Atmosphere

Research

25 pages, 2617 KiB

Open AccessArticle

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

by Hirut Getachew Feleke, Michael J. Savage, Kindie Tesfaye Fantaye and Fasil Mequanint Rettie

Atmosphere 2023, 14(3), 497; https://doi.org/10.3390/atmos14030497 - 3 Mar 2023

Cited by 4 | Viewed by 2920

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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23 pages, 5549 KiB

Open AccessArticle

Basin Runoff Responses to Climate Change Using a Rainfall-Runoff Hydrological Model in Southeast Australia

by Newton Muhury, Gebiaw T. Ayele, Sisay Kebede Balcha, Mengistu A. Jemberie and Ermias Teferi

Atmosphere 2023, 14(2), 306; https://doi.org/10.3390/atmos14020306 - 3 Feb 2023

Cited by 5 | Viewed by 2313

Abstract

The effects of climate change have been observed in the Murrumbidgee River basin, which is one of the main river basins in the southeast region of Australia. The study area is the largest and most important agricultural production area within the Murray Darling [...] Read more.

The effects of climate change have been observed in the Murrumbidgee River basin, which is one of the main river basins in the southeast region of Australia. The study area is the largest and most important agricultural production area within the Murray Darling Basin (MDB). It produces more than AUD 1.9 billion of agricultural products annually and accounts for about 46% of Australia’s total agricultural production. Since Australia’s economy largely depends on its natural resources, climate change adversely impacts the economy in various ways. According to the Intergovernmental Panel on Climate Change’s fifth assessment report (IPCC, AR5), the adaptive capacity and adaptation processes have increased in Australia. The country has implemented policies and management changes in both rural and urban water systems to adapt to future drought, unexpected floods, and other climatic changes. In this study, future catchment runoff has been estimated using the hydrological model, Simplified Hydrolog (SIMHYD), which is integrated with data from three different General Circulation Models (GCMs) and future emission scenarios. Two different representative concentration pathway (RCP) emission scenarios, RCP 4.5 and RCP 8.5, have been used to obtain downscaled future precipitation and evapotranspiration data for the period of 2016 to 2100. Modeling results from the two emission scenarios showed an anticipated warmer and drier climate for the Murrumbidgee River catchment. Runoff in the Murrumbidgee catchment is affected by various dams and weirs, which yields positive results in runoff even when the monthly rainfall trend decreases. The overall runoff simulation result indicated that the impact of climate change is short and intense. The result of the Simplified Hydrolog (SIMHYD) modeling tool used in this study under the RCP 4.5 scenario for the period 2016 to 2045 indicates a significant future impact from climate change on the volumes of runoff in the Murrumbidgee River catchment. For the same period, the climate change prediction showed a decrease in total annual rainfall within the range of 2% to 62%. This reduction in rainfall is projected to decrease river runoff in the upper catchments (e.g., Tharwa, and Yass) by 17% to 58% over the projected periods. However, the runoff trends in the lower sub-catchments (e.g., Borambola) have increased by 137% to 87% under RCP 4.5 and RCP 8.5, respectively. This increasing potential runoff trend in the lower Murrumbidgee catchments gives an indication to build irrigation dams for dry season irrigation management. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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15 pages, 2869 KiB

Open AccessArticle

Early Warning Signals of Dry-Wet Transition Based on the Critical Slowing Down Theory: An Application in the Two-Lake Region of China

by Hao Wu, Pengcheng Yan, Wei Hou, Jinsong Wang and Dongdong Zuo

Atmosphere 2023, 14(1), 126; https://doi.org/10.3390/atmos14010126 - 6 Jan 2023

Viewed by 1646

Abstract

In recent years, the dry-wet transition (DWT), which often leads to regional floods and droughts, has become increasingly frequent in the Poyang Lake basin and the Dongting Lake basin (hereinafter referred to as the two-lake region). This study aims to investigate the early [...] Read more.

