# 16. THE 2012 NORTH CHINA FLOODS: EXPLAINING AN EXTREME RAINFALL EVENT IN THE CONTEXT OF A LONGER-TERM DRYING TENDENCY

TIANJUN ZHOU, FENGFEI SONG, RENPING LIN, XIAOLONG CHEN, AND XIANYAN CHEN

*Introduction.* North China, including Beijing (~39°52′N, 116°28′E), Tianjin (~39°10′N, 117°10′E), and part of Hebei Province, experienced severe flooding in the summer of 2012. During 21–22 July, Beijing received a regionally averaged total precipitation of 190.3 mm, and the center of the rainfall event received 460.0 mm; the observations of 11 stations broke the historical records (CMA 2013). The flood-affected area in Beijing was about 16 000 km², and the damage-suffering population was estimated at 1.9 million with 77 people dead. The direct economic loss is more than 10 billion Chinese renminbi.

Flooding events are not unusual in China, but have been uncommon in North China in recent decades. The East Asian summer monsoon (EASM) circulation has been weakening since the end of the 1970s, which led to a drying tendency in North China (see Zhou et al. 2009 for a review). The occurrence of the 2012 flood in the context of a multidecadal drying tendency has received great attention. In this study, we analyze the 2012 North China flood in the context of summer monsoon changes in the past 62 years (1951–2012). We examine whether climate change may have played a role in either the 2012 extreme rainfall or the recent multidecadal trend of decreased precipitation in North China.

*Observed heavy rainfall in North China.* A heavy rainfall occurred on 21–22 July 2012 where the major rain belt extended from Southwest China to North China and was centered in Beijing (Fig. 16.1a). The Beijing area received a regionally averaged rainfall of 190.3 mm, as measured by 20 stations in the area. The Hebei Town station of Fangshan District in the western mountain area of Beijing received the largest amount of rainfall and recorded 460.0 mm within 24 hours (CMA 2013). The 24-hour precipitation accumulation in the western mountain area was nearly equal to the area's

<figure><img src="image_39.png" /><figcaption>**FIG. 16.1.** (a) The accumulated precipitation (mm) from 21 July 2012 08:00 China Standard Time (CST) to 22 July 2012 08:00 CST. (b) Time series of the July maximum one-day precipitation (RXIDAY) index in Beijing station (station number: 54511). The red solid line is the climatological mean (1981–2000) threshold of the 95th percentile of all precipitation days in July (mm day⁻¹). The text denotes the date when the RXIDAY value appeared, e.g., "0721" means the RXIDAY occurred on 21 July. (c) July (green) and annual total (white) precipitation (mm) in Beijing station, the red solid line denotes the mean total annual precipitation during 1981–2010.</figcaption></figure>
annual precipitation and far more than the area's average for a single rainfall event. Note, the mean July precipitation for the period 1981–2010 is 160.5 mm, and the mean total annual precipitation is 541.8 mm at *Beijing station* (station number 54511; Fig. 16.1c). The average 24-hour rainfall in the Tianjin area was 98.6 mm, with 294.7 mm of total precipitation at the rainfall center on 21–22. Flooding was observed in part of the Haihe River valley in Hebei province (CMA 2013).

The maximum one-day precipitation (RX1DAY) index of July during 1951–2012 at Beijing is examined in Fig. 16.1b. Since the original daily precipitation amount is defined as the accumulation of precipitation starting at 20:00 China Standard Time (CST) one day and ending at 20:00 CST the next day, to catch the heavy rainfall that occurred starting 21 July 08:00 CST, a two-day running mean is applied to the original daily data. The RX1DAY of 2012 was

the strongest since 1995, but not unprecedented in the past 62 years. RX1DAY indices stronger than the 2012 case are seen in the historical record (Fig. 16.1b). The total July precipitation was more than 30% of the annual precipitation at the Beijing station (Fig. 16.1c).

*Was the extreme rainfall of July 2012 due to climate change?* Precipitation in North China is known to be directly linked to the strength of the EASM circulation, which is measured by a commonly used index (Guo 1983) and has been weakening since the end of the 1970s (Fig. 16.2a), as reported in previous studies (e.g., Yu et al. 2004; Yu and Zhou 2007; Zhou et al. 2009). The declining trend can also be regarded as an inter-decadal variation due to the short data length. The EASM circulation of 2012 is greater than most years following 1980, but still weaker than the period 1951–79 (Fig. 16.2a,b). Following the decreasing trend of the EASM circulation, the July precipitation in

<figure><img src="image_40.png" /><figcaption>FIG. 16.2. (a) Normalized Jul EASM index derived from NCEP/NCAR reanalysis based on Guo (1983). The green line indicates the PDO index derived from http://jisao.washington.edu/pdo/PDO.latest. (b) Normalized Jul precipitation amount averaged over North China (35°N–43°N, 110°E–122°E; 23 stations included; dimensionless). The green line indicates the PDO. (c) Linear trends of July precipitation during 1951–2011 (mm yr⁻¹; the absolute values of anomalies larger than 0.4 are statically significant at the 5% level), the green box indicates the North China region (35°N–43°N, 110°E–122°E). (d) Anomalies of Jul precipita- tion in 2012 relative to 1981–2010.</figcaption></figure>
North China has also been decreasing since the end of 1970s. However, as with only five other Julies in the last 30 years, July 2012 was above normal. More importantly, July 2012 was the strongest event in the last 15 years (Fig. 16.2b).

While North China precipitation and the EASM circulation index are directly correlated, they are inversely correlated with the phase of the Pacific Decadal Oscillation (PDO), or “Inter-decadal Pacific Oscillation” (IPO; Power et al. 1999). A negative phase of the PDO corresponds to a stronger EASM circulation (Fig. 16.2a) and more precipitation in North China (Fig. 16.2b). The PDO affects the EASM circulation through changing land-sea thermal contrast (Li et al. 2010). The recent transition of the PDO from a positive to a negative phase (Zhu et al. 2011; Liu et al. 2012) provides a large-scale background for the stronger summer monsoon in 2012.

The precipitation anomaly of July 2012 is compared to the long-term trend in Fig. 16.2c. Following the weakening tendency of the EASM circulation (Fig. 16.2a), decreasing precipitation trends are seen in most stations of North China (Fig. 16.2c). The extreme precipitation of July 2012 is in contrast to the long-term trend, and most stations in North China witnessed a positive July 2012 precipitation anomaly (Fig. 16.2d). There is insufficient data to conclude whether this indicates a recovery of the EASM after experiencing a weakening stage since the end of the 1970s (Liu et al. 2012).

Following the declining trend of the EASM circulation, the average rainfall amount and frequency have significantly decreased but the rainfall intensity has increased in North China (Yu et al. 2010). July 2012 is in contrast to the long-term trend in rainfall amount but consistent with rainfall intensity trends.

To understand the relationship of the July North China rainfall event with climate change, the July precipitation averaged in North China derived from the historical simulation and Representative Concentration Pathway (RCP) projection of 39 Coupled Model Intercomparison Project Phase 5 (CMIP5) models are analyzed (Supplementary Fig. S16.1). The models show an increasing trend from 1950–2000, in contrast to the decreasing trend observed during this period. The inability of CMIP5 models to replicate the observations is partly due to the inconsistency of PDO phase transition, since the CMIP5 models were not initialized with contemporary observations and, therefore, would not reproduce the observed phase transition of the PDO event. Under RCP4.5

and RCP8.5 scenarios, the July precipitation in North China is projected to increase in the future by CMIP5 models. However, the credibility of the projection is reduced due to the weakness of historical simulation. Due to the inability of climate models to reproduce the observed trend, it is difficult to make any conclusions about the role of climate change in the trend of decreasing (increasing) precipitation amount (intensity) observed in North China over the last three decades. In addition, the performance of global models in reproducing regional precipitation changes in North China may be limited by their low resolutions. Whether a dynamical downscaling using a higher resolution regional model can improve the simulation deserves further study

*Concluding remarks.* Although the damage caused by the 2012 floods in North China was large, the amount of precipitation was not unprecedented in the past 62 years. The flood occurred in the background of a longer-term drying tendency. Since the late 1970s, the total summer rainfall amounts have significantly decreased, but the rainfall intensity of single events has increased in North China associated with the weakening tendency of the EASM circulation partly due to the phase transition of the PDO. We are unable to confirm or reject the role of climate change in the 21–22 July 2012 rainfall event due to the inability of the CMIP5 models to accurately replicate observations in this region of China. The CMIP5 models show an increasing trend from 1950 to 2000, in contrast to the decreasing trend observed during this period. Both the mean and extreme precipitation in North China are projected to increase in the future, but the creditability of the projection is limited by the weakness of models to simulate the climatology of EASM and the design of CMIP5 projection experiments, which were not initialized with contemporary ocean observations and would not be able to reproduce the observed phase transition of the PDO.

In addition, we should note that the inability of CMIP5 models to replicate the observations is not due entirely to the unmodelled phase transition of the PDO. The trend of the PDO during 1971–2012 is nearly zero, but the precipitation trend in North China is nonzero, which is -1.44 mm day⁻¹ and statistically significant at the 5% level. This suggests an underlying trend caused by processes other than the PDO. Further studies are needed to understand the underlying processes.
17. CONTRIBUTION OF ATMOSPHERIC CIRCULATION
CHANGE TO THE 2012 HEAVY RAINFALL IN
SOUTHWESTERN JAPAN

YUKIKO IMADA, MASAHIRO WATANABE, MASATO MORI, MASAHIDE KIMOTO, HIDEO SHIOGAMA, AND MASAYOSHI ISHII

*Introduction.* From 11 July to 14 July 2012, during the late Japanese “Baiu” rainy season, the southwestern part of Japan experienced an extraordinary heavy rainfall. This event was record breaking at many sites (detailed rainfall analysis is shown in Supplementary Fig. S17.1), and caused devastating damage with 31 deaths, 3 missing persons, floods, mudslides, and damaged homes in the southwestern part of the mainland of Japan.

In a normal rainy season from June to July, a persistent rain band called the “Baiu front” supplies a large amount of water to southwestern Japan and some parts of East Asia (Fig. 17.1a). The Baiu front is associated with the large-scale Asian summer monsoon circulation and a development of the northwestern Pacific subtropical high (PASH). In 2012, water vapor supply to Kyushu, the southernmost part of Japan, was extremely large (Fig. 17.1b). The intense convergence over these areas was forced by the anomalously strong PASH and an anomalous cyclonic circulation over the South China Sea, both inducing southerly

convergence flows, which transported a large amount of water vapor into southwestern Japan (Fig. 17.1b).

The stronger PASH during the 2012 rainy season was characterized by a dominant teleconnection pattern over East Asia called the Pacific-Japan (PJ) pattern (Nitta 1987). The PJ pattern consists of a meridional dipole of circulation between the tropical and midlatitude northwestern Pacific (highlighted in Fig. 17.2b by red plus and cross symbols), and is an important teleconnection pattern affecting the East Asian summer climate. Contours in Fig. 17.2a show that convection was more active than normal in the tropics [negative outgoing longwave radiation (OLR)] to the east of the Philippines and Indonesia, associated with a descending high-pressure anomaly over the subtropical area (corresponding to the PASH region) and a low-pressure anomaly to the north of the PASH region. This pressure pattern in 2012 was located to the east of the typical PJ pattern, which tends to intensify heavy rainfall especially in southwestern Japan (Fig. 17.2b, as described hereinafter in detail). In

addition, the emergence of another teleconnection pattern called the circumglobal teleconnection (CGT; Ding and Wang 2005; Yasui and Watanabe 2010), which is characterized by a dominant wave train along the Asian Jet located around 30°N–40°N, amplified the PASH in the rainy season of 2012 (the observed wave train can be found in Supplementary Fig. S17.2). These two independent teleconnection patterns both have barotropic structure and affect the PASH activity and Japanese rainfall.

<figure><img src="image_41.png" /><figcaption>FIG. 17.1. (a) A climatology of the Japanese rainy season (15 Jun–15 Jul) from the JRA-25 reanalysis (1979–2011) column-averaged water vapor flux (kg kg⁻¹ m s⁻¹, arrows) and its divergence (×10⁹ kg kg⁻¹ s⁻¹, shading). (b) Same as (a) but for anomalies in 2012.</figcaption></figure>
<figure><img src="image_42.png" /><figcaption>FIG. 17.2. (a) Same as Fig. 17.1b but for Z500 (m, shading) and OLR (contour, 4-W m⁻² interval). (b) Regression coefficient of column-averaged water vapor divergence onto the observed PJ index from 1979 to 2012 (×10⁹ kg kg⁻¹ s⁻¹). White lines denote a 90% significance level. (c) Same as (a) but for a 100-member ensemble-mean of ALL run for the 2012 rainy season. (d) Histogram (bars) and PDF (curve) estimated by the kernel method (Silverman 1986; Kimoto and Ghil 1993) of the PJ index; 1979–2011 climatology based on the JRA-25 (dashed) and 10-member ensemble of ALL-LNG (green), 100-member ALL (red), and NAT (blue) runs for 2012. Triangle indicates an observed anomaly in the 2012 rainy season.</figcaption></figure>

The importance of evaluating the extent to which anthropogenic global warming affects specific extreme weather events has been increasing. In this context, Pall et al. (2011) conducted huge ensemble experiments with an atmospheric general circulation model (AGCM) under two specific boundary conditions corresponding to the period of an extreme event, the observed forcing and a counterfactual forcing without anthropogenic climate change, and evaluated the probabilistic difference in event occurrence rates as the contribution of anthropogenic effect by analyzing the huge pool of AGCM ensemble experiments.

We basically adopted the methodology of Pall et al. (2011). In the case of rainfall events over Japan, most AGCMs do not have the ability to reproduce such regional phenomena but may possibly represent large-scale circulations such as the PJ pattern and the CGT.

