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The current drought plaguing the Southwest US (SWUS) underscores the need for long-term precipitation predictability to inform sustainable planning of future ecological and economic systems. Precipitation predictability requires understanding the teleconnections and intercorrelations of a suite of climate indices that are known to impact the SWUS. However, decision criteria about the selection of El Niño and southern oscillation (ENSO) and non-ENSO indices, definition of winter months, geographical extent, temporal scale, computation of what constitutes a long-term mean, and determination of the study period, have not been systematically examined, yet have important consequences on the appropriate characterization of SWUS winter precipitation predictability. Here, we used Pearson’s correlations, Mann–Kendall tests, descriptive statistics, and principal component analyses to explore the statistical relationships between natural modes of climate variability and observed SWUS precipitation. We found no statistically significant persistent changes in the patterns of precipitation for a suite of SWUS geographical designations. Our results show that the choice of the temporal scale has an important impact on the determination of the strength of the climate signal. We show that ENSO indices were the primary determinants of SWUS precipitation, although inconsistencies persisted depending on the choice of ENSO index, the selection of SWUS geographical designation, and the chosen winter month combination. Non-ENSO indices in isolation were found inadequate to explain SWUS precipitation outcomes. Our analysis also indicates the predictability of SWUS precipitation must consider neutral ENSO events when non-ENSO modes are found to play an important role. We recommend the undertaking of a coordinated multi-decadal suite of numerical modeling experiments that systematically account for the individual and total impacts of this critical set of climate indices to improve understanding of past precipitation outcomes and by extension, improve predictability for a future for which tens of millions of people will require advanced planning.

Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ∼53% of diurnally averaged total electric demand, ranging from ∼35% during early morning to ∼65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.

Changes in land use management practices may have multiple effects on microclimate and soil properties that affect soil greenhouse gas (GHG) emissions. Soil surface GHG emissions need to be better quantified in order to assess the total environmental costs of current and possible alternative land uses in the Missouri River Floodplain (MRF). The objective of this study was to evaluate soil GHG emissions (CO2, CH4, N2O) in MRF soils under long-term agroforestry (AF), row-crop agriculture (AG) and riparian forest (FOR) systems in response to differences in soil water content, land use, and N fertilizer inputs. Intact soil cores were obtained from all three land use systems and incubated under constant temperature conditions for a period of 94 days using randomized complete block design with three replications. Cores were subjected to three different water regimes: flooded (FLD), optimal for CO2 efflux (OPT), and fluctuating. Additional N fertilizer treatments for the AG and AF land uses were included during the incubation and designated as AG-N and AF-N, respectively. Soil CO2 and N2O emissions were affected by the land use systems and soil moisture regimes. The AF land use resulted in significantly lower cumulative soil CO2 and N2O emissions than FOR soils under the OPT water regime. Nitrogen application to AG and AF did not increase cumulative soil CO2 emissions. FLD resulted in the highest soil N2O and CH4 emissions, but did not cause any increases in soil cumulative CO2 emissions compared to OPT water regime conditions. Cumulative soil CO2 and N2O emissions were positively correlated with soil pH. Soil cumulative soil CH4 emissions were only affected by water regimes and strongly correlated with soil redox potential.

Empirical estimations of urban effects on the diurnal temperature cycle were carried out for Phoenix, Arizona, through a framework capable of estimating the mean urban effect on air temperature. The analysis of pre-urban and urban differences in hourly temperature data at Sky Harbor International Airport during dry tropical conditions in June and January revealed a significant urban influence. Minimum temperature was most influenced by urbanization with the mean minimum temperature during the urban period exceeding that of the pre-urban period by 4.4°C (2.4°C) in June (January). A significant urban heat sink in January maximum temperatures was evident as the mean maximum temperature during the pre-urban period exceeded that of the urban period by 1.5°C. The greater thermal inertia due to urban growth around the airport and the growth of the airport itself has also had an effect on nocturnal cooling rates. Pre-urban January minimum temperatures typically occurred between 06:00 and 07:00 local standard time (LST) in the pre-urban period and between 07:00 and 08:00 LST in the urban period. Similarly, in June, the minimum temperature occurred on average around 05:00 LST during the pre-urban period and between 05:00 and 06:00 LST during the urban period. This apparent decrease in cooling rates was modeled well by the first harmonic fit to the average hourly temperature data.

Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ∼53% of diurnally averaged total electric demand, ranging from ∼35% during early morning to ∼65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.

Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate‐emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short‐term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.