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

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State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences lihm@mail.iap.ac.cn Rucong Yu State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, <strong>China</strong> Meteorological Administration Tianjun Zhou State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences Using daily station data over <strong>China</strong>, we studied the trends, inter-annual and decadal variations in July-August (hereinafter JA) precipitation and temperature extremes, and probed into possible association between precipitation and temperature. Our results show that except for the precipitation intensity, which show consistent increase trends over most part of <strong>China</strong>, other extreme precipitation indices show positive trends along the Yangtze River valley and negative trends over North <strong>China</strong> for the last 50 years, there’s no significant trend for <strong>China</strong> as a whole. Extreme temperature indices based on daily minimum temperature show coherent increasing trend, which is corresponding to the global warming. Daily maximum temperature is mostly affected by precipitation, significant decrease trends were found along mid-low Yangtze River valley. For <strong>China</strong> as a whole, the dominant increase of daily extreme temperature occurred after 1990s. Besides long-term trend, JA precipitation and temperature extremes also show significant decadal variations. <strong>The</strong> DTR (diurnal temperature range) and ETR (extreme temperature range) tend to decrease over most part of <strong>China</strong>, they experienced a decadal shift starting from late 1970s. <strong>The</strong> difference (1980-2001 mean minus 1958-1979 mean) spatial pattern of extreme temperature and precipitation indices is consistent with their long-term trends. <strong>The</strong> probability of cold nights (warm nights) is lower (higher) after 1980s than before. Changes of winter extreme rainfall over Southwest Western Australia and the linkage to the Southern Annular Mode Speaker: Yun Li Yun Li CSIRO Mathematical and Information Sciences, Private Bag 5, Wembley, WA 6913, Australia Yun.Li@csiro.au Southwest Western Australia (SWWA) is an austral winter rainfall dominated region. Since late 1960s, rainfall over SWWA has experienced a substantial drying trend with a winter rainfall decrease of some 25%, putting further constraints on water resources in an already dry area. <strong>The</strong> decline is manifest as a reduction of high-intensity extreme rainfall events. <strong>The</strong>re 15
has been vigorous debate as to what is forcing the drying trend, i.e., whether it is driven by secular forcings such as increasing atmospheric CO2 concentration or whether it is part of multidecadal variability. Previous results from greenhouse warming experiments suggest that the SWWA rainfall shows a drying trend with an increasing mid-latitude mean sea level pressure (MSLP) under increasing atmospheric CO2, which is expressed as an upward trend of a dominant atmospheric circulation mode in Southern Hemisphere called Southern Annual Mode (SAM) or the Antarctic Oscillation (AAO). We present our recent results on statistical modeling winter extreme rainfall over SWWA and its associated changes with the SAM. <strong>The</strong> possibility of the rainfall reduction being a part of multidecadal variability was discussed by using outputs of the CSIRO Mark 3 climate model in an experiment without CO2 forcing. Our results offer the qualified support for the argument that the SAM may contribute to the drying trend. This work was supported by the Indian Ocean Climate Initiative of the Western Australian state government, and CSIRO WfHC Flagship-Climate Program. Estimation and Extrapolation of Climate Normals and Climatic Trend Speaker: Robert E. Livezey Robert E. Livezey Climate Services Division, Office of Climate, Water, and Weather Services, NWS/NOAA/DOC robert.e.livezey@noaa.gov WMO-recommended 30-yr normals are no longer generally useful for the design, planning, and decision-making purposes they were intended. <strong>The</strong>y not only have little relevance to the future climate, but are often unrepresentative of the current climate. <strong>The</strong> reason for this is rapid global climate change over the last 30 years that is likely to continue into the future. It is demonstrated that simple empirical alternatives already are available that not only produce reasonably accurate normals for the current climate but also often justify their extrapolation several years into the future. This result is tied to the condition that recent trends in the climate are approximately linear or have a substantial linear component. This condition is generally satisfied for the U. S. climate division data, but will also have wide applicability globally. One alternative, Optimum Climate Normals (OCN) are multi-year averages not fixed at 30 years like WMO normals, but adapted climate record by record based on easily-estimated characteristics of the records. <strong>The</strong> OCN works well except with very strong trends or longer extrapolations with more moderate trends. In these cases least squares linear trend fits to the period since the mid-1970s are viable alternatives. An even better alternative is the use of hinge fit normals, based on modeling the time dependence of large-scale climate change. Here, longer records can be exploited to stabilize estimates of modern trends. Related issues are the need to avoid arbitrary trend fitting and to account for trends in studies of ENSO impacts. Given these results we recommend that (a) the WMO and national climate services 16
Page 1 and 2: 1Oth International
Page 3: Preface It is truly a great pleasur
Page 6 and 7: Speaker: Dan Cooley................
Page 8 and 9: Speaker: Qian Li ..................
Page 10 and 11: 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 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 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
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
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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
Page 114 and 115:
Iberian Peninsula and with the corr
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89% during the 10 days of mid-June,
Page 118 and 119:
The paper examines
Page 120 and 121:
Special AMS Session:Verification of
Page 122 and 123:
Verification of Climate Forecasts (
Page 124 and 125:
the regional climate change is repr
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model has been applied to the serie
Page 128 and 129:
are analyzed. It is shown that thes
Page 130 and 131:
stations in Iran. The</stro
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The study of the U
Page 134 and 135:
Development and analysis of a daily
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Fractal widely exists in nature, an
Page 138 and 139:
Rescue of historical climate data:
Page 140 and 141:
D. Semeradova Institute of Landscap
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Kulkarni Ashwini, Sabade S S and Kr
Page 144 and 145:
The magnitude of t
Page 146 and 147:
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
Page 166 and 167:
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
Page 176 and 177:
Calibrating and evaluating reanalys
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To explore an optimal climate decis
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