A53D-1433
Cloudsat, CALIOP, MODIS, AIRS, OMI, and POLDER Data Search and Visualization Available to Facilitate Multi Instrument Cloud Studies Along the A-Train Path
Now that the A-Train suite of datasets have become more mature, new and innovative science utilizing the various products has become more reliable and challenging. To perform multi-satellite research with A-Train data originating from heterogeneous missions, scientists must access, subset, visualize, and analyze user specified datasets, in ways unique to the dataset. Then, the datasets need to be co-registered. The A-Train Data Depot (ATDD) has been developed to save each scientist the effort and expense of developing these functions individually. The ATDD, operational for over a year, successfully serves co-registered data, as spatially and temporally specified by the researcher, from the Cloudsat, CALIOP, AIRS, MODIS, and now OMI data instruments. Currently, the ATDD provides data visualization and access, using the GIOVANNI data exploration tool, for: 8 Cloudsat, CALIOP, MODIS, and AIRS products that include cloud and aerosol, atmospheric temperature, and water vapor profile parameters; and, 10 MODIS, AIRS, and OMI products that include cloud pressure, water vapor, cloud optical thickness and aerosol, horizontally plotted parameters (+/-100 km from the profile data). In addition, a significant step, overlaying OMI, POLDER, MODIS, and AIRS 2-D cloud pressure data on the vertical profiles, was implemented. Once parameters of interest for cloud studies are visualized, various collocated and subsetted data sets as well as PNG image files can be downloaded. This user specified ‘production' visualization of A-Train data greatly aids researchers by conveniently availing them of specific data of interest, while affording more time for research. http://disc.gsfc.nasa.gov/atdd/
A53D-1434
Vertical Structure of Tropical Convection and Precipitation
Cloudsat cloud radar data are used to investigate the vertical structure of cloud systems across the West and East Pacific of the ITCZ. Deep convective systems look structurally similar in both regions, and cover roughly 29 percent and 25 percent of the West and East Pacific, respectively. Shallow convective systems cover 4.5 percent and 2.4 percent of the East and West Pacific, respectivlely, and are individually larger in the East Pacific. Cloud radar data are also collocated with precipitation rates from Advanced Microwave Scanning Radiometer (AMSR) to examine differences in cloud top PDFs for different rainrate regimes. Heavily precipitating clouds mostly have high tops (peaks of 13-14 km in the East Pacific and 15-16 km in the West Pacific) that are two to three km deeper than moderately raining or non-raining high clouds. Moderately raining clouds and non-raining clouds are much more bottom-heavy in the East Pacific. We also show that rainrate increases with cloud height, especially for clouds higher than 12 km. A nearly tenfold increase in rainrate is observed as cloud tops ascend from 12 km to near the tropical tropopause. Finally, we show that low clouds contribute 28 percent to total rainfall in the West Pacific and 40 percent in the East Pacific, but that they contribute 51 percent and 67 percent to total rain area, respectively. This is qualitatively consistent with the more bottom-heavy vertical velocity profile in the East Pacific.
A53D-1435
Joint Variability of Airborne Passive Microwave and Ground-based Radar Observations Obtained in the TRMM Kwajalein Experiment
The Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) held July-September 1999 in the west Pacific was designed to obtain an empirical physical characterization of precipitating convective clouds over the tropical ocean. The majority of the precipitation was from mixed-phase clouds. Coordinated data sets were obtained from aircraft and ground-based sensors including passive microwave measurements by the Advanced Microwave Precipitation Radiometer (AMPR) instrument on the NASA DC-8 aircraft and S-band volumetric radar data by the KPOL radar. The AMPR and KPOL data sets were processed to yield a set of 25,049 matching observations at ~ 2 km x 2 km horizontal spatial resolution and within 6 min. The TRMM satellite Microwave Imager (TMI) has a similar set of channels to AMPR but coarser spatial resolution (19 GHz: 35 km, 85 GHz: 7.7 km). During KWAJEX, the 0 deg C level height was nearly constant at ~ 4800 m. Hence, two potential sources of uncertainty in relating passive microwave brightness temperatures (Tbs) to surface precipitation, inhomogeneous beam filling and variations in depth of the rain layer are much smaller sources of error in the KWAJEX data set than for TMI. TRMM was originally designed to yield monthly rainfall estimates over 5 deg x 5 deg grid boxes. The use of these data to yield instantaneous rainrate products at smaller spatial scales is more sensitive to the detailed characteristics of the joint distributions of passive microwave Tbs versus rain rate. KWAJEX data sets reveal poor correlations, very wide scatter, and weak modes in these distributions. The spread of emission Tb values for a given rain-layer reflectivity (e.g., 75 K at 30 dBZ for 19 GHz) is similar or larger within convective compared to stratiform precipitation regions. This result implies that the enhancement in emission Tbs associated with partially melted ice particles can occur whether the particles are concentrated within a thin layer in stratiform regions or are more dispersed in the column in convective regions. There is little information in either ice-layer radar reflectivity or scattering (85 GHz) Tbs on the underlying quantitative surface rain rates at the spatial scale examined.
A53D-1436
Characteristics of Non-precipitating Clouds as Observed by the Tropical Rainfall Measuring Mission Satellite
Satellites are the only platform currently available to practically measure precipitation on a global scale. It is important to identify errors and uncertainties in satellite estimates of global precipitation so that these can be taken into account in merged products and other applications of these data. The Tropical Rainfall Measuring Mission (TRMM) satellite carries two instruments to measure precipitation: the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR). Berg et al. (2006, J. Climate) examined differences in rainfall detection between the TMI and PR over the East China Sea where the TMI detected rain and the PR did not. Berg et al. theorized that aerosol loading suppresses precipitation in these clouds. They suggest that for a relatively high liquid water path (LWP) cloud (1 kg m-2) with a plausible cloud droplet concentration for a polluted cloud (based on field observations from the Asian Particle Environmental Change Studies), the reflectivity could be reduced to approximately 18 dBZ – just below the PR minimum detection threshold. Although not observable by PR, clouds with a reflectivity near 18 dBZ would be observable by an S-band WSR-88D radar which has an operational minimum detectable threshold of 5 dBZ in precipitation mode. We tested their hypothesis by examining coastal radar data, visible and IR satellite data, and upper air sounding data. The Taiwan coastal S-band WSR-88D radar did not detect any echoes > 10 dBZ in the East China Sea features identified as raining in TMI but not PR. Infrared satellite and sounding observations indicate that these areas of spurious TMI precipitation occur where there are liquid-phase clouds with tops just below the freezing level and a low cloud base very near the surface, i.e. the cloud layer is unusually thick. These thick warm-phase clouds have a high enough LWP to be detected by the TMI. Since the reflectivity is below the detection threshold of the coastal radar, we infer that these are non-precipitating clouds. Including these non-precipitating clouds as precipitating may contribute to a positive bias in TRMM TMI rainfall estimates, particularly in regions where average rain rates are low. Within the global TRMM domain, clouds with radiative characteristics similar to the spurious TMI rainfall events in the East China Sea occur ~1% of the time (regionally as high as 10%) in a wide variety of settings including coastal regions and open ocean regions far removed from land including the southern oceans. These types of clouds do not occur in marine stratocumulus regions off the west coasts of South America, North America, and Africa. The variety of locations and lack of correlation with aerosol optical depth globally indicates that aerosols may be a contributing factor but are not the sole cause of the occurrence of low-cloud-top, high LWP clouds.
A53D-1437
Characteristics of boundary-layer structure and cloud properties revealed by satellite data
This study uses MODIS cloud-top temperature, MISR stereoscopic cloud-top height, (AMSR-E) sea surface temperature, and AIRS tropospheric temperature to estimate the jump in temperature at the marine boundary layer-free troposphere interface as well as the magnitude of decoupling between the cloud layer and subcloud layer in marine stratocumulus regions. The contributions of inversion strength, decoupling, sea surface temperature, and horizontal advection to variations in low-level cloud amount and optical depth are investigated in order to determine the relative importance of changes in internal boundary-layer structure and external boundary layer forcing on cloud properties. A mixed-layer model is used to explore the observed relationships as well assess the significance of decoupling in the stratocumulus region.
