Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

AbstractA new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data

Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake

Long-term, global offline (land-only) simulations with a dynamic vegetation phenology model are used to examine the control of hydroclimate over vegetation-related quantities. First, with a control simulation, the model is shown to capture successfully (though with some bias) key observed relationships between hydroclimate and the spatial and temporal variations of phenological expression. In subsequent simulations, the model shows that: (i) the global spatial variation of seasonal phenological maxima is controlled mostly by hydroclimate, irrespective of distributions in vegetation type, (ii) the occurrence of high interannual moisture-related phenological variability in grassland areas is determined by hydroclimate rather than by the specific properties of grassland, and (iii) hydroclimatic means and variability have a corresponding impact on the spatial and temporal distributions of gross primary productivity (GPP)

The Spring 1985 high precision baseline test of the JPL GPS-based geodetic system

The Spring 1985 High Precision Baseline Test (HPBT) was conducted. The HPBT was designed to meet a number of objectives. Foremost among these was the demonstration of a level of accuracy of 1 to 2:10 to the 7th power, or better, for baselines ranging in length up to several hundred kilometers. These objectives were all met with a high degree of success, with respect to the demonstration of system accuracy in particular. The results from six baselines ranging in length from 70 to 729 km were examined for repeatability and, in the case of three baselines, were compared to results from colocated VLBI systems. Repeatability was found to be 5:10 to the 8th power (RMS) for the north baseline coordinate, independent of baseline length, while for the east coordinate RMS repeatability was found to be larger than this by factors of 2 to 4. The GPS-based results were found to be in agreement with those from colocated VLBI measurements, when corrected for the physical separations of the VLBI and CPG antennas, at the level of 1 to 2:10 to the 7th power in all coordinates, independent of baseline length. The results for baseline repeatability are consistent with the current GPA error budget, but the GPS-VLBI intercomparisons disagree at a somewhat larger level than expected. It is hypothesized that these differences may result from errors in the local survey measurements used to correct for the separations of the GPS and VLBI antenna reference centers

Comparison of bevel and tulip triggered pultruded tubes for energy absorption

The crushing behavior of E-glass/polyester and E-glass/vinyl ester pultruded tubes has been found to be significantly different for tulip triggered specimens as compared with bevel triggered specimens. Up to 100% more energy per unit weight was absorbed by tulip triggered tubes. In addition, the crushing was more controlled and predictable with the tulip trigger. The morphology of the material in the crushing zone differed in the amount and the pattern of fracture. The fracture pattern and crushing behavior initiated by both triggers were found not to change during crushing. The difference in crushing appeared to arise from the different abilities of the tubes to support load because of the different geometry of individual load-carrying structures that resulted from triggering.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29526/1/0000613.pd

Effects of material properties and crush conditions on the crush energy absorption of fiber composite rods

Crushing along the fiber axis of undirectional E-glass fiber composites rods was examined to determine the effects of fiber volume fraction, fiber diameter, matrix compressive yield strength, crush rate, fiber surface treatment, and crush plate geometry. The volume specific energy absorption was found to increase with fiber content, fiber diameter, matrix yield strength, and crush rate. The crush load stability was found to be independent of fiber content and fiber diameter but not of matrix yield strengt. The crush load became less stable as the yield strength increased. The crush behavior of specimens containing clean fibers was about the same as with sized fibers, but specimens with a release agent on the fiber surface crushed with less energy absorption that decreased even as the fiber content increased, but the crush load was more stable than with sized or clean fibers. The volume specific energy absorption was greater when the rod specimens were crushed against concave surfaces than against a flat plate. A relatively simple model was able to account for the dependence of the energy absorption on fiber volume fraction and matrix yield strength.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31082/1/0000759.pd

Updated respiration routines alter spatio-temporal patterns of carbon cycling in a global land surface model

We updated the routines used to estimate leaf maintenance respiration (MR) in the Energy Land Model (ELM) using a comprehensive global respiration data base. The updated algorithm includes a temperature acclimating base rate, an updated instantaneous temperature response, and new plant functional type specific parameters. The updated MR algorithm resulted in a very large increase in global MR of 16.1 Pg (38%), but the signal was not geographically uniform. The increase was concentrated in the tropics and humid warm-temperate forests. The increase in MR led to large but proportionally smaller decreases in global net primary production (19%) and in average global leaf area index (15%). The effect on global gross primary production (GPP) was a more modest 5.7 Pg (4%). A detailed site level analysis also demonstrated a wide range of effects the updated algorithm can have on the seasonal cycle of GPP. Output from the updated and old models did not differ markedly in how closely they matched a suite of benchmarks. Given the substantial impact on the land surface carbon cycle, a neutral influence on model benchmarks, and better alignment with empirical evidence, an MR algorithm similar to the one presented here should be adopted into ELM

Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations

Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales

Assessing simulation ecosystem processes for climate variability research at Glacier National Park

Glacier National Park served as a test site for ecosystem analyses that involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter–cooler, drier–warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur