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This study evaluates the ability of four versions BCC (Beijing Climate Center or National Climate Center) models (BCC AGCM2.1, BCC AGCM2.2, BCC CSM1.1 and BCC CSM1.1m) in simulating the MJO phenomenon using the outputs of the AMIP (Atmospheric Model Inter- comparison Project) and historical runs. In general, the models can simulate some major characteristics of the MJO, such as the intensity, the periodicity, the propagation, and the temporal/spatial evolution of the MJO signals in the tropics. There are still some biases between the models and the observation/reanalysis data, such as the overestimated total intraseasonal variability, but underestimated MJO intensity, shorter signiﬁcant periodicity, and excessive westward propagation. The differences in the ability of simulating the MJO between AMIP and historical experiments are also signiﬁcant. Compared to the AMIP runs, the total intraseasonal variability is reduced and more realistic, however the ratio between the MJO and its westward counterpart decreases in the historical runs. This unrealistic simulation of the zonal propagation might have been associated with the greater mean precipitation over the Paciﬁc and corresponded to the exaggeration of the South Paciﬁc Convergence Zone structure in precipitation mean state. In contrast to the T42 versions, the improvement of model resolution demonstrate more elaborate topography, but the enhanced westward propagation signals over the Arabia Sea followed. The underestimated (overestimated) MJO variability over eastern Indian Ocean (Paciﬁc) was assumed to be asso- ciated with the mean state. Three sets of sensitive experiments using BCC CSM1.1m turn out to support this argument.
et al., 2008; Aiyyer and Molinari, 2008; Camargo et al.,
2009), only a handful of modeling studies have exam- ined the relationship between TS activity and MJO, par- ticularly over the western North Pacific (WNP) (e.g. Vitart, 2009; Satoh et al., 2012) and the eastern North Pacific (Jiang et al., 2012). The limited number of mod- eling studies indicates the difficulties of climate mod- els in reproducing the spatial and temporal variability of the MJO and TS. This reflects various deficiencies of current climatemodels including insufficient hori- zontal resolution and uncertainties in the parameterized moist physics. Progress on these issues is presented by Vitart (2009) who examined the changes in TS den- sity and the landfall risk according to the MJO prop- agation. The large-scale patterns of low-level vortic- ity and the mid-level relative humidity also propagate eastward in the model hindcasts, suggesting important large-scale regulatory mechanisms for the TS devel- opment. The very-high resolution GCM runs of Satoh
Evidence is increasing that suggests ocean models may be necessary to capture dynamical ocean feedbacks important for initializing and maintaining the MJO. Webber et al. [2010, 2012] highlight the important role of ocean dynamics particularly in the Indian Ocean, where a tropical ocean internal wave response to the MJO leads to SST anomalies with the potential to feedback on the atmosphere and trigger further MJO events. Anomalous easterlies in the equatorial Indian Ocean can act to force a westward propagating downwelling (upwelling) Rossby wave, and SST increases (decreases) in phase with the passage of the wave [Seiki et al., 2013; Shinoda et al., 2013]. Drushka et al.  demonstrate that mixed layer depth variations on MJO time scales modulate the heat budget by ∼40% in the warm pool region. These studies imply that to accurately model the MJO, ocean dynamics may need to be simulated adequately enough to resolve internal waves as well as SST anomalies forced by waves. Developing a better picture for how MJO forcing impacts the ocean, and how this may feedback onto the MJO, is necessary for improving MJO prediction and modeling. This study extends previous work by carrying out daily initialized MJO simulations with a global coupled Met Oﬃce Uniﬁed Model (MetUM) conﬁguration and by using a more complex ocean model than has been previously applied to MJO and air-sea interactions investigations on medium range time scales. The experimental setup, outlined in section 2, permits us to examine the inﬂuence of the subsurface ocean on MJO simulations. As MetUM uncoupled operational forecast models already have a good general rep- resentation of the MJO on these time scales [Gottschalck et al., 2010], we consider the model a suitable
Abstract. The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameter- izations and model performance. BCC-CSM1.1 and BCC- CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simula- tions from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea- ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th cen- tury, the stratospheric quasi-biennial oscillation (QBO), the Madden–JulianOscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2- MR shows significant improvements in many aspects includ- ing the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diur- nal cycle of precipitation, interannual variations of SST in
