scholarly journals Evaluation and Future Projection of Chinese Precipitation Extremes Using Large Ensemble High-Resolution Climate Simulations

2019 ◽  
Vol 32 (8) ◽  
pp. 2169-2183 ◽  
Author(s):  
Weili Duan ◽  
Naota Hanasaki ◽  
Hideo Shiogama ◽  
Yaning Chen ◽  
Shan Zou ◽  
...  
Keyword(s):  
High Resolution ◽  
General Circulation ◽  
Southern China ◽  
Coupled Model ◽  
Circulation Model ◽  
Internal Variability ◽  
Precipitation Extremes ◽  
Large Ensemble ◽  
Extreme Precipitation Events ◽  
The Difference

AbstractEvaluation of Chinese precipitation extremes is conducted based on large ensemble projections of the present climate and 4-K-warmer climates derived from a high-resolution atmospheric general circulation model. The model reproduced the overall trend and magnitude of total precipitation and extreme precipitation events for China reasonably well, revealing that this dataset can represent localized precipitation extremes. Precipitation extremes are more frequent and more severe in future projections under 4-K-warmer climates than in the representative concentration pathway 8.5 (RCP8.5) scenario of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our results show that using a large-ensemble simulation can improve the ability to estimate with high precision both the precipitation mean and the precipitation extremes compared with small numbers of simulations, and the averaged maximum yearly precipitation will be likely to increase by approximately 18% under a +4-K future in southern China compared with the past. Finally, uncertainty evaluation in future precipitation projections indicates that the component caused by the difference in six ΔSST patterns is more important in southern China compared with the component due to the atmospheric internal variability. All these results could provide valuable insights in simulating and predicting precipitation extremes in China.

scholarly journals Does the Atmospheric Global Model MRI-AGCM3.2 Perform Better than CMIP6 Atmospheric Models in Simulating Precipitation?

2021 ◽  
Author(s):  
Shoji Kusunoki ◽  
Tosiyuki Nakaegawa ◽  
Ryo Mizuta
Keyword(s):  
East Asia ◽  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Monthly Precipitation ◽  
Atmospheric Models ◽  
Extreme Precipitation Events ◽  
Precipitation Events ◽  
Better Than

Abstract The performance of the Meteorological Research Institute-Atmospheric General Circulation model version 3.2 (MRI-AGCM3.2) in simulating precipitation is compared with that of global atmospheric models registerred to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The MRI-AGCM3.2 with the grid size of 20-km and 60-km and 36 CMIP6 models are forced with observed sea surface temperature for 20-year period from 1995 to 2014. The horizontal resolution of the MRI-AGCM3.2 is relatively finer than CMIP6 models. As for global domain, the reproducibility of MRI-AGCM3.2 models are better than or equal to CMIP6 models in simulating geographical distribution of annual precipitation and intense precipitation events. Models with higher horizontal resolution tend to be better than those with lower resolution in simulating global precipitation. As for East Asia, the performance of MRI-AGCM3.2 models are better than or equal to CMIP6 models in simulating summertime monthly precipitation and the seasonal march in the Japanese rainy season, and extreme precipitation events. Higher horizontal resolution models also tend to perform better than lower resolution models in simulating precipitation over East Asia. The advantage of models with higher horizontal resolution over those with lower resolution in reproducing precipitation is more evident over East Asia than over the globe.

scholarly journals Variability and Predictability of North Atlantic Hurricane Frequency in a Large Ensemble of High-Resolution Atmospheric Simulations

2019 ◽  
Vol 32 (11) ◽  
pp. 3153-3167 ◽  
Author(s):  
Wei Mei ◽  
Youichi Kamae ◽  
Shang-Ping Xie ◽  
Kohei Yoshida
Keyword(s):  
North Atlantic ◽  
General Circulation ◽  
Decadal Variability ◽  
Intraseasonal Variability ◽  
Vertical Wind Shear ◽  
Circulation Model ◽  
Internal Variability ◽  
Potential Index ◽  
The Difference ◽  
Highly Correlated

Abstract Variability of North Atlantic annual hurricane frequency during 1951–2010 is studied using a 100-member ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The ensemble mean results well capture the interannual-to-decadal variability of hurricane frequency in best track data since 1970, and suggest that the current best track data might underestimate hurricane frequency prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the main development region (MDR) accounts for more than 80% of the SST-forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a useful predictor; a 1°C increase in this SST difference produces 7.05 ± 1.39 more hurricanes. The hurricane frequency also exhibits strong internal variability that is systematically larger in the model than observations. The seasonal-mean environment is highly correlated among ensemble members and contributes to less than 10% of the ensemble spread in hurricane frequency. The strong internal variability is suggested to originate from weather to intraseasonal variability and nonlinearity. In practice, a 20-member ensemble is sufficient to capture the SST-forced variability.

scholarly journals Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate

2018 ◽  
Vol 115 (11) ◽  
pp. 2681-2686 ◽  
Author(s):  
S. Sandeep ◽  
R. S. Ajayamohan ◽  
William R. Boos ◽  
T. P. Sabin ◽  
V. Praveen
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Coupled Model ◽  
Circulation Model ◽  
Central India ◽  
Wind Speeds ◽  
Northern India ◽  
Extreme Precipitation Events ◽  
Poleward Shift ◽  
Atmospheric Vortices

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ∼600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ∼60% and continental genesis increasing by ∼10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India.

scholarly journals Climate Drift in the CMIP5 Models*

2013 ◽  
Vol 26 (21) ◽  
pp. 8597-8615 ◽  
Author(s):  
Alexander Sen Gupta ◽  
Nicolas C. Jourdain ◽  
Jaclyn N. Brown ◽  
Didier Monselesan
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Deep Ocean ◽  
Internal Variability ◽  
Correction Method ◽  
Physical Models ◽  
Cmip5 Models ◽  
Steric Sea Level ◽  
Biogeochemical Models ◽  
The Difference

