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TITLE
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A dynamically downscaled projection of past and future microclimates
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AUTHORS
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Ofir Levy1, Lauren B. Buckley2, Timothy H. Keitt3, Michael J. Angilletta Jr.1
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AFFILIATIONS
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School of Life Sciences, Arizona State University, Tempe, AZ 85287
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Department of Biology, University of Washington, Seattle, WA 98195
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Section of Integrative Biology, University of Texas, Austin, TX 78712
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corresponding author(s): Ofir Levy ([email protected])
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INTRODUCTION
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Large sets of environmental data have become essential ingredients for ecological forecasting,
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because ecological phenomena such as species distributions depend on physical properties of the
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environment (Porter et al. 2000, Parmesan and Yohe 2003, Root et al. 2003, Parmesan 2006,
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Sutherland 2006, Elith and Leathwick 2009, Franklin 2009, Kearney and Porter 2009, Kearney et
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al. 2009, Buckley et al. 2010, Anderson 2013). Statistical models relate the probability that a
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species occurs at a site to the temperature or humidity (Franklin 2009). Mechanistic models
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describe how these climatic variables influence the fitness of organisms (Mitchell et al. 2008,
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Kearney and Porter 2009, Buckley 2010, Kearney et al. 2010, Levy et al. 2012); for example,
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spatial and temporal variation in temperature and rainfall were used to predict the spread of an
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invasive species (Kearney et al. 2008) and a disease vector (Kearney and Porter 2009). Similar
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climatic data were used to predict how species will respond to climate change (Deutsch et al.
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2008, Buckley 2010, Sinervo et al. 2011, Radchuk et al. 2013, Vasseur et al. 2014). These
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applications contributed to the development of a macroecological discipline that seeks to
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understand linkages between processes at local and continental scales (Keith et al. 2012,
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Heffernan et al. 2014).
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Despite methodological and computational advances, ecological forecasting still suffers
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from the poor resolutions of environmental data (Levy et al. 2014). Models of organismal
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processes often rely on data as coarsely resolved as 1000 km2 over space (Potter et al. 2013).
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Yet, variation at the resolution of a meter or less can influence activity and energetics (Sears
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2010). Likewise, temperatures have been resolved to periods of months (Kearney and Porter
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2004, Buckley 2008, Deutsch et al. 2008, Kearney et al. 2014), but variations among minutes,
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hours, and days affect survival and reproduction. The resolution of data can either quantitatively
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(Kearney et al. 2012) or qualitatively (Vasseur et al. 2014) alter predictions about organismal
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performance and species distributions. For example, Kearney and colleagues (2012)
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demonstrated that climatic extremes are crucial to accurately predict the survival, growth, and
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reproduction of lizards, especially when the climate constrains thermoregulation. Moreover,
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extreme climatic events can cause dramatic ecological changes (Denny et al. 2009).We need to
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infer environmental conditions at resolutions pertaining to organisms while amassing data for
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long periods and large areas. Only then can ecologists model the dynamics of macrosystems at a
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sufficient grain to capture interactions across local and regional scales (e.g., Bucklin et al. 2013).
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Organisms move among microclimates to minimize environmental stress. Extreme
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temperatures can be avoided by seeking sun or shade, climbing objects or burrowing
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underground, and minimizing or maximizing contact with surfaces. This behavioral
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thermoregulation enables animals to survive in changing climates (Kearney et al. 2009).
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However, global or regional models of climate offer only a few variables relevant to
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microclimates. Other models, which incorporate heat-balance equations, are more suitable for
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calculating microclimates relevant to organisms (Porter et al. 1973). Hence, we developed a
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microclimate model to predict temperatures and wind speeds in microhabitats above or below
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ground.
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For a given climate, the spatial distribution of microclimates depends on the structure of
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the landscape, including vegetation, elevation, and substrate. As climates warm, changes in the
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distribution of microclimates will affect opportunities for thermoregulation (Adolph 1990,
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Carrascal et al. 1992, Attum and Eason 2006). Loss of vegetation not only affects
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thermoregulation but also increases the risk of predation by reducing refuges and increasing need
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to shuttle between patches (Attum and Eason 2006, Scheinin et al. 2012). Furthermore, while a
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longer growing season or greater resource supply benefits organisms (Post and Stenseth 1999,
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Pettorelli et al. 2005a, Pettorelli et al. 2005b, Garel et al. 2011), phenological shifts in vegetation
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can generate a mismatch between the presence of food and the timing of reproduction (Tveraa et
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al. 2013). Therefore, we also modeled changes in vegetation cover along with microclimatic
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variables.
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Both statistical and dynamic approaches have been used to enhance the spatial and
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temporal resolution of environmental data (Giorgi et al. 2001). Statistical downscaling links
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coarse-scale climates to fine-scale climates, based on features such as topography and land use
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(Wilby and Wigley 1997, Tabor and Williams 2010). This approach demands less computational
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power than dynamical downscaling does; however, statistically downscaled data have limited
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value for forecasting when statistical relationships change as climates change (Giorgi et al.
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2001). Dynamical downscaling relies on regional models built from first principles of physics,
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enabling one to simulate climate at a higher resolution within a region (Maraun et al. 2010).
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Although computationally expensive and time consuming, these simulations capture processes
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relevant to organisms, such as extreme weather, surface-atmosphere interactions, vegetation
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cover, and land use (Fowler et al. 2007, Stefanova et al. 2012). In most cases, the greater time
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and energy invested in dynamic downscaling should yield a more realistic forecast of climate
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(Fowler et al. 2007). However, computational costs of dynamical downscaling grow dramatically
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at finer spatial scales, limiting the practical resolution for each application.
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Here, we introduce data describing past and future microclimates and vegetation cover.
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These data were generated by dynamically downscaling the output of a global circulation model
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to spatial and temporal resolutions of 36 km and 1 h, respectively. We also modeled hourly
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changes in vegetation. Then, we modeled microclimates that differ in shade and height above or
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below the ground (see our framework in Figure 1). These data are more suitable than previously
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published data for studying the ecology of organisms.
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METADATA
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CLASS I. DATA SET DESCRIPTORS
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A. Data set identity: MIC_CLIM_36, microclimate and vegetation cover data at 36-km/1-rh
resolution of past and future climates
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B. Data identification code: MIC_CLIM_36 v1.0
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C. Data description:
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1. Originator(s):
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Ofir Levy
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School of Life Sciences, Arizona State University, Tempe, AZ 85287
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Lauren B. Buckley
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Department of Biology, University of Washington, Seattle, WA 98195
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Timothy Keitt
Section of Integrative Biology, University of Texas, Austin, TX 78712
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Michael J. Angilletta Jr.
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School of Life Sciences, Arizona State University, Tempe, AZ 85287
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2. Abstract
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Ecological forecasting requires information about the climatic conditions experienced by
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organisms. Despite impressive methodological and computational advances, ecological
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forecasting still suffers from poor resolutions of environmental data. Published data comprise
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relatively few layers of surface climate and suffer from coarse temporal resolution. Hence,
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models using these data might underestimate heterogeneity of microclimates and miss biological
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consequences of climatic extremes. Moreover, we currently lack predictions about vegetation
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cover in future environments, a key factor for estimating the spatial heterogeneity of
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microclimates and hence the capacity for behavioral thermoregulation. Here, we describe
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microclimates and vegetation for the past and the future at spatial and temporal resolutions of 36
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km (approximately 0.3°) and 1 h, respectively. We used the Weather Research & Forecasting
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model to downscale published, bias-corrected predictions of a global-circulation model from a
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resolution of 0.9° latitude and 1.25° (approximately 100 km in latitude and 130 km in longitude).
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Output from this model was used as input for a microclimate model, which generated
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temperatures and wind speeds for 1980-1999 and 2080-2099 at various heights, as well as soil
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temperatures at various depths and shade intensities. We also predicted the percentage of green
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vegetation and the percentage of shade given the angle of the sun. These data were evaluated
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using several criteria, each of which shed light on a different aspect of value to researchers. The
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metadata describe the modeling protocol, microclimate calculations, computer programs, and the
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evaluation process.
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D. Key words
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climate change, vegetation, shade, downscaling, soil temperatures, wind speed, microclimate,
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extreme events, stochasticity
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CLASS II. RESEARCH ORIGIN DESCRIPTORS
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A. Project description
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1. Identity: A database of ground microclimate layers related to temperature, wind speed,
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radiation, and vegetation cover.
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2. Originator(s): as above.
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3. Period of study: 1980-1999, 2080-2099
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4. Objectives: Provide climatic data suitable for analyses of organismal ecology.
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5. Abstract: as above.
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6. Source(s) of funding: Grants from the National Science Foundation to M. J. Angilletta (EF-
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1065638), L. B. Buckley (EF-1065638), and T. H. Keitt (EF-1064901). O. Levy was also
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supported by a Rothschild Post-Doctoral fellowship.
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B. Specific subproject description
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1. Site description: Data were obtained by downscaling output from a global climate model to a
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resolution of 36 km and then calculating microclimatic conditions. The domain was centered at
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38.3°N, -99.6°W, with a grid dimension of 130 x 200 points (Figure 1).
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Site type: land coordinates.
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Habitat: Various layers above and below the ground (3 to 198 cm above and below the ground
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surface, at 3-cm intervals from 3 to 30 cm,18-cm intervals from 30 to 174 cm, and 198 cm),
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under five levels of shade (0 to 100% at 25% intervals)
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2. Research methods
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Detailed Description of Climate and Simulations of Microclimates
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Bias-corrected predictions of a global model
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For past and future climates, we used bias-corrected output from a global climate simulation
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based on version 1 of NCAR's Community Earth System Model (CESM1) (dataset 316.1;
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Monaghan et al. 2014). The CESM1 is a coupled global climate model that provides state-of-the-
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art computer simulations of the Earth's climate system (Hurrell et al. 2013). The model consists
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of five components that simultaneously simulate the Earth's atmosphere, ocean, land, land-ice,
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and sea-ice, plus one central coupler component. Using the CESM, researchers can simulate and
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study the Earth's past, present, and future climates at different resolutions or under different
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scenarios (e.g., Marsh et al. 2013, Moore et al. 2013). Based on its ability to simulate global
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patterns of observed temperature and rainfall, the CESM was ranked at the top of all global
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circulation models (Knutti et al. 2013).
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The original predictions of the CESM1 participated in phase 5 of the Coupled Model
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Intercomparison Experiment as part of the Intergovernmental Panel on Climate Change Fifth
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Assessment Report (IPCC AR5). The data include all variables needed for the initial and
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boundary conditions to simulate regional climates with the Weather Research and Forecasting
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model (Advanced Research WRF, version 3.4; Skamarock et al. 2008). Monaghan and
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colleagues (2014) used a bias-correction protocol (Bruyère et al. 2013) to make the monthly
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means of each variable match the European Centre for Medium-Range Weather Forecasts
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Interim Reanalysis (Dee et al. 2011) fields for 1981-2005. We downloaded data for 1979-1999
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(past climate) and 2079-2099 (future climate), assuming a radiative forcing of +8.5 W m-2 at year
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2100 (Riahi et al. 2011).
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Dynamical downscaling
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We used the WRF Model (www.wrf-model.org), developed by the National Center for
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Atmospheric Research, to downscale the bias-corrected predictions of the CESM1. Using the
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global predictions as input, the model uses physical calculations to predict climate at finer spatial
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and temporal scales. This non-hydrostatic model, serves atmospheric research and operational
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forecasting, operates across scales from tens of meters to thousands of kilometers, and resolves
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fine-scale weather (Skamarock et al. 2008). Similar to the CESM model, the WRF model
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comprises several components for physical calculations, such as for radiation, cumulus, and land
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surface; the model also operates under different configurations, physics, and parameterizations
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(e.g., Katragkou et al. 2015).
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The ability of regional climate models, such as the WRF, to downscale depends on lateral
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boundary conditions at large scales, and regional forcings, such as topography, land cover, and
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land use. We chose a domain with a Lambert Conformal projection with a grid dimension of 130
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x 200 points, centered at 38.3°N, -99.6°W (Figure 1). A horizontal resolution of 36 km was used
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with 51 vertical levels. Based on the Courant-Friedrichs-Lewy stability criteria, we allowed the
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time step to range between 90-200 seconds (Courant et al. 1928). We chose the following
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options for our simulation: (1) WRF Single-Moment 5-class microphysics scheme (Hong et al.
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2004); (2) the New Goddard shortwave radiation scheme (Chou and Suarez 1999); (3) the Rapid
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Radiative Transfer Model (Mlawer et al. 1997) longwave radiation schemes; (4) the Kain-Fritsch
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convective parameterization (Kain 2004); (5) and the Noah Microphysics (Noah-MP) land-
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surface model (Niu et al. 2011). Examples of layers for mean near-surface temperature, specific
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humidity, and height-specific wind speed are given in Figure 2, along with predicted changes
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between past and future climates.
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Noah-MP, which depends on the Noah land-surface model (Chen et al. 1996), predicts
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runoff, soil moisture, snow, and skin temperature that match observations better than the original
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model (Yang et al. 2011). This newer model calculates energy balance separately for the canopy
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and soil surfaces and better handles the dynamics of multilayer snow and frozen soil. Moreover,
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the Noah-MP model simulates short-term dynamics of vegetation by explicitly computing
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photosynthetically active radiation, canopy temperature, and related fluxes of energy, water, and
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carbon (Niu et al. 2011). The dynamic leaf model (Dickinson et al. 1998, Yang and Niu 2003)
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describes carbon budgets for soil and vegetation (leaf, wood, and root) and accounts for foliage
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dynamics caused by cold, drought, senescence, herbivory, and mechanical stress (Niu et al.
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2011). We ran the WRF model with dynamic vegetation to estimate daily and annual changes
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under past and future climates.
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In long-term simulations, downscaling errors occur as the simulated climate drifts away
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from the global driving fields. To solve this problem, the simulation needs to retain the large-
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scale features of the driving climate while predicting small-scale features (Liu et al. 2012). We
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therefore constrained the WRF model to our input data using spectral nudging. In this technique,
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the forcing by the input data is not only stipulated at the lateral boundaries but also in the
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interior. However, this interior forcing is mostly confined to higher altitudes, so that the
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atmospheric state at lower levels is free to adjust to surface properties (von Storch et al. 2000).
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Moreover, spectral nudging affects large-scale deviations without affecting climate at small
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scales, hence minimizing bias while the WRF model develops climatic features at small scales
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(Liu et al. 2012). We nudged horizontal winds at all vertical levels and temperature and
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geopotential height at levels above the planetary boundary layer, as suggested by Liu et al
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(2012).
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Microclimate model
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Model description
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We used the output of the WRF model to calculate the microclimates available in each grid cell.
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Specifically, we used output variables that relate to the heat balance of the ground (Table 1) to
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predict temperatures and wind speeds for 1980-1999 and 2080-2099 at various heights, as well
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as soil temperatures at various depths and shade intensities. For each hour, we first calculated the
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ground temperature (Tg) depending on the proportion of shade, SHD (0.00, 0.25, 0.50, 0.75, or
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1.00), and then calculated air and soil temperatures. Since the details of the heat flux model are
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described in the technical notes for Noah-MP (http://www.jsg.utexas.edu/noah-mp/files/Noah-
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MP_Technote_v0.2.pdf), we describe only those features relevant to our calculations of
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microclimate.
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Ground temperature calculation
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At each time step, we solved for surface fluxes and ground temperatures that balance the energy
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budget (Figure 3):
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(1) S - L - H - E - G = 0,
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where S (W m-2) is the solar radiation absorbed by ground, L (W m-2) is the net flux of longwave
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radiation, H (W m-2) is the flux of sensible heat, E (W m-2) is the flux of latent heat, and G (W m-
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2
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fluxes have positive values when directed toward the sky.
) is the flux of soil heat. Solar radiation is positive toward the ground, and all other surface
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Net solar radiation
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The solar radiation absorbed by ground (S) was calculated using the shortwave downward flux
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(Sg, W m-2) and the ground albedo (𝛼𝑔 , dec. %):
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(2) 𝑆 = (1 − 𝛼𝑔 )(1 − 𝑆𝐻𝐷)𝑆𝑔 .
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Net longwave radiation
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The net flux of longwave radiation (L), the difference between incoming and outgoing longwave
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radiation, depends on the longwave downward flux (𝐿𝑎𝑡𝑚 , W m-2), ground temperature (Tg, K),
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vegetation temperature (𝑇𝑣 , K), and emissivities of the ground (𝜖𝑔 , dec. %) and vegetation (𝜖𝑣 ,
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dec. %):
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(3) 𝐿 = 𝜖𝑔 𝜎𝑇𝑔4 − (1 − 𝑆𝐻𝐷)𝜖𝑔 𝐿𝑎𝑡𝑚 − 𝑆𝐻𝐷(𝜖𝑔 (1 − 𝜖𝑣 )𝐿𝑎𝑡𝑚 + 𝜖𝑔 𝜖𝑣 𝜎𝑇𝑣4 ),
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where 𝜎 is the Stefan-Boltzmann constant (5.67∙10-8 W m-2 k-4).
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Sensible heat flux
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The flux of sensible heat (H) was calculated as a function of the coefficient for sensible heat (𝑐𝑠ℎ ,
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W m-2 K-1), ground temperature (Tg, K), and air temperatures in the canopy (𝑇𝑐 , K) and at 2 m
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height (T2, K):
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(4) 𝐻 = 𝑐𝑠ℎ ((𝑇𝑔 − 𝑇𝑐 )𝑆𝐻𝐷 + (𝑇𝑔 − 𝑇2 )(1 − 𝑆𝐻𝐷)).
We calculated csh using air density (𝜌𝑎𝑖𝑟 , kg m-3), the heat capacity of dry air (Cp) at
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constant pressure (1004.64 J kg-1 k-1), and the aerodynamic resistance for sensible heat (rah, s m-
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1
):
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𝐶𝑝
(5) 𝑐𝑠ℎ = 𝜌𝑎𝑖𝑟 𝑟 .
𝑎ℎ
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The aerodynamic resistance, rah, was derived from Monin-Obukhov similarity theory
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applied to the surface layer (Brutsaert 1982 , Arya 1988) and depends on the air temperature at a
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height of 2 m, the wind speed at a height of 10 m, and the roughness height (i.e., the height
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above the ground at which wind speed is zero). In principle, the resistance declines as wind
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speed increases and roughness height decreases. In the model, roughness height was based on the
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vegetation type. A map of vegetation types was generated by the WRF Preprocessing System
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(geogrid.exe) by interpolating data from a higher resolution map.
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Latent heat flux
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The flux of latent heat (E) was calculated using the coefficient of evaporative heat flux (cev, W
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m-2 Pa-1), the saturation vapor pressure at a given ground temperature (es(Tg), Pa), relative
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humidity of the air within soil or snow at the surface (𝜙𝑠𝑟𝑓 , dec. %), vapor pressure of air in the
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canopy (𝑒𝑐 , Pa), and vapor pressure of air above bare ground (𝑒𝑏 , Pa):
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(6) 𝐸 = 𝑐𝑒𝑣 (𝑒𝑠 (𝑇𝑔 )𝜙𝑠𝑟𝑓 − 𝑒𝑐 𝑆𝐻𝐷 − 𝑒𝑏 (1 − 𝑆𝐻𝐷)).
We calculated cev using air density (𝜌𝑎𝑖𝑟 ,kg m-3), the aerodynamic resistance for water
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vapor (raw, s m-1), the surface-resistance of ground (𝑟𝑠𝑟𝑓 , s m-1), and the psychrometric constant 𝛾
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(Pa k-1):
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(7) 𝑐𝑒𝑣 = 𝛾(𝑟
𝜌𝑎𝑖𝑟 𝐶𝑝
𝑎𝑤 +𝑟𝑠𝑟𝑓 )
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.
The psychrometric constant was calculated as
𝐶𝑝 𝑃𝑠𝑟𝑓
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(8) 𝛾 =
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where Psrf is the surface pressure (Pa), and λ is the latent heat of vaporization or sublimation (J
0.622𝜆
,
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kg-1) based on air temperature at 2 m above ground, T2: λ equaled hvap or hsub when T2 was
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greater than 0°C or less than 0°C, respectively ( hvap = 2.51∙106 and hsub = 2.84∙106).
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Heat flux in soil
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The heat flux to the soil (G) was calculated as
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(9) 𝐺 = 𝑐𝑔ℎ (𝑇𝑔 − 𝑇𝑠𝑜𝑖𝑙1 ),
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where cgh is the coefficient of ground heating (W m-2 K-1), Tg is the ground temperature (K), and
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Tsoil1 is the soil temperature near the surface at the preceding time (K).
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We calculated cgh using thermal conductivity (W m-1 K-1) of the soil layer based on the
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temperature and other properties, and the depth (Δz1, m) of the reference layer of soil or snow:
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(10) 𝑐𝑔ℎ = 𝛥𝑧1 .
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2𝑘
1
When snow layers existed, we set Tsoil1 and Δz1 as the temperature and depth of the top
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layer of snow. Otherwise, we used Tsoil1 and Δz1 from our microclimate model. Since a Δz1 of 3
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cm resulted in computational instabilities, we used Δz1 = 6 cm and Tsoil1 as the average
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temperature of the first two layers. Soil properties at each location were based on a static map of
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soil types, generated by the geogrid.exe program.
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Solving for ground temperature
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Equation 1 was solved by Newton-Raphson’s method of iteration. First, we calculated the solar
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radiation absorbed by the ground, which is independent of ground temperature. Then, we
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performed 10 iterations of the model. In the first iteration, L, H, E, and G are calculated from the
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ground temperature at the preceding hour. The change in ground temperature, ΔTg, was
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calculated as
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𝑆−𝐿−𝐻−𝐸−𝐺
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(11) 𝛥𝑇𝑔 =
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where
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Tg is updated as Tg = Tg + ΔTg, and L, H, E, and G are updated according to the new Tg. Once
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ground temperature was calculated, if there was snow on the ground (Hsno > 0.05 m) and the
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ground temperature exceeded 0°C, we set the ground temperature to 0°C and re-evaluated L, H
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and E based on Eqs. 3, 4, and 6. The flux of soil heat was re-calculated as G = S – (L + H + E).
