Kevin Lawrence

Potential Impacts of Climate Change on the Distribution of North American Trees

ABSTRACT Currently predicted change in climate could strongly affect plant distributions during the next century. Here we determine the present-day climatic niches for 130 North American tree species. We then locate the climatic conditions of these niches on maps of predicted future climate, indicating where each species could potentially occur by the end of the century. A major unknown in this work is the extent to which populations of trees will actually track climate shifts through migration. We therefore present two extreme scenarios in which species either move entirely into future climatic niches or do not move out of their current niches. In the full-dispersal scenario, future potential ranges show decreases and increases in size, with an average decrease of 12% and a northward shift of 700 kilometers (km). In the no-dispersal scenario, potential ranges decrease in size by 58% and shift northward by 330 km. Major redistribution pressures appear to be in order under both dispersal scenarios.

Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Temperature and Precipitation for 1961–2003

Abstract The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961–2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.1° and 1.6°C, respectiv...

Customized Spatial Climate Models for North America

Over the past two decades, researchers at Natural Resources Canadas Canadian Forest Service, in collaboration with the Australian National University (ANU), Environment Canada (EC), and the National Oceanic and Atmospheric Administration (NOAA), have made a concerted effort to produce spatial climate products (i.e., spatial models and grids) covering both Canada and the United States for a wide variety of climate variables and time steps (from monthly to daily), and across a range of spatial resolutions. Here we outline the method used to generate the spatial models, detail the array of products available and how they may be accessed, briefly describe some of the usage and impact of the models, and discuss anticipated further developments. Our initial motivation in developing these models was to support forestry-related applications. They have since been utilized by a wider range of agencies and researchers. This article is intended to further raise awareness of the strengths and weaknesses of these clim...

Beyond Traditional Hardiness Zones: Using Climate Envelopes to Map Plant Range Limits

ABSTRACT Traditional plant hardiness zone maps identify areas that are relatively homogeneous with respect to climatic conditions that affect plant survival. Plants are typically categorized according to the most northerly, and sometimes the most southerly, zone in which they can successfully grow. This approach suffers from a number of limitations, including the coarse spatial nature of the zones and the relatively unsystematic assignment of plants to zones. Here we propose using climate envelopes to map the potential ranges of plant species in North America in wild and cultivated settings. We have initiated a major data-gathering effort that currently includes over 1.8 million georeferenced observations for more than 4100 plant species. We demonstrate the approach using sugar maple (Acer saccharum) and show the ease with which predicted climate-change impacts can be incorporated into the models.

High resolution interpolation of climate scenarios for the conterminous USA and Alaska derived from general circulation model simulations

Projections of future climate were selected for four well-established general circulation models (GCM) forced by each of three greenhouse gas (GHG) emissions scenarios, namely A2, A1B, and B1 from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES). Monthly data for the period 1961-2100 were downloaded mainly from the web portal of Third Coupled Model Intercomparison Project (Phase 3) of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and subsets of data covering North America were extracted. Climate variables included monthly mean daily maximum and minimum temperatures, precipitation, incident surface solar radiation, wind speed, and specific humidity. All variables were expressed as changes relative to the simulated monthly means for 1961-1990, which corrected for GCM bias in reproducing past climate and allowed future projected trends to be compared directly. The downscaling procedure used the ANUSPLIN software package to fit a two-dimensional spline function to each months change data for each climate variable at a spatial resolution of 5 arcminutes (0.0833o) longitude and latitude. The A2 emission scenario invariably generated the greatest warming by 2100 and the B1 the least. Alaska is projected to undergo the greatest regional increases in temperature and precipitation. Differences across the projections were generally greater from the different GHG forcings than those resulting from the different GCMs, although the consistency varied spatially. Gridded datasets are publicly available. The downscaled change factors from this study are being used with historical climatology developed from the PRISM climate data set to develop the climate projections for the RPA scenarios in the USDA FS RPA assessment. A companion report and data set will be issued by Natural Resources Canada (Canadian Forest Service) for Canada.Data for this publication: Conterminous US (http://dx.doi.org/10.2737/RDS-2011-0023) and Alaska (http://dx.doi.org/10.2737/RDS-2011-0022)

