1. Introduction
The twenty-first-century projections generated by phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012) are analyzed here to assess climate change in North America (NA). This study accompanies two companion papers (Sheffield et al. 2013a, hereafter Part I; Sheffield et al. 2013b, hereafter Part II) that assess the CMIP5 models’ potential to accurately simulate regional climate in the twentieth century. Additionally, it provides an overview and is a first step toward integrating the understanding of climate projection results from the individual papers in the Journal of Climate special collection entitled “North American climate in CMIP5 experiments.” This paper first examines the changes in the continent-wide distribution of seasonal precipitation and temperature in simulations making use of representative concentration pathway 8.5 (RCP8.5; Taylor et al. 2012). It then focuses on a select set of regional climate features. These changes are considered in the context of the ability of models to accurately simulate current climate, discussed in the two companion papers (Part I and Part II), which is generally comparable to that of CMIP3 models, with some improvement noted for individual models.
Previous projections of NA climate change (e.g., CMIP3) have been evaluated as part of earlier climate assessments (Solomon et al. 2007). The CMIP3 consensus projection indicated that, by 2080–99, annual mean temperature increases are very likely across NA with the greatest changes in northern Canada and Alaska, where 10°C mean wintertime temperature increases are projected in some scenarios (Solomon et al. 2007). Increases in annual mean precipitation are projected for the northern tier of the United States, northward into Canada, with projected decreases for the southwest United States, east Pacific warm pool, Caribbean, and adjacent land areas (e.g., Neelin et al. 2006; Seager et al. 2007; Seager and Vecchi 2010).
Beyond mean state changes, CMIP3 models predict a general increase in precipitation intensity (e.g., Diffenbaugh et al. 2005; Mahajan et al. 2012), particularly in the northern tier of the United States and Canada (Tebaldi et al. 2006). Increases in the duration and severity of drought are projected in regions such as Central America and midlatitude NA (e.g., Sheffield and Wood 2008), of which increased temperatures and evapotranspiration are major components (Francina et al. 2010; Gutzler and Robbins 2011; Wehner et al. 2011). A general increase in heat waves, decrease in cold extremes, decrease in frost days, and increase in length of the growing season have been projected across large portions of NA (Meehl and Tebaldi 2004; Diffenbaugh et al. 2005; Biasutti et al. 2012; Christiansen et al. 2011; Diffenbaugh and Scherer 2011; Duffy and Tebaldi 2012; Lau and Nath 2012), projected trends that are generally consistent with observed trends in such quantities over the last century (Alexander et al. 2006). Decreases in the duration of the snowpack have been projected for many regions, in particular low altitude areas of the Pacific Northwest and Rockies (e.g., Brown and Mote 2009; Elsner et al. 2010). Such changes are likely to lead to earlier spring snowmelt in many areas of the west (e.g., Hay et al. 2011). While model agreement is good on projected overall snow water equivalent declines in many areas by the end of the twenty-first century, some models show increases in snowpack along the Arctic Rim by 2100 (e.g., Brown and Mote 2009), particularly at the height of the winter season, even though the length of the snow season shortens (e.g., Räisänen 2008).
The projected response of NA climate in future emission scenarios is often more nuanced on the regional and local scales than for the continental-scale features, especially when considering the evolution during the seasonal cycle. For example, Rauscher et al. (2008) noted an earlier onset of the midsummer drought in Mexico and Central America in model projections for the end of the twenty-first century. Previous studies project a redistribution of precipitation in monsoon regimes such as the southwest United States with reduced spring rainfall and increased late rainy season rainfall (Seth et al. 2010, 2011; Biasutti and Sobel 2009). Ruiz-Barradas and Nigam (2010) showed that projections for the twenty-first century indicate a wetter north-central United States during spring (increase in number of extreme springs) and a drier southwest United States but little consistency in summer rainfall tendencies among models in these same regions. The uncertainty in projected summer precipitation extends to adjacent land areas of the Gulf of Mexico (Biasutti et al. 2012). Studies using CMIP3 projections suggest that, while the total number of North Atlantic tropical cyclones (TCs) will decrease and the number of intense hurricanes will increase, changes in North Atlantic TC activity remain uncertain. This is likely because climate models produce differing patterns of tropical SST change and different representations of tropical Atlantic SST relative to the tropical mean SST, which has been suggested to be a strong regulator of Atlantic TC activity (e.g., Latif et al. 2007; Swanson 2008; Vecchi et al. 2008; Wang and Lee 2008; Knutson et al. 2010; Vecchi et al. 2011; Zhao and Held 2012). Past studies using CMIP3-class models have generally indicated that climate projections for the twenty-first century at the local and regional levels remain a substantial challenge.
The present study provides a summary of projected twenty-first-century NA climate change in the updated state-of-the-art climate and Earth system models used in CMIP5. The results contained herein are contributed by members of the CMIP5 Task Force of the National Oceanographic and Atmospheric Administration (NOAA) Modeling, Analysis, Predictions and Projections Program (MAPP). Where appropriate, we make reference to individual papers submitted in parallel with this comprehensive study to the Journal of Climate special collection entitled “North American climate in CMIP5 experiments.” These individual contributions provide further depth to and physical interpretation of the findings summarized here. The current paper is one of three papers (with Part I and Part II) that synthesize the results and form the core of the special collection, and they represent an initial step toward integrating our understanding of CMIP5 evaluations and projections for North America. We largely focus on RCP8.5 in a core set of 17 CMIP5 models.
Section 2 provides a brief introduction to CMIP5 as well as the primary climate change experiment (RCP8.5). Section 3 presents an assessment of continental climate changes over the twenty-first century, and section 4 assesses regional climate changes. How intraseasonal variability will change in the twenty-first century is assessed in section 5. Changes in Atlantic and east Pacific TC activity are examined in section 6. Multidecadal trends in interannual to decadal hydroclimate variability are analyzed in section 7. Conclusions and a discussion are presented in section 8.
2. CMIP5 models and experiments
We use CMIP5 multimodel datasets of historical climate and climate change experiments (Taylor et al. 2012). These are long-term century-scale projections of climate based on coupled simulations that include a representation of future atmospheric composition from the RCPs (Meinshausen et al. 2011). Table 1 summarizes information on the models used in this study. As noted in Taylor et al. (2012), in addition to physical improvements made in many models, one advantage provided by the CMIP5 experiments versus the CMIP3 effort is that the horizontal resolution of the atmospheric components of the coupled models has significantly increased. About one-third of the models have atmospheric resolution of approximately 1.5° latitude or less, an improvement over CMIP3 where only about 10% of models met this criterion. This higher resolution is of some help in discerning the regional structure of hydroclimate variables over NA. However, in regions of complex topography and coastlines, the resolution of CMIP5 models remains insufficient for resolving important dynamic and thermodynamic features. Where appropriate in the text, we provide contrasts between the current CMIP5 results and those previously derived from CMIP3.
CMIP5 models evaluated in this study and their attributes.
Results based on RCP8.5 will be highlighted here, as it represents one of the core concentration pathways used for the CMIP5 project (Taylor et al. 2012). This experiment represents a high concentration pathway in which radiative forcing due to anthropogenic factors reaches 8.5 W m−2 by 2100 (e.g., Meinshausen et al. 2011, Fig. 4) and continues to grow thereafter. Selected analyses also provide a comparison to a more moderate mitigation pathway (RCP4.5) in which stabilization at 4.5 W m−2 occurs around 2050, and then forcing remains fixed. In terms of the time evolution and value of globally averaged radiative forcing at 2100, RCP8.5 and RCP4.5 most closely resemble the A2 and B1 scenarios for CMIP3 used in the International Panel on Climate Change (IPCC) Fourth Assessment Report (Solomon et al. 2007, Fig. 10.26), respectively. The projection experiments are compared to historical runs of the same models forced by observed trace gases, natural and anthropogenic aerosols, solar forcing, and other agents from the mid-nineteenth century onward (Taylor et al. 2012). A more comprehensive analysis of model performance in the historical runs is provided in the companion papers (Part I and Part II), which provide an additional baseline for comparison with the results shown here. No downscaling or bias correction is used before presentation of results. The exception to lack of downscaling is contained within the supplementary material, where a high-resolution model is used to assess future changes in tropical cyclone activity. Further, the use of downscaling in past and potential future studies is referenced at certain other points in the manuscript.
Multimodel ensemble mean (MEM) differences are highlighted for most of the analyses, as the MEM produces demonstrably superior results in historical climate assessment to those from an individual model (e.g., Gleckler et al. 2008; Pierce et al. 2009). We also use intermodel variability about the MEM to assess model consensus, including the likelihood of specific climate changes, with aspects demonstrating lack of model consensus summarized in the conclusions. In places, the methodology used is more diverse than a simple MEM analysis, given that this paper represents a synthesis of ideas from individual papers in the special collection. Ideally, we would like to compare common future and historical base periods among analyses. Unfortunately, from a practical standpoint this was not always possible. In the analysis of historical simulations in Part I and Part II, the base periods were often determined by the availability of the observational data to assess the models, which were application specific. Further, we assess projected changes in phenomena that have different time scales that range from synoptic to decadal. For example, assessment of interannual to decadal variability requires a longer record than assessment of tropical intraseasonal variability to assess statistical significance.
For consistency with Part I and Part II, our analysis concentrates on the core set of CMIP5 model highlighted by asterisks in Table 1. Part I discussed the selection criteria for these models, which meet the need to include contributions from a large and diverse set of modeling groups and model types. The number of models used in a particular analysis shown below is often limited by availability of data at the time of this study or local storage space, although we try to be as comprehensive as possible. For example, for many of the analyses requiring high-resolution data, a smaller subset of models was used because of the lack of RCP8.5 data availability. Further, because of the large number of contributors from different institutions, overall coordination and unified model selection were not always possible. For some analyses, the number of models used was significantly lower than that of the core set, or the RCP4.5 scenario was used rather than RCP8.5. In these cases, while the results are still enlightening, we have placed the details of these analyses into the supplementary material. These include analyses of moisture transport and diurnal temperature range changes, as well as an analysis of tropical cyclone activity change using a downscaling technique with a high-resolution model. We also occasionally include a more expansive set than the core models in an individual analysis, although we comment on how results would differ if a smaller subset including only core models were used.
3. Continental climate
a. Temperature and precipitation
We first examine projected changes on the continental scale at the end of the twenty-first century relative to the twentieth century climate. Part I noted that CMIP5 models have success in capturing the broad-scale features of NA surface temperature and precipitation in current climate, although with some regional-scale biases. Figure 1 shows the 17-member MEM December–February (DJF) and June–August (JJA) precipitation changes during 2070–99 for RCP8.5 relative to a 1961–90 base period. For models that have more than one run with the same forcing, the average is taken over all runs prior to forming the MEM. A two-sided t test comparing the MEM change to a standard error associated with interannual variability is shown at the 95% confidence level. Note that this tests only the sampling error associated with interannual variability in forming the MEM. Figure 2 shows the model agreement for the precipitation changes, along with two additional criteria for evaluation of significance that are described in the figure caption.
