Predicted Northward Expansion of the Geographic Range of the Tick Vector Amblyomma americanum in North America under Future Climate Conditions
2019; National Institute of Environmental Health Sciences; Volume: 127; Issue: 10 Linguagem: Inglês
10.1289/ehp5668
ISSN1552-9924
AutoresIrina Sagurova, Antoinette Ludwig, Nicholas H. Ogden, Yann Pelcat, Guillaume Dueymes, Philippe Gachon,
Tópico(s)Mosquito-borne diseases and control
ResumoVol. 127, No. 10 ResearchOpen AccessPredicted Northward Expansion of the Geographic Range of the Tick Vector Amblyomma americanum in North America under Future Climate Conditions Irina Sagurova, Antoinette Ludwig, Nicholas H. Ogden, Yann Pelcat, Guillaume Dueymes, and Philippe Gachon Irina Sagurova ESCER (Étude et Simulation du Climat à l'Échelle Régionale) centre, Université du Québec à Montréal, Montréal, Québec, Canada , Antoinette Ludwig Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada , Nicholas H. Ogden Address correspondence to Nicholas H. Ogden, National Microbiology Laboratory, Public Health Agency of Canada, 3200 Sicotte, St. Hyacinthe (QC) J2S 2M2, Canada. Email: E-mail Address: [email protected] Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada , Yann Pelcat Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada , Guillaume Dueymes ESCER (Étude et Simulation du Climat à l'Échelle Régionale) centre, Université du Québec à Montréal, Montréal, Québec, Canada , and Philippe Gachon ESCER (Étude et Simulation du Climat à l'Échelle Régionale) centre, Université du Québec à Montréal, Montréal, Québec, Canada Strategic Research Chair on Hydro-Meteorological Risks under Climate Change, Department of Geography, Université du Québec à Montréal, Montréal, Québec, Canada Published:31 October 2019CID: 107014https://doi.org/10.1289/EHP5668Cited by:20AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick's range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick's range.Results:The predicted geographic scope of temperature suitability [≥3,285 annual cumulative degree days (DD) >0°C] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to 1,000km by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668IntroductionAnthropogenic climate change (Cook et al. 2013; IPCC 2018) is likely to drive changes in the geographic ranges of arthropod disease vectors, including those of tick vectors in North America (Ogden et al. 2005; Minigan et al. 2018; Springer et al. 2015). This likelihood is because the survival of tick populations depends on both biotic and abiotic conditions. Temperature plays a critical role in the tick life cycle by determining development rates of eggs and engorged states (Koch 1983) and affecting tick questing activity (Haile and Mount 1987). Subzero air temperatures are not lethal for ticks if they can find refuges in their environment, particularly in the surface layer of the soil (Burks et al. 1996). However, due to its effects on development and activity, temperature determines the length of the tick life cycle. Even where habitats provide refuges from subzero temperatures, a threshold temperature condition occurs below which the tick populations cannot survive, i.e., temperature conditions are too low for the tick to complete its life cycle before it dies, given a particular daily probability that a tick survives (Ogden et al. 2005; Ludwig et al. 2016). Temperature may, therefore, be a limiting factor of the geographic ranges of ticks, and a warming climate may facilitate their establishment in regions previously climatically unsuitable.Throughout the 20th century, the geographic range of Amblyomma americanum has expanded from the southeastern United States northward to locations in Michigan and New York states that are close to the Canadian border (Springer et al. 2014). This range expansion may have been driven by anthropogenic climate change, which has resulted in a warming trend in the late 20th century in North America (Crowley 2000; Stott et al. 2000; Blunden and Arndt 2019), although there have been no efforts to date to attribute changes in geographic distribution to climate change. This range expansion has had public health impact, at least in terms of increased incidence of spotted fever group rickettsioses (Dahlgren et al. 2016). A. americanum is a recognized public health threat, known for its aggressive host-seeking behavior and vector competence for a wide range of zoonotic pathogens, including Francisella tularensis (the cause of tularemia; Goddard and Varela-Stokes 2009), Ehrlichia chaffeensis (the cause of human monocytic ehrlichiosis; Brouqui 1998), Rickettsia rickettsii (the cause of Rocky Mountain spotted