極端降雨與氣候變遷

1. 極端降雨與氣候變遷 鄭克聲教授 國立臺灣大學 生物環境系統工程學系

2. 內容綱要 • 序論 • 氣候模式與氣候變遷情境 • 氣候變遷對極端降雨之影響 • 序率暴雨模擬模式 • 氣候變遷對設計雨量之影響 • 結論 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

3. Historic tropical cyclone tracks The tracks of nearly 150 years of tropical cyclones. The map is based on all storm tracks available from the National Hurricane Center and the Joint Typhoon Warning Center through September 2006. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

4. What is climate change? • Climate is constantly changing. • Annual rainfall in Taiwan changes from one year to another. • Global mean temperature changes from one year to another. • Natural phenomena are physical and stochastic. • Changes with respect to statistical properties. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

5. Variations in global mean temperature (1961 – 2010, CRU) Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

6. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

7. Effect of climate change on 10% bin rainfall percentages 1st 10% bin rainfall percentage 大武站，1961 - 2010 Global mean temperature (C) Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

8. 6th 10% bin rainfall percentage 大武站，1961 - 2010 Global mean temperature (C) Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

9. Top 10% bin rainfall percentage 大武站，1961 - 2010 Global mean temperature (C) Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

10. The effect of record length on change detection Change detected ! ? Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

11. The effect of record length on change detection • Stationary or non-stationary? So, what should we do? Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

12. Understanding and identifying causes (not evidences) of climate change are crucial for planning adaptive measures. • A proactive (預設發生) and responsible approach is needed. Or, understanding our inability to fully understand or model the complexity of the Earth system. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

13. Importance of climate change impacts on water resources management • Key factors in water resources management and design • Design rainfall depth • Flood of 100-year return period • Drought of specific return periods • Climate models generally do not yield reliable projections for extreme parameters. Hydrological extremes at site- or regional scale in space and event scale in time GCMs are more skillful at predicting means (averages) of precipitation or temperature than any higher order statistics. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

14. 內容綱要 • 序論 • 氣候模式與氣候變遷情境 • 氣候變遷對極端降雨之影響 • 序率暴雨模擬模式 • 氣候變遷對設計雨量之影響 • 結論 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

15. 氣候學家 A. S. Monin將「氣候」定義為「大氣-海洋-陸地系統狀態於數十年的之統計系集」。然而氣象學家對於應取多長的時間來計算氣候特性的系集平均有許多不同的看法，目前世界氣象組織將之定義為30年。 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

16. 全球氣候模式(GCMs) • 目前主要用來研究氣候變遷的氣候模式為全球大氣-海洋耦合模式(coupled atmosphereoceangeneral circulation models, AOGCMs)，它考慮了大氣運動、表面洋流、溫鹽環流、以及大氣/海洋交互作用，除此之外，大氣化學、植被及土壤的影響也都考慮在內，是目前模擬溫室氣體影響未來氣候最先進的工具。 • GCMs是三維的數值模型，具有150到300公里的水平解析度與10到30層的的垂直分層。 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

17. 氣候變遷情境 • What Are Climate Change Scenarios? • Scenarios are plausible(合理且可能) combinations of conditions that can represent possible future situations. • We create climate change scenarios because predictions of climate change at the regional scale have a high degree of uncertainty. • By regional scale, we typically mean the sub-continental scale to country level to provincial level. 情境有不同層次，例如模式輸入(因子)情境與模式輸出情境。 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

18. Although it is likely that temperatures will eventually rise in most regions of the world, changes at the regional scale in many other key variables, such as precipitation, are uncertain for most regions. Even where the direction of change is certain or likely, there is uncertainty about the magnitude and path of change. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

19. It is critical to keep in mind that regional climate change scenarios are not a prediction of future climate change, but rather a tool to communicate what could happen as a result of human-induced climate change and to facilitate understanding of how different systems could be affected by climate change. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

