Spatial predictions of near-surface air temperature ( $T_{air}$ ) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather stations in Antarctica is sparse, remote sensing methods have large potential due to their capabilities and accessibility. Based on the MODIS land surface temperature (LST) data, $T_{air}$ at the exact time of satellite overpass was modelled at a spatial resolution of 1 km using data from 32 weather stations. The performance of a simple linear regression model to predict $T_{air}$ from LST was compared to the performance of three machine learning algorithms: Random Forest (RF), generalized boosted regression models (GBM) and Cubist. In addition to LST, auxiliary predictor variables were tested in these models. Their relevance was evaluated by a Cubist-based forward feature selection in conjunction with leave-one-station-out cross-validation to reduce the impact of spatial overfitting. GBM performed best to predict $T_{air}$ using LST and the month of the year as predictor variables. Using the trained model, $T_{air}$ could be estimated with a leave-one-station-out cross-validated $R^2$ of 0.71 and a RMSE of 10.51 ${}^{\circ }$ C. However, the machine learning approaches only slightly outperformed the simple linear estimation of $T_{air}$ from LST ( $R^2$ of 0.64, RMSE of 11.02 ${}^{\circ }$ C). Using the trained model allowed creating time series of $T_{air}$ over Antarctica for 2013. Extending the training data by including more years will allow developing time series of $T_{air}$ from 2000 on.

Abiotic and biotic factors control ecosystem biodiversity, but their relative contributions remain unclear. The ultraoligotrophic ecosystem of the Antarctic Dry Valleys, a simple yet highly heterogeneous ecosystem, is a natural laboratory well-suited for resolving the abiotic and biotic controls of community structure. We undertook a multidisciplinary investigation to capture ecologically relevant biotic and abiotic attributes of more than 500 sites in the Dry Valleys, encompassing observed landscape heterogeneities across more than 200 km². Using richness of autotrophic and heterotrophic taxa as a proxy for functional complexity, we linked measured variables in a parsimonious yet comprehensive structural equation model that explained significant variations in biological complexity and identified landscape-scale and fine-scale abiotic factors as the primary drivers of diversity. However, the inclusion of linkages among functional groups was essential for constructing the best-fitting model. Our findings support the notion that biotic interactions make crucial contributions even in an extremely simple ecosystem.

A set of Python-based tools, WRF4PALM, has been developed for offline nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high-resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model and requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/, last access: 23 April 2021). This paper describes and validates the new free and open-source WRF4PALM tools (available at https://github.com/dongqi-DQ/WRF4PALM, last access: 23 April 2021). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology.

Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica’s environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km². AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R${}^2$ of 0.78 and a RMSE of 3.32 ${}^{\circ }$C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution.

Intermittent fog occurrences supply significant amounts of moisture to plants in the form of fog drip onto the soil surface thereby prompting interest in their statistical behavior at multiple timescales. A comparison of rainfall and fog measurements collected at an inland tropical cloud forest in Kenya and a coastal rangeland in Northern California is presented to explore whether fog occurrences have similar intermittency characteristics as rainfall. The results confirm that both rainfall and fog show approximate power law relations for distributions of dry period and event size consistent with predictions from self‐organized criticality. Moreover, the spectral exponents of the on‐off time series of the fog and rainfall exhibited an approximate f^−0.8 over a broad range of frequencies f, which is remarkably close to scaling exponents across sites experiencing different rainfall generation mechanisms. These results suggest that fog intermittency shares some properties of critical behavior documented in numerous rainfall studies.