Effects of a Warming Climate on Daily Snowfall Events in the Northern Hemisphere

Transcription

1 1SEPTEMBER 2016 D A N C O E T A L Effects of a Warming Climate on Daily Snowfall Events in the Northern Hemisphere JAMES F. DANCO a Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey ANTHONY M. DEANGELIS Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California BRYAN K. RANEY Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey ANTHONY J. BROCCOLI Department of Environmental Sciences, and Institute for Earth, Ocean, and Atmospheric Sciences, Rutgers University, New Brunswick, New Jersey (Manuscript received 29 September 2015, in final form 27 May 2016) ABSTRACT Using simulations performed with 24 coupled atmosphere ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of Northern Hemisphere daily snowfall events under the RCP8.5 emissions scenario are analyzed for the periods of and and compared to the historical period of The overall frequency of daily snowfall events is simulated to decrease across much of the Northern Hemisphere, except at the highest latitudes such as northern Canada, northern Siberia, and Greenland. Seasonal redistributions of daily snowfall event frequency and average daily snowfall are also projected to occur in some regions. For example, large portions of the Northern Hemisphere, including much of Canada, Tibet, northern Scandinavia, northern Siberia, and Greenland, are projected to experience increases in average daily snowfall and event frequency in midwinter. But in warmer months, the regions with increased snowfall become fewer in number and are limited to northern Canada, northern Siberia, and Greenland. These simulations also show changes in the frequency distribution of daily snowfall event intensity, including an increase in heavier snowfall events even in some regions where the overall snowfall decreases. The projected changes in daily snowfall event frequency exhibit some dependence on the temperature biases of the individual models in certain regions and times of the year, with colder models typically toward the positive end of the distribution of event frequency changes and warmer models toward the negative end, particularly in regions near the transition zone between increasing and decreasing snowfall. 1. Introduction Snow is an important aspect of weather and climate with physical, ecological, and societal impacts. The presence of snow cover increases Earth s surface albedo, which has a a Current affiliation: School of Meteorology, University of Oklahoma, Norman, Oklahoma. Corresponding author address: James F. Danco, Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ jdanco@ou.edu major cooling effect on the climate, and it has a dramatic influence on the hydrological cycle in the midlatitudes and in the Arctic through spring melting (Vavrus 2007). Accumulated snow is a source of ground and surface water supply via natural (e.g., lakes and rivers) and engineered (e.g., reservoirs and aqueducts) storage and distribution systems. Over one-sixth of the world s population relies on melting snow and ice for their water supply (Barnett et al. 2005). Snowfall, which can be defined as the accumulation of snow during a given time period, greatly disrupts transportation and causes automobile accidents (Eisenberg and Warner 2005), and it has been linked with increases in heart attacks (Franklin et al. 1996). DOI: /JCLI-D Ó 2016 American Meteorological Society

2 6296 J O U R N A L O F C L I M A T E VOLUME 29 Adverse economic effects include canceled events, roof and building damage, snow removal costs, and damaging floods from rapid melting; in fact, during , major snowstorms caused a total of $21.6 billion in property losses in the United States (Changnon and Changnon 2006). Global mean surface temperature has increased during the past half-century, primarily due to the emission of greenhouse gases as a result of human activities, and this trend is likely to continue and perhaps accelerate in the coming decades (IPCC 2013). If there were no changes in the amount of precipitation, rising temperatures would produce less snowfall almost everywhere around the globe because a higher fraction of the total precipitation would fall as rain rather than snow. However, the expected response is not this simple because it is virtually certain that global precipitation will increase with increased global temperature, at a likely rate of 1% to 3% per 8C of warming, with precipitation increases likely occurring in high latitudes and currently moist midlatitude regions under the RCP8.5 greenhouse gas forcing scenario (Collins et al. 2013). This is primarily due to exponential increases in atmospheric moisture content, resulting from exponential increases in the water-holding capacity of the atmosphere by ;7% per 8C of warming, and to increased transport of water vapor from the tropics (Sun et al. 2007; Collins et al. 2013). In the winter months of December February (DJF), model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) indicate especially strong increases in precipitation in middle and high latitudes compared to other months of the year. Mean DJF precipitation is projected to increase by at least 10% throughout much of the Northern Hemisphere where snow falls and by greater than 50% across far northern Canada and northern Asia by under the RCP8.5 forcing scenario (Collins et al. 2013; see Fig. 1 in their box 12.1). Extreme precipitation events in particular are exhibiting strong changes. Hartmann et al. (2013) found that there have likely been increases in either the frequency or intensity of heavy precipitation in North America and Europe since the middle of the twentieth century, and in Asia increases are being observed in more regions than decreases. Furthermore, Collins et al. (2013) concluded that a global shift to more intense precipitation events and fewer weak events is likely to continue as temperatures increase, especially over most midlatitude landmasses and wet tropical regions. The total effect of global warming on snowfall is a delicate balance between these competing effects of increased temperature and increased precipitation. A number of recent studies have examined projected future changes in snowfall and the mechanisms associated with them. Kapnick and Delworth (2013) analyzed the response of snowfall to an idealized increase in carbon dioxide concentration in two versions of a GFDL climate model that differ primarily in resolution. They found that snowfall is simulated to decrease over most continental locations, but that increases in snowfall are simulated in high-latitude and high-elevation regions where the contribution of increased precipitation dominates the reduced fraction of precipitation that falls in the form of snow. Krasting et al. (2013) examined trends in annual, seasonal, and monthly Northern Hemisphere snowfall in a multimodel ensemble of CMIP5 simulations of projected future climate under the RCP4.5 emissions scenario. Similarly, the study discovered that total annual snowfall is projected to decrease over areas in which winter temperatures are relatively mild and even a modest amount of warming will decrease the amount of precipitation that falls as snow. At higher latitudes, where the climate is typically cold enough to remain below freezing and still support snow even with moderate warming, they found that the simulated increase in winter precipitation is sufficient to offset the decrease in snow fraction that results from rising temperatures. This includes large regions of Eurasia and North America in the midwinter but is more limited to the northernmost regions in warmer months. The study also found that the 2108C isotherm in the simulated late twentieth-century climate approximately separates the regions of increasing and decreasing snowfall (Krasting et al. 2013). Other studies have focused on higher intensity snowfall events. Rather than using model output directly, Notaro et al. (2014) processed climate model output using a statistical downscaling approach to examine projected trends in snowfall and snow depth over central and eastern North America. As in the studies cited previously, they found that annual snowfall is expected to decrease across this region. However, their analysis also indicated that daily snowfall events over much of this region are projected to become less frequent but more intense. O Gorman (2014) found that warming from CMIP5 model simulations causes widespread decreases in annual mean snowfall across the middle latitudes from to , with increases confined to extreme northern Canada, Greenland, and northern Siberia. In contrast, extreme snowfall events exhibit a much more muted response. For example, in the multimodel median, mean snowfall decreases by 65% for monthly climatological temperatures just below freezing while the 99.99th percentile of daily snowfall only decreases by 8% (O Gorman 2014). This study will specifically examine how the frequency distribution of daily snowfall events in the Northern Hemisphere will be affected by increasing temperatures,

3 1SEPTEMBER 2016 D A N C O E T A L thus extending the work of Krasting et al. (2013) that examined snowfall on monthly and annual time scales. The approach will be somewhat different from that of O Gorman (2014) by focusing on spatial variations in the response of daily snowfall. As in both of these previous studies, a key issue will be the interplay between changes in frequency distribution of precipitation amount and changes in the fraction of precipitation falling as snow. Section 2 of this paper includes a description of the methodology, including the model output and observed data used in the analysis. Section 3 discusses the projected future changes in daily snowfall and how well they match the findings from previous studies. Section 4 examines the extent to which daily snowfall projections may be affected by the temperature biases of the models. Section 5 summarizes the findings of the study while identifying questions that could be the subject of future research. 2. Data and methodology This analysis uses output from an ensemble of climate model simulations coordinated under CMIP5, promoted by the World Climate Research Programme (Taylor et al. 2012). The CMIP5 output was downloaded using the Program for Climate Model Diagnosis and Intercomparison web portal ( llnl.gov/), and two experimental designs are considered. The first is a historical simulation of twentiethcentury climate and the second is a twenty-first-century climate simulation using the RCP8.5 forcing scenario, which increases greenhouse gas emissions by a factor of about 3 over the course of the twenty-first century (Riahi et al. 2011). A total of 24 coupled climate models and 37 ensemble members had daily snowfall data available (in terms of mass per unit area per unit time) and thus are used in this study. Table 1 provides a list of the models used, the number of ensemble members from each model, and each model s original resolution. Since the models were run at a wide range of spatial resolutions, their output is interpolated to a common 18 latitude by 18 longitude grid to facilitate comparison among the models. This resolution is chosen to approximate the highest resolution among the models. All grid boxes that contain more than 50% water (based on the GFDL-ESM2M land fractions that are interpolated down to resolution) are discarded because the impacts of snowfall are confined mainly to land regions. As noted by Krasting et al. (2013), thecmip5 models determine precipitation type using a variety of methods. The snowfall output from the models is used regardless of any differences in methods of determining precipitation type. Since the model output provides no information about snow density, snowfall is determined from its water equivalent by assuming a uniform 10:1 snow-to-liquid ratio, as in Krasting et al. (2013). Since this study uses climate model simulations from CMIP5, it is important that any biases in the CMIP5 models are identified and considered when examining their simulations of Northern Hemisphere snowfall. Krasting et al. (2013) compared CMIP5 snowfall output in the Northern Hemisphere to estimates of observed snowfall and found that a positive snowfall bias exists in the multimodel ensemble over the western half of North America and much of Eurasia, except for central Europe where snowfall is underestimated. However,themodelsdoreasonablycapturethepatterns of relative maxima and minima of snowfall. A general cold bias exhibited by CMIP5 models, particularly in cold regions and months, can at least partially explain the positive bias in snowfall in most of the Northern Hemisphere. For example, an evaluation of European temperatures and their projected changes from the CMIP5 ensemble found that, on average, these models have a cold bias in winter, especially in northern Europe (Cattiaux et al. 2013). Another study by Su et al. (2013) compared CMIP5 model output to ground observations in the eastern Tibetan Plateau for the period of and found that the majority of models have cold biases averaging C for the months of December May, and less than 18C for June October. A substantial wintertime cold bias has also been shown to exist among CMIP5 models in the very high latitudes of North America (Sheffield et al. 2013). CMIP5 simulations suffer from biases in precipitation as well. An analysis of the precipitation biases of 17 CMIP5 models over North America for found that the models have a mean positive bias of 12% in DJF, with an overestimation of precipitation in more humid and cooler regions and an underestimation in drier regions (Sheffield et al. 2013). Another study found that most CMIP5 models have a positive bias in precipitation over regions of complex topography, such as western North and South America and southern Africa and Asia, and a negative bias over arid regions (Mehran et al. 2014). To determine the temperature biases associated with each model and how such biases may impact the response of daily snowfall, historical land-only monthly temperature data from the Climatic Research Unit (CRU) TS v dataset over the period of arealsousedinthisstudy(harris et al. 2014). They were downloaded from the CRU website ( crudata.uea.ac.uk/cru/data/hrg/) and have a native resolution of 0.58 latitude longitude.

4 6298 J O U R N A L O F C L I M A T E VOLUME 29 TABLE 1. List of CMIP5 models included in the analysis. The number of ensemble members used and the horizontal resolution are indicated for each model. Model acronym Model name Modeling center (or group) Number of ensemble members Horizontal resolution (8 latitude by 8 longitude) BNU-ESM Beijing Normal University Earth System Model College of Global Change and Earth System Science, Beijing Normal University CanESM2 Second Generation Canadian Earth System Model Canadian Centre for Climate Modeling and Analysis CCSM4 Community Climate System Model, version 4 National Center for Atmospheric Research CMCC-CESM Centro Euro-Mediterraneo per I Cambiamenti Climatici Carbon Cycle Earth System Model CMCC-CM Centro Euro-Mediterraneo per I Cambiamenti Climatici Climate Model CMCC-CMS Centro Euro-Mediterraneo per I Cambiamenti Climatici Climate Model with a resolved stratosphere CNRM-CM5 Centre National de Recherches Meteorologiques Climate Model, version 5 CSIRO-Mk3.6.0 Commonwealth Scientific and Industrial Research Organization Mark, version FGOALS-g2 Flexible Global Ocean Atmosphere Land System Model, gridpoint version 2 GFDL-CM3 Geophysical Fluid Dynamics Laboratory Climate Model, version 3 GFDL-ESM2G Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics (GOLD) component (ESM2G) GFDL-ESM2M Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model 4 (MOM4) component (ESM2M) HadGEM2-CC Hadley Centre Global Environment Model, version 2 Carbon Cycle HadGEM2-ES Hadley Centre Global Environment Model, version 2 Earth System IPSL-CM5A-LR Institut Pierre-Simon Laplace Coupled Model, version 5A, low resolution IPSL-CM5A-MR Institut Pierre-Simon Laplace Coupled Model, version 5A, mid resolution Centro Euro-Mediterraneo per I Cambiamenti Climatici Centro Euro-Mediterraneo per I Cambiamenti Climatici Centro Euro-Mediterraneo per I Cambiamenti Climatici Centre National de Recherches Meìteìorologiques / Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS,Tsinghua University NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory Met Office Hadley Centre Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais) Institut Pierre-Simon Laplace Institut Pierre-Simon Laplace

5 1SEPTEMBER 2016 D A N C O E T A L TABLE 1. (Continued) Model acronym Model name Modeling center (or group) Number of ensemble members Horizontal resolution (8 latitude by 8 longitude) IPSL-CM5B-LR Institut Pierre-Simon Laplace Coupled Model, version 5B, low resolution MIROC5 Model for Interdisciplinary Research on Climate, version 5 MIROC-ESM Model for Interdisciplinary Research on Climate, Earth System Model MIROC-ESM-CHEM Model for Interdisciplinary Research on Climate, Earth System Model, Chemistry Coupled MPI-ESM-LR Max Planck Institute Earth System Model, low resolution MPI-ESM-MR Max Planck Institute Earth System Model, medium resolution MRI-CGCM3 Meteorological Research Institute Coupled Atmosphere Ocean General Circulation Model, version 3 NorESM1-M Norwegian Earth System Model, version 1 (intermediate resolution) Institut Pierre-Simon Laplace Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Max-Planck-Institut fu r Meteorologie Max-Planck-Institut fu r Meteorologie Meteorological Research Institute Norwegian Climate Centre

6 6300 J O U R N A L O F C L I M A T E VOLUME Projected future changes in daily snowfall a. Spatial patterns We begin by examining projected future changes in daily snowfall event frequency for events exceeding a given threshold amount. The historical time period used is , and it is compared to two future time periods: and To enable each model to have the same weight regardless of the number of available ensemble members from that model, an ensemble mean event frequency x i for each model i at each grid cell is calculated by averaging the frequency of daily snowfall events at or above a given threshold across the ensemble members. The model-mean percent change in frequency between two time periods is then computed as å N i51 x future i 2 å N i51 å N x historical i i51 x historical i 3 100, (1) where N is the number of models and the superscripts indicate the time period. This calculation is done for all months of the year ( annual ) and for a midwinter period (January February) chosen to be two months in length so that the sample size is large enough to get reasonably robust results. A map of the projected percent change in the annual number of measurable daily snowfall events, defined here as events $ 0.25 cm, from to is shown in Fig. 1a. The analysis indicates that measurable snowfall events decrease in frequency in most regions of the Northern Hemisphere where snow occurs, with the percent decrease in frequency especially large across the southern United States and Mexico, much of central and southern Europe, and central and southern Asia, where decreases of more than 30% are projected. The only areas with little change or a slight increase in measurable snowfall events are in some very high-latitude regions, including far northern Canada, northeastern Greenland, and northeastern Siberia. This projected response would be expected with a warmer planet as higher temperatures cause more precipitation events to become rain instead of snow, except at the very coldest regions where temperatures average so far below freezing that they can rise substantially and still be cold enough for snow, and thus the increase in precipitation becomes the dominant factor. Figure 1a indicates that this transition between increasing and decreasing changes in measurable snowfall events by generally occurs near the 2108C isothermin the historical period in northeastern Siberia and far northern Canada and near the 2208C isotherm in Greenland. The majority of models project an increase in events in far northern Canada, northeastern Greenland, and northeastern Siberia (Fig. 1b). In most of the rest of the Northern Hemisphere, there is nearly unanimous agreement among all the models that measurable snowfall events will decrease (Fig. 1b). By (Fig. 1c), the changes discussed above become even more pronounced as the climate continues to warm. Thus the frequency of measurable daily snowfall events once again decreases throughout almost the entire Northern Hemisphere, but now the drop is even greater. The only regions where these events are projected to increase are the same very high-latitude areas as the earlier time period, and the transition between increasing and decreasing events occurs near the same climatological temperatures as in the earlier time period. The map of the percentage of models projecting a decrease by (Fig. 1d) is very similar to Fig. 1b, but the models have 95% 100% agreement in even more areas than in the earlier time period. Examining changes in the frequency of events for higher thresholds yields substantially different results. For example, the geographic boundary between positive and negative changes in the frequency of annual events $ 10 cm day 21 from to is much farther south, and it generally coincides with an annual average temperature over of about 08C(Fig. 2a). Many more places are projected to have a higher number of these events, including much of Canada, Greenland, northern Asia, and the Tibetan Plateau region. The likely reason for this disparity in response between measurable events and larger events is that warming increases absolute humidity and leads to higher sea surface temperatures in moisture source regions, which leads to a disproportionate increase in heavier precipitation events (Allen and Ingram 2002; Sun et al. 2007; Trenberth 2011; Collins et al. 2013) and thus more heavy snowfall events during the instances when it is below freezing. The dependence of changes in frequency of $10 cm day 21 events on temperature results in disparities among regions of similar latitude. Regions with more continental climates are more likely to have projected increases in $10 cm day 21 events while regions with stronger marine influence such as Europe and the west coast of North America (even as far north as southern Alaska) have projected decreases. The effects of elevation are also clearly seen in the Tibetan Plateau region of central Asia, where the frequency of these events increases despite decreases in places at similar latitudes to the east and west. Since the elevation here is very high, and the marine influence is minimal, temperatures are able to remain cold enough for snow even with projected future warming. In the case of $10 cm day 21 events, there are

7 1SEPTEMBER 2016 D A N C O E T A L FIG. 1. Percent change in frequency of measurable daily snowfall events ($0.25 cm) in all seasons simulated by the CMIP5 multimodel ensemble for the periods of (a) and (c) The changes are expressed relative to a reference period of [see Eq. (1)]. Grid boxes that had less than five events in the reference period are shaded gray. Line contours display the model-mean annual temperature over the reference period at intervals of 108C. In addition, the percent of models projecting a decrease in frequency of measurable events, excluding any models that had zero events in both the historical and future time periods, is given for the periods (b) and (d) few areas with nearly unanimous agreement among the models in the projected sign of change, and there is considerable disagreement in many areas (Fig. 2b). By , the frequency of events $10 cm day 21 is projected to decrease even further in the United States, Europe, and central Asia outside of Tibet, while the opposite occurs in high-latitude or high-elevation regions, including northern and central Canada, Greenland, northern Asia, and Tibet (Fig. 2c). In some of the highest latitudes, percent increases even approach and exceed 100%. Thus the trends in the earlier time period are amplified in the later time period during which higher temperatures are simulated. Interestingly, however, the geographic boundary between increasing and decreasing frequency is not projected to retreat poleward very much. This is likely because the competing effects of warming temperatures and rising precipitation remain nearly the same close to the boundary, so while both of these variables increase, they each continue to have an equal but opposite effect on snowfall. As a result, they offset each other, and the net result is little movement of the boundary. There is considerably better model agreement in the sign of change of.10 cm day 21 events for compared to , with 95% 100% of models projecting an increase or decrease in many regions (Fig. 2d). The patterns seen in Figs. 2b and 2d hold true for even larger intensity thresholds and in January February only (not shown). Raising the intensity threshold further to 25 cm day 21 results in the geographic boundary between increasing

8 6302 J O U R N A L O F C L I M A T E VOLUME 29 FIG. 2.AsinFig. 1, but for daily snowfall events $ 10 cm. and decreasing frequency shifting even farther equatorward (Fig. 3a), including large portions of Asia and North America in projected increases (extending as far south as the northern United States). Note that the fewer number of these events results in a larger range for the percent change metric and a noisier spatial pattern. The boundary between increasing and decreasing events at this intensity occurs at even warmer average annual temperatures over , generally around 58C. As with the $ 10 cm day 21 events, by , the geographic boundary between increasing and decreasing frequency experiences little movement, and areas with a projected decrease in the earlier time period are projected to experience even fewer $ 25 cm day 21 events, while regions with an increase are projected to experience even more $ 25 cm day 21 events (Fig. 3b). Focusing next on midwinter (i.e., January February), the frequency of larger snowfall events is generally projected to increase in more regions than it does when all months are considered. For instance, by , the frequency of January February days with snowfall $ 10 cm is simulated to rise in not only Canada, Greenland, and much of northern and central Asia, but also in parts of northern Europe and the northern United States (Fig. 4a). High latitudes are projected to experience a substantial increase in these events, greater than 100%, which is much higher than their projected annual increase. Because midwinter months are colder, more regions are able to stay cold enough for snow that might be too warm in other months of the year, and since a greater fraction of precipitation is still able to stay as snow, the increase in precipitation becomes an even more dominant factor over warming temperatures. The boundary between increasing and decreasing events coincides with an average January February temperature over of about 2108C. By , the geographic boundary between increasing and decreasing frequency of midwinter events $ 10 cm day 21 stays in roughly the same position as it was during the earlier time period (Fig. 4b), similar to what was seen with

9 1SEPTEMBER 2016 D A N C O E T A L FIG. 3. (a),(b) As in Figs. 1a and 1c, but for daily snowfall events $ 25 cm. annual events, but the magnitude of the changes goes up considerably. To conduct a more regional analysis, percent changes in the frequency of measurable and heavy daily snowfall events for the entire year are computed over each of the 20 Northern Hemisphere regions used in the study by Krasting et al. (2013), plus three additional regions in interior North America (Table 2; see Fig. 5 for a geographic representation of the regions). As might be expected from Fig. 1, every region is projected to experience a decrease in the annual number of measurable snowfall events by , with the exception of northeastern Greenland where a small increase is projected. As was also evident in the maps, regions with a projected increase in events are more common for heavier events than for all measurable events, especially for events $ 25 cm day 21. Colder regions are also more likely to show projected increases in snowfall events (note that regions are sorted from warmest to coldest model-mean annual climatological temperature in Table 2). However, the dependence on climatological temperature is less clear for large snowfall events than for all measurable events, as some regions with projected decreases in the frequency of large events are colder than regions with projected increases in these events. This is likely at least partly because regions with similar annual mean temperatures can have very different snowfall climatologies if they have maritime versus continental climates, such as British Columbia versus southern Québec and New Brunswick (Table 2). Furthermore, the spread between the 25th and 75th percentiles among the ensemble members is much greater for large events than for all measurable events, indicating a larger uncertainty in the effects of a warming climate on high-intensity snowfall events. This is especially true over Europe and central and FIG. 4. (a),(b) As in Figs. 2a and 2c, but for midwinter (January February) events and the midwinter temperature only.

10 6304 J O U R N A L O F C L I M A T E VOLUME 29 TABLE 2. Percent change in the frequency of daily snowfall events at or above specified thresholds from all seasons. The changes are expressed relative to the period and are organized by future time period and region. Regions are sorted top to bottom from warmest to coldest model-mean annual climatological temperature over The model-mean percent frequency changes in snowfall (as described in the text) are shown. Adjacent numbers in parentheses represent the 25th and 75th percentile frequency changes among the 37 individual ensemble members. Undefined values ( ) occurred if there were no days with snowfall in the historical period for many models. Region $0.25 cm $10 cm $25 cm Southeastern U.S. (#1) 233 (247, 230) 271 (280, 269) 238 (258, 214) 274 (291, 272) 234 (255, 112) 258 (284, 0) South Central U.S. (#21) 224 (231, 220) 256 (265, 252) 226 (234, 217) 259 (272, 249) 227 (247, 28) 257 (279, 243) U.S. Mid-Atlantic (#2) 222 (231, 219) 255 (266, 252) 227 (238, 219) 263 (274, 259) 223 ( ) 249 (282, 231) Caucasus Mountains region (#15) 225 (233, 222) 256 (269, 253) 223 (239, 224) 261 (279, 259) 215 (237, 130) 257 (288, 244) Central and Southern Europe (#16) 226 (236, 224) 260 (272, 257) 221 (234, 214) 252 (269, 242) 217 (230, 147) 228 (251, 164) U.S. West Coast (#8) 223 (234, 219) 255 (271, 254) 234 (257, 230) 272 (287, 271) 243 (265, 233) 278 (292, 252) North Central U.S. (#22) 213 (217, 210) 237 (245, 234) 24 (219, 14) 228 (249, 222) 10 (227, 131) 26 (239, 114) New England (#3) 215 (220, 212) 242 (252, 238) 218 (225, 211) 250 (259, 245) 26 (213, 113) 227 (247, 28) Japan (#12) 212 (215, 28) 232 (237, 226) 212 (224, 28) 237 (255, 231) 0 (236, 120) 23 (244, 134) Baltic Sea region (#17) 217 (224, 213) 243 (254, 239) 211 (223, 25) 235 (244, 230) 25 (225, 135) 225 (217, 1100) Southern Québec and New Brunswick (#4) 211 (214, 29) 231 (235, 226) 27 (214, 21) 235 (245, 227) 121 (21, 131) 26 (225, 125) British Columbia (#9) 215 (220, 211) 239 (252, 234) 226 (240, 223) 256 (270, 253) 237 (258, 214) 269 (284, 234) Southern Canada (#23) 28 (211, 26) 223 (227, 221) 19 (26, 123) 11 (220, 114) 123 (14, 191) 134 (210, 1134) Labrador (#5) 28 (211, 26) 226 (232, 221) 12 (25, 17) 219 (231, 212) 135 (16, 168) 142 (0, 162) Tibet (#14) 26 (27, 23) 217 (219, 212) 113 (15, 122) 121 (11, 128) 118 (236, 189) 169 (252, 1110) Northern Scandinavia (#18) 212 (218, 28) 232 (243, 225) 22 (213, 111) 214 (228, 23) 0 (256, 162) 18 (250, 1331) Southern Alaska (#10) 212 (218, 210) 233 (245, 228) 215 (226, 211) 240 (261, 240) 220 (247, 26) 241 (273, 236) Eastern Hudson Bay region (#7) 25 (27, 24) 218 (221, 214) 19 (22, 116) 13 (212, 116) 125 (223, 1134) 155 (222, 1151) Northeastern Québec (#6) 24 (25, 22) 214 (218, 211) 114 (16, 125) 18 (25, 125) 189 (132, 1174) 1119 (162, 1350) Northeastern Siberia (#11) 22 (24, 11) 210 (215, 23) 112 (0, 123) 116 (28, 124) 129 (216, 191) 153 (211, 196) Northwestern Siberia (#13) 23 (25, 21) 210 (215, 25) 131 (119, 150) 184 (170, 1134) 171 (211, ) 1130 (285, ) Southeastern Greenland (#19) 21 (23, 12) 25 (27, 21) 13 (21, 111) 16 (22, 120) 18 (22, 130) 119 (15, 162) Northeastern Greenland (#20) 13 (11, 17) 14 (11, 18) 133 (121, 148) 180 (150, 1115) 169 (145, 1118) 1188 (1162, 1323)

