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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Procedures for analysis of spatial relationships among ship survey data and sea surface temperature

The establishment of a limited spatial data base for the U.S. eastern seaboard vicinity is discussed along with the demonstration of computer assisted analysis techniques for investigating spatial patterns and relationships among ship survey data and remotely sensed sea surface temperature. Ship survey variables included concentrations of two zooplankton, two icthyoplankton, and two fish species, in addition to physical data of depth to bottom and surface and bottom water temperatures. Continuous spatial distributions of these data were created by both weighted nearest neighbor and iterative smoothing interpolation techniques. Maps of surface water temperature were created by digitizing GOES satellite images. All mapped data were spatially registered by conversion of latitude and longitude coordinates to rotated Lambert conic conformal rectilinear coordinates and stored in grid format of approximately one hundred square kilometers per cell. The analysis of these data include the generation of statistical summaries and maps describing the joint occurrence among variables.

Berry, J. K.↗
NASA NTRS1988

Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern

The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas.

Franklin, Janet↗
NASA NTRS2012

Spatiotemporal Interpolation of Elevation Changes Derived from Satellite Altimetry for Jakobshavn Isbrae, Greenland

Estimation of ice sheet mass balance from satellite altimetry requires interpolation of point-scale elevation change (dHdt) data over the area of interest. The largest dHdt values occur over narrow, fast-flowing outlet glaciers, where data coverage of current satellite altimetry is poorest. In those areas, straightforward interpolation of data is unlikely to reflect the true patterns of dHdt. Here, four interpolation methods are compared and evaluated over Jakobshavn Isbr, an outlet glacier for which widespread airborne validation data are available from NASAs Airborne Topographic Mapper (ATM). The four methods are ordinary kriging (OK), kriging with external drift (KED), where the spatial pattern of surface velocity is used as a proxy for that of dHdt, and their spatiotemporal equivalents (ST-OK and ST-KED).

Jakobshavn Isbrae↗
NASA NTRS2013

Spatial and Temporal Dust Source Variability in Northern China Identified Using Advanced Remote Sensing Analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas.

spectral mixing analysis↗
NASA NTRS2015

Long-Term Changes/Trends in Surface Temperature and Precipitation During the Satellite Era (1979-2012)

During the post-1979 period in which the satellite-based precipitation measurements with global coverage are available, global mean surface temperature rapidly increased up to late 1990s, followed by a period of temperature hiatus after about 19981999. Comparing observed surface temperature trends against the simulated ones by the CMIP5 historical experiments especially in the zonal mean context suggests that although the anthropogenic greenhouse-gases (GHG) forcing has played a major role, in addition to the anthropogenic aerosols and various natural forcings, the effects from decadal-to-interdecadal scale internal modes specifically the Pacific Decadal Oscillation(PDO) are also very strong. Evident temperature changes associated with the PDOs phase shift are seen in the Pacific basin, with decadal-scale cooling in the tropical central-eastern Pacific and most of the east basin and concurrent warming in the subtropics of both hemispheres,even though the PDOs net effect on global mean temperature is relatively weak. The Atlantic Multidecadal Oscillation(AMO) also changed its phase in the mid-1990s, and hence its possible impact is estimated and assessed as well.However, comparisons with CMIP5 simulations suggest that the AMO may have not contributed as significantly as the PDO in terms of the changes trends in global surface temperature, even though the data analysis technique used here suggests otherwise. Long-term precipitation changes or trends during the post-1979 period are further shown to have been modulated by the two major factors:anthropogenic GHG and PDO, in addition to the relatively weak effects from aerosols and natural forcings. The spatial patterns of observed precipitation trends in the Pacific,including reductions in the tropical central-eastern Pacific and increases in the tropical western Pacific and along the South Pacific Convergence Zone, manifest the PDOs contributions.Removing the PDO effect from the total precipitation trends makes the spatial structures of precipitation trends more similar to those simulated by CMIP5 historical full forcing experiments particularly in the context of zonal-mean results. This also confirms that in spite of the PDO effect specifically on regional scales, the anthropogenic GHG signals are still discernible in observed precipitation during the time period. Following the increase of GHG, precipitation tends to increase roughly along the climatological ITCZ and decrease south of the equator and in the subtropics of both hemispheres.

