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NASA NTRS2022

Turbulent Heat Flux, Downward Longwave Radiation, and Large-Scale Atmospheric Circulation Associated with Wintertime Barents–Kara Sea Extreme Sea Ice Loss Events

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Nino–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Arctic↗

NASA NTRS2001

Technical Report Series on Global Modeling and Data Assimilation: The Climate of the FVCCM-3 Model - Volume 20

This document describes the climate of version 1 of the NASA-NCAR model developed at the Data Assimilation Office (DAO). The model consists of a new finite-volume dynamical core and an implementation of the NCAR climate community model (CCM-3) physical parameterizations. The version of the model examined here was integrated at a resolution of 2 degrees latitude by 2.5 degrees longitude and 32 levels. The results are based on assimilation that was forced with observed sea surface temperature and sea ice for the period 1979-1995, and are compared with NCEP/NCAR reanalyses and various other observational data sets. The results include an assessment of seasonal means, subseasonal transients including the Madden Julian Oscillation, and interannual variability. The quantities include zonal and meridional winds, temperature, specific humidity, geopotential height, stream function, velocity potential, precipitation, sea level pressure, and cloud radiative forcing.

Suarez, Max J.↗

NASA NTRS2004

Seasonal-to-Interannual Variability and Land Surface Processes

Atmospheric chaos severely limits the predictability of precipitation on subseasonal to interannual timescales. Hope for accurate long-term precipitation forecasts lies with simulating atmospheric response to components of the Earth system, such as the ocean, that can be predicted beyond a couple of weeks. Indeed, seasonal forecasts centers now rely heavily on forecasts of ocean circulation. Soil moisture, another slow component of the Earth system, is relatively ignored by the operational seasonal forecasting community. It is starting, however, to garner more attention. Soil moisture anomalies can persist for months. Because these anomalies can have a strong impact on evaporation and other surface energy fluxes, and because the atmosphere may respond consistently to anomalies in the surface fluxes, an accurate soil moisture initialization in a forecast system has the potential to provide additional forecast skill. This potential has motivated a number of atmospheric general circulation model (AGCM) studies of soil moisture and its contribution to variability in the climate system. Some of these studies even suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are quite small relative to soil moisture impacts. The model results, though, are strongly model-dependent, with some models showing large impacts and others showing almost none at all. A validation of the model results with observations thus naturally suggests itself, but this is exceedingly difficult. The necessary contemporaneous soil moisture, evaporation, and precipitation measurements at the large scale are virtually non-existent, and even if they did exist, showing statistically that soil moisture affects rainfall would be difficult because the other direction of causality - wherein rainfall affects soil moisture - is unquestionably active and is almost certainly dominant. Nevertheless, joint analyses of observations and AGCM results do reveal some suggestions of land-atmosphere feedback in the observational record, suggestions that soil moisture can affect precipitation over seasonal timescales and across certain large continental areas. The strength of this observed feedback in nature is not large but is still significant enough to be potentially useful, e.g., for forecasts. This talk will address all of these issues. It will begin with a brief overview of land surface modeling in atmospheric models but will then focus on recent research - using both observations and models - into the impact of land surface processes on variability in the climate system.

Koster, Randal↗

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 NTRS1999

Global and Regional Axial Ocean Angular Momentum Signals and Length-of-Day Variations (1985-1996)

Changes in ocean angular momentum about the polar axis (M) are related to fluctuations in zonal currents (relative component M(sub r)) and latitudinal shifts in mass (planetary component M(sub Omega)). Output from a 1 deg ocean model is used to calculate global M(sub r), M(sub Omega), and M time series at 5-day intervals for the period January 1985-April 1996. The annual cycle in M(sub r), M(sub Omega), and M is larger than the semiannual cycle, and M(sub Omega) amplitudes are nearly twice those of M(sub r). Year-to-year modulation of the seasonal cycle is present, but interannual variability is weak. The spectrum of M is red (background slope between omega(sup (-1) and omega(sup -2)) at subseasonal periods, implying a white or blue spectrum for the external torque on the ocean. Comparisons with previous studies indicate the importance of direct atmospheric forcing in inducing subseasonal M signals, relative to instabilities and other internal sources of rapid oceanic signals. Regional angular momentum estimates show that seasonal variability tends to be larger at low latitudes but there are many local maxima due to the spatial structure of zonal current and mass variability. At seasonal timescales, latitudes approximately 20 S - 10 N contribute substantial variability to M(sub Omega), while signals in M(sub r) can be traced to Antarctic Circumpolar Current transports and associated circulation. Variability in M is found to be small when compared with similar time series for the atmosphere and the solid Earth, but ocean signals are significantly coherent with atmosphere-solid Earth residuals, implying a measurable oceanic impact on length-of-day variations.

