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DOE OSTI2014

Nuclear Energy Response in the EMF27 Study

The nuclear energy response for mitigating global climate change across eighteen 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.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2023

Risks of Synchronized Low Yields Are Underestimated in Climate and Crop Model Projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Atmospheric dynamics↗

DOE OSTI2020

Forecasted attribution of the human influence on Hurricane Florence

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm’s core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Solar constant secular changes

A recent model for solar constant secular changes is used to calculate a 'proxy' solar constant for: (1) the past four centuries, based upon the sunspot record, (2) the past nine centuries, based upon C-14 observations and their relation to solar activity, and (3) the next decade, based upon a dynamo theory model for the solar cycle. The proxy solar constant data is tabulated as it may be useful for climate modelers studying global climate changes.

Schatten, Kenneth H.↗

NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗

DOE OSTI2021

Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

Abstract The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Detection and attribution of vegetation greening trend in China over the last 30 years

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. Here, we use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAI GS ) estimated by satellite datasets (average trend of 0.0070yr -1 , ranging from 0.0035yr -1 to 0.0127yr -1 ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS (P<0.05), and marginally significantly (P=0.07) correlated with the residual of LAI(GS) trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. In conclusion, this result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

The seasonal climate niche predicts phenology and distribution of an ephemeral annual plant, Mollugo verticillata

Many short-lived species complete their life cycles during brief seasonal windows of favourable environmental conditions. Such species may persist in the face of climate warming by migration to track their seasonal climate niche in space and/or by phenological shifts to track favourable conditions in time within the year. To describe the seasonal climate niche of the short-lived annual Mollugo verticillata in California, we used data from herbarium specimens and historic climate records to estimate environmental conditions at the location, month and year of each collection. We used these data in a MaxEnt framework to construct a seasonal species distribution model (SDM) of the species’ climate niche within the total climate space available across all seasons and locations in California. The model provides fine-scale spatial and temporal predictions of habitat suitability, predicting both where and when the species should be observed. We compared the predictions of the model to those from a conventional SDM based on mean annual climate data. Both models showed that M. verticillata is limited to warm environments within California. However, the seasonal SDM also predicted phenology by mapping climate suitability across the state for each month of the year. Mollugo verticillata is limited to warm months, and its seasonal climate niche shifts in space across California in the course of the year. We used the seasonal SDM to map the predicted future species distribution for each month of the year under three warming scenarios. The species is predicted to expand its range and occur earlier in the year in most locations; in the warmest locations, seasonal suitability is predicted to decline in the warmest months, which may result in bimodal phenology with a mid-summer gap. Synthesis. We developed a novel species distribution model using herbarium records and monthly weather data, which predicts not only where a short-lived species should be found but also when during the year it is predicted to occur in those areas. Furthermore, this model can be used to predict how climate change will affect the species distribution in space as well as seasonal phenology across the landscape.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Mid-21st Century Ozone Air Quality and Health Burden in China Under Emissions Scenarios and Climate Change

Despite modest emissions reductions of air pollutants in recent years, China still suffers from poor air quality, and the outlook for future air quality in China is uncertain. We explore the impact of two disparate 2050 emissions scenarios relative to 2015 in the context of a changing climate with the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 (GFDL-AM3) chemistry-climate model. We impose the same near-term climate change for both emission scenarios by setting global sea surface temperature (SST) and sea ice cover (SIC) to the average over 2010–2019 and 2046–2055, respectively, from a three-member ensemble of GFDL coupled climate model simulations under the RCP8.5 (Representative Concentration Pathway) scenario. By the 2050s, annual mean surface ozone increases throughout China by up to 8 ppbv from climate change alone (estimated by holding air pollutants at 2015 levels while setting SIC and SST to 2050 conditions in the model) and by 8–12 ppbv in a scenario in which emissions of ozone precursors nitrogen oxides (NO (sub x) ) and anthropogenic volatile organic compounds (VOCs) increase by ~10%. In a scenario in which NO (sub x) and anthropogenic VOC emissions decline by 60%, annual mean surface ozone over China decreases by 16–20 ppbv in the 2050s relative to the 2010s. The ozone increase from climate change alone results in an additional 62 000 premature deaths in China as compared to 330 000 fewer premature deaths by the 2050s under a strong emissions mitigation scenario. In springtime over Southwestern China in the 2050s, the model projects 9–12 ppbv enhancements to surface ozone from the stratosphere (diagnosed with a model tracer) and from international anthropogenic emissions (diagnosed by differencing AM3 simulations with the same emissions within China but higher versus lower emissions in the rest of the world). Our findings highlight the effectiveness of emissions controls in reducing the health burden in China due to air pollution, and also the potential for climate change and rising global emissions to offset, at least partially, some of the ozone decreases attained with regional emission reductions in China.