In recent years, the dry-wet transition (DWT), which often leads to regional floods and droughts, has become increasingly frequent in the Poyang Lake basin and the Dongting Lake basin (hereinafter referred to as the two-lake region). This study aims to investigate the early warning signals (EWSs) for DWT events. Firstly, based on the standardized precipitation index (SPI) at 161 meteorological stations in the two-lake region from 1961 to 2020, the two-lake region is divided into four sub-regions by the Rotational Empirical Orthogonal Function (REOF) analysis method. Then, the occurrence time of the DWT events in each sub-region is determined by the moving t-test (MTT) technique. Finally, by using two indicators (variance and the auto-correlation coefficient) to describe the critical slowing down (CSD) phenomenon, the EWSs denoting the DWT events in all sub-regions are investigated. The results reveal that there was a significant dry-to-wet (wet-to-dry) event around 1993 (2003) in the two-lake region during the last 60 years. The phenomenon of CSD, where the auto-correlation coefficient and variance increases are found in all sub-regions around 10 years before the DWT, suggests that it can be taken as an EWS for the DWT events. This study confirms the effectiveness of applying the slowing down theory in investigating the EWSs for abrupt changes in the two-lake region, aiming to provide a theoretical basis for effective prevention and mitigation against disasters in this region. Moreover, it is expected to be well-applied to the middle and lower reaches of the Yangtze River. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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14 pages, 3392 KiB

Open AccessArticle

Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models

by Muhammad Amjad, Ali Khan, Kaniz Fatima, Osama Ajaz, Sajjad Ali and Khusro Main

Atmosphere 2023, 14(1), 88; https://doi.org/10.3390/atmos14010088 - 31 Dec 2022

Cited by 5 | Viewed by 4155

Abstract

In this paper, the average monthly temperature of the Karachi region, Pakistan, has been modelled. The time period of the procured dataset is from January 1989 to December 2018. The Autoregressive Integrated Moving Average (ARIMA) modelling technique in conjunction with the Box–Jenkins approach [...] Read more.

In this paper, the average monthly temperature of the Karachi region, Pakistan, has been modelled. The time period of the procured dataset is from January 1989 to December 2018. The Autoregressive Integrated Moving Average (ARIMA) modelling technique in conjunction with the Box–Jenkins approach has been applied to forecast the average monthly temperature of the study area. A total of 83.33% of the trained dataset is used for construction of the model, and the remaining 16.67% of the dataset is used for the validation of the model. The best-fitted model is identified as ARIMA (2, 1, 4), generated on the basis of minimum values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) procedures. The accuracy parameters considered are Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Both parameters show that the model is 98.152% and 98.413% accurate, respectively. In addition, the Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier (ARCH-LM) test has been conducted to check the presence of heteroscedasticity in the residuals of the identified model. This test shows no heteroscedasticity present in the residual series. By means of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, the most appropriate orders of the ARIMA model are determined and evaluated. The model has been employed to investigate the time series variables’ precise impact on the scale of the regional warming scenario. Accordingly, the created model can help in determining future strategies related to weather conditions in the Karachi region. From the forecast result, it is found that the average temperature seems to show an increasing trend. Such an increasing trend can potentially upset the weather conditions and economic activities of the coastal area of Pakistan. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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12 pages, 4127 KiB

Open AccessArticle

Contributions of Multiple Water Vapor Sources to the Precipitation in Middle and Lower Reaches of Yangtze River Based on Precipitation Recycle Ratio

by Zeng-Ping Zhang, Xi-Yu Wang, Min Liu, Bi-Cheng Huang, Yong-Ping Wu, Guo-Lin Feng and Gui-Quan Sun

Atmosphere 2022, 13(12), 1957; https://doi.org/10.3390/atmos13121957 - 23 Nov 2022

Cited by 2 | Viewed by 1508

Abstract

Global warming weakened the summer monsoon and increased the evaporation, leading to more contribution of local evaporation moisture to the local precipitation for the monsoon areas. However, the descriptions of the contribution of the local moisture to the total precipitation and its characteristics [...] Read more.

Global warming weakened the summer monsoon and increased the evaporation, leading to more contribution of local evaporation moisture to the local precipitation for the monsoon areas. However, the descriptions of the contribution of the local moisture to the total precipitation and its characteristics have not been known very well. In this paper, taking the middle and lower Reaches of the Yangtze River (MLRYR) as a case and using the precipitation recycling process model, we analyzed the characteristics of the contribution of the local moisture to the total precipitation and the possible reasons. The results show that: the seasonal difference in precipitation recycling rates is obvious, the precipitation recycling rates in spring and summer are small (18.30% and 19.30%), the maximum in autumn is 30.50%, and the precipitation recycling rates in all seasons except summer show a significant upward trend (about 1.70%/10a). Additionally, the water vapor input into MLRYR from four boundaries significantly reduced except for the eastern boundary, and the water vapor contribution from the South and East borders is in summer, and the water vapor contribution from the North and West borders is in autumn, winter and spring. We suggest that the model of the precipitation recycling rate is important to evaluate the contribution of different water vapor sources, and help to further improve the ability of river water prediction in flood season. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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11 pages, 1903 KiB

