scholarly journals An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales

2011 ◽  
Vol 24 (14) ◽  
pp. 3609-3623 ◽  
Author(s):  
Fiona Johnson ◽  
Seth Westra ◽  
Ashish Sharma ◽  
Andrew J. Pitman
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Large Scale ◽  
Low Frequency ◽  
General Circulation Models ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Max Planck ◽  
Reservoir Storage ◽  
Low Frequency Variability

Abstract Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and amplitude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for individual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4); when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables.

scholarly journals Stochastic Parameterization Schemes for Use in Realistic Climate Models

2011 ◽  
Vol 68 (2) ◽  
pp. 284-299 ◽  
Author(s):  
Joel Culina ◽  
Sergey Kravtsov ◽  
Adam H. Monahan
Keyword(s):  
General Circulation ◽  
Ad Hoc ◽  
Climate Models ◽  
Low Frequency ◽  
General Circulation Models ◽  
Planetary Scale ◽  
Low Frequency Variability ◽  
Quasigeostrophic Model ◽  
Low Dimensional ◽  
O 10

Abstract Stochastic parameterizations of fast-evolving, subgrid-scale processes are increasingly being used in a range of models from conceptual models to general circulation models. However, stochastic terms are generally included in an ad hoc fashion. In this study, a systematic method—“Hasselmann’s method”—of stochastic parameterization is developed through the direct application of rigorously justified limit theorems that predict the effective slow dynamics in systems with coupled slow and fast variables. The multiple Hasselmann models form a hierarchy of models ordered by the time scales over which they are expected to provide good approximations to the slowly evolving variables. Adaptable, efficient algorithms for integrating these reduced models are developed that require minimal changes to the unreduced model. Hasselmann’s method is tested on an O(10 000)-dimensional (planetary and synoptic scale) quasigeostrophic model of atmospheric low-frequency variability. Low-dimensional deterministic and stochastic models in the planetary-scale modes alone are derived, which accurately generate the statistics of the corresponding modes of the unreduced model, including the statistical signatures of jet regime behavior. It is shown that deterministic nonlinearity through slow forcing averaged with respect to the fast modes distribution dominates over multiplicative noise in generating the regime behavior.

scholarly journals Testing the Annular Mode Autocorrelation Time Scale in Simple Atmospheric General Circulation Models

2008 ◽  
Vol 136 (4) ◽  
pp. 1523-1536 ◽  
Author(s):  
Edwin P. Gerber ◽  
Sergey Voronin ◽  
Lorenzo M. Polvani
Keyword(s):  
Time Scale ◽  
General Circulation ◽  
Simple Procedure ◽  
Low Frequency ◽  
General Circulation Models ◽  
Low Frequencies ◽  
Annular Mode ◽  
Fluctuation Dissipation ◽  
Low Frequency Variability ◽  
Autocorrelation Time

Abstract A new diagnostic for measuring the ability of atmospheric models to reproduce realistic low-frequency variability is introduced in the context of Held and Suarez’s 1994 proposal for comparing the dynamics of different general circulation models. A simple procedure to compute τ, the e-folding time scale of the annular mode autocorrelation function, is presented. This quantity concisely quantifies the strength of low-frequency variability in a model and is easy to compute in practice. The sensitivity of τ to model numerics is then studied for two dry primitive equation models driven with the Held–Suarez forcings: one pseudospectral and the other finite volume. For both models, τ is found to be unrealistically large when the horizontal resolutions are low, such as those that are often used in studies in which long integrations are needed to analyze model variability on low frequencies. More surprising is that it is found that, for the pseudospectral model, τ is particularly sensitive to vertical resolution, especially with a triangular truncation at wavenumber 42 (a very common resolution choice). At sufficiently high resolution, the annular mode autocorrelation time scale τ in both models appears to converge around values of 20–25 days, suggesting the existence of an intrinsic time scale at which the extratropical jet vacillates in the Held and Suarez system. The importance of τ for computing the correct response of a model to climate change is explicitly demonstrated by perturbing the pseudospectral model with simple torques. The amplitude of the model’s response to external forcing increases as τ increases, as suggested by the fluctuation–dissipation theorem.

scholarly journals Imprint of chaotic ocean variability on transports in the southwestern Pacific at interannual timescales

Ocean Science ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 487-507
Author(s):  
Sophie Cravatte ◽  
Guillaume Serazin ◽  
Thierry Penduff ◽  
Christophe Menkes
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Initial Perturbation ◽  
Low Frequency ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Atmospheric Forcing ◽  
Ocean Variability

