Climate change affects ecosystems, communities and individual organisms. Plants, for instance, are primary producers which often adapt to extreme environmental conditions with different strategies, especially in arid areas ¹ . Many plant species in the region exhibit adaptation to extremes of heat and drought. In the United Arab Emirates (UAE), for example, vegetation may be considered already highly resilient to climate change ² . However, some of these species may be close to their physiological tolerance limits. This presents a natural challenge to living organisms ¹ . As such, the present study attempted to assess temperature patterns, in order to further investigate the overall impact of climate change on land cover and land use dynamics.

With the expected change in climate, such species will experience additional stressors, leading to reduced land cover and therefore degradation due to overgrazing and land use change ³ . The UAE climate is classified as hyper-arid, with different bio-climatic zones ⁴ and hence a variety of environmental challenges, including very low land cover coupled with major shifts in land use ⁵ . In the north-eastern regions of the UAE, the average precipitation is higher while the temperatures are lower ⁴ . Additionally, and in comparison, these conditions are similar to those of other areas in the Arabian Gulf, where the mean annual temperatures increase along a north–south gradient from Kuwait and Riyadh towards Dibba and Muscat. But there may be patterns in rainfall ¹ as well as temperature. Consequently, there exists a need to assess patterns in climatic conditions, especially with the potential impact of climate change regionally as well as globally.

Climate change has also been impacting the UAE. The UAE’s tropical and dry climate is subject to impacts from the Indian Ocean across the Sea of Oman ⁶ . Across the UAE, using 2012 statistics performed by the Ministry of Environment and Water ⁶ , the temperature increased by 0.6°C to 2.7°C. Raising temperature, for instance, has been observed in Abu Dhabi with an increase of around 2.3°C between 1982 and 2013, while an increase of 2.7°C between 2013 and 1975 in Dubai ⁶ . Other cities have also experienced increases between 0.6 and 1.8°C during the past 30–40 years ⁶ . The Gulf temperatures are also changing. During the last 50 years, the Arabian gulf surface temperature increased 0.2°C per decade. In other parts of the Arabian Gulf, the increase was estimated to be thrice the global average or 0.6°C per decade ⁷ . Additionally, sea surface temperatures, sea surface salinity and sea level were projected to change ⁸ . Wind patterns were also estimated to change across the region ⁸ , which normally affects temperature. Further, and in terms of energy supply, for instance, the UAE 2030 strategic plan envisages to generate more than quarter of its electricity from clean energy sources ⁶ . It is therefore believed that predicting temperature changes in different parts of the UAE is necessary, especially with the widespread use of time series analyses.

Auto Regressive Integrated Moving Average (ARIMA) based models are used in time series analyses to predict changes in climatic conditions ⁹ . ARIMA is a class of models that predicts a given time series based on historical values ¹⁰ . It is a modeling approach in which three predictors (i.e. p, d and q) are calculated to be the basis for the forecasting process. ARIMA has been used to predict solar radiation ⁹ , weather ¹¹ , evapo-transpiration ¹² , wind speed ¹³ and rain & temperature ¹⁴ . Generally, the goodness of fit of the model is tested against standardized residuals, the auto-correlation function (ACF), and the partial autocorrelation function (PACF). The ACF plot shows the correlation of the series with itself at different lags, while the PACF plot shows the amount of auto-correlation at a given lag that is not explained by lower-order auto-correlations ¹⁵ . For evaluation purposes, the model is compared with Monte Carlo simulations, using mean absolute error, R-square error, root mean square error and sum of square error. ARIMA performed better than Adaptive Network Based Fuzzy Inference System in a study to forecast weather in Bangladesh ¹¹ , Arca et al. ¹² ; however, a lower performance in modeling evapo-transpiration when compared to other modeling approaches was reported. The relationship between circulation type frequencies and temperature indices revealed a warming effect for both winter and summer ¹⁶ . The number of days with extreme high temperatures in the wheat growing regions of the Netherlands, for instance, significantly increased since the early 1900s ¹⁷ , while the number of extreme low temperature events dropped during the same period. It is therefore useful to use standardized indices such as STI (standardized temperature index) to compare historical values across and within years.

