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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Consistent Representation of Cloud Overlap and Cloud Subgrid Vertical Heterogeneity

Many global climate models underestimate the cloud cover and overestimate the cloud albedo, especially for low-level clouds. We determine how a correct representation of the vertical structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low-level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential-random overlap that represents the cloud overlap between layers and the subgrid cloud properties over several vertical scales, with a single value of the overlap parameter. Starting from a coarse vertical grid, representative of atmospheric models, this algorithm is used to generate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We confirm that the frequently used maximum-random overlap leads to a significant error by underestimating the low-level cloud cover with a relative error of about 50%, that can lead to an error of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

A Joint Analysis of GPS Displacement and GRACE Geopotential Data for Simultaneous Estimation of Geocenter Motion and Gravitational Field

Gravitational potential data from GRACE are being used to study mass redistribution within and between the atmosphere, hydrosphere, cryosphere, and solid Earth. The GRACE data are made available in a reference frame with its origin at the center of mass of the Earth system (geocenter) while many other geophysical models and data sets refer to a reference frame attached to the Earth's surface. Changes in the offset between these reference frames (geocenter motion) must be accounted for when GRACE data are used to quantify surface mass changes. In this study, we developed a technique for co‐estimation of geocenter motion and gravitational potential field seamlessly from degree 1 to 90 by simultaneously inverting a set of globally‐distributed GPS displacement time series and the temporally‐varying GRACE gravity data. We found that the effect of geocenter motion was evident particularly in the GPS time series of horizontal displacements. Our estimates of geocenter motion are most consistent with the Satellite Laser Ranging (SLR) results within 1 mm in X and Z components and a submillimeter in Y component, when compared to monthly variability averaged over the period of 2003–2016. The overall magnitude of the degree‐1 (l = 1) surface mass load is estimated to be ~3 cm in equivalent water height annually migrating south‐westward from Europe (December–January) to the South Pacific (June–July). Our results also show that dense GPS network data improve water storage recovery in major river basins in the United States and Europe by contributing significantly to the recovery of higher‐degree (l ≥ ~20) geopotential coefficients.

Razeghi, Mahdiyeh↗
NASA NTRSDate not supplied

Development of a Global Reference Surface Reflectance and BRDF Datasets from Geostationary Satellite Observations and AERONET Measurements

Surface reflectances and their dependency on illumination-view geometries (i.e., BRDF) are the foundation of many high-level satellite products for land and water monitoring. Yet it is difficult to evaluate the quality of satellite-based surface reflectances with ground-based measurements due to the spatial scale differences. In order to fill the gap, here we develop a reference dataset of surface reflectance and BRDF at the global AERONET sites with data streams from operational geostationary sensors including Himawari 8/9 AHI, GK-2A AMI, and GOES 16/17 ABI. Taking the top-of-atmosphere (TOA) reflectance and the site measured atmospheric aerosol optical depth (AOD) as the main inputs, we apply the GeoNEX-AC algorithm to performance accurate atmospheric correction and derive 10-minute surface reflectance and daily Ross-Thick-Li-Sparse (RTLS) BRDF parameters at AERONET sites where coincident AOD measurements and TOA observations are available from 2016 (for Himawari) or 2018 (for GOES) onwards. The algorithm ensures that the retrieved surface BRDF parameters, along with the site-measured AOD, allow the atmospheric radiative transfer model, SHARM, accurately simulate the observed TOA reflectance at diurnal and longer time scales. They are our best estimates of the surface optical properties and thus can serve as the “reference” to evaluate the performance of operational atmospheric correction algorithms (where AOD is assumed unknown and needs to be retrieved). The reference BRDF also allow us to evaluate the spectral band ratios between the SWIR (e.g., 2200 nm) and the visible (e.g., 650 nm) regions, which are commonly used in operational atmospheric correction algorithms. Finally, we demonstrate that the reference dataset can be used to develop potential data synergies between different GEO satellites as well as GEO-LEO sensors.

