Projected climate reshuffling based on multivariateclimate-availability, climate-analog, and climate-velocityanalyses: implications for community disaggregation

Alejandro Ordonez & John W. Williams

Received: 1 August 2012 /Accepted: 15 March 2013 /Published online: 20 April 2013# Springer Science+Business Media Dordrecht 2013

Abstract There is a need for biologically relevant metrics of climate risk for regional- toglobal-scale climate vulnerability assessments and adaptation planning. Here, we develop,combine, and compare univariate and multivariate forms of several metrics (climate-avail-ability, climate-analog, and two forms of climate-velocity) used to assess the risks arisingfrom future climate change, using downscaled climate projections for Wisconsin (USA) as acase study. Climate-availability and climate-analog analyses show little or no overlapbetween late-20th-century and projected late-21st-century climates for Wisconsin, and largedifferences among variables in the distance, bearing, and velocity of projected climatechange. There is a strong negative correlation between geographic and climatic distancesto closest analogs, creating a tradeoff when climate velocity is assessed using multivariateanalog-based approaches: some locations have no good analogs anywhere in future climatespace and so analog-based methods pick nearby locations, resulting in low velocity esti-mates. local velocities projected for Wisconsin are higher than global means. In this region,lake effects, not topographic heterogeneity, exert the strongest influences on regionalpatterns of climate-velocity and analogs. The multivariate analog-based velocities arecorrelated with univariate velocity measures that are scaled to local spatial heterogeneity,with the magnitude and correlation analog-based velocities estimates most similar to those ofthe intervariable mean of climate velocities. Because species are differentially sensitive toparticular dimensions of climate change, and vary in their dispersal capacity, the strongdifferences among climate variables in the spatial direction, distance, and rate of projectedclimate change provide a powerful mechanism for community restructuring.

Climatic Change (2013) 119:659–675DOI 10.1007/s10584-013-0752-1

Electronic supplementary material The online version of this article (doi:10.1007/s10584-013-0752-1)contains supplementary material, which is available to authorized users.

A. Ordonez (*) : J. W. WilliamsDepartment of Geography, Center for Climatic Research, University of Wisconsin,Madison, WI 53706, USAe-mail: ordonezglori@wisc.edu

J. W. Williamse-mail: jww@geography.wisc.edu

1 Introduction

21st-century climate change driven by anthropogenic greenhouse gas emissions is expectedto alter the distribution and diversity of species, and the ecosystem services provided bythese species (Parmesan 2006; Parmesan and Yohe 2003). Determining and communicatingthe potential effects of this climatic reshuffling on the distribution of species, diversitypatterns, phenology and ecosystem services is a fundamental scientific challenge (Cleland etal. 2007; Ohlemüller 2011; Ohlemüller et al. 2006; Parmesan 2006; Thomas et al. 2004) withdirect implications for the management of species and ecosystems.

A common approach to assess the exposure of species to climate change (sensu Dawsonet al. 2011) is using the contemporary spatial distribution of individual species (or groups ofthem) to describe their realized climatic niches, then applying this niche to project futurepotential distributions of species based on future climate scenarios (e.g., Loarie et al. 2008;Thomas et al. 2004). A key challenge is that empirical species distribution models borrowstrength from the contemporary correlations among climate variables, yet this correlationstructure is likely to change over this century as it has in the past (Jackson et al. 2009),weakening the predictive ability of empirical models, as novel (no-analog) combinations ofclimatic variables emerge (Ackerly et al. 2010; Williams et al. 2007).

A complementary approach for assessing the impacts of climate change is shifting thefocus from species-oriented models to more general, yet biologically relevant, analyses ofthe spatial rate, magnitude, and direction of climate change. As climates change, thecurrently realized climate could shrink, expand or disappear; and in some cases, entirelynew (no-analog) climatic realizations could emerge (Ackerly et al. 2010; Ohlemüller et al.2006; Veloz et al. 2012; Williams et al. 2007). Climate-analog analyses can be used toidentify the direction and bearing of future analogs, providing a measure of spatial displace-ment (Ohlemüller et al. 2006; Veloz et al. 2012). In parallel, climate-velocity analyses havebeen developed to measure the spatial rate of climate change, which provides an initialapproximation of how quickly species would have to migrate in order to track climate shifts(Burrows et al. 2011; Chen et al. 2011; Loarie et al. 2009; Sandel et al. 2011). Together,analog and velocity analyses provide simple, yet powerful, tools for assessing how climatechange could affect species distributions.

Here, we compare and integrate these complementary metrics to assess the differentaspects of risk arising from future climate change. For this, we first begin by looking atunivariate indices of climate risk (changes in the availability of climate space plus the spatialvelocity, displacement and bearing of individual variables). Then we summarize thesechanges using multivariate climate-analog and climate-velocity methods and compare them,with a focus on visualizing and assessing climate exposure in multiple climatic dimensions.We then assess the transferability of univariate metrics of climatic changes to themultidimensional space describing regional changes in climatic conditions. This is animprovement from previous works, in that we jointly assess climatic similarity (locationand direction of analogs) and two forms of estimating climate velocity (analog-basedvelocity estimates versus local velocity estimates in which temporal changes in climatevariables are normalized against local spatial climatic gradients; Loarie et al. 2009).

