Joint Global Change Research Institute

About: Joint Global Change Research Institute is a facility organization based out in Riverdale Park, Maryland, United States. It is known for research contribution in the topics: Greenhouse gas & Climate change. The organization has 197 authors who have published 934 publications receiving 62390 citations.

The GGCMI Phase 2 emulators : Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Abstract: . Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

An open-access CMIP5 pattern library for temperature and precipitation: description and methodology

Abstract: . Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squares regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60–90° N/S). Bias and mean errors between modeled and pattern-predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5 °C, but the choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. This paper describes our library of least squares regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.495632 .

Power sector investment implications of climate impacts on renewable resources in Latin America and the Caribbean.

Abstract: Climate change mitigation will require substantial investments in renewables. In addition, climate change will affect future renewable supply and hence, power sector investment requirements. We study the implications of climate impacts on renewables for power sector investments under deep decarbonization using a global integrated assessment model. We focus on Latin American and Caribbean, an under-studied region but of great interest due to its strong role in international climate mitigation and vulnerability to climate change. We find that accounting for climate impacts on renewables results in significant additional investments ($12–114 billion by 2100 across Latin American countries) for a region with weak financial infrastructure. We also demonstrate that accounting for climate impacts only on hydropower—a primary focus of previous studies—significantly underestimates cumulative investments, particularly in scenarios with high intermittent renewable deployment. Our study underscores the importance of comprehensive analyses of climate impacts on renewables for improved energy planning. Substantial investment will be required in renewables to implement climate change mitigation. Here, the authors focus on Latin America and the Caribbean and find that climate impacts on renewables would result in additional investments $12-114 billion by 2100.

Nuclear energy response in the EMF27 study

Abstract: The nuclear energy response for mitigating global climate change across 18 participating models of the EMF27 study is investigated. Diverse perspectives on the future role of nuclear power in the global energy system are evident in the broad range of nuclear power contributions from participating models of the study. In the Baseline scenario without climate policy, nuclear electricity generation and shares span 0–66 EJ/year and 0–25 % in 2100 for all models, with a median nuclear electricity generation of 39 EJ/year (1,389 GWe at 90 % capacity factor) and median share of 9 %. The role of nuclear energy increased under the climate policy scenarios. The median of nuclear energy use across all models doubled in the 450 ppm CO2e scenario with a nuclear electricity generation of 67 EJ/year (2,352 GWe at 90 % capacity factor) and share of 17 % in 2100. The broad range of nuclear electricity generation (11–214 EJ/year) and shares (2–38 %) in 2100 of the 450 ppm CO2e scenario reflect differences in the technology choice behavior, technology assumptions and competitiveness of low carbon technologies. Greater clarification of nuclear fuel cycle issues and risk factors associated with nuclear energy use are necessary for understanding the nuclear deployment constraints imposed in models and for improving the assessment of the nuclear energy potential in addressing climate change.