CL4.31

The Antarctic Oscillation (AAO) is the dominant mode of the southern extratropical atmospheric mass variability which has potential influences on the Northern Hemisphere (NH). This study reveals a significantly negative correlation between the September-October (SO) AAO index and the occurrence rate of following January-February (JF) wet and cold weather in the Middle and Lower Reaches of Yangtze River Basin (MLRY) in China. The latter is quantified by a Precipitation-Temperature (PT) Index. JF PT is modulated by both northerly air flow in the lower troposphere and southerly air flow in the lower-middle troposphere. The SO AAO stimulates Southern Ocean Dipole (SOD) pattern-like SST anomalies, which induces a North Atlantic Oscillation (NAO)-like atmospheric response in the following JF through ocean-air interaction. As for the northerly flow, the JF NAO-like pattern triggers an eastward propagating wave train, influencing the intensity of East Asian Winter Monsoon (EAWM) and subsequently the northerly cold flow to MLRY. As for southerly flow, the variation of JF SOD regulates the local meridional cell, in turn modulating the Middle East Jet Stream (MEJS) along with the NAO-like pattern, influencing the intensity of precipitation and the wet and warm flow over Southern China and the adjacent regions. In addition to the tropospheric processes, the stratospheric Quasi Biennial Oscillation (QBO) serves as the ‘bridge’ for linking SOD to NH climate, inducing the JF PT response to SOD SST. To summarize, SO AAO affects the JF PT in MLRY by modulating both cold-dry northerly air flow and warm-wet southerly air flow through ocean-atmosphere interactions and stratospheric pathway.

How to cite: Yuan, Z., Qin, J., Li, S., Huang, S., Mbululo, Y., and Rehman, A.: Impact of Boreal Autumn Antarctic Oscillation on Winter Wet and Cold Weather in East Asia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-351, https://doi.org/10.5194/egusphere-egu21-351, 2021.

The retreat of Arctic sea ice for the last four decades is a primary manifestation of the climate system response to increasing atmospheric greenhouse gas concentrations. This retreat is frequently considered as a possible driver of atmospheric circulation anomalies at mid-latitudes. However, the year-to-year evolution of the Arctic sea ice cover is also characterized by significant fluctuations attributed to internal climate variability. It is unclear how the atmosphere will respond to a near-total retreat of summer Arctic sea ice, a reality that might occur in the foreseeable future. This study uses sensitivity experiments  with higher and lower horizontal resolution configurations of three global coupled climate models to investigate the local and remote atmospheric responses to a reduction in Arctic sea ice cover during the preceding summer. Recognizing that these responses likely depend on the model itself and on its horizontal resolution, and that the model’s internally-generated climate variability may obscure the atmospheric response, we design a protocol to compare each source separately. After imposing a 15-month albedo perturbation resulting in a sudden summer Arctic sea ice loss, the remote mid-latitude climate response has a very low signal-to-noise ratio such that internal climate variability dominates the uncertainty of the response, regardless of the atmospheric variable. Indeed, more than 28, 165 and 210 members are needed to detect a robust response in surface air temperature, precipitation and sea level pressure to sea ice loss in Europe, respectively. Finally, we find that horizontal resolution plays a secondary role in the uncertainty of the atmospheric response to substantial perturbation of Arctic sea ice. These findings suggest that even with higher resolution model configurations, it is important to have large ensemble sizes to increase the signal to noise ratio for the mid-latitude atmospheric response to sea ice changes.

How to cite: Delhaye, S., Fichefet, T., Massonnet, F., Docquier, D., Roberts, C., Keeley, S., Senan, R., Msadek, R., Chripko, S., García-Serrano, J., and Bretonnière, P.-A.: Role of the internal climate variability in the atmospheric response to a sudden summer Arctic sea ice loss, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2528, https://doi.org/10.5194/egusphere-egu21-2528, 2021.

Assimilation of sea ice concentration satellite products has successfully been used to initialize sea ice models and coupled NWP systems. Sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. We have examined the potential for sea ice thickness observations to improve forecast skill on timescales from days to a year ahead in two state-of-the-art coupled GCMs.

Here we examine the influence of Arctic sea-ice thickness observations on the potential predictability of the sea-ice and atmospheric circulation using idealised ‘data denial’ experiments. We perform paired sets of ensembles with the HadGEM3 and EC-Earth GCMs using different initial conditions retrieved from present-day control runs.

One set of ensembles start with complete information about the sea-ice conditions and is treated as “truth”, and one set has degraded sea ice information. We investigate how the pairs of ensembles, all started in January, predict the subsequent evolution of the sea-ice state, sea level pressure and circulation within the Arctic with the aim of quantifying the value of sea-ice observations for improving predictions.

We show that accurate initialization of sea ice thickness improves the model prediction skill during the first month of simulation and that several sea ice state and atmospheric variables present a re-emergence of skill in September. Prediction skill of several oceanic variables is also observed. The two models present a good agreement in terms of the regions where they show either a skill gain or loss.

How to cite: Flocco, D., Hawkins, E., Ponsoni, L., Massonnett, F., Feltham, D., and Fichefet, T.: Sea ice and atmospheric potential predictability in coupled GCMs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2590, https://doi.org/10.5194/egusphere-egu21-2590, 2021.

The influence of Rossby wave sources (RWS) emitted on the Northeastern Pacific Ocean (NePO) in the Northern Hemisphere during summer is analysed in the ERA5 reanalysis and a large ensemble performed with the EC-Earth3 model. Using extreme years composites of precipitation, surface temperature and geopotential height, we found a causal influence of the Rossby waves generated over the NePO on a global climate response. Both the reanalysis ERA5 and the EC-Earth3 large ensemble show that RWS triggers wave-like patterns arising from the upper troposphere NePO region. We show that an increased Rossby wave sources intensity is related with a) negative temperature anomalies over western North America, b) positive temperature anomalies over eastern North America, c) increased precipitation over Northern Europe during summer and d) sea-ice concentration decrease in the Arctic.  We also show that the North Atlantic plays a very important role hindering or permitting that Rossby waves generated in the Pacific reach the Atlantic and modulate the atmospheric conditions over Europe. Such conditions were found in ERA5 and EC-Earth3 large ensemble during colder and icier conditions over the North Atlantic.

How to cite: Fuentes-Franco, R., Koenigk, T., Docquier, D., Graef, F., and Wyser, K.: Exploring the influence of the North Pacific Ocean Rossby wave sources on interannual variability of summer precipitation and surface temperature over the Northern Hemisphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3270, https://doi.org/10.5194/egusphere-egu21-3270, 2021.

Skillful sea-ice prediction in the Antarctic Ocean remains a big challenge due to paucity of sea-ice observations and insufficient representation of sea-ice processes in climate models. This study demonstrates that the Antarctic sea-ice concentration (SIC) prediction is significantly improved using a coupled general circulation model (SINTEX-F2) in which the model’s SIC and sea-ice thickness (SIT) are initialized with the ocean/sea-ice reanalysis product (C-GLORSv7). It is found that the wintertime SIT initialization adds positive values to the prediction skills of the summertime SIC, most effectively in the Weddell Sea where the SIT climatology and variability are the largest among the Antarctic Seas. Examination of the SIT balance during low sea-ice years of the Weddell Sea shows that negative SIT anomalies initialized in June retain the memory throughout austral winter (July-September) owing to horizontal advection of the SIT anomalies by sea-ice velocities. The negative SIT anomalies continue to develop in austral spring (October-December) owing to more incoming solar radiation via ice-albedo feedback and the associated warming of mixed layer. This results in further sea-ice decrease during austral summer (January-March). Concomitantly, the model reasonably reproduces atmospheric circulation anomalies in the Amundsen-Bellingshausen Seas as well as the Weddell Sea during the development of the negative sea-ice anomalies. These results provide solid evidence that the wintertime SIT initialization benefits skillful summertime sea-ice prediction in the Antarctic Seas.

How to cite: Morioka, Y., Iovino, D., Cipollone, A., Masina, S., and Behera, S.: Improved sea-ice prediction in the Weddell Sea using sea-ice thickness initialization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3638, https://doi.org/10.5194/egusphere-egu21-3638, 2021.

As climate warms sea ice loss may become a potent climate change feedback, both in the Arctic and at lower latitudes. For instance, extreme events over Europe and North America, such as drought or warm spells, have been attributed to sea ice minima in recent years. Yet a comprehensive understanding of the local or remote impact of sea ice loss on climate is lacking, with the predicted atmospheric and oceanic response to sea ice loss differing between climate studies. In particular, the impact of varying geographical distribution of sea ice loss on regional climatic changes remains uncertain.

Here, we assess the sensitivity of the atmospheric response to various patterns of sea ice loss, at a pan-Arctic or regional scale, by analyzing a set of idealised AMIP-like simulations. Depending on where sea ice is reduced, we find that climatic anomalies can vary widely among experiments, especially the zonal-mean component of the tropospheric circulation: for instance, the subpolar jet and polar cell can strengthen or weaken with sea ice loss, depending on its geographical distribution. We demonstrate that the geometry of the sea ice loss, in particular the degree to which sea ice extent changes is zonally symmetric or asymmetric, controls this disparate climatic response through an atmospheric feedback mechanism. In this feedback mechanism, changes in poleward eddy heat flux and latent heat release over the Arctic in response to a specific sea ice loss pattern can either warm or cool the Arctic troposphere. We discuss the implications of our results for interpreting the apparent discrepancies in the climate response to Arctic sea ice variability among studies.

How to cite: Levine, X., Cvijanovic, I., Ortega, P., Donat, M., and Tourigny, E.: A mechanism predicting the climate response to sea ice loss from its geometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7251, https://doi.org/10.5194/egusphere-egu21-7251, 2021.

Results from CMIP5 have previously suggested that ensemble regression techniques or model selection may provide solutions to the challenge of making projections of future Antarctic sea ice area (SIA) in the presence of large historical biases. Here, we revisit and extend such analysis incorporating the CMIP6 ensemble, which shows modest improvements in some aspects of sea ice simulation and in particular a reduction of inter-model spread in historical SIA. We focus on the strongest forcing scenarios analysed, CMIP5 RCP85 and CMIP6 SSP5.85.

In summer (February) the historical climatology of SIA is a strong linear constraint on projections of SIA in both generations. This is because the strong forcing leads to the loss of the majority of summer SIA in each model, so that the models that start with greater SIA exhibit greater reductions. Differences between CMIP5 and CMIP6 are largely explained by the fact that, compared to CMIP6, CMIP5 contains many more models that have very large positive biases in historical SIA and do not lose the majority of ice.

In winter (September), a much smaller proportion of SIA is lost, but inter-model spread in SIA climatology still explains just under half the variance in projections of SIA change, in both CMIP5 and CMIP6. The mean historical winter climatology is similar between generations, as is the regression slope of SIA change against SIA climatology.  However, there is a greater reduction of SIA in CMIP6 than CMIP5. We find this to be statistically related to greater global mean warming in CMIP6 than CMIP5, and therefore potentially to the larger climate sensitivity in the CMIP6 ensemble.

These findings imply that, depending on season, a different balance of local (SIA climatology) and global (GMST change) drivers can be used to explain the inter-model and inter-generation spread in projections of SIA loss. They also firmly tie our ability to project Antarctic SIA loss to our understanding of the fidelity of higher CMIP6 climate sensitivity. Questions remain about whether models are correct in their simulation of Antarctic SIA sensitivity to global surface temperature.

How to cite: Holmes, C., Bracegirdle, T., and Holland, P.: Does CMIP6 better constrain projections of 21st century Antarctic sea ice loss?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7586, https://doi.org/10.5194/egusphere-egu21-7586, 2021.

Polar lows (PLs) are important mesoscale (horizontal diameter up to 1000 km) maritime weather systems at high latitudes, forming pole ward from the polar front. We consider the possible prognostic criteria of PLs, in particular, the kinematic helicity as a quadratic characteristic related to the integral vortex formations and the kinematic vorticity number (KVN). To calculate such characteristics we use reanalysis data and the results of numerical simulation with the WRF-ARW model (Version 4.1.) for the PLs over the Nordic (Norwegian and Barents) seas. For comparison, experimental data are used.

Our estimate of helicity is based on the connection of an integral helicity (IH) in the Ekman layer with the geostrophic wind velocity, due to the good correlation between IH and half the sum of the wind velocity squared. We have chosen IH averaged over preselected area covering the locality of PLs genesis. This area was moving along with the centre of PL during the numerical simulation.

The genesis of PLs can be divided into three stages: (i) an initial development stage, in which a number of small vortices appear in a shear zone; (ii) a late development stage, characterized by the merger of vortices; (iii) a mature stage, in which only a single PL is present. Approximately one day before PL formation, a significant increase in helicity was observed. The average helicity bulk density of large-scale motions has values of 0.3 – 0.4 ms^-2. The local changes in helicity are adjacent to the front side of the PLs. The IH criterion described facilitates the identification of the PLs genesis area. For a more detailed analysis of the PL genesis, it is recommended to apply KVN, which is the additional indicator of PL size and intensity. At the moment of maximum intensity of PLs KVN can reach values of 12 – 14 units. The advantage of using KVN is also in its clear change directly in the centre of the emerging PLs, which allows to precisely indicates the limits of the most intense part of PLs.

The main challenge is to make the operational forecast of PLs possible through the selection of the prognostic integral characteristics of PLs, sufficient for PLs identification and for analysis of their size and intensity in a convenient, usable and understandable way. The criteria associated with vorticity and helicity are reflected in the PLs genesis and development quite clearly. At this time, such a claim is only a hypothesis, which must be tested using a larger set of cases. Future work will need to extend these analyses to other active PL basins. Also, it would be interesting to compare the representation of PLs by using any other criteria. It is intended to use our combined criteria as a precursor to machine learning-based PLs identification procedure where satellite image analysis and capture of particular cloud patterns are currently applied in most of the cases. It would eliminate the time consuming first stage of collecting data sets.

This work was supported by the Russian Science Foundation (project No. 19-17-00248).

How to cite: Vazaeva, N., Chkhetiani, O., and Kurgansky, M.: Prognostic criteria for Polar Lows, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12282, https://doi.org/10.5194/egusphere-egu21-12282, 2021.

Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high amounts of carbon emissions. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean sometime during the 2030s—much earlier than the average projection from the global climate models. Our results are also consistent with projections from bivariate regressions of sea ice extent and carbon emissions. 

How to cite: Rudebusch, G. and Diebold, F.: Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13126, https://doi.org/10.5194/egusphere-egu21-13126, 2021.

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. 

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. 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.

How to cite: Hay, S. and Kusnher, P.: The relative roles of Arctic and Antarctic sea-ice loss in the response to greenhouse warming, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16310, https://doi.org/10.5194/egusphere-egu21-16310, 2021.

How to cite: Rendfrey, T. and Payne, A.: Connecting Antarctic Sea Ice and Mid-latitude Precipitation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16393, https://doi.org/10.5194/egusphere-egu21-16393, 2021.