|By Herring, Stephanie, EDITOR|
Using a variety of methodologies, six extreme events of the previous year are explained from a climate perspective.
E very year, the Bulletin of the AMS publishes an annual report on the State of the Climate [e.g., see the Blunden and Arndt (2012) supplement to this issue]. That report does an excellent job of documenting global weather and climate conditions of the previous year and putting them into accurate historical perspective. But it does not address the causes. One of the reasons is that the scientists working at understanding the causes of various extreme events are generally not the same scientists analyzing the magnitude of the events and writing the State of the Climate. Another reason is that explaining the causes of specific extreme events in near-real time is severely stretching the current state of the science.
Our report is a way to foster the growth of this science. Other reports, such as those by the
This first edition of what is intended to be an annual report starts out with an assessment on causes of historical changes in temperature and precipitation extremes worldwide to provide a long-term perspective for the events discussed in 2011. That section also considers the use of the term "extreme" in climate science so as to provide a context for the extreme events discussed in the rest of the report. The report then goes on to examine only six extreme events assessed by teams of experts from around the world. We are not attempting to be comprehensive nor does our selection of extreme events reflect any judgment about the importance of the events discussed here relative to the many other extreme events around the world in 2011.
By choosing a few noteworthy events to analyze there could be a risk of selection bias if the events chosen are thought of as representative of the weather observed in 2011, which they are not. However, our purpose here is to provide some illustrations of a range of possible methodological approaches rather than to be comprehensive. We hope that the examples we have chosen will serve to stimulate the development of attribution science and lead to submissions that, in future years, look at different regions and a wider range of extreme events. Developing objective criteria for defining extreme weather and climate events ahead of time, and applying predetermined methodologies, should minimize the risk of bias resulting from selective choice of criteria based on what actually occurred (e.g., Stott et al. 2004).
Currently, attribution of single extreme events to anthropogenic climate change remains challenging (Seneviratne et al. 2012). In the past it was often stated that it simply was not possible to make an attribution statement about an individual weather or climate event. However, scientific thinking on this issue has moved on and now it is widely accepted that attribution statements about individual weather or climate events are possible, provided proper account is taken of the probabilistic nature of attribution (
One analogy of the effects of climate change on extreme weather is with a baseball player (or to choose another sport, a cricketer) who starts taking steroids and afterwards hits on average 20% more home runs (or sixes) in a season than he did before (Meehl 2012). For any one of his home runs (sixes) during the years the player was taking steroids, you would not know for sure whether it was caused by steroids or not. But you might be able to attribute his increased number to the steroids. And given that steroids have resulted in a 20% increased chance that any particular swing of the player's bat results in a home run (or a six), you would be able to make an attribution statement that, all other things being equal, steroid use had increased the probability of that particular occurrence by 20%. The job of the attribution assessment is to distinguish the effects of anthropogenic climate change or some other external factor (steroids in the sporting analogy) from natural variability (e.g., in the baseball analogy, the player's natural ability to hit home runs or the configuration of a particular stadium).
There have been relatively few studies published in the literature that attempt to explain specific extreme events from a climate perspective and this report covers some of the main methodological approaches that have been published to date. A position paper produced for the World Climate Research Program (Stott et al. 2012) reviewed some of these studies including attribution assessments of the 2000
This report also considers other approaches distinct from those that seek to apportion changed odds. Analyzing how temperatures within particular flow patterns have changed helps to illustrate how longterm climate change is altering the typical weather associated with a particular flow regime. Such a regime-based approach (Cattiaux et al. 2010a) has shown how the cold northwestern European winter of 2009/10, associated largely with a very negative North Atlantic Oscillation (NAO), would have been even colder were it not for a long-term warming associated with ongoing climate change. Other related approaches involve using statistical models or climate models to tease apart the effects of climate variability and long-term warming on the observed occurrence of particular extreme weather events. By not quantifying the link to human emissions, such analyzes do not fully answer the attribution question, but they do help to put extreme events into a climate perspective.
While the report includes three examples of the odds-based attribution analyzes discussed earlier, the challenges of running models and analyzing data in time for this report have meant that only the final analysis (of the cold
One important aspect we hope to help promote through these reports is a focus on the questions being asked in attribution studies. Often there is a perception that some scientists have concluded that a particular weather or climate event was due to climate change whereas other scientists disagree. This can, at times, be due to confusion over exactly what is being attributed. For example, whereas Dole et al. (2011) reported that the 2010 Russian heatwave was largely natural in origin, Rahmstorf and Coumou (2011) concluded it was largely anthropogenic. In fact, the different conclusions largely reflect the different questions being asked, the focus on the magnitude of the heatwave by Dole et al. (2011) and on its probability by Rahmstorf and Coumou (2011), as has been demonstrated by Otto et al. (2012). This can be particularly confusing when communicated to the public.
