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NASA NTRS2018

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast coupled forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 (World Ocean Atlas 2013) SSS), 3) Aquarius and 4) SMAP initialization. Coupled hindcasts are generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development.

Kovach, R.↗
NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino.

SST↗
NASA NTRS2018

Assessing the Impact of Large Volcanic Eruptions of the Last Millennium (850-1850 CE) on Australian Rainfall Regimes

Explosive volcanism is an important natural climate forcing, impacting global surface temperatures and regional precipitation. Although previous studies have investigated aspects of the impact of tropical volcanism on various ocean–atmosphere systems and regional climate regimes, volcanic eruptions remain a poorly understood climate forcing and climatic responses are not well constrained. In this study, volcanic eruptions are explored in particular reference to Australian precipitation, and both the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO). Using nine realizations of the last millennium (LM) (850–1850CE) with different time-evolving forcing combinations, from the NASA GISS ModelE2-R, the impact of the six largest tropical volcanic eruptions of this period are investigated. Overall, we find that volcanic aerosol forcing increased the likelihood of El Niño and positive IOD conditions for up to four years following an eruption, and resulted in positive precipitation anomalies over north-west (NW) and south-east (SE) Australia. Larger atmospheric sulfate loading during larger volcanic eruptions coincided with more persistent positive IOD and El Niño conditions, enhanced positive precipitation anomalies over NW Australia, and dampened precipitation anomalies over SE Australia.

Blake, Stephanie A. P.↗
NASA NTRS2018

Impacts of NASA's Earth Observations on Subseasonal and Seasonal Forecasts

NASA's suite of Earth-observing satellites provides a unique view of many processes on Earth, with relevance on timescales ranging from hours to weeks and even years. NASA's observations span all parts of the Earth system: atmospheric, ocean, land and cryosphere, and include physical, chemical and biological components. This presentation explores use of NASA observations in extended-range prediction, from many days to months, using the Goddard Earth Observing System (GEOS) assimilation and predictive modeling capabilities. The skill of a weather forecast is linked to the fidelity of the initialization process (data assimilation) and the realistic representation of "fast" physical processes in the model. At longer time horizons, the slower "feedback" processes in the Earth System begin to take a more prominent role in the accuracy of the forecast. Simultaneously, forecast-skill attribution transitions away from feature-based metrics that emphasize smaller scales (e.g., representations of fronts and vortices) to metrics that emphasize the statistical distributions of large-scale features (e.g., ENSO diagnostics and teleconnections). This presentation will summarize studies performed using the GEOS-S2S (subseasonal to seasonal) system that explore the impacts of NASA observations on the fidelity of the forecasts. The GEOS-S2S system is configured for the atmosphere-ocean-land-ice model and is initialized using in-situ and space-based observations, including atmospheric aerosols and ozone which are not typically analyzed in such systems. The GEOS-S2S model routinely includes aerosol feedbacks, which systematically impact the realism of the forecasts, providing a first example of how suitable NASA observations impact the performance of the GEOS-S2S system. Studies in which a stratospheric chemistry module is activated in the GEOS-S2S system allow the impacts of ozone radiative feedbacks to be isolated. Space-based observations of sea-surface temperature and altimetry are routinely analyzed for the initialization of the GEOS-S2S system; recent advances allow the use of NASA's sea-surface salinity data, which are shown to impact the long-range skill of the forecasts.

Pawson, Steven↗
NASA NTRS2019

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

El Nino↗
NASA NTRS2019

Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models

This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5-7 decades with modeled rates ranging from 0.01 to 2.18 mm year-1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.

Groundwater storage↗
NASA NTRS2019

El Niño Related Tropical Land Surface Water and Energy Response in MERRA-2

The El Niño Southern Oscillation (ENSO) is a coupled Earth system circulation phenomena that reaches all around the globe. The heat added to the atmosphere by increased precipitation produces circulation changes that have global reach and, over time, warms the entire tropical band, and much of the Earth. Many studies have noted that El Niño causes warm and dry (and sometimes drought) conditions over tropical land masses. We develop a composite analysis of El Niño to identify the predominate features of tropical land response. This analysis shows that the land’s lagged response is related to a reduction clouds that leads to increase surface shortwave radiation that increases the surface temperature. The precipitation lag is somewhat longer, and then leads to a reduction in soil water and, in concert with increased SW induced surface warming, leads to increased sensible heating of the atmosphere above. The M2AMIP simulation generally captures these features, but the response is strongest with increased temporal and spatial proximity to the El Niño peak warming. The regionality of these features is also discussed, and it is noted that even the strongest individual El Niño events can vary from this composite mechanistic paradigm.

