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DOE OSTI2022

A model for $F_L$ structure function at low values of $Q^2$ and $x$ - revisited

A reanalysis of the model for the longitudinal structure function F_L (x,Q^2) F L ( x , Q 2 ) at low x x and low Q^2 Q 2 was undertaken, in view of the advent of the EIC. The model includes the kinematic constraint F_L\sim Q^4 F L ∼ Q 4 as Q^2\rightarrow Q 2 → 0. It is based on the photon-gluon fusion mechanism suitably extrapolated to the region of low Q^2 Q 2 . Revised model was critically updated, extended to the EIC kinematic region, and e.g. contains new parameterisations of the parton distribution functions.

Badelek, Barbara↗

DOE OSTI2024

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Assessment of Model Hub Height Wind Speed Performance Using DOE Lidar Buoy Data

As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.

17 WIND ENERGY↗

NASA NTRS2013

Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing

Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation ensembles is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional Downscaling Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each downscaling system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each downscaling system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the downscaling, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.

Reanalysis↗

NASA NTRS2015

Climate Model Diagnostic Analyzer

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

cloud computing↗

NASA NTRS2013

Improved Understanding of the Modeled QBO Using MLS Observations and MERRA Reanalysis

The Quasi-Biennial Oscillation (QBO) dominates the variability of the tropical stratosphere on interannual time scales. The QBO has been shown to extend its influence into the chemical composition of this region through dynamical mechanisms. We have started our analysis using the realistic QBO internally generated by the Goddard Earth Observing System Version 5 (GEOS-5) general circulation model coupled to a comprehensive stratospheric and tropospheric chemical mechanism forced with observed sea surface temperatures over the past 33 years. We will show targeted comparisons with observations from NASAs Aura satellite Microwave Limb Sounder (MLS) and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis to provide insight into the simulation of the primary and secondary circulations associated with the QBO. Using frequency spectrum analysis and multiple linear regression we can illuminate the resulting circulations and deduce the strengths and weaknesses in their modeled representation. Inclusion of the QBO in our simulation improves the representation of the subtropical barriers and overall tropical variability. The QBO impact on tropical upwelling is important to quantify when calculating trends in sub-decadal scale datasets.

MERRA↗

NASA NTRS2017

WCRP Task Team for the Intercomparison of Reanalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. Several centers have emerged to produce new atmospheric reanalyses in various forms every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses. Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While WCRPs involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses, a central team of reanalyses expertise is not in place in the WCRP structure. The differences among reanalyses and their inherent uncertainties are some of the most important questions for both users and developers of reanalyses. Therefore, a collaborative effort to systematically assess and intercompare reanalyses would be a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to the TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means 2)To communicate new developments and best practices among the reanalyses producing centers 3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment 4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific community. This presentation outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far.

WDA↗

NASA NTRS2018

WCRP Task Team for the Intercomparison of ReAnalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. New atmospheric reanalyses in various forms are produced every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses, and regional reanalyses are emerging in addition to global reanalyses. Systems of data information are being developed to facilitate the intercomparison of reanalyses among themselves and with observations and climate change experiments (e.g. CREATE-IP and WRIT). Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While the World Climate Research Programme (WCRP) involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses. However, these efforts have previously not included an analysis of the differences among reanalyses and their inherent uncertainties which are important questions for both users and developers of reanalyses. Therefore, the WCRP Task Team for the Intercomparison of ReAnalyses (TIRA) task team has been formed as collaborative effort to address reanalyses role in WCRP. This constitutes a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means)To communicate new developments and best practices among the reanalyses producing centers3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific communityThis contribution outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far. Results for some pilot intercomparisons will also be discussed.

Bosilovich, Michael↗

NASA NTRS2014

Impact of Satellite Viewing-Swath Width on Global and Regional Aerosol Optical Thickness Statistics and Trends

We use the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical thickness (AOT) product to assess the impact of reduced swath width on global and regional AOT statistics and trends. Alongtrack and across-track sampling strategies are employed, in which the full MODIS data set is sub-sampled with various narrow-swath (approximately 400-800 km) and single pixel width (approximately 10 km) configurations. Although view-angle artifacts in the MODIS AOT retrieval confound direct comparisons between averages derived from different sub-samples, careful analysis shows that with many portions of the Earth essentially unobserved, spatial sampling introduces uncertainty in the derived seasonal-regional mean AOT. These AOT spatial sampling artifacts comprise up to 60%of the full-swath AOT value under moderate aerosol loading, and can be as large as 0.1 in some regions under high aerosol loading. Compared to full-swath observations, narrower swath and single pixel width sampling exhibits a reduced ability to detect AOT trends with statistical significance. On the other hand, estimates of the global, annual mean AOT do not vary significantly from the full-swath values as spatial sampling is reduced. Aggregation of the MODIS data at coarse grid scales (10 deg) shows consistency in the aerosol trends across sampling strategies, with increased statistical confidence, but quantitative errors in the derived trends are found even for the full-swath data when compared to high spatial resolution (0.5 deg) aggregations. Using results of a model-derived aerosol reanalysis, we find consistency in our conclusions about a seasonal-regional spatial sampling artifact in AOT Furthermore, the model shows that reduced spatial sampling can amount to uncertainty in computed shortwave top-ofatmosphere aerosol radiative forcing of 2-3 W m(sup−2). These artifacts are lower bounds, as possibly other unconsidered sampling strategies would perform less well. These results suggest that future aerosol satellite missions having significantly less than full-swath viewing are unlikely to sample the true AOT distribution well enough to obtain the statistics needed to reduce uncertainty in aerosol direct forcing of climate.

remote sensing↗

DOE OSTI2023

Climate, Variability, and Climate Sensitivity of “Middle Atmosphere” Chemistry Configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6))

Abstract Simulating whole atmosphere dynamics, chemistry, and physics is computationally expensive. It can require high vertical resolution throughout the middle and upper atmosphere, as well as a comprehensive chemistry and aerosol scheme coupled to radiation physics. An unintentional outcome of the development of one of the most sophisticated and hence computationally expensive model configurations is that it often excludes a broad community of users with limited computational resources. Here, we analyze two configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)) with simplified “middle atmosphere” chemistry at nominal 1 and 2° horizontal resolutions. Using observations, a reanalysis, and direct model comparisons, we find that these configurations generally reproduce the climate, variability, and climate sensitivity of the 1° nominal horizontal resolution configuration with comprehensive chemistry. While the background stratospheric aerosol optical depth is elevated in the middle atmosphere configurations as compared to the comprehensive chemistry configuration, it is comparable among all configurations during volcanic eruptions. For any purposes other than those needing an accurate representation of tropospheric organic chemistry and secondary organic aerosols, these simplified chemistry configurations deliver reliable simulations of the whole atmosphere that require 35% and 86% fewer computational resources at nominal 1 and 2° horizontal resolution, respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Bias Correction and Statistical Downscaling of Solar Radiation Using NA-CORDEX and the NSRDB

The current state-of-art for estimating long-term PV production uses long-term estimates of solar radiation variables, such as global horizontal irradiance (GHI), from previous years. This data is used in models such as the System Advisor Model (SAM) or PYSyst to predict annual production for a PV plant. This information is then used to estimate the production over the next 20 years (a typical plant lifetime) under the assumption that the variability over the current period is representative of the future. As the PV industry moves to extend plant lifetimes to 50 years the current assumptions of representativeness of weather may not be appropriate. This is especially true as our climate changes rapidly. To assess long-term PV production, future projections for solar radiation based on projected carbon emissions are readily available in regional and global climate models. However, climate model projections contain inherent biases that may need to be corrected for accurate analysis of future projections of climate variables. Several studies have analyzed projections of solar radiation for future years, however the accuracy of the model output compared to current and historic data has not been widely studied. Chen (2021) showed that available climate models do not accurately represent solar radiation in some cases, over-projecting GHI at the surface while under-projecting its obstructions, such as clouds and aerosols. This works aims to (1) increase understanding of the accuracy of solar radiation currently available in global and regional climate models and (2) implement bias correction through linear models based on reanalysis data compared to observed solar radiation. The latter aim will be conducted using available observed solar radiation data and modeled data from several regional climate models (RCMs). The bias correction method will be applied to projections of solar radiation resulting in a more accurate representation of the future of solar production.

