Greenbelt, MD, United States
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Yang Q.,University of Washington | Wang M.,University of Washington | Overland J.E.,National Oceanic and Atmospheric Administration | Wang W.,National Oceanic and Atmospheric Administration | And 2 more authors.
Monthly Weather Review | Year: 2017

The impacts of model physics and initial sea ice thickness on seasonal forecasts of surface energy budget and air temperature in the Arctic during summer were investigated based on Climate Forecast System, version 2 (CFSv2), simulations. The model physics changes include the enabling of a marine stratus cloud scheme and the removal of the artificial upper limit on the bottom heat flux from ocean to sea ice. The impact of initial sea ice thickness was examined by initializing the model with relatively realistic sea ice thickness generated by the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Model outputs were compared to that from a control run that did not impose physics changes and used Climate Forecast System Reanalysis (CFSR) sea ice thickness. After applying the physics modification to either sea ice thickness initialization, the simulated total cloud cover more closely resembled the observed monthly variations of total cloud cover except for the midsummer reduction. Over the Chukchi-Bering Seas, the model physics modification reduced the seasonal forecast bias in surface air temperature by 24%. However, the use of initial PIOMAS sea ice thickness alone worsened the surface air temperature predictions. The experiment with physics modifications and initial PIOMAS sea ice thickness achieves the best surface air temperature improvement over the Chukchi-Bering Seas where the area-weighted forecast bias was reduced by 71% from 1.05Kdown to20.3K compared with the control run. This study supports other results that surface temperatures and sea ice characteristics are highly sensitive to the Arctic cloud and radiation formulations in models and need priority in model formulation and validation. © 2017 American Meteorological Society.

Chen L.-C.,The Interdisciplinary Center | van den Dool H.,National Oceanic and Atmospheric Administration | Becker E.,National Oceanic and Atmospheric Administration | Becker E.,Innovim LLC | Zhang Q.,National Oceanic and Atmospheric Administration
Journal of Climate | Year: 2017

In this study, precipitation and temperature forecasts during El Niño-Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982-2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

Collow T.W.,Rutgers University | Collow T.W.,INNOVIM LLC. | Robock A.,Rutgers University | Wu W.,Brookhaven National Laboratory
Journal of Geophysical Research: Atmospheres | Year: 2014

This study investigates the influences of soil moisture and vegetation on 30 h convective precipitation forecasts using the Weather Research and Forecasting model over the United States Great Plains with explicit treatment of convection. North American Regional Reanalysis (NARR) data were used as initial and boundary conditions. We also used an adjusted soil moisture (uniformly adding 0.10 m3/m3 over all soil layers based on NARR biases) to determine whether using a simple observationally based adjustment of soil moisture forcing would provide more accurate simulations and how the soil moisture addition would impact meteorological parameters for different vegetation types. Current and extreme (forest and barren) land covers were examined. Compared to the current vegetation cover, the complete removal of vegetation produced substantially less precipitation, while conversion to forest led to small differences in precipitation. Adding 0.10 m3/m 3 to the soil moisture with the current vegetation cover lowered the near surface temperature and increased the humidity to a similar degree as using a fully forested domain with no soil moisture adjustment. However, these temperature and humidity effects on convective available potential energy and moist enthalpy nearly canceled each other out, resulting in a limited precipitation response. Although no substantial changes in precipitation forecasts were found using the adjusted soil moisture, the similarity found between temperature and humidity forecasts using the increased soil moisture and those with a forested domain highlights the sensitivity of the model to soil moisture changes, reinforcing the need for accurate soil moisture initialization in numerical weather forecasting models. ©2014. American Geophysical Union. All Rights Reserved.

Gao Z.,Institute of Meteorological science of Jilin Province | Gao Z.,Latitude | Hu Z.-Z.,5830 University Research Court | Jha B.,5830 University Research Court | And 6 more authors.
Climate Dynamics | Year: 2014

