Search Results

You are looking at 61 - 70 of 93 items for

  • Author or Editor: David A. Marks x
  • All content x
Clear All Modify Search
Ralph A. Kahn, John A. Ogren, Thomas P. Ackerman, Jens Bösenberg, Robert J. Charlson, David J. Diner, Brent N. Holben, Robert T. Menzies, Mark A. Miller, and John H. Seinfeld

We briefly but systematically review major sources of aerosol data, emphasizing suites of measurements that seem most likely to contribute to assessments of global aerosol climate forcing. The strengths and limitations of existing satellite, surface, and aircraft remote sensing systems are described, along with those of direct sampling networks and ship-based stations. It is evident that an enormous number of aerosol-related observations have been made, on a wide range of spatial and temporal sampling scales, and that many of the key gaps in this collection of data could be filled by technologies that either exist or are expected to be available in the near future. Emphasis must be given to combining remote sensing and in situ active and passive observations and integrating them with aerosol chemical transport models, in order to create a more complete environmental picture, having sufficient detail to address current climate forcing questions. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative would provide an organizational framework to meet this goal.

Full access
James R. Campbell, David A. Peterson, Jared W. Marquis, Gilberto J. Fochesatto, Mark A. Vaughan, Sebastian A. Stewart, Jason L. Tackett, Simone Lolli, Jasper R. Lewis, Mayra I. Oyola, and Ellsworth J. Welton
Open access
Ali H. Omar, David M. Winker, Mark A. Vaughan, Yongxiang Hu, Charles R. Trepte, Richard A. Ferrare, Kam-Pui Lee, Chris A. Hostetler, Chieko Kittaka, Raymond R. Rogers, Ralph E. Kuehn, and Zhaoyan Liu

Abstract

Descriptions are provided of the aerosol classification algorithms and the extinction-to-backscatter ratio (lidar ratio) selection schemes for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products. One year of CALIPSO level 2 version 2 data are analyzed to assess the veracity of the CALIPSO aerosol-type identification algorithm and generate vertically resolved distributions of aerosol types and their respective optical characteristics. To assess the robustness of the algorithm, the interannual variability is analyzed by using a fixed season (June–August) and aerosol type (polluted dust) over two consecutive years (2006 and 2007). The CALIPSO models define six aerosol types: clean continental, clean marine, dust, polluted continental, polluted dust, and smoke, with 532-nm (1064 nm) extinction-to-backscatter ratios Sa of 35 (30), 20 (45), 40 (55), 70 (30), 65 (30), and 70 (40) sr, respectively. This paper presents the global distributions of the CALIPSO aerosol types, the complementary distributions of integrated attenuated backscatter, and the volume depolarization ratio for each type. The aerosol-type distributions are further partitioned according to surface type (land/ocean) and detection resolution (5, 20, and 80 km) for optical and spatial context, because the optically thick layers are found most often at the smallest spatial resolution. Except for clean marine and polluted continental, all the aerosol types are found preferentially at the 80-km resolution. Nearly 80% of the smoke cases and 60% of the polluted dust cases are found over water, whereas dust and polluted continental cases are found over both land and water at comparable frequencies. Because the CALIPSO observables do not sufficiently constrain the determination of the aerosol, the surface type is used to augment the selection criteria. Distributions of the total attenuated color ratios show that the use of surface type in the typing algorithm does not result in abrupt and artificial changes in aerosol type or extinction.

Full access
Anthony G. Barnston, Huug M. van den Dool, Stephen E. Zebiak, Tim P. Barnett, Ming Ji, David R. Rodenhuis, Mark A. Cane, Ants Leetmaa, Nicholas E. Graham, Chester R. Ropelewski, Vernon E. Kousky, Edward A. O'Lenic, and Robert E. Livezey

The National Weather Service intends to begin routinely issuing long-lead forecasts of 3-month mean U. S. temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of up to 1 yr is attributed to advances in data observing and processing, computer capability, and physical understanding—particularly, for tropical ocean-atmosphere phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the U.S forecasts that are made largely on their basis.

