Displays

The strong decline of Arctic sea ice in recent years has raised growing interest in seasonal-to-interannual predictions of Arctic sea ice. Previous studies have revealed a large predictability gap between potential and operational forecast skill of Arctic sea ice, which could indicate a strong potential for improvement of operational sea ice predictions or hint at a systematic overestimation of sea ice memory in current climate models.

Here, we assess and compare memory of Arctic sea ice in terms of lagged correlations of sea ice area anomalies on seasonal to interannual time scales in a large model ensemble (MPI Grand Ensemble) as well as several reanalysis and observational products. While the different datasets show good agreement for short-term memory on time scales of a few months on which persistence is the dominant source of memory, we find substantial differences between model and observational memory behaviour on longer time scales. In particular, we find that memory from the summer sea ice minimum into the following year is significantly overestimated in the model, as lagged correlation values in all observational datasets are outside the range of model variability. Reanalysis data show correlation values that lie in between observational and model mean values, underpinning the hybrid nature of reanalyses combining observations and model behaviour. Extending the analysis of sea ice memory to a regional scale provides further information on the spatial origin of specific memory features in the different datasets and helps in understanding differences between model and real-world behaviour on a physical process level.

How to cite: Gieße, C., Notz, D., and Baehr, J.: Memory of Arctic sea ice in model simulations, observations, and reanalyses, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8003, https://doi.org/10.5194/egusphere-egu2020-8003, 2020.

The METROMS is a coupled ocean and sea ice model based on the Regional Ocean Modeling System (ROMS) and the Los Alamos sea ice model CICE.  It was employed for seasonal forecast of the September Arctic sea ice extent (SIE) in 2019 in the Sea Ice Prediction Network (SIPN), using a regional configuration of grid resolution 20km for the Arctic, the so-called Arctic-20km configuration. In the present study, we investigate the impact of model initialization and sea ice data assimilation on the seasonal forecast of the September Arctic SIE. The ERA5 atmospheric forcing is used to driver the model. The preliminary results indicate that model initialization plays a very important role in the seasonal prediction of September Arctic SIE. Experiments using different model initializations from climate monthly mean (CMM) and actual monthly mean (AMM) indicate that the AMM generally has a much higher prediction skill. The prediction skill also increases with decreasing prediction time. With a reasonable model initialization, SIC assimilation can significantly improve the prediction skill, particularly within two months. On the contrary, SIT assimilation tends to provide relatively small contribution to the September SIE prediction when model is reasonably initialized, due mostly to the fact that no data is available in the summer period. 

How to cite: Wang, K., Li, Q., Wang, C., Debernard, J., and Keeley, S.: Effect of model initialization and sea ice data assimilation on the seasonal forecast of September Arctic sea ice extent in a coupled ocean-sea ice model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13429, https://doi.org/10.5194/egusphere-egu2020-13429, 2020.

The Arctic has lost more than 50% multiyear sea ice (MYI) area during 1999-2017. Observation analysis suggests that if the decline of the MYI coverage continues, changes in the Arctic ice cover (i.e. area and volume) will be more controlled by seasonal ice than the effect of global warming. To investigate how large and where the source of Arctic prediction skill is given a large losses of thick MYI during the last two decades, we explore the decadal prediction skills and sensitivity to sea ice thickness (SIT) initialization from the EC-Earth3 Climate Prediction System with Anomaly Initialization (EC-Earth3-CPSAI). Three sets of ensemble hind-cast experiments following the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP) are carried out in which the predictions start from: 1) a baseline system with ocean only initialization; 2) with ocean and sea ice concentration (SIC) initialization; 3) with ocean, SIC and SIT initialization. The hind-cast experiments are initialized and validated based on the ERA-Interim-reanalysis for the atmosphere and ORAS5 for ocean and sea-ice, with a focus period 1997-2016. All initialized experiments show better agreement with ORAS5 than the CMIP6 historical run (i.e. the Free run) for the first winter sea ice forecast. The SIT initialized experiments show the best skill in predicting SIT (or volume) and the added value by greatly reducing errors of near surface air temperature over the Greenland and its surrounding waters. In the Central Arctic, the Beaufort and East Siberian Seas, there are only minor differences in prediction skills on seasonal to decadal time scales between the ocean-only initialized and the SIT initialized experiments, indicating that the source of predictability in these regions are mainly from the ocean; while the ocean-only initialization degrades skill with larger RMSE than the Free run, e.g. during the ice-freezing season in the GIN and Barents Seas, or at  the summer minimum in the Kara Sea, the added value from the SIT initialized experiment is present, and it may have long-term effect (>4 years) probably associated with sea-ice recirculation. In all cases, the improvement from the ocean-only initialization to also including SIC initialization is found negligible, even somehow degrading the skills. This highlights the important use of SIT in predicting changes in the Arctic sea ice cover at various time scales during the study period. Therefore, the sea-ice initialization with constraint on SIT is recommended as the most effective initialization strategy in our EC-Earth3-CPSAI for present climate prediction from seasonal to decadal time scales.

How to cite: Tian, T., Yang, S., Karami, P., Massonnet, F., Kruschke, T., and Koenigk, T.: Assessment of decadal prediction skills and sensitivity to sea-ice thickness initialization in the Arctic when seasonal ice becomes dominant in the Arctic , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13516, https://doi.org/10.5194/egusphere-egu2020-13516, 2020.

Climate models predict that sea ice cover will shrink--even disappear-- in most regions of the Arctic basin by the end of the century, triggering local and remote responses in the surface climate via atmospheric and oceanic circulation changes. In particular, it has been suggested that seasonal anomalies over Europe and North America in recent years could have been caused by record low Arctic sea ice cover. Despite an intense research effort toward quantifying its effect, the contribution of regional sea ice loss to climate change and its mechanisms of action remain controversial. 

In this study, we prescribe sea ice loss in individual sectors of the Arctic within a climate model, and study its effect on climatic anomalies in the Northern Hemisphere. Using the EC-EARTH3.3 model in its atmospheric-only and fully coupled configuration, and following the PAMIP protocol, sea ice cover is set to either its present day state, or a hypothetical future distribution of reduced sea ice cover in the Arctic. This pan-Arctic sea ice loss experiment is then complemented by 8 regional sea ice loss experiments.

Comparing those experiments, we assess the contribution of sea ice loss in each region of the Arctic to climate change over Europe, Siberia and North America. We find that sea ice loss in some sectors of the Arctic appears to matter more for Northern Hemisphere climate change than others, even after normalizing for differences in surface cover. Furthermore, the climatic effect of regional sea ice loss is compared to that of a pan-Arctic sea ice loss, whose associated climate anomalies are found to be strikingly different from that expected from a simple linear response to regional sea ice loss. We propose a mechanism for this nonlinear climate response to regional sea ice loss, which considers regional differences in the strength of the thermal inversion over the Arctic, as well as the relative proximity of each Arctic region to features critical for stationary wave genesis (e.g. the Tibetan plateau).

How to cite: Levine, X., Cvijanovic, I., Ortega, P., and Donat, M.: Assessing the climate response to regional sea ice change across all Arctic regions., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14616, https://doi.org/10.5194/egusphere-egu2020-14616, 2020.

Climate change in the Arctic is embedded in the global climate system leading to phenomenon like Arctic Amplification and linkages to the mid-latitudes. A major forcing emerges from changed surface conditions like declining sea ice cover (SIC) and rising sea surface temperatures (SST). We performed time-slice model experiments with the global atmosphere-only model ECHAM6 and changed SIC and SST to either high or low states, respectively. These experiments are compared to reanalysis data and analysed aiming at a separation between the influences of SIC and SST, while focusing on linkages between the Arctic and mid-latitudes in winter.

We identify five significant regimes in the Atlantic-Eurasian sector with the k-means clustering method. The regimes include different blocking patterns, situation with strong low pressure influence and the North Atlantic Oscillation in its two phases. Their frequency of occurrence is discussed for winter months. In the reanalysis we observe an increase of blocking patterns in early winter of the most recent decades. This is reproduced by our experiments with increased SST, where blocking becomes more dominant overall. In late winter, an increased frequency of occurrence of the North Atlantic Oscillation in its negative phase is observed. This and the overall temporal behaviour of regimes in recent years is best represented if SST and SIC are changed to their more recent state simultaneously. Therefore, our results suggest that increased SSTs and reduced SIC together act on observed linkages between polar regions and mid-latitudes.

How to cite: Jaiser, R., Akperov, M., Timazhev, A., Romanowsky, E., Handorf, D., and Mokhov, I.: Regime behaviour in the Atlantic-Eurasian Arctic related to sea ice and sea surface temperature changes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17763, https://doi.org/10.5194/egusphere-egu2020-17763, 2020.

Wintertime Barents Sea ice cover has been strongly linked to heat transport through the Barents Sea opening and Barents Sea heat content. Previous studies have shown predictability at seasonal timescales with short lead times. However, studies that have used statistical prediction have focused on a small set of predictors in the vicinity of the Barents Sea. Here we will extend the analysis further south following the path of the Norwegian Atlantic Current and show that monthly predictability with lead times up to 1-2 years can be achieved in CMIP6 models using Climate Response Function (CRF's). We further examine the effects of model resolution and coupling in the predictability and compare the results to CRF derived from observations. Our results suggest that higher resolution generally leads to stronger predictability and the fully coupled system provides the most realistic response function. The ocean provides a narrow range of lead times corresponding to an advective timescale, while coupling to the atmosphere broadens the lead times that are important for prediction. Finally, we show that even the upstream sea surface temperatures provide relatively high predictability of the Barents Sea ice cover both in the models and in the observations.

How to cite: Guo, C. and Nummelin, A.: Linear predictability of Barents Sea ice cover: effects of coupling and resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18270, https://doi.org/10.5194/egusphere-egu2020-18270, 2020.

Extratropical cyclones are a key player for the global energy budget as they transport a large amount of moisture and heat from mid- to high-latitudes. One of the main corridors for cyclones entering the Arctic from the North Atlantic is the Barents Sea, a region that has experienced the largest decrease in winter sea ice during the past decades. On the one hand, some studies showed that moisture transported by cyclones to the Arctic can lead to drastic temperature increases and sea ice melt. On the other hand, it has been suggested that the location of the sea ice edge can influence the tracks of cyclones. Therefore, it is crucial to understand what controls cyclone tracks through the Barents Sea into the Arctic to explain and potentially predict climate variability at high latitudes.

To address this question, we track cyclones from 1979 to 2018 in the ERA-Interim data set, characterizing and quantifying them depending on their genesis location and path. The focus is on cyclones entering the Barents Sea from the North Atlantic as they carry the most moisture into the Arctic. Despite a clear declining trend in sea ice in the Barents Sea, our results show neither significant changes in cyclone frequency nor in their tracks. However, we find that the large-scale flow and in particular the presence or absence of blocking in the Barents Sea influence the cyclone frequency in this region, providing a potential mechanism that controls high latitude climate variability.

How to cite: Madonna, E., Hes, G., Michel, C., Li, C., and Siew, P. Y. F.: Understanding cyclone variability in the Barents Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10690, https://doi.org/10.5194/egusphere-egu2020-10690, 2020.

The impact of the Atlantic water inflow (AW inflow) into the Barents Sea on the regional cyclone activity in winter is analyzed in 10 ensemble simulations with the coupled Arctic atmosphere-ocean-sea ice model HIRHAM-NAOSIM for the 1979–2016 period. The model shows a statistically robust connection between AW inflow and climate variability in the Barents Sea. The analysis reveals that anomalously high AW inflow leads to changed baroclinicity in the lower troposphere via changed static stability and wind shear, and thus favorable conditions for cyclogenesis in the Barents/Kara Seas. The frequency of occurrence of cyclones, but particularly of intense cyclones, is increased over the Barents Sea. Furthermore, the cyclones in the Barents Sea become larger (increased radius) and stronger (increased intensity) in response to an increased AW inflow into the Barents Sea, compared to years of anomalously low AW inflow.

The authors acknowledge the support by the Russian-German project funded by the Federal Ministry of Education and Research of Germany and Ministry of Science and Higher Education of the Russian Federation (grant 05.616.21.0109 (RFMEFI61619X0109)).

How to cite: Akperov, M., Semenov, V. A., Mokhov, I. I., Dorn, W., and Rinke, A.: Impact of Atlantic water inflow on winter cyclone activity in the Barents Sea: Insights from coupled regional climate model simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-456, https://doi.org/10.5194/egusphere-egu2020-456, 2020.

The winter North Atlantic oscillation (NAO), is a crucial part of our understanding of Eurasian and Atlantic climate variability and predictability. However, both the statistical forecast model and the coupled model showed the limited forecasting skill for the winter NAO. In this study, we developed effective prediction schemes based on the interannual increment prediction method and verified their performance based on the climate hindcasts of the coupled ocean–atmosphere climate models（DEMETER, ENSEMBLES，CFSV2）. This approach utilizes the year-to-year increment of a variable (i.e. a difference in a variable between the current year and the previous year, e.g. DY of a variable) as the predictand rather than the anomaly of the variable. The results demonstrate that the new schemes can generally improve prediction skill of the winter NAO compared to the raw coupled model’s output（DEMETER, ENSEMBLES，CFSV2）. Also, the new schemes show higher skill in prediction of abnormal NAO cases than the climatological prediction. Scheme-I uses just the NAO in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme-II is a hybrid prediction model that contains two predictors: the NAO derived from the coupled models, and the observed preceding autumn Atlantic sea surface temperature in the form of year-to-year increments. Scheme-II shows an even better prediction skill of the winter NAO than Scheme-I. Besides, a new statistical forecast scheme was also developed using observed North Atlantic sea surface temperature and Eurasian snow cover in the preceding autumn to predict the upcoming winter NAO. The statistical prediction model showed high predictive skill in reproducing the interannual and interdecadal variability of NAO in boreal winter.

How to cite: Fan, K.: Skillful prediction of the winter North Atlantic Oscillation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3872, https://doi.org/10.5194/egusphere-egu2020-3872, 2020.

According to the reanalysis data of recent years, the Siberian snow-albedo feedback is found to play a crucial role on the spring East Asian dust cycle by influence local energy budget and circulation. By analyzing the CESM Last Millennium Ensemble conducted by National Center for Atmospheric Research (NCAR), we found that the spring East Asian dust burden is significantly correlated with the snow-albedo over Siberia during the past millennium. The correlation coefficient between the snow depth over Siberia and the East Asian dust burden reaches to 0.56. The cloud fraction over Siberia is also correlated with the dust burden with a coefficient of 0.40. The Siberian snow cover reflects shortwave radiation and cools down the lower and middle troposphere, which leads to more clouds and snows occurring over Siberia. The increased cloud cover therefore reflects more shortwave to cool down the surface as a positive feedback. The cooling of lower troposphere over Siberia induces cyclonic wind anomalies around the region, enhances the westerly winds over the East Asian deserts which locate on the south side of Siberia and finally promotes the East Asian dust cycle.

How to cite: Liu, H.: The teleconnection between the Siberian snow-albedo feedback and the spring East Asian dust cycle : based on Last Millennium Ensemble, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4673, https://doi.org/10.5194/egusphere-egu2020-4673, 2020.

We show evidence that tropical atmospheric variability over the central tropical Pacific modulates the circulation over the western Arctic and the North Atlantic-European sector, impacting the sea ice concentration over the Arctic and the summer precipitation especially over Nordic European countries (NEC). Our results, based on the ERA5 reanalysis, suggest the occurrence of a teleconnection mechanism (similar to the Pacific North American pattern) between the tropical Pacific in early spring and summer precipitation over NEC, and we propose two indices as predictors for NEC summer precipitation based on geopotential height anomalies at 500hPa over the western tropical Pacific during March. After successfully cross-validate an empirical model with both indices as predictors, we show that these indices allow predicting the observed tercile of summer precipitation over big portions of NEC in most of the summers within the 1979-2018 period, with a Heidke skill score greater than 90%.

Furthermore, we analysed CMIP6 simulations, and we found that models that show strong ENSO variability, reproduce the observed link of tropical variability in early spring with precipitation over NEC and ice concentration over the Arctic. In turn, CMIP6 simulations with weak ENSO variability fail to reproduce this observed connection.

How to cite: Fuentes Franco, R. and Koenigk, T.: Tropical sources of predictability for summer precipitation over Nordic European countries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5576, https://doi.org/10.5194/egusphere-egu2020-5576, 2020.

Natural variability of Arctic climate is studied on the basis of preindustrial run with climate model INM-CM5-0.  The length of run is 1200 years. Temperature in Arctic shows significant peaks at periods of 60 and 15 years. Model climate oscillations are studied using technique of calculation of energy generation and impact to phase change.

60-year oscillation is generated mainly by advection of Atlantic water to Arctic ocean. Anomaly of oceanic currents associated with the oscillation are generated by gradients of density. Before warm phase there is negative anomaly of density near coasts and continental slope. This leads to enhancing of Atlantic water inflow to Arctic ocean, warming, increasing of density near slope and turning to negative phase of oscillation. Cyclonic vorticity over warm Bartents and Kara seas leads to wind currents that enhance inflow of Atlantic water to Arctic.

15-year oscillation is also generated by advection of Atlantic water to Arctic ocean, but anomalies of currents are generated mainly by wind stress. Before warm Arctic we have cold and fresh North Atlantic, that leads to positive NAO, it induces wind currents that transport more Atlantic water to Arctic ocean. This leads to Arctic warming, decrease of NAO and turn to opposite phase of oscillation. Warming of North Atlantic happens 3-4 years after maximum of Arctic warming. The response of Atlantic meridional streamfunction to the oscillation is studied.

"Ideal model" potential predictability experiments started from synthetic state preceding warm Arctic (cold and fresh North Atlantic) show that this oscillation can be predicted for time interval up to 10 years.

How to cite: Volodin, E.: The mechanism of 60-year and 15-year Arctic climate oscillations in climate model INM-CM5-0, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7265, https://doi.org/10.5194/egusphere-egu2020-7265, 2020.