4𝜖𝑔 𝜎𝑇𝑔3 +𝑐𝑠ℎ +𝑐𝑒𝑣
𝑑𝑒𝑠 (𝑇𝑔 )
𝑑𝑡
𝑑𝑒𝑠 (𝑇𝑔 )
+𝑐𝑔ℎ
𝑑𝑡
,
is the change in the saturation vapor pressure as a function of temperature (Pa k-1),
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Air temperature and wind speeds calculations
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We calculated air temperatures (Tair) and wind speed (WIND) at different heights above the
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ground. We assumed that wind speed decreases logarithmically with a decrease in height:
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(12) 𝑊𝐼𝑁𝐷ℎ =
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where WINDh is the wind speed at height h (m sec-1, ranging from 3 to 198 cm as described
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above), Z0 is the roughness height (m), and wind* is the friction velocity. We calculated friction
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velocity as
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(13) 𝑤𝑖𝑛𝑑 ∗= 𝑊𝐼𝑁𝐷10𝑚 √𝐶𝑚 ,
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where Cm is the drag coefficient of momentum derived from Monin-Obukhov’s similarity theory.
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Air temperatures were calculated with a transition function between the air temperature at
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𝑤𝑖𝑛𝑑∗
𝐾
ℎ
𝑙𝑛 (𝑍 ),
0
2 m (T2) and the ground temperature (Tg):
ℎ
+1)
𝑍0
2
𝑙𝑛( +1)
𝑍0
𝑙𝑛(
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(14) 𝑇𝑎𝑖𝑟,ℎ = 𝑇𝑔 +
(𝑇2 − 𝑇𝑔 ),
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assuming weak or no free convention (Sellers 1965, Kearney et al. 2014). As defined above, Z0 is
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the roughness height in meters.
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Calculating soil temperatures
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At each time, we calculated soil temperatures based on the temperature of the ground surface.
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We divided the substrate column into 0 to 3 layers of snow (Niu et al. 2011) and 3-cm layers of
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soil down to a depth of 198 cm. For each hour, we used soil properties at each location and
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predicted temperatures of soil and snow to calculate the thermal conductivity (k, W m-1 k-1) and
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the volumetric heat capacity (ρc, J m-3 k-1) of each layer. These variables enabled us to solve the
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partial differential equation for heat transfer:
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(15) 𝜌𝑐 𝜕𝑡 =
339
where Fz is the heat flux (W m-2) at depth z, calculated as
340
(16) 𝐹𝑧 = − [𝑘 𝜕𝑧 ].
341
𝜕𝑇
−𝜕𝐹𝑧
𝜕𝑧
𝜕
𝜕𝑇
= 𝜕𝑧 [𝑘 𝜕𝑧 ],
𝜕𝑇
Equation 16 was solved numerically to yield soil temperatures. We used the heat flux into
342
soil (G) as the upper boundary condition, and the temperature of deep soil (8 m) as the lower
343
boundary condition. We assumed that deep soil temperature equaled the mean ground radiant
344
temperature (Trad, K) of the previous 150 days, as implemented in the WRF model. The radiant
345
temperature is the net flux of infra-red heat in the ground surface, and is part of the WRF output.
346
Additional details of the numerical solution can be found in the technical notes for the Noah-MP.
347
348
CLASS III. DATA SET STATUS AND ACCESSIBILITY
349
A. Status
350
1. Latest update: 25 June 2015.
351
2. Latest archive date: 25 June 2015.
352
3. Metadata status: up to date as of 25 June 2015.
16
353
4. Data verification: up to date as of 25 June 2015.
354
B. Accessibility
355
1. Storage location and medium: Data are stored in the Knowledge Network for Biocomplexity
356
(KNB). See: Ofir Levy, Lauren B. Buckley, Timothy Keitt, and Michael J. Angilletta Jr. 2016.
357
Data from a dynamically downscaled projection of past and future microclimates covering North
358
America from 1980-1999 and 2080-2099. KNB Data Repository. doi:10.5063/F1Z899CZ.
359
2. Contact person(s): Ofir Levy.
360
3. Copyright restrictions: None.
361
4. Proprietary restrictions: None.
362
5. Costs: None.
363
364
CLASS IV. DATA STRUCTURAL DESCRIPTORS
365
A. Data set files
366
1. Identity: The files’ name template is
367
[variable]_[location_id]_MIC_CLIM_36_[climate_scenario]_[latitude]_[longitude].nc
368
To help users find data for a specific location, files are organized in 22 geographical regions.
369
2. Size: Each file is up to 35 megabytes, depending on the variable and its number of dimensions
370
(see Table 2).
371
3. Format and storage mode: The MIC_CLIM_36 dataset is provided in netCDF format. Each
372
file is a time-series estimates of one variable (see Table 2) in one location for either past or future
373
climates (1980-1999 and 2080-2099). The microclimate data include various layers above and
374
below the ground (3 to 198 cm above and below the ground surface, at 3-cm intervals from 3 to
17
375
30cm,18-cm intervals from 30 to 174cm, and at 198 cm), under five levels of shade (0 to 100%
376
at 25% intervals).
377
378
B. Variable information
379
Variable identity and definition: See Table 2.
380
381
C. Data anomalies
382
WRF evaluation
383
We compared hourly predictions of the WRF model for 1980 to 1990 with the corresponding
384
data from the NCEP Climate Forecast System Reanalysis (ds093.1), at a spatial resolution of
385
0.312° lat x 0.313° lon (Saha et al. 2010), and from the Global Land Data Assimilation System
386
reanalysis (Version 2), at a spatial resolution of 0.25° lat x 0.25° lon (Rodell and Beaudoing
387
2013). Since coarser data were used for bias correction, comparing our predictions to a product
388
at higher resolution enables us to see how well the WRF model captured the dynamics of
389
climate. Although bias correction was also based on reanalysis data, the deviation from the mean
390
at each of point in time remained unlinked to these data; hence we should always expect to
391
observe differences between the predictions of the WRF model and the reanalysis data. We used
392
several criteria for evaluation, each of which shed light on a different aspect of the data. We
393
limited our evaluation to air temperature at 2 m, specific humidity at 2 m, and wind speed at 10
394
m, because these variables relate most significantly to microclimates that concern ecologists. We
395
also compared the mean fraction of green vegetation in each season (hereafter, seasonal
396
vegetation) predicted by the Noah-MP (1985-1990) to that derived by Gutman and Ignatov
397
(1998; data set ds093.1 available at
18
398
http://www.emc.ncep.noaa.gov/mmb/gcp/sfcimg/gfrac/index.html). These researchers derived
399
the fraction of green vegetation from the Normalized Difference Vegetation Index, which
400
overestimates green vegetation in grasslands and shrublands (Montandon and Small 2008).