Abstract Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

Tropical Atmospheric Variability Forced by Oceanic Internal Variability

2007 ◽  
Vol 20 (4) ◽  
pp. 765-771 ◽  
Author(s):  
Markus Jochum ◽  
Clara Deser ◽  
Adam Phillips
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Rainfall Variability ◽  
Circulation Model ◽  
Internal Variability ◽  
Tropical Pacific ◽  
Atmospheric Variability ◽  
Instability Waves ◽  
Tropical Instability Waves ◽  
The Tropical Pacific

Abstract Atmospheric general circulation model experiments are conducted to quantify the contribution of internal oceanic variability in the form of tropical instability waves (TIWs) to interannual wind and rainfall variability in the tropical Pacific. It is found that in the tropical Pacific, along the equator, and near 25°N and 25°S, TIWs force a significant increase in wind and rainfall variability from interseasonal to interannual time scales. Because of the stochastic nature of TIWs, this means that climate models that do not take them into account will underestimate the strength and number of extreme events and may overestimate forecast capability.

scholarly journals Effect of AMOC collapse on ENSO in a high resolution general circulation model

2017 ◽  
Vol 50 (7-8) ◽  
pp. 2537-2552 ◽  
Author(s):  
Mark S. Williamson ◽  
Mat Collins ◽  
Sybren S. Drijfhout ◽  
Ron Kahana ◽  
Jennifer V. Mecking ◽  
...  
Keyword(s):  
High Resolution ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model

scholarly journals Evaluating climate field reconstruction techniques using improved emulations of real-world conditions

2014 ◽  
Vol 10 (1) ◽  
pp. 1-19 ◽  
Author(s):  
J. Wang ◽  
J. Emile-Geay ◽  
D. Guillot ◽  
J. E. Smerdon ◽  
B. Rajaratnam
Keyword(s):  
Graphical Models ◽  
Real World ◽  
General Circulation ◽  
Circulation Model ◽  
Internal Variability ◽  
Field Reconstruction ◽  
Spatial Skill ◽  
Reconstruction Methods ◽  
Climate Field Reconstruction ◽  
Global Mean

Abstract. Pseudoproxy experiments (PPEs) have become an important framework for evaluating paleoclimate reconstruction methods. Most existing PPE studies assume constant proxy availability through time and uniform proxy quality across the pseudoproxy network. Real multiproxy networks are, however, marked by pronounced disparities in proxy quality, and a steep decline in proxy availability back in time, either of which may have large effects on reconstruction skill. A suite of PPEs constructed from a millennium-length general circulation model (GCM) simulation is thus designed to mimic these various real-world characteristics. The new pseudoproxy network is used to evaluate four climate field reconstruction (CFR) techniques: truncated total least squares embedded within the regularized EM (expectation-maximization) algorithm (RegEM-TTLS), the Mann et al. (2009) implementation of RegEM-TTLS (M09), canonical correlation analysis (CCA), and Gaussian graphical models embedded within RegEM (GraphEM). Each method's risk properties are also assessed via a 100-member noise ensemble. Contrary to expectation, it is found that reconstruction skill does not vary monotonically with proxy availability, but also is a function of the type and amplitude of climate variability (forced events vs. internal variability). The use of realistic spatiotemporal pseudoproxy characteristics also exposes large inter-method differences. Despite the comparable fidelity in reconstructing the global mean temperature, spatial skill varies considerably between CFR techniques. Both GraphEM and CCA efficiently exploit teleconnections, and produce consistent reconstructions across the ensemble. RegEM-TTLS and M09 appear advantageous for reconstructions on highly noisy data, but are subject to larger stochastic variations across different realizations of pseudoproxy noise. Results collectively highlight the importance of designing realistic pseudoproxy networks and implementing multiple noise realizations of PPEs. The results also underscore the difficulty in finding the proper bias-variance tradeoff for jointly optimizing the spatial skill of CFRs and the fidelity of the global mean reconstructions.

scholarly journals Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

2017 ◽  
Vol 24 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiong Deng ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Circulation Model ◽  
Scale Interactions ◽  
Climate Signals ◽  
Coupled Data Assimilation ◽  
Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

scholarly journals The Challenge of Using Future SWOT Data for Oceanic Field Reconstruction

2016 ◽  
Vol 33 (1) ◽  
pp. 119-126 ◽  
Author(s):  
Lucile Gaultier ◽  
Clément Ubelmann ◽  
Lee-Lueng Fu
Keyword(s):  
High Resolution ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Two Dimensional ◽  
Satellite Mission ◽  
The Future ◽  
Resolution Model ◽  
Mapping Techniques ◽  
Ocean Topography

AbstractConventional altimetry measures a one-dimensional profile of sea surface height (SSH) along the satellite track. Two-dimensional SSH can be reconstructed using mapping techniques; however, the spatial resolution is quite coarse even when data from several altimeters are analyzed. A new satellite mission based on radar interferometry is scheduled to be launched in 2020. This mission, called Surface Water and Ocean Topography (SWOT), will measure SSH at high resolution along a wide swath, thus providing two-dimensional images of the ocean surface topography. This new capability will provide a large amount of data even though they are contaminated with instrument noise and geophysical errors. This paper presents a tool that simulates synthetic observations of SSH from the future SWOT mission using SSH from any ocean general circulation model (OGCM). SWOT-like data have been generated from a high-resolution model and analyzed to investigate the sampling and accuracy characteristics of the future SWOT data. This tool will help explore new ideas and methods for optimizing the retrieval of information from future SWOT missions.

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