http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf

The aim of this research is to evaluate the temperature outputs of climate forecasting systems over Iran. The analysis is provided based on Atmosphere-Ocean Coupled General Circulation Models from North America Multi Model Ensemble (NMME). The skill of NMME individual models are evaluated in differe...

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Main Authors: H. Najafi, A. R. Massah Bavani, P. Irannejad, A. W. Robertson
Format: Article
Language:English
Published: Iranian Society of Irrigation and Water 2018-09-01
Series:هواشناسی کشاورزی
Subjects:
Online Access:http://www.agrimet.ir/article_69417_7a9ddbceeead91b0185eaec5ab3fe826.pdf
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spelling doaj-6469bfb38c8f44e1b3d94b22704e8d442020-11-25T00:11:41ZengIranian Society of Irrigation and Waterهواشناسی کشاورزی2345-34192588-60022018-09-0161193010.22125/agmj.2018.113708.69417http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdfH. Najafi0A. R. Massah Bavani1P. Irannejad2A. W. Robertson3Ph. D. in Water Resources Engineering, College of Aburaihan, University of Tehran, IranAssociate Professor, College of Aburaihan, University of Tehran, IranAssociate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, IranHead of Climate Group, International Research Institute for Climate and Society (IRI), Erath Institute, Columbia University in New York, USAThe aim of this research is to evaluate the temperature outputs of climate forecasting systems over Iran. The analysis is provided based on Atmosphere-Ocean Coupled General Circulation Models from North America Multi Model Ensemble (NMME). The skill of NMME individual models are evaluated in different initializations, of lead times (0-month, 1-month and 2-month) for October-December (OND), December-February (DJF), and February-April (FMA) target seasons. Temperatures at 2m from Climate Research Unit (CRU) dataset are used as reference observation over 1982-2010. Pearson correlation, Mean Error and Root Mean Squared Error are calculated as deterministic verification criteria for seasonal forecast verification. In addition, Relative Operating Characteristic (ROC) score is calculated as a categorical measure for below-normal and above-normal conditions. The results suggest that correlation between NMME forecasts and CRU is higher in FMA (compared to DJF and OND). CFSv2 has a significant skill in the south of Iran in FMA (correlation ≥ 0.9, ROC≥ 0.7). Spatial pattern of NMME biases is similar in three target seasons. GFDL-FLOR-B01 bias is lowest among all evaluated NMME models. At longer lead times; skill of some models is dropped for forecasting temperature in some river basins in Iran. Given large temperature biases found in NMME individual models, applying Model Output Statistics is recommended. Developing Multi-model Ensemble (MME) can also help to improve seasonal forecasts in Iran’s river basins for agriculture and water resources management applications.http://www.agrimet.ir/article_69417_7a9ddbceeead91b0185eaec5ab3fe826.pdfSeasonal Forecasting of TemperatureNorth American Multi-Model Ensemble (NMME)Iran’s river basins
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language English
format Article
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author H. Najafi
A. R. Massah Bavani
P. Irannejad
A. W. Robertson
spellingShingle H. Najafi
A. R. Massah Bavani
P. Irannejad
A. W. Robertson
http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
هواشناسی کشاورزی
Seasonal Forecasting of Temperature
North American Multi-Model Ensemble (NMME)
Iran’s river basins
author_facet H. Najafi
A. R. Massah Bavani
P. Irannejad
A. W. Robertson
author_sort H. Najafi
title http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
title_short http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
title_full http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
title_fullStr http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
title_full_unstemmed http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
title_sort http://www.agrimet.ir/article_69413_1f753aa38d785ddda126e4772b34e416.pdf
publisher Iranian Society of Irrigation and Water
series هواشناسی کشاورزی
issn 2345-3419
2588-6002
publishDate 2018-09-01
description The aim of this research is to evaluate the temperature outputs of climate forecasting systems over Iran. The analysis is provided based on Atmosphere-Ocean Coupled General Circulation Models from North America Multi Model Ensemble (NMME). The skill of NMME individual models are evaluated in different initializations, of lead times (0-month, 1-month and 2-month) for October-December (OND), December-February (DJF), and February-April (FMA) target seasons. Temperatures at 2m from Climate Research Unit (CRU) dataset are used as reference observation over 1982-2010. Pearson correlation, Mean Error and Root Mean Squared Error are calculated as deterministic verification criteria for seasonal forecast verification. In addition, Relative Operating Characteristic (ROC) score is calculated as a categorical measure for below-normal and above-normal conditions. The results suggest that correlation between NMME forecasts and CRU is higher in FMA (compared to DJF and OND). CFSv2 has a significant skill in the south of Iran in FMA (correlation ≥ 0.9, ROC≥ 0.7). Spatial pattern of NMME biases is similar in three target seasons. GFDL-FLOR-B01 bias is lowest among all evaluated NMME models. At longer lead times; skill of some models is dropped for forecasting temperature in some river basins in Iran. Given large temperature biases found in NMME individual models, applying Model Output Statistics is recommended. Developing Multi-model Ensemble (MME) can also help to improve seasonal forecasts in Iran’s river basins for agriculture and water resources management applications.
topic Seasonal Forecasting of Temperature
North American Multi-Model Ensemble (NMME)
Iran’s river basins
url http://www.agrimet.ir/article_69417_7a9ddbceeead91b0185eaec5ab3fe826.pdf
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