The air temperature over Arctic sea ice can fall strongly below 0°C, while for adjacent areas of open water, sea surface temperature remains close to freezing. This creates a strong temperature gradient across the sea ice edge. Transports of cold air masses from the sea ice toward open ocean water, known as marine cold air outbreaks (MCAOs), modify vertical stability of the atmospheric column and thus can create conditions favorable for the formation of hazardous maritime cyclones (polar lows), which pose risks to marine and coastal infrastructure and society. For marine management, MCAO predictions would be highly beneficial. Previous studies analyze the genesis of MCAOs, while predictability and large-scale drivers of MCAOs remain poorly understood. 

We investigate (i) the ability of the Earth System Model from the Max-Planck Institute for Meteorology (MPI-ESM) to predict MCAOs at a seasonal timescale and (ii) options to improve predictability of MCAOs through their large-scale drivers. To identify MCAO preconditions, we utilize the atmospheric reanalysis ERA-Interim using lagged cross-correlation analysis, composite analysis, and causal effect network (CEN).

Our results show that the MPI-ESM has high prediction skill for MCAOs over the Barents Sea (BS), Greenland-Iceland-Norwegian Seas and the Labrador Sea for about 2-2.5 weeks ahead starting from the November and February initial conditions. This holds for the prediction skill analyzed from daily model output. For MCAO properties such as extreme MCAO values occurring during a month, or the frequency of MCAO events per month, we find high prediction skill for up to a month ahead. Whereas the lagged cross-correlation analysis indicates a relationship between September and October atmospheric circulation and sea ice conditions with November BS-MCAOs, the CEN identifies the causal link only from the Arctic sea ice cover.

How to cite: Polkova, I., Afargan-Gerstman, H., Domeisen, D., King, M., Ruggieri, P., Athanasiadis, P., Dobrynin, M., and Baehr, J.: Autumn Arctic predictors and predictions for winter marine cold air outbreaks over the Barents Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7280, https://doi.org/10.5194/egusphere-egu2020-7280, 2020.

How to cite: Niraula, B.: Sea-ice edge forecast using damped persistence of probability anomaly, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7502, https://doi.org/10.5194/egusphere-egu2020-7502, 2020.

The Arctic amplified warming under global warming is one of the prominent climate change events during the past several decades. Arctic sea ice retreat contributed the majority of the near-surface warming, and little to the mid-troposphere warming. The remote factors might contribute to or modulate the aloft Arctic warming.

Here we performed a multi-model joint-analysis to study the role of the Pacific decadal oscillation, which is one of the most important recurring ocean-atmosphere variability in the climate system, in the tropospheric Arctic warming. In the multi-model simulation, PDO reduced the Arctic warming trend during 1979-2013 significantly in spring, Autumn and early winter season from the near-surface to the upper troposphere. The reduction of warming reaches 0.3 / 0.2 °C per decade in the upper / lower troposphere.

How to cite: Suo, L., Gao, Y., Gastineau, G., Liang, Y.-C., Ghosh, R., Tian, T., and Zhang, Y.: Modulation of PDO in the Arctic tropospheric warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9281, https://doi.org/10.5194/egusphere-egu2020-9281, 2020.

The skill of the decadal climate prediction is analyzed based on recent ensemble experiments from the CMIP5 and CMIP6 decadal climate prediction projects (DCPP) and the Community Earth System Model (CESM) Large Ensemble (LENS) Project. The experiments are initialized every year at November 1 for the period of 1960-2005 in the CMIP5 DCPP experiments and 1960-2016 for the CMIP6 DCPP models as well as the CESM LENS decadal prediction. The CMIP5/6 ensemble has 10 members for each model and the CESM ensemble has 40 members. For the considered models un-initialized (historical) ensembles with the same forcings exist. The advantage of initialization is analyzed by comparing these two sets of experiments.

We find that the models agree that for lead-times between 4-10 years little effect of initialization is found except in the North Atlantic sub-polar gyre region (NASPG). This well-known result is found for all the models and is robust to temporal and spatial smoothing. In the sub-polar gyre region the ensemble mean of the forecast explains 30-40 % more of the observed variance than the ensemble mean of the historical non-initialized experiments even for lead-times of 10 years.

However, the skill in the NASPG seems to a large degree to be related to the shift towards warmer temperatures around 1996. Weak or no skill is found when the sub-periods before and after 1996 are considered. We further analyze the characteristics of other climate indicators than surface temperature as well as the NAO to understand the cause and implication of the prediction skill.

How to cite: Yang, S. and Christiansen, B.: The decadal climate prediction skill with focus on the North Atlantic region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11243, https://doi.org/10.5194/egusphere-egu2020-11243, 2020.

