A Decomposition of Feedback Contributions to the Arctic Temperature Biases in the CMIP5 Climate Models

2020 ◽  
Author(s):  
Tae-Won Park ◽  
Doo-Sun Park
Keyword(s):  
Water Vapor ◽  
Cloud Fraction ◽  
Kara Sea ◽  
Specific Humidity ◽  
The Arctic ◽  
Physical Processes ◽  
Albedo Feedback ◽  
Cloud Forcing ◽  
Arctic Sea ◽  
Arctic Temperature

<p>The systematic temperature biases over the Arctic Sea in the CMIP5 models are decomposed into partial biases due to physical and dynamical processes, based upon the climate feedback-responses analysis method (CFRAM). In the frame of the CFRAM, physical processes are also divided into water vapor, cloud, and albedo feedbacks. Though the Arctic temperature biases largely depend on models, considerable cold bias are found in most of models and ensemble mean. Overall, temperature biases corresponding to physical and dynamical processes tend to cancel each other out and total biases equal to their sums are geographically similar to those related to physical processes. For the physics-related biases, a contribution of albedo feedback is the largest, followed by cloud and water vapor feedbacks in turn. Quantitative contributions of the processes to temperature biases are evaluated from area-mean values over the entire Arctic Sea, Barents-Kara Sea, and Beaufort Sea. While relationships between total and partial biases over the Arctic Sea show the large model-dependency, in the local-scale, total temperature biases over Barents-Kara Sea and Beaufort Sea are made from consistent contributions among models. An overestimate (underestimate) of specific humidity and cloud fraction in models are responsible for an overall warm (cold) biases through longwave heating rates of the greenhouse effect. Shortwave cloud forcing by cloud fraction biases offsets a substantial part of biases related to longwave cloud forcing, while shortwave effect of specific humidity bias plays a minor role on water vapor feedback. The fact that geographical distribution of sea-ice biases is mostly opposite to that of partial temperature bias due to albedo feedback indicates that the biased simulation of sea-ice in models are the main contributors in albedo feedback.</p>

Arctic Ocean Sea Ice Area Extent Cyclicity and Non-Stationarity

2020 ◽  
Author(s):  
Reginald Muskett ◽  
Syun-Ichi Akasofu
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Wave Pattern ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Weather System ◽  
Sea Ice Extent ◽  
Temperature Variations ◽  
Arctic Sea ◽  
Arctic Temperature

<p>Arctic sea ice is a key component of the Arctic hydrologic cycle. This cycle is connected to land and ocean temperature variations and Arctic snow cover variations, spatially and temporally. Arctic temperature variations from historical observations shows an early 20th century increase (i.e. warming), followed by a period of Arctic temperature decrease (i.e. cooling) since the 1940s, which was followed by another period of Arctic temperature increase since the 1970s that continues into the two decades of the 21st century. Evidence has been accumulating that Arctic sea ice extent can experience multi-decadal to centennial time scale variations as it is a component of the Arctic Geohydrological System. </p><p><br>We investigate the multi-satellite and sensor daily values of area extent of Arctic sea ice since SMMR on Nimbus 7 (1978) to AMSR2 on GCOM-W1 (2019). From the daily time series we use the first year-cycle as a wave-pattern to compare to all subsequent years-cycles through April 2020 (in progress), and constitute a derivative time series. In this time series we find the emergence of a multi-decadal cycle, showing a relative minimum during the period of 2007 to 2014, and subsequently rising. This may be related to an 80-year cycle (hypothesis). The Earth’s weather system is principally driven the solar radiation and its variations. If the multi-decadal cycle in Arctic sea ice area extent that we interpret continues, it may be linked physically to the Wolf-Gleissberg cycle, a factor in the variations of terrestrial cosmogenic isotopes, ocean sediment layering and glacial varves, ENSO and Aurora.</p><p>Our hypothesis and results give more evidence that the multi-decadal variation of Arctic sea ice area extent is controlled by natural physical processes of the Sun-Earth system. </p>

scholarly journals The Intraseasonal and Interannual Variability of Arctic Temperature and Specific Humidity Inversions

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 214 ◽  
Author(s):  
Lejiang Yu ◽  
Qinghua Yang ◽  
Mingyu Zhou ◽  
Xubin Zeng ◽  
Donald H. Lenschow ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Intraseasonal Variability ◽  
Intraseasonal Oscillation ◽  
Lower Troposphere ◽  
Specific Humidity ◽  
The Arctic ◽  
Self Organizing Map ◽  
Eastern Hemisphere ◽  
Temperature And Humidity ◽  
Arctic Temperature

Temperature and humidity inversions are common in the Arctic’s lower troposphere, and are a crucial component of the Arctic’s climate system. In this study, we quantify the intraseasonal oscillation of Arctic temperature and specific humidity inversions and investigate its interannual variability using data from the Surface Heat Balance of the Arctic (SHEBA) experiment from October 1997 to September 1998 and the European Centre for Medium-Range Forecasts (ECMWF) Reanalysis (ERA)-interim for the 1979–2017 period. In January 1998, there were two noticeable elevated inversions and one surface inversion. The transitions between elevated and surface-based inversions were associated with the intraseasonal variability of the temperature and humidity differences between 850 and 950 hPa. The self-organizing map (SOM) technique is utilized to obtain the main modes of surface and elevated temperature and humidity inversions on intraseasonal time scales. Low (high) pressure and more (less) cloud cover are related to elevated (surface) temperature and humidity inversions. The frequency of strong (weak) elevated inversions over the eastern hemisphere has decreased (increased) in the past three decades. The wintertime Arctic Oscillation (AO) and Arctic Dipole (AD) during their positive phases have a significant effect on the occurrence of surface and elevated inversions for two Nodes only.

scholarly journals Arctic Energy Budget in Relation to Sea Ice Variability on Monthly-to-Annual Time Scales

2015 ◽  
Vol 28 (16) ◽  
pp. 6335-6350 ◽  
Author(s):  
F. Krikken ◽  
W. Hazeleger
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Energy Budget ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Albedo Feedback ◽  
Arctic Sea ◽  
Cloud Ice

Abstract The large decrease in Arctic sea ice in recent years has triggered a strong interest in Arctic sea ice predictions on seasonal-to-decadal time scales. Hence, it is important to understand physical processes that provide enhanced predictability beyond persistence of sea ice anomalies. This study analyzes the natural variability of Arctic sea ice from an energy budget perspective, using 15 climate models from phase 5 of CMIP (CMIP5), and compares these results to reanalysis data. The authors quantify the persistence of sea ice anomalies and the cross correlation with the surface and top-of-atmosphere energy budget components. The Arctic energy balance components primarily indicate the important role of the seasonal ice–albedo feedback, through which sea ice anomalies in the melt season reemerge in the growth season. This is a robust anomaly reemergence mechanism among all 15 climate models. The role of the ocean lies mainly in storing heat content anomalies in spring and releasing them in autumn. Ocean heat flux variations play only a minor role. Confirming a previous (observational) study, the authors demonstrate that there is no direct atmospheric response of clouds to spring sea ice anomalies, but a delayed response is evident in autumn. Hence, there is no cloud–ice feedback in late spring and summer, but there is a cloud–ice feedback in autumn, which strengthens the ice–albedo feedback. Anomalies in insolation are positively correlated with sea ice variability. This is primarily a result of reduced multiple reflection of insolation due to an albedo decrease. This effect counteracts the ice-albedo effect up to 50%. ERA-Interim and Ocean Reanalysis System 4 (ORAS4) confirm the main findings from the climate models.

scholarly journals A Stochastic Dynamical Model of Arctic Sea Ice

2017 ◽  
Vol 30 (13) ◽  
pp. 5119-5140 ◽  
Author(s):  
Woosok Moon ◽  
J. S. Wettlaufer
Keyword(s):  
Sea Ice ◽  
Multiplicative Noise ◽  
Numerical Solutions ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Radiative Loss ◽  
Albedo Feedback ◽  
Wide Range ◽  
Arctic Sea

The noise forcing underlying the variability in the Arctic ice cover has a wide range of principally unknown origins. For this reason, the analytical and numerical solutions of a stochastic Arctic sea ice model are analyzed with both additive and multiplicative noise over a wide range of external heat fluxes Δ F0, corresponding to greenhouse gas forcing. The stochastic variability fundamentally influences the nature of the deterministic steady-state solutions corresponding to perennial and seasonal ice and ice-free states. Thus, the results are particularly relevant for the interpretation of the state of the system as the ice cover thins with Δ F0, allowing a thorough examination of the differing effects of additive versus multiplicative noise. In the perennial ice regime, the principal stochastic moments are calculated and compared to those determined from a stochastic perturbation theory described previously. As Δ F0 increases, the competing contributions to the variability of the destabilizing sea ice–albedo feedback and the stabilizing longwave radiative loss are examined in detail. At the end of summer the variability of the stochastic paths shows a clear maximum, which is due to the combination of the increasing influence of the albedo feedback and an associated “memory effect,” in which fluctuations accumulate from early spring to late summer. This is counterbalanced by the stabilization of the ice cover resulting from the longwave loss of energy from the ice surface, which is enhanced during winter, thereby focusing the stochastic paths and decreasing the variability. Finally, common examples in stochastic dynamics with multiplicative noise are discussed wherein the choice of the stochastic calculus (Itô or Stratonovich) is not necessarily determinable a priori from observations alone, which is why both calculi are treated on equal footing herein.

scholarly journals Improving model-satellite comparisons of sea ice melt onset with a satellite simulator

2021 ◽  
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Clara Burgard ◽  
Dirk Notz
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Satellite Observations ◽  
The Arctic ◽  
Physical Processes ◽  
Ice Melt ◽  
Brightness Temperatures ◽  
Arctic Sea

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and therefore must define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator (ARC3O) to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using CESM2 ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.

scholarly journals Irtysh River Drove Arctic Sea Ice Expansion 3 Million Years Ago

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
David Shultz
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Fresh Water ◽  
Kara Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Irtysh River ◽  
Earth’S Climate

The Siberian river’s creation caused a massive influx of fresh water into the Kara Sea and radically changed the Arctic Ocean and Earth’s climate.

scholarly journals Moisture and dynamical interactions maintaining decoupled Arctic mixed-phase stratocumulus in the presence of a humidity inversion

2011 ◽  
Vol 11 (5) ◽  
pp. 13469-13524 ◽  
Author(s):  
A. Solomon ◽  
M. D. Shupe ◽  
P. O. G. Persson ◽  
H. Morrison
Keyword(s):  
Water Vapor ◽  
Mixed Layer ◽  
Potential Temperature ◽  
Cloud Layer ◽  
Mixed Phase ◽  
Specific Humidity ◽  
The Arctic ◽  
Entrainment Zone ◽  
Atmospheric Radiation Measurement ◽  
Temperature Inversions

Abstract. Observations suggest that processes maintaining subtropical and Arctic stratocumulus differ, due to the different environments in which they occur. For example, specific humidity inversions (specific humidity increasing with height) are frequently observed to occur coincident with temperature inversions in the Arctic, while they do not occur in the subtropics. In this study we use nested LES simulations of decoupled Arctic Mixed-Phase Stratocumulus (AMPS) clouds observed during the DOE Atmospheric Radiation Measurement Program's Indirect and SemiDirect Aerosol Campaign (ISDAC) to analyze budgets of water components, potential temperature, and turbulent kinetic energy. These analyses quantify the processes that maintain decoupled AMPS, including the role of the humidity inversions. The results show the maintenance of liquid clouds in both the shallow upper entrainment zone (temperature and humidity inversion) due to a down gradient transport of water vapor by turbulent fluxes into the cloud layer and direct condensation by radiative cooling, and in the updrafts of the mixed-layer eddies below cloud top due to buoyant destabilization. These processes cause at least 20 % of the cloud liquid water to extend into the inversion. The redistribution of water vapor from the top of the humidity inversion to the base of the humidity inversion maintains the cloud layer while the mixed layer-entrainment zone system is continually losing total water. In this decoupled system, the humidity inversion is the only source of water vapor for the cloud system since water vapor from the surface layer is not efficiently transported into the mixed layer. Sedimentation of ice is the dominant sink of moisture from the mixed layer.

A remarkable pogonophoran from a desalted shallow near the mouth of the Yenisey River in the Kara Sea, with the description of a new species of the genus Galathealinum (Annelida: Pogonophora: Frenulata)

2020 ◽  
Vol 29 (1) ◽  
pp. 138-154
Author(s):  
R.V. Smirnov ◽  
O.V. Zaitseva ◽  
A.A. Vedenin
Keyword(s):  
New Species ◽  
Arctic Ocean ◽  
Anterior Part ◽  
Morphological Characters ◽  
Kara Sea ◽  
The Arctic ◽  
External Layer ◽  
The Arctic Ocean ◽  
A New Species ◽  
Unusual Finding

A new species of Pogonophora obtained from one station at a depth of 25 m from near the Dikson Island in the Kara Sea is described. Galathealinum karaense sp. nov. is one of the largest pogonophorans, the first known representative of the rare genus Galathealinum Kirkegaard, 1956 in the Eurasian part of the Arctic Ocean and a highly unusual finding for the desalted shallow of the Yenisey Gulf. Several characters occurring in the new species are rare or unique among the congeners: under-developed, hardly discernible frills on the tube segments, extremely thin felted fibres in the external layer of the tube, and very faintly separated papillae in the anterior part of the trunk. Morphological characters useful in distinguishing species within the genus Galathealinum are defined and summarised in a table. Diagnosis of the genus Galathealinum is emended and supplemented by new characters. Additionally, three taxonomic keys are provided to the species of Galathealinum and to the known species of the Arctic pogonophorans using either animals or their empty tubes only, with the brief zoogeographical information on each Arctic species.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

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