ABSTRACT 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

monitoring temperature patterns in Antarctica. SIMILAR CONTENT BEING VIEWED BY OTHERS HIGH-RESOLUTION GRIDS OF DAILY AIR TEMPERATURE FOR PERU - THE NEW PISCOT V1.2 DATASET Article Open

access 01 December 2023 HIGH-RESOLUTION SURFACE TEMPERATURE CHANGES FOR PORTUGAL UNDER CMIP6 FUTURE CLIMATE SCENARIOS Article Open access 26 July 2024 WORLDWIDE CONTINUOUS GAP-FILLED MODIS

LAND SURFACE TEMPERATURE DATASET Article Open access 04 March 2021 BACKGROUND & SUMMARY Local near-surface air temperature variations and extreme temperatures in Antarctica resulting

from mesoscale climate processes can have a large effect on the biodiversity1,2,3 and have a significant influence on hydrological4 and glaciological processes5. With a globally warming

climate, Antarctica has been the focus of climate research for its impacted melting ice shelves, sea ice changes, and surface mass balance6,7,8,9. There has been a growing focus throughout

several regions of Antarctica on localized climate extremes caused by foehn wind warming events10,11,12 impacting the ice shelf mass balance13 and thereby being an important component in the

breakup of ice shelves14. Hydrological extremes in areas of high biodiversity have also been linked to foehn warming events15. Shorter term climate perturbations and extreme meteorological

events have an important indirect influence on species distribution and ecosystem functioning through its contribution to temporal availability of meltwater16,17. The availability of a

high-resolution air temperature dataset over multiple decades is therefore important to analyse spatio-temporal variability and understand the effects of these mesoscale temperature

variations and trends on the physical and biological systems across entire Antarctica. Hourly _in-situ_ measurements of air temperature are available from Automatic Weather Stations (AWS),

but with a low spatial density due to cost and logistical issues in operating in such a remote location. Therefore, AWS data are not sufficient for a comprehensive spatio-temporal analysis

of climate variables. Atmospheric reanalysis products such as ERA5 also provide Antarctic-wide near-surface temperatures with a high temporal resolution, but with a grid spacing of 31 km,

which is not sufficient for resolving local patterns and processes causing near-surface temperature variability. Numerical weather prediction models such as The Antarctic Mesoscale

Prediction System (AMPS), provide both a high temporal and spatial resolution air temperature with up to an hourly temporal and approximately 1 km spatial resolution18, but solely for

selected regions such as the Antarctic Peninsula and the Ross Sea Region and currently only available for a limited span of years. Remote sensing data in the thermal bands can be used as

proxies for air temperature19, and may therefore be a solution to this demand for high spatial-temporal resolution products capable of estimating mesoscale dynamics. The MODerate-resolution

Imaging Spectroradiometer (MODIS) is onboard the Aqua and Terra satellites and with their polar orbits, they provide Antarctic wide imagery of a large range of products several times a day

with 1 km spatial grid resolution20. Studies from all over the world including the polar regions have shown that the MODIS land surface temperature (LST) and ice surface temperature (IST)

products are good proxies for air temperatures21,22,23,24,25,26,27,28,29,30,31,32. As _in-situ_ skin temperature measurements are sparse for Antarctica, AWS measurements of air temperature

have in previously studies been used for validating the MODIS skin temperature products. Wang _et al_.27 compared AWS measurements with MODIS LST over the Lambert Glacier Basin in East

Antarctica and proposed that there is great potential in using MODIS LST for improving the reconstruction of spatio-temporal variability of temperature in Antarctica. A study of the MODIS

IST by Shuman _et al_.33 concluded that the MODIS IST product has the consistency to provide knowledge of the surface temperature of the Greenland Ice sheet. They furthermore suggested an

empirical correction to refine the correlation between air temperature measured at 2 m height by AWS and MODIS IST values due to a cold bias between the MODIS LST and IST and air temperature

measured in AWS at 1 m to 3 m height in arctic regions29,33,34. This apparent cold bias is due to a near-surface temperature inversion with thermal stratification near the snow surface29.

