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Climate Risk Management in North-East of Iran By Statistical Downscaling Direct on GCMs Output Speaker: Sina Samadi Sina Samadi Climatological Research Institute(CRI)-I.R.of Iran Meteorological Organization(IRIMO) sisamadi@yahoo.com In climate change studies the global circulation models of Atmosphere (GCMAs) enable one to simulate the global climate, with the field variables being represented on a grid points 300 km apart. Unfortunately, the most recent generation of general circulation models (GCMs) still has serious problems when modeling over Iran. However, these models are able to reproduce the main patterns of atmospheric circulation, such as those derived from a principal component analysis of the sea level pressure anomaly field. GCMs Models are benefit for detecting Climate change and zone on a special parameter. Even if global climate models in the future are run at high resolution there will remain the need to ‘downscale’ the results from such models to individual sites or localities for impact studies. A Statistical Model (SM) has been developed to downscale large scale predictors given by Global Climate Models (GCMs). This is a complementary approach to the dynamical modeling of regional climate change using high resolution nested models. It allows bridging the scale difference (“downscaling”) between coarse grid Coupled Atmosphere-Ocean GCMs and the finest temporal and spatial scales required for regional and environmental impact studies of climate changeGeneral Circulation Models (GCMs) indicate that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So–called “downscaling” techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what impact assessors require. Many downscaling techniques have been developed in recent years, all having in common the need to establish statistical links between the large-scale circulation and the observed data at a local or regional scale. We know several centers in the world are producing climate data in deferent scenarios. So, choose the best one that is suitable in selection area and then use them for detecting change in climate parameters is the main propose of this paper. In this paper, we try to find correlation between observed data and models outputs to find the best one for downscale monthly precipitation over the North-East of Iran. We use SDSM tools for that changing scale. It was found that the precipitation characteristics (mean, variance, and empirical distribution) were better reproduced by the downscaled results than by the GCM direct output. With using Precipitation parameter constructed for the future (2005–2050) in SPI (Standard Precipitation Index) we could detect climate change in this region for future and it will improve climate risk management. Keyword: Climate Change, Downscaling, , GCM, Management, SDSM, Risk 69
Analysing decadal rainfall perfomance in Uganda Speaker: Senkunda Samuel Senkunda Samuel Uganda Department of Meteorology senkunds@yahoo.com <strong>The</strong> generation of climatological and weather information that can be applied in advising the planners and decision makers mostly involves data analysis, after which most appropriate inferencing can be done.This paper will look at the ten day rainfall analysis for Uganda for the past ten years, using accumulated decadal rainfall data.Data from a total of 16 active synoptic stations across the country, has been analysed whereby accumulated ten day rainfall graphs have been generated. A Preview of Iran`s Climate Using SIAP Method Speaker: Behrooz Sari Sarraf Dr.Behrooz Sari Sarraf Islamic Azad University_Ahar Branch sarisarraf@tabrizu.ac.ir Abbas Hoseiny Shamechi Islamic Azad University_Ahar Branch Mrs.Masoomeh Emamgholizadeh University of Tabriz In the existing study, we have used the precipitation records of synoptic stations of Iran for a long term period, after retrieving the data and applying homogenity tests.Using the SIAP equation, we have drawn zoning map of IRAN based on standardized values and the coefficient of variation of precipitation have been studied and finally the coefficients of variation map of Iran has beenstudied and finally the coefficients of variation map of Iran has been prepared. According to the results obtained, in the years 1966 and 1973 all over Iran has experienced droughts and since 1995 until 2001 a very long desiccation in most parts of Iran has dominated. Key words:Zoning, Drought,coefficient of variation,Siap 70
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1Oth International
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Preface It is truly a great pleasur
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Speaker: Dan Cooley................
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Speaker: Qian Li ..................
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Speaker: Ediang Okuku Archibong....
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Speaker: Yi Wang...................
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Climatological Dataset.............
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Bridging the gap between simulated
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Invited Session: Spatial patterns o
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Joint Program on the Science and Po
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e to adopt a different calibrated l
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those retained, and [iii] performan
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A range of existing statistical app
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palaeo-environmental data from sout
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Whilst it has been noted that such
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State Key Laboratory of Numerical M
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address new policies for changing c
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Page 48 and 49: We present applications of several
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Page 52 and 53: ichard@stats.ucl.ac.uk Nadja Leith
Page 54 and 55: Answering questions about empirical
Page 56 and 57: In a series of climate change impac
Page 58 and 59: climatological annual frequency of
Page 60 and 61: Detection of urban warming in recen
Page 62 and 63: Detection of Human Influence on pan
Page 64 and 65: Extending attribution studies: post
Page 66 and 67: Francis W. Zwiers, Gabriele C. Hege
Page 68 and 69: 1, In the middle-pattern of El Niñ
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Page 74 and 75: A multiple surrogate data study of
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Page 98 and 99: Rajesh J. Department of Atmospheric
Page 100 and 101: Andrew Moore Uni. of California at
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Fractal widely exists in nature, an
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Rescue of historical climate data:
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D. Semeradova Institute of Landscap
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Kulkarni Ashwini, Sabade S S and Kr
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The magnitude of t
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Richard E. Chandler University Coll
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convective cloud clusters when they
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1999; Smith et al, 2001; Gibbons, 2
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dmanatsa@buse.ac.zw Wisemen Chingom
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The Relation Betwe
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conditioned on some indices of the
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ari@ludens.elte.hu J. Bartholy Dept
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ainfall as predictand. Additionally
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Impact of ENSO PHASES on Amazonia S
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time lag. Both perturbed analysis a
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Relationship Between Ensemble Mean
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ways were considered. Four zones, f
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- Monthly ratios of synthetic and o
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Total ozone trends are typically st
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Impact of Urban Expansion on Region
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Calibrating and evaluating reanalys
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To explore an optimal climate decis
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