A53D-1438
Expanding Curtain Observations of Cloud Vertical Structure and Layering to Model-Relevant Spatial Scales
Clouds, representing perhaps the most obvious physical manifestations of atmospheric dynamics at work, remain in many ways an enigmatic and unifying intellectual challenge to researchers of all disciplines within the atmospheric sciences. Given the universally acknowledged importance of cloud systems in determining the state of current and future climate through radiative, chemical, dynamic, and thermodynamic processes tied intimately to the hydrological cycle, it is no wonder that so much recent attention has been given to better understanding the non-linear feedbacks involving clouds and ways to improve their handling in numerical weather prediction (NWP) models. In terms of operational community interests, knowledge of cloud vertical structure, ceiling (cloud base) height, and phase is key to aviation safety assurance in the private, commercial, and defense-agency sectors alike. The launch of the NASA Earth System Science Pathfinder CloudSat (cloud radar; 3 mm wavelength) mission in 2006 changed forever the way we view cloud systems from the space platform—providing vertically-resolved ‘cuts' through the cloudy troposphere. The Cloud Profiling Radar (CPR) system resolves nearly all radiatively significant cloud structures present in the column at vertical resolutions sufficient to afford scientists the opportunity to examine new hypotheses on cloud formation (leading potentially to new/improved cloud process parameterizations) and make observationally-based discoveries bordering on the frontiers of our current understanding. At the same time, the non-scanning nature of the CPR (providing so-called ‘curtain' observations) represents in some respects a frustrating tease to the potential of a three-dimensional scanning system, relegating its utility to the realms of research as opposed to full spatial environmental characterization and data assimilation. This research examines ways to extend via statistical methods the curtain slices provided by CloudSat into the horizontal to construct pseudo three-dimensional information. These statistics are based on cloud-type classification, which are identifiable from cloud top observations by conventional 2-D observing systems. Preliminary cloud-type-dependent vertical structures, based on the CloudSat Level-2 Cloud Water Content (CWC) product, are presented for an assortment of cloud classifications. Such statistics can then be applied to the vertically-integrated liquid/ice water content as retrieved by 2-D sensors to distribute this water in the column according to type-dependency. In addition, the degree to which cloud layer base heights can be extended into the cross-track direction (e.g., given an observation of similar cloud-type from a conventional 2-D optical radiometer) can be assessed via correlation lengths computed along the CloudSat track. The effective result is a pseudo 3-D swath of cloud water content of potential use to operational support and numerical weather prediction analysis and/or validation. Preliminary results from the currently available compilation of CloudSat data are presented to illustrate conceptually the potential and limitations of such approaches.
A53D-1439
Evaluating Simulated Clouds With NASA A-Train Observations
As computer resources mature, operational forecasts are being initiated at higher spatial resolution, allowing for the simulation of cloud systems through parameterization schemes that predict the evolution of their microphysical properties. Clouds are a dominant component of sensible weather by affecting the diurnal temperature cycle, the heating or cooling of a vertical column, and the distribution of precipitation. The NASA A- Train, a satellite constellation consisting of high resolution instruments that are nearly coincident in time and space, provides an opportunity to compare observed cloud properties to those produced by high resolution model forecasts. Verification data provided by the NASA A-Train include products derived from the MODIS aboard Aqua and the CloudSat Cloud Profiling Radar. Distributions of cloud top and cloud profile properties from MODIS are compared to model derived properties during the evolution of a midlatitude cyclone from March 2007. A radiative transfer model is used to compare CloudSat radar reflectivity to reflectivity simulated from WRF cloud profiles. Sensitivities in the simulation of clouds are examined through experiments with modified parameters focusing on changes in the ice phase components.
A53D-1440
Evaluating CloudSat Ice Water Retrievals Using a Cloud Resolving Model: Sensitivities to Frozen Particle Properties and Implications for Model-Data Comparisons
The sensitivities of CloudSat ice water content retrievals to frozen particle characteristics are tested by generating CloudSat-like retrievals from profiles of known ice water content. First, `truth' values of total ice water content are generated by a cloud-resolving model (MM5). The MM5 model profiles are generated using the Reisner- Thompson microphysical parameterization scheme, which allows for the existence of multiple types of frozen particles (cloud ice, snow and graupel). Next, a 94-GHz reflectivity simulator, called QuickBeam, is used to generate a CloudSat-like view of the model generated profiles. Since reflectivity is highly dependent on the characteristics of the scattering particles (e.g., density, size distribution), a set of tests are performed to determine the sensitivity of the reflectivity to the assumed properties of cloud ice and snow particles. Finally, the CloudSat ice water content retrieval algorithm is applied to the profiles of 94-GHz reflectivity, producing 'simulated retrieved' values of ice water content, which can be compared to the `truth' values. The comparisons suggest that CloudSat ice water content retrievals are sensitive to the frozen particle properties often parameterized in models (e.g., particle density, particle size distributions). The sensitivity tests provide a better understanding of how the different components of the frozen water mass impact the ice water content retrieved by CloudSat. Such information is important when comparing the measurements to modeled frozen water mass quantities, including those from various levels of sophistication in global climate models. Additionally, we demonstrate how information gained in this study may be used for improving the retrieval system. A simple height-based retrieval correction that effectively corrects for the vertically varying characteristics of frozen particles is examined.
A53D-1441
Use of High-Resolution Satellite Observations to Evaluate Cloud and Precipitation Statistics from Cloud-Resolving Model Simulations
The cloud and precipitation statistics simulated by 3D Goddard Cumulus Ensemble (GCE) model for different environmental conditions, i.e., the South China Sea Monsoon Experiment (SCSMEX), CRYSTAL-FACE, and KAWJEX are compared with Tropical Rainfall Measuring Mission (TRMM) TMI and PR rainfall measurements and as well as cloud observations from the Earth's Radiant Energy System (CERES) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. It is found that GCE is capable of simulating major convective system development and reproducing total surface rainfall amount as compared with rainfall estimated from the soundings. The model presents large discrepancies in rain spectrum and vertical hydrometer profiles. The discrepancy in the precipitation field is also consistent with the cloud and radiation observations. The study will focus on the effects of large scale forcing and microphysics to the simulated model- observation discrepancies.
A53D-1442
Evaluation of microphysics and precipitation-type frequencies in long-term three-dimensional cloud-resolving model simulations using passive and active microwave sensors from the TRMM satellite
With significant improvements in computational power over the last decades, cloud-resolving model (CRM) simulations can now be conducted on larger scales for longer time periods to better understand cloud- precipitation systems. However, even after the decadal development of CRMs, there are many uncertainties in cloud microphysics processes and cloud-precipitation structures due to the lack of routine observations. Therefore, we need to establish a practical CRM evaluation framework using frequent observations from satellites. This evaluation framework consists of i) multi-satellite simulators and ii) the construction of statistical composites that can be used to effectively evaluate cloud-precipitation systems. First, simulated cloud- precipitation structures and microphysics processes are converted to satellite-consistent radar reflectivity and microwave brightness temperature using microwave and radar simulators in the Satellite Data Simulator Unit (SDSU). Second, the CRM-computed and satellite-observed radar reflectivities and microwave brightness temperatures are used to construct two statistical composites. One combines TRMM (Tropical Rainfall Measuring Mission) PR (precipitation radar) 13.8-GHz radar echo-top heights and TRMM VIRS (visible/infrared scanner) 10.8-micron brightness temperatures. This composite categorizes precipitating clouds into shallow warm, cumulus congestus, deep stratiform, and deep convective clouds. The other composite combines multi- frequency TMI (TRMM microwave imager) brightness temperatures. The combination of low- and high-frequency channels reveals the performance of the model cloud microphysics in terms of liquid and ice precipitation amounts. In this study, long-term CRM simulations are performed using the Goddard Cumulus Ensemble (GCE) model for three cases: ARM TWP-ICE (Tropical Warm Pool International Cloud Experiment), SCSMEX (South China Sea Monsoon Experiment), and KWAJEX (Kwajalein Experiment). Results from the proposed evaluation framework for cloud microphysics and cloud-precipitation structures will be shown for these different cases.
A53D-1443
Validation of Microphysical Schemes in a CRM Using TRMM Satellite
The microphysical scheme in the Goddard Cumulus Ensemble (GCE) model has been the most heavily developed component in the past decade. The cloud-resolving model now has microphysical schemes ranging from the original Lin type bulk scheme, to improved bulk schemes, to a two-moment scheme, to a detailed bin spectral scheme. Even with the most sophisticated bin scheme, many uncertainties still exist, especially in ice phase microphysics. In this study, we take advantages of the long-term TRMM observations, especially the cloud profiles observed by the precipitation radar (PR), to validate microphysical schemes in the simulations of Mesoscale Convective Systems (MCSs). Two contrasting cases, a midlatitude summertime continental MCS with leading convection and trailing stratiform region, and an oceanic MCS in tropical western Pacific are studied. The simulated cloud structures and particle sizes are fed into a forward radiative transfer model to simulate the TRMM satellite sensors, i.e., the PR, the TRMM microwave imager (TMI) and the visible and infrared scanner (VIRS). MCS cases that match the structure and strength of the simulated systems over the 10-year period are used to construct statistics of different sensors. These statistics are then compared with the synthetic satellite data obtained from the forward radiative transfer calculations. It is found that the GCE model simulates the contrasts between the continental and oceanic case reasonably well, with less ice scattering in the oceanic case comparing with the continental case. However, the simulated ice scattering signals for both PR and TMI are generally stronger than the observations, especially for the bulk scheme and at the upper levels in the stratiform region. This indicates larger, denser snow/graupel particles at these levels. Adjusting microphysical schemes in the GCE model according the observations, especially the 3D cloud structure observed by TRMM PR, result in a much better agreement.
