Journal articles on the topic 'Atmospheric circulation Vietnam Mathematical models'

Abstract Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequence of atmospheric circulation patterns over the United Kingdom between 1946 and 2007. It is demonstrated that the influence of the seasonal pattern variability on transition processes is dominated by the long-term effects revealed by the introduced methods. Despite the differences in the mathematical assumptions implied by both presented methods, almost identical symmetrical changes of the cyclonic and anticyclonic pattern probabilities are identified in the analyzed data, with the confidence intervals being smaller than in the case of the local Gaussian kernel smoothing algorithm. Analysis results are investigated with respect to model sensitivity and compared to a standard analysis technique based on a local Gaussian kernel smoothing. Finally, the implications of the discussed strategies on long-range predictability of the data-fitted Markovian models are discussed.

Abstract. We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July–August daily mean temperature at 2 m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performance of climate models in simulating local weather extremes.

Abstract. Numerical general circulation models of the atmosphere are generally required to conserve mass and energy for their application to climate studies. Here we draw attention to another conserved global integral, viz. the component of angular momentum (AM) along the Earth's axis of rotation, which tends to receive less consideration. We demonstrate the importance of global AM conservation in climate simulations with the example of the Community Atmosphere Model (CAM) with the finite-volume (FV) dynamical core, which produces a noticeable numerical sink of AM. We use a combination of mathematical analysis and numerical diagnostics to pinpoint the main source of AM non-conservation in CAM–FV. We then present a method to enforce global conservation of AM, and we discuss the results in a hierarchy of numerical simulations of the atmosphere of increasing complexity. In line with theoretical expectations, we show that even a crude, non-local enforcement of AM conservation in the simulations consistently results in the mitigation of certain persistent model biases.

AbstractThis study compares the predictability of surface wind components by linear statistical downscaling using data from both observations and comprehensive models [regional climate models (RCM) and NCEP-2 reanalysis] in three domains: North America (NAM), Europe–Mediterranean Basin (EMB), and East Asia (EAS). A particular emphasis is placed on predictive anisotropy, a phenomenon referring to unequal predictability of surface wind components in different directions. Simulated predictability by comprehensive models is generally close to that found in observations in flat regions of NAM and EMB, but it is overestimated relative to observations in mountainous terrain. Simulated predictability in EAS shows different structures. In particular, there are regions in EAS where predictability simulated by RCMs is lower than that in observations. Overestimation of predictability by comprehensive models tends to occur in regions of low predictability in observations and can be attributed to small-scale physical processes not resolved by comprehensive models. An idealized mathematical model is used to characterize the predictability of wind components. It is found that the signal strength along the direction of minimum predictability is the dominant control on the strength of predictive anisotropy. The biases in the model representation of the statistical relationship between free-tropospheric circulation and surface winds are interpreted in terms of inadequate simulation of small-scale processes in regional and global models, and the primary cause of predictive anisotropy is attributed to such small-scale processes.

Geophysical cybernetics allows for exploring weather and climate modification (geoengineering) as an optimal control problem in which the Earth’s climate system is considered as a control system and the role of controller is given to human operators. In mathematical models used in climate studies control actions that manipulate the weather and climate can be expressed via variations in model parameters that act as controls. In this paper, we propose the “instability-sensitivity” approach that allows for determining feasible control variables in geoengineering. The method is based on the sensitivity analysis of mathematical models that describe various types of natural instability phenomena. The applicability of this technique is illustrated by a model of atmospheric baroclinic instability since this physical mechanism plays a significant role in the general circulation of the atmosphere and, consequently, in climate formation. The growth rate of baroclinic unstable waves is taken as an indicator of control manipulations. The information obtained via calculated sensitivity coefficients is very beneficial for assessing the physical feasibility of methods of control of the large-scale atmospheric dynamics and for designing optimal control systems for climatic processes. It also provides insight into potential future changes in baroclinic waves, as a result of a changing climate.