401
Moreover, while the monthly data developed by Gutman and Igantov (1998) remain constant
402
among years, the Noah-MP predicts hourly changes in green vegetation. Examples of layers of
403
mean vegetation cover during January, April, July, and October of 1980-1999 and predicted
404
changes by 2080-2099 are shown in Figure 4.
405
406
Seasonal means
407
We calculated the biases for summer (June–August) and winter (December–February). Bias
408
measures the mean difference between a prediction and an observation (𝑓 ̅ − 𝑜̅ ), hence the
409
average error in the predictions.
410
In general, spatial patterns of predictions from the WRF model agree with those of the
411
reanalyses of NCEP and GLDAP data (Figure 2, 5). Bias values vary spatially and seasonally
412
(NCEP – Figure 6, GLDAS – Figure 7). During summer, a negative bias in green vegetation
413
appears at mid latitudes of the United States, where grasslands dominate the landscape. The
414
frequency distributions of the bias values are presented in Figures 8 (NCEP) and 9 (GLDAP).
415
416
Annual frequency of values
417
Bias tells us the mean accuracy of predictions but does not assess the distributions of predicted
418
values (Perkins et al. 2007). Since climate change will affect the frequency and magnitude of
419
extreme events as well as the mean, we need to predict climatic distributions accurately,
420
including relatively rare events (Perkins et al. 2007). To evaluate the overlap in the distribution
19
421
of variables between the WRF predictions and the reanalysis data, we calculated a probability
422
density function (PDF) score for each variable. First, we divided the hourly data into bins of 1°C
423
for temperature, 0.001 kg kg-1 for specific humidity, and 1 m s-1 for U and V wind components.
424
Then, for each variable, we calculated the relative frequency of each bin compared to the other
425
bins, arriving at the skill score devised by Perkins et al. (2007):
426
PDF skill score = ∑𝑖 min(PDF𝑊𝑅𝐹,𝑖 , PDF𝑂,𝑖 ),
427
where PDFWRF,i is the relative frequency of bin i in the WRF predictions and PDFO,i is the
428
relative frequency of bin i in the reanalysis data. A skill score value of 1 represents a perfect
429
match between the PDFs and a score of 0 represent no overlap. Importantly, the skill score is less
430
sensitive to a mismatch in the frequency of very rare events, because they contribute very little to
431
the score. Since observations of green vegetation cover are constant among years, we did not
432
analyze skill score for this variable. We calculated the skill score separately for each year
433
between 1980 and 1989, and then calculated the mean score among years.
434
The PDF skill scores demonstrate a strong match between the predicted and observed
435
distributions of values, with most scores exceeding 0.85 (NCEP - Figures 10-11, GLDAS –
436
Figures 12-13). Lower scores for wind velocities across the Rocky Mountains suggest that,
437
although the general characteristics of climate have been captured in that area, our domain’s
438
resolution is too crude to capture the circulation of air within these complex terrains.
439
440
Microclimate Model simplifications
441
Simplifications in our model scheme for soil
442
Our model excluded water balance in the soil, but used soil moisture from the WRF model to
443
calculate the thermal properties of water in soil and snow. These variables were obtained using a
20
444
weighted average of evaporative water loss between exposed and shaded ground (Niu et al.
445
2011). The values might have differed slightly if we had calculated them directly, because
446
evaporative water loss varies among microhabitats; hence, soil moisture and thermal properties
447
of soil derived from its moisture should differ as well (Breshears et al. 1998). Thermal
448
properties, such as thermal conductivity and heat capacity, affect the proportion of radiation that
449
leads to evaporation instead of heating. Hence, under warm conditions, our calculations (and
450
those of Noah-MP) may over-estimate the moisture of exposed soils and under-estimate the
451
moisture of shaded soils. As a consequence, our results are conservative for exposed
452
microhabitats, because moisture reduces thermal variation throughout the day (Ashcroft and
453
Gollan 2013). Under moist conditions, canopy cover only weakly affects the temperatures of soil
454
and air (Ashcroft and Gollan 2013). However, under dry conditions, evaporative heat loss is
455
minor, and large differences in temperature should occur between exposed and shaded soils.
456
457
Simplifications in our model scheme under canopy
458
Our model calculates temperatures for a constant level of shade throughout the day. For most
459
gaps in the canopy, the depth of shade changes as the sun moves. Hence, our conditions of 0%
460
and 100% shade represent the extremes of radiative conditions, which capture the full gradient of
461
climatic conditions that interest ecologists.
462
463
Microclimate Model validation
464
For validation purposes, we compared the predictions of the model for 2624 sites across the
465
simulated domain to observations from weather stations. Daily minimal and maximal
466
temperatures at each site were obtained from the Daily Global Historical Climatology Network,
21
467
version 3.20 (Peterson and Vose 1997). These data, which include temperatures for nearly
468
15,000 stations around the globe (Peterson and Vose 1997), constitute the most comprehensive
469
set of daily observations available. For comparison, we calculated the bias and PDF skill score
470
between each station and the closest coordinate on the WRF domain. We compared each
471
station’s air temperatures to our predictions for air above bare-ground (2624 sites). We also
472
compared each station’s soil temperatures at 5 cm (15 sites), 10 cm (52 sites), and 20 cm (14
473
sites) below ground to our predictions for 6, 9, and 18 cm below bare ground. We excluded
474
stations either outside our domain or with fewer than three years of data for comparison. Our
475
predictions strongly match the observations: minimal air temperature (PDF score = 0.80 ± 0.07,
476
bias = 1.0 ± 2.38 °C; mean ± SD; Figure 14); maximal air temperature (PDF score = 0.80 ± 0.08,
477
bias = -0.85 ± 2.58 °C, mean ± SD; Figure 14); minimal soil temperature (PDF score = 0.75 ±
478
0.04, bias = -1.22 ± 1.43 °C, mean ± SD; Figure 15); and maximal soil temperature (PDF score =
479
0.76 ± 0.05, bias = -0.38 ± 1.70 °C, mean ± SD; Figure 15).
