Regimes of tropical low-level clouds are commonly identified according to large-scale subsidence and lower tropospheric stability (LTS). This definition alone is insufficient for the distinction between regimes and limits the comparison of low-level clouds from CloudSat radar observations and the ECHAM5 GCM run with the COSP radar simulator. Comparisons of CloudSat radar cloud altitude-reflectivity histograms for stratocumulus and shallow cumulus regimes, as defined above, show nearly identical reflectivity profiles, because the distinction between the two regimes is dependent upon atmospheric stability below 700 hPa and observations above 1.5 km. Regional subsets, near California and Hawaii, for example, have large differences in reflectivity profiles than the dynamically defined domain; indicating different reflectivity profiles exist under a given large-scale environment. Regional subsets are better for the evaluation of low-level clouds in CloudSat and ECHAM5 as there is less contamination between 2.5 km and 7.5 km from precipitating hydrometeors which obscured cloud reflectivities.

An investigation of the impact of the subgrid-scale variability of cloud liquid water on the autoconversion process as parameterized in a general circulation model is presented in this paper. For this purpose, a prognostic statistical probability density distribution (PDF) of the subgrid scale variability of cloud water is incorporated in a continuous autoconversion parameterization. Thus, the revised autoconversion rate is calculated by an integral of the autoconversion equation over the PDF of total water mixing ratio from the saturation vapor mixing ratio to the maximum of total water mixing ratio. An evaluation of the new autoconversion parameterization is carried out by means of one year simulations with the ECHAM5 climate model. The results indicate that the new autoconversion scheme causes an increase of the frequency of occurrence of high autoconversion rates and a decrease of low ones compared to the original scheme. This expected result is due to the emphasis on areas of high cloud liquid water in the new approach, and the non-linearity of the autoconversion with respect to liquid water mixing ratio. A similar trend as in the autoconversion is observed in the accretion process resulting from the coupling of both processes. As a consequence of the altered autoconversion, large-scale surface precipitation also shows a shift of occurrence from lower to higher rates. The vertically integrated cloud liquid water estimated by the model shows slight improvements compared to satellite data. Most importantly, the artificial tuning factor for autoconversion in the continuous parameterization could be reduced by almost an order of magnitude using the revised parameterization.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19976号 / 工博第4220号 / 新制||工||1653(附属図書館) / 33072 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 堀 智晴, 准教授 KIM SUNMIN / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

ROCKE-3D is a contemporary Global Circulation Model allowing research into the complex processes behind the climate of planets. Using ROCKE-3D one can construct a model coupling atmosphere, land, and ocean revealing how the climate of a planet evolves over time. When constructing a model using ROCKE-3D, 2 different types of oceans can be used, one shallow without horizontal heat transfer and one fully coupled dynamic ocean. Simulations created using the different ocean types give rise to different results. In this project, the fractional habitability of the model 'planets' will be calculated using various methods in order to evaluate the differences between ocean types in ROCKE-3D simulations. There is also a hope to better understand how parameters such as rotation period and insolation, are affected when using different ocean types. The results show a large difference in predicted habitability fraction using two approximations for the ocean heat transport indicating that the Qflux approximation produces unrealistic models and should be avoided. / ROCKE-3D är en modern Global Circulation Model som tillåter forskning in i de komplexa processer som bygger planeters klimat. Vid användning av ROCKE-3D kan modeller som kopplar atmosfärer, land och hav konstrueras, detta kan avslöja hur en planets klimat utvecklas över tid. När en model konstrueras med ROCKE-3D kan 2 olika typer av hav användas, ett utan horisontell värmeöverföring som kallas för ett "Qflux" hav och ett fullt kopplat dynamiskt hav. Simulationer skapade med de olika havstyperna ger skillnad i resultat. I det här projektet, beräknas modell-planeternas fractional habitability för att evaluera skillnaden mellan havstyp i ROCKE-3D simulationer. Det finns även ett hopp för en bättre förståelse av hur parametrar, rotationsperiod och solinstrålning, påverkar när det är att föredra användningen av en viss havstyp. Resultaten visar att skillnaden i ""fractional habitability" mellan simulationer skapta med de 2 havstyperna minskar när solinstrålningen ökar, vilket visar att användandet av ett "Qflux" hav i skapandet av simulationer vid låg solinstrålning borde avrådas.