In general, CGT is said to be atmospheric internal variability that cannot be attributed to SST forcing (Kosaka et al. 2009). On the other hand, the PJ pattern is a teleconnection from the tropics and has the potential to respond to SST variability, although the PJ pattern is also affected by the stochastic CGT. In terms of global warming, Kusunoki et al. (2006) demonstrated an increase in Baiu rainfall due to the El Niño-like tropical SST changes associated with the intensification of the PASH using time-slice experiments of future projections with a high-resolution AGCM. Therefore, in this study, we focused on the extent to which the anthropogenic SST change raises the level of the PJ occurrence in 2012 relative to natural variability.

plinary Research on Climate version 5 (MIROC5, T85L40, Watanabe et al. 2010). The model was integrated over the recent four years (2009–12), with observed SST and sea ice under the anthropogenic external conditions during the period. In order to assess the influence of possible anthropogenic effects, another 100-member ensemble was generated under anthropogenic forcing fixed at conditions from the year 1850, with modified boundary conditions (SST and sea ice) in which possible human-induced components were removed (counterfactual run under the natural external conditions, called the NAT run, see Shiogama et al. 2013 for details). The difference in the SST boundary conditions between the ALL and NAT runs is characterized by the El Niño-like warming trend pattern in the tropical Pacific, as is the case for most 20th century experiments with coupled GCMs. To validate the model reproducibility of the

*Method.* We conducted a 100-member AGCM ensemble experiment (factual run, called the ALL run) by an atmospheric component model of the Model for Interdisciplinary Research on Climate version 5 (MIROC5, T85L40, Watanabe et al. 2010).
interannual variability, the ALL run for 1946–2011 was replicated with a reduced ensemble size of 10 (ALL-LNG run).

**Validation.** Most AGCMs generally have difficulty in reproducing local rainfall variability associated with the Baiu front but have the potential to represent the background circulation. Anomaly correlation maps of rainfall and pressure patterns (500-hPa geopotential height; Z500) between the ALL-LNG and observed anomalies are shown in Supplementary Fig. S17.3a, b, respectively, and they indicate that high correlation is distributed only over a limited area in the equatorial oceans for precipitation, while a significant correlation for Z500 extends to midlatitudes. Therefore, we focused on the specific PJ pattern in 2012. Figure 17.2c shows simulated anomalies of OLR and Z500 in an ensemble mean of the ALL run as in Fig. 17.2a. The ensemble captured the observed PJ pattern responding to active convection in the tropics. We defined a PJ index as the difference of Z500 anomalies between 20°N, 150°E and 32°N, 163°E (plus and cross symbols, respectively, in Fig. 17.2a; the index is defined as the former minus the latter and a positive value corresponds stronger PASH). This index represents the 2012 extreme rainfall well because an observed water vapor supply regressed onto the PJ index shows an enhanced Baiu rainfall over southwestern Japan (Fig. 17.2b). The PJ index for 2012 is the third highest since 1979 (the time series can be found in Supplementary Fig. S17.3c), where the other two extreme cases (1995 and 2006) also involved disastrous heavy Baiu rainfall. The model ensemble average of this index from the ALL-LNG run is positively correlated with the index calculated from observations (r = 0.35, exceeding the 90% significant level), indicating that the "PJ pattern responds to the SST forcing to a degree through a convection change in the tropical western Pacific and that there is a potential to detect human influences through our method using the

AGCM. The smallness of the correlation suggests that atmospheric natural variability that is independent from the SST variability also plays an important role in this region. Because the top/bottom 15 cases of the ALL-LNG run diagnosed by the PJ index clearly show the CGT pattern, which intensifies/depresses the PJ pattern (the pattern for the top 15 cases can be seen in Supplementary Fig. S17.2b), our AGCM also has the ability to capture this stochastic atmospheric internal variability. The shape of the probability density function (PDF) of the PJ index based on the model (green curve in Fig. 17.2d) and that derived from observations (dashed curve) are sufficiently similar that we consider our model suitable for the purpose of this study.

**Results.** We showed the PDFs for the PJ index from ALL and NAT runs with red and blue curves, respectively, in Fig. 17.2d to evaluate the anthropogenic effect to an occurrence rate of the 2012 circulation pattern. The PDFs of both ALL and NAT runs are shifted to the positive PJ pattern relative to the climatological distribution (green). On the other hand, the difference between the ALL and NAT runs is subtle and an ensemble mean of the ALL run is slightly larger than that of the NAT run; it implies that human-induced El Niño-like SST change slightly intensifies the "PJ pattern through the enhanced PASH. These results suggest that our results support the indication of Kusunoki et al. (2006), but in the case of the 2012 event, the effect of anthropogenic global warming is too small to be detected compared with the variability due to the natural variability of the SST.

**Conclusions.** The extremeness of the Japanese heavy rainfall of 2012 was mainly caused by the oceanic natural variability and probabilistic atmospheric natural variability rather than by anthropogenic climate change.
18. LIMITED EVIDENCE OF ANTHROPOGENIC INFLUENCE
ON THE 2011–12 EXTREME RAINFALL OVER SOUTHEAST
AUSTRALIA

ANDREW D. KING, SOPHIE C. LEWIS, SARAH E. PERKINS, LISA V. ALEXANDER, MARKUS G. DONAT, DAVID J. KAROLY, AND MITCHELL T. BLACK

*Introduction.* The 2010–12 period was characterized by well-above-average rainfall over much of Australia. The wetter conditions brought an end to the 13-year drought in southeastern (SE) Australia, although they also led to severe flooding across large areas of New South Wales and Victoria in early 2012. Both total and extreme rainfall were above average in the austral warm seasons (October–March) of 2010–11 and 2011–12 across SE Australia [2011–12 spatial anomalies are shown in Figs. 18.1a and 18.1b for rain-

fall totals and maximum consecutive five-day rain-
fall (Rx5day; Zhang et al. 2011) respectively]. Many
stations across this region broke daily and multiday
rainfall records as well as monthly records during
February and March 2012 (Bureau of Meteorology
2012; Ganter and Tobin 2013).

ENSO is the primary driver of climate variability in eastern Australia (Nicholls et al. 1997) with greater rainfall totals during La Niña seasons than El Niño seasons in SE Australia (Fig. 18.1c). There is an asym-

<figure><img src="image_43.png" /></figure>

FIG. 18.1. Maps of Oct 2011–Mar 2012 (a) total rainfall and (b) Rx5day anomalies as a percentage of the Oct–Mar mean seasonal rainfall and Rx5day, respectively, for the period 1900–2012. The boxed region represents our area of study. Scatter plots of Oct–Mar Niño-3.4 SSTAs against area-averaged Oct–Mar (c) total rainfall and (d) Rx5day values for southeast Australia. Lines of best fit, calculated using ordinary least squares regression, are shown for warm SSTA (red) and cool SSTA (blue) seasons with corresponding slope values. The correlation (Spearman’s Rank, r) and the significance of the fit (p-value) are also shown. The recent 2010–11 and 2011–12 seasons are marked in green.
metry whereby negative SST anomalies have a greater effect than positive SST anomalies on rainfall totals. Similar asymmetric relationships were reported by Power et al. (2006) for the Australian continent as a whole and by Cai et al. (2010) for SE Queensland.

