Monthly Minimum Air Temperature Research Articles

The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.

GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning

Abstract. An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature, and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Assessing the Correlation between Land Cover Conversion and Temporal Climate Change—A Pilot Study in Coastal Mediterranean City, Fethiye, Turkey

The rapid growth and expansion of urbanized landscapes in cities has resulted in an increase in air temperature and has lowered the bioclimatic comfort levels in urban landscapes. Recent studies to estimate the climatic response of urban landscape conversion have mostly examined the relationship between land use/land cover (LULC) change and land surface temperature (LST) data collected using advanced remote sensing (RS) techniques instead of atmospheric temperature. In this respect, four decadal Landsat images from the 1980s were used to investigate the impact of landscape transformation on atmospheric temperature. The mean and average minimum and maximum monthly air temperature datasets were used in the analysis. The CORINE (Coordination of Information on Environment) index was used to determine LULC diversity in an urban development boundary and urban periphery. Consequently, clustered LULC change values for the last three decades were integrated with decadal air temperature anomalies. The findings revealed an important relationship between monthly mean air temperature and land changes over recent decades, which resulted in an increase in urban fabric land use, deforestation land cover changes and conversion of permanent crop fields to artificial green houses for earlier vegetable production; the R-sqr values for these regressions were 97.7%, 88.5% and 90.6% respectively. On the other hand, the most important increasing temperature trends were obtained for the average monthly minimum air temperature, which supports the global warming concerns of the IPCC (Intergovernmental Panel on Climate Change) and related studies, which have concluded that an increased nighttime temperature results in urban heat islands (UHIs). The results should be used to support better urban landscape plans and architectural designs to improve human thermal comfort for sustainable urban life in Mediterranean cities. Street geometry and orientation to wind breeze, the Height/Width H/W ratio of buildings, and sizes of open and green spaces should be examined carefully in urban planning and design for climate adaptation.

Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.

Estimations of Several Climatic Values in the Freezing Season

The purpose of the author is to obtain the method to estimate the several climatic values based on the average monthly air temperature. For this purpose, the author investigated the relation between the air temperature and several climatic values, using the climatic data of meteorological offices in Japan. The results are as follows ;1) The relation between the average monthly minimum air temperature and the number of days under 0°C (Fig. 1), the length of the period under 0°C (Fig. 2) or the average calendar-day at the end of the freezing season (Fig. 4) can be shown by the tendency curve with little deviations.2) The average calendar-day leaving 0°C in minimum air temperature in spring (the end of freezing season) is closely related to the number of days under 0°C. Using the Fig. 6 showing the relation in each month, the author obtained Fig. 7, by which the number of freezing days after that day can be estimated.3) Regarding the minimum air temperature, the number of days under 0°C at 1.5 meter level is not always closely related to it near the ground (Fig. 8). But the deviations from the tendency-curve show the relation form a geographical pattern (Fig. 9).4) The relations between the average calendar-day with last-frost and the average monthly minimum air temperature are known (Fig. 10). From the figure the retarding-rate of the former per 1°C in the latter can be estimated as shown in Fig. 11. And the distribution of the climatic last-frost day will be obtained in micro-pattern corresponding to the distribution of average minimum air temperature.