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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.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Satellite Remote Sensing of Aerosol Forcing

The role of aerosol forcing remains one of the largest uncertainties in estimating man's impact on the global climate system. One school of thought suggests that remote sensing by satellite sensors will provide the data necessary to narrow these uncertainties. While satellite measurements of direct aerosol forcing appear to be straightforward, satellite measurements of aerosol indirect forcing will be more complicated. Pioneering studies identified indirect aerosol forcing using AVHRR data in the biomass burning regions of Brazil. We have expanded this analysis with AVHRR to include an additional year of data and assimilated water vapor fields. The results show similar latitudinal dependence as reported by Kaufman and Fraser, but by using water vapor observations we conclude that latitude is not a proxy for water vapor and the strength of the indirect effect is not correlated to water vapor amounts. In addition to the AVHRR study we have identified indirect aerosol forcing in Brazil at much smaller spatial scales using the MODIS Airborne Simulator. The strength of the indirect effect appears to be related to cloud type and cloud dynamics. There is a suggestion that some of the cloud dynamics may be influenced by smoke destabilization of the atmospheric column. Finally, this study attempts to quantify remote sensing limitations due to the accuracy limits of the retrieval algorithms. We use a combination of numerical aerosol transport models, ground-based AERONET data and ISCCP cloud climatology to determine how much of the forcing occurs in regions too clean to determine from satellite retrievals.

Remer, Lorraine↗
NASA NTRS2012

Multi-Factor Impact Analysis of Agricultural Production in Bangladesh with Climate Change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

Ruane, Alex C.↗
NASA NTRS2015

Lidar Measurements of Atmospheric CO2 From Regional to Global Scales

Atmospheric CO2 is a critical forcing for the Earth's climate and the knowledge on its distributions and variations influences predictions of the Earth's future climate. Large uncertainties in the predictions persist due to limited observations. This study uses the airborne Intensity-Modulated Continuous-Wave (IMCW) lidar developed at NASA Langley Research Center to measure regional atmospheric CO2 spatio-temporal variations. Further lidar development and demonstration will provide the capability of global atmospheric CO2 estimations from space, which will significantly advances our knowledge on atmospheric CO2 and reduce the uncertainties in the predictions of future climate. In this presentation, atmospheric CO2 column measurements from airborne flight campaigns and lidar system simulations for space missions will be discussed. A measurement precision of approx.0.3 ppmv for a 10-s average over desert and vegetated surfaces has been achieved. Data analysis also shows that airborne lidar CO2 column measurements over these surfaces agree well with in-situ measurements. Even when thin cirrus clouds present, consistent CO2 column measurements between clear and thin cirrus cloudy skies are obtained. Airborne flight campaigns have demonstrated that precise atmospheric column CO2 values can be measured from current IM-CW lidar systems, which will lead to use this airborne technique in monitoring CO2 sinks and sources in regional and continental scales as proposed by the NASA Atmospheric Carbon and Transport â€" America project. Furthermore, analyses of space CO2 measurements shows that applying the current IM-CW lidar technology and approach to space, the CO2 science goals of space missions will be achieved, and uncertainties in CO2 distributions and variations will be reduced.

Lin, Bing↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRS2008

U.S. Eastern Continental Shelf Carbon Cycling (USECoS): Modeling, Data Assimilation, and Analysis

Although the oceans play a major role in the uptake of fossil fuel CO2 from the atmosphere, there is much debate about the contribution from continental shelves, since many key shelf fluxes are not yet well quantified: the exchange of carbon across the land-ocean and shelf-slope interfaces, air-sea exchange of CO2, burial, and biological processes including productivity. Our goal is to quantify these carbon fluxes along the eastern U.S. coast using models quantitatively verified by comparison to observations, and to establish a framework for predicting how these fluxes may be modified as a result of climate and land use change. Our research questions build on those addressed with previous NASA funding for the USECoS (U.S. Eastern Continental Shelf Carbon Cycling) project. We have developed a coupled biogeochemical ocean circulation model configured for this study region and have extensively evaluated this model with both in situ and remotely-sensed data. Results indicate that to further reduce uncertainties in the shelf component of the global carbon cycle, future efforts must be directed towards 1) increasing the resolution of the physical model via nesting and 2) making refinements to the biogeochemical model and quantitatively evaluating these via the assimilation of biogeochemical data (in situ and remotely-sensed). These model improvements are essential for better understanding and reducing estimates of uncertainties in current and future carbon transformations and cycling in continental shelf systems. Our approach and science questions are particularly germane to the carbon cycle science goals of the NASA Earth Science Research Program as well as the U.S. Climate Change Research Program and the North American Carbon Program. Our interdisciplinary research team consists of scientists who have expertise in the physics and biogeochemistry of the U.S. eastern continental shelf, remote-sensing data analysis and data assimilative numerical models.