In these analyses, we use downscaled climate datasets from Wisconsin (WICCI 2011) asa case study of these species-independent metrics of climate change risk at a mesoclimaticscale. Wisconsin is useful for this purpose because most studies of climate-velocity to datehave focused solely on univariate analyses of temperature and on topographic relief as aprimary differentiator between low-velocity and high-velocity regions and do so at largespatial scales (Ackerly et al. 2010; Loarie et al. 2009; Sandel et al. 2011; but see Dobrowski

660 Climatic Change (2013) 119:659–675

et al. 2012). Wisconsin, by contrast, is a good example of a region characterized by severalstrong spatial gradients in mesoclimate that are primarily associated with non-topographicfactors, and occupies the crossroads between two orthogonal climatic gradients: a north-south gradient in temperature and an east-west gradient in precipitation and moistureavailability, each of which is strongly modified by proximity to Lakes Superior andMichigan (Curtis 1959). Wisconsin also includes four of the largest ecoregions of NorthAmerica (Wiken et al. 2011) representing ecosystems with both historical and economicimportance. Hence, we use Wisconsin both as a general testing ground for developing testmultivariate climate-risk analyses for regional climate vulnerability assessments and adap-tation planning while also discussing the regional implications of projected climate changes.

2 Methods

2.1 Data: recent and projected future climates

Analyses are based on late-20th-century (1961 to 2000) observational datasets anddownscaled late-21st-century climate projections (2081 to 2100) obtained from theWisconsin Initiative on Climate Change Impacts (WICCI 2011). Late-20th-centuryclimatic conditions (average precipitation and maximum and minimum temperatureat monthly resolution between 1961 and 2000) were obtained from WICCI as griddeddata at a 5-arcmin resolution (ca. 8 km). For the climate-analog analyses, we use theMaurer et al. (2002) gridded data, which describes the same variables as WICCI inmonthly resolution at a spatial resolution of 7.5arcmin (ca. 12 km) across NorthAmerica.

Projected future climatic conditions for Wisconsin are based on the 4th AssessmentReport (AR4) from the Intergovernmental Panel on Climate Change (IPCC 2007)downscaled by WICCI to a 5-arcmin (ca. 8 km) resolution (WICCI 2011). Statisticaldownscaling of future predictions was done for three IPCC SRES scenarios (A1B:rapid economic growth and global development, A2: rapid economic growth andregional development, and B1: sustainable economic growth and global development)and 15 global circulation models (Notaro et al. 2011; Veloz et al. 2012; WICCI 2011).The use of these regional downscaled predictions allowed us to capture some topo-graphical effects on local climate while the use of several global circulation modelspermitted the incorporation of intermodel uncertainties. By comparing recent condi-tions to late 21st-century (2081 to 2100 means) we aim to determine the potentiallong-term effects of climatic change in the area of interest; and by using alternativeGCM’s, we obtained a range and the mean of expected future conditions under similarscenarios.

For univariate analyses, we focused on 19 biologically meaningful variables (orbioclimatic variables sensu Hijmans et al. 2005) commonly used in ecological nichemodeling to predict changes in the past and future size of distribution ranges, andspatial-temporal rates of climatic tracking (e.g., Chen et al. 2011; Loarie et al. 2009;Pearson 2006). These variables represent annual trends (mean annual temperature,total annual precipitation), seasonality (annual range in temperature and precipitation,and seasonal temperature means and total precipitation) and extreme or limitingenvironmental factors (coldest and warmest month temperature; and wettest and driestquarters precipitation). An eight-variable subset (list as described in the next section)was used for multivariate analyses.

Climatic Change (2013) 119:659–675 661

2.2 Univariate climatic availability, bearing and distance

Univariate histograms were used to describe shifts in climate availability for each biocli-matic variable in Wisconsin under current and future conditions, for each IPCC scenario.These analyses allow the identification of changes in the availability, distribution andcoverage of specific climatic conditions and displacement of climatic extremes.

To underscore the potential complexity of 21st-century climate change (and ecologicalresponses to climate change), we identified contemporary analogs for individual climatevariables by comparing each grid-cell in the WICCI domain to all cells in the NorthAmerican domain and calculating the Standardized Euclidean Distances (or SED andexplained in detail in section 2.4; Williams et al. 2007). Univariate SEDs were used todetermine similarity in climatic conditions; spatial distance (in km) and bearing. Univariateclimatic analogs were determined by selecting the geographically closest cell within the top1,000 analogs. This geographical constraint is necessary when determining univariateanalogs because the search for climatic analogs is under-constrained when only a singledimension is used. Hence, the chosen analog in the univariate analyses represents thespatially closest climatic analog.

2.3 local velocities of climate change

The velocity of climate change was calculated as the ratio of the projected temporal rate of

change divided by the local spatial gradient for the same climate factor ΔClimVar year=ΔClim Var km= ¼ km

year .

This form of climate velocity (hereafter identified as local velocity) represents the localrate of movement of a climate isocline across a spatially varying climatic gradient.Because projected temporal changes are normalized by spatial variability, local velocitiestend to be lower in topographically heterogeneous regions (Loarie et al. 2009; Sandel etal. 2011). From a biological perspective, climate-velocity provides an estimate of howslow or how fast organisms and populations would need to disperse to track theircurrent climatic position, making it a useful metric of climate risk (Ackerly et al. 2010;Sandel et al. 2011).

Local gradients were determined following Loarie et al. (2009). Spatial gradientsi:e:;ClimVar km=ð Þ were calculated from a 3×3 grid cell neighborhood using theaverage-maximum technique (Burrough and McDonnell 1998), adjusted for latitudinalvariations in grid cell dimensions. Temporal gradients i:e:;ClimVar year=ð Þ weredetermined for each climate change scenario as the slope of a linear model fittedagainst the period of interest (that is through all years between 2000 and 2100).Temporal trends were estimated per emissions scenario as the mean across 15 GCM’s.