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

The most important component of the climate system over the Indian Ocean region is the southwest monsoon, which dictates the life and economy of billions of people in the tropics. Being a phenomena that involves interaction between atmosphere, ocean and land, the southwest monsoon is strongly influenced by upper ocean, primarily through warm sea surface temperature (SST). This is particularly true about the southeastern Arabian Sea (SEAS) and the onset of southwest monsoon over the peninsular India. A localized patch of warm water, known as the Arabian Sea mini warm pool (ASMWP), forms in the SEAS during February–March. It remain as the warmest spot in the northern Indian Ocean till early April. A large region, surrounding the SEAS, attains SST exceeding 30°C during April–May, with often the ASMWP as its core. The ASMWP is believed to have a critical impact on the air-sea interaction during the onset phase of southwest monsoon and on the formation of the onset vortex, during late May or early June. This thesis addresses the formation mechanisms of ASMWP, using a high-resolution Ocean General Circulation Model (OGCM) of the Indian Ocean. In addition to the formation of ASMWP, the SEAS is characterized by several features in its hydrography and circulation, which have been invoked in the past to explain the preferential warming of this oceanic region. During November–January, the prevailing surface currents transport low-salinity water from the Bay of Bengal into the SEAS and leads to strong haline stratification in the upper layer and formation of barrier layer (layer between mixed layer and isothermal layer). The vertical distribution of temperature in the SEAS exhibit inversions (higher subsurface temperature than that at surface) during December–February. A high in sea level and anticyclonic eddies develop in the SEAS during December and they propagate westward. These eddies modify the hydrography through downwelling and play an important role in the redistribution of advected low-salinity water within the SEAS. The seasonally reversing coastal and equatorial currents present in and around SEAS also have a major contribution in setting up the hydrography, through the advection and redistribution of cooler low-salinity water. These features make the SEAS a unique oceanographic region. The first hypothesis on the formation of ASMWP, which has been suggested by diagnostic studies, is based on the barrier layer mechanism. The barrier layer, caused by the influx of low-salinity water at surface, is argued to maintain a shallow mixed layer which can warm more efficiently. In addition, presence of barrier layer can prevent mixed layer cooling, by cutting off the interaction of mixed layer with cooler thermocline water below. However, a coupled model study have shown that there is no significant impact on the ASMWP formation from barrier layer, but only a weak warming effect during it mature phase during April. The second hypothesis, which is based on an OGCM study, has suggested that the temperature inversions present within the barrier layer can heat the mixed layer through turbulent entrainment and in turn lead to the formation of ASMWP during February–March. Both hypotheses rule out the possibility of air-sea heat fluxes being the primary reason in its formation. The strong salinity stratification in the SEAS during December–March is central to the hypotheses about formation of the ASMWP. Observational studies have only limited success in assessing the contribution from barrier layer and temperature inversions, as the ASMWP always form in their presence. OGCMs offer a better alternative. However, modelling processes in the northern Indian Ocean, especially that in the SEAS, is a challenging problem. Previous Indian Ocean models have had serious difficulties in simulating the low-salinity water in the Bay of Bengal and its intrusion into the SEAS. The northward advection of low-salinity water in the SEAS, along the west coast of India, is used to be absent in model simulations. Moreover, the coarse resolution inhibited those models from simulating faster surface currents and vigorous eddies as seen in the observations. In this thesis, we use an OGCM of the Indian Ocean, based on the recent version of Modular Ocean Model (MOM4p0), to study the ASMWP. The model has high resolutions in the horizontal (1/4o x 1/4o) and vertical (40 levels, with 5 m spacing in upper 60 m), and has been forced with daily values momentum, heat and freshwater fluxes. The turbulent (latent and sensible) and long wave heat fluxes have been calculated as a function of model SST. The freshwater forcing consists of precipitation, evaporation and river runoff, and there are no surface restoring or flux adjustments. The river runoff has been distributed over several grid points about the river mouth instead of discharging into a singe grid point, which has resulted in remarkable improvements in salinity simulation. The model simulates the Indian Ocean temperature, salinity and circulation remarkably well. The pattern of model temperature distribution and evolution matches very well with that in the observations. Significant improvements have been made in the salinity simulation, including the Bay of Bengal freshwater plume and intrusion of low-salinity water from the bay into the SEAS. The salinity distribution within the SEAS is also well represented in the model. The use of appropriate horizontal friction parameters has resulted in the simulation of realistic currents. The observed features in the SEAS, including the life cycle of the ASMWP, low-salinity water, barrier layer, temperature inversions, eddies and currents are well represented in the model. Present study has unraveled the processes involved in the life cycle of barrier layer and temperature inversions in the SEAS. Presence of low-salinity water is necessary for their formation. Barrier layer develops in the SEAS during November, after the intrusion of low-salinity water from the Bay of Bengal. The barrier layer is thickest during January–February, and it dissipates during March–April. The variations and peak of barrier layer thickness is controlled by variations in isothermal layer depth, which in turn is dominated by the downwelling effects of anticyclonic eddies. The intense solar heating during March–April leads to the formation of shallow isothermal layer and results in the dissipation of barrier layer. Temperature inversions starts developing in the SEAS during December, reaches its peak during January–February and dissipates in the following months. Advection of cooler low-salinity water over warmer salty water and penetrating shortwave radiation is found to cause temperature inversions within the SEAS, whereas winter cooling is also important to the north and south of the SEAS. There is significant variation in the magnitude, depth of occurrence and formation mechanisms of temperature inversions within the SEAS. Analysis of model mixed layer heat budget has shown that the SEAS SST is mainly controlled by atmospheric forcing, including the life cycle of ASMWP. It has also shown that the heating from temperature inversions do not contribute to the formation of ASMWP. In an experiment in which a constant salinity of 35 psu was maintained over the entire model domain, the ASMWP evolved very similar to that in the standard run, suggesting that the salinity effects are not necessary for the formation of ASMWP. Examination of wind field show that the winds over the SEAS during November–February are low due to the blocking of northeasterly winds by Western Ghats. Several process experiments by modifying the wind and turbulent heat fluxforcing fields have shown that these low winds lead to the formation of ASMWP in the SEAS during February–March. The low winds reduce latent heat loss, resulting in net heat gain by the ocean. This helps the SEAS to keep warmer SST while the surrounding region experience intense cooling under the strong dry northeasterly winds. As the winds are weak over the SEAS, the mixed layer is not able to feel the stratification beneath and the mixed layer depth is determined by solar heating, with or without salinity effects. In addition, the weak winds are not able to entrain the temperature inversions present in the barrier layer. The winds are weak during March–April too, and the air-sea heat fluxes dictate the SST evolution during this period. Therefore, during November–April, the SEAS acts as a low wind heat-dominated regime, where the evolution of sea surface temperature is solely determined by atmospheric forcing. We show that, in such regions, the evolution of surface layer temperature is not dependent on the characteristics of subsurface ocean, including the presence of barrier layer and temperature inversions.

