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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Algorithm Development and Validation for Satellite-Derived Distributions of DOC and CDOM in the US Middle Atlantic Bight

In coastal ocean waters, distributions of dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) vary seasonally and interannually due to multiple source inputs and removal processes. We conducted several oceanographic cruises within the continental margin of the U.S. Middle Atlantic Bight (MAB) to collect field measurements in order to develop algorithms to retrieve CDOM and DOC from NASA's MODIS-Aqua and SeaWiFS satellite sensors. In order to develop empirical algorithms for CDOM and DOC, we correlated the CDOM absorption coefficient (a(sub cdom)) with in situ radiometry (remote sensing reflectance, Rrs, band ratios) and then correlated DOC to Rrs band ratios through the CDOM to DOC relationships. Our validation analyses demonstrate successful retrieval of DOC and CDOM from coastal ocean waters using the MODIS-Aqua and SeaWiFS satellite sensors with mean absolute percent differences from field measurements of < 9 %for DOC, 20% for a(sub cdom)(355)1,6 % for a(sub cdom)(443), and 12% for the CDOM spectral slope. To our knowledge, the algorithms presented here represent the first validated algorithms for satellite retrieval of a(sub cdom) DOC, and CDOM spectral slope in the coastal ocean. The satellite-derived DOC and a(sub cdom) products demonstrate the seasonal net ecosystem production of DOC and photooxidation of CDOM from spring to fall. With accurate satellite retrievals of CDOM and DOC, we will be able to apply satellite observations to investigate interannual and decadal-scale variability in surface CDOM and DOC within continental margins and monitor impacts of climate change and anthropogenic activities on coastal ecosystems.

Mannino, Antonio↗
NASA NTRS2002

Relationship of Global Precipitation Measurement (GPM) Mission to Global Change Research

In late 2001, the Global Precipitation Measurement (GPM) mission was approved as a new start by the National Aeronautics and Space Administration (NASA). This new mission is motivated by a number of scientific questions that are posed over a range of space and time scales that generally fall within the discipline of the global water and energy cycle (GWEC). Recognizing that satellite rainfall datasets are now a foremost tool for understanding global climate variability out to decadal scales and beyond, for improving weather forecasting, and for producing better predictions of hydrometeorological processes including short-term hazardous flooding and seasonal fresh water resources assessment, a comprehensive and internationally sanctioned global measuring strategy has led to the GPM mission. The GPM mission plans to expand the scope of rainfall measurement through use of a multi-member satellite constellation that will be contributed by a number of world nations. This talk overviews the GPM scientific research program that has been fostered within NASA, then focuses on scientific progress that is being made in various research areas in the course of the mission formulation phase that are of interest to the global change scientific community. This latter part of the talk addresses research issues that have become central to the GPM science implementation plan concerning: (1) the rate of global water cycling through the atmosphere and surface and the relationship of precipitation variability to the sustained rate of the water cycle; (2) the relationship between climate change and cloud macrophysical- microphysical processes; and (3) the general improvement in measuring precipitation at the fundamental microphysical level that will take place during the GPM era and an explanation of how these improvements are expected to come about.

Smith, Eric A.↗
DOE OSTI2024

Changing windstorm characteristics over the US Northeast in a single model large ensemble

Abstract Extreme windstorms pose a significant hazard to infrastructure and public safety, particularly in the highly populated US Northeast (NE). However, the influence climate change and changing land use will have on these events remains unclear. A large ensemble generated using the Max-Planck Institute (MPI) Earth system model is used to generate projections of NE windstorms under different shared socioeconomic pathways (SSPs) and to attribute changes to projected land use land cover (LULC) change, externally forced changes and internal climate variability. To reduce the influence of coarse grid cell resolution and uncertainties in surface roughness lengths, windstorms are identified using simultaneous widespread exceedance of local 99th percentile 10 m wind speeds (U 99 ). Projected declines in forest cover in the NE and the resulting reductions in surface roughness length under SSP3-7.0 lead to projections of large increases in U 99 and derived windstorm intensity and scale. However, these projected changes in regional LULC under SSP3-7.0 are unprecedented in a historical context and may not be realistic. After corrections are applied to remove the influence of LULC on wind speeds, regionally averaged U 99 exhibit declines for most of the single model initial-condition large ensemble (SMILE) members which are broadly proportional to the radiative forcing and global air temperature increase in the SSPs, with a median value of −0.15 ms −1 °C −1 . While weak cyclones are projected to decline in frequency in the NE, intense cyclones and the resulting windstorms and indices of socioeconomic loss do not. Where present, significant trends in these loss indices are positive, and some MPI SMILE members generate future windstorms that are unprecedented in the historical period.