fever; Levin et al. 2017) and Heartland virus (Savage et al. 2016). Recently, it has been suggested that the bite of A. americanum may trigger red meat allergy (Commins et al. 2011).A number of studies have assessed associations between A. americanum tick population occurrence and density and environmental predictors (Koch and Burg 2006; Schulze et al. 2001; Willis et al. 2012). Studies have also explored the potential effects of climate change on the spatial distribution of the tick (Springer et al. 2015), suggesting possible northward range expansion that may affect northern U.S. states and southern Canada, although A. americanum is not yet considered established in Canada and has not been detected in extensive field surveillance conducted in recent years to track the expansion of I. scapularis ticks (Bouchard et al. 2015). However, in recent years a small number of specimens, likely imported by migratory birds or travelers, were detected in passive tick surveillance, which suggests that if environmental conditions are, or become, suitable in northern U.S. states and Canada, this tick species could become established (Gasmi et al. 2018). By the end of the 21st century, Canada would "very likely" face a mean annual temperature rise in the range of 2–4°C in comparison with current climate (Romero-Lankao et al. 2014) and potentially higher than 5°C under a high greenhouse gas emissions scenario (Ogden and Gachon 2019). If indeed temperature conditions are a major determinant of the northern limit of the range of this tick, then A. americanum may expand its range northward into northern U.S. states and Canada in the future and bring with it the range of diseases and health issues with which it is associated. Consequently, here we evaluate current and future climate suitability for A. americanum in the northern United States and Canada to better prepare public health system for possible emergence of these diseases and health issues.There are two main modeling approaches to understanding and estimating the relationships between environmental variables, such as climate, and the environmental suitability for arthropods, such as A. americanum. The most commonly used are "pattern matching" approaches, such as ecological niche modeling that infer environmental suitability from observed species-distribution data. This approach has been used to predict current environmental suitability in North America for A. americanum and to project impacts of climate change on the possible geographic distribution of the tick (Bouzek et al. 2013; Springer et al. 2015; Minigan et al. 2018; Kessler et al. 2019; Pascoe et al. 2019). An alternative approach is the use of mechanistic simulation models that identify how environmental factors such as temperature affect population processes in the tick life cycle (Haile and Mount 1987; Mount et al. 1993; Ogden et al. 2005; Ludwig et al. 2016). Both methods have advantages and disadvantages that have been reviewed elsewhere (Mannelli et al. 2016). In previous studies on I. scapularis ticks, simulations of a deterministic simulation model calibrated with temperature-dependent parameters were used to define suitable temperature conditions for establishment of tick populations (Ogden et al. 2005) and then to project future spatial distribution of the species under climate change (Ogden et al. 2006, 2008). A similar dynamic population model was developed by Ludwig et al. (2016), which describes the transformations that A. americanum ticks undergo as they pass through the four stages of their life cycle (egg, larvae, nymph, and adult). Feeding tick stages are hardening, questing, feeding, and engorged ticks; tick abundance in a specific simulated area depends on seasonally variable environmental conditions, including temperature and day length, while accounting for effects of temperature-independent diapause.In this study, we used this mechanistic model to investigate the relationship between the size of A. americanum tick populations and temperature conditions at 36 locations in southeastern Canada and the eastern United States. From these simulations we defined threshold temperature conditions for tick population survival and evaluated how that the northern limit of the region with suitable climate conditions for the tick in North America may change with climate change.MethodsWe ran simulations of the dynamic population model (Ludwig et al. 2016) using climate data from each of 36 meteorological stations located in southeastern Canada and northeastern United States (from latitudes 35 to 60°N, and from longitudes 55 to 95°W) to define a temperature threshold for survival