In this work, authors examine the variabilities of precipitation and surface air temperature (T2m) in Northeast China during 1948-2012, and their global connection, as well as the predictability. It is noted that both the precipitation and T2m variations in Northeast China are dominated by interannual and higher frequency variations. However, on interdecadal time scales, T2m is shifted significantly from below normal to above normal around 1987/1988. Statistically, the seasonal mean precipitation and T2m are largely driven by local internal atmospheric variability rather than remote forcing. For the precipitation variation, circulation anomalies in the low latitudes play a more important role in spring and summer than in autumn and winter. For T2m variations, the associated sea surface pressure (SLP) and 850-hPa wind (uv850) anomalies are similar for all seasons in high latitudes with significantly negative correlations for SLP and westerly wind anomaly for uv850, suggesting that a strong zonal circulation in the high latitudes favors warming in Northeast China. The predictability of precipitation and T2m in Northeast China is assessed by using the Atmospheric Model Inter-comparison Project type experiments which are forced by observed sea surface temperature (SST) and time-evolving greenhouse gas (GHG) concentrations. Results suggest that T2m has slightly higher predictability than precipitation in Northeast China. To some extent, the model simulates the interdecadal shift of T2m around 1987/1988, implying a possible connection between SST (and/or GHG forcing) and surface air temperature variation in Northeast China on interdecadal time scales. Nevertheless, the precipitation and T2m variations are mainly determined by the unpredictable components which are caused by the atmospheric internal dynamic processes, suggesting low predictability for the climate variation in Northeast China. © 2013 Springer-Verlag (outside the USA).

Jha B.,5830 University Research Court | Jha B.,Innovim LLC | Kumar A.,5830 University Research Court | Hu Z.-Z.,5830 University Research Court
Climate Dynamics | Year: 2016

In this analysis, an update in the estimate of predictable component in the wintertime seasonal variability of atmosphere documented by Kumar et al. (J Clim 20: 3888–3901, 2007) is provided. The updated estimate of seasonal predictability of 200-hPa height (Z200) was based on North American Multi-Model Ensemble (NMME) forecast system. The seasonal prediction systems participating in the NMME have gone through an evolution over a 10-year period compared to models that were used in the analysis by Kumar et al. (J Clim 20: 3888–3901, 2007). The general features in the estimates of predictable signal conform with previous results—estimates of predictability remain high in the tropical latitudes and decrease towards the extratropical latitudes; and predictability in the initialized coupled seasonal forecast systems is still primarily associated with ENSO variability. As the horizontal and vertical resolution of the models used in the current analysis is generally higher, it did not have a marked influence on the estimate of the relative amplitude of predictable component. Although the analysis indicates an increase in the estimate of predictable component, however, it maybe related to the increase in ENSO related SST variance over 1982–2000 relative to 1950–2000 (over which the analysis of Kumar et al. in J Clim 20: 3888–3901, 2007 was). The focus of the analysis is wintertime variability in Z200 and its comparison with results in Kumar et al. (J Clim 20: 3888–3901, 2007), some analyses for summertime variability in Z200, and further, for sea surface temperature, 2-m temperature and precipitation are also presented. © 2016 Springer

Peng P.,5830 University Research Court | Kumar A.,5830 University Research Court | Jha B.,INNOVIM LLC
Climate Dynamics | Year: 2014

In this study, the climate mean, variability, and dominant patterns of the Northern Hemisphere wintertime mean 200 hPa geopotential height (Z200) in a CMIP and a set of AMIP simulations from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) are analyzed and compared with the NCEP/NCAR reanalysis. For the climate mean, it is found that a component of the bias in stationary waves characterized with wave trains emanating from the tropics into both the hemispheres can be attributed to the precipitation deficit over the Maritime continent. The lack of latent heating associated with the precipitation deficit may have served as the forcing of the wave trains. For the variability of the seasonal mean, both the CMIP and AMIP successfully simulated the geographical locations of the major centers of action, but the simulated intensity was generally weaker than that in the reanalysis, particularly for the center over the Davis Strait-southern Greenland area. It is also noted that the simulated action center over Aleutian Islands was southeastward shifted to some extent. The shift was likely caused by the eastward extension of the Pacific jet. Differences also existed between the CMIP and the AMIP simulations, with the center of actions over the Aleutian Islands stronger in the AMIP and the center over the Davis Strait-southern Greenland area stronger in the CMIP simulation. In the mode analysis, the El Nino-Southern Oscillation (ENSO) teleconnection pattern in each dataset was first removed from the data, and a rotated empirical orthogonal function (REOF) analysis was then applied to the residual. The purpose of this separation was to avoid possible mixing between the ENSO mode and those generated by the atmospheric internal dynamics. It was found that the simulated ENSO teleconnection patterns from both model runs well resembled that from the reanalysis, except for a small eastward shift. Based on the REOF modes of the residual data, six dominant modes of the reanalysis data had counterparts in each model simulation, though with different rankings in explained variance and some distortions in spatial structure. By evaluating the temporal coherency of the REOF modes between the reanalysis and the AMIP, it was found that the time series associated with the equatorially displaced North Atlantic Oscillation in the two datasets were significantly correlated, suggesting a potential predictability for this mode. © 2014 Springer-Verlag (outside the USA).

Furtado J.C.,Atmospheric and Environmental Research Inc. | Cohen J.L.,Atmospheric and Environmental Research Inc. | Butler A.H.,University of Colorado at Boulder | Riddle E.E.,National Oceanic and Atmospheric Administration | And 2 more authors.
Climate Dynamics | Year: 2015

Observational studies and modeling experiments illustrate that variability in October Eurasian snow cover extent impacts boreal wintertime conditions over the Northern Hemisphere (NH) through a dynamical pathway involving the stratosphere and changes in the surface-based Arctic Oscillation (AO). In this paper, we conduct a comprehensive study of the Eurasian snow–AO relationship in twenty coupled climate models run under pre-industrial conditions from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our analyses indicate that the coupled climate models, individually and collectively, do not capture well the observed snow–AO relationship. The models lack a robust lagged response between October Eurasian snow cover and several NH wintertime variables (e.g., vertically propagating waves and geopotential heights). Additionally, the CMIP5 models do not simulate the observed spatial distribution and statistics of boreal fall snow cover across the NH including Eurasia. However, when analyzing individual 40-year time slices of the models, there are periods of time in select models when the observed snow–AO relationship emerges. This finding suggests that internal variability may play a significant role in the observed relationship. Further analysis demonstrates that the models poorly capture the downward propagation of stratospheric anomalies into the troposphere, a key facet of NH wintertime climate variability irrespective of the influence of Eurasian snow cover. A weak downward propagation signal may be related to several factors including too few stratospheric vortex disruptions and weaker-than-observed tropospheric wave driving. The analyses presented can be used as a roadmap for model evaluations in future studies involving NH wintertime climate variability, including those considering future climate change. © 2015 Springer-Verlag Berlin Heidelberg

Pan C.,The Interdisciplinary Center | Flynn L.,National Oceanic and Atmospheric Administration | Buss R.,Innovim Company LLC | Wu X.,National Oceanic and Atmospheric Administration | And 2 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Launched on October 28, 2011, the S-NPP satellite carried an ozone mapping and profiler suite (OMPS) of sensors. OMPS opened its aperture door on January 26, 2012 to begin its Earth observation mission. After an early orbit checkout of the sensors for a couple of months and an intensive evaluation of the sensor data for several more months, an initial on-orbit calibration for OMPS was established using data acquired during these periods. To date in 2013, this sensor system calibration has been applied to produce OMPS nadir sensor data records (SDRs) and the resulting ozone environment data records (EDRs). This paper provides an evaluation of the combined performance of the orbital OMPS nadir sensors coupled with the ground data processing system for the current SDR's provisional status, and offers lessons learned during the first 1.5 years of operation. Examples of the sensors' short-term and limited long-term responses are provided, including cross-comparisons of OMPS EDRs with concurrent solar backscatter ultraviolet instrument (SBUV2) data from other NOAA orbital sensors, to illustrate the on-orbit stability of the data products despite some secular changes to the calibration and sensor. © 2008-2012 IEEE.

Lynnes C.,NASA | Vollmer B.,NASA | Olsen E.,Jet Propulsion Laboratory | Wolfe R.,NASA | And 2 more authors.
HPDC 2010 - Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing | Year: 2010

NASA provides a wide variety of Earth-observing satellite data products to a diverse community. These data are annotated with quality information in a variety of ways, with the result that many users struggle to understand how to properly account for quality when dealing with satellite data. To address this issue, a Data Quality Screening Service (DQSS) is being implemented for a number of datasets. The DQSS will enable users to obtain data files in which low-quality pixels have been filtered out, based either on quality criteria recommended by the science team or on the user's particular quality criteria. The objective is to increase proper utilization of this critical quality data in science data analysis of satellite data products. Copyright 2010 ACM.

Collow T.W.,INNOVIM LLC | Collow T.W.,National Oceanic and Atmospheric Administration | Wang W.,National Oceanic and Atmospheric Administration | Kumar A.,National Oceanic and Atmospheric Administration | Zhang J.,University of Washington
Monthly Weather Review | Year: 2015

Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean-atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified tomore physically reasonable settings-namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice-ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently. © 2015 American Meteorological Society.

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