Regional Atmospheric Climate Model Research Articles

Spatiotemporal variability and trends in rainfall and temperature extremes using ERA5 reanalysis and CORDEX–Africa model in Southern Ethiopia

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C–44 °C in Areka, 31.64 °C–36.8 °C in Gesuba, and 29.19 °C–36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%–99.3 %) than the annual rainfall (CV = 33%–79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.

Firn air content changes on Antarctic ice shelves under three future warming scenarios

Abstract. The Antarctic firn layer provides pore space in which an estimated 94 % to 96 % of the surface melt refreezes or is retained as liquid water. Future depletion of firn pore space by increased surface melt, densification and formation of low-permeability ice slabs can potentially lead to meltwater ponding, hydrofracturing and ice-shelf disintegration. Here, we investigate the 21st-century evolution of total firn air content (FAC) and accessible FAC (i.e. the pore space that meltwater can reach) across Antarctic ice shelves. We use the semi-empirical IMAU Firn Densification Model (IMAU-FDM) with an updated dynamical densification expression to cope with changing climate forcing. The firn model is forced by general circulation model output of the Community Earth System Model version 2 (CESM2) for three climate emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5), dynamically downscaled to a 27 km horizontal resolution by the Regional Atmospheric Climate Model version 2.3p2 (RACMO2.3p2). To estimate accessible FAC, we prescribe a relationship between ice-slab thickness and permeability. In our simulations, ice shelves on the Antarctic Peninsula and the Roi Baudouin Ice Shelf in Dronning Maud Land are particularly vulnerable to total FAC depletion (> 50 % decrease by 2100), even for low-emission (SSP1-2.6) and intermediate-emission (SSP2-4.5) scenarios. In the high-emission (SSP5-8.5) scenario in particular, the formation of ice slabs further reduces accessible FAC on ice shelves with low accumulation rates (current rates of < 500 mmw.e.yr-1), including many East Antarctic ice shelves and the Filchner–Ronne, Ross, Pine Island and Larsen C ice shelves. These results underline the potentially large vulnerability of low-accumulation ice shelves to firn air depletion through ice-slab formation.

Towards an intelligent malaria outbreak warning model based intelligent malaria outbreak warning in the northern part of Benin, West Africa

BackgroundMalaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.MethodsClimate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).ResultsTwo ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.ConclusionThese results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

What recent global atmospheric reanalyses and regional climate models can represent observed snow accumulation on Antarctica?

Recent compiled surface mass balance observations on the Antarctic ice sheet are used to examine the performance of four global reanalysis products and two regional climate models (RCMs), i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI), the fifth generation ECMWF reanalysis (ERA5), the Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), Modèle Atmosphérique Régional (MAR), and the Regional Atmospheric Climate Model (RACMO). All these models perform similarly well in reproducing spatial patterns of snow accumulation from the coast to inland plateau. But they differ in their magnitude with respective to in-situ observations with either evident overestimation (MAR) or underestimation (ERAI and CFSR) in the interior of the Antarctica. By comparison, MERRA2 and ERA5 at each elevation bin of 200 m match best with observations. Among the models, ERAI shows the smallest amount of snow accumulation averaged over the entire ice sheet. All models present some skills for reproducing the inter-annual variability in observed snow accumulation. By comparison, ERA5 probably provides the most realistic representation of variability in Antarctic snow accumulation since 1979.

