The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

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Detection of Human Influence on pan-Arctic Precipitation Speaker: Seung-Ki Min Seung-Ki Min Climate Research Division, Environment Canada seung-ki.min@ec.gc.ca Xuebin Zhang Climate Research Division, Environment Canada Francis Zwiers Climate Research Division, Environment Canada <strong>The</strong> Arctic and the northern sub-polar region are critical areas for climate change. <strong>The</strong> fluctuation of regional fresh water inflow to the Arctic Ocean modulates the deep ocean circulation and thus exerts strong influence on global climate. This region also warms faster under global warming. By comparing observations to simulations from 22 coupled climate models, we detect anthropogenic influence in the space-time precipitation patterns over the northern high-latitude (north of 55N) lands during the second half of the 20th century. <strong>The</strong> detection is also robust to the removal of Arctic Oscillation related variability in the observed precipitation. This regional-scale precipitation detection result represents a new and strong evidence that human activity has contributed to the pan-Arctic climate change. Impact of carbonaceous aerosols on attributable warming and future prediction Speaker: Toru Nozawa Toru Nozawa National Institute for Environmental Studies nozawa@nies.go.jp Hideo Shiogama National Institute for Environmental Studies Tatsuya Nagashima National Institute for Environmental Studies Toshihiko Takemura Research Institute for Applied Mechanics, Kyushu University Impacts of historical changes in carbonaceous aerosols on past attributable warming and future prediction are investigated using an optimal fingerprinting method (Allen and Stott, 45
2003) with a series of numerical simulations performed by a coupled ocean-atmosphere general circulation model (CGCM) called by MIROC. Observationally constrained attributable warming in the 20th century is similar to that estimated with the other CGCMs (Stott et al., 2006) if the historical changes in carbonaceous aerosols do not considered in MIROC as in the other CGCMs. When we consider historical changes in carbonaceous aerosols, on the other hand, observationally constrained attributable warming increases and uncertainties in attributable anthropogenic signals become larger. Scaled future prediction gets more uncertainties when we use scaling factors estimated with carbonaceous aerosol forcings. This implies that the other process, such as carbon cycle, atmospheric chemistry, dynamical vegetation, etc. might also have significant impact on the past attributable warming and the scaled future prediction. Robustness of climate change signals in near term predictions up to the year 2030 Speaker: Hideo Shiogama Hideo Shiogama National Institute for Environmental Studies, Tsukuba, Japan shiogama.hideo@nies.go.jp Toru Nozawa National Institute for Environmental Studies, Tsukuba, Japan Seita Emori National Institute for Environmental Studies, Tsukuba, Japan This is the first study to examine whether human contributions to changes in extreme temperature indices have larger amplitudes than natural variability in near future (up to 2030) climate prediction. We performed 10 runs of the initial condition perturbed ensemble of a coupled atmosphere-ocean general circulation model. In the near future, over most land areas, all 10 runs show agreement in predicting more frequent occurrences of warm nights and warm days, and less frequent cold nights and cold days, suggesting that human influences in them have become larger than natural variability. Area weighted fraction of grid boxes where all runs agree the direction of changes over the land is little sensitive to ensemble sizes (for warm nights, 96% and 93% for 4 runs and 10 runs, respectively). It was also suggested that the changes in the frequency of warm and cold extremes are mainly due to shifts in seasonal mean temperatures. Additionally snow cover affects the frequency of cold extremes in some areas. 46
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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....
Page 12 and 13: Speaker: Yi Wang...................
Page 14 and 15: Climatological Dataset.............
Page 16 and 17: Bridging the gap between simulated
Page 18 and 19: Invited Session: Spatial patterns o
Page 20 and 21: Joint Program on the Science and Po
Page 22 and 23: e to adopt a different calibrated l
Page 24 and 25: those retained, and [iii] performan
Page 26 and 27: A range of existing statistical app
Page 28 and 29: palaeo-environmental data from sout
Page 30 and 31: Whilst it has been noted that such
Page 32 and 33: State Key Laboratory of Numerical M
Page 34 and 35: address new policies for changing c
Page 36 and 37: and show promise for analyzing tren
Page 38 and 39: 1901-2005 with complete data of all
Page 40 and 41: Homogenization of daily meteorologi
Page 42 and 43: as Homogen Data, Gap, Index, Statis
Page 44 and 45: Penalized maximal t-test for detect
Page 46 and 47: Iran,Tehran, Azad university, Resea
Page 48 and 49: We present applications of several
Page 50 and 51: A 50-member ensemble is produced wi
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 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
Page 70 and 71: convergence zone (ITCZ). Th
Page 72 and 73: stationary waves occur in mid-latit
Page 74 and 75: A multiple surrogate data study of
Page 76 and 77: Linear and non-linear reconstructio
Page 78 and 79: Pseudo-proxy tests of the regulariz
Page 80 and 81: Parallel Sessions:Regional Studies
Page 82 and 83: Iterative Interpolation of Daily Pr
Page 84 and 85: disturbance in the natural ecosyste
Page 86 and 87: Climate Risk Management in North-Ea
Page 88 and 89: Analyzing of synoptic intence showe
Page 90 and 91: chosen since it completely defines
Page 92 and 93: The Roles of Ensem
Page 94 and 95: The diagram shows
Page 96 and 97: Empirical probabilistic seasonal fo
Page 98 and 99: Rajesh J. Department of Atmospheric
Page 100 and 101: Andrew Moore Uni. of California at
Page 102 and 103: Parallel Sessions:Simulation tools
Page 104 and 105: data can ever be generated. In fact
Page 106 and 107: egion can be explained by the rando
Page 108 and 109: on instantaneous time scales, respe
Page 110 and 111: Key words:- principal component reg
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een analysed with geostatistical to
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Iberian Peninsula and with the corr
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89% during the 10 days of mid-June,
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The paper examines
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Special AMS Session:Verification of
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Verification of Climate Forecasts (
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the regional climate change is repr
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model has been applied to the serie
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are analyzed. It is shown that thes
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stations in Iran. The</stro
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The study of the U
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Development and analysis of a daily
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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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