This thesis examines the structure of the Tropical Easterly Jet (TEJ) in a General Circulation Model (GCM). The TEJ is observed only during the Indian summer monsoon period and is strongest during July and August. The jet structure simulated by an atmospheric GCM (CAM-3.1) in July has been compared with reanalysis data. The simulated TEJ was displaced westward by ~ 25◦ when compared to observations. The removal of orography had no impact on the jet structure. This demonstrated that the Tibetan Plateau did not play an important role in the location and structure of the jet. The changes in cumulus scheme in the GCM had a large influence on the location of the jet maxima. To examine the factors which control the location and structure of the jet, a series of experiments were conducted using an aqua-planet version of the model. The impact of different sea surface temperature (SST) profiles was studied. The rainfall in the GCM was primarily in the regions where the SST attained a maximum. By altering the location of SST maximum (and hence the rainfall maximum), the impact of location of rainfall maximum on the location and structure of the jet was studied. When the rainfall maximum was located close to the equator, it did not generate a strong jet but had an influence on the vertical structure of the jet. A large number of simulations were conducted with multiple rainfall maxima and the need for these was demonstrated since only then was the observed jet structure well simulated. Based on the simulations, it was concluded that the simulation of the TEJ by CAM-3.1 was unrealistic because of large unrealistic rainfall over Saudi Arabia in this GCM. Equatorial heating has been shown to be important to simulate proper jet structure. The zonal structure of the jet was also influenced by rainfall in the Pacific Ocean. Although the aqua-planet configuration of the CAM-3.1 GCM provided several useful insights, the simulation was not perfect on account of errors in the simulation of the temperature profile in the lower troposphere. An ideal-physics configuration of the GCM was used. This removed the cumulus physics and instead imposed the observed heating pro-files. Both upper tropospheric friction and radiative-convective atmospheric temperatures were required to simulate the TEJ. The problems with the simulation of structure in the jet exit region was corrected by using radiative-convective atmospheric temperatures that were qualitatively similar to those observed in northern hemisphere summer time. The ideal-physics configuration reconfirmed that the Saudi Arabian rainfall was responsible for the westward shift of the TEJ in the simulations. The ideal-physics simulations showed that the simple analytical model proposed by Gillin1980 was not suitable for the simulation of TEJ. The above the simulations indicate that a shift in the location of the jet is related to a shift in the rainfall pattern. Based on this insight one would expect that the jet location will be different in good and bad monsoon periods. This is indeed the case. In July 2002 the Indian monsoon failed after beginning well in June. In June the TEJ is consequently located west ward compared to July. The same situation prevails even in good and poor monsoon years. In a good monsoon year (July 1988) the jet maximum is located westward when compared to a bad monsoon year (July 2002). In this thesis we have clearly demonstrated the role of anomalous rainfall on the location of the TEJ. This thesis has shown that an accurate simulation of the TEJ depends upon the accurate simulation of various rainfall centers that act as multiple heat sources in the atmosphere. The rainfall in the equatorial region does not influence the strength of the TEJ but alters the vertical structure of the jet. The strength the jet is dependent on the intensity of rainfall and the latitudinal distance from the equator. The complex vertical structure of the jet is not simulated by simple analytical models of the jet.