A strong asymmetric ENSO-extreme rainfall teleconnection also exists for the SE Australia region (Fig. 18.1d) for area-averaged values of Rx5day. King et al. (2013) observed an asymmetric ENSO-extreme rainfall relationship for eastern Australia. The 2010–11 and 2011–12 seasons are two of the four largest Rx5day totals in the 1900–2012 series. This begs the question whether anthropogenic effects on the climate have affected the ENSO-extreme rainfall relationship, thus altering the probability of extreme rainfall in this area.

*Data and methods.* Observed monthly total rainfall and Rx5day amounts were calculated from the Australian Water Availability Project (AWAP) gridded dataset of daily rainfall (Jones et al. 2009) interpolated onto a regular 0.5° grid. Anomalies from the 1900–2012 means of total rainfall and Rx5day were calculated for each grid box separately. SSTs in the Niño-3.4 region (5°N–5°S, 170°W–120°W) were used as an index for ENSO from the HadISST dataset (Rayner et al. 2003). Observed total and Rx5day anomalies were averaged across the SE Australia region as defined by the boxed area (25°S–40°S, 135°E–154°E) in Fig. 18.1a,b and plotted against Niño-3.4 SSTs (Fig. 18.1c,d). As Rx5day anomalies at individual gridboxes were calculated relative to the gridbox mean values, the area-average Rx5day values are not biased towards wetter areas of SE Australia. The boxed region includes Australia's largest cities (i.e., Sydney, Melbourne, and Brisbane), with more than three-quarters of the country's population, and the entire Murray-Darling Basin, an area accounting for over 40% of Australia's agricultural production in terms of gross value (Nicholls 2004).

To make inferences about potential anthropogenic impacts, historical runs from models in the Coupled Model Intercomparison Project phase 5 (CMIP5) archive (Taylor et al. 2012) were analyzed and compared with observations. Ten models were selected based on the availability of data in the Australian node of the CMIP5 data repository and on their ability to capture variability in ENSO (see Supplementary Table S18.1). As ENSO strongly drives Australian rainfall variability, models that do not capture ENSO variability cannot replicate the observed teleconnection. These ten models adequately capture the amplitude

of variability in Niño-3.4 region surface air temperatures seen in the observational HadCRUT4 dataset (Morice et al. 2012). The absolute SE Australia Rx5day amounts were taken from data downloaded from Environment Canada's CLIMDEX website (http://www.cccma.ec.gc.ca/data/climdex/; Sillmann et al. 2013). Rx5day anomalies were first calculated for each grid box in each model run and then averaged over the SE Australia domain. The anomalies were calculated in the same way as for the observations, so the area-averaged Rx5day anomaly values are not biased towards wetter areas. Sea surface temperature anomalies (SSTAs) in the Niño-3.4 region and Rx5day anomaly values for SE Australia were calculated for each individual model run and examined against each other per model. All models had greater Rx5day values in La Niña seasons than El Niño seasons. Models that did not have a stronger relationship between Niño-3.4 SSTAs and Rx5day when SSTAs are negative than when they are positive were removed leaving five models to be analyzed in more detail (models in bold in Supplementary Table S18.1). The area-averaged Rx5day anomaly values for each model were normalized against the model mean values over the historical period. Niño-3.4 SSTAs and normalized values of Rx5day could then be plotted together for the five models studied.

To examine for possible changes in extreme rainfall and in the ENSO-extreme rainfall teleconnection, the relationships in the modeled 1861–90 and 1976–2005 periods were compared. The earlier period (1861–90) was used to represent a time of much lower anthropogenic influence on the climate than the later period. Values of Rx5day in La Niña seasons only (defined as October–March seasons with SSTAs less than -0.5°C) were used to plot probability distribution functions (PDFs) in the two 30-year periods and these were examined for anthropogenic signals. Any low-frequency variability in the model runs would be unlikely to influence our results as multiple model runs were considered together. To examine the effect of ENSO variability, PDFs of La Niña Rx5day values and all other seasons' values through the entire historical period were also analyzed.

A Kolmogorov-Smirnov (KS) test was used to quantify differences between the respective sets of PDFs. Each PDF was bootstrapped 1000 times based on sub-samples of 50% of the smaller data sample being examined. Fractional Attributable Risk (FAR; Allen 2003) was calculated to examine change in risk of extreme rainfall events associated with anthropogenic influences and ENSO variability. FAR was
<figure><img src="image_44.png" /><figcaption>FIG. 18.2. (a) and (b) Scatter plots of Oct–Mar Niño-3.4 SSTAs against area-averaged Oct–Mar Rx5day values for southeast Australia for five CMIP5 models. Lines of best fit, calculated using ordinary least squares regression, are shown for warm SSTA (red) and cool SSTA (blue) seasons with corresponding slope values. The correlation (Spearman's Rank, r) and the significance of the fit (p-value) are also shown. These scatter plots are for the (a) 1861–90 and (b) 1976–2005 periods. PDFs of (c) La Niña-only normalized Rx5day values in the 1861–90 (black line) and 1976–2005 (red line) periods from the five CMIP5 models and of (d) La Niña-only (black line) and El Niño and Neutral (red line) normalized Rx5day values for the entire historical period from the five CMIP5 models.</figcaption></figure>

calculated using Rx5day values above 150% of the mean Rx5day over the entire period to compare the frequency of extreme events between the early and late periods, and La Niña and all other events. The 150% threshold permitted the inclusion of only extreme rainfall events and provided a reasonable sample size for the FAR calculation.

All analysis involving both observations and models was done over October–March periods, coinciding with the peak in the annual ENSO cycle and the warm season in SE Australia when more of the extreme rainfall events occur.

*Results.* The analysis was conducted using the sub-selection of five CMIP5 models (those in bold in Supplementary Table S18.1), as the relationship in La Niña seasons is reproduced to some degree. The ENSO-extreme rainfall relationship is plotted for all five models combined, for the 1861–90 and 1976–2005 periods (Figs. 18.2a,b). There is little obvious change in the relationship between the two periods. On average, Rx5day values are 1.9% greater in the latter period

than the earlier one. The increase is slightly weaker in La Niña seasons (1.3%) than El Niño (October–March with SSTAs greater than 0.5°C) seasons (3.4%).

