A climatology of cold air outbreaks over North ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D12107, doi:10.1029/2011JD015711, 2011

A climatology of cold air outbreaks over North America: WACCM and ERA‐40 comparison and analysis D. D. Wheeler,1 V. L. Harvey,2 D. E. Atkinson,3,4 R. L. Collins,5 and M. J. Mills2,6 Received 28 January 2011; revised 1 April 2011; accepted 8 April 2011; published 23 June 2011.

[1] A climatology of cold air outbreaks (CAOs) over North America is presented on the basis of a 50 year simulation of the Whole Atmosphere Community Climate Model (WACCM). This climatology is compared to a similar CAO climatology based on 45 years (1957–2002) of European Centre for Medium‐Range Weather Forecasts 40 Year Re‐Analysis Project (ERA‐40) data. A CAO is identified at a given grid point if the following criteria are met: (1) the surface temperature is lower than 1.5 standard deviations below the 31 day climatological running mean, (2) the standard deviation in temperature is greater than 2 K, and (3) conditions 1 and 2 are satisfied over a contiguous area of ∼5° longitude by 5° latitude. WACCM and ERA‐40 comparisons are shown for CAO frequency, temperature anomaly from 31 day climatological mean, geographical location, minimum temperature, and areal extent. Overall, CAOs in WACCM occur ∼30% less frequently than in ERA‐40 but cover ∼30% greater area and are 1–2 K lower. In midwinter, WACCM CAOs form at lower latitudes and penetrate to lower latitudes compared to CAOs in ERA‐40. Citation: Wheeler, D. D., V. L. Harvey, D. E. Atkinson, R. L. Collins, and M. J. Mills (2011), A climatology of cold air outbreaks over North America: WACCM and ERA‐40 comparison and analysis, J. Geophys. Res., 116, D12107, doi:10.1029/2011JD015711.

1. Introduction [2] A cold air outbreak (CAO) is an equatorward surge of extremely cold air from polar to subtropical latitudes over the continents during winter [e.g., Walsh et al., 2001, and references therein]. In the Northern Hemisphere over Europe, Asia, and North America, CAOs are associated with baroclinic weather systems characterized by cold surface anticyclones located to the west of warm cyclones [e.g., Colucci and Davenport, 1987]. This study focuses on CAOs over North America. These CAOs have a significant impact on human health, property, agriculture, and the economy [Cellitti et al., 2006]. For example, while current attention is often focused on damage to citrus crops in Florida [Rogers and Rohli, 1991], CAOs have been impli-

1 Laboratory for Atmospheric and Space Physics, Department of Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, Colorado, USA. 2 Laboratory for Atmospheric and Space Physics, University of Colorado at Boulder, Boulder, Colorado, USA. 3 International Arctic Research Center, Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, Alaska, USA. 4 Now at Department of Geography, University of Victoria, Victoria, British Columbia, Canada. 5 Geophysical Institute, Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, Alaska, USA. 6 Now at Earth System Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA.

Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2011JD015711

cated in frost events associated with the major famines in the Aztec Empire during the 1450s [Hassig, 1981; Therrell et al., 2004]. [3] Climatologically, these events are part of the large‐ scale meridional redistribution of heat because they are balanced by poleward warm air advection to the east of the low‐pressure system [e.g., Konrad and Colucci, 1989]. Long‐term climate modeling studies suggest that, in general, the frequency of CAOs in the Northern Hemisphere will decrease by 50–100% owing to global warming [Vavrus et al., 2006; Intergovernmental Panel on Climate Change (IPCC), 2007]. However, Vavrus et al. [2006] stress that their predictions are based on CAOs “defined with respect to late‐twentieth‐century climatic condition.” Vavrus et al. [2006] compared CAOs in seven general circulation models (GCMs) over three 20 year periods separated by 50 years, (centered on 1990, 2055, and 2090) and estimated that CAOs will decrease in frequency by 50 to 100% over the Northern Hemisphere (∼85% over North America) during the twenty‐ first century. Vavrus et al. highlighted the fact there is no standardized definition of CAOs and recognized the inherent challenge in defining anomalous weather events. [4] Previous methods used to identify extreme CAOs typically require anomalously cold temperatures, high pressures, northerly winds, or a combination thereof, to persist at a given location for a specified length of time. For example, Walsh et al. [2001] used the National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP) reanalysis data [Kalnay et al., 1996] to identify CAOs over North America and Europe. They

