Daily Temperature Time Series Research Articles

Investigation of Temperature Multifractrality According to Zugspitze Weather Station Data

The main multifractal properties of time series of mean, maximum and minimum daily temperatures are analyzed using the method of multifractal fluctuation analysis. As initial data, we used the results of instrumental temperature observations made at the Zugspitze meteorological station in the period from August 1, 1900 to January 31, 2023. In general, variations in the mean, maximum and minimum daily temperatures demonstrate multifractal behavior, especially for small time scales, up to about 90 days An analysis of the generalized Hurst exponent found that the considered time series have a long-term positive correlation and that the multifractality is weaker with large fluctuations. The singularity spectrum for all time series is truncated to the left, which means that the time series have a multifractal structure that is insensitive to local fluctuations of large values.

Warming surface and Lake heatwaves as key drivers to harmful algal Blooms: A case study of Lake Dianchi, China

Extreme heat events such as marine and atmospheric heatwaves, have catastrophic impacts on marine and terrestrial ecosystems. Recent studies have shown that the continuous warming of lake surfaces and enhancement of lake heatwaves (LHWs) have generated great research interest globally. However, the effects of LHWs on lake ecosystems, especially cyanobacterial harmful blooms (cHABs), remain unclear. This study reconstructed the daily Lake surface water temperature (LSWT) time series in Lake Dianchi from 1951 to 2020 using random forest. Trends and characteristics of LSWT and LHWs were identified, and their critical driving effects on cHABs were analyzed. Results showed that the LSWT in Lake Dianchi significantly increased with a rate of 0.26 °C/10a and with an abrupt changing point in 1992, coinciding with the first report of severe cHABs in Lake Dianchi. From 1951 to 2020, 141 LHWs were detected, of which 94.3 % occurred after 1992. Among all the potential climatic and water quality driving factors on Chlorophyll-a (Chla) variation, LSWT is the most important. Chla concentration and the intensity of LHWs were significantly correlated. The complex and strong interactions between LHWs and cHABs enable the suppression of cHABs only when both remain low. A case study in Lake Dianchi highlighted the critical driving effects of LSWT and LHWs on cHABs, suggesting that continuously warming surface and enhancing LHWs in the future will further challenge more specific and effective lake management strategies to suppress cHABs.

Combining Landsat TIR‐imagery data and ERA5 reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the Canadian Subarctic

AbstractArctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.

Past and future discharge and stream temperature at high spatial resolution in a large European basin (Loire basin, France)

Abstract. This paper presents retrospective simulations (1963–2019) and future projections (1976–2100) of daily time series of discharge and stream temperature for 52 278 reaches (median length of 1.3 km) over the Loire River basin (105 km2) in France, using a physical process-based thermal model coupled with a semi-distributed hydrological model. Retrospective simulations are based on the 8 km gridded Safran meteorological reanalysis over France. Twenty-first century projections are based on a subset of the 8 km gridded and bias-corrected DRIAS-2020 dataset over France. The discharge and stream temperature dataset stands out from existing ones thanks to its large scale and its high spatial resolution and the use of a physical process-based thermal model. The whole dataset is freely available and can be downloaded in NetCDF format from https://doi.org/10.57745/LBPGFS (Seyedhashemi et al., 2022a).

Future temperature-related excess mortality under climate change and population aging scenarios in Canada

ObjectiveClimate change is expected to increase global temperatures. How temperature-related mortality risk will change is not completely understood, and how future demographic changes will affect temperature-related mortality needs to be clarified. We evaluate temperature-related mortality across Canada until 2099, accounting for age groups and scenarios of population growth.MethodsWe used daily counts of non-accidental mortality for 2000 to 2015 for all 111 health regions across Canada, incorporating in the study both urban and rural areas. A two-part time series analysis was used to estimate associations between mean daily temperatures and mortality. First, current and future daily mean temperature time series simulations were developed from Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from past and projected climate change scenarios under Shared Socioeconomic Pathways (SSPs). Next, excess mortality due to heat and cold and the net difference were projected to 2099, also accounting for different regional and population aging scenarios.ResultsFor 2000 to 2015, we identified 3,343,311 non-accidental deaths. On average, a net increase of 17.31% (95% eCI: 13.99, 20.62) in temperature-related excess mortality under a higher greenhouse gas emission scenario is expected for Canada in 2090–2099, which represents a greater burden than a scenario that assumed strong levels of greenhouse gas mitigation policies (net increase of 3.29%; 95% eCI: 1.41, 5.17). The highest net increase was observed among people aged 65 and over, and the largest increases in both net and heat- and cold-related mortality were observed in population scenarios that incorporated the highest rates of aging.ConclusionCanada may expect net increases in temperature-related mortality under a higher emissions climate change scenario, compared to one assuming sustainable development. Urgent action is needed to mitigate future climate change impacts.