The performance of five ENSO prediction systems is examined: Two are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty Earth Observatory and the nonsimple coupled model of the National Centers for Environmental Prediction (NCEP)]; one is a hybrid coupled model (the Scripps Institution for Oceanography-Max Planck Institute for Meteorology system with a full ocean general circulation model and a statistical atmosphere); and two are statistical (canonical correlation analysis and constructed analogs, used at the Climate Prediction Center of NCEP). With increasing physical understanding, dynamically based forecasts have the potential to become more skillful than purely statistical ones. Currently, however, the two approaches deliver roughly equally skillful forecasts, and the simplest model performs about as well as the more comprehensive models. At a lead time of 6 months (defined here as the time between the end of the latest observed period and the beginning of the predict and period), the SST forecasts have an overall correlation skill in the 0.60s for 1982–93, which easily outperforms persistence and is regarded as useful. Skill for extra-tropical surface climate is this high only in limited regions for certain seasons. Both types of forecasts are not much better than local higher-order autoregressive controls. However, continual progress is being made in understanding relations among global oceanic and atmospheric climate-scale anomaly fields.

It is important that more real-time forecasts be made before we rush to judgement. Performance in the real-time setting is the ultimate test of the utility of a long-lead forecast. The National Weather Service's plan to implement new operational long-lead seasonal forecast products demonstrates its effectiveness in identifying and transferring “cutting edge” technologies from theory to applications. This could not have been accomplished without close ties with, and the active cooperation of, the academic and research communities.

Full access
Evan A. Kalina, Sergey Y. Matrosov, Joseph J. Cione, Frank D. Marks, Jothiram Vivekanandan, Robert A. Black, John C. Hubbert, Michael M. Bell, David E. Kingsmill, and Allen B. White

Abstract

Dual-polarization scanning radar measurements, air temperature soundings, and a polarimetric radar-based particle identification scheme are used to generate maps and probability density functions (PDFs) of the ice water path (IWP) in Hurricanes Arthur (2014) and Irene (2011) at landfall. The IWP is separated into the contribution from small ice (i.e., ice crystals), termed small-particle IWP, and large ice (i.e., graupel and snow), termed large-particle IWP. Vertically profiling radar data from Hurricane Arthur suggest that the small ice particles detected by the scanning radar have fall velocities mostly greater than 0.25 m s−1 and that the particle identification scheme is capable of distinguishing between small and large ice particles in a mean sense. The IWP maps and PDFs reveal that the total and large-particle IWPs range up to 10 kg m−2, with the largest values confined to intense convective precipitation within the rainbands and eyewall. Small-particle IWP remains mostly <4 kg m−2, with the largest small-particle IWP values collocated with maxima in the total IWP. PDFs of the small-to-total IWP ratio have shapes that depend on the precipitation type (i.e., intense convective, stratiform, or weak-echo precipitation). The IWP ratio distribution is narrowest (broadest) in intense convective (weak echo) precipitation and peaks at a ratio of about 0.1 (0.3).

Full access
Mark A. Bourassa, Sarah T. Gille, Cecilia Bitz, David Carlson, Ivana Cerovecki, Carol Anne Clayson, Meghan F. Cronin, Will M. Drennan, Chris W. Fairall, Ross N. Hoffman, Gudrun Magnusdottir, Rachel T. Pinker, Ian A. Renfrew, Mark Serreze, Kevin Speer, Lynne D. Talley, and Gary A. Wick