Consider an air mass whose total water concentration is changing, this observation alone cannot reveal the process driving the moistening or dehydration. However, simulta- neous measurements of isotopes can reveal information on the relevant processes acting to change the hydrology [Webster and Heymsfield, 2003; Galewsky et al., 2007; D. Noone et al., Pairing measurements of the water vapor isotope ratio with humidity to deduce atmospheric moisten- ing an dehydration in the tropical mid-troposphere, submitted to Journal of Climate, 2012]. The classical example of how this manifests is through the equation that describes Rayleigh distillation, where the immediate loss of moisture through condensation produces a reduction in the ratio between the heavy and common water isotopologues and moisture that falls along a predictable trajectory in the water-isotope space [Friedman et al., 1964; Jouzel, 1986; Noone, 2012]. Devia- tions from this trajectory reveal processes acting on the vapor such as mixing from an additional moisture source or pre- cipitation efficiency that is less than 1 [Worden et al., 2007]. Therefore a series of simple models derived from the Ray- leigh distillation equation and end-member mixing models can be applied toward understanding information on the history of water vapor at a given location in space and time (Noone et al., submitted manuscript, 2012). Within the framework of these simple models that describe joint changes in water and dD, a water budget for the MJO is developed. While similar information can be derived through classical meteorological fields by way of trajectory analysis and careful budgeting of fluxes, these techniques do not provide direct measures of the characteristic history of a vapor mass. [ 12 ] In the first part of this paper a series of diagnostic
The coupled AOGCM used in this study is the FGOALS-g2 developed at State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), which participates in CMIP5 and PMIP3. The model includes four components, the Grid Atmospheric Model of IAP/LASG version 2.0 (GAMIL2.0, Li et al., 2013b), the LASG/IAP Climate system Ocean Model version 2.0 (LICOM2.0, Liu et al., 2012), the im- proved version based on the CICE (Community Ice CodE) model version 4 named CICE4-LASG (Wang et al., 2010), and the Community Land Model version 3 (CLM3, Oleson et al., 2004). The GAMIL2.0 employs a hybrid horizontal grid, with Gaussian grid of 2.8 ◦ between 65.58 ◦ S and 65.58 ◦ N and weighted equal-area grid poleward of 65.58 ◦ and 26 ver- tical layers up to 0.01 hPa. The major differences between GAMIL2.0 and its previous version GAMIL1.0 (Wang et al., 2004) are the upgraded cloud-related processes, for exam- ple the deep convection parameterizations, convective cloud fraction and microphysical schemes. The ocean model LI- COM2.0 has a horizontal resolution of 1 ◦ × 1 ◦ × (0.5 ◦ merid- ional resolution in the tropics) and 30 layers in vertical (10 m each layer in the upper 150 m). The two-step shape- preserving advection scheme (TSPAS – Yu, 1994) has been introduced and the physical processes have been updated or improved, including the mixing schemes, solar penetration scheme and other physical processes (Liu et al., 2012). The resolution of CICE4-LASG and CLM3 is set to the same as the ocean model LICOM2.0 and the atmospheric model GAMIL2.0, respectively. These four components are coupled by the National Center for Atmospheric Research (NCAR) coupler version 6 (CPL6, Craig et al., 2005). More details of FGOALS-g2 are described in Li et al. (2013a). In brief, FGOALS-g2 simulates a better annual cycle of SST along the equatorial Pacific when compared to its previous version. The characteristics of El Ni˜no-Southern Oscillation (ENSO), including the amplitude, period and phase-locking, are well reproduced in the model, as well as the frequency of tropi- cal land precipitation, East Asian monsoon and the Madden– Julianoscillation (MJO).
In climate research, the maritime continent (MC) is one of the most crucial regions to study . MC can play a key role as heat and moisture source and can impact global atmospheric circulations and contribute to climate change . MC is known to be the most active convective region where convection is influenced by local, regional and global atmospheric conditions such as monsoon, tropical convection, intra-seasonal oscillations and complex structure of the topography of land and sea around it [3,4]. The intertropical convergence zone (ITCZ) and its correlations with sea surface temperature (SST) and active precipitation are mostly responsible for convection in the MC region . Despite its importance, inter-scale interactions of intra-seasonal and mesoscale variability forced by diurnal affects (e.g. oceanic circulation, wind, temperature and precipitation) errors are often found in the climatemodels of MC . The MC has a signature seasonal cycle in its rainfall characterizing a typical monsoon climate . Due to intricate topography of the region and vast unpopulated land areas dense rain gauge network is absent in most of the places of MC and gauge observation in the oceanic areas are also not became possible to obtain so far . Satellite observation is the best available solution in such case to attain adequate temporal and spatial coverage of rainfall data . Therefore, in this study computer model is used using the satellite data to investigate SST and rainfall. In the southern equator principally, rainy season peaks in DJF (December-February) and the drier season peaks in July-August .
Seo, K-H., and Kim, K-Y (2003): Propagation and initiation mechanisms of the Madden-JulianOscillation. Journal of Geophysical Research, 108, 13, 4384. Shinoda, T and Hendon, H. (2002): Rectified Wind Forcing and Latent Heat Flux Produced by the Madden-JulianOscillation. Journal of Climate, 15, 3000-3508. Slingo, J. M., Sperber, K. R., Boyle, Ceron, J. P., Dix, M., Dugas, B., Ebisuzaki, W., Fyfe, J., Gregory, D., Gueremy, J. F., Hack, J., Harzallah, A., Inness, P. M., Kitoh, A., Lau, K. M., McAvaney, B., Madden, R. A., Matthews, A. J., Palmer, Park, C. K., Randall, D. and Renno, N. (1996): Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic test. Climate Dynamics, 12, 325-358.
If changes in the MJO-rainfall relationship are observed as a function of ENSO state, these changes could be due to one or both of two factors: (1) changes in the rainfall response to the MJO due to ENSO-related changes in the climate system and/or (2) changes in the MJO itself due to its statistical relationship with ENSO. It has been suggested that the second possibility might manifest through a change in the frequency distribution of MJO phases with ENSO state (Pohl & Matthews, 2007). Therefore, we perform a double- signi ﬁ cance test to discern between these two possibilities. First, we perform the basic test described above which tests if anomalies are signi ﬁ cant relative to random climate variations (regardless of ENSO state). Second, we repeat this test but restrict the simulated Monte Carlo phase vector to only values that occurred during the ENSO state of interest. This ensures that the changing distribution of MJO phases in El Niño and La Niña (i.e., the statistical relationship between the MJO and ENSO indices) are implicitly taken into account. By performing the Monte Carlo double-signi ﬁ cance test using only MJO phase values from the ENSO state of interest, we preserve the MJO-ENSO statistical relationship as part of the test. Therefore, any signi ﬁ cant results are those which exceed any in ﬂ uence of this relationship. Both tests are carried out at the 5% signi ﬁ - cance level, and if both tests prove the composites to be statistically signi ﬁ cant, then they could not have arisen due to the MJO-ENSO statistical relationship alone. Consequently, they re ﬂ ect a real change in the MJO-rainfall relationship.
Sperber et al (1997) stated that of those AMIP models they analysed, the Goddard Laboratory for Atmospheres (GLA) and United Kingdom Meteorological Office (UKMO) simulated the most realistic MJO. The GLA model produced a good prototype of convection over the western and central Pacific Ocean and into the SPCZ, and was better at feigning the eastward propagation. Both models exhibited the baroclinic structure observed in the real MJO. They concluded that the models suggest wave-CISK as the eastward propagation mechanism rather than evaporative feedback. When Wang et al (2001) re-investigated the AMIP they also found that some models reasonably well reproduced the main aspects of the MJO.
The GPCP and MULTI precipitation responses dis- agree over the eastern Antarctic Peninsula and the Weddell Sea where the GPCP response shows a local maximum, which is absent in MULTI. The maximum is absent in ERA-40 too (not shown). The high topogra- phy of the Antarctic Peninsula creates a precipitation shadow region on the eastern side of the peninsula (Turner et al. 1995), therefore the maximum appears unexpected, at least in the northern part of the region where a more positive SAM index is associated with the more frequent passage of air from west to east of the peninsula (Marshall et al. 2006). MULTI does not re- produce the equatorward extension of the moistening response over the eastern Pacific Ocean evident in GPCP. This is most probably related to the models’ failure to reproduce the negative pressure anomaly in this region. Another area of disagreement between MULTI and GPCP is along the east coast of South America between 208 and 408S. Here, GPCP shows a negative precipitation response, which is absent in MULTI. Gillett et al. (2006) show one station in this region with significant drying response. Silvestri and Vera (2003) showed that the response in this region is largest in late spring. They suggested that the response is linked to a SAM-related positive pressure anomaly that blocks the moisture transport by cyclones. If so, this mechanism is apparently missing in the models.
