1,721,019 research outputs found
Projecting Changes in Tanzania Rainfall for the 21st century: Scenarios, Downscaling and Analysis”
No full textA Non-Homogeneous hidden Markov Models (NHMM) is developed using a 40-years record (1950-1990) of daily rainfall amount at eleven stations in Tanzania and re-analysis atmospheric fields of Temperature (T) at 1000 hPa, Geo Potential Height (GPH) at 1000 hPa, Meridional Winds (MW) and Zonal Winds (ZW) at 850 hPa, and Zonal Winds along the Equator, and from 10 to 1000 hPa along the vertical. The NHMM fitted is then used for predicting future rainfall patterns under global warming scenario (RCP8.5), using predictors from the CMCC-CMS simulations from 1950-2100. The model directly includes a consideration of seasonality through changes in the driving variables thus addressing the question of how future changes in seasonality of precipitation can also be modeled. The results of the simulations obtained by using the downscaling model NHMM, with predictors derived from the simulations of CMCC-CMS CGM, in the worst conditions of global warming as simulated by RCP8.5 scenario, seems to indicate that, as a consequence of increase of CO2 concentration and temperature, Tanzania should be subjected to a reduction of total annual rainfall; this reduction is concentrated in the wet seasons, both MAM and OND, mainly as a consequence of decreasing of seasonal number of wet days. The tendency towards drier conditions is partially compensated by a slight increasing of precipitation in the dry season JJAS. Frequency and Intensity of extreme events don't show any evident trend during the 21 century. An investigation on the causes of such hydrologic changes, and specifically on the role of Intertropical Convergence Zone ITCZ and Indian Ocean dipole IOD is pursued
Northen hemisphere meridional and zonal temperature gradients and their relation to hydrologic extremes at mid-latitude: trends, variability and link to climate modes in observation and simulations
No full textThe jet stream dynamics and the associated mid-latitude storm track are modulated by large scale ocean-land boundary conditions, which depend
on both the evolution of the known interannual and multi-decadal natural variability and on changes in meridional and zonal surface temperature
gradients due to anthropogenic forcing. Here, within the framework of the Lorenz (1984) low-order atmospheric model, the Equator-to-Pole
temperature Gradient (EPG) and the Ocean-Land temperature Contrast (OLC) are considered as drivers of mid-latitudinal circulation.
The historical trends of the seasonal NH Equator-to-Pole temperature Gradient (EPG) and the Ocean-Land temperature Contrast (OLC) are
explored, as well as their probability structure, and their potential relation to anthropogenic warming. The connection between dierent
combinations of EPG and OLC and precipitation patterns at mid-latitudes are shown. Then, these variables and the relations described above are
examined in two CGCM simulations of the 20th century.
The results show that there exist systematic biases in the temporal simulation of the gradients at hand, which is likely propagated to the
simulation of precipitation. However, we show that if a model is able to reasonably capture the observed relationship between the gradients and
precipitation, then it produces large-scale spatial distributions of rainfall that are consistent with observed patterns. Therefore, an eort to address
the biases in simulating EPG and OLC could lead to improved temporal and spatial simulations of precipitation in the models
Vulnerability,resilience and adaptation of coastal regions to climate change: hydrological changes in Agro-pontino region by using the nonhomogeneous hidden Markov model-
No full textInsights from low order model of increasing complexity: probability and temporal structure of climate extreme
No full textThis study provides insight to changes in the probability and temporal
structure of mid-latitude circulation features under possible shift of average
conditions to a more El Ni ̃ no-like or La Ni ̃ na-like state.
I Analysis shows that the response to dierent El Ni ̃no events in mid-latitudes is
highly variable, depending on the corresponding changes to the combination
of Equator-to-Pole Gradient and Ocean-Land Contrast.
I Types of ENSO events and their potential impacts could be classified based on
corresponding fF,Gg and the associated pdf’s of energy (X2 + Y2 + Z2), as well
as the attractor’s properties.
I The general impact of El Ni ̃no conditions is dissipation and organization of the
eddies via enhancement of the jet stream.
I La Ni ̃na conditions cannot be considered as the opposite of El Ni ̃no conditions.
Similar responses in mid-latitudes are possible.
I The question whether ENSO information is transmitted to mid-latitudes via
EPG or OLC changes, or both (ultimately seen as fF,Gg combinations) needs
further investigation.
I Seasonality eects on ENSO impact in mid-latitudes are important.
I Many impacts identified in the present study are qualitatively consistent with
high-order GCM results, thus illustrating the ability of low order models to
represent atmospheric processes in a correct manner.
I Hence, introducing low-frequency modes interactively in a plausible way may
be useful in understanding the behavior and potential outcomes associated
with low frequency forcings in the coupled ocean-atmosphere system
Stochastic downscaling of daily rainfall: analysis of future hydroclimatic changes and their impact on the Pontinia plain using Nonhomogeneous Hidden Markov Model and Dynamic Hierarchical Bayesian Network Model.
