Seasonality of biological and physical systems as indicators of climatic variation and change - Jake F. Weltzin; Julio L. Betancourt; Benjamin I. Cook; Theresa M. Crimmins; Carolyn A. F. Enquist; Michael D. Gerst; John E. Gross; Geoffrey M. Henebry; Rebecca A. Hufft; Melissa A. Kenney; John S. Kimball; Bradley C. Reed; et al. (Climatic Change, 2020)

Terrestrial primary productivity indicators for inclusion in the National Climate Indicators System - Matthew O. Jones; Steven W. Running; John S. Kimball; Nathaniel P. Robinson; Brady W. Allred (Climatic Change, 2020)

Genetic analysis of threshability and other spike traits in the evolution of cultivated emmer to fully domesticated durum wheat - Jyoti S. Sharma; Katherine L. D. Running; Steven S. Xu; Qijun Zhang; Amanda R. Peters Haugrud; Sapna Sharma; Phillip E. McClean; Justin D. Faris (Molecular Genetics and Genomics, 2019)

Variation in stability of elk and red deer populations with abiotic and biotic factors at the species‐distribution scale - Farshid S. Ahrestani; William K. Smith; Mark Hebblewhite; Steven Running; Eric Post (Ecology, 2016)
Stability in population dynamics is an emergent property of the interaction between direct and delayed density dependence, the strengths of which vary with environmental covariates. Analysis of variation across populations in the strength of direct and delayed density dependence can reveal variation in stability properties of populations at the species level. We examined the stability properties of 22 elk/red deer populations in a two‐stage analysis. First, we estimated direct and delayed density dependence applying an AR (2) model in a Bayesian hierarchical framework. Second, we plotted the coefficients of direct and delayed density dependence in the Royama parameter plane. We then used a hierarchical approach to test the significance of environmental covariates of direct and delayed density dependence. Three populations exhibited highly stable and convergent dynamics with strong direct, and weak delayed, density dependence. The remaining 19 populations exhibited more complex dynamics characterized by multi‐annual fluctuations. Most (15 of 19) of these exhibited a combination of weak to moderate direct and delayed density dependence. Best‐fit models included environmental covariates in 17 populations (77% of the total). Of these, interannual variation in growing‐season primary productivity and interannual variation in winter temperature were the most common, performing as the best‐fit covariate in six and five populations, respectively. Interannual variation in growing‐season primary productivity was associated with the weakest combination of direct and delayed density dependence, while interannual variation in winter temperature was associated with the strongest combination of direct and delayed density dependence. These results accord with a classic theoretical prediction that environmental variability should weaken population stability. They furthermore suggest that two forms of environmental variability, one related to forage resources and the other related to abiotic conditions, both reduce stability, but in opposing fashion: one through weakened direct density dependence and the other through strengthened delayed density dependence. Importantly, however, no single abiotic or biotic environmental factor emerged as generally predictive of the strengths of direct or delayed density dependence, nor of the stability properties emerging from their interaction. Our results emphasize the challenges inherent to ascribing primacy to drivers of such parameters at the species level and distribution scale.

Decrease in winter respiration explains 25% of the annual northern forest carbon sink enhancement over the last 30 years - Zhen Yu; Jingxin Wang; Shirong Liu; Shilong Piao; Philippe Ciais; Steven W. Running; Benjamin Poulter; James S. Rentch; Pengsen Sun (Global Ecology and Biogeography, 2016)
Aim Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying microclimate, but the impacts of change in snow cover on the annual C budget at a large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Location Non‐permafrost region of the northern forest area. Methods Here, we used site‐based eddy covariance flux data to investigate the relationship between depth of snow cover and ecosystem respiration ( R eco ) during winter. We then used the Biome‐BGC model to estimate the effect of reductions in winter snow cover on the C balance of northern forests in the non‐permafrost region. Results According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028 to 1.53 gC·m −2 ·day −1 , accounting for 44 ± 123% of the annual C budget. Model simulation showed that over the past 30 years, snow‐driven change in winter C fluxes reduced non‐growing season CO 2 emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter R eco significantly decreased by 0.33 gC·m −2 ·day −1 ·year −1 in response to decreasing depth of snow cover, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Main conclusion Soil temperature is primarily controlled by snow cover rather than by air temperature as snow serves as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model‐simulated results show that both R eco and NEE are significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming as less C is emitted to the atmosphere.

