Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses

Chernokulsky, А. V.; Мохов, И. И.

doi:10.1155/2012/542093

Cited by 67 publications

(54 citation statements)

References 73 publications

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“…The comparisons provided by LIVVkit include the annual cycle of monthly averaged low, high, and total clouds for both the model and ISCCP Fig. 7 exhibit a seasonal cycle, which is consistent with findings over the entire Arctic (Chernokulsky and Mokhov, 2012). However, the CESM produces total clouds that are considerably too few and capture no summer minimum, 5 also consistent with noted CESM biases observed for the whole Arctic region (Barton et al, 2012).…”

supporting

confidence: 83%

LIVVkit 2.1: Automated and extensible ice sheet model validation

Evans¹,

Kennedy²,

Lu³

et al. 2018

Preprint

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Abstract. A collection of scientific analyses, metrics, and visualizations for robust validation of ice sheet models is presented using the LIVVkit package, version 2.1. This software collection targets stand-alone ice sheet or coupled Earth system models, and handles datasets and operations that require high-performance computing and storage. LIVVkit aims to enable efficient and fully reproducible workflows for post-processing, analysis, and visualization of observational and model-derived datasets in a shareable format, whereby all data, methodologies, and output are distributed to users for evaluation. We demonstrate 5LIVVkit validation for a Greenland ice sheet simulation using the coupled Community Earth System Model, CESM, as well as an idealized stand-alone high-resolution ice sheet model, CISM-Albany. As one example of the capability, LIVVkit analyzes the degree to which models capture the surface mass balance (SMB) and identifies potential sources of bias, using recently available in-situ and remotely sensed data as comparison. Related fields within atmosphere and land surface models, e.g. surface temperature, radiation, and cloud cover, are also diagnosed. Applied to the CESM1.0, LIVVkit identifies a positive 10 SMB bias that is focused largely around Greenland's southwest region that is due to insufficient ablation.

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supporting

confidence: 83%

LIVVkit 2.1: Automated and extensible ice sheet model validation

Evans¹,

Kennedy²,

Lu³

et al. 2018

Preprint

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“…However, the vertical dimension should be accounted for in such a comparison as well as the fact that parts of the trajectories may be outside the Arctic region. There are additional causes for the apparent discrepancy between satellite-and reanalysis-derived annual cycle of Arctic cloudiness which are explained in Chernokulsky et al (2012). Nevertheless, the results of the trajectory analysis shown in Fig.…”

Section: Main Drivers Of the Annual Cycle Of N Accmentioning

confidence: 94%

Pan-Arctic aerosol number size distributions: seasonality and transport patterns

et al. 2017

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Abstract. The Arctic environment has an amplified response to global climatic change. It is sensitive to human activities that mostly take place elsewhere. For this study, a multi-year set of observed aerosol number size distributions in the diameter range of 10 to 500 nm from five sites around the Arctic Ocean (Alert, Villum Research Station -Station Nord, Zeppelin, Tiksi and Barrow) was assembled and analysed.A cluster analysis of the aerosol number size distributions revealed four distinct distributions. Together with Lagrangian air parcel back-trajectories, they were used to link the observed aerosol number size distributions with a variety of transport regimes. This analysis yields insight into aerosol dynamics, transport and removal processes, on both an intraand an inter-monthly scale. For instance, the relative occurrence of aerosol number size distributions that indicate new particle formation (NPF) event is near zero during the dark months, increases gradually to ∼ 40 % from spring to summer, and then collapses in autumn. Also, the likelihood of Arctic haze aerosols is minimal in summer and peaks in April at all sites.The residence time of accumulation-mode particles in the Arctic troposphere is typically long enough to allow tracking them back to their source regions. Air flow that passes at low altitude over central Siberia and western Russia is associated with relatively high concentrations of accumulationmode particles (N acc ) at all five sites -often above 150 cm −3 .

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“…Bromwich et al (2007) report the largest differences in three products are related to clouds and their associated radiation impacts; ERA-40 captures the cloud variability better than NCEP-R1 and the Japanese 25-yr Reanalysis Project (JRA-25), but the ERA-40 and JRA-25 clouds are too optically thin for shortwave radiation. In a comparison of the seasonal climatology for clouds of eight different reanalyses to different satellite and surface observations, Chernokulsky and Mokhov (2012) report that NCEP-R1, NCEP-R2, and JRA-25 have less total cloud fraction (TCF) than observations during the whole year; other reanalyses are in close agreement with observations during summer and have noticeably higher TCF values than observations during winter. Zib et al (2012) evaluate cloud fraction and radiative fluxes compared to surface observations for five models and also find strong biases.…”

Section: Introductionmentioning

confidence: 91%

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

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Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses

Cited by 67 publications

References 73 publications

LIVVkit 2.1: Automated and extensible ice sheet model validation

LIVVkit 2.1: Automated and extensible ice sheet model validation

Pan-Arctic aerosol number size distributions: seasonality and transport patterns

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

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