Bosilovich, Michael G.↗
NASA NTRS2020

Representation of Tropical Storms by the Modern-Era Retrospective Analysis for Research and Applications Version 2

This study examines the veracity of the tropical cyclone (TC) statistics estimated from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global atmospheric reanalysis, focusing on the climatological-mean genesis regions, tracks and their lifetime maximum intensity, as well as the interannual and intraseasonal variations in TC activity. The results are validated against the International Best Track Archive for Climate Stewardship (IBTrACS) data for 1980-2016. MERRA-2 represents the spatial distribution of the TC genesis location and the tracks realistically well over all main development regions (MDRs), but with notable discrepancies including too weak TC activity over the Arabian Sea and the South China Sea and too strong activity over the North Indian Ocean. Over the western North Pacific and the North Atlantic, MERRA-2 underestimates recurving TCs steered by background westerlies in the mid-latitudes and thereby exaggerates northwestward moving TCs, resulting in an overestimate of the landfall probability in East Asia and North America. Excessive development of TCs over the Bay of Bengal also tends to exaggerate the landfall probability in India. In spite of the discrepancies in the annual TC number, the seasonal variation of TC genesis is realistic in MERRA-2. MERRA-2 also captures the TC intensity relationship between the minimum pressure and the maximum surface wind speed at the mature stage, although the maximum intensity is weaker than in the observations. While MERRA-2 tends to reproduce the interannual variations of the observed TC number and the power dissipation index (PDI), the level of accuracy depends on the region. MERRA-2 describes the changes in the TC genesis region and tracks realistically according to the different phases of El Nino and the Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), although it is less realistic over the North Indian Ocean.

International Best Track Archive for Climate Stewa↗
NASA NTRS2020

Designing an Optimal Ensemble Strategy for GMAO S2S Forecast System

The NASA Global Modeling and Assimilation Office (GMAO) Sub-seasonal to Seasonal (S2S) prediction system is being readied for a major upgrade. An important factor in successful extended range forecasting is the definition of the ensemble. Our overall strategy is to run a relatively large ensemble of about 40 members up to 3 months (focusing on the sub-seasonal forecast problem), after which we sub-sample the ensemble, and continue the forecast with about 10 members (up to 12 months). Here we present the results of our testing of various ways to generate the initial perturbations and the validation of a stratified sampling approach for choosing the members of the smaller ensemble. For the initialization of the ensemble we propose a combination of lagged and burst initial conditions. To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states (chosen randomly from the corresponding season) separated by 1-10 days. We consider perturbing separately the atmosphere and the ocean, or both. By varying the separation times between the analysis states, we are able to produce perturbations that resemble well-known modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread with, however, considerable differences in the timing of the impacts on spread for the atmospheric and oceanic perturbations.Our initial (larger) ensemble size was determined so as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes (namely the NAO, PNA and AO). Since it is not feasible for us to run with the larger ensemble beyond about 3 months, we employ a stratified sampling procedure that identifies the emerging directions of error growth to subset the ensemble. By comparing the results from the stratified ensemble with that of the randomly sampled ensemble of the same size, we find that the former provides substantially better estimates the mean of the original large ensemble.

Borovikov, Anna↗
NASA NTRS2020

GEOS S2S Version 3: The New NASA/GMAO High Resolution Seasonal Prediction System

The NASA/Goddard Global Modeling and Assimilation Office (GMAO) released Version 2 of the Subseasonal to Seasonal (GEOS-S2S) forecast system in the fall of 2017, and it has been producing near-real time subseasonal to seasonal forecasts and a weakly coupled atmosphere-ocean data assimilation record since then. A new version of the coupled modeling and analysis system (Version 3) was released by the GMAO at the end of 2019. The new version runs at higher oceanic resolution than the previous (approximately 1/2 degree for the atmosphere, 1/4 degree for the ocean), and includes interactive earth system model components not typically present in seasonal prediction systems (two moment cloud microphysics for aerosol indirect effect and an interactive aerosol model). The weakly coupled atmosphere-ocean data assimilation system now includes assimilation of sea surface salinity, that has been shown to result in improved ocean mixed layer simulation and ENSO prediction skill.

