Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada

(1)

Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada

A. Bootsma

¹

, S. Gameda

¹

and D. W. McKenney

²

1Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario K1A 0C6; ²Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie,

Ontario, Canada P6A 5M7. Received 5 March 2004, accepted 14 February 2005.

Bootsma, A., Gameda, S. and McKenney, D. W. 2005. Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada. Can. J. Soil Sci. 85: 329–343. Agroclimatic indices (heat units and water deficits) were determined for the Atlantic region of Canada for a baseline climate (1961 to 1990 period) and for two future time periods (2010 to 2039 and 2040 to 2069). Climate scenarios for the future periods were primarily based on outputs from the Canadian General Circulation Model (GCM) that included the effects of aerosols (CGCMI-A), but variability introduced by multiple GCM experiments was also examined. Climatic data for all three periods were interpolated to a grid of about 10 to 15 km. Agroclimatic indices were computed and mapped based on the gridded data. Based on CGCMI-A scenarios interpolated to the fine grid, average crop heat units (CHU) would increase by 300 to 500 CHU for the 2010 to 2039 period and by 500 to 700 CHU for the 2040 to 2069 period in the main agricultural areas of the Atlantic region. However, increases in CHU for the 2040 to 2069 period typically varied from 450 to 1650 units in these regions when variability among GCM experiments was considered, resulting in a projected range of 2650 to 4000 available CHU. Effective growing degree-days above 5°C (EGDD) typically increased by about 400 units for the 2040 to 2069 period in the main agricultural areas, resulting in available EGDD from 1800 to over 2000 units. Uncertainty introduced by multiple GCMs increased the range from 1700 to 2700 EGDD. A decrease in heat units (cooling) is anticipated along part of the coast of Labrador. Anticipated changes in water deficits (DEFICIT), defined as the amount by which potential evapotranspiration exceeded precipitation over the growing season, typically ranged from +50 to –50 mm for both periods, but this range widened from +50 to –100 mm when variability among GCM experiments was considered. The greatest increases in deficits were expected in the central region of New Brunswick for the 2040 to 2069 period. Our interpolation procedures estimated mean winter and summer temperature changes that were 1.4°C on average lower than a statistical downscaling procedure (SDSM) for four locations. Increases in precipitation during summer and autumn averaged 20% less than SDSM. During periods when SDSM estimated relatively small changes in temperature or precipitation, our interpolation procedure tended to produce changes that were larger than SDSM. Additional investigations would be beneficial that explore the impact of a range of scenarios from other GCM models, other downscaling methods and the potential effects of change in climate variability on these agroclimatic indices. Potential impacts of these changes on crop yields and production in the region also need to be explored.

Key words: Crop heat units, effective growing degree-days, water deficits, climate change scenarios, statistical downscaling, spatial interpolation

Bootsma, A., Gameda, S. et McKenney, D. W. 2005. Incidence d’un éventuel changement climatique sur quelques indices agroclimatiques dans les provinces canadiennes de l’Atlantique. Can. J. Soil Sci. 85: 329–343. Les auteurs ont déterminé les indices agroclimatiques (nombre de degrés-jours et déficit hydrique) de la région canadienne de l’Atlantique pour une période de base (1961 à 1990) et deux périodes à venir (2010 à 2039 et 2040 à 2069). Les scénarios climatologiques des deux périodes à venir reposent principalement sur les données du modèle de circulation générale (MCG) pour le Canada tenant compte des effets des aérosols (CGCMI-A), mais on s’est aussi penché sur la variabilité introduite par les multiples expériences effectuées avec ce mod- èle. Les données climatologiques pour les trois périodes ont été interpolées sur une grille aux mailles de 10 à 15 km environ. Après calcul, on a cartographié les indices agroclimatiques d’après les données de la grille. Selon les scénarios du CGCMI-A interpolés sur la grille, le nombre moyen de degrés-jours (DJ) passerait de 300 à 500 entre 2010 et 2039 puis de 500 à 700 entre 2040 à 2069 dans les principales zones agricoles de la région de l’Atlantique. Lorsqu’on tient compte de la variabilité des expériences réalisées avec le modèle cependant, entre 2040 et 2069, l’accroissement du nombre de DJ varierait généralement de 450 à 1 650, si bien que le nombre de DJ disponibles se situerait entre 2 650 et 4 000. Dans les principales zones agricoles, le nombre de degrés-jours de croissance (DJC) supérieurs à 5 °C augmente d’environ 400 unités entre 2040 et 2069, ce qui en porterait la totalité entre 1 700 et 2 700. On prévoit aussi une baisse du nombre de degrés-jours (refroidissement) le long d’une partie de la côte du Labrador. En ce qui concerne le déficit hydrique, à savoir l’écart entre l’évapotranspiration et les précipitations potentielles durant la période végétative, les prévisions varient généralement de +50 à –50 mm au cours des deux périodes, mais l’écart passe de +50 à –100 mm quand on prend en compte la variabilité des expériences effectuées avec le modèle. Les plus fortes hausses au niveau du déficit hydrique devraient survenir dans le centre du Nouveau-Brunswick entre 2040 et 2069. Les méthodes d’interpolation donnent un écart de température moyen de 1,4 °C inférieur à celui obtenu avec les méthodes statistiques de réduction d’échelle (MSRE) pour

329

Abbreviations: CCIS, Canadian Climate Impacts Scenario Project; CHU, crop heat units; DEFICIT, water deficit; EGDD, effective growing degree-days above 5°C; GCM, General Circulation Model; GGD, growing degree-days; GHG, greenhouse gas; P, precipitation; PE, potential evapotranspiration; RCM, regional climate model; SDSM, statistical downscaling procedure Can. J. Soil. Sci. Downloaded from cdnsciencepub.com by 134.122.89.123 on 04/21/21 For personal use only.

(2)

Agriculture is an important sector of the economy in the Atlantic region of Canada that is sensitive to climate change.

Projected changes in climate are likely to have both positive and negative effects on agriculture in the region (Bootsma 1997). Assessments of the potential impacts of climate change on agriculture are needed by scientists, policy mak- ers, producers and others to make decisions on policies and management practices that will minimize negative impacts of expected changes in climate and take advantage of the positive impacts or opportunities (Smit and Skinner 2002).

Various agroclimatic indices have been used in the past to assess the climatic conditions for production of cultivated crops in Canada. Water deficits (DEFICIT, defined as PE – P, where PE and P are the seasonal potential evapotranspiration and precipitation, respectively) and effective growing degree-days above 5°C (EGDD) have been used as principal climate variables to rate the climatic suitability of land for production of spring-seeded small grains (Agronomic Interpretations Working Group 1995). EGDD are a modification of growing degree-days (GDD) that reflect a shorter growing season for spring cereals and include consideration of the influence of daylength at high latitudes. Crop heat units (CHU), also known as corn heat units, have been wide- ly used to rate the suitability of various regions for the production of corn and soybeans (Major et al. 1976; Chapman and Brown 1978; Bootsma et al. 1992, 1999; Brown and Bootsma 1993). These indices were adopted for our study because of their important influence on crop performance and common acceptance in the past as indicators of crop suitability.