Using AWS measurements as ground truth for creating a validated air temperature dataset from MODIS surface temperature products was published by Hooker _et al_.30 but Antarctica was excluded

from their global dataset. Recently Zhang _et al_.31 developed an Antarctic Near-Surface Air Temperature based on MODIS observations and AWS measurements but in monthly means and a 5.6 km

spatial grid resolution which is not enough to resolve these mesoscale warming phenomena. The previous case studies in Antarctica are also only based on either MODIS LST or MODIS IST which

does not provide comprehensive temperature information for identifying warming that extend along the terrestrial costal margin. Meyer _et al_.28 showed the utility of training machine

learning models and a linear regression technique to make predictions of the near-surface air temperature over Antarctica based on MODIS LST values for 2013. The models were trained on air

temperature measured at 3 m height at the exact time of a satellite overpass. It was concluded that the methods were promising but further research needed to include more training samples

from a longer period to improve the models. The study by Meyer _et al_.28 further showed that a linear model was comparable to the more advanced machine learning algorithms. Tree-based

machine learning models have large issues with extrapolation outside the range of the original training dataset35,36. With a shift in the distribution of measured temperatures in Antarctica

in the past decades and an increasing number of extreme high temperatures37 a model must be robust towards these extremes and capable of extrapolation. This would favour the applicability of

a linear model as simpler but yet robust model for this purpose, over machine learning models. Here, we develop AntAir ICE, a near-surface air temperature dataset for the entire terrestrial

Antarctica, the ice shelves, and the seasonal sea ice around Antarctica by using MODIS IST and LST as a proxy for near-surface temperature. This study uses air temperature measured by 117

AWSs at 3 m height for ground truth for this calibration. The linear relation between the daily mean of MODIS IST and LST and daily mean air temperature measured at 3 m height was

established and then applied on the full MODIS record for the past 19 years. The developed AntAir ICE dataset has a spatial grid resolution of 1 km and represents daily average air

temperature. The dataset is developed for the purpose of researching local temperature variations and extremes that are currently not well represented in available numerical climate models

and reanalysis products. METHODS The flowchart (Fig. 1) shows the schematic overview of the processing steps to generate the near-surface air temperature dataset, AntAir ICE. REMOTE SENSING

DATA The MODIS sensor delivers the land surface temperature and ice surface temperature as a standard product also referred to as skin temperature or surface radiometric temperature38. The

MODIS IST product is a sea ice product available for ice shelves and surrounding sea ice and the MODIS LST product is available for continental Antarctica. The MODIS skin temperature is

estimated using a generalized split-window algorithm from the emissivity from MODIS sensor radiance data product band 31 and 3239. The emissivity for the LST product is estimated based on

information about the land cover type, the lower-boundary air surface temperature and atmospheric column water vapour39, whereas the IST algorithm uses a fixed snow/ice emissivity40. In the

presence of clouds, the MODIS cloud mask product, MOD35 and MYD35, is used for removing cloud contaminated pixels in both the MODIS LST and IST product. For MODIS IST a pixel is considered

cloud free if the cloud mask indicates that there is a 66% chance or higher that the pixel is clear and this probability is increased to 95% or higher for the MODIS LST products40. For the

MODIS all pixels are furthermore quality controlled and the quality information for LST is saved in the products quality flag. Besides a swath for each satellite overpass, the MODIS LST and

MODIS IST products are compiled into a night and daytime product by selecting the best quality measurements for that day. The MODIS LST product is divided into night and daytime products

based on local solar time40 and the products over Antarctica are therefore not reflecting the solar position but a time interval, whereas the MODIS IST products are divided into a day and

night product based on the solar zenith angle, and 0° to 85° is defined as daytime40. In this study, the MOD11A1 and MYD11A1 LST products41 containing both a daily daytime and nighttime skin

temperature product each at a 1 km grid resolution, were used. The data is retrieved from the NASA LPDAAC website (https://lpdaac.usgs.gov/) part of collection 6 and automatically

downloaded using the R package RGISTools42. For IST the day products40 MOD29P1D, MYD29P1D, and night products MOD29P1N and MYD29P1N that is available daily and in a 1 km grid resolution from

collection 6 was used. The data was accessed through NASA NSIDC DAAC website (https://nsidc.org/) and downloaded using the available python script from the NSIDC DAAC Data Access Tool. It

is a well-known issue that the MODIS LST product still contains pixels with clouds that the cloud mask product cannot detect43,44. In this case, LST or IST values reflect cloud top

temperatures which are usually far below the skin temperature measured on surrounding days. A threshold of two times the standard deviation below the mean was applied for each pixel to

remove this cloud contamination as also used in previous studies of MODIS skin temperature products44. Further preprocessing was applied to the MODIS IST since a snow or ice surface with a

skin temperature above 0 °C is not possible in the MODIS IST product45, but the MODIS IST is known to have these wrongly estimated pixels44,45. Following the procedure from Yu _et al_.44,

all pixels with a MODIS IST above 0 °C were removed. AUTOMATIC WEATHER STATION DATA Measurements of daily mean air temperature from AWS (Tair) were used as the ground truth in this study for

modelling and validation. The spatial distribution of the available AWS from five different providers used for AntAir ICE throughout Antarctica are illustrated in Fig. 2. Data from AWS were

obtained from Meteo-Climatological Observatory of Italy46, the Long-Term Ecological Research program47, the United States Department of Agriculture or the Antarctic Meteorological Research

