A new 200‐year spatial reconstruction of West Antarctic surface mass balance

High‐spatial resolution surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) spanning 1800‐2010 is reconstructed by means of ice core records combined with the outputs of the European Centre for Medium‐range Weather Forecasts “Interim” reanalysis (ERA‐Interim) and the latest polar version of the Regional Atmospheric Climate Model (RACMO2.3p2). The reconstruction reveals a significant negative trend (‐1.9 ± 2.2 Gt yr‐1 decade‐1) in the SMB over the entire WAIS during the 19th century, but a statistically significant positive trend of 5.4 ± 2.9 Gt yr‐1 decade‐1 between 1900 and 2010, in contrast to insignificant WAIS SMB changes during the 20th century reported earlier. At regional scales, the Antarctic Peninsula (AP) and western WAIS show opposite SMB trends, with different signs in the 19th and 20th centuries. The annual resolution reconstruction allows us to examine the relationships between SMB and large‐scale atmospheric oscillations. Although SMB over the AP and western WAIS correlates significantly with the Southern Annular Mode (SAM) due to the influence of the Amundsen Sea Low (ASL) and El Niño/Southern Oscillation (ENSO) during 1800‐2010, the significant correlations are temporally unstable, associated with the phase of SAM, ENSO and the Pacific decadal oscillation (PDO). In addition, the two climate modes seem to contribute little to variability in SMB over the whole WAIS on decadal‐centennial time scales. This new reconstruction also serves to identify unreliable precipitation trends in ERA‐Interim, and thus has potential for assessing the skill of other reanalyses or climate models to capture precipitation trends and variability

Snow accumulation variability over the West Antarctic Ice Sheet since 1900: a comparison of ice core records with ERA-20C reanalysis

This study uses a set of 37 firn core records over the West Antarctic Ice Sheet (WAIS) to test the performance of ERA-20C reanalysis for snow accumulation and quantify temporal variability in snow accumulation since 1900. The firn cores are allocated to four geographical areas demarcated by drainage divides (i.e., Antarctic Peninsula (AP), western WAIS, central WAIS and eastern WAIS) to calculate stacked records of regional snow accumulation. Our results show that the inter-annual variability in ERA-20C precipitation minus evaporation (P-E) agrees well with the corresponding ice core snow accumulation composites in each of the four geographical regions, suggesting its skill for simulating snow accumulation changes before the modern satellite era (pre-1979). Snow accumulation experiences significantly positive trends for the AP and eastern WAIS, a negative trend for the western WAIS, and no significant trend for the central WAIS from 1900 to 2010. The contrasting trends are associated with changes in the large-scale moisture transport driven by a deepening of the low-pressure systems and anomalies of sea ice in the Amundsen Sea Low (ASL) region

Cold-Season Surface Energy Balance on East Rongbuk Glacier, Northern Slope of Mt. Qomolangma (Everest)

As the highest peak on the earth, Mt. Qomolangma provides an unparalleled platform to study glacier-atmosphere interaction. Although glacier surface energy balance (SEB) on Mt. Qomolangma was examined during warm season, relevant knowledge during cold season is still unknown, which prevents a complete understanding of all-season glacier SEB on it. Based on an in-situ observation from October 2007 to January 2008, this study presents a cold-season glacier SEB result at 6,523 m above sea level on Mt. Qomolangma and identifies its atmospheric control. Our results show that the observational period experienced strong winds and deficient clouds. Near-surface wind speeds usually exceeded 10 m s−1, resulting in a substantial sensible heat transport toward glacier and thus enhancing outgoing longwave radiation, which, under the combined effect of deficient clouds, eventually caused an increase in longwave radiative loss. The large solar zenith angle and relatively high albedo of the glacier surface led to a small absorption of solar irradiance, which, in combination with the strong longwave radiation loss, resulted in a semi-permanent surface radiative loss. Uncommon over the highly reflective glacier surface, clouds decreased the incident solar radiation more than increased the longwave radiation, demonstrating that the clouds' shading effect surpassed its greenhouse effect. As a vital heat sink, the turbulent latent heat induced an average sublimation rate of 0.8 mm water equivalent per day. This study provides valuable insights into the atmospheric control on the cold-season glacier-atmosphere interaction at high altitudes on Mt. Qomolangma when meteorological variables are subject to the westerlies

