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DOE OSTI2020

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Process-Based Model Evaluation Using Surface Energy Budget Observations in Central Greenland

Energy exchange at the Greenland ice sheet surface governs surface temperature variability, a factor critical for representing surface melt. Physical processes link driving forces to subsequent surface energy budget responses, including radiative, turbulent, and ground heat fluxes, and ultimately control surface temperature evolution. A reanalysis product (ERA-Interim, ERA-I), operational model (Climate Forecast System version 2, CFSv2), and climate model (Community Earth System Model, CESM) are evaluated using a comprehensive set of surface energy budget observations and process-based relationships obtained at Summit, Greenland. Simulated downwelling longwave radiation is underestimated, which is linked to a deficiency of liquid-bearing clouds. Lower than observed surface albedo, especially in ERA-I, compensates for summer deficiencies in downwelling longwave radiation. In winter, such deficiencies are compensated by an overestimation of the sensible heat flux. Process-based relationships convey that all three models underestimate the response of surface temperature to changes in radiative forcing, primarily due to an overactive ground heat flux response in ERA-I, turbulent heat fluxes in CFSv2, and sensible heat flux in CESM. Cross comparison of three distinct models indicates that the ground heat flux response for ERA-I, CFSv2, and CESM is too high, too low, and comparatively accurate, respectively, signifying the benefit of using an advanced representation of snow properties. Relatively small biases in CESM surface albedo suggest that advances in the representation of cloud microphysics result in more realistic radiative forcing. These results provide insight into model strengths and deficiencies, indicating the importance of representing physical processes when portraying cloud impacts on surface temperature variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A Community Emissions Data System (CEDS) for Historical Emissions

Historical emission estimates for anthropogenic aerosol and precursor compounds are key data needed for Earth system models, climate models, and atmospheric chemistry and transport models; both for general analysis and assessment and also for model validation through comparisons with observations. Current global emission data sets have a number of shortcomings, including timeliness and transparency. Satellite and other earth-system data are increasingly available in near real-time, but global emission estimates lag by 5-10 years. The CEDS project will construct a data-driven, open source framework to produce annually updated emission estimates. The basic methodologies to be used for this system have been used for SO2 (Smith et al. 2011, Klimont, Smith and Cofala 2013), and are designed to complement existing inventory efforts. The goal of this system is to consistently extend current emission estimates both forward in time to recent years and also back over the entire industrial era. The project will produce improved datasets for global and (potentially) regional model, allow analysis of trends across time, countries, and sectors of emissions and emission factors, and facilitate improved scientific analysis in general. Consistent estimation of uncertainty will be an integral part of this system. This effort will facilitate community evaluation of emissions and further emission-related research more generally.

Smith, Steven J.↗

DOE OSTI2015

A Community Emissions Data System (CEDS) for Historical Emissions

Smith, Steven J.↗

DOE OSTI2021

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High-Resolution Global Nonhydrostatic Model

Current climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30-years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by~35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low-level SSTP but increase over high-level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ~50%/°C and ~8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

An automatic performance model-based scheduling tool for coupled climate system models

The prediction of the climate system is highly depended on the efficient integration of observations and simulations of the Earth. This is regarded as a canonical example of the cyber-physical system. The climate system model, the simulation engine in this cyber-physical system, is one of most challenging applications in scientific computing. It utilizes the multi-physics simulation that couples multiple components, conducts decadal to millennium simulations, and has long been an important application on supercomputers. However, current climate system models suffer from the inefficient task scheduling methods resulting in an intolerable simulation time. Take the Community Earth System Model (CESM), the most widely used climate system model, as an example, one major reason that CESM suffers from bad performances is the huge overhead to rationally distribute processes among the coupled heterogeneous components. According to the report of NCAR, every percent improvement in CESM performance frees up to the equivalent of $250,000 in computing resources in their scientific experiments. To address such challenge, our paper firstly constructs a lightweight and accurate performance model for effectively capturing and predicting the heterogeneous time-to-solution performance of end-to-end CESM components with a given simulation configuration. Then, based on the performance model, we further propose an efficient scheduling strategy based on rectangular packing method to determine the best process layout among different components, and the process numbers assigned to each component. Our evaluations show that we can achieve 58% average run time reductions on CESM comparing to the widely used sequential process layout for a scale of 144 to 480 cores on typical CPU clusters. And we can save 4 million CPU hours when we conduct one standard scientific experiment (a 2870-year simulation), which equals to save $40,089 with a charge of $0.01 per CPU hour. Meanwhile, 26% extra performance improvements could also be gained in our methods comparing to the heuristic branch and bound algorithm with the guidance of the known curve-fitting performance model.

58 GEOSCIENCES↗

DOE OSTI2021

Task Parallelism to Optimize Performance of Environmental Modeling Software

Climate modeling is an integral part of environmental research, from studying rare phenomena to predicting future climate trends. The need for more accurate models is only growing, but as climate modeling capabilities advance, existing workflows require optimization to recoup performance. A solution comes in the form of task parallelism, a novel programming capability that provides an opportunity for optimization at execution time by allowing tasks to be executed in parallel, reducing runtime significantly. Using Parsl, an intuitive and scalable parallel scripting library for Python, we implement task parallelism within support software to aid in the continuous advancement of climate modeling technology.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

The Impact of Model Resolution on the Southern Hemisphere in Community Climate System Model Version 4 Idealized Climate Simulations

Model resolution plays a large role in accurately simulating the Southern Hemisphere circulation in both the ocean and atmosphere. Resolving the mesoscale field is important as it has been shown to have a significant impact on the large-scale climate in eddy-rich regions, which are regions of large CO2 absorption. The presence of ocean and atmospheric mesoscale features can affect sea surface temperatures, the strength and location of storm tracks, and many other air-sea processes. Additionally, with an improvement in resolution, the eddy kinetic energy in the ocean can be expected to change considerably. The significance model resolution has on the Southern Hemisphere is examined using the Community Climate System Model, Version 4, eddy-parameterizing and eddy-resolving simulations. The CO2 concentrations and ozone levels are specified independently to better understand how the mesoscale field responds to extreme changes in external forcing and the resulting climate impacts. Overall, in the eddy-parameterizing simulations, the ozone forcing is found to be more important than the changes in CO2 concentrations. However, in the case of the eddy-resolving simulations, the CO2 concentrations are found to be more dominant, especially in eddy-rich regions. These results demonstrate the need for an increase in model resolution for climate prediction.

Daher, Houraa (ORCID:0000000200177346)↗

DOE OSTI2022

MSU Disentanglement Analysis Software

This software is used to disentangle the forced-versus-unforced components of tropospheric temperature change over the satellite era (after 1979) using maps of surface temperature change as a predictor. In general, the software assembles training datasets (from pre-computed surface temperature trend maps and domain averaged tropospheric warming rates), trains statistical/machine learning (ML) algorithms, applies the trained statistical/ML model to climate model data and observations, and then saves the results. A leave-one-out approach is used in which the statistical/ML models are iteratively trained on (N- 1) climate model and then applied to the remaining climate model (and observations). Each model includes a large ensemble (i.e., >10) of model simulations. The software relies on scikit-learn ridge regression, PLS regression, and neural network algorithms.

Po-Chedley, StephenD↗

DOE OSTI2014

Characteristics of tropical cyclones in high‐resolution models in the present climate

Abstract The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Understanding the Biases in Global Monsoon Simulations from the Perspective of Atmospheric Energy Transport

Understanding global monsoon (GM) variability and projecting its future changes rely heavily on climate models. However, climate models generally show pronounced biases in GM simulations, and the reasons for this remain unclear. Here, in this study, we evaluate the performance of 20 pairs of climate models that participated in both phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 of CMIP (CMIP6) and identify the sources of their GM simulation biases from an energy transport perspective. The multimodel mean improvement in CMIP6 compared to CMIP5 is demonstrated by the increasing skill scores for various GM metrics from 0.20–0.79 to 0.48–0.83. More specifically, the dry biases in the Northern Hemisphere Summer Monsoon (NHSM) precipitation in CMIP5 [root-mean-square error (RMSE): 1.85 mm day −1 ] are reduced in CMIP6 (RMSE: 1.66 mm day −1 ). This higher simulation skill is associated with higher skill in simulating the precipitation-solstitial mode, monsoon intensity, and monsoon domains. The improvement in the NHSM precipitation simulation results from that in the meridional transport of atmospheric energy. Atmospheric energy budget analysis shows that the negative biases in downward surface longwave radiation and northward energy transport are smaller in CMIP6 than in CMIP5 in the boreal summer, resulting in a more realistic interhemispheric thermal contrast and meridional gradient of moist static energy. However, a major weakness of the CMIP6 models is found in the Southern Hemisphere Summer Monsoon precipitation simulation due to the positive bias in the top-of-the-atmosphere downward longwave radiation. This study shows that reasonably reproducing the meridional global atmospheric energy transportation is necessary for skillful GM simulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Oscillations in a simple climate–vegetation model

We formulate and analyze a simple dynamical systems model for climate–vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate–vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various timescales is discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Linking models of human behaviour and climate alters projected climate change

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Lastly, our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Climate Change Modeling:Computational Opportunities and Challenges

High- delity climate models are the workhorses of modern climate change sciences. In thisarticle, the authors focus on several computational issues associated with climate changemodeling, covering simulation methodologies, temporal and spatial modeling restrictions,the role of high-end computing, as well as the importance of data-driven regional climateimpact modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Probabilistic Modeling of Climate Change Impacts on Renewable Energy and Storage Requirements for NM's Energy Transition Act (SAND Report)

This report provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico’s Integrated Resource Plan for 2020 – 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~25,000 – 30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Vetting new models of climate responses to geoengineering: The Seventh Meeting of the Geoengineering Model Intercomparison Project; Newry, Maine, 26 July 2017

On July 27, 2017, a group of approximately 30 scientists met in Newry, Maine to discuss the latest science and future directions in the Geoengineering Model Intercomparison Project (http://climate.envsci.rutgers.edu/GeoMIP/) (GeoMIP). This international effort, which is an official part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), is the largest source of information about the robust climate model response to solar geoengineering, a collection of proposed technologies designed to quickly, temporarily offset the effects of global warming.

Kravitz, Benjamin S.↗

DOE OSTI2019

Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) Science Implementation Plan

Boreal regions have experienced warming faster than the rest of the Earth, and the arctic ice cover has declined at a rate faster than predicted by most climate models. Uncertainties in climate projections for this region are large, in part due to the lack of observational guidance to constrain the treatment of aerosol-cloud-precipitation linkages in climate models. To remedy this shortfall, field campaigns have been conducted to collect integrated, comprehensive measurements under specific atmospheric regimes. While considerable efforts have been devoted to subtropical marine boundary-layer clouds, as well as to clouds in the stable arctic environment, very few targeted observations exist of convective boundary-layer clouds that form over open water when cold airmasses are advected off ice-covered regions or boreal continents. There is an urgent need to shed light on the dynamics and microphysical properties of clouds and precipitation in the high-latitude marine boundary layer during cold-air outbreaks. Despite the common occurrence of linear and cellular cloud patterns that typify cold-air outbreaks, little is known about the properties of these clouds; how they vary with surface, environmental, and aerosol conditions; the role of cold-air outbreaks in the global atmospheric and ocean circulation; and the accuracy of the treatment of this atmospheric regime in climate models. Thus, we proposed to conduct the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), to focus on marine boundary-layer clouds during cold-air outbreaks. COMBLE will deploy the first ARM Mobile Facility (AMF1) and an AMF “satellite” station in the far North Atlantic in January–May 2020. COMBLE will take advantage of the synergy with several coincident campaigns, notably MOSAIC, which will characterize the source airmasses of cold-air outbreaks over the arctic ice, and (AC) 3 , which will operate several aircraft between northern Scandinavia and the MOSAIC deployment to document evolution of these airmasses. COMBLE and its sister campaigns will compose a four-member array of supersites between northern Scandinavia and the Arctic, each with profiling and in situ cloud, precipitation, radiation, and aerosol measurements. COMBLE will be guided by six science themes. The first five deal with boundary-layer convection in cold-air outbreaks: (1) the fetch-dependent mesoscale organization of clouds and precipitation, including linear and cellular convection; (2) surface heat and momentum fluxes and vertical profiles of temperature, humidity, wind, and turbulence; (3) vertical structure of clouds and precipitation; (4) the sources and sinks of aerosol, including ice-nucleating particles, and the role of cloud-active aerosol on cloud processes and radiative fluxes; and (5) the influence of these four themes on polar cyclogenesis and polar low vertical structure. The overarching sixth theme is that COMBLE will provide integrated data sets of dynamical, thermodynamic, and cloud microphysical characteristics of marine boundary-layer convection in cold-air outbreaks, including cloud and aerosol properties, that will enable constraining high-resolution numerical simulations, developing process-level understanding, and, subsequently, evaluating and improving representations of shallow convection in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Climate Sensitivity of the Community Climate System Model, Version 4

Equilibrium climate sensitivity of the Community Climate System Model Version 4 (CCSM4) is 3.20°C for 1° horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1° resolution is 1.72°C, which is about 0.2°C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. We use the radiative kernel technique to show that from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude, and the shortwave cloud feedback increases. These two warming effects are partially canceled by cooling due to slight decreases in the global mean water-vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed-layer, slab ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab ocean model version for both CCSM3 and CCSM4. We argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.

54 ENVIRONMENTAL SCIENCES↗