Weile Wang↗
DOE OSTI2023

Continental United States climate projections based on thermodynamic modification of historical weather

Abstract Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Understanding of Jupiter's Atmosphere After the Galileo Probe Entry

Instruments on the Galileo probe measured composition, cloud properties, thermal structure. winds, radiative energy balance, and electrical properties of the Jovian atmosphere. As expected the probe results confirm some expectations about Jupiter's atmosphere, refute others, and raise new questions which still remain unanswered. This talk will concentrate on those aspects of the probe observations which either raised new questions or remain unresolved. The Galileo probe observations of composition and clouds provided some of the biggest surprises of the mission. Helium abundance measured by the probe differed significantly from the remote sensing derivations from Voyager. discrepancy between the Voyager helium abundance determinations for Jupiter and the Galileo probe value have now led to a considerably increased helium determination for Saturn. Global abundance of N in the form of ammonia was observed to be supersolar by approximately the same factor as carbon, in contrast to expectations that C/N would be significantly larger than solar. This has implications for the formation and evolution of Jupiter. The cloud structure was not what was generally anticipated, even though most previous remote sensing results below the uppermost cloud referred to 5 micron hot spots, local regions with reduced cloud opacity. The Galileo probe descended in one of these hot spots. Only a tenuous, presumed ammonium hydrosulfide, cloud was detected, and no significant water cloud or super-solar water abundance was measured. The mixing ratios as a function of depth for the condensibles ammonia, hydrogen sulfide, and water, exhibited no apparent correlation with either condensation levels or with each other, an observation that is still a puzzle, although there are now dynamical models of hot spots which show promise in being able to explain such behavior. Probe tracked zonal winds show that wind magnitude increases with depth to pressures of about 4 bars, with the winds extending to at least as deep as the probe made measurements, 22 bars. Models of hot spot dynamics raise the possibility that the variation with depth of the probe measured zonal winds between 0.4 and 4 bars reflect the dynamics of the hot spot rather than the global wind pattern. Galileo upper atmosphere measurements established that there is a sharp temperature rise with altitude between about 350 and 800 km above the 1 bar pressure level, with the upper atmosphere reaching, temperatures near 900 K. The energy sources for this upper atmosphere heating are not clearly established, but various mechanisms have been proposed. These and other aspects of the Galileo probe data will be discussed.

Young, Richard E.↗
NASA NTRS2003

Understanding of Jupiter's Atmosphere after the Galileo Probe Entry

Instruments on the Galileo probe measured composition, cloud properties, thermal structure, winds, radiative energy balance, and electrical properties of the Jovian atmosphere. As expected the probe results confirm some expectations about Jupiter's atmosphere, refute others, and raise new questions which still remain unanswered. This talk will concentrate on those aspects of the probe observations which either raised new questions or remain unresolved. The Galileo probe observations of composition and clouds provided some of the biggest surprises of the mission. Helium abundance measured by the probe differed significantly from the remote sensing derivations from Voyager. Discrepancy between the Voyager helium abundance determinations for Jupiter and the Galileo probe value have now led to a considerably increased helium determination for Saturn. Global abundance of N in the form of ammonia was observed to be super-solar by approximately the same factor as carbon, in contrast to expectations that C/N would be significantly larger than solar. This has implications for the formation and evolution of Jupiter. The cloud structure was not what was generally anticipated, even though most previous remote sensing results below the uppermost cloud referred to 5 micron hot spots, local regions with reduced cloud opacity. The Galileo probe descended in one of these hot spots. Only a tenuous, presumed ammomium hydrosulfide, cloud was detected, and no significant water cloud or super-solar water abundance was measured. The mixing ratios as a function of depth for the condensibles ammonia, hydrogen sulfide, and water, exhibited no apparent correlation with either condensation levels or with each other, an observation that is still a puzzle, although there are now dynamical models of hot spots which show promise in being able to explain such behavior. Probe tracked zonal winds show that wind magnitude increases with depth to pressures of about 4 bars, with the winds extending to at least as deep as the probe made measurements, 22 bars. Models of hot spot dynamics raise the possibility that the variation with depth of the probe measured zonal winds between 0.4 and 4 bars reflect the dynamics of the hot spot rather than the global wind pattern. Galileo upper atmosphere measurements established that there is a sharp temperature rise with altitude between about 350 and 800 km above the 1 bar pressure level, with the upper atmosphere reaching temperatures near 900 K. The energy sources for this upper atmosphere heating are not clearly established, but various mechanisms have been proposed. These and other aspects of the Galileo probe data will be discussed.

