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Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs).Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the “single-column approximation” which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.

Aman Gupta↗

DOE OSTI2022

A framework for detection and attribution of regional precipitation change: Application to the United States historical record

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortages with uncertainty-aware and physics-informed machine learning

Focal Area(s): The focus area is predictive modeling through the use of AI techniques and AI-derived model components with a particular emphasis on extreme weather in Atmospheric Science and power outages and shortages in Energy Science. Science Challenge: Predicting weather extremes (e.g., heavy precipitation, strong wind, and large hailstones), and weather-related power system outages and shortages can mitigate economic losses, save lives, support renewables integration, and improve power system resiliency. However, currently, the poor reliability and large uncertainty associated with the weather extreme prediction in the current climate models make the problem intractable. The key challenges are: (1) physical factors like green-house gases (GHGs), aerosols, and land use and land cover (LULC) can significantly impact extreme storms, but the understanding of these impacts is limited, particularly globally; (2) the convective permitting resolutions needed to model severe convective storms and their impacts are computationally prohibitive with global climate models (GCMs); (3) interactions between weather extremes and power system outages are complex and subject to great uncertainty. Current outage prediction models are short lead (~ 3 days), which do not allow for long-time planning of energy production and distribution. Moreover, we have limited capacity to predict weather events leading to sustained shortages in a renewable-energy-dominated power system. These challenges drive motivation for mechanistic understanding and reliable and efficient predictive modeling of extremes and their impacts from the sub-seasonal to long term projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Thresholds in Atmosphere-Soil Moisture Interactions: Results from Climate Model Studies

The potential predictability of the effects of warm season soil moisture anomalies over the central U.S. has been investigated using a series of GCM (Global Climate Model) experiments with the NCAR (National Center for Atmospheric Research) CCM3 (Community Climate Model version 3)/LSM (Land Surface Model). Three different types of experiments have been made, all starting in either March (representing precursor conditions) or June (conditions at the onset of the warm season): (1) 'anomaly' runs with large, exaggerated initial soil moisture reductions, aimed at evaluating the physical mechanisms by which soil moisture can affect the atmosphere; (2) 'predictability' runs aimed at evaluating whether typical soil moisture initial anomalies (indicative of year-to-year variability) can have a significant effect, and if so, for how long; (3) 'threshold' runs aimed at evaluating if a soil moisture anomaly must be of a specific size (i.e., a threshold crossed) before a significant impact on the atmosphere is seen. The 'anomaly' runs show a large, long-lasting response in soil moisture and also quantities such as surface temperature, sea level pressure, and precipitation; effects persist for at least a year. The 'predictability' runs, on the other hand, show very little impact of the initial soil moisture anomalies on the subsequent evolution of soil moisture and other atmospheric parameters; internal variability is most important, with the initial state of the atmosphere (representing remote effects such as SST anomalies) playing a more minor role. The 'threshold' runs, devised to help resolve the dichotomy in 'anomaly' and 'predictability' results, suggest that, at least in CCM3/LSM, the vertical profile of soil moisture is the most important factor, and that deep soil zone anomalies exert a more powerful, long-lasting effect than do anomalies in the near surface soil zone. We therefore suggest that soil moisture feedbacks may be more important in explaining prolonged decadal to century-long droughts evident in the historic and recent prehistoric records, but less important on a seasonal to interannual time-scale.

Oglesby, Robert J.↗

DOE OSTI2021

Evaluation of the Causes of Wet-Season Dry Biases Over Amazonia in CAM5

This study investigates the causes of pronounced low precipitation bias over Amazonia in the Community Atmosphere Model version 5 (CAM5), a common feature in many global climate models. Our analysis is based on a suite of 3-day long hindcasts starting every day at 00Z from 1997 to 2012 and an AMIP simulation for the same period. The Amazonia dry bias appears by the second day in the hindcasts and is very robust for all the seasons with the largest bias magnitude during the wet season (December–February). The bias pattern and magnitude do not change much during different dynamical wind regimes on sub-seasonal time scales. We further classify the diurnal cycle of precipitation near the LBA sites from observations and hindcasts into three convective regimes: no precipitation, late afternoon deep convection, and nighttime deep convection. CAM5 can only simulate the late afternoon convective regime and completely fails to simulate the nighttime convection, which is mostly from propagating convective systems originating from remote locations. CAM5 mainly underestimates precipitation in the late afternoon and nighttime convective regimes, which occur during ~67% of wet season days and account for ~75% of accumulated precipitation amount in observations. The persistent warm temperature bias and slightly higher moisture below 850 mb likely trigger deep convection too frequently, resulting in an earlier but weaker rainfall peak in the diurnal cycle. Finally, shallow convection may not effectively transport moisture from boundary layer to the free atmosphere, which also leads to weaker deep convection events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Decomposing Cloud Radiative Feedbacks by Cloud-Top Phase

Changes in cloud scattering properties and emissivity that arise from atmospheric warming cause substantial radiative feedbacks in model projections of anthropogenic climate change, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. We test this hypothesis by developing a method to decompose cloud radiative feedbacks by cloud-top phase. The method is applied to an ensemble of six state-of-the-art global climate models run with prescribed sea surface temperature. In these simulations, the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from −0.17 to −0.01 W m −2 K −1 , while the overall net cloud feedback ranges from 0.02 to 0.91 W m −2 K −1 . The multimodel mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 19% of the magnitude of the multimodel mean of the overall cloud feedback (−0.10 vs 0.52 W m −2 K −1 ). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale.

Climate change↗

NASA NTRS2024

Assessing the Impact of Climate Change on Atmospheric Rivers: A Modeling Perspective

Atmospheric rivers (ARs) play a crucial role in the poleward transport of water vapor, and the AR-associated precipitation is a critical component of global water supplies, making it critical that we understand how ARs may change in the future. To approach this issue, integrations of the NASA Goddard Institute for Space Studies global climate model ModelE version 2.1 (GISSE2.1) are employed. Multiple configurations of the model simulating different climates are analyzed: (1) the last-glacial maximum; (2) present day; (3) the end of the 21st century. The thermodynamic and dynamic components of changes to AR frequency are analyzed using a decomposition method. This method utilizes differences in distinct AR seasonal climatology frequencies derived from various vertically integrated water vapor transport (IVT) thresholds to resolve AR frequency into its components. Global mean state changes in poleward AR frequency for different climates are dominated by precipitable water vapor (PWV) changes. A set of idealized cold and warm climates in which present day sea surface temperatures are uniformly changed are considered for a targeted analysis of the south Pacific Ocean basin. For this analysis, frequency and distribution of AR events in the model runs are analyzed by comparing them to changes in the jet stream as well as the Eulerian storm tracks and low-level baroclinicity. Latitudinal shifts in the ARs in the south Pacific Ocean basin using our integrations are not as tightly coupled to these two storm-related climatological metrics in the midlatitudes but fare better on the poleward side of the storm tracks.

Atmospheric rivers↗

NASA NTRS1981

Do initial conditions matter? A comparison of model climatologies generated from different initial states

A coarse mesh (8 by 10) 7 layer global climate model was used to compute 15 months of meteorological history in two perpetual January experiments on a water planet (without continents) with a zonally symmetric climatological January sea surface temperature field. In the first of the two water planet experiments the initial atmospheric state was a set of zonal mean values of specific humidity, temperature, and wind at each latitude. In the second experiment the model was initialized with globally uniform mean values of specific humidity and temperature on each sigma level surface, constant surface pressure (1010 mb), and zero wind everywhere. A comparison was made of the mean January climatic states generated by the two water planet experiments. The first two months of each 15 January run were discarded, and 13 month averages were computed from months 3 through 15.