Open AccessArticle

Extreme Precipitation Strongly Impacts the Interaction of Skewness and Kurtosis of Annual Precipitation Distribution on the Qinghai–Tibetan Plateau

by Tong Guo

Atmosphere 2022, 13(11), 1857; https://doi.org/10.3390/atmos13111857 - 8 Nov 2022

Cited by 2 | Viewed by 2237

Abstract

Characterizing extreme precipitation precisely is crucial for predicting vegetation response to drought or storms. However, current precipitation generators in vegetation models do not simulate the occurrence and amount of extreme precipitation well. This study examined the effects of extreme precipitation on the skewness, [...] Read more.

Characterizing extreme precipitation precisely is crucial for predicting vegetation response to drought or storms. However, current precipitation generators in vegetation models do not simulate the occurrence and amount of extreme precipitation well. This study examined the effects of extreme precipitation on the skewness, kurtosis, and skewness–kurtosis interaction of annual precipitation distribution. The examination was based on theoretical calculations and monitoring data from 78 meteorological stations on the Qinghai–Tibetan Plateau (QTP). The results showed that extreme precipitation generally increased the skewness and kurtosis of annual precipitation distribution. A higher mean annual precipitation amplified the effects of precipitation extremes on promoting skewness and kurtosis in normal distribution scenarios. In contrast, these effects tended to be saturated for scenarios of higher mean annual precipitation in probability-based distributions. A reduction of dry days in a year markedly intensified the interaction of the skewness and the kurtosis, while the skewness–kurtosis interaction weakened with decreased maximum daily precipitation in a year. Moreover, the effect of extreme precipitation on the skewness–kurtosis interaction was stronger in arid or low-altitude areas. This study illustrates the fact that considering the skewness and kurtosis of annual precipitation distributions will be very helpful for simulating extreme precipitation on the QTP in the future. This will allow us to better understand the impact of climate change on alpine plants. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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13 pages, 1910 KiB

Open AccessArticle

Study on the Complexity Reduction of Observed Sequences Based on Different Sampling Methods: A Case of Wind Speed Data

by Xiaowei Huai, Pengcheng Yan, Li Li, Zelin Cai, Xunjian Xu and Xiaohui Hu

Atmosphere 2022, 13(11), 1746; https://doi.org/10.3390/atmos13111746 - 23 Oct 2022

Cited by 1 | Viewed by 1450

Abstract

Many studies have confirmed that the complexity of a time sequence is closely related to its predictability, but few studies have proposed methods to reduce the time sequence complexity, which is the key to improving its predictability. This study analyzes the complexity reduction [...] Read more.

Many studies have confirmed that the complexity of a time sequence is closely related to its predictability, but few studies have proposed methods to reduce the time sequence complexity, which is the key to improving its predictability. This study analyzes the complexity reduction method of observed time sequences based on wind speed data. Five sampling methods, namely the random method, average method, sequential method, max method and min method, are used to obtain a new time sequence with a low resolution from a high resolution time sequence. The ideal time sequences constructed by mathematical functions and the observed wind speed time sequences are studied. The results show that the complexity of ideal time series of periodic sequences, chaotic sequences and random sequences increases in turn, and the complexity is expressed by the approximate entropy (ApEn) exponent. Furthermore, the complexity of the observed wind speed is closer to the complexity of a random sequence, which indicates that the wind speed sequence is not easy to predict. In addition, the complexity of sub-time series change with different sampling methods. The complexity of sub-time series obtained by the average method is the lowest, which indicates that the average method can reduce the complexity of observed data effectively. Therefore, the complexity of sub-time series sampled from the high-resolution wind speed data is reduced by using the average method. The method that can reduce the complexity of wind speed substantially will help to choose the appropriate wind speed data, thus improving the predictability. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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17 pages, 3807 KiB

Open AccessArticle

Comparison of Urban Canopy Schemes and Surface Layer Schemes in the Simulation of a Heatwave in the Xiongan New Area

by Yiguo Xu, Wanquan Gao, Junhong Fan, Zengbao Zhao, Hui Zhang, Hongqing Ma, Zhichao Wang, Yan Li and Lei Yu

Atmosphere 2022, 13(9), 1472; https://doi.org/10.3390/atmos13091472 - 10 Sep 2022

Cited by 5 | Viewed by 1819

Abstract

Due to rapid growth and expansion, Xiongan New Area is at risk for heatwaves in the present and future induced by the urban heat island effect. Based on eight combined schemes, including two common WRF surface layer schemes (MM5 and Eta) and urban [...] Read more.

