An Intraseasonal Mode Linking Wintertime Surface Air Temperature over Arctic and Eurasian Continent

2021 ◽  
pp. 1-47
Keyword(s):  
Air Temperature ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Free Atmosphere ◽  
Temperature Anomalies ◽  
Downward Longwave Radiation

Abstract Key processes associated with the leading intraseasonal variability mode of wintertime surface air temperature (SAT) over Eurasia and the Arctic region are investigated in this study. Characterized by a dipole distribution in SAT anomalies centered over north Eurasia and the Arctic, respectively, and coherent temperature anomalies vertically extending from the surface to 300hPa, this leading intraseasonal SAT mode and associated circulation have pronounced influences on global surface temperature anomalies including the East Asian winter monsoon region. By taking advantage of realistic simulations of the intraseasonal SAT mode in a global climate model, it is illustrated that temperature anomalies in the troposphere associated with the leading SAT mode are mainly due to dynamic processes, especially via the horizontal advection of winter mean temperature by intraseasonal circulation. While the cloud-radiative feedback is not critical in sustaining the temperature variability in the troposphere, it is found to play a crucial role in coupling temperature anomalies at the surface and in the free-atmosphere through anomalous surface downward longwave radiation. The variability in clouds associated with the intraseasonal SAT mode is closely linked to moisture anomalies generated by similar advective processes as for temperature anomalies. Model experiments suggest that this leading intraseasonal SAT mode can be sustained by internal atmospheric processes in the troposphere over the mid-to-high latitudes by excluding forcings from Arctic sea ice variability, tropical convective variability, and the stratospheric processes.

scholarly journals EC-Earth3-AerChem, a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

2020 ◽  
Author(s):  
Twan van Noije ◽  
Tommi Bergman ◽  
Philippe Le Sager ◽  
Declan O'Donnell ◽  
Risto Makkonen ◽  
...  
Keyword(s):  
20Th Century ◽  
Southern Hemisphere ◽  
Air Temperature ◽  
Atmospheric Chemistry ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Air Temperature ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model and describe in detail how it differs from the other EC-Earth3 configurations, and what the new features are compared to the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under pre-industrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The mean energy imbalance at the top of the atmosphere in the pre-industrial control simulation is −0.10 ± 0.25 W m−2 and shows no significant drift. The corresponding mean global surface air temperature is 14.05 ± 0.16 °C, with a small drift of −0.075 ± 0.009 °C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 °C and its transient climate response at 2.1 °C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread among ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared to the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts, the ensemble mean surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.35 °C in the Northern Hemisphere and 1.29 ± 0.05 °C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant climate effects from the 20th century onwards. For the SSP3-7.0 shared socio-economic pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 °C above the pre-industrial mean. A 0.5 °C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 °C.

scholarly journals Unraveling the Teleconnection Mechanisms that Induce Wintertime Temperature Anomalies over the Northern Hemisphere Continents in Response to the MJO

2016 ◽  
Vol 73 (9) ◽  
pp. 3557-3571 ◽  
Author(s):  
Kyong-Hwan Seo ◽  
Hyun-Ju Lee ◽  
Dargan M. W. Frierson
Keyword(s):  
North America ◽  
Eastern Europe ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Temperature Variations ◽  
Temperature Anomalies ◽  
Air Temperature Variations

Abstract Significant extratropical surface air temperature variations arise as a result of teleconnections induced by the Madden–Julian oscillation (MJO). The authors elucidate the detailed physical processes responsible for the development of temperature anomalies over Northern Hemisphere continents in response to MJO-induced heating using an intraseasonal perturbation thermodynamic equation and a wave activity tracing technique. A quantitative assessment demonstrates that surface air temperature variations are due to dynamical processes associated with a meridionally propagating Rossby wave train. Over East Asia, a local Hadley circulation causes adiabatic subsidence following MJO phase 3 to be a main driver for the warming. Meanwhile, for North America and eastern Europe, horizontal temperature advection by northerlies or southerlies is the key process for warming or cooling. A ray-tracing analysis illustrates that Rossby waves with zonal wavenumbers 2 and 3 influence the surface warming over North America and a faster wavenumber 4 affects surface temperature over eastern Europe. Although recent studies demonstrate the impacts of the Arctic Oscillation, Arctic sea ice melting, and Eurasian snow cover variations on extremely cold wintertime episodes over the NH extratropics, the weather and climate there are still considerably modulated through teleconnections induced by the tropical heat forcing. In addition, the authors show that the MJO is a real source of predictability for strong warm/cold events over these continents, suggesting a higher possibility of making a skillful forecast of temperature extremes with over 1 month of lead time.

scholarly journals Pacific sea surface temperature anomalies as important boundary forcing in driving the interannual Warm Arctic-Cold Continent pattern over the North American sector

2021 ◽  
pp. 1-43
Author(s):  
Weina Guan ◽  
Xianan Jiang ◽  
Xuejuan Ren ◽  
Gang Chen ◽  
Qinghua Ding
Keyword(s):  
North American ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
The Arctic ◽  
Surface Boundary ◽  
The North