Spar, J.↗

NASA NTRS2017

Assessing Climate Change Impacts on the Stability of Small Tidal Inlets: Part 2- Data Rich Environments

Climate change (CC) is likely to affect the thousands of bar-built or barrier estuaries (here referred to as Small tidal inlets - STIs) around the world. Any such CC impacts on the stability of STIs, which governs the dynamics of STIs as well as that of the inlet-adjacent coastline, can result in significant socio-economic consequences due to the heavy human utilisation of these systems and their surrounds. This article demonstrates the application of a process based snap-shot modelling approach, using the coastal morphodynamic model Delft3D, to 3 case study sites representing the 3 main STI types; Permanently open, locationally stable inlets (Type 1), Permanently open, alongshore migrating inlets (Type 2) and Seasonally/Intermittently open, locationally stable inlets (Type 3). The 3 case study sites (Negombo lagoon - Type 1, Kalutara lagoon - Type 2, and Maha Oya river - Type 3) are all located along the southwest coast of Sri Lanka. After successful hydrodynamic and morphodynamic model validation at the 3 case study sites, CC impact assessment are undertaken for a high end greenhouse gas emission scenario. Future CC modified wave and riverflow conditions are derived from a regional scale application of spectral wave models (WaveWatch III and SWAN) and catchment scale applications of a hydrologic model (CLSM) respectively, both of which are forced with IPCC Global Climate Model output dynamically downscaled to approximately 50 km resolution over the study area with the stretched grid Conformal Cubic Atmospheric Model CCAM. Results show that while all 3 case study STIs will experience significant CC driven variations in their level of stability, none of them will change Type by the year 2100. Specifically, the level of stability of the Type 1 inlet will decrease from 'Good' to 'Fair to poor' by 2100, while the level of (locational) stability of the Type 2 inlet will also decrease with a doubling of the annual migration distance. Conversely, the stability of the Type 3 inlet will increase, with the time till inlet closure increasing by approximately 75%. The main contributor to the overall CC effect on the stability of all 3 STIs is CC driven variations in wave conditions and resulting changes in longshore sediment transport, not Sea level rise as commonly believed.

IPC↗

DOE OSTI2021

Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble

The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Furthermore, compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Floods due to Atmospheric Rivers along the U.S. West Coast: The Role of Antecedent Soil Moisture in a Warming Climate

Precipitation extremes are projected to become more frequent along the U.S. West Coast due to increased atmospheric river (AR) activity, but the frequency of less intense precipitation events may decrease. Antecedent soil moisture (ASM) conditions can have a large impact on flood responses, especially if prestorm precipitation decreases. Taken together with increased antecedent evaporative demand due to warming, this would result in reduced soil moisture at the onset of extreme precipitation events. In this work, we examine the impact of ASM on AR-related floods in a warming climate in three basins that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. Additionally, we ran the Distributed Hydrology Soil Vegetation Model (DHSVM) over the three river basins using forcings downscaled from 10 global climate models (GCMs). We examined the dynamic role of ASM by comparing the changes in the largest 50, 100, and 150 extreme events in two periods, 1951–2000 and 2050–99. In the Chehalis basin, the projected fraction of AR-related extreme discharge events slightly decreases. In the Russian basin, this fraction increases, however, and more substantially so in the Santa Margarita basin. This is due to increases in AR-related extreme precipitation events, as well as the fact that the relationship of extreme precipitation to extreme discharge is strengthened by projected increases in year-to-year volatility of annual precipitation in California, which increases the likelihood of concurrent occurrence of large storms and wet ASM conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa

We report the Eastern Africa region experiences frequent extreme precipitation events that can cause destruction of property and environment, and loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March–May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weakness of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total-wet day precipitation and consecutive wet days, and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Baselining the Indirect Effect by Improving Quantification of Sea Spray and Marine Sources at Ascension Island (Final Report)

Oceans cover two-thirds of the Earth and understanding the interactions of aerosols with clouds in these large marine regions requires quantifying the man-made contributions to the budget of cloud-drop forming particles (known as cloud condensation nuclei, or CCN) relative to the non-manmade “baseline” conditions and understanding the meteorology of boundary layer clouds. Modeling studies have shown substantial uncertainties and sensitivities to natural marine CCN sources, meaning that to reduce uncertainties in indirect effects we must be able to better quantify the CCN budget in ocean regions. While models provide important constraints on these uncertainties, actually reducing uncertainties requires substantial observations in open-ocean and coastal regions in order to establish the baseline on which manmade emissions are added. The tropical South Atlantic Ocean is one of the least-sampled regions of the planet, making the comprehensive measurements of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign provides the longest record of aerosol size distribution measurements from a differential mobility analyzer in a cloud-influenced marine location in the ARM database, with 17 months of ground-based aerosol size distribution measurements. This project addressed the research topic of Aerosol-Cloud Interactions (ACI) by supporting three publications from the LASIC measurements: 1. The ARM measurements were combined with a new value-added technique that was pioneered by the Russell group for quantifying sea salt. This quantification of sea salt uses supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available, observationally-constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available. 2. The fitted modes of the distribution were used to investigate the signatures of cloud processing for very clean to very smoky aerosol conditions, revealing not only differences in the particles that activate in clouds but also in the mechanisms that control that droplet formation process. In clean air, the size required to form a cloud droplet is influenced by the number of particles, as well as how quickly particles take up water during growth in cloud. 3. Building on this aerosol characterization, cloud and meteorological conditions were used to evaluate aerosol-related changes in cloud albedo and optical depth. We introduced a new method of decomposing the impact of aerosols on clouds known as the Twomey effect by incorporating retrieved supersaturation from two independent sets of observations to constrain the feedback of aerosol particles on cloud properties. The method quantifies the reduction of the Twomey effect at high aerosol concentrations, which has never been explained quantitatively by observations. In addition, the results provide the first direct validation for the conditions observed in the tropical South Atlantic of a parcel-based approach that is embedded in many climate models. Together these findings illustrate how ARM extended field campaigns in stratocumulus-covered regions can be used to constrain ACI processes with direct observations, providing specific radiative effects without models. By making such process-specific constraints available to improve ACI in global climate models, ARM observations play a key role in supporting model development.

58 GEOSCIENCES↗

DOE OSTI2020

Warm Arctic, Cold Siberia Pattern: Role of Full Arctic Amplification Versus Sea Ice Loss Alone

The effect of future Arctic amplification (AA) on the extratropical atmospheric circulation remains unclear in modeling studies. Using a collection of coordinated atmospheric and coupled global climate model perturbation experiments, we find an emergent relationship between the high-latitude 1,000–500 hPa thickness response and an enhancement of the Siberian High in winter. This wave number-1-like sea level pressure anomaly pattern is linked to an equatorward shift of the eddy-driven jet and a dynamical cooling response in eastern Asia. Additional simulations, where AA is imposed directly into the model domain by nudging, demonstrate how the sea ice forcing is insufficient by itself to capture the vertical extent of the warming and by extension the amplitude of the response in the Siberian High. Additionally, this study demonstrates the importance of the vertical extent of the tropospheric warming over the polar cap in revealing the “warm Arctic, cold Siberia” anomaly pattern in future projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

TRMM On Orbit Attitude Control System Performance

This paper presents an overview of the Tropical Rainfall Measuring Mission (TRMM) Attitude Control System (ACS) along with detailed in-flight performance results for each operational mode. The TRMM spacecraft is an Earth-pointed, zero momentum bias satellite launched on November 27, 1997 from Tanegashima Space Center, Japan. TRMM is a joint mission between NASA and the National Space Development Agency (NASDA) of Japan designed to monitor and study tropical rainfall and the associated release of energy. Launched to provide a validation for poorly known rainfall data sets generated by global climate models, TRMM has demonstrated its utility by reducing uncertainties in global rainfall measurements by a factor of two. The ACS is comprised of Attitude Control Electronics (ACE), an Earth Sensor Assembly (ESA), Digital Sun Sensors (DSS), Inertial Reference Units (IRU), Three Axis Magnetometers (TAM), Coarse Sun Sensors (CSS), Magnetic Torquer Bars (MTB), Reaction Wheel Assemblies (RWA), Engine Valve Drivers (EVD) and thrusters. While in Mission Mode, the ESA provides roll and pitch axis attitude error measurements and the DSS provide yaw updates twice per orbit. In addition, the TAM in combination with the IRU and DSS can be used to provide pointing in a contingency attitude determination mode which does not rely on the ESA. Although the ACS performance to date has been highly successful, lessons were learned during checkout and initial on-orbit operation. This paper describes the design, on-orbit checkout, performance and lessons learned for the TRMM ACS.

Robertson, Brent↗

NASA NTRS2015

Downscaling GISS ModelE Boreal Summer Climate over Africa

The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.

Regional climate models↗