We hope that this new venture will help develop the means of communicating assessments of the extent to which natural and anthropogenic factors contribute to the extreme weather or climate events of a particular year. As such we seek your reactions to this report, which will be invaluable in determining how we should continue in future years. It will also help inform the dialog about how best to enable a wider public to appreciate the links between the weather they are experiencing and the effects of longterm climate change.
HISTORI CAL CONT EXT
Francis W. Zwiers-Pacific Climate Impact s Consortium,
T he occurrence of high-impact extreme weather and climate variations invariably leads to questions about whether the frequency or intensity of such events have changed, and whether human influence on the climate system has played a role. Research on these questions has intensified in recent years, culminating in two recent assessments (Karl et al. 2008; Field et al. 2012), and in proposals to formalize "event attribution" as a global climate service activity (Stott et al. 2012). In order to provide historical context for later sections, this section discusses the extent to which human influence has caused long-term changes in the frequency and intensity of some types of extremes.
The nature of extreme events. The term "extreme" is used in a number of contexts in climate science. It refers to events that may in fact not be all that extreme, such as the occurrence of a daily maximum temperature that exceeds the 90th percentile of daily variability as estimated from a climatological base period, or it may refer to rare events that lie in the far tails of the distribution of the phenomenon of interest. A characteristic of extremes is that they are understood within a context-and thus seasonal or annual means may be "extreme" just as an unusual short-term event, such as a daily precipitation accumulation, may be extreme. Certain phenomena, such as tropical cyclones that have been classified on the Saffir-
Challenges in detection and attribution of extremes. The discussion in this section reflects the fact that most detection and attribution research on long-term changes in the probability and frequency of extremes thus far has focused on short duration events that can be monitored using long records of local daily temperature and precipitation observations. These changes are generally captured as indices that document the frequency or intensity of extremes in the observed record rather than focusing on individual rare events. In contrast, many of the events considered in later sections of this report are individual events, often of longer duration than the extremes considered here, and are also usually events with longer return periods. Nevertheless, the finding that human influence is detectable in some types of short duration events that can be conveniently monitored from meteorological observations provides important context for the interpretation of other types of events. For example, feedbacks and physical processes that influence individual large events (
While not discussed in this section, the detection and attribution of changes in the mean state of the climate system often also provides important context for the understanding of individual extreme events. An example is the European 2003 heat wave, which can be characterized both by very extreme warm daily maximum and minimum temperatures, and by an extremely warm summer season. The demonstration that human factors had influenced the climate of southern
The frequency and intensity of extremes can be affected by both the internal variability of the climate system and external forcing, and the mechanisms involved can be both direct (e.g., via a change in the local energy balance) and indirect (e.g., via circulation changes). This makes the attribution of events to causes very challenging, since extreme events in any location are rare by definition. However, global-scale data make it possible to determine whether broadly observed changes in the frequency and intensity of extremes are consistent with changes expected from human influences, and inconsistent with other possibilities such as climate variability. Results from such detection and attribution studies provide the scientific underpinning of work determining changes in the likelihood of individual events.
Observed changes in extremes. We briefly consider historical changes in frequency and intensity of daily temperature and precipitation extremes. There is a sizable literature on such events, in part because reliable long-term monitoring data are gathered operationally by meteorological services in many countries. Many other areas remain understudied, such as whether there have been changes in the complex combinations of factors that trigger impacts in humans and ecosystems (e.g., Hegerl et al. 2011), or areas that are subject to greater observational and/or process knowledge uncertainty, such as the monitoring and understanding of changes in tropical cyclone frequency and intensity (e.g.,
Changes in extreme temperature and the intensification of extreme precipitation events are expected consequences of a warming climate. A warmer climate would be expected to have more intense warm temperature extremes, including longer and more intense heat waves and more frequent record-breaking high temperatures than expected without warming. It would also be expected to show less intense cold temperature extremes and fewer record-breaking low temperatures than expected before. Both of these expected changes in the occurrence of record-breaking temperatures have indeed been observed (e.g., Alexander et al. 2006; Meehl et al. 2009). Further, a warmer atmosphere can, and does, contain more water vapor, as has been observed and attributed to human influence (Santer et al. 2007; Willett et al. 2007; Arndt et al. 2010). This implies that more moisture is available to form precipitation in extreme events and to provide additional energy to further intensify such events. About two-thirds of locations globally with long, climate-quality instrumental records [e.g., as compiled in the Hadley Centre Global Climate Extremes dataset (HadEX); Alexander et al. 2006] show intensification of extremes in the far tails of the precipitation distribution during the latter half of the twentieth century (Min et al. 2011).
