scholarly journals Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1052
Author(s):  
Juyoung Hong ◽  
Khadijeh Javan ◽  
Yonggwan Shin ◽  
Jeong-Soo Park
Keyword(s):  
Averaging Method ◽  
Coupled Model ◽  
Model Averaging ◽  
Extreme Rainfall ◽  
Simulation Models ◽  
Uncertainty Assessment ◽  
Return Level ◽  
Multimodel Ensemble ◽  
The Past ◽  
Period 2

Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.

scholarly journals Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm

2021 ◽  
Author(s):  
Xingpo Liu ◽  
Chengfei Xia ◽  
Yifan Tang ◽  
Jiayang Tu ◽  
Huimin Wang
Keyword(s):  
Uncertainty Quantification ◽  
Parameter Optimization ◽  
Likelihood Estimation ◽  
Extreme Rainfall ◽  
Uncertainty Assessment ◽  
Return Level ◽  
Assessment Procedure ◽  
Rainfall Frequency ◽  
Storm Drainage ◽  
Credible Intervals

Abstract A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.

scholarly journals Changes in the Tropical Pacific SST Trend from CMIP3 to CMIP5 and Its Implication of ENSO

2012 ◽  
Vol 25 (21) ◽  
pp. 7764-7771 ◽  
Author(s):  
Sang-Wook Yeh ◽  
Yoo-Geun Ham ◽  
June-Yi Lee
Keyword(s):  
Coupled Model ◽  
Research Programme ◽  
Tropical Pacific ◽  
Multimodel Ensemble ◽  
Phase 3 ◽  
Intercomparison Project ◽  
Sst Anomaly ◽  
Earth System Models ◽  
Background State ◽  
The Tropical Pacific

This study assesses the changes in the tropical Pacific Ocean sea surface temperature (SST) trend and ENSO amplitude by comparing a historical run of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP) phase-5 multimodel ensemble dataset (CMIP5) and the CMIP phase-3 dataset (CMIP3). The results indicate that the magnitude of the SST trend in the tropical Pacific basin has been significantly reduced from CMIP3 to CMIP5, which may be associated with the overestimation of the response to natural forcing and aerosols by including Earth system models in CMIP5. Moreover, the patterns of tropical warming over the second half of the twentieth century have changed from a La Niña–like structure in CMIP3 to an El Niño–like structure in CMIP5. Further analysis indicates that such changes in the background state of the tropical Pacific and an increase in the sensitivity of the atmospheric response to the SST changes in the eastern tropical Pacific have influenced the ENSO properties. In particular, the ratio of the SST anomaly variance in the eastern and western tropical Pacific increased from CMIP3 to CMIP5, indicating that a center of action associated with the ENSO amplitude has shifted to the east.

scholarly journals On numerical solver selection and related uncertainty terminology

2009 ◽  
Vol 12 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Petra Claeys ◽  
Ann van Griensven ◽  
Lorenzo Benedetti ◽  
Bernard De Baets ◽  
Peter A. Vanrolleghem
Keyword(s):  
Fluid Dynamics ◽  
Mathematical Models ◽  
Process Design ◽  
Uncertainty Assessment ◽  
Environmental Modelling ◽  
Chemical Systems ◽  
The Past ◽  
Numerical Solver ◽  
Physical And Chemical ◽  
Insight Into

Mathematical models provide insight into numerous biological, physical and chemical systems. They can be used in process design, optimisation, control and decision support, as acknowledged in many different fields of scientific research. Mathematical models do not always yield reliable results and uncertainty should be taken into account. At present, it is possible to identify some factors contributing to uncertainty, and the awareness of the necessity of uncertainty assessment is rising. In the fields of Environmental Modelling and Computational Fluid Dynamics, for instance, terminology related to uncertainty exists and is generally accepted. However, the uncertainty due to the choice of the numerical solver and its settings used to compute the solution of the models did not receive much attention in the past. A motivating example on the existence and effect of numerical uncertainty is provided and clearly shows that we can no longer ignore it. This paper introduces a new terminology to support communication about uncertainty caused by numerical solvers, so that scientists become perceptive to it.

scholarly journals Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

2021 ◽  
Vol 13 (21) ◽  
pp. 4464
Author(s):  
Jiawen Xu ◽  
Xiaotong Zhang ◽  
Chunjie Feng ◽  
Shuyue Yang ◽  
Shikang Guan ◽  
...  
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Mean Squared Error ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Longwave Radiation ◽  
Energy System ◽  
General Circulation Models ◽  
The Individual

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

Workflow and tools to analyse the PMIP4-CMIP6 ensemble

2021 ◽  
Author(s):  
Anni Zhao ◽  
Chris Brierley
Keyword(s):  
Case Studies ◽  
Coupled Model ◽  
Important Case ◽  
Earth System ◽  
Model Intercomparison ◽  
Past Climate ◽  
The Past ◽  
The Earth ◽  
Intercomparison Project

&lt;p&gt;Experiment outputs are now available from the Coupled Model Intercomparison Project&amp;#8217;s 6&lt;sup&gt;th&lt;/sup&gt; phase (CMIP6) and the past climate experiments defined in the Model Intercomparison Project&amp;#8217;s 4&lt;sup&gt;th&lt;/sup&gt; phase (PMIP4). All of this output is freely available from the Earth System Grid Federation (ESGF). Yet there are overheads in analysing this resource that may prove complicated or prohibitive. Here we document the steps taken by ourselves to produce ensemble analyses covering past and future simulations. We outline the strategy used to curate, adjust the monthly calendar aggregation and process the information downloaded from the ESGF. The results of these steps were used to perform analysis for several of the initial publications arising from PMIP4. We provide post-processed fields for each simulation, such as climatologies and common measures of variability. Example scripts used to visualise and analyse these fields is provided for several important case studies.&lt;/p&gt;

A Physics-Based Proxy for Surface and Subsurface Coupled Simulation Models

2021 ◽  
Author(s):  
Changdong Yang ◽  
Jincong He ◽  
Song Du ◽  
Zhenzhen Wang ◽  
Tsubasa Onishi ◽  
...  
Keyword(s):  
Network Model ◽  
Reservoir Simulation ◽  
Coupled Model ◽  
Simulation Models ◽  
Production Performance ◽  
Proxy Model ◽  
Surface Network ◽  
Run Time ◽  
Multi Phase ◽  
Subsurface Reservoir

Abstract Full-physics subsurface simulation models coupled with surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run-time of the coupled model to enable rapid decision-making for reservoir management. In the coupled model the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves which are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the CPU intensive reservoir simulation is replaced with an IPR database constructed from a data pool of one or multiple simulation runs. The IPR database captures well performance that represents subsurface reservoir dynamics. The proxy model can then be used to predict the production performance of new scenarios – for example new drilling sequence – by intelligently looking up the appropriate IPR curves for oil, gas and water phases for each well and solving it with the surface network. All necessary operational events in the surface network and field management logic (such as facility constraints, well conditional shut-in, and group guide rate balancing) for the full-coupled model can be implemented and honored. In the proposed proxy model, while the reservoir simulation component is eliminated for efficiency. The entirety of the surface network model is retained, which offers certain advantages. It is particularly suitable for investigating the impact of different surface operations, such as maintenance schedule and production routing changes, with the aim of minimizing production capacity off-line due to maintenance. Replacing the computationally intensive subsurface simulation with the appropriate IPR significantly improves the run time of the coupled model while preserving the essential physics of the reservoir. The accuracy depends on the difference between the scenarios that the proxy is trained on and the scenarios being evaluated. Initial testing with a complex reservoir with more than 300 wells showed the accuracy of the proxy model to be more than 95%. The computation speedup could be an order of magnitude, depending largely on complexity of the surface network model. Prior work exists in the literature that uses decline curves to replicate subsurface model performance. The use of the multi-phase IPR database and the intelligent lookup mechanism in the proposed method allows it to be more accurate and flexible in handling complexities such as multi-phase flow and interference in the surface network.

Dietary Changes Among Breastfeeding Mothers

2020 ◽  
pp. 089033442095928
Author(s):  
Marina Iacovou ◽  
Peter R. Gibson ◽  
Jane G. Muir
Keyword(s):  
Online Survey ◽  
Mixed Methods Design ◽  
Milk Chocolate ◽  
Cross Sectional ◽  
Dietary Changes ◽  
Food Avoidance ◽  
Infant Crying ◽  
The Past ◽  
Period 2 ◽  
Food And Beverage

Background Breastfeeding mothers have been avoiding foods in their diet based on ancient beliefs that it can prevent/reduce unsettled infant crying–fussing behavior. Research aims This study aimed to explore (1) the prevalence of maternal dietary changes during the postpartum period; (2) the demographic and infant feeding differences between women who made dietary changes and those who did not; (3) the reasons for dietary change; and (4) what specific foods were avoided. Methods A prospective, cross-sectional 2-group comparison using an online survey mixed-methods design was advertised via social media and Australian websites. Anonymous volunteers who were presently breastfeeding or had breastfed for any length of time in the past were eligible. Results Of 1,262 participants, 966 (77%) avoided foods/beverages in their diet. The most commonly avoided beverages were alcohol (79%) and coffee (44%), and the most commonly avoided foods were chili (22%), milk-chocolate (22%), cabbage (20%), onion (20%), and garlic (16%). Reasons for dietary avoidance related to baby being unsettled (31%), baby having wind/gas (29%), colic (11%), and crying (10%). Of 245 participants who removed dairy, 80 (33%) did not substitute with calcium-rich alternatives. Food and beverage avoidance commenced as early as 1 week postpartum and continued until mean ( SD) infant age of 9 (5) months. Conclusions It is commonplace for breastfeeding mothers to avoid foods and beverages for reasons associated with infantile colic. Of major concern is the duration of food avoidance during a time of increased nutritional requirements. This information may assist in improving the nutritional support given to breastfeeding mothers.

Precipitation and Temperature Forecast Performance at the Weather Prediction Center

2014 ◽  
Vol 29 (3) ◽  
pp. 489-504 ◽  
Author(s):  
David R. Novak ◽  
Christopher Bailey ◽  
Keith F. Brill ◽  
Patrick Burke ◽  
Wallace A. Hogsett ◽  
...  
Keyword(s):  
Short Range ◽  
Deterministic Model ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Extreme Rainfall Events ◽  
Forecast Performance ◽  
The Past ◽  
Medium Range

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

Anthropogenic and Natural Contributions to the Lengthening of the Summer Season in the Northern Hemisphere

2018 ◽  
Vol 31 (17) ◽  
pp. 6803-6819 ◽  
Author(s):  
Bo-Joung Park ◽  
Yeon-Hee Kim ◽  
Seung-Ki Min ◽  
Eun-Pa Lim
Keyword(s):  
Northern Hemisphere ◽  
Coupled Model ◽  
Atlantic Multidecadal Oscillation ◽  
Summer Season ◽  
Season Length ◽  
The Past ◽  
External Forcings ◽  
Regional Trends ◽  
Intercomparison Project

Observed long-term variations in summer season timing and length in the Northern Hemisphere (NH) continents and their subregions were analyzed using temperature-based indices. The climatological mean showed coastal–inland contrast; summer starts and ends earlier inland than in coastal areas because of differences in heat capacity. Observations for the past 60 years (1953–2012) show lengthening of the summer season with earlier summer onset and delayed summer withdrawal across the NH. The summer onset advance contributed more to the observed increase in summer season length in many regions than the delay of summer withdrawal. To understand anthropogenic and natural contributions to the observed change, summer season trends from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations forced with the observed external forcings [anthropogenic plus natural forcing (ALL), natural forcing only (NAT), and greenhouse gas forcing only (GHG)] were analyzed. ALL and GHG simulations were found to reproduce the overall observed global and regional lengthening trends, but NAT had negligible trends, which implies that increased greenhouse gases were the main cause of the observed changes. However, ALL runs tend to underestimate the observed trend of summer onset and overestimate that of withdrawal, the causes of which remain to be determined. Possible contributions of multidecadal variabilities, such as Pacific decadal oscillation and Atlantic multidecadal oscillation, to the observed regional trends in summer season length were also assessed. The results suggest that multidecadal variability can explain a moderate portion (about ±10%) of the observed trends in summer season length, mainly over the high latitudes.

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