20. 氣候變遷情境之類別 • Synthetic climate change scenarios • Analogue climate change scenarios • Scenarios based on climate model output. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

21. Synthetic scenarios(人為設定情境) • Synthetic climate change scenarios are changes in key variables selected to test the sensitivity of a system to possible changes in climate. • An example is combinations of 1°, 2° and 4° increases in temperature combined with no change and increases and decreases of 10% and 20% in precipitation. • Different changes can be assumed for different seasons. • These scenarios are most useful for testing the sensitivity of systems to changes in individual variables and combined changes. • Analysts should be careful to keep synthetic changes consistent with what is possible under climate change and avoid implausible combinations of variables. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

22. Analogue scenarios(類比情境) • Analogue, or past climates, can be created from historical instrumental records of climate or from paleoclimate reconstructions. • The instrumental record is a complete multi-decadal record of often daily or sub-daily weather observations at each station and thus could provide better information on daily and even diurnal climate variability and regional distribution of climate than many climate models. • Their disadvantages include inaccuracies in the estimation of past climates, low temporal resolution (e.g., they may estimate seasonal or annual climates), and incomplete coverage. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

23. GCM-output scenarios(氣候模式(輸出)情境) • Climate models are mathematical representations of the climate. Although there are many uncertainties with climate models, they do enable us to simulate how global and regional climates may change as result of anthropogenic influences on the climate. • They model change on a regional scale, typically estimating change in grid boxes that are approximately several hundred kilometers wide. • GCMs provide only an average change in climate for each grid box, even though real climates can vary quite considerably within several hundred kilometers. GCMs are deterministic models. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

24. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

25. 氣候模式(因子)情境 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

26. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

27. IPCC AR4 – 23個GCMs • 雖然氣候模式已經十分複雜，這些模式仍無法包含所有可能影響氣候的因子。一般來說，氣候模式模擬空間尺度越大的準確度越高，模擬區域的氣候特性就比較困難。此外氣候模式模擬百年氣溫變化趨勢與溫室氣體、懸浮微粒的變化趨勢相當一致，但模擬數十年內的氣候變化則變異仍大。總之，時間及空間尺度越小，則氣候模式的模擬能力就越差。 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

28. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

29. Scale for assessing state of knowledge Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

30. Assessing uncertainties of climate change Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

31. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

32. Downscaling from GCMs • Downscaling is a way to obtain higher spatial resolution output based on GCMs • Options include: • Combine low-resolution monthly GCM output with high-resolution observations • Use statistical downscaling • Easier to apply • Assumes fixed relationships across spatial scales • Use regional climate models (RCMs) • High resolution • Capture more complexity • Limited applications • Computationally very demanding Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

33. Downscaling methods Scenarios Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

34. Impact assessment by statistical downscaling • Statistical downscaling utilizes relationships between GCM outputs and historical data to produce finer spatial and temporal resolution climate data at the regional or site level. • GCM outputs • Local observations • Temporal downscaling is conducted mostly from monthly to daily scale. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

35. Brown et al., 2008. IRI, Columbia University Dynamical downscaling is valuable where local topography and land use or vegetation have significant influence on regional climate. Statistical downscaling tends to be a better value than dynamical downscaling for hydrologic applications, being as effective and less expensive. Temporal precipitation downscaling to resolutions of daily scale is an active research area, with statistical approaches showing the most promise. Statistical downscaling is especially useful for temporal downscaling from monthly to daily values. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

36. Statistical downscaling is based on empirical relationships found in past climate observations and thus, may or may not hold in the possible future climates. Statistical downscaling assumes no future change in predictor/predictand relationship. An advantage of statistical models is the ability to characterize and incorporate the uncertainty of the downscaled results. This is particularly important in hydrologic modeling applications, where uncertainties in climate change scenarios and downscaling has been found to outweigh uncertainties in hydrological model parameters (Wilby and Harris 2006; Menzel et al 2006). Bias correction also needs to be considered! Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

37. Key considerations in climate change impact assessment • Projection of means versus ensemble projections. • Characterizing the uncertainties. • Downscaling should preserve statistical properties of different moments and at different scales. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

38. Downscaling Independent simulations of monthly rainfalls will yield monthly rainfall series that preserve statistical properties of rainfall data at monthly scale and mean (but not standard deviation) at yearly scale. Also, it fails to preserve the autocorrelation structure of monthly rainfall series. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

39. Bias and uncertainties • Let’s define = bias of T . [Note: is an estimator of .] Then, 均方差 = 變異數 + (偏差量)2 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

40. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

41. Uncertainties Bias Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

42. 內容綱要 • 序論 • 氣候模式與氣候變遷情境 • 氣候變遷對極端降雨之影響 • 序率暴雨模擬模式 • 氣候變遷對設計雨量之影響 • 結論 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

43. 氣候變遷對極端降雨之影響 • Chronological approach • Nonparametric change detection methods • High-tail approach • The approach is based on the premise that changes in extremes are expected to be proportionately greater than changes in the mean value, so changes in moderately extreme values should become more robustly detectable (Hegerl et al. 2004). Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

44. Fujibe et al. (2005) divided 4-hour precipitation data in a given month into ten categories (or bins) of equal cumulative rainfall amounts, and assessed the trend of precipitation amount in individual 10% bins over a period of 100 years. • Lau and Wu (2007) used a similar approach to detect trends in tropical rainfall characteristics. • Liu et al. (2009) also investigated interannual changes in the cumulative rainfall percentages of the top 10% bin rainfalls with respect to changes in global mean temperature. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

45. The Clausius-Clapeyron relation • The Clausius-Clapeyron relation indicates that specific humidity would increase roughly exponentially with temperature. Thus, the Clausius-Clapeyron relation expresses the saturation vapor pressure es by where L is the latent heat of vaporization and R is the gas constant. At temperatures typical of the lower troposphere,  0.065 K-1. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

46. The CC-relation leads to about 7% rate increase (the CC-rate of increase) in the saturation vapor pressure of the lower-troposphere for each 1-K increase in temperature T. • A few studies have found that rainfall extremes (defined by the top 10% bin rainfalls or the very high percentiles of daily rainfalls) increase at a rate faster than that implied by the CC-relation. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

47. Lenderink and van Meijgaard (2008) analyzed a 99-year record of hourly and daily precipitation data at De Bilt in the Netherlands, and found that one-hour precipitation extremes increase twice as fast with rising temperatures as expected from the Clausius-Clapeyron equation when daily mean temperatures exceed 12C. • O’Gorman and Schneider (2009) assessed how precipitation extremes change in simulations with different climate models, and found that changes in the 99.9% percentile of daily precipitation range from 1.3% K-1 to 30% K-1. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

48. Although the above studies detected or projected various degrees of changes in rainfall extremes with respect to climate changes, the uncertainties of such changes were not quantitatively assessed. • Allen and Strainforth (2002) argued that a climate forecast is intrinsically five-dimensional, spanning space, time, and probability, and the main uncertainty in multi-decade climate prediction is not in the initial state nor in the external driving, but in the climate system’s response. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

49. Therefore, it is imperative to provide a quantitative assessment on the uncertainties in detected or projected changes in precipitation extremes. • In this study, we assess the effect of changes in global mean temperature on changes in rainfall extremes in Taiwan by investigating the joint distribution of changes in global mean temperature and changes in rainfall extremes, and make interpretation on the resultant changes from a stochastic point of view. Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

50. Definition of rainfall extremes • Allen and Ingram (2002) and O’Gorman and Schneider (2009) used 90%, 99%, and 99.9% percentiles of daily rainfall to characterize rainfall extremes. • Threshold rainfall amounts of the top 10% bins were used by Fujibe (2005), Lau and Wu (2007) and Liu et al. (2009). Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU