Analysis of Climate Variability: Applications of Statistical TechniquesH. von Storch, A. Navarra This volume has grown from an Autumn School about "Analysis of Climat~ Variability - Applications of Statistical techniques" on Elba in November 1993. We have included those lectures which referred explicitly to appli cations of statistical techniques in climate science, since we felt that general descriptions of statistical methods, both at the introductory and at advanced level, are already available. We tried to stress the application side, discussing many examples dealing with the analysis of observed data and with the eval uation of model results (Parts I and II). Some effort is also devoted to the treatment of various techniques of pattern analysis (Part III). Methods like teleconnections, EOF, SSA, CCA and POP are becoming routine tools for the climate researcher and it is probably important for graduate students to be exposed to them early in their academic career in a hopefully clear and concise way. A short subject index is included at the end of the volume to assist the reader in the search of selected topics. Rather than attempting to reference every possible occurrence of some topic we have preferred to indicate the page where that topic is more extensively discussed. The Autumn School was part of the training and education activities of the European Programme on Climatology and Natural Hazards (EPOCH), and is continued under the subsequent research programme (ENVIRONMENT 1990-1994). It aimed at students in general, taking first and second year courses at the graduate level. |
Contents
II | 3 |
III | 4 |
IV | 6 |
V | 7 |
VI | 8 |
VII | 9 |
IX | 11 |
X | 13 |
LXXXVIII | 161 |
LXXXIX | 162 |
XC | 163 |
XCI | 166 |
XCII | 168 |
XCIV | 169 |
XCV | 171 |
XCVI | 173 |
XI | 15 |
XII | 18 |
XIII | 24 |
XIV | 26 |
XV | 27 |
XVI | 29 |
XVII | 31 |
XVIII | 35 |
XIX | 39 |
XX | 46 |
XXI | 49 |
XXII | 50 |
XXIII | 53 |
XXIV | 54 |
XXVI | 55 |
XXVIII | 56 |
XXX | 57 |
XXXI | 58 |
XXXIII | 59 |
XXXIV | 65 |
XXXV | 67 |
XXXVI | 70 |
XXXVII | 73 |
XXXVIII | 76 |
XXXIX | 77 |
XLI | 79 |
XLIII | 80 |
XLIV | 81 |
XLVI | 83 |
XLVII | 84 |
XLVIII | 86 |
L | 87 |
LI | 90 |
LII | 93 |
LIII | 94 |
LIV | 95 |
LV | 96 |
LVI | 97 |
LVII | 98 |
LVIII | 99 |
LIX | 101 |
LXI | 106 |
LXII | 107 |
LXIII | 108 |
LXIV | 109 |
LXV | 111 |
LXVI | 116 |
LXVII | 117 |
LXVIII | 119 |
LXIX | 121 |
LXX | 122 |
LXXI | 123 |
LXXII | 126 |
LXXIII | 128 |
LXXIV | 131 |
LXXV | 133 |
LXXVI | 135 |
LXXVII | 137 |
LXXVIII | 138 |
LXXIX | 139 |
LXXX | 140 |
LXXXI | 142 |
LXXXII | 143 |
LXXXIII | 144 |
LXXXIV | 145 |
LXXXVI | 147 |
LXXXVII | 153 |
XCVII | 176 |
C | 177 |
CII | 178 |
CIII | 179 |
CIV | 180 |
CVII | 181 |
CVIII | 184 |
CIX | 187 |
CXI | 188 |
CXII | 191 |
CXIII | 193 |
CXIV | 194 |
CXV | 196 |
CXVI | 197 |
CXVII | 198 |
CXVIII | 199 |
CXIX | 200 |
CXX | 203 |
CXXIII | 205 |
CXXIV | 206 |
CXXV | 208 |
CXXVI | 212 |
CXXVII | 214 |
CXXVIII | 215 |
CXXIX | 217 |
CXXX | 222 |
CXXXI | 224 |
CXXXII | 227 |
CXXXIII | 228 |
CXXXIV | 231 |
CXXXV | 232 |
CXXXVII | 235 |
CXXXVIII | 236 |
CXXXIX | 238 |
CXL | 240 |
CXLII | 243 |
CXLIII | 246 |
CXLIV | 250 |
CXLV | 252 |
CXLVII | 255 |
CXLVIII | 257 |
CL | 261 |
CLII | 262 |
CLIII | 265 |
CLIV | 266 |
CLV | 268 |
CLVII | 269 |
CLIX | 270 |
CLX | 271 |
CLXI | 272 |
CLXII | 273 |
CLXIII | 274 |
CLXV | 279 |
CLXVI | 285 |
CLXVII | 287 |
CLXVIII | 290 |
CLXIX | 291 |
CLXXI | 292 |
CLXXII | 294 |
CLXXIII | 295 |
CLXXIV | 298 |
CLXXV | 301 |
CLXXVI | 302 |
305 | |
CLXXVIII | 337 |
339 | |
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Common terms and phrases
Atlantic Atmos atmospheric variables average Briffa Chapter chronology circulation climate change climate research climate variability climatology coefficients components covariance matrix cross-covariance cross-spectrum matrix defined dendroclimatology described distribution dynamical eigenvalues eigenvector empirical distribution function EOF analysis equations estimated extratropical field significance Figure Folland forecasts Frankignoul frequency function geopotential height global grid points hindcast independent Jones linear Livezey maps methods modes Monte Carlo MSSA multivariate noise normal Northern Hemisphere null hypothesis observed ocean orthogonal oscillation parameters patterns period phase precipitation prediction predictors region regression rejected relationship represent response Sahel Sahel rainfall sample scale score sea surface temperature sea-level pressure seasonal Section serial correlation signal simulations Singular Value Decomposition skill spatial spectral spectrum SST anomaly ST-PCs statistical stochastic Storch subspace techniques teleconnection thermocline tree-ring tropical UKHI values variance Vautard wavenumber weather wind winter Zwiers
Popular passages
Page 334 - WIGLEY, TML, JONES, PD and KELLY, PM, 1986. Empirical climate studies, warm world scenarios and the detection of climatic change induced by radiatively active gases.
Page 334 - Willebrand, J., 1978: Temporal and spatial scales of the wind field over the North Pacific and North Atlantic.
Page 335 - Storch, 1992. The atmospheric circulation and sea surface temperature in the North Atlantic area in winter: their interaction and relevance for Iberian precipitation.
Page 329 - Hoskins, 1976: Baroclinic instability on the sphere: normal modes of the primitive and quasigeostrophic equations. J. Atmos. Sci., 33, 1454-1477.
Page 322 - MacCracken, MC, and H. Moses, 1982: The first detection of carbon dioxide effects: Workshop Summary, 8-10 June 1981, Harpers Ferry, West Virginia. Bull.
Page 335 - Pegram. 1979. Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models.