Mannino, Antonio↗
DOE OSTI2022

Translator Plan: A Coordinated Vision for Fiscal Years 2023-2025

Translators serve a unique role in the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility, offering scientific input through various leadership and service roles. The Translators direct the creation of value-added products (VAPs) and analysis tools that make ARM measurements more accessible to the scientific community. Translators also serve as liaisons between users and the ARM infrastructure, collecting information about priorities and communicating ARM data and services. A key group focus is supporting the DOE Atmospheric System Research (ASR) program scientists and ASR’s efforts towards a process-level understanding of cloud-aerosol interactions, and in reducing uncertainty in global climate model projections. The ARM Translator Group (Table 1) consists of the five Translators, a representative of software development, and one from the Data Quality Office (DQO). Additionally, the ARM Engineering and Process Manager participates in this group and provides input and direction from ARM and its programmatic priorities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Estimating Dust and Water Ice Content of the Martian Atmosphere From THEMIS Data

Researchers at JPL and Arizona State University conducted a comparative study of three candidate algorithms for estimating components of the Martian atmosphere, using raw (uncalibrated) data collected by the Thermal Emission Imaging System (THEMIS). THEMIS is an instrument onboard the Mars Odyssey spacecraft that acquires image data in five visible and nine infrared (IR) wavelength bands. The algorithms under study used data collected from eight of the nine IR bands to estimate the dust and water ice content of the atmosphere. Such an algorithm could be used in onboard data processing to trigger other algorithms that search for features of scientific interest and to reduce the volume of data transmitted to Earth. The algorithms studied were based on regression models. In the study, the optical depths estimated by these algorithms were compared with optical depths estimated in ground-based processing using fully calibrated data from both THEMIS and the Thermal Emission Spectrometer (TES). TES is an instrument onboard the Mars Global Surveyor spacecraft that also observes the planet at infrared wavelengths, but at a lower spatial resolution than THEMIS does. Of the algorithms studied, the one that performed best was based on a Gaussian Support Vector Machine regression model. The test results indicated that this algorithm, operating on the raw data, had error rates that were within the uncertainty associated with the estimates obtained by the groundbased analysis of the fully calibrated data. This level of fidelity demonstrates that these algorithms are sufficiently accurate for use in an onboard setting.

Bandfield, Joshua↗
NASA NTRS2016

P21C-2113: Constraining Hesperian Martian PCO2 from Mineral Analysis at Gale Crater

Carbon dioxide is an essential atmospheric component in martian climate models that attempt to reconcile a faint young sun with widespread evidence of liquid water at the planet's surface in the Noachian and Early Hesperian. Current estimates of ancient martian CO levels, derived from global inventories of carbon, and orbital detections of Noachian and Early Hesperian clay mineralbearing terrains indicate CO levels that are unable to support warm and wet conditions. These estimates are subject to various sources of uncertainty however. Mineral and contextual sedimentary environmental data collected by the Mars Science Laboratory rover Curiosity in Gale Crater provide a more direct means of estimating the atmospheric partial pressure of CO (P ) coinciding with a long-lived lake system in Gale crater at approximately 3.5 Ga. Results from a reaction transport model, which simulates mineralogy observed within the Sheepbed member at Yellowknife Bay by coupling mineral equilibria with carbonate precipitation kinetics and rates of sedimentation, indicate atmospheric levels in the 10's mbar range. At such low P levels, climate models are unable to warm Hesperian Mars anywhere near the freezing point of water and other gases are required to raise atmospheric pressure to prevent lakes from boiling away. Thus, lacustrine features of Gale formed in a cold environment by a mechanism yet to be determined, or the climate models lack an essential component that would serve to elevate surface temperatures, at least temporally and/or locally, on Hesperian Mars. Our results also impose restrictions on the potential role of atmospheric CO in inferred warmer conditions of the Noachian.

Bristow, Thomas↗
NASA NTRS2022

Constraining Warm Dark Matter and Population III Stars with the Global 21 cm Signal

Upcoming ground- and space-based experiments may have sufficient accuracy to place significant constraints upon high-redshift star formation, Reionization, and dark matter (DM) using the global 21 cm signal of the intergalactic medium. In the early universe, when the relative abundance of low-mass DM halos was important, measuring the global signal would place constraints on the damping of structure formation caused by DM having a higher relic velocity (warm dark matter, or WDM) than in cold dark matter. Such damping, however, can be mimicked by altering the star formation efficiency (SFE) and can be difficult to detect because of the presence of Population III stars with unknown properties. We study these various cases and their degeneracies with the WDM mass parameter mX using a Fisher matrix analysis. We study the mX = 7 keV case and a star formation model that parameterizes the SFE as a strong function of halo mass and include several variations of this model along with three different input noise levels for the likelihood; we also use a minimum halo virial temperature for collapse near the molecular cooling threshold. We find that when the likelihood includes only Population II stars, mX is constrained to an uncertainty of ∼0.4 keV for all models and noise levels at the 68% confidence level. When the likelihood includes weak Population III stars, mX ∼ 0.3 keV, and if Population III star formation is relatively efficient, mX ∼ 0.1 keV uncertainty, with tight Population III star formation parameter constraints. Our results show that the global 21 cm signal is a promising test-bed for WDM models, even in the presence of strong degeneracies with astrophysical parameters.

Cosmology↗
NASA NTRS2010

Multifidelity Analysis and Optimization for Supersonic Design

Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.

Kroo, Ilan↗
NASA NTRSDate not supplied

GMAO OSSE Framework in Support of PBL Mission Science

The planetary boundary layer is the bottom layer of the troposphere where most of human activities take place. Atmospheric pollutants are capped by the temperature inversion layer in PBL. The thickness of PBL, ranging from tens of meter to several kilometer, affects pollutant dispersion and air quality. The thickness of PBL typically shows a strong diurnal cycle but it is difficult to model and predict its change because complicated dynamic and thermodynamic processes involved with air-surface exchange of temperature and moisture, convective mixing, surface friction, topography, advection and radiation heating and cooling influence the PBL height. Various parameterization schemes for PBL were developed but the underlying processes of PBL are neither clearly understood nor represented in NWP models and adding large uncertainty into weather and climate predictions. There are no systematic global observations to provide information on thermodynamic structure of PBL and efforts to explore new spaceborne instruments and measurement techniques are growing. This study aims to develop a PBL OSSE framework leveraging existing GMAO’s OSSE system that utilizes GEOS data assimilation and global forecast model (1) to evaluate existing and potential new observation types for the PBL structure analysis and prediction and (2) to test sensitivity of PBL parameterization schemes to PBL forecasts.

Min-Jeong Kim↗
DOE OSTI2025

Extratropical Cloud Feedback Constrained by Cloud Sources and Sinks in Cyclones

Constraining cloud feedback in global climate models (GCMs) using observations is important for establishing accurate predictions of future climate. Uncertainty in shortwave cloud feedback (SW FB ) dominates uncertainty in total cloud feedback. Recent studies show a shift toward more positive extratropical SW FB in the latest generations of GCMs leading to the emergence of very high equilibrium climate sensitivity (ECS). In this study, we use precipitation efficiency and albedo susceptibility to constrain liquid water path (LWP) response to warming and SW FB in the Southern Ocean (SO; 50°–80°S). We analyze precipitation in extratropical cyclones (ECs) to learn about extratropical condensed water sink processes, combined with observations of clouds and moisture convergence, and use the analysis to better understand and constrain SW FB . We utilize a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model, version 6 (CAM6), to provide a constraint on SW FB based on observations from Clouds and the Earth’s Radiant Energy System (CERES) and Multisensor Advanced Climatology of LWP (MAC-LWP). We apply Gaussian process regression to emulate the model response to all parameters perturbed in the PPE. Confronting the emulator output with observations provides a new estimated response of Earth to global warming. Furthermore, our new estimates of SO LWP reduce the PPE range by 66%–72%, which results in a shortwave cloud radiative effect estimated range that is 27%–34% less than the PPE range. Observations suggest a more positive SO SW FB than the Community Earth System Model, version 2 (CESM2), and consequently do not reject the high climate sensitivity GCMs emerging from the Coupled Model Intercomparison Project phase 6 (CMIP6).

Atmosphere↗
NASA NTRS2012

Surface Temperature Data Analysis

Small global mean temperature changes may have significant to disastrous consequences for the Earth's climate if they persist for an extended period. Obtaining global means from local weather reports is hampered by the uneven spatial distribution of the reliably reporting weather stations. Methods had to be developed that minimize as far as possible the impact of that situation. This software is a method of combining temperature data of individual stations to obtain a global mean trend, overcoming/estimating the uncertainty introduced by the spatial and temporal gaps in the available data. Useful estimates were obtained by the introduction of a special grid, subdividing the Earth's surface into 8,000 equal-area boxes, using the existing data to create virtual stations at the center of each of these boxes, and combining temperature anomalies (after assessing the radius of high correlation) rather than temperatures.