Molod, Andrea↗
NASA NTRS2020

Designing an Optimal Ensemble Strategy for GMAO S2S Forecast System

GMAO Sub/Seasonal prediction system (S2S) is being readied for a major upgrade to GEOS-S2S Version 3. An important factor in successful extended range forecast is the definition of an ensemble For initialization of the ensemble we propose a combination of lagged and burst initial conditions. We plan to run a relatively large ensemble of 40 members for sub-seasonal forecast (up to 3 months), at which point we sub-sample the ensemble, and continue the forecast with 10 members (up to 12 months). Here we present the results of the extensive testing of various ways to generate the perturbations to the initial conditions and the validation of the stratified sampling strategy we chose.To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states separated by 1-10 days, randomly chosen from a corresponding season. We considered perturbing separately only the atmospheric fields or only the ocean or both of the forecast initial conditions. Considering varying separation times between the analysis states, we were able to produce perturbations sampling various modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread.Our ensemble size for sub-seasonal forecasts was determined as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes, NAO, PNA and AO. It is not feasible to run equally large ensemble for seasonal forecasts. Using a stratified sampling procedure we can identify the emerging directions of error growth. By comparing the stratified ensemble with randomly sampled ensemble of the same size, we were able to show that the former better estimates the mean of the original large ensemble.

Borovikov, Anna↗
NASA NTRS2019

Editorial: North Pacific Environment and Paleoclimate from the Late Pleistocene to Present

The vast area of the North Pacific, spanning ~55° longitude, represents a challenge for documenting and understanding the geologic history of ocean, atmosphere, and terrestrial environmental change. This special issue highlights site-specific analyses to address various questions and provides clues that arise in response to continued North Pacific warming today in a rapidly changing climate (Figure 1). The emergence of new methods and novel application of existing methods serve to enhance our fundamental understanding of natural modes of climate variability driven by ocean and atmospheric circulation patterns, including the role of external (e.g., insolation) vs. internal forcing [e.g., El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO)]. Included in this volume are geographically diverse studies that present new and traditional methods for the interpretation of climate records drawn from pollen, macrofossils, tree rings, diatoms, grain size, and glacial studies. Many provide new geographic comparisons, such as Kamchatka and Hawaii, or emerge from novel geologic archives and environments, such as the marine shelf, a thermokarst lake, and a fossil forest.

Jones, Miriam C.↗
NASA NTRS2018

TPSAS-NF1676L-29978-DND

Energy within an atmospheric column changes due to absorption of shortwave irradiance, emission of longwave irradiance, enthalpy released by precipitation, convergence of dry static and kinetic energy flux, and surface sensible heat flux. When satellite derived irradiance, precipitation, and surface sensible heat flux data products, as well as tendency and horizontal advection of dry static and kinetic energy fluxes from reanalysis are used to compute the atmospheric energy balance, energy fluxes does not balance exactly, resulting in an energy balance residual. A study by Kato et al. (2016) shows that large residuals exist over tropical oceans. In this study, we examine how the energy balance residual changes with time. If the energy balance residual for a given region is constant every year, this implies that annual anomalies of energy flux components balance over the region. If, however, the energy balance residual changes with time, an analysis of the dependence on atmospheric or cloud properties may provide information to understand the reason for the residual. A preliminary result indicates that significant interannual variabilities in energy balance residual exist when annual energy balance residuals over the entire ocean is examined. Temporal variability of regional energy budget residual and covariability of the energy budget residual with the multivariate ENSO index and the occurrence of specific cloud types will further be investigated.

Seiji Kato↗
NASA NTRSDate not supplied

Diagnosing Changes In Earth's Top-Of-Atmosphere Radiation Budget Since 2000

Earth’s radiation budget (ERB) is a key indicator of the climate system as it describes the delicate balance between how much of the sun’s energy reaching Earth is absorbed and how much thermal infrared radiation is emitted back to space. It is now well established that there is a positive net imbalance in ERB of 0.5-1 Wm-2. This imbalance, referred to as Earth’s Energy Imbalance (EEI), is shaped by natural and anthropogenic climate forcings and the climate system’s response to the forcing.When averaged over a long period, a positive EEI leads to an increase in surface temperature, sea levelrise, ocean heat content, melting of snow and sea-ice, and an acceleration of the hydrological cycle.EEI is also influenced by internal climate variability at interannual (e.g., El Niño/Southern Oscillation(ENSO)) and longer timescales (e.g., Pacific Decadal Oscillation (PDO)). Here we use CERES data toshow that since 2000, EEI has been increasing at a rate of 0.04+-0.03 Wm-2 per yr, corresponding to 0.8 Wm-1 over the 20-year CERES record. To understand the underlying causes for this increase, we analyze the CERES data in the context of internal climate variability indices and regional pattern changes. We also perform a partial radiative perturbation analysis that decomposes the TOA SW and LW radiation variations in terms of contributions by clouds, aerosols, surface albedo, temperature,water vapor and other gases (e.g., ozone, carbon dioxide, methane, etc.).

Norman G Loeb↗
NASA NTRSDate not supplied

From the Upper Troposphere Through the Stratosphere: How Satellite Measurements Help Us Decode the Past to Better Project the Future

Decades of observations of key chemical species are allowing us to more fully understand processes important to transport and composition from the upper troposphere through the stratosphere. This talk will focus on a few examples and how they are informing our understanding of variability, trends, and future projections. The growth of the Antarctic ozone hole (late 1970s – mid 1990s) caused a dynamical perturbation to the Southern Hemisphere stratospheric circulation visible in ozone, one of our longest and best observed chemical constituents, and illustrates the connection between chemical change and the coupled radiative and dynamical response that challenges chemistry-climate models (CCMs). The quasi-biennial oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere, however its impacts on stratospheric circulation and composition can be traced globally. The QBOs timing with respect to the seasonal cycle in each hemisphere is significant in understanding its impact on up to decadal scale variability. The El Nino Southern Oscillation (ENSO), which dominates tropical tropospheric interannual variability, also affects the stratosphere and its influence is visible in our growing record of tropospheric and stratospheric composition measurements. I will discuss how our knowledge of response has grown and how representation of these observed responses in CCMs is key both to understanding recent trends and to reducing uncertainty in future changes of composition.

Luke David Oman↗
NASA NTRS2020

Large-Scale Influences on Atmospheric River Induced Extreme Precipitation Events Along the Coast of Washington State

Transient, narrow plumes of strong water vapor transport, referred to as AtmosphericRivers (ARs), are responsible for much of the precipitation along the west coast of the UnitedStates. The most intense precipitation events are almost always induced by an AR on the coast ofOregon and Washington and can result in detrimental impacts on society due to mudslides andflooding. In order to accurately predict AR events on numerical weather prediction, subseasonal,and seasonal timescales, it is important to understand the large-scale impacts on extreme ARevents. Here, characteristics of ARs that result in an extreme precipitation event are compared totypical ARs on the coast of Washington State. In addition to more intense water vapor transport,notable differences in the synoptic forcing are present during extreme precipitation events thatare not present during typical AR events. Subseasonal and seasonal teleconnection patterns areknown to influence the weather in the Pacific Northwest and are investigated here. The MaddenJulian Oscillation (MJO) plays a role in determining the strength of precipitation associated withan AR on the Washington Coast. Phase 5 of the MJO (convection centered over the maritimecontinent) is the most common phase during an extreme precipitation event, while phase 2(convection over the Indian Ocean) discourages an extreme event from occurring. Interactionsbetween El Niño Southern Oscillation (ENSO) and the propagation speed of the MJO result inextreme events during phase 1 of the MJO and El Niño but phase 8 during neutral ESNOconditions.