Westervelt, D. M.↗

DOE OSTI2026

Watershed response of the Feather River Basin, California, United States of America to future climate changes

Increasing mean annual temperatures under climate change are expected to reduce seasonal snowpack, increase evapotranspiration (ET), and alter summer baseflow in headwater watersheds worldwide. Strong regional variability in hydrologic responses highlights the need for catchment-scale, physically based models to assess future flood risk and water availability. This study examines climate-driven changes in the hydrologic response of the Upper Feather River watershed in the Sierra Nevada Mountains, California. Four priority climate models and two representative concentration pathways (RCP4.5 and RCP8.5) are used to evaluate future hydrologic responses of the watershed. Hydrologic processes are simulated using an objectively calibrated Soil and Water Assessment Tool Plus model for a historical baseline (1986–2005) and a future period 2070–2099), driven by observed and projected precipitation and temperature. Results indicate a declining contribution of snowfall to annual precipitation, with peak snowfall and water yield shifting 1–3 months earlier. Long-term annual maximum flows are projected to increase considerably, whereas low-flow responses are mixed, with both increases and decreases projected by the end of the century. These findings highlight the need for adaptive watershed management to enhance flood protection, water storage, and drought resilience. Future water resource planning should also account for one-to-three-month shifts in peak water yield and surface runoff due to changes in snowmelt timing and a lower snowfall-to-rainfall ratio under climate change. The modeling framework and insights are transferable to other snow-dominated headwater watersheds experiencing climate-driven change.

Tigabu, T [UC Davis]↗

DOE OSTI2016

Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24 (~4 km) grid resolution for the baseline (1966 2005) and future (2011 2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snow-covered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. Furthermore, the projected hydrological changes described in this study have implications for various aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Numerical Modeling of Glaciers in Martian Paleoclimates

Numerous geologic features suggest the presence of ice flow on the surface of mars. These features include lobate debris aprons, concentric crater fill, and lineated valley fill. The lateral extent of these features can range from 100 meters to over 20 km. Previous work has demonstrated that these features could not have formed in current Martian conditions. It has long been speculated that changes in Mars orbital properties, namely its obliquity, eccentricity, and argument of perihelion, can result in dramatic changes to climate. Recent climate model studies have shown that at periods of increased obliquity north polar water ice is mobilized southward and deposited at low ad mid latitudes. Mid latitude accumulation of ice would provide the necessary conditions for rock glaciers to form. A time-marching, finite element glacier model is used to demonstrate the ability of ice and ice-rock mixtures to flow under Martian paleoclimate conditions. Input to this model is constrained by the NASA Ames Mars General Circulation Model (MGCM).

Colaprete, A.↗

NASA NTRS2015

Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP in Sub-Saharan Africa

Agriculture in Sub-Saharan Africa (SSA) is experiencing climate change-related effects that call for integrated regional assessments, yet capacity for these assessments has been low. The Agricultural Model Intercomparison and Improvement Project (AgMIP) is advancing research on integrated regional assessments of climate change that include climate, crop, and economic modeling and analysis. Through AgMIP, regional integrated assessments are increasingly gaining momentum in SSA, and multi-institutional regional research teams (RRTs) centered in East, West, and Southern· Africa are generating new information on climate change impacts and adaptation in selected agricultural systems. The research in Africa is organized into four RRTs and a coordination team. Each of the RRTs in SSA is composed of scientists from the Consultative Group of International Agricultural Research (CGIAR) institutions, National Agriculture Research institutes (NARs), and universities consisting of experts in crop and economic modeling, climate, and information technology. Stakeholder involvement to inform specific agricultural systems to be evaluated, key outputs, and the representative agricultural pathways (RAPs), is undertaken at two levels: regional and national, in order to contribute to decision making at these levels. Capacity building for integrated assessment (lA) is a key component that is undertaken continuously through interaction with experts in regional and SSA-wide workshops, and through joint creation of tools. Many students and research affiliates have been identified and entrained as part of capacity building in IA. Bi-monthly updates on scholarly publications in climate change in Africa also serve as a vehicle for knowledge-sharing. With 60 scientists already trained and actively engaged in IA and over 80 getting monthly briefs on the latest information on climate change, a climate-informed community of experts is gradually taking shape in SSA. (See Part 2, Appendices 3-5 in this volume for AgMIP Regional Workshop reports.)

