Quantitative evaluation of the seasonal variations in climate model cloud regimes

2012 ◽  
Vol 41 (9-10) ◽  
pp. 2679-2696 ◽  
Author(s):  
Yoko Tsushima ◽  
Mark A. Ringer ◽  
Mark J. Webb ◽  
Keith D. Williams
Keyword(s):  
Quantitative Evaluation ◽  
Seasonal Variations ◽  
Climate Model ◽  
Model Cloud ◽  
Cloud Regimes

scholarly journals Cloud Regime Variability over the Azores and Its Application to Climate Model Evaluation

2015 ◽  
Vol 28 (24) ◽  
pp. 9707-9720 ◽  
Author(s):  
Jasmine Rémillard ◽  
George Tselioudis
Keyword(s):  
High Pressure ◽  
Climate Model ◽  
Cumulus Clouds ◽  
Low Clouds ◽  
Shallow Cumulus ◽  
Cloud Regime ◽  
Wide Range ◽  
Model Cloud ◽  
Cloud Regimes ◽  
Regime Analysis

Abstract From its location on the subtropics–midlatitude boundary, the Azores is influenced by both the subtropical high pressure and the midlatitude baroclinic storm regimes and therefore experiences a wide range of cloud structures, from fair-weather scenes to stratocumulus sheets and deep convective systems. This work combines three types of datasets to study cloud variability in the Azores: a satellite analysis of cloud regimes, a reanalysis characterization of storminess, and a 19-month field campaign that occurred on Graciosa Island. Combined analysis of the three datasets provides a detailed picture of cloud variability and the respective dynamic influences, with emphasis on low clouds that constitute a major uncertainty source in climate model simulations. The satellite cloud regime analysis shows that the Azores cloud distribution is similar to the mean global distribution and can therefore be used to evaluate cloud simulation in global models. Regime analysis of low clouds shows that stratocumulus decks occur under the influence of the Azores high pressure system, while shallow cumulus clouds are sustained by cold-air outbreaks, as revealed by their preference for postfrontal environments and northwesterly flows. An evaluation of climate model cloud regimes from phase 5 of CMIP (CMIP5) over the Azores shows that all models severely underpredict shallow cumulus clouds, while most models also underpredict the occurrence of stratocumulus cloud decks. It is demonstrated that the regime analysis can assist in the selection of case studies representing specific climatological cloud distributions. With all the tools now in place, a methodology is suggested to better understand cloud–dynamics interactions and attempt to explain and correct climate model cloud deficiencies.

scholarly journals Sunset–sunrise difference in solar occultation ozone measurements (SAGE II, HALOE, and ACE–FTS) and its relationship to tidal vertical winds

2015 ◽  
Vol 15 (2) ◽  
pp. 829-843 ◽  
Author(s):  
T. Sakazaki ◽  
M. Shiotani ◽  
M. Suzuki ◽  
D. Kinnison ◽  
J. M. Zawodny ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Seasonal Variations ◽  
Climate Model ◽  
Transport Model ◽  
Stratospheric Aerosol ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Latitude Range ◽  
Solar Occultation ◽  
Vertical Winds

Abstract. This paper contains a comprehensive investigation of the sunset–sunrise difference (SSD, i.e., the sunset-minus-sunrise value) of the ozone mixing ratio in the latitude range of 10° S–10° N. SSD values were determined from solar occultation measurements based on data obtained from the Stratospheric Aerosol and Gas Experiment (SAGE) II, the Halogen Occultation Experiment (HALOE), and the Atmospheric Chemistry Experiment–Fourier transform spectrometer (ACE–FTS). The SSD was negative at altitudes of 20–30 km (−0.1 ppmv at 25 km) and positive at 30–50 km (+0.2 ppmv at 40–45 km) for HALOE and ACE–FTS data. SAGE II data also showed a qualitatively similar result, although the SSD in the upper stratosphere was 2 times larger than those derived from the other data sets. On the basis of an analysis of data from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) and a nudged chemical transport model (the specified dynamics version of the Whole Atmosphere Community Climate Model: SD–WACCM), we conclude that the SSD can be explained by diurnal variations in the ozone concentration, particularly those caused by vertical transport by the atmospheric tidal winds. All data sets showed significant seasonal variations in the SSD; the SSD in the upper stratosphere is greatest from December through February, while that in the lower stratosphere reaches a maximum twice: during the periods March–April and September–October. Based on an analysis of SD–WACCM results, we found that these seasonal variations follow those associated with the tidal vertical winds.

