ENSO, seasonal rainfall patterns and simulated maize yield variability in Zimbabwe

1998 ◽  
Vol 90 (1-2) ◽  
pp. 39-50 ◽  
Author(s):  
J.G Phillips ◽  
M.A Cane ◽  
C Rosenzweig
2018 ◽  
Vol 23 (4) ◽  
pp. 05018001 ◽  
Author(s):  
Sathyanathan Rangarajan ◽  
Deeptha Thattai ◽  
Sai Rutwik Reddy Yellasiri ◽  
Revanth Vytla ◽  
Nishanth Tedla ◽  
...  

2020 ◽  
Vol 143 (1-2) ◽  
pp. 177-191
Author(s):  
Peter Hoffmann ◽  
Arne Spekat

AbstractThis study looks into the question to what extent long-term change patterns of observed temperature and rainfall over Europe can be attributed to dynamical causes, in other words: Are the observed changes due to a change in frequency of the patterns or have the patterns’ dynamical properties changed? By using a combination of daily meteorological data and a European weather-type classification, the long-term monthly mean temperature and precipitation were calculated for each weather-type. Subsequently, the observed weather-type sequences were used to construct analogue time series for temperature and precipitation which only include the dynamical component of the long-term variability since 1961. The results show that only a fraction of about 20% of the past temperature rise since 1990, which for example amounted to 1 °C at the Potsdam Climate Station can be explained by dynamical changes, i.e. most of the weather-types have become warmer. Concerning long-term changes of seasonal rainfall patterns, a fraction of more than 60% is considerably higher. Moreover, the results indicate that for rainfall compared with temperature, the decadal variability and trends of the dynamical component follow the observed ones much stronger. Consequently, most of the explained seasonal rainfall variances can be linked to changes in weather-type sequences in Potsdam and over Europe. The dynamical contribution to long-term changes in annual and seasonal rainfall patterns dominates due to the fact that the alternation of wet and dry weather-types (e.g. the types Trough or High pressure over Central Europe), their frequencies and duration has significantly changed in the last decades.


2013 ◽  
Vol 486 ◽  
pp. 412-419 ◽  
Author(s):  
Fanghua Hao ◽  
Siyang Chen ◽  
Wei Ouyang ◽  
Yushu Shan ◽  
Shasha Qi

2021 ◽  
Author(s):  
David Lafferty ◽  
Ryan Sri ◽  
Iman Haqiqi ◽  
Thomas Hertel ◽  
Klaus Keller ◽  
...  

Abstract Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Previous studies typically focus their uncertainty analyses on climatic variables and are silent on how these uncertainties propagate into human systems through their subsequent incorporation into econometric models. Here, we use a multi-model ensemble of statistically downscaled and bias-corrected climate models, as well as the corresponding CMIP5 parent models, to analyze uncertainty surrounding annual maize yield variability in the United States. We find that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest historically observed yield shocks. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.


OALib ◽  
2016 ◽  
Vol 03 (09) ◽  
pp. 1-9 ◽  
Author(s):  
Kabongo Tshiabukole ◽  
Pongi Khonde ◽  
Muliele Muku ◽  
Kizungu Vumilia ◽  
Kiasala Lunekua ◽  
...  

2019 ◽  
Vol 177 (11) ◽  
pp. 5551-5565 ◽  
Author(s):  
K. V. Narasimha Murthy ◽  
T. Amaranatha Reddy ◽  
K. Vijaya Kumar

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