Cloudy Boundary Layer: A Climatology from Space
A MEaSUREs Cloudy Boundary Layer Data Record

The Intergovernmental Panel on Climate Change (IPCC) has reiterated that clouds remain the largest source of uncertainty in climate projections. Clouds in the atmospheric boundary layer, and in particular, subtropical clouds such as stratocumulus, cumulus and the transitions between them, appear to play a key role in cloud-climate feedbacks. Unfortunately, current climate models are not able to represent these clouds in a realistic manner, which leads to a large uncertainty in current climate projections.

A new multi-sensor multi-parameter Earth System Data Record of state of the art observations of the cloudy boundary layer was created as part of the MEaSUREs Program. This data record is intended to help better understand the physics of the cloudy boundary layer from a global perspective, and for the evaluation of models of varying complexity from simple bulk models to fully coupled global models. This project is focused on four key variables: boundary layer water vapor, liquid water path, boundary layer height and light surface precipitation.

  • Multi-Sensor Ultra-high Resolution (MUR) SST Field

    Mapping sea surface temperature (SST) from satellite measurements and surface observation (ship an bouy data). SST data are useful for weather forecasting, fisheries management, ocean acoustic communication, and scientific studies of climate and marine life.

  • GOZCARDS: Global Ozone Chemistry And Related trace gas Data records for the Stratosphere

    The Global Ozone Chemistry And Related trace gas Data records for the Stratosphere (GOZCARDS) MEaSUREs project provides stratospheric composition data from Earth system data records. The science focus of this data includes ozone decline and recovery. High-quality long-term ozone and related trace gas data records are needed to (a) evaluate and understand composition changes in the atmosphere and (b) constrain model representations of atmospheric dynamics and photochemistry.

  • JPL MEaSUREs Gridded Sea Surface Height Anomalies Version 1609

    These are gridded Sea Surface Height Anomalies (SSHA) above a 10 year time mean surface, on 1/6th degree grid every 5 days. It contains the fully corrected heights, but delayed 1-3 months. If you require data sooner there is an interim dataset available, but with slightly lower accuracy at The gridded data are derived from the SSHA data of TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 as reference data from the level 2 swath data found at, plus ERS-1, ERS-2, Envisat, SARAL-AltiKa, CRyosat-2, depending on the date, from the RADS database. The gridding is done by the kriging method. The date given in the data is the center of the 5 day window.

  • Boundary Layer Water Vapor

    The majority of atmospheric water vapor lies within the boundary layer. However, each of the existing spaceborne methods for remote sensing of water vapor has limitations that preclude them from accurately measuring water vapor within the planetary boundary layer. In this dataset, boundary layer column water vapor (BL-CWV) is derived from the synergy of microwave and near-infrared imagery (Millán et al. 2016). Microwave radiometry provides information on the total column water vapor by measuring at the strong water vapor absorption line near 22 GHz, while near-infrared imagery can provide water vapor above the clouds by measuring the solar radiation reflected near the 0.94-μm water vapor band. Their difference provides the BL-CWV when the cloud top is capped at the boundary layer top.

  • Liquid Water Path

    The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently provides 28 years (1988 – 2015) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) inter-calibrated 0.25°-resolution retrievals (Elsaesser et al. 2017). Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2 and GMI. In order to mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and monthly-mean diurnal cycle. In addition to a longer record and five additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrieval products, a correction for a CLWP bias (based on matchups to clear-sky MODIS scenes), and the construction of a total (cloud+rain) water path (TWP) record. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TWP field can be used as a quality-control screen, where uncertainty increases as the ratio of CLWP to TWP decreases. For regions where confidence in CLWP is highest (i.e. CLWP:TWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from –15% (e.g. global oceanic stratocumulus decks) to +5-10% (e.g. portions of the higher-latitude oceans, storm tracks, and shallower convection regions straddling the ITCZ).

  • Boundary Layer Height

    We have produced a global climatology of boundary layer height derived from 10 years (2006-2015) of GPS radio occultation (RO) observations from the COSMIC and TerraSAR-X missions. The boundary layer height is obtained from each GPS RO vertical profile of refractivity from the height where the vertical refractivity gradient has the largest magnitude (Ao et al. 2012). Only vertical profiles that reach within 500 m of the surface are used to derive their boundary layer height, which are then averaged over 2 deg x 2 deg latitude-longitude bins to produce the climatology shown here. Boundary layer heights derived from GPS RO are most accurate when the boundary layer is capped with a strong inversion layer and have been shown to agree very well with radiosondes and other satellite measurements over the Southeastern Pacific Ocean (Xie et al. 2012; Ho et al. 2015).

  • surface light precipitation

    Surface Light Precipitation

    Conventional space-based precipitation observations such as passive microwave imagers and low-frequency precipitation radar are unable to observe light rainfall. However, the CloudSat Cloud Profiling Radar (CPR) is extremely sensitive to light rainfall due to its relatively high 94 GHz operating frequency. While the CPR is not capable of estimating heavy rainfall due to the saturation of its signal, its observations of light rainfall provide a useful complement to the conventional precipitation observing system (Lebsock and L'Ecuyer 2011). CloudSat observations have demonstrated the ubiquity of rainfall in liquid-phase clouds over the ocean. This dataset provides a gridded-climatology from 2006 to 2016 from the CPR of the frequency of occurrence and mean rainfall rate at the surface falling from low-level marine clouds. The dataset is designed specifically for studies of marine boundary layer stratocumulus and cumulus clouds.

Ao, C. O., D. E. Waliser, S. K. Chan, J.-L. Li, B. Tian, F. Xie, and A. J. Mannucci (2012), Planetary boundary layer heights from GPS radio occultation refractivity and humidity profiles, J. Geophys. Res., 117, D16117, doi:10.1029/2012JD017598.

Elsaesser, G., C. O’Dell, M. Lebsock, R. Bennartz, T. Greenwald, The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) , submitted to Journal of Climate.

Ho, S., L. Peng, R.A. Anthes, Y. Kuo, and H. Lin (2015) Marine Boundary Layer Heights and Their Longitudinal, Diurnal, and Interseasonal Variability in the Southeastern Pacific Using COSMIC, CALIOP, and Radiosonde Data. J. Climate, 28, 2856–2872, doi: 10.1175/JCLI-D-14-00238.1.

Lebsock, M.D., and T.S. L'Ecuyer (2011), The retrieval of warm rain from CloudSat, J. Geophys. Res., 116, D20209, doi:10.1029/2011JD016076.

Millán, L., M. Lebsock, E. Fishbein, P. Kalmus, J. Teixeira (2016), Quantifying Marine Boundary Layer Water Vapor Beneath Low Clouds with Near-Infrared and Microwave Imagery, J. Appl. Meteorol. Clim., 55, 213–225, doi:10.1175/JAMC-D-15-0143.1.

Xie, F., D. L. Wu, C. O. Ao, A. J. Mannucci, and E. R. Kursinski (2012), Advances and limitations of atmospheric boundary layer observations with GPS occultation over southeast Pacific Ocean, Atmos. Chem. Phys., 12, 903–918, doi:10.5194/acp-12-903-2012.

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