scholarly journals The Community Climate System Model Version 4

2011 ◽  
Vol 24 (19) ◽  
pp. 4973-4991 ◽  
Author(s):  
Peter R. Gent ◽  
Gokhan Danabasoglu ◽  
Leo J. Donner ◽  
Marika M. Holland ◽  
Elizabeth C. Hunke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Frequency Distribution ◽  
Arctic Sea Ice ◽  
Climate System ◽  
System Model ◽  
The Arctic ◽  
Community Climate System Model ◽  
Climate System Model ◽  
Arctic Sea

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.

scholarly journals Recent advances in understanding the Arctic climate system state and change from a sea ice perspective: a review

2014 ◽  
Vol 14 (7) ◽  
pp. 10929-10999 ◽  
Author(s):  
R. Döscher ◽  
T. Vihma ◽  
E. Maksimovich
Keyword(s):  
Global Warming ◽  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Climate System ◽  
Arctic Climate ◽  
The Arctic ◽  
Atlantic Sector ◽  
Sea Ice Decline ◽  
Arctic Sea

Abstract. The Arctic sea ice is the central and essential component of the Arctic climate system. The depletion and areal decline of the Arctic sea ice cover, observed since the 1970's, have accelerated after the millennium shift. While a relationship to global warming is evident and is underpinned statistically, the mechanisms connected to the sea ice reduction are to be explored in detail. Sea ice erodes both from the top and from the bottom. Atmosphere, sea ice and ocean processes interact in non-linear ways on various scales. Feedback mechanisms lead to an Arctic amplification of the global warming system. The amplification is both supported by the ice depletion and is at the same time accelerating the ice reduction. Knowledge of the mechanisms connected to the sea ice decline has grown during the 1990's and has deepened when the acceleration became clear in the early 2000's. Record summer sea ice extents in 2002, 2005, 2007 and 2012 provided additional information on the mechanisms. This article reviews recent progress in understanding of the sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric changes being the major driver of sea ice change. Feedbacks due to reduced ice concentration, surface albedo and thickness allow for additional local atmosphere and ocean influences and self-supporting feedbacks. Large scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Northward heat fluxes in the ocean are clearly impacting the ice margins, especially in the Atlantic sector of the Arctic. Only little indication exists for a direct decisive influence of the warming ocean on the overall sea ice cover, due to an isolating layer of cold and fresh water underneath the sea ice.

Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3

2013 ◽  
Vol 40 (10) ◽  
pp. 2121-2124 ◽  
Author(s):  
Marika M. Holland ◽  
Edward Blanchard-Wrigglesworth ◽  
Jennifer Kay ◽  
Steven Vavrus
Keyword(s):  
Sea Ice ◽  
Climate System ◽  
System Model ◽  
Initial Value ◽  
Community Climate System Model ◽  
Climate System Model ◽  
Antarctic Sea Ice ◽  
Community Climate

scholarly journals Simulation of sea ice in the NCAR Climate System Model

1997 ◽  
Vol 25 ◽  
pp. 107-110 ◽  
Author(s):  
John W. Weatherly ◽  
Thomas W. Bettge ◽  
Bruce P. Briegleb
Keyword(s):  
Sea Ice ◽  
Ad Hoc ◽  
Climate System ◽  
System Model ◽  
The Arctic ◽  
Climate Simulation ◽  
Climate System Model ◽  
Polar Climate ◽  
Ice Drift ◽  
Cavitating Fluid

The Climate System Model (CSM) developed at the National Center for Atmospheric Research (NCAR) consists of atmosphere, land and ocean models, as well as a dynamic-thermodynamic sea-ice model. The results of sea-ice simulation using the first coupled climate simulation with the CSM is presented. It was found that the simulated total-ice areas in both hemispheres compared well with observations for winter, but were too large for summer. The numerical solution of the cavitating fluid dynamics was found to allow excessive ridging of ice, and an ad hoc correction was implemented. The ice velocities were realistic for the Antarctic, but for the Arctic were turned toward Alaska and Siberia by modeled winds and currents. This ice-drift pattern was reflected by ice thickness, which lacks the observed ridging near Greenland. The results illustrate the sensitivity of sea ice to the simulation of polar climate and the challenge of modeling the entire climate system.

