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We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface mass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979–2016). The updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation, a revised topography, tuned parameters in the cloud scheme to generate more precipitation towards the AIS interior and modified snow properties reducing drifting snow sublimation and increasing surface snowmelt. Comparisons of RACMO2 model output with several independent observational data show that the existing biases in AIS temperature, radiative fluxes and SMB components are further reduced with respect to the previous model version. The model-integrated annual average SMB for the ice sheet including ice shelves (minus the Antarctic Peninsula, AP) now amounts to 2229 Gt y−1, with an interannual variability of 109 Gt y−1. The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼ 5.5 km) AP simulation, results remain comparable to earlier studies. The updated model provides a new, high-resolution data set of the contemporary near-surface climate and SMB of the AIS; this model version will be used for future climate scenario projections in a forthcoming study.

In sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978–December 2010 reveal an overall positive trend in ice extents of 17 100 ± 2300 km2 yr−1. Much of the increase, at 13 700 ± 1500 km2 yr−1, has occurred in the region of the Ross Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has (like the Arctic) instead experienced significant sea ice decreases, with an overall ice extent trend of −8200 ± 1200 km2 yr−1. When examined through the annual cycle over the 32-yr period 1979–2010, the Southern Hemisphere sea ice cover as a whole experienced positive ice extent trends in every month, ranging in magnitude from a low of 9100 ± 6300 km2 yr−1 in February to a high of 24 700 ± 10 000 km2 yr−1 in May. The Ross Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but the magnitudes of the two trends differ, and in some cases these differences allow inferences about the corresponding changes in sea ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.

The Greenland ice sheet is one of the largest contributors to global mean sea-level rise today and is expected to continue to lose mass as the Arctic continues to warm. The two predominant mass loss mechanisms are increased surface meltwater run-off and mass loss associated with the retreat of marine-terminating outlet glaciers. In this paper we use a large ensemble of Greenland ice sheet models forced by output from a representative subset of the Coupled Model Intercomparison Project (CMIP5) global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The simulations are part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We estimate the sea-level contribution together with uncertainties due to future climate forcing, ice sheet model formulations and ocean forcing for the two greenhouse gas concentration scenarios RCP8.5 and RCP2.6. The results indicate that the Greenland ice sheet will continue to lose mass in both scenarios until 2100, with contributions of 90±50 and 32±17 mm to sea-level rise for RCP8.5 and RCP2.6, respectively. The largest mass loss is expected from the south-west of Greenland, which is governed by surface mass balance changes, continuing what is already observed today. Because the contributions are calculated against an unforced control experiment, these numbers do not include any committed mass loss, i.e. mass loss that would occur over the coming century if the climate forcing remained constant. Under RCP8.5 forcing, ice sheet model uncertainty explains an ensemble spread of 40 mm, while climate model uncertainty and ocean forcing uncertainty account for a spread of 36 and 19 mm, respectively. Apart from those formally derived uncertainty ranges, the largest gap in our knowledge is about the physical understanding and implementation of the calving process, i.e. the interaction of the ice sheet with the ocean.

There are an increasing number of digital elevation models (DEMs) available worldwide for deriving elevation differences over time, including vertical changes on glaciers. Most of these DEMs are heavily post-processed or merged, so that physical error modelling becomes difficult and statistical error modelling is required instead. We propose a three-step methodological framework for assessing and correcting DEMs to quantify glacier elevation changes: (i) remove DEM shifts, (ii) check for elevation-dependent biases, and (iii) check for higher-order, sensor-specific biases. A simple, analytic and robust method to co-register elevation data is presented in regions where stable terrain is either plentiful (case study New Zealand) or limited (case study Svalbard). The method is demonstrated using the three global elevation data sets available to date, SRTM, ICESat and the ASTER GDEM, and with automatically generated DEMs from satellite stereo instruments of ASTER and SPOT5-HRS. After 3-D co-registration, significant biases related to elevation were found in some of the stereoscopic DEMs. Biases related to the satellite acquisition geometry (along/cross track) were detected at two frequencies in the automatically generated ASTER DEMs. The higher frequency bias seems to be related to satellite jitter, most apparent in the back-looking pass of the satellite. The origins of the more significant lower frequency bias is uncertain. ICESat-derived elevations are found to be the most consistent globally available elevation data set available so far. Before performing regional-scale glacier elevation change studies or mosaicking DEMs from multiple individual tiles (e.g. ASTER GDEM), we recommend to co-register all elevation data to ICESat as a global vertical reference system.