climate data↗

NASA NTRSDate not supplied

High Latitude Atmospheric Rivers: Teleconnections and Impacts

Atmospheric rivers (ARs) are long, narrow synoptic scale features that act as moisture transport vehicles by moving water vapor poleward. They are typically found in storm tracks and often have profound implications for regional hydroclimate. For high latitude locales such as Antarctica and the Arctic, ARs are fewer in number but often have an outsized impact by either producing significant snow accumulation events, or, depending on their thermal characteristics, initiating melt events on ice sheets, ice shelves, and sea ice. In this study, we apply both reanalysis and climate model data to evaluate high latitude ARs for both the Arctic and Antarctic in the context of teleconnections and modes of variability. Specifically, we fold in both MERRA-2 and ERA5 reanalysis as well as historical and climate change simulations from the CESM2 (Community Earth System Model, Version 2) and E3SMv2 (Energy Exascale Earth System Model, v2) with multiple ensemble members to better understand natural variability versus climate change signals. An overview of AR climatology and impacts will be presented, including a baseline uncertainty quantification analysis using data from ARTMIP (the Atmospheric River Tracking Method Intercomparison Project). AR impacts during specific modes of variability will be characterized by diagnosing precipitation and boundary layer temperature associated with Ars influencing the cryosphere. Modes of variability investigated include the SAM (Southern Annular Mode), PDO (Pacific Decadal Oscillation), PSA2 (Pacific South American Mode 2), the IOD (Indian Ocean Dipole), and the AO (Arctic Oscillation).

Atmospheric Rivers↗

DOE OSTI2022

Limitations of reanalysis data for wind power applications

Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7-year-long data set of hourly, plant-level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1-year of records (2019) to evaluate an operational, high-resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

17 WIND ENERGY↗

NASA NTRSDate not supplied

Use of three-cornered hat error estimates in MERRA-2 to guide an improved reanalysis-Part 1

The three-cornered hat (3CH) method estimates the uncertainties of three different co-located model or observational data sets (Anthes and Rieckh, 2018; Sjoberg et al., 2021). Rieckh et al. (2021) used the 3CH method to compare the random error statistics of different global forecast and reanalysis models, as well as radio occultation (RO) and radiosonde observations. That study showed that the MERRA-2 reanalysis, while having smaller errors in the stratosphere than its predecessor MERRA, had larger errors in the troposphere than many of the other data sets analyzed. The MERRA-2 errors were particularly large in the tropics. In a collaborative effort between UCAR’s COSMIC (Constellation Observing System for Meteorology, Ionosphere and Meteorology) program and NASA’s Global Modeling and Assimilation Office (GMAO), we carried out further 3CH error diagnostics to help isolate the causes of these larger errors and help guide the development of an improved reanalysis. This presentation summarizes random error statistics associated with MERRA-2, ECMWF’s ERA5 reanalysis, and COSMIC-2 (C2) RO observations. We compute 3CH error variance estimates of refractivity, as well as temperature and specific humidity using UCAR’s COSMIC Data Analysis and Archive Center (CDAAC) improved 1D-variational (1D-Var) retrieval (wetPf2) over 15 latitude bands from 45S to 45N. The 1D-Var retrievals of specific humidity and temperature for C2 use NCEP’s Global Forecast System (GFS) as the background. Anthes et al. (2021) showed that it gives accurate estimates of temperature and specific humidity in the tropics and subtropics, even in the challenging environment of intense Hurricane Dorian (2019). This presentation confirms the previous results that MERRA-2 has significantly larger errors in the tropics and subtropics than either C2 or ERA5. Its errors are larger between 30S and 30N compared to 30-45 N-S latitudes, and are also larger over land compared to oceans. Most of the MERRA-2 refractivity errors come from specific humidity, except over land below 3 km where temperature errors are large. These results suggest that moist convection and atmospheric boundary layer physics in MERRA-2 may be responsible for a significant part of the higher uncertainties. These results are being used to guide GMAO in developing an improved next-generation reanalysis, as shown in a companion presentation submitted to this conference (El Akkraoui et al., 2021), which extends this study and describes improvements to MERRA-2 leading to the next GMAO reanalysis.

Jeremiah Sjoberg↗

NASA NTRSDate not supplied

Evaluating the Global Water and Energy Cycles in Reanalyses Using Ensemble Spread

Reanalyses provide continuous observation based global data over weather and climate time scales through observational assimilation. Ensemble forecasts are a component of modern data assimilation systems, providing information to the analysis process. Storing this spread over the duration of a climate reanalysis will provide insight to the variations of the model that underlies the reanalysis data. Global water and energy cycles (WECs) are a critical aspect of both weather and climate, yet there are some large gaps in the observation of key components as well as their representation in models and reanalyses. NASA’s Water and Energy cycle Studies (NEWS) program is developing a corrected and balanced budget data set that is closed at monthly and regional scales during the 21st century, so far. In this paper, we will evaluate reanalysis WEC components against the initial NEWS collection of observation, but integrate the analysis spread from the reanalysis data assimilation as a new perspective on the evaluation. The initial evaluation will cover a few more recent years using ERA5 10 member spread. As the GMAO’s production of the Modern-Era Retrospective analysis for Research and Applications 21st Century (MERRA-21C) progresses, the effort will include the MERRA-21C 32 member spread. Ultimately, we will explore the potential of the ensemble spread as a measure of uncertainty in the reanalysis.