The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.

Abstract. Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.

The Be River Catchment was studied to quantify the potential impact of climate change on the streamflow using a multi-model ensemble approach. Climate change scenarios (A1B and B1) were developed from an ensemble of four GCMs (general circulation models) (CGCM3.1 (T63), CM2.0, CM2.1 and HadCM3) that showed good performance for the Be River Catchment through statistical evaluations between 15 GCM control simulations and the corresponding time series of observations at annual and monthly levels. The Soil and Water Assessment Tool (SWAT) was used to investigate the impact on streamflow under climate change scenarios. The model was calibrated and validated using daily streamflow records. The calibration and validation results indicated that the SWAT model was able to simulate the streamflow well, with Nash–Sutcliffe efficiency exceeding 0.78 for the Phuoc Long station and 0.65 for the Phuoc Hoa station, for both calibration and validation at daily and monthly steps. Their differences in simulating the streamflow under future climate scenarios were also investigated. The results indicate a 1.0–2.9 °C increase in annual temperature and a −4.0 to 0.7% change in annual precipitation corresponding to a change in streamflow of −6.0 to −0.4%. Large decreases in precipitation and runoff are observed in the dry season.

Abstract The low-frequency response to changes in external forcing or other parameters for various components of the climate system is a central problem of contemporary climate change science. The fluctuation–dissipation theorem (FDT) is an attractive way to assess climate change by utilizing statistics of the present climate; with systematic approximations, it has been shown recently to have high skill for suitable regimes of an atmospheric general circulation model (GCM). Further applications of FDT to low-frequency climate response require improved approximations for FDT on a reduced subspace of resolved variables. Here, systematic mathematical principles are utilized to develop new FDT approximations on reduced subspaces and to assess the small yet significant departures from Gaussianity in low-frequency variables on the FDT response. Simplified test models mimicking crucial features in GCMs are utilized here to elucidate these issues and various FDT approximations in an unambiguous fashion. Also, the shortcomings of alternative ad hoc procedures for FDT in the recent literature are discussed here. In particular, it is shown that linear regression stochastic models for the FDT response always have no skill for a general nonlinear system for the variance response and can have poor or moderate skill for the mean response depending on the regime of the Lorenz 40-model and the details of the regression strategy. New nonlinear stochastic FDT approximations for a reduced set of variables are introduced here with significant skill in capturing the effect of subtle departures from Gaussianity in the low-frequency response for a reduced set of variables.

The Earth radiation and energy budget is calculated by a manifold of rather complex Global Circulation Models. Their outcome mostly cannot identify integral radiation or energy budget relations. Therefore it is reasonable to look at more basic models to identify the main aspects of the model results. The simplest one of all of those is a one-dimensional one-layer model. However, most of these models — two are discussed here — suffer the drawback that they do not include essential contributions and relations between the atmospheric layer and the Earth. The one-dimensional one-layer model presented here integrates sensible and latent heat, the absorption of solar radiation and the direct emission of the long wave radiation to space in addition to the standard correlations. For the atmospheric layer two different long wave fluxes are included, top of atmosphere to space and bulk emission to Earth. The reflections of long wave radiation are taken into account. It is shown that this basic model is in excellent agreement with the observed integrated global energy budget.

Abstract The amplification of thermohaline circulation (THC) anomalies resulting from heat and freshwater forcing at the ocean surface is investigated in a zonally averaged coupled ocean–atmosphere model. Optimal initial conditions of surface temperature and salinity leading to the largest THC growth are computed, and so are the structures of stochastic surface temperature and salinity forcing that excite maximum THC variance (stochastic optimals). When the THC amplitude is defined as its sum of squares (equivalent to using the standard L2 norm), the nonnormal linearized dynamics lead to an amplification with a time scale on the order of 100 yr. The optimal initial conditions have a vanishing THC anomaly, and the complex amplification mechanism involves the advection of both temperature and salinity anomalies by the mean flow and of the mean temperature and salinity by the anomaly flow. The L2 characterization of THC anomalies leads to physically interesting results, yet to a mathematically singular problem. A novel alternative characterizing the THC amplitude by its maximum value, as often done in general circulation model studies, is therefore introduced. This complementary method is shown to be equivalent to using the L-infinity norm, and the needed mathematical approach is developed and applied to the THC problem. Under this norm, an amplification occurs within 10 yr explained by the classic salinity advective feedback mechanism. The analysis of the stochastic optimals shows that the character of the THC variability may be very sensitive to the spatial pattern of the surface forcing. In particular, a maximum THC variance and long-time-scale variability are excited by a basin-scale surface forcing pattern, while a significantly higher frequency and to some extent a weaker variability are induced by a smooth and large-scale, yet mostly concentrated in polar areas, surface forcing pattern. Overall, the results suggest that a large THC variability can be efficiently excited by atmospheric surface forcing, and the simple model used here makes several predictions that would be interesting to test using more complex models.

Abstract A multiscale model of the MJO is developed here that accounts, in a simplified fashion, for both the upscale transfer from synoptic to planetary scales of momentum and temperature from wave trains of thermally driven equatorial synoptic-scale circulations in a moving convective envelope as well as direct mean heating on planetary scales. This model involves idealized thermally driven congestus synoptic-scale fluctuations in the eastern part of the moving wave envelope and convective superclusters in the western part of the envelope. The model self-consistently reproduces qualitatively many of the detailed structural features of the planetary circulation in the observations of the MJO, including the vertical structure in both the westerly onset region and the strong westerly wind burst region, as well as the horizontal quadrupole planetary vortex structure. The westerly midlevel inflow in the strong westerly region and the quadrupole vortex are largely produced in the model by the upscale transport of momentum to the planetary scales, while the midlevel easterly jet in the westerly onset region is substantially strengthened by this process. The role of wave trains of tilted organized synoptic-scale circulations is crucial for this fidelity with observations. The appeal of the multiscale models developed below is their firm mathematical underpinnings, simplicity, and analytic tractability while remaining self-consistent with many of the features of the observational record.

Earth system science (ESS) is an approach to: ‘obtain a scientific understanding of the entire Earth system on a global scale by describing how its component parts and their interactions have evolved, how they function, and how they may be expected to continue to evolve on all timescales’ (Bretherton, 1998). The aim of this review is to introduce some key examples showing the role of Earth surface processes, the traditional subject of geomorphology, within the interacting Earth system. The paper considers three examples of environmental systems in which geomorphology plays a key role: (1) links between topography, tectonics, and atmospheric circulation; (2) links between geomorphic processes and biogeochemical cycles; and (3) links between biological processes and the Earth’s surface. Key research needs are discussed, including the requirement for better opportunities for interdisciplinary collaboration, clearer mathematical frameworks for Earth system models, and more sophisticated interaction between natural and social scientists.

Abstract Climate change (CC) is likely to have a long-term influence on regional water resources, including surface water and groundwater. Therefore, quantifying the CC influence is indispensable for proper management of water resources. This study scrutinized the influence of CC on river discharge and groundwater recharge (GWR) in Ho Chi Minh City (HCMC), Vietnam, utilizing the Soil and Water Assessment Tool (SWAT). The calibrated SWAT was utilized to simulate the discharge and GWR under projected climate scenarios in reliance on an ensemble of seven General Circulation Models (GCMs) derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways (SSPs), including SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results pointed out that the climate of HCMC is warmer and wetter in the 21st century. Under the CC influence, the future discharge is envisaged to rise from 0.1 to 4.5% during the near-future period of 2030s (2021–2045), 8.1 to 11.6% during the mid-future period of 2055s (2046–2070), and 7.7 to 19.6% during the far-future period of 2080s (2071–2095) under the three SSP scenarios. In addition, the GWR is prognosticated to have rising trends of 0.9–4.9%, 5.3–7.9%, and 5.7– 13.5% during the near-future, mid-future, and far-future periods, respectively. Furthermore, uncertainties in the discharge and GWR projections connected with SSP scenarios and CMIP6 GCMs are considerable.

Abstract. The dominant "convection" model of atmospheric circulation is based on the premise that hot air expands and rises, to be replaced by colder air, thereby creating horizontal surface winds. A recent theory put forward by Makarieva and Gorshkov (2007, 2013) maintains that the primary motive force of atmospheric circulation derives from the intense condensation and sharp pressure reduction that is associated with regions where a high rate of evapotranspiration from natural closed-canopy forests provides the "fuel" for cloud formation. The net result of the "biotic pump" theory is that moist air flows from ocean to land, drawn in by the pressure changes associated with a high rate of condensation. To test the physics underpinning the biotic pump theory, namely that condensation of water vapour, at a sufficiently high rate, results in an uni-directional airflow, a 5 m tall experimental apparatus was designed and built, in which a 20 m3 body of atmospheric air is enclosed inside an annular 14 m long space (a "square donut") around which it can circulate freely, allowing for rotary air flows. One vertical side of the apparatus contains some 17 m of copper refrigeration coils, which cause condensation. The apparatus contains a series of sensors measuring temperature, humidity and barometric pressure every five seconds, and air flow every second. The laws of Newtonian physics are used in calculating the rate of condensation inside the apparatus. The results of more than one hundred experiments show a highly significant correlation, with r2 > 0.9, of airflow and the rate of condensation. The rotary air flows created appear to be consistent both in direction and velocity with the biotic pump hypothesis, the critical factor being the rate change in the partial pressure of water vapour in the enclosed body of atmospheric air. Air density changes, in terms of kinetic energy, are found to be orders of magnitude smaller than the kinetic energy of partial pressure change. The consistency of the laboratory experiments, in confirming the physics of the biotic pump, has profound implications for current mathematical climate models, not just in terms of predicting the consequences of widespread deforestation, but also for better understanding the atmospheric processes which lead to air mass convection.

AbstractThe full, three-dimensional Coriolis force includes the familiar sine-of-latitude terms as well as frequently dropped cosine-of-latitude terms [nontraditional Coriolis terms (NCT)]. The latter are often ignored because they couple the zonal and vertical momentum equations that in the large-scale limit of weak vertical velocity are considered insignificant almost everywhere. Here, we ask whether equatorial mesoscale clouds that fall outside the large-scale limit are affected by the NCT. A simple scaling indicates that a Lagrangian parcel convecting at 10 m s−1 through the depth of the troposphere should be deflected over 2 km to the west. To understand the real impact of NCT, we develop a mathematical framework that describes an azimuthally symmetric convective circulation with an analytical expression for an incompressible poloidal flow. Because the model incorporates the full three-dimensional flow associated with convection, it uniquely predicts not only the westward tilt of clouds but also a meridional diffluence of western cloud outflow. To test these predictions, we perform a set of cloud-resolving simulations whose results show preferential lifting of surface parcels with positive zonal momentum and zonal asymmetry in convective strength. RCE simulations show changes to the organization of coherent precipitation regions and a decrease in mean convective intensity of approximately 2 m s−1 above the freezing level. An additional pair of dry cloud-resolving simulations designed to mimic the steady-state flow of the model show maximum perturbations to the upper-level zonal flow of 8 m s−1. Together, the numerical and analytic results suggest the NCT consequentially alter equatorial mesoscale convective circulations and should be considered in conceptual models.