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

In recent years, airborne microplastics have been identified in a range of remote environments. However, data throughout the Southern Hemisphere, in particular Antarctica, are largely absent to date. We collected snow samples from 19 sites across the Ross Island region of Antarctica. Suspected microplastic particles were isolated and their composition confirmed using micro-Fourier transform infrared spectroscopy (µFTIR). We identified microplastics in all Antarctic snow samples at an average concentration of 29 particles L−1, with fibres the most common morphotype and polyethylene terephthalate (PET) the most common polymer. To investigate sources, backward air mass trajectories were run from the time of sampling. These indicate potential long-range transportation of up to 6000 km, assuming a residence time of 6.5 d. Local sources were also identified as potential inputs into the environment as the polymers identified were consistent with those used in clothing and equipment from nearby research stations. This study adds to the growing body of literature regarding microplastics as a ubiquitous airborne pollutant and establishes their presence in Antarctica.

We present a new glacier inventory for high-mountain Asia named \"Glacier Area Mapping for Discharge from the Asian Mountains\" (GAMDAM). Glacier outlines were delineated manually using 356 Landsat ETM+ scenes in 226 path-row sets from the period 1999–2003, in conjunction with a digital elevation model (DEM) and high-resolution Google EarthTM imagery. Geolocations are largely consistent between the Landsat imagery and DEM due to systematic radiometric and geometric corrections made by the United States Geological Survey. We performed repeated delineation tests and peer review of glacier outlines in order to maintain the consistency and quality of the inventory. Our GAMDAM glacier inventory (GGI) includes 87 084 glaciers covering a total area of 91 263 ± 13 689 km2 throughout high-mountain Asia. In the Hindu Kush–Himalaya range, the total glacier area in our inventory is 93% that of the ICIMOD (International Centre for Integrated Mountain Development) inventory. Discrepancies between the two regional data sets are due mainly to the effects of glacier shading. In contrast, our inventory represents significantly less surface area (−24%) than the recent global Randolph Glacier Inventory, version 4.0 (RGI), which includes 119 863 ± 9201 km2 for the entirety of high Asian mountains. Likely causes of this disparity include headwall definition, effects of exclusion of shaded glacier areas, glacier recession since the 1970s, and inclusion of seasonal snow cover in the source data of the RGI, although it is difficult to evaluate such effects quantitatively. Further rigorous peer review of GGI will both improve the quality of glacier inventory in high-mountain Asia and provide new opportunities to study Asian glaciers.

Earlier large-scale Greenland ice sheet sea-level projections (e.g. those run during the ice2sea and SeaRISE initiatives) have shown that ice sheet initial conditions have a large effect on the projections and give rise to important uncertainties. The goal of this initMIP-Greenland intercomparison exercise is to compare, evaluate, and improve the initialisation techniques used in the ice sheet modelling community and to estimate the associated uncertainties in modelled mass changes. initMIP-Greenland is the first in a series of ice sheet model intercomparison activities within ISMIP6 (the Ice Sheet Model Intercomparison Project for CMIP6), which is the primary activity within the Coupled Model Intercomparison Project Phase 6 (CMIP6) focusing on the ice sheets. Two experiments for the large-scale Greenland ice sheet have been designed to allow intercomparison between participating models of (1) the initial present-day state of the ice sheet and (2) the response in two idealised forward experiments. The forward experiments serve to evaluate the initialisation in terms of model drift (forward run without additional forcing) and in response to a large perturbation (prescribed surface mass balance anomaly); they should not be interpreted as sea-level projections. We present and discuss results that highlight the diversity of data sets, boundary conditions, and initialisation techniques used in the community to generate initial states of the Greenland ice sheet. We find good agreement across the ensemble for the dynamic response to surface mass balance changes in areas where the simulated ice sheets overlap but differences arising from the initial size of the ice sheet. The model drift in the control experiment is reduced for models that participated in earlier intercomparison exercises.

The urban-rural transformation from dichotomy to integration is a gradual process. Like rural areas in many countries, Chinese rural society is experiencing a decline in all spheres due to depopulation, aging, lack of economic opportunity, and so on. Aiming at solving the serious rural issues, China proposed the implementation of a rural revitalization strategy and the promotion of an integrated urban-rural development for the first time in 2017. This proposal marks the transformation of the urban-rural relationship, and the integrated urban-rural development reflects a significant conceptual change. Researches on issues of rural decline are urgently needed to determine the most effective method for rural revitalization and development from the perspective of the urban-rural dynamics. In this context, this paper focuses on studying the theory, technology and management of rural revitalization and development. We construct a theoretical framework for urban-rural integration based on population-land-industry-right between the urban and rural systems, regarding land engineering for land capacity building as the technical support and the rural land system reform and reconstruction as the policy support for management. This research will provide theoretical support for the implementation of China’s rural revitalization strategy.