Using high resolution climate models to explore future changes in post-tropical cyclone precipitation
Explore the source record for details and available documents.
Erica Bower↗
SEARCH · Search NASA
Results for “Climate Change Modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Modeling Integrated Impacts of Climate Change and Grazing on Mongolia’s Rangelands
Explore the source record for details and available documents.
Virginia Kowal↗
Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling
Abstract We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.
54 ENVIRONMENTAL SCIENCES↗
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.
54 ENVIRONMENTAL SCIENCES↗
Assessing the Impact of Climate Change on Atmospheric Rivers: A Modeling Perspective
Atmospheric rivers (ARs) play a crucial role in the poleward transport of water vapor, and the AR-associated precipitation is a critical component of global water supplies, making it critical that we understand how ARs may change in the future. To approach this issue, integrations of the NASA Goddard Institute for Space Studies global climate model ModelE version 2.1 (GISSE2.1) are employed. Multiple configurations of the model simulating different climates are analyzed: (1) the last-glacial maximum; (2) present day; (3) the end of the 21st century. The thermodynamic and dynamic components of changes to AR frequency are analyzed using a decomposition method. This method utilizes differences in distinct AR seasonal climatology frequencies derived from various vertically integrated water vapor transport (IVT) thresholds to resolve AR frequency into its components. Global mean state changes in poleward AR frequency for different climates are dominated by precipitable water vapor (PWV) changes. A set of idealized cold and warm climates in which present day sea surface temperatures are uniformly changed are considered for a targeted analysis of the south Pacific Ocean basin. For this analysis, frequency and distribution of AR events in the model runs are analyzed by comparing them to changes in the jet stream as well as the Eulerian storm tracks and low-level baroclinicity. Latitudinal shifts in the ARs in the south Pacific Ocean basin using our integrations are not as tightly coupled to these two storm-related climatological metrics in the midlatitudes but fare better on the poleward side of the storm tracks.
Atmospheric rivers↗
Accelerated Nutrient Cycling and Increased Light Competition Will Lead to 21st Century Shrub Expansion in North American Arctic Tundra
We present that recent changes in species composition, and increases in shrub abundance in particular, have been reported as a result of warming in Arctic tundra. Despite these changes, the driving factors that control shrubification and its future trajectory remain uncertain. Here we used an ecosystem model, ecosys, to mechanistically represent the processes controlling recent and 21st century changes in plant functional type using RCP8.5 climate forcing across North American Arctic tundra. Recent and projected warming was modeled to deepen the active layer (spatially averaged by ~0.35 m by 2100) and thereby increase nutrient availability. Shrub productivity was modeled to increase across much of the tundra, particularly in Alaska and tundra-boreal ecotones. Deciduous and evergreen shrubs increased from ~45% of total tundra ecosystem net primary productivity (NPP) in recent decades to ~70% by 2100. The increased canopy cover of shrubs reduced incoming shortwave radiation for low-lying plants, causing declines in graminoids NPP from a current 35% of tundra NPP to 18%, and declines in nonvascular plants from 20% to 12%. The faster-growing deciduous shrubs modeled with less efficient nutrient conservation dominated much of the low Arctic by 2100 where nutrient cycling became more rapid, while the slower-growing evergreen shrubs modeled with more efficient nutrient conservation dominated a wider latitudinal range that extended to the high Arctic where nutrient cycling remained slower. Lastly, we conclude that high-latitude vegetation dynamics over the 21st century will depend strongly on soil nutrient dynamics, diversity in plant traits controlling nutrient uptake and conservation, and light competition.
54 ENVIRONMENTAL SCIENCES↗
Estimating Uncertainty in Simulated ENSO Statistics
Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.
54 ENVIRONMENTAL SCIENCES↗
A Collaborative Proposal: Simulating and Understanding Abrupt Climate-Ecosystem Changes During Holocene with NCAR-CCSM3
We have made significant progress in our proposed work in the last 4 years (3 years plus 1 year of no cost extension). In anticipation of the next phase of study, we have spent time on the abrupt changes since the last glacial maximum. First, we have performed further model-data comparison based on our baseline TRACE-21 simulation and made important progress towards the understanding of several major climate transitions. Second, we have made a significant effort in processing the model output of TRACE-21 and have put this output on a website for access by the community. Third, we have completed many additional sensitivity experiments. In addition, we have organized synthesis workshops to facilitate and promote transient model-data comparison for the international community. Finally, we have identified new areas of interest for Holocene climate changes.