Detection and attribution of changes in intensity and frequency of extremes. A number of studies (e.g., Christidis et al. 2005, 2010; Zwiers et al. 2011; Morak et al. 2011, 2012) have now used various types of detection and attribution methods to determine whether the changes in temperature extremes predicted by climate models in response to historical greenhouse gas increases and other forcings are detectable in observations. The accumulating body of evidence on the human contribution to changes in temperature extremes is robust, and leads to the assessment that "it is likely that anthropogenic influences have led to warming of extreme daily minimum and maximum temperatures on the global scale" (Seneviratne et al. 2012). Results tend to show that the climate models used in studies simulate somewhat more warming in daytime maximum temperature extremes than observed, while underestimating the observed warming in cold extremes in many locations on the globe. It remains to be determined if this model-data difference occurs consistently across all models, or whether it is specific to the small set of phase 3 of the
Heavy and extreme precipitation events have also received a considerable amount of study. Heavy precipitation has been found to contribute an increasing fraction of total precipitation over many of the regions for which good instrumental records are available (Groisman et al. 2005; Alexander et al. 2006; Karl and Knight 1998; Kunkel et al. 2007; Peterson et al. 2008; Gleason et al. 2008), indicating an intensification of precipitation extremes. Direct examination of precipitation extremes, such as the largest annual 1-day accumulation, or the largest annual 5-day accumulation, also shows that extreme precipitation has been intensifying over large parts of the global landmass for which suitable records are available (Alexander et al. 2006; Min et al. 2011; Figs. 1 and 2), with an increase in the likelihood of a typical 2-yr event of about 7% over the 49-yr period from 1951 to 1999 (Min et al. 2011). It should be noted, however, that the spatial extent of regions for which long records of daily and pentadal precipitation accumulations are available is still severely limited (e.g., Alexander et al. 2006; see also Fig. 1), and that spatial patterns of change are still noisy.
The intensification of extreme precipitation is an expected consequence of human influence on the climate system (e.g., Allen and Ingram 2002; Trenberth et al. 2003) and is simulated by models over the latter half of the twentieth century in response to anthropogenic forcing, albeit with weaker amplitude than observed, which is at least partly due to differences in the spatial scales resolved by climate models and station-based local records (Chen and
Few detection and attribution studies that include observations of temperature or precipitation extremes in the first decade of the twenty-first century have yet been performed (exceptions include Morak, et al. (2011, 2012, manuscript submitted to J. Climate), who detect anthropogenic influence in the frequency of occurrence of temperature extremes in data that extend to 2005]. However, studies of changes in extremes that include more recent observations show that ongoing changes in temperature extremes are regionally consistent with those observed in the latter half of the twentieth century. Examples include studies of the frequencies of warm and cold days and nights in
Natural low frequency internal variability of the climate system also affects the intensity and frequency of temperature and precipitation extremes, generally with a mixed pattern of increasing and decreasing responses depending on regions and seasons. For example, El Niño strongly influences both temperature and precipitation extremes globally (Kenyon and Hegerl 2008, 2010; see Fig. 3) and can alter the likelihood of rare damaging wintertime precipitation events by more than a factor of 4 in some parts of
THE ABSENCE OF A ROL E OF CLIMAT E CHANG E IN THE 2011 THAILAN D FLOO DS
Geert Jan van Oldenborgh-KNMI ,
T hailand experienced severe flooding in 2011. During and after an unusually wet monsoon (July-September) in northern
Flooding event s are not uncommon in
Observed rainfall anomaly and return time. We use the Global Precipitation Climatology Centre (GPCC) V5 1° rainfall analyzes (Schneider et al. 2011) to estimate historical rainfall over
Figure 4a shows the time series of rainfall in the middle and upper Chao Phraya basin, approximated by the region 15°-20°N, 99°-101°E, which is shown by the box in Fig 5. In this estimate the monsoon season 2011 is the wettest in the record, but comparable to 1995. To estimate the return time we fitted a generalized Pareto distribution (GPD; Coles 2001) to the highest 80% of the distribution before 2011. This gives a central estimate of a return value of 140 years although the 95% confidence interval encompasses a range from 50 to several thousand years. In terms of large-scale meteorology, the 2011 monsoon was not very different from previously observed seasons.
