Designing ecological climate change impact assessments to reflect key climatic drivers

Abstract

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.

Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador)

“…The performance of statistical bias correction methods to downscale the meteorological variables of the GCMs is considered satisfactory in different hydro-climatological studies [39]. The observed data for the period 1975-2004 used as the base period were also corrected according to Sofaer et al [40] (see Supplementary Materials, Tables S3 and S4). Data download, spatial downscaling and bias correction of GCMs and the observed data were carried out in the CCT software.…”

Bio-economics of Indian hybrid Bt cotton and farmer suicides

“…Humans have adapted some natural systems for agricultural purposes (e.g., corn, coffee, cotton, rice), but mismanagement may threaten their sustainability. It is increasingly recognized that large crossscale problems in agriculture (and natural systems) are bioeconomic in nature, and that extant methodological barriers can be surmounted with mechanistic processbased models of mass-energy flow dynamics [44,45]. Physiologically based demographic models (PBDMs) have been used to capture the time-varying, age-stage structured dynamics of species and their interactions, enabling assessment of the bioeconomy of individuals to multi-species populations across geographic space, time, and technology and climate change (see [46]).…”

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

“…We estimated models based on observed climatic conditions (Maurer, Brekke, Pruitt, & Duffy, ). To assess climate change impacts we compared future distributions under forecasted climate to distributions under simulated past climate (“hindcast”), an approach that avoids conflating climate model biases with the impacts of climate change (Sofaer et al., ). Climate forecasts and hindcasts were obtained from 10 randomly selected CMIP5 GCMs under Representative Concentration Pathway 8.5 (See Fig.…”