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NASA NTRS1993

GEWEX Cloud Systems Study (GCSS)

The Global Energy and Water Cycle Experiment (GEWEX) Cloud Systems Study (GCSS) program seeks to improve the physical understanding of sub-grid scale cloud processes and their representation in parameterization schemes. By improving the description and understanding of key cloud system processes, GCSS aims to develop the necessary parameterizations in climate and numerical weather prediction (NWP) models. GCSS will address these issues mainly through the development and use of cloud-resolving or cumulus ensemble models to generate realizations of a set of archetypal cloud systems. The focus of GCSS is on mesoscale cloud systems, including precipitating convectively-driven cloud systems like MCS's and boundary layer clouds, rather than individual clouds, and on their large-scale effects. Some of the key scientific issues confronting GCSS that particularly relate to research activities in the central U.S. are presented.

Moncrieff, Mitch↗

NASA NTRS2023

Robustness of Gridded Precipitation Products for Vietnam Basins using the Comprehensive Assessment Framework of Rainfall

The use of satellite–based precipitation products (SPPs) have become increasingly prevalent as key inputs to provide regional rainfall for improving hydrological simulations in data–spare regions. This study introduces a new approach – Comprehensive Assessment Framework of Rainfall (CAFR) to evaluate six satellite–based precipitation products (SPPs) for eleven basins with different sizes across Vietnam (2007–2015). These SPPs include the Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final run Version 6 (GPM IMERGF V6), Multi–Source Weighted–Ensemble Precipitation (MSWEP) V2.2, Soil Moisture to Rain (SM2RAIN) – Advanced SCATterometer (ASCAT) V1.5, Asian Precipitation–Highly–Resolved Observational Data Integration Towards Evaluation (APHRODITE) V1901, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) V2.0, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – Climate Data Record (CDR) V1.0. With the proposed CAFR: (1) IMERGF is suggested to have the best performance overall, especially when simulating flood peaks; (2) SM2RAIN–ASCAT demonstrates the best skills in metrics related to the dry season; (3) For streamflow simulations, SPPs' performance is sensitive to basin size, with larger basins showing better performance skills. In this study, we demonstrate the capability of our proposed framework to better understand SPP applications in hydrological modeling.

Comprehensive Assessment Framework of Rainfall (CA↗

NASA NTRS2023

Impact of Design Factors for ESA CCI Satellite Soil Moisture Data Assimilation over Europe

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

Soil moisture↗

NASA NTRS2010

Tropical Cyclones, Hurricanes, and Climate: NASA's Global Cloud-Scale Simulations and New Observations that Characterize the Lifecycle of Hurricanes

One of the primary interests of Global Change research is the impact of climate changes and climate variability on extreme weather events, such as intense tropical storms and hurricanes. Atmospheric climate models run at resolutions of global weather models have been used to study the impact of climate variability, as seen in sea surface temperatures, on the frequency and intensity of tropical cyclones. NASA's Goddard Earth Observing System Model, version 5 (GEOS-5) in ensembles run at 50 km resolution has been able to reproduce the interannual variations of tropical cyclone frequency seen in nature. This, and other global models, have found it much more difficult to reproduce the interannual changes in intensity, a result that reflects the inability of the models to simulate the intensities of the most extreme storms. Better representation of the structures of cyclones requires much higher resolution models. Such improved representation is also fundamental to making best use of satellite observations. In collaboration with NOAA's Geophysical Fluid Dynamics Laboratory, GEOS-5 now has the capability of running at much higher resolution to better represent cloud-scale resolutions. Global simulations at cloud-permitting resolutions (10- to 3.5-km) allows for the development of realistic tropical cyclones from tropical storm 119 km/hr winds) to category 5 (>249km1hr winds) intensities. GEOS-5 has produced realistic rain-band and eye-wall structures in tropical cyclones that can be directly analyzed against satellite observations. For the first time a global climate model is capable of representing realistic intensity and track variability on a seasonal scale across basins. GEOS-5 is also used in assimilation mode to test the impact of NASA's observations on tropical cyclone forecasts. One such test, for tropical cyclone Nargis in the Indian Ocean in May 2008, showed that observations from Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU-A) on Aqua substantially reduced forecast track errors. Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. SA is also bringing several state of the art instruments in recent field campaigns to peer under the clouds and study the inner workings of the tropical storms. With the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth science field experiment in 2010 that includes the Global Hawk Unmanned Airborne System (UAS) configured with a suite of in situ and remote sensing instruments that are observing and characterizing the lifecycle of hurricanes, we expect significant improvement in our understanding of the track and intensification processes with the assimilation of the satellite and field campaign observations of meteorological parameters in the numerical prediction models.

Putman, William M.↗

NASA NTRS2002

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China. Multiple observation platforms (e.g., upper-air soundings, Doppler radar, ships, wind profilers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional scale model (with grid size of 20 km) and Goddard Cumulus Ensemble (GCE) model (with 1 km grid size) are used to perform multi-day integration to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil-precipitation interaction and feedback associated with a flood event that occurred in and around China's Yantz River during SCSMEX Sensitivity tests on various land surface models, sea surface temperature (SST) variations, and cloud processes are performed to understand the precipitation processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. These tests have indicated that the land surface model has a major impact on the circulation over the S. China Sea. Cloud processes can effect the precipitation pattern while SST variation can effect the precipitation amounts over both land and ocean. The exact location (region) of the flooding can be effected by the soil-rainfall feedback. The GCE-model results captured many observed precipitation characteristics because it used a fine grid size. For example, the model simulated rainfall temporal variation compared quite well to the sounding-estimated rainfall. The results show there are more latent heat fluxes prior to the onset of the monsoon. However, more rainfall was simulated after the onset of the monsoon. This modeling study indicates the latent heat fluxes (or evaporation) have more of an impact on precipitation processes and rainfall in the regional climate model simulations than in the cloud-resolving model simulations. Research is underway to determine if the difference in the grid sizes or the moist processes used in these two models is responsible for the differing influence of surface fluxes an precipitation processes.

Tao, W.-K.↗

NASA NTRS2019

The Subseasonal Experiment (SubX): A Multi-Model Subseasonal Prediction Experiment

SubX is a multi-model subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced seventeen years of retrospective (re-) forecasts and more than a year of weekly real-time forecasts. The re-forecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation three weeks ahead of time in specific regions. The SubX multi-model ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden-Julian Oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated with skillful predictions of the MJO four weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones which can help emergency management and aid organizations to plan for disasters. (Capsule Summary) A research to operations project in service of developing better operational subseasonal forecasts.

Precipitation↗

NASA NTRS2026

South Asian Monsoon Response to Regional Aerosol Emission Reductions: Insights From RAMIP

Anthropogenic aerosol and associated precursor gas emissions are already declining in many regions and are likely to decline significantly by 2050, with major implications for regional climate. Unlike greenhouse gases, aerosol impacts are spatially heterogeneous and can influence climate both near emission sources and through remote teleconnections. This is particularly important for the South Asian monsoon system, where both local and remote aerosol changes can significantly affect precipitation patterns. Using simulations from the Regional Aerosol Model Intercomparison Project, we examine how local and remote aerosol emission reductions influence South Asian climate across both pre-monsoon and monsoon seasons, including weakening of the elevated heat pump (EHP) mechanism during pre-monsoon months. Our analysis employs 10-member ensembles from 10 CMIP6-era models to compare three experiments with global, South Asian, and East Asian aerosol reductions relative to a high-emission baseline (SSP3-7.0). This experimental design allows us to isolate and quantify the distinct impacts of regional emission changes. Results reveal that global aerosol reductions produce a larger all-India precipitation increase (+0.28 mm day −1 ) than South Asian reductions alone (+0.19 mm day −1 ), with the strongest regional responses over the northern Bay of Bengal, the Western Ghats and Indo-Gangetic Plains. East Asian reductions show uncertain but modest precipitation reductions of 0.2-0.6 mm day −1 over parts of west-central and eastern Indian regions. Pre-monsoon carbonaceous aerosol reductions systematically weaken the EHP, cooling the mid-troposphere over the Himalayan foothills by up to 0.4 K and producing localised anomalous descent that opposes the climatological pre-monsoon ascent. Substantial inter-model diversity exists in the spatial patterns and magnitudes of these responses, highlighting key uncertainties in aerosol–monsoon interactions. The South Asian precipitation response is driven by enhanced land–sea thermal contrast, while the pre-monsoon EHP weakening is attributable exclusively to local South Asian carbonaceous aerosol forcing rather than remote teleconnections. These findings have direct implications for air quality and climate adaptation planning across South Asia as regional aerosol emissions diverge under different development pathways.

cmip6↗

NASA NTRS2018

Coordinating AgMIP Data and Models Across Global and Regional Scales for 1.5°C and 2.0°C Assessments

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some bread basket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’.

interdisciplinary↗

NASA NTRS2011

Preface for the ILWS Special Issue of JASTP

During the week of October 4-9,2009, about 160 participants from 19 countries met at the Itamambuca resort area of Ubatuba, Sao Paulo, Brazil to discuss the "Influence of solar variability on geophysical and heliospheric phenomena" Conference, organized by the International Living With a Star (ILWS) Program of NASA and by the National Institute of Space Research (INPE) of Brazil. This special issue collects 38 papers from that Conference, with 3 papers dealing with solar topics, 12 with interplanetary and cosmic rays,12 with magnetospheric, 9 with the low-latitude ionosphere and 2 with solar terrestrial system and climate. Furthermore six of the review papers presented at that Conference are being published in a special issue of the SpaceScience Reviews journal. The combined ensemble of papers describes the complex series of phenomena that link the Sun to the Earth.

Gonzalez, Walter D.↗

NASA NTRS2011

Assimilation of Terrestrial Water Storage from GRACE in a Snow-Dominated Basin

Terrestrial water storage (TWS) information derived from Gravity Recovery and Climate Experiment (GRACE) measurements is assimilated into a land surface model over the Mackenzie River basin located in northwest Canada. Assimilation is conducted using an ensemble Kalman smoother (EnKS). Model estimates with and without assimilation are compared against independent observational data sets of snow water equivalent (SWE) and runoff. For SWE, modest improvements in mean difference (MD) and root mean squared difference (RMSD) are achieved as a result of the assimilation. No significant differences in temporal correlations of SWE resulted. Runoff statistics of MD remain relatively unchanged while RMSD statistics, in general, are improved in most of the sub-basins. Temporal correlations are degraded within the most upstream sub-basin, but are, in general, improved at the downstream locations, which are more representative of an integrated basin response. GRACE assimilation using an EnKS offers improvements in hydrologic state/flux estimation, though comparisons with observed runoff would be enhanced by the use of river routing and lake storage routines within the prognostic land surface model. Further, GRACE hydrology products would benefit from the inclusion of better constrained models of post-glacial rebound, which significantly affects GRACE estimates of interannual hydrologic variability in the Mackenzie River basin.

Forman, Barton A.↗

NASA NTRS2024

Development of A Multidecadal Land Reanalysis Over High Mountain Asia

Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.

Fadji Z. Maina↗

NASA NTRS2021

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Michael Notaro↗

NASA NTRS2021

Mid-Pliocene Atlantic Meridional Overturning Circulation simulated in PlioMIP2

In the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), coupled climate models have been used to simulate an interglacial climate during the mid-Piacenzian warm period (mPWP; 3.264 to 3.025 Ma). Here, we compare the Atlantic Meridional Overturning Circulation (AMOC), poleward ocean heat transport and sea surface warming in the Atlantic simulated with these models. In PlioMIP2, all models simulate an intensified mid-Pliocene AMOC. However, there is no consistent response in the simulated Atlantic ocean heat transport nor in the depth of the Atlantic overturning cell. The models show a large spread in the simulated AMOC maximum, the Atlantic ocean heat transport and the surface warming in the North Atlantic. Although a few models simulate a surface warming of ~8–12°C in the North Atlantic, similar to the reconstruction from Pliocene Research, Interpretation and Synoptic Mapping (PRISM) version 4, most models appear to underestimate this warming. The large model spread and model–data discrepancies in the PlioMIP2 ensemble do not support the hypothesis that an intensification of the AMOC, together with an increase in northward ocean heat transport, is the dominant mechanism for the mid-Pliocene warm climate over the North Atlantic.

Pliocene Model Intercomparison Project Phase 2 (Pl↗

NASA NTRS2018

Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields Under Climate Change

A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

Basso, B.↗

NASA NTRS2021

The MJO-QBO Relationship in a GCM with Stratospheric Nudging

Observational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

Madden-Julian oscillation (MJO)↗

NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗

NASA NTRS2010

Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

Land surface (or "skin") temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. Here we assimilate LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) into the Noah and Catchment (CLSM) land surface models using an ensemble-based, off-line land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LST typically exhibit different mean values and variability. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation ("open loop") are comparable to each other and superior to that of ISCCP retrievals. For LST, RMSE values are 4.9 K (CLSM), 5.6 K (Noah), and 7.6 K (ISCCP), and anomaly correlation coefficients (R) are 0.62 (CLSM), 0.61 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over open loop) of up to 0.7 K in RMSE and 0.05 in anomaly R. The skill of surface turbulent flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Reichle, Rolf H.↗

NASA NTRS2016

Solar Rotational Modulations of Spectral Irradiance and Correlations with the Variability of Total Solar Irradiance

Aims: We characterize the solar rotational modulations of spectral solar irradiance (SSI) and compare them with the corresponding changes of total solar irradiance (TSI). Solar rotational modulations of TSI and SSI at wavelengths between 120 and 1600 nm are identified over one hundred Carrington rotational cycles during 2003-2013. Methods: The SORCE (Solar Radiation and Climate Experiment) and TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics)/SEE (Solar EUV Experiment) measured and SATIRE-S modeled solar irradiances are analyzed using the EEMD (Ensemble Empirical Mode Decomposition) method to determine the phase and amplitude of 27-day solar rotational variation in TSI and SSI. Results: The mode decomposition clearly identifies 27-day solar rotational variations in SSI between 120 and 1600 nm, and there is a robust wavelength dependence in the phase of the rotational mode relative to that of TSI. The rotational modes of visible (VIS) and near infrared (NIR) are in phase with the mode of TSI, but the phase of the rotational mode of ultraviolet (UV) exhibits differences from that of TSI. While it is questionable that the VIS to NIR portion of the solar spectrum has yet been observed with sufficient accuracy and precision to determine the 11-year solar cycle variations, the temporal variations over one hundred cycles of 27-day solar rotation, independent of the two solar cycles in which they are embedded, show distinct solar rotational modulations at each wavelength.

Spectral Solar Irradiance↗