The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel "double decomposition method" that employs the "cloud radiative kernel" and "cloud regime" concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies. Climate models project that rising levels of greenhouse gas emissions will raise Earth's global mean surface air temperature. However, the amount of warming to be expected remains highly uncertain. A main underlying cause for this uncertainty is the representation of atmospheric clouds in these models. In particular, the response of clouds to global warming, known as the "cloud feedback" over the Southern Ocean has been linked to higher levels of projected warming. To address the uncertainty, we apply a novel method to two satellite data sets. This method teases apart contributions from changing cloud properties such as their horizontal spatial coverage and their ability to reflect sunlight, to the cloud feedback, and then further dissects each of these contributions into those arising from changes occurring both within groups of cloud types and changes occurring between these groups. Results suggest that a general shift from thick storm-track clouds to thinner oceanic low-level clouds exacerbates warming in the Southern Ocean but are partially counterbalanced by cloud thickening in part due to stronger near-surface winds in the same region. Our novel methods and results can potentially be used to evaluate climate models and aid in tracing their shortcomings to poorly represented physical processes. MODIS satellite observations indicate that in the Southern Ocean the interannual cloud optical depth feedback is slightly positive More frequently occurring thin low-level clouds and fewer storm-track clouds contribute to a positive feedback in the Southern Ocean Strengthened near-surface winds contribute to a negative low-level cloud feedback in the Southern Ocean

Predicting heavy precipitation remains scientifically challenging. Here we combine Atmospheric Infrared Sounder (AIRS) temperature and moisture soundings and weather forecast winds to predict the formation of thermodynamic conditions favourable for convection in the hours following satellite overpasses. Here we treat AIRS retrievals as air parcels that are moved adiabatically to generate time-varying fields. Over much of the Central-Eastern Continental U.S. during the non-winter months of 2019-2020, our derived convective available potential energy alone predicts intense precipitation. For hourly precipitation above the all-hours 99.9th percentile, performance is marginally lower than forecasts from a convection permitting model, but similar to the ERA5 reanalysis and substantially better than using the original AIRS soundings. Our results illustrate how mesoscale advection is a major contributor to developing heavy precipitation in the region. Enhancing the full AIRS record as described here would provide an alternative approach to quantify multi-decade trends in heavy precipitation risk. Integrating weather-forecast wind data with satellite-based temperature and moisture soundings leads to better predictions of heavy hourly precipitation over the central and eastern continental United States, according to an analysis of Atmospheric Infrared Sounder (AIRS) data.

The 3-D fields of temperature (T) and specific humidity (q) retrieved by instruments such as the Atmospheric Infrared Sounder (AIRS) are predictive of convection, but convection often triggers during the multi-hour gaps between satellite overpasses. Here we fill the hours after AIRS overpasses by treating AIRS retrievals as air parcels which are moved adiabatically along numerical weather prediction (NWP) wind trajectories. The approach is tested in a simulation experiment that samples 3-D European Reanalysis-5 (ERA5) T and q following the real-world AIRS time–space sampling from March–November 2019 over much of the continental US. Our time-resolved product is named ERA5-FCST, in correspondence to the AIRS forecast product we are using it to test, named AIRS-FCST. ERA5-FCST errors may arise since processes such as radiative heating and NWP sub-grid convection are ignored. For bulk atmospheric layers, ERA5-FCST captures 59 %–94 % of local hourly variation in T and q. We then consider the relationship between convective available potential energy (CAPE), convective inhibition (CIN), and ERA5 precipitation. The 1∘ latitude–longitude ERA5-FCST grid cells in our highest CAPE and lowest CIN bins are more than 50 times as likely to develop heavy precipitation (> 4 mm hr−1), compared with the baseline probability from randomly selecting a location. This is a substantial improvement compared with using the original CAPE and CIN values at overpass time. The results support the development of similar FCST products for operational atmospheric sounders to provide time-resolved thermodynamics in rapidly changing pre-convective atmospheres.