A variety of methods have been developed for downscaling the coarse-scale GCM projections to regional or local (site- specific) scales, each with various strengths and weaknesses (Wilby and Wigley 1997). Regional climate models (RCMs) are computationally very expensive to run and therefore avail- ability of data sets is very limited (Caya and Laprise 1999;

IPCC-TGCIA 1999). Statistical downscaling procedures are relatively easy and cheaper to apply for specific sites (IPCC 2001a) but become problematic when constructing finely gridded scenarios for a region. Lines et al. (2003) used a statistical downscaling technique to construct scenarios of temperature and precipitation for selected locations in the Atlantic region.

However, no comparisons were made with other procedures for developing scenarios at regional/local scales. In the absence of appropriate downscaling methods available for generating finely gridded data for regional-scale mapping, various spatial interpolation procedures have been employed in developing high-resolution baseline climatologies and climate

change scenarios (Kittel et al. 1997; IPCC 2001a; McKenney et al. 2001; Canadian Institute for Climate Studies 2002; Price et al. 2004). Brklacich and Curran (2002) concluded that several different methods of interpolating macroscale GCM outputs to a regional scale in western Canada all resulted in similar temperature and precipitation values at the regional scale. However, they did not make comparisons with more rigorous spatial and temporal downscaling techniques such as described by Wilby et al. (2002) and by Semenov and Barrow (1997).

The purpose of our study was to (i) develop and test an approach/methodology for assessing impacts of climate change scenarios from GCM model experiments, and (ii) evaluate potential impacts of greenhouse gas (GHG) induced climate changes on several agroclimatic indices that are of significance to agricultural production in the region, using a plausible GCM scenario interpolated to a fine scale.

To date there has been little research done on the impacts of future climate change projections on agro-climatic indices in the region. Nor has there been extensive evaluation of procedures that examine spatial distributions of agro-climates under change scenarios in the region.

METHODOLOGY General Procedures

In our study, agro-climatic indices were calculated from 30- yr monthly climatic normals data for a baseline period (1961 to 1990) and two future 30-yr time periods (2010 to 2039 and 2040 to 2069) at a fine grid scale (500 arc seconds).

Temperature and precipitation scenarios for the future time periods were generated by interpolating the output from a Canadian GCM model experiment to the fine grid scale and applying the changes to the observed baseline monthly climate data for the 1961 to 1990 period interpolated to the same grid. Comparisons were made between temperature and precipitation scenarios created using our interpolation procedure and the statistical downscaling procedures of Wilby et al. (2002) as employed by Lines et al. (2003) for several locations in the region.

Description of Study Area

The study area included the three maritime provinces (New Brunswick, Nova Scotia, and Prince Edward Island), and Newfoundland and Labrador. The geographic boundaries of this region are approximately 52°40′W to 68°00′W and 43°30′N to 60°30′(Fig. 1). However, climate station data and General Circulation Model (GCM) grid point data out- quatre endroits, en hiver et en été. La hausse des précipitations en été et en automne était d’en moyenne 20 % plus faible que celle calculée avec les MSRE. Durant les périodes où les MSRE estiment des changements relativement minimes pour la température et les précipitations, la technique d’interpolation a tendance à donner des variations plus importantes. Il serait bon d’entreprendre des études supplémentaires pour vérifier l’incidence d’autres scénarios avec le MCG, d’autres techniques de réduction d’échelle et les répercussions éventuelles d’une modification de la variabilité climatique sur ces indices agroclimatiques. On devrait aussi approfondir les conséquences potentielles de tels changements sur le rendement des culture et la production dans ces régions.

Mots clés: Degrés-jours, degrés-jours de croissance, déficit hydrique, changement climatique, réduction d’échelle, interpolation spatiale

Can. J. Soil. Sci. Downloaded from cdnsciencepub.com by 134.122.89.123 on 04/21/21 For personal use only.

(3)

side of this range were also included in the data analyses, e.g., a portion of Quebec. The climate is predominantly a modified continental type, moderated somewhat by the proximity to the Atlantic Ocean (Chapman and Brown 1978). Winters tend to be cold and snowy, while growing season conditions are generally moderately cool with ample moisture. Most of the land areas suitable for agriculture are located in the three Maritime Provinces. Figure 2 shows areas for which, according to 1996 Census data (Statistics Canada 1997), Soil Landscape Polygons (Agriculture and Agri-Food Canada 1994) had at least 2.5% of the land area (1% in Newfoundland and Labrador) under cultivated crops or improved pasture. Climatic changes are likely to have the greatest impact in these areas, since this is where most of the agricultural land is concentrated.

Baseline Climate and Climate Change Scenarios The baseline climate data used for this study was 30-yr monthly climate normals for the 1961 to 1990 period (Environment Canada 1994) for daily maximum and minimum air temperature and total precipitation. The locations of climate stations in the region with available data for this

period are shown in Fig. 1. Monthly mean values of each variable were interpolated to a grid of 500 arc seconds (approximately 10 to 15 km), using Digital Elevation Model (DEM) data and a thin plate smoothing spline surface fitting technique (Hutchinson 1995). Interpolations were made using a software package called ANUSPLIN (Hutchinson 2000). ANUSPLIN is a program that fits multi-dimensional thin plate splines to noisy multi-variate data. Surfaces were created using trivariate splines with latitude, longitude and elevation as the independent variables. Complete details on the interpolation procedures used are available in McKenney et al. (2001). This procedure has been used to develop spatially continuous climate “surfaces” for many regions in the world (Hutchinson 1995; New et al. 1999;

Price et al. 2000). Results of interpolations have also been used to produce an updated version of the Plant Hardiness Zones map for Canada (McKenney et al. 2001). The gridded climate normals data for 1961 to 1990 are available from the Canadian Climate Impacts Scenario Project (CCIS) web site (Canadian Institute for Climate Studies 2002).

Climate change scenarios for temperature and precipitation for the periods 2010 to 2039 and 2040 to 2069 were con- Fig. 1.Location of study area, with locations of climate stations (dots) and GCM grid points (+) identified. Numbered grid points were used to examine variability among GCMs.

(4)

structed, based on the output of the first generation coupled Canadian General Circulation Model (CGCMI-A) (Boer et al.

2000b). This model has been demonstrated to provide realistic global patterns of seasonal mean surface air temperature and precipitation (Flato et al. 2000). Furthermore, the model shows reasonable agreement with observed global temperature changes over the 20th century. Precipitation trends are much less evident in the model, and are masked by large natural variability (Boer et al. 2000a). The Canadian GCM data are provided on a grid of 3.75° latitude by 3.75° longitude (Fig. 1).

Grid points that fall over water in Fig. 1 are treated as sea and the remainder as land-based locations in the GCM, as indicated by the sea-land mask available from Canadian Centre for Climate Modelling and Analyses web site (CCCma 2004).