La Niña has a statistically significant but small effect of rainfall in the area: the linear correlation coefficient with the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST1) Niño-3.4 index is about -0.25 (between -0.07 and -0.39 with 95% confidence), slightly weaker than the spring teleconnection to
The second method is to use climate models rather than statistical models, which in principle can give a physics-based estimate of the change in PDF. A full analysis would have to involve a validation of the representation of the Southeast Asian monsoon in these models. Here we simply note that the 17 climate models available in the CMIP5 archive (
Conclusions. Although the damage caused by the 2011 floods on the Chao Phraya river in
EXCEPTIONAL WARMING IN THE WEST ERN PA CIFI C-IN DIAN OCEAN WARM POOL HAS CONTRI BUTED TO MOR E FR EQUENT DRO UGHTS IN EAST ERN
I n 2011,
An increased frequency of East African droughts. Here we present 1979-2010 GPCP data (
Dry areas, based in the 1999-2011 anomalies, were identified for the March-June and June-September seasons. These regions are shown with brown (March-June) and blue (June-September) polygons in Fig. 6a. The background shading in Fig. 6a shows 2005 Gridded Population of the World population densities. The region impacted is one of the most densely populated areas of
Has ocean warming led to decreased East African rainfall during La Niña episodes? While the La Niña event of 2010/11 played a central role in triggering the 2010/11 food crisis, it is impossible to unambiguously attribute a single event to anthropogenic climate change. There has been recent research, however, that has emphasized that the long-term trend in IPWP SSTs (Williams and Funk 2011), rainfall, and winds could interact dangerously with interannual La Niña climate events. The latter observation helped trigger effective early warning of the 2011 East African food crisis (Ververs 2012; Funk 2011). More recent SST-driven climate simulations have emphasized the important role of post-1999 warming in the
How much has the IPWP been warming? Figure 7 shows the recent IPWP warming, as measured by SSTs and an air temperature index. Also shown is a new CMIP5 multimodel ensemble IPWP SST average, based on 55 simulations from five models running the historical climate experiment (
We can confirm the exceptional warming in the IPWP with an independent index we computed by averaging selected long-running GHCN v3 (Lawrimore et al. 2011) air temperature stations.3 The 2001-11 air temperature index recorded a 0.5°C increase since 1950, a large increase when compared with the 1890-1970 standard deviation of decadal averages of the air temperature index. Both SSTs and terrestrial station data converge on substantial warming.
Between 1864 and 2011, 10-yr running averages of the IPWP SSTs are highly correlated with global
Conclusions. The ~0.7°C IPWP warming, given the already warm state of the region, is likely to have had substantial dynamic impacts, as supported by recent modeling experiments (
It is interesting to note that while SST-driven simulations of the 2011 March-May (MAM) season clearly show the important role played by the warm western Pacific (
DID HUMAN INFL UENCE ON CLIMAT E MA KE THE 2011 TEXAS DRO UGHT MOR E PRO BABLE?
I n 2011, the state of
As with other extreme events discussed in this volume, we pose this question: Was the likelihood of either the heat wave or the drought altered by human influence on global climate? This question is portentous because an affirmative answer implies that such events, with their severe impacts on ecosystems and economics, may become more frequent. Here we endeavor to quantify the change in the likelihood of the heat wave and drought since the 1960s to the present, a period during which there has been a significant anthropogenic influence on climate. We analyze a very large ensemble of simulations from a global climate model (GCM), with greenhouse gas concentrations and other climate forcings representative of the 1960s and present day (Pall et al. 2011; Otto et al. 2012). Through the use of public volunteered distributed computing (Allen 1999; Massey et al. 2006), we obtain an ensemble size that is large enough to examine the tails of the distribution of climate variables (see the later section on the changing odds of warm Novembers and cold Decembers in
Along with anthropogenic greenhouse gases and other climate forcings, natural sources of interannual variability will result in differences in probability distributions between years. The El Niño-Southern Oscillation (ENSO), for one, is considered to be a key driver of drought conditions in the central
Data and methods. Values of observed monthly temperature and precipitation for the years 1895-2011 and spatially averaged over the state of
The atmospheric and land surface climate of the decades 1960-70 and 2000-10 were simulated with the
Because simulations under 2011 forcing conditions were not available, we chose 2008 as a proxy for 2011, and compared it to the years 1964, 1967, and 1968. The years 1964 and 2008 were similar with respect to sea surface temperature patterns in the tropical and northern Pacific, as given by the Niño-3.4 and Pacific decadal oscillation (PDO) indices, respectively. The years 1967 and 1968 were also La Niña years (though weaker than 1964) and had negative values of the PDO index. The inclusion of three La Niña years from the 1960s allows us to examine interannual variability not driven by
A spatial, weighted average was calculated from the 27 GCM grid boxes that fell within
Results. The GCM captured the inverse correlation between temperature and precipitation that is evident in the observations (Fig. 8), though the model in general generated a climate that was too dry and too warm. Between 1964 and 2008, the simulated ensembles show shifts towards warmer and slightly drier conditions (Fig. 8). The relationship is similar between 1967-68 and 2008 (not shown).
The return period for a given low precipitation event was slightly longer for the years in the 1960s than for 2008 (Fig. 9, top; e.g., a simulated 100-yr return period MAMJJA precipitation under 1964 conditions has a 25-yr return period under 2008 conditions). This may indicate an increased contribution of precipitation deficit to drought conditions in 2008, but larger sample sizes and a more in depth analysis including looking at other years are required before firmer conclusions can be drawn.
For extreme heat events, the difference between the years in the 1960s and 2008 was much more pronounced, with the return period of a particular extreme heat event being more than an order of magnitude shorter for 2008 than for any of the 3 years from the 1960s (Fig. 9, lower panel). As an example, 100-yr return period MAMJJA and JJA heat events under 1964 conditions had only 5- and 6-yr return periods, respectively, under 2008 conditions.
