Observational challenges in evaluating climate models

Collins, Michael R.; AchutaRao, Krishna; Ashok, Karumuri; Bhandari, S. M.; Mitra, Ashis K.; Prakash, Satya; Srivastava, Rajesh; Turner, Andrew G.

doi:10.1038/nclimate2012

Cited by 59 publications

(46 citation statements)

References 10 publications

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“…The climatological precipitation rate of the MME is greater than the GPCP data but smaller than the CMAP data over the South China Sea (SCS), and approximately equivalent to the GPCP data but smaller than the CMAP data off the eastern coast of the Philippines. The uncertainty among precipitation datasets has been noted by many previous studies (e.g., Collins et al 2013;Sperber et al 2013;Jourdain et al 2013). …”

Section: Model Evaluation On the Mean State And The Interannual Wnpshmentioning

confidence: 93%

“…To examine the impact of the natural variability of the climate system, multiple realizations from three models (IPSL-CM5A-LR, MIROC5, and MPI-ESM-LR) are analyzed. Given the uncertainty of observation (Collins et al 2013;Sperber et al 2013;Jourdain et al 2013), multiple observational and reanalysis data (hereafter referred to as observation) are employed to evaluate the models. The rainfall data used in this study include the Global Precipitation Climatology Project (GPCP) version 2 precipitation data (Adler et al 2003) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) precipitation data (Xie and Arkin 1997).…”

Section: Model Data and Methodsmentioning

confidence: 99%

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Responses of the Western North Pacific Subtropical High to Global Warming under RCP4.5 and RCP8.5 Scenarios Projected by 33 CMIP5 Models: The Dominance of Tropical Indian Ocean–Tropical Western Pacific SST Gradient

Zhou

2014

Journal of Climate

113

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Using the outputs of 33 coupled models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5), the changes of the western North Pacific subtropical high (WNPSH) in the 2050-99 period under representative concentration pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) scenarios relative to the 1950-99 period are analyzed. Under both scenarios, the projected changes in the WNPSH intensity are approximately zero in the multimodel ensemble mean (MME), and large intermodel spread is seen. About half of the models project an enhanced WNPSH and about half of the models project a weakened WNPSH under both scenarios. As revealed by both diagnostic studies and numerical simulations, the projected change in the WNPSH intensity is dominated by the change in the zonal sea surface temperature (SST) gradient between the tropical Indian Ocean (TIO) and the tropical western Pacific (TWP). A stronger (weaker) warming in the TIO is in favor of an enhanced (weakened) WNPSH, and a weaker (stronger) warming over the TWP is also in favor of an enhanced (weakened) WNPSH. The projected change of the WNPSH modulates the climate change over eastern China. Under both RCP4.5 and RCP8.5 scenarios, all of the models with a significantly increased (decreased) WNPSH intensity are associated with a significant increase in the precipitation over the northern (southern) part of eastern China and an enhanced (weakened) southerly wind.

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Section: Model Evaluation On the Mean State And The Interannual Wnpshmentioning

confidence: 93%

Section: Model Data and Methodsmentioning

confidence: 99%

Responses of the Western North Pacific Subtropical High to Global Warming under RCP4.5 and RCP8.5 Scenarios Projected by 33 CMIP5 Models: The Dominance of Tropical Indian Ocean–Tropical Western Pacific SST Gradient

Zhou

2014

Journal of Climate

113

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“…Kundzewicz and Gerten [] noted that detection and attribution of changes in observed data, projections for the future, and assessing and reducing uncertainty are the grand challenges related to assessment of climate change impacts on freshwater resources. Uncertainty in observed data sets can be as large as coming from the climate model simulations [ M. Collins et al ., ; Kannan et al ., ], which affect the understanding of processes and impact assessment of water resources. Moreover, uncertainty in observations poses challenges in evaluation of climate models [ M. Collins et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Climatic uncertainty in Himalayan water towers

Mishra

2015

JGR Atmospheres

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The Himalayan water towers (e.g., Indus, Ganges, and Brahmaputra) play a major role in water resource availability and affect a significant population in the South Asia region. Climatic uncertainty in the region not only hampers progress toward process understanding but also decision making. Observational data sets show uncertainty (standard deviation in mean temperature in data sets) of 0.2 to 0.5°C in winter (January-February-March-April) and the monsoon season (June-July-August-September) air temperature. Observational uncertainty in precipitation in the winter and monsoon seasons ranged between 6 and 17% (with respect to ensemble mean seasonal precipitation) in the Indus, Ganges, and Brahmaputra river basins. The Coordinated Regional Climate Downscaling Experiment (CORDEX) South Asia regional climate models (CORDEX-RCMs) show larger uncertainty (1-3.6°C in temperature and 18-60% in precipitation) than that of the observations. Moreover, RCMs exhibit a large cold bias (6-8°C) and are not able to reproduce the observed warming in the Himalayan water towers. In fact, the CORDEX-RCMs overestimate observed warming by threefold in Ganges and Brahmaputra basins, which raises a question on their reliability on future warming trends in the region. The CORDEX-RCMs overestimate the area that experienced significant warming and fail to reproduce precipitation trends in both magnitude as well as direction. In observational data sets, uncertainty in precipitation and air temperature increases with elevation, which may be associated with sparse observations. However, the CORDEX-RCMs showed larger uncertainty at the lower elevations in both precipitation as well as temperature. The host general circulation models show a better performance in simulating winter climate than the CORDEX-RCMs, which suggest that an improved representation of elevation may not necessarily improve the model's performance. While observations show significant warming in the Indus basin and decline in the monsoon season precipitation in the Ganges basin, reliability of future climate projections and their impacts on water resources in the region will depend on improvements in the models and observations in coming years.

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“…Compared to the pre-industrial period, selected CMIP5 models show an increase in mean monsoon rainfall of 5-20 percent in a 4 °C world (Jourdain et al 2013). A significant uncertainty remains (see also hashed areas in Figure OR3), compare Collins et al (2013) and Sperber et al (2012).…”

Section: Monsoonmentioning

confidence: 96%