Assessment of the Regional Arctic System Model Intra-Annual Ensemble Predictions of Arctic Sea Ice

2020 ◽  
Author(s):  
Wieslaw Maslowski ◽  
Younjoo Lee ◽  
Anthony Craig ◽  
Mark Seefeldt ◽  
Robert Osinski ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Weather Prediction ◽  
Arctic Sea Ice ◽  
System Model ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
The Past ◽  
Arctic Sea

<p>The Regional Arctic System Model (RASM) has been developed and used to investigate the past to present evolution of the Arctic climate system and to address increasing demands for Arctic forecasts beyond synoptic time scales. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model configured over the pan-Arctic domain with horizontal resolution of 50 km or 25 km for the atmosphere and land and 9.3 km or 2.4 km for the ocean and sea ice components. As a regional model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which for simulations of the past to present are derived from global atmospheric reanalyses, such as the National Center for Environmental Predictions (NCEP) Coupled Forecast System version 2 and Reanalysis (CFSv2/CFSR). This dynamical downscaling approach allows comparison of RASM results with observations, in place and time, to diagnose and reduce model biases. This in turn allows a unique capability not available in global weather prediction and Earth system models to produce realistic and physically consistent initial conditions for prediction without data assimilation.</p><p>More recently, we have developed a new capability for an intra-annual (up to 6 months) ensemble prediction of the Arctic sea ice and climate using RASM forced with the routinely produced (every 6 hours) NCEP CFSv2 global 9-month forecasts. RASM intra-annual ensemble forecasts have been initialized on the 1<sup>st</sup> of each month starting in 2019 with forcing for each ensemble member derived from CSFv2 forecasts, 24-hr apart from the month preceding the initial forecast date.  Several key processes and feedbacks will be discussed with regard to their impact on model physics, the representation of initial state and ensemble prediction skill of Arctic sea ice variability at time scales from synoptic to decadal. The skill of RASM ensemble forecasts will be assessed against available satellite observations with reference to reanalysis as well as hindcast data using several metrics, including the standard deviation, root mean square difference, Taylor diagrams and integrated ice-edge error.</p>

scholarly journals The calculation of surface temperature and albedo of Arctic sea ice from AVHRR

1993 ◽  
Vol 17 ◽  
pp. 391-397 ◽  
Author(s):  
R. Lindsay ◽  
D. Rothrock
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Calculations ◽  
Arctic Sea ◽  
Arctic Atmosphere ◽  
Using Data ◽  
Cover Thickness ◽  
The Relationship

The temperature and albedo distributions of Arctic sea ice are calculated from images obtained from the AVHRR satellite sensor. The temperature estimate uses a split window correction incorporating regression coefficients appropriate for the arctic atmosphere. The albedo estimate is found assuming a clear and dry atmosphere. Both estimates are made with published correction techniques. Inherent errors due to the uncertainty of the atmospheric interference produced by humidity, aerosols, and diamond dust are judged to be 2–5°C in surface temperature and 0.10–0.20 in surface albedo. Cloudy regions are masked out manually using data from all five channels. The relationship between temperature and albedo is shown for a sample scene. A simple model of a surface composed of only cold, bright ice and warm, dark water is inadequate. Model calculations based on the surface energy balance allow us to relate albedo and temperature to ice thickness and snow-cover thickness and to further assess the accuracy of the surface estimates.

scholarly journals Simulation of observed climate changes in 1850–2014 with climate model INM-CM5

2018 ◽  
Vol 9 (4) ◽  
pp. 1235-1242 ◽  
Author(s):  
Evgeny Volodin ◽  
Andrey Gritsun
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Changes ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ocean Heat Uptake ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
Global Mean

Abstract. Climate changes observed in 1850–2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and 2000–2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is the correct reproduction of the slowdown in global warming in 2000–2014 that we attribute to a change in ocean heat uptake and a more accurate description of the total solar irradiance in the CMIP6 protocol. The model is able to reproduce the correct behavior of global mean temperature in 1980–2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations in just one model run; the model underestimates Arctic sea ice loss by a factor of 2.5. The spatial pattern of the model mean surface temperature trend during the last 30 years looks close to the one for the ERA-Interim reanalysis. The model correctly estimates the magnitude of stratospheric cooling.

scholarly journals Variations of the Mid-Pacific Trough and Their Relations to the Asian–Pacific–North American Climate: Roles of Tropical Sea Surface Temperature and Arctic Sea Ice

2018 ◽  
Vol 31 (6) ◽  
pp. 2233-2252 ◽  
Author(s):  
Kaiqiang Deng ◽  
Song Yang ◽  
Mingfang Ting ◽  
Chundi Hu ◽  
Mengmeng Lu
Keyword(s):  
United States ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
The Arctic ◽  
Boreal Summer ◽  
Arctic Sea ◽  
Second Mode