11 1SEPTEMBER 2016 D A N C O E T A L FIG. 5. Regions used for regional analysis of projected daily snowfall changes. Regions 1 20 are from Krasting et al. (2013), while regions are new regions in interior North America defined for this study. eastern Asia, where the sign of the projected change in extreme events varies considerably among the ensemble members (Table 2). The time of year also has a large influence on the change in daily snowfall events. Compared with events throughout the year, simulated percent changes in the number of January February events are considerably more positive in colder regions and considerably less negative in warmer regions (Table 3). For example, while measurable events during the entire year decrease in every region but one, in midwinter they are projected to stay the same or increase by in the three northeastern Canadian regions, southern Canada, northern Scandinavia, Tibet, both regions in northern Siberia, and both regions in Greenland. Once again, regions with a colder January February climatological temperature are much more likely to have projected increases in snowfall events during these months, but there are some regions

12 6306 J O U R N A L O F C L I M A T E VOLUME 29 TABLE 3. As in Table 2, but for midwinter (January February) daily snowfall events and regions sorted by model-mean midwinter climatological temperature. Region $0.25 cm $10 cm $25 cm Southeastern U.S. (#1) 229 (243, 224) 268 (279, 264) 231 (253, 16) 273 (291, 271) 228 (260, 183) 256 (291, 188) South Central U.S. (#21) 221 (228, 214) 249 (259, 245) 221 (235, 29) 253 (268, 245) 215 (238, 149) 256 (275, 225) U.S. Mid-Atlantic (#2) 216 (228, 212) 245 (260, 239) 220 (240, 28) 256 (268, 249) 218 (247, 125) 243 (273, 211) U.S. West Coast (#8) 217 (230, 210) 247 (264, 241) 231 (258, 225) 268 (286, 264) 243 (284, 223) 277 (293, 257) Caucasus Mountains region (#15) 219 (227, 216) 249 (263, 244) 218 (239, 220) 256 (274, 253) 28 (252, 145) 254 (290, 240) Central and Southern Europe (#16) 221 (231, 217) 254 (267, 249) 216 (233, 25) 240 (260, 232) 211 (235, 173) 28 (246, 1160) New England (#3) 28 (211, 24) 225 (233, 218) 214 (221, 28) 237 (254, 228) 0 (224, 132) 23 (238, 120) British Columbia (#9) 28 (212, 24) 226 (240, 215) 224 (237, 219) 253 (268, 244) 241 (254, 216) 274 (288, 242) North Central U.S. (#22) 24 (210, 0) 221 (228, 215) 112 (210, 133) 18 (231, 118) 120 (228, 1116) 168 (210, 1220) Baltic Sea region (#17) 28 (211, 25) 228 (238, 222) 11 (210, 17) 215 (231, 23) 212 (265, 191) 0 (246, 1460) Japan (#12) 27 (211, 23) 220 (228, 212) 26 (220, 113) 221 (248, 23) 23 (244, 139) 116 (229, 198) Southern Québec and New Brunswick (#4) 21 (23, 12) 27 (213, 22) 14 (29, 18) 213 (230, 11) 135 (212, 165) 129 (219, 187) Southern Alaska (#10) 23 (27, 0) 217 (232, 28) 210 (227, 13) 231 (260, 222) 218 (241, 114) 235 (273, 214) Northern Scandinavia (#18) 0 (25, 16) 26 (215, 13) 117 (11, 145) 133 (112, 170) 237 (287, 0) 253 (291, ) Southern Canada (#23) 12 (21, 13) 0 (26, 16) 141 (111, 172) 178 (139, 1126) 133 (242, ) 1213 (147, ) Labrador (#5) 12 (21, 16) 0 (27, 18) 115 (13, 123) 18 (213, 120) 166 (119, 1133) 1114 (141, 1250) Tibet (#14) 16 (14, 110) 111 (18, 117) 159 (136, 1105) 1147 (190, 1246) 167 (229, ) 1280 (194, ) Eastern Hudson Bay region (#7) 19 (16, 111) 119 (111, 129) 138 (115, 171) 176 (139, 1133) 185 (272, ) 1245 (29, ) Northeastern Québec (#6) 111 (16, 115) 127 (119, 132) 137 (121, 170) 169 (137, 1143) 1315 (13, 12350) 1614 (1264, ) Northeastern Siberia (#11) 111 (15, 119) 122 (118, 132) 135 (111, 159) 172 (126, 1101) 182 (120, ) 1165 (143, ) Southeastern Greenland (#19) 11 (22, 13) 13 (23, 15) 17 (22, 118) 118 (16, 128) 114 (24, 144) 130 (17, 181) Northwestern Siberia (#13) 17 (13, 112) 121 (113, 127) 1122 (1108, 1609) 1746 (1559, 12850) (, ) (, ) Northeastern Greenland (#20) 18 (13, 114) 125 (112, 133) 142 (113, 160) 1108 (151, 1152) 186 (130, 1167) 1220 (1122, 1468)