480
We also evaluated the differences between open and shaded microhabitats. In open
481
microhabitats, more shortwave radiation reaches the ground during the day but more longwave
482
radiation escapes the ground; therefore, maximal ground temperatures should be higher and
483
minimal ground temperatures should be lower compared to temperatures in vegetated
484
microhabitats. Accordingly, our microclimate model predicted that maximal and minimal
485
temperatures of bare ground were 3.52 ± 1.41 °C (mean ± SD) warmer and 0.22 ± 0.94 °C
486
colder, respectively, compared to temperatures in vegetated microhabitats (Figures 16-17).
487
CLASS V. SUPPLEMENTAL DESCRIPTORS
488
A. Data acquisition
489
1. Data forms or acquisition methods: N/A.
22
490
2. Location of completed data forms: N/A.
491
3. Data entry verification procedures: N/A.
492
B. Quality assurance/quality control procedures: See Class IV.C – Data anomalies section.
493
C. Related material: N/A
494
D. Computer programs and data-processing algorithms:
495
Computer programs and data-processing algorithms:
496
read_wrf_output.f90 – split output maps from Weather Research & Forecasting Model to land
497
location files. Due to memory limits, the script creates several files for each location.
498
microclimate_model.f90 – microclimate model as described above.
499
locations.txt – a table of the coordinates in our domain (location_id, latitude, longitude and
500
elevation).
501
read_location_data.R – an R function that shows how to read the netCDF files, given a
502
coordinate, climate scenario, and a list of variables.
503
These are available in the Supporting Information, DataS1.zip.
504
E. Archiving: N/A.
505
F. History of data set usage:
506
Levy, O., L. B. Buckley, T. H. Keitt, C. D. Smith, K. O. Boateng, D. S. Kumar, and M. J.
507
Angilletta. 2015. Resolving the life cycle alters expected impacts of climate change.
508
Proceedings of the Royal Society B 282:20150837. doi: 10.1098/rspb.2015.0837
509
510
Levy, O., L. B. Buckley, T. H. Keitt, and M. J. Angilletta. 2016. Ontogeny constrains phenology:
511
opportunities for activity and reproduction interact to dictate potential phenologies in a
512
changing climate. Ecology Letters:Early View. doi:10.1111/ele.12595
23
513
24
514
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TABLES
765
Table 1: Output from the WRF model that we used as input for the microclimate model
Variable
Description
Symbol Units
ALBEDO
ground albedo
𝛼𝑔
dec. %
EAH
air vapor pressure of canopy
𝑒𝑐
Pa
EAIR
vapor pressure of bare ground
𝑒𝑏
Pa
EMG
emissivity of ground
𝜖𝑔
dec. %
EMISS
emissivity of surface
𝜖𝑠
dec. %
EMV
emissivity of vegetation
𝜖𝑣
dec. %
GLW
downward flux of near-infrared radiation
𝐿𝑎𝑡𝑚
W m-2
ISNOW
number of snow layers
Isnow
PSFC
surface pressure
𝑃𝑠𝑟𝑓
Pa
QAIR
specific humidity at surface (2-m height)
𝑞𝑠𝑟𝑓
kg kg-1
RHOAIR
air density
𝜌𝑎𝑖𝑟
kg m-3
SMOIS
soil water
𝜃𝑠𝑜𝑖𝑙
m3/m3
SNEQVO
snow mass
SNOWH
water-equivalent of snow
𝐻𝑠𝑛𝑜
m
SNICE
layer of frozen water in the snow
𝜃𝑖𝑐𝑒
mm
SNLIQ
layer of liquid water in the snow
𝜃𝑙𝑖𝑞
mm
SWDOWN
downward flux of visible radiation (W m-2)
𝑆𝑔
W m-2
T2B
temperature above bare surface (2-m height)
𝑇2𝑏
K
TAH
air temperature in canopy
𝑇𝑐
K
𝑚𝑠𝑛𝑜𝑤 mm
37
TG *
temperature of bulk ground
𝑇𝑔𝑏
K
TRAD
ground radiant temperature
Trad
K
TSLB *
soil temperature
𝑇𝑠𝑜𝑖𝑙
K
TSNO
snow temperature
𝑇𝑠𝑛𝑜𝑤
K
TV
temperature of a leaf in the vegetation
𝑇𝑣
K
U10
east-west component of wind speed (10-m height)
𝑢10
m/s
V10
north-south component of wind speed (10-m height)
𝑣10
m/s
ZSNSO
depth from the bottom of a layer to snow surface
𝑧𝑏𝑜𝑡
M
766
* for initial conditions only
767
Bolded variables were not included in the output of the WRF model by default.
768
38
769
Table 2: Variables derived from the WRF and microclimate models that are provided in the
770
online dataset
Variable
Description
Units
Dimensions
Source
ALBEDO
ground albedo
dec. %
time
WRF
gap between canopies
dec. %
time
WRF
Pa
time
WRF
Pa
time
WRF
dec. %
time
WRF
W m-2
time
WRF
0,1,or 2
time
WRF
surface pressure
Pa
time
WRF
specific humidity at
kg kg-1
time
WRF
(the fraction of area
where visible solar
radiation can reach the
BGAP
ground)
EAH
air vapor pressure of
canopy
vapor pressure of bare
ground
fraction of green
EAIR
FVEG
vegetation
GLW
downward flux of
longwave radiation
ISNOW
number of snow layers.