Polar regions have great sensitivity to climate forcing; however, understanding of the physical processes coupling the atmosphere and ocean in these regions is relatively poor. Improving our knowledge of high-latitude surface fluxes will require close collaboration among meteorologists, oceanographers, ice physicists, and climatologists, and between observationalists and modelers, as well as new combinations of in situ measurements and satellite remote sensing. This article describes the deficiencies in our current state of knowledge about air–sea surface fluxes in high latitudes, the sensitivity of various high-latitude processes to changes in surface fluxes, and the scientific requirements for surface fluxes at high latitudes. We inventory the reasons, both logistical and physical, why existing flux products do not meet these requirements. Capturing an annual cycle in fluxes requires that instruments function through long periods of cold polar darkness, often far from support services, in situations subject to icing and extreme wave conditions. Furthermore, frequent cloud cover at high latitudes restricts the availability of surface and atmospheric data from visible and infrared (IR) wavelength satellite sensors. Recommendations are made for improving high-latitude fluxes, including 1) acquiring more in situ observations, 2) developing improved satellite-flux-observing capabilities, 3) making observations and flux products more accessible, and 4) encouraging flux intercomparisons.

Full access
David J. Diner, Robert T. Menzies, Ralph A. Kahn, Theodore L. Anderson, Jens Bösenberg, Robert J. Charlson, Brent N. Holben, Chris A. Hostetler, Mark A. Miller, John A. Ogren, Graeme L. Stephens, Omar Torres, Bruce A. Wielicki, Philip J. Rasch, Larry D. Travis, and William D. Collins

A comprehensive and cohesive aerosol measurement record with consistent, well-understood uncertainties is a prerequisite to understanding aerosol impacts on long-term climate and environmental variability. Objectives to attaining such an understanding include improving upon the current state-of-the-art sensor calibration and developing systematic validation methods for remotely sensed microphysical properties. While advances in active and passive remote sensors will lead to needed improvements in retrieval accuracies and capabilities, ongoing validation is essential so that the changing sensor characteristics do not mask atmospheric trends. Surface-based radiometer, chemical, and lidar networks have critical roles within an integrated observing system, yet they currently undersample key geographic regions, have limitations in certain measurement capabilities, and lack stable funding. In situ aircraft observations of size-resolved aerosol chemical composition are necessary to provide important linkages between active and passive remote sensing. A planned, systematic approach toward a global aerosol observing network, involving multiple sponsoring agencies and surface-based, suborbital, and spaceborne sensors, is required to prioritize trade-offs regarding capabilities and costs. This strategy is a key ingredient of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) framework. A set of recommendations is presented.

Full access
Peter J. Shellito, Sujay V. Kumar, Joseph A. Santanello Jr., Patricia Lawston-Parker, John D. Bolten, Michael H. Cosh, David D. Bosch, Chandra D. Holifield Collins, Stan Livingston, John Prueger, Mark Seyfried, and Patrick J. Starks

Abstract

The utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.

Restricted access
Robin W. Pascal, Margaret J. Yelland, Meric A. Srokosz, Bengamin I. Moat,, Edward M. Waugh, Daniel H. Comben, Alex G. Cansdale, Mark C. Hartman, David G. H. Coles, Ping Chang Hsueh, and Timothy G. Leighton

Abstract

Waves and wave breaking play a significant role in the air–sea exchanges of momentum, sea spray aerosols, and trace gases such as CO2, but few direct measurements of wave breaking have been obtained in the open ocean (far from the coast). This paper describes the development and initial deployments on two research cruises of an autonomous spar buoy that was designed to obtain such open-ocean measurements. The buoy was equipped with capacitance wave wires and accelerometers to measure surface elevation and wave breaking, downward-looking still and video digital cameras to obtain images of the sea surface, and subsurface acoustic and optical sensors to detect bubble clouds from breaking waves. The buoy was free drifting and was designed to collect data autonomously for days at a time before being recovered. Therefore, on the two cruises during which the buoy was deployed, this allowed a variety of sea states to be sampled in mean wind speeds, which ranged from 5 to 18 m s−1.

Full access
Sandrine Bony, Robert Colman, Vladimir M. Kattsov, Richard P. Allan, Christopher S. Bretherton, Jean-Louis Dufresne, Alex Hall, Stephane Hallegatte, Marika M. Holland, William Ingram, David A. Randall, Brian J. Soden, George Tselioudis, and Mark J. Webb

Abstract

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

Full access