Center at the University of Wisconsin48, Institute for Marine and Atmospheric Research, Utrecht University49 and Antarctic Climate Data Collected by Australian Agencies50. There is a total

of 117 stations (compared to 70 stations used for AntAir version 1 by Meyer _et al_.51) available in this study. All stations provided 15-minute to hourly sampling intervals of air

temperature in 2 or 3 m height and only station records with the complete 24-hour period of measurements aggregated into daily averages. The temperature sensors on the stations have a shield

for natural ventilation that prevents heating from direct sunlight and the uncertainty lies within ± 0.5 °C52. COMPILATION OF TRAINING AND VALIDATION DATA For each AWS location a 19-year

record of MODIS skin temperature was derived by extracting the MODIS time series of the grid cell containing the station. The daily mean of either MODIS LST or IST were matched with the Tair

for the corresponding station depending on the location of the station. For LST only days with 4 available scenes with good quality were selected based on the pixel’s qualify flag. There

were in total 47,865 cloud free data points of matching daily MODIS LST and measured Tair within the period from 2003 to 2021. For the IST the day and night products are based on the solar

zenith angle and due to the high latitude, there will only be a day (or night) product available during the austral summer (austral winter). However, due to the extent of Antarctica, this is

only valid for the most southern part of Antarctica. A latitude threshold of 75°S was therefore chosen. For 75°S latitude and northwards there must be either a day and night product

together or all 4 scenes available. For 75°S to 90°S latitude one scene during the summer period and at least two scenes during the winter period were selected. There were in total 38,809

cloud free data points of matching daily MODIS IST and measured Tair within the period from 2003 up to and including 2021. Every third year was used for model validation (2003, 2006, 2009,

2012, 2015, 2018, 2021), and the rest for fitting the model. MODEL TRAINING AND VALIDATION A linear regression model between the daily skin temperature from MODIS and Tair was derived using

the MODIS data as predictor and the corresponding air temperature as measured by the stations as the response. Due to the difference in the MODIS LST and IST products’ temperature range and

environment, two separate models were made for each product referred to as LMLST and LMIST. The linear relation between the measured Tair and daily mean MODIS LST and MODIS IST are shown in

Fig. 3a,b respectively (p-value < 0.01). The trained models were then used to predict the held-back validation data for the purpose of model testing. The models were used to make

predictions for the entire spatial time series for each product and then spatially merged into one product. The final predictions are the continues near-surface air temperature dataset in a

daily temporal resolution and 1 km grid resolution for the period 2003–2021, AntAir ICE. The daily mean near-surface air temperature from the LMLST and the LMIST model will be denoted

TAntAir in the following. DATA RECORDS The AntAir ICE dataset is published on PANGEA53. The geographic coverage of the AntAir ICE dataset includes the entire continent of Antarctica, the

surrounding ice shelves, and partly sea ice in 1 km spatial resolution and a daily average temporal resolution for the period 2003–2021, inclusive. The dataset is in GeoTIFF format and in

the Antarctic Polar Stereographic projection (EPSG 3031) with one file per day. Each day is a spatial raster group with two layers; the first layer is the predicted air temperature for that

day in degree Celsius using a scaling factor of 0.1, the second layer is the number of available MODIS scenes for each grid cell for that day, ranging from 0 to 4. Areas with cloud

contamination or without sea ice are marked with no data. Files are named AntAir_ICE_<YYYY>_<DOY>.tif, where <YYYY> represents the year and <DOY> represents the day

of the year. Files are divided into quarters with January, February, March as 1, April, May, June as 2, July, August, September as 3 and October, November, and December as 4, for each year

(2003–2021) and compressed to a ZIP files. Data are also available on New Zealand modelling consortium open environmental digital library (https://www.envlib.org) with access through the

tethysts python package. TECHNICAL VALIDATION Temporal validation of the LMLST and LMIST models was done based on every third year (2003, 2006, 2009, 2012, 2015, 2018, 2021) of measurements

from all AWS and on days with no clouds and good quality according to the flag. As performance measures, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R2) and Mean

Bias (MB) were used (Table 1). The LMLST model has a MAE of 2.26 °C and a R2 value of 0.97 which indicates that the model could well estimate the overall patterns in air temperature. The