2.4 Multivariate climatic novelty, bearing, distance, and velocity

Novelty of future climates for Wisconsin was assessed using a series of multivariatequantitative climate-analog analyses, based on a subset of eight bioclimatic variables derivedfrom the WICCI data, selected to represent changes in both temperature and precipitation.Mean annual temperature, mean summer (JJA) daily temperature, mean winter (DJF) dailytemperature, and temperature seasonality (standard deviation of monthly mean temperatures)were used to describe temperature-related changes while total annual precipitation, precip-itation seasonality (represented by the coefficient of variation in monthly precipitation over ayear), and both mean summer and winter precipitation were used to represent precipitation-

662 Climatic Change (2013) 119:659–675

related changes. Principal components analysis (PCA) indicates that these eight variablesrepresent 2 orthogonal axes (PC-1 and PC-2) that capture 82 % of the spatial climaticvariability in the region, primarily representing seasonal temperature (PC-2) and moisture(PC-1) availability on plant distributions and abundance.

To identify contemporary climatic analogs for 21st-century climates, each grid-cell in theWICCI late 21st-climatic projections were compared to the gridded observations in theMaurer et al. (2002) dataset for North America by calculating Standardized EuclideanDistances (or SED, Veloz et al. 2012; Williams et al. 2007):

SEDi;j ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

X

nk¼1 bk;i � ak;i2

� �

S2k;j

.

r

ð1Þ

Here, n determines the number of climatic variables used to estimate similarity, and ak,iand bk,i represent the current and future climatic conditions in the ith cell. Distances were

standardized using the mean historic (1965–2000) interannual variability S2k;j

� �

across the

time series of evaluated GCMs. This standardization puts all variables on a common scaleand allows searching for analogs across multiple climate variables simultaneously (Williamset al. 2007). For each grid cell, the spatial distance and bearing to its closest present climaticanalog was recorded based on the climatic dissimilarity between the late-21st-centuryclimatology of each Wisconsin grid cell and its closest 20th-century North American analog.Bearings are reported as the direction from the closest 20th-century analog to the targetWisconsin grid cell so that they follow the projected trajectory of climate change over thiscentury.

Changes in the climatic similarity between 20th-century and late-21st-centuryWisconsin climates were evaluated by calculating the minimum SED (SEDmin) foreach Wisconsin grid cell when compared to all 20th-century North America climatol-ogies. We compared SEDmin estimates against two alternative SED thresholds (SEDt)used to assess when SEDmin is large enough to represent a truly novel climate (novelclimates are defined by SEDmin>SEDt). Threshold climatic distances were determinedbased on the distribution of SEDmin for pairs of grid cells from the same biome(Williams et al. 2007), here using 20th-century North America climatologies andWiken et al. (2011) biome distributions. We used the 95 % (SEDt=1.934), and99 % percentiles (SEDt=2.487) of the within-biome SEDmin distributions in order todefine a both a restrictive (95th percentile) and relaxed (99th percentile) SEDt forassessing whether a particular 21st-century climatology lacked a modern analog.Comparisons of these SEDt to the SEDt values calculated using the receiver-operating-characteristic (ROC, the method used in Williams et al. 2007) showed thatusing the 95th and 99th percentiles results in higher values of SEDt (ROC-SEDt=1.54)and hence more conservative determinations of no-analog climates.

We determined whether locations with no-analog climates also experience different localvelocities for the eight variables used to determine climatic analogs. For this, weimplemented a PERMANOVA (Anderson 2001) to determine if the presence/absence of aclimatic analog could be discriminated on the bases of the multiple combinations of climatic-velocities. Unlike MANOVAS, PERMANOVAs do not assume either normality or homo-scedasticity, nor require assumptions about the underlying distribution (i.e., normality) orspread (i.e., variance) of the data within treatment groups. Tests of main effects wereperformed using a permutation of residuals under a reduced model, with 999 permutationson a data matrix of average distance measures.

Climatic Change (2013) 119:659–675 663

The spatial displacement between grid-cells and their future climate analogs has beenused as an index of climate change risks to biodiversity (Ohlemüller et al. 2006; Veloz et al.2012). Here, we take the next logical step and convert these displacements to estimates ofclimate velocity, simply by dividing by the time between the midpoints of the two timewindows (i.e., 1980 to 2090). We analyzed the tradeoff between climatic dissimilarity(SEDmin) and climate-analog velocity (here analog velocity) for each emission scenariousing the non-parametric association (Spearman’s rank correlations) between thesevariables.

2.5 Comparison of climatic-velocities: local velocity vs. analog-based velocity

We evaluated the match between local- and analog-based velocities as a way to assessthe transferability of multidimensional regional-scale metric of climatic displacementover time (analog-based velocity) to local-scale univariate summaries of climatevelocities (local velocity). This comparison is necessary since analog-based approachesdiffer from local velocity methods in two important respects. First, the analog-basedmethod is multivariate and so its estimates of climate displacement will be jointlyinfluenced by fast-changing and slow-changing climate variables. Second, the twoapproaches differ in spatial scale. The analog-based velocities are calculated byestimating regional-scale displacement of climates while the local-method is basedon local-scale differences in climate between grid cells and their immediately adjacentneighbors. It is unclear whether one approach is more biologically meaningful thanthe other; they represent different scales of change, so here we assess whether theyare similar.

We compare the two metrics using the non-parametric association (Spearman’s rankcorrelations) between the mean and maximum climate-velocity (Loarie method) of theclimatic variables used to define the location of the climate-analog, versus the analog-based estimates of climate-velocity. We also compared the local velocity and analog velocityfor individual variables.

3 Results

3.1 Univariate climatic availability, bearing and distance

Measures of climatic availability (using univariate histograms, Fig. 1 and Appendix S1)indicated a bimodal distribution of temperature (for annual and seasonal means) andunimodal distributions of precipitation (for annual and seasonal totals). The availableclimates are projected to shift towards higher temperatures (rightward shift of future annualand seasonal temperatures, Fig. 1) with little overlap between historic and late-21st-centuryclimates. Wisconsin precipitation is projected to shift towards higher annual and seasonaltotals (Fig. 1), with the strongest shift in the spring and weakest in the summer. Overall, thereis more overlap between historic and late 21st-century distributions of precipitation inWisconsin than for temperature (Fig. 1).

Univariate climate-analog analyses show a highly idiosyncratic pattern among climatevariables (Fig. 2 and Appendix S2). Temperature variables (Fig. 2) show a consistent north-northwestward trend with median distances between 343 and 515 km. In contrast, annualprecipitation analogs are nearby (<90 km) and weakly distributed to the northwest (Fig. 2).

664 Climatic Change (2013) 119:659–675

Furthermore, among the temperature-related variables, the maximum and minimum temper-ature of the coldest and warmest quarter shows relatively large changes (>300 km) and apronounced northward bearing. In contrast, isothermality and mean diurnal range showsrelatively little change (distances between 32 and 53 km across scenarios, Appendix S2),

4 6 8 10 12 14 16Mean annual daily mean temperature

−10 −5 0Winter daily mean temperature [Dec−Jan−Feb]

2 4 6 8 10 12 14Spring daily mean temperature [Mar−Apr−May]

16 18 20 22 24 26 28Summer daily mean temperature [Jun−Jul−Aug]

6 8 10 12 14 16Fall daily mean temperature [Sep−Oct−Nov]

700 800 900 1000 1100Total annual precipitation

50 100 150 200 250Winter total precipitation [Dec−Jan−Feb]

150 200 250 300Spring total precipitation [Mar−Apr−May]

200 250 300 350Summer total precipitation [Jun−Jul−Aug]

150 200 250 300Fall total precipitation [Sep−Oct−Nov]

late 20th−centurylate 21st−century [A1B]late 21st−century [A2]late 21st−century [B1]

Fig. 1 Histogram of current and future climatic availability for annual and seasonal mean temperature (leftcolumn) and total precipitation (right column). Height of the line at each point corresponds to the proportion of thearea inWisconsin for that climate variable. Late 20th-century climates (1961–2000) are represented with the blackline, and projected future climates (derived fromWICCI downscaled predictions for the late 21st century) are theensemble prediction of 15GCMs under three IPCCAR4 emission scenarios (red: A1B, blue: A2, and yellow: B1).Temperature is measured in degrees Celsius, precipitation as totals (in mm) per year or season

Climatic Change (2013) 119:659–675 665

with bearings evenly distributed across all directions. Precipitation changes showed aunimodal pattern with median distances always lower than 100 km (Appendix S2). How-ever, bearings varied widely among precipitation-related variables, as indicated by wettest

N

S

EW10 %

20 %30 %

40 %50 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 515 kmA2: 573 kmB1: 343 km

Mean annual daily mean temperaturea)

N

S

EW9 %

18 %27 %

36 %45 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 494 kmA2: 529 kmB1: 325 km

Winter daily mean temperature [Dec Jan Feb]b)

N

S

EW8 %

16 %24 %

32 %40 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 526 kmA2: 649 kmB1: 307 km

Summer daily mean temperature [Jun Jul Aug]c)

N

S

EW2 %

4 %6 %

8 %10 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 87 kmA2: 71 kmB1: 45 km

Total annual precipitationd)

N

S

EW3 %

6 %9 %

12 %15 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 90 kmA2: 95 kmB1: 76 km

Winter total precipitation [Dec Jan Feb]e)

N

S

EW3 %

6 %9 %

12 %15 %

0 250 500 750 1000

Distance to nearest analog [km]

A1B: 38 kmA2: 50 kmB1: 27 km

Summer total precipitation [Jun Jul Aug]f)

Fig. 2 Histograms of the spatial distance (histograms) and directional bearing (rose plots inserts) for climateanalogs based on individual variables: annual, winter and summer mean temperature (left column) and totalannual, winter and summer precipitation (right column). Direction of bearings are oriented from the contem-porary analog towards the WI grid cell, i.e. along the direction of climate change

666 Climatic Change (2013) 119:659–675

quarter analogs showing an easterly bearing while driest quarter analogs showing a west-northwest bearing (Appendix S2).

3.2 Local velocity of climate change

Estimates of local climate velocity for annual and seasonal velocities range from 0.2to 6.6 km/yr. (Fig. 3). Kolmogorov–Smirnov tests show a consistent and significantmatch in the distribution of climatic change velocity between A1B, A2, and B1scenarios within each one of the evaluated climatic variables (p>0.05 for allBonferroni corrected pairwise comparisons). Mean annual temperature velocity wason average 2.5 times higher than annual precipitation velocity, and the fastest climaticvariable in 20–21 % of the evaluated locations (Appendix S3). Velocities of maximumand mean winter temperature were a close second (14 to 18 % and 13 % 17 % of thelocations in the state respectively; Appendix S3). Temporal variability was consistent-ly similar to or higher than spatial variability for most climate variables (AppendixS3), particularly for temperature-related variables, but spatial heterogeneity stronglyinfluenced the geographical patterns of climate velocity.

Areas of maximum velocity for mean annual temperature, summer temperature, andwinter temperature (Appendix S3) were located in the northern lakes and forestregions (Lake Superior clay plains, Ontonagon lobe moraines, Gogebic Iron Range,and the Chequamegon moraine and outwash plains). Velocity of temperature season-ality was largely homogenous across Wisconsin, except for a zone of lower velocitiesnearer to Lake Michigan (Appendix S3). Precipitation-related measures of climate-velocity show fairly divergent patterns, with the highest velocities for the precipitationof the wettest month and wettest quarter in the northern and eastern part of Wiscon-sin, the velocities of the precipitation of the driest month in the northern part ofWisconsin, and no clear area of highest velocities for annual precipitation and winterprecipitation (Appendix S3).

3.3 Multivariate climatic novelty, bearing, distance and velocity

Distribution of the bearings and distances of Wisconsin grid cells to their closestNorth American multivariate analogs was similar among climate-change scenarios(Fig. 4a), except that closest analogs are nearer for the relatively mild B1 scenario.Both distances and bearings for A1B and A2 scenarios show a very similar unimodaldistribution; while distances for B1 scenario is unimodal but with bimodal northwestand northeast bearings (Fig. 4a-inset). For most Wisconsin grid-cells, the climate-analog analyses suggest that the predominant spatial direction of climate change willbe north and northeastward, with contemporary analogs found in Illinois, IowaMissouri, Kansas, and Oklahoma. For a second population of Wisconsin grid-cells,mostly in the eastern part of the state, the direction of climate change is predomi-nantly north to northwestward, with present analogs found in Michigan, Indiana,Ohio, and West Virginia.

Higher greenhouse gases not only lead to a higher spatial displacement between Wis-consin grid cells and their closest future analogs (Fig. 4a), the climatic similarity between theWisconsin grid cells and their closest analogs decreases (Fig. 4b). Interestingly, the spatialdistances between Wisconsin grid cells and their closest analogs were roughly the samebetween the A1B and A2 scenarios (Fig. 4a), but the climatic similarity of these analogsdecreased (Fig. 4b), suggesting that as radiative forcing increases, Wisconsin climates move

Climatic Change (2013) 119:659–675 667

increasingly outside the domain of late-20th-century climates in North America. MeanSEDmin significantly differs (using paired t-tests) across all emission scenarios; a trendmanifested by the increase in SEDmin for Wisconsin grid cells with higher greenhouse gasconcentrations (Fig. 4b).

0.03 0.1 1 10 100

Mean annual daily mean temperatureA1B: 5.9km/yrA2: 6.6km/yrB1: 4km/yr

a)

0.03 0.1 1 10 100

Winter daily mean temperature [Dec−Jan−Feb]A1B: 5.6km/yrA2: 6km/yrB1: 3.9km/yr

b)

0.03 0.1 1 10 100

Spring daily mean temperature [Mar−Apr−May]A1B: 4.9km/yrA2: 5.4km/yrB1: 3.4km/yr

c)

0.03 0.1 1 10 100

Summer daily mean temperature [Jun−Jul−Aug]A1B: 5.1km/yrA2: 6km/yrB1: 3.4km/yr

d)

0.03 0.1 1 10 100

Fall daily mean temperature [Sep−Oct−Nov]

Velocity [km/yr]

A1B: 5.7km/yrA2: 6.6km/yrB1: 4km/yr

e)

0.03 0.1 1 10 100

Total annual precipitationA1B: 1.5km/yrA2: 1.3km/yrB1: 0.9km/yr

f)

0.03 0.1 1 10 100

Winter total precipitation [Dec−Jan−Feb]A1B: 1.5km/yrA2: 1.6km/yrB1: 1.2km/yr

g)

0.03 0.1 1 10 100

Spring total precipitation [Mar−Apr−May]A1B: 2.9km/yrA2: 2.3km/yrB1: 1.4km/yr

h)

0.03 0.1 1 10 100

Summer total precipitation [Jun−Jul−Aug]A1B: 0.4km/yrA2: 0.7km/yrB1: 0.2km/yr

i)

0.03 0.1 1 10 100

Fall total precipitation [Sep−Oct−Nov]

Velocity [km/yr]

A1B: 0.8km/yrA2: 0.9km/yrB1: 0.5km/yr

j)late 21st century [A1B]late 21st century [A2]late 21st century [B1]

Fig. 3 Histograms of the local velocity of change (km/yr) for annual and seasonal mean temperature (a–e)and total precipitation (f–j). Plotted predictions are based on three IPCC climate scenarios (red: A1B, blue: A2and yellow: B1). Local velocity in km/yr. (x-axis log-scaled) is estimated as the ratio between spatial andtemporal rates of change as specified by Loarie et al., (2009). The numbers at upper left in each plot indicatethe mean velocity for each scenario in Wisconsin

668 Climatic Change (2013) 119:659–675

N

S

EW

5 %

10 %

15 %

0 200 400 600 800 1000 1200 1400

Distance to nearest analog [km]

A1B: 472 kmA2: 454 kmB1: 362 km

a)

0 2 4 6 8Standardized Euclidean Distance (SED)

2.487 Non analog region99 percentileAnalog region

3.52 4.442.3

late 21st century [A1B]late 21st century [A2]late 21st century [B1]

b)

Fig. 4 Histograms of (a) spatial with directional histograms (rose plots inserts), and (b) geographical distancefor the climates of each Wisconsin grid cell and its closest analog in the North American late-20th-centuryobserved climate dataset from Maurer et al. (2002). These multivariate climate analog analyses are based on8 climate variables (see Methods). Bearings are oriented from the contemporary analog towards the Wisconsingrid-cell (i.e., along the direction of climate change). High SEDmin indicate that future climates in Wisconsinhas no close analog in North America. High SEDmin indicate that future climate in Wisconsin has no closeanalog in North America. Red, blue, and yellow dashed lines and numbers indicate median climaticdisplacement. Histograms indicate frequency of minimum climatic distances to late-21st-century climates inWisconsin based on ensemble of 15 GCM WICCI-downscaled grids. Paired t-tests between SEDmin distri-butions for late 20th-century and late 21st century climates are A1B scenario: t(2502)=220.02, p<0.001; A2scenario: t(2502)=157.46, p<0.001; and B1 scenario: t(2502)=241.5, p<0.001. Late-20th-century climaticsurfaces were obtained from Maurer et al. (2002)

