Sea Ice Concentration Distribution Research Articles

A Comparison of the Impacts of Two Consecutive Double-Peaked La Niña Events on Antarctic Sea Ice in Austral Spring

Abstract Among nine La Niña events since 1980, there are seven double-peaked La Niña events that typically persist for 2 years and peak twice in the two consecutive boreal winters. In the study, the individual impacts of the first and second peak episodes of such La Niña on the Antarctic sea ice in austral spring (September–November) were compared. The results suggest a difference. The first episode induces a tripolar distribution of sea ice concentration (SIC) with a negative anomaly in the Bellingshausen Sea sandwiched with positive anomalies in the Ross Sea and the northeastern Weddell Sea. The second causes an SIC reduction in most parts of the Southern Ocean except for the eastern Ross–western Amundsen Seas where an increase is observed. Mechanistically, the first episode forces one single Rossby wave train to propagate southeastward, causing a strong cyclone anomaly over the eastern Ross–Amundsen–Bellingshausen Seas along with a weak anticyclone over the Weddell Sea. In comparison, the second La Niña excites two branches of Rossby wave trains emanating from the southeastern tropical Indian Ocean and the central equatorial Pacific, respectively, which induce three anomalous anticyclones and two anomalous cyclones over the Southern Ocean. These different atmospheric circulation anomalies shape their different sea ice distributions between the two La Niña episodes through both dynamic and thermodynamic processes. The modeling results from CAM5 verify these differences. Significance Statement Under global warming, the double-peaked La Niña occurs more frequently. The first and second La Niña episodes in such double-peaked La Niña are distinct from each other not only in their onset and developing mechanisms but also in their climate impacts. Based on observational analyses and model experiments, the study investigated the distinctive impacts of the first and second episodes on the austral spring Antarctic sea ice. The results reveal that the first episode excites one single southeastward-propagated Rossby wave train, while the second episode forces two branches of Rossby wave trains. These different atmospheric responses in the Southern Hemisphere shape the distinct sea ice distributions both dynamically and thermodynamically. The study also indicates the diversity of tropical–Antarctic teleconnections.

Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2.

Antarctic Sea Ice Area in CMIP6

AbstractFully coupled climate models have long shown a wide range of Antarctic sea ice states and evolution over the satellite era. Here, we present a high‐level evaluation of Antarctic sea ice in 40 models from the most recent phase of the Coupled Model Intercomparison Project (CMIP6). Many models capture key characteristics of the mean seasonal cycle of sea ice area (SIA), but some simulate implausible historical mean states compared to satellite observations, leading to large intermodel spread. Summer SIA is consistently biased low across the ensemble. Compared to the previous model generation (CMIP5), the intermodel spread in winter and summer SIA has reduced, and the regional distribution of sea ice concentration has improved. Over 1979–2018, many models simulate strong negative trends in SIA concurrently with stronger‐than‐observed trends in global mean surface temperature (GMST). By the end of the 21st century, models project clear differences in sea ice between forcing scenarios.

Impact of Enthalpy-Based Ensemble Filtering Sea Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model

AbstractThe non-Gaussian probability distribution of sea ice concentration makes it difficult to directly assimilate sea ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea ice state, any direct assimilation adjustment on sea ice states is easily overridden by model physics. A new approach implements sea ice data assimilation in enthalpy space where a sea ice model represents a nonlinear function that transforms a positive-definite space into the sea ice concentration subspace. Results from observation–assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea ice observations” into the model: while improving sea ice variability itself, it consistently improves the estimates of all “climate” components. The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model.

Interdecadal variability of the East Asian summer monsoon in an AGCM

It is well known that significant interdecadal variation of the East Asian summer monsoon (EASM) occurred around the end of the 1970s. Whether these variations can be attributed to the evolution of global sea surface temperature (SST) and sea ice concentration distribution is investigated with an atmospheric general circulation model (AGCM). The model is forced with observed monthly global SST and sea ice evolution through 1958–1999. A total of four integrations starting from different initial conditions are carried out. It is found that only one of these reproduces the observed interdecadal changes of the EASM after the 1970s, including weakened low-level meridional wind, decreased surface air temperature and increased sea level pressure in central China, as well as the southwestward shift of the western Pacific subtropical high ridge and the strengthened 200-hPa westerlies. This discrepancy among these simulated results suggests that the interdecadal variation of the EASM cannot be accounted for by historical global SST and sea ice evolution. Thus, the possibility that the interdecadal timescale change of monsoon is a natural variability of the coupled climate system evolution cannot be excluded.

Sensitivity of numerical prognoses to Antarctic sea ice distribution

To examine the effect of Antarctic sea ice concentration upon the atmosphere on synoptic timescales, a general circulation model was used in conjunction with the Australian Bureau of Meteorology's Global Assimilation and Prediction system analyses and the Defense Meteorological Satellite Program's Special Sensor Microwave/Imager sea ice data to perform a suite of 5‐day forecasts. As well as using a generic July sea ice concentration, further experiments were conducted with 0, 10, 25, 50, 80, and 100% sea ice concentrations. The 100% forecast was used as the control. Results show that the central pressure, structure and tracks of individual cyclones are sensitive to the “switch on” of different sea ice conditions. Composites of all forecasts made with each concentration showed considerable and statistically significant anomalies in the surface temperatures and turbulent heat fluxes over the sea ice. The magnitudes of these changes varied monotonically with the area of open water. The largest changes were simulated closest to the coast for all concentrations, except for the generic July sea ice run, which displayed maxima over the outer pack. Significant westerly anomalies were also induced over the ice in all cases. Mean sea level pressure (MSLP) anomalies occurred in all forecasts. Again, the generic July sea ice run displayed a distribution of the MSLP anomaly different from all other runs, with maxima occurring in the central to outer pack. All other forecasts displayed maxima at the coast. These results suggest that sea ice concentration induces anomalies in the atmospheric parameters in timescales of less than 5 days. Further, use of a realistic distribution of sea ice concentration produces results distinct from the constant concentration forecasts. Hence it is suggested that real‐time Antarctic sea ice data may be of considerable benefit to numerical weather prediction and analysis.