Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth

Abstract Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.

Download Full-text

Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System

Monthly Weather Review ◽

10.1175/mwr-d-12-00057.1 ◽

2013 ◽

Vol 141 (4) ◽

pp. 1375-1394 ◽

Cited By ~ 79

Author(s):

Wanqiu Wang ◽

Mingyue Chen ◽

Arun Kumar

Keyword(s):

Sea Ice ◽

Seasonal Prediction ◽

Arctic Sea Ice ◽

Prediction Skill ◽

Forecast System ◽

Sea Ice Extent ◽

Term Trend ◽

Fully Coupled ◽

Long Term Trend

Abstract While fully coupled atmosphere–ocean models have been used to study the seasonal predictability of sea ice variations within the context of models’ own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere–ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January–September and a negative SIE bias for the forecast for October–December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed.

Download Full-text

Record high Pacific Arctic seawater temperatures and delayed sea ice advance in response to episodic atmospheric blocking

Scientific Reports ◽

10.1038/s41598-020-77488-y ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Tsubasa Kodaira ◽

Takuji Waseda ◽

Takehiko Nose ◽

Jun Inoue

Keyword(s):

Sea Ice ◽

Arctic Region ◽

Arctic Sea Ice ◽

Sea Surface ◽

The Arctic ◽

Atmospheric Blocking ◽

Sea Ice Extent ◽

The Pacific ◽

Arctic Sea ◽

Highly Correlated

AbstractArctic sea ice is rapidly decreasing during the recent period of global warming. One of the significant factors of the Arctic sea ice loss is oceanic heat transport from lower latitudes. For months of sea ice formation, the variations in the sea surface temperature over the Pacific Arctic region were highly correlated with the Pacific Decadal Oscillation (PDO). However, the seasonal sea surface temperatures recorded their highest values in autumn 2018 when the PDO index was neutral. It is shown that the anomalous warm seawater was a rapid ocean response to the southerly winds associated with episodic atmospheric blocking over the Bering Sea in September 2018. This warm seawater was directly observed by the R/V Mirai Arctic Expedition in November 2018 to significantly delay the southward sea ice advance. If the atmospheric blocking forms during the PDO positive phase in the future, the annual maximum Arctic sea ice extent could be dramatically reduced.

Download Full-text

Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

The Cryosphere Discussions ◽

10.5194/tcd-8-1383-2014 ◽

2014 ◽

Vol 8 (1) ◽

pp. 1383-1406 ◽

Cited By ~ 1

Author(s):

P. J. Hezel ◽

T. Fichefet ◽

F. Massonnet

Keyword(s):

Sea Ice ◽

Radiative Forcing ◽

Climate Models ◽

Global Climate ◽

Arctic Sea Ice ◽

Global Climate Models ◽

The Arctic ◽

Sea Ice Extent ◽

Arctic Sea ◽

Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

Download Full-text

September Arctic Sea Ice minimum prediction – a new skillful statistical approach

10.5194/esd-2018-61 ◽

2018 ◽

Author(s):

Monica Ionita ◽

Klaus Grosfeld ◽

Patrick Scholz ◽

Renate Treffeisen ◽

Gerrit Lohmann

Keyword(s):

Sea Ice ◽

Statistical Model ◽

Climate Models ◽

Global Climate ◽

Arctic Sea Ice ◽

The Arctic ◽

Polar Regions ◽

Sea Ice Extent ◽

Important Indicator ◽

Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

Download Full-text

Pan-Arctic and Regional Sea Ice Predictability: Initialization Month Dependence

Journal of Climate ◽

10.1175/jcli-d-13-00614.1 ◽

2014 ◽

Vol 27 (12) ◽

pp. 4371-4390 ◽

Cited By ~ 78

Author(s):

J. J. Day ◽

S. Tietsche ◽

E. Hawkins

Keyword(s):

Sea Ice ◽

Climate Models ◽

Global Climate ◽

Regional Analysis ◽

Early Summer ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Sea Ice Extent ◽

The North ◽

Arctic Sea

Abstract Seasonal-to-interannual predictions of Arctic sea ice may be important for Arctic communities and industries alike. Previous studies have suggested that Arctic sea ice is potentially predictable but that the skill of predictions of the September extent minimum, initialized in early summer, may be low. The authors demonstrate that a melt season “predictability barrier” and two predictability reemergence mechanisms, suggested by a previous study, are robust features of five global climate models. Analysis of idealized predictions with one of these models [Hadley Centre Global Environment Model, version 1.2 (HadGEM1.2)], initialized in January, May and July, demonstrates that this predictability barrier exists in initialized forecasts as well. As a result, the skill of sea ice extent and volume forecasts are strongly start date dependent and those that are initialized in May lose skill much faster than those initialized in January or July. Thus, in an operational setting, initializing predictions of extent and volume in July has strong advantages for the prediction of the September minimum when compared to predictions initialized in May. Furthermore, a regional analysis of sea ice predictability indicates that extent is predictable for longer in the seasonal ice zones of the North Atlantic and North Pacific than in the regions dominated by perennial ice in the central Arctic and marginal seas. In a number of the Eurasian shelf seas, which are important for Arctic shipping, only the forecasts initialized in July have continuous skill during the first summer. In contrast, predictability of ice volume persists for over 2 yr in the central Arctic but less in other regions.

