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Title: Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia

Journal Article · · Climate Dynamics
ORCiD logo [1];  [1];  [1]
  1. George Mason Univ., Fairfax, VA (United States). Center for Ocean-Land-Atmosphere Studies
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Systematic error and forecast skill for temperature and precipitation in two regions of Southern Asia are investigated using hindcasts initialized May 1 from the North American Multi-Model Ensemble. We focus on two contiguous but geographically and dynamically diverse regions: the Extended Indian Monsoon Rainfall (70–100E, 10–30 N) and the nearby mountainous area of Pakistan and Afghanistan (60–75E, 23–39 N). Forecast skill is assessed using the Sign test framework, a rigorous statistical method that can be applied to non-Gaussian variables such as precipitation and to different ensemble sizes without introducing bias. We find that models show significant systematic error in both precipitation and temperature for both regions. The multi-model ensemble mean (MMEM) consistently yields the lowest systematic error and the highest forecast skill for both regions and variables. However, we also find that the MMEM consistently provides a statistically significant increase in skill over climatology only in the first month of the forecast. While the MMEM tends to provide higher overall skill than climatology later in the forecast, the differences are not significant at the 95% level. We also find that MMEMs constructed with a relatively small number of ensemble members per model can equal or outperform MMEMs constructed with more members in skill. This suggests some ensemble members either provide no contribution to overall skill or even detract from it.

Research Organization:
George Mason Univ., Fairfax, VA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
FG02-00ER41132
OSTI ID:
1623606
Journal Information:
Climate Dynamics, Vol. 53, Issue 12; ISSN 0930-7575
Publisher:
Springer-VerlagCopyright Statement
Country of Publication:
United States
Language:
English

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  • DelSole, Timothy; Yang, Xiaosong; Tippett, Michael K.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 139, Issue 670 https://doi.org/10.1002/qj.1961
journal May 2012
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Cited By (3)

Potential predictability of boreal winter precipitation over central-southwest Asia in the North American multi-model ensemble journal October 2019
Probabilistic predictions of the Indian monsoon rainfall in below-normal category using NMME global model products journal September 2018
Forecast Customization System (FOCUS): A Multimodel Ensemble-Based Seasonal Climate Forecasting Tool for the Homogeneous Climate Zones of Myanmar journal December 2019

Figures / Tables (14)

Table 1(p. 3)table Table 1
Fig. 1(p. 4)figure Fig. 1
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Fig. 11(p. 14)figure Fig. 11
Fig. 12(p. 16)figure Fig. 12
Fig. 13(p. 17)figure Fig. 13

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Meteorology & Atmospheric Sciences
Monsoon
ENSO
Forecast skill