The normalized Rx5day values in La Niña seasons only were selected to form PDFs for each of the 30-year periods (Fig. 18.2c). These PDFs are based on 124 samples for the 1861–90 period and a sample size of 119 for 1976–2005. There is some difference between the two PDFs in terms of their shape, but a KS-test suggests the two PDFs are not significantly different (at the 5% level). Examining values above a 150% threshold in the two periods gives a FAR value of +64% (with a standard deviation of ±22% calculated through bootstrapping) for extreme Rx5day values due to anthropogenic climate change in the recent period. It is worth noting that the FAR value is strongly sensitive to choice of threshold, selection of models, and region of study. Therefore, a robust anthropogenic influence cannot be detected.

Taking all normalized Rx5day values across the historical period in La Niña seasons and comparing with those values from neutral and El Niño seasons,
the PDFs shown in Fig. 18.2d were plotted. These PDFs are formed from 637 samples representing La Niña Rx5day values and 1523 samples representing El Niño and neutral Rx5day values. These PDFs are significantly different. FAR values show an increase in the likelihood of Rx5day values above a 150% threshold of 58% (with a standard deviation of ±15% across the 1000 estimates) in La Niña seasons compared with El Niño and neutral seasons combined. This FAR value is robust to the choice of threshold used.

*Conclusions*. We examined SE Australia extreme rainfall and its teleconnection with ENSO in observations and a selection of CMIP5 models. In observations, the magnitude of anomalously cool SSTs in the Niño-3.4 region has a far greater effect on Rx5day in SE Australia compared to the magnitude of anomalously warm SSTs. Five CMIP5 models were selected as the focus for our study as they possess aspects of ENSO variability and an asymmetric ENSO-extreme

rainfall relationship. Using these models, we found little evidence of significant change in the ENSO-extreme rainfall relationship between 1861–90 and 1976–2005. The PDFs of Rx5day values in La Niña seasons also show nonsignificant differences between the same periods. There is little robust change in the risk of extreme rainfall events between 1861–90 and 1976–2005. Interannual variability related to ENSO has played a greater role than any long-term trend on the magnitude of extreme rainfall events in southeast Australia over the period 1861–2005. In summary, we detect limited evidence of a change in the relationship between ENSO and SE Australia extreme rainfall, or of a change in extreme rainfall itself, that may be attributed to anthropogenic climate change. Similar analysis of the austral summer mean rainfall anomalies in 2010–11 and 2011–12 also show some influence from La Niña with no apparent influence from anthropogenic climate change in the observed rainfall anomalies.

# 19. AN ATTRIBUTION STUDY OF THE HEAVY RAINFALL OVER EASTERN AUSTRALIA IN MARCH 2012

NIKOLAOS CHRISTIDIS, PETER A. STOTT, DAVID J. KAROLY, AND ANDREW CIAVARELLA

*Introduction*. Heavy rains at the end of summer 2012 across eastern Australia (Bureau of Meteorology 2012) led to swollen rivers and widespread flooding that swamped agricultural land, caused loss of life, and forced tens of thousands of people to evacuate their homes. The event came only a year after the catastrophic floods in Queensland, when the largest part of the north-eastern state was declared a disaster zone. In the aftermath of two major floods in consecutive years, the question arises whether the odds of heavy rain in eastern Australia are set to increase under the synergy between internal climate variability and externally forced climate change. Here we investigate the possible contributions of the ENSO and the long-term effect of human influences on the climate to the heavy rainfall in March 2012 over eastern Australia (10°S–45°S, 140°E–160°E). The anthropogenic contribution is estimated with a new state-of-the-art system for Attribution of extreme weather and Climate Events (ACE; Christidis et al. 2013), built on HadGEM3-A, the latest atmospheric model from the Hadley Centre. We concentrate on March, as the main flooding occurred in the beginning of that month and the total rainfall in the region was exceptionally high

in comparison with the summer months (Fig. 19.1a). In fact, March 2012 ranks as the third wettest after 2011 and 1956 in the Global Historical Climatology Network (GHCN) dataset used to provide rainfall observations for this study (Peterson and Vose 1997). Although the heavy rainfall event examined here is not unprecedented, the adverse impacts associated with two consecutive wet years in the region make it an interesting case to study.

*The influence of ENSO*. A correlation between the phase of ENSO and rainfall in Australia has long been identified (McBride and Nicholls 1983). La Niña episodes, characterized by warm SST anomalies over the West Pacific are associated with wetter-than-normal conditions over eastern Australia. While La Niña conditions prevailed at the end of 2011 and beginning of 2012, they had considerably weakened by March, and ENSO changed phase in April. Figure 19.1b illustrates the relationship between March precipitation in the region and the Southern Oscillation Index (SOI) from summer to early autumn. An increase in rainfall with the SOI is evident, and the slope of the least-square fit is found to be significantly different
<figure><img src="image_45.png" /></figure>

**FIG. 19.1.** GHCN observations of rainfall anomalies relative to 1961–1990 over eastern Australia. (a) 1900–2012 Mar rainfall time series; (b) the relationship between Mar rainfall and the Dec–Mar mean value of the Southern Oscillation Index (from http://www.bom.gov.au/climate/current/soi2.shtml); (c) the relationship between Dec–Mar rainfall and spring north Australian SST anomalies (from HadISST) for years with 7< SOI <12 (marked by the vertical dotted lines in panel b). The year 2012 is highlighted in red. The p-values (panels b and c) refer to testing of the hypothesis that the least square fit (plotted in black) has a zero trend.

Australia by 0.1 K decade⁻¹ over the last 50 years, consistent with the multidecadal anthropogenic global ocean warming (Glecker et al. 2012; Pierce et al. 2006). While the SST warming is indicative of more wet summers over eastern Australia, which could also be exacerbated by La Niña conditions, it is important to assess whether the occurrence of extreme rainfall also becomes more frequent. So far, we have only assumed that long-term externally forced climate change might have contributed to the heavy rainfall in 2012 through the warming of the ocean. We next attempt to investigate the overall contribution of human influences in more detail and examine how they may have changed the odds of above average and extreme rainfall in March 2012.

from zero. While the La Niña episode early in the year is likely to increase the chances of a wet March, the extremely high rainfall amount (marked with a red cross in Fig. 19.1b) is unlikely to be attributable solely to ENSO.

