Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a p...Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.展开更多
The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been perf...The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.展开更多
The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over t...The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over the United States in establishing outbreak-favorable environments. The predictability of Rossby wavetrain phasing is strongly related to numerous climate-scale interannual variability indices, which are predictable many months in advance. To formalize the relationship between interannual variability indices and seasonal tornado outbreak frequency, indices derived from monthly mean Northern Hemisphere 500-hPa and 1000-hPa geopotential height fields and Ni？o 3.4 indices for ENSO phase were compared to annual tornado outbreak seasonal frequencies. Statistical models predicting seasonal outbreak frequency were established using linear（stepwise multivariate linear regressione SMLR） and nonlinear（support vector regressione SVR） statistical modeling techniques.The stepwise methodology revealed predictors that are important in establishing outbreak-favorable environments at long lead times. Additionally, the results of the statistical modeling revealed that the nonlinear SVR technique reduced root mean square errors produced by the control SMLR technique by 28% and provided more consistent forecasts. A preliminary physical analysis revealed that years with high outbreak frequencies were associated with the presence of 500-mb troughs over the central and western US during the peak of outbreak season, while lower frequencies were consistent with ridging over the US or northwest flow over the Plains. These patterns support the results of the statistical modeling, which demonstrate the utility of geopotential height variability as a predictability measure of outbreak frequency.展开更多
Thirty years of monthly mean anomalies of sea level(SL) at 15 Japanese coastal stations, sea sur-face temperature (SST) and sea level pressure (SLP) in or over the northern Pacific were analyzed bycanonical correlatio...Thirty years of monthly mean anomalies of sea level(SL) at 15 Japanese coastal stations, sea sur-face temperature (SST) and sea level pressure (SLP) in or over the northern Pacific were analyzed bycanonical correlation analysis (CCA) to study the relationship between the interdecadal SL variationand large scale climate state. Given two time-varying fields this technique identifies the pair ofspacial patterns with optimally correlated time series.The results show that there are two important air-sea interactive processes in the extratropicalPacific region for the variation of the SL at the Japanese coast on interdecadal scale. One is theocean heating or cooling of the atmosphere over the Kuroshio extension region, which results in ahuge SLP anomalous vortex with planetary spacial scale big enough to change the global climate. An-other is the large Kuroshio meander phenomenon controlled by the large-scale wind-stress curls oneyear earlier in the adjacent region of the Hawaiian Islands. The first process展开更多
Because of its vast volume and heat capacity, the ocean contains most of the memory of the earth's ocean-atmosphere coupled system. It has been suggested that the ocean may delay global warming by absorbing large ...Because of its vast volume and heat capacity, the ocean contains most of the memory of the earth's ocean-atmosphere coupled system. It has been suggested that the ocean may delay global warming by absorbing large amounts of heat, that it may cause abrupt climate change due to its disrupted thermohaline circulation, and that it may set the time-scales for various climate oscillations. Although the slow pace and persistence of oceanic variations give hope to long-range prediction, there still exist large uncertainties in climate predictability. Presently available observations and models are generally inadequate for studying and predicting long-term climate changes. However, some short-term fluctuations such as ENSO have been well studied and shown to be highly predictable even with simplified models.展开更多
文摘Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41130103 and 41210007)
文摘The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.
基金supported by the National Science Foundation under Grant No.DGE-0947419 at Mississippi State University
文摘The predictability of dangerous atmospheric phenomena such as tornado outbreaks has generally been limited to a week or less. However, recent work has demonstrated the importance of the Rossby wavetrain phasing over the United States in establishing outbreak-favorable environments. The predictability of Rossby wavetrain phasing is strongly related to numerous climate-scale interannual variability indices, which are predictable many months in advance. To formalize the relationship between interannual variability indices and seasonal tornado outbreak frequency, indices derived from monthly mean Northern Hemisphere 500-hPa and 1000-hPa geopotential height fields and Ni？o 3.4 indices for ENSO phase were compared to annual tornado outbreak seasonal frequencies. Statistical models predicting seasonal outbreak frequency were established using linear（stepwise multivariate linear regressione SMLR） and nonlinear（support vector regressione SVR） statistical modeling techniques.The stepwise methodology revealed predictors that are important in establishing outbreak-favorable environments at long lead times. Additionally, the results of the statistical modeling revealed that the nonlinear SVR technique reduced root mean square errors produced by the control SMLR technique by 28% and provided more consistent forecasts. A preliminary physical analysis revealed that years with high outbreak frequencies were associated with the presence of 500-mb troughs over the central and western US during the peak of outbreak season, while lower frequencies were consistent with ridging over the US or northwest flow over the Plains. These patterns support the results of the statistical modeling, which demonstrate the utility of geopotential height variability as a predictability measure of outbreak frequency.
基金This project was supported by the Dinector's funds of the Chiese Academy of Seiences.
文摘Thirty years of monthly mean anomalies of sea level(SL) at 15 Japanese coastal stations, sea sur-face temperature (SST) and sea level pressure (SLP) in or over the northern Pacific were analyzed bycanonical correlation analysis (CCA) to study the relationship between the interdecadal SL variationand large scale climate state. Given two time-varying fields this technique identifies the pair ofspacial patterns with optimally correlated time series.The results show that there are two important air-sea interactive processes in the extratropicalPacific region for the variation of the SL at the Japanese coast on interdecadal scale. One is theocean heating or cooling of the atmosphere over the Kuroshio extension region, which results in ahuge SLP anomalous vortex with planetary spacial scale big enough to change the global climate. An-other is the large Kuroshio meander phenomenon controlled by the large-scale wind-stress curls oneyear earlier in the adjacent region of the Hawaiian Islands. The first process
基金the National Basic Research Program of China under contract No. 2007CB816005the National Natural Science Foundation of China under contract No.40730843.
文摘Because of its vast volume and heat capacity, the ocean contains most of the memory of the earth's ocean-atmosphere coupled system. It has been suggested that the ocean may delay global warming by absorbing large amounts of heat, that it may cause abrupt climate change due to its disrupted thermohaline circulation, and that it may set the time-scales for various climate oscillations. Although the slow pace and persistence of oceanic variations give hope to long-range prediction, there still exist large uncertainties in climate predictability. Presently available observations and models are generally inadequate for studying and predicting long-term climate changes. However, some short-term fluctuations such as ENSO have been well studied and shown to be highly predictable even with simplified models.