LMIST model does have a slightly lower, but still very high R2 value of 0.93 and a MAE of 2.66 °C. Since the final AntAir ICE temperature dataset is containing days with cloud contaminated

scenes and lower quality pixels, the RMSE, MAE, R-squared and MB were additionally calculated for the validation years for the full non-filtered days (Table 1). The models performance

slightly decreased for both, the LMLST model (MAE = 3.48 °C, R2 = 0.92) and LMIST model (MAE = 3.52 °C, and R2 = 0.82) for the entire dataset but the models were still capable of predicting

air temperature within reasonable error. The relationship between the missing MODIS scenes and the performance of the models is illustrated in Fig. 4 where it is clear that the LMIST model

is not as sensitive to requiring all 4 scenes as the LMLST model. The daily availability of 4 cloud free scenes in the MODIS LST is also more occurrent (Fig. 4a) than for the MODIS IST (Fig.

 4e). For the LMLST model, the R-squared was low for pixels with only one available scene (R2 = 0.64) but it increases drastically when two scenes were available (R2 = 0.87). As the final

datasets contain information in the metadata about the number of available scenes the end user can choose to not include the cloud contaminated days. The continued validation and comparison

were based on the temporal validation set with four available scenes and good quality flags. The newly published Antarctic AWS dataset AntAWS52 contains quality controlled daily temperature

records from 216 AWS records from the period 1980–2021. For validation AntAir ICE, temperatures for the temporal validation period were compared to the 176 available AWS records from AntAWS

using performance measures MAE, RMSE, R2 and MB (Table 1). The R-squared values were still high compared to the two models individually and similarly the MAE for cloud free days (MAE = 2.73 

°C) was only slightly increased when the quality controlled AntAWS dataset is used for validation. SEASONAL VARIATION Due to the location of Antarctica, there is a large seasonal variation

driven by the change in incoming solar radiation and it is therefore important to test the LMLST and the LMIST models’ performance for each season in the validation years (Table 1). There

was a clear trend that the summer months, December, January, and February, (DJF) have the lowest RMSE and MAE for both LMLST and LMIST models. The spring season, September, October, and

November (SON) also showed a low RMSE and MAE for both models. The autumn, March, April, and May (MAM) and winter months, June, July, and August, (JJA) had the highest RMSE and MAE for both

models. This larger bias is expected due to the highly variable temperature conditions in winter caused by wind, which is only partly reflected in AntAir ICE, however, the MAE did not exceed

3 °C or any season for either the LMLST or LMIST models. The bias, calculated as the difference between the Tair from the AWS measurements and the TAntAir, for each season for the temporal

validation years is illustrated in Fig. 5. The box plot indicates that for the majority of data, the bias was close to zero. TREND COMPARISON To analyse how the AntAir ICE is comparing to

Tair time series and trend, the records of daily mean air temperature in the AWS Henry, AmeryG3, AWS09 and Minna Bluff were compared using the AWS data from AntAWS52 along with the temporal

trend in annual mean for both the station and correlating AntAir ICE grid cell (Fig. 6). The annual mean from AntAir ICE also includes cloud interfered days where not all four scenes were

available, nevertheless the correlation between the Tair from the AWS measurements and the TAntAir is significant for all four stations throughout the year. The annual mean trends were

furthermore very similar and significant for all stations except AmeryG3. At AmeryG3 and Minna bluff there were cold spikes that could be related to cloud contamination. COMPARISON WITH ERA5

REANALYSIS ERA554 is a widely used climate reanalysis dataset for air temperature, however, with a grid spacing of 31 km, it is in some cases not capable of resolving local processes

causing near-surface temperature variability. By comparing AntAir ICE to ERA5 performance in multiple Antarctic regions this study can illustrate how the high resolution of AntAir ICE is

improving the understanding of spatial temperature patterns. The ERA5 temperature data were obtained from ECMWF (https:// apps.ecmwf.int/datasets/) from 2003 to 2021. Using a similar method

as Zhu _et al_.55 the hourly 2 m temperature record from the ERA5 was extracted from the nearest grid locations of all 117 AWS and a daily mean was produced, TERA5. Every third year was used

for model validation (2003, 2006, 2009, 2012, 2015, 2018) were selected for comparing ERA5 to the _in-situ_ measured Tair, which excluding 2021 the same years used for validation of AntAir

ICE. To account for the elevation difference between the selected ERA5 grid cell and AWS location, a correction in elevation using the dry adiabatic lapse rate of 9.8 °C/km was applied for

all 117 stations, and the MAE, RMSE, and R2 from the comparison was calculated (Table 2). The lapse rate correction improves ERA5 performance significantly, with a RMSE = 4.31 °C, MAE = 3.02

