Future circulation changes off West Antarctica: Sensitivity of the Amundsen Sea Low to projected anthropogenic forcing

The Amundsen Sea Low (ASL) is a major driver of West Antarctic climate variability, with the potential to accelerate the loss of glacial ice. Using the 11 global climate models which most reliably simulate the seasonality in ASL location, we assess the ASL sensitivity to projected future changes using the CMIP5 historical (1951–2000) and representative concentration pathway experiment RCP8.5 (2051–2100). For the first time, we show that the future ASL will likely migrate poleward in summer (December, January, and February) and autumn (March, April, and May), and eastward in autumn and winter (June, July, and August). The autumn-winter changes drive colder southerly winds over the Ross Sea and warmer northerly winds toward the Antarctic Peninsula. This is consistent with recent trends in ERA-Interim reanalysis meridional winds (1979–2014) and reconstructed temperature (1957–2006), suggesting that the impact of anthropogenic forcing on the ASL is likely to play an important role on both past and future patterns of West Antarctic climate variability

The Amundsen Sea low

We develop a climatology of the Amundsen Sea low (ASL) covering the period 1979–2008 using ECMWF operational and reanalysis fields. The depth of the ASL is strongly influenced by the phase of the Southern annular mode (SAM) with positive (negative) mean sea level pressure anomalies when the SAM is negative (positive). The zonal location of the ASL is linked to the phase of the mid-tropospheric planetary waves and the low moves west from close to 110°W in January to near 150°W in June as planetary waves 1 to 3 amplify and their phases shift westwards. The ASL is deeper by a small, but significant amount, during the La Niña phase of El Niño-Southern Oscillation (ENSO) compared to El Niño. The difference in depth of the low between the two states of ENSO is greatest in winter. There is no statistically significant difference in the zonal location of the ASL between the different phases of ENSO. Over 1979–2008 the low has deepened in January by 1.7 hPa dec−1 as the SAM has become more positive. It has also deepened in spring and autumn as the semi-annual oscillation has increase in amplitude over the last 30 years. An increase in central pressure and eastward shift in March has occurred as a result of a cooling of tropical Pacific SSTs that altered the strength of the polar front jet

Regional disparities and seasonal differences in climate risk to rice labour.

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p ≪ 0.01, R 2 > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress

Twentieth century increase in snowfall in coastal West Antarctica

The Amundsen Sea sector of the West Antarctic ice sheet has been losing mass in recent decades; however, long records of snow accumulation are needed to place the recent changes in context. Here we present 300 year records of snow accumulation from two ice cores drilled in Ellsworth Land, West Antarctica. The records show a dramatic increase in snow accumulation during the twentieth century, linked to a deepening of the Amundsen Sea Low (ASL), tropical sea surface temperatures, and large-scale atmospheric circulation. The observed increase in snow accumulation and interannual variability during the late twentieth century is unprecedented in the context of the past 300 years and evidence that the recent deepening of the ASL is part of a longer trend

Kernel Learning for Explainable Climate Science

The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes from the complex spatial-temporal distribution of precipitation across the basin. In this work we propose Gaussian processes with structured non-stationary kernels to model precipitation patterns in the UIB. Previous attempts to quantify or model precipitation in the Hindu Kush Karakoram Himalayan region have often been qualitative or include crude assumptions and simplifications which cannot be resolved at lower resolutions. This body of research also provides little to no error propagation. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. This allows the posterior function samples to adapt to the varying precipitation patterns inherent in the distinct underlying topography of the Indus region. The input dependent lengthscale is governed by a latent Gaussian process with a stationary squared-exponential kernel to allow the function level hyperparameters to vary smoothly. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint spatio-temporal reconstruction. We benchmark our model with a stationary Gaussian process and a Deep Gaussian processes.Comment: 16th Bayesian Modelling Applications Workshop at UAI, 2022 (Eindhoven, Netherlands