Results of the first of three ensembles that included the effect of aerosols were extracted from the IPCC Data Distribution Centre (1999) CD-ROM. Greenhouse gas (GHG) forcing corresponds to that observed from 1850 to 1990 and increases at a rate 1% per year thereafter until year 2100, also known as the IS92a emission scenario (Boer et al. 2000b). In this scenario, the GHG forcing increase is equivalent to a doubling of CO₂ by around 2050 compared to the 1980s. Using only one GCM scenario in our study for estimating the potential effects of climate change is not ideal, but it does provide an indication of one plausible outcome. The IS92a emission scenario results in global surface temperature projections that are slightly lower than the average of the full envelope of the latest available

(SRES) scenarios by the IPCC (IPCC 2001a). We examined scatter plots of temperature and precipitation changes from a wide range of GCM experiments (Canadian Institute for Climate Studies 2002) to determine the effects of variability in GCM outputs on agroclimatic indices.

Mean monthly differences in temperature between the GCM simulations (for each of the two future time periods) and the historic simulation of the GCM for the 1961 to 1990 period were interpolated from the coarse GCM grid to the finer regional grid scale using the thin plate spline methods of ANUSPLIN. For precipitation, the ratios of the simulated GCM values for the future time period over the simulated baseline period were interpolated. The ANUSPLIN interpolation approach is detailed in Price et al. (2004).

After several trials with different ANUSPLIN settings, fixed signal models based on a latitude and longitude interpolation were used. Fixed signal models are appropriate when there is a poor statistical relationship between the dependent and independent variables (in this case geographic position and the GCM change field). The signal in thin plate spline models is akin to the degrees of freedom in standard regression models. Initial model runs generated SIGNAL = 1 models, which result in an exact interpolation between the data points (GCM grid cells). These models generate steep gradients of temperature differences and precipitation ratios between grid cells, which, in our opinion, would not be appropriate. Such models would infer that Fig. 2.Principal areas (indicated as shaded) in the Atlantic Provinces with agricultural land under cultivated crops or improved pasture.

(5)

there are no errors in the GCM scenario, which is unlikely.

GCM outputs are not considered to be precise forecasts of future climate, but only as plausible scenario of what could happen in the future given a trajectory of economic activity and population levels (IPCC 2001b). Also, note again that it is the temperature differences and precipitation ratios rela- tive to the simulated 1961 to 1990 GCM output that were interpolated, not the absolute values of these variables. This is standard practice for climate change studies to help remove any biased representation of current climate that may be present in a GCM output. Trivariate spline models (interpolations using longitude, latitude and elevation) were also not used because GCM models are not developed with elevation dependencies. Separate trials affirmed this lack of elevation influences in GCM change fields. In our study, the fixed signal model, where SIGNAL = 0.6, was used to produce spatially coherent and smoothly varying models of the change fields. Further details of the underlying mathematics for thin plate splines are provided by Wahba (1990), Hutchinson and Gessler (1994) or Hutchinson (1995).

Temperature differences and precipitation ratios were applied to the gridded mean monthly maximum and minimum air temperature and precipitation data, respectively, for the 1961 to 1990 period to construct gridded data for the two future time periods. While the application of more sophisticated downscaling techniques such as statistical downscaling (Wilby and Wigley 1997) is advantageous for producing scenarios, particularly within the land-sea inter- face of this region, the simpler interpolation procedures was used here to provide one plausible outcome of climate change in the region. To indicate how downscaling methods might affect the results, we compared our gridded data (2040 to 2069 period) with results using the Statistical Downscaling Model (SDSM, Wilby et al. 2002) as reported by Lines et al. (2003), for four locations that were arbitrari- ly selected. The gridded scenario data generated in our study are available from the CCIS Project web site (Canadian Institute for Climate Studies 2002).

Calculation of Agro-climatic Indices

Calculations of CHU, EGDD and DEFICIT were made using the gridded monthly climatic normals for average daily maximum (T_max) and minimum (T_min) air temperature and total precipitation (P) as input data. Initially, 365 daily average values of T_max and T_min were generated from monthly average values using the Brooks sine wave interpolation procedure (Brooks 1943). Average daily values of P were generated by dividing the monthly value by the number of days in the month.

Average daily values of CHU were computed after Brown and Bootsma (1993) using the following formula:

Y_max= 3.33 (T_max– 10.0) – 0.084 (T_max– 10.0)² (if T_max< 10.0, Y_max= 0.0)

Y_min= 1.8 (T_min– 4.44) (if T_min< 4.44, Y_min= 0.0) where Y_maxand Y_min are the contributions to CHU from average daily maximum (T_max) and minimum (T_min) air temperatures, respectively.

Then, average daily CHU = (Y_max+ Y_min)/2.0

Average daily CHU was accumulated between average starting and stopping dates to obtain long-term seasonal averages (CHU_norm). Starting dates were based on the date when the average mean daily temperature (T_mean) was 11.0°C in spring. This date corresponds closely to the average planting date for corn in the region (Bootsma 1991).

Stopping dates were based on the date when average mean daily minimum temperature was 5.8°C in the fall, which corresponds closely to the date of 10% probability of occur- rence of killing frost (–2°C) in this region. CHU values were adjusted using a linear relationship (Fig. 3), to account for differences in average CHU calculated from daily averages of T_maxand T_min(generated from monthly normals using sine wave interpolation data) and those calculated from 30 yr of daily maximum and minimum air temperature. The linear relationship in Fig. 3 was determined by regressing values for CHU_ave calculated from daily maximum and minimum air temperature data from 33 locations in the Atlantic region in a previous study (Bootsma 1991) against CHU_norm. It was assumed that the linear relationship in Fig.

3 could be extrapolated to higher CHU values under the climate change scenarios. This was a reasonable assumption since relationships continued to be linear above 2800 CHU based on data from locations in Quebec (Bootsma et al.

1999) and in Ontario (Bootsma, unpublished data).

To compute EGDD, average daily values of GDD > 5°C (GDD) were first calculated from interpolated mean daily air temperatures (T_mean) using the formula:

Average daily GDD = T_mean– 5.0 (if T_meanis < 5.0, GDD = 0.0)

Average daily GDD were then summed from 10 d after T_mean exceeded 5.0°C in spring to the day before the average date of the first fall frost (0°C). These starting dates for accumulating Fig. 3.Relationship between CHU calculated from sine-derived

average daily temperature data (CHU_norm) and CHU calculated from daily temperature data (CHU_ave).

(6)

EGDD are more appropriate for the growing period of spring- seeded small-grain cereals than GDD, which are designed to represent the growth period for perennial forage crops (Chapman and Brown 1978). Calculating GDD from mean daily air temperatures may involve some error in the spring and fall periods, since averages of daily temperatures include days when the temperature was below the base value. It also disregards some daily temperatures above 5°C before the starting and after the ending dates. However, this procedure may be considered sufficiently accurate for classifying the average GDD climates for agriculture (Chapman and Brown 1978). Average fall frost dates were estimated from monthly temperature normals, station elevation and astronomical data as described by Sly et al. (1971). An adjustment was then made to accumulated GDD to account for the effect of longer daylengths in promoting earlier maturity in cereal crops at latitudes higher than 49°N. This adjustment was based on the relationship used in the land suitability rating system for spring-seeded small grains in Canada (Agronomic Interpretations Working Group 1995).

The water deficit (DEFICIT) was calculated by subtract- ing average daily precipitation (P) from potential evapotranspiration (PE) and accumulating values over the same period as EGDD. Latent evaporation (LE), defined as the volume of water (cm³) that evaporates from a Bellani atmometer, was estimated from T_max, T_minand solar radia-

tion at the top of the atmosphere (Q_o) using Baier and Robertson (1965) Formula I and converted to PE (mm) by using the conversion factor of 0.086 mm cm^–3(Baier 1971).

Q_owas estimated from latitude and time of year using procedures described by Robertson and Russelo (1968). While the use of average daily P and PE values are not realistic from the point of view of the hydrological cycle, average seasonal DEFICIT values have been successfully related to water deficits or irrigation requirements determined from water budgeting procedures using daily data (Sly and Coligado 1974). In a more recent study of agroclimatic indices in Ontario and Quebec (Bootsma et al. 2004), a close regression relationship was established between average seasonal DEFICIT values as computed in this study with an aridity index based on a daily water budgeting technique (R²

= 0.82). The aridity index is very similar to the irrigation requirements used by Shields and Sly (1984), and has been used as a primary classifier in the Soil Water Regime Classification System for Canada (Expert Committee on Soil Survey 1991). Therefore, we consider the water deficit as applied in this study as a useful indicator of climatic moisture regimes under present and future climates.

Mapping of Indices

Maps of each index were generated for the baseline climate (1961 to 1990) and for the two future periods (2010 to 2039 Fig. 4.Comparison of CGCMI-A scenario with other GCM models for the March to May period (source: Canadian Institute for Climate Studies 2000).

(7)

and 2040 to 2069) using ArcView GIS (ESRI 1996) software. Changes in indices from the baseline period to the future scenarios were also mapped in this manner.

RESULTS AND DISCUSSION Climate Change Scenarios

Monthly mean changes in temperature projected by the Canadian GCM for the grid points used in this study were greatest for the 2040 to 2069 period. In general, the greatest temperature increases occurred over the northern part of Quebec and Labrador. Changes for T_max ranged from an increase of 5.5°C in March in northern Quebec to a decrease of –6.3°C in May off the Labrador coast. Changes in T_minwere even greater, ranging from an increase of 8.1°C in February to a decrease of –6.4°C in May. Temperature increases were generally largest in the December to April period, partly due to a positive feedback with albedo as snow cover is reduced (F.

Zwiers, Canadian Centre for Climate Modelling and Analysis, personal communications). The largest decreases in temperature were generally for grid points off the Labrador coast in the North Atlantic Ocean/Labrador Sea, between 53.8 and 57.5°N and tended to be during the period from April to August.

Temperature changes averaged over all GCM grid points and months were 1.3 and 1.6°C for T_maxand T_min, respectively, for the 2040 to 2069 period. For the 2010 to 2039 period, these averages were 0.8 and 1.0°C, respectively.

Changes in precipitation were generally small, i.e., the ratio of average monthly precipitation for the future scenario divided by the value for baseline period was frequently close to 1. Average ratios for all grid points and months were 1.0 (no change) for the 2010 to 2039 period and 1.04 for the 2040 to 2069 period. The highest ratios (1.4) occurred at some of the most northern grid points in September in both periods. The lowest ratios (less than 0.75) occurred in August for the 2010 to 2039 period for some grid points south of the Atlantic region over the Atlantic Ocean and New England states. For the 2040 to 2069 period, the lowest precipitation ratio (0.74) occurred in February for a grid point north of Labrador. Statistical tests on the significance of the changes in climate were not available since (i) GCM scenarios are not accompanied by error estimates, (ii) we dealt only with average temperature and precipitation values, and (iii) we did not include results from multiple GCMs in constructing the scenarios.

Comparisons of temperature and precipitation scenarios generated by the CGCMI ensemble used in our study in relation to 26 other CGCMI ensembles and other GCM model outputs for the 2040 to 2069 period are shown in Figs. 4 and 5 for a grid point located in eastern New Brunswick (46.4°N, 67.5°W). These scatter plots were extracted from the CCIS Project web site (Canadian Institute for Climate Studies 2002), and were based on the IS92a emission scenario, some with and some without sul- Fig. 5.Comparison of CGCMI-A scenario with other GCM models for the June to August period (source: Canadian Institute for Climate Studies 2000).

(8)

phate aerosols. For the spring period (Fig. 4), average temperature change for the GCM scenario used in this study was near the mid-point of the range of other GCM models, while the increase in precipitation was somewhat greater than most other models. For the summer period (Fig. 5), the scenario we used was near the mid-point of the other GCM models for temperature, but at the low end of the range for precipitation. Different results may be expected from other grid points in the region. For example, for a grid point located near 46.4°N, 60°W, our scenario had the lowest increase in summer mean temperature, while the change in precipitation was near the mid-point of the other GCM experiments.

Choosing different GCM scenarios would therefore affect the patterns of anticipated changes in the agroclimatic indices mapped for the region. The fact that the GCM scenario used in this study had relatively low precipitation compared to other GCM outputs for the central New Brunswick area suggests that water deficits computed for future periods for this region would likely be on the extreme high end compared to values calculated from other GCM experiments.

Effects of Downscaling on Climate Scenarios Comparisons were made between scenario data from the CGCMI coarse grid, the fine grid data generated by ANUS- PLIN and the downscaled data using SDSM for the 2040 to 2069 period for four locations (Table 1). All GCM grid

points used in these comparisons were considered as land- based points. Mean winter and summer temperature changes using SDSM were 1.4°C on average higher than the fine grid and the GCM grid values. During the spring period, SDSM temperature changes were relatively small (< 1.8°C) compared to the summer and winter periods and were negative for two locations (Fredericton A and Goose A). SDSM changes during spring were 2.0°C on average lower than the fine grid and the GCM grid values. Precipitation increases for the fine grid and the GCM grid tended to be larger than SDSM in winter and spring, when changes in precipitation using the SDSM were usually small or negative. During the summer to autumn period, precipitation increases using SDSM were relatively large in comparison to the other times of the year and averaged 20% more than the fine grid and the GCM grid values. Overall, the changes in precipitation values for the fine grid were similar to those of the GCM grid.

These comparisons indicate that downscaling procedures may significantly influence the values and distribution patterns of the climate change scenarios generated from the GCM data and suggest that further studies to evaluate the effect of downscaling methods on the study results would be beneficial. Statistical downscaling attempts to account for the influences of regional and local physiographic features on large-scale climate by establishing empirical relationships between large-scale atmospheric variables (predictors)

Table 1. Changes in mean temperature (T_mean) and precipitation for the 2040 to 2069 period compared to 1961 to 1990 for CGCMI grid box, nearest fine grid and SDSM at four locations

Change in T_mean(ºC) Change in precip (%)

CGCMI CGCMI Location (climate station) Period^z coarse grid^y Nearest fine grid^x SDSM^y coarse grid^y Nearest fine grid^x SDSM^y

Charlottetown A Winter 2.78 2.46 3.88 9.00 7.37 4.09

(46.283°N lat., Spring 2.93 2.56 1.80 8.00 8.20 3.38

63.133°W long.) Summer 2.27 2.01 3.55 –2.00 –1.77 11.85

Autumn 2.05 1.99 1.37 –8.00 –7.40 15.54

Annual 2.52 2.26 2.65 2.00 1.60 9.07

Fredericton A Winter 2.92 2.67 5.24 7.00 6.10 5.67

(45.867°N lat., Spring 2.53 2.56 -0.72 7.00 6.93 16.77

66.533°W long.) Summer 2.33 2.14 3.89 –11.00 –7.07 24.84

Autumn 2.03 1.93 3.10 –8.00 –7.63 33.98

Annual 2.47 2.33 2.88 –2.00 -0.42 20.77

Gander A Winter 2.35 1.59 2.61 5.00 4.90 –1.69

(48.950°N lat., Spring 2.08 1.08 0.66 8.00 10.27 1.51

54.567°W long.) Summer 1.95 0.80 2.89 2.00 4.37 19.99

Autumn 1.85 1.12 1.68 2.00 3.00 15.09

Annual 2.05 1.15 1.96 4.00 5.63 8.65

Goose A Winter 3.20 2.67 4.81 2.00 3.37 –5.42

(53.317°N lat., Spring 2.85 2.16 -0.55 15.00 15.27 1.19

60.417°W long.) Summer 2.17 1.68 2.30 –4.00 –3.10 7.59

Autumn 1.68 1.39 3.50 2.00 3.33 8.18

Annual 2.45 1.97 2.51 3.00 4.72 3.56

zWinter = DJF; Spring = MAM; Summer = JJA; Autumn = SON.

yDerived from the study by Lines et al. (2003).

xDerived from ANUSPLIN.

Nearest fine grid point co-ordinates: Charlottetown A – 46.347°N, 63.181°W; Fredericton A – 45.931°N, 66.514°W; Gander A – 48.986°N, 54.569°W; Goose A – 53.292°N, 60.403°W.

CGCMI co-ordinates: Charlottetown A – 46.389°N, 63.750°W; Fredericton A – 46.389°N, 67.500°W; Gander A – 50.100°N, 56.250°W; Goose A – 53.810°N, 60.000°W.

(9)

and local variables such as site-specific temperature and precipitation (predictands) (Wilby and Wigley 1997; IPCC 2001a), and is therefore expected to be an improvement over spatial interpolation of coarse grid GCM data using ANUS- PLIN. However, statistical downscaling depends on being able to establish significant predictor-predictand relationships, and assumes that these will be valid under future climate conditions. Studies have shown that different downscaling methods yield varying results (Wilby and Wigley 1997; Hellström et al. 2001; Wood et al. 2004) and considerable opportunities remain for comparing different methods. The overall effects of downscaling procedures on accumulated indices such as CHU and DEFICIT also require additional study, since the seasonal distribution of the changes could also be an important consideration.

Effect of Climate Change Scenarios on Agroclimatic Indices

Average CHU for the baseline climate (Fig. 6) are typically in the 2400 to 2600 CHU range in the main agricultural areas in our study (Fig. 2). These increase to 2600 to 3000 CHU for the 2010 to 2039 period, and to 3000 to 3200 CHU for 2040 to 2069. The increases are in the 300 to 500 and 500 to 700 CHU category, respectively, for the two future time periods (Fig. 7). Only the southeastern part of Labrador indicated a significant decrease in CHU. Areas with less than an average of 2100 CHU are generally unsuitable for silage corn production and areas with less than 2300 CHU are unsuitable for grain corn and soybean production in eastern Canada (Smith et al. 1982; Bootsma et al. 1992). The projected increases in CHU from the Canadian GCM scenario suggest that by the 2040 to 2069 period, production of these crops could expand into areas in northern New Brunswick, eastern Nova Scotia and southern Newfoundland that are presently unsuitable because of limitations imposed by the climate.

Patterns for changes in EGDD (Figs. 8 and 9) are very similar to the pattern of change for CHU. For the 1961 to 1990 period, EGDD are in the 1200 to 1600 heat unit cate- gories in the main agricultural regions (Fig. 8), and these increase by 200 to 300 units for the 2010 to 2039 period and by more than 400 units for the 2040 to 2069 period (Fig. 9).

Consequently, by 2040 to 2069, EGDD in the main agricultural areas are estimated to range from 1800 to over 2000 units. Only the eastern part of Labrador indicated a decrease in EGDD. Projected increases in growing degree-days above 5°C could result in increased potential yields (yields under no moisture stress) of forage crops for the region as the longer, warmer growing season would allow for additional cuts in a season (Bootsma et al. 1994). Whether or not the increased potential yields would translate into higher actual productivity would depend on the moisture regime under the changed climate. The impact of increased EGDD on yields of spring-seeded small-grain cereals would likely be small, as shifts towards earlier planting and ripening dates would likely result in little change in overall exposure to heat units.

Water deficits (DEFICIT) for the baseline period varied from over 100 mm in central New Brunswick to a surplus (negative value) in excess of 100 mm in the more humid regions

Fig. 6.Average crop heat units (CHU) for baseline period (1961 to 1990) and two future time periods.

Fig. 7.Change in crop heat units (CHU) for two future time periods compared to 1961 to 1990 baseline period.

(10)

such as the south-western tip of Nova Scotia, Cape Breton Island and south-eastern parts of Newfoundland and Labrador (Fig. 10). Patterns for deficits/surpluses were similar for the future time periods. The greatest increases in deficits were in New Brunswick, with a large region in the interior indicating increases in the 25 to 50-mm class for the 2040 to 2069 period (Fig. 11). In areas of eastern Nova Scotia and in much of Newfoundland, precipitation surpluses increased by 25 to 50 mm for this time period. Slight to moderate increases in surpluses were evident for much of the Maritime Provinces and Newfoundland for the 2010 to 2039 period. Slight increases in deficits in most of Labrador for 2010 to 2039 were mostly reversed in the 2040 to 2069 period.

Effects of GCM Variability on Climate Scenarios To further investigate the variability introduced into the temperature and precipitation scenarios by using results

from multiple GCM experiments, we visually examined the scatter plots available on the CCIS web site (Canadian Institute for Climate Studies 2002) for six GCM grid points in the region (shown in Fig. 1) for the 2040 to 2069 period.

The grid points selected were as follows: points 1, 2 and 3 were all at 46.39°N and at 67.75, 63.75 and 60.0°W, respectively; points 4, 5 and 6 were all at 50.1°N and at 67.75, 63.75 and 56.75°W, respectively. These grid points were all at the centre of the grid boxes for the Canadian GCM. For all other GCMs, the value ascribed came from the grid box within which it fell for the GCM in question (note: this procedure was revised for the scatter plots in Figs. 4 and 5, which were created by “re-gridding” all data from non- Canadian GCM-derived scenarios to the Canadian GCM grid). Points 3 and 5 were the only ones treated as being over water in the Canadian GCM. The remaining four points were considered as land based. However, the land-sea mask Fig. 8.Average effective growing degree-days (EGDD) for baseline period (1961 to 1990) and two future time periods.

(11)

for non-Canadian GCMs was not identified and could be different from the Canadian mask.

We examined the typical range in temperature and precipitation changes for the spring, summer and annual periods for the results from 32 GCM experiments contained in the scatter plots. The plots were based on a new set of GHG emission scenarios created by IPCC, known as the SRES scenarios, which replace the old IS92 set of scenarios (Canadian Institute for Climate Studies 2002). Although there were some differences in the ranges for different grid points and seasons, most of the temperature changes fell within a range of +1.5 to +5.0°C. Some subtle differences included somewhat higher ranges in temperature during spring (up to about +5.5°C) versus summer (up to +4.0°C) for grid points furthest inland (points 1 and 4). These points also tended to have slightly higher increases in the upper range for annual temperature (i.e., typically 4.5 to 5.0°C) than points further east (3.5 to 4.0°C).

Changes in annual precipitation for the 32 GCM experiments for the 2040 to 2069 period were typically in an envelope that ranged from about –2% to +12%. For the summer period, a range in values from about –12% to +10% was quite usual.

Effects of GCM Variability on Agroclimatic Indices To explore the impacts of variability in the outputs of GCM experiments on agroclimatic indices for the 2040 to 2069 period, we conducted sensitivity analyses of CHU, EGDD and DEFICIT to changes in temperature and precipitation.

Annual temperature and precipitation changes typical of the

range of values derived from multiple GCMs in the previous section were artificially imposed on the monthly baseline values for 1961 to 1990. For the baseline data, we used the fine grid points that were nearest to the six GCM grid points previously identified. For CHU and EGDD, temperatures were incrementally increased from +1.5°C to +5.0°C. For DEFICIT, precipitation and temperature were simultaneous- ly altered as follows: (i) precipitation was changed by –5%, while temperatures were increased by +2.0 and +4.0°C, and (ii) precipitation was changed by +12%, while temperatures were increased by +2.0, +3.5 and +5.0°C. These artificial scenarios were considered to adequately represent the range of values displayed by scatter plots of multiple GCMs.

Sensitivity analyses indicated that increases in CHU values averaged over grid points 1, 2 and 4 were typically 300 CHU/°C at small temperature increases (i.e., +1.5°C) to 330 CHU/°C for larger temperature increases (i.e., +5.0°C).

Thus, while CHU increases based on the Canadian GCM scenario were typically 500 to 700 units (Fig. 7), the variability introduced by the full suite of GCM experiments project a range of increases from about 450 to 1650 CHU.

Consequently, projections for available CHU in the main agricultural regions for the 2040 to 2069 period range from about 2650 to 4000 CHU. Grid points 3, 5 and 6 had less than an average of 1500 CHU available for the baseline (1961 to 1990) climate, far fewer than needed for corn and soybean production. For these points, total available CHU projected for the 2040 to 2069 period range from about 1600 to over 3000 units, i.e., from an inadequate to an adequate Fig. 9.Change in effective growing degree-days (EGDD) for two future time periods compared to 1961 to 1990 baseline period.

(12)

amount needed to mature the earliest hybrids/varieties available at the present time.

Increases in EGDD averaged over the six grids ranged from about 175 units/°C at low temperature increases (i.e., +1.5 to +2.0°C), to 200 units/°C at high temperature increases (i.e., +5 to +6°C). The grids that were furthest inland (points 1 and 4) had the highest rates of increase (about 200 to 245 EGDD/°C), while grid point 6 near the east coast of Newfoundland had the lowest (about 145 to 160 EGDD/°C).

Thus, while the Canadian GCM scenario used with our interpolation procedures indicated typical increases of EGDD of about 400 units for the 2040 to 2069 period in the main agricultural areas (Fig. 9), a full suite of GCM experiments suggest a plausible range of increase in EGDD of 300 to 1100 units. The projected range of available EGDD would therefore be about 1700 to 2700 units. This is not including additional uncertainty that would be introduced by downscaling methods.

Analysis of the variability in DEFICIT introduced in response to a range of artificial scenarios was more complex than for CHU or EGDD, since DEFICIT is influenced by changes in temperature, precipitation and the length of the growing season. The complexity is shown by the fact that increasing temperature while holding precipitation changes constant resulted in a wide range of changes in water deficits for different grid points. For example, increasing temperature from +2 to +4°C while precipitation changes were held constant at –5% resulted in a decrease in DEFICIT of 82 mm for grid point 2 and an increase of 31 mm for grid point 4. Water deficits decreased with increasing temperature for all grid points except point 4. Evidently, in most cases, the increased length of the growing season at higher temperatures resulted in greater increases in P than in PE. When averaged over all six grid points, the changes in DEFICIT from the 1961 to 1990 values were as follows: for –5% change in precipitation and +2°C and +4°C change in Fig. 10.Average water deficits (mm) for baseline period (1961 to 1990) and two future time periods.

(13)

temperature, average DEFICIT increased by 22 and 12 mm, respectively; for +12% change in precipitation and +2.0°C, +3.5°C and +5.0°C change in temperature, average DEFICIT decreased by 60, 70 and 93 mm, respectively. The typical range in DEFICIT changes for most grid points using the artificial scenarios was from +25 mm to –90 mm.

The exceptions were grid point 4 which had a maximum increase of 80 mm and grid point 2 with a maximum decrease of 246 mm (i.e., increased surplus). Thus, while results from the Canadian GCM scenario used in our study typically indicated changes in DEFICIT ranging from +50 mm to –50 mm for most areas (Fig. 11), the variability introduced by multiple GCMs suggest that a range +50 to –100 mm would be realistic.

CONCLUSIONS

The climatic changes in the Atlantic region within the next 50 yr, projected by the Canadian GCM model and a “busi- ness as usual” emission scenario for GHGs, are likely to have significant impacts on agroclimatic indices which affect crop production in the region. Heat units are expected to increase significantly in most agricultural areas of the region, while only slight to moderate changes in water deficits are anticipated. Sensitivity analyses applied to an envelope of typical ranges in GCM outputs indicate the need to evaluate potential impacts on agroclimatic indices using results from more GCMs and for a range of GHG emission scenarios. Expanded analyses would provide better indica- tions of the variations and uncertainties that may be associ- ated with regional climate change scenarios and their impacts (IPCC-TGCIA 1999). In this study, we have assumed that there will be little or no change in variability of the climate in the future. Further analyses are needed to

determine if expected changes in variability projected by different GCMs would affect the general nature of our conclusions. There is also a need to expand the analysis to other agricultural regions in Canada.

The procedures used to apply climate data and climate change scenarios to the fine grid were based on relatively straightforward interpolation of mean monthly data. Our study indicated that methods of downscaling daily data can significantly influence the results. Therefore, there is a need to evaluate the impacts of various downscaling methods such as stochastic weather generators, statistical downscaling and/or high-resolution regional climate models (Wilby and Wigley 1997) on the patterns of change in agroclimatic indices for the region. The interpolation method applied in this study is a practical approach towards development of regional scenarios, recognizing that future climate scenarios are only intended to be plausible alternatives of the future (IPCC 2001b). The spatial estimates of the baseline climate are robust and, importantly, include topographic dependencies. Nevertheless, improvements in spatially explicit climate change scenarios could likely be achieved through the use of more rigorous downscaling procedures. Since it is generally impractical to use statistical downscaling at a grid density desirable for agricultural or any landscape-scale applications, we suggest that a combination of downscaling and spatial interpolation could be investigated as a practical way of developing improved scenarios.

The anticipated changes in heat and moisture are likely to have a significant impact on the potential yields of crops grown in the region. The additional heat units will likely promote higher yields in heat-loving crops such as corn and soybeans. Increased heat units accompanied by a longer growing season will likely allow for additional cuts of some forage Fig. 11.Change in water deficits (mm) for two future time periods compared to 1961 to 1990 baseline period.

(14)

crops, and thus promote higher yields provided that moisture is not limiting (Bootsma et al. 1994). The impact of increased heat is likely to be small for spring cereals due to shifts towards earlier dates for both planting and maturity. Crop yields may be impacted either positively or negatively by the changes in water deficits or surpluses in the region. Negative impacts on yield are probable in those areas/crops where significant deficits exist and are expected to increase. Increased surpluses in some areas may impact yields of crops negatively that are sensitive to excess moisture.

Additional work is needed to evaluate the impacts of the potential changes in agroclimate on crop yields for the region, using either empirical or mechanistic crop growth models. Our scenarios can be useful to assess potential changes in average yields spatially. However, to assess changes in temporal yield variability would require using daily climate data as input into suitable models, rather than 30-yr averages. Assessment of potential impacts of climate change on other aspects of crop production, such as pest and disease infestation, crop lodging and harvest losses, will likely require development of suitable impact models that also require daily climate for input data.

ACKNOWLEDGEMENTS

The technical assistance of the following individuals with software development, data analyses and mapping is acknowledged with thanks: Dirk Anderson, Daniel Sabourin, Allan Jones and Janet Cummings, AAFC, Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario; Kathy Campbell and Y-Q.

Yang, Canadian Forest Service, Sault Ste. Marie, Ontario;

Jeremy Goodier and Randy Hutson, co-op students, Algonquin College of Applied Arts and Technology, Ottawa, Ontario.

Thanks are also expressed to Drs. R. de Jong, H. N.

Hayhoe and P. Schut, AAFC, Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario, for their helpful review comments on this work. The co-operation of M. Pancura, Meteorological Services of Canada, in providing data from the SDSM study for the Atlantic region is also gratefully acknowledged. This research was supported in part by the Government of Canada’s Climate Change Action Fund.

Agriculture and Agri-Food Canada. 1994.Soil landscapes of Canada, Version 1.9, National Soil DataBase, Agriculture and Agri-Food Canada, Research Branch, Ottawa, ON.

Agronomics Interpretations Working Group. 1995.Land suitability rating system for agricultural crops: 1. Spring-seeded small grains. W. W. Pettapiece, ed. Tech. Bull. 1995-6E. Centre for Land and Biological Resources Research, Agriculture and Agri-Food Canada, Ottawa, ON. 90 pp., 2 maps.

Baier, W. 1971.Evaluation of latent evaporation estimates and their conversion to potential evaporation. Can. J. Plant Sci. 51:

255–266.

Baier, W. and Robertson, G. W. 1965.Estimation of latent evaporation from simple weather observations. Can. J. Plant Sci. 45:

276–284.

Boer, G. J., Flato, G. M., Reader, M. C. and Ramsden, D.

2000a.A transient climate change simulation with historical and projected greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the 20th century.

Climate Dynam. 16: 405–425.

Boer, G. J., Flato, G. and Ramsden, D. 2000b.A transient climate change simulation with greenhouse gas and aerosol forcing:

projected climate to the twenty-first century. Climate Dynam. 16:

427–450.

Bootsma, A. 1991.Risk analyses of heat units available for corn production in the Maritime provinces. Agriculture Canada, Research Branch Tech. Bull. 1991-8E, 49 pp.

Bootsma, A. 1997.A review of impacts of climate variability and change on agriculture in Atlantic Canada. Pages 79–97 in R. W.

Shaw, ed. Climate change and climate variability in Atlantic Canada. Workshop Proceedings, December 3 to 6, 1996.

Environment Canada – Atlantic Region Occasional Report no. 9, Environment Canada, Sackville, NB.

Bootsma, A., Anderson, D. and Gameda, S. 2004. Potential impacts of climate change on agroclimatic indices in southern regions of Ontario and Quebec. Eastern Cereal and Oilseed Research Centre, Research Branch, Agriculture and Agri-Food Canada, Ottawa, ECORC Tech. Bull. Contrib. No. 03–284. 20 pp.

+ maps. [Online] Available: http://res2.agr.ca/ecorc/tb03284/

index_e.htm [2004 May 05].

Bootsma, A., Boisvert, J. and Dumanski, J. 1994.Climate-based estimates of potential forage yields in Canada using a crop growth model. Agric. For. Meteorol. 67: 151–172.

Bootsma, A., Gordon, R., Read, G. and Richards, W. G. 1992.

Heat units for corn in the Maritime Provinces. Atlantic Committee on Agrometeorology Publ. 92–1, 8 pp.

Bootsma, A., Tremblay, G. and Filion, P. 1999.Risk analyses of heat units available for corn and soybean production in Quebec.

Agriculture and Agri-Food Canada, Research Branch, Eastern Cereal and Oilseed Research Centre, Ottawa, ON. Technical Bulletin, ECORC Contrib. No. 991396. 127 pp. [Online] Available:

http://res2.agr.gc.ca/ecorc/clim2/index_e.htm [2003 Jun. 23].

Brklacich, M. and Curran, P. 2002.Impacts of climatic change on Canadian agriculture: an evaluation of impact assessment procedures. Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada. Final report for Government of Canada, Climate Change Action Fund CCAF Project A318, Natural Resources Canada, Ottawa, ON. 42 pp.

Brooks, C. E. P. 1943.Interpolation tables for daily values of meteorological elements. Q. J. R. Meteorol. Soc. 69: 160–162.

Brown, D. M. and Bootsma, A. 1993.Crop heat units for corn and other warm-season crops in Ontario. Ontario Ministry of Agriculture and Food Factsheet No. 93-119, Agdex 111/31, 4 pp.

[Online] Available: http://www.gov.on.ca/OMAFRA/english/

crops/facts/93-119.htm#c1 [2004 Jul. 30].

Caya, D. and Laprise, R. 1999.A semi-implicit semi-Lagrangian regional climate model: The Canadian RCM. Mon. Wea. Rev. 127:

341–362.

Canadian Institute for Climate Studies. 2002.Web site for the Canadian Climate Impacts Scenarios Project (CCIS). [Online]

Available: http://www.cics.uvic.ca/scenarios/index.cgi [2003 Apr.

24].

CCCma. 2004. Canadian Centre for Climate Modelling and Analyses, University of Victoria, Victoria, BC. [Online] Available:

http://www.cccma.bc.ec.gc.ca/ [2004 Jun. 30].

Chapman, L. J. and Brown, D. M. 1978.The climates of Canada for agriculture. Canada Land Inventory Report No. 3. Revised 1978. Environment Canada, Lands Directorate. 24 pp.

Environment Canada 1994.Canadian monthly climate data and 1961–1990 normals on CD-ROM, Version 3.0E. Environment Canada, Atmospheric Environment Service, Downsview, ON.

ESRI. 1996.ArcView GIS, The Geographic Information System for Everyone. Environmental Systems Research Institute, Inc., Redlands, CA.

(15)

Expert Committtee on Soil Survey. 1991.Soil water investiga- tion methods manual. Compiled by R.G. Eilers, Chairman of Soil Water Interest Group, Land Resource Research Centre, Research Branch, Agriculture Canada, Ottawa, ON. 29 pp.

Flato, G. M., Boer, G. J., Lee, W. G., McFarlane, N. A., Ramsden, D., Reader, M. C. and Weaver, A. J. 2000. The Canadian Centre for Climate Modelling and Analysis Global Coupled Model and its Climate. Climate Dynam. 16: 451–467.

Hellström, C., Chen, C., Achberger, C. and Räisänen, J. 2001.

Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation.

Climate Res. 19: 45–55.

Hutchinson, M. F. 1995.Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Inf. Syst.9: 385–403 Hutchinson, M. F. 2000.ANUSPLIN Version 4.1 user guide.

Centre for Resource and Environmental Studies, Australian National University, Canberra ACT 0200, Australia.

Hutchinson, M. F. and Gessler, P. E. 1994.Splines-more than just a smooth interpolator. Geoderma62: 45–67.

IPCC. 2001a. Climate change 2001: The scientific basis.

Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. J. T. Houghton et al., eds. Cambridge University Press, Cambridge, UK. 881 pp.

[Online] Available: http://www.ipcc.ch/ [2005 Feb. 07].

IPCC. 2001b.Climate change 2001: Impacts, adaptation, and vulner- ability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. J.

McCarthy et al., eds. Cambridge University Press, Cambridge, UK.

1032 pp. [Online] Available: http://www.ipcc.ch/ [2005 Feb. 07].

IPCC Data Distribution Centre. 1999.Providing climate change and related scenarios for impacts assessments. Climatic Research Unit, University of East Anglia, Norwich, UK, CD-ROM Version 1.0, 1999 Apr.

IPCC-TGCIA. 1999.Guidelines on the use of scenario data for climate impact and adaptation assessment. Version 1. Prepared by T. R. Carter, M. Hulme, and M. Lal, Intergovernmental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment. 69 pp. [Online] Available: http://ipcc-ddc.cru.uea.ac.

uk/guidelines/index.html [2005 Feb. 07].

Kittel, T. G. F., Royle, J. A., Daly, C., Rosenbloom, N. A., Gibson, W. P., Fisher, H. J., Schimel, D. S., Berliner, L. M. and VEMAP2 Participants. 1997.A gridded historical (1895–1993) bioclimate dataset for the conterminous United States. Pages 219–222 in Proceedings of the 10th Conference on Applied Climatology, 1997 Oct. 20–24. Reno, NV, American Meteorological Society, Boston, MA.

Lines, G. S., Pancura, M. and Lander, C. 2003.Building climate change scenarios of temperature and precipitation in Atlantic Canada using the Statistical Downscaling Model (SDSM).

American Meteorological Society, Combined Preprints CD-Rom, 83rd AMS Annual Meeting, 14th Symposium on Global Change and Climate Variations, Poster Session P2.23, 9–13 February, 2003, Long Beach, CA.

McKenney, D. W., Hutchinson, M. F., Kesteven, J. L. and Venier, L. A. 2001.Canada’s plant hardiness zones revisited using modern climate interpolation techniques. Can. J. Plant Sci. 81:

129–143.

Major, D. J., Pelton, W. L., Shaykewich, C. F., Gage, S. H. and Green, D. G. 1976.Heat units for corn in the prairies. Agriculture Canada, Ottawa, ON. Canadex factsheet 111.070. 5 pp.

New, M., Hulme, M. and Jones, P. 1999.Representing twentieth- century space–time climate variability. Part I. Development of a 1961–90 mean monthly terrestrial climatology. J. Climate 12:

829–856.

Price, D., McKenney, D. W., Nalder, I., Hutchinson, M. F. and Kestevan, J. 2000.A comparison of two statistical methods for interpolating monthly mean climate. Agric. For. Meteorol. 101: 81–94.

Price, D. T., McKenney, D. W., Papadopol, P., Logan, T. and Hutchinson, M. F. 2004.High resolution future scenario climate data for North America. 26th Annual Conference on Agric. and Forest Meterol. Proceedings, Amer. Meterol. Society, 2004 Sep.

23–26. Vancouver, BC.

Robertson, G. W. and Russelo, D. A. 1968.Astrometeorological estimator for estimating time when sun is at any elevation, elapsed time between the same elevations in the morning and afternoon, and hourly and daily values of solar energy. Qo. Tech. Bull. 14, Agrometeor. Section, Plant Research Inst., Canada Department of Agriculture, Ottawa, ON. 22 pp.

Semenov, M. A. and Barrow, E. M. 1997.Use of a stochastic weather generator in the development of climate change scenarios.

Climatic Change 35: 397–414.

Shields, J. A. and Sly, W. K. 1984.Aridity indices derived from soil and climatic parameters. Research Branch, Agriculture Canada, Ottawa, ON. Technical Bulletin 1984-14E. 18 pp.

Sly, W. K. and Coligado, M. C. 1974.Agroclimatic maps for Canada – derived data: Moisture and critical temperatures near freezing. Tech.

Bull. 81 Agrometeorol. Research and Service, Chem. and Biol. Res.

Inst., Research Branch, Canada Dept. Agriculture, Ottawa, ON. 31 pp.

+ maps.

Sly, W., Robertson, G. W. and Coligado, M. C. 1971.Estimation of probable dates of temperatures near freezing from monthly temperature normals, station elevation, and astronomical data. Canada Department of Agriculture, Research Branch, Plant Research Institute, Agrometeorology Section, Ottawa, ON. Tech. Bull. 79, 21 pp.

Smit, B. and Skinner, M. W. 2002.Adaptation options in agriculture to climate change: a typology. Mitigation and Adaptation Strategies for Global Change 7: 82–114.

Smith, P. J., Bootsma, A. and Gates, A. D. 1982.Heat units in relation to corn maturity in the Atlantic region of Canada. Agric.

Meteorol. 26: 201–213.

Statistics Canada. 1997. Agricultural profile of the Atlantic Provinces (data and analytical products: 1996 Census of Agriculture).

Statistics Canada, Catalogue No. 95-175-XPB, Ottawa, ON

Wahba, G. 1990.Spline models for observational data. CBMS- NSF Regional Conference Series in Applied Mathematics 59, SIAM, Philadelphia, PA.

Wilby, R. L. and Wigley, T. M. L. 1997.Downscaling general circulation model output: a review of methods and limitations.

Prog. Phys. Geog. 21: 530–548.

Wilby, R. L., Dawson, C. W. and Barrow, E. M. 2002.SDSM - a decision support tool for the assessment of regional climate change impacts. Environ. Modelling Softw. 17: 145–157.

Wood, A. W., Leung, L. R., Sridhar, V. and Lettenmaier, D. P.

2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate models outputs. Climatic Change62: 189–216.

(16)

Can. J. Soil. Sci. Downloaded from cdnsciencepub.com by 134.122.89.123 on 04/21/21