Climatic Change (2013) 119:659–675 669

Comparisons to late 20th-century North American climates (Fig. 4b) show thatWisconsin 21st-century projected climate is increasingly dissimilar to historic climatesin North America with the A2 scenario having the highest, and the B1 scenario thelowest median dissimilarities. Setting a no-analog threshold to the 99th percentile(SEDt =2.487) suggests that 85 % of Wisconsin grid-cells will lack close analogs inthe 21st century under the A2 scenario, versus 76 % under the A1B scenario and43 % under the B1 scenario. If the no-analog threshold is set to a lower value (i.e.,95-percentile of within biome SEDmin=1.934) 92 % of Wisconsin grid-cells will lackclose analogs in the 21st century under the A2 scenario, versus 85 % under the A1Bscenario and 65 % under the B1 scenario.

local velocities differed between areas with and without analog climates, withhigher climate-velocities in areas without analog climates. Comparisons of individualand summaries of climate-velocities indicate a tendency for significant univariate(PERANOVAs) and multivariate (PERMANOVAs) differences between sites withand without climate analogs (highly significant main effects for PERMANOVA andPERANOVA contrasts, Appendix S4); a trend driven by faster climate-velocities inareas where future climates do not have contemporary analogs (i.e., grids whereSEDmin was higher than SEDt).

Comparisons between SEDmin and analog-based velocities showed a significant and negativeassociation (Spearman’s rank correlation rho A1B=-0.346, A2=-0.453 and B1=−0.368;Fig. 5a). Joint maps of these two variables (Fig. 5b) show areas of small climatic dissimilarityand high velocity (light purple area in the northern lakes region), large climatic dissimilarity andslow velocity (light red area in southwestern Wisconsin, centered on the Fox River Valley), andsome areas with small climatic dissimilarity and slow velocity (black area in the easternpostglacial driftless region). This tradeoff between climatic dissimilarity and velocities(Fig. 5a) highlights how some locations have good climatic analogs, but far away (the lightpurple) while other areas are projected to move into future climates with no good historicalanalogs (light red areas).

3.4 Climate-velocities: local velocities vs. analog velocities

While maximum and mean local velocities were consistently faster than analogvelocities, minimum local velocity estimates were always slower than analogvelocities (chi-squared test p<0.001 for all contrasts and scenarios). Mean localvelocity showed the closest numerical and spatial resemblance to the analog velocities,relative to the other local velocity summary (Fig. 5c–d). Nonparametric measure ofstatistical dependence showed that analog-based climate velocities were positively butweakly correlated with mean and maximum local velocities, but and in the case ofminimum local velocity the association was negative (Appendix S5). Spearman-rankcorrelation coefficients of analog-based velocities vs. local velocities indicated asignificant and positive association with most variables (Appendix S6). Multivariateanalog-based velocities were more strongly correlated with local velocities for meanannual temperature than for total annual precipitation. However, the opposite patternwas found between summer and winter temperature and precipitation, with analogvelocity correlation with local velocities weaker for temperature than for precipitation.In the case of univariate comparisons, Bonferroni-corrected Spearman rank correla-tions showed a strong association between analog velocities and local velocities (p<0.05 for almost all comparisons, Appendix S6), with a pattern of local velocitiesbeing slightly higher than analog velocities.

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4 Discussion

4.1 Climatic analogs: availability, bearing, distance, and novelty

Future climate change will alter the spatial distribution of habitats, biomes and climaticallysuitable areas (Ohlemüller 2011; Saxon et al. 2005; Williams et al. 2007). The resultspresented here indicate an increasing likelihood of no-analog climates emerging in

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Fig. 5 Associations between (a–b) climatic dissimilarity (SED) or (c–d) local velocities and multivariateanalog-based velocities (see methods), evaluated in both (a–c) bivariate and (b–d) geographical space. Scatterplot (panel a) represents the tradeoff between climatic dissimilarity and analog velocities while (panel c)compares mean local velocity for 8 variables to analog velocities for three IPCC climate scenarios (red: A1B,blue: A2 and yellow: B1). The box-plot inset represents the median, interquartile and range of the multivariateanalog-based velocities (y-axis, analog based velocity) and climatic dissimilarity (x-axis, minimum standard-ized Euclidean distance) or local velocities (y-axis, local velocity). Geographical representations of theinteraction between (b) climatic dissimilarity and velocities or (d) analog vs. local velocities represent thespatial combination of areas with fast-slow analog-based velocity, and high-low climatic similarity or localvelocity using a Red-Blue composite (Blue scale for analog-based velocity and Red scale for climaticdissimilarity or local velocity). White areas represents lake covered regions

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Wisconsin with higher-end climate-change scenarios, as there is little or no overlap betweenlate-20th-century and projected late-21st-century climates at the state level (Fig. 4b).

In accordance with Veloz et al. (2012), the magnitude of projected temperature changesmeans that the late-21st century climates projected for Wisconsin are largely beyond therange of climates observed for the state in the recent past. Furthermore, the intervariabledifferences in the direction, bearing, and velocities of climate change lead to a projectedreshuffling of associations among climatic variables (Dobrowski et al. 2012). These newclimatic combinations would be in the form of appearing (novel) climates, and/or currentclimatic conditions decreasing or disappearing (Ackerly et al. 2010; Ohlemüller 2011;Ohlemüller et al. 2006; Saxon et al. 2005; Williams et al. 2011). The reported changes inthe direction, bearing, and velocity of climate change differ strongly among variables wouldlead to the emergence of novel climates, particularly in eastern Wisconsin where theintersection of rising temperatures with regional lake effects creates climate regimes withno close analog elsewhere in North America.