Recent changes in Antarctic sea ice

In contrast to the Arctic, total sea ice extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×103 km2 per decade (1.5% per decade; p<0.01) for 1979–2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen–Bellingshausen) Sea. Sea ice variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the ice concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross Sea sector, where the SIE is significantly correlated with the depth of the Amundsen Sea Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical sea surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating sea ice. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability

Antarctic sea ice increase consistent with intrinsic variability of the Amundsen Sea Low

We investigate the relationship between atmospheric circulation variability and the recent trends in Antarctic sea ice extent (SIE) using Coupled Model Intercomparison Project Phase 5 (CMIP5) atmospheric data, ECMWF Interim reanalysis fields and passive microwave satellite data processed with the Bootstrap version 2 algorithm. Over 1979–2013 the annual mean total Antarctic SIE increased at a rate of 195 × 103 km2 dec−1 (1.6 % dec−1), p < 0.01. The largest regional positive trend of annual mean SIE of 119 × 103 km2 dec−1 (4.0 % dec−1) has been in the Ross Sea sector. Off West Antarctica there is a high correlation between trends in SIE and trends in the near-surface winds. The Ross Sea SIE seasonal trends are positive throughout the year, but largest in spring. The stronger meridional flow over the Ross Sea has been driven by a deepening of the Amundsen Sea Low (ASL). Pre-industrial control and historical simulations from CMIP5 indicate that the observed deepening of the ASL and stronger southerly flow over the Ross Sea are within the bounds of modeled intrinsic variability. The spring trend would need to continue for another 11 years for it to fall outside the 2 standard deviation range seen in 90 % of the simulations

Summer Drivers of Atmospheric Variability Affecting Ice Shelf Thinning in the Amundsen Sea Embayment, West Antarctica

Satellite data and a 35-year hindcast of the Amundsen Sea Embayment summer climate using the Weather Research and Forecasting model are used to understand how regional and large-scale atmospheric variability affects thinning of ice shelves in this sector of West Antarctica by melting from above and below (linked to intrusions of warm water caused by anomalous westerlies over the continental shelf edge). El Nino episodes are associated with an increase in surface melt but do not have a statistically significant impact on westerly winds over the continental shelf edge. The location of the Amundsen Sea Low and the polarity of the Southern Annular Mode (SAM) have negligible impact on surface melting, although a positive SAM and eastward shift of the Amundsen Sea Low cause anomalous westerlies over the continental shelf edge. The projected future increase in El Nino episodes and positive SAM could therefore increase the risk of disintegration of West Antarctic ice shelves

Sea ice detection using concurrent multispectral and synthetic aperture radar imagery

Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatio-temporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery remains problematic due to the presence of ambiguous signal and noise within the image. Conversely, ice and water are easily distinguishable using multispectral imagery (MSI), but in the polar regions the ocean's surface is often occluded by cloud or the sun may not appear above the horizon for many months. To address some of these limitations, this paper proposes a new tool trained using concurrent multispectral Visible and SAR imagery for sea Ice Detection (ViSual\_IceD). ViSual\_IceD is a convolution neural network (CNN) that builds on the classic U-Net architecture by containing two parallel encoder stages, enabling the fusion and concatenation of MSI and SAR imagery containing different spatial resolutions. The performance of ViSual\_IceD is compared with U-Net models trained using concatenated MSI and SAR imagery as well as models trained exclusively on MSI or SAR imagery. ViSual\_IceD outperforms the other networks, with a F1 score 1.60\% points higher than the next best network, and results indicate that ViSual\_IceD is selective in the image type it uses during image segmentation. Outputs from ViSual\_IceD are compared to sea ice concentration products derived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight how ViSual\_IceD is a useful tool to use in conjunction with PMW data, particularly in coastal regions. As the spatial-temporal coverage of MSI and SAR imagery continues to increase, ViSual\_IceD provides a new opportunity for robust, accurate sea ice coverage detection in polar regions.Comment: 34 pages, 10 figures, 2 table