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Yu Liu, Xuepeng Zhao, Weiliang Li, and Xiuji Zhou

Abstract

The Stratospheric Aerosol and Gas Experiment II (SAGE II) aerosol products from 1998 to 2004 have been analyzed for the tendency of changes in background stratospheric aerosol properties. The aerosol extinction coefficient E has apparently increased in the midlatitude lower stratosphere (LS) in both hemispheres, at an annual rate that is as great as 2%–5%. Positive changes in the aerosol surface area density S in the midlatitude LS are most distinct, with a rate of increase that is as high as 5%–6% annually. At the same time, there has been a secular decrease in aerosol effective radius R, especially in the tropical LS, at a rate of up to −2.5% yr−1. Corresponding to these trends, the aerosol number concentration is inferred to have increased by roughly 5%–10% yr−1 in the tropical LS and by 4%–8% yr−1 in the midlatitude LS. Changes in aerosol mass are also deduced, with rates of increase in the midlatitude LS that are in the range of 1%–5% yr−1. The large uncertainty in operational S product is the major factor influencing the trend in S, aerosol number concentrations, and mass. The authors’ global assessment supports the speculation of Hofmann et al. on the basis of local observations that the cause of an increase in lidar backscatter over a similar period was a consequence of aerosol particle growth due to enhanced anthropogenic sulfur dioxide emissions. Moreover, it is found that an increase in the injection rate of condensation nuclei from the troposphere to the stratosphere at tropical latitudes is required to sustain the increase in stratospheric aerosol concentrations identified in this analysis.

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Scott Elliott, Xuepeng Zhao, Richard P. Turco, Chih-Yue Jim Kao, and Mei Shen

Abstract

Atmospheric photochemistry lies at the heart of global-scale pollution problems, but it is a nonlinear system embedded in nonlinear transport and so must be modeled in three dimensions. Total earth grids are massive and kinetics require dozens of interacting tracers, taxing supercomputers to their limits in global calculations. A matrix-free and noniterative family scheme is described that permits chemical step sizes an order of magnitude or more larger than time constants for molecular groupings, in the 1-h range used for transport. Families are partitioned through linearized implicit integrations that produce stabilizing species concentrations for a mass-conserving forward solver. The kinetics are also parallelized by moving geographic loops innermost and changes in the continuity equations are automated through list reading. The combination of speed, parallelization, and automation renders the programs naturally modular. Accuracy lies within 1% for all species in week-long fidelity tests. A 50-species, 150-reaction stratospheric module tested in a spectral GCM benchmarks at 10 min CPU time per day and agrees with lower-dimensionality simulations. Tropospheric nonmethane hydrocarbon chemistry will soon be added, and inherently three-dimensional phenomena will be investigated both decoupled from dynamics and in a complete chemical GCM.

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Hyoun-Myoung Cho, Shaima L. Nasiri, Ping Yang, Istvan Laszlo, and Xuepeng “Tom” Zhao

Abstract

Analyses show that several existing Moderate Resolution Imaging Spectroradiometer (MODIS) dust detection techniques, including an approach based on simple brightness temperature difference thresholds, the D-parameter method, and the multichannel image (MCI) algorithm, may be more effective for detection of highly concentrated dust plumes than for thin dust layers. Using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud and aerosol classification as a reference, the sensitivities of six MODIS radiative parameters (including brightness temperature differences, and standard deviation and ratios of reflectances) to cloud, clear sky, and dust layers are examined in this paper. Reflectance ratios and the standard deviation of reflectances were confirmed to be useful in the discrimination of dust from cloud and underlying ocean surface, while brightness temperature differences alone were not sufficient to separate dust from cloud and clear sky over the ocean surface. Using a collocated MODIS and CALIPSO training dataset from 2008, visible and infrared MODIS radiative parameters from six latitude bands and four seasons were combined using linear and quadratic discriminant analyses to develop a new algorithm for the detection of optically thin dust over the ocean. The validation using collocated MODIS and CALIPSO data from 2009 shows that the present algorithm is effective in detecting thin dust layers having optical thicknesses between 0.1 and 2.0, but that it tends to misclassify optically thicker dust layers as clouds.

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Andrew K. Heidinger, Michael J. Foster, Andi Walther, and Xuepeng (Tom) Zhao

The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoids spatial or temporal averaging of data, thus maintaining the flexibility to conduct multidimensional analysis. Comparison of this format against the unsampled record demonstrates the ability to reproduce the pixel distribution to a high level of accuracy. AVHRR PATMOS-x is composed of data from 17 different sensors. An examination of cloud amount and total-sky albedo time series demonstrates that intersatellite biases are less than 2%. The comparison of the cloud amount time series to the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) demonstrates a high degree of correlation, indicating that sensor-to-sensor differences are also not contributing significantly to the observed climate variability in PATMOS-x. AVHRR PATMOS-x data are hosted by the National Climatic Data Center (NCDC) (available at www.ncdc.noaa.gov/cdr/operationalcdrs.html).

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John J. Bates, Jeffrey L. Privette, Edward J. Kearns, Walter Glance, and Xuepeng Zhao

Abstract

The key objective of the NOAA Climate Data Record (CDR) program is the sustained production of high-quality, multidecadal time series data describing the global atmosphere, oceans, and land surface that can be used for informed decision-making. The challenges of a long-term program of sustaining CDRs, as contrasted with short-term efforts of traditional 3-yr research programs, are substantial. The sustained production of CDRs requires collaboration between experts in the climate community, data management, and software development and maintenance. It is also informed by scientific application and associated user feedback on the accessibility and usability of the produced CDRs. The CDR program has developed a metric for assessing the maturity of CDRs with respect to data management, software, and user application and applied it to over 30 CDRs. The main lesson learned over the past 7 years is that a rigorous team approach to data management, employing subject matter experts at every step, is critical to open and transparent production. This approach also makes it much easier to support the needs of users who want near-real-time production of CDRs for monitoring and users who want to use CDRs for tailored, derived information, such as a drought index.

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