In this work, we make use of an inter-model comparison and of a perfect model approach, in which model outputs are used as true reference states, to assess the impact that denying sea ice information has on the prediction of atmospheric processes, both over the Arctic and at mid-latitude regions. To do so, two long-term control runs (longer than 250 years) were generated with two state-of-the-art General Circulation Models (GCM), namely EC-Earth and HadGEM. From these two reference states, we have identified three different years in which the Arctic sea ice volume (SIV) was (i) maximum, (ii) minimum and (iii) a representative case for the mean state. By departing from each of these three dates (not necessarily the same for the two models), we generated a set of experiments in which the control runs are restarted both from original and climatological sea ice conditions. Here, climatological sea ice conditions are estimated as the time-average of sea ice parameters from the respective long-term control runs. The experiments are 1-year long and all of them start in January when ice is still thin, snow depth is small, air-ocean temperatures contrast the most and, therefore, the heat conductive flux in sea ice (at the surface) is nearly maximum. To robustly separate the response to degrading the initial sea ice state from background internal variability, each of the two counterfactual experiments (reference and climatological) consists of 50 ensembles members. Threstatedese ensembles are generated by adding small random perturbations to the sea surface temperature (EC-Earth) or to the air temperature (HadGEM) fields. Preliminary results reinforce the importance of having the right sea ice state for improving the (sub-)seasonal prediction of atmospheric parameters (e.g., 2m-temperature and geopotential) and circulation (e.g., Westerlies and Jet Stream) not only over the Arctic, but also at mid-latitude regions.

How to cite: Ponsoni, L., Flocco, D., Massonnet, F., Delhaye, S., Hawkins, E., and Fichefet, T.: The impact of denying sea ice information on the predictability of atmospheric processes over the Arctic and at mid-latitude regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11692, https://doi.org/10.5194/egusphere-egu2020-11692, 2020.

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.

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^st 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.

How to cite: Maslowski, W., Lee, Y., Craig, A., Seefeldt, M., Osinski, R., Cassano, J., and Clement Kinney, J.: Assessment of the Regional Arctic System Model Intra-Annual Ensemble Predictions of Arctic Sea Ice , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12849, https://doi.org/10.5194/egusphere-egu2020-12849, 2020.

The Arctic Oscillation (AO) is one of the main modes of variability of the Northern Hemisphere winter, also referred as Northern Annular Mode (NAM). The positive phase of the AO is characterized by warming/cooling over Northern Eurasia and the United States and cooling over Canada, especially over eastern Canada. Its positive phase is also characterized by very dry conditions over the Mediterranean and wet conditions over Northern Europe. A positive trend of the AO is observed for the period 1951-2011 and it is captured in CMIP5 models only when GHG-only forcing are included. In CMIP5 models the change expected is mostly mitigated by the effects of the aerosols. When considering AR5 scenarios, the AO is projected to become more positive in the future, though with a large spread among the models.

Overall the spread in the representation of the AO variability and trend is large also in experiments with present-day conditions, likely associated with the large internal variability. Unique tools to identify and measure the role of the internal variability in the model representation of the large-scale modes of variability are large ensembles where multiple members are built with different initial conditions.

Here we use the NCAR Community Model Large Ensemble (CESM-LE) composing the historical period (1920-2005) to the future (2006-2100) in a RCP8.5 scenario to measure the role of the internal variability in shaping AO variability and changes. Potential predictability of the AO index is quantified in the historical and future periods, evidencing how the members spread remain large without specific trends in these characteristics. Preliminary results indicate that the internal variability has large influence on the AO changes and related implications for the Northern Hemisphere climate.

How to cite: Cherchi, A., Oliveri, P., and van Delden, A.: Internal variability of the Arctic Oscillation and its projections, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13027, https://doi.org/10.5194/egusphere-egu2020-13027, 2020.

Planetary waves with zonal wavenumbers k ≤ 3 dominate poleward atmospheric energy transport and its associated Arctic warming and moistening impacts in reanalysis data. Previous work suggests planetary waves generated by tropical warm pool Sea-Surface Temperatures (SSTs) and midlatitude synoptic waves (k ≥ 4) can drive Arctic energy transport. Here, we investigate tropical and midlatitude drivers of Arctic planetary wave transport using an idealised aquaplanet model. First, we show that the zonally-symmetric model qualitatively captures the main characteristics of observed planetary wave transport, as well as its impacts in the Arctic. Next, we show that an idealised tropical warm pool, driven by regional SST forcing, amplifies but is not the dominant source of Arctic planetary wave transport. Finally, lag-regressions using reanalysis and model data suggest midlatitude synoptic waves compensate rather than drive Arctic planetary wave transport. The results do not support the simple geometric effect of midlatitude synoptic waves aliasing onto Arctic planetary waves on a sphere, but rather point towards more complex scale interactions and local drivers of Arctic planetary wave transport.

How to cite: Dunn-Sigouin, E., Li, C., and Kushner, P.: Investigating tropical and midlatitude drivers of Arctic atmospheric energy transport, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15262, https://doi.org/10.5194/egusphere-egu2020-15262, 2020.

