Local Regimes of Atmospheric Variability: A Case Study of Southern California

2006 ◽  
Vol 19 (17) ◽  
pp. 4308-4325 ◽  
Author(s):  
Sebastien Conil ◽  
Alex Hall

Abstract The primary regimes of local atmospheric variability are examined in a 6-km regional atmospheric model of the southern third of California, an area of significant land surface heterogeneity, intense topography, and climate diversity. The model was forced by reanalysis boundary conditions over the period 1995–2003. The region is approximately the same size as a typical grid box of the current generation of general circulation models used for global climate prediction and reanalysis product generation, and so can be thought of as a laboratory for the study of climate at spatial scales smaller than those resolved by global simulations and reanalysis products. It is found that the simulated circulation during the October–March wet season, when variability is most significant, can be understood through an objective classification technique in terms of three wind regimes. The composite surface wind patterns associated with these regimes exhibit significant spatial structure within the model domain, consistent with the complex topography of the region. These regimes also correspond nearly perfectly with the simulation’s highly structured patterns of variability in hydrology and temperature, and therefore are the main contributors to the local climate variability. The regimes are approximately equally likely to occur regardless of the phase of the classical large-scale modes of atmospheric variability prevailing in the Pacific–North American sector. The high degree of spatial structure of the local regimes and their tightly associated climate impacts, as well as their ambiguous relationship with the primary modes of large-scale variability, demonstrate that the local perspective offered by the high-resolution model is necessary to understand and predict the climate variations of the region.

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.


2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.


1990 ◽  
Vol 14 ◽  
pp. 364 ◽  
Author(s):  
Tetsuzo Yasunari ◽  
Akio Kitoh ◽  
Tatsushi Tokioka

Observational studies have shown that Eurasian snow-cover anomalies during winter-through-spring seasons have a great effect on anomalies in atmospheric circulation and climate in the following summer season through snow albedo feedback (Hahn and Shukla, 1976; Dey and Bhanu Kumar, 1987). Morinaga and Yasunari (1987) have revealed that large-scale snow-cover extent over central Asia in late winter, which particularly has a great effect on the circulation over Eurasia in the following season, is closely related to the Eurasian pattern circulation (Wallace and Gutzler, 1981) in the beginning of winter. Some atmospheric general circulation models (GCM) have suggested that not only the albedo effect of the snow cover but also the snow-hydrological process are important in producing the atmospheric anomalies in the following seasons (Yeh and others, 1984; Barnett and others, 1988). However, more quantitative evaluations of these effects have not yet been examined. For example, it is not clear to what extent atmospheric anomalies are explained solely by snow-cover anomalies. Spatial and seasonal dependencies of these effects are supposed to be very large. Relative importance of snow cover over Tibetan Plateau should also be examined, particularly relevant to Asian summer monsoon anomalies. Moreover, these effects seem to be very sensitive to parameterizations of these physical processes (Yamazaki, 1988). This study focuses on these problems by using some versions of GCMs of the Meteorological Research Institute. The results include the evaluation of total snow-cover feedbacks as part of internal dynamics of climatic change from 12-year GCM integration, and of the effect of anomalous snow cover over Eurasia in late winter on land surface conditions and atmospheric circulations in the succeeding seasons.


2019 ◽  
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/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 (5.7 K) than its predecessor CanESM2 (3.8 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations are contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6), and will be employed for climate science and service applications in Canada.


2000 ◽  
Vol 24 (4) ◽  
pp. 499-514 ◽  
Author(s):  
Richard Washington

The atmosphere is known to be forced by a variety of energy sources, including radiation and heat fluxes emanating from the boundary layer associated with sea-surface temperature anomalies and land-surface features. The atmosphere is also subject to internal variability which is essentially unforced and is thought to be a basic characteristic of fluids. Whereas much work has been done in quantifying the links between external forcing of the atmosphere and its long-term response as well as the influence of boundary layer forcing in determining organized, large-scale modes of planetary-scale circulation, less is known about the importance of internal variability or chaos in determining the evolution of weather and climate. General circulation models (GCMs) now provide for this possibility. Multiple evolutions of the climate system may be computed in GCM simulations. Where these simulations are identical except for the conditions by which the model is initialized, the degree of departure in the evolution of climate from one model run to the next corresponds precisely to the degree of internal variability or chaos present in the model atmosphere. A methodology for quantifying this chaotic forcing is considered and is applied to century-long integrations of the UK Meteorological Office model HADAM2A.