Kihara, Job↗

NASA NTRS1988

Integration of space and in situ observations to study global climate change

The use of model-based global data sets of atmospheric circulation for studying fundamental dynamical and physical processes is discussed, focusing on limitations of the available model-based data sets. Data from the Global Weather Experiment in 1979 were analyzed by two authorized level IIIb data centers in 1980 and in 1981. The analyses led to difference in data-sparse regions such as the tropics. Study areas which can be addressed by an internally-consistent long-term multivariate data set for the atmospheric circulation are considered, including mean climate, forcing for the ocean models, global hydrological cycle, atmospheric energetics, intraseasonal variability, land surface processes, and structure and variability of vertical velocity, divergence, and diabatic heating. It is concluded that the most comprehensive technique for integrating space and in situ observations to produce this type of data set would be a four-dimensional data assimilation system with a realistic physical model of the type employed in operational numerical weather prediction.

Bengtsson, L.↗

DOE OSTI2021

Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest

Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. Furthermore, this phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO 2 and warming conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Bioenergy in Energy Transformation and Climate Management

Unlike fossil fuels, biomass is a renewable resource that can sequester carbon during growth, be converted to energy, and then re-grown. Biomass is also a flexible fuel that can service many end-uses. This paper explores the importance of bioenergy to potential future energy transformation and climate change management. Using a model comparison of fifteen models, we characterize and analyze future dependence on, and the value of, bioenergy in achieving potential long-run climate objectives—reducing radiative forcing to 3.7 and 2.8 W/m2 in 2100 (approximately 550 and 450 ppm carbon dioxide equivalent atmospheric concentrations). Model scenarios project, by 2050, bioenergy growth of 2 to 10% per annum reaching 5 to 35 percent of global primary energy, and by 2100, bioenergy becoming 15 to 50 percent of global primary energy. Non-OECD regions are projected to be the dominant suppliers of biomass, as well as consumers, with up to 35 percent of regional electricity from biopower by 2050, and up to 70 percent of regional liquid fuels from biofuels by 2050. Bioenergy is found to be valuable to many models with significant implications for mitigation costs and world consumption. The availability of bioenergy, in particular biomass with carbon dioxide capture and storage (BECCS), notably affects the cost-effective global emissions trajectory for climate management by accommodating prolonged near-term use of fossil fuels. We also find that models cost-effectively trade-off land carbon and nitrous oxide emissions for the long-run climate change management benefits of bioenergy. Overall, further evaluation of the viability of global large-scale bioenergy is merited.

Rose, Steven K.↗

DOE OSTI2013

Global vegetation model diversity and the risks of climate-driven ecosystem shifts

Climate change is modifying global biogeochemical cycles, and is expected to exert increasingly large effects in the future. How these changes will in turn affect and interact with the structure and function of particular ecosystems is unclear, however, both because of scientific uncertainties and the very diversity of global vegetation models in use. Writing in Environmental Research Letters, Warszawski et al. (1) aggregate results from a group of models, across a range of emissions scenarios and climate data, to investigate these risks. Although the models frequently disagree about which specific regions are at risk, they consistently predict a greater chance of ecosystem restructuring with more warming; this risk roughly doubles between 2 and 3 °C increases in global mean temperature. The innovative work of Warszawski et al. represents an important first step towards fully consistent multi-model, multi-scenario assessments of the future risks to global ecosystems.

climate change↗

NASA NTRS2019

North American Extreme Precipitation Events and Related Large-Scale Meteorological Patterns: a Review of Statistical Methods, Dynamics, Modeling, and Trends

This paper surveys the current state of knowledge regarding Large-Scale Meteorological Patterns (LSMPs) associated with short-duration (less than one week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some 4 promising analysis techniques have been identified and the LSMP perspective is useful for evaluating model dynamics.

Mathew Barlow↗