Download Full-text

Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model

Annals of Glaciology ◽

10.1017/aog.2020.13 ◽

2020 ◽

Vol 61 (82) ◽

pp. 97-105

Author(s):

Jun Ono ◽

Yoshiki Komuro ◽

Hiroaki Tatebe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Predictive Skill ◽

The Pacific ◽

The Impact

AbstractThe impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

Download Full-text

Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes

Journal of Climate ◽

10.1175/jcli-d-16-0391.1 ◽

2016 ◽

Vol 29 (24) ◽

pp. 9179-9188 ◽

Cited By ~ 18

Author(s):

Erica Rosenblum ◽

Ian Eisenman

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

Sea Ice Extent ◽

Arctic Sea ◽

Volcanic Forcing ◽

Global Mean Surface Temperature ◽

Ice Retreat ◽

Sea Ice Retreat ◽

Global Mean

Abstract The downward trend in Arctic sea ice extent is one of the most dramatic signals of climate change during recent decades. Comprehensive climate models have struggled to reproduce this trend, typically simulating a slower rate of sea ice retreat than has been observed. However, this bias has been widely noted to have decreased in models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) compared with the previous generation of models (CMIP3). Here simulations are examined from both CMIP3 and CMIP5. It is found that simulated historical sea ice trends are influenced by volcanic forcing, which was included in all of the CMIP5 models but in only about half of the CMIP3 models. The volcanic forcing causes temporary simulated cooling in the 1980s and 1990s, which contributes to raising the simulated 1979–2013 global-mean surface temperature trends to values substantially larger than observed. It is shown that this warming bias is accompanied by an enhanced rate of Arctic sea ice retreat and hence a simulated sea ice trend that is closer to the observed value, which is consistent with previous findings of an approximately linear relationship between sea ice extent and global-mean surface temperature. Both generations of climate models are found to simulate Arctic sea ice that is substantially less sensitive to global warming than has been observed. The results imply that much of the difference in Arctic sea ice trends between CMIP3 and CMIP5 occurred because of the inclusion of volcanic forcing, rather than improved sea ice physics or model resolution.

Download Full-text

Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System

Journal of Climate ◽

10.1175/jcli-d-17-0093.1 ◽

2017 ◽

Vol 30 (21) ◽

pp. 8429-8446 ◽

Cited By ~ 16

Author(s):

Zhiqiang Chen ◽

Jiping Liu ◽

Mirong Song ◽

Qinghua Yang ◽

Shiming Xu

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Forecast System ◽

Climate Forecast System ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

Download Full-text

The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming

10.5194/egusphere-egu21-16310 ◽

2021 ◽

Author(s):

Stephanie Hay ◽

Paul Kusnher

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

Climate Model Simulations ◽

Arctic Sea Ice Loss

Antarctic sea ice has gradually increased in extent over the forty-year-long satellite record, in contrast with the clear decrease in sea-ice extent seen in the Arctic over the same time period. However, state-of-the-art climate models ubiquitously project Antarctic sea-ice to decrease over the coming century, much as they do for Arctic sea-ice. Several recent years have also seen record low Antarctic sea-ice. It is therefore of interest to understand what the climate response to Antarctic sea-ice loss will be.&#160;We have carried out new fully coupled climate model simulations to assess the response to sea-ice loss in either hemisphere separately or coincidentally under different albedo parameter settings to determine the relative importance of each. By perturbing the albedo of the snow overlying the sea ice and the albedo of the bare sea ice, we obtain a suite of simulations to assess the linearity and additivity of sea-ice loss. We find the response to sea-ice loss in each hemisphere exhibits a high degree of additivity, and can simply be decomposed into responses due to loss in each hemisphere separately.&#160;We find that the response to Antarctic sea-ice loss exceeds that of Arctic sea-ice loss in the tropics, and that Antarctic sea-ice loss leads to statistically significant Arctic warming, while the opposite is not true.With these new simulations and one in which CO2 is instantaneously doubled , we can further characterize the response to sea-ice loss from each hemisphere using an extension to classical pattern scaling that includes three controlling parameters. This allows us to simultaneously compute the sensitivity patterns to Arctic sea-ice loss, Antarctic sea-ice loss, and to tropical warming. The statistically significant response to Antarctic sea-ice loss in the Northern Hemisphere extratropics is found to be mediated by tropical warming and small amounts of Arctic sea-ice loss.

Download Full-text

Record high Pacific Arctic seawater temperatures and delayed sea ice advance in response to episodic atmospheric blocking

10.5194/egusphere-egu21-9312 ◽

2021 ◽

Author(s):

Tsubasa Kodaira ◽

Takuji Waseda ◽

Takehiko Nose ◽

Jun Inoue

Keyword(s):

Sea Ice ◽

Arctic Region ◽

Arctic Sea Ice ◽

Sea Surface ◽

The Arctic ◽

Atmospheric Blocking ◽

Sea Ice Extent ◽

The Pacific ◽

Arctic Sea ◽

Highly Correlated

Arctic sea ice is rapidly decreasing during the recent period of global warming. One of the significant factors of the Arctic sea ice loss is oceanic heat transport from lower latitudes. For months of sea ice formation, the variations in the sea surface temperature over the Pacific Arctic region were highly correlated with the Pacific Decadal Oscillation (PDO). However, the seasonal sea surface temperatures recorded their highest values in autumn 2018 when the PDO index was neutral. It is shown that the anomalous warm seawater was a rapid ocean response to the southerly winds associated with episodic atmospheric blocking over the Bering Sea in September 2018. This warm seawater was directly observed by the R/V Mirai Arctic Expedition in November 2018 to significantly delay the southward sea ice advance. If the atmospheric blocking forms during the PDO positive phase in the future, the annual maximum Arctic sea ice extent could be dramatically reduced.

Download Full-text