北京大学：《气候影响评价》课程教学资源（PPT课件讲稿，英文版）ClimateImpact-chp06-09

Chapter 6 Chap 6 Climate/Change Impact methods Index. variables Statistical methods GCMs with downscaling Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts Chap. 6 Climate/Change Impact methods • Index, variables • Statistical methods • GCMs with downscaling

Chapter 6 6.1 Index 1. Variables for element characteristics Precipitation regime Temperature Humidity/evaporation Wind Hazards: drought. flood, frost etc 2. Variables for element changes Difference Ratio Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.1 Index 1. Variables for element characteristics Precipitation regime Temperature Humidity/evaporation Wind Hazards: drought, flood, frost, etc. 2. Variables for element changes Difference Ratio

Chapter6 6.2 Statistical methods 1. The value of statistics Can not provide definitive answers, but can be of great help in establishing relationships and quantifying uncertainties within climatic data 2. Some typical problems Mean state Correlation: linear, auto", cross-(spatial) Stationary/cyclo-stationary/non-stationary Quality of forecasts Time and spatial characteristics Pairs of characteristic patterns Model evaluation Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods 1. The value of statistics Can not provide definitive answers, but can be of great help in establishing relationships and quantifying uncertainties within climatic data 2. Some typical problems • Mean state • Correlation: linear, auto-, cross-(spatial) • Stationary/cyclo-stationary/non-stationary • Quality of forecasts • Time and spatial characteristics • Pairs of characteristic patterns • Model evaluation

Chapter 6 6.2 Statistical methods 3. Mathematical models Certain relationships Random relationships Stationary- stable mean Non-stationary- varying mean Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods 3. Mathematical models • Certain relationships • Random relationships • Stationary– stable mean • Non-stationary– varying mean

Chapter 6 6.2 Statistical methods 4. Main aspects for analysis Probability and distributions Confirmation/test. validation Fitting statistical models Time series analysis scale: length, time step duration stationary/non-stationary Spatial analysis pecific topics EOF/PC/CCA/SVD/POP Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods 4. Main aspects for analysis • Probability and distributions • Confirmation/test: validation • Fitting statistical models • Time series analysis scale: length, time step, duration stationary/non-stationary • Spatial analysis • Specific topics EOF/PC/CCA/SVD/POP

Chapter 6 6.2 Statistical methods time series analysis methods (1) Graphical depiction: trends/cycles Different functⅰonS near exp Inverse po Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis methods: (1) Graphical depiction: trends/cycles Different functions: linear exp ln inverse poly-

Chapter 6 6.2 Statistical methods time series analysis methods (2)Autocorrelation: temporal autocorrelation e.g. today's wind speed is similar to yesterdays wind speed this is the persistence or inertia of the climate system Diurnal/annual cycles should be removed before estimating autocorrelation X-average (X)/ fitting and removing an analytical function such as a series of sinusoids or polynomials The most natural way to visualize autocorrelation in a time series is by plotting the autocorrelation as a function of lag time. that is the autocorrelation function Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis methods (2) Autocorrelation: temporal autocorrelation • e.g. today’s wind speed is similar to yesterday’s wind speed this is the persistence or inertia of the climate system • Diurnal/annual cycles should be removed before estimating autocorrelation • X-average(X)/ fitting and removing an analytical function, such as a series of sinusoids or polynomials • The most natural way to visualize autocorrelation in a time series is by plotting the autocorrelation as a function of lag time, that is the autocorrelation function

Chapter 6 6.2 Statistical methods time series analysis models Component decomposition (1)Alinear trend an annual cycle (3)diurnal cycle (4)autocorrelation (5)a random component Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis models Component decomposition (1)A linear trend (2) an annual cycle (3) a diurnal cycle (4) autocorrelation (5) a random component

Chapter 6 6.2 Statistical methods time series analysis models Spectrum analysis (1) Discrete Fourier Transform (2) The power spectrum (3)Cross spectrum analysis (4) Filtering (5) Wavelets Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis models Spectrum analysis (1) Discrete Fourier Transform (2) The power spectrum (3) Cross spectrum analysis (4) Filtering (5) Wavelets

Chapter 6 6.2 Statistical methods ° spatial analysis Grid Interpolation Relationships between related fields Geostatistics: Kriging Assessment of Climate/Change Impacts

Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • spatial analysis Grid Interpolation Relationships between related fields Geostatistics: Kriging