Ponte, Rui M.↗

NASA NTRS2004

Meridional Propagation of the MJO/ISO and Prediction of Off-equatorial Monsoon Variability

In this study we examine the links between tropical heating, the Madden Julian Oscillation (MJO)/Intraseasonal Oscillation (ISO), and the off-equatorial monsoon development. We examine both observations and idealized "MJO heating" experiments employing the NASA Seasonal-Interannual Prediction Project (NSIPP) atmospheric general circulation model (AGCM). In the simulations, the model is forced by climatological SST and an idealized eastward propagating heating profile that is meant to mimic the canonical heating associated with the MJO in the Indian Ocean and western Pacific. The observational analysis highlights the strong link between the Indian summer monsoon and the tropical ISO/MJO activity and heating. Here we focus on the potential for skillful predictions of the monsoon on subseasonal time scales associated with the meridional propagation of the ISOMJO. In particular, we show that the variability of the Indian summer monsoon lags behind the variability of tropical ISOMJO heating by about 15 days when the tropical heating is around 60E and 90E. This feature of the ISOMJO is reproduced in the AGCM experiments with the idealized eastward propagating MJO-like heating, suggesting that models with realistic ISOM0 variability should provide useful skill of monsoon breaks and surges on subseasonal time scales.

Wu, Man Li C.↗

NASA NTRS2012

Climate Variability and Weather Extremes: Model-Simulated and Historical Data

Extremes in weather and climate encompass a wide array of phenomena including tropical storms, mesoscale convective systems, snowstorms, floods, heat waves, and drought. Understanding how such extremes might change in the future requires an understanding of their past behavior including their connections to large-scale climate variability and trends. Previous studies suggest that the most robust findings concerning changes in short-term extremes are those that can be most directly (though not completely) tied to the increase in the global mean temperatures. These include the findings that (IPCC 2007): There has been a widespread reduction in the number of frost days in mid-latitude regions in recent decades, an increase in the number of warm extremes, particularly warm nights, and a reduction in the number of cold extremes, particularly cold nights. For North America in particular (CCSP SAP 3.3, 2008): There are fewer unusually cold days during the last few decades. The last 10 years have seen a lower number of severe cold waves than for any other 10-year period in the historical record that dates back to 1895. There has been a decrease in the number of frost days and a lengthening of the frost-free season, particularly in the western part of North America. Other aspects of extremes such as the changes in storminess have a less clear signature of long term change, with considerable interannual, and decadal variability that can obscure any climate change signal. Nevertheless, regarding extratropical storms (CCSP SAP 3.3, 2008): The balance of evidence suggests that there has been a northward shift in the tracks of strong low pressure systems (storms) in both the North Atlantic and North Pacific basins. For North America: Regional analyses suggest that there has been a decrease in snowstorms in the South and lower Midwest of the United States, and an increase in snowstorms in the upper Midwest and Northeast. Despite the progress already made, our understanding of the basic mechanisms by which extremes vary is incomplete. As noted in IPCC (2007), Incomplete global data sets and remaining model uncertainties still restrict understanding of changes in extremes and attribution of changes to causes, although understanding of changes in the intensity, frequency and risk of extremes has improved. Separating decadal and other shorter-term variability from climate change impacts on extremes requires a better understanding of the processes responsible for the changes. In particular, the physical processes linking sea surface temperature changes to regional climate changes, and a basic understanding of the inherent variability in weather extremes and how that is impacted by atmospheric circulation changes at subseasonal to decadal and longer time scales, are still inadequately understood. Given the fundamental limitations in the time span and quality of global observations, substantial progress on these issues will rely increasingly on improvements in models, with observations continuing to play a critical role, though less as a detection tool, and more as a tool for addressing physical processes, and to insure the quality of the climate models and the verisimilitude of the simulations (CCSP SAP 1.3, 2008).

Schubert, Siegfried D.↗

NASA NTRS2022

Turbulent Heat Flux, Downward Longwave Radiation and Large-Scale Atmospheric Circulation Associated with the Wintertime Barents-Kara Sea Extreme Sea Ice Loss Events

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents-Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents-Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, though turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6 to 10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden-Julian Oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Turbulent heat flux↗

NASA NTRS2010

Soil Moisture Controls on Rainfall and Temperature Variability: A Modeler Searches Through Observational Data

The degree to which atmospheric processes respond to variations in soil moisture - a potentially important but largely untapped element of subseasonal to seasonal prediction - can be determined easily and directly for an atmospheric model but cannot be determined directly for nature through an analysis of observations. In atmospheric models) directions of causality can be artificially manipulated; we can avoid difficulties associated with the fact that atmospheric variations have a much larger impact on land state variations than vice-versa. In nature) on the other hand) the dominant direction of causality (the atmosphere forcing the ground) cannot be artificially "turned off") and the statistics associated with this dominant direction overwhelm those of the feedback signal. Observational data) however) do allow a number of indirect measures of landatmosphere feedback. This seminar reports on a series of joint analyses of observational and model data designed to illuminate the degree of land-atmosphere feedback present in the real world. The indirect measures do in fact suggest that feedback in nature, though small) is significant - enough to warrant the development of realistic land initialization strategies for subseasonal and seasonal forecasts.

Koster, Randal↗

NASA NTRS2009

The GLACE-2 Experiment

A major motivation for the study of the coupled land-atmosphere system is the idea that soil moisture anomalies may affect future meteorological variables through their effects on future surface energy and water budgets. If true, the accurate initialization of soil moisture in a subseasonal or seasonal forecast system may improve forecast skill, making the forecast products more valuable to society. The Global Land-Atmosphere Coupling Experiment (GLACE-2) project is examining, with a wide variety of models, the degree to which subseasonal (out to two months) precipitation and air temperature forecasts improve through the realistic initialization of soil moisture. For the first time ever, a global consensus should emerge regarding the value of land initialization for forecasts, perhaps motivating national forecast centers to make full use of land moisture initialization in their operations. Participants in GLACE-2 perform two series of forecasts, each consisting of 100 2-month forecast ensembles (10 members per ensemble) covering ten boreal spring and summer start-dates in each of the years 1986-1995. Series 1 utilizes realistic land surface state initialization, provided through a decadal offline simulation using realistic meteorological forcing, as provided by the Global Soil Wetness Project - Phase 2 (GSWP-2), a research activity of the Global Land-Atmosphere System Study (GLASS) of GEWEX. Series 2 is identical to Series 1 in every way except for the fact that it does not benefit from realistic land state initialization. Through the comparison of Series 1 and 2, we isolate the impact of land initialization on the forecasts. Optional extensions to these base runs include forecasts covering additional years, using alternative meteorological forcing for the land initialization. To date, GLACE-2 has garnered participation from eleven modeling groups, covering 13 atmospheric models. Analysis of available results is already well underway, both at the individual modeling institutions and at the NASA Goddard Space Flight Center, which is coordinating the project. Analysis focuses on two elements of the forecast problem: (i) the quantification of model-specific "predictability" (i.e., the degree to which simulated atmospheric chaos will foil a forecast, even under the assumption of"perfect" model physics, initialization data, and validation data) and its decay with time; and (ii) the quantification of forecast skill, determined through a comparison of predicted precipitation and air temperature against observations. Indeed, the specific contribution of land initialization to both these elements is isolated through a comparison of the Series 1 and 2 forecasts. We examine the two elements at four different forecast leads: 1-15, 16-30, 31-45, and 46-60 days. Statistics-based approaches for enhancing skill (essentially using observational statistics to reduce the impact of the models' climatic biases) are also tested. In the present talk, we provide an update of progress in GLACE-2, featuring quantifications of predictability and forecast skill for a number of the participating models and providing a "first look" at the desired consensus view of land impacts on subseasonal forecasts.

Koster, Randal↗

NASA NTRS2010

The Second Phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2)

Koster, Randal D.↗

NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗

NASA NTRS2020

Prediction skill of the 2012 U.S. Great Plains flash drought in Subseasonal Experiment (SubX) models

Rapid-onset droughts, known as flash 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. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (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 once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer 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 atmospheric circulation anomalies.

Anthony M DeAngelis↗

NASA NTRS2020

Downward Migration of the Zonal‐Mean Circulation in the Tropical Atmosphere

The annular modes of the extratropical atmosphere have received much attention for quantifying variability of the jet streams and storm tracks, despite the fact that the midlatitude circulation itself does not vary uniformly with longitude. While tropical fluctuations in geopotential height have lower amplitude than in the extratropics, they exhibit stronger zonal coherence, or dynamical annularity. A simple index is developed to characterize zonal‐mean anomalies of the tropical circulation. It reveals that anomalies in geopotential height and zonal wind migrate downward from the upper troposphere to the surface on a time scale of about 10 days. These features are distinguishable from known modes of tropical variability, the Madden‐‐Julian Oscillation in particular. Evidence from reanalysis and idealized model experiments confirms that this downward migration is quite generic and driven by mechanically forced variations in the strength of the Hadley circulation on subseasonal time scales.

Atmospheric dynamics↗

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 NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗

NASA NTRS2018

Local Versus Remote Contributions of Soil Moisture to Near-Surface Temperature Variability

Soil moisture variations have a straightforward impact on overlying air temperatures, wetter soils can induce higher evaporative cooling of the soil and thus, locally, cooler temperatures overall. Not known, however, is the degree to which soil moisture variations can affect remote air temperatures through their impact on the atmospheric circulation. In this talk we describe a two-pronged analysis that addresses this question. In the first segment, an extensive ensemble of NASA/GSFC GEOS-5 atmospheric model simulations is analyzed statistically to isolate and quantify the contributions of various soil moisture states, both local and remote, to the variability of air temperature at a given local site. In the second segment, the relevance of the derived statistical relationships is evaluated by applying them to observations-based data. Results from the second segment suggest that the GEOS-5-based relationships do, at least to first order, hold in nature and thus may provide some skill to forecasts of air temperature at subseasonal time scales, at least in certain regions.

Koster, R.↗

NASA NTRS2023

A SmallSat Concept to Resolve Diurnal and Vertical Variations of Aerosols, Clouds, and Boundary Layer Height

A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

John E. Yorks↗