13 1SEPTEMBER 2016 D A N C O E T A L that have smaller projected increases (or even projected decreases) in heavy midwinter events compared to regions that are warmer. The spread in model projections is greater for large midwinter events as well (Table 3). b. Daily snowfall histograms Histograms depicting daily snowfall changes are constructed for each of the 23 regions from the previous subsection. The same historical and future time periods are used as well. Daily snowfall events at each grid point in the region are categorized into 12 bins, with the lowest including events, 0.25 cm, defined as a trace (T), which is approximately the cutoff between measurable and nonmeasurable snowfall. The second bin ranges from T to5cmandthenextninehavea uniform width of 5 cm. The last bin includes daily snowfall events greater than 50 cm. The model-mean frequency difference between two time periods is then computed for each bin and for each region as å N å G i51 j51 x future i, j 2 å N NG å G i51 j51 x historical i, j, (2) where x i, j is the ensemble mean event frequency for each model i at each grid cell j, N is the number of models, G is the number of land grid cells in the region, and the superscripts indicate the time period. The frequency distribution is normalized by the total number of land grid points in the region in order to effectively compare regions of different sizes. The model median differences using the ensemble means for each model are also computed. Additionally, the maximum, minimum, and individual differences among all 37 separate ensemble members are calculated and shown in the histograms. These difference histograms are computed for each of six 2-month intervals (January February through November December), and for each future period ( and ). The July August interval is only analyzed for the two Greenland regions, because in all other regions these months are too warm for substantial amounts of snow to fall. Since it is not feasible to show histograms for all 23 regions and all 2-month intervals, selected histograms are analyzed to illustrate some examples of each type of snowfall response. In some regions that are examined, the climate is warm enough that an increase in temperature will cause a large decrease in daily snowfall for all bins in all months of the year, due to less precipitation falling as snow and more as rain. The only bin that increases in such regions is 0 T, as the frequency of days with no snow must go up when the overall frequency of daily snowfall events goes down. Regions exhibiting this behavior include the southeastern United States, the U.S. Mid-Atlantic, the south-central United States, the U.S. West Coast, British Columbia, southern Alaska, and central and southern Europe. A difference histogram for the last of these regions for January February for serves as an example (Fig. 6a). As is clearly shown in this figure, a sharp decline in daily snowfall event frequency is projected by most of the models in this region by , and all 37 ensemble members simulate that the frequency of days with zero snowfall will go up and the frequency of days with T 5 cm will go down. The average daily snowfall is projected to decrease substantially as well. By , this downward trend in daily event frequency is larger, with mean midwinter daily snowfall in central and southern Europe reduced by more than half (Fig. 6b). This dramatic decrease in daily snowfall in these regions is due to relatively high winter temperatures that make the climate marginal for snow. Thus the higher temperatures that models simulate in the future periods would allow for more precipitation that would have fallen as snow to instead fall as rain. While increases in precipitation are likely in at least some of these regions (Sun et al. 2007; Collins et al. 2013), this is not enough to offset the effects of increasing temperatures. Decreases in snowfall are projected to be even more dramatic in these regions in the warmer months such as the fall transition months (November December) and the spring transition months (March April), during which the fraction of precipitation that falls as snow is even more sensitive to rising temperatures (not shown). For example, by the later time period, average daily snowfall in these transition months in the U.S. mid-atlantic is projected to be less than a third of what it was in In other regions that are slightly farther north and colder, including the New England, Japan, Caucasus Mountains, and Baltic Sea regions, average snowfall is still simulated to decrease in all months of the year, but in January February the frequency of larger snowfall events stays about the same or even slightly increases. Figure 7a shows a clear example of this pattern, with the frequency of small and moderate midwinter snow events in New England dropping by but the occurrence of large events $ 30 cm day 21 slightly increasing in the ensemble mean. Midwinter snowfall is also projected to decrease in Japan by , but the frequency of events $ 25 cm day 21 goes up slightly (Fig. 7b). As discussed earlier, this is likely due to higher atmospheric moisture and higher sea surface temperatures causing a greater number of intense precipitation events (Allen and Ingram 2002; Sun et al. 2007; Trenberth 2011; Collins et al. 2013), including heavy snowfall events when temperatures are cold enough.

