Weakened Interannual Variability in the Tropical Atlantic

Climate variability in the Tropical Atlantic is complex with strong ocean-atmosphere coupling, where the sea surface temperature (SST) variability impacts the hydroclimate of the surrounding continents. We observe a decrease in the variability of the Tropical Atlantic after 1970 in both CMIP6 models and observations. Most of the Tropical Atlantic interannual variability is explained by its equatorial (Atlantic Zonal Mode, AZM) and meridional (Atlantic Meridional Mode, AMM) modes of variability. The observed wind relaxation after 1970 in both the equatorial and Tropical North Atlantic (TNA) plays a role in the decreased variability. Concerning the AZM, a widespread warming trend is observed in the equatorial Atlantic accompanied by a weakening trend of the trade winds. This drives a weakening in the Bjerknes Feedback by deepening the thermocline in the eastern equatorial Atlantic and increasing the thermal damping. Even though individually the TNA and Tropical South Atlantic (TSA) show increased variability, the observed asymmetric warming in the Tropical Atlantic and relaxed northeast trade winds after the 70s play a role in decreasing the AMM variability. This configuration leads to positive Wind-Evaporation-SST (WES) feedback, increasing further the TNA SST, preventing AMM from changing phases as before 1970. Associated with it, the African Sahel shows a positive precipitation trend and the Intertropical Convergence Zone tends to shift northward, which acts on maintaining the increased precipitation.

Analyzing trend and prediction of precipitation in Germany using non-parametrical tests and machine learning algorithms

The study explores analyzes the temporal changes in precipitation using the data from 1881 to 2020 across Germany at the regional level. Man-Kendall and Hamad-Rao modification tests were employed to analyze the precipitation trend,while Pettit test was used for detecting the change point in the time frame. Machine learning methods like k-nearest neighbour, Support vector machine and Random forest algorithms were applied for prediction. Most of the regions showed an increasing trend annually and seasonally in 0.05 significance level while some negative can be seen in summer. Furthermore, Based on Pettit test, most of the change points were detected after 1940 in several regions. In the prediction of precipitation, k-NN algorithm showed better performance in terms of mean absolute error rather than Support vector machine and Random forest algorithms.

The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

Assessing impact of weather variability and changing climate on oil-palm yield in major growing regions of southern Thailand

Oil palm yield is very responsive to weather fluctuations in the growing season. The purpose of this study was to investigate the relationship between yield variation and climate trends in the major oil palm-growing regions, especially in Southern Thailand (Chumphon; CP, Ranong; RN, Krabi; KB, Trang; TR, Satun; ST, Phang-Nga; PN, SuratThani; SR and Nakhon Si Thammarat; NS) where oil palm has been grown in a large plantation. Monthly weather variables from 16 agricultural meteorological stations were analyzed by linear and non-linear regressions over 28 years in each major oil palm-producing region. To evaluate the trends of changes in weather parameters and yield, a statistical model was developed for estimating oil palm yield based on climatic trends during 1994-2017. The results showed that warming trends were observed at all major oil palm-growing regions. There were pieces of evidence of significant correlation in temperature trends which had the strongest values in KB (Tmax, R2=0.534**) and PN (Tmin, R2=0.670**). The highest trends of ET and RH were also markedly increased in SR (R2=0.618**). Whereas precipitation trend had slightly increasing changes in CP (R2=0.220**) and PN (R2=0.233**). In addition, the annual trends in the values of Heliothermal Index, Dryness Index and Cool Night Index were markedly increased in NS, RN and KB, respectively. Comparing climate variables and yield variations over 19 years, the study indicated that the relationships between observed yield and estimated yield had highly significant differences in CP (R2=0.468**), SR (R2=0.735***) and NS (R2=0.579***), but there was lower value in KB (R2=0.098*) than those of the other regions. Therefore, this study indicates that recent climate trends have had an implicit effect on oil palm yield in the major producing regions in Southern Thailand. This study could be a guideline to further planning for oil palm management.

