Effects of the horizontal resolution of climate models on the simulation of extreme hourly precipitation in the Tibetan Plateau and surrounding areas

2021 ◽  
Author(s):  
Qing Bao ◽  
Lei Wang ◽  
Yimin Liu ◽  
Guoxiong Wu ◽  
Jinxiao Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Boreal Summer ◽  
The Tibetan Plateau ◽  
Standard Resolution ◽  
Surrounding Areas

<p>Extreme precipitation events, represented by the extreme hourly precipitation (EHP), often occur in the Tibetan Plateau and surrounding areas (TPS) as a result of the complex topography and unique geographical location of this region and can lead to large losses of human life. Previous studies have shown that the performance of extreme precipitation simulations can be improved by increasing the resolution of the model, although the mechanisms are not yet not clear. In this study, we firstly compared the most recent high-quality satellite precipitation product  with station data from Nepal, which is located on the southern edge of the Tibetan Plateau. The results showed that the GPM dataset can reproduce extreme precipitation well and we therefore used these data as a benchmark for climate models of the TPS. We then evaluated the fidelity of global climate models in the representation of the boreal summer EHP in the TPS using datasets from the CMIP6 High-Resolution Model Intercomparison Project (HighResMIP). We used four global climate models with standard (about 100 km) and enhanced (up to 25 km) resolution configurations to simulate the EHP. The models with a standard resolution largely underestimated the intensity of EHP, especially over the southern edge of the Tibetan Plateau. The EHP can reach up to 50 mm h<sup>−1</sup>in the TPS, whereas the maximum simulated EHP was <35 mm h<sup>−1</sup> for all the standard resolution models. The mean intensity of EHP is about 5.06 mm h<sup>−1</sup> in the GPM satellite products, whereas it was <3 mm h<sup>−1</sup> in standard resolution models. The skill of the simulation of EHP is significantly improved at increased horizontal resolutions. The high-resolution models with a horizontal resolution of 25 km can reproduce the geographical distribution of the intensity of EHP in the TPS. The intensity–frequency distribution of EHP also resembles that from GPM products, showing the same features up to 50 mm h<sup>−1</sup>, although it slightly overestimates heavy precipitation events. Finally, we propose possible physical linkages between the simulation of EHP and the impacts of the resolution of the model and physical processes. Phenomena over the Indian Ocean at different timescales and the diurnal variation of precipitation in the TPS are used to propose possible physical linkages as they may play an important part in the simulation of EHP in the TPS. Further analysis shows that an increase in the horizontal resolution helps to accurately reproduce the features of water vapor transport on days with extreme precipitation, the northward-propagating intraseasonal oscillation over the Indian and western Pacific Ocean monsoon regions in the boreal summer, the intensity and number of tropical cyclones over the southern Asian monsoon regions, and the peak time and amplitude of the diurnal cycle of precipitation. This increase in accuracy contributes to the improvements in the simulation of EHP in the TPS. This study suggests improvements to increase the horizontal resolution of the GCMs and lay a solid foundation for the accurate reproduction and prediction of EHP in the TPS.</p>

scholarly journals Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1771 ◽  
Author(s):  
Kun Jia ◽  
Yunfeng Ruan ◽  
Yanzhao Yang ◽  
Chao Zhang
Keyword(s):  
Tibetan Plateau ◽  
Climate Models ◽  
Global Climate ◽  
Precipitation Change ◽  
Multiple Criteria ◽  
Global Climate Models ◽  
The Tibetan Plateau ◽  
Spatial Distributions ◽  
Near Term ◽  
Far Future

In this study, the performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) global climate models (GCMs) in simulating precipitation over the Tibetan Plateau (TP) was assessed using data from 1961 to 2005 by an improved score-based method, which adopts multiple criteria to achieve a comprehensive evaluation. The future precipitation change was also estimated based on the Delta method by selecting the submultiple model ensemble (SMME) in the near-term (2006–2050) and far future (2051–2095) periods under Representative Concentration Pathways (RCP) scenarios RCP4.5 and RCP8.5. The results showed that most GCMs can reasonably simulate the precipitation pattern of an annual cycle; however, all GCMs overestimated the precipitation over TP, especially in spring and summer. The GCMs generally provide good simulations of the temporal characteristics of precipitation, while they did not perform as well in reproducing its spatial distributions. Different assessment criteria lead to inconsistent results; however, the improved rank score method, which adopts multiple criteria, provided a robust assessment of GCMs performance. The future annual precipitation was projected to increase by ~6% in the near-term with respect to the period 1961–2005, whereas increases of 12.3% and 16.7% are expected in the far future under RCP4.5 and RCP8.5 scenarios, respectively. Similar spatial distributions of future precipitation changes can be seen in the near-term and far future periods under the two scenarios, and indicate that the most predominant increases occurred in the north of TP. The results of this study are expected to provide valuable information on climate change, and for water resources and agricultural management in TP.

Extending the paleoglaciological record of the southeastern Tibetan Plateau by combining geochronological and high-resolution remote sensing techniques

2020 ◽  
Author(s):  
Arjen P. Stroeven ◽  
Ramona A.A. Schneider ◽  
Robin Blomdin ◽  
Natacha Gribenski ◽  
Marc W. Caffee ◽  
...  
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Luminescence Dating ◽  
The Tibetan Plateau ◽  
Exposure Dating ◽  
Southeastern Margin ◽  
Downstream Communities

<p>Paleoglaciological data is a crucial source of information towards insightful paleoclimate reconstructions by providing vital boundary conditions for regional and global climate models. In this context, the Third Pole Environment is considered a key region because it is highly sensitive to global climate change and its many glaciers constitute a diminishing but critical supply of freshwater to downstream communities in SE Asia. Despite its importance, extents of past glaciation on the Tibetan Plateau remain poorly documented or controversial largely because of the lack of well define glacial chronostratigraphies and reconstructions of former glacier extent. This study contributes to a better documentation of the extent and improved resolution of the timing of past glaciations on the southeastern margin of the Tibetan Plateau. We deploy a high-resolution TanDEM-X Digital Elevation Model (12 m resolution) to produce maps of glacial and proglacial fluvial landforms in unprecedented detail. Geomorphological and sedimentological field observations complement the mapping while cosmogenic nuclide exposure dating of quartz samples from boulders on end moraines detail the timing of local glacier expansion. Additionally, samples for optically stimulated luminescence dating were taken from extensive and distinct terraces located in pull-apart basins downstream of the end moraines to determine their formation time. We compare this new dataset with new and published electron spin resonance ages from terraces. Temporal coherence between the different chronometers strengthens the geochronological record while divergence highlights limitations in the applicability of the chronometers to glacial research or in our conceptual understanding of landscape changes in tectonic regions. Results highlight our current understanding of paleoglaciation, landscape development, and paleoclimate on the SE Tibetan Plateau.</p>

Evaluation of global climate models for downscaling applications centred over the Tibetan Plateau

2016 ◽  
Vol 37 (2) ◽  
pp. 657-671 ◽  
Author(s):  
Jianwei Xu ◽  
Yanhong Gao ◽  
Deliang Chen ◽  
Linhong Xiao ◽  
Tinghai Ou
Keyword(s):  
Tibetan Plateau ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
The Tibetan Plateau

scholarly journals Characteristics and Scenarios Projection of Climate Change on the Tibetan Plateau

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhenchun Hao ◽  
Qin Ju ◽  
Weijuan Jiang ◽  
Changjun Zhu
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Climate Models ◽  
Global Climate ◽  
Surface Air Temperature ◽  
Global Climate Models ◽  
The Tibetan Plateau ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation ◽  
Ipcc Ar4

The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) presents twenty-two global climate models (GCMs). In this paper, we evaluate the ability of 22 GCMs to reproduce temperature and precipitation over the Tibetan Plateau by comparing with ground observations for 1961~1900. The results suggest that all the GCMs underestimate surface air temperature and most models overestimate precipitation in most regions on the Tibetan Plateau. Only a few models (each 5 models for precipitation and temperature) appear roughly consistent with the observations in annual temperature and precipitation variations. Comparatively, GFCM21 and CGMR are able to better reproduce the observed annual temperature and precipitation variability over the Tibetan Plateau. Although the scenarios predicted by the GCMs vary greatly, all the models predict consistently increasing trends in temperature and precipitation in most regions in the Tibetan Plateau in the next 90 years. The results suggest that the temperature and precipitation will both increase in all three periods under different scenarios, with scenario A1 increasing the most and scenario A1B increasing the least.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

2021 ◽  
Author(s):  
SOURABH SHRIVASTAVA ◽  
RAM AVTAR ◽  
PRASANTA KUMAR BAL
Keyword(s):  
Machine Learning ◽  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Summer Monsoon Rainfall ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Model Ensemble ◽  
The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

Forecast and Uncertainty Analysis of Extreme Precipitation in China from Ensemble of Multiple Climate Models

2021 ◽  
Author(s):  
Peng Deng ◽  
Jianting Zhu
Keyword(s):  
Extreme Precipitation ◽  
Flood Control ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Model Averaging ◽  
Statistical Distribution ◽  
Global Climate Models ◽  
Effective Measure

Abstract Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

scholarly journals Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Sicheng He ◽  
Jing Yang ◽  
Qing Bao ◽  
Lei Wang ◽  
Bin Wang
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
East China ◽  
Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

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