14 6308 J O U R N A L O F C L I M A T E VOLUME 29 FIG. 6. Histograms depicting the change in frequency of daily snowfall events (January February) as a function of intensity for the periods of (a) and (b) for central and southern Europe. The ordinate is displayed on a nonlinear scale in order to facilitate the representation of values that can span several orders of magnitude. The histogram bars represent the mean difference in frequency among the ensemble means of all the models for that particular intensity bin [see Eq. (2)], and each white circle represents the median difference in frequency among the ensemble means. Within a bin, the narrow black tick marks represent the difference in frequency of each multimodel ensemble member for that bin, while the upper and lower whiskers display the maximum and minimum difference, respectively, among all the members. All grid cells containing more than 50% water are masked out, and all of the frequency differences are divided by the total number of land grid points in the region. Average daily snowfall is displayed on each histogram for the historical and future period. The north-central United States, southern Canada, southern Québec and New Brunswick, Labrador, eastern Hudson Bay, Tibet, and northern Scandinavia are regions where the models project an overall increase or little change in daily January February snowfall by For example, an increase or little difference in snowfall event frequency in midwinter is projected for all bins except for the lightest snowfall events (T 5 cm day 21 ) in the north-central United States, with no change in average midwinter daily snowfall (Fig. 8a). In these places, it is cold enough that some warming is not enough to greatly decrease the fraction of precipitation that falls as snow, and the increase in precipitation is just as important or the more dominant factor. In the warmer transition months of November December and March April, a decrease or little change in overall snowfall is projected, but the large event frequency still increases (e.g., Fig. 8b). This is FIG. 7.AsinFig. 6, but for the (a) New England and (b) Japan regions, for

15 1SEPTEMBER 2016 D A N C O E T A L FIG. 8. As in Fig. 6, but for the north-central United States for (a) January February and (b) March April for comparable to what is projected to happen in regions slightly farther south in the midwinter (e.g., Figs. 7a,b) and is likely occurring for similar reasons. These projections also demonstrate that increases in midwinter snowfall in these regions are likely to be offset by decreases in snowfall during warmer months, resulting in a shortening of the overall snowfall season, as was found by Krasting et al. (2013). By , the regions projected to experience an increase in average daily snowfall in midwinter no longer include the north-central United States, southern Québec and New Brunswick, and northern Scandinavia, because the temperatures are finally warm enough to cause a substantial drop in the fraction of precipitation that falls as snow. But this does not stop the more intense events from increasing. For instance, in southern Québec and New Brunswick, average daily snowfall in January February drops by , but the frequency of events $ 20 cm day 21 still increases (Fig. 9a). And, while average daily snowfall in November December plummets by over 40% from what it was in , daily events $ 35 cm slightly increase (Fig. 9b). For places as far north as northeastern Québec, northwestern Siberia, and northeastern Siberia, where the fraction of precipitation that falls as snow is even less sensitive to warming, the frequency of daily snowfall events is simulated to rise in the midwinter months as well as the transition months due to the dominating effect of higher atmospheric moisture and thus greater precipitation. However, even in climates this cold, the FIG. 9. As in Fig. 6, but for for southern Québec and New Brunswick for (a) January February and (b) November December.

16 6310 J O U R N A L O F C L I M A T E VOLUME 29 FIG. 10. As in Fig. 6, but for northwestern Siberia for (a),(b) November December and (c),(d) September October for (left) and (right) warm months of September October and May June receive less snowfall overall. Once again though, in some cases these months are still projected to have an increase in large events. As an example, daily snowfall events in northwestern Siberia are projected to increase in November December by for all measurable bins, and average daily snowfall goes up (Fig. 10a), but in September October the frequency of small events decreases greatly and events $ 10 cm day 21 increase (Fig. 10c). This is yet another example of a shortening of the snowfall season that is likely to occur in many regions, even where snowfall in colder months may increase. By , a similar pattern is projected, but increases in snowfall during November April and decreases during May October are even more dramatic (e.g., Figs. 10b,d), because as temperatures continue to rise, their effects on snowfall should continue to strengthen. The two Greenland regions are the only ones among those examined where daily snowfall is simulated to increase by for all 2-month intervals and nearly all bins, with the exception of July August. For example, the frequency of events increases for northeastern Greenland in September October for all measurable bins, and the average daily snowfall increases as well (Fig. 11a). By , average daily snowfall is projected to increase even more in northeastern Greenland in September October (Fig. 11b). In cold months the increase is even more dramatic; for instance, in northeastern Greenland average midwinter daily snowfall in the later time frame is about 30% higher than what it was in the earlier time frame (not shown). In southeastern Greenland, however, some subtle negative effects of increased temperatures on snowfall are evident where average daily snowfall is projected to slightly decline from