Conclusions. We are assessing how the combined impact of changing atmospheric composition and surface temperatures have affected the risk of extreme hot and dry conditions in
We found that extreme heat events were roughly 20 times more likely in 2008 than in other La Niña years in the 1960s and indications of an increase in frequency of low seasonal precipitation totals. With 2008 serving as our proxy for 2011, this suggests that conditions leading to droughts such as the one that occurred in
However, there are two main factors in the model driving the differences in the 1960s and 2008 probability distributions of precipitation and temperature. One factor is the effects of external climate forcings, dominated by the increase in greenhouse gas concentrations due principally to anthropogenic emissions. The second factor is the difference in the SST/sea-icefraction fields between the years. However, the difference in SST/sea-ice-fraction fields itself has a contribution from increased anthropogenic greenhouse gases, and a second contribution that is due to natural variability. We chose to compare years with similar values of the Niño-3.4 and PDO in order to reduce the contribution due to natural variability; however, other SST patterns may have played significant roles (e.g. McCabe et al. 2004; Schubert et al. 2009).
Progress toward quantifying attribution will include analysis of more years to further evaluate the natural variability and test the robustness of the results presented here. Furthermore, we will explore uncertainty in atmospheric response using perturbed physics ensembles.
Modeling studies such as this allow us to quantify how much the probability of extreme hot and dry conditions in
CONTRI BUTION OF ATMOSP HERI C CIR CULATION TO REMAR KABLE EUROP EAN TEMP ERAT URES OF 2011
Were 2011 temperatures anomalously warm compared to those expected from their flow analogues? We use in situ measurements provided by the European Climate Assessment dataset at more than 2500 stations over the period 1948-2011 (Klein-Tank et al. 2002). Similarly to C10, 306 stations are selected on the basis of (i) an altitude lower than 800 m, (ii) the availability of more than 90% of daily values between
Winter 2010/11 was particularly cold in northern
In 2011, despite important deficits in soil moisture at the end of spring (comparable to those that preceded summer 2003 heat waves), summer temperatures turned out to be close to normal over most of western
The contribution of the large-scale dynamics to temperature anomalies of 1948-2011 is estimated from the same flow-analogue approach as used in C10. For each day, we selected the 10 days with the most correlated atmospheric circulation among days of other years but within a moving window of 31 calendar days (for details, see Lorenz 1969; Yiou et al. 2007). The following results are insensitive to (i) the number of selected days (here 10) and (ii) the metrics used for assessing analogy (here Spearman's rank correlation). Further methodological details can be found in C10 and Vautard and Yiou (2009). Circulations are derived from sea level pressure (SLP) anomalies of
For all seasons of 2011, mean analog temperatures (i.e., averaged over the 10 analog days) were lower than observed ones at respectively 76%, 88%, 86%, and 89% of western
At the intraseasonal time scale, observed temperatures of 2011 were 29% of the time above the maximum of the 10 analog temperatures, and 77% above the median (Fig. 11a). This is significantly higher than the expected statistical values, respectively 1/11 = 9% (2.5-20%) and 1/2 = 50% (35%-65%) (brackets indicate 95% confidence intervals obtained from binomial quantiles assuming 40 independent days among the 396 of Fig. 11a). The heat waves of late April, late August, and late September were largely underestimated by the analogues, despite relatively high correlations between observed and analog SLP during these three periods (not shown). Overall, the analog temperature of year 2011 reaches 0.7s, suggesting that large-scale circulations contributed to ~33% of the observed anomaly (Fig. 11b).
Conclusions. 2011 fits into the pattern of recent years where observed temperatures are distinctly warmer than analog temperatures. This is true for seasons with cold anomalies which are not as cold as expected from flow-analogues (e.g., winter 2009/10; see C10) and warm seasonal anomalies, that are hotter than the corresponding analog seasons (e.g., autumn- winter 2006/07; see Yiou et al. 2007). In addition, high interannual correlations between observed and analog temperatures confirm that the North Atlantic dynamics remains the main driver of European temperature variability, especially in wintertime.
HAV E THE ODDS OF WARM NOV EMBER TEMP ERAT URES AN D OF COL D DECEMBER TEMP ERAT URES IN CENTRAL ENGLAN D CHANG ED?
N. Massey-Atmospheric, Oceanic and Planetary Physics,
T he Central England Temperature (CET) data set is the oldest continuously running temperature dataset in the world (Manley 1974) and records temperatures over a central area of
The emergent science of probabilistic event attribution is becoming an increasingly important method of evaluating the extent of how this humaninfluenced climate change is affecting localized weather events. Studies into the European heat wave of 2003 (Stott et al. 2004), the
We follow a similar methodology to Pall et al. (2011), which uses very large ensembles of global climate models (GCMs) to assess the change in risk of autumn flooding in the
Method. Weatherathome is a volunteer-distributed computing project that uses idle computing time from a network of "citizen scientists" home computers to run an RCM embedded within a GCM. The models used are HadAM3P, an atmosphere only, mediumresolution (1.875° × 1.25°, 19 levels, 15-min time step) GCM and HadRM3P, a high-resolution (0.44° × 0.44°, 19 levels, 5-min time step) RCM. Both models have been developed by the
Each volunteer's computer runs both models for a model year at a time, with initial conditions being provided by model runs previously completed by other volunteers. In this way, very large ensembles of RCMs can be computed, on the order of thousands, which in turn allows greater confidence when examining the tails of the distribution of climate variables.