GPCP↗
NASA NTRS1991

Intraseasonal oscillations in the global atmosphere. II - Southern Hemisphere

A systematic examination of oscillatory modes in the global atmosphere is completed by studying 12 years of 500 mb geopotential heights in the Southern Hemisphere. The data were band-pass filtered to focus on intraseasonal (IS) phenomena, and spatial empirical orthogonal functions (EOFs) were obtained. The leading principal components were subjected to singular spectrum analysis in order to identify nonlinear IS oscillations with high statistical confidence. In the Southern Hemisphere, the dominant mode has a period of 23 days, with spatial patterns carried by the second and third winter EOF of the IS band. It has a zonal wavenumber-four structure. The 40-day mode is second, and is dominated by wavenumbers three and four, while a 16-day mode is too weak to separate its spatial behavior from the previous two. The IS dynamics in the Southern Hemisphere is more complex and dominated by shorter wavenumbers than the Northern Hemisphere. No statistically significant correlations between the Southern Hemisphere and the tropics or the Northern Hemisphere are apparent in the IS band.

Ghil, Michael↗
DOE OSTI2025

Amplified Mesoscale and Submesoscale Variability and Increased Concentration of Precipitation under Global Warming over Western North America

Abstract Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. Significance Statement The precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041–80 period. As compared to present-day climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future.

Climate change↗
NASA NTRS2009

The Green Sahara: Climate Change, Hydrologic History and Human Occupation

Archaeology can provide insight into interactions of climate change and human activities in sensitive areas such as the Sahara, to the benefit of both disciplines. Such analyses can help set bounds on climate change projections, perhaps identify elements of tipping points, and provide constraints on models. The opportunity exists to more precisely constrain the relationship of natural solar and climate interactions, improving understanding of present and future anthropogenic forcing. We are beginning to explore the relationship of human occupation of the Sahara and long-term solar irradiance variations synergetic with changes in atmospheric-ocean circulation patterns. Archaeological and climate records for the last 12 K years are gaining adequate precision to make such comparisons possible. We employ a range of climate records taken over the globe (e.g. Antarctica, Greenland, Cariaco Basin, West African Ocean cores, records from caves) to identify the timing and spatial patterns affecting Saharan climate to compare with archaeological records. We see correlation in changing ocean temperature patterns approx. contemporaneous with drying of the Sahara approx. 6K years BP. The role of radar images and other remote sensing in this work includes providing a geographically comprehensive geomorphic overview of this key area. Such coverage is becoming available from the Japanese PALSAR radar system, which can guide field work to collect archaeological and climatic data to further constrain the climate change chronology and link to models. Our initial remote sensing efforts concentrate on the Gilf Kebir area of Egypt.

remote sensing↗
NASA NTRS2006

Atmospheric Teleconnection over Eurasia Induced by Aerosol Radiative Forcing during Boreal Spring

The direct effects of aerosols on global and regional climate during boreal spring are investigated based on numerical simulations with the NASA Global Modeling and Assimilation Office finite-volume general circulation model (fvGCM) with Microphyics of Clouds with the Relaxed Arakawa Schubert Scheme (McRAS), using aerosol forcing functions derived from the Goddard Ozone Chemistry Aerosol Radiation and Transport model (GOCART). The authors find that anomalous atmospheric heat sources induced by absorbing aerosols (dust and black carbon) excite a planetary-scale teleconnection pattern in sea level pressure, temperature, and geopotential height spanning North Africa through Eurasia to the North Pacific. Surface cooling due to direct effects of aerosols is found in the vicinity and downstream of the aerosol source regions, that is, South Asia, East Asia, and northern and western Africa. Significant atmospheric heating is found in regions with large loading of dust (over northern Africa and the Middle East) and black carbon (over Southeast Asia). Paradoxically, the most pronounced feature in aerosol-induced surface temperature is an east west dipole anomaly with strong cooling over the Caspian Sea and warming over central and northeastern Asia, where aerosol concentrations are low. Analyses of circulation anomalies show that the dipole anomaly is a part of an atmospheric teleconnection pattern driven by atmospheric heating anomalies induced by absorbing aerosols in the source regions, but the influence was conveyed globally through barotropic energy dispersion and sustained by feedback processes associated with the regional circulations. The surface temperature signature associated with the aerosol-induced teleconnection bears striking resemblance to the spatial pattern of observed long-term trend in surface temperature over Eurasia. Additionally, the boreal spring wave train pattern is similar to that reported by Fukutomi et al. associated with the boreal summer precipitation seesaw between eastern and western Siberia. The results of this study raise the possibility that global aerosol forcing during boreal spring may play an important role in spawning atmospheric teleconnections that affect regional and global climates.