The Last Interglacial period (130-115 ka BP, hereafter LIG) is often considered as a prime example to study the effect of warmer-than-present temperatures on polar ice sheets evolution. As the debate mainly focuses on the causes and tipping point of a potential collapse of the West Antarctic Ice Sheet (hereafter WAIS), few investigations examine the consequences of a wais collapse in terms of atmospheric circulation. However, a knowledge of the state of the atmosphere is necessary to use proxy data recorded in ice cores. By analysing a new ice core drilled in Skytrain ice rise and using climate modeling, the WACSWAIN (WArm Climate Stability of West Antarctic ice sheet in the last Interglacial) aims to reconstruct WAIS extent during the LIG. Here, we use simulations from the atmospheric general circulation model HadCM3 with different WAIS configurations. We show that changes in temperature are directly linked to changes in orography through thermodynamic effects, as well as a linear sea ice extent rise over the Pacific Ocean with the WAIS reduction explained by a reversal of meridional winds turning southwards as the WAIS disappears. At the Skytrain ice rise, we show that not only the isotopic thermometer can be applied, but we also suggest that the water stable isotope record imprinted in the ice core will allow us to quantify the wais reduction.

How to cite: Goursaud, S., Sime, L., and Wolff, E.: Inferring the Last Interglacial West Antarctic Ice Sheet from the coupling of an ice core water stable isotope record and an atmospheric general circulation model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16787, https://doi.org/10.5194/egusphere-egu2020-16787, 2020.

Arctic sea ice variability and long-term trend play a major role in affecting the climate of polar and lower latitudes via complex coupling with the polar atmospheric circulation and the North Atlantic Ocean circulation. Moreover, sea ice conditions in the Arctic have direct impacts on socio-economy (e.g. the key shipping regions) and on the ecosystem. Understanding and improving predictions of Arctic sea ice on seasonal to decadal time scales is therefore crucial. We investigate the skill of decadal climate prediction simulations of the EC-Earth3 model (T255L91, ORCA1L75) with a focus on Arctic sea ice. In line with the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP), we launched 59 hindcasts/forecasts from 1960 to 2018. Each hindcast/forecast has 15 ensemble members which were initialized on 1 November and integrated for 10 years (+ 2 months). Anomaly initialization approach for the ocean and sea-ice (based on data from the ORA-S5-reanalysis) and full-field initialization for the atmosphere/land surface (based on ERA-Interim/ERA-Land) were applied. We first present a comparison of our hindcasts to observations for global key parameters and provide quantitative estimates of hindcast skill by using common deterministic metrics such as correlation and the Mean Squared Error Skill Score. We focus particularly on the skill regarding sea ice concentration and area in the Arctic’s sub-basins and its relation to the temperature and circulation of lower troposphere as well as the mean state of the ocean outside the Arctic. We also explore relevant processes and how the ocean state and natural climate variability can affect our prediction skills to improve the prediction system.

How to cite: Karami, P., Kruschke, T., Tian, T., Koenigk, T., and Yang, S.: Prediction skill of Arctic sea ice in decadal climate simulations of the EC-Earth3 model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17838, https://doi.org/10.5194/egusphere-egu2020-17838, 2020.

The Sea Ice Drift Forecast Experiment (SIDFEx) is a Year of Polar Prediction (YOPP) community effort to solicit, collect, and analyze sea ice drift forecasts, based on various methods, on a regular basis. SIDFEx is inspired by research and operational needs to forecast future positions of assets drifting in Arctic sea ice. Beside a number of sea-ice buoys of the International Arctic Buoy Programme, current targets include the MOSAiC drift campaign main site (and distributed network) for which consensus forecasts are delivered every six hours. A systematic assessment of real drift forecasting capabilities across operational and research forecast systems is meant to improve our physical understanding of sea ice and to identify and resolve model shortcomings.

Since the launch of SIDFEx in 2017, thirteen groups have started contributing drift forecasts to SIDFEx on a regular basis. Most groups derive their 2-days to seasonal-range forecasts by means of diagnostic tracking based on prediction drift fields of coupled or uncoupled general circulation models. Some groups submit ensembles of drift trajectories instead of single (deterministic) trajectories, and several groups submit their forecasts in real-time. We present results from around 75,000 individual forecasts, how they have been used for real-time support of the MOSAiC Arctic drift campaign since autumn 2019, and what they reveal about current models' capabilities to forecast sea-ice drift and deformation.

How to cite: Goessling, H. F. and the SIDFEx Team: Making Use and Sense of 75,000 Forecasts of the Sea Ice Drift Forecast Experiment (SIDFEx), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18867, https://doi.org/10.5194/egusphere-egu2020-18867, 2020.