Coburn, Jacob (ORCID:0000000309538117)↗
DOE OSTI2025

E3SM‐GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human‐Earth Feedback Research

Abstract Modeling human‐environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate‐responsive land use and CO 2 emission projections for the next 5‐year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes. Plain Language Summary Earth system models help us understand how humans are changing the climate. Currently, these models do not include human systems, so predetermined greenhouse gas, aerosol, and land use change data are input to these models. These data do not reflect human responses to changes projected by Earth system models. We have added a human component to an Earth system model to represent human responses to environmental change and calculate corresponding greenhouse gas and land use change data instead of using predetermined data. Including this human component changes projections of land use, land carbon storage, and regional climate. Key Points We have incorporated a novel, advanceable human component in an Earth system model to simulate human‐Earth feedbacks Including terrestrial productivity feedbacks from the Earth to the human systems affects land change, crop prices, carbon, and climate Regional effects of including terrestrial productivity feedbacks are greater than global effects because land change is the main driver

E3SM↗
NASA NTRSDate not supplied

Dynamical and Trace Gas Responses of the Quasi-Biennial Oscillation to Historical and Future Climate Change

The Quasi-Biennial Oscillation (QBO) dominates the variability of the tropical stratosphere. The QBO can be seen in stratospheric zonal wind, temperature, and composition, but it also has a number of teleconnections—including to the polar vortices, extratropical surface winter climate, and the Madden-Julian Oscillation. Thus, its future trends will have a signature on the surface. Recent multi-model investigations (Richter et al. 2019, Butchart et al. 2020) suggest that a weakening of the QBO amplitude is very likely, but changes to QBO period and stability remain unclear, due in part to its highly parameterized nature. We investigate these aspects using multiple configurations of the NASA Goddard Institute for Space Studies Model E2.2 (Rind et al., 2020; Orbe et al. 2020). As a high-top model with gravity wave drag partly sourced from (parameterized) convection, Model E2.2 includes key pathways for climate forcings to influence the QBO. Overall, both the period and amplitude decrease in response to increased CO2, with convection feedbacks critical for the period response, and ozone feedbacks enhancing the amplitude response. We use these results to interpret QBO trends in the historical and SSP integrations. Lastly, we examine the effect of volcanic eruptions on the QBO

Quasi-Biennial Oscillation↗
NASA NTRS2000

Characteristics of the East Asian Winter Climate Associated with the Westerly Jet Stream and ENSO

In this study, the influences of the East Asian jet stream (EAJS) and El Nino/Southern Oscillation (ENSO) on the interannual variability of the East Asian winter climate are examined with a focus on the relative climate impacts of the two phenomena. Although the variations of the East Asian winter monsoon and the temperature and precipitation of China, Japan, and Korea are emphasized, the associated changes in the broad-scale atmospheric circulation patterns over Asia and the Pacific and in the extratropical North Pacific sea surface temperature (SST) are also investigated. It is demonstrated that there is no apparent relationship between ENSO and the interannual variability of EAJS core. The EAJS and ENSO are associated with distinctly different patterns of atmospheric circulation and SST in the Asian-Pacific regions. While ENSO causes major climate signals in the Tropics and over the North Pacific east of the dateline, the EAJS produces significant changes in the atmospheric circulation over East Asia and western Pacific. In particular, the EAJS explains larger variance of the interannual signals of the East Asian trough, the Asian continental high, the Aleutian low, and the East Asian winter monsoon. When the EAJS is strong, all these atmospheric systems intensify significantly. The response of surface temperature and precipitation to EAJS variability and ENSO is more complex. In general, the East Asian winter climate is cold (warm) and dry (wet) when the EAJS is strong (weak) and it is warm during El Nino years. However, different climate signals are found during different La Nina years. In terms of linear correlation, both the temperature and precipitation of northern China, Korea, and central Japan are more significantly associated with the EAJS than with ENSO.

Yang, Song↗
DOE OSTI2025

Distinctive Pattern of Global Warming in Ocean Heat Content

Abstract Huge heat anomalies in the atmosphere and ocean in recent years are not yet explained. Strong characteristic patterns in temperatures for upper layers of the ocean occurred from 2000 to 2023 in the presence of global warming from increasing atmospheric greenhouse gases. Here, we show that the deep tropics are warming, although sharply modulated by El Niño–Southern Oscillation events, with strong heating in the extratropics near 40°N and 40°–45°S but little heating near 20°N and 25°–30°S. The heating is most clearly manifested in zonal-mean ocean heat content and is evident in sea surface temperatures. The strongest heating is in the Southern Hemisphere, where aerosol effects are small. Estimates are made of the contributions to heating of top-of-atmosphere (TOA) radiation, atmospheric energy transports, surface fluxes of energy, and redistribution of energy by surface winds and ocean currents. The patterns of change are not directly related to TOA radiation but are evident in net surface energy fluxes and inferred ocean heat transports, underscoring their coupled origin. Changes in the atmospheric circulation through a poleward shift in ocean jet streams and storm tracks are reflected in surface wind-driven ocean Ekman transports. As well as human-induced climate change, internal natural variability is likely in play. Hence, the atmosphere and ocean currents are systematically redistributing heat from global warming, profoundly affecting local climates. Significance Statement As the climate changes, it has been difficult to discern meaningful patterns. Distinctive patterns of change have occurred in the ocean when examined as zonal averages around latitude bands. Most excess heat from global warming resides in the ocean and, since 2005, has become focused into bands near 40°N and 40°S, with little net warming in the subtropics. The strongest warming is in the Southern Hemisphere, although sea surface temperatures have increased more in the Northern Hemisphere. Changes in the atmospheric circulation through a poleward shift in the jet stream and storm tracks are primarily responsible along with corresponding changes in ocean currents. These changes are linked through surface exchanges of energy via heat, moisture, and wind stress.

Trenberth, Kevin E. [National Center for Atmospher↗
NASA NTRS2019

Climate Change Amplification of Natural Drought Variability: The Historic Mid-Twentieth Century North American Drought in a Warmer World

In the mid-twentieth century (1948-1957), North America experienced a severe drought forced by cold tropical Pacific sea surface temperatures (SSTs). If these SSTs recurred, it would likely cause another drought, but in a world substantially warmer than the one in which the original event took place. We use a 20-member ensemble of the GISS climate model to investigate the drought impacts of a repetition of the mid-twentieth century SST anomalies in a significantly warmer world. Using observed SSTs and mid-twentieth century forcings (Hist-DRGHT), the ensemble reproduces the observed precipitation deficits during the cold season (October–March) across the Southwest, Southern Plains, and Mexico and during the warm season (April–September) in the Southern Plains and the Southeast. Under analogous SST forcing and enhanced warming (Fut-DRGHT, ≈3 K above pre-industrial), cold season precipitation deficits are ameliorated in the Southwest and Southern Plains and intensified in the Southeast, while during the warm season precipitation deficits are enhanced across North America. This occurs primarily from greenhouse gas forced trends in mean precipitation, rather than changes in SST teleconnections. Cold season runoff deficits in Fut-DRGHT are significantly amplified over the Southeast, but otherwise similar to Hist-DRGHT over the Southwest and Southern Plains. In the warm season, however, runoff and soil moisture deficits during Fut-DRGHT are significantly amplified across the southern US, a consequence of enhanced precipitation deficits and increased evaporative losses due to warming. Our study highlights how internal variability and greenhouse gas forced trends in hydroclimate are likely to interact over North America, including how changes in both precipitation and evaporative demand will affect future drought.

Benjamin I Cook↗
NASA NTRS2005

Surface Dust Redistribution on Mars as Observed by the Mars Global Surveyor

The global redistribution of dust by the atmosphere is geologically and climatologically important. Dust deposition and removal at the surface represents ongoing sedimentary geology: a vestige of aeolian processes responsible for the concentration of vast dustsheets and potentially for ancient layered units at various locations on Mars. The varying amount of dust on the surface has also long been hypothesized as a factor in determining whether regional or global dust storms occur in a given year. Indeed, the atmosphere has a very short, sub-seasonal time-scale (or memory) and as such, any inter-annual variability in the climate system that is not simply ascribable to stochastic processes, must involve changing conditions on the surface. An excellent, multi-year dataset is provided by the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) and the Mars Orbiter Camera Wide Angle imager (MOC-WA). This dataset allows investigation into the degree to which surface dust deposits on Mars really change: over decadal time scales, over the course of the annual cycle, and as a result of global and regional dust storms. The MGS mapping orbit data set extends over almost 3 Martian years at the time of writing. These data sets include one global dust storm and smaller regional storms (one in the first TES mapping year and two in the third).

Szwast, M. A.↗
DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Impact of Spatial Sampling on Continuity of MODIS-VIIRS Land Surface Reflectance Products: A Simulation Approach

With the increasing need to construct long-term climate-quality data records to understand, monitor, and predict climate variability and change, it is vital to continue systematic satellite measurements along with the development of new technology for more quantitative and accurate observations. The Suomi National Polar-orbiting Partnership mission provides continuity in monitoring the Earths surface and its atmosphere in a similar fashion as the heritage MODIS instruments onboard the National Aeronautics and Space Administrations Terra and Aqua satellites. In this paper, we aim at quantifying the consistency of Aqua MODIS and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Reflectance (LSR) and NDVI products as related to their inherent spatial sampling characteristics. To avoid interferences from sources of measurement and/or processing errors other than spatial sampling, including calibration, atmospheric correction, and the effects of the bidirectional reflectance distribution function, the MODIS and VIIRSLSR products were simulated using the Landsat-8s Operational Land Imager (OLI) LSR products. The simulations were performed using the instruments point spread functions on a daily basis for various OLI scenes over a 16-day orbit cycle. It was found that the daily mean differences due to discrepancies in spatial sampling remain below 0.0015 (1) in absolute surface reflectance at subgranule scale (i.e., OLI scene size).We also found that the MODISVIIRS product intercomparisons appear to be minimally impacted when differences in the corresponding view zenith angles (VZAs) are within the range of -15deg to -35deg (VZA(sub v) - VZA(sub m)), where VIIRS and MODIS footprints resemble in size. In general, depending on the spatial heterogeneity of the OLI scene contents, per-grid-cell differences can reach up to 20.Further spatial analysis of the simulated NDVI and LSR products revealed that, depending on the user accuracy requirements for product intercomparisons, spatial aggregations may be used. It was found that if per-grid-cell differences on the order of 10(in LSR or NDVI) are tolerated, the product intercomparisons are expected to be immune from differences in spatial sampling.

VIIRS↗
NASA NTRS1998

A common-sense climate index: is climate changing noticeably?

We propose an index of climate change based on practical climate indicators such as heating degree days and the frequency of intense precipitation. We find that in most regions the index is positive, the sense predicted to accompany global warming. In a few regions, especially in Asia and western North America, the index indicates that climate change should be apparent already, but in most places climate trends are too small to stand out above year-to-year variability. The climate index is strongly correlated with global surface temperature, which has increased as rapidly as projected by climate models in the 1980s. We argue that the global area with obvious climate change will increase notably in the next few years. But we show that the growth rate of greenhouse gas climate forcing has declined in recent years, and thus there is an opportunity to keep climate change in the 21st century less than "business-as-usual" scenarios.

Time↗
NASA NTRS2013

Ground Water and Climate Change

As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.

climate change↗
NASA NTRS2023

The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Climate change↗
DOE OSTI2024

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↗
DOE OSTI2024

Land Processes Can Substantially Impact the Mean Climate State

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling

Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 4% since 1998. The North Pacific ocean basin has increased nearly 19%. These trend analyses follow earlier results showing decadal declines in global ocean chlorophyll and primary production. To understand the causes of these changes and trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The model utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. This enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll. A full discussion of the changes and trends, possible causes, modeling approaches, and data assimilation will be the focus of the seminar.

Gregg, Watson↗
NASA NTRS2018

The Roles of Climate Change and Climate Variability in the 2017 Atlantic Hurricane Season

The 2017 hurricane season was extremely active with six major hurricanes, the third most on record. The sea-surface temperatures (SSTs) over the eastern Main Development Region (EMDR), where many tropical cyclones (TCs) developed during active months of August/September, were approximately 0.96 degrees Centigrade above the 1901-2017 average (warmest on record): about 0.42 degrees Centigrade from a long-term upward trend and the rest (around 80 percent) attributed to the Atlantic Meridional Mode (AMM). The contribution to the SST from the North Atlantic Oscillation over the EMDR was a weak warming, while that from ENSO was negligible. Nevertheless, ENSO, the NAO, and the AMM all contributed to favorable wind shear conditions, while the AMM also produced enhanced atmospheric instability. Compared with the strong hurricane years of 2005-2010, the ocean heat content (OHC) during 2017 was larger across the tropics, with higher SST anomalies over the EMDR and Caribbean Sea. On the other hand, the dynamical/thermodynamical atmospheric conditions, while favorable for enhanced TC activity, were less prominent than in 2005-2010 across the tropics. The results suggest that unusually warm SST in the EMDR together with the long fetch of the resulting storms in the presence of record-breaking OHC were key factors in driving the strong TC activity in 2017.

SST↗