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>

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

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 Can we reconstruct Arctic sea ice back to 1900 with a hybrid approach?

2008 ◽  
Vol 4 (4) ◽  
pp. 955-979 ◽  
Author(s):  
S. Brönnimann ◽  
T. Lehmann ◽  
T. Griesser ◽  
T. Ewen ◽  
A. N. Grant ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Historical Data ◽  
Arctic Sea Ice ◽  
Coupled Processes ◽  
The Arctic ◽  
Statistical Reconstruction ◽  
Historical Climatology ◽  
Arctic Sea ◽  
The Future

Abstract. The variability and trend of Arctic sea ice since the mid 1970s is well documented and linked to rising temperatures. However, much less is known for the first half of the 20th century, when the Arctic also underwent a period of strong warming. For studying this period in atmospheric models, gridded sea ice data are needed as boundary conditions. Current data sets (e.g., HadISST) provide a historical climatology, but may not be suitable when interannual-to-decadal variability is important, as they are interpolated and relaxed towards a (historical) climatology to fill in gaps, particularly in winter. Regional historical sea ice information exhibits considerable variability on interannnual-to-decadal scales, but is only available for summer and not in gridded form. Combining the advantages of both types of information could be used to constrain model simulations in a more realistic way. Here we discuss the feasibility of reconstructing year-round gridded Arctic sea ice from 1900 to 1953 from historical information and a coupled climate model. We decompose sea ice variability into centennial (due to climate forcings), decadal (coupled processes in the ocean-sea ice system) and interannual time scales (atmospheric circulation). The three time scales are represented by a historical climatology from HadISST (centennial), a closest analogue approach using the coupled control run of the CCSM-3.0 model (decadal), and a statistical reconstruction based on high-pass filtered data (interannual variability), respectively. Results show that differences in the model climatology, the length of the control run, and inconsistent historical data strongly limit the quality of the product. However, with more realistic and longer simulations becoming available in the future as well as with improved historical data, useful reconstructions are possible. We suggest that hybrid approaches, using both statistical reconstruction methods and numerical models, may find wider applications in the future.

scholarly journals Benefits of sea ice thickness initialization for the Arctic decadal climate prediction skill in EC-Earth3

2020 ◽  
Author(s):  
Tian Tian ◽  
Shuting Yang ◽  
Mehdi Pasha Karami ◽  
François Massonnet ◽  
Tim Kruschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Central Arctic Ocean ◽  
Ice Volume ◽  
Arctic Sea

Abstract. A substantial part of Arctic climate predictability at interannual time scales stems from the knowledge of the initial sea ice conditions. Among all the variables characterizing sea ice, sea ice volume, being a product of sea ice area/concentration (SIC) and thickness (SIT), is the most sensitive parameter for climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the benefits of specific initialized variables at regional scales, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean-only; ocean plus SIC; and ocean plus SIC and SIT initialization. The increased skill from ocean plus SIC initialization is small in most regions, compared to ocean-only initialization. In the marginal ice zone covered by seasonal ice, skills regarding winter SIC are mainly gained from the initial ocean temperature anomalies. Consistent with previous studies, the Arctic sea ice volume anomalies are found to play a dominant role for the prediction skill of September Arctic sea ice extent. Winter preconditioning of SIT for the perennial ice in the central Arctic Ocean results in increased skill of SIC in the adjacent Arctic coastal waters (e.g. the Laptev/East Siberian/Chukchi Seas) for lead time up to a decade. This highlights the importance of initializing SIT for predictions of decadal time scale in regional Arctic sea ice. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from being the sea ice volume playing the major role to a more ocean-related processes.

Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales

2020 ◽  
Author(s):  
Lejiang Yu ◽  
Sharon Zhong
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Time Scales ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Atmospheric Circulations ◽  
The Pacific ◽  
Arctic Sea

<p>The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science<br>community. A majority of climate analyses performed to date have used monthly or seasonal data. Here,<br>however, we analyze daily sea ice data for 1979–2016 using the self-organizing map (SOM) method to further<br>examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice<br>variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara<br>Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more<br>than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be<br>associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation<br>(AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the<br>Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the<br>Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave<br>radiation associated with the advection are consistent with the melt-season sea ice anomalies observed<br>in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice<br>on multiple (synoptic, intraseasonal, and interannual) time scales.</p>

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 recent weakening of winter temperature association between Arctic and Asia

2022 ◽  
Author(s):  
Bingyi Wu ◽  
Zhenkun Li ◽  
Jennifer A. Francis ◽  
Shuoyi Ding
Keyword(s):  
Sea Ice ◽  
Phase Ii ◽  
Arctic Sea Ice ◽  
Winter Temperature ◽  
The Arctic ◽  
Arctic Warming ◽  
The Past ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
Over Time