The generation of USDA-equivalent extreme minimum temperature models and a comparison with Canada’s plant hardiness zones

Hardiness zones are widely used in North America to support the trade of plants and recommendations on local use of perennial plant species. In Canada, two zonation approaches are in use, a made-in-Canada model that integrates seven climate variables and the United States Department of Agriculture’s (USDA) extreme minimum temperature map/model. In this paper we develop and present several extreme minimum temperature models for the 1961–1990 and 1971–2000 climate normal periods and annual models for the winter seasons of 1961 through 2000. These models are similar in nature to the USDA plant hardiness model/map. We compare these models with a recent update of the Canadian plant hardiness zones developed with the same mathematical interpolation techniques (thin plate smoothing splines). Individual Canadian zones typically span five to nine USDA equivalent sub-zones in total, although most of the area (>75%) of each zone generally spans 3–4 USDA sub-zones. We note that there is no simple transformation of on...

Projecting climate change in the United States: A technical document supporting the Forest Service RPA 2010 Assessment

A set of climate change projections for the United States was developed for use in the 2010 USDA Forest Service RPA Assessment. These climate projections, along with projections for population dynamics, economic growth, and land use change in the United States, comprise the RPA scenarios and are used in the RPA Assessment to project future renewable resource conditions 50 years into the future. This report describes the development of the historical and projected climate data set. The climate variables are monthly total precipitation in millimeters (mm), monthly mean daily maximum air temperature in degrees Celsius (°C), and monthly mean daily minimum air temperature in degrees Celsius (°C). Downscaled climate data were developed for the period 2001-2100 at the 5-arcminute grid scale (approximately 9.3 km by 7.1 km grid size at 40 degree N) for the conterminous United States. These data were also summarized at the U.S. county level. Computed monthly mean daily potential evapotranspiration (mm) and mean grid cell elevation in meters (m) are also included in the data set. The scenarios used here from the IPCC Special Report on Emissions Scenarios are A1B, A2, and B2. The A1B and A2 scenarios were used to drive three climate models: the Third Generation Coupled Global Climate Model, version 3.1, medium resolution; the Climate System Model, Mark 3.5 (T63); and the Model for Interdisciplinary Research on Climate, version 3.2, (T42), all used in the Fourth IPCC Assessment. The B2 scenario was used to drive three earlier generation climate models: the Second Generation Coupled Global Climate Model, version 2, medium resolution; the Climate System Model, Mark 2; and the UKMO Hadley Centre Coupled Model, version 3, all used in the IPCC Third Assessment. Monthly change factors were developed from global climate model output using the delta method. The coarse-resolution change factors were downscaled to a 5-arcminute resolution grid using ANUSPLIN. The 30-year mean historical climatology (1961-1990) was developed using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data at 2.5-arcminute resolution and aggregated to the 5-arcminute resolution grid. The downscaled change factors were combined with the PRISM observed climatology to develop nine future climate projections for the conterminous United States. These projection data and the change factor data are available through the U.S. Forest Service data archive website (http://www.fs.usda.gov/rds/archive/).

A Comparison of Two Approaches for Generating Spatial Models of Growing-Season Variables for Canada

AbstractThis study produced annual spatial models (or grids) of 27 growing-season variables for Canada that span two centuries (1901–2100). Temporal gaps in the availability of daily climate data—the typical and preferred source for calculating growing-season variables—necessitated the use of two approaches for generating these growing-season grids. The first approach, used only for the 1950–2010 period, employed a computer script to directly calculate the suite of growing-season variables from existing daily climate grids. Since daily grids were not available for the remaining years, a second approach, which employed a machine-learning method called boosted regression trees (BRT), was used to generate statistical models that related each growing-season variable to a suite of climate and water-related predictors. These BRT models were used to generate grids of growing-season variables for each year of the study period, including the 1950–2010 period to allow comparison between the two approaches. Mean abs...