Greenland ice sheet rainfall climatology, extremes and atmospheric river rapids

Greenland rainfall has come into focus as a climate change indicator and from a variety of emerging cryospheric impacts. This study first evaluates rainfall in five state-of-the-art numerical prediction systems (NPSs) (CARRA, ERA5, NHM-SMAP, RACMO, MAR) using in situ rainfall data from two regions spanning from land onto the ice sheet. The new EU Copernicus Climate Change Service (C3S) Arctic Regional ReAnalysis (CARRA), with a relatively fine (2.5 km) horizontal grid spacing and extensive within-model-domain observational initialization, has the lowest average bias and highest explained variance relative to the field data. ERA5 inland wet bias versus CARRA is consistent with the field data and other research and is presumably due to more ERA5 topographic smoothing. A CARRA climatology 1991–2021 has rainfall increasing by more than one-third for the ice sheet and its peripheral ice masses. CARRA and in situ data illuminate extreme (above 300 mm per day) local rainfall episodes. A detailed examination CARRA data reveals the interplay of mass conservation that splits flow around southern Greenland and condensational buoyancy generation that maintains along-flow updraft ‘rapids’ 2 km above sea level, which produce rain bands within an atmospheric river interacting with Greenland. CARRA resolves gravity wave oscillations that initiate as a result of buoyancy offshore, which then amplify from terrain-forced uplift. In a detailed case study, CARRA resolves orographic intensification of rainfall by up to a factor of four, which is consistent with the field data

A Daily 1-km Resolution Greenland Rainfall Climatology (1958–2020) From Statistical Downscaling of a Regional Atmospheric Climate Model

A daily, gridded 1-km rainfall climatology (1958–2020) for Greenland is presented, including the Greenland ice sheet (GrIS), the peripheral glaciers and ice caps (GIC), and ice-free tundra. It is obtained by statistically downscaling the 5.5 km output of the regional atmospheric climate model version 2 to a resolution of 1 km, using the elevation dependence of snowfall fraction. Based on this new product, the average total annual rainfall in Greenland during 1958–2020 is estimated to be 111.4 ± 11.2 Gt/year, of which 28.6 ± 6.1 Gt/year falls on the GrIS, 11.4 ± 1.4 Gt/year on the GIC, and 71.4 ± 9.0 Gt/year on the tundra. The downscaled 1 km product better resolves especially the relatively small GIC, more than doubling (+124%) their rainfall compared to the 5.5 km product. The relatively warm southern regions of Greenland have the highest rainfall amounts, with annual values locally exceeding 1,000 mm. We confirm a significant positive trend in Greenland rainfall (>40 mm/decade), notably in the northwest and mainly due to an increase in rainfall fraction (>3.5%/decade) during July and August. For the whole of Greenland, during 1991–2020 the seasonal rainfall peak has shifted from July to August, with significant increases in September, which receives more rain than June. An analysis of rainfall fraction and near-surface temperature shows that local warming rates are a good predictor of recent rainfall changes

Quantifying Rainfall in Greenland: A Combined Observational and Modeling Approach

This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients

Assessment of MODIS Surface Temperature Products of Greenland Ice Sheet Using In-Situ Measurements

Satellite-based data have promoted the research progress in polar regions under global climate change, meanwhile the uncertainties and limitations of satellite-derived surface temperatures are widely discussed over Greenland. This study validated the accuracy of ice surface temperature (IST) from the moderate-resolution imaging spectroradiometer (MODIS) over the Greenland ice sheet (GrIS). Daily MODIS IST was validated against the observational surface temperature from 24 automatic weather stations (AWSs) using the mean bias (MB), the root mean square (RMSE), and the correlation coefficient (R). The temporal and spatial variability over the GrIS spanning from March 2000 to December 2019 and the IST melt threshold (−1 °C) were analyzed. Generally, the MODIS IST was underestimated by an average of −2.68 °C compared to AWSs, with cold bias mainly occurring in winter. Spatially, the R and RMSE performed the better accuracy of MODIS IST on the northwest, northeast, and central part of the GrIS. Furthermore, the mean IST is mainly concentrated between −20 °C and −10 °C in summer while between −50 °C and −30 °C in winter. The largest positive IST anomalies (exceeds 3 °C) occurred in southwestern GrIS during 2010. IST shows the positive trends mainly in spring and summer and negative in autumn and winter

The surface energy balance of Austre Lovénbreen, Svalbard, during the ablation period in 2014

The ability to simulate the surface energy balance is key to studying land–atmosphere interactions; however, it remains a weakness in Arctic polar sciences. Based on the analysis of meteorological data from 1 June to 30 September 2014 from an automatic weather station on the glacier Austre Lovénbreen, near Ny–Ålesund, Svalbard, we established a surface energy balance model to simulate surface melt. The results reveal that the net shortwave radiation accounts for 87% (39 W m–2) of the energy sources, and is controlled by cloud cover and surface albedo. The sensible heat equals 6 W m–2 and is a continuous energy source at the glacier surface. Net longwave radiation and latent heat account for 31% and 5% of heat sinks, respectively. The simulated summer mass balance equals –793 mm w.e., agreeing well with the observation by an ultrasonic ranger

Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches

High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects