Julie Ann Winkler

"The Global Partners Program: A New International Volunteer Opportunity for AMS Members" published on 01 Mar 2024 by American Meteorological Society.

Editorial: Hydroclimatology of the Great Lakes region of North America

Daily solar radiation is a critical input for estimating plant growth and development, yet this variable is infrequently measured compared to other climate variables. This study evaluates the sensitivity of simulated maize and soybean production from the CERES-Maize and CROPGRO-Soybean modules of the Decision Support System for Agrotechnology Transfer (DSSAT) to daily solar radiation estimates obtained from traditional (stochastic, empirical, and mechanistic models) and non-traditional (satellite estimation, reanalysis datasets, and regional climate model simulations) approaches, using as an example radiation estimates for Hancock, Wisconsin, USA. When compared to observations, radiation estimates obtained from empirical and mechanistic models and a satellite-based dataset generally had smaller biases than other approaches. Daily solar radiation estimates from a reanalysis dataset and regional climate model simulations overestimate incoming daily solar radiation. When the radiation estimates were used as an input to CERES-Maize, no significant differences were found for maize yield obtained from the different radiation estimates compared to yield from observed radiation, even though differences were found in the daily values of leaf area index, crop evapotranspiration, and crop dry weight (biomass). In contrast, significant differences were found in simulated soybean yield from CROPGRO-Soybean for the majority of the radiation estimates.

The persistence of high values of relative humidity for several hours to a few days has wide‐ranging implications for natural and human systems. Among its many impacts, persistent high relative humidity contributes to reduced atmospheric visibility, fog frequency and duration, road and powerline icing, the occurrence of foliar plant disease and the temperature at which heat‐related morbidity and mortality can occur. Despite this, the characteristics of humidity spells—including shifts with climate change—are rarely investigated and consequently poorly understood. Hourly relative humidity, precipitation and temperature for 35 locations in the Lower Peninsula (LP) of Michigan during 2003–2017, and for seven locations during 1973–2017, were used to assess, by biweekly periods, spatial, intra‐annual and inter‐annual variations in the characteristics of relative humidity spells. Two humidity thresholds that accommodate a wide range of applications were used: (1) high relative humidity (HRH), defined as hourly values ≥60%, and extremely high relative humidity (EHRH), defined as hourly values ≥85%. Extended HRH and EHRH spells are frequent across the LP, with HRH spells often spanning 36 hr or longer and EHRH spells commonly persisting more than 12 hr. The probability of HRH and EHRH spells generally decreased with time during 1973–2017, particularly later in the period. Although most spells initiate in the late afternoon and early evening hours, this diurnal preference is less evident as spell length increases, especially for EHRH spells. Furthermore, precipitation is more likely to occur with EHRH spells compared to HRH spells. The orientation and strength of spatial gradients in average annual spell frequency are highly dependent on the choice of humidity and duration thresholds and the temperatures accompanying the spells. This study represents the first comprehensive analysis of the persistence of humidity spells and illustrates the potential usefulness of temporally‐flexible climatological summaries relevant for a wide range of applications.

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km²), we found that the models built at the next-largest extent (500 km²) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species’ habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

An emerging literature discusses the effects of short-term temperature fluctuations on public opinion toward climate change. Yet, prior literature has not explored potential opinion-influencing effects of temporal patterns of temperature fluctuations or the interdependence between temperature anomalies and the direction and magnitude of short-term trends. This study uses an extreme warm spell that occurred during a survey of Michigan residents to evaluate the influence of complex temperature effects on public support for government involvement in the agricultural sector's adaptation to climate change. Comparison of several alternatives for capturing the influence of temperature fluctuations on survey responses (some drawn from the literature, some newly constructed) shows a temporary increase in support for government assistance for adaptation after the onset of a warm spell, but a longer exposure to extreme temperatures does not necessarily lead to more support for adaptation policies. Conditional on other attributes of temperature abnormalities (e.g., direction of trend), abnormal heat might even lead to reduced support for adaptation policies.

Understanding the impacts of climate change on agriculture is essential to ensure adequate future food production. Controlled growth experiments provide an effective tool for assessing the complex effects of climate change. However, a review of the use of climate projections in 57 previously published controlled growth studies found that none considered within-season variations in projected future temperature change, and few considered regional differences in future warming. A fixed, often arbitrary, temperature perturbation typically was applied for the entire growing season. This study investigates the utility of employing more complex climate change scenarios in growth chamber experiments. A case study in potato was performed using three dynamically downscaled climate change projections for the mid-twenty-first century that differ in terms of the timing during the growing season of the largest projected temperature changes. The climate projections were used in growth chamber experiments for four elite potato cultivars commonly planted in Michigan’s major potato growing region. The choice of climate projection had a significant influence on the sign and magnitude of the projected changes in aboveground biomass and total tuber count, whereas all projections suggested an increase in total tuber weight and a decrease in specific gravity, a key market quality trait for potato, by mid-century. These results demonstrate that the use of more complex climate projections that extend beyond a simple incremental change can provide additional insights into the future impacts of climate change on crop production and the accompanying uncertainty.