Impact of Dynamics and Atmospheric State on Cloud Vertical Overlap

2008 ◽  
Vol 21 (8) ◽  
pp. 1758-1770 ◽  
Author(s):  
Catherine M. Naud ◽  
Anthony Del Genio ◽  
Gerald G. Mace ◽  
Sally Benson ◽  
Eugene E. Clothiaux ◽  
...  

Abstract The observation and representation in general circulation models (GCMs) of cloud vertical overlap are the objects of active research due to their impacts on the earth’s radiative budget. Previous studies have found that vertically contiguous cloudy layers show a maximum overlap between layers up to several kilometers apart but tend toward a random overlap as separations increase. The decorrelation length scale that characterizes the progressive transition from maximum to random overlap changes from one location and season to another and thus may be influenced by large-scale vertical motion, wind shear, or convection. Observations from the U.S. Department of Energy Atmospheric Radiation Measurement program ground-based radars and lidars in midlatitude and tropical locations in combination with reanalysis meteorological fields are used to evaluate how dynamics and atmospheric state influence cloud overlap. For midlatitude winter months, strong synoptic-scale upward motion maintains conditions closer to maximum overlap at large separations. In the tropics, overlap becomes closer to maximum as convective stability decreases. In midlatitude subsidence and tropical convectively stable situations, where a smooth transition from maximum to random overlap is found on average, large wind shears sometimes favor minimum overlap. Precipitation periods are discarded from the analysis but, when included, maximum overlap occurs more often at large separations. The results suggest that a straightforward modification of the existing GCM mixed maximum–random overlap parameterization approach that accounts for environmental conditions can capture much of the important variability and is more realistic than approaches that are only based on an exponential decay transition from maximum to random overlap.

2017 ◽  
Vol 30 (11) ◽  
pp. 4021-4035 ◽  
Author(s):  
Bin Wang ◽  
Ja-Yeon Moon

Abstract Modulation of tropical cyclone (TC) genesis by the Madden–Julian oscillation (MJO) has been quantitatively diagnosed by using a climatological genesis potential index (GPI). Analysis of TC genesis during November–April of 1979–2014 indicates the most effective factors controlling intraseasonal TC genesis are 850-hPa relative vorticity weighted by the Coriolis parameter fζr850 and 500-hPa vertical motion ω500. The total vertical wind shear and maximum potential intensity are unimportant, and the role of 600-hPa relative humidity is greatly represented by ω500. The MJO modulates TC genesis primarily through changing low-level vorticity induced by its Rossby wave gyres and meridional shears of equatorial zonal winds. A new intraseasonal GPI (ISGPI) is proposed to quantify the MJO’s modulation of TC genesis. The ISGPI significantly improves representation of intraseasonal variation of TC genesis in the tropics and in each subregion of the southern Indian Ocean, Australian monsoon, and South Pacific. In the hot spots of the Southern Hemisphere TC genesis zone, the probability of TC genesis can differ by a factor of 5–19 as a result of MJO modulation. The results suggest that the large-scale factors controlling TC genesis may vary with different time scales, and the climatological GPI may not be quite applicable for diagnoses of climate variability and future change of TC genesis potential. To simulate realistic impacts of the MJO on TC genesis, general circulation models must reproduce not only realistic eastward propagation but also the MJO low-level circulation structure. Application of the new ISGPI may have a large potential to improve dynamical subseasonal prediction of TC genesis.


2004 ◽  
Vol 85 (12) ◽  
pp. 1903-1916 ◽  
Author(s):  
Thomas J. Phillips ◽  
Gerald L. Potter ◽  
David L. Williamson ◽  
Richard T. Cederwall ◽  
James S. Boyle ◽  
...  

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.


2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.


2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.


2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.


2006 ◽  
Vol 24 (8) ◽  
pp. 2075-2089 ◽  
Author(s):  
A. Chakraborty ◽  
R. S. Nanjundiah ◽  
J. Srinivasan

Abstract. A theory is proposed to determine the onset of the Indian Summer Monsoon (ISM) in an Atmospheric General Circulation Model (AGCM). The onset of ISM is delayed substantially in the absence of global orography. The impact of orography over different parts of the Earth on the onset of ISM has also been investigated using five additional perturbed simulations. The large difference in the date of onset of ISM in these simulations has been explained by a new theory based on the Surface Moist Static Energy (SMSE) and vertical velocity at the mid-troposphere. It is found that onset occurs only after SMSE crosses a threshold value and the large-scale vertical motion in the middle troposphere becomes upward. This study shows that both dynamics and thermodynamics play profound roles in the onset of the monsoon.


2020 ◽  
Vol 50 (4) ◽  
pp. 1045-1064 ◽  
Author(s):  
Steven L. Morey ◽  
Ganesh Gopalakrishnan ◽  
Enric Pallás Sanz ◽  
Joao Marcos Azevedo Correia De Souza ◽  
Kathleen Donohue ◽  
...  