Hansen, James↗
DOE OSTI2020

Cooperation or Rivalry? Impact of alternative development pathways on India's long-term electricity generation and associated water demands

The world has witnessed a significant shift in the last decade in the way countries interact with each other. A cooperation based international paradigm is being challenged by countries engaging in rivalry with each other in the name of their national interests. What developmental pathway the world charts in the future is going to be path defining for global energy and emissions use. India, expected to play a critical role in the emissions mitigation debate, would contribute to and would be impacted by these pathways. In India specific literature, however, little attention has been paid to development pathways related uncertainties and their impact on energy futures. We seek to address this gap by modelling India's electricity futures and associated water demands under different development pathways. Our analysis shows that alternative development pathways would have significantly different implications for the energy access, electricity generation mix, associated water withdrawals, and carbon dioxide emissions in India. We conclude by arguing that energy, emissions and water policies should be conceived with an explicit understanding of uncertainties related to potential development pathways that the world and India can chart.

India, Electricity-Water Nexus, Development Pathwa↗
NASA NTRS2005

Global Partitioning of NOx Sources Using Satellite Observations: Relative Roles of Fossil Fuel Combustion, Biomass Burning and Soil Emissions

This document contains the following abstract for the paper "Global partitioning of NOx sources using satellite observations: Relative roles of fossil fuel combustion, biomass burning and soil emissions." Satellite observations have been used to provide important new information about emissions of nitrogen oxides. Nitrogen oxides (NOx) are significant in atmospheric chemistry, having a role in ozone air pollution, acid deposition and climate change. We know that human activities have led to a three- to six-fold increase in NOx emissions since pre-industrial times, and that there are three main surface sources of NOx: fuel combustion, large-scale fires, and microbial soil processes. How each of these sources contributes to the total NOx emissions is subject to some doubt, however. The problem is that current NOx emission inventories rely on bottom-up approaches, compiling large quantities of statistical information from diverse sources such as fuel and land use, agricultural data, and estimates of burned areas. This results in inherently large uncertainties. To overcome this, Lyatt Jaegle and colleagues from the University of Washington, USA, used new satellite observations from the Global Ozone Monitoring Experiment (GOME) instrument. As the spatial and seasonal distribution of each of the sources of NOx can be clearly mapped from space, the team could provide independent topdown constraints on the individual strengths of NOx sources, and thus help resolve discrepancies in existing inventories. Jaegle's analysis of the satellite observations, presented at the recent Faraday Discussion on "Atmospheric Chemistry", shows that fuel combustion dominates emissions at northern mid-latitudes, while fires are a significant source in the Tropics. Additionally, she discovered a larger than expected role for soil emissions, especially over agricultural regions with heavy fertilizer use. Additional information is included in the original extended abstract.

Jaegle, Lyatt↗
NASA NTRS2023

Validity of the Landsat Surface Reflectance Archive for Aquatic Science: Implications for Cloud-Based Analysis

Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset ( N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

Landsat, lakes, harmful algal blooms↗
NASA NTRS2015

An Assessment of Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100 Using NASA's MERRA and CMIP5 Simulations

Background: In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2 emission (DOE/EIA "Annual Energy Outlook 2013"). Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climate zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE. Establishing the current variability and potential changes to future building climate zones is very important for increasing the energy efficiency of buildings and reducing energy costs and emissions in the future. Objectives: This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the DOE/ASHRAE building climate zone maps and then using MERRA to define the last 30 years of variability in climate zones for the Continental US. An atmospheric assimilation is a global atmospheric model optimized to satellite, atmospheric and surface in situ measurements. Using MERRA as a baseline, we then evaluate the latest Climate Model Inter-comparison Project (CMIP) climate model Version 5 runs to assess potential variability in future climate zones under various assumptions. Methods: We derive DOE/ASHRAE building climate zones using surface and temperature data products from MERRA. We assess these zones using the uncertainties derived by comparison to surface measurements. Using statistical tests, we evaluate variability of the climate zones in time and assess areas in the continental US for statistically significant trends by region. CMIP 5 produced a data base of over two dozen detailed climate model runs under various greenhouse gas forcing assumptions. We evaluate the variation in building climate zones for 3 different decades using an ensemble and quartile statistics to provide an assessment of potential building climate zone changes relative to the uncertainties demonstrated using MERRA. Findings and Conclusions: These results show that there is a statistically significant increase in the area covered by warmer climate zones and a tendency for a reduction of area in colder climate zones in some limited regions. The CMIP analysis shows that models vary from relatively little building climate zone change for the least sensitive and conservation assumptions to a warming of at most 3 zones for certain areas, particularly the north central US by the end of the 21st century.

Stackhouse, Paul W., Jr.↗