The evolution of future Antarctic surface melt using PISM-dEBM-simple

Abstract. It is virtually certain that Antarctica's contribution to sea-level rise will increase with future warming, although competing mass balance processes hamper accurate quantification of the exact magnitudes. Today, ocean-induced melting underneath the floating ice shelves dominates mass losses, but melting at the surface will gain importance as global warming continues. Meltwater at the ice surface has crucial implications for the ice sheet's stability, as it increases the risk of hydrofracturing and ice-shelf collapse that could cause enhanced glacier outflow into the ocean. Simultaneously, positive feedbacks between ice and atmosphere can accelerate mass losses and increase the ice sheet's sensitivity to warming. However, due to long response times, it may take hundreds to thousands of years until the ice sheet fully adjusts to the environmental changes. Therefore, ice-sheet model simulations must be computationally fast and capture the relevant feedbacks, including the ones at the ice–atmosphere interface. Here we use the novel surface melt module dEBM-simple (a slightly modified version of the “simple” diurnal Energy Balance Model) coupled to the Parallel Ice Sheet Model (PISM, together referred to as PISM-dEBM-simple) to estimate the impact of 21st-century atmospheric warming on Antarctic surface melt and ice dynamics. As an enhancement compared to the widely adopted positive degree-day (PDD) scheme, dEBM-simple includes an implicit diurnal cycle and computes melt not only from the temperature, but also from the influence of solar radiation and changes in ice albedo, thus accounting for the melt–albedo feedback. We calibrate PISM-dEBM-simple to reproduce historical and present-day Antarctic surface melt rates given by the regional atmospheric climate model RACMO2.3p2 and use the calibrated model to assess the range of possible future surface melt trajectories under Shared Socioeconomic Pathway SSP5-8.5 warming projections until the year 2100. To investigate the committed impacts of the enhanced surface melting on the ice-sheet dynamics, we extend the simulations under fixed climatological conditions until the ice sheet has reached a state close to equilibrium with its environment. Our findings reveal a substantial surface-melt-induced speed-up in ice flow associated with large-scale elevation reductions in sensitive ice-sheet regions, underscoring the critical role of self-reinforcing ice-sheet–atmosphere feedbacks in future mass losses and sea-level contribution from the Antarctic Ice Sheet on centennial to millennial timescales.

Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019)

The spatiotemporal distribution and variability of surface water remained poorly known at the pan-Greenland scale, and its dominant features (supraglacial rivers, lakes, and water-filled crevasses) were rarely studied collectively. We present pan-GrIS surface water extent and volume for a relatively cool (2018) and a relatively warm (2019) summer, using 10 m resolution Sentinel-2 imagery, a semi-automated multi-scale water extraction algorithm, and the Regional Atmospheric Climate Model (RACMO). While the 10 m resolution of Sentinel-2 imagery prevents inclusion of all water-filled crevasses and narrow rivers, our findings include: (1) strong interannual differences are observed in total surface water area (4903km2 vs. 9988 km2), volume (3.5 km3 vs. 6.8 km3), and mean elevation limit (1407 m a.s.l. vs. 1545 m a.s.l.) in response to a low (cool) (265 Gt/yr) and high (warm) (510 Gt/yr) runoff year; (2) large spatial contrasts in surface water extent, volume, elevation limit, and drainage pattern among the eight major GrIS basins; (3) supraglacial rivers dominate GrIS surface water appearance, accounting for 57%/48% of total surface water area/volume, respectively, over the two years (in contrast, water-filled crevasses and supraglacial lakes account for 33%/32% and 10%/20%, respectively); (4) ratio of remotely sensed water volume to RACMO-simulated cumulative surface runoff declines during the cool (2.6%) vs. the warm (1.8%) year; and from north (4.5%) to south (1.1%) Greenland. In summary, this study reveals strong temporal and spatial differences in GrIS surface water extent, volume, and drainage pattern and raises prospects for improved understanding of pan-Greenland Ice Sheet surface hydrology.

Coupled hydrological and hydrodynamic modelling application for climate change impact assessment in the Nemunas river watershed–Curonian Lagoon–southeastern Baltic Sea continuum

Abstract. We analyse the cumulative impacts of climate change in a complex basin–lagoon–sea system continuum, which covers the Nemunas river basin, Curonian Lagoon, and the southeastern part of the Baltic Sea. A unique, state-of-the-art coupled modelling system was developed using hydrological and hydrodynamic models. The results of four regional downscaled models from the Rossby Centre high-resolution regional atmospheric climate model have been bias-corrected using in situ measurements and were used as forcing to assess the changes that the continuum will undergo until the end of this century. Results show that the Curonian Lagoon will be subjected to higher river discharges that in turn increase the outgoing fluxes into the Baltic Sea. Through these higher fluxes, both the water residence time and saltwater intrusion into the lagoon event frequency will decrease. Most of these changes will be more pronounced in the northern part of the lagoon, which is more likely to be influenced by the variations in the Nemunas river discharge. Its delta area may be susceptible to flooding as a result of the elevated discharge during winter. The southern part of the lagoon will experience lesser changes. While water temperatures in the entire lagoon and the southeastern Baltic Sea will steadily increase and salinity will decrease, the foreseen changes in the physical characteristics will not cause significant shifts in the ecosystem functioning but may affect the nutrient retention capacity. However, some ecosystem services such as ice fishing are expected to vanish completely due to the loss of ice cover.