The results examine the changing frequency of warm Novembers and cold Decembers since the 1960s. Two periods are analyzed, the 2000s and the 1960s which both use sea surface temperatures (SST) and sea ice fractions (SIF) from the HadISST observational dataset (
Validation and bias correction. To analyze the results from the regional modeling experiment, four separate ensembles are formed from the data. Each data point in each ensemble is the mean of 27 grid boxes from the regional model, corresponding to 9 grid boxes centered over
Figure 12a shows quantiles of temperatures in the ensembles of 1960s Novembers and Decembers against the corresponding quantiles in the CET dataset. Figure 12b shows the same for the 2000s ensembles. The solid lines are the raw ensemble data, whereas the dashed lines are the result of applying a simple bias correction to ensure the means and standard deviations of the ensembles match the means and standard deviations of the observed CET dataset. The same bias correction is applied to both the 1960s and 2000s.
After the bias correction, there is good agreement between the ensembles and observations, giving confidence that any change in return time is representative of the change in return time in the observations.
Results and conclusions. Figure 13a shows the return times of warm temperatures in November in both the 1960s ensemble (blue) and 2000s ensemble (red). The temperature of a 100-yr event in Novembers in the 2000s has increased to 10.42°C from 8.97°C. The warm November of 2011, which is the second warmest in the CET, has a monthly mean temperature of 9.6°C. This corresponds to a return period of 20 years in the 2000s, but a return period of 1250 years in the 1960s, an approximately 62 times increase in occurrence.
Figure 13b shows the return times of cold temperatures in December in both the 1960s and 2000s. Although the occurrence of a cold December in the 2000s has decreased from the 1960s, the difference in temperature of the 100-yr event is 0.87°C. The cold December of 2010, which is the second coldest December and coldest since 1890, has a monthly mean temperature of -0.7°C, which has a return period of 139 years in the 1960s and a return period of 278 in the 2000s. Therefore, a cold December of -0.7°C is half as likely to occur in the 2000s when compared to the 1960s.
LENGT HENED ODDS OF THE COL D
T he winter of 2010/11 began with the coldest December in the
Figure 14 shows how the early part of the 2010/11 winter compares to the other winters in the central
Has human influence on climate changed the chances of cold winters? The main tool we use to address this question is the Met Office Hadley Centre attribution system (Christidis et al. 2012, manuscript submitted to J. Climate). This is based on HadGEM3-A, the atmospheric component of the model used for seasonal forecasting at the Met Office (Arribas et al. 2011) and which has a resolution of 1.25° longitude by 1.875° latitude and 38 vertical levels. We compare a 100-member ensemble of model simulations forced with observed SSTs and sea ice and current levels of greenhouse gases with two alternative 100-member ensembles in which human influence has been subtracted from the SSTs and sea ice and in which greenhouse gases and aerosols are reduced to preindustrial levels following a similar methodology to that of Pall et al (2011). Here, estimates of the change in SST due to human influence are derived from transient simulations of three coupled climate models, HadGEM1, HadGEM2-ES, and HadCM3. Further details of the attribution system are given in Christidis et al (2012, manuscript submitted to J. Climate).
Verification of model statistics against observations helps assess the trustworthiness of the attribution system. Based on a five-member ensemble of simulations forced with observed SSTs from 1960 to 2010, Christidis et al. (2012, manuscript submitted to J. Climate) concluded that the model has a realistic representation of
Change of odds in the model. The change of odds of cold December and January temperatures in 2010/11 attributable to climate change can be seen in Fig. 15 (top), which shows the ratio of the probability of such cold temperatures in the current world (P1) to the world had human influence not affected climate (P0). The three estimates, based on attributable SST changes derived from the HadGEM1, HadGEM2-ES, and HadCM3 models, have median values of approximately 0.5, indicating that human influence has halved the probability of temperatures as cold as seen in 2010/11 with 5th-95th-percentile uncertainty ranges of 0.24-0.80, 0.25-0.70, and 0.26-0.82 depending on which coupled model is used to define the change in SSTs. In summary, model results indicate that human influence has reduced the odds by at least 20% and possibly by as much as 4 times with a best estimate that the odds have been halved.
Change of odds estimated from the CET record. An observationally based consistency check of these numbers is obtained by calculating empirically the number of times prior to
For the single month of
Conclusions. The winter of 2010/11 was a rare weather event, even in the context of the 352 years of the central
"Climate is what a boxer trains for but weather throws the punches" (
The section on historical context summarizes the evidence that human influence has affected trends and long-term behavior of temperature and precipitation extremes around the globe, thus altering the types and frequencies of punches for which our boxer must train. This is to be anticipated from theoretical expectations of a warmer world. The recent IPCC SREX report (Field et al. 2012) concluded that "it is likely that anthropogenic influences have led to warming of extreme daily minimum and maximum temperature at the global scale" and that "there is medium confidence that anthropogenic influences have contributed to intensification of extreme precipitation at the global scale." But even if human influence is making a particular type of event more likely on average, because of natural variability it does not necessarily follow that its likelihood is greater every year. So while it has been argued that in the anthropocene4 all extreme weather or climate events that occur are altered by human influence on climate (Trenberth 2011), and although it is difficult to prove that a particular extreme weather or climate event was not in some way influenced by climate change, this does not mean that climate change can be blamed for every extreme weather or climate event. After all, there has always been extreme weather.
The contributions in this article examining some of the specific extreme weather or climate events of 2011 demonstrate the importance of understanding the interplay of natural climate variability and anthropogenic climate change on their occurrence. We should not expect that climate change plays the major role in every extreme weather or climate event and indeed the rainfall associated with the devastating
The development of a regular attribution service whose results are available shortly after the month or season in question depends on the implementation of an established methodology. For example, the same circulation regime-based technique used to analyze the very cold northwestern European winter of 2009/10 (Cattiaux et al. 2010a) was used to investigate European seasonal temperatures in 2011. All four seasons were warmer in many parts of
Another approach that supports a regular attribution service is based on estimating the changed probabilities of extreme weather or climate events from ensembles of atmosphere only climate models with different sea surface temperatures (SSTs) and altered concentrations of greenhouse gases and other climate forcings. This technique has been used to show that human-induced greenhouse gas emissions have increased the risk of the
An important future development of such attribution systems is to allow the changed risk of extreme weather or climate events to be calculated quickly and disseminated on a regular basis. The Weather Risk Attribution Forecast (WRAF) system, which is based on a seasonal forecasting modeling system, has been trialled in this way, providing regularly updated estimates of risks of temperature and precipitation extremes. It will be crucial to understand the strength and limitations of such systems for the weather and climate events to which they are being applied. This should include an assessment of the reliability of the models being used (Christidis et al. 2012, manuscript submitted to J. Climate).
Providing such attribution results in time for this issue has proved extremely challenging given the delays involved in collecting observations, running models and analyzing data. Two analyzes presented here used preexisting climate model simulations to compare event statistics for recent years with years from the 1960s. While this approach does not explicitly calculate the extent of changes attributable to human influence because natural external forcing and natural internal variability could have contributed to the change in the likelihood of events since the 1960s, it does address how the long-term warming trend has affected weather odds. By carefully choosing years with patterns of SSTs similar to those of 2011, it was possible to determine that heat waves such as the one that affected
It has been questioned whether attribution studies might neglect many of the regions most vulnerable to extreme weather because of the greater difficulties of collecting climate observations and undertaking climate modeling in developing countries (Hulme 2011). Therefore the analysis of the East African drought of 2011 is particularly interesting because it demonstrates the potential for attribution in tropical regions that lack robust international exchange of climate observations. Low-latitude regions generally have higher ratios between the signal of climate change in temperature and variability than other regions (Mahlstein et al. 2011) and there appears to be potential skill in seasonal forecasting of impactrelevant metrics such as the onset of seasonal rains in
2011 was a year during which the weather threw plenty of punches [see Blunden and Arndt's (2012) supplement to this issue]. While much work remains to be done in attribution science, to develop better observational datasets, to improve methodologies, to make further progress in understanding and to assess and improve climate models, the contributions in this article demonstrate the potential that already exists for meaningful assessments of the connection between specific extreme weather or climate events that occurred in a particular year and climate change. Whether readers react with excitement at the possibilities already demonstrated, or with irritation at the gaps and limitations still present, our hope as editors is that this initial selection of investigations encourages further development of the capability to produce timely and reliable assessments of recent extreme weather or climate events. Such an enterprise is much further advanced for climate monitoring-as shown by the maturity of the annual State of the Climate report (e.g., Blunden and Arndt 2012)-but even there important uncertainties exist and new assessments of past years will emerge, just as they will for attribution as understanding develops. By developing the scientific underpinning, the ability to put recent extreme weather or climate events into the longerterm context of climate change should improve as each year goes by.
1 Likely indicates probability greater than 66%; see IPCC guidance on uncertainty language (Mastrandrea et al. 2010), which also includes guidance on expression of levels of confidence.
2 See Mastrandrea et al. (2010) for a description of IPCC confidence language used in the IPCC Fifth Assessment, including the Special Report on Extremes (Field et al. 2012).
3 Bombay/Mombassa, Madras, Port Blair, Mannar, Trincomalee, Puttalam,
4 The anthropocene is the most recent geological era in which human activities have had a significant global impact on the Earth's ecosystems (Crutzen 2002).