Three semi-permanent cloud bands exist in the Southern Hemisphere extending southeastward from the equator, through the tropics, and into the subtropics. The most prominent of these features occurs in the South Pacific and is referred to as the South Pacific Convergence Zone (SPCZ). Similar convergence zones, with less intensity, exist in the South Atlantic (SACZ) and Indian (SICZ) oceans. We attempt to explain the physical mechanisms that promote the diagonal orientation of the SPCZ and the processes that determine the timescales of its variability. It is argued that the slowly varying sea surface temperature patterns produce upper tropospheric wind fields that vary substantially in longitude. Regions where 200 hPa zonal winds decrease with longitude (i.e., negative zonal stretching deformation, or dU/dx<0) reduce the group speed of the eastward propagating synoptic (3-6 day period) Rossby waves and locally increase the wave energy density. Such a region of wave accumulation occurs in the vicinity of the SPCZ, thus providing a physical basis for the diagonal orientation and earlier observations that the zone acts as a "graveyard" of propagating synoptic disturbances. In essence, dU/dx=0 demarks the boundary of the graveyard while regions where dU/dx<0 denote the graveyard itself. Composites of the life cycles of synoptic waves confirm this hypothesis. From the graveyard hypothesis comes a more general theory accounting for the SPCZ's spatial orientation and its longer term variability influenced by the El Niño-Southern Oscillation (ENSO), or alternatively, the changing background SST associated with different phases of ENSO.

The Georgia General Assembly, like many states, has enacted pre-defined, comprehensive, drought-mitigation apparatus, but they need rainfall outlooks. Global circulation models (GCMs) provide rainfall outlooks, but they are too spatially course for jurisdictional impact assessment. To wed these efforts, spatially averaged, time-smoothed, daily precipitation observations from the National Weather Service cooperative network are fitted to eight points of 700 mbar atmospheric data from the NCEP/NCAR Reanalysis Project for climate downscaling and drought prediction in the Apalachicola-Chattahoochee-Flint (ACF) river basin. The domain is regionalized with a factor analysis to create specialized models. All models complied well with mathematical assumptions, though the residuals were somewhat skewed and flattened. All models had an R-squared > 0.2. The models revealed map points to the south to be especially influential. A leave-one-out cross-validation showed the models to be unbiased with a percent error of < 20%. Atmospheric parameters are estimated for 2008–2011 with GCMs and empirical extrapolations. The transfer function was invoked on both these data sets for drought predictions. All models and data indicate drought especially for 2010 and especially in the south.

No description available.