One-degree resolution mascon solution over Antarctic derived from GRACE Level-2 data

The mass loss of the Antarctic Ice Sheet (AIS) is an important contributor to global sea-level rise in response to the warming ocean and atmospheric temperatures as well as the changes in current systems and precipitation patterns. In this study, a regional mascon method is developed to squeeze more mass change signals, in which the pseudo-observations of the geopotential are generated from the unfiltered GRACE Level-2 data whereas the regularization matrix is constructed with the prior information derived from filtered GRACE Level-2 data. A series of mascon solutions with 1°×1° equal-area resolution over AIS is derived from the updated Tongji-Grace2018 model spanning April 2002 to December 2016. Compared to the filtering results from P4M6 decorrelation and 100 km Gaussian filtering, our mascon solutions can effectively suppress the strips, improve the spatial resolution over AIS, and get a stronger signal with an improvement of 116.86% in the Antarctic Peninsula Ice Sheet (APIS), and more coincide with the features of glaciers and ice streams, such as the most striking ice mass loss in Totten, Getz, Thwaites and Pine Island, and the ice mass gain in Kamb Ice Stream. During the period from 2002 to 2016, the mass change rates from our mascon solution are −103.6 ± 5.6 Gt/yr, 63.0 ± 4.3 Gt/yr, −143.3 ± 4.9 Gt/yr and −23.29 ± 1.2 Gt/yr in AIS, East AIS, West AIS, and APIS, respectively. The mass change signals at the basin scale are with even more distinguishing features, with the highest mass gain rates of 18.03 ± 1.88 Gt/yr and 14.55 ± 0.60 Gt/yr at Basin 7 and Basin 18, and the highest mass loss rates of −58.57 ± 2.48 Gt/yr and −44.12 ± 2.27 Gt/yr at Basin 21 and Basin 22. Relative to the cumulated surface mass balance from the regional atmospheric climate model, the correlation coefficients of our mascon solutions are 0.91, 0.94, and 0.96 in East AIS, West AIS, and APIS.

Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar

Abstract. During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a−1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a−1, which matches closely to the 2.75 m w.e. a−1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a−2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models.

Evaluation of climate change, urbanization, and low-impact development practices on urban flooding

The Personal Computer Storm Water Management Model was used in this study to evaluate the potential impacts of climate change, urbanization, and low-impact developments (LIDs) on urban flooding in Robe town, Ethiopia. To achieve the objective, four scenarios were developed in order to simulate changes in peak runoff, inundated volume, and the performance of existing drainage systems. The findings revealed that as urbanization increased from 10% to 70%, the inundated volume of nodes and peak runoff increased from 35,418 to 52,118 × 103 m3 and 89.4–111.96 m3/s, respectively. Furthermore, the peak runoff in response to climate change is increased by 46.9%, 34.8%, and 37.5%, respectively, as a result of the Rossby Centre Regional Climate Model version 4 (RCA4), Regional Atmospheric Climate Model (RACMO22T), and the hydrostatic version of the regional model (REMO2009). Overall, the findings showed that existing drainage systems were unable to collect and convey the amplified inundation from different simulated scenarios, and the Welmel sub-city to roundabout was threatened by increased flooding, causing significant damage to properties and infrastructure. The implemented LIDs are capable of reducing the expected peak runoff, flooding magnitude, and flooded junctions in climate change and urbanization scenarios; however, combining both mitigation measures can further reduce the study area. The implementation of a mitigation strategy with adequate drainage systems will be required to mitigate the flooding risks in Robe town.