of A. americanum populations. Under current climate, the region where the meteorological stations are located (Figure 1) is characterized by a north–south latitudinal gradient in temperature (being warmer in the south) with temperatures also being higher the closer that the stations are to the Atlantic coastline. The deduced temperature threshold was used to evaluate the tick's potential range in the United States and Canada under future climatic conditions. Our focus was investigating possible northward range expansion of the tick, but northward contraction of the southern limit of the tick may also be possible. If so then the impact on distribution in the southern United States may be small because the tick is likely established in Mexico at present (Sosa-Gutierrez et al. 2016), but further studies targeted to the southern part of the tick's range are required to explore this impact.Figure 1. Geographic locations of the 36 meteorological stations (using abbreviations of the station names) that provided temperature and day-length data for the dynamic population model simulations. QC, Quebec, ON, Ontario, NL, Newfoundland and Labrador, NB, New Brunswick, NS, Nova Scotia.Determining a Lower Temperature Threshold for A. americanum Population SurvivalModel calibration and simulations.In this study, we used a dynamic population model of A. americanum (Ludwig et al. 2016) in STELLA version 10.0.2 (High Performance Systems, Inc.) to estimate the number of ticks, once the model reached equilibrium after an 85-y simulation, at each of 36 sites in eastern North America (Table 1). The logic of the modeling approach is that as cold temperatures in winter do not greatly affect tick mortality rates as long as the woodland habitats provide appropriate refuges (Brunner et al. 2012), and climatic changes would likely act mostly through temperature effects on tick activity, rates of development, and thus life cycle length. Given constant per capita mortality rates of the ticks, a lower climatic temperature threshold will exist at which the life cycle of the tick reaches a length such that the probability that a larvae survives to be a mated, egg-laying, adult female falls below 1, i.e., the basic reproduction number (R0) of the tick falls below 1, and self-sustaining populations cannot persist (Ogden et al. 2014).Table 1 Meteorological stations that provided temperature data for simulations with their location, altitude, mean number of degree days >0°C (DD>0°C) and maximum number of feeding larvae at equilibrium in simulations of the dynamic population model of A. americanum.Table 1 lists meteorological stations in the first column; the corresponding location (in °N and in °W), altitude (in meters), mean annual number of degree days greater than 0 degrees Celcius, and maximum feeding larvae at equilibrium are listed in the other columns.StationLocation (°N and °W)Altitude (m)Mean DD>0°CMaximum feeding larvae at equilibriumaCanada Ontario Barrie WPCC44°22'N, 79°41'W221.03,274.90* Cornwall45°00'N, 74°44'W64.03,542.7217,154 Drummond Centre45°01'N, 76°15'W145.03,182.90 Hamilton A43°10'N, 79°56'W237.73,439.730,410 London Int'l Airport43°01'N, 81°09'W278.03,431.617,203 Ottawa Macdonald-Cartier Int'l A45°19'N, 75°40'W114.03,285.212,204 Peterborough Trent U44°22'N, 78°18'W198.13,280.20* Sault Ste Marie A46°29'N, 84°30'W192.02,725.90 Sudbury A46°37'N, 80°47'W348.42,786.10 Toronto Lester B. Pearson Int'l A43°40'N, 79°37'W173.43,530.0164,968 Québec Bromptonville45°29'N, 71°57'W130.03,052.00 Chapais 249°47'N, 74°51'W396.22,136.40 Farnham45°18'N, 72°54'W68.03,274.30 Hemmingford Four Winds45°04'N, 73°43'W61.03,289.40* Montreal/Pierre Elliott Trudeau Int'l A45°28'N, 73°45'W36.03,349.354,080 Quebec/ Jean Lesage Int'l A46°48'N, 71°23'W74.42,785.70 Rimouski48°27'N, 68°31'W35.72,651.60 Trois Rivières Aqueduc46°23'N, 72°37'W54.93,027.10 New Brunswick Fredericton A45°52'N, 66°31'W20.72,933.20 Moncton A46°06'N, 64°41'W70.72,830.00 Saint John A45°19'N, 65°53'W108.82,682.50 Prince Edward Island Charlottetown A46°17'N, 63°07'W48.82,824.90 Nova Scotia Halifax Stanfield Int'l A44°52'N, 63°30'W145.42,994.00 Sydney A46°10'N, 60°02'W61.92,727.70 Yarmouth A43°49'N, 66°05'W43.02,958.70United States Albany Int'l A42°44'N, 73°47'W85.33,639.0353,952 Columbus Ohio State University A40°04'N, 83°04'W275.84,173.91,060,376 Concord Municipal A43°07'N, 71°17'W105.53,343.457,345 Detroit City A42°24'N, 83°00'W190.83,924.61,054,015 Frankfort Capital City A38°11'N, 84°54'W245.14,723.62,010,296 Harrisburg Capital City A40°13'N, 76°51'W103.64,336.11,315,079 Hartford Bradley Int'l A41°56'N, 72°40'W53.33,944.4908,993 Lansing Capital City A42°46'N, 84°34'W256.33,623.6330,843 Raleigh A35°53'N, 78°46'W126.85,849.12,828,503 Richmond Int'l A37°30'N, 77°19'W50.05,457.03,020,711 Trenton Mercer Co A40°16'N, 74°48'W56.14,377.51,320,764Note: A, airport; and Int'l A, international airport.aAt three sites indicated by asterisks, the number of larvae was declining at the end of the simulations indicating that the populations were dying out. For simplicity, the equilibrium value shown for these sites is zero.The model comprises 14 states corresponding to stages or phases in the tick life cycle. These stages are eggs, questing (i.e., host-seeking) larvae, feeding larvae, engorged larvae, questing nymphs, feeding nymphs, engorged nymphs, questing adult females, feeding adult females and engorged and egg-laying adult females, plus a "hardening" phase for newly hatched larvae, and newly molted nymphs and adults (Figure 2). Temperature affects tick questing activity as well as the duration of development from one tick life stage to the next: from engorged females to egg laying (the preoviposition period), egg development to hatching of larvae (the preeclosion period), development of engorged larvae to questing nymphs, and development of engorged nymphs to adults. All parameters were the same as those in Ludwig et al. (2016) with the exception of mortality rates among feeding ticks. The proportions of feeding ticks of each stage that die due to grooming were adjusted to produce realistic adult tick infestations of deer in comparison with observations (a maximum 50–75 adult ticks per deer in U.S. locations; e.g., Bloemer et al. 1988, Durden et al. 1991), when the numbers of deer and rodent hosts were set at those seen in 2 km2 in parts of the eastern United States where the hosts are abundant (Ogden et al. 2005). a) Mortality rate of feeding larvae 0.65+(0.049*ln(1.01+FeedLHostL))b) Mortality rate of feeding nymphs 0.45+(0.049*ln(1.01+FeedNHostN))c) Mortality rate of feeding adults 0.35+(0.049*ln(1.01+FeedAHostA))*1210FeedL=Number of feeding larvaeFeedN=Number of feeding nymphsFeedA=Number of feeding adultsHostL=Number of hosts for larvae (200 rodents)HostN=Number of hosts for nymphs (200 rodents)HostA=Number of hosts for adults (40 deer)Figure 2. A diagram of the model showing each stage or phase of the tick life cycle as boxes linked by life cycle processes (hatching of eggs, host finding, development and molting of ticks) shown by solid arrows. Arrows of different styles indicate mortality that affects each life stage, influences of temperature and day length on life cycle processes, and density-dependent effects on mortality and reproduction rates. Temperature affects questing activity of questing larvae, nymphs, and adults, and acts on development rates from one life stage to the next: the preoviposition period of engorged adult females (POP), the preeclosion period of eggs (PEP), and the development of engorged larvae to nymphs (L to N) and from engorged nymphs to adults (N to A). Effects of day length on questing activity were also included in the model.The values on the left-hand side of the equations are the proportions of feeding ticks of each stage that die due to grooming. The right-hand side components describe additional mortality due to density dependent effects on increasing grooming and possible density-dependent acquired resistance to ticks (Ludwig et al. 2016) calibrated as for I. scapularis ticks (Ogden et al. 2005) in the absence of data specifically for A. americanum.Simulations were run with tick development and questing activity rates calculated for each meteorological station (as described in Ludwig et al. 2016), using the following site-specific historical climate data: The monthly mean temperatures (climatological values computed over the 1981–2010 period) were provided by Environment and Climate Change Canada (ECCC) ( http://climate.weather.gc.ca/climate_normals/) for Canadian sites and by the National Oceanic and Atmospheric Administration ( https://www.ncdc.noaa.gov/cdo-web/datatools/normals) for sites in the United States. The mean annual number of DD>0°C (mean DD>0°C) was used as an index of the annual temperature conditions at a site for comparison with the numbers of ticks at model equilibrium. Mean DD>0°C values for the 1981–2010 period were taken from the ECCC website ( http://climate.weather.gc.ca/climate_normals/) for Canadian sites and calculated using daily mean temperature provided by the National Centers for Environmental Information website of the NOAA ( https://www.ncdc.noaa.gov/cdo-web/datatools/normals) for sites in the United States. Day length also affects questing activity of A. americanum (reviewed in Ludwig et al. 2016), and for the simulations, day-length data for 2010 for all sites were obtained from the Astronomical Applications Department of the United States Naval Observatory website ( http://aa.usno.navy.mil/data/).The model output data were managed in R (version 3.4.0; R Development Core Team). A tick population was considered at equilibrium if the maximum annual numbers of ticks of a particular stage was stable for the 10 y at the end of the 85-y simulation period. Sites with zero or crashing populations at the end of the simulation were considered as having a climate unsuitable for A. americanum.DD threshold for tick population survival.As in previous studies (Ogden et al. 2005, 2006; Springer et al. 2015), the mean annual number of DD>0°C served as an index of the seasonally variable temperature conditions at any particular location. The outcome from the dynamic population model (the maximum annual number of feeding larvae, nymphs, and adult ticks at equilibrium) was the dependent variable in a regression model, where the mean DD>0°C was an explanatory variable. The Spearman's rank correlation test (Dahmen and Hall 1990) at the 1% significance level (p 0°C at which the number of ticks in the model was zero was considered to be the lowest temperature threshold for tick population survival. However, simulation results from the individual meteorological stations were also inspected, because DD>0°C is not a perfect index of how seasonally variable temperature conditions may affect tick population survival (Ogden et al. 2005, and see Results section).Using this threshold, the predicted current distribution of temperature suitability for A. americanum in North America was mapped using gridded observational data series at 10-km resolution from the Australian National University SPLINe (ANUSPLIN) data sets over Canada (Hutchinson et al. 2009; McKenney et al. 2011), and the North American Land Data Assimilation System (NLDAS) data sets over the United States (Xia et al. 2012).As a sensitivity analysis of variation in the geographic scope of predicted temperature suitability, the limit of the occurrence of temperature suitability was mapped using DD>0°C values higher and lower than the value selected as described above. The selection of these values was guided by the standard error of the coefficient in the regression model of number of larvae against DD>0°C.Empirical validation of the life cycle model.First, the results of 11 simulations run using environmental data from stations in the United States were compared with county-level distribution maps of A. americanum in the United States (Springer et al. 2014). Using records from literature and databases published between the years 1898 and 2012, Springer et al. (2014) classified counties as having established A. americanum populations (six ticks or two life stages) or reported A. americanum (fewer than six ticks of a single life stage or number of ticks not specified), or as A. americanum being absent (no report). We considered that counties classified by Springer et al. (2014) as "established" and "reported" as having suitable temperature conditions for survival of A. americanum if they were south of counties considered to have "established" A. americanum populations. Second, we assessed the extent to which counties that were considered "established" and "reported" by Springer et al. (2014) were within the geographic range of the region currently predicted to be climatically suitable for the tick, and we measured the sensitivity of the temperature limit to detect "established" and "reported" counties in the United States. Estimates of specificity were not attempted because the data in Springer et al. (2014) are presence only, and because it was expected that temperature suitability for A. americanum will have a much wider geographic scope than the complete ecological niche that includes characteristics of habitat and host densities.Sensitivity analysis of mortality rates.The sensitivity of simulation outcomes of tick models of this type to changes in parameter values has been extensively studied (Ogden et al. 2005; Wu et al. 2013; Ludwig et al. 2016), and mortality rates of both feeding and questing ticks are key parameters determining the numbers of ticks at model equilibrium. Values for these parameters in the A. americanum model are estimated from other tick species, so it is important to assess how changes in the values may affect the number of ticks at equilibrium in simulations. For example, if mortality rates are set higher than those actually occurring in nature, in simulations the number of ticks would be artificially low. This possibility is not of great importance for our objectives here unless it affects the temperature threshold for A. americanum population survival deduced from the simulations at the meteorological stations. To explore this possibility, we performed a local sensitivity analysis to assess the impact of increasing and decreasing mortality rates of parasitic and nonparasitic tick stages on the model outcome of the numbers of feeding larvae at equilibrium. Feeding larvae were chosen over feeding nymphs and adults because feeding larvae had the most linear relationship with DD>0°C above the threshold value for population survival. The model was run as before for 85 y with temperatures and day-length data for four meteorological stations (Ottawa and Hamilton in Canada, and Albany and Detroit in the United States) that have temperature conditions close to the deduced DD>0°C limit for A. americanum population survival. For each meteorological station, three simulations were run: one with mortality rates unchanged, one with mortality rates increased by 5%, and one with mortality rates decreased by 5%. The outcomes of the simulations (maximum annual number of larvae at model equilibrium) were then explored graphically to see if changing the mortality rates altered the intercept of the relationship between DD>0°C and number of larvae.Potential Distribution Changes under Future ClimateWe estimated future spatial distribution of the estimated DD>0°C limit for A. americanum populations over North America using average values of daily mean temperature simulations over North America from an ensemble of six regional climate model (RCM) simulations in order to minimize uncertainties related to intramodel variability. These RCM simulations are part of the COordinated Regional climate Downscaling EXperiment (CORDEX; Mearns et al. 2017) under the initiative of the World Climate Research Programme's Working Group on Regional Climate and the Working Group on Coupled Modelling. Each simulation covers the 1971–2000, 2011–2040, 2041–2070, and 2071–2100 periods and is available at a horizontal resolution of approximately 50km. Our series of climate projections are based on two Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5 (Van Vuuren et al. 2011), to represent uncertainties about future greenhouse gas emissions. RCP4.5 is a medium stabilization scenario leading to a stable radiative forcing level of 4.5°W/m2 by the year 2100 (relative to the year 1750; Moss et al. 2010). RCP8.5 is a high emission scenario leading to a rising radiative forcing level of 8.5°W/m2 by the year 2100 (Van Vuuren et al. 2011). The six simulations of RCMs driven by various global climate models are described in Table 2.Table 2 The regional climate models (RCMs) and driving models used to perform simulations of historic (1971–2000) and future (2011–2040, 2041–2070, 2071–2100) temperature trends over North America.Table 2 lists simulation number in the first column; the corresponding name, responsible institution, reference for regional climate model and global climate model (driving conditions) are listed in the other columns.Simulation numberRegional Climate ModelGlobal Climate Model (driving conditions)NameResponsible institutionReferencesNameResponsible institutionReferences1Canadian Regional Climate Model (CRCM5)Université du Québec à Montréal, CanadaMartynov et al. 2013; Šeparović et al. 2013Second generation Canadian Earth System Model (CanESM2)Canadian Centre for Climate Modelling and Analysis (CCCma) of Environment and Climate Change Canada (ECCC), Canada http://climate-modelling.canada.ca/2Canadian Regional Climate Model (CRCM5)Université du Québec à Montréal, CanadaMartynov et al. 2013; Šeparović et al. 2013Fifth version of Max Planck Institute Earth System Model (ECHAM5/MPI-M&MPI-ESM-LR)Max Planck Institute for Meteorology, Germany https://www.mpimet.mpg.de/3Canadian Regional Climate Model (CanRCM4)CCCma of ECCC, CanadaScinocca et al. 2016Canadian Regional Climate Model (CanRCM4)CCCma of ECCC, Canada4High Resolution Limited Area Model (HIRHAM5)Danish Meteorological Institute, Denmark, and Alfred Wegener Institute Foundation for Polar and Marine Research, GermanyBøssing Christensen et al. 2007High Resolution Limited Area Model (HIRHAM5)Danish Meteorological Institute, Denmark and Alfred Wegener Institute Foundation for Polar and Marine Research, GermanyBøssing Christensen et al. 20075Rossby Centre regional atmospheric model (RCA4)Rossby Centre, SwedenStrandberg et al. 2015CanESM2CCCma of ECCC, Canada http://climate-modelling.canada.ca/6Rossby Centre reg
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