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[1] The ice cloud estimates in current global models exhibit significant inconsistency, resulting in a significant amount of uncertainties in climate forecasting. Vertically resolved ice water content (IWC) is recently available from new satellite products, such as CloudSat, providing important observational constraints for evaluating the global models. To account for the varied nature of the model parameterization schemes, it is valuable to develop methods to distinguish the cloud versus precipitating ice components from the remotely sensed estimates in order to carry out meaningful model-data comparisons. The present study develops a new technique that partitions CloudSat total IWC into small and large ice hydrometeors, using the ice particle size distribution (PSD) parameters provided by the retrieval algorithm. The global statistics of CloudSat-retrieved PSD are analyzed for the filtered subsets on the basis of convection and precipitation flags to identify appropriate particle size separation. Results are compared with previous partitioning estimates and suggest that the small particles contribute to ∼25–45% of the global mean total IWC in the upper to middle troposphere. Sensitivity measures with respect to the PSD parameters and the retrieval algorithm are presented. The current estimates are applied to evaluate the IWC estimates from the European Centre for Medium-Range Weather Forecasts model and the finite-volume multiscale modeling framework model, pointing to specific areas of potential model improvements. These results are discussed in terms of applications to model diagnostics, providing implications for reducing the uncertainty in the model representation of cloud feedback and precipitation.

Much like the better-known EI Nino-Southern Oscillation, the Madden-Julian Oscillation (MJO) is a global-scale atmospheric phenomenon. The MJO involves periodic, systematic changes in the distribution of clouds and precipitation over the western Pacific and Indian oceans, along with differences in wind intensity over even more extensive areas, including the north and subtropical Atlantic Ocean. The lead authors of this paper developed a sophisticated mathematical technique for mapping the spatial and temporal behavior of changes in the atmosphere produced by the MJO. In a previous paper, we applied this technique to search for modulation of airborne particle amount in the eastern hemisphere associated with the "wet" (cloudy) vs. "dry" phases of the MJO. The study used primarily AVHRR, MODIS, and TOMS satellite-retrieved aerosol amount, but concluded that other factors, such as cloud contamination of the satellite signals, probably dominated the observed variations. The current paper looks at MJO modulation of desert dust transport eastward across the Atlantic from northern Africa, a region much less subject to systematic cloud contamination than the eastern hemisphere areas studied previously. In this case, a distinct aerosol signal appears, showing that dust is transported westward much more effectively during the MJO phase that favors westward-flowing wind, and such transport is suppressed when the MJO reduces these winds. Aside form the significant achievement in identifying such an effect, the result implies that an important component of global dust transport can be predicted based on the phase of the MJO. As a consequence, the impact of airborne dust on storm development in the Atlantic, and on dust deposition downwind of the desert sources, can also be predicted and more accurately modeled.

[1] The study objective is to explore the relationship between the Madden-Julian Oscillation (MJO) and intraseasonal sea surface temperature (SST) variability in the eastern tropical Pacific Ocean. Previous studies have illustrated the connection between MJO and the production of zonally large (>2000 km), persistent (∼weeks) SST anomalies. Those studies suggested that vertical processes, such as advection and entrainment, forced remotely by winds in the western Pacific (via Kelvin waves) may be the mechanism controlling SST changes. To overcome limitations in situ observations (e.g., sparse and missing data/quantities) and to develop a more comprehensive physical understanding, this study examines the relationship using an ocean general circulation model. A simulation was conducted in which the model was forced by idealized MJO conditions constructed from observed forcing fields. Analysis of the model simulation shows an equatorial Kelvin wave initiated in the western Pacific Ocean. However, analysis of the model's mixed layer temperature heat budget shows that in the eastern Pacific meridional advection plays the major role in the sea surface temperature change, 61.8% of the warming phase and 70.7% of the cooling phase percent heat budget. Zonal advection is the second most important term to the warming phase (20.5%) with the vertical advection and mixing term being second for the cooling phase (37.6%). In addition, the results indicate that the primary component of the meridional advection is the advection of the mean meridional temperature gradient by MJO-forced meridional current anomalies. The implications and caveats of these results are discussed in relationship to results in prior studies.

[1] Present-day shortcomings in the representation of upper tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global-scale measurements. With the relatively recent addition of satellite-derived products from Aura/Microwave Limb Sounder (MLS) and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their "cloud" ice and related fields. Using this information as background, we (1) discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of remaining questions and suggestions for pathways forward.