CMIP5 17-member multimodel, multirun ensemble-mean precipitation change (mm day−1) for RCP8.5 for 2070–99 relative to 1961–90 base period for (top) DJF and (bottom) JJA. Models used: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, FGOALS-s2, GFDL CM3, GFDL-ESM2M, GISS-E2-R, HadGEM2-CC, INM-CM4.0, IPSL-CM5A, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M (see Table 1). The red line is the 4 mm day−1 contour of the 1961–90 climatology. Grid points are cross hatched where the MEM does not pass a two-sided t test for differences of the mean with respect to interannual variability at the 95% level (see text). All models are interpolated to a common 2.5° by 2.5° latitude–longitude grid as in the corresponding climatology figure in Part I (Fig. 1).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
CMIP5 17-member multimodel, multirun ensemble-mean precipitation change (mm day−1) for RCP8.5 for 2070–99 relative to 1961–90 base period for (top) DJF and (bottom) JJA. Models used: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, FGOALS-s2, GFDL CM3, GFDL-ESM2M, GISS-E2-R, HadGEM2-CC, INM-CM4.0, IPSL-CM5A, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M (see Table 1). The red line is the 4 mm day−1 contour of the 1961–90 climatology. Grid points are cross hatched where the MEM does not pass a two-sided t test for differences of the mean with respect to interannual variability at the 95% level (see text). All models are interpolated to a common 2.5° by 2.5° latitude–longitude grid as in the corresponding climatology figure in Part I (Fig. 1).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
CMIP5 17-member multimodel, multirun ensemble-mean precipitation change (mm day−1) for RCP8.5 for 2070–99 relative to 1961–90 base period for (top) DJF and (bottom) JJA. Models used: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, FGOALS-s2, GFDL CM3, GFDL-ESM2M, GISS-E2-R, HadGEM2-CC, INM-CM4.0, IPSL-CM5A, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M (see Table 1). The red line is the 4 mm day−1 contour of the 1961–90 climatology. Grid points are cross hatched where the MEM does not pass a two-sided t test for differences of the mean with respect to interannual variability at the 95% level (see text). All models are interpolated to a common 2.5° by 2.5° latitude–longitude grid as in the corresponding climatology figure in Part I (Fig. 1).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The (top) DJF and (bottom) JJA plots of model agreement on sign of end-of-century precipitation change for the CMIP5 RCP8.5 scenario for the years 2070–99, relative to a base period of 1961–90. Red colors indicate the number of models (out of 17) that agree on a negative precipitation change; blue colors indicate the number of models that agree on a positive precipitation change. The color shaded areas (12 or more models agreeing on sign) pass a binomial test rejecting the hypothesis of 50% probability of either sign at the 95% level; areas not passing at this level are left unshaded. Stippled areas use a version of the Neelin et al. (2006) criterion to show grid points where more than half (9 or more) of the models both pass a two-tailed t test at the 95% confidence level and agree on sign. Tebaldi et al. (2011) use a modified version of this test that is effectively the same over the region shown here. Both of these criteria use significance tests on individual models that are more restrictive than the t test in Fig. 1 or the binomial test, which test characteristics of the ensemble rather than individual models (for comparisons of significance tests, see Langenbrunner and Neelin 2013).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The (top) DJF and (bottom) JJA plots of model agreement on sign of end-of-century precipitation change for the CMIP5 RCP8.5 scenario for the years 2070–99, relative to a base period of 1961–90. Red colors indicate the number of models (out of 17) that agree on a negative precipitation change; blue colors indicate the number of models that agree on a positive precipitation change. The color shaded areas (12 or more models agreeing on sign) pass a binomial test rejecting the hypothesis of 50% probability of either sign at the 95% level; areas not passing at this level are left unshaded. Stippled areas use a version of the Neelin et al. (2006) criterion to show grid points where more than half (9 or more) of the models both pass a two-tailed t test at the 95% confidence level and agree on sign. Tebaldi et al. (2011) use a modified version of this test that is effectively the same over the region shown here. Both of these criteria use significance tests on individual models that are more restrictive than the t test in Fig. 1 or the binomial test, which test characteristics of the ensemble rather than individual models (for comparisons of significance tests, see Langenbrunner and Neelin 2013).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The (top) DJF and (bottom) JJA plots of model agreement on sign of end-of-century precipitation change for the CMIP5 RCP8.5 scenario for the years 2070–99, relative to a base period of 1961–90. Red colors indicate the number of models (out of 17) that agree on a negative precipitation change; blue colors indicate the number of models that agree on a positive precipitation change. The color shaded areas (12 or more models agreeing on sign) pass a binomial test rejecting the hypothesis of 50% probability of either sign at the 95% level; areas not passing at this level are left unshaded. Stippled areas use a version of the Neelin et al. (2006) criterion to show grid points where more than half (9 or more) of the models both pass a two-tailed t test at the 95% confidence level and agree on sign. Tebaldi et al. (2011) use a modified version of this test that is effectively the same over the region shown here. Both of these criteria use significance tests on individual models that are more restrictive than the t test in Fig. 1 or the binomial test, which test characteristics of the ensemble rather than individual models (for comparisons of significance tests, see Langenbrunner and Neelin 2013).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
Figures 1 and 2 indicate increases in MEM wintertime precipitation along the west coast of NA from California to Alaska, as well as along the NA east coast from the Mid-Atlantic states northward. Model agreement for these changes is high north of about 40°N, where all but one or two models agree on the sign of changes for all locations. Comparison to a similar ensemble of 16 CMIP3 models indicates that, while the large-scale pattern of precipitation increases at middle to high latitudes and precipitation decreases in the subtropics are similar between the two intercomparisons, one notable difference is that the boundary between these changes has shifted slightly south. This yields projected precipitation increases over parts of California in CMIP5, passing the binomial test for agreement on sign at levels exceeding 95% at points seen in Fig. 2. High interannual variance over coastal land points prevents these from passing the stricter Neelin et al. (2006) criterion. Area averages pass significance tests on the model ensemble (Neelin et al. 2013), which points out a relationship between the extension of storm-track-associated precipitation in this region and the regional manifestation of jet stream increases at the steering level. For such differences at the boundaries of precipitation features, it remains an open question whether the CMIP5 ensemble should be given any additional weight in assessment relative to the CMIP3 ensemble.
Summertime MEM precipitation changes are characterized by higher precipitation amounts in Alaska and the Yukon, where the models all agree on the sign of the changes. All models also suggest precipitation increases along the Arctic coast across the entire length of NA. The MEM indicates reduced summertime precipitation in the east Pacific warm pool and the Caribbean, with agreement of all models in the vicinity of major Caribbean islands, the Yucatan, and in southwestern Mexico adjacent to the east Pacific warm pool. The agreement on these changes for the Caribbean and Mexico was high for CMIP3 models (e.g., Neelin et al. 2006) and is reinforced as a region of even higher intermodel agreement in CMIP5. Because of the model disagreement in projections of future tropical cyclone activity for the Atlantic and east Pacific (shown in section 6 below), it is unlikely that changes in tropical cyclone activity are responsible for these precipitation decreases given the strong model agreement in the precipitation change. Figure S1 (in the supplemental material) provides maps of the MEM percentage precipitation change and its corresponding multimodel standard deviation for the core models used here.
Figure 3 shows the comparable MEM changes for surface air temperature (2-m level) during JJA and DJF. As expected, warming is projected across all regions of NA, with the greatest warming concentrated during wintertime at high latitude regions, where the MEM temperature increase peaks near 15°C in the vicinity of Hudson Bay. Land regions warm more than ocean regions, associated in part with ocean heat storage causing a lag relative to the ongoing greenhouse gas increase, as in the CMIP3-based assessments (Meehl et al. 2007). Over the lower 48 United States and most of Canada, the MEM warming exceeds 5°C in JJA, with an intermodel standard deviation slightly over 1°C in the United States increasing to 2°C in northern Canada. In DJF, both the ensemble mean warming and the intermodel standard deviation have a strong poleward gradient, with warming around 4°C (standard deviation of 1°C) in the southern United States, increasing to over 12°C (standard deviation of over 3°C) in far northern Canada.