Abstract Arctic warming and its association with the mid-latitudes have been hot topic over the past two decades. Although many studies have explored these issues it is not clear that how their linkage has changed over time. The results show that winter low tropospheric temperatures in Asia experienced two phases over the past two decades. Phase I (2007/2008 to 2012/2013) was characterized by a warm Arctic and cold Eurasia, and phase II by a warm Arctic and warm Eurasia (2013/2014 to 2018/2019). A strengthened association in winter temperature between the Arctic and Asia occurred during phase I, followed by a weakened linkage during phase II. Simulation experiments forced by observed Arctic sea ice variability largely reproduce observed patterns, suggesting that Arctic sea ice loss contributes to phasic (or low-frequency) variations in winter atmosphere and make the Arctic-Asia temperature association fluctuate over time. The weakening of the Arctic-Asia linkage post-2012/2013 was associated with amplified and expanded Arctic warming. The corresponding anomalies in SLP resembled a positive phase North Atlantic Oscillation (NAO) during phase II. This study implies that the phasic warm Arctic-cold Eurasia and warm Arctic-warm Eurasia patterns would alternately happen in the context of Arctic sea ice loss, which increase the difficulty to correctly predict Asian winter temperature.

Arctic connections between sea ice, ocean dynamics and biogeochemistry in the UK Earth System Model (UK ESM1): present climate and future scenarios

2020 ◽  
Author(s):  
Yevgeny Aksenov ◽  
Andrew Yool ◽  
Julien Palmieri ◽  
Katya Popova ◽  
Stephen Kelly ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Arctic Sea Ice ◽  
Ocean Dynamics ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Present Climate ◽  
Arctic Sea ◽  
The Uk

<p>We present analysis of Arctic sea ice and ocean dynamics in the ensemble of the UK Earth System Model (UK ESM1) simulations completed under the Coupled Model Intercomparison Project Phase 6 (CMIP6) protocol. The focus of the investigation is on the future changes in the Arctic sea ice and oceanic connections and on the impact of the nutrient advection on the Arctic marine biogeochemistry and ecosystems. Changes in the balance of the oceanic inflows from the North Atlantic and North Pacific Oceans are found to have a first order effect on the watermasses and nutrients balances in the central Arctic Ocean. The simulations show that the total primary production in the Arctic Ocean is increased by 100% in the 2090s as compared to the present climate. This is caused by higher nutrients availability in the Atlantic inflowing waters and prolonged ice- free season. The faster connections through the Arctic and milder oceanic environment allows species to survive through the winter and from the second half of the century the Arctic Ocean could become a key oceanic gateway connecting the global oceans. The study is supported from the project APEAR (NE/R012865/1) NERC-BMBF and from the NERC ACSIS Programme (NE/N018044/1).</p>

scholarly journals How Tropical Pacific Surface Cooling Contributed to Accelerated Sea Ice Melt from 2007 to 2012 as Ice Is Thinned by Anthropogenic Forcing

2019 ◽  
Vol 32 (24) ◽  
pp. 8583-8602 ◽  
Author(s):  
Ian Baxter ◽  
Qinghua Ding ◽  
Axel Schweiger ◽  
Michelle L’Heureux ◽  
Stephen Baxter ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Model Simulation ◽  
Wave Train ◽  
Tropical Pacific ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Surface Cooling ◽  
The Past ◽  
Arctic Sea

Abstract Over the past 40 years, the Arctic sea ice minimum in September has declined. The period between 2007 and 2012 showed accelerated melt contributed to the record minima of 2007 and 2012. Here, observational and model evidence shows that the changes in summer sea ice since the 2000s reflect a continuous anthropogenically forced melting masked by interdecadal variability of Arctic atmospheric circulation. This variation is partially driven by teleconnections originating from sea surface temperature (SST) changes in the east-central tropical Pacific via a Rossby wave train propagating into the Arctic [herein referred to as the Pacific–Arctic teleconnection (PARC)], which represents the leading internal mode connecting the pole to lower latitudes. This mode has contributed to accelerated warming and Arctic sea ice loss from 2007 to 2012, followed by slower declines in recent years, resulting in the appearance of a slowdown over the past 11 years. A pacemaker model simulation, in which we specify observed SST in the tropical eastern Pacific, demonstrates a physically plausible mechanism for the PARC mode. However, the model-based PARC mechanism is considerably weaker and only partially accounts for the observed acceleration of sea ice loss from 2007 to 2012. We also explore features of large-scale circulation patterns associated with extreme melting periods in a long (1800 yr) CESM preindustrial simulation. These results further support that remote SST forcing originating from the tropical Pacific can excite significant warm episodes in the Arctic. However, further research is needed to identify the reasons for model limitations in reproducing the observed PARC mode featuring a cold Pacific–warm Arctic connection.

scholarly journals Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales

2019 ◽  
Vol 32 (5) ◽  
pp. 1461-1482 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Mingyu Zhou ◽  
Donald H. Lenschow ◽  
Bo Sun
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Time Scales ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Multiple Time Scales ◽  
Atmospheric Circulations ◽  
The Pacific ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science community. A majority of climate analyses performed to date have used monthly or seasonal data. Here, however, we analyze daily sea ice data for 1979–2016 using the self-organizing map (SOM) method to further examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave radiation associated with the advection are consistent with the melt-season sea ice anomalies observed in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice on multiple (synoptic, intraseasonal, and interannual) time scales.

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