[1] We compare matched retrievals of upper tropospheric water vapor (UTWV) mixing ratios from the Microwave Limb Sounder (MLS) instrument on the Aura satellite, and the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite. Because each instrument's sampling is affected by tropical conditions, about half of mutually observed scenes in the tropics yield simultaneous successful retrievals from both systems. The fraction of mutually retrieved scenes drops to 30% at higher latitudes where clouds significantly inhibit AIRS sounding. Essentially all scenes observed by MLS in extratropical and polar regions yield successful retrievals. At 250 hPa in the tropics, measurements from the two instruments are highly correlated, the differences of their means (q) are smaller than 10%, and the standard deviations of their differences (σq) are 30% or less. At 300 hPa, MLS means are drier by 10–15%, and σq is 40–60%, indicating that responses of MLS and AIRS to UTWV perturbations are not one-to-one. Root mean square agreement is also poorer over the poles at 300 hPa and at 200 and 150 hPa at lower latitudes. In these regions, ∣q∣ = 10% or more, and σq = 40–70%. Correlations between the two data sets are 0.7–0.9 at 300 and 250 hPa globally and at 200 hPa in the tropics. This high correlation indicates that σq of 50% or greater comes mainly from systematic differences in sensitivity of the two instruments, especially for small and large UTWV amounts; larger values of σq are generally not due to large random errors from either instrument. An AIRS low-end sensitivity threshold of 15–20 ppmv leads to poorer agreement under the driest conditions. Disagreement at 300 hPa likely comes from overestimation by MLS for the wettest conditions of >400 ppmv. While MLS is biased slightly dry overall at 300 hPa, it is biased wet in the wettest regions, particularly those associated with deep convection. These sensitivity differences explain nonunity slopes of linear fits to the two data sets. MLS everywhere has a greater dynamic range than AIRS, with larger maxima and smaller minima. Good agreement at 250 hPa suggests AIRS uncertainties of 25% up to the reported 250–200 hPa layer in the tropics and extratropics, consistent with previous comparisons with balloon- and aircraft-borne instruments. The agreement at 250 hPa also indicates that MLS is reliable from its reported 215-hPa level upward in altitude.

[1] The A-Train satellite constellation has dramatically increased the temporal and spatial coverage of atmospheric ice water content estimates. The new data are derived by retrieval algorithms designed to estimate atmospheric cloud ice water content from remotely sensed measurements. Such retrieval algorithms rely on simplifying assumptions regarding the characteristics of ice particles in the atmosphere. In this study, 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. CloudSat actively measures vertical profiles of radar reflectivity in clouds with a 94-GHz cloud-profiling radar. Ice water content is retrieved in each cloudy profile at temperatures below 0°C. To assess the CloudSat radar-only ice water content retrieval algorithm (version 5.0 in Release 3 [R03] and version 5.1 in Release 4 [R04] of 2B-CWC-RO), we apply a 94-GHz reflectivity simulator to profiles of ice water content generated by a cloud-resolving numerical model and comprising various frozen particle species (ice, snow, and graupel). The CloudSat ice water content retrieval algorithm is applied to the profiles of simulated reflectivity, and the results are compared to the modeled profiles of known frozen water mass. The results from each version of the algorithm are shown to be sensitive to the characteristics of the frozen particle size distributions and particle densities. Tests of version 5.0 indicate that height varying information could improve retrievals. Despite the addition of a height varying component implemented in version 5.1, similar positive biases are indicated in the tests of each algorithm.

[1] The large-scale response of the Indo-Pacific Ocean to atmospheric forcing associated with the Madden-Julian Oscillation (MJO) is examined using an ocean general circulation model forced by canonical MJO conditions constructed from observations. The results show that for a number of equatorial areas, ocean advection processes can play an important role in determining the ocean response. In addition, mixed layer depth (MLD) variations are considerable and contribute to the sea surface temperature (SST) variability. SST variability also develops in the eastern equatorial Pacific via the propagation of ocean Kelvin waves. With regards to this variability the results suggest that advective processes, namely, meridional advection, play the most significant role. Sea level variability in the equatorial regions and eastern sides of the Indian and Pacific basins associated with the MJO is considerable. In conjunction with these sea level variations are also large variations in the Indonesian Throughflow. The results also show that the MJO forcing produces a low-frequency rectified signal consisting of a weak cooling in the equatorial western Pacific and Indian Ocean regions, a relatively larger warming around the maritime continent, a fair amount of MLD shallowing in most of the above regions, and a westward equatorial Pacific Ocean current anomaly. In addition, the heat flux variations associated with the MJO produce systematic variations in the east-west zonal gradient of Indian Ocean SST, which could influence the evolution of the Indian Ocean Zonal Mode. The implications and caveats associated with these results, the caveats associated with the model and forcing framework, and areas necessitating further study are discussed.

[1] Comparison of ocean buoy observations and model calculations of incoming clear-sky surface shortwave radiation is performed in order to assess the buoys' general reliability under operating conditions. The buoy data employed for this study come from several experimental and operational deployments conducted by Woods Hole Oceanographic Institution (WHOI) and Pacific Marine Environmental Laboratory (PMEL). WHOI deployments include the Frontal Air-Sea Interaction Experiment, Marine Light Mixed Layer Experiment, Coupled Ocean-Atmosphere Response, Subduction Experiment, Arabian Sea Experiment, Pan-American Climate Study, and Biowatt. PMEL deployments include the Tropical Atmosphere Ocean moored buoy array in the tropical Pacific Ocean. These moorings and their associated shortwave measurements represent the vast majority of open-ocean in situ shortwave observations available to date. Two separate schemes were used to filter the cloudy samples from the buoy shortwave time series, one based on satellite values of cloudiness and a second scheme based on the buoy observations themselves and on a number of additional constraints. The clear-sky model calculations of surface shortwave were computed using the single-column radiation code from the National Center for Atmospheric Research Community Climate Model, version 3. The primary uncertainty associated with the model calculations is the specification of the aerosol amount. In general, there was a fairly high level of agreement between the buoy and modeled values of clear-sky surface shortwave. However, there were a few buoys that exhibited significant model-data discrepancies (e.g., model-data biases exceeding 10%, or ∼40 W m−2). The possible reasons for these discrepancies were investigated. In some cases, unaccounted for aerosol variability in the model was found to be the most probable cause, indicating that observations were likely to be reliable. In other cases, the discrepancies appeared to result from sensor tilt associated with wind, currents, or deployment/mounting problems and/or were possibly due to aerosol buildup on the sensor.