This climatic reshuffling, and the emergence of novel climatic regimes provide a poten-tially strong driving mechanism for disaggregation of existing species associations, assem-bly into novel associations and other unexpected ecological responses. This linkage betweennovel climates and novel species associations assumes interspecific differences in climaticrequirements (causing them to differentially track particular dimensions of climate change),or climatic track capabilities via dispersal. This differential sensitivity to particular aspects ofclimate change resulting in individualistic responses across species is supported by thepaleoecological record (Clark 1998; Cleland et al. 2007; Davis and Shaw 2001; Nogues-Bravo et al. 2010; Williams et al. 2011), and recent meta-analyses focusing on the effects oflate 20th-century climatic change on eastern North America bird (Zuckerberg et al. 2009)and plant (Woodall et al. 2009; Zhu et al. 2012) species.

4.2 Local velocity of climate change

For many variables, the mean local climate velocities reported for Wisconsin are higher thanLoarie et al. (2009) global averages (0.08 to 1.26 km/yr. for mean annual temperature), butsimilar to estimates for Europe based on the location of climate analogs (1.3 to 4 km/yr formean annual temperature; Ohlemüller et al. 2006). The likelihood of species reshuffling ispotentially amplified both by the high climate velocities for Wisconsin relative to globalaverages, and by the strong differences among variables in spatial patterns. Relatively highlocal velocities are consistent with the expectation that highest velocities will be in areas ofrelatively low topographic relief. Such landscapes may be particularly susceptible to thenegative effects of high velocities (e.g., reduced residence time, Loarie et al. 2009); lowerpersistence of endemic and small ranged species (Sandel et al. 2011); incomplete rangefilling and species extinctions (e.g., García-Valdés et al. 2013; Nogues-Bravo et al. 2010;Svenning and Skov 2004), especially if high velocity areas coincide with species-richregions. Furthermore, local velocities for winter temperatures mean annual temperature arehigher than the rest of evaluated climatic variables (Fig. 3), suggesting that climate velocitiesbased solely on mean annual temperature can underestimate climate risk.

Our analyses confirm the importance of spatiotemporal heterogeneity in determiningregional patterns of climate-velocity and hence the potential ecological impacts associatedwith limitations on the ability of species to track rapid climate change (e.g., Burrows et al.2011; Loarie et al. 2009; Sandel et al. 2011). However, the spatial patterns of climate changevelocity often differed among climatic variables (Appendix S3), as a result of topographyexerting a differential control on different variables; resulting in climate variables expressing

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a richly varied spatial mosaic of projected climate velocities in the state (often but not alwaysinfluenced by proximity to the Great Lakes). Assuming that most species respond to multipleclimate influences, intervariable differences in velocity and spatial pattern tends to increasethe rate of dispersal required to track climate change at any given location.

4.3 Climate-velocities: Local-method vs. Analog-based estimates

By comparing both univariate (climate-velocity) and multivariate (climate-analog)metrics, we were able to determine the occurrence of significant associations betweenanalog and climate-velocities, and between analog and climate-velocities summaries(e.g., mean, minimum, maximum). Analog velocity estimates of climatic displacementunderestimated maximum and overestimated minimum local velocity, indicating howthe use of a regional scale multivariate metric smooths out the range of variabilityseen across individual variables. However, analog velocity estimates matched meanlocal velocity estimates showing how this metric can be used to simultaneouslydescribe the local rates of climatic variations across multiple variables. This, togetherwith the significant higher local velocities (of individual variables, summary statisticsand combination of climatic factors) of areas without analog climates shows thecomplementarity of these analyses, and the possibility of simultaneously using bothto establish the magnitude, rate, and direction of climatic change across multipleclimate dimensions.

5 Conclusion

In summary, we report substantial differences among climate variables in the magnitude,direction and rate of projected change, both at the scale of individual grid cells and acrossthe state of Wisconsin. Different metrics highlight different forms of climate risk and thecombination of these complementary approaches allows for a more comprehensive anal-ysis of climate exposure. General conclusions from these analyses include: 1) there is littleoverlap between the late 20th-century climates of Wisconsin and those projected for theend of this century, 2) some regions, particularly in eastern Wisconsin, lack good analogsanywhere in North America today, 3) climate velocities for Wisconsin are higher than theglobal average, 4) in this region, lake effects, not topography, exert the primary influenceon the spatial patterns of climate analogs and velocities, 5) there is a general congruence inthe magnitude of velocities estimated using analog- and local-based approaches, and 6)there appears to be a tradeoff between geographic distance and multivariate analogvelocity, with some locations able to find climate analogs spatially far away and otherregions unable to find analogs.

Given that species are differentially sensitive to particular aspects of the climate systemand also vary in their ability to track fast rates of climate change; intervariable differences inthe climatic rate and direction of change become an important mechanism by which climatechange can trigger the disaggregation of existing species associations and the reshuffling ofspecies into new mixtures.

Acknowledgments Wisconsin Focus on Energy and its Environment and Economic Research Program andthe Bryson Climate, People, and Environment Program at the Nelson Institute Center for Climatic Research atthe University of Wisconsin-Madison supported this work. We thank Drs. M. Notaro, D. Lorenz, and D.Vimont for advice and access to the WICCI datasets, and three anonymous referees for their useful commentsthat helped improve this manuscript. This is CCR publication Number 1074.

