Weather Sensitive High Spatio-Temporal Resolution Transportation Electric Load Profiles For Multiple Decarbonization Pathways

Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to generating hourly electric load profiles that considers charging strategies and evolving sensitivity to temperature. The approach consists of downscaling annual state-scale sectoral load projections from the multi-sectoral Global Change Analysis Model (GCAM) into hourly electric load profiles leveraging high resolution climate and population datasets. Profiles are developed and evaluated at the Balancing Authority scale, with a 5-year increment until 2050 over the Western U.S. Interconnect for multiple decarbonization pathways and climate scenarios. The datasets are readily available for production cost model analysis. Our open source approach is transferable to other regions

Genetics of Dispersal

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal-related phenotypes or evidence for the micro-evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment-dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non-additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non-equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context-dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.Peer reviewe

Is voice a marker for autism spectrum disorder? A systematic review and meta-analysis

Individuals with Autism Spectrum Disorder (ASD) tend to show distinctive, atypical acoustic patterns of speech. These behaviours affect social interactions and social development and could represent a non-invasive marker for ASD. We systematically reviewed the literature quantifying acoustic patterns in ASD. Search terms were: (prosody OR intonation OR inflection OR intensity OR pitch OR fundamental frequency OR speech rate OR voice quality OR acoustic) AND (autis* OR Asperger). Results were filtered to include only: empirical studies quantifying acoustic features of vocal production in ASD, with a sample size > 2, and the inclusion of a neurotypical comparison group and/or correlations between acoustic measures and severity of clinical features. We identified 34 articles, including 30 univariate studies and 15 multivariate machine-learning studies. We performed metaanalyses of the univariate studies, identifying significant differences in mean pitch and pitch range between individuals with ASD and comparison participants (Cohen’s d of 0.4-0.5 and discriminatory accuracy of about 61-64%). The multivariate studies reported higher accuracies than the univariate studies (63-96%). However, the methods used and the acoustic features investigated were too diverse for performing meta-analysis. We conclude that multivariate studies of acoustic patterns are a promising but yet unsystematic avenue for establishing ASD markers. We outline three recommendations for future studies: open data, open methods, and theory-driven research

GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County

<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>.</p><p>GCAM-USA scenarios (see <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details):</p><ul><li>`BAU_Climate` - a business-as-usual scenario without IRA incentives</li><li>`business_as_usual_ira_ccs_climate` - a business-as-usual scenario with IRA incentives for CCS technology</li><li>`NetZeroNoCCS_Climate` - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>`net_zero_ira_ccs_climate` - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><p>Climate pathways (see <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details):</p><ul><li>`rcp45cooler` - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>`rcp85hotter` - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><p>Fields in the data files:</p><ul><li>`time` - hourly timestamp in UTC representing the preceding hour of data</li><li>`load_MWh` - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>`temperature_celsius` - mean temperature within this county and balancing authority in degrees Celsius</li><li>`FIPS` - FIPS code for the county</li><li>`balancing_authority` - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>`State` - the state abbreviation for this county</li></ul><p> </p><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). </p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p&gt

GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County

<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p&gt

Divergent urban land trajectories under alternative population projections within the Shared Socioeconomic Pathways

Population change is a main driver behind global environmental change, including urban land expansion. In future scenario modeling, assumptions regarding how populations will change locally, despite identical global constraints of Shared Socioeconomic Pathways (SSPs), can have dramatic effects on subsequent regional urbanization. Using a spatial modeling experiment at high resolution (1 km), this study compared how two alternative US population projections, varying in the spatially explicit nature of demographic patterns and migration, affect urban land dynamics simulated by the Spatially Explicit, Long-term, Empirical City development (SELECT) model for SSP2, SSP3, and SSP5. The population projections included: (1) newer downscaled state-specific population (SP) projections inclusive of updated international and domestic migration estimates, and (2) prevailing downscaled national-level projections (NP) agnostic to localized demographic processes. Our work shows that alternative population inputs, even those under the same SSP, can lead to dramatic and complex differences in urban land outcomes. Under the SP projection, urbanization displays more of an extensification pattern compared to the NP projection. This suggests that recent demographic information supports more extreme urban extensification and land pressures on existing rural areas in the US than previously anticipated. Urban land outcomes to population inputs were spatially variable where areas in close spatial proximity showed divergent patterns, reflective of the spatially complex urbanization processes that can be accommodated in SELECT. Although different population projections and assumptions led to divergent outcomes, urban land development is not a linear product of population change but the result of complex relationships between population, dynamic urbanization processes, stages of urban development maturity, and feedback mechanisms. These findings highlight the importance of accounting for spatial variations in the population projections, but also urbanization process to accurately project long-term urban land patterns

Continental United States climate projections based on thermodynamic modification of historical weather

Abstract Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events

Continental United States climate projections based on thermodynamic modification of historical weather

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Abstract Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance