scholarly journals A multi-diagnostic approach to cloud evaluation

2017 ◽  
Vol 10 (7) ◽  
pp. 2547-2566 ◽  
Author(s):  
Keith D. Williams ◽  
Alejandro Bodas-Salcedo
Keyword(s):  
General Circulation ◽  
Model Development ◽  
Model Performance ◽  
Coupled Model ◽  
Cloud Amount ◽  
General Circulation Models ◽  
Diagnostic Techniques ◽  
Model Errors ◽  
Intercomparison Project ◽  
Model Development Process

Abstract. Most studies evaluating cloud in general circulation models present new diagnostic techniques or observational datasets, or apply a limited set of existing diagnostics to a number of models. In this study, we use a range of diagnostic techniques and observational datasets to provide a thorough evaluation of cloud, such as might be carried out during a model development process. The methodology is illustrated by analysing two configurations of the Met Office Unified Model – the currently operational configuration at the time of undertaking the study (Global Atmosphere 6, GA6), and the configuration which will underpin the United Kingdom's Earth System Model for CMIP6 (Coupled Model Intercomparison Project 6; GA7). By undertaking a more comprehensive analysis which includes compositing techniques, comparing against a set of quite different observational instruments and evaluating the model across a range of timescales, the risks of drawing the wrong conclusions due to compensating model errors are minimized and a more accurate overall picture of model performance can be drawn. Overall the two configurations analysed perform well, especially in terms of cloud amount. GA6 has excessive thin cirrus which is removed in GA7. The primary remaining errors in both configurations are the in-cloud albedos which are too high in most Northern Hemisphere cloud types and sub-tropical stratocumulus, whilst the stratocumulus on the cold-air side of Southern Hemisphere cyclones has in-cloud albedos which are too low.

scholarly journals A multi-diagnostic approach to cloud evaluation

2017 ◽  
Author(s):  
Keith D. Williams ◽  
Alejandro Bodas-Salcedo
Keyword(s):  
General Circulation ◽  
Model Development ◽  
Model Performance ◽  
Coupled Model ◽  
Cloud Amount ◽  
General Circulation Models ◽  
Diagnostic Techniques ◽  
Model Errors ◽  
Intercomparison Project ◽  
Model Development Process

Abstract. Most studies evaluating cloud in general circulation models present new diagnostic techniques or observational datasets, or apply a limited set of existing diagnostics to a number of models. In this study, we use a range of diagnostic techniques and observational datasets to provide a thorough evaluation of cloud, such as might be carried out during a model development process. The methodology is illustrated by analysing two configurations of the Met Office Unified Model – the currently operational configuration at the time of undertaking the study (Global Atmosphere 6, GA6), and the configuration which will underpin the United Kingdom's Earth System Model for CMIP6 (Coupled Model Intercomparison Project 6) (GA7). By undertaking a more comprehensive analysis which includes compositing techniques, comparing against a set of quite different observational instruments and evaluating the model across a range of timescales, the risks of drawing the wrong conclusions due to compensating model errors are minimised and a more accurate overall picture of model performance can be drawn. Overall the two configurations analysed perform well, especially in terms of cloud amount. GA6 has excessive thin cirrus which is removed in GA7. The primary remaining errors in both configurations are the in-cloud albedos which are too high in most northern hemisphere cloud types and sub-tropical stratocumulus, whilst the stratocumulus on the cold air side of southern hemisphere cyclones has in-cloud albedo's which are too low.

Evaluation of the tail of the probability distribution of daily and sub-daily precipitation in CMIP6 models

2021 ◽  
pp. 1-61
Author(s):  
Jesse Norris ◽  
Alex Hall ◽  
J. David Neelin ◽  
Chad W. Thackeray ◽  
Di Chen
Keyword(s):  
General Circulation ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Lower Troposphere ◽  
General Circulation Models ◽  
Intercomparison Project ◽  
Cloud Tops ◽  
The Tropics ◽  
Fraction Models

AbstractDaily and sub-daily precipitation extremes in historical Coupled-Model-Intercomparison-Project-Phase-6 (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01–10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes the multi-model median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3-D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r=–0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r=–0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These inter-model differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible 21st-Century projections.

scholarly journals An automatic and effective parameter optimization method for model tuning

2015 ◽  
Vol 8 (11) ◽  
pp. 3579-3591 ◽  
Author(s):  
T. Zhang ◽  
L. Li ◽  
Y. Lin ◽  
W. Xue ◽  
F. Xie ◽  
...  
Keyword(s):  
Simplex Method ◽  
General Circulation ◽  
Model Development ◽  
Model Performance ◽  
Parameter Tuning ◽  
General Circulation Models ◽  
Development Stage ◽  
Optimum Combination ◽  
Downhill Simplex Method ◽  
Downhill Simplex

Abstract. Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals An Assessment of Future Southern Hemisphere Blocking Using CMIP5 Projections from Four GCMs

2016 ◽  
Vol 29 (21) ◽  
pp. 7599-7611 ◽  
Author(s):  
Simon Parsons ◽  
James A. Renwick ◽  
Adrian J. McDonald
Keyword(s):  
Southern Hemisphere ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Southern Annular Mode ◽  
Model Intercomparison ◽  
Future Projections ◽  
South Pacific Ocean ◽  
Blocking Activity ◽  
Intercomparison Project

AbstractThis study is concerned with blocking events (BEs) in the Southern Hemisphere (SH), their past variability, and future projections. ERA-Interim (ERA-I) is used to compare the historical output from four general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5); the output of the representative concentration pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) projections are also examined. ERA-I shows that the higher latitudes of the South Pacific Ocean (SPO) are the main blocking region, with blocking occurring predominantly in winter. The CMIP5 historical simulations also agree well with ERA-I for annual and seasonal BE locations and frequencies. A reduction in BEs is observed in the SPO in the 2071–2100 period in the RCP4.5 projections, and this is more pronounced for the RCP8.5 projections and occurs predominantly during the spring and summer seasons. Preliminary investigations imply that the southern annular mode (SAM) is negatively correlated with blocking activity in the SPO in all seasons in the reanalysis. This negative correlation is also observed in the GCM historical output. However, in the RCP projections this correlation is reduced in three of the four models during summer, suggesting that SAM may be less influential in summertime blocking in the future.

scholarly journals GCM Simulations of the Climate in the Central United States

2005 ◽  
Vol 18 (7) ◽  
pp. 1016-1031 ◽  
Author(s):  
Kenneth E. Kunkel ◽  
Xin-Zhong Liang
Keyword(s):  
United States ◽  
General Circulation ◽  
Coupled Model ◽  
Scalar Fields ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Model Intercomparison ◽  
Central United States ◽  
Intercomparison Project ◽  
Precipitation Changes

Abstract A diagnostic analysis of relationships between central U.S. climate characteristics and various flow and scalar fields was used to evaluate nine global coupled ocean–atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). To facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses. Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central United States, although no model exhibits small differences from the observationally based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except the third-generation Hadley Centre CGCM (HadCM3), appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central United States. The CMIP doubled-CO2 transient runs show warming (1°–5°C) for all models and seasons and variable precipitation changes over the central United States. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the twentieth century and the model variations in the control simulations.

scholarly journals Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method

2019 ◽  
Vol 11 (16) ◽  
pp. 4283 ◽  
Author(s):  
Patricia Jitta Abdulai ◽  
Eun-Sung Chung
Keyword(s):  
General Circulation ◽  
Standardized Precipitation Index ◽  
Model Performance ◽  
Coupled Model ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Drought Indices ◽  
Ensemble Averaging ◽  
Total Uncertainty ◽  
Uncertainty Range

The consequence of climate variations on hydrology remains the greatest challenging aspect of managing water resources. This research focused on the quantitative approach of the uncertainty in variations of climate influence on drought pattern of the Cheongmicheon watershed by assigning weights to General Circulation Models (GCMs) based on model performances. Three drought indices, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are used for three durations 3-, 6- and 9-months. This study included 27 GCMs from Coupled Model Intercomparison Project 5 (CMIP5) and considered three future periods (2011–2040, 2041–2070 and 2071–2100) of the concentration scenario of Representation Concentration Pathway (RCP) 4.5. Compared to SPEI and SDI, SPI identified more droughts in severe or extreme categories of shorter time scales than SPEI or SDI. The results suggested that the discrepancy in temperature plays a significant part in characterizing droughts. The Reliability Ensemble Averaging (REA) technique was used to give a mathematical approximation of associated uncertainty range and reliability of future climate change predictions. The uncertainty range and reliability of Root Mean Square Error (RMSE) varied among GCMs and total uncertainty ranges were between 50% and 200%. This study provides the approach for realistic projections by incorporating model performance ensemble averaging based on weights from RMSE.

scholarly journals Quantifying Uncertainty in the Modelling Process; Future Extreme Flood Event Projections Across the UK

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Cameron Ellis ◽  
Annie Visser-Quinn ◽  
Gordon Aitken ◽  
Lindsay Beevers
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Extreme Value ◽  
Extreme Flood ◽  
Emissions Scenarios ◽  
Rcp 8.5 ◽  
Intercomparison Project ◽  
The Uk ◽  
Model Chain

With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK by the 2080s (2069–2098). Capturing uncertainty in the hydroclimatic modelling chain, flow projections were extracted from the EDgE (End-to-end Demonstrator for improved decision-making in the water sector in Europe) multi-model ensemble: five Coupled Model Intercomparison Project (CMIP5) General Circulation Models and four hydrological models forced under emissions scenarios Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 (5 × 4 × 2 chains). Uncertainty in extreme value parameterisation was captured through consideration of two methods: generalised extreme value (GEV) and generalised logistic (GL). The method was applied across 192 catchments and aggregated to eight regions. The results suggest that, by the 2080s, many regions could experience large increases in extreme runoff, with a maximum mean change signal of +34% exhibited in East Scotland (1:2-year RP). Combined with increasing urbanisation, these estimates paint a concerning picture for the future UK flood landscape. Model chain uncertainty was found to increase by the 2080s, though extreme value (EV) parameter uncertainty becomes dominant at the 1:30-year RP (exceeding 60% in some regions), highlighting the importance of capturing both the associated EV parameter and ensemble uncertainty.

scholarly journals Arctic sea ice in the PlioMIP ensemble: is model performance for modern climates a reliable guide to performance for the past or the future?

2015 ◽  
Vol 11 (2) ◽  
pp. 1263-1312 ◽  
Author(s):  
F. W. Howell ◽  
A. M. Haywood ◽  
B. L. Otto-Bliesner ◽  
F. Bragg ◽  
W.-L. Chan ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Climate Models ◽  
Model Performance ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Intercomparison Project ◽  
Model Tuning

Abstract. Eight general circulation models have simulated the mid-Pliocene Warm Period (mPWP, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced and displays greater variability within the ensemble compared to the pre-industrial. This variability is highest in the summer months, when the model spread in the mid-Pliocene is more than three times larger than the rest of the year. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. It is suggested that the weaker relationship between pre-industrial Arctic sea ice and temperatures is likely due to the tuning of climate models to achieve an optimal pre-industrial sea ice cover, which may also affect future predictions of Arctic sea ice. Model tuning for the pre-industrial does not appear to be best suited for simulating the different climate state of the mid-Pliocene. This highlights the importance of evaluating climate models through simulation of past climates, and the urgent need for more proxy evidence of sea ice during the Pliocene.

scholarly journals An automatic and effective parameter optimization method for model tuning

2015 ◽  
Vol 8 (5) ◽  
pp. 3791-3822
Author(s):  
T. Zhang ◽  
L. Li ◽  
Y. Lin ◽  
W. Xue ◽  
F. Xie ◽  
...  
Keyword(s):  
General Circulation ◽  
Model Development ◽  
Computational Cost ◽  
Model Performance ◽  
Objective Evaluation ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Optimization Method ◽  
Development Stage ◽  
Optimum Combination

Abstract. Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

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