Estimating climate change effects on net primary production of rangelands in the United States - Matthew C. Reeves; Adam L. Moreno; Karen E. Bagne; Steven W. Running (Climatic Change, 2014)

Impacts of climate change on August stream discharge in the Central-Rocky Mountains - Jason C. Leppi; Thomas H. DeLuca; Solomon W. Harrar; Steven W. Running (Climatic Change, 2012)

The Effects of Urbanization on Net Primary Productivity in Southeastern China - Dengsheng Lu; Xiaofeng Xu; Hanqin Tian; Emilio Moran; Maosheng Zhao; Steven Running (Environmental Management, 2010)

Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models - JAMES T. RANDERSON; FORREST M. HOFFMAN; PETER E. THORNTON; NATALIE M. MAHOWALD; KEITH LINDSAY; YEN‐HUEI LEE; CYNTHIA D. NEVISON; SCOTT C. DONEY; GORDON BONAN; RETO STÖCKLI; CURTIS COVEY; STEVEN W. RUNNING; et al. (Global Change Biology, 2009)
With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon‐LAnd Model Intercomparison Project (C‐LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) – Carnegie‐Ames‐Stanford Approach′ (CASA′) and carbon–nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1–3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO 2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4 Pg C yr −1 for CASA′ vs. 1.2 Pg C yr −1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988–2004 based on comparison with atmospheric inversion results from TRANSCOM ( r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C‐LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community‐wide platform for model‐data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C‐LAMP are publicly available on the Earth System Grid.

FLUXNET and modelling the global carbon cycle - ANDREW D. FRIEND; ALMUT ARNETH; NANCY Y. KIANG; MARK LOMAS; JÉRÔME OGÉE; CHRISTIAN RÖDENBECK; STEVEN W. RUNNING; JEAN‐DIEGO SANTAREN; STEPHEN SITCH; NICOLAS VIOVY; F. IAN WOODWARD; SÖNKE ZAEHLE (Global Change Biology, 2007)
Measurements of the net CO 2 flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO 2 source–sink patterns, CO 2 flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO 2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model‐data comparison. Flux measurements made over four vegetation types were used to calibrate the land‐surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land‐surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO 2 uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite‐measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO 2 measurements. Combining the PEM, DGVM, and inversion results suggests that CO 2 fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO 2 uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion's global NEE estimate of −1.2 Pg [C] yr −1 for 1982–1995 is compatible with the PEM‐ and DGVM‐predicted trends in NPP. There is, thus, a convergence in understanding derived from process‐based models, remote‐sensing‐based observations, and inversion of atmospheric data. Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night‐time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land‐atmosphere CO 2 fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO 2 uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO 2 variability. A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO 2 exchange.

Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century - CÉLINE BOISVENUE; STEVEN W. RUNNING (Global Change Biology, 2006)
Changes to forest production drivers (light, water, temperature, and site nutrient) over the last 55 years have been documented in peer‐reviewed literature. The main objective of this paper is to review documented evidence of the impacts of climate change trends on forest productivity since the middle of the 20th century. We first present a concise overview of the climate controls of forest production, provide evidence of how the main controls have changed in the last 55 years, followed by a core section outlining our findings of observed and documented impacts on forest productivity and a brief discussion of the complications of interpreting trends in net primary production (NPP). At finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground‐based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. Of the 49 papers reporting forest production levels we reviewed, 37 showed a positive growth trend, five a negative trend, three reported both a positive and a negative trend for different time periods, one reported a positive and no trend for different geographic areas, and two reported no trend. Forests occupy ≈52% of the Earth's land surface and tend to occupy more temperature and radiation‐limited environments. Less than 7% of forests are in strongly water‐limited systems. The combined and interacting effects of temperature, radiation, and precipitation changes with the positive effect of CO 2 , the negative effects of O 3 and other pollutants, and the presently positive effects of N will not be elucidated with experimental manipulation of one or a few factors at a time. Assessments of the greening of the biosphere depend on both accurate measurements of rates (net ecosystem exchange, NPP), how much is stored at the ecosystem level (net ecosystem production) and quantification of disturbances rates on final net biome production.

Mapping and Modeling the Biogeochemical Cycling of Turf Grasses in the United States - Cristina Milesi; Steven W. Running; Christopher D. Elvidge; John B. Dietz; Benjamin T. Tuttle; Ramakrishna R. Nemani (Environmental Management, 2005)

Site‐level evaluation of satellite‐based global terrestrial gross primary production and net primary production monitoring - David P. Turner; William D. Ritts; Warren B. Cohen; Thomas K. Maeirsperger; Stith T. Gower; Al A. Kirschbaum; Steve W. Running; Maosheng Zhao; Steven C. Wofsy; Allison L. Dunn; Beverly E. Law; John L. Campbell; et al. (Global Change Biology, 2005)
Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site‐level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite‐based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty‐five 1 km 2 cells at each site, with 8‐day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ∼30 m resolution), and process‐based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m −2 yr −1 at an arctic tundra site to 550 g C m −2 yr −1 at a temperate deciduous forest site. There was not a consistent over‐ or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over‐ and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.

A generalized, bioclimatic index to predict foliar phenology in response to climate - William M. Jolly; Ramakrishna Nemani; Steven W. Running (Global Change Biology, 2005)
The phenological state of vegetation significantly affects exchanges of heat, mass, and momentum between the Earth's surface and the atmosphere. Although current patterns can be estimated from satellites, we lack the ability to predict future trends in response to climate change. We searched the literature for a common set of variables that might be combined into an index to quantify the greenness of vegetation throughout the year. We selected as variables: daylength (photoperiod), evaporative demand (vapor pressure deficit), and suboptimal (minimum) temperatures. For each variable we set threshold limits, within which the relative phenological performance of the vegetation was assumed to vary from inactive (0) to unconstrained (1). A combined Growing Season Index (GSI) was derived as the product of the three indices. Ten‐day mean GSI values for nine widely dispersed ecosystems showed good agreement ( r >0.8) with the satellite‐derived Normalized Difference Vegetation Index (NDVI). We also tested the model at a temperate deciduous forest by comparing model estimates with average field observations of leaf flush and leaf coloration. The mean absolute error of predictions at this site was 3 days for average leaf flush dates and 2 days for leaf coloration dates. Finally, we used this model to produce a global map that distinguishes major differences in regional phenological controls. The model appears sufficiently robust to reconstruct historical variation as well as to forecast future phenological responses to changing climatic conditions.

Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari - William M. Jolly; Steven W. Running (Global Change Biology, 2004)
We utilized an ecosystem process model to investigate the influence of precipitation and soil water potential on vegetation phenology in the semi‐arid, drought‐deciduous ecosystems in the Kalahari region of South Africa. The timing of leaf flush was assumed to be the first day during which a rainfall event exceeded that day's estimate of potential evapotranspiration after a defined dry season. Leaf senescence was assumed to be a dynamic feedback between soil water potential and net plant carbon gain and was determined by dynamically modeling the effects of concomitant trends in soil water potential and net primary production on leaf area index (LAI). Model predictions of LAI were compared with satellite‐derived normalized difference vegetation indices (NDVI) for 3 years at two sites along the Kalahari transect. The mean absolute error for the prediction of modeled leaf flush date compared with leaf flush dates estimated from NDVI were 10.0 days for the Maun site and 39.3 days for the Tshane site. Correlations between model predicted 10‐day average LAI and 10‐day composite NDVI for both Maun and Tshane were high ( ρ =0.67 and 0.74, respectively, P

Investigating the Balance between Timber Harvest and Productivity of Global Coniferous Forests under Global Change - Galina Churkina; Steven Running (Climatic Change, 2000)

The Earth Observing System and Forest Management - Steven W. Running; Lloyd Queen; Michele Thornton (Journal of Forestry, 2000)
Inexpensive, readily available, and frequently updated: These are the important characteristics of biophysical data coming from the first satellite launched as part of the Earth Observing System. For forest managers, the data can help detect and monitor fires, determine seasonal and annual productivity across ownerships, even estimate national carbon balances for environmental policy plans. Although the variables do not distinguish all forest cover types, processing modifications will soon allow more detailed calculations.

Modeled responses of terrestrial ecosystems to elevated atmospheric CO 2 : a comparison of simulations by the biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) - Yude Pan; Jerry M. Melillo; A. David McGuire; David W. Kicklighter; Louis F. Pitelka; Kathy Hibbard; Lars L. Pierce; Steven W. Running; Dennis S. Ojima; William J. Parton; David S. Schimel (Oecologia, 1998)

Contrasting Climatic Controls on the Estimated Productivity of Global Terrestrial Biomes - Galina Churkina; Steven W. Running (Ecosystems, 1998)