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Massively parallel processor

A brief description is given of the Massively Parallel Processor (MPP). Major applications of the MPP are in the area of image processing (where the operands are often very small integers) from very high spatial resolution passive image sensors, signal processing of radar data, and numerical modeling simulations of climate. The system can be programmed in assembly language or a high level language. Information on background, status, architecture, programming, hardware reliability, applications, and the MPP's development as a national resource for parallel algorithm research are presented in outline form.

Source record↗

Microphysical fundamentals governing cirrus cloud growth: Modeling studies

For application to Global Climate Models, large scale numerical models of cirrus cloud formation and maintenance need to be refined to more reliably simulate the effects and feedbacks of high level clouds. A key aspect is how ice crystal growth is initiated in cirrus, which has started a cloud microphysical controversy between camps either believing that heterogeneous or homogeneous drop freezing is predominantly responsible for cold cirrus ice crystal nucleation. In view of convincing evidence for the existence of highly supercooled cloud droplets in the middle and upper troposphere, however, it is concluded that active ice nuclei are rather scarce at cirrus cloud altitudes, and so a new understanding of cirrus cloud formation is needed. This understanding is sought through an examination of cirrus cloud growth models.

Sassen, Kenneth↗

How well can regional fluxes be derived from smaller-scale estimates?

Regional surface fluxes are essential lower boundary conditions for large scale numerical weather and climate models and are the elements of global budgets of important trace gases. Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of km. A classical difficulty is that fluxes have been measured directly only at points or along lines. The process of scaling up observations limited in space and/or time to represent larger areas was done by assigning properties to surface classes and combining estimated or calculated fluxes using an area weighted average. It is not clear that a simple area weighted average is sufficient to produce the large scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture, and other properties. Surface type was classified as lake or land with aircraft borne infrared thermometer, and flight level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries.

Moore, Kathleen E.↗

How well can regional fluxes be derived from smaller-scale estimates?

Regional surface fluxes are essential lower boundary conditions for large scale numerical weather and climate models and are the elements of global budgets of important trace gases. Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of km. A classical difficulty is that fluxes have been measured directly only at points or along lines. The process of scaling up observations limited in space and/or time to represent larger areas was done by assigning properties to surface classes and combining estimated or calculated fluxes using an area weighted average. It is not clear that a simple area weighted average is sufficient to produce the large scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture, and other properties. Surface type was classified as lake or land with aircraft borne infrared thermometer, and flight level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries.

Moore, Kathleen E.↗

The Child's Tantrum: El Nino. The Origin of the El Nino-Southern Oscillation

In 1997, a child's tantrums caught the world's attention. These tantrums took the form not of crying and foot stamping, but of droughts and floods. Obviously, this was no ordinary child. It was, in fact, The Child, or El Nino, as it was, named in the late 1800s by South American observers, who noted that its timing coincided with the Christmas holiday. El Nino is a reversal in sea surface temperature (SST) distributions that occurs once every few years in the tropical Pacific. When it coincides with a cyclical shift in air pressure, known as the Southern Oscillation, normal weather patterns are drastically altered. The combined phenomenon is known as El Nino-Southern Oscillation (ENSO). Although ENSO is a regular phenomenon, it was unusually strong in 1997. It produced heavy rainfall and floods in California and bestowed spring-like temperatures on the Midwest during the winter. These drastic changes in normal weather patterns captured the public imagination, from news reports to jokes on late-night talk shows. Naturally, people wanted to. know as much, about El Nino as possible. Fortunately, scientists had at their disposal new satellites and ocean sensors that provided an unprecedented level of information. Consequently, not only was the 1997 ENSO the strongest in recent memory, but it was also the most thoroughly studied. Prominent groups such as the NASA Seasonalto-Interannual Prediction Project (NSIPP) combined numerous aspects of climate modeling into a single, predictive endeavor.

Picault, Joel↗

Multi-Spectral Cloud Retrievals from Moderate Image Spectrometer (MODIS)

MODIS observations from the NASA EOS Terra spacecraft (1030 local time equatorial sun-synchronous crossing) launched in December 1999 have provided a unique set of Earth observation data. With the launch of the NASA EOS Aqua spacecraft (1330 local time crossing! in May 2002: two MODIS daytime (sunlit) and nighttime observations are now available in a 24-hour period allowing some measure of diurnal variability. A comprehensive set of remote sensing algorithms for cloud masking and the retrieval of cloud physical and optical properties has been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate modeling, climate change studies, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. An overview of the instrument and cloud algorithms will be presented along with various examples, including an initial analysis of several operational global gridded (Level-3) cloud products from the two platforms. Statistics of cloud optical and microphysical properties as a function of latitude for land and Ocean regions will be shown. Current algorithm research efforts will also be discussed.

Platnick, Steven↗

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral instruments produce high-resolution and information-dense radiance spectra, thus offering the opportunity to reduce uncertainties in retrieval products. An optimal estimation-based cirrus cloud optical and microphysical product retrieval is under development for the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Earth Surface Mineral Dust Source Investigation (EMIT) instruments, and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder). The spectral coverage of all three instruments includes the ultraviolet, visible, and near-infrared. In this study a very fast radiative transfer model, the Principal Component-based Radiative Transfer Model in the solar spectral region (PCRTM-Solar), is used for forward modeling computations. This will reduce the computational burden in the forward radiance and Jacobian calculations as the cost function is minimized, enabling the entire AVIRIS, EMIT, and CLARREO-Pathfinder spectral range to be used in the optimal estimation framework. Using the entire spectrum will maximize the information content, resulting in a more robust and more accurate retrieval. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water. Preliminary results will be shown.

Jeffrey Mast↗

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.

Cirrus cloud↗

Sensitivity of Regional Climate to Deforestation in the Amazon Basin

The deforestation results in several adverse effect on the natural environment. The focus of this paper is on the effects of deforestation on land-surface processes and regional climate of the Amazon basin. In general, the effect of deforestation on climate are likely to depend on the scale of the defrosted area. In this study, we are interested in the effects due to deforestation of areas with a scale of about 250 km. Hence, a meso-scale climate model is used in performing numerical experiments on the sensitivity of regional climate to deforestation of areas with that size. It is found that deforestation results in less net surface radiation, less evaporation, less rainfall, and warmer surface temperature. The magnitude of the of the change in temperature is of the order 0.5 C, the magnitudes of the changes in the other variables are of the order of IO%. In order to verify some of he results of the numerical experiments, the model simulations of net surface radiation are compared to recent observations of net radiation over cleared and undisturbed forest in the Amazon. The results of the model and the observations agree in the following conclusion: the difference in net surface radiation between cleared and undisturbed forest is, almost, equally partioned between net solar radiation and net long-wave radiation. This finding contributes to our understanding of the basic physics in the deforestation problem.

Eltahir, Elfatih A. B.↗

GCSS Idealized Cirrus Model Comparison Project

The GCSS Working Group on Cirrus Cloud Systems (WG2) is conducting a systematic comparison and evaluation of cirrus cloud models. This fundamental activity seeks to support the improvement of models used for climate simulation and numerical weather prediction through assessment and improvement of the "process" models underlying parametric treatments of cirrus cloud processes in large-scale models. The WG2 Idealized Cirrus Model Comparison Project is an initial comparison of cirrus cloud simulations by a variety of cloud models for a series of idealized situations with relatively simple initial conditions and forcing. The models (16) represent the state-of-the-art and include 3-dimensional large eddy simulation (LES) models, two-dimensional cloud resolving models (CRMs), and single column model (SCM) versions of GCMs. The model microphysical components are similarly varied, ranging from single-moment bulk (relative humidity) schemes to fully size-resolved (bin) treatments where ice crystal growth is explicitly calculated. Radiative processes are included in the physics package of each model. The baseline simulations include "warm" and "cold" cirrus cases where cloud top initially occurs at about -47C and -66C, respectively. All simulations are for nighttime conditions (no solar radiation) where the cloud is generated in an ice supersaturated layer, about 1 km in depth, with an ice pseudoadiabatic thermal stratification (neutral). Continuing cloud formation is forced via an imposed diabatic cooling representing a 3 cm/s uplift over a 4-hour time span followed by a 2-hour dissipation stage with no cooling. Variations of these baseline cases include no-radiation and stable-thermal-stratification cases. Preliminary results indicated the great importance of ice crystal fallout in determining even the gross cloud characteristics, such as average vertically-integrated ice water path (IWP). Significant inter-model differences were found. Ice water fall speed is directly related to the shape of the particle size distribution and the habits of the ice crystal population, whether assumed or explicitly calculated. In order to isolate the fall speed effect from that of the associated ice crystal population, simulations were also performed where ice water fall speed was set to the same constant value everywhere in each model. Values of 20 and 60 cm/s were assumed. Current results of the project will be described and implications will be drawn. In particular, this exercise is found to strongly focus the definition of issues resulting in observed inter-model differences and to suggest possible strategies for observational validation of the models. The next step in this project is to perform similar comparisons for well observed case studies with sufficient high quality data to adequately define model initiation and forcing specifications and to support quantitative validation of the results.

Starr, David OC.↗

The need to operationalize climate modelling

We report climate models have changed the world. Starting out as simple globally averaged representations of vertical energy exchanges in the atmosphere in the 1950s, today’s climate models are highly complex numerical representations of the Earth system based on the fundamental laws of physics. Their development has largely been driven by scientific curiosity, with the many approximate virtual representations of Earth available today enabling the interrogation of the past and present climate system and the development of an understanding of the influence of human activity on its future. It is this understanding that underpins the climate mitigation targets enshrined in the Paris Agreement to limit global mean temperature increases.

54 ENVIRONMENTAL SCIENCES↗

Estimation of Mesoscale Atmospheric Latent Heating Profiles from TRMM Rain Statistics Utilizing a Simple One-Dimensional Model

In this study, a model is developed to estimate mesoscale-resolution atmospheric latent heating (ALH) profiles. It utilizes rain statistics deduced from Tropical Rainfall Measuring Mission (TRMM) data, and cloud vertical velocity profiles and regional surface thermodynamic climatologies derived from other available data sources. From several rain events observed over tropical ocean and land, ALH profiles retrieved by this model in convective rain regions reveal strong warming throughout most of the troposphere, while in stratiform rain regions they usually show slight cooling below the freezing level and significant warming above. The mesoscale-average, or total, ALH profiles reveal a dominant stratiform character, because stratiform rain areas are usually much larger than convective rain areas. Sensitivity tests of the model show that total ALH at a given tropospheric level varies by less than +/- 10 % when convective and stratiform rain rates and mesoscale fractional rain areas are perturbed individually by 1 15 %. This is also found when the non-uniform convective vertical velocity profiles are replaced by one that is uniform. Larger variability of the total ALH profiles arises when climatological ocean- and land-surface temperatures (water vapor mixing ratios) are independently perturbed by +/- 1.0 K (+/- 5 %) and +/- 5.0 K (+/- 15 %), respectively. At a given tropospheric level, such perturbations can cause a +/- 25 % variation of total ALH over ocean, and a factor-of-two sensitivity over land. This sensitivity is reduced substantially if perturbations of surface thermodynamic variables do not change surface relative humidity, or are not extended throughout the entire model evaporation layer. The ALH profiles retrieved in this study agree qualitatively with tropical total diabatic heating profiles deduced in earlier studies. Also, from January and July 1999 ALH-profile climatologies generated separately with TRMM Microwave Imager and Precipitation Radar rain statistics, it is shown that ALH profiles can be retrieved utilizing diverse satellite-derived rain products that offer convective and stratiform discrimination. Therefore, the ALH retrieval model developed in this study can be used to make regional estimates of total diabatic heating profiles in the future Global Precipitation Measurement mission, and to assimilate these profiles into numerical weather forecast and climate models.

Iacovazzi, Robert A., Jr.↗

Estimation of Mesoscale Atmospheric Latent Heating Profiles from TRMM Rain Statistics Utilizing a Simple One-Dimensional Model

In this study, a model is developed to estimate mesoscale-resolution atmospheric latent heating (ALH) profiles. It utilizes rain statistics deduced from Tropical Rainfall Measuring Mission (TRMM) data, and cloud vertical velocity profiles and regional surface thermodynamic climatologies derived from other available data sources. From several rain events observed over tropical ocean and land, ALH profiles retrieved by this model in convective rain regions reveal strong warming throughout most of the troposphere, while in stratiform rain regions they usually show slight cooling below the freezing level and significant warming above. The mesoscale-average, or total, ALH profiles reveal a dominant stratiform character, because stratiform rain areas are usually much larger than convective rain areas. Sensitivity tests of the model show that total ALH at a given tropospheric level varies by less than +/- 10 % when convective and stratiform rain rates and mesoscale fractional rain areas are perturbed individually by +/- 15 %. This is also found when the non-uniform convective vertical velocity profiles are replaced by one that is uniform. Larger variability of the total ALH profiles arises when climatological ocean- and land-surface temperatures (water vapor mixing ratios) are independently perturbed by +/- 1.0 K (+/- 5%) and +/- 5.0 K (+/- 15%), respectively. At a given tropospheric level, such perturbations can cause a +/- 25% variation of total ALH over ocean, and a factor-of-two sensitivity over land. This sensitivity is reduced substantially if perturbations of surface thermodynamic variables do not change surface relative humidity, or are not extended throughout the entire model evaporation layer. The ALH profiles retrieved in this study agree qualitatively with tropical total diabatic heating profiles deduced in earlier studies. Also, from January and July 1999 ALH-profile climatologies generated separately with TRMM Microwave Imager and Precipitation Radar rain statistics, it is shown that ALH profiles can be retrieved utilizing diverse satellite-derived rain products that offer convective and stratiform discrimination. Therefore, the ALH retrieval model developed in this study can be used to make regional estimates of total diabatic heating profiles in the future Global Precipitation Measurement mission, and to assimilate these profiles into numerical weather forecast and climate models.

Iacovazzi, Robert A., Jr.↗

Update and Status of the Mars Climate Modeling Center at NASA Ames Research Center

NASA Ames Research Center has a long-established history in the study and numerical modeling of Mars’ atmosphere and climate. Such studies began with the late Jim Pollack and have continued under the leaderships of Bob Haberle, Jeff Hollingsworth, and now Melinda Kahre. Our group has grown to more than 10 members, including civil servants, research scientists, postdocs, and students. While we still prioritize science and the model development that supports it, we have recently enhanced efforts to make our codes and output publicly available and boost community engagement through the hosting of modeling tutorials, etc. Here our goal is to present the status of the Ames Mars GCM, our ongoing science projects, the tools we have recently made publicly available, and our plans for releases and continued community engagement.

M. A. Kahre↗

Software Simplifies the Sharing of Numerical Models

To ease the sharing of climate models with university students, Goddard Space Flight Center awarded SBIR funding to Reston, Virginia-based Parabon Computation Inc., a company that specializes in cloud computing. The firm developed a software program capable of running climate models over the Internet, and also created an online environment for people to collaborate on developing such models.

Source record↗

Assessing Global Ocean Circulation Sensitivity to Changes in Antarctic Ice Discharge

The principal goal of this research is to assess how projected increases in meltwater discharge and iceberg calving from the Antarctic Ice Sheet will impact climate in the next 250 years. In short, the sensitivity of the global ocean circulation and climate, and in particular the Atlantic Meridional Overturning Circulation (AMOC), to large increases in iceberg calving and meltwater discharges to the Southern Ocean are poorly understood. The requirement to investigate this topic is heightened by growing evidence that the West Antarctic Ice Sheet (WAIS) is vulnerable to rapid retreat and collapse through marine-ice-sheet-instability (MISI) on multidecadal-to-centennial timescales. Future model simulations of the AIS show the potential for rapid ice sheet retreat in the next 50 – 300 years, with such a collapse associated with the discharge of enormous volumes of ice and meltwater to the Southern Ocean. This project uses a combination of an ice sheet/shelf model and a fully coupled (ocean-atmosphere-seaice) model (CESM 1.2) to explore the sensitive of global climate to future changes in runoff from the Antarctic continent under future greenhouse gas emissions scenarios IPCC RCP4.5 and 8.5. Overall, accounting for Antarctic meltwater reduces the projected global mean anthropogenic greenhouse warming by up to 2 deg C. during peak WAIS collapse and by ~1 deg. for most of the simulation. In addition, the increased meltwater contribution raises sub-surface ocean temperatures at the Antarctic ice sheet margin by more than 1ºC, such that the ice shelf basal melt rates increase beyond those currently projected. In contrast, the model predicts a strong cooling of both the surface air and ocean temperatures over the Southern Ocean that results in a massive expansion and thickening of the perennial sea-ice. The slow-down in anthropogenic warming also results in the onset of ice-free conditions in the Arctic being delayed in the model by several decades, and the projected slowdown in the AMOC to reduce.

58 GEOSCIENCES↗

Modeling and Observational Framework for Diagnosing Local Land-Atmosphere Coupling on Diurnal Time Scales

Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph A., Jr.↗

Multimodel simulation of vertical gas transfer in a temperate lake

In recent decades, several lake models of varying complexity have been developed and incorporated in numerical weather prediction systems and climate models. To foster enhanced forecasting ability and verification, improvement of these lake models remains essential. This especially applies to the limited simulation capabilities of biogeochemical processes in lakes and greenhouse gas exchanges with the atmosphere. Here we present multi-model simulations of physical variables and dissolved gas dynamics in a temperate lake (Harp Lake, Canada). The five models (ALBM, FLake, LAKE, LAKEoneD, MTCR-1) considered within this most recent round of the Lake Model Intercomparison Project (LakeMIP) all captured the seasonal temperature variability well. In contrast, none of the models is able to reproduce the exact dates of ice-cover and ice-off, leading to considerable errors in the simulation of eddy diffusivity around those dates.We then conducted an additional modeling experiment with a diffusing passive tracer to isolate the effect of the eddy diffusivity on gas concentration. Remarkably, sophisticated k -" models do not demonstrate a significant difference in the vertical diffusion of a passive tracer compared to models with much simpler turbulence closures. All models simulate less intensive spring overturn compared to autumn. Reduced mixing in the models consequently leads to the accumulation of the passive tracer distribution in the water column. The lake models with a comprehensive biogeochemical module, such as ALBM and LAKE, predict dissolved oxygen dynamics adequate to the observed data. However, for the surface carbon dioxide concentration the correlation between modeled (ALBM, LAKE) and observed data is weak (~0.3). Overall our results indicate the need to improve the representation of physical and biogeochemical processes in lake models, thereby contributing to enhanced weather prediction and climate projection capabilities.

Guseva, Sofya↗