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

AbstractIn this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Corrigendum to “A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network”

Corrigendum to “A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network”

Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling.

CLIMBra - Climate Change Dataset for Brazil

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

Statistical properties of the entropy from ordinal patterns.

The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair entropy-statistical complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon's entropy for any model under which the true normalized entropy is neither zero nor one. We obtain the asymptotic distribution from the central limit theorem (assuming large time series), the multivariate delta method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon's entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon's entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results.

Projections of temperature-attributable suicide under climate change scenarios in Japan

BACKGROUND AND AIM: Recent studies have projected all-cause mortality attributable to non-optimal temperature under climate change scenarios. However, the impact of climate change on mental health remains to be assessed. The short-term association between suicide and temperature has been reported, but there is a lack of future projections of temperature-attributable suicide. We projected the excess temperature-related suicide mortality in Japan under three climate change scenarios until the 2090s. METHODS: Daily time series of mean temperature and the number of suicide deaths in 1973-2015 were collected for 47 prefectures in Japan. A two-stage time-stratified case-crossover analysis was used to estimate the temperature-suicide association. We obtained the modeled daily temperature series using five general circulation models (GCMs) under three climate change scenarios from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways scenarios (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. We projected the excess temperature-related suicide mortality until 2099 for each scenario and evaluated the net relative changes compared with the 2010s. RESULTS: During 1973-2015, there was a total of 1,049,592 suicides in Japan. Net increases in temperature-related excess suicide mortality were estimated under all scenarios, with the highest increase under the high-emission scenario (SSP5-8.5). The net change in 2090-99 compared with 2010-19 was 0.6% (95% empirical confidence interval [eCI]: 0.1, 1.6) for a low emission scenario (SSP1-2.6), 1.3% (95% eCI: 0.6, 2.4) for an intermediate scenario (SSP2-4.5), and 2.4% (95% eCI: 0.7, 3.9) for a high emission scenario (SSP5-8.5). The increases were greater the higher the emission scenarios were. The increase in excess temperature-related suicide mortality was minimized under the SSP1-2.6 scenario assuming mitigation strategies. CONCLUSIONS: The comparison of projection results under different scenarios underscores the importance of mitigation, with the higher the emissions, the greater impact on the temperature-related suicide mortality. Keywords:Climate change, non-optimal temperature, mental health, projections, DLNM

Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions

Rényi entropy based on characteristic function has been used as an information measure contained in wide-sense and real stationary vector autoregressive and moving average (VARMA) processes. These classes of processes have been extended by fractionally integrated VARMA (VARFIMA) ones, composed of a VARMA process, a vector of fractional differencing parameters, and independent and identically distributed multivariate normal random errors. Such processes have often been used to explicitly account for persistence to incorporate long-term correlations into multivariate data. The purpose of this paper is to extend Rényi entropy from VARMA to VARFIMA processes, addressing long-memory behavior of time series by adding a fractional differencing parameter. The characteristic function of the process can be derived directly from the asymptotic form of the impulse response function using the Wold representation. Then, assuming multivariate Gaussian white noise with known fractional differencing, autoregressive and moving average matrix parameters, the differential and Rényi entropies and Kullback–Leibler and Rényi divergences were obtained by evaluating the variance-covariance matrix identified with VARFIMA process distribution. The influences of the fractional differencing parameters on the Rényi entropy increment were analyzed, as were comparisons between VARFIMA processes using the Kullback–Leibler and Rényi divergences. Finally, numerical examples and an application to U.S. daily temperature time series are presented.

Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.

Continuity of daily temperature time series in the transition from conventional to automated stations for the Colombian coffee network

AbstractThe transition from conventional weather stations (CWSs) to automated weather stations (AWSs) of the Colombian coffee network has required testing their performance and adjusting the temperature measurements to ensure the continuity of the historical CWS series. In this study, the mean (Tmean), minimum (Tmin), and maximum temperature (Tmax) measurements of CWS and AWS operating in parallel at 36 locations between 2014 and 2019 were compared, and the biases of the daily temperature differences (CWS − AWS), the agreement index (d), and the percentage of data within the allowed range (PR05) were calculated. The most consistent method for calculating Tmean and Tmax for CWS was selected for use on the AWS data. With the standard normal homogeneity test and with the metadata, we found that the series of temperature differences between CWS and AWS was not homogeneous, instrument failures and sensor changes being the main causes of the lack of homogeneity. The statistical analyses indicated that the AWS data need to be adjusted to be continuous with the CWS series. To correct the temperature bias, two approaches were applied: quantile mapping and the additive constant. The results suggest that the quantile mapping adjustments improve the average bias at all stations but do not necessarily bring the percentage to within ±0.5°C. In Tmin and Tmean, 12 AWSs can give continuity with the historical series of the CWS, and for the rest of the stations and variables, the series of the AWSs are independent of the CWSs.