AbstractThree simulations of the circulation in the Gulf of Mexico (the “Gulf”) using different numerical general circulation models are compared with results of recent large-scale observational campaigns conducted throughout the deep (>1500 m) Gulf. Analyses of these observations have provided new understanding of large-scale mean circulation features and variability throughout the deep Gulf. Important features include cyclonic flow along the continental slope, deep cyclonic circulation in the western Gulf, a counterrotating pair of cells under the Loop Current region, and a cyclonic cell to the south of this pair. These dominant circulation features are represented in each of the ocean model simulations, although with some obvious differences. A striking difference between all the models and the observations is that the simulated deep eddy kinetic energy under the Loop Current region is generally less than one-half of that computed from observations. A multidecadal integration of one of these numerical simulations is used to evaluate the uncertainty of estimates of velocity statistics in the deep Gulf computed from limited-length (4 years) observational or model records. This analysis shows that the main deep circulation features identified from the observational studies appear to be robust and are not substantially impacted by variability on time scales longer than the observational records. Differences in strengths and structures of the circulation features are identified, however, and quantified through standard error analysis of the statistical estimates using the model solutions.


2019 ◽  
Vol 12 (11) ◽  
pp. 4823-4873 ◽  
Author(s):  
Neil C. Swart ◽  
Jason N. S. Cole ◽  
Viatcheslav V. Kharin ◽  
Mike Lazare ◽  
John F. Scinocca ◽  
...  

Abstract. The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.


2009 ◽  
Vol 22 (10) ◽  
pp. 2713-2725 ◽  
Author(s):  
Celeste M. Johanson ◽  
Qiang Fu

Abstract Observations show that the Hadley cell has widened by about 2°–5° since 1979. This widening and the concomitant poleward displacement of the subtropical dry zones may be accompanied by large-scale drying near 30°N and 30°S. Such drying poses a risk to inhabitants of these regions who are accustomed to established rainfall patterns. Simple and comprehensive general circulation models (GCMs) indicate that the Hadley cell may widen in response to global warming, warming of the west Pacific, or polar stratospheric cooling. The combination of these factors may be responsible for the recent observations. But there is no study so far that has compared the observed widening to GCM simulations of twentieth-century climate integrated with historical changes in forcings. Here the Hadley cell widening is assessed in current GCMs from historical simulations of the twentieth century as well as future climate projections and preindustrial control runs. The authors find that observed widening cannot be explained by natural variability. This observed widening is also significantly larger than in simulations of the twentieth and twenty-first centuries. These results illustrate the need for further investigation into the discrepancy between the observed and simulated widening of the Hadley cell.


2010 ◽  
Vol 23 (5) ◽  
pp. 1127-1145 ◽  
Author(s):  
A. Bellucci ◽  
S. Gualdi ◽  
A. Navarra

Abstract The double–intertropical convergence zone (DI) systematic error, affecting state-of-the-art coupled general circulation models (CGCMs), is examined in the multimodel Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) ensemble of simulations of the twentieth-century climate. The aim of this study is to quantify the DI error on precipitation in the tropical Pacific, with a specific focus on the relationship between the DI error and the representation of large-scale vertical circulation regimes in climate models. The DI rainfall signal is analyzed using a regime-sorting approach for the vertical circulation regimes. Through the use of this compositing technique, precipitation events are regime sorted based on the large-scale vertical motions, as represented by the midtropospheric Lagrangian pressure tendency ω500 dynamical proxy. This methodology allows partition of the precipitation signal into deep and shallow convective components. Following the regime-sorting diagnosis, the total DI bias is split into an error affecting the magnitude of precipitation associated with individual convective events and an error affecting the frequency of occurrence of single convective regimes. It is shown that, despite the existing large intramodel differences, CGCMs can be ultimately grouped into a few homogenous clusters, each featuring a well-defined rainfall–vertical circulation relationship in the DI region. Three major behavioral clusters are identified within the AR4 models ensemble: two unimodal distributions, featuring maximum precipitation under subsidence and deep convection regimes, respectively, and one bimodal distribution, displaying both components. Extending this analysis to both coupled and uncoupled (atmosphere only) AR4 simulations reveals that the DI bias in CGCMs is mainly due to the overly frequent occurrence of deep convection regimes, whereas the error on rainfall magnitude associated with individual convective events is overall consistent with errors already present in the corresponding atmosphere stand-alone simulations. A critical parameter controlling the strength of the DI systematic error is identified in the model-dependent sea surface temperature (SST) threshold leading to the onset of deep convection (THR), combined with the average SST in the southeastern Pacific. The models featuring a THR that is systematically colder (warmer) than their mean surface temperature are more (less) prone to exhibit a spurious southern intertropical convergence zone.


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