scholarly journals Contributions of aerosol‐cloud interactions to mid‐Piacenzian seasonally sea ice‐free Arctic Ocean

2019 ◽  
Vol 46 (16) ◽  
pp. 9920-9929 ◽  
Author(s):  
Ran Feng ◽  
Bette L. Otto‐Bliesner ◽  
Yangyang Xu ◽  
Esther Brady ◽  
Tamara Fletcher ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

scholarly journals The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3

2011 ◽  
Vol 24 (13) ◽  
pp. 3484-3519 ◽  
Author(s):  
Leo J. Donner ◽  
Bruce L. Wyman ◽  
Richard S. Hemler ◽  
Larry W. Horowitz ◽  
Yi Ming ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
System Component ◽  
Dynamical Core ◽  
Atmospheric Component ◽  
Clear Sky ◽  
Aerosol Cloud ◽  
Droplet Activation ◽  
Aerosol Cloud Interactions ◽  
Physical Parameterizations

Abstract The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.

scholarly journals A satellite-based estimate of aerosol-cloud microphysical effects over the Arctic Ocean

2018 ◽  
Author(s):  
Lauren M. Zamora ◽  
Ralph A. Kahn ◽  
Klaus B. Huebert ◽  
Andreas Stohl ◽  
Sabine Eckhardt
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Transport Model ◽  
Regional Scale ◽  
The Arctic ◽  
Energy Budgets ◽  
Precipitation Frequency ◽  
Aerosol Cloud ◽  
Full Dataset ◽  
The Arctic Ocean

Abstract. Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol-cloud interactions. Here we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify regional-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (~ 25 W m−2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain much of this signal – for example, explaining up to 91 % of the CF differences between the full dataset and the clean-condition subset. After normalizing for meteorological regime, aerosol microphysical effects have small but significant regional-scale impacts on CF, CP, and precipitation frequency. These effects indicate that dominant aerosol-cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.

scholarly journals A satellite-based estimate of combustion aerosol cloud microphysical effects over the Arctic Ocean

2018 ◽  
Vol 18 (20) ◽  
pp. 14949-14964 ◽  
Author(s):  
Lauren M. Zamora ◽  
Ralph A. Kahn ◽  
Klaus B. Huebert ◽  
Andreas Stohl ◽  
Sabine Eckhardt
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Large Scale ◽  
Transport Model ◽  
The Arctic ◽  
Energy Budgets ◽  
Precipitation Frequency ◽  
Aerosol Cloud ◽  
The Arctic Ocean ◽  
Combustion Aerosols

Abstract. Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol–cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify large-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (∼10 W m−2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40 % of the CF differences between the full dataset and the clean-condition subset, compared to between 57 % and 91 % of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol–cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Societal implications of a changing Arctic Ocean

AMBIO ◽  
2021 ◽  
Author(s):  
Henry P. Huntington ◽  
Andrey Zagorsky ◽  
Bjørn P. Kaltenborn ◽  
Hyoung Chul Shin ◽  
Jackie Dawson ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Indigenous Peoples ◽  
Global Climate ◽  
Rapid Change ◽  
The Arctic ◽  
Cultural Continuity ◽  
Societal Implications ◽  
Arctic Ecosystems ◽  
The Many

AbstractThe Arctic Ocean is undergoing rapid change: sea ice is being lost, waters are warming, coastlines are eroding, species are moving into new areas, and more. This paper explores the many ways that a changing Arctic Ocean affects societies in the Arctic and around the world. In the Arctic, Indigenous Peoples are again seeing their food security threatened and cultural continuity in danger of disruption. Resource development is increasing as is interest in tourism and possibilities for trans-Arctic maritime trade, creating new opportunities and also new stresses. Beyond the Arctic, changes in sea ice affect mid-latitude weather, and Arctic economic opportunities may re-shape commodities and transportation markets. Rising interest in the Arctic is also raising geopolitical tensions about the region. What happens next depends in large part on the choices made within and beyond the Arctic concerning global climate change and industrial policies and Arctic ecosystems and cultures.

scholarly journals Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas
Keyword(s):  
Radiative Forcing ◽  
Cloud Fraction ◽  
Future Climate Change ◽  
Satellite Measurements ◽  
Aerosol Cloud ◽  
Fine Mode ◽  
Sampling Biases ◽  
Aerosol Cloud Interactions ◽  
Significant Underestimation ◽  
Weak Satellite

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

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