Michael Bosilovich↗

DOE OSTI2025

Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0

This dataset presents a high-resolution historical streamflow reanalysis for NHDPlusV2 stream reaches across Puerto Rico (PR) spanning 1950 - 2019. The reanalysis is generated using the calibrated VIC-RAPID hydrologic modeling framework at the Hydrologic Unit Code Sub-basin (HUC08) scale, forced with sub-daily and daily meteorological forcings from Daymet. Runoff is simulated on 1- and 6-km grids, and the resulting total runoff is routed through the NHDPlusV2 river network using the RAPID routing model to produce Naturalized Streamflow Reanalysis. Where complete observational records are available over 1980 - 2019, streamflows are assimilated (substituted) and subsequently routed downstream through the river network to produce Assimilated Streamflow Reanalysis. The dataset includes streamflow outputs from eight distinct hydrologic modeling configurations along with key performanc evaluation metrics at daily and monthly scales, supporting a wide range of water resource applications. This dataset is derived to support the Non-Powered Dam Assessment, as well as 9505 Secure Water Assessment projects for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Ghimire et al. (2023), Kao et al. (2024), and Ghimire et al. (2025).

13 HYDRO ENERGY↗

DOE OSTI2021

Assessing the Representation of Synoptic Variability Associated With California Extreme Precipitation in CMIP6 Models

Abstract Days of extreme precipitation over California are evaluated in Coupled Model Intercomparison Project Phase 6 (CMIP6) models and the ERA‐Interim reanalysis. In the current climate, the model spread in composited precipitation on extreme precipitation days is closely related to the magnitude of composited integrated vapor transport (IVT) across models, a proxy for the intensity of atmospheric rivers. Most models underestimate the magnitude of IVT associated with extreme precipitation, according to ERA‐Interim. This is due mostly to the contribution of moisture, which almost all models overestimate, while the contribution of lower‐tropospheric wind speed is generally closer to the reanalyses. Moreover, most models underestimate the variance in the latitude of maxima of numerous variables among days of extreme California precipitation. That is, in the general circulation models there is a lack of diversity in the latitude of the disturbances bringing winter precipitation to California. In the future climate, most models project a decrease in the frequency of southward‐displaced disturbances among California extreme precipitation days. Hence, the greatest increases in extreme precipitation are over northern California. However, the historical underestimate of the latitudinal variance of disturbances calls into question the reliability of these projections. This bias should be especially considered for dynamical downscaling efforts over the region.

Norris, Jesse↗

NASA NTRS2014

Air Pollution Over the Ganges Basin and Northwest Bay of Bengal in the Early Postmonsoon Season Based on NASA MERRAero Data

The MERRA Aerosol Reanalysis (MERRAero) has been recently developed at NASA's Global Modeling Assimilation Office. This reanalysis is based on a version of the Goddard Earth Observing System-5 (GEOS-5) model radiatively coupled with Goddard Chemistry, Aerosol, Radiation, and Transport aerosols, and it includes assimilation of bias-corrected aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on both Terra and Aqua satellites. In October over the period 2002-2009, MERRAero showed that AOT was lower over the east of the Ganges basin than over the northwest of the Ganges basin: this was despite the fact that the east of the Ganges basin should have produced higher anthropogenic aerosol emissions because of higher population density, increased industrial output, and transportation. This is evidence that higher aerosol emissions do not always correspond to higher AOT over the areas where the effects of meteorological factors on AOT dominate those of aerosol emissions. MODIS AOT assimilation was essential for correcting modeled AOT mainly over the northwest of the Ganges basin, where AOT increments were maximal. Over the east of the Ganges basin and northwest Bay of Bengal (BoB), AOT increments were low and MODIS AOT assimilation did not contribute significantly to modeled AOT. Our analysis showed that increasing AOT trends over northwest BoB (exceeding those over the east of the Ganges basin) were reproduced by GEOS-5, not because of MODIS AOT assimilation butmainly because of the model capability of reproducing meteorological factors contributing to AOT trends. Moreover, vertically integrated aerosol mass flux was sensitive to wind convergence causing aerosol accumulation over northwest BoB.

AOT↗