Abstract: The field of sustainability science has grown significantly over the past two decades in terms of both conceptual development and empirical research. Systems-focused analysis is critical to building generalizable knowledge in the field, yet much relevant research does not take a systems view. Systems-oriented analytical frameworks can help researchers conceptualize and analyze sustainability-relevant systems, but existing frameworks may lack access or utility outside a particular research tradition.

In this article, we outline the human–technical–environmental (HTE) framework, which provides analysts from different disciplinary backgrounds and fields of study a common way to advance systems-focused research on sustainability issues. We detail a step-by-step guide for the application of the HTE framework through a matrix-based approach for identifying system components, studying interactions among system components, and examining interventions targeting components and/or their interactions for the purpose of advancing sustainability. We demonstrate the applicability of the HTE framework and the matrix-based approach through an analysis of an empirical case of coal-fired power plants and mercury pollution, which is relevant to large-scale sustainability transitions.

Based on this analysis, we identify specific insights related to the applicability of upstream and downstream leverage points, connections between energy markets and the use of pollution control technologies, and the importance of institutions fitting both biophysical dynamics and socioeconomic and political dynamics. Further application of the HTE framework and the identification of insights can help develop systems-oriented analysis, and inform societal efforts to advance sustainability, as well as contribute to the formulation of empirically grounded middle-range theories related to sustainability systems and sustainability transitions. We conclude with a discussion of areas for further development and application of the HTE framework.

Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long‐term future climate developed by integrating a spatial disaggregation (SD) pattern-scaling technique and a bias-correction (BC) delta method. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3). The anomalies or deltas are derived by spatially disaggregating zonal climate projections from the MIT Integrated Global System Modeling (IGSM) framework based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models.

Four emission scenarios are considered to represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. This global dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

Quantitative assessments clearly indicate the ability of the dataset to represent the expected large-scale climate features across various regions of the globe. This large‐ensemble, high-resolution dataset can be used for assessing impacts of climate change from a risk-based perspective across different applications, including hydropower, water resources, wind power resources to name a few.

Significance: The vast subtropical oceans play a leading role in the global storage of organic carbon into the deep ocean. There, biological production is limited by the availability of surface nutrients due to the large-scale ocean circulation pushing nutrient-rich waters at depth. The transfer of nutrients into the sunlit layer is achieved by fine-scale vertical motions, at the expense of the layers beneath. We show that subsurface layers are substantially replenished by the lateral turbulent transport of nutrients along density surfaces, on 10 to 100 km scales. This nutrient relay, involving both vertical and lateral transport, ultimately fuels biological production and sustains an associated sequestration of carbon in the subtropics.

Abstract: The expansive gyres of the subtropical ocean account for a significant fraction of global organic carbon export from the upper ocean. In the gyre interior, vertical mixing and the heaving of nutrient-rich waters into the euphotic layer sustain local productivity, in turn depleting the layers below. However, the nutrient pathways by which these subeuphotic layers are themselves replenished remain unclear.

Using a global, eddy-permitting simulation of ocean physics and biogeochemistry, we quantify nutrient resupply mechanisms along and across density surfaces, including the contribution of eddy-scale motions that are challenging to observe. We find that mesoscale eddies (10 to 100 km) flux nutrients from the shallow flanks of the gyre into the recirculating interior, through time-varying motions along density surfaces. The subeuphotic layers are ultimately replenished in approximately equal contributions by this mesoscale eddy transport and the remineralization of sinking particles.

The mesoscale eddy resupply is most important in the lower thermocline for the whole subtropical region but is dominant at all depths within the gyre interior. Subtropical gyre productivity may therefore be sustained by a nutrient relay, where the lateral transport resupplies nutrients to the thermocline and allows vertical exchanges to maintain surface biological production and carbon export.