape and physical properties. Our proposed method extracts individual clouds by cloud water and cloud ice, and classifies them into six types by their altitude and upward flow. We applied the method to time-varying atmospheric simulation data, and attempted to visualize atmospheric phenomena on the tropics such as developing cumulonimbus and tropical cyclone. Two case studies clearly visualize the behavior of individual cloud type and clarify that some cloud types have a relationship with rainfall during active weather phenomena. The proposed method has the potential to analyze such phenomena that develop in the vertical direction as well as the horizontal direction.

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