Institution
Cooperative Institute for Research in the Atmosphere
About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.
Topics: Snow, Data assimilation, Aerosol, Tropical cyclone, Precipitation
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors reviewed existing knowledge with regard to organic aerosol (OA) of importance for global climate modelling and defined critical gaps needed to reduce the involved uncertainties, and synthesized the information to provide a continuous analysis of the flow from the emitted material to the atmosphere up to the point of the climate impact of the produced organic aerosols.
Abstract: The present paper reviews existing knowledge with regard to Organic Aerosol (OA) of importance for global climate modelling and defines critical gaps needed to reduce the involved uncertainties. All pieces required for the representation of OA in a global climate model are sketched out with special attention to Secondary Organic Aerosol (SOA): The emission estimates of primary carbonaceous particles and SOA precursor gases are summarized. The up-to-date understanding of the chemical formation and transformation of condensable organic material is outlined. Knowledge on the hygroscopicity of OA and measurements of optical properties of the organic aerosol constituents are summarized. The mechanisms of interactions of OA with clouds and dry and wet removal processes parameterisations in global models are outlined. This information is synthesized to provide a continuous analysis of the flow from the emitted material to the atmosphere up to the point of the climate impact of the produced organic aerosol. The sources of uncertainties at each step of this process are highlighted as areas that require further studies.
2,863 citations
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Western Kentucky University1, Cooperative Institute for Research in Environmental Sciences2, University of Nebraska–Lincoln3, Purdue University4, University of Queensland5, Pennsylvania State University6, Cooperative Institute for Research in the Atmosphere7, Agriculture and Agri-Food Canada8, John Deere9, Oak Ridge National Laboratory10, University of Alabama in Huntsville11, University of Delaware12, University of Georgia13, Chinese Academy of Sciences14, University of Colorado Boulder15, Texas A&M University16, Tuskegee University17
TL;DR: In this article, the authors provide an overview and synthesis of some of the most notable types of land cover changes and their impacts on climate, including agriculture, deforestation and afforestation, desertification, and urbanization.
Abstract: Land cover changes (LCCs) play an important role in the climate system. Research over recent decades highlights the impacts of these changes on atmospheric temperature, humidity, cloud cover, circulation, and precipitation. These impacts range from the local- and regional-scale to sub-continental and global-scale. It has been found that the impacts of regional-scale LCC in one area may also be manifested in other parts of the world as a climatic teleconnection. In light of these findings, this article provides an overview and synthesis of some of the most notable types of LCC and their impacts on climate. These LCC types include agriculture, deforestation and afforestation, desertification, and urbanization. In addition, this article provides a discussion on challenges to, and future research directions in, assessing the climatic impacts of LCC.
560 citations
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TL;DR: Subjective measurements and variable procedures make existing tropical cyclone databases insufficiently reliable to detect trends in the frequency of extreme cyclones.
Abstract: Subjective measurements and variable procedures make existing tropical cyclone databases insufficiently reliable to detect trends in the frequency of extreme cyclones.
468 citations
01 Dec 2004
TL;DR: In this paper, a spatially distributed snow-evolution modeling system called SnowModel is proposed for application in landscapes, climates, and conditions where snow occurs, which is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind.
Abstract: SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowp...
388 citations
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TL;DR: In this article, a method of estimating snow bulk density is presented and then used to convert snow depth to snow water equivalent (SWE), which is grounded in the fact that depth varies over a range that is many times greater than that of bulk density, and estimates derived from measured depths and modeled densities generally fall close to measured values of SWE.
Abstract: In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against t...
381 citations
Authors
Showing all 332 results
Name | H-index | Papers | Citations |
---|---|---|---|
Graeme L. Stephens | 83 | 341 | 25365 |
Sonia M. Kreidenweis | 82 | 315 | 23612 |
Graham Feingold | 73 | 221 | 17294 |
William R. Cotton | 69 | 257 | 18298 |
Jeffrey L. Collett | 60 | 248 | 12016 |
Glen E. Liston | 58 | 186 | 13824 |
James P. Kossin | 54 | 140 | 16400 |
Christian D. Kummerow | 51 | 191 | 13514 |
Armin Sorooshian | 51 | 216 | 8678 |
William C. Malm | 47 | 123 | 9664 |
Christopher W. O'Dell | 46 | 137 | 6383 |
John A. Knaff | 44 | 118 | 7296 |
Raymond W. Arritt | 41 | 122 | 9312 |
Timothy G. F. Kittel | 39 | 80 | 6097 |
Thomas H. Vonder Haar | 36 | 120 | 4545 |