Due to rapid growth and expansion, Xiongan New Area is at risk for heatwaves in the present and future induced by the urban heat island effect. Based on eight combined schemes, including two common WRF surface layer schemes (MM5 and Eta) and urban canopy schemes (SLAB, UCM, BEP and BEP + BEM), simulation performance for 2-m temperature, 2-m relative humidity and 10-m wind during a heatwave in July 2019 was compared and analyzed. The simulation performance is ranked from best to worst: 2-m temperature, 2-m relative humidity, 10-m wind direction and 10-m wind speed. MM5 simulate 2-m temperature and 10-m wind speed better than Eta, but 2-m relative humidity worse. MM5 coupling BEP + BEM provides the highest simulation performance for 2-m air temperature, 10-m wind direction and 10-m wind speed but the worst for 2-m relative humidity. MM5 and Eta produce nearly opposite results for wind direction and wind speed. Due to the Anxin station close to Baiyang Lake, lake-land breeze affects the simulation findings, worsening the correlation between simulated 10-m wind and observation. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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16 pages, 7792 KiB

Open AccessArticle

Multi-Model Ensemble Prediction of Summer Precipitation in China Based on Machine Learning Algorithms

by Jie Yang, Ying Xiang, Jiali Sun and Xiazhen Xu

Atmosphere 2022, 13(9), 1424; https://doi.org/10.3390/atmos13091424 - 2 Sep 2022

Cited by 6 | Viewed by 2339

Abstract

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a [...] Read more.

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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11 pages, 1499 KiB

Open AccessArticle

Response of Natural Gas Consumption to Temperature and Projection under SSP Scenarios during Winter in Beijing

by Jingjing Min, Yan Dong and Hua Wang

Atmosphere 2022, 13(8), 1178; https://doi.org/10.3390/atmos13081178 - 25 Jul 2022

Viewed by 1818

Abstract

The present study investigates the response of natural gas consumption to temperature on the basis of observations during heating season (middle November–middle March) for the period 2002–2021 in Beijing, China, and then estimates temperature-related changes in the gas consumption under future scenarios by [...] Read more.

The present study investigates the response of natural gas consumption to temperature on the basis of observations during heating season (middle November–middle March) for the period 2002–2021 in Beijing, China, and then estimates temperature-related changes in the gas consumption under future scenarios by using climate model simulations from the Coupled Model Intercomparison Project Phase 6. Observational evidence suggests that the daily natural gas consumption normalized by gross domestic product is linearly correlated with the daily average temperature during heating season in the past two decades in Beijing. Hence, a linear regression model is built to estimate temperature-related changes in the natural gas consumption under future scenarios. Corresponding to a rising trend in the temperature, the natural gas consumption shows a decrease trend during 2015–2100 under both the SSP245 and the SSP585 scenarios. In particular, the temperature would increase rapidly from early 2040s to the end of 21st century under the SSP585 scenario, leading to an obvious reduction in the natural gas consumption for heating in Beijing. Relative to that in the present day (1995–2014), the natural gas consumption would show a reduction of approximately 9% (±4%) at the end of 21st century (2091–2100) under the SSP245 scenario and approximately 22% (±7%) under the SSP585 scenario. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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10 pages, 4200 KiB

Open AccessArticle

A Comprehensive Evaluation Model for Local Summer Climate Suitability under Global Warming: A Case Study in Zhejiang Province

by Kuo Wang, Zhihang Xu, Gaofeng Fan, Dawei Gao, Changjie Liu, Zhenyan Yu, Xia Yao and Zhengquan Li

Atmosphere 2022, 13(7), 1075; https://doi.org/10.3390/atmos13071075 - 7 Jul 2022

Cited by 3 | Viewed by 1477

Abstract

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is [...] Read more.

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is proposed. In this paper, a summer suitable index (SSI) for Zhejiang is calculated, including four secondary indices: a summer cool index (SCI), a comfort days index (CDI), a good air days index (GADI) and a vegetation cover index (VCI). Using a local evaluation criterion, summer climate suitable areas are distinguished objectively according to the SSI. The results show that especially suitable regions account for 4.97% of Zhejiang Province, very suitable regions account for 22.2%, suitable regions account for 39.58%, and general regions account for 33.25%. The summer climate suitable areas are located mainly in high mountains and hills and coastal island areas while plain areas cannot be considered a suitable destination for summer tourism. By comparison and discussion, the SSI is demonstrated to capture summer climate suitability well. In contrast to a fixed evaluation index, benchmark values obtained for the SSI depend on the local climate and the index is straightforward to apply. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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12 pages, 3486 KiB

Open AccessArticle

Self-Organized Criticality of Precipitation in the Rainy Season in East China

by Zhonghua Qian, Yuxin Xiao, Luyao Wang and Qianjin Zhou

Atmosphere 2022, 13(7), 1038; https://doi.org/10.3390/atmos13071038 - 29 Jun 2022

Viewed by 1632

Abstract

Based on daily precipitation data from 1960 to 2017 in the rainy season in east China, to a given percentile threshold of one observation station, the time that precipitation spends below threshold is defined as quiet time

τ

. The probability density functions [...] Read more.

Based on daily precipitation data from 1960 to 2017 in the rainy season in east China, to a given percentile threshold of one observation station, the time that precipitation spends below threshold is defined as quiet time

τ

. The probability density functions

τ

in different thresholds follow power-law distributions with exponent

β

of approximately 1.2 in the day, pentad and ten-day period time scales, respectively. The probability density functions

τ

in different regions follow the same rules, too. Compared with sandpile model, Γ function describing the collapse behavior can effectively scale the quiet time distribution of precipitation events. These results confirm the assumption that for observation station data and low-resolution precipitation data, even in China, affected by complex weather and climate systems, precipitation is still a real world example of self-organized criticality in synoptic. Moreover, exponent

β

of the probability density function

τ

, mean quiet time

{\bar{τ}}_{q}

and hazard function

H_{q}

of quiet times can give sensitive regions of precipitation events in China. Usual intensity precipitation events (UPEs) easily occur and cluster mainly in the middle Yangtze River basin, east of the Sichuan Province and north of the Gansu Province. Extreme intensity precipitation events (EPEs) more easily occur in northern China in the rainy season. UPEs in the Hubei Province and the Hunan Province are more likely to occur in the future. EPEs in the eastern Sichuan Province, the Guizhou Province, the Guangxi Province and Northeast China are more likely to occur. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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10 pages, 2342 KiB

Open AccessArticle

Decomposition of Trend and Interdecadal Variation of Evaporation over the Tropical Indian Ocean in ERA5

by Bicheng Huang, Tao Su, Zengping Zhang, Yongping Wu and Guolin Feng

Atmosphere 2022, 13(3), 496; https://doi.org/10.3390/atmos13030496 - 19 Mar 2022

Viewed by 2046

Abstract

Based on ERA5 from 1980 to 2018, we compare and analyze the trend and interdecadal variation of evaporation anomalies over the tropical Indian Ocean by the evaporation decomposition method. This method mainly decomposes the evaporation anomalies into the Newtonian cooling, stability, relative humidity, [...] Read more.

Based on ERA5 from 1980 to 2018, we compare and analyze the trend and interdecadal variation of evaporation anomalies over the tropical Indian Ocean by the evaporation decomposition method. This method mainly decomposes the evaporation anomalies into the Newtonian cooling, stability, relative humidity, wind speed, and transfer coefficient terms. The annual mean evaporation anomalies show an increasing trend (0.083 mm/d/decade). The Newtonian cooling term (0.026 mm/d/decade), the relative humidity term (0.032 mm/d/decade), and the wind speed term (0.026 mm/d/decade) play a major role in the increasing trend. The interdecadal variation of evaporation anomalies shows decreases in the 1980s and after the early 2000s, and an increase in the 1990s. The decreased evaporation anomalies in the 1980s are affected by the transfer coefficient term, which is associated with the North Atlantic Oscillation (NAO). The increased evaporation anomalies in the 1990s and the decreased evaporation anomalies since the early 2000s are largely controlled by the wind speed term, which are dominated by the Atlantic Multidecadal Oscillation (AMO). The Pacific Decadal Oscillation (PDO) may have important impacts on the interdecadal increase of evaporation anomalies by affecting the wind speed in the 1990s. Full article

(This article belongs to the Special Issue Multi-Scale Climate Change: Recent Trends, Current Progress and Future Directions)

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