A53D-1444
Latent Heating Properties of Cloud Regimes Determined by Cluster Analysis of Satellite and Model Data
The regime-averaged latent heating properties of several distinct cloud regimes of the tropical West Pacific are examined. Regimes are determined through k-means cluster analysis (Jakob and Tselioudis 2003) for both satellite and model data, and are based on joint histograms of cloud top pressure, cloud optical depth, and rainrate. Satellite observations use the MODIS and AMSR-E instruments onboard the Aqua satellite as well as cloud radar profiles from CloudSat. Cloud regimes effectively delineate the strength of convective activity in the region of interest. Regime-averaged properties and the progression with time of each regime provide insight into the mechanisms behind the tropical circulation and may prove useful for development of latent heat profile retrievals in the future.
A53D-1445
Evaluating cloud-layer fractions and associated atmosphere profiles of Goddard Multi-scale Modeling Framework using the A-Train constellation of satellites
Although cloud fractions defined at different layers are a first-order parameter that controls earth's energy and water budget and climate system, their global-scale measurements were strictly limited by passive space-born remote sensing. However, the emergence of space-born Cloud Profile Radar (CPR) onboard CloudSat has begun to uncover global three-dimensional cloud structures. CPR measures the radar backscattering signals from optically thick large-particle clouds at the millimeter wavelength (94GHz). The backscattering signals provide more accurate pictures of vertical profiles of cloud layers that can be used to evaluate the cloud parameterization of weather/climate models in details. This study evaluates Goddard Multi-scale Modeling Framework (MMF) using the cloud-layer fraction derived from CloudSat radar reflectivity. The Goddard MMF is the general circulation model (GCM) that explicitly resolves convective eddies and condensates by the embedded two-dimensional cloud resolving model at each GCM column. Thus, it allows more direct comparison with high-resolution satellite observations. We use the cloud radar simulators in QuickBeam and Satellite Data Simulator Unit (SDSU) to derive satellite-consistent product of radar reflectivity and brightness temperature from the MMF-simulated cloud and precipitation condensates. Since the explicitly simulated cloud fractions and radiative processes feed back to the tendencies of thermodynamic variables in GCM, we will also examine AIRS-derived water vapor and temperature profiles, CERES-derived top-of-atmosphere outgoing longwave radiation and shortwave radiation, AMSR-E-derived precipitation, and examine how the errors in cloud-layer fractions in Goddard MMF are linked to the simulated energy and water cycles.
A53D-1446
Assimilation of GOES-Derived Cloud Fields Into MM5
This approach for the assimilation of GOES-derived cloud data into an atmospheric model (the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, or MM5) was performed in two steps. In the first step, multiple linear regression equations were developed using a control MM5 simulation to develop relationships for several dependent variables in model columns that had one or more layers of clouds. In the second step, the regression equations were applied during an MM5 simulation with assimilation in which the hourly GOES satellite data were used to determine the cloud locations and some of the cloud properties, but with all the other variables being determined by the model data. The satellite-derived fields used were shortwave cloud albedo and cloud top pressure. Ten multiple linear regression equations were developed for the following dependent variables: total cloud depth, number of cloud layers, depth of the layer that contains the maximum vertical velocity, the maximum vertical velocity, the height of the maximum vertical velocity, the estimated 1-h stable (i.e., grid scale) precipitation rate, the estimated 1-h convective precipitation rate, the height of the level with the maximum positive diabatic heating, the magnitude of the maximum positive diabatic heating, and the largest continuous layer of upward motion. The horizontal components of the divergent wind were adjusted to be consistent with the regression estimate of the maximum vertical velocity. The new total horizontal wind field with these new divergent components was then used to nudge an ongoing MM5 model simulation towards the target vertical velocity. Other adjustments included diabatic heating and moistening at specified levels. Where the model simulation had clouds when the satellite data indicated clear conditions, procedures were taken to remove or diminish the errant clouds. The results for the period of 0000 UTC 28 June – 0000 UTC 16 July 1999 for both a continental 32-km grid and an 8-km grid over the Southeastern United States indicate a significant improvement in the cloud bias statistics. The main improvement was the reduction of high bias values that indicated times and locations in the control run when there were model clouds but when the satellite indicated clear conditions. The importance of this technique is that it has been able to assimilate the observed clouds in the model in a dynamically sustainable manner. Acknowledgments. This work was partially funded by the following grants: a GEWEX grant from NASA , the Cooperative Agreement between the University of Alabama in Huntsville and the Minerals Management Service on Gulf of Mexico Issues, a NASA applications grant, and a NSF grant.