Climate Change Impact On Lake Tana Water Storage, Upper Blue Nile Basin, Ethiopia

Temperature and precipitation trend fluctuations influence the components of the hydrological cycle and the availability of water supplies and their resulting shifts in the balance of lake water (lake level). Quantile mapping was applied to correct temperature biases, and power transformation was applied for rainfall correction. The performance of the HBV model was evaluated through calibration and validation using objective functions (RVE, NSE) and provide RVE of 3.7%, -1.27%,1.05%, -0.72%,8.9% and -0.68 during calibration and RVE of -1.5%, 6.93%, -3.04%,8.796%, -5.89% and 8.5 % during validation for Gumara, Kiltie, Koga, Gilgel Abay, Megech and Rib respectively, While the model provided NS of 0.79,0.63,0.72,0.803,0.68 and 0.797 during calibration and NSE of 0.8,0.64,0.7,0.82,0.801 and 0.82 during validation for Gumara, Kiltie, Koga, Gilgel Abay, Megech, and Rib respectively. The simulated Lake level showed adequate agreement to the observed with NS and RVE of 0.7 and 6.44 % respectively. The result confirmed that over lake evaporation and rainfall increase for all future scenarios. The ungauged surface inflow is also increased shortly scenarios while gauged surface inflow increased for RCP4.5 (the 2070s) and RCP8.5 (2040s) and decreased for RCP4.5 (2040s) and RCP8.5 (2070s). The decreased in gauged surface water inflow is due to a decrease in inflow for Gilgel Abay, Koga and Gumara gauged catchments. Lake storage results showed a decrease in all future scenarios of all-time horizons.

Geospatial Assessment of Flood and Drought on the Chure Region of Western Nepal

Flood troubles people of central chure region and its vicinity almost every year. Besides, the area is also becoming susceptible to the drought currently. In that perspective, it is necessary to assess the vulnerability of floods and drought in the chure region. So this research focused on the assessment of flood and drought geospatially in Butwal city which was the major city and experience flood and drought every year. This study mainly records floods, calculates drought, finds their present condition, studies their trend, and determines drought and flood vulnerable areas within Butwal. Climate data, imagery, soil data, and socioeconomic data, and other relevant information were analyzed and extracted past drought conditions through Temperature and precipitation trend analysis and SPEI model. NDVI, NDDI, NDWI were used to find drought vulnerability status, and AHP and MCDA were used to find flood vulnerability. The city faced extreme drought in 2005, 2011, 2012, and 2013. And, Flood of 1970 and 1981 are the major floods. The area was frequently affected by floods in the past but no flood has been recorded after 2017. About 43% of the area was found high to very highly vulnerable to drought and 68% of the land was found vulnerable to extremely vulnerable to flood. So geospatial vulnerability assessment can enhance the planning decision and effectiveness of activities of the preventive measures by the stakeholders.

Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China

ERA5 is the latest fifth-generation reanalysis global atmosphere dataset from the European Centre for Medium-Range Weather Forecasts, replacing ERA-Interim as the next generation of representative satellite-observational data on the global scale. ERA5 data have been evaluated and applied in different regions, but the performances are inconsistent. Meanwhile, there are few precise evaluations of ERA5 precipitation data over long time series have been performed in Chinese mainland. This study evaluates the temporal-spatial performance of ERA5 precipitation data from 1979 to 2018 based on gridded-ground meteorological station observational data across China. The results showed that ERA5 data could capture the annual and seasonal patterns of observed precipitation in China well, with correlation coefficient values ranging from 0.796 to 0.945, but ERA5 slightly overestimated precipitation in the summer. Nonetheless, the results also showed that the accuracy of the precipitation products was strongly correlated with topographic distribution and climatic divisions. The performance of ERA5 shows spatial inherently across China that the highest correlation coefficient values locate in eastern, Northwestern and North China and the lowest biases locate in Southeast China. This study provides a reliable data assessment of the ERA5 data and precipitation trend analyses in China. The results provide accuracy references for the further use of precipitation satellite data for hydrological calculations and climate numerical simulations.

Precipitation Trends in the Ganges-Brahmaputra-Meghna River Basin, South Asia: Inconsistency in Satellite-Based Products

The Ganges-Brahmaputra-Meghna (GBM) river basin is the world’s third largest. Literature show that changes in precipitation have a significant impact on climate, agriculture, and the environment in the GBM. Two satellite-based precipitation products, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Multi-Source Weighted-Ensemble Precipitation (MSWEP), were used to analyze and compare precipitation trends over the GBM as a whole and within 34 pre-defined hydrological sub-basins separately for the period 1983–2019. A non-parametric Modified Mann-Kendall test was applied to determine significant trends in monsoon (June–September) and pre-monsoon (March–May) precipitation. The results show an inconsistency between the two precipitation products. Namely, the MSWEP pre-monsoon precipitation trend has significantly increased (Z-value = 2.236, p = 0.025), and the PERSIANN-CDR monsoon precipitation trend has significantly decreased (Z-value = −33.071, p < 0.000). However, both products strongly indicate that precipitation has recently declined in the pre-monsoon and monsoon seasons in the eastern and southern regions of the GBM river basin, agreeing with several previous studies. Further work is needed to identify the reasons behind inconsistent decreasing and increasing precipitation trends in the GBM river basin.