**SST warming to the north of Australia.** Australian rainfall is also influenced by the SSTs to the north of the country, and it is found that warm anomalies during spring are associated with heavy summer rainfall across eastern Australia (Nicholls 1984; Evans and Boyer-Souchet 2012). Figure 19.1c depicts the relationship between summer-to-early-autumn rainfall and the spring SSTs around northern Australia (0°–22°S, 94°E–174°E) taken from the HadISST dataset (Rayner et al. 2003). Both external forcings and variability may contribute to warm SSTs. In order to minimize the effect of variability due to ENSO (Catto et al. 2012) we examine only years with SOI between 7 and 12 (marked by the vertical dotted lines in Fig. 19.1b), i.e., similar conditions to the event we consider here. We find a significant increase in rainfall with SST, which could explain wetter conditions in December–March 2012 given the warm ocean temperature. The observations suggest an increase in the SSTs north of

**Anthropogenic influence.** The new Hadley Centre ACE system has already been employed to study several high-impact extreme events in recent years (Christidis and Stott 2012; Christidis et al. 2013; Lott et al. 2013). We carried out a new experiment that comprises ensembles of 600 simulations with HadGEM3-A over the period September 2011–August 2012. We have one ensemble that represents the actual climate where the effect of all external forcings is accounted for and two ensembles that provide two possible representations of a hypothetical “natural” climate without the effect of human influences. In simulations of the natural climate, an estimate of the anthropogenic change in the SSTs has been subtracted from the prescribed observations and the sea ice has been adjusted accordingly. The estimate of the change in the SST comes from two Earth System models, HadGEM2-ES (Jones et al. 2011) and CanESM2 (Gillett et al. 2012), hence, we have two different representations of the natural climate without human influences. Details on the ACE experimental setup are given in Christidis et al. (2013). We assessed the model skill in reproducing high rainfall during March over eastern Australia on the basis of reliability diagrams, as in previous work. Using GHCN observations and a five-member
ensemble of multidecadal simulations of the actual climate, we find that the forecast probability is consistent with the observed frequency of heavy rainfall in the reference region, which suggests the model can represent well drivers of high rainfall events and is therefore a reliable tool for our analysis.

Figure 19.2 illustrates the regional rainfall distributions for March 2012 with and without anthropogenic effects as well as the change in the odds of above average, heavy, and extreme precipitation (defined in the figure caption) due to human influences. Note that the observed rainfall in March 2012 was three standard deviations above the climatological average, i.e., well above the extreme threshold. Our results (Figs. 19.2a and 19.2c) indicate a

small but statistically significant (as inferred from a Kolmogorov-Smirnov test) shift in the rainfall distribution towards wetter conditions under the effect of anthropogenic forcings. For the calculation of the change in the odds, the probabilities of exceeding a threshold are computed using the generalized Pareto distribution if the threshold lies at the tails, and a Monte Carlo bootstrap procedure is employed to estimate the sampling uncertainty (Christidis et al. 2013). Figures 19.2b and 19.2d show that the uncertainty in our estimate of the change in the odds increases as the threshold becomes more extreme, which implies that larger samples would be required to estimate small probabilities with greater precision. Our analysis provides evidence for increasing odds of above average rainfall relative to the natural climate. Therefore, although there is no evident trend in rainfall observations (Fig. 19.1a), once we constrain the SSTs and thus minimize the effect of natural variability, we begin to see an emerging signal of wetter months under the influence of anthropogenic forcings, as also indicated in Fig. 19.1c. The odds of heavy and extreme precipitation also seem to increase, as the largest part

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /><figcaption>Fig. 19.2. Normalized distributions of the rainfall over eastern Australia in Mar 2012 (panels a and c) constructed with the ACE ensembles with (red histograms) and without (blue histograms) the effect of human influences. Simulations of the natural climate use estimates of the change in the SST and sea ice due to anthropogenic forcings from HadGEM2-ES and CanESM2 (blue histograms in panels a and c respectively). The normalized distributions of the change in the odds of above average, heavy, and extreme rainfall in the region in Mar 2012 due to anthropogenic forcings are shown in panels (b) and (d) for the two versions of the natural climate. The thresholds for average, heavy, and extreme rainfall correspond to the 1960–2010 climatological mean and the rainfall levels of one and two standard deviations above the mean. The best estimate of the change in the odds, approximated by the median of the distribution, is also noted in panels (b) and (d).</figcaption></figure>

of the corresponding distributions in Figs. 19.2b and 19.2d lies over positive values, but the uncertainty in these cases is much larger. The results for these higher thresholds are also more sensitive to the SSTs prescribed in the simulations of the natural climate with the CanESM2 version providing a much stronger indication of an increase in heavy and extreme rainfall. It should be noted that the anthropogenic warming in north Australian SSTs computed with CanESM2 is 0.54 K higher than the HadGEM2-ES estimate, indicative of a greater separation between the rainfall distributions with and without the effect of human influences.

**Conclusions.** The La Niña episode in early 2012 is unlikely to entirely explain why March 2012 was the third wettest in the observational record in eastern Australia. Warm north Australian SSTs, however, are expected to result in wetter conditions and given the continuing warming trend of the ocean, increased rainfall over the eastern part of the country could become more common. Using the ACE methodology we find that the overall long-term effect of human
influences on the climate increases the chances of
above-average rainfall by 5%–15% (best estimate),
although the impact on extreme precipitation is much
more uncertain. Even when all climatic forcings are
accounted for, the observed rainfall in March 2012

lies in the far tail of the expected rainfall distribu-
tion from the ACE ensembles. Therefore, despite the
potential contribution of all factors examined here,
the extreme magnitude of the event appears to arise
mainly from unforced internal climatic variations.

20. THE ROLE OF CLIMATE CHANGE IN THE TWO-DAY
EXTREME RAINFALL IN GOLDEN BAY, NEW ZEALAND,
DECEMBER 2011

SAM M. DEAN, SUZANNE ROSIER, TREVOR CAREY-SMITH, AND PETER A. STOTT

Introduction. In December 2011, a torrent of water fell from the sky in Golden Bay and Nelson, overwhelming local streams and inundating the landscape with mud and debris. Barren scars of earth littered the hillsides of the region, a testimony to the power of the event (see Supplementary figures). The culprit was a low-pressure weather system that transported moist air directly from the subtropics to New Zealand (Fig. 20.1a and Supplementary animation). When this “atmospheric river” collided with the coastal hills of Golden Bay and Nelson, the rainfall amounts were staggering—in the township of Takaka 453 mm was recorded in just 24 hours and 674 mm in 48 hours. This greatly exceeds any previous record at this site, and from the available observations, it has been estimated that the 48-hour total is a one in 500-year event. The event was also the largest 48-hour accumulation ever recorded in an urban area in New Zealand.

Strikingly, the rain fell
predominantly on coastal
hills within 2 km–5 km of
the coast (all <500 m eleva-
tion), such that none of the
major rivers with head-
waters in the mountains
reached extreme flood lev-

els. Such high rainfall in coastal areas is damaging,
predominantly because of higher population density.
The event was unusual due to its rare combination of
high humidity in the lower atmosphere accompanied
by only moderate winds (Doyle and Harvey 2012).
A stationary high east of New Zealand also played
a key role in slowing the progress of the deep low.

<figure><img src="image_50.png" /><figcaption>FIG. 20.1. Observations of the Dec 2011 Golden Bay and Nelson extreme rainfall event. (a) Vertical integral of the specific humidity (kg m⁻²) for 14 Dec 2011 from the ERA-Interim reanalysis. The location of the Takaka rain gauge is indicated by the red cross, while the dashed lines indicate the box over which the large-scale fields were averaged. (b) Vertical integral of the specific humidity (kg m⁻²) from ERA-Interim averaged for the box in (a) plotted against all daily rainfall amounts (09:00 to 09:00 NZST) measured at Takaka that exceeded 130 mm for the period 1979–2012. Colors indicate the orientation of the vertical integral of the moisture flux. Note that the Dec 2011 event lies in the top right of the figure.</figcaption></figure>
**Was this event influenced by climate change?** Pall et al. (2011) have recently demonstrated how the risk of a monthly scale flooding event in England and Wales changed due to the emission of anthropogenic greenhouse gases (GHGs). However, it is difficult to apply their techniques to the short duration rainfall events that are typical of flooding in New Zealand, largely because of the higher spatial and temporal variability involved.