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References

Ackerly DD, Loarie SR, Cornwell WK, Weiss SB, Hamilton H, Branciforte R, Kraft NJB (2010) Thegeography of climate change: implications for conservation biogeography. Divers Distrib 16:476–487

Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecology26:32–46

Burrough PA, McDonnell RA (1998) Principles of geographical information systems (Spatial InformationSystems). Oxford University Press, New York

Burrows MT, Schoeman DS, Buckley LB, Moore P, Poloczanska ES, Brander KM, Brown C, Bruno JF,Duarte CM, Halpern BS, Holding J, Kappel CV, Kiessling W, O’Connor MI, Pandolfi JM, Parmesan C,Schwing FB, Sydeman WJ, Richardson AJ (2011) The pace of shifting climate in marine and terrestrialecosystems. Science 334:652–655

Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated withhigh levels of climate warming. Science 333:1024–1026

Clark JS (1998) Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord. AmNat 152:204–224

Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response toglobal change. Trends in Ecol Evol 22:357–365

Curtis JT (1959) The vegetation of Wisconsin. University of Wisconsin Press, MadisonDavis MB, Shaw RG (2001) Range shifts and adaptive responses to Quaternary climate change. Science

292:673–679Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conser-

vation in a changing climate. Science 332:53–58Dobrowski SZ, Abatzoglou J, Swanson AK, Greenberg JA, Mynsberge AR, Holden ZA, Schwartz MK (2012)

The climate velocity of the contiguous United States during the 20th century. Global Change Biol:(Inpress)

García-Valdés R, Zavala MA, Araújo MB, Purves DW (2013) Chasing a moving target: projecting climatechange-induced shifts in non-equilibrial tree species distributions. J of Ecol:n/a-n/a

Hijmans R, Cameron S, Parra J, Jones P, Jarvis A (2005) Very high resolution interpolated climate surfaces forglobal land areas. Int J Climatol 25:1965–1978

IPCC (2007) Climate Change 2007: Synthesis Report. In: Core Writing Team, Pachauri RK, Reisinger A (eds)Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IntergovernmentalPanel on Climate Change. IPCC, Geneva, Switzerland, 104 pp

Jackson ST, Betancourt JL, Booth RK, Gray ST (2009) Ecology and the ratchet of events: climate variability,niche dimensions, and species distributions. Proc Natl Acad Sci U S A 106:19685–19692

Loarie SR, Carter BE, Hayhoe K, McMahon S, Moe R, Knight CA, Ackerly DD (2008) Climate change andthe future of California’s endemic flora. PLoS One 3:e2502

Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD (2009) The velocity of climate change.Nature 462:1052–1055

Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based datasetof land surface fluxes and states for the conterminous United States. J Clim 15:3237–3251

Nogues-Bravo D, Ohlemuller R, Batra P, Araujo MB (2010) Climate predictors of late Quaternary extinctions.Evolution 64:2442–2449

Notaro M, Lorenz DJ, Vimont D, Vavrus S, Kucharik C, Franz K (2011) 21st century Wisconsin snowprojections based on an operational snow model driven by statistically downscaled climate data. Int JClimatol 31:1615–1633

Ohlemüller R (2011) Running out of climate space. Science 334:613–614Ohlemüller R, Gritti ES, Sykes MT, Thomas CD (2006) Towards European climate risk surfaces: the extent

and distribution of analogous and non-analogous climates 1931–2100. Glob Ecol Biogeogr 15:395–405Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst

37:637–669Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems.

Nature 421:37–42Pearson RG (2006) Climate change and the migration capacity of species. Trends Ecol Evol 21:111–113Sandel B, Arge L, Dalsgaard B, Davies RG, Gaston KJ, Sutherland WJ, Svenning JC (2011) The influence of

late Quaternary climate-change velocity on species endemism. Science 334:660–664Saxon E, Baker B, Hargrove W, Hoffman F, Zganjar C (2005) Mapping environments at risk under different

global climate change scenarios. Ecol Lett 8:53–60Svenning JC, Skov F (2004) Limited filling of the potential range in European tree species. Ecol Lett 7:565–573

674 Climatic Change (2013) 119:659–675

Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de SiqueiraMF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature427:145–148

Veloz S, Williams J, Lorenz D, Notaro M, Vavrus S, Vimont D (2012) Identifying climatic analogs forWisconsin under 21st-century climate-change scenarios. Clim Chang 112:1037–1058

WICCI (2011) Wisconsin’s changing climate: impacts and adaptation. nelson institute for environmentalstudies. University of Wisconsin-Madison and the Wisconsin Department of Natural Resources, Madison

Wiken E, Jiménez Nava F, Griffith G (2011) North American Terrestrial Ecoregions—Level III. Commissionfor Environmental Cooperation, Montreal

Williams JW, Blois JL, Shuman BN (2011) Extrinsic and intrinsic forcing of abrupt ecological change: casestudies from the late Quaternary. J Ecol 99:664–677

Williams JW, Jackson ST, Kutzbacht JE (2007) Projected distributions of novel and disappearing climates by2100 AD. Proc Natl Acad Sci U S A 104:5738–5742

Woodall CW, Oswalt CM, Westfall JA, Perry CH, Nelson MD, Finley AO (2009) An indicator of treemigration in forests of the eastern United States. Forest Ecol Manage 257:1434–1444

Zhu K, Woodall CW, Clark JS (2012) Failure to migrate: lack of tree range expansion in response to climatechange. Glob Chang Biol 18:1042–1052

Zuckerberg B, Woods AM, Porter WF (2009) Poleward shifts in breeding bird distributions in New YorkState. Glob Chang Biol 15:1866–1883

Climatic Change (2013) 119:659–675 675