The analogy made between the unreliability of an individual‟s memory and a country‟s past history is very meaningful and suggests that one could always fabricate stories around them to make them sound more attractive and plausible. Martha‟s first memory, “an innocently arranged lie” (p. 8) was assembling her counties of English jigsaw puzzle, sometimes with a piece missing. This was not a false memory, but still not unprocessed since she could not recall the details. Once the missing parts are found and the puzzle completes, she would feel happy. “Staffordshire has been found, and her jigsaw, her England, and her heart had been whole again” (p. 9). The word “whole” here takes on an ironic interpretation because when her father abandoned the house ostensibly to find the missing part of the puzzle; Nottinghamshire, and he never returned, neither the jigsaw nor her heart was made whole again, “this all seemed-what?-not untruthful, but irrelevant, not a way of filling the exact, unique, fretsaw-cut hole within her. She asked for Nottinghamshire” (p. 26). This image, as Guignery maintains, “provides a metaphor for the essence of history and memory, whose wholeness is a mere illusion” (The Fiction of Julian Barnes, 2006, p. 106). Thus, the first part of the novel lays the conceptual setting for the creation of the theme park and theoretically justifies the rationale behind such a project.
We therefore proceed by examining whether changes to the lateral melt scheme may also impact the simulation of sea ice. The current representation of lateral melt in CMIP5 mod- els is heavily parametrized (Table 1), with the formulation described in Sect. 2.4 being the most complex parametriza- tion available in the CMIP5 models. Tsamados et al. (2015) showed that a more advanced concentration-dependent lat- eral melt parametrization significantly impacted the decom- position of sea ice melt processes, resulting in reduced sea ice concentrations around the ice edge in the Arctic. In the Antarctic, heat flux from solar heating of open water areas has been cited as the major cause of sea ice decay (Nihashi and Ohshima, 2001), with this melting potential available for both lateral and bottom melt. Recent studies have also sug- gested that floe size should also impact sea ice concentra- tion through processes such as floe–floe collisions and lateral growth (Horvat and Tziperman, 2015; Zhang et al., 2015).
Figure 1 shows the equatorial zonal mean zonal wind for the different models, the ERA-Interim reanalysis and the FU Berlin dataset. In the models’ stratosphere, QBO-like oscil- lations can be recognized. How much these resemble the ob- served QBO will be assessed based on a set of characteristic metrics. The most obvious one is the mean period; however, the QBO has a structure in latitude and height and the be- haviour of easterly and westerly phases differs. Furthermore, it is not a classic harmonic oscillation with one single restor- ing force, which leads to a variety of periods (Dunkerton, 2016). There might be an interaction with the semiannual os- cillation or the 11-year solar cycle as well as the annual cycle in the troposphere that can influence timing of phase changes and descent of the shear zones. To assess the different aspects of the QBO that are seen in the zonal wind observations, we propose a set of characteristic metrics, including the height of the maximum amplitude, the latitudinal and vertical ex- tent, and the descent rates of each shear zone (Table 3, first row).
The land-sea surface temperature contrast on the western coast of Sumatra Island was examined using observation data obtained from the pre-Years of the Maritime Continent (YMC) field campaign from November to December 2015. Surface observations showed that, on most days, strong daytime solar radiation caused a pronounced diurnal cycle in surface air temperatures on the island, even during the local active phase of the Madden-JulianOscillation (MJO). Sudden drops in surface air temperature occurred frequently on the island in the late afternoon and over the sea at nighttime, accompanied by precipitation. Temperatures on the island were higher than those over the sea during the daytime and lower in the night and early morning. Prior to the local active phase of the MJO, dual maxima in the land-sea surface air temperature contrast occurred in the evening and early morning. During the local active phase of the MJO, in spite of cloudy conditions, there were still large land-sea temperature contrasts during the daytime and in the early morning. In addition to the nighttime radiative cooling of the land surface, decreases in air temperature over the land due to precipitation cooling and the lower solar insolation in the MJO active phase caused the larger temperature differences in the morning. These results suggest that the decrease in air temperature caused by precipitation cooling had a substantial effect on the land-sea surface air temperature contrast on the western coast of Sumatra Island, particularly during an active phase of the MJO.