A two-dimensional mathematical model of moist air convection in the sub-cloud and cloud layers is proposed. A theoretical analysis of the influence of near ground atmospheric parameters on the development of sub-cloud and cloud convection is provided, and the criteria of convection development are considered. As a rule, this relationship is parameterized in general circulation, regional or mesoscale models of the atmosphere. Therefore, achieving a more complete and correct understanding of this relationship can lead to an improvement in the accuracy of weather forecasts. The mathematical model describes the system of the equations of motion, heat conductivity and the continuity equations for a two-dimensional vertical plane. The approximate analytical solution of the system of equations is obtained. Expressions for the estimation of the convection height and height of maximum velocity are derived for vertical and horizontal components of updraft wind and for vertical distribution of temperature. From the expressions obtained, the criterion of sub-cloud convection development is derived. The expressions for the convection parameters at a condensation level have also been formulated, from which the criterion of cloud development through convection is derived. It is established that the development of cloud convection depends on absolute values of the dew point deficit in a near-surface layer and, in a greater degree, on vertical gradients of water vapor mass fraction. It is shown that at certain critical values of a vertical gradient of water vapor mass fraction “explosive convective growth” is observed. The application of the obtained results to artificial stimulation of convection by means of air heating in the near-ground atmosphere has shown that the success of such an application and the required air heating-up depend on: (i) the vertical temperature gradient; (ii) the vertical dew-point gradient; and (iii) the value of the dew point deficit in the near-ground layer. The analysis performed has shown the possibility of successful stimulation of artificial convection under specific favorable atmospheric conditions.

The phenomenon of climate change in recent years has led to significant changes in climatic elements and as a result the status of surface and groundwater resources, especially in arid and semi-arid regions, this issue has sometimes caused a significant decline in groundwater resources. In this paper, the effects of climate change on the status of groundwater resources in Marvdasht plain have been investigated. Water supply of different parts of this region is highly dependent on groundwater resources and therefore the study of groundwater changes in future periods is important in the development of this plain and the management of its water resources. In order to evaluate the effects of climate change, the output of atmospheric circulation models (GCM) has been used. Then, in order to adapt the output scale of these models to the scale required by local studies of climate change, precipitation and temperature data have been downscaled by LARS-WG model. Downscaled information was used to determine the amount of feed and drainage of the aquifer in future periods. To investigate changes in groundwater levels at different stages, a neural network dynamic model has been developed in MATLAB software environment. It is also possible to study and compare other points using other scenarios and mathematical modeling. The results of the study, assuming the current state of development in the region, indicate a downward trend in the volume of the aquifer due to climate change and its effects on resources and uses of the study area. The results also introduce Scenario A2 as the most critical scenario related to climate change, which also shows the largest aquifer decline in neural network modeling.

The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. Additionally, it provides insights for the improvement of corresponding interaction processes in general circulation models. In this regard, the global picture of the interdependence patterns of extreme-rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular, the “monsoon jump” over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon, we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.

Abstract. On scales of ≈ 10 days (the lifetime of planetary-scale structures), there is a drastic transition from high-frequency weather to low-frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; thus, in GCM (general circulation model) macroweather forecasts, the weather is a high-frequency noise. However, neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developed that use empirical data to force the statistics and climate to be realistic so that even a two-parameter model can perform as well as GCMs for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the large stochastic memories that we quantify. Since macroweather temporal (but not spatial) intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the ScaLIng Macroweather Model (SLIMM). SLIMM is based on a stochastic ordinary differential equation, differing from usual linear stochastic models (such as the linear inverse modelling – LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes that there is no low-frequency memory, SLIMM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner, notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful stochastic forecasts of natural macroweather variability is to first remove the low-frequency anthropogenic component. A previous attempt to use fGn for forecasts had disappointing results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill – with no adjustable parameters – show excellent agreement with hindcasts, and these show some skill even on decadal scales. We also compare our forecast errors with those of several GCM experiments (with and without initialization) and with other stochastic forecasts, showing that even this simplest two parameter SLIMM is somewhat superior. In future, using a space–time (regionalized) generalization of SLIMM, we expect to be able to exploit the system memory more extensively and obtain even more realistic forecasts.