Kim, Maeng-Ki↗
NASA NTRS2020

Synergistic Use of SMAP and OCO-2 Data in Assessing the Responses of Ecosystem Productivity to the 2018 U.S. Drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF could well capture the spatial extent and dynamics of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2=0.44, p<0.001) and GOSIF (R2=0.76, p<0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

Soil Moisture Active Passive↗
NASA NTRS1989

Contrasting landform perception with varied radar illumination geometries and at simulated resolutions of Venera and Magellan

The high sensitivity of imaging radars to slope at moderate to low incidence angles enhances the perception of linear topography on images. It reveals broad spatial patterns that are essential to landform mapping and interpretation. As radar responses are strongly directional, the ability to discriminate linear features on images varies with their orientation. Landforms that appear prominent on images where they are transverse to the illumination may be obscure to indistinguishable on images where they are parallel to it. Landform detection is also influenced by the spatial resolution in radar images. Seasat radar images of the Gran Desierto Dunes complex, Sonora, Mexico; the Appalachian Valley and Ridge Province; and accreted terranes in eastern interior Alaska were processed to simulate both Venera 15 and 16 images (1000 to 3000 km resolution) and image data expected from the Magellan mission (120 to 300 m resolution. The Gran Desierto Dunes are not discernable in the Venera simulation, whereas the higher resolution Magellan simulation shows dominant dune patterns produced from differential erosion of the rocks. The Magellan simulation also shows that fluvial processes have dominated erosion and exposure of the folds.

Ford, J. P.↗
NASA NTRS1997

Predicting Photosynthetic Fluxes from Spectral Reflectance of Leaves and Canopies

The central hypothesis of this study has been that photosynthetic efficiency and capacity can be predicted from 'physiological reflectance indices' derived from spectral reflectance of leaves and canopies. I have approached this topic with a combination of laboratory and field experiments, and have also explored the potential of deriving a meaningful physiological index from imaging spectrometry (e.g. AVIRIS). A few highlights are presented below. The main emphasis has been on the 'Photochemical Reflectance Index' (PRI), derived from reflectance at 531 nm and 570 nm. Unlike most 'conventional' vegetation indices (e.g. NDVI), PRI changes rapidly both with illumination and physiological state, because it detects the interconversion of xanthophyll cycle pigments, which serve as photoregulatory pigments and control energy distribution for the photosynthetic system. This approach has differed dramatically from most remote sensing in that it has emphasized temporal variation in narrow-band spectral signatures, instead of spatial patterns of broadband indices. Our primary conclusion has been that PRI works well as an index of photosynthetic light-use efficiency at the leaf scale, much in the same way as the fluorescence index DeltaF/Fm. However, unlike DeltaF/Fm which must be measured at close scales, PRI can be sampled at a range of spatial scales, presenting the possibility of monitoring photosynthetic fluxes remotely.

Gamon, John A.↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
DOE OSTI2024

Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH 4 ). The FLUXNET-CH 4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH 4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH 4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH 4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH 4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH 4 emissions and reduce bias by 79% (from 76 to 16 TgCH 4 y -1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predicting Large‐Scale Systematic Missing Pipe Attributes in Water Distribution Networks

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Poff, Jason W. [Oregon State Univ., Corvallis, OR ↗
NASA NTRS2013

A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.

multiscale↗
NASA NTRS2016

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

MERRA↗
NASA NTRS2014

Implications of Arctic Sea Ice Decline for the Earth System

Arctic sea ice decline has led to an amplification of surface warming and is projected to continue to decline from anthropogenic forcing, although the exact timing of ice-free summers is uncertain owing to large natural variability. Sea ice reductions affect surface heating patterns and the atmospheric pressure distribution, which may alter midlatitude extreme weather patterns. Increased light penetration and nutrient availability during spring from earlier ice breakup enhances primary production in the Arctic Ocean and its adjacent shelf seas. Ice-obligate marine mammals may be losers, whereas seasonally migrant species may be winners from rapid sea ice decline. Tundra greening is occurring across most of the Arctic, driven primarily by warming temperatures, and is displaying complex spatial patterns that are likely tied to other factors. Sea ice changes are affecting greenhouse gas exchanges as well as halogen chemistry in the Arctic. This review highlights the heterogeneous nature of Arctic change, which is vital for researchers to better understand.

Decline↗