A53D-1447
Preparation for Evaluating Cloud Simulations from GCMs Using the A-Train Data
The launch of CloudSat and CALIPSO provides significant opportunities to properly evaluate the vertical structures of cloud macro- and micro-physical properties from model simulations. The new observational datasets will lead to the improvement of the representation of clouds and precipitation in climate models. The vertical distribution of cloud fraction combining CloudSat and CALIPSO measurements is sorted by dynamical regime using the monthly mean pressure velocity at 500 hPa from 30S to 30N. The simulations from two leading U.S. GCMs are grouped by the different dynamical regimes and compared to the observations. In this preliminary study, it has been shown that the two models underestimate the occurrence of low-level clouds in regimes of strong subsidence and overestimate that of high clouds in the ascent regime, while the satellite observations show a reasonable circulation with the basic atmospheric features in tropical regions. This result will help to analyze the regional distributions of different cloud regimes and their effects on the energy and hydrological field. In order to widely evaluate the cloud simulations from GCMs using the observations from A-Train and other satellites, a combined CFMIP ISCCP/CloudSat/CALIPSO simulator is under development. The CloudSat simulator will convert model clouds into radar reflectivity similar to the observations, and the early results from the CloudSat simulator are presented. A vertical subgrid precipitation overlap module and a statistical summary module are proposed with the least input and the fewest assumptions.
A53D-1448
Evaluation of a New Mixed-Phase Cloud Microphysics Parameterization with a Single Column Model, CAPT Forecasts and M-PACE Observations
Most global climate models generally prescribe the partitioning of condensed water into liquid droplets and ice crystals in mixed-phase clouds according to a temperature-dependent function, which affects modeled cloud phase, cloud lifetime and radiative properties. In this study we evaluate a new mixed-phase cloud microphysics parameterization (for ice nucleation and water vapor deposition) against the Atmospheric Radiation Measurement (ARM) Mixed-phase Arctic Cloud Experiment (M-PACE) observations using the NCAR Community Atmospheric Model Version 3 (CAM3) running in the single column mode (SCAM) and in the CCPP-ARM Parameterization Testbed (CAPT) forecasts. It is found that SCAM with the new physically-based cloud microphysical scheme produces a more realistic simulation of the cloud phase structure and the partitioning of condensed water into liquid droplets against observations during the M-PACE than the standard CAM with an oversimplified cloud microphysics. CAM3 in the CAPT forecasts significantly underestimates the observed boundary layer mixed- phase cloud fraction. The simulation of the boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when the new scheme is used. The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes. Both SCAM simulations and CAPT forecasts suggest that the ice number concentration could play an important role in the simulated mixed-phase cloud microphysics, and thereby needs to be realistically represented in global climate models.
A53D-1449
Evaluation of Diurnal Cycle of Convection in a GCM with Satellite Data
Clouds are one of the most important components of the climate system, regulating the radiation budget of the earth. In the simulation of the global climate using General Circulation Models (GCM), cloud feedbacks contribute to a major uncertainty on account of poorly represented cloud related processes in the model. In particular, the representation of convection and convective clouds constitutes at the same time a crucial component of GCMs and a main source of uncertainty. Satellite observations provide the most comprehensive view of cloud related quantities at a global scale, and are an important data source for the evaluation of parameterization schemes. We present here some experiments with ECHAM5 GCM and their comparison with satellite observations. This study focuses on the diurnal cycle of convection for monsoon months over two regions in the Indian subcontinent. The first area being over land and the other in the ocean, the model's capability in producing two distinct behaviors is analyzed. Sensitivity studies with ECHAM5 are carried out in order to examine the influence of different parameters of the convection parameterization in this model. The International Satellite Cloud Climatology Project (ISCCP) products and data from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument are used as verification data.