54 ENVIRONMENTAL SCIENCES↗
Pathways to Mexico’s climate change mitigation targets: A multi-model analysis
Mexico’s climate policy sets ambitious national greenhouse gas (GHG) emission reduction targets—30% versus a business-as-usual baseline by 2020, 50% versus 2000 by 2050. However, these goals are at odds with recent energy and emission trends in the country. Both energy use and GHG emissions in Mexico have grown substantially over the last two decades. Here, we investigate how Mexico might reverse current trends and reach its mitigation targets by exploring results from energy system and economic models involved in the CLIMACAP-LAMP project. To meet Mexico’s emission reduction targets, all modeling groups agree that decarbonization of electricity is needed, along with changes in the transport sector, either to more efficient vehicles or a combination of more efficient vehicles and lower carbon fuels. These measures reduce GHG emissions as well as emissions of other air pollutants. The models find different energy supply pathways, with some solutions based on renewable energy and others relying on biomass or fossil fuels with carbon capture and storage. The economy-wide costs of deep mitigation could range from 2% to 4% of GDP in 2030, and from 7% to 15% of GDP in 2050. Our results suggest that Mexico has some flexibility in designing deep mitigation strategies, and that technological options could allow Mexico to achieve its emission reduction targets, albeit at a cost to the country.
09 BIOMASS FUELS↗
The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility
The Variable Infiltration Capacity (VIC) model is a macroscale semi-distributed hydrologic model. VIC development began in the early 1990s and the model has since been used extensively for basin- to global-scale applications that include hydrologic dataset construction, trend analysis of hydrologic fluxes and states, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact assessment. Ongoing operational applications of the VIC model include the University of Washington's drought monitoring and forecasting systems and NASA's Land Data Assimilation System. This paper documents the development of VIC version 5 (VIC-5), which includes a major reconfiguration of the legacy VIC source code to support a wider range of modern hydrologic modeling applications. The VIC source code has been moved to a public GitHub repository to encourage participation by the broader user and developer communities. The reconfiguration has separated the core physics of the model from the driver source code, whereby the latter is responsible for memory allocation, preprocessing and post-processing, and input–output (I–O). VIC-5 includes four drivers that use the same core physics modules, but which allow for different methods for accessing this core to enable different model applications. Finally, VIC-5 is distributed with robust test infrastructure, components of which routinely run during development using cloud-hosted continuous integration. The work described here provides an example to the model development community for extending the life of a legacy model that is being used extensively. The development and release of VIC-5 represents a significant step forward for the VIC user community in terms of support for existing and new model applications, reproducibility, and scientific robustness.
42 ENGINEERING↗
Climate change scenario planning in Alaska's National Parks: Stakeholder involvement in the decision-making process
This article studies the participation of stakeholders in climate change decision-making in Alaska s National Parks. We place stakeholder participation within literatures on environmental and climate change decision-making. We conducted participant observation and interviews in two planning workshops to investigate the decision-making process, and our findings are three-fold. First, the inclusion of diverse stakeholders expanded climate change decision-making beyond National Park Service (NPS) institutional constraints. Second, workshops of the Climate Change Scenario Planning Project (CCSPP) enhanced institutional understandings of participants attitudes towards climate change and climate change decision-making. Third, the geographical context of climate change influences the decisionmaking process. As the first regional approach to climate change decision-making within the NPS, the CCSPP serves as a model for future climate change planning in public land agencies. This study shows how the participation of stakeholders can contribute to robust decisions, may move climate change decision-making beyond institutional barriers, and can provide information about attitudes towards climate change decision-making.
Adaptation↗
Integrating Arctic Plant Functional Types in a Land Surface Model Using Above‐ and Belowground Field Observations
Abstract Accurate simulations of high‐latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO 2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Many such models represent high‐latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine Arctic‐specific PFTs. The newly developed PFTs include: (1) nonvascular mosses and lichens, (2) deciduous and evergreen shrubs of various height classes, including an alder PFT, (3) graminoids, and (4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario and constant PFT fractional areas showed alder‐dominated plant communities gaining more biomass and lichen‐dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.
54 ENVIRONMENTAL SCIENCES↗
Agriculture and Climate Change in Global Scenarios: Why Don't the Models Agree
Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs makes direct use of weather inputs. Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes of key variables such as prices, production, and trade. These divergent outcomes arise from differences in model inputs and model specification. The goal of this paper is to review climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By providing common productivity drivers that include climate change effects, differences in model outcomes are reduced. All models show higher prices in 2050 because of negative productivity shocks from climate change. The magnitude of the price increases, and the adaptation responses, differ significantly across the various models. Substantial differences exist in the structural parameters affecting demand, area, and yield, and should be a topic for future research.
Nelson, Gerald↗
The role of sea ice dynamics in global climate change
The topics covered include the following: general characteristics of sea ice drift; sea ice rheology; ice thickness distribution; sea ice thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic Sea ice; and sensitivity of sea ice models to climate change.