AbstractThe leading interannual mode of winter surface air temperature over the North American (NA) sector, characterized by a “Warm Arctic, Cold Continents” (WACC) pattern, exerts pronounced influences on NA weather and climate, while its underlying mechanisms remain elusive. In this study, the relative roles of surface boundary forcing versus internal atmospheric processes for the formation of the WACC pattern are quantitatively investigated using a combined analysis of observations and large-ensemble atmospheric global climate model simulations. Internal atmospheric variability is found to play an important role in shaping the year-to-year WACC variability, contributing to about half of the total variance. An anomalous SST pattern resembling the North Pacific Mode is identified as a major surface boundary forcing pattern in driving the interannual WACC variability over the NA sector, with a minor contribution from sea ice variability over the Chukchi- Bering Seas. Findings from this study not only lead to improved understanding of underlying physics regulating the interannual WACC variability, but also provide important guidance for improved modeling and prediction of regional climate variability over NA and the Arctic region.

scholarly journals EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

2021 ◽  
Vol 14 (9) ◽  
pp. 5637-5668
Author(s):  
Twan van Noije ◽  
Tommi Bergman ◽  
Philippe Le Sager ◽  
Declan O'Donnell ◽  
Risto Makkonen ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Southern Hemisphere ◽  
Air Temperature ◽  
Atmospheric Chemistry ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Air Temperature ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average −0.09 W m−2 with a standard deviation due to interannual variability of 0.25 W m−2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘C with an interannual standard deviation of 0.17 ∘C, exhibiting a small drift of 0.015 ± 0.005 ∘C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘C, and its transient climate response is estimated at 2.1 ∘C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.05 ∘C with a standard deviation across ensemble members of 0.35 ∘C in the Northern Hemisphere and 1.29 ± 0.02 ∘C with a corresponding standard deviation of 0.05 ∘C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘C above the preindustrial mean. A 0.5 ∘C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘C.

The response of Northern Hemisphere polar lows to2climate change in a 25 km high-resolution global climate model

2020 ◽  
Author(s):  
Len Shaffrey ◽  
Helene Bresson ◽  
Kevin Hodges ◽  
Giuseppe Zappa
Keyword(s):  
High Resolution ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Heavy Precipitation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Lows

<p>Polar lows are small, intense cyclones that form at high latitudes during winter. Their high wind speeds and heavy precipitation can have substantial impacts on shipping, coastal communities and infrastructure. However, climate models typically have low resolutions and therefore poorly simulate Polar Lows. This reduces the confidence that can be placed in future projections of extreme high latitude weather and associated risks.</p><p>In this study, Polar Lows are assessed for the first time in a high-resolution (25 km) global climate atmosphere-only model, N512 HadGEM3-GA3, for both present-day and future RCP 8.5 climate scenarios. Using an objective tracking algorithm, the representation of Polar Lows in the N512 HadGEM3-GA3 present-day simulation is found to agree reasonably well the NCEP-CFS reanalysis. RCP8.5 scenario conditions are generated by adding SST changes between 1990-2010 and 2090-2110 from the RCP8.5 experiments with the HadGEM2-ES model to observed SSTs from the present-day climate. In the RCP8.5 N512 HadGEM-GA3 simulations, the number of Northern Hemisphere Polar Lows are projected to substantially decrease (by over 60%) by the end of the 21st century, which is largely due to an increase in atmospheric static stability. However, new regions of Polar Low activity along the northern Russian coastlines are found where the Arctic sea ice is projected to retreat.</p>

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

2015 ◽  
Vol 28 (14) ◽  
pp. 5477-5509 ◽  
Author(s):  
Mitchell Bushuk ◽  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Spring Sea Ice ◽  
Phase Relationships

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

scholarly journals Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations

2014 ◽  
Vol 119 (13) ◽  
pp. 8169-8188 ◽  
Author(s):  
Paul Glantz ◽  
Adam Bourassa ◽  
Andreas Herber ◽  
Trond Iversen ◽  
Johannes Karlsson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
The Arctic ◽  
Model Simulations ◽  
Climate Model Simulations

scholarly journals The Granular Sea Ice Model in Spherical Coordinates and Its Application to a Global Climate Model

2007 ◽  
Vol 20 (24) ◽  
pp. 5946-5961 ◽  
Author(s):  
Jan Sedlacek ◽  
Jean-François Lemieux ◽  
Lawrence A. Mysak ◽  
L. Bruno Tremblay ◽  
David M. Holland
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Climate Model ◽  
Heat Budget ◽  
Global Climate ◽  
Global Climate Model ◽  
Internal Heat ◽  
The Arctic ◽  
Spherical Coordinates ◽  
Ice Model

Abstract The granular sea ice model (GRAN) from Tremblay and Mysak is converted from Cartesian to spherical coordinates. In this conversion, the metric terms in the divergence of the deviatoric stress and in the strain rates are included. As an application, the GRAN is coupled to the global Earth System Climate Model from the University of Victoria. The sea ice model is validated against standard datasets. The sea ice volume and area exported through Fram Strait agree well with values obtained from in situ and satellite-derived estimates. The sea ice velocity in the interior Arctic agrees well with buoy drift data. The thermodynamic behavior of the sea ice model over a seasonal cycle at one location in the Beaufort Sea is validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) datasets. The thermodynamic growth rate in the model is almost twice as large as the observed growth rate, and the melt rate is 25% lower than observed. The larger growth rate is due to thinner ice at the beginning of the SHEBA period and the absence of internal heat storage in the ice layer in the model. The simulated lower summer melt is due to the smaller-than-observed surface melt.

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