 °C, and R2 = 0.92, which were a higher value for RMSE and MAE and a lover R-squared than the LMLST and LMIST models’ temporal validation. The spatial variations in AntAir ICE and ERA5

performance measured as MAE when compared to _in-situ_ measured Tair (Fig. 7a,b) indicated a very similar MAE for the two temperature datasets. For TAntAir 70% of the AWS locations have a

MAE below 3 °C whereas it was 67.5% for ERA5. The AWS located in West Antarctica seemed to have a lower MAE for TAntAir than for TERA5. The Ross Sea Region around Ross Island have for TERA5

two AWS locations with very high MAE, which could be due to the very complex terrain that ERA5 was not as capable of resolving. The spatial variations in AntAir ICE and ERA5 performance

measured as R2 were very similar for the two datasets with most stations above R2 = 0.8 (Fig. 7c,d). For the stations located in West Antarctica and the Antarctica Peninsula the mean bias

were lower for AntAir ICE than for ERA5 (Fig. 7d,e). The East Antarctic Plateau stations also had a low mean bias in AntAir ICE (Fig. 7e) showing a good performance. To compare the

similarity spatially between ERA5 and AntAir ICE the annual mean from 2003 to 2021 was calculated for both datasets and AntAir ICE was resampled to ERA5 resolution (Fig. 8a,b). The

difference between the annual mean in Fig. 8c shows a very similar pattern but there is a clear higher warming pattern in AntAir ICE along the Transantarctic Mountains and a colder bias on

the Antarctic Peninsula. To investigate the structural similarity in the two annual means a structural similarity index (SSIM) was used. SSIM can be used as a tool to assess perceived

changes in structural information between two spatial inputs and the metric has been found to be a more meaningful assessment of structural changes compared to a traditional metrics like

Mean Square Error (MSE). A SSIM of 1 represents a perfect match and it was found that the mean SSIM between AntAir ICE and ERA5 annual mean was SSIM = 0.74. There was a clear difference in

the structural similarity index (Fig. 8d) and difference between ERA5 and AntAir ICE in areas with complex terrain. Especially around the Transantarctic Mountains there was a warmer trend in

AntAir ICE which could be due to this area is exposed to warming katabatic outflows and these warming patterns were not resolved in ERA5. This was also supported by the lower MAE and MB in

AntAir ICE than ERA5 for the stations in this area (Fig. 7). To illustrate the uniqueness of this high-resolution near-surface air temperature dataset in comparison to the ERA5, the

accumulative sum of days with temperature above 0 °C for each pixel in three different regions of Antarctica for the full 19-year temperature record for DJF were calculated from ERA5 and

AntAir ICE respectively (Fig. 9). It is clear to see how AntAir ICE resolves inter-valley variability in the McMurdo Dry valleys in the Ross Sea Region (Fig. 9c) compared to ERA5 (Fig. 9d).

The sea ice areas in the Antarctic Peninsula seemed to have a higher occurrence of above 0 °C for ERA5 (Fig. 9f) than for AntAir ICE (Fig. 9e). This is caused by the fact that AntAir ICE

were only available for that area when sea ice was present due to the nature of the MODIS IST product whereas ERA5 provides air temperature for the full period independently of sea ice. This

detailed spatial resolution highlights its potential for monitoring of temperature patterns in Antarctica. CODE AVAILABILITY Python 3.8 was used for conversion of the MODIS products from

HDF files to raster and all data handling and processing was thereafter done in R version 4.0.0. All data processing and modelling procedures are available as R scripts on a public Github

repository: https://github.com/evabendix/AntAir-ICE. Using this code it is possible to download new available MODIS LST and IST scenes and apply the model to continue the near-surface air

temperature dataset. REFERENCES * Cary, S. C., McDonald, I. R., Barrett, J. E. & Cowan, D. A. On the rocks: the microbiology of Antarctic Dry Valley soils. _Nature Reviews Microbiology_

8, 129–138 (2010). Article  CAS  PubMed  Google Scholar  * Chown, S. L. _et al_. The changing form of Antarctic biodiversity. _Nature_ 522, 431–438 (2015). Article  ADS  CAS  PubMed  Google

Scholar  * Singh, J., Singh, R. P. & Khare, R. Influence of climate change on Antarctic flora. _Polar Science_ 18, 94–101 (2018). Article  ADS  Google Scholar  * Herbei, R. _et al_.

Hydrological controls on ecosystem dynamics in Lake Fryxell, Antarctica. _PloS one_ 11, e0159038 (2016). Article  PubMed  PubMed Central  Google Scholar  * Cook, A., Fox, A., Vaughan, D.