This paper presents some results of rainfall forecast in the monsoon onset period in South Vietnam, with the use of ensemble Kalman filter to assimilate observation data into the initial field of the model. The study of rainfall forecasts are experimented at the time of Southern monsoon outbreaks for 3 years (2005, 2008 and 2009), corresponding to 18 cases. In each case, there are five trials, including satellite wind data assimilation, upper-air sounding data assimilation, mixed data (satellite wind+upper-air sounding data) assimilation and two controlled trials (one single predictive test and one multi-physical ensemble prediction), which is equivalent to 85 forecasts for one trial. Based on the statistical evaluation of 36 samples (18 meteorological stations and 18 trials), the results show that Kalman filter assimilates satellite wind data to forecast well rainfall at 48 hours and 72 hours ranges. With 24 hour forecasting period, upper-air sounding data assimilation and mixed data assimilation experiments predicted better rainfall than non-assimilation tests. The results of the assessment based on the phase prediction indicators also show that the ensemble Kalman filter assimilating satellite wind data and mixed data sets improve the rain forecasting capability of the model at 48 hours and 72 hour ranges, while the upper-air sounding data assimilation test produces satisfactory results at the 72 hour forecast range, and the multi-physical ensemble test predicted good rainfall at 24 hour and 48 hour forecasts. The results of this research initially lead to a new research approach, Kalman Filter Application that assimilates the existing observation data into input data of the model that can improve the quality of rainfall forecast in Southern Vietnam and overall country in general.References Bui Minh Tuan, Nguyen Minh Truong, 2013. 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Abstract: This study has selected a vortex tracking algorithm scheme for simulating the activity of tropical cyclone in the Vietnam East Sea by CCAM model. The results show that the CCAM model is able to simulate well the large scale in each month through a reasonable description of the movement rules of the tropical cyclone in the study area. Then, this vortex tracking algorithm scheme was applied to test the seasonal forecast with the outputs of the CCAM model with a resolution of 20km for September 2018 and October 2018. The obtaining results are forecasted quite closely in terms of both quantity and high potential occurrence areas of the tropical cyclone when compared with reality. In particular, for October 2018, although the activity area of ​​the tropical cyclone - YUTU is significantly different from the multi-year average activity position, the seasonal forecast results are obtained from the 120 members of the CCAM model captured this difference. 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Kieu, Seasonal forecasting of tropical cyclone activity in the coastal region of Vietnam using RegCM4.2, Clim. Res. 62 (2015) 115-129. https://doi.org/10. 3354/cr01267.

Several examples illustrating the energy balance associated with a mixing process at the interface of a planar dynamical model describing two-phase perfect fluid circulating around a circle with a sufficiently large radius within a central gravitational field are presented. The model is associated with the spatial and temporal structure of the zonally averaged global-scale atmospheric longitudinal circulation around the Earth. The fluid is supposed to be irrotational and pressure on a outer layer is constant. It is postulated that the total fluid depth is small compared to the radius of the circle and the gravity vector is directed to the center of the circle. Under these assumptions, this problem can be associated with a spatial and temporal structure of the zonally averaged global-scale atmospheric longitudinal circulation around equatorial plane. The model is the subject to the rigid lid approximation to the external boundary conditions for the outer fluid layer. One of the novelties in this work is the derivation of the nonlinear shallow water model by means of the average velocity. This introduction simplifies essentially further potential studies of mixing criteria associated with nonlinear mathematical models representing shallow water equations.