The mid-Pacific trough (MPT), occurring in the upper troposphere during boreal summer, acts as an atmospheric bridge connecting the climate variations over Asia, the Pacific, and North America. The first (second) mode of empirical orthogonal function analysis of the MPT, which accounts for 20.3% (13.4%) of the total variance, reflects a change in its intensity on the southwestern (northeastern) portion of the trough. Both modes are significantly correlated with the variability of tropical Pacific sea surface temperature (SST). Moreover, the first mode is affected by Atlantic SST via planetary waves that originate from the North Atlantic and propagate eastward across the Eurasian continent, and the second mode is influenced by the Arctic sea ice near the Bering Strait by triggering an equatorward wave train over the northeast Pacific. A stronger MPT shown in the first mode is significantly linked to drier and warmer conditions in the Yangtze River basin, southern Japan, and the northern United States and wetter conditions in South Asia and northern China, while a stronger MPT shown in the second mode is associated with a drier and warmer southwestern United States. In addition, an intensified MPT (no matter whether in the southwestern or the northeastern portion) corresponds to more tropical cyclones (TCs) over the western North Pacific (WNP) and fewer TCs over the eastern Pacific (EP) in summer, which is associated with the MPT-induced ascending and descending motions over the WNP and the EP, respectively.

scholarly journals Comparison of Leads Mapping in the Arctic Ocean Between Landsat and MODIS Ice Surface Temperature Products

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Xiaoping Pang ◽  
Pei Fan ◽  
Xi Zhao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Single Channel ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Landsat 8 ◽  
Ice Surface ◽  
Arctic Sea ◽  
Split Window Algorithm ◽  
Ice Surface Temperature

<p><strong>Abstract.</strong> Leads are linear or wedge-shaped openings in the sea ice cover. They account for about half of the sensible heat transfer from the Arctic Ocean to the atmosphere in winter, though the sea surface area covered by them is only 1%~2% of the total sea ice area, thus monitoring leads changes and mapping leads distributions become an essential role on Arctic researches. Sea ice surface temperature (IST) product from Moderate Resolution Imaging Spectroradiometer (MODIS) is the most used source of leads monitoring and mapping, however, due to the coarse spatial resolution (1km at swath level), it suffers from mixed pixel effect when describes the temperature variations on thin leads (10m~100m), thus an IST product with a finer spatial resolution is needed. Though several surface temperature retrieval algorithms had been introduced based on Landsat 8 thermal imagery, none of them were validated in Arctic sea ice region. Given that the special weather conditions such as air temperature inversion were not taken into consideration, these algorithms may not always suitable for IST acquisition in Arctic. In this paper, we applied five mainstream IST algorithms (three split window algorithms and two single channel methods) on Arctic sea ice, compared the Landsat 8 IST with corresponding MODIS IST product, and validated all the satellite ISTs by in situ temperature measurements from drifting buoys. Compared to the buoy ISTs, the single channel method through web-based atmosphere correction tool provided by Barsi et al. (2003) offers the best accuracy. The split window algorithm proposed by Du et al. (2015) ranks the second, but constrained by the banding effect due to the stripe noise. Split window algorithm introduced by Jiménez-Muñoz et al. (2014) coincides with MODIS IST product best. All of the three methods mentioned above have slightly better accuracy than MODIS IST, particular in thin leads areas, which indicated that Landsat based leads map will provide us a better insight of Arctic sea ice. All the satellite ISTs tend to underestimate the surface temperature than those measured by buoys.</p>

scholarly journals Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multi-model Large Ensemble

2021 ◽  
pp. 1-64
Author(s):  
Yu-Chiao Liang ◽  
Claude Frankignoul ◽  
Young-Oh Kwon ◽  
Guillaume Gastineau ◽  
Elisa Manzini ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
General Circulation ◽  
Cold Season ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Variability ◽  
Large Ensemble ◽  
Near Surface ◽  
Arctic Sea

AbstractTo examine the atmospheric responses to Arctic sea-ice variability in the Northern Hemisphere cold season (October to following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily-varying sea-ice, sea-surface temperature, and radiative forcings prescribed during the 1979-2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multi-model ensemble mean (MMEM) shows decreasing sea-level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea-ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drives a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual co-variability between sea-ice extent in the Barents-Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the co-variability in MMEMs. The interannual sea-ice decline followed by a negative North Atlantic Oscillation-like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea-ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

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