Allison B Marquardt Collow↗
NASA NTRSDate not supplied

Global Precipitation Means and Variations with the New Version of GPCP

Knowledge of global precipitation means, patterns and variations is essential for understanding the global water cycle. The observation-based global analysis of the Global Precipitation Climatology Project (GPCP) has been a key input to many such studies, including those related to means of the global (and regional) water and energy cycles. A new version (Version 3.1) of the GPCP monthly analysis is now available (1983-2019), with finer spatial resolution (0.5º latitude/longitude), updated satellite algorithms, latest gauge analysis over land from the Global Precipitation Climatology Center (GPCC), and with ocean climatologies adjusted using information from the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission and CloudSat. The presentation will give key findings from the new analysis, compare with the previous version, link to studies of the water cycle and compare to CMIP6 climate model results. The GPCP Monthly V3.1 analysis uses a Tropical Composite Climatology (TCC) using 22 years (1998-2019) of TRMM and GPM-based surface precipitation estimates from passive microwave, radar and combined passive microwave and radar observations to adjust the long-term mean values in the tropics over ocean. At higher latitudes over ocean climatological values of merged CloudSat and GPM combined passive microwave and radar surface precipitation estimates are used. The result of the new algorithms and procedures is an ocean mean value 60º N to 60º S of 3.2 mm/d, an increase of 7% from the older V2.3. Over land the gauge analysis (from GPCC in Germany), combined with satellite estimates results in a small decrease (~ 0.5%) from the previous version, giving a total global precipitation mean of 2.81 mm/d for V3.1, an increase of 4.5%. Variations from inter-annual to trend scales over most of the ocean are driven by the Colorado State University (CSU) Goddard Profiling (GPROF) algorithm applied to SSMI/SSMIS satellite data. The new GPCP version retains a near zero trend of global precipitation, with significant positive trends in the deep tropics along the Pacific ITCZ and elsewhere, countered by middle latitude decreases, very similar to the previous version of the GPCP Monthly analysis. The pattern of trends is similar to that of AMIP climate model results, driven by observed SSTs for the period in question, but very different from results for “free-running” CMIP historical ensembles, likely due in part to the relatively short comparison period and effects of inter-decadal variations in the GPCP and AMIP results that are not in the “history” models. Interannual variations are also nearly the same in the new version, but with finer detail, although ENSO variations over the ocean have slightly larger amplitude than before, an effect likely related to the change in ocean satellite algorithm used in Version 3. For precipitation intensity (percentiles) at the monthly scale, especially in the tropics, GPCP shows a positive trend for the upper one third of the percentiles (Pct ≥ 70th) and a much weaker positive trend for the lowest percentiles (Pct ≤ 10th), while negative trends appear for the middle one-half percentiles (20th-65th). AMIP results agree with those from the GPCP in terms of the sign of the changes/trends for high and intermediate percentiles. The CMIP historical results also agree in the sign of the trends, but the trends are weaker. Comparisons with other types of CMIP historical simulations including the GHG-only, aerosol-only, and nature-only runs suggest that the observed changes/trends in precipitation amount and intensity during the GPCP period are dominated by a combination of the effects of the Pacific Decadal Oscillation (PDO) and anthropogenic GHG-related surface warming.

Robert Adler↗
NASA NTRS2020

Revisiting the Relationship between Atlantic Dust and Tropical Cyclone Activity using Aerosol Optical Depth Reanalyses: 2003-2018

Previous studies have noted a relationship between African dust and Atlantic tropical cyclone (TC) activity. However, due to the limitations of past dust analyses, the strength of this relationship remains uncertain. The emergence of aerosol reanalyses, including the Navy Aerosol Analysis and Prediction System (NAAPS) aerosol optical depth (AOD) reanalysis, NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and ECMWF Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), enables an investigation of the relationship between African dust and TC activity over the tropical Atlantic and Caribbean in a consistent temporal and spatial manner for 2003–2018. Although June–July–August (JJA) 550 nm dust AOD (DAOD) from all three reanalysis products correlates significantly over the tropical Atlantic and Caribbean, the difference in DAOD magnitude between products can be as large as 60 % over the Caribbean and 20 % over the tropical North Atlantic. Based on the three individual reanalyses, we have created an aerosol multi-reanalysis consensus (MRC). The MRC presents overall better root mean square error over the tropical Atlantic and Caribbean compared to individual reanalyses when verified with ground-based AErosol RObotic NETwork (AERONET) AOD measurements. Each of the three individual reanalyses and the MRC have significant negative correlations between JJA Caribbean DAOD and seasonal Atlantic accumulated cyclone energy (ACE), while the correlation between JJA tropical North Atlantic DAOD and seasonal ACE is weaker. Possible reasons for this regional difference are provided. A composite analysis of 3 high-JJA-Caribbean-DAOD years versus 3 low-JJA-Caribbean-DAOD years reveals large differences in overall Atlantic TC activity. We also show that JJA Caribbean DAOD is significantly correlated with large-scale fields associated with variability in interannual Atlantic TC activity including zonal wind shear, mid-level moisture, and sea surface temperature (SST), as well as the El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), implying confounding effects of these factors on the dust–TC relationship. We find that seasonal Atlantic DAOD and the AMM, the leading mode of coupled Atlantic variability, are inversely related and intertwined in the dust–TC relationship. Overall, DAOD in both the tropical Atlantic and Caribbean is negatively correlated with Atlantic hurricane frequency and intensity, with stronger correlations in the Caribbean than farther east in the tropical North Atlantic.

Dust particles↗