Ecological Applications, 26(5), 2016, pp. 1321–1337 © 2016 by the Ecological Society of America The effects of climate downscaling technique and observational data set on modeled ecological responses 1,4 1 2 3 AFSHIN POURMOKHTARIAN, CHARLES T. DRISCOLL, JOHN L. CAMPBELL, KATHARINE HAYHOE, AND 3 ANNE M. K. STONER 1Department of Civil and Environmental Engineering, Syracuse University, Syracuse, New York 13244 USA 2US Forest Service, Northern Research Station, Durham, New Hampshire 03824 USA 3Climate Science Center, Texas Tech University, Lubbock, Texas 79409 USA Abstract. Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeo- chemical responses to changing climate are affected by the selection of the climate down- scaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction- Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere– ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observa- tions (1/8º resolution grid vs. individual weather station) to generate the high- resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs). The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chem- istry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station- based and grid- based obser- vations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/ or methods that do not resolve sub-monthly shifts in the distribution of temperature and/ or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. Key words: climate change; ecological modeling; forested watershed; Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA; hydrology; LTER; net primary productivity; statistical downscaling; stream nitrate; uncertainty analysis; variability. INTRODUCTION Any model analysis is subject to multiple sources of uncertainty. For biogeochemical models, uncertainties Dynamic forest biogeochemical models are useful stem from simplifications and assumptions regarding the tools to understand, evaluate, and predict the interactive hydrological, biological, and geochemical processes effects of climate change, atmospheric CO increases, 2 depicted in the model (structural uncertainty) (Gupta et al. and atmospheric deposition of anthropogenic pollutants 2012), as well as inaccurate parameterizations due to lack on the hydrology and water quality of watersheds. of data or uncertain observations (parametric uncertainty) Models have the ability to simulate the dynamics of (Wellen et al. 2015). When models are used to assess the energy, water, and element cycles in terrestrial ecosystems potential impacts of climate change on a terrestrial eco- over spatio-temporal scales that are difficult to achieve system, uncertainties in climate projections enter the analy- through direct observation and experimentation. sis. These include uncertainties in estimates of future emissions due to human activities (human or scenario Manuscript received 27 April 2015; revised 19 October 2015; accepted 1 December 2015. Corresponding Editor: D. S. Schimel. uncertainty) (Stott and Kettleborough 2002), the ability 4E-mail: [email protected] of the atmosphere–ocean general circulation models 1321 1322 AFSHIN POURMOKHTARIAN ET AL. Ecological Applications Vol. 26, No. 5 (AOGCMs), or global climate models, to simulate the and selected data set for training, and helps guide the response of the climate system to human forcings (struc- application of biogeochemical watershed models in tural and parametric uncertainty) (Andrews et al. 2012), climate change research. and uncertainties in the methods and/or models used to translate large- scale change into high- resolution projec- METHODS tions (downscaling uncertainty) (Gutiérrez et al. 2012, Gutmann et al. 2014). The latter is the focus of this study. Site description Application of biogeochemical models to assess cli- This study was conducted using long- term data from mate change impacts on forest ecosystems necessitates a relatively undisturbed reference watershed (watershed inputs of high- resolution simulated climatic variables 6) at the Hubbard Brook Experimental Forest (HBEF) covering a historical and future time period. Due to their in the White Mountains of New Hampshire, USA coarse resolution (typically ~1–2° in latitude and longi- (43°56′ N, 71°45′ W) (Likens and Bormann 1995). The tude) and recognized biases in the absolute values of HBEF was established as a center for hydrological temperature and precipitation, direct AOGCM projec- research in 1955 by the U.S. Forest Service, and joined tions are usually inadequate to assess climate change the National Science Foundation Long- Term Ecological impacts on small watersheds, particularly in complex Research (LTER) network in 1987. The HBEF (http:// mountainous terrain that may be affected by highly www.hubbardbrook.org/data/dataset_search.php) has localized topography and weather patterns. Often, sta- one of the longest and most extensive records on mete- tistical techniques have been applied to downscale coarse orology, hydrology, and biogeochemistry in the U.S. resolution AOGCM output to a finer spatial resolution, (Likens and Bormann 1995, Campbell et al. 2011). The matching long- term observations (Hayhoe et al. 2004, climate is cool- temperate, humid continental, with an 2007, 2008, Stoner et al. 2012). Numerous studies have annual mean precipitation of ~1400 mm that is distrib- evaluated the effect of different statistical downscaling uted evenly over the year. Watershed 6 is 0.13 km2 and techniques on hydrological model output (e.g., Chen has complex terrain with an elevation range of 549– et al. 2011, 2013, Liu et al. 2011, Gutmann et al. 2014), 792 m. Soils are largely well- drained Spodosols, with but we are not aware of previous studies that have con- bedrock at an average depth of 1–2 m. Vegetation is sidered the influence of different downscaling techniques mostly northern hardwoods (Johnson et al. 2000). A on productivity and biogeochemical processes of for- detailed description of the site and long- term monitoring ested watershed ecosystems or the effects of the choice program is provided by Likens and Bormann (1995). of observations for training downscaling models. We assessed the effects of three different downscaling Ecosystem model PnET- BGC methods and two sets of observations, which were used to train those techniques, on simulations of hydrology, PnET- BGC is a deterministic forest-soil- water model water quality, and forest growth under potential future that simulates energy, water, and element fluxes at the changing climate using a forest biogeochemical watershed small watershed scale. PnET- BGC has been used to model (PnET-BGC). Previous work has shown that assess climate change impacts on northern hardwood PnET- BGC simulations are relatively sensitive to forests (Campbell et al. 2009, 2011, Wu and Driscoll changes in meteorological inputs (temperature, precipita- 2009, Pourmokhtarian et al. 2012) as well as atmospheric tion, and photosynthetically active radiation [PAR]; deposition and land disturbance on soil and surface (Pourmokhtarian et al. 2012). The goals of this study were waters across northern forest ecosystems (Chen and to: (1) evaluate variability in climate change projections Driscoll 2005). The model was developed by linking a associated with the application of different downscaling water, carbon, and nitrogen model (PnET- CN) (Aber techniques and sets of observations, which were used to and Driscoll 1997, Aber et al. 1997) to a biogeochemical train those techniques; (2) assess this variability in the (BGC; Gbondo- Tugbawa et al. 2001) sub- model, which context of other sources of variability in climate projec- enables the simultaneous simulation of major biotic and tions (i.e., AOGCM variability, scenario variability); and abiotic processes of major elements (Ca2+, Mg2+, K+, (3) quantify the implications of this variability in simula- Na+, C, N, P, S, Si, Al3+, Cl−, and F−) (Gbondo- tions of the response of a forest ecosystem in montane Tugbawa et al.