Adler, R. F., and Coauthors, 2003: The Version 2.1
Aguilar, E., and Coauthors, 2009: Changes in temperature and precipitation extremes in western central
Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
Allen, M. R., 1999: Do-it-yourself climate prediction. Nature, 401, 642.
Arndt, D. S.,
Arribas, A., and Coauthors, 2011: The GloSea4 ensemble prediction system for seasonal forecasting. Mon. Wea. Rev., 139, 1891-1910.
Barnett, T. P.,
Blunden, J., and
_____, _____, and
Chen, C.-T., and T.
Choi, G., and Coauthors 2009: Changes in means and extreme events of temperature and precipitation in the Asia-Pacific Network region, 1955-2007. Int. J. Climatol., 29, 1956-1975.
Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Verlag, 208 pp.
Crutzen, P. J., 2002: Geology of mankind. Nature, 415, 23, doi:10.1038/415023a.
Dole, R., and Coauthors, 2011: Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., 38, L06702, doi:10.1029/2010GL046582.
Field, C. B., and Coauthors, Eds., 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Summary for Policymakers. A Special Report of Working Groups I and II of the
Folkins, I., and
Funk, C., 2011: We thought trouble was coming. Nature, 476, 7.
Gleason, K. L.,
Graham, R., and Y. Biot, 2011: Targeting climate research and services to development needs in
Hegerl, G. C.,
Held, I. M., and
Hulme, M., 2011: Is weather event attribution necessary for adaptation? Science, 334, 764-765.
Jones, R. G.,
Karl, T. R., and
Kenyon, J., and G. C. Hegerl, 2008: The influence of
_____, and _____, 2010: Influence of modes of climate variability on global precipitation extremes. J. Climate, 23, 6248-6262.
Kistler, R., and Coauthors, 2001: The NCEP-NCAR 50- Year Reanalysis. Bull.
Klein-Tank, A., and Coauthors, 2002: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, 1441-1453, doi:10.1002/joc.773.
Kunkel, K. E.,
Lawrimore, J. H.,
Lorenz, E., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636-646.
Manley, G., 1974:
Massey, N., and Coauthors, 2006: Data access and analysis with distributed federated data servers in climateprediction.net. Adv. Geosci., 8, 49-56.
Mastrandrea, M. D., and Coauthors, 2010: Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties.
McCabe, G. J.,
Meehl, G. A., cited 2012: As animated in steroids, baseball, and climate change: What do home runs and weather extremes have in common? UCAR video. [Available online at http://www2.ucar.edu /atmosnews/attribution/steroids-baseball-climatechange.]
Min, S.-K., X. Zhang,
_____, _____, and
Nakicenovic, N., and
Otto, F. E. L.,
Pall, P., T. Aina,
Palmer, T. N., and
Peterson, T. C., X. Zhang,
Pierce, D. W.,
Rahmstorf, S., and
Robock, A., 2000: Volcanic eruptions and climate. Rev. Geophys., 38, 191-219, doi:10.1029/1998RG000054.
Santer, B. D., and Coauthors, 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci.
Sato, M., cited 2011: Forcings in
Schiermeier, Q., 2011: Climate and weather: Extreme measures. Nature, 477, 148-149, doi:10.1038/477148a.
Schneider, U., A. Becker,
Schubert, S., and Coauthors, 2009: A USCLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results. J. Climate, 22, 5251-5272, doi:10.1175/2009JCLI3060.1.
Seneviratne S.I., T. Corti,
_____, and Coauthors, 2012: Changes in climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation,
Stott, P. A.,
_____, and Coauthors, 2011: Attribution of weather and climate-related extreme events. Climate Science for
_____, _____, and _____, 2012: An overview of the CMIP5 experimental design. Bull.
Trenberth, K. E., 2011: Attribution of climate variations and trends to human influences and natural variability. WIREs Climate Change, 2, 925-930, doi:10.1002/wcc.142.
_____, A. Dai,
_____, G. Burgers, and
Vautard, R., and
_____, and Coauthors, 2007: Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit. Geophys. Res. Lett., 34, L07711, doi:10.1029/2006GL028001.
Ververs, M. J., 2012: The East African food crisis: Did regional early warning systems function? J. Nutrition, 142, 131-133, doi:10.3945/jn.111.150342.
Willett, K. M.,
Williams, P., and
_____, and Coauthors, 2011: Recent summer precipitation trends in the
Xie, P., and
Yu, L., and
Zhou, Y. P.,
Zwiers, F. W., X. Zhang and Y. Feng, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Climate, 24, 881-892, doi:10.1175/2010JCLI3908.1.
Thomas C. Peterson,
CORRESPONDING EDITOR: Thomas C. Peterson,
E-mail: [email protected]
The abstract for this article can be found in this issue, following the table of contents.
DOI :10.1175/BAMS -D-12-00021.1
In final form
|Copyright:||(c) 2012 American Meteorological Society|
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