Abstract. The southwestern Pacific Ocean sits at a bifurcation where southern subtropical waters are redistributed equatorward and poleward by different ocean currents. The processes governing the interannual variability of these currents are not completely understood. This issue is investigated using a probabilistic modeling strategy that allows disentangling the atmospherically forced deterministic ocean variability and the chaotic intrinsic ocean variability. A large ensemble of 50 simulations performed with the same ocean general circulation model (OGCM) driven by the same realistic atmospheric forcing and only differing by a small initial perturbation is analyzed over 1980–2015. Our results show that, in the southwestern Pacific, the interannual variability of the transports is strongly dominated by chaotic ocean variability south of 20∘ S. In the tropics, while the interannual variability of transports and eddy kinetic energy modulation are largely deterministic and explained by the El Niño–Southern Oscillation (ENSO), ocean nonlinear processes still explain 10 % to 20 % of their interannual variance at large scale. Regions of strong chaotic variance generally coincide with regions of high mesoscale activity, suggesting that a spontaneous inverse cascade is at work from the mesoscale toward lower frequencies and larger scales. The spatiotemporal features of the low-frequency oceanic chaotic variability are complex but spatially coherent within certain regions. In the Subtropical Countercurrent area, they appear as interannually varying, zonally elongated alternating current structures, while in the EAC (East Australian Current) region, they are eddy-shaped. Given this strong imprint of large-scale chaotic oceanic fluctuations, our results question the attribution of interannual variability to the atmospheric forcing in the region from pointwise observations and one-member simulations.

scholarly journals Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Seasonal Prediction ◽  
General Circulation Models ◽  
Meridional Wind ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Continental Scale ◽  
Predictive Skill ◽  
Murray Darling Basin

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.

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

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.

An applied mathematics perspective on stochastic modelling for climate

2008 ◽  
Vol 366 (1875) ◽  
pp. 2427-2453 ◽  
Author(s):  
Andrew J Majda ◽  
Christian Franzke ◽  
Boualem Khouider
Keyword(s):  
Stochastic Model ◽  
General Circulation ◽  
Applied Mathematics ◽  
Low Frequency ◽  
Lattice Models ◽  
Stochastic Modelling ◽  
General Circulation Models ◽  
Teleconnection Patterns ◽  
Slowing Down ◽  
Low Dimensional

Systematic strategies from applied mathematics for stochastic modelling in climate are reviewed here. One of the topics discussed is the stochastic modelling of mid-latitude low-frequency variability through a few teleconnection patterns, including the central role and physical mechanisms responsible for multiplicative noise. A new low-dimensional stochastic model is developed here, which mimics key features of atmospheric general circulation models, to test the fidelity of stochastic mode reduction procedures. The second topic discussed here is the systematic design of stochastic lattice models to capture irregular and highly intermittent features that are not resolved by a deterministic parametrization. A recent applied mathematics design principle for stochastic column modelling with intermittency is illustrated in an idealized setting for deep tropical convection; the practical effect of this stochastic model in both slowing down convectively coupled waves and increasing their fluctuations is presented here.

Nycthemeral Rhythm of the Frequency and Biomechanical Energy of High Frequency Intraocular Pressure Fluctuations

2013 ◽  
Author(s):  
Vincent Libertiaux ◽  
William P. Seigfreid ◽  
Massimo A. Fazio ◽  
Juan F. Reynaud ◽  
Claude F. Burgoyne ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
High Frequency ◽  
Pressure Fluctuations ◽  
Low Frequency ◽  
Pulse Amplitude ◽  
Mean Value ◽  
Multiple Time Scales ◽  
Dynamic Contour Tonometry ◽  
Multiple Time ◽  
Ocular Pulse

The optic nerve head (ONH) is the site of insult in glaucoma, the second leading cause of blindness worldwide. Intraocular pressure (IOP) is commonly regarded as a major factor in the onset and progression of the disease1 and lowering IOP is the only clinical treatment that has been shown to retard the onset and progression of glaucoma2. However, many patients continue to progress even at an epidemiologically-determined normal level of IOP3. This suggests that in addition to the mean value of IOP, IOP fluctuations could be a factor in glaucomatous pathophysiology. The importance of low frequency fluctuations of clinically-measured mean IOP remains controversial. These studies all rely on snapshot measurements of mean IOP at each time point, and those measurements are taken at relatively infrequent intervals (hourly at the most frequent, but usually monthly or longer). Recently however, there has been some interest in ocular pulse amplitude, or the fluctuation in IOP associated with the cardiac cycle, which can be measured by Dynamic Contour Tonometry (DCT). DCT provides continuous measurement of IOP, but only for a period of tens of seconds in which a patient can tolerate corneal contact without blinking or eye movement, which ironically are two of the most common sources of large high frequency IOP fluctuations according to our telemetric data collected from monkeys4 and previous human studies. In a recent report, continuous IOP telemetry was used in three nonhuman primates to characterize IOP dynamics at multiple time scales for multiple 24-hour periods5.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

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.

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