Fonda, Mark↗
NASA NTRS2020

Prediction Skill of the 2012 U.S. Great Plains Flash Drought in Subseasonal Experiment (SubX) Models

Flash droughts refer to droughts that develop much more rapidly than normal (i.e., on the order of weeks to a few months). Such droughts can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. We investigated the prediction skill of U.S. flash droughts at subseasonal lead times in global forecast systems participating in the Subseasonal Experiment (SubX) project. An additional comprehensive set of hindcasts with NASA?s GEOSv2.1, a model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. Here we focus on results for the 2012 Great Plains flash drought, noting that the findings based on this event are generally applicable to other U.S. flash droughts. The prediction skill of the SubX models is quite variable. While the skill is limited to less than 2 weeks in most models, it is considerably higher (3-4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization, and 2) the satisfactory representation of quasi-stationary cross-North Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary Rossby waves.

DeAngelis, Anthony M.↗
NASA NTRS1993

In-flight measurement of the National Oceanic and Atmospheric Administration (NOAA)-10 static Earth sensor error

Analysis performed in the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) measures error in the static Earth sensor onboard the National Oceanic and Atmospheric Administration (NOAA)-10 spacecraft using flight data. Errors are computed as the difference between Earth sensor pitch and roll angle telemetry and reference pitch and roll attitude histories propagated by gyros. The flight data error determination illustrates the effect on horizon sensing of systemic variation in the Earth infrared (IR) horizon radiance with latitude and season, as well as the effect of anomalies in the global IR radiance. Results of the analysis provide a comparison between static Earth sensor flight performance and that of scanning Earth sensors studied previously in the GSFC/FDD. The results also provide a baseline for evaluating various models of the static Earth sensor. Representative days from the NOAA-10 mission indicate the extent of uniformity and consistency over time of the global IR horizon. A unique aspect of the NOAA-10 analysis is the correlation of flight data errors with independent radiometric measurements of stratospheric temperature. The determination of the NOAA-10 static Earth sensor error contributes to realistic performance expectations for missions to be equipped with similar sensors.

Harvie, E.↗
NASA NTRS2011

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

Hu, Chuanmin↗
DOE OSTI2020

Flux Seasonality Metrics Database: A companion dataset for FLUXNET annual product

This dataset include standardized phenological transition dates, and derived season lengths of active, dormant and transition seasons of ecosystem carbon and energy fluxes, as reported in FLUXNET. Such metrics are unique in that they characterize the seasonal dynamics of these fluxes in their own terms rather than in reference to traditional phenology indicators that mostly relate to vegetation structural characteristics. The Flux Seasonality Metrics Database (FSMD) generated 17 standardized seasonality metrics for 4 different fluxes including gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE) and sensible heat (H) reported in FLUXNET2015 Dataset for about 200 sites and 1500 site-years of data globally. Each metric is reported as an estimate and a confidence interval, which is a function of data coverage and flux variability. We anticipate that the FSMD will facilitate new model validation exercises, and stimulate new research in global change and Earth science disciplines where land-atmosphere exchange dynamics play a central role.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Model Sensitivity Studies of the Decrease in Atmospheric Carbon Tetrachloride

Carbon tetrachloride (CCl4) is an ozone-depleting substance, which is controlled by the Montreal Protocol and for which the atmospheric abundance is decreasing. However, the current observed rate of this decrease is known to be slower than expected based on reported CCl4 emissions and its estimated overall atmospheric lifetime. Here we use a three-dimensional (3-D) chemical transport model to investigate the impact on its predicted decay of uncertainties in the rates at which CCl4 is removed from the atmosphere by photolysis, by ocean uptake and by degradation in soils. The largest sink is atmospheric photolysis (74% of total), but a reported 10% uncertainty in its combined photolysis cross section and quantum yield has only a modest impact on the modelled rate of CCl4 decay. This is partly due to the limiting effect of the rate of transport of CCl4 from the main tropospheric reservoir to the stratosphere, where photolytic loss occurs. The model suggests large interannual variability in the magnitude of this stratospheric photolysis sink caused by variations in transport. The impact of uncertainty in the minor soil sink (9%of total) is also relatively small. In contrast, the model shows that uncertainty in ocean loss (17%of total) has the largest impact on modelled CCl4 decay due to its sizeable contribution to CCl4 loss and large lifetime uncertainty range (147 to 241 years). With an assumed CCl4 emission rate of 39 Gg year(exp -1), the reference simulation with the best estimate of loss processes still underestimates the observed CCl4 (overestimates the decay) over the past 2 decades but to a smaller extent than previous studies. Changes to the rate of CCl4 loss processes, in line with known uncertainties, could bring the model into agreement with in situ surface and remote-sensing measurements, as could an increase in emissions to around 47 Gg year(exp -1). Further progress in constraining the CCl4 budget is partly limited by systematic biases between observational datasets. For example, surface observations from the National Oceanic and Atmospheric Administration (NOAA) network are larger than from the Advanced Global Atmospheric Gases Experiment (AGAGE) network but have shown a steeper decreasing trend over the past 2 decades. These differences imply a difference in emissions which is significant relative to uncertainties in the magnitudes of the CCl4 sinks.

Carbon Trechloride↗
NASA NTRS2006

The Relationship Between Global Mean Sea Level Rise and the Reference Frame

The Terrestrial Reference Frame is the fundamental means by which we relate observations in space and time. For example, in order to generate a homogeneous and consistent time series of geo-referenced altimeter measurements over the span of the Topex/Poseidon and Jason-1 missions, we must examine carefully the role of improvements in measurement modelling, force modelling, and improved reference frame realizations. In this paper, we quantify the effects of improvements in force modelling, for example the use of new GRACE-derived gravity models, the effect of time-variable gravity derived from GRACE on altimeter satellite orbits. In addition, we examine the effects of modelling geocenter in altimeteric satellite POD, and look at how the application of atmospheric loading might affect the time-series of precise orbits for Topex/Poseidon and Jason-1.

Lemoine, F.↗
NASA NTRS1993

Modeling and data assimilation for Mars Observer

The Mars Observer mission will return a substantial set of meteorological data for the atmosphere of Mars, principally in the form of thermal infrared soundings from the Pressure Modulator Infrared Radiometer (PMIRR). The dataset will enable global studies of a range of dynamical and physical phenomena in the martian atmosphere. The solar orbital configuration of Mars Observer, however, means that the coverage of the martian atmosphere and surface by remote sounding instruments will be asynchronous. This leads to difficulties of interpretation when studying synoptic-scale phenomena with timescales of a few days. In an attempt to overcome such problems we propose to use data assimilation techniques, as currently employed for operational weather forecasting on the Earth, in conjunction with a Martian General Circulation Model (MGCM) that is under development at Oxford and Reading Universities. An overview of the model and assimilation scheme will be given, with reference to problems encountered so far in applying such methods to remote-sensing observations of the martian atmosphere.

Lewis, Stephen R.↗
NASA NTRS2003

Spectral Retrieval of Latent Heating Profiles from TRMM PR Data: Comparison of Look-Up Tables

The primary goal of the Tropical Rainfall Measuring Mission (TRMM) is to use the information about distributions of precipitation to determine the four dimensional (i.e., temporal and spatial) patterns of latent heating over the whole tropical region. The Spectral Latent Heating (SLH) algorithm has been developed to estimate latent heating profiles for the TRMM Precipitation Radar (PR) with a cloud- resolving model (CRM). The method uses CRM- generated heating profile look-up tables for the three rain types; convective, shallow stratiform, and anvil rain (deep stratiform with a melting level). For convective and shallow stratiform regions, the look-up table refers to the precipitation top height (PTH). For anvil region, on the other hand, the look- up table refers to the precipitation rate at the melting level instead of PTH. For global applications, it is necessary to examine the universality of the look-up table. In this paper, we compare the look-up tables produced from the numerical simulations of cloud ensembles forced with the Tropical Ocean Global Atmosphere (TOGA) Coupled Atmosphere-Ocean Response Experiment (COARE) data and the GARP Atlantic Tropical Experiment (GATE) data. There are some notable differences between the TOGA-COARE table and the GATE table, especially for the convective heating. First, there is larger number of deepest convective profiles in the TOGA-COARE table than in the GATE table, mainly due to the differences in SST. Second, shallow convective heating is stronger in the TOGA COARE table than in the GATE table. This might be attributable to the difference in the strength of the low-level inversions. Third, altitudes of convective heating maxima are larger in the TOGA COARE table than in the GATE table. Levels of convective heating maxima are located just below the melting level, because warm-rain processes are prevalent in tropical oceanic convective systems. Differences in levels of convective heating maxima probably reflect differences in melting layer heights. We are now extending our study to simulations of other field experiments (e.g. SCSMEX and ARM) in order to examine the universality of the look-up table. The impact of look-up tables on the retrieved latent heating profiles will also be assessed.

Shige, Shoichi↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
NASA NTRS1989

Background stratospheric aerosol reference model

In this analysis, a reference background stratospheric aerosol optical model is developed based on the nearly global SAGE 1 satellite observations in the non-volcanic period from March 1979 to February 1980. Zonally averaged profiles of the 1.0 micron aerosol extinction for the tropics and the mid- and high-altitudes for both hemispheres are obtained and presented in graphical and tabulated form for the different seasons. In addition, analytic expressions for these seasonal global zonal means, as well as the yearly global mean, are determined according to a third order polynomial fit to the vertical profile data set. This proposed background stratospheric aerosol model can be useful in modeling studies of stratospheric aerosols and for simulations of atmospheric radiative transfer and radiance calculations in atmospheric remote sensing.

Mccormick, M. P.↗
NASA NTRS2018

A GEOS-Based OSSE for the "MISTiC Winds" Concept

The Goddard Earth Observing System (GEOS) atmospheric model and data assimilation system are used to perform an Observing System Simulation Experiment (OSSE) for the proposed MISTiC Wind mission. The GEOS OSSE includes a reference simulation (the Nature Run), from which the pseudo-observations are generated. These pseuo-observations span the entire suite of in-situ and space space-based observations presently used in operational weather prediction, with the addition of the MISTiC-Wind dataset. New observation operators have been constructed for the MISTiC Wind data, including both the radiances measured in the 4-micron part of the solar spectrum and the winds derived from these radiances. The OSSE examines the impacts on global forecast skill of adding these observations to the current operational suite, showing substantial improvements in forecasts when the wind information are added. It is shown that a constellation of four MISTiC Wind satellites provides more benefit than a single platform, largely because of the increased accuracy of the feature-derived wind measurements when more platforms are used.

McCarty, W.↗
DOE OSTI2023

Advances in understanding the changes of tropical rainfall annual cycle: a review

Abstract Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under a global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, spatial pattern shift, phase and wet/dry season length changes. Here, amplitude refers to the local summer-winter rainfall contrast and phase refers to the timing of rainfall annual cycle. In a warming climate, the amplitude is enhanced, more evidently over ocean, while the phase is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The spatial pattern shift under warming is marked by two features: equatorward shift of the ITCZ throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter may be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress in the research of tropical rainfall annual cycle change, many gaps remain in understanding its mechanisms, quantifying and attributing its historical emergence, narrowing the inter-model uncertainty of its future projections, and evaluating its global impact, motivating future work guided by some directions proposed in this review.

Song, Fengfei (ORCID:0000000230041749)↗