Evidence of rapid adaptation integrated into projections of temperature-related excess mortality

Few studies have used empirical evidence of past adaptation to project temperature-related excess mortality under climate change. Here, we assess adaptation in future projections of temperature-related excess mortality by employing evidence of shifting minimum mortality temperatures (MMTs) concurrent with climate warming of recent decades. The study is based on daily non-external mortality and daily mean temperature time-series from 11 Spanish cities covering four decades (1978–2017). It employs distributed lag non-linear models (DLNMs) to describe temperature-mortality associations, and multivariate mixed-effect meta-regression models to derive city- and subperiod-specific MMTs, and subsequently MMT associations with climatic indicators. We use temperature projections for one low- and one high-emission scenario (ssp126, ssp370) derived from five global climate models. Our results show that MMTs have closely tracked mean summer temperatures (MSTs) over time and space, with meta-regression models suggesting that the MMTs increased by 0.73 °C (95%CI: 0.65, 0.80) per 1 °C rise in MST over time, and by 0.84 °C (95%CI: 0.76, 0.92) per 1 °C rise in MST across cities. Future projections, which include adaptation by shifting MMTs according to observed temporal changes, result in 63.5% (95%CI: 50.0, 81.2) lower heat-related excess mortality, 63.7% (95%CI: 30.2, 166.7) higher cold-related excess mortality, and 11.2% (95%CI: −5.5, 39.5) lower total temperature-related excess mortality in the 2090s for ssp370 compared to estimates that do not account for adaptation. For ssp126, assumptions on adaptation have a comparatively small impact on excess mortality estimates. Elucidating the adaptive capacities of societies can motivate strengthened efforts to implement specific adaptation measures directed at reducing heat stress under climate change.

Creation of a representative climatological database for Hungary from 1870 to 2020

Climate studies, particularly those that are related to climate change, require long, high-quality controlled data sets, which are representative both spatially and temporally. Changing the conditions of measurements, for example relocating the station, or changing the frequency and timing of measurements, or changing the instruments used can cause breaks in the time series. To avoid these problems, data errors and inhomogeneities are eliminated and the data gaps are filled by using the MASH (Multiple Analysis of Series for Homogenization, Szentimrey, 1999, 2008) homogenization procedure. The Hungarian meteorological observation network was upgraded significantly in the last decades. Homogenization of the data series raises the question of how to homogenize long and short data series together within the same process. It is possible to solve this with the MASH method due it has solid mathematical foundations, which make it suitable for such purposes. The solution includes the synchronization of the common parts’ inhomogeneities within three (or more) different MASH processing of the three (or more) datasets with different lengths depending on the time periods and elements. After the homogenization process, the station data series were interpolated to a 0.1 degree regular grid covering the whole area of Hungary. The MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari, 2007) program system was used for this purpose. The MISH procedure was developed specifically for the interpolation of various meteorological elements. In the case of mean temperature, we also renewed the MISH modeling, as compared to previous years, the number of homogenized stations doubled due to the new work, so it was expedient to model the climate statistical parameters with this extended station system. Time series of daily mean temperature and precipitation sum for the period 1870–2020 for Hungary were used in this study. As a result, the longest ever homogenized, gridded daily data sets became available for Hungary. The method described here can also be applied to produce representative datasets for other meteorological elements.

Benchmarking the performance of homogenization algorithms on synthetic daily temperature data

AbstractThis paper describes a new homogenization algorithm validation methodology, and its use to assess the skill of eight different algorithms, when applied to synthetic daily temperature time series. These algorithms were ACMANT, Climatol (in both daily and monthly configurations), DAP, HOM, MAC‐D, MASH and SpliDHOM. Algorithms were tested on benchmark data replicating daily temperature variability in four regions in North America: Wyoming, the South East, the North East and the South West. These benchmarks contained plausible spatial and temporal correlation, differing station densities and both abrupt and gradual inhomogeneities. Algorithm ability was assessed both according to detection of inhomogeneities and correction of their effects, investigating bias, root‐mean‐square error (RMSE), linear trend recovery, station extremes and variability recovery. Inhomogeneities with a magnitude greater than 1°C were those most commonly detected with smaller inhomogeneities, and those that were not constant over time, proving harder to identify. Regional RMSE was always reduced by all algorithms and regional bias was reduced in over half of the region/scenario pairs. Trend recovery was variable, but the correct sign of regional trends was retained by all algorithms. Areas for future algorithm improvement include working with autocorrelated data and correcting moments higher than the mean. The data are available from https://www.metoffice.gov.uk/hadobs/benchmarks and the validation code from https://github.com/RachelKillick/Daily_benchmarks allowing the extension of this work and its application to new algorithms.

Quality control and filling of daily temperature and precipitation time series in Colombia

Quality control and filling of daily temperature and precipitation time series in Colombia

Estimating the uncertainty of Australian area‐average temperature anomalies

AbstractAn important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850.