scholarly journals Evaluation of Downscaled CMIP5 Coupled with VIC Model for Flash Drought Simulation in a Humid Subtropical Basin, China

2018 ◽  
Vol 31 (3) ◽  
pp. 1075-1090 ◽  
Author(s):  
Yuqing Zhang ◽  
Qinglong You ◽  
Changchun Chen ◽  
Jing Ge ◽  
Muhammad Adnan
Keyword(s):  
Heat Waves ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Atmospheric Conditions ◽  
Multimodel Ensemble ◽  
Infiltration Capacity ◽  
Coupled Models ◽  
Vic Model ◽  
Ensemble Mean ◽  
Meteorological Observations

Abstract Compared to traditional drought events, flash droughts evolve rapidly during short-term extreme atmospheric conditions, with a lasting period of one pentad to several weeks. There are two main categories of flash droughts: the heat wave flash drought (HWFD), which is mainly caused by persistent high temperatures (heat waves), and the precipitation deficit flash drought (PDFD), which is mainly triggered by precipitation deficits. The authors’ previous research focused on the characteristics and causes of flash drought based on meteorological observations and Variable Infiltration Capacity (VIC) model simulations in a humid subtropical basin (Gan River basin, China). In this study, the authors evaluated the downscaled phase 5 of the Coupled Model Intercomparison Project (CMIP5) models’ simulations, coupled with the VIC model (CMIP5–VIC) in reproducing flash droughts in a humid subtropical basin in China. Most downscaled CMIP5–VIC simulations can reproduce the spatial patterns of flash droughts with respect to the benchmarks. The coupled models fail to readily replicate interannual variation (interannual pentad change), but most models can reflect the interannual variability (temporal standard deviation) and long-term average pentads of flash droughts. It is difficult to simultaneously depict both the spatial and temporal features of flash droughts within only one coupled model. The climatological patterns of the best multimodel ensemble mean are close to those of the all-model ensemble mean, but the best multimodel ensemble mean has a minimal bias range and relatively low computational burden.

scholarly journals Reply to “Comment on ‘Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations’”

2017 ◽  
Vol 30 (23) ◽  
pp. 9773-9782 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Low Frequency ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Multimodel Ensemble ◽  
Internal Climate Variability ◽  
Instrumental Record ◽  
Ensemble Mean ◽  
Intercomparison Project ◽  
The Individual

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al.

scholarly journals Southern Ocean Wind Stress in CMIP5 Models: Role of Wind Fluctuations

2020 ◽  
Vol 33 (4) ◽  
pp. 1209-1226 ◽  
Author(s):  
Xia Lin ◽  
Xiaoming Zhai ◽  
Zhaomin Wang ◽  
David R. Munday
Keyword(s):  
Southern Ocean ◽  
Time Scales ◽  
Wind Stress ◽  
Surface Wind ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Coupled Models ◽  
Surface Wind Stress ◽  
Stress Changes ◽  
The Mean

AbstractThe Southern Ocean (SO) surface wind stress is a major atmospheric forcing for driving the Antarctic Circumpolar Current and the global overturning circulation. Here the effects of wind fluctuations at different time scales on SO wind stress in 18 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated. It is found that including wind fluctuations, especially on time scales associated with synoptic storms, in the stress calculation strongly enhances the mean strength, modulates the seasonal cycle, and significantly amplifies the trends of SO wind stress. In 11 out of the 18 CMIP5 models, the SO wind stress has strengthened significantly over the period of 1960–2005. Among them, the strengthening trend of SO wind stress in one CMIP5 model is due to the increase in the intensity of wind fluctuations, while in all the other 10 models the strengthening trend is due to the increasing strength of the mean westerly wind. These discrepancies in SO wind stress trend in CMIP5 models may explain some of the diverging behaviors in the model-simulated SO circulation. Our results suggest that to reduce the uncertainty in SO responses to wind stress changes in the coupled models, both the mean wind and wind fluctuations need to be better simulated.

scholarly journals Diagnosing Relationships between Mean State Biases and El Niño Shortwave Feedback in CMIP5 Models

2018 ◽  
Vol 31 (4) ◽  
pp. 1315-1335 ◽  
Author(s):  
Samantha Ferrett ◽  
Matthew Collins ◽  
Hong-Li Ren
Keyword(s):  
El Niño ◽  
El Nino ◽  
Coupled Model ◽  
Heat Fluxes ◽  
Cmip5 Models ◽  
Coupled Models ◽  
El Niño Events ◽  
Mean State ◽  
Cloud Response ◽  
El Nino Events

The rate of damping of tropical Pacific sea surface temperature anomalies (SSTAs) associated with El Niño events by surface shortwave heat fluxes has significant biases in current coupled climate models [phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. Of 33 CMIP5 models, 16 have shortwave feedbacks that are weakly negative in comparison to observations, or even positive, resulting in a tendency of amplification of SSTAs. Two biases in the cloud response to El Niño SSTAs are identified and linked to significant mean state biases in CMIP5 models. First, cool mean SST and reduced precipitation are linked to comparatively less cloud formation in the eastern equatorial Pacific during El Niño events, driven by a weakened atmospheric ascent response. Second, a spurious reduction of cloud driven by anomalous surface relative humidity during El Niño events is present in models with more stable eastern Pacific mean atmospheric conditions and more low cloud in the mean state. Both cloud response biases contribute to a weak negative shortwave feedback or a positive shortwave feedback that amplifies El Niño SSTAs. Differences between shortwave feedback in the coupled models and the corresponding atmosphere-only models (AMIP) are also linked to mean state differences, consistent with the biases found between different coupled models. Shortwave feedback bias can still persist in AMIP, as a result of persisting weak shortwave responses to anomalous cloud and weak cloud responses to atmospheric ascent. This indicates the importance of bias in the atmosphere component to coupled model feedback and mean state biases.

scholarly journals An evaluation of CMIP5 precipitation simulations using ground observations over ten river basins in China

2021 ◽  
Author(s):  
Xiuqin Yang ◽  
Bin Yong ◽  
Zhiguo Yu ◽  
Yuqing Zhang
Keyword(s):  
Coupled Model ◽  
Single Mode ◽  
River Basins ◽  
Cmip5 Models ◽  
Multimodel Ensemble ◽  
Intercomparison Project ◽  
Precipitation Indices ◽  
Improved Performance ◽  
Model Ensembles ◽  
Statistical Metrics

Abstract Using the precipitation measurements obtained from 2,419 ground meteorological stations over China from 1960 to 2005 as benchmark, the performance of 21 single-mode precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using Taylor diagrams and several statistical metrics. Based on statistical metrics, the models were ranked in terms of their ability to reproduce similar patterns in precipitation relative to the observations. Except in Southeast and Pearl river basins, research results show that all model ensemble means overestimate in the rest of the river basins, especially in Southwest and Northwest. The performance of CMIP5 models is quite different among each river basin; most models show significant overestimation in Northwest and Yellow and significant underestimations in Southeast and Pearl. The simulations are more reliable in Songhua, Liao, Yangtze, and Pearl than in other river basins according to spatial distribution and interannual variability. No individual model performs well in all the river basins both spatially and temporally. In Songhua, Liao, Yangtze, and Pearl, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles.

scholarly journals Atmospheric and Oceanic Conditions Associated with Southern Australian Heat Waves: A CMIP5 Analysis

2014 ◽  
Vol 27 (20) ◽  
pp. 7807-7829 ◽  
Author(s):  
Ariaan Purich ◽  
Tim Cowan ◽  
Wenju Cai ◽  
Peter van Rensch ◽  
Petteri Uotila ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Heat Wave ◽  
Heat Waves ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Sea Level Pressure ◽  
Near Surface ◽  
Wave Statistics ◽  
High Emission ◽  
Mean State

Abstract Atmospheric and oceanic conditions associated with southern Australian heat waves are examined using phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. Accompanying work analyzing modeled heat wave statistics for Australia finds substantial increases in the frequency, duration, and temperature of heat waves by the end of the twenty-first century. This study assesses the ability of CMIP5 models to simulate the synoptic and oceanic conditions associated with southern Australian heat waves, and examines how the classical atmospheric setup associated with heat waves is projected to change in response to mean-state warming. To achieve this, near-surface temperature, mean sea level pressure, and sea surface temperature (SST) from the historical and high-emission simulations are analyzed. CMIP5 models are found to represent the synoptic setup associated with heat waves well, despite showing greater variation in simulating SST anomalies. The models project a weakening of the pressure couplet associated with future southern Australian heat waves, suggesting that even a non-classical synoptic setup is able to generate more frequent heat waves in a warmer world. A future poleward shift and strengthening of heat wave–inducing anticyclones is confirmed using a tracking scheme applied to model projections. Model consensus implies that while anticyclones associated with the hottest future southern Australian heat waves will be more intense and originate farther poleward, a greater proportion of heat waves occur in association with a weaker synoptic setup that, when combined with warmer mean-state temperatures, gives rise to more future heat waves.

Projecting Future Air Quality Under Energy Transition Scenarios over the U.S. using Online-Coupled Models

2021 ◽  
Author(s):  
Yang Zhang ◽  
Kai Wang ◽  
Daniel Schuch
Keyword(s):  
Air Quality ◽  
Linear Regression ◽  
Surface Ozone ◽  
Energy Transition ◽  
Air Pollutant ◽  
Atmospheric Composition ◽  
Atmospheric Conditions ◽  
Coupled Models ◽  
Ensemble Mean ◽  
Multi Linear Regression

<p> </p><p>Online-coupled meteorology-chemistry models provide powerful tools for more realistically simulation of current and future air quality with feedbacks between atmospheric composition and meteorology that cannot be considered in offline-coupled models. In this work, several state-of-science online-coupled models are applied to generate the best possible predictions of surface ozone (O<sub>3</sub>) and fine particulate matter (PM<sub>2.5</sub>) concentrations under current emission and climate conditions. Two ensemble methods are used to further reduce the model biases and errors including a simple ensemble mean based on an average of ensemble members, and a weighted ensemble mean based on the multi-linear regression. The skills of individual models and their ensembles are evaluated using available surface network data.  Compared to individual models and the simple ensemble mean, the weighted ensemble predictions based on the multi-linear regression perform the best overall for both O<sub>3</sub> and PM<sub>2.5</sub>. The model with best performance is selected to apply for future years to project the changes in air quality under various energy transition scenarios to support the development of emission control strategies. These results illustrate the current capability of the online-coupled models and the potential of weighted ensemble in generating the best possible estimates of air pollutant concentrations under current and future atmospheric conditions. </p>

Benefits of CMIP5 Multimodel Ensemble in Reconstructing Historical Ocean Subsurface Temperature Variations

2016 ◽  
Vol 29 (15) ◽  
pp. 5393-5416 ◽  
Author(s):  
Lijing Cheng ◽  
Jiang Zhu
Keyword(s):  
Decadal Variability ◽  
Heat Content ◽  
Coupled Model ◽  
Mapping Method ◽  
Optimal Interpolation ◽  
Cmip5 Models ◽  
Subsurface Temperature ◽  
Sea Level Changes ◽  
Multimodel Ensemble

Abstract A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advanced mapping method is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the ocean mean state, long-term trends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of this method is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.

scholarly journals Process-level improvements in CMIP5 models and their impact on tropical variability, Southern Ocean and monsoons

2017 ◽  
Author(s):  
Axel Lauer ◽  
Colin Jones ◽  
Veronika Eyring ◽  
Martin Evaldsson ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Earth System ◽  
Evaluation Tool ◽  
Coupled Models ◽  
Improvement Project ◽  
Inter Tropical Convergence Zone ◽  
Rainfall Analysis ◽  
Systematic Biases

Abstract. The performance of improved versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian (SAM) and West African (WAM) monsoons, the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs. The analysis shows that the tropical precipitation in three out of four models is significantly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Inter-Tropical Convergence Zone. Simulated cloud amounts and cloud-radiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability of precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower resolution models may be a result of unrealistic intensity distributions.

scholarly journals Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons

2018 ◽  
Vol 9 (1) ◽  
pp. 33-67 ◽  
Author(s):  
Axel Lauer ◽  
Colin Jones ◽  
Veronika Eyring ◽  
Martin Evaldsson ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Southern Ocean ◽  
West African Monsoon ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Earth System ◽  
Evaluation Tool ◽  
Coupled Models ◽  
Improvement Project ◽  
Rainfall Analysis ◽  
Systematic Biases

Abstract. The performance of updated versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The Earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian monsoon (SAM) and the West African monsoon (WAM), the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs. The analysis shows that the tropical precipitation in three out of four models is clearly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Intertropical Convergence Zone (ITCZ). Simulated cloud amounts and cloud–radiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability in precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest that the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower-resolution models may be a result of unrealistic intensity distributions.

scholarly journals ENSO Asymmetry in CMIP5 Models

2014 ◽  
Vol 27 (11) ◽  
pp. 4070-4093 ◽  
Author(s):  
Tao Zhang ◽  
De-Zheng Sun
Keyword(s):  
Coupled Model ◽  
La Niña ◽  
Eastern Pacific ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Coupled Models ◽  
Zonal Winds ◽  
La Nina ◽  
Enso Asymmetry ◽  
Intercomparison Project

Abstract The El Niño–La Niña asymmetry is evaluated in 14 coupled models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The results show that an underestimate of ENSO asymmetry, a common problem noted in CMIP3 models, remains a common problem in CMIP5 coupled models. The weaker ENSO asymmetry in the models primarily results from a weaker SST warm anomaly over the eastern Pacific and a westward shift of the center of the anomaly. In contrast, SST anomalies for the La Niña phase are close to observations. Corresponding Atmospheric Model Intercomparison Project (AMIP) runs are analyzed to understand the causes of the underestimate of ENSO asymmetry in coupled models. The analysis reveals that during the warm phase, precipitation anomalies are weaker over the eastern Pacific, and westerly wind anomalies are confined more to the west in most models. The time-mean zonal winds are stronger over the equatorial central and eastern Pacific for most models. Wind-forced ocean GCM experiments suggest that the stronger time-mean zonal winds and weaker asymmetry in the interannual anomalies of the zonal winds in AMIP models can both be a contributing factor to a weaker ENSO asymmetry in the corresponding coupled models, but the former appears to be a more fundamental factor, possibly through its impact on the mean state. The study suggests that the underestimate of ENSO asymmetry in the CMIP5 coupled models is at least in part of atmospheric origin.

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