& Ferrigno, J. Retreating glacier fronts on the Antarctic Peninsula over the past half-century. _Science_ 308, 541–544 (2005). Article  ADS  CAS  PubMed  Google Scholar  * Siegert, M.

Vulnerable Antarctic ice shelves. _Nature Climate Change_ 7, 11–12 (2017). Article  ADS  Google Scholar  * Favier, V. _et al_. Antarctica-regional climate and surface mass budget. _Current

Climate Change Reports_ 3, 303–315 (2017). Article  Google Scholar  * Steig, E. J. _et al_. Recent climate and ice-sheet changes in West Antarctica compared with the past 2,000 years.

_Nature Geoscience_ 6, 372–375 (2013). Article  ADS  CAS  Google Scholar  * Steig, E. J. _et al_. Warming of the Antarctic ice-sheet surface since the 1957 International Geophysical Year.

_nature_ 457, 459–462 (2009). Article  ADS  CAS  PubMed  Google Scholar  * Elvidge, A. D. _et al_. Foehn jets over the Larsen C ice shelf, Antarctica. _Quarterly Journal of the Royal

Meteorological Society_ 141, 698–713 (2015). Article  ADS  Google Scholar  * Zou, X., Bromwich, D. H., Nicolas, J. P., Montenegro, A. & Wang, S. H. West Antarctic surface melt event of

January 2016 facilitated by föhn warming. _Quarterly Journal of the Royal Meteorological Society_ 145, 687–704 (2019). Article  ADS  Google Scholar  * Speirs, J. C., McGowan, H. A.,

Steinhoff, D. F. & Bromwich, D. H. Regional climate variability driven by foehn winds in the McMurdo Dry Valleys, Antarctica. _International Journal of Climatology_ 33, 945–958 (2013).

Article  ADS  Google Scholar  * Turton, J. V., Kirchgaessner, A., Ross, A. N., King, J. C. & Kuipers Munneke, P. The influence of föhn winds on annual and seasonal surface melt on the

Larsen C Ice Shelf, Antarctica. _The Cryosphere_ 14, 4165–4180 (2020). Article  ADS  Google Scholar  * Cape, M. _et al_. Foehn winds link climate‐driven warming to ice shelf evolution in

Antarctica. _Journal of Geophysical Research: Atmospheres_ 120, 11,037–011,057 (2015). Google Scholar  * Doran, P. T. _et al_. Hydrologic response to extreme warm and cold summers in the

McMurdo Dry Valleys, East Antarctica. _Antarctic Science_ 20, 499–509 (2008). Article  ADS  Google Scholar  * Convey, P. _et al_. The spatial structure of Antarctic biodiversity. _Ecological

monographs_ 84, 203–244 (2014). Article  Google Scholar  * Convey, P., Coulson, S., Worland, M. & Sjöblom, A. The importance of understanding annual and shorter-term temperature

patterns and variation in the surface levels of polar soils for terrestrial biota. _Polar Biology_ 41, 1587–1605 (2018). Article  Google Scholar  * Powers, J. G., Manning, K. W. &

Lambertson, M. M. 3.5 Application of the weather research and forecasting (wrf) model in antarctica. (2005). * Comiso, J. C. Variability and trends in Antarctic surface temperatures from _in

situ_ and satellite infrared measurements. _Journal of Climate_ 13, 1674–1696 (2000). Article  ADS  Google Scholar  * Barnes, W. L., Xiong, X. & Salomonson, V. V. Status of terra MODIS

and aqua MODIS. _Advances in Space Research_ 32, 2099–2106 (2003). Article  ADS  Google Scholar  * Colombi, A., De Michele, C., Pepe, M., Rampini, A. & Michele, C. D. Estimation of daily

mean air temperature from MODIS LST in Alpine areas. _EARSeL eProceedings_ 6, 38–46 (2007). Google Scholar  * Neteler, M. Estimating daily land surface temperatures in mountainous

environments by reconstructed MODIS LST data. _Remote sensing_ 2, 333–351 (2010). Article  ADS  Google Scholar  * Benali, A., Carvalho, A., Nunes, J., Carvalhais, N. & Santos, A.