Here it is suggested that progress might be made on this question by using a methodology that is capable of identifying all occurrences of the synoptic situation matching the Golden Bay/Nelson event within large ensembles of climate-model simulations that alternatively exclude and include the impact of GHGs. If the observed precipitation distribution for these events is well simulated by the model for the past climate, then any change in the rainfall distribution due to GHGs can be considered.

A formal attribution based on precipitation requires a large ensemble from a regional climate model capable of resolving the fine structure of atmospheric rivers, and this is an objective of the Australia/New Zealand “weatherathome” experiment (http://www.weatherathome.net). As a first step, this paper tests the proposed methodology in a simplified manner by considering the daily data available from the Coupled Model Intercomparison Project 5 (CMIP5) archive of global climate models (Taylor et al. 2012). Our analysis relies on the relationship between rainfall extremes at midlatitudes and the large-scale moisture fluxes associated with atmospheric rivers (Junker et al. 2008; Ralph and Dettinger 2012). Here we derive the relationship between the large-scale circulation and Golden Bay extreme rainfall events using station observations and moisture fluxes from the ERA-Interim reanalyses (Dee et al. 2011). We then apply these constraints to the CMIP5 models and consider the impact on the humidity available for precipitation.

**Observations of rainfall and moisture.** In Fig. 20.1b, all observations are 09:00 New Zealand Standard Time (NZST) 24-hour rainfall accumulations, which have exceeded 130 mm in Takaka (1979–2012). This corresponds to all observations at or above the 99.8th percentile of the rainfall distribution, an arbitrary threshold for an extreme. For each of these 26 events, the vertical integral of the meridional and zonal moisture fluxes from ERA-Interim were used to calculate an orientation and magnitude for the integrated moisture flux averaged over the dashed box shown in Fig. 20.1a. The direction of origin for the moisture

fluxes (Fig. 20.1b, colors) lies in a narrow range of angles between north and northwest. This is a direct consequence of the topography that surrounds this location.

The vertical integral of specific humidity derived from ERA-Interim, and averaged over the same area, is plotted against the extreme precipitation amounts in Fig. 20.1b. Rainfall extremes are hypothesized to increase with the availability of moisture (Allen and Ingram 2002; Trenberth et al. 2003), and the moderate positive correlation in this very small sample of observations suggests some support for this conclusion. The observations also suggest that high moisture flux magnitudes are a prerequisite for extreme precipitation (not shown), but there is no similar linear relationship between moisture fluxes and extreme precipitation (not shown).

Altogether, this analysis suggests that an angle constraint on moisture flux direction, combined with a minimum magnitude of moisture flux and specific humidity, may be used to define large-scale synoptic conditions that are a prerequisite for rainfall extremes at this location. Thresholds used for the integrated specific humidity and moisture flux are the minimum values observed for Takaka extreme events, 23 kg m⁻² and 400 kg m⁻¹ s⁻¹ respectively. However, these basic criteria overpredict extreme precipitation: for 1979–2012 ERA-Interim has 315 events that match the criteria while only 26 extreme events (>130 mm day⁻¹) are observed at Takaka.

**Is there evidence from global climate models that the total moisture available for this event has changed as a result of GHG emissions?** Daily pressure, humidity, and wind fields were acquired from the CMIP5 archive for climate models providing historical simulations (1950–2005) that included all natural and anthropogenic forcings (ALL) as well as matching simulations that included only natural forcings (NAT). The only four climate models that met these criteria were BNU-ESM, GFDL-ESM2M, IPSL-CM5A-LR, and IPSL-CM5A-MR (see PCMDI 2013 and links therein for model descriptions). From these, the vertical integrals of moisture flux and specific humidity were calculated for the dashed box in Fig. 20.1a. Potential extreme rainfall events were identified using the constraints for moisture flux and humidity described previously. The number of events that match these synoptic criteria for a Takaka rainfall extreme in the ALL simulations range from 469 to 738, which can be compared to an estimate of 525 from ERA-Interim for a period of 55 years. This suggests the models do a
reasonable job of reproducing the frequency of these synoptic conditions. All four models show an increase in the number of events for the ALL simulations compared to the NAT, with a range of 8% to 32%. In all cases, this is dominated by an increase in the number of events that exceed the moisture thresholds, rather than because of changes in the occurrence of synoptic lows. This gives confidence that these increases in the ALL simulations are a thermodynamic response to the emission of GHGs.

Figure 20.2 shows the percentage change in the vertically integrated specific humidity for these events for ALL relative to NAT. The plot is created by calculating the quantiles for the smaller number of events in the NAT simulations (a percentile for every value) and then interpolating the events in the ALL simulations onto these percentiles. While all four model distributions are noisy at this scale, there is a consistent trend for the models to increase the specific humidity, particularly in the high tail of the distribution. To calculate the change at the actual value of the Takaka event, we reduce this noise by averaging over the 16 quantile values centered on 45 kg m⁻². All four models increase the integrated specific humidity in

the ALL simulations relative to the NAT, with a range of 1.3%–5.4% and an average of 3.6%.

It is hypothesized that rainfall extremes should increase with global warming, primarily as the availability of moisture in the atmosphere increases by about 7% per degree of warming following the Clausius-Clapeyron constraint. For this particular event, the integrated specific humidity increases from NAT to ALL by an average 6% per degree of warming (range of 2%–10%), where the change in temperature is calculated over a region covering both Golden Bay and the moisture source region for the event. For midlatitudes, the increase in precipitation expected to have occurred may be somewhat more or less than these changes in moisture due to the way vertical winds and the vertical temperature structure change locally (O’Gorman and Schneider 2009; Pall et al. 2007).

*Conclusions*. This analysis indicates that the total moisture available for precipitation in the Golden Bay/Nelson extreme rainfall event of December 2011 was 1%–5% higher as a result of the emission of anthropogenic greenhouse gases. Theory and observations imply that this should have led to increased precipitation for this location.

This analysis also found that the number of synoptic events with enough moisture to cause extreme rainfall in Takaka has increased, which indicates there may have been an elevated risk of this type of event. In this study, no attempt has been made to adequately assess the uncertainty on these results. A much larger ensemble of a higher resolution model, such as in the “weathera-thome” experiment, is required for formal attribution statements about precipitation extremes.