CMIP5 17-member multimodel, multirun ensemble-mean surface air temperature (2-m level) change (°C; contour interval shown on color bar) for RCP8.5 for 2070–99 relative to the 1901–60 base period for (top) DJF and (bottom) JJA. All grid points pass the two-tailed t test for the multimodel ensemble mean at the 95% level. Contours for the standard deviation among individual ensemble member surface temperature change are superimposed (contour interval of 1°C).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
CMIP5 17-member multimodel, multirun ensemble-mean surface air temperature (2-m level) change (°C; contour interval shown on color bar) for RCP8.5 for 2070–99 relative to the 1901–60 base period for (top) DJF and (bottom) JJA. All grid points pass the two-tailed t test for the multimodel ensemble mean at the 95% level. Contours for the standard deviation among individual ensemble member surface temperature change are superimposed (contour interval of 1°C).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
CMIP5 17-member multimodel, multirun ensemble-mean surface air temperature (2-m level) change (°C; contour interval shown on color bar) for RCP8.5 for 2070–99 relative to the 1901–60 base period for (top) DJF and (bottom) JJA. All grid points pass the two-tailed t test for the multimodel ensemble mean at the 95% level. Contours for the standard deviation among individual ensemble member surface temperature change are superimposed (contour interval of 1°C).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
While we cannot provide extensive detail on all aspects of projected regional precipitation and temperature change here, Tables S1–S10 (in the supplemental material) provide the MEM and multimodel standard deviation of precipitation and temperature changes as a function of season for the regions defined in Fig. 4. Comparing the individual models or the standard deviation of the model ensemble provides additional information regarding how much confidence should be placed in the MEM mean for a particular region. For instance, in central North America in JJA (Table S9), the intermodel standard deviation is roughly double the MEM value, indicating low confidence that this should be interpreted as significantly different from zero. This is consistent with the summary statistics in Fig. 2, in which only a small portion of this region passes either the binomial test for agreement on sign or the Neelin et al. (2006) criterion that includes a model-by-model t test for significance with respect to interannual variability.
The 30-yr means from the historical (1971–2000) and RCP8.5 experiment (2071–2100) for regionally averaged runoff and evapotranspiration (mm day−1). Six regions were defined for the NA continent: Central America (CAM), western North America (WNA), central North America (CNA), eastern North America (ENA), Alaska/northwest Canada (ALA), and northeast Canada (NEC). The circles represent individual climate models. The triangles represent the MEM values. Precipitation balances runoff plus evapotranspiration over decadal time scales by assuming no change in water storage. The diagonal lines represent contours of precipitation. A shift in the MEM toward the top right indicates an increase in precipitation. Values are calculated for 15 core models (BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, GFDL CM3, GFDL-ESM2G, GISS-E2-R, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) using one ensemble member each.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The 30-yr means from the historical (1971–2000) and RCP8.5 experiment (2071–2100) for regionally averaged runoff and evapotranspiration (mm day−1). Six regions were defined for the NA continent: Central America (CAM), western North America (WNA), central North America (CNA), eastern North America (ENA), Alaska/northwest Canada (ALA), and northeast Canada (NEC). The circles represent individual climate models. The triangles represent the MEM values. Precipitation balances runoff plus evapotranspiration over decadal time scales by assuming no change in water storage. The diagonal lines represent contours of precipitation. A shift in the MEM toward the top right indicates an increase in precipitation. Values are calculated for 15 core models (BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, GFDL CM3, GFDL-ESM2G, GISS-E2-R, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) using one ensemble member each.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The 30-yr means from the historical (1971–2000) and RCP8.5 experiment (2071–2100) for regionally averaged runoff and evapotranspiration (mm day−1). Six regions were defined for the NA continent: Central America (CAM), western North America (WNA), central North America (CNA), eastern North America (ENA), Alaska/northwest Canada (ALA), and northeast Canada (NEC). The circles represent individual climate models. The triangles represent the MEM values. Precipitation balances runoff plus evapotranspiration over decadal time scales by assuming no change in water storage. The diagonal lines represent contours of precipitation. A shift in the MEM toward the top right indicates an increase in precipitation. Values are calculated for 15 core models (BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, GFDL CM3, GFDL-ESM2G, GISS-E2-R, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) using one ensemble member each.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
b. Evapotranspiration and runoff
Future changes in precipitation and how it is partitioned into evaporation and runoff have implications for water availability and the occurrence of extreme hydrological events such as floods and droughts. CMIP3 projections indicated that changes in precipitation coupled with increased potential for evaporation from higher atmospheric moisture holding capacity leads to the drying of subtropical regions including Central America and the southwestern United States (Wang 2005; Seager et al. 2007; Sheffield and Wood 2008) and an increased potential for flooding because of more frequent and intense precipitation events, changes in snow accumulation and melt timing, and changes in antecedent soil moisture conditions (e.g., Hamlet and Lettenmaier 2007; Das et al. 2011). Here we analyze changes in the terrestrial water budget on a regional basis by calculating 30-yr averages in annual mean precipitation, evapotranspiration, and runoff between 1971–2000 and 2071–2100 for 15 core models for six regions of NA (Fig. 4). Changes in water storage (i.e., surface water, soil moisture, groundwater, etc.) over the 30-yr periods are assumed to be small compared to the other terms. Thus, precipitation should equal the sum of evapotranspiration and runoff.
For western and eastern NA regions, an increase in MEM annual precipitation is projected, consistent with Fig. 1. The precipitation increases in these regions are apportioned more to evapotranspiration than to runoff (which is important for understanding changes in the availability of water), although models tend to overestimate evapotranspiration in historical simulations (Part I). In the central region, annual mean precipitation increases are more modest. In high latitudes (Alaska–northwest Canada and northeast Canada) the MEM precipitation is projected to increase, consistent with Fig. 1, and is mostly partitioned into increases in runoff, rather than increases in evapotranspiration. This is likely because 1) higher temperatures will increase the proportion of rainfall to snowfall and will melt the snowpack earlier and faster in the spring and 2) the increased precipitation will come in more intense events. In Central America, precipitation is projected to decrease with most of the decrease manifesting in decreasing runoff.
c. Snow
Reductions in snow cover extent and amount are expected in the future as a result of increasing temperatures modified by changes in precipitation and their seasonal interactions. This has important implications for water supply, hydropower generation, and ecosystems and feedbacks with the underlying soil and permafrost (Lawrence and Slater 2010) and to the climate system through changes in albedo (Qu and Hall 2007). CMIP3 projections (Räisänen 2008) indicated that warming reduces the snow season length from both ends across NA, but midwinter snow water equivalent (SWE) is expected to increase in high latitude colder regions because of increased winter snowfall but decrease in regions to the south, where temperature effects on precipitation phase and melting dominate any changes in precipitation amount.
Changes in snow are calculated using the CMIP5 model SWE values. Figure 5 (top) shows the seasonal cycle of changes between 2070–99 and 1971–2000 in monthly mean SWE averaged over NA for 15 core models. All models project a decrease in SWE throughout the year with maximum changes during the peak of the snow season in January–April. The MEM decrease in SWE averaged over NA is about −30 mm (with models ranging from −80 to −10 mm) in the spring and about −10 mm in the summer (ranging from 0 to −65 mm). Spatially, the majority of NA (south of 70°N) is projected to experience a decline in SWE where increasing temperatures have a dominant effect by reducing the ratio of snowfall to rainfall and accelerating melting (Fig. 5, bottom). These reductions are concentrated in the Rocky Mountains to southern Alaska, in the eastern provinces of Canada, and with lower magnitude in the Canadian Prairies. North of 70°N, SWE is projected to increase in places because of increasing precipitation, which outweighs the effects of increasing temperature. Uncertainties across models are likely associated with differences in the temperature projections, to which modeled snow is highly sensitive (Räisänen 2008). At higher latitudes, the sign of the change is uncertain in transitional regions because of the competing effects of increasing snowfall and warming temperatures and in regions of increasing SWE where the magnitude of the precipitation increase is also quite uncertain.
Changes in SWE (mm) from 14 CMIP5 core models (one ensemble member each) from 1971–2000 to 2071–2100 for the RCP8.5 scenario. (top) Mean monthly change in SWE averaged over North America (25°–80°N, 170°–65°W) and (bottom) spatial distribution of change in winter–spring [November–May (NDJFMAM)] SWE (shading) and coefficient of variation (CV) of changes in SWE across models (contours). Some of the models have spuriously high snow accumulations at isolated grid cells and these are filtered out. The models are as follows: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, GISS-E2-R, INM-CM4.0, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
Changes in SWE (mm) from 14 CMIP5 core models (one ensemble member each) from 1971–2000 to 2071–2100 for the RCP8.5 scenario. (top) Mean monthly change in SWE averaged over North America (25°–80°N, 170°–65°W) and (bottom) spatial distribution of change in winter–spring [November–May (NDJFMAM)] SWE (shading) and coefficient of variation (CV) of changes in SWE across models (contours). Some of the models have spuriously high snow accumulations at isolated grid cells and these are filtered out. The models are as follows: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, GISS-E2-R, INM-CM4.0, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
Changes in SWE (mm) from 14 CMIP5 core models (one ensemble member each) from 1971–2000 to 2071–2100 for the RCP8.5 scenario. (top) Mean monthly change in SWE averaged over North America (25°–80°N, 170°–65°W) and (bottom) spatial distribution of change in winter–spring [November–May (NDJFMAM)] SWE (shading) and coefficient of variation (CV) of changes in SWE across models (contours). Some of the models have spuriously high snow accumulations at isolated grid cells and these are filtered out. The models are as follows: BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5.1, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, GISS-E2-R, INM-CM4.0, MIROC5, MIROC-ESM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
d. Growing season length
Projected warming will likely impact temperature-sensitive flora and fauna, as well as agriculture. We calculate changes in biophysically relevant temperature thresholds including the last spring freeze, the first autumn freeze, and the growing season length at 2071–2100. The growing season length is defined as the number of days between the last spring freeze and the first autumn freeze in the same year. A hard freeze occurs when the daily maximum temperature drops below −2°C. (Schwartz et al. 2006). An analysis of 14 core models (Fig. 6) indicates that the growing season will increase across the NA continent by the end of the century, although substantial variability in the magnitude of these changes exists on a regional basis. All changes are statistically significant at the 95% level relative to interannual variability in the observations (see Part I) with implications for impacts on agriculture and natural vegetation. The largest changes occur over the western United States and northern Mexico, where MEM increases of 40 days or more are projected. It should be noted that these same regions have complex terrain and are characterized by some of the largest negative biases in historical simulations (Part I), as well as the largest multimodel standard deviation in growing season length change of up to 8 days. In the central United States and Canada, increases of about 3–5 weeks are projected. The lengthening of the growing season is caused by both last spring freezes that are earlier and first autumn freezes that are later (not shown), but the change in the former is generally larger. A complementary analysis detailing changes in frost days is shown in the supplementary material.
(bottom) Projected changes in growing season length for 14 core CMIP5 models (BCC_CSM1.1, CanESM2, CCSM4, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, HadGEM2-ES, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MRI-CGCM3, MPI-ESM-LR, and NorESM1-M; all for the first ensemble member) for RCP8.5. Multimodel standard deviations are also shown as contours. Also shown, are the multimodel ensemble growing season lengths for (top) the historical runs (1971–2000) and (middle) RCP8.5 (2071–2100). Changes are calculated as the difference between the mean for 2071–2100 and 1971–2000. We define the growing season length following Schwartz et al. (2006), which is the number of days between the last spring freeze of the year and the first hard freeze of the autumn in the same year. A hard freeze is defined when the daily maximum temperature drops below −2°C. Values were calculated on the model grid, interpolated to 2.0° resolution, and then averaged over 1971–2000 for the historical and 2071–2100 for the RCP8.5 scenario.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
(bottom) Projected changes in growing season length for 14 core CMIP5 models (BCC_CSM1.1, CanESM2, CCSM4, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, HadGEM2-ES, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MRI-CGCM3, MPI-ESM-LR, and NorESM1-M; all for the first ensemble member) for RCP8.5. Multimodel standard deviations are also shown as contours. Also shown, are the multimodel ensemble growing season lengths for (top) the historical runs (1971–2000) and (middle) RCP8.5 (2071–2100). Changes are calculated as the difference between the mean for 2071–2100 and 1971–2000. We define the growing season length following Schwartz et al. (2006), which is the number of days between the last spring freeze of the year and the first hard freeze of the autumn in the same year. A hard freeze is defined when the daily maximum temperature drops below −2°C. Values were calculated on the model grid, interpolated to 2.0° resolution, and then averaged over 1971–2000 for the historical and 2071–2100 for the RCP8.5 scenario.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
(bottom) Projected changes in growing season length for 14 core CMIP5 models (BCC_CSM1.1, CanESM2, CCSM4, CSIRO Mk3.6.0, GFDL CM3, GFDL-ESM2M, HadGEM2-ES, INM-CM4.0, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MRI-CGCM3, MPI-ESM-LR, and NorESM1-M; all for the first ensemble member) for RCP8.5. Multimodel standard deviations are also shown as contours. Also shown, are the multimodel ensemble growing season lengths for (top) the historical runs (1971–2000) and (middle) RCP8.5 (2071–2100). Changes are calculated as the difference between the mean for 2071–2100 and 1971–2000. We define the growing season length following Schwartz et al. (2006), which is the number of days between the last spring freeze of the year and the first hard freeze of the autumn in the same year. A hard freeze is defined when the daily maximum temperature drops below −2°C. Values were calculated on the model grid, interpolated to 2.0° resolution, and then averaged over 1971–2000 for the historical and 2071–2100 for the RCP8.5 scenario.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
e. Extreme events
To assess the projected changes in the frequency of occurrence (FOC) of extreme persistent dry–wet events over NA, we use the eight core models that contain three or more runs for both the historical and RCP8.5 experiments (see caption). Because these events are rare, three runs from each model are needed in order to produce enough events for meaningful statistics. The methodology used to define extreme events is the same as in Part I and repeated in the Fig. 7 caption. Note that because these calculations account for only liquid precipitation, results in the coldest northern regions are questionable and not discussed.
The difference in FOC for RCP8.5 projection runs minus historical runs both calculated using the historical climatology defined by the 6-month standardized precipitation index (SPI6) averaged over positive (wet) events minus negative (dry) events for (a) CanESM2, (b) CCSM4, (c) CNRM-CM5.1, (d) CSIRO Mk3.6.0, (e) HadGEM2-ES, (f) IPSL-CM5A-LR, (g) MIROC5, (h) MPI-ESM-LR, and (i) the equally weighted ensemble mean. In (a)–(i), contours are shown at −0.4, −0.2, −0.1, 0.1, 0.2, and 0.4. Negative values are dashed. Values that are statistically significant at the 5% level are color shaded. (j) As in (i), but anomalies are computed with respect to the RCP8.5 projected climatology and the contour interval is 0.02. Meteorological drought is measured by precipitation (P) deficit, and the index used to classify drought is the SPI6. The SPI6 is computed by following the method outlined by McKee et al. (1993, 1995). The FOC is the number of extreme events that last at least 9 months divided by the total number of events. An event is defined as extreme if the SPI6 reaches the threshold of ±0.8. Statistics are calculated during 1850–2005 for the historical period and 2006–2100 for RCP8.5.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The difference in FOC for RCP8.5 projection runs minus historical runs both calculated using the historical climatology defined by the 6-month standardized precipitation index (SPI6) averaged over positive (wet) events minus negative (dry) events for (a) CanESM2, (b) CCSM4, (c) CNRM-CM5.1, (d) CSIRO Mk3.6.0, (e) HadGEM2-ES, (f) IPSL-CM5A-LR, (g) MIROC5, (h) MPI-ESM-LR, and (i) the equally weighted ensemble mean. In (a)–(i), contours are shown at −0.4, −0.2, −0.1, 0.1, 0.2, and 0.4. Negative values are dashed. Values that are statistically significant at the 5% level are color shaded. (j) As in (i), but anomalies are computed with respect to the RCP8.5 projected climatology and the contour interval is 0.02. Meteorological drought is measured by precipitation (P) deficit, and the index used to classify drought is the SPI6. The SPI6 is computed by following the method outlined by McKee et al. (1993, 1995). The FOC is the number of extreme events that last at least 9 months divided by the total number of events. An event is defined as extreme if the SPI6 reaches the threshold of ±0.8. Statistics are calculated during 1850–2005 for the historical period and 2006–2100 for RCP8.5.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The difference in FOC for RCP8.5 projection runs minus historical runs both calculated using the historical climatology defined by the 6-month standardized precipitation index (SPI6) averaged over positive (wet) events minus negative (dry) events for (a) CanESM2, (b) CCSM4, (c) CNRM-CM5.1, (d) CSIRO Mk3.6.0, (e) HadGEM2-ES, (f) IPSL-CM5A-LR, (g) MIROC5, (h) MPI-ESM-LR, and (i) the equally weighted ensemble mean. In (a)–(i), contours are shown at −0.4, −0.2, −0.1, 0.1, 0.2, and 0.4. Negative values are dashed. Values that are statistically significant at the 5% level are color shaded. (j) As in (i), but anomalies are computed with respect to the RCP8.5 projected climatology and the contour interval is 0.02. Meteorological drought is measured by precipitation (P) deficit, and the index used to classify drought is the SPI6. The SPI6 is computed by following the method outlined by McKee et al. (1993, 1995). The FOC is the number of extreme events that last at least 9 months divided by the total number of events. An event is defined as extreme if the SPI6 reaches the threshold of ±0.8. Statistics are calculated during 1850–2005 for the historical period and 2006–2100 for RCP8.5.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
We first calculated the difference in FOC of extreme precipitation events using each experiment’s own climatology as a baseline for that experiment. Therefore, the historical climatology is from 1850 to 2005 and the RCP8.5 climatology is from 2006 to 2100. Each model shows little difference in the FOCs for both positive (wet) and negative (dry) events (not shown). The MEM difference between the positive and negative events (Fig. 7j) also shows no robust change between the model projections and historical data when the different climatologies are used. When the historical climatology is used as a baseline for the RCP8.5 experiment instead of the RCP8.5 climatology, all models but HadGEM2-ES show a decrease in the number of positive events in Mexico and the southwest United States and an increase in such events in the northeastern United States. The opposite is true for negative (dry) events. This shows the impact of the changing climatology between the experiments. These results are illustrated in Figs. 7a–h for individual models and in Fig. 7i for the MEM. The patterns among the models are similar, but they differ in magnitude and the precise location of the zero line. For example, IPSL-CM5A-LR shows an increase in dry events over the southern United States and Mexico, but the CCSM4 indicates that only Mexico is impacted. A metric showing the difference between area averages of the northeast quadrant (94°–75°W, 35°–48°N) and the southwest quadrant (123°–95°W, 15°–35°N) of NA is also given (Table 2). The metric values also have a small range between 0.31 and 0.48 when HadGEM2-ES is removed. With the spread taken into consideration, we conclude that more droughts are projected over Mexico and more persistent wet spells are projected over the northeast United States (Fig. 7i).
Metric showing the difference between the area average of the northeast quadrant (94°–75°W, 35°–48°N) and the southwest quadrant (123°–95°W, 15°–35°N) of North America in Fig. 7, which shows the FOC differences between projection and historical experiments for positive minus negative events.
The projected changes of extreme surface temperature during 2081–2100 relative to 1981–2000 are shown in Fig. 8, calculated using one ensemble member from each of the 11 models described in the caption (10 are core models). Daily maximum surface temperatures Tmax are used to compute the number of days per year that exceed 90° (Fig. 8a) and 100°F (Fig. 8b), respectively. The MEM projections show an increase of 60%–300% (50–80 days) annually with Tmax warmer than 90°F in the southern United States and northern Mexico. In the southeastern United States, southern Texas, and northern Mexico, the number of days with Tmax warmer than 90°F is projected to increase to nearly 80. The MEM projections also show that the number of days with Tmax warmer than 100°F will increase 80%–400% (40–80 days) in parts of the south-central and southwestern United States. Across the southern United States and northern Mexico, the change in frequency of extreme surface temperatures are robust, suggested by the MEM projections having greater difference than the intermodel spread of the changes. However, greater uncertainty exists in other areas where MEM increases have the same magnitude as the standard deviation, in particular increases of 90°F days in the northeastern United States and northern Rockies and 100°F day changes across the northern half of the United States.
The MEM changes (color shading) of (a) Tmax > 90°F and (b) Tmax > 100°F between RCP8.5 for the period 2081–2100 and historical simulations for the period 1981–2000 and its standard deviation (contours) across 11 CMIP5 models; the units are number of days. The 11 models we used are CanESM2, CCSM4, GFDL CM3, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MPI-ESM-LR, and MRI-CGCM3.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The MEM changes (color shading) of (a) Tmax > 90°F and (b) Tmax > 100°F between RCP8.5 for the period 2081–2100 and historical simulations for the period 1981–2000 and its standard deviation (contours) across 11 CMIP5 models; the units are number of days. The 11 models we used are CanESM2, CCSM4, GFDL CM3, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MPI-ESM-LR, and MRI-CGCM3.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
The MEM changes (color shading) of (a) Tmax > 90°F and (b) Tmax > 100°F between RCP8.5 for the period 2081–2100 and historical simulations for the period 1981–2000 and its standard deviation (contours) across 11 CMIP5 models; the units are number of days. The 11 models we used are CanESM2, CCSM4, GFDL CM3, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MPI-ESM-LR, and MRI-CGCM3.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
4. Regional climate
a. North Pacific and North Atlantic storm tracks
The projected change in Northern Hemisphere storm-track (ST) activity is examined based on 6-hourly data provided by 15 of the 17 core models (Fig. 9). Here, ST activity is defined based on meridional wind and SLP variance statistics computed using a 24-h difference filter (Wallace et al. 1988) that highlights the synoptic time scale (1.2–6 days).
(a) Black solid contour: winter (DJF) climatological (1980–99) storm-track activity, as indicated by the variance of 24-h difference bandpass-filtered meridional wind υ at 250-hPa level (contour level of 400 m2 s−2), based on the MEM of 15 CMIP5 models (core models in Table 1 except GISS-E2-R and HadCM3). The filter used is the 24-h difference filter (Wallace et al. 1988), which highlights synoptic variability with periods of 1.2–6 days. Colored lines: projected change (2081–2100 mean minus 1980–99 mean) based on RCP8.5 (contour interval of 20 m2 s−2) with solid (dashed) lines for positive (negative) values. Color shading: grid boxes over which more than 80% of CMIP5 models agree on the sign of the projected change. (b) As in (a), but for summer (JJA; contour level of 150 m2 s−2 and interval of 10 m2 s−2). (c) As in (a), but for 500-hPa level (contour level of 200 m2 s−2 and interval of 10 m2 s−2). (d) As in (c), but for JJA (contour level of 50 m2 s−2 and interval of 5 m2 s−2). (e) As in (a), but for variance of SLP (contour level of 120 hPa2 and interval of 5 hPa2). (f) As in (e), but for JJA (contour level of 30 hPa2 and interval of 2.5 hPa2).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
(a) Black solid contour: winter (DJF) climatological (1980–99) storm-track activity, as indicated by the variance of 24-h difference bandpass-filtered meridional wind υ at 250-hPa level (contour level of 400 m2 s−2), based on the MEM of 15 CMIP5 models (core models in Table 1 except GISS-E2-R and HadCM3). The filter used is the 24-h difference filter (Wallace et al. 1988), which highlights synoptic variability with periods of 1.2–6 days. Colored lines: projected change (2081–2100 mean minus 1980–99 mean) based on RCP8.5 (contour interval of 20 m2 s−2) with solid (dashed) lines for positive (negative) values. Color shading: grid boxes over which more than 80% of CMIP5 models agree on the sign of the projected change. (b) As in (a), but for summer (JJA; contour level of 150 m2 s−2 and interval of 10 m2 s−2). (c) As in (a), but for 500-hPa level (contour level of 200 m2 s−2 and interval of 10 m2 s−2). (d) As in (c), but for JJA (contour level of 50 m2 s−2 and interval of 5 m2 s−2). (e) As in (a), but for variance of SLP (contour level of 120 hPa2 and interval of 5 hPa2). (f) As in (e), but for JJA (contour level of 30 hPa2 and interval of 2.5 hPa2).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
(a) Black solid contour: winter (DJF) climatological (1980–99) storm-track activity, as indicated by the variance of 24-h difference bandpass-filtered meridional wind υ at 250-hPa level (contour level of 400 m2 s−2), based on the MEM of 15 CMIP5 models (core models in Table 1 except GISS-E2-R and HadCM3). The filter used is the 24-h difference filter (Wallace et al. 1988), which highlights synoptic variability with periods of 1.2–6 days. Colored lines: projected change (2081–2100 mean minus 1980–99 mean) based on RCP8.5 (contour interval of 20 m2 s−2) with solid (dashed) lines for positive (negative) values. Color shading: grid boxes over which more than 80% of CMIP5 models agree on the sign of the projected change. (b) As in (a), but for summer (JJA; contour level of 150 m2 s−2 and interval of 10 m2 s−2). (c) As in (a), but for 500-hPa level (contour level of 200 m2 s−2 and interval of 10 m2 s−2). (d) As in (c), but for JJA (contour level of 50 m2 s−2 and interval of 5 m2 s−2). (e) As in (a), but for variance of SLP (contour level of 120 hPa2 and interval of 5 hPa2). (f) As in (e), but for JJA (contour level of 30 hPa2 and interval of 2.5 hPa2).
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
Near the tropopause (250 hPa), the models project a strengthening of ST activity on the poleward flank of the historical ST peak and a slight weakening on the equatorward flank during winter. In summer, the models project a significant decrease in upper-tropospheric ST activity south of the ST peak and weak increase north of it. These results are consistent with previous studies based on CMIP3 (e.g., Yin 2005; Teng et al. 2008) that indicate a poleward shift of the ST under global warming. In the midtroposphere (500 hPa) and near the surface, the models generally project a significant decrease in ST activity extending from the Pacific across North America into the Atlantic in both seasons. This contrasting upper-level and near-surface change in winter can be related to contrasting projected change in the meridional temperature gradient. Near the surface, the temperature gradient is projected to significantly decrease because of greater warming at high latitudes, while the temperature gradient near the tropopause is projected to increase because of warming in the tropical upper troposphere and cooling in the polar lower stratosphere due to increases in greenhouse gases. The general decrease in ST activity in summer can be related to a projected decrease in mean available potential energy because of a decrease in the midlatitude temperature gradient and an increase in static stability.
As discussed in Chang et al. (2012), the significant near-surface ST activity decrease over NA represents one of the largest differences between CMIP5 and CMIP3 ST projections over the globe. As seen in Table 3, during winter (DJF), CMIP5 models project a −9.9% ± 3.6% MEM change in sea level pressure (SLP) variance in winter over the region roughly covering the contiguous United States and −19.8% ± 6.9% change in summer (JJA), with 14 out of 15 models projecting a decrease in winter and all 15 models projecting a decrease in summer. On the other hand, 11 CMIP3 models project −0.4% ± 4.0% change in the same quantity in winter [based on the Special Report on Emissions Scenarios (SRES) A2 scenario] and −9.2% ± 6.3% change in summer, with 7 out of 11 models projecting a decrease in winter and 10 of 11 models projecting a decrease in summer. We have also examined 8 other CMIP5 models not listed in Table 3, and all of them showed a decrease for both seasons. More details are given in Chang (2013), who showed that models projecting a larger decrease in ST activity over North America also project a more northward intrusion of the decrease in subtropical precipitation into southern United States.
Projected percentage change in 24-h difference filtered SLP variance for DJF and JJA over the region 120°–60°W, 30°–50°N. Difference is between 2081–2100 from the RCP8.5 experiment and 1980–99 from the historical experiment.
We now provide a complementary analysis to that above using the Hodges (1994, 1995) cyclone-tracking scheme on 6-hourly mean SLP data to assess changes in extratropical cyclone activity along the U.S. East Coast. Part I presented the historical (1979–2004) predictions of western Atlantic extratropical cyclones during the cool season (November–March), which show substantial skill at simulating the distribution of cyclone activity, although with modest underprediction of amplitude. Colle et al. (2013) highlighted the details of this historical cyclone analysis and the twenty-first-century predictions in this region using these 15 CMIP5 models. Figures 10b–d show the MEM difference in cool season cyclone-track density for each of the three separate 30-yr future periods in RCP8.5 (2009–38, 2038–69, and 2069–98) and the historical period (1979–2004; Fig. 10a). Only a slight decrease in cyclone activity is projected over parts of the western Atlantic storm track for 2009–38 (Fig. 10b); however, Colle et al. (2013) show that this reduction may be more widespread if only the highest-resolution CMIP5 models are considered. The MEM reduction in cyclone density is more apparent for the 2038–69 period, with a reduction of 5%–15%, primarily along the southern half of the cyclone storm track, which is near the Gulf Stream boundary. Meanwhile, a slight increase in cyclone density is projected to the north over parts of northern New England and Nova Scotia, enhanced in the highest-resolution models (not shown). Future changes relative to the historical period continue to increase in size and amplitude (10%–20%) for the 2069–98 period. The results above are very similar if only the 12 of 15 models contained in the core model list (Table 1) are used. Figure S5 provides a commentary analysis of changes in cyclone intensity.
(a) Cyclone-track density for the MEM (color shaded) and spread (contoured every 0.3) for 15 CMIP5 models showing the number of cyclones per cool season (November–March) per 50 000 km2 for 1979–2004. (b)–(d) As in (a), but the difference in cyclone density (future minus historical) and percentage change between (b) 2009–38, (c) 2038–68, and (d) 2069–98 and the historical 1979–2004 period. The dashed box in (a) is an averaging region used in the supplemental information to assess changes in cyclone intensity. The 15 models used are BCC_CSM1.1, CanESM2, CNRM-CM5.1, EC-EARTH, GFDL-ESM2M, HadGEM2-ES, HadGEM2-CC, INM-CM4.0, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MRI-CGCM3, MPI-ESM-LR, and NorESM1-M.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1
(a) Cyclone-track density for the MEM (color shaded) and spread (contoured every 0.3) for 15 CMIP5 models showing the number of cyclones per cool season (November–March) per 50 000 km2 for 1979–2004. (b)–(d) As in (a), but the difference in cyclone density (future minus historical) and percentage change between (b) 2009–38, (c) 2038–68, and (d) 2069–98 and the historical 1979–2004 period. The dashed box in (a) is an averaging region used in the supplemental information to assess changes in cyclone intensity. The 15 models used are BCC_CSM1.1, CanESM2, CNRM-CM5.1, EC-EARTH, GFDL-ESM2M, HadGEM2-ES, HadGEM2-CC, INM-CM4.0, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MRI-CGCM3, MPI-ESM-LR, and NorESM1-M.
Citation: Journal of Climate 27, 6; 10.1175/JCLI-D-13-00273.1