Estimating air surface temperature in Portugal using MODIS LST data. _Remote Sensing of Environment_ 124, 108–121 (2012). Article  ADS  Google Scholar  * Emamifar, S., Rahimikhoob, A. &

Noroozi, A. A. Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree. _International Journal of Climatology_ 33, 3174–3181 (2013). Article

  ADS  Google Scholar  * Hengl, T., Heuvelink, G., Perčec Tadić, M. & Pebesma, E. J. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. _Theoretical

and applied climatology_ 107, 265–277 (2012). Article  ADS  Google Scholar  * Kilibarda, M. _et al_. Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km

resolution. _Journal of Geophysical Research: Atmospheres_ 119, 2294–2313 (2014). Article  ADS  Google Scholar  * Wang, Y., Wang, M. & Zhao, J. A comparison of MODIS LST retrievals with

_in situ_ observations from AWS over the Lambert Glacier Basin, East Antarctica (2013). * Meyer, H. _et al_. Mapping daily air temperature for Antarctica based on MODIS LST. _Remote Sensing_

8, 732 (2016). Article  ADS  Google Scholar  * Adolph, A. C., Albert, M. R. & Hall, D. K. Near-surface temperature inversion during summer at Summit, Greenland, and its relation to

MODIS-derived surface temperatures. _The Cryosphere_ 12, 907–920 (2018). Article  ADS  Google Scholar  * Hooker, J., Duveiller, G. & Cescatti, A. A global dataset of air temperature

derived from satellite remote sensing and weather stations. _Scientific data_ 5, 1–11 (2018). Article  Google Scholar  * Zhang, X. _et al_. Spatiotemporal Reconstruction of Antarctic

Near-Surface Air Temperature from MODIS Observations. _Journal of Climate_ 35, 5537–5553 (2022). Article  ADS  Google Scholar  * Comiso, J. C., Hall, D. K. & Rigor, I. in _Taking the

Temperature of the Earth_ 151–184 (Elsevier, 2019). * Shuman, C. A., Hall, D. K., DiGirolamo, N. E., Mefford, T. K. & Schnaubelt, M. J. Comparison of near-surface air temperatures and

MODIS ice-surface temperatures at Summit, Greenland (2008–13). _Journal of Applied Meteorology and Climatology_ 53, 2171–2180 (2014). Article  ADS  Google Scholar  * Hall, D. K. _et al_. A

multilayer surface temperature, surface albedo, and water vapor product of Greenland from MODIS. _Remote Sensing_ 10, 555 (2018). Article  ADS  PubMed  PubMed Central  Google Scholar  *

Qiao, H. _et al_. An evaluation of transferability of ecological niche models. _Ecography_ 42, 521–534 (2019). Article  ADS  Google Scholar  * Meyer, H. & Pebesma, E. Predicting into

unknown space? Estimating the area of applicability of spatial prediction models. _Methods in Ecology and Evolution_ 12, 1620–1633 (2021). Article  Google Scholar  * Turner, J. _et al_.

Extreme temperatures in the Antarctic. _Journal of Climate_ 34, 2653–2668 (2021). Article  ADS  Google Scholar  * Dash, P., Göttsche, F.-M., Olesen, F.-S. & Fischer, H. Land surface

temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. _International Journal of remote sensing_ 23, 2563–2594 (2002). Article  ADS  Google

Scholar  * Wan, Z. New refinements and validation of the MODIS land-surface temperature/emissivity products. _Remote Sensing of Environment_ 112, 59–74 (2008). Article  ADS  Google Scholar 

* Hall, D. & Riggs, G. MODIS/Terra Sea Ice Extent and IST Daily L3 Global 4km EASEGrid Day, Version 6. Boulder, Colorado USA. _NASA National Snow and Ice Data Center Distributed Active

Archive Center._ https://doi.org/10.5067/MODIS/MOD29E1D.006 (2015). * Wan, Z., Hook, S. & Hulley, G. _MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid

V006 NASA EOSDIS Land Processes DAAC._ https://doi.org/10.5067/MODIS/MOD11A1.006 (2013). * Pérez-Goya, U., Montesino-SanMartin, M., Militino, A. F. & Ugarte, M. D. RGISTools:

Downloading, Customizing, and Processing Time Series of Remote Sensing Data in R. _arXiv preprint arXiv:2002.01859_ (2020). * Phan, T. N. & Kappas, M. Application of MODIS land surface

temperature data: a systematic literature review and analysis. _Journal of Applied Remote Sensing_ 12, 041501 (2018). Article  ADS  Google Scholar  * Yu, X. _et al_. Assessment of MODIS

Surface Temperature Products of Greenland Ice Sheet Using _In-Situ_ Measurements. _Land_ 11, 593 (2022). Article  Google Scholar  * Hall, D. K. _et al_. Variability in the surface

temperature and melt extent of the Greenland ice sheet from MODIS. _Geophysical Research Letters_ 40, 2114–2120 (2013). Article  ADS  Google Scholar  * MeteoClimatological Observatory at MZS

and Victoria Land’ of PNRA http://www.climantartide.it (2002). * Doran, P. T., Dana, G. L., Hastings, J. T. & Wharton, R. A. Jr. McMurdo LTER: LTER automatic weather network (LAWN).