Low-Earth-orbiting (LEO) hyperspectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to 6 h to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite-observing time to generate future soundings of temperature ( T ) and specific humidity ( q ) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi National Polar-Orbiting Partnership ( Suomi NPP ) and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time. Significance Statement Advection of low-Earth-orbiting (LEO) satellite observations of temperature and specific humidity forward in time exhibits skill in determining where and when convection eventually initiates. This approach provides a foundation for a new nowcasting methodology leveraging thermodynamic soundings derived from hyperspectral infrared (IR) sounders on LEO satellite platforms. This method may be useful for creating time-resolved soundings with the constellation of LEO satellites until hyperspectral infrared soundings are widely available from geostationary platforms.

A two‐decade climatology of height‐resolved horizontal variance scaling exponents ( 𝛼 ) for temperature ( T ) and specific humidity ( q ) is described using Aqua Atmospheric Infrared Sounder (AIRS) sounding profiles. The AIRS Team Version 6 (V6), Version 7 (V7), and Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieval algorithms are compared to European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) reanalyses. Large‐scale T exponents derived between 600 and 1,200 km ( 𝛼 L ) show close agreement between V6, V7, and CLIMCAPS algorithms. However, small‐scale q exponents derived between 150 and 400 km ( 𝛼 S ) are in poor agreement, including an unrealistically steep 𝛼 S in the planetary boundary layer (PBL) in the V7 and CLIMCAPS algorithms that is caused by a combination of algorithm damping and overconstraint by the first guess fields. ERA5 and MERRA‐2 reanalyses have large values of 𝛼 L and 𝛼 S for both T and q that indicate reduced small‐scale variability in the reanalysis fields. Differences in 𝛼 S between free‐running MERRA‐2 AMIP and MERRA‐2 are negligible, implying that suppressed small‐scale variability in reanalyses is imposed by the background model and not caused by the data assimilation process. AIRS has positively skewed T distributions in the tropical‐free troposphere that is consistent with positively buoyant air parcels in convection, and negative skewness in the PBL that is related to the existence of cold pools, behavior that is mostly absent in ERA5 and MERRA‐2. AIRS provides a global view of scale‐dependent variance and skewness that is useful for subgrid parameterization development and validation of weather and climate prediction models.

Abstract. We describe a cloud mask simulation experiment developed for the PREFIRE mission. The basis of the cloud mask is a principal component (PC) methodology (PC-MSK) adapted from the algorithm heritage of the upcoming Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission. Simulated clear-sky and cloudy-sky PREFIRE radiances are calculated from the Goddard Earth Observing System (GEOS) meteorological fields and include a variety of complex cloud configurations. The simulation experiment is based on local training that is adjusted along segments of simulated orbits that mimic actual PREFIRE orbits. A numerically stable method of separating clear sky from cloudy sky is achieved using Otsu’s binary classification method and requires no a priori thresholding estimate for multimodal histograms. Comparisons are made against a machine-learning cloud mask (ML-MSK) developed for the PREFIRE mission. The global hit rate of PC-MSK (92.6 %) compares favorably to the hit rate of ML-MSK (95.3 %). The Arctic hit rate of PC-MSK (86.7 %) compares favorably to the hit rate of ML-MSK (89.4 %) and both cloud masks are shown to meet mission requirements for PREFIRE cloud detection. The simulation experiment demonstrates the potential for accurate cloud masking with PREFIRE despite a low number of information-containing PCs compared to those obtained from hyperspectral infrared sounders. We conclude with a discussion about clear-sky and cloudy-sky training sets that are suitable for an operational version of PC-MSK and their development during the post-launch checkout time period.

The Aqua, SNPP (Suomi National Polar-orbiting Partnership), and JPSS (Joint Polar Satellite System) satellites carry a combination of hyperspectral infrared sounders (AIRS, Atmospheric Infrared Sounder, and CrIS, Cross-track Infrared Sounder) and high-spatial-resolution narrowband imagers (MODIS, Moderate Resolution Imaging Spectroradiometer, and VIIRS, Visible Infrared Imaging Radiometer Suite). They provide an opportunity to acquire high-quality, long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at the pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS version 7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) version 2 for sounders and the standard Aqua MODIS collection 6.1 and the NASA MODIS–VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at the sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found to be highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at the sounder FOV shows good agreement between the standard and continuity products from different satellites. However, the impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA's progress toward building a continuous cloud data record with sufficient quality to investigate clouds' role in global environmental change.

The Polar Radiant Energy in the Far-Infrared Experiment (PREFIRE) is a low-cost cubesat-based Earth Science mission selected for rapid implementation through NASAs Earth Ventures-Instrument program. Two cubesats carrying thermal infrared spectrometers (TIRS) in polar orbits will test long-held assumptions regarding radiative balance at long wavelengths where the cold-dry Arctic and Antarctic surfaces expel significant amounts of the earth's radiant energy. Thermal radiometry with better than one micron sampling at wavelengths beyond 15 microns will provide critical observational bounds for inputs into glacier melt and climate change models through improvements in surface emissivities, longwave greenhouse effect parameterizations, and cloud presence/types. Additional information on potential far-infrared surface/atmosphere feedbacks during rapid melt processes may be captured with sub-diurnal revisits of targeted areas. A baseline mission with two cubesats in different orbits provides a means for optimization of sub-diurnal sampling for science purposes. In this presentation we provide details of orbit studies that inform expected sampling metrics. Candidate orbits are modeled in SGP and then TIRS sampling is projected into surface footprints with appropriate geo-location. Matching algorithms then compile statistics for time-delayed revisits between and within the two orbits. Binning of results by latitude allows for abstraction of precession issues as well as for correlations with surface types as defined by radiometric classification schemes. Multiple acceptable pairs of science-driven orbits were found that optimize sampling in areas known to experience rapid melt events. The final choice of PREFIRE orbital parameters includes the systems trade-space, particularly the power budget, as well as the availability of launch opportunities.

Earth’s climate is strongly influenced by energy deficits at the poles that emit more thermal energy than they receive from the sun. Energy exchanges between the surface and atmosphere influence the local environment while heat transport from lower latitudes drives midlatitude atmospheric and oceanic circulations. In the Arctic, in particular, local energy imbalances induce strong seasonality in surface–atmosphere heat exchanges and an acute sensitivity to forced climate variations. Despite these important local and global influences, the largest contributions to the polar atmospheric and surface energy budgets have not been fully characterized. The spectral variation of far-infrared radiation that makes up 60% of polar thermal emission has never been systematically measured impeding progress toward consensus in predicted rates of Arctic warming, sea ice decline, and ice sheet melt. Enabled by recent advances in sensor miniaturization and CubeSat technology, the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will document, for the first time, the spectral, spatial, and temporal variations of polar far-infrared emission. Selected under NASA’s Earth Ventures Instrument (EVI) program, PREFIRE will utilize new lightweight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 μ m to quantify Earth’s far-infrared spectrum. Estimates of spectral surface emissivity, water vapor, cloud properties, and the atmospheric greenhouse effect derived from these measurements offer the potential to advance our understanding of the factors that modulate thermal fluxes in the cold, dry conditions characteristic of the polar regions.

The subgrid spatial variability of water vapor is an important geophysical parameter for modeling tropical convention and cloud processes in atmospheric models. This study maps sub-kilometer spatial structures in total atmospheric column water vapor with visible to shortwave infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA's AVIRIS-NG spectrometer enable the highest-resolution measurement to date of water vapor's spatial variability and scaling properties. We find second-order structure function scaling exponents consistent with prior studies of convective atmospheres. Airborne lidar data show that this total column measurement provides information about variability in the lower troposphere. We conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat-CALIPSO (CCL) observations using four years of data (2007-10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (Delta T1000-300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%-95% and 22%-32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and Delta T1000-300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

Abstract Research on heatwaves has gained significant impetus over the past decade due to a warming planet and rapid 21st century urbanization. This study examines driving factors influencing heatwave trends and interannual variability across Southern California (SoCal) from 1950–2020. Inland urban areas of Los Angeles county are the most susceptible to heatwaves with strong increasing trends in frequency, duration, and intensity that are closely tied to nighttime warming. Coastal and rural areas are less impacted but show a significant increase in heatwave frequency over the past two decades. Heatwave nighttime temperatures combined with high humidity have been increasing at a rapid rate of ~1°C/decade since the 1980s—elevating heat stress and mortality risk to vulnerable urban communities. The increased nighttime humidity is associated with an anomalous moisture source off the coast of Baja California that has intensified over the past decade and is linked to ocean warming trends and changes in the California current system. Heatwaves are starting earlier and ending later in the year for urban regions. This augments public health risks and sets the stage for more intense fall wildfires by enhancing the drying of fuels. Droughts and heatwaves are strongly linked, particularly in inland urban and rural areas that have a high statistical probability of heatwaves increasing in frequency (42%), duration (26%), and daily mean temperature (2.2%) during severe drought conditions. Better understanding of heatwave climate drivers and underlying physical processes could help with prediction skill, in addition to providing effective data‐driven recommendations for mitigation efforts in SoCal's vulnerable urban regions.

The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will, for the first time, systematically document the far-infrared (15–54 µm) spectral region from space. The environmental sampling characteristics of the PREFIRE CubeSats, defined in terms of surface temperature (Tsfc) and column water vapor (CWV) are evaluated for a range of possible orbit scenarios for both clear-sky and all-sky conditions over a variety of surface types (land, ocean, sea ice, snow, glacier ice) at both poles. Using NASA Aqua’s Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) retrievals to define the climatological ranges of Tsfc and CWV, the fraction of environmental regimes observed by distinct PREFIRE configurations are evaluated. The sampling rates within any single year for two-orbit CubeSat launches spanning both polar regions are ~75% for clear-sky and ~85% for all-sky compared to the AIRS/AMSU climatology. Decreasing mission duration from 12 to 3 months decreases sampling much more (10%–20%) than decreasing the swath width from 15 to 8 footprints (6%–9%). For a single CubeSat launch, a 98° orbital inclination provides slightly better sampling than either 93° or 103°. For a two-orbit CubeSat launch, a combination of 93° + 98° is somewhat preferable to 103° + 98°. Finally, a 50% data loss rate simulated by dropping out every other orbit leads to only a modest 7%–8% reduction in sampling from full data coverage. This statistical analysis demonstrates that low-cost platforms could offer similar coverage as present-day flagship missions for sampling wide-ranging Tsfc and CWV states over polar regions.

Two upcoming missions are scheduled to provide novel spaceborne observations of upwelling far‐infrared spectra. In this study, the accuracy of ice cloud property retrievals using spaceborne middle‐to‐far‐infrared (MIR‐FIR) measurements is examined toward a better understanding of retrieval biases and uncertainties. Theoretical sensitivity studies demonstrate that the MIR‐FIR spectra are sensitive to ice cloud properties, thereby providing a robust means for retrieving cloud properties under nighttime conditions. However, the temperature dependence of the ice refractive index and relevant ice particle shape models need to be incorporated into the retrieval procedure to avoid systematic biases in inferring cloud optical thickness and effective particle radius. Furthermore, prior information of subpixel cloud fractions is essential to mitigation of substantial systematic retrieval biases due to inconsistent subpixel cloud fractions.

New methods for optimizing data storage and transmission are required as orbital imaging spectrometers collect ever-larger data volumes due to increases in optical efficiency and resolution. In Earth surface investigations, storage and downlink volumes are the most important bottleneck in the mission’s total data yield. Excising cloud-contaminated data onboard, during acquisition, can increase the value of downlinked data and significantly improve the overall science performance of the mission. Threshold-based screening algorithms can operate at the acquisition rate of the instrument but require accurate and comprehensive predictions of cloud and surface brightness. To date, the community lacks a comprehensive analysis of global data to provide appropriate thresholds for screening clouds or to predict performance. Moreover, prior cloud screening studies have used universal screening criteria that do not account for the unique surface and cloud properties at different locations. To address this gap, we analyzed the Hyperion imaging spectrometer’s historical archive of global Earth reflectance data. We selected a diverse subset spanning space (with tropical, midlatitude, arctic, and Antarctic latitudes), time (2005–2017), and wavelength (400–2500 nm) to assure that the distributions of cloud data are representative of all cases. We fit models of cloud reflectance properties gathered from the subset to predict locally and globally applicable thresholds. The distributions relate cloud reflectance properties to various surface types (land, water, and snow) and latitudinal zones. We find that taking location into account can significantly improve the efficiency of onboard cloud screening methods. Models based on this dataset will be used to screen clouds onboard orbital imaging spectrometers, effectively doubling the volume of usable science data per downlink. Models based on this dataset will be used to screen clouds onboard NASA's forthcoming mission, the Earth Mineral Dust Source InvesTigation (EMIT).

In this paper, we describe how researchers and weather forecasters work together to make satellite sounding data sets more useful in severe weather forecasting applications through participation in National Oceanic and Atmospheric Administration (NOAA)’s Hazardous Weather Testbed (HWT) and JPSS Proving Ground and Risk Reduction (PGRR) program. The HWT provides a forum for collaboration to improve products ahead of widespread operational deployment. We found that the utilization of the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) soundings was improved when the product developer and forecaster directly communicated to overcome misunderstandings and to refine user requirements. Here we share our adaptive strategy for (1) assessing when and where NUCAPS soundings improved operational forecasts by using real, convective case studies and (2) working to increase NUCAPS utilization by improving existing products through direct, face-to-face interaction. Our goal is to discuss the lessons we learned and to share both our successes and challenges working with the weather forecasting community in designing, refining, and promoting novel products. We foresee that our experience in the NUCAPS product development life cycle may be relevant to other communities who can then build on these strategies to transition their products from research to operations (and operations back to research) within the satellite meteorological community.

Shifts in deep tropical convection and midlatitude jet streams both manifest themselves in high cloud anomalies. Such anomalies may play a significant role in local to global climate processes. This work investigates how high cloud properties covary with two primary interannual modes of variability in the Southern Hemisphere (SH): El Ntho-Southern Oscillation (ENSO) and the southern annular mode (SAM). In contrast to several recent studies that utilize the latest remote sensing datasets (e.g., CloudSat), we employ a novel combination of imager and sounder data from legacy satellite instruments. Using these legacy data, we confirm the poleward shift of high cloud fields in the SH midlatitudes with SAM seen in other recent studies and characterize the opposing impacts of SAM and ENSO on the South Pacific convergence zone and Southern Hemisphere storm tracks. Furthermore, we demonstrate that the standard deviation of brightness temperature data from the window channel acts as a surrogate for high cloud fraction in the tropics and midlatitudes. Our results reconcile apparent differences in recent studies and suggest that brightness temperature standard deviations are climate relevant, in addition to being largely insensitive to instrument calibration.

Observations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks lambda for different cloud types, with respect to the interannual variability within the A-Train era (July 2002-June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter lambda(GG) from regressing the global-mean cloud-induced TOA radiation anomaly Delta R-G with the global-mean surface temperature change Delta T-GS; 2) the local feedback parameter lambda(LL) from regressing the local Delta R with the local surface temperature change Delta T-S; and 3) the local feedback parameter lambda(GL) from regressing global Delta R-G with local Delta T-S. Observations show significant temporal variability in the magnitudes and spatial patterns in lambda(GG) and lambda(GL), whereas lambda(LL) remains essentially time invariant for different cloud types. The global-mean net lambda(GG) exhibits a gradual transition from negative to positive in the A-Train era due to a less negative lambda(GG) from low clouds and an increased positive lambda(GG) from high clouds over the warm pool region associated with the 2015/16 strong El Nino event. Strong temporal variability in lambda(GL) is intrinsically linked to its dependence on global Delta R-G, and the scaling of lambda(GL) with surface temperature change patterns to obtain global feedback lambda(GG) does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.