0 – no snow cover
1 – snow cover is higher
than 5 cm
2 – snow cover is higher
than 20 cm
PSFC
QAIR
surface (2-m height)
RHOAIR
air density
kg m-3
time
WRF
SMOIS
soil water
m3/m3
time,height
WRF
SWDOWN
downward flux of
W m-2
time
WRF
K
time
WRF
shortwave radiation
TAH
air temperature in canopy
39
Tair
air temperature
K
time,shade,height
microclimate
Tsoil
soil/snow temperature
K
time,shade,depth
microclimate
Tsurface
ground temperature
K
time,shade
microclimate
TV
temperature of a leaf in
the vegetation
gap within the canopy
K
time
WRF
dec. %
time
WRF
m s-1
time,height
microclimate
(the fraction of visible
solar radiation that can
reach the ground through
WGAP
the canopy)
WIND10
wind velocity
771
772
40
773
FIGURE LEGENDS
774
Figure 1: The objectives and approaches in our study. First, we gathered climate data for the past
775
and future at relatively coarse resolutions. Second, we downscaled the data to resolutions of 1 hr
776
and 36 km. Finally, we calculated temperatures under 5 shade conditions and 19 heights above
777
and below the ground.
778
779
Figure 2: Predicted microclimates for past climate (1980-1990) derived from the observation-
780
based NCEP and GLDAS reanalysis and our climate simulation with the WRF model. Data are
781
mean values of air temperature at 2 m, specific humidity at 2 m, and wind velocity at 10 m. The
782
mean difference between past and future (2080-2090) predictions are shown in the right panels.
783
784
Figure 3: The components of the surface-energy balance in the models of open and shaded
785
microhabitats. Net solar radiation (S, yellow arrow) reaches open microhabitats. Net longwave
786
radiation (L, red arrows) is the net difference between radiation from the ground and radiation
787
from the sky (open) or canopy (shade). Sensible heat flux (H, orange arrows) is the exchange of
788
heat between the ground and the air at 2-m height (open) or at the canopy (shade). Latent heat (E,
789
grey arrows) is the evaporative heat loss from the ground to the air. The heat flux into the soil
790
(G, green arrows) is the exchange of heat between the soil and the ground. At each time step,
791
these fluxes were used to calculate the ground temperature. Then, the heat exchange between soil
792
layers (F, blue arrows) was calculated using partial differential equation for heat transfer, with
793
the heat flux into the soil (G) as the upper boundary condition and the deep soil temperature (8 m
794
depth) as the lower boundary condition.
795
796
Figure 4: Predicted annual cycle of vegetation cover for past climate (left panels, 1980-1990).
41
797
Data are mean predictions for January, April, July and October. The mean difference between
798
past and future (2080-2090) predictions are shown in the right panels.
799
800
Figure 5: The annual mean percentage of green vegetation cover (1980-1990) as derived from
801
the Normalized Difference Vegetation Index (left panel) and as predicted by the Noah-MP
802
dynamic vegetation model (middle panel). The mean difference between past and future (2080-
803
2090) predictions are shown in the right panel.
804
805
Figure 6: Biases for air temperature and specific humidity at 2 m, wind velocities at 10 m, and
806
the percentage of green vegetation were derived from data generated by the observation-based
807
NCEP reanalysis (left panels) and by our WRF simulation (right panels). Data were averaged for
808
the period of 1980-1990.
809
810
Figure 7: Biases for air temperature and specific humidity at 2 m, wind velocities at 10 m, and
811
the percentage of green vegetation were derived from data generated by the observation-based
812
GLDAP reanalysis (left panels) and by our WRF simulation (right panels). Data were averaged
813
for the period of 1980-1990.
814
815
Figure 8: Frequency distributions of the biases depicted in Figure 6.
816
817
Figure 9: Frequency distributions of the biases depicted in Figure 7.
818
819
Figure 10: Probability density functions of skill scores between the observation-based NCEP
42
820
reanalysis and our WRF simulation for air temperature and specific humidity at 2 m, and wind
821
velocities at 10 m.
822
823
Figure 11: Frequency distributions of the probability density functions depicted in Figure 10.
824
825
Figure 12: Probability density functions of skill scores between the observation-based GLDAS
826
reanalysis and our WRF simulation for air temperature and specific humidity at 2 m, and wind
827
velocities at 10 m.
828
829
Figure 13: Frequency distributions of the probability density functions depicted in Figure 12.
830
831
Figure 14: Probability density functions of skill scores and the biases for minimum and
832
maximum air temperature were derived from data gathered in stations and by our microclimate
833
simulation.
834
835
Figure 15: Probability density functions of skill scores and the biases for minimum and
836
maximum soil temperature were derived from data gathered in stations and by our microclimate
837
simulation.
838
839
Figure 16: Temperature differences between open and fully shaded ground surfaces’ minimum
840
and maximum temperatures were derived from our microclimate simulation.
841
842
Figure 17: Frequency distributions of temperature differences between open and fully shaded
43
843
ground surfaces’ minimum and maximum temperatures as shown in Figure 16.
844
44
845
Figure 1
846
847
848
45
849
Figure 2
850
46
851
Figure 3
852
853
47
854
Figure 4
855
856
48
857
Figure 5
858
859
860
49
861
Figure 6
862
863
864
865
50
866
Figure 7
867
868
51
869
Figure 8
870
52
871
Figure 9
872
53
873
Figure 10
874
875
876
877
54
878
Figure 11
879
880
55
881
Figure 12
882
883
56
884
Figure 13
885
886
57
887
Figure 14
888
889
890
58
891
Figure 15
892
893
59
894
Figure 16
895
896
897
60
898
Figure 17
899
900
901
61