_Antarct. j. US_ 30, 276–280 (1995). Google Scholar  * Lazzara, M. A., Weidner, G. A., Keller, L. M., Thom, J. E. & Cassano, J. J. Antarctic automatic weather station program: 30 years

of polar observation. _Bulletin of the American Meteorological Society_ 93, 1519–1537 (2012). Article  ADS  Google Scholar  * Institute for Marine and Atmospheric Research, Utrecht

University. https://www.projects.science.uu.nl/iceclimate/aws/antarctica.php (2021). * Barens-Keoghan, I. Antarctic Climate Data Collected by Australian Agencies, Ver. 1, Australian

Antarctic Data Centre https://data.aad.gov.au/metadata/records/Antarctic_Meteorology (2000). * Meyer, H. _et al_. AntAir: satellite-derived 1km daily Antarctic air temperatures since

2003-2016 (monthly means). _PANGAEA_ https://doi.org/10.1594/PANGAEA.902193 (2019). * Wang, Y. _et al_. The AntAWS dataset: a compilation of Antarctic automatic weather station observations.

_Earth System Science Data Discussions_ 2022, 1–26 (2022). Google Scholar  * Nielsen, E. S. B., Katurji, M., Zawar-Reza, P. & Meyer, H. Antarctic daily mesoscale air temperature dataset

derived from remotely sensed land and ice surface temperature – AntAir ICE. _PANGAEA_, https://doi.org/10.1594/PANGAEA.954750 (2023). * Hersbach, H. _et al_. ERA5 hourly data on single

levels from 1940 to present. _Copernicus Climate Change Service (C3S) Climate Data Store (CDS)_ https://doi.org/10.24381/cds.adbb2d47 (2023). * Zhu, J. _et al_. An assessment of ERA5

reanalysis for Antarctic near-surface air temperature. _Atmosphere_ 12, 217 (2021). Article  ADS  Google Scholar  * Matsuoka, K., Skoglund, A. & Roth, G. Quantarctica [data set].

_Norwegian Polar Insitute_ (2018). * Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J. & Morin, P. The reference elevation model of Antarctica. _The Cryosphere_ 13, 665–674 (2019).

Article  ADS  Google Scholar  Download references ACKNOWLEDGEMENTS The authors appreciate the contribution of weather station data from Meteo-Climatological Observatory of Italy, the

Long-Term Ecological Research program, the United States Department of Agriculture or the Antarctic Meteorological Research Center at the University of Wisconsin, Institute for Marine and

Atmospheric Research, Utrecht University and Antarctic Climate Data Collected by Australian Agencies. This research was supported by the New Zealand Antarctic Science Platform (ANTA1801,

program: Projecting Ross Sea Region Ecosystem Changes in a Warming World). This work was also co-funded by Royal Society of New Zealand grant number RDF-UOC1701 This study was conducted

using the Palma II HPC system from the University of Münster. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Centre for Atmospheric Research, School of Earth and Environment at University of

Canterbury, Christchurch, New Zealand Eva Bendix Nielsen, Marwan Katurji & Peyman Zawar-Reza * Institute of Landscape Ecology at University of Münster, Münster, Germany Hanna Meyer

Authors * Eva Bendix Nielsen View author publications You can also search for this author inPubMed Google Scholar * Marwan Katurji View author publications You can also search for this

author inPubMed Google Scholar * Peyman Zawar-Reza View author publications You can also search for this author inPubMed Google Scholar * Hanna Meyer View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS E.N., H.M., M.K. and P.Z.-R. planned the work. E.N. developed model with supervision from H.M. Dataset preparation and validation

was done by E.N. with supervision from H.M., M.K. and P.Z.-R. E.N. wrote the manuscript with contributions and supervision from M.K., P.Z.-R. and H.M. Resources was made available by H.M.

and M.K. CORRESPONDING AUTHOR Correspondence to Eva Bendix Nielsen. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S

NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under

a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate

credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article

are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and

your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this

licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Nielsen, E.B., Katurji, M., Zawar-Reza, P. _et al._ Antarctic daily

mesoscale air temperature dataset derived from MODIS land and ice surface temperature. _Sci Data_ 10, 833 (2023). https://doi.org/10.1038/s41597-023-02720-z Download citation * Received: 27

January 2023 * Accepted: 06 November 2023 * Published: 27 November 2023 * DOI: https://doi.org/10.1038/s41597-023-02720-z SHARE THIS ARTICLE Anyone you share the following link with will be

able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing

initiative