No full textThe Nonhomogeneous Hidden Markov Model is an established technique that usually provides excellent results
for the downscaling of multi-site precipitation. However, the selection of the number of states is subjective and
results in a model that can be over parameterized and overfit leading to por performance in applications. A
dynamic hierarchical Bayesian network model (DHBN) that is continuous and is not based on discretization into
states is tested here and compared against NHMM for the downscaling of daily precipitation for Pontinia Plain.
This región is a relevant example of coastal area particularly vulnerable to hydrological changes. The winter
(October-March) wet season is considered. Weather states and atmospheric variables from CMIP5 GCM are used
as exogenous predictors. The daily rainfall occurrence and amount at 32 stations over the region for the winters of
1916-2004 is used as the primary data. Rainfall variability is described in terms of occurrence of ’weather state’
as classified by a Hidden Markov Model, and associated to variables representing the main characteristics of large
scale atmospheric circulation as obtained by reanalysis data. A nonhomogeneous hidden Markov model (NHHM)
and a DHBN model are used to make future projections of the downscaled precipitation as by using the GCM’s
simulations under different global warming scenarios.The spatial interaction between the sites is modeled through
the underlying covariance function and the uncertainty in the model parameters is explicitly represented in their
posterior distribution. Preliminary results show that the seasonal statistics are adequately captures for the 20th
century runs. The structural differences between the two models are discussed
Extreme precipitation in the south and south-east Mediterranean climate structure and predictability
No full textAs part of a Global Flood project, we assess the conditions that lead to the changing frequency and spatial structure of extreme daily precipitation events in this region for the 6 month winter period from Oct-March. Spatial and temporal trends in the ECA&D data for this region are analyzed in the precipitation frequency and intensity for events exceeding the 99th percentile of daily precipitation. The associated climatic conditions (SST, atmospheric circulation patterns, canonical moisture sources and moisture transport patterns) are analyzed using re-analysis data to establish the concurrent and season ahead conditions associated with the leading space and time patterns. Analogs of these patterns are investigated for GCM integrations for future climate (seasonal forecasts as well as IPCC scenarios) to assess potential predictability and outcomes
21st Projections of precipitation extremes in the Mediterranean from a medium resolution GCM
No full textPrecipitation extremes simulated by a medium resolution GCM ( INMCM3.0) are analyzed for
the Mediterranean region. A structured analysis of low frequency variability in the control
and forced ( corresponding to the IPCC scenarios) model runs is performed. The preliminary
results of the analysis of rainfall patterns under global warming conditions, during the
extended winter ONDJFM season, show an increase of rainfall extremes in both frequency
and intensity in northern Europe and a decrease in the most part of Mediterranean. In the
latter region an increase of dry conditions is also observed. The change in the rainfall patterns
can be explained by a northward shift of the North Atlantic winter storm track. This
shift is related changes in meridional and zonal surface temperature gradients ( Equator-
Pole and Ocean-Land contrast, respectively) due to anthropogenic forcing. Changes in the
inter-annual and multi-decadal natural variability are also noted
Decadal variability in Floods and Extreme Rainfall
No full textDecadal variability in climate extremes associated with floods is of particular
interest for infrastructure development and for insurance programs. From an analysis of US
data we note that changes in insurance rates and in the construction of flood control
infrastructure emerge soon after a period where there is a high incidence of regional
flooding. This leads to the question of whether there is clustering in the incidence of
anomalous flooding (or its absence) at decadal scales
Homogeneous & non-homogeneous hidden markov downscaling model for projection of hydroclimate changes in Tanzania
No full textClimate change poses a number of problems to water resources. The precipitation change, expressed in amount of rainfall and occurrence of wet/dry days, could cause seasonal and annual shifts in the climatology of the area and bring up an extreme events intensification.
Therefore, the aim of the proposed work is to develop a Statistical Downscaling Methodology on the entire Tanzania territory, where water supply by superficial and underground sources play a key role. So, Any change in hydrologic cycle could constitute an hazard and adversely affect the sustainability and also the future economic development of the area.
A Hidden Markov Model (HMM) is here used to describe annual daily rainfall occurrence at 11 gauge stations in Tanzania, in east Africa, yearly along the period 1950-1990. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Five ‘‘hidden’’ rainfall states are identified. These states are able to capture the typical Tanzanian seasonality conditions; and further the occurrence and amount of precipitations proper of the different stations.
Moreover, a Non-homogeneous Hidden Markov Model (NHMM) is then applied to downscale daily precipitation occurrence at the 11 stations, using daily large scale predictors extracted from the NCAR-NCEP reanalysis General Circulation Model (GCM) dataset as: Geo Potential Height (GPH), Temperature (T), Zonal and Meridional Winds (ZW & MW) and Vertical Equator Winds from 10 to 1000 hPa (ZEW).
The calibration (1950-1980) and validation (1981-1990) tests, for different predictor combinations, evidence that a considerably betterment in fitting the historical data is obtained by using the NHMM, which results able to simulate the seasonal rainfall pattern, characteristic of Tanzania. Then, the NHMM provides a useful diagnostic and predictive tool (a) in linking the statistics of daily rainfall occurrence and amount at the station level to the large-scale atmospheric patterns and (b) it can be used with the goal to make future projections of the downscaled precipitation by using the GCM’s simulations (CMIP5) under different global warming scenarios
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