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Biomass allocation in plants is the foundation for understanding dynamics in ecosystem carbon balance, species competition, and plant-environment interactions. However, existing work on plant allometry has mainly focused on trees, with fewer studies having developed allometric equations for grasses. Grasses with different life histories can vary in their carbon investment by prioritizing the growth of specific organs to survive, outcompete co-occurring plants, and ensure population persistence. Further, because grasses are important fuels for wildfire, the lack of grass allocation data adds uncertainty to process-based models that relate plant physiology to wildfire dynamics. To fill this gap, we conducted a greenhouse experiment with 11 common California grasses varying in photosynthetic pathway and growth form. We measured plant sizes and harvested above- and belowground biomass throughout the life cycle of annual species, while for the establishment stage of perennial grasses to quantify allometric relationships for leaf, stem, and root biomass, as well as plant height and canopy area. We used basal diameter as a reference measure of plant size. Overall, basal diameter is the best predictor for leaf and stem biomass, height, and canopy area. Including height as another predictor can improve model accuracy in predicting leaf and stem biomass and canopy area. Fine root biomass is a function of leaf biomass alone. Species vary in their allometric relationships, with most variation occurring for plant height, canopy area, and stem biomass. We further explored potential trade-offs in biomass allocation across species between leaf and fine root, leaf and stem, and allocation to reproduction. Consistent with our expectation, we found that fast-growing plants allocated a greater fraction to reproduction. Additionally, plant height and specific leaf area negatively influenced the leaf-to-stem ratio. However, contrary to our hypothesis, there were no differences in root-to-leaf ratio between perennial and annual or C4 and C3 plants. Our study provides species-specific and functional-type-specific allometry equations for both above- and belowground organs of 11 common California grass species, enabling nondestructive biomass assessment in California grasslands. These allometric relationships and trade-offs in carbon allocation across species can improve ecosystem model predictions of grassland species interactions and environmental responses through differences in morphology.

Vegetation demographic models (VDMs) endeavor to predict how global forests will respond to climate change. This requires simulating which trees, if any, are able to recruit under changing environmental conditions. We present a new recruitment scheme for VDMs in which functional-type-specific recruitment rates are sensitive to light, soil moisture and the productivity of reproductive trees. We evaluate the scheme by predicting tree recruitment for four tropical tree functional types under varying meteorology and canopy structure at Barro Colorado Island, Panama. We compare predictions to those of a current VDM, quantitative observations and ecological expectations. We find that the scheme improves the magnitude and rank order of recruitment rates among functional types and captures recruitment limitations in response to variable understory light, soil moisture and precipitation regimes. Our results indicate that adopting this framework will improve VDM capacity to predict functional-type-specific tree recruitment in response to climate change, thereby improving predictions of future forest distribution, composition and function.

Plant community composition influences carbon, water, and energy fluxes at regional to global scales. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

The fate of tropical forests under climate change is unclear as a result, in part, of the uncertainty in projected changes in precipitation and in the ability of vegetation models to capture the effects of drought-induced mortality on aboveground biomass (AGB). We evaluated the ability of a terrestrial biosphere model with demography and hydrodynamics (Ecosystem Demography, ED2-hydro) to simulate AGB and mortality of four tropical tree plant functional types (PFTs) that operate along light- and water-use axes. Model predictions were compared with observations of canopy trees at Barro Colorado Island (BCI), Panama. We then assessed the implications of eight hypothetical precipitation scenarios, including increased annual precipitation, reduced inter-annual variation, El Niño-related droughts and drier wet or dry seasons, on AGB and functional diversity of the model forest. When forced with observed meteorology, ED2-hydro predictions capture multiple BCI benchmarks. ED2-hydro predicts that AGB will be sustained under lower rainfall via shifts in the functional composition of the forest, except under the drier dry-season scenario. These results support the hypothesis that inter-annual variation in mean and seasonal precipitation promotes the coexistence of functionally diverse PFTs because of the relative differences in mortality rates. If the hydroclimate becomes chronically drier or wetter, functional evenness related to drought tolerance may decline.