Hibler, William D., III↗
Integrated Human Futures Modeling in Egypt
The Integrated Human Futures Project provides a set of analytical and quantitative modeling and simulation tools that help explore the links among human social, economic, and ecological conditions, human resilience, conflict, and peace, and allows users to simulate tradeoffs and consequences associated with different future development and mitigation scenarios. In the current study, we integrate five distinct modeling platforms to simulate the potential risk of social unrest in Egypt resulting from the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile in Ethiopia. The five platforms simulate hydrology, agriculture, economy, human ecology, and human psychology/behavior, and show how impacts derived from development initiatives in one sector (e.g., hydrology) might ripple through to affect other sectors and how development and security concerns may be triggered across the region. This approach evaluates potential consequences, intended and unintended, associated with strategic policy actions that span the development-security nexus at the national, regional, and international levels. Model results are not intended to provide explicit predictions, but rather to provide system-level insight for policy makers into the dynamics among these interacting sectors, and to demonstrate an approach to evaluating short- and long-term policy trade-offs across different policy domains and stakeholders. The GERD project is critical to government-planned development efforts in Ethiopia but is expected to reduce downstream freshwater availability in the Nile Basin, fueling fears of negative social and economic impacts that could threaten stability and security in Egypt. We tested these hypotheses and came to the following preliminary conclusions. First, the GERD will have an important short-term impact on water availability, food production, and hydropower production in Egypt, depending on the short- term reservoir fill rate. Second, the GERD will have a very small impact on water availability in the Nile Basin over the longer term. Depending on the GERD fill rate, short-term (e.g., within its first 5 years of operation) annual losses in Egyptian food production may peak briefly at 25 percent. Long-term (e.g., 15 to 30 year) cumulative losses in Egypt's food production may be less than 3 percent regardless of the fill rate, with the GERD having essentially no impact on projected annual food production in Egypt about 25 years after opening. For the quick fill rates, the short-term losses may be sufficient to create an important decrease in overall household health among the general population, which, along with other economic stressors and different strategies employed by the government, could lead to social unrest. Third, and perhaps most importantly, our modeling suggests that the GERD's effect on Egypt's food and water resources is small when compared to the effect of projected Egyptian population and economic growth (and the concomitant increase in water consumption). The latter dominating factors are exacerbated in the modeling by natural climate variability and may be further exacerbated by climate change. Our modeling suggests that these growth dynamics combine to create long-term water scarcity in Egypt, regardless of the Ethiopian project. All else being equal, filling strategies that employ slow fill rates for the GERD (e.g., 8 to 13 years) may mitigate the risks in future scenarios for Egypt somewhat, but no policy or action regarding the GERD is likely to significantly alleviate the projected water scarcity in Egypt's Nile Basin. However, general beliefs among the Egyptian populace regarding the GERD as a major contributing factor for scarcities in Egypt could make Ethiopia a scapegoat for Egyptian grievances -- contributing to social unrest in Egypt and generating undesirable (and unnecessary) tension between these two countries. Such tension could threaten the constructive relationships between Egypt and Ethiopia that are vital to maintaining stability and security within and between their respective regional spheres of influence, Middle East and North Africa, and the Horn of Africa.
97 MATHEMATICS AND COMPUTING↗
Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales
We have successfully built up, in consistency and completeness, the Cloud-Aerosol-Radiation (CAR) Ensemble Modeling System. Then we have published the CAR general paper to present a general description of the CAR system and illustrate its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud-aerosol-radiation interactions. For demonstration purpose, the evaluation is based on several CAR standalone experiments driven by CIRC Phase I data or ERA-Interim observational analysis data (ERI) 2004 global data, and coupled climate model experiments by using CWRF, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes.
58 GEOSCIENCES↗
Evaluation of a Cloud Resolving Model Using TRMM Observations for Multiscale Modeling Applications
The climate change simulation community is moving toward use of global cloud resolving models (CRMs), however, current computational resources are not sufficient to run global CRMs over the hundreds of years necessary to produce climate change estimates. As an intermediate step between conventional general circulation models (GCMs) and global CRMs, many climate analysis centers are embedding a CRM in each grid cell of a conventional GCM. These Multiscale Modeling Frameworks (MMFs) represent a theoretical advance over the use of conventional GCM cloud and convection parameterizations, but have been shown to exhibit an overproduction of precipitation in the tropics during the northern hemisphere summer. In this study, simulations of clouds, precipitation, and radiation over the South China Sea using the CRM component of the NASA Goddard MMF are evaluated using retrievals derived from the instruments aboard the Tropical Rainfall Measuring Mission (TRMM) satellite platform for a 46-day time period that spans 5 May - 20 June 1998. The NASA Goddard Cumulus Ensemble (GCE) model is forced with observed largescale forcing derived from soundings taken during the intensive observing period of the South China Sea Monsoon Experiment. It is found that the GCE configuration used in the NASA Goddard MMF responds too vigorously to the imposed large-scale forcing, accumulating too much moisture and producing too much cloud cover during convective phases, and overdrying the atmosphere and suppressing clouds during monsoon break periods. Sensitivity experiments reveal that changes to ice cloud microphysical parameters have a relatively large effect on simulated clouds, precipitation, and radiation, while changes to grid spacing and domain length have little effect on simulation results. The results motivate a more detailed and quantitative exploration of the sources and magnitude of the uncertainty associated with specified cloud microphysical parameters in the CRM components of MMFs.
Posselt, Derek J.↗