scholarly journals Sunset–sunrise difference in solar occultation ozone measurements (SAGE II, HALOE, and ACE–FTS) and its relationship to tidal vertical winds

2014 ◽  
Vol 14 (11) ◽  
pp. 16043-16083
Author(s):  
T. Sakazaki ◽  
M. Shiotani ◽  
M. Suzuki ◽  
D. Kinnison ◽  
J. M. Zawodny ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Seasonal Variations ◽  
Climate Model ◽  
Transport Model ◽  
Stratospheric Aerosol ◽  
Chemical Transport Model ◽  
Latitude Range ◽  
Solar Occultation ◽  
Vertical Winds ◽  
Chemistry Experiment

Abstract. This paper contains a comprehensive investigation of the sunset–sunrise difference (SSD; i.e., the sunset-minus-sunrise value) of the ozone mixing ratio in the latitude range of 10° S–10° N. SSD values were determined from solar occultation measurements based on data obtained from the Stratospheric Aerosol and Gas Experiment (SAGE) II, the Halogen Occultation Experiment (HALOE), and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The SSD was negative at altitudes of 20–30 km (–0.1 ppmv at 25 km) and positive at 30–50 km (+0.2 ppmv at 40–45 km) for HALOE and ACE–FTS data. SAGE II data also showed a qualitatively similar result, although the SSD in the upper stratosphere was two times larger than those derived from the other datasets. On the basis of an analysis of data from the Superconducting Submillimeter Limb Emission Sounder (SMILES), and a nudged chemical-transport model (the Specified Dynamics version of the Whole Atmosphere Community Climate Model: SD–WACCM), we conclude that the SSD can be explained by diurnal variations in the ozone concentration, particularly those caused by vertical transport by the atmospheric tidal winds. All datasets showed significant seasonal variations in the SSD; the SSD in the upper stratosphere is greatest from December through February, while that in the lower stratosphere reaches a maximum twice: during the periods March–April and September–October. Based on an analysis of SD–WACCM results, we found that these seasonal variations follow those associated with the tidal vertical winds.

scholarly journals Initial Tendencies of Cloud Regimes in the Met Office Unified Model

2008 ◽  
Vol 21 (4) ◽  
pp. 833-840 ◽  
Author(s):  
K. D. Williams ◽  
M. E. Brooks
Keyword(s):  
Initial Temperature ◽  
Climate Model ◽  
Deep Convection ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Systematic Bias ◽  
Cloud Regime ◽  
Tropical Deep Convection ◽  
Cloud Regimes ◽  
Temperature Tendency

Abstract The Met Office unified forecast–climate model is used to compare the properties of simulated climatological cloud regimes with those produced in short-range forecasts initialized from operational analyses. The regimes are defined as principal clusters of joint cloud-top pressure–optical depth histograms. In general, the cloud regime properties are found to be similar at all forecast times, including the climatological mean. This suggests that weaknesses in the representation of fast local processes are responsible for errors in the simulation of the cloud regimes. The increased horizontal resolution of the model used for numerical weather prediction generally has little impact on the cloud regimes, although the simulation of tropical shallow cumulus is improved, while the relative frequency of tropical deep convection and cirrus compare less favorably with observations. Analysis of the initial temperature tendency profiles for each cloud regime indicates that some of the initial temperature tendency, which leads to a systematic bias in the model climatology, is associated with a particular cloud regime.

Extratropical Cyclone Clouds in the GFDL Climate Model: Diagnosing Biases and the Associated Causes

2019 ◽  
Vol 32 (20) ◽  
pp. 6685-6701 ◽  
Author(s):  
Catherine M. Naud ◽  
James F. Booth ◽  
Jeyavinoth Jeyaratnam ◽  
Leo J. Donner ◽  
Charles J. Seman ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Climate Model ◽  
Extratropical Cyclones ◽  
Moist Convection ◽  
Radiative Balance ◽  
Tuning Parameters ◽  
Model Cloud ◽  
Low Levels ◽  
Cloud Parameterization ◽  
High Level

Abstract The clouds in Southern Hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat, and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of “AM4,” a model GFDL that will utilize for CMIP6, and the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low levels (again by differing amounts), especially in the post–cold frontal (PCF) region, when compared with observations. In focusing only on the models, it is seen that their differences in high and midlevel clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud-parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting that other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.

Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators

2012 ◽  
Vol 25 (15) ◽  
pp. 5190-5207 ◽  
Author(s):  
J. E. Kay ◽  
B. R. Hillman ◽  
S. A. Klein ◽  
Y. Zhang ◽  
B. Medeiros ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Marine Boundary Layer ◽  
Climate Model ◽  
Substantial Improvement ◽  
Satellite Observations ◽  
Cloud Properties ◽  
Community Atmosphere Model ◽  
Realistic Representation ◽  
Model Cloud ◽  
Atmosphere Model

Abstract Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain intermodel differences in cloud feedbacks and climate sensitivity. More generally, this study demonstrates that simulator-facilitated evaluation of cloud properties, such as amount by vertical level and optical depth, can robustly expose large and at times radiatively compensating climate model cloud biases.

Analysis of diagnostic climate model cloud parametrizations using large-eddy simulations

2015 ◽  
Vol 141 (691) ◽  
pp. 2199-2205 ◽  
Author(s):  
J. Rosch ◽  
T. Heus ◽  
M. Brueck ◽  
M. Salzmann ◽  
J. Mülmenstädt ◽  
...  
Keyword(s):  
Climate Model ◽  
Large Eddy Simulations ◽  
Large Eddy ◽  
Model Cloud

scholarly journals Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models

2010 ◽  
Vol 49 (9) ◽  
pp. 1971-1991 ◽  
Author(s):  
Dominique Bouniol ◽  
Alain Protat ◽  
Julien Delanoë ◽  
Jacques Pelon ◽  
Jean-Marcel Piriou ◽  
...  
Keyword(s):  
Water Content ◽  
Large Scale ◽  
Climate Model ◽  
Weather Forecast ◽  
Cloud Fraction ◽  
Forecast Models ◽  
Ice Water Content ◽  
Model Cloud ◽  
Intermittent Sampling ◽  
Ice Water

Abstract The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

scholarly journals Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 867
Author(s):  
Dong Wang ◽  
Jiahong Liu ◽  
Weiwei Shao ◽  
Chao Mei ◽  
Xin Su ◽  
...  
Keyword(s):  
River Basin ◽  
Seasonal Variations ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Poor Performance ◽  
Model Ensemble ◽  
Downscaling Method ◽  
Simulated Precipitation ◽  
Hanjiang River

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

Analytical Predictions of Seasonal Variations in Flexible Pavements: Minnesota Road Research Project Site

2000 ◽  
Vol 1730 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Bjorn Birgisson ◽  
Jill Ovik ◽  
David E. Newcomb
Keyword(s):  
Moisture Content ◽  
Seasonal Variations ◽  
Climate Model ◽  
Climatic Factors ◽  
Flexible Pavements ◽  
Temperature History ◽  
Research Project ◽  
Analysis And Design ◽  
Project Site ◽  
Strength And Stiffness

The mechanistic analysis and design of flexible pavements is very dependent on knowledge of traffic loading, materials, and climatic factors. Seasonal variation of climate factors such as temperature, temperature history, and precipitation affects the subsurface conditions of the pavement layers, including the in situ temperature, moisture content, and state of moisture. In turn, these subsurface conditions have a direct relationship with the pavement strength and stiffness, causing seasonal variations in both strength and pavement layer moduli. Many agencies are now moving toward a mechanistic-empirical pavement design, in which the design inputs include, as a minimum, the seasonal variations in pavement layer moduli. The ability to analytically predict and quantify the climatic effects on pavement strength and stiffness has been investigated by numerous researchers, but few comparisons with measured field data have been completed, because of a lack of pavement sites with extensive arrays of monitoring instrumentation. Detailed is a comparison between field results and predictions obtained from an analytical tool, called the enhanced integrated climate model (ICM). The climatic factors used as inputs into the model include temperature, rainfall, wind speed, and solar radiation. The ICM is used to predict seasonal variations in temperature, moisture content, and layer moduli at two representative flexible pavement test sections at the Minnesota Road Research Project site.

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