Understanding how climate warming will affect the demographic rates of different ecotypes is critical to predicting shifts in species distributions. Here, we present results from a common garden, climate change experiment in which we measured seedling recruitment of lodgepole pine, a widespread North American conifer that is also planted globally. Seeds from a low-elevation provenance had more than three-fold greater recruitment to their third year than seeds from a high-elevation provenance across sites within and above its native elevation range and across climate manipulations. Heating halved recruitment to the third year of both low- and high-elevation seed sources across the elevation gradient, while watering more than doubled recruitment, alleviating some of the negative effects of heating. Demographic models based on recruitment data from the climate manipulations and long-term observations of adult populations revealed that heating could effectively halt modeled upslope range expansion except when combined with watering. Simulating fire and rapid postfire forest recovery at lower elevations accelerated lodgepole pine expansion into the alpine, but did not alter final abundance rankings among climate scenarios. Regardless of climate scenario, greater recruitment of low-elevation seeds compensated for longer dispersal distances to treeline, assuming colonization was allowed to proceed over multiple centuries. Our results show that ecotypes from lower elevations within a species' range could enhance recruitment and facilitate upslope range shifts with climate change.

Mountain meadows have high biodiversity and help regulate stream water release following the snowmelt pulse. However, many meadows are experiencing woody plant encroachment, threatening these ecosystem services. While there have been field surveys of individual meadows and remote sensing-based landscape-scale studies of encroachment, what is missing is a broad-scale, ground-based study to understand common regional drivers, especially at high elevations, where land management has often played a less direct role. With this study, we ask: What are the climate and landscape conditions conducive to woody plant encroachment at the landscape scale, and how has historical climate variation affected tree recruitment in subalpine meadows over time? We measured density of encroaching trees across 340 subalpine meadows in the central Sierra Nevada, California, USA, and used generalized additive models (GAMs) to determine the relationship between landscape-scale patterns of encroachment and meadow environmental properties. We determined ages of trees in 30 survey meadows, used observed climate and GAMs to model the relationship between timing of recruitment and climate since the early 1900s, and extrapolated recruitment patterns into the future using downscaled climate scenarios. Encroachment was high among meadows with lodgepole pine (Pinus contorta Douglas ex Loudon var. murrayana (Balf.) Engelm.) in the immediate vicinity, at lower elevations, with physical conditions favoring strong soil drying, and with maximum temperatures above or below average. Climatic conditions during the year of germination were unimportant, with tree recruitment instead depending on a 3-yr seed production period prior to germination and a 6-yr seedling establishment period following germination. Recruitment was high when the seed production period had high snowpack, and when the seedling establishment period had warm summer maximum temperatures, high summer precipitation, and high snowpack. Applying our temporal model to downscaled output from four global climate models indicated that the average meadow will shift to forest by the end of the 21st century. Sierra Nevada meadow encroachment by conifers is ubiquitous and associated with climate conditions increasingly favorable for tree recruitment, which will lead to substantial changes in subalpine meadows and the ecosystem services they provide.

Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long life spans, the importance of climate‐sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. Using spatially explicit, stochastic population models combined with data from long‐term forest surveys, we explored whether the climate‐sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high‐elevation North American conifers. Empirically observed, warming‐driven declines in recruitment led to rapid modelled population declines at the low‐elevation, ‘warm edge’ of subalpine forest and slow emergence of populations beyond the high‐elevation, ‘cool edge’. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modelled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above tree line, and, ultimately, expansion into the alpine. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high‐elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

Land-atmosphere interactions are important to climate prediction, but the underlying effects of surface forcing of the atmosphere are not well understood. In the U.S. Southern Great Plains, grassland/pasture and winter wheat are the dominant land covers but have distinct growing periods that may differently influence land-atmosphere coupling during spring and summer. Variables that influence surface flux partitioning can change seasonally, depending on the state of local vegetation. Here we use surface observations from multiple sites in the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains Climate Research Facility and statistical modeling at a paired grassland/agricultural site within this facility to quantify land cover influence on surface energy balance and variables controlling evaporative fraction (latent heat flux normalized by the sum of sensible and latent heat fluxes). We demonstrate that the radiative balance and evaporative fraction are closely related to green leaf area at both winter wheat and grassland/pasture sites and that the early summer harvest of winter wheat abruptly shifts the relationship between evaporative fraction and surface state variables. Prior to harvest, evaporative fraction of winter wheat is strongly influenced by leaf area and soil-atmosphere temperature differences. After harvest, variations in soil moisture have a stronger effect on evaporative fraction. This is in contrast with grassland/pasture sites, where variation in green leaf area has a large influence on evaporative fraction throughout spring and summer, and changes in soil-atmosphere temperature difference and soil moisture are of relatively minor importance.

Scientists estimate that U.S. Corn Belt crop yields will increase or decrease, on average, and become more variable with climate change. Corn and soybeanfarming dominates this region, but studies typically do not assess the joint impact of new distributions of corn and soybean yields on markets. We use a structural economic model with projections of climate-driven yield changes to simulate these effects. Our findings suggest that a narrow focus on a single crop in this key growing region risks underestimating the impact on price distributions and average crop receipts, and can lead to incorrect signs on estimated impacts.

Climate niche models project that subalpine forest ranges will extend upslope with climate warming. These projections assume that the climate suitable for adult trees will be adequate for forest regeneration, ignoring climate requirements for seedling recruitment, a potential demographic bottleneck. Moreover, local genetic adaptation is expected to facilitate range expansion, with tree populations at the upper forest edge providing the seed best adapted to the alpine. Here, we test these expectations using a novel combination of common gardens, seeded with two widely distributed subalpine conifers, and climate manipulations replicated at three elevations. Infrared heaters raised temperatures in heated plots, but raised temperatures more in the forest than at or above treeline because strong winds at high elevation reduced heating efficiency. Watering increased season-average soil moisture similarly across sites. Contrary to expectations, warming reduced Engelmann spruce recruitment at and above treeline, as well as in the forest. Warming reduced limber pine first-year recruitment in the forest, but had no net effect on fourth-year recruitment at any site. Watering during the snow-free season alleviated some negative effects of warming, indicating that warming exacerbated water limitations. Contrary to expectations of local adaptation, low-elevation seeds of both species initially recruited more strongly than high-elevation seeds across the elevation gradient, although the low-provenance advantage diminished by the fourth year for Engelmann spruce, likely due to small sample sizes. High- and low-elevation provenances responded similarly to warming across sites for Engelmann spruce, but differently for limber pine. In the context of increasing tree mortality, lower recruitment at all elevations with warming, combined with lower quality, high-provenance seed being most available for colonizing the alpine, portends range contraction for Engelmann spruce. The lower sensitivity of limber pine to warming indicates a potential for this species to become more important in subalpine forest communities in the coming centuries.

Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off-line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture-evaporative fraction (EF) and LAI-EF correlations in off-line CLM4.5 and reduced the root-mean-square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of -1.3°C, and a corresponding dry bias in precipitation was reduced from -111 mm to -23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

© 2015 Springer-Verlag Berlin Heidelberg In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine-scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind-driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs' ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high-resolution historic climatic record, we developed multiple fine-scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under 'normal' combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020-2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high-resolution alternative to downscaled GCM outputs for near-term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean-atmosphere dynamics that are not represented by coarse-scale GCMs.

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO) and compared to a naturally warmer, but otherwise similar subalpine site in the Valles Caldera National Preserve, New Mexico (NM). Observed snow accumulation was 30% lower on average and snow melted out 16 days earlier in the heated plots compared to the controls. Soil temperature during snowmelt was 3°C greater on average and soil moisture was 4% lower on average in heated plots compared to controls. In NM, snow accumulation was 23% lower, snow melted 23 days earlier, soil temperature was 0.6°C greater, and soil moisture was 13% lower on average relative to CO controls. In order to estimate differences in energy and mass balance fluxes at the snow–atmosphere interface in control versus warmer plots, the 1-D, physically based snowmelt model, SNOWPACK, was used. Model results indicated that heaters alter radiative, turbulent and mass fluxes by amounts comparable to the differences between CO and NM fluxes. The proportion of the energy flux associated with latent heat exchange during snowmelt was 9–27% of the total energy flux in heated models and 19–22% of NM models compared to 3–7% in control models. Thus, sublimation loss to the atmosphere was greater in both experimentally and naturally warmer cases relative to the control case. We conclude that IR heaters can provide alterations to the timing and magnitude of snow accumulation and snowmelt consistent with conditions observed at a warmer analog site and with climate and hydrology model projections. Impacts of IR heating on energy partitioning and sublimation should be considered when designing manipulations of the snowpack, as reductions in snowmelt water may alter biological or ecological processes.

Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long-term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long-lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long-term, large-scale global change experiments with process studies and modeling. Long-term global change manipulative experiments, especially in high-priority ecosystems such as tropical forests and high-latitude regions, are essential to maximize information gain concerning future states of the earth system. The long-term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long-term experiments and process studies together with information from long-term observations, surveys, and space-for-time studies along environmental and biological gradients. Future research programs with coordinated long-term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long-term ecosystem dynamics in response to global change.