Predicting Groundwater Level Using Climate Change Scenarios in the Southern Part of Mali

Groundwater is mainly demanded in all the activities for the population of the southern part especially in the Koda catchment, the studied area. These resources are affected by various factors especially climate change. Therefore, knowing the impact of projected climate change on groundwater recharge is an important issue for water resources management, especially for those responsible for the Koda catchment. In this work, the impact of climate change on groundwater resources in the study area in Mali, West Africa is investigated. The Hydrogeological modeling was performed using the Gardenia model, and the monthly precipitation and temperature data were used as the Baseline. These data considered the past 30-year period (1987-2016) and the projections for the next 30 years (2021-2050). Projected precipitation and air temperatures, extracted from the Rossby Centre regional Atmospheric climate model (RCA 4) statistically downscaled from the GCM-IHEC-EC-EARTH and the GCM-MPI-M-MPI-ESM-LR under the Representative Concentration Pathways RCP 4.5 and RCP 8.5 and corrected with the Multiscale Quantile Mapping bias correction method, were used as input data to the gardenia model. Potential evapotranspiration (PET) values estimated from Blaney Criddle method and groundwater levels measured in three piezometers were used to calibrate the Gardenia model. The outputs display the reduction of groundwater level in the three piezometers in the Koda catchment for all the two Regional Climate Models (RCMs) during the periods of rainy season from July to October. From the results of GCM IHEC-EC-EARTH, the projected decline in GWL reaches 1.09 m for the RCP 4.5 and it up to 1.26 m for the RCP 8.5 in the study area while the GCM MPI-M-MPI-ESM-LR presentes the decline in groundwater level (GWL) during winter season from about 0.62 m for the RCP 4.5 up to 1.93 m for the RCP 8:5. Both RCMs project a reduction trend of groundwater recharge over time. It is noticeable that this decline is greater in RCP8.5 for all the three piezometers. The results also show that the average groundwater recharge (90 mm) in the future (2021-2050) is lower (180 mm) than that of the current drought (1987-2016), which could lead to severe drought events. The projected impacts of climate change would have a significant impact on groundwater in the period of 2029-2039; this situation could have a negative impact the socioeconomic activities especially on agriculture, which depends on water resources. The results will help also to take some adaptation measures to climate change, the famers could have a possibility to know the period of groundwater recharge where they have more water infiltration therefore, where to seek crops that need less or more water. The study area presents numerous potential of groundwater, the results could be a tool for groundwater management and to determine the favorable sites to implant new boreholes.

Systematic Calibration of a Convection‐Resolving Model: Application Over Tropical Atlantic

AbstractNon‐hydrostatic km‐scale weather and climate models show significant improvements in simulating clouds and precipitation, especially of convective nature. However, even km‐scale models need to parameterize some physical processes and are thus subject to the corresponding parameter uncertainty. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate‐change scenario simulations. In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters' sensitivities are evaluated with respect to a set of validation fields. Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows a methodology previously used for regional climate simulations. This includes simulations considering the interaction of all pairs of parameters, and the exploitation of a quadratic‐form metamodel to emulate the simulations. In the current set‐up with 5 parameters, 51 simulations are required to build the metamodel. The model is calibrated for the year 2016 and validated in two different years using slightly different model setups (domain and resolution). Both years demonstrate significant improvements, in particular for outgoing shortwave radiation, with reductions of the bias by a factor of 3–4. The results thus show that parameter calibration is a useful and efficient tool for model improvement. Calibrating over a larger domain might help to further improve the overall performance, but could potentially also lead to compromises among different regions and variables, and require more computational resources.

Evaluation of future wind power potential and their projected changes in the Black Sea and possible stable locations for wind farms

This study aimed to evaluate the possible impacts of climate change on the wind power potential and assign possible stable locations in the Black Sea until the end of the 21st century. The wind fields simulated by a regional climate model (Rossby Centre regional atmospheric climate model, version RCA4) were analyzed considering two future periods (2021–2060, 2061–2100) under RCP4.5 and RCP8.5 scenarios. The temporal variability of the wind power density (WPD) was investigated using both spatial and local analyses for the future period (2021–2100). Three temporal variability indices (namely the coefficient of variation (CV), monthly (MV), and seasonal variability (SV)) were in agreement that the mean WPD in the eastern basin is significantly more variable than in other parts of the basin. Considering the impact of climate change, the future projections for both climate scenarios indicate changes, and the eastern basin will experience more changes under the RCP8.5 scenario. For the most predictable future developments of the wind power potential, 15 reference points along the Black Sea were analyzed using the intra- and inter-annual variability of the mean WPD. In the future, strong, durable, and stable wind resources in the western basin will ensure reliable, permanent, and sustainable WPD.

Variability in Antarctic surface climatology across regional climate models and reanalysis datasets

Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques.

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

AbstractA 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.

