Nathaniel A. Brunsell

Timing of climate variability and grassland productivity

Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and high-intensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing.

Warm spring reduced carbon cycle impact of the 2012 US summer drought

Significance Carbon uptake by terrestrial ecosystems mitigates the impact of anthropogenic fossil fuel emissions on atmospheric CO2 concentrations, but the strength of this carbon sink is highly sensitive to large-scale extreme climate events. In 2012, the United States experienced the most severe drought since the Dust Bowl period, along with the warmest spring on record. Here, we quantify the impact of this climate anomaly on the carbon cycle. Our results show that warming-induced earlier vegetation activity increased spring carbon uptake, and thus compensated for reduced carbon uptake during the summer drought in 2012. This compensation, however, came at the cost of soil moisture depletion from increased spring evapotranspiration that likely enhanced summer heating through land-atmosphere coupling. The global terrestrial carbon sink offsets one-third of the world’s fossil fuel emissions, but the strength of this sink is highly sensitive to large-scale extreme events. In 2012, the contiguous United States experienced exceptionally warm temperatures and the most severe drought since the Dust Bowl era of the 1930s, resulting in substantial economic damage. It is crucial to understand the dynamics of such events because warmer temperatures and a higher prevalence of drought are projected in a changing climate. Here, we combine an extensive network of direct ecosystem flux measurements with satellite remote sensing and atmospheric inverse modeling to quantify the impact of the warmer spring and summer drought on biosphere-atmosphere carbon and water exchange in 2012. We consistently find that earlier vegetation activity increased spring carbon uptake and compensated for the reduced uptake during the summer drought, which mitigated the impact on net annual carbon uptake. The early phenological development in the Eastern Temperate Forests played a major role for the continental-scale carbon balance in 2012. The warm spring also depleted soil water resources earlier, and thus exacerbated water limitations during summer. Our results show that the detrimental effects of severe summer drought on ecosystem carbon storage can be mitigated by warming-induced increases in spring carbon uptake. However, the results also suggest that the positive carbon cycle effect of warm spring enhances water limitations and can increase summer heating through biosphere–atmosphere feedbacks.

Differential effects of extreme drought on production and respiration: synthesis and modeling analysis

Extremes in climate may severely impact ecosys- tem structure and function, with both the magnitude and rate of response differing among ecosystem types and processes. We conducted a modeling analysis of the effects of extreme drought on two key ecosystem processes, production and res- piration, and, to provide a broader context, we complemented this with a synthesis of published results that cover a wide variety of ecosystems. The synthesis indicated that across a broad range of biomes, gross primary production (GPP) was generally more sensitive to extreme drought (defined as pro- portional reduction relative to average rainfall periods) than was ecosystem respiration (ER). Furthermore, this differen- tial sensitivity between production and respiration increased as drought severity increased; it occurred only in grassland ecosystems, and not in evergreen needle-leaf and broad-leaf forests or woody savannahs. The modeling analysis was de- signed to enable a better understanding of the mechanisms underlying this pattern, and focused on four grassland sites arrayed across the Great Plains, USA. Model results consis- tently showed that net primary productivity (NPP) was re- duced more than heterotrophic respiration (Rh) by extreme drought (i.e., 67 % reduction in annual ambient rainfall) at all four study sites. The sensitivity of NPP to drought was di- rectly attributable to rainfall amount, whereas the sensitivity of Rh to drought was driven by soil drying, reduced carbon (C) input and a drought-induced reduction in soil C content - a much slower process. However, differences in reductions in NPP and Rh diminished as extreme drought continued, due to a gradual decline in the soil C pool leading to further re- ductions in Rh. We also varied the way in which drought was imposed in the modeling analysis; it was either imposed by simulating reductions in rainfall event size (ESR) or by re- ducing rainfall event number (REN). Modeled NPP and Rh decreased more by ESR than REN at the two relatively mesic sites but less so at the two xeric sites. Our findings suggest that responses of production and respiration differ in magni- tude, occur on different timescales, and are affected by dif- ferent mechanisms under extreme, prolonged drought.

Length Scale Analysis of Surface Energy Fluxes Derived from Remote Sensing

Abstract Wavelet multiresolution analysis was used to examine the variation in dominant length scales determined from remotely sensed airborne- and satellite-derived surface energy flux data. The wavelet cospectra are computed between surface radiometric temperature, fractional vegetation, and derived energy fluxes at airborne (12 m) and Advanced Very High Resolution Radiometer (AVHRR) (1000 m) resolutions. Length scale analysis of high-resolution data shows that small-scale variability in temperature dominates over other effects. Analysis of coarse-resolution data shows that small-scale variations in vegetation are important, although the large-scale variation in radiometric temperature dominates the derived fluxes. This is determined to be a result of the fact that, at smaller scales, the incoming solar radiation effect is muted by the small-scale variability in vegetation, temperature, and albedo, whereas at coarser scales, the large-scale effect of incoming radiation on temperature dominates over the ...

Vegetation Phenology in Global Change Studies

Global change, encompassing natural and anthropogenic changes to the Earth system at sub-annual to geologic time scales, has strong interactions with vegetation phenology. In this chapter we will refer to global change as alterations to the Earth system that are certainly or probably influenced by human activity, primarily since the industrial revolution. This form of global change includes irrefutable anthropogenic alterations to terrestrial land cover and alterations to the global climate that are probably anthropogenically influenced. Within this context we discuss three aspects of vegetation phenology: the influence of vegetation phenology on general circulation models (GCMs); a wavelet analysis of phenological patterns and associated evidence of likely phenological responses to direct human-induced land cover alteration; and third, serious challenges regarding the use of phenological data and concepts in global change research.

Land surface response to precipitation events using MODIS and NEXRAD data

We have developed a wavelet‐based information theoretic approach to examine the interaction between precipitation (PPT) forcing events and the land surface response. Combining Next Generation Weather Radar (NEXRAD) PPT with Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation (NDVI) and surface temperature (T s) data over the Missouri Basin in the north‐central USA, we are able to address the spatial and temporal fluctuations surrounding the hydrometeorology of grassland ecosystems. Information theory metrics of entropy and mutual information content are combined with a wavelet multi‐resolution analysis to examine to what extent the observed PPT signal directly determines the spatial distribution of the land surface temperature and vegetation and how this relationship varies with spatial scale. Results indicate that (1) there is a reduction in the temporal variance of the wavelet coefficients as the signal is transferred from the PPT into the surface temperature and finally the vegetation signal, (2) there are significant correlations as a function of spatial resolution between PPT–NDVI and PPT–T s signals which generally increase with spatial resolution, while there is little correlation between the NDVI and T s signals as a function of resolution, and (3) the scale‐wise entropy and the mutual information content of the signals increase for all fields as the spatial resolution increases. This provides a methodology for determining the relative impact of regional climatology and local land–atmosphere interactions as a function of spatial scale.

Coupling terrestrial and atmospheric water dynamics to improve prediction in a changing environment

Humans have profoundly influenced their environment. It has been estimated that nearly one-third of the global land cover has been modified while approximately 40% of the photosynthesis has been appropriated. As the interface between the subsurface and the atmosphere is altered, it is imperative that we understand the influence this alteration has in terms of changing regional and global climates. Land surface heterogeneity is sometimes a principal modulator of local and regional climates and, as such, there are potential aggregation and teleconnection effects ranging in scales from soil pores to the general atmospheric circulation when the land surface is altered across a range of scales. The human fingerprint on land surface processes is critical and must also be accounted for in the discourse on land-atmosphere coupling as it pertains to climate and global change as well as local processes such as evapotranspiration and streamflow. It is at this pivotal interface where hydrologists, atmospheric scientists and ecologists must understand how their disciplines interact and influence each other.Fluxes across the land-surface directly influence predictions of ecological processes, atmospheric dynamics, and terrestrial hydrology. However, many simplifications are made in numerical models when considering terrestrial hydrology from the view point of the atmosphere and visa-versa. While this may be a necessity in the current generation of operational models used for forecasting, it can create obstacles to the advancement of process understanding. These simplifications can limit the numerical prediction capabilities on how water partitions itself throughout all phases of the water cycle. The feedbacks between terrestrial and atmospheric water dynamics are not well understood or represented by the current generation of operational land-surface and atmospheric models. This can lead to erroneous spatial patterns and anomalous temporal persistence in land-atmosphere exchanges and atmospheric water cycle predictions. Cross-disciplinary efforts are needed not only to identify but also to quantify feedbacks between terrestrial and atmospheric water at appropriate spatiotemporal scales. This is especially true as today’s young scientists set their sights on improving process understanding and prediction skill from both research and operational models used to describe such linked systems.In recognition of these challenges, a junior faculty and early career scientist forum was recently held at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado with the intent of identifying and characterizing feedback interactions, and their attendant spatial and temporal scales, important for coupling terrestrial and atmospheric water dynamics. The primary focus of this forum is on improved process understanding, rather than operational products, as the possibility of incorporating more realistic physics into operational models is computationally prohibitive. We approached the subject of improved predictability through better process understanding by focusing on the following three framework questions described and discussed below.

Two methods for estimating limits to large-scale wind power generation

Significance Wind turbines generate electricity by removing kinetic energy from the atmosphere. We show that the limited replenishment of kinetic energy from aloft limits wind power generation rates at scales sufficiently large that horizontal fluxes of kinetic energy can be ignored. We evaluate these factors with regional atmospheric model simulations and find that generation limits can be estimated from the ‟preturbine” climatology by comparatively simple means, working best when the atmosphere between the surface and hub height is naturally well-mixed during the day. Our results show that the reduction of wind speeds and limited downward fluxes determine the limits in large-scale wind power generation to less than 1 W⋅m−2. Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way.

EO-1 Hyperion Reflectance Time Series at Calibration and Validation Sites: Stability and Sensitivity to Seasonal Dynamics

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

How can we use MODIS land surface temperature to validate long-term urban model simulations?

High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.