In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.

While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.

Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.

How to cite: Koenigk, T. and Dekker, E.: Sea ice representation in CMIP6 simulations with EC-Earth3-Veg, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20372, https://doi.org/10.5194/egusphere-egu2020-20372, 2020.

Spatial predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. However, with sea ice variability likely to increase under continued anthropogenic warming, increasingly complex tools are required in order to make accurate forecasts. In this study, predictions of both Arctic and Antarctic summer sea ice extents are made using a complex network statistical approach. This method exploits statistical relationships within geo-spatial time series data in order to construct regions of spatio-temporal homogeneity -- nodes, and subsequently derive teleconnection links between them. The nodes and links of the networks here are generated from monthly sea ice concentration fields in June(November), July(December) and August(January) for Arctic(Antarctic) forecasts, hence lead times extend from 1 to 3 months. Network information is then utilised within a linear Gaussian Process Regression forecast model; a Bayesian inference technique. Network teleconnection weights are used to generate priors over functions in the form of a random walk covariance kernel; the hyperparameters of which are determined by the empirical Bayesian approach of type-II maximum likelihood. We also show predictions of all other months in order to ascertain the presence of a spring predictability barrier in observational data, for both hemispheres.

How to cite: Gregory, W., Tsamados, M., Stroeve, J., and Sollich, P.: Random Walks through Climate Networks: Sea Ice Prediction with Bayesian Inference, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20595, https://doi.org/10.5194/egusphere-egu2020-20595, 2020.

Sea ice is a critical component of the Earth system, playing an important role in high-latitude
surface radiation balance and heat, moisture and momentum exchange between atmosphere
and ocean. In recent years, rapid changes have been occurring in Arctic sea ice, including
decline in ice extent/area, decreasing in ice thickness and volume, and shifting towards a first-
year ice (FYI) dominated, rather than multi-year ice (MYI) dominated ice pack. These are one
of the most well-known and striking examples of climate change. However, representing
these changes in the model is still in question since most of our knowledge is based on MYI.
CICE is a sea ice model developed at Los Alamos National Laboratory since 1994. It is
widely used to simulate the growth, melt and movement of sea ice, and to resolve the
biogeochemical processes. Its column version, Icepack, has been separated from CICE after
CICE V5.1.2, which provides additional opportunity for simulating the evolution of drifting
sea ice floes. How about the representation of sea ice in a column model (Icepack) and a 3d
model (CICE)? In 2012, an ice mass balance buoy (IMB) and a Spectral Radiation Buoy
(SRB) were deployed on FYI near the North Pole, and later drifted towards Fram Strait. These
buoys collected a complete summer melt season of in-band (350-800 nm) spectral solar
radiation and sea ice mass balance data. In this study, we apply the Icepack (version 1.1.1)
and CICE (version 5.1.2) to investigate the seasonal evolution of sea ice in 2012 in these two models, and
assess how well the physical processes are represented in CICE and Icepack, with the focus
on the surface changes.

How to cite: Wang, C., Granskog, M. A., Debernard, J. B., and Wang, K.: Sea ice representation in sea ice model of CICE and Icepack, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21935, https://doi.org/10.5194/egusphere-egu2020-21935, 2020.

The changes in Eurasian vegetation not only have important effects on regional climate, but also have effects on global temperatures and the carbon cycle. In this study, the interannual linkage between spring vegetation growth over Eurasia and winter sea-ice cover over the Barents Sea (SICBS), as well as the prediction of spring Euraisan vegetation are investigated. The Normalized Difference Vegetation Index (NDVI) derived from the advanced very high resolution radiometer is used as the proxy of vegetation growth. During 1982–2015, the winter SICBS is significantly correlated with the spring NDVI over Eurasia (NDVIEA). The positive (negative) winter SICBS anomalies tend to increase (decrease) the spring NDVIEA. The increased winter SICBS corresponds to higher winter surface air temperature and soil temperature over most parts of Eurasia, and in turn, corresponds to less winter snow cover and less snow water equivalent. The persistent less and thinner snow cover from winter to spring over Eurasia, especially over Western and Central Siberia, tends to induce increased surface air temperature through decreased surface albedo and less snowmelt latent heat. Subsequently, the increased surface air temperature corresponding to increased SICBS contributes to higher vegetation growth over Eurasia in spring and vice versa. Based on this linkage, seasonal predictions of spring NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter SICBS, winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

How to cite: Ji, L. and Fan, K.: Winter arctic sea-ice cover variability and the prediction of spring vegetation growth over Eurasia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22339, https://doi.org/10.5194/egusphere-egu2020-22339, 2020.