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selected the ten most negative 1, 3, and 5 day average surface temperature anomalies from the respective 52 year mean. In their analysis, temperature anomalies were spatial averages in three (two) regions over North America (Europe) and events could not occur within 1 week of each other. Jeong et al. [2006] identified CAOs over Asia using 43 years of data from NCEP and 177 surface weather stations. In their study, CAO detection required multiple criteria to be satisfied. These included the southeastward displacement of the Siberian surface anticyclone, surface temperatures to be 1.5 standard deviations below the 31 day running mean, and strong northerly winds. Cellitti et al. [2006] used a 55 year data record at 17 U.S. weather stations and selected the top thirty coldest 5 day average surface temperatures that occurred simultaneously at 3 or more stations. Vavrus et al. [2006] used seven general circulation models and required that surface temperature is two standard deviations below the single year December‐ January‐February mean for at least 2 consecutive days. These studies and references therein demonstrate that a variety of techniques have been used to identify extreme CAOs. [5] Against the general backdrop of widespread interest in the future trajectory of extreme weather events, it is important to clearly understand how models represent these features in the current climate. Thus the particular focus of this study is the characterization of CAOs as they appear in the Whole Atmosphere Community Climate Model (WACCM). WACCM is a comprehensive numerical model that spans the range of altitude from the Earth’s surface to the thermosphere [Garcia et al., 2007]. Our use of WACCM, a model that explicitly couples the lower, middle, and upper atmosphere, is motivated by a broader desire to understand the processes that underlie CAOs both at the surface and in the free troposphere and in the middle atmosphere above. The primary objective in this study is to assess how CAOs appear near the surface in WACCM, and lay a foundation for future studies of coupling between the troposphere and stratosphere. We first describe our CAO algorithm and validate it by comparison with a recent study of historical CAOs in the NCEP reanalysis [Cellitti et al., 2006]. We then present differences between CAOs observed in the European Centre for Medium‐Range Weather Forecasts 40 Year Re‐Analysis Project (ERA‐40) analyses and CAOs simulated by WACCM.

2. Data and Numerical Methods

Figure 1. Mercator plots of ERA‐40 daily average surface temperature over North America on (a) 28 January 1996, (b) 31 January 1996, (c) 3 February 1996, and (d) 5 February 1996. The thick white contour is the freezing line. The white dots indicate ERA‐40 grid points that satisfy the CAO criterion. The large gray dots connected by the thick gray contour represent the track of the CAO center of mass on previous days. The gray dot with the inset white dot is the location of the CAO center of mass on each of the days shown.

2.1. WACCM [6] WACCM is a coupled chemistry climate model that extends from the surface to 140 km [e.g., Garcia et al., 2007, and references therein]. WACCM is based on the Community Atmosphere Model version 3 in the lower atmosphere [Collins et al., 2004]. The model was run using SSTs and constituent boundary conditions as described by Marsh et al. [2007]. The WACCM output has a horizontal resolution of 4° latitude by 5° longitude on 66 vertical hybrid sigma levels. The WACCM horizontal resolution does not fully resolve the height and extent of the Rocky Mountains that play a key role in steering cold air masses southward. This limitation should be kept in mind in the interpretation of the results shown in this work. The vertical

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Figure 2. Histograms of daily surface temperature at four geographic sites for WACCM (blue) and ERA‐40 (red). Normalized values comprise 45 years of data on 1 January (see section 2.4 for details). Vertical lines indicate means and ±1.5 standard deviations. The colored boxes in the maps in the upper left corners indicate the geographic area associated with each histogram. resolution is ∼1.5 km in the troposphere and lower stratosphere, increasing to 2 km at the stratopause and 3.5 km in the mesosphere [Sassi et al., 2004]. WACCM provides a daily average for each model variable. In this study, we use the temperature at the lowest level (i.e., at the ground level) to investigate CAOs. This version of WACCM does not assimilate observations thus the timing of individual CAO events is unrelated to their occurrence in ERA‐40. Therefore, this work is limited to presenting statistical comparisons of CAO characteristics. In this work, we run WACCM for 50 years, we omit the first 5 years during model spin‐up so that the WACCM record is the same length as ERA‐40. 2.2. ERA‐40 Reanalysis [7] ERA‐40 reanalysis are used in this work [Uppala et al., 2005] for comparison to WACCM. The model assimilates sea‐surface temperatures (SSTs) from the Hadley Centre sea ice and SST data set prior to 1981 and the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation SST analysis [Hurrell et al., 2008, and references therein]. The reanalysis spans 45 years (1957– 2002). Model output is provided four times daily at 0 Z, 6 Z, 12 Z, and 18 Z. Model output have a horizontal resolution of 2.5° longitude by 2.5° latitude. Variables are provided near the surface (at the 2 m level) and at 23 pressure levels that extend from 1000 hPa up to 1 hPa. In this study, we use daily averaged near surface temperatures.

2.3. Numerical Algorithm to Identify CAOs [8] In developing our algorithm, we conducted several case studies of individual CAO events. We explored a variety of indices (i.e., deviations from daily averages over 45 years, deviations from monthly averages over 45 years, deviations from running averages over yearly cycles, daily temperature tendencies, meridional heat fluxes, surface pressure tendencies) and their combinations to identify CAOs and evaluated them on the basis of visual inspection of the weather maps. From this investigation, we concluded that the most reliable parameter found to consistently identify CAOs was the deviation of temperature from a 31 day climatological running mean that is centered on each day. Specifically, we use a threshold of 1.5 standard deviations below the mean, and require that the standard deviation exceed 2 K, to identify a CAO at a given grid point. This constraint avoids identifying CAOs in regions where there is little variability, typically over the ocean. Having identified a grid point that satisfies this criterion, we further require that the CAO grid points are contiguous in space. For WACCM, CAO grid points must span a longitude by latitude area of 5° by 4° (corresponding to four grid points) and for ERA‐40 an area of 5° by 5° (corresponding to nine grid points). The spatial criteria removes stray grid points that satisfy the CAO criterion but are isolated from the contiguous CAO air mass. Benefits of this algorithm are

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Figure 3. Same as Figure 2 but for only CAO grid points.

that, in addition to demarking strong CAOs that occur in the winter, it (1) reliably identifies moderate CAOs (when minimum temperatures are near freezing) that tend to occur in November and March and (2) is not biased by a single winter average being unusually cold or warm. [9] We demonstrate the fidelity of the CAO algorithm for an event that occurred in January and February 1996. This CAO is the third strongest since 1948 [Cellitti et al., 2006]; however, results are representative of moderate CAOs. In this work, CAO area is defined as the area weighted longitude and latitude of all CAO grid points identified between 225 and 300°E longitude and between 20 and 70°N latitude. CAO strength is defined as the largest temperature anomaly (difference from the 31 day climatological mean) at any individual CAO grid point between the aforementioned longitude and latitude bounds. [10] Figure 1 shows ERA‐40 surface temperature over North America on 4 select days between 28 January and 5 February 1996. The freezing line is indicated by the thick white contour. Grid points that satisfy the CAO criterion are indicated by white dots. We define the center of mass as the average area weighted longitude and latitude of all CAO grid points identified between 225 and 300°E longitude and between 20 and 70°N latitude. The CAO center of masses on previous days are given by large gray dots. The CAO center of mass on the days shown in each panel is indicated by the gray dot with the inset white dot. The thick gray contour represents the CAO “track” as the movement of the center of mass from day to day.

[11] On 28 January (Figure 1a), grid points meeting the CAO criterion extend from British Columbia to Manitoba and southward into Montana. On this day, the CAO center of mass is located along the border between British Columbia and Alberta. The solid gray dots indicate that the CAO onset occurred 2 days earlier (26 January). During this “initial phase” of the CAO, the freezing line extends from northern California to the Great Plains and from northern Georgia to Maine. By 31 January (Figure 1b), the cold air mass had been driven southeastward over the United States and CAO grid points are identified by the algorithm as far south as northern Texas and Alabama. During this “growth phase,” the CAO center of mass is over the North Dakota/ Canadian border and the number of CAO grid points more than doubled. On 3 February (Figure 1c), CAO grid points are identified over most of the continental United States. The center of mass is located between Nebraska and Iowa and CAO grid points are flagged as far south as the Gulf of Mexico. During this “mature phase,” the CAO reaches its largest spatial extent and freezing temperatures extend south to the coast of the Gulf of Mexico. Figure 1d shows the CAO during the “decay phase” on 5 February. Freezing temperatures in Florida represent this phase of the CAO. CAO grid points extend from southern Texas to Miami and from the Ohio Valley to Maine. On this day, the CAO center of mass is located over South Carolina. The gray dots and gray contour represent the CAO “track” over the life cycle of the event. The performance of the algorithm during this case study demonstrates that CAOs are properly identified.

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Figure 4. Latitude‐time section of the percent of longitude points flagged as CAOs between 225 and 300°E for (a) WACCM and (b) ERA‐40. Data are daily 45 year averages. The white contour is 5%. Dates range from 1 October to 30 April. Solid black lines denote the approximate latitude boundaries of the contiguous United States.

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spanning 10° longitude by 8° latitude and is represented by the blue box centered on the blue diamond in the inset maps. ERA‐40 areas span 10° longitude by 10° latitude (25 grid points) are indicated by the red boxes and diamonds. Thus, the histograms are composed of 12,555 WACCM and 34,875 ERA‐40 temperature values. We normalize the histograms by 9 for WACCM and 25 for ERA‐40 to yield a histogram representing an equivalent single grid point. The vertical lines in each panel show histogram means and standard deviations. Figure 2a shows temperature comparisons over Saskatchewan, Montana, and North Dakota. The CAO center of mass is typically located in this region during the “growth” phase. Here, mean WACCM and ERA‐40 temperatures agree to within 0.2 K; however, the shape of the histograms show a bimodal pattern in WACCM with more frequent temperature extremes. WACCM temperature is higher than ERA at the peak of the histogram but also shows more frequent temperatures lower than 240 K. Over South Dakota and Nebraska (Figure 2b) and Missouri and Arkansas (Figure 2c), WACCM mean temperatures are ∼2 K higher than ERA‐40. This is reflected in a systematic rightward shift in the histograms. Over Georgia and Alabama (Figure 2d), WACCM mean temperature is ∼2 K higher than ERA‐40 but the shape of the histogram indicates that this is due to more frequent high temperatures. [14] Next, we explore the potential impact of the temperature differences on the identification of CAOs in WACCM compared to in ERA‐40. WACCM (ERA‐40) CAOs are defined relative to WACCM (ERA‐40) mean temperatures. Figure 3 shows histograms of daily surface temperature (as in Figure 2) but is based only on CAO flagged grid points. With the exception of the northern‐most point at the U.S.‐Canadian border (Figure 3a), mean CAO temperatures in WACCM are within 2 K compared to those in ERA‐40 and the shapes of the histograms are similar. Figure 3a shows that at the northern‐most point the mean temperature of WACCM CAOs is ∼4 K lower compared to CAOs in ERA‐40. There is good agreement at temperatures above the mean but WACCM shows higher frequencies of CAOs colder than 240 K (−27°F).

3. WACCM and ERA‐40 CAO Climatologies [12] In addition to the case study shown in Figure 1, we compared our results to 28 extreme CAO events identified by Cellitti et al. [2006], who used the NCEP reanalysis. CAO composites were reproduced using ERA‐40 from 10 days prior to 10 days following day zero (the peak of the CAO). The composite plots are in excellent agreement (not shown) giving further confidence that our algorithm correctly and consistently identifies CAOs. 2.4. Statistics of Daily Surface Air Temperatures [13] Surface air temperature (2m height) comparisons were conducted between WACCM and ERA‐40 in regions of frequent CAO activity. Figure 2 shows surface air temperature comparisons between WACCM and ERA‐40 at four geographic locations situated along a typical CAO track. Histograms show the normalized frequency of occurrence of daily surface temperature for 1 January ±15 days in four regions from the 45 year data records (1395 total days). Each geographic area in WACCM consists of 9 grid points

3.1. Latitude‐Time Sections [15] In this section, we examine 45 year average annual cycles of CAO occurrence frequency (Figure 4) and strength (Figure 5) over North America as a function of latitude. These results are based on CAO‐flagged grid points between 225 and 300°E longitude. Figure 4 shows the average percent of CAO‐flagged longitude points between 1 October and 30 April in WACCM (Figure 4a) and ERA‐40 (Figure 4b). Black dashed lines at 50 and 30°N correspond approximately to the northern and southern borders of the contiguous United States. The thick white contour indicates 5% frequencies. Higher values (greens, yellows, reds) indicate more frequent CAOs at a given time of the year and latitude. CAOs that are longitudinally broad also accumulate to give high occurrence frequencies. The maps have strong meridional striations. The tilt of the striations reflects the equatorward movement of CAOs from Canada across the United States over the course of about a week. Individual CAO events are evident in the map as single

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southward (from 60 to 40°N) between October and January and then northward (from 40 to 55°N) from January to April. In midwinter, WACCM CAOs are 5 K colder than ERA‐40 over the United States (i.e., south of 50°N) and ∼7–10 K colder than ERA‐40 over Canada (i.e., north of 50°N). These results are consistent with the histograms shown in Figure 3a. 3.2. Maps of North America [17] Finally, we document the geographical distribution of CAOs over North America in WACCM and ERA‐40. Figure 6 shows Mercator maps of the 45 year average CAO frequency of occurrence rate. Frequency of occurrence is defined as the average number times a grid point is flagged as being part of a CAO each year. Both WACCM (Figure 6a) and ERA‐40 (Figure 6b) show that maximum CAO frequencies occur along a northwest‐to‐southeast axis that extends from western Canada to the southeastern United States. The geographic distribution in Figure 6 is similar to that shown in the work of Vavrus et al. [2006, Figure 2].

Figure 5. Same as Figure 4 but for the 45 year daily average temperature anomaly at CAO grid points. The anomaly is defined as the difference between daily temperature and a 31 day climatological running mean. The white contour is 10 K.

striations. Overall, WACCM and ERA‐40 frequencies are in agreement. In Figures 4a and 4b at 50°N, the average percent of longitude points flagged as CAOs increases from less than 5% in October to 7% in November. From December through March, 7–10% of longitude points are CAOs. Values decrease to less than 7% in April. Poleward of ∼60°N, from November through March, WACCM occurrence frequencies are 50% lower than in ERA‐40. Over the United States from December through March, WACCM occurrence frequency maxima are shifted ∼5° equatorward compared to in ERA‐40. These results suggest that in midwinter WACCM CAOs form at lower latitudes and penetrate to lower latitudes compared to CAOs in ERA‐40. WACCM frequencies are 1–2% larger than ERA‐40. [16] Figure 5 presents latitude‐time maps of CAO strength defined as the temperature deviation from the 31 day climatological running mean in WACCM (Figure 5a) and ERA‐40 (Figure 5b). The thick white contour denotes temperature anomalies of 10 K. Both WACCM and ERA‐40 show a seasonal pattern where anomalously cold air moves

Figure 6. Mercator plots (as in Figure 1) of the average number of CAO days per year for (a) WACCM and (b) ERA‐40. The thick (thin) white contours represent even (odd) numbers of days.

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3.3. WACCM and ERA‐40 CAO Statistics [19] Table 1 summarizes 45 years of CAO statistics in WACCM and ERA‐40. Data are based on grid points flagged as CAOs between 225 and 300°E longitude and between 25 and 70°N latitude for days between 1 October and 30 April (212 days) each year. We compare the WACCM and ERA‐40 CAOs in terms of frequency of occurrence, spatial extent, temperature anomalies, and minimum temperatures. In WACCM, there are an average of 82 CAO days per year (39% of the time) compared to 129 days (61% of the time) in ERA‐40. CAOs appear larger in WACCM (average value of 3.3 million km2) than in ERA‐40 (average value of 2 million km2). For reference, the area of the continental United States is ∼8 million km2. Histograms (summarized in Table 1 by values in the lowest, middle, and highest thirds and in the highest 10% categories) of CAO area are consistent with the average, where WACCM indicates consistently larger CAOs than ERA‐40. Two thirds of CAOs in WACCM are smaller than 3.7 million km2 while two thirds of CAOs in ERA‐40 are smaller than 2.2 million km2. Thus, CAOs in WACCM occur ∼30% less frequently than ERA‐40 but are ∼30% larger in WACCM. Finally CAOs in WACCM are generally ∼2 K colder (both in terms of anomaly and minimum temperature) than ERA‐40. Again the bias is consistent in both the mean and the distributions.

4. Conclusions

Figure 7. Same as Figure 6 but for the 45 year average temperature anomaly at CAO grid points. The thick (thin) white contours represent even (odd) Kelvin.

The overall geographic distribution of CAOs is similar between WACCM and ERA‐40. At 50°N each year, ∼15 CAOs occur over Alberta and ∼10 occur over Quebec. Over the United States, both WACCM and ERA‐40 indicate ∼10– 12 CAOs are identified each year. Differences include (1) 10% higher frequencies in WACCM in British Columbia, (2) 10% lower frequencies in WACCM in Montana, and (3) 5% higher frequencies in WACCM over the Ohio River Valley and Alabama and Georgia. Differences in CAO frequencies between WACCM and ERA‐40 likely arise owing to differences in the vertical and horizontal resolution of the models. [18] Figure 7 is the same as Figure 6 but for CAO strength defined as the temperature deviation from the 31 day climatological running mean (as shown in Figure 5). CAOs are generally 1–5 K colder in WACCM relative to ERA‐40, consistent with Figure 5. However, CAOs in WACCM are warmer than those in ERA‐40 over Hudson Bay. In ERA‐40, CAOs are warmer over the Great Lakes. This regional signature is not observed in WACCM likely due to the coarse grid resolution.

[20] A climatology of CAOs over North America is presented on the basis of a 45 year simulation of WACCM. The climatology is compared to CAOs in 45 years of ERA‐40 data. A CAO is identified at a given grid point if the following criteria are met: (1) the temperature is lower than 1.5 standard deviations below the 31 day climatological running mean, (2) the standard deviation in temperature is greater than 2 K, and (3) the CAO grid points span a contiguous area of ∼5° longitude by 5° latitude. Composite analysis of 28 extreme CAO events shows excellent agreement with previous results, which gives confidence in the CAO identification algorithm. WACCM and ERA‐40 comparisons are shown for temperature as well as CAO diagnostics of frequency, strength, geographical location, minimum temperature, and areal extent. [21] CAOs occur ∼30% less frequently in WACCM than in ERA‐40. Most CAOs in WACCM occur between late December and mid‐March. Most CAOs in ERA‐40 occur between December and February. CAOs in WACCM are 5 K colder over the United States and 7–10 K colder over Canada compared to CAOs in ERA‐40. However, differences in CAO strength vary with longitude. WACCM CAOs are colder than ERA‐40 in British Columbia but warmer over Hudson Bay. WACCM CAOs are colder over the Great Lakes and in New England. The difference over the Great Lakes is likely a consequence of finer horizontal resolution in ERA‐40 that captures the local meteorology. On average, CAOs in WACCM are ∼30% larger than CAOs in ERA‐40. WACCM CAOs that exceed 3 million km2 occur over nearly twice as often compared to ERA‐40. [22] While this study shows a similar geographic pattern of occurrence of CAOs in both model and reanalysis data, the character of the CAOs differs in frequency of occurrence, areal extent, and strength. The differences in frequency of occurrence between the model and reanalysis are of similar order to

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Table 1. WACCM and ERA‐40 CAO Statistics Based on 45 Years of Data From 1 October to 30 Aprila WACCM: 82 CAO d/yr Area (million km2) Temperature anomaly (K) Minimum temperature (K)

ERA‐40: 129 CAO d/yr

Mean

Standard Deviation

Mean

Standard Deviation

3.3 11.4 261.3

2.0 3.6 13.9

2.0 8.9 262.9

1.8 2.9 16.0

WACCM: 82 CAO d/yr Mean

ERA‐40: 129 CAO d/yr

Maximum

Minimum

Mean

Maximum

Minimum

0.5 2.0 3.7 6.2

0.5 1.5 4.0 6.0

0.9 2.2 13.4 13.4

0.1 0.9 2.2 4.3

Area Lowest third Middle third Highest third Highest 10%

1.4 2.8 5.6 7.7

2.0 3.7 15.8 15.8

Lowest third Middle third Highest third Highest 10%

7.7 10.9 15.6 18.6

9.3 12.7 24.9 24.9

Temperature Anomaly 3.9 9.3 12.7 16.4

5.9 8.4 12.3 14.3

7.1 10.1 18.1 18.1

3.9 7.1 10.1 13.0

Lowest third Middle third Highest third Highest 10%

276.7 261.4 245.9 235.4

Temperature Minimum 267.5 296.8 255.6 267.4 220.6 255.6 220.6 242.8

280.9 263.4 244.5 235.1

272.2 255.2 225.7 225.7

295.2 272.2 255.2 240.3

a Diagnostics include the average number of CAO days per year, mean and 1 standard deviation in CAO area, average temperature anomalies in CAOs, and minimum CAO temperatures.

predicted changes in frequency of occurrence of CAOs. This study has indicated that WACCM, despite some issues linked to its coarser spatial resolution, can serve to represent large‐ scale weather events with reasonable accuracy. [ 23 ] Acknowledgments. We thank ECMWF for producing the ERA‐40 reanalysis and NCAR for distributing the reanalysis data. We thank Dan Marsh and Rolando Garcia of NCAR for helpful discussions about the WACCM model. The authors acknowledge support from the U.S. National Science Foundation under grant ARC 0632387 as part of the United States International Polar Year Program.

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