The drastic thickening of the barrier layer in the marginal sea off the western coast of Sumatra during the passage of the Madden-Julianoscillation (MJO) observed during December 2015 is investigated. Before the MJO arrival, the halocline above a depth of 20 m was very strong, and the barrier layer thickness was 5 – 10 m based on R/V Mirai observations. During the MJO forcing of 13 – 16 December, the isothermal layer drastically deepened from 20 to 100 m. Meanwhile, the mixed layer deepening lagged behind the isothermal layer deepening by 1 day, and the barrier layer underwent dramatic thickening to 60 m within 24 h. An evaluation of the vertical salinity gradient tendency showed that the dramatic thickening of the barrier layer was due to the vertical oceanic mixing by the atmospheric MJO forcing and the vertical stretching by the oceanic downwelling coastal Kelvin wave intruding from the open ocean. In addition, an evaluation of the vertical temperature gradient tendency showed that the temperature inversion in the barrier layer formed by losing heat to the atmosphere due to the MJO forcing and downward advection of the temperature gradient due to the downwelling Kelvin wave resulting in the dramatic isothermal layer deepening. One of the important factors in the drastic barrier layer thickening was the atmospheric external forcing and the oceanic internal wave being in phase. The downwelling oceanic Kelvin wave continuously lowered the thermocline from the middle of November to the end of December, and the salinity stratification in the vicinity of the thermocline was continuously mitigated by the vertical stretching. Under such conditions, the MJO forcing caused vertical mixing of the freshwater with strong salinity stratification and temperature stratification near the surface. The combination of the two distinct processes caused the drastic thickening of the barrier layer, and the barrier layer thickness reached a maximum of 85 m 5 days after the MJO arrival.
the equatorial Indian ocean , whereas the 10 - 20-day mode is believed to be associated with westward propagation clouds, rainfall and winds from the west Pacific. During the summer monsoon season (June to September), a substantial component of this variability of convection and rainfall over the Indian region arises from the fluctuations on the intraseasonal scale between active spells with good rainfall and weak spells or breaks with little rainfall . The interannual variability of the sub-seasonal fluctuations during the monsoon season is large and long intense breaks have an impact on the seasonal monsoon rainfall over the country . It is recognized that intraseasonal variation can have an impact on the seasonal total rainfall of Indian monsoon. Madden and Julian named it the “40 - 50-day oscillation” because of its preferred time scale -. There can be multiple MJO events within a season, and so the MJO is best described as intraseasonal tropical climate variability. Since then it has been called the “30 - 60-day oscillation” and the “intraseasonal oscillation”, but the term “MJO” has now emerged as a favorite in the global climate change scenario. A comprehensive review of MaddenJulianOscillation can be found elsewhere . Earlier there was no universally accepted definition for MJO. The Real-time Multivariate MJO index (RMM) of Wheeler and Hendon is now accepted as the standard definition for MJO . The MJO cycle, as de- fined by the RMM index, is split up into 8 phases, for convenience. Each phase corresponds to 1/8th of the full cycle. An individual MJO event can last anywhere between 30 and 60 days.
Introduction. Large-scale signals in the ocean-atmosphere system are an important object of research in climatology. Modern climate paradigm is based on idea of the existence of some stable states (modes) in the atmosphere, and the change of the climate conditions is considered as phase-to-phase transition. Large- scale atmospheric signals, their spatial-temporal structure and variability are determined by multivariate statistical analysis. The first leading mode of climate variability in the Atlantic-European region defined by principal component analysis (PCA) of atmospheric pressure pattern is the North Atlantic Oscillation (NAO). The second leading mode is the East Atlantic teleconnection pattern or East Atlantic Oscillation (EAO).
To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
We introduced implicit models based on Jeffrey’s rule of conditioning, binary vot- ing and three that use the popular wpq query expansion approach. The simulated approach used to test the model assumes the role of a searcher ‘viewing’ relevant documents and relevance paths between granular representations of documents. The simulation passes the information it viewed to the implicit models, which use this evidence of relevance to select terms to best describe this information. We investi- gated the degree to which each of the models improved search effectiveness and learned relevance. From the six models tested, the Jeffrey’s model provided the high- est levels of precision and the highest rate of learning.