Large ensemble of downscaled historical daily snowfall from an earth system model to 5.5 km resolution over Dronning Maud Land, Antarctica

Abstract. We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; https://doi.org/10.5281/zenodo.4287517; Ghilain et al., 2021). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century.

Sensitivity of Antarctic surface climate to a new spectral snow albedo and radiative transfer scheme in RACMO2.3p3

Abstract. This study investigates the sensitivity of modeled surface melt and subsurface heating on the Antarctic ice sheet to a new spectral snow albedo and radiative transfer scheme in the Regional Atmospheric Climate Model (RACMO), version 2.3p3 (Rp3). We tune Rp3 to observations by performing several sensitivity experiments and assess the impact on temperature and melt by incrementally changing one parameter at a time. When fully tuned, Rp3 compares well with in situ and remote sensing observations of surface mass and energy balance, melt, near-surface and (sub)surface temperature, albedo and snow grain specific surface area. Near-surface snow temperature is especially sensitive to the prescribed fresh snow specific surface area and fresh dry snow metamorphism. These processes, together with the refreezing water grain size and subsurface heating, are important for melt around the margins of the Antarctic ice sheet. Moreover, small changes in the albedo and the aforementioned processes can lead to an order of magnitude overestimation of melt, locally leading to runoff and a reduced surface mass balance.

Revisiting Ice Flux and Mass Balance of the Lambert Glacier–Amery Ice Shelf System Using Multi-Remote-Sensing Datasets, East Antarctica

Due to rapid global warming, the relationship between the mass loss of the Antarctic ice sheet and rising sea levels are attracting widespread attention. The Lambert–Amery glacial system is the largest drainage system in East Antarctica, and its mass balance has an important influence on the stability of the Antarctic ice sheet. In this paper, the recent ice flux in the Lambert Glacier of the Lambert–Amery system was systematically analyzed based on recently updated remote sensing data. According to Landsat-8 ice velocity data from 2018 to April 2019 and the updated Bedmachine v2 ice thickness dataset in 2021, the contribution of ice flux approximately 140 km downstream from Dome A in the Lambert Glacier area to downstream from the glacier is 8.5 ± 1.9 Gt·a−1, and the ice flux in the middle of the convergence region is 18.9 ± 2.9 Gt·a−1. The ice mass input into the Amery ice shelf through the grounding line of the whole glacier is 19.9 ± 1.3 Gt·a−1. The ice flux output from the mainstream area of the grounding line is 19.3 ± 1.0 Gt·a−1. Using the annual SMB data of the regional atmospheric climate model (RACMO v2.3) as the quality input, the mass balance of the upper, middle, and lower reaches of the Lambert Glacier was analyzed. The results show that recent positive accumulation appears in the middle region of the glacier (about 74–78°S, 67–85°E) and the net accumulation of the whole glacier is 2.4 ± 3.5 Gt·a−1. Although the mass balance of the Lambert Glacier continues to show a positive accumulation, and the positive value in the region is decreasing compared with values obtained in early 2000.

Mapping Firn Saturation Over Greenland Using NASA’s Soil Moisture Active Passive Satellite

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using <i>L</i>-band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization <i>L</i>-band brightness temperature (<inline-formula><tex-math notation="LaTeX">$T_{V}^{B}$</tex-math></inline-formula>) imagery (2015&#x2013;2019) generated using observations collected over Greenland by NASA&#x2019;s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer <i>L</i>-band brightness temperature model. We map water-saturated firn layers via a &#x201C;firn saturation&#x201D; parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA&#x2019;s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.

Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

Abstract. Accurately estimating the surface melt volume of the Antarctic Ice Sheet is challenging and has hitherto relied on climate modeling or observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations with a regional climate model by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs) and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations, (2) correcting Moderate Resolution Imaging Spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C ice shelves, Antarctica, cross-validation shows a high accuracy (root-mean-square error of 0.95 mm w.e. d−1, mean absolute error of 0.42 mm w.e. d−1, and a coefficient of determination of 0.95). Moreover, the deep MLP model outperforms conventional machine learning models and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18), the deep MLP model does not show improved agreement with AWS observations; this is likely because surface melt is largely driven by factors (e.g., air temperature, topography, katabatic wind) other than albedo within the corresponding coarse-resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. However, more work is required to refine the method, especially for complicated and heterogeneous terrains.