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  • 1
    Publication Date: 2013-07-01
    Description: While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available datasets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Climate Research Facility as inputs into a Gauss–Newton optimal estimation scheme, in which an assumed guess of the entrainment rate is iteratively adjusted through intercomparison of modeled cloud attributes to their observed counterparts. The forward model in this algorithm is the explicit mixing parcel model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for the uncertainty in the retrieved entrainment rate is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on 3 months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km−1 for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
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  • 2
    Publication Date: 2012-04-01
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
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  • 3
    Publication Date: 2011-05-15
    Description: Yearly percentiles of geostrophic wind speeds serve as a widely used proxy for assessing past storm activity. Here, daily geostrophic wind speeds are derived from a geographical triangle of surface air pressure measurements and are used to build yearly frequency distributions. It is commonly believed, however unproven, that the variation of the statistics of strong geostrophic wind speeds describes the variation of statistics of ground-level wind speeds. This study evaluates this approach by examining the correlation between specific annual (seasonal) percentiles of geostrophic and of area-maximum surface wind speeds to determine whether the two distributions are linearly linked in general. The analyses rely on bootstrap and binomial hypothesis testing as well as on analysis of variance. Such investigations require long, homogeneous, and physically consistent data. Because such data are barely existent, regional climate model–generated wind and surface air pressure fields in a fine spatial and temporal resolution are used. The chosen regional climate model is the spectrally nudged and NCEP-driven regional model (REMO) that covers Europe and the North Atlantic. Required distributions are determined from diagnostic 10-m and geostrophic wind speed, which is calculated from model air pressure at sea level. Obtained results show that the variation of strong geostrophic wind speed statistics describes the variation of ground-level wind speed statistics. Annual and seasonal quantiles of geostrophic wind speed and ground-level wind speed are positively linearly related. The influence of low-pass filtering is also considered and found to decrease the quality of the linear link. Moreover, several factors are examined that affect the description of storminess through geostrophic wind speed statistics. Geostrophic wind from sea triangles reflects storm activity better than geostrophic wind from land triangles. Smaller triangles lead to a better description of storminess than bigger triangles.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 4
    Publication Date: 2013-02-01
    Description: Global atmospheric reanalyses have become a common tool for both validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the Twentieth Century Reanalysis (20CR), which assimilates only surface pressure reports, sea ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, the authors compare the variability and long-term trends in northeast Atlantic storminess derived from 20CR and from observations. A well-established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both variability and long-term trends derived from 20CR and from observations are inconsistent. In particular, both time series show opposing trends during the first half of the twentieth century: both storm indices share a similar behavior only for the more recent periods. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis, most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record, at least over the northeast Atlantic. The results imply and reconfirm previous findings that care is needed in general when global reanalyses are used to assess long-term changes.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 5
    Publication Date: 2011-02-15
    Description: Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 6
    Publication Date: 2011-09-01
    Description: Observations of the scale dependence of height-resolved temperature T and water vapor q variability are valuable for improved subgrid-scale climate model parameterizations and model evaluation. Variance spectral benchmarks for T and q obtained from the Atmospheric Infrared Sounder (AIRS) are compared to those generated by state-of-the-art numerical weather prediction “analyses” and “free-running” climate model simulations with spatial resolution comparable to AIRS. The T and q spectra from both types of models are generally too steep, with small-scale variance up to several factors smaller than AIRS. However, the two model analyses more closely resemble AIRS than the two free-running model simulations. Scaling exponents obtained for AIRS column water vapor (CWV) and height-resolved layers of q are also compared to the superparameterized Community Atmospheric Model (SP-CAM), highlighting large differences in the magnitude of CWV variance and the relative flatness of height-resolved q scaling in SP-CAM. Height-resolved q spectra obtained from aircraft observations during the Variability of the American Monsoon Systems Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) demonstrate changes in scaling exponents that depend on the observations’ proximity to the base of the subsidence inversion with scale breaks that occur at approximately the dominant cloud scale (~10–30 km). This suggests that finer spatial resolution requirements must be considered for future satellite observations of T and q than those currently planned for infrared and microwave satellite sounders.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 7
    Publication Date: 2013-10-01
    Description: This study presents a summary of the properties of deep convective updraft and downdraft cores over the central plains of the United States, accomplished using a novel and now-standard Atmospheric Radiation Measurement Program (ARM) scanning mode for a commercial wind-profiler system. A unique profiler-based hydrometeor fall-speed correction method modeled for the convective environment was adopted. Accuracy of the velocity retrievals from this effort is expected to be within 2 m s−1, with minimal bias and base core resolution expected near 1 km. Updraft cores are found to behave with height in reasonable agreement with aircraft observations of previous continental convection, including those of the Thunderstorm Project. Intense updraft cores with magnitudes exceeding 15 m s−1 are routinely observed. Downdraft cores are less frequently observed, with weaker magnitudes than updrafts. Weak, positive correlations are found between updraft intensity (maximum) and updraft diameter length (coefficient r to 0.5 aloft). Negligible correlations are observed for downdraft core lengths and intensity.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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