2012 ◽  
Vol 25 (11) ◽  
pp. 3970-3984 ◽  
Author(s):  
Jonathan M. Eden ◽  
Martin Widmann ◽  
David Grawe ◽  
Sebastian Rast

The ability of general circulation models (GCMs) to correctly simulate precipitation is usually assessed by comparing simulated mean precipitation with observed climatologies. However, to what extent the skill in simulating average precipitation indicates how well the models represent temporal changes is unclear. A direct assessment of the latter is hampered by the fact that freely evolving climate simulations for past periods are not set up to reproduce the specific evolution of internal atmospheric variability. Therefore, model-to-real-world comparisons of time series of daily, monthly, or annual precipitation are not meaningful. Here, for the first time, the authors quantify GCM skill in simulating precipitation variability using simulations in which the temporal evolution of the large-scale atmospheric state closely matches that of the real world. This is achieved by nudging the atmospheric states in the ECHAM5 GCM, but crucially not the precipitation field itself, toward the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Global correlation maps between observed and simulated seasonal precipitation allow areas in which simulated future precipitation changes are likely to be meaningful to be identified. In many areas, correlations higher than 0.8 are found. This means also that in these regions the simulated precipitation is a very good predictor for the true precipitation, and thus a statistical correction of the simulated precipitation, which can include a downscaling component, can provide useful estimates for local-scale precipitation. The authors show that a simple scaling of the simulated precipitation performs well in a cross validation and thus appears to be a promising alternative to standard statistical downscaling approaches.


1990 ◽  
Vol 14 ◽  
pp. 364-364 ◽  
Author(s):  
Tetsuzo Yasunari ◽  
Akio Kitoh ◽  
Tatsushi Tokioka

Observational studies have shown that Eurasian snow-cover anomalies during winter-through-spring seasons have a great effect on anomalies in atmospheric circulation and climate in the following summer season through snow albedo feedback (Hahn and Shukla, 1976; Dey and Bhanu Kumar, 1987). Morinaga and Yasunari (1987) have revealed that large-scale snow-cover extent over central Asia in late winter, which particularly has a great effect on the circulation over Eurasia in the following season, is closely related to the Eurasian pattern circulation (Wallace and Gutzler, 1981) in the beginning of winter.Some atmospheric general circulation models (GCM) have suggested that not only the albedo effect of the snow cover but also the snow-hydrological process are important in producing the atmospheric anomalies in the following seasons (Yeh and others, 1984; Barnett and others, 1988).However, more quantitative evaluations of these effects have not yet been examined. For example, it is not clear to what extent atmospheric anomalies are explained solely by snow-cover anomalies. Spatial and seasonal dependencies of these effects are supposed to be very large. Relative importance of snow cover over Tibetan Plateau should also be examined, particularly relevant to Asian summer monsoon anomalies. Moreover, these effects seem to be very sensitive to parameterizations of these physical processes (Yamazaki, 1988).This study focuses on these problems by using some versions of GCMs of the Meteorological Research Institute. The results include the evaluation of total snow-cover feedbacks as part of internal dynamics of climatic change from 12-year GCM integration, and of the effect of anomalous snow cover over Eurasia in late winter on land surface conditions and atmospheric circulations in the succeeding seasons.


2017 ◽  
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner

Abstract. Model output statistics (MOS) methods empirically relate an environmental variable of interest to predictions from general circulation models (GCMs). This variable often belongs to a spatial scale not resolved by the GCM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the GCM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In so doing, the MOS method is tested for catchments areas covering four orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduces the mean absolute error of the former in the range of 5 to 11 percent, which is consistently reproduced at the subcatchment scale. The results further indicate that bias corrected runoff from the H-TESSEL land surface model is an interesting option when it comes to seasonal streamflow forecasting in large river basins.


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.


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