Abstract On a global scale, climate change is projected to have detrimental impacts on water availability. This situation will become more severe owing to accumulated impacts of climate change and anthropogenic activities. This study aims to investigate climate change impact on water availability in the upper Dong Nai River Basin using the Soil and Water Assessment Tool (SWAT) and Water Evaluation and Planning (WEAP) models. Future rainfall scenarios were downscaled from five different general circulation models under RCP4.5 and RCP8.5 using the Long Ashton Research Station Weather Generator (LARS-WG) tool. Under the climate change impact, annual river discharge in the study region is generally projected to have upward trends in the future, except for the near-future period of 2030s under RCP4.5. In addition, dry-seasonal river discharge is expected to be increased in the future. Considering the baseline condition of water use, there was an annual water shortage of approximately 32.9 × 103 m3, which mostly occurred in the dry season from January to March. Climate change may reduce the water shortage in the study region ranging from 7.0 to 30.1% in the future. Under the combined impacts of climate change and increasing water demand, the water shortage will vary from −18.6 to 6.0% in the future. The results can provide valuable insights to implement appropriate future water resources planning and management in the study region.

The use of physico-mathematical models for the numerical prediction of weather changes on the global scale is described. The accuracy of the predictions is assessed in relation to the limitations of both the observa­tional data and the representation in the model of the many interactive physical and dynamical processes that govern the evolution of the major features of the atmospheric circulation. The concept of predictability in relation to the evolution of atmospheric disturbances is discussed. Given an adequate global coverage of observations and continued improvement in the models, it should be possible to extend the range of useful forecasts up to about 14 days; this may prove to be the limit of deterministic predictability set by the random nature of atmospheric fluctuations. However, some relatively stable atmospheric states, such as those that produce long dry summers, may possess greater predictability. Furthermore, it may well be possible to predict the general character of the weather for some weeks or months ahead even if the day-to-day variations are unpredictable this far ahead. Because the longer-range forecasts are bound to show considerable variability in skill and reliability, it will be desirable to assign them a confidence rating based on the rate at which an ensemble of model forecasts diverge when starting from slightly different initial states.

Abstract When quadrupling the atmospheric CO2 concentration in relation to pre-industrial levels, most global climate models show an initially strong net radiative feedback that significantly reduces the energy imbalance during the first two decades after the quadrupling. Afterwards, the net radiative feedback weakens, needing more surface warming than before to reduce the remaining energy imbalance. Such weakening radiative feedback has its origin in the tropical oceanic stratiform cloud cover, linked to an evolving spatial warming pattern. In the classical linearized energy balance framework, such variation is represented by an additional term in the planetary budget equation. This additional term is usually interpreted as an ad-hoc emulation of the cloud feedback change, leaving unexplained the relationship between this term and the spatial warming pattern. I use a simple non-linearized energy balance framework to justify that there is a physical interpretation of this term: the evolution of the spatial pattern of warming is explained by changes in the ocean’s circulation and energy uptake. Therefore, the global effective thermal capacity of the system also changes, leading to the additional term. In reality, the clouds respond to what occurs in the ocean, changing their radiative effect. In the equation, the term is now a concrete representation of the ocean’s role. Additionally, I derive for the first time an explicit mathematical expression of the net radiative feedback and its temporal evolution in the linearized energy balance framework. This mathematical expression supports the new proposed interpretation. As a corollary, it justifies the twenty-year time scale used to study the variation of the net radiative feedback.

Abstract In this paper, the responses of hydro-meteorological drought to changing climate in the Be River Basin located in Southern Vietnam are investigated. Climate change scenarios for the study area were statistically downscaled using the Long Ashton Research Station Weather Generator tool, which incorporates climate projections from Coupled Model Intercomparison Project 5 (CMIP5) based on an ensemble of five general circulation models (Can-ESM2, CNRM-CM5, HadGEM2-AO, IPSL-CM5A-LR, and MPI-ESM-MR) under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The Soil and Water Assessment Tool model was employed to simulate streamflow for the baseline time period and three consecutive future 20 year periods of 2030s (2021–2040), 2050s (2041–2060), and 2070s (2061–2080). Based on the simulation results, the Standardized Precipitation Index and Standardized Discharge Index were estimated to evaluate the features of hydro-meteorological droughts. The hydrological drought has 1-month lag time from the meteorological drought and the hydro-meteorological droughts have negative correlations with the El Niño Southern Oscillation and Pacific Decadal Oscillation. Under the climate changing impacts, the trends of drought severity will decrease in the future; while the trends of drought frequency will increase in the near future period (2030s), but decrease in the following future periods (2050 and 2070s). The findings of this study can provide useful information to the policy and decisionmakers for a better future planning and management of water resources in the study region.

<p style='text-indent:20px;'>The global structure of the atmosphere and the oceans is a continuous source of intriguing challenges in geophysical fluid dynamics (GFD). Among these, jets are determinant in the air and water circulation around the Earth. In the last fifty years, thanks to the development of more and more precise and extensive observations, it has been possible to study in detail the atmospheric formations of the giant-gas planets in the solar system. For those planets, jets are the dominant large scale structure. Starting from the 70s, various theories combining observations and mathematical models have been proposed in order to describe their formation and stability. In this paper, we propose a purely algebraic approach to describe the spontaneous formation of jets on a spherical domain. Analysing the algebraic properties of the 2D Euler equations, we give a characterization of the different jets' structures. The calculations are performed starting from the discrete Zeitlin model of the Euler equations. For this model, the classification of the jets' structures can be precisely described in terms of reductive Lie algebras decomposition. The discrete framework provides a simple tool for analysing both from a theoretical and and a numerical perspective the jets' formation. Furthermore, it allows to extend the results to the original Euler equations.</p>

Formulation of the problem. A national climate program, complying with the Association Agreement between Ukraine and the EU, is aimed at long-term adaptation measures to climate change, providing for the development of legislation in the field of environmental protection, sectoral strategies, improving air and water quality. These measures require identification of climatic factors’ impact on hydrological characteristics of water bodies in any area in order to protect and improve them, as well as a comprehensive approach to the rational management of water resources. Analysis of previous research. Using mathematical models of atmospheric circulation allows us to obtain generalized calculations of the average global and regional air temperature, characterized by significant errors in case of different models. Problems of further research. Modern changes in climatic conditions in Ukraine are characterized by locality and rapidity: increasing number of anomalous atmospheric phenomena, frequency of droughts, dry winds which, according to preliminary estimates, may lead to reduction of water resources, changes in internal annual redistribution of water temperature and runoff against the background of increasing thermal resources of the territory. The purpose. The article analyses climatic indicators’ influence on the main hydrological characteristics on the example of the Psyol river basin within Ukraine. Research methods. Research methods are presented by statistical and cartographic analysis (correlation analysis method). The initial information is the results of observations contained in the State Climate Cadastre and the State Water Cadastre for the Dnieper Valley. Presentation of the main research material. Analysis of climate change in the Psyol river basin indicates a decrease in water consumption, both in the main river of the basin and in its main tributaries. In addition to changes in temperature and humidity, the reasons for this process were the shifts that occurred in the nature of the intra-annual distribution of the runoff. During the late XX – early XXI century the share of spring floods decreased due to the reduced period of ice phenomena and corresponding decrease in the thickness of the snow cover on the rivers. As a result, the most important component of the annual runoff of rivers began to decrease rapidly. The role of groundwater in the formation of water runoff increased against the background of a predominant decrease in precipitation. This is especially true for medium-size and small rivers, subject to significant anthropogenic pressure: over-regulation of channels, creation of artificial lakes, their shallowing. Practical value. Based on the main provisions of the national environmental policy of Ukraine on the use of water resources and the implementation of European principles of the Water Framework Directive 2000/60 / EC (ERVD) the study of changes in hydrological regime of rivers is of practical importance for sustainable management. Research results. The study of the impact of changes in climatic indicators on hydrological characteristics makes it possible to determine the reference conditions and classes of ecological status of rivers under conditions of anthropogenic pressure against the background of further changes in climatic indicators that will have different trends.