STI is calculated in the same way as was reported by McKee and Kleist ¹⁸ for the standardized precipitation index (SPI). Specifically, SPI was calculated based on the Palmer Drought Severity Index, which is widely used for drought forecasting ^{19, 20} and evapo-transpiration assessment ²¹ . Such standardization process allows for comparison across space and time at any location ²² . Both SPI and STI have been developed into RStudio packages ²³ , and in the present work we limit our analyses to STI. The basis of which has been that assessing temperature anomalies may be useful for climate forecasting purposes. Temperature anomalies may also be used as indicators of climate change ²⁴ . Trends in annual mean temperature anomalies for the globe show rapid and steady warming through the early 1940s ²⁵ . As a consequence, the present study aimed to assess the use of ARIMA modeling approach coupled with STI to predict temperature anomalies across four bio-climatic UAE regions. Historical data is used for the ARIMA forecasting and STI categorization of climatic events.

Here, we assessed the use of ARIMA modeling approach coupled with STI to predict temperature anomalies across four UAE bio-climatic regions. ARIMA modeling proved to be valid to assess such predictive powers. Drought forecasting was validated through ARIMA modeling ²⁸ . In the present study, the ARIMA forecasting assessment showed that all zones, except Dubai, were predicted to have constant trend of average temperatures until 2030. For Dubai, however, there was a slight increasing trend. The ARIMA predictors (i.e. p, d and q) were in the order of 2,0,2. The estimates reported by Koenker and Schorfheide ²⁹ suggest a generally upward sloping trend of the temperature series during their study period. ACF and PACF trends showed that the data were not autocorrelated. Weather data were used in similar attempts (e.g. 30, 31). It was also used to predict long-term variability in solar irradiation and surface air temperature ³² . Surface air temperature was closely predicted by ARIMA modeling ³² . Increasing trends were also reported about the maximum temperature in north-eastern Bangladesh ³³ . Globally, temperature changes had similar ranges when compared to various climate sensitivities ³⁴ . When using various variants of ARIMA, some outperformed others based on Mean Absolute Percentage Error, Maximum Absolute Percentage Error and Mean Absolute Error ³⁵ . It is important to highlight that ARIMA was used by Corchado and Fyfe ³⁶ for comparisons with other modeling approaches.

The STI assessment of the historical data for all four bioclimatic zones showed that "near normal" events were the highest in Sharjah and Al-Ain (171 and 168, respectively). Further, Dubai had 34 "moderately hot" events, while Abu Dhabi has experienced only 12 such events.

Increased frequencies of warm days and warm nights, higher extreme temperature values were reported by Donat et al. ²⁶ . They added that the warming trends were stronger in the 1970s. In the present assessment, the scatter plots of the STI values were around zero for both Abu Dhabi and Al-Ain, while these values were skewed toward the upper limits for Dubai and Sharjah. An STI assessment in the Himalayas revealed significant positive trends of extreme monsoonal temperatures in most regions studied ³⁷ .

Extreme events were reported to be impacted by the changing climate. It was projected that temperatures in the Arabian Gulf are likely to come close or even exceed critical limits ³⁸ . It was also highlighted that this region will be a regional hot spot if no major mitigation measures are in place. Significant negative trends of the number of days when temperature was below its 10th percentile and daily temperature range was also reported ³⁹ .

The spatial and temporal variations of the extreme events and the changing temperature reported in the present work are critical. They impact various facets of our environment. Such impact can be detrimental to human health ⁴⁰ coral assemblages ⁴¹ , coral survival ⁴² , plants, birds, butterflies ⁴³ , wheat stress ⁴⁴ and much more. Targeting climate change causes (mitigation) and addressing its impact (adaptation) are urgently needed; locally, regionally and globally, as the region will remain a hot spot, if no major mitigation measures are in place ³⁸ .

ARIMA is a valid model to forecast temperature. This was true using the UAE historical data to predict temperature into the future. Much of the findings reported here agreed with previously published work. Some parts of the UAE will suffer more in terms of increased extreme events than others. STI can be used to further analyse temperature events and support ARIMA modeling approaches. Further assessments are needed to link between historical temperature data and the extracted ARIMA and STI outputs on the one hand and projected changes in daily/monthly temperatures on the other hand. Further, a detailed analysis of temperature fluctuations at finer spatial and temporal scales are also needed to strengthen any modeling approach.