<figure><img src="image_51.png" /></figure>

FIG. 20.2. Percentage change in the vertical integral of specific humidity for an ALL simulation relative to a NAT simulation for each of four CMIP5 models over the period 1950–2005. This is shown plotted against the values of the vertical integral of specific humidity for the ALL simulations, such that the value for the Golden Bay/Nelson event (45 kg m⁻²) calculated from ERA-Interim can be shown by the dashed line. The colored dots are the respective values for the change in vertical integral of specific humidity when 16 quantile values are averaged about the value of the Golden Bay/ Nelson event. In the case of IPSL-CM5A-LR, there are only two points above 45 kg m⁻² available for the calculation.
21. CONCLUSIONS AND EPILOGUE

THOMAS C. PETERSON, PETER A. STOTT, STEPHANIE C. HERRING, AND MARTIN P. HOERLING

To help understand the difficulty of determining the anthropogenic contribution to specific extreme events, consider this driving analogy (UCAR 2012). “Adding just a little bit of speed to your highway commute each month can substantially raise the odds that you’ll get hurt some day. But if an accident does occur, the primary cause may not be your speed itself: it could be a wet road or a texting driver.” Similarly, while climate models may indicate a human effect is causing increases in the chances of having extremely high precipitation in a region (much like speeding increases the chances of having an accident), natural variability can still be the primary factor in any individual extreme event. The difficulty in determining the precise sensitivity of, according to our analogy, driving speed on risks of accidents in particular conditions (wet roads, texting drivers) can explain why somewhat different analyses of the same meteorological event can reach somewhat different conclusions about the extent to which human influence has altered the likelihood and magnitude of the event.

For example, one assessment of seasonal changes in heavy five-day precipitation events in Australia finds little modeled evidence of long-term changes in such events once ENSO is factored in, while another analysis that examined the human influence on total monthly precipitation for March in roughly the same area found that it increased the probability of above average rainfall by 5%–15% (“Limited Evidence of Anthropogenic Influence on the 2011–12 Extreme Rainfall over Southeast Australia” and “An Attribution Study of the Heavy Rainfall over Eastern Australia in March 2012” in this report). These differences arose despite the two analyses having an author in common. However, the bottom line of the event attribution was basically the same: that the heavy rain last year in eastern Australia was predominately due to natural variability. But for rare events, the results of these analyses of Australian rainfall illustrate how attribution studies that examine the effects of human influences on an extreme event can be dependent on both the methodology used in the assessment as well as the choice of metrics.

Among all the extreme events that occurred around the world in 2012, the subset of events analyzed in this paper was not chosen at random. While “The Extreme March–May 2012 Warm Anomaly over

the Eastern United States: Global Context and Multi-model Trend Analysis” (in this report) used objective criteria to search the globe to determine the event to analyze, other analysis topics were selected based on a variety of factors similar to what drives a great deal of scientific research, including the researchers’ interests and subjective estimation of what events present tractable problems. It is likely no accident that the majority of the submissions focus on events related to temperature and precipitation, given that these are two variables where the observational record is strong. We also see a natural bias towards addressing local events as scientists in Australia, China, France, Japan, the Netherlands, New Zealand, Spain, the United Kingdom, and the United States examined extreme events impacting themselves, their friends, and their neighbors. A few groups examined events in distant locations, including Arctic sea ice and drought in the Horn of Africa, which they deemed important. It is also noteworthy that only one group examined a cold event, which global warming might theoretically be expected to decrease in frequency or intensity. So, there is a danger in drawing too strong a conclusion from a small sample of 19 analyses of 12 events that were not chosen at random. That said, approximately half of the analyses found some evidence that anthropogenic climate change was a contributing factor to the extreme event examined, though the effects of natural fluctuations of weather and climate on the evolution of many of the extreme events played key roles as well.

In this second issue of the annual *BAMS* attribution report, an important innovation is that we have now included multiple analyses of a single event. The differences in the results also provide insights into the structural uncertainty of event attribution, that is, the uncertainty that arises directly from the differences in analysis methodology. For example, there are four different studies of the wet summer in northern Europe using different methodological approaches, observational datasets, and climate models. While these studies provide complementary perspectives into the possible roles of North Atlantic sea surface temperatures, Arctic sea ice reduction, atmospheric circulation, and an enhanced moisture carrying capacity of the atmosphere, these studies also highlight certain deficiencies of current models, observations, and methods. As a result, the attribution conclusions
for such relatively complex events remain somewhat equivocal. The results in general contained in this special issue are not necessarily the final word nor the definitive treatments on these cases. Other studies, using different and perhaps improved tools (e.g. new models) will undoubtedly come forth and will further test the efficacy of surmised and plausible causal factors. In the manner that has become common in our field to "re-analyze" historical data as new data, analysis methods and models come forward, so too we foresee a rich science enterprise of "re-attribution". A priority for further research is to develop modeling systems that have been shown to be sufficiently reliable with respect to the specific type of extreme being assessed to generate less equivocal and more robust results for such complex events.

With the increasing sophistication of event attribution studies comes a greater focus on assessing the capability of the various current approaches to provide robust answers to such difficult questions as whether there is a link between anthropogenic climate change and the extremely wet summer in northern Europe in 2012, which was not only an extreme event in itself but also the sixth summer in a row in which UK rainfall was higher than the 1981-2010 average.

Where does the work on event attribution go from here? At the September 2012 workshop on the Attribution of Climate and Weather Extremes: Assessing, Anticipating and Communicating Climate Risks, which was held at Oxford University, it became clear that there was a broad range of stakeholders interested in the results of this science. For example, the insurance and legal sectors are keenly interested in understanding how the risks associated with extreme events are changing (Stott and Walton 2013, manuscript submitted to *Weather*). Climate forecasting services are likewise keenly interested in knowing the causes of extreme events. The process

of clarifying the proximate and underlying causes is seen as key to providing a narrative of their origins, which advances predictive understanding. It is here where the dual challenges of improved prediction and improved explanation of causes intertwine.

This information is critical to preparing for future events, and the need to make this information accessible, timely, and relevant for the user is broadly recognized. For example, one of the key drivers of the Global Framework for Climate Services (GFCS) is to mainstream the provision of “information that governments, organizations, and individuals can use to manage climate risks and opportunities” (WMO 2012). One-fourth of the World Meteorological Organization (WMO) Commission for Climatology is dedicated to improving climate information for adaptation and risk management (WMO 2013). And, this is in addition to action being taken at the national and local scales by various governmental and nongovernmental groups. Extreme events are, of course, a key source of climate risk (Schiermeier 2011).

As the science underpinning the attribution of extreme events matures, it will nurture and make possible the creation of operational climate attribution systems (Stott et al. 2013). Then, as attribution of extreme events become part of routine climate services and part of the GFCS, scientists will need to continue outreach into our stakeholder communities. Climate risk reduction is a key driver of many climate adaptation activities (van Aalst et al. 2008). Therefore, many communities will need to better understand exactly what attribution of extreme event science can and cannot say. To return to the opening analogy, this means answering the question of how the change in the driver's speed was responsible for changing the odds of colliding with a texting driver on a wet road, which would be the extreme event we are trying to attribute.
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