Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

Chisanga, Charles Bwalya; Phiri, Elijah; Chinene, Vernon R. N.

doi:10.9734/bjast/2017/33531

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…In summary we can say that ANN is an attractive and workable alternative to complex physics-based coupled models that additionally require a large amount of tuning effort. The ANN-based models can be postprocessed with appropriate bias correction procedures to enhance their performance (Chen et al 2000;Chisanga et al 2017). 1 2 3 4 12 4 1 2 3 4 12 24 36 48 60 72 5 1 2 3 4 6 12 24 6 1 2 3 4 6 12 18 24 30 36 48 60 72 7 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 24 36 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 24 30 36 42 48 54 60 72 84…”

Section: Discussionmentioning

confidence: 99%

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Patil

Deo

2018

Journal of Atmospheric and Oceanic Technology

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The prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

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Section: Discussionmentioning

confidence: 99%

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Patil

Deo

2018

Journal of Atmospheric and Oceanic Technology

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“…The climate data for this study were obtained from the website of Climate Change, Agriculture and Food Security (CCAFS) (www.ccafs-climate.org/data/ (accessed on 31 May 2021)), developed by Consultative Group on International Agricultural Research (CGIAR), under a research program addressing the climate change impact on agricultural production. The AgMERRA, a high-resolution (0.25 • × 0.25 • ) daily time-series climate dataset, has been used in CCAFS data portal to simulate future climate data through CMIP5 GCMs, for the purpose of agriculture modeling [20][21][22][23]. Therefore, the AgMIP Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) baseline (historical) data from 1980-2004 (25 years) were used as an available observed data on this website to generate the simulated baseline, and future climate data of 40 years (2020-2059).…”

Section: Climatic Datasets and Correction Methodologymentioning

confidence: 99%

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Jha

Kalita

Cooke

2021

Climate

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The change in future climate will have a prominent impact on crop production and water requirement. Crop production is directly related to climatic variables. Temperature, solar radiation, wind, precipitation, CO2 concentration and other climatic variables dictate crop yield. This study, based on long-term historical data, investigates the patterns and changes in climatic variables (precipitation, temperature, and solar radiation) that would most significantly affect the future crop production in many parts of the world, and especially in India, where most farmers depend on rainfall for rice production. Statistical analyses—box and whisker plot, mean absolute error, Taylor diagram, double mass curve, Mann–Kendall trend test, and projected climate change—were used to assess the significance of the climatic factors for the purpose of agricultural modeling. Large variability in precipitation may cause the flash floods and affect the farming, and at the same time, increase in temperature from baseline period will lead to high water requirement by crops, and may cause drought if rainfall does not occur. Decrease in solar radiation will affect crop growth and development, and thus, would hamper the crop production. The results of this study would be useful in identifying the negative issues arising from climate change in future agricultural practices in Bihar, India. Furthermore, the results can also help in developing management strategies to combat the climate change impact on crop production.

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“…The BC technique can be applied to correct both the historical and future time periods using the GCM output (Ho et al, 2012;Hawkins et al, 2013b;Chisanga et al, 2017) as presented in Equation 1.…”

Section: Bias Correctionmentioning

confidence: 99%

“…The CF assumes the daily variance correction is to the same degree during the future and baseline and the corrected daily time series data is computed by the equation below which considers changes in variance as reported by Ho et al (2012) and Chisanga et al (2017). (2) Where and denote the standard deviation ( ) in the future time segment of the GCM output and observed time series, respectively.…”

Section: Bias Correctionmentioning

confidence: 99%

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Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

Chisanga¹,

Phiri²,

Chinene³

2017

Sci. Res. Essays

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Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

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Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

Cited by 5 publications

References 29 publications

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Assessment of Climatic Parameters for Future Climate Change in a Major Agricultural State in India

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

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