Engineering topics

Cullather, Richard I.

Publications and source records attributed to Cullather, Richard I..

14 records

DOE OSTI2024

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Metrics for Improved Reanalyses in Polar Regions

Atmospheric reanalyses are widely used for a variety of scientific endeavors in the Arctic and Antarctic. Reanalyses are used as boundary conditions for a regional and process-based models, for climate model validation, and for diagnostic analysis of physical processes, weather and climatic events. However, reanalyses are typically global and often do not account for specific, regional considerations, such as for polar regions. In this work, we provide a brief evaluation of a prototype for a new GMAO reanalysis, which incorporates higher spatial resolution, an updated approach for data assimilation, and a revised atmospheric model. We identify differences in the representation of the Arctic atmosphere in comparison to recent reanalyses. Furthermore, we provide a forum for Arctic scientists to consider the future improvements for reanalyses, and seek feedback for the following questions: 1) What are important performance factors to consider in evaluating new reanalyses? 2) What physical processes should be incorporated into new reanalyses? 3) What spatio-temporal scales should be considered?

Parker, Chelsea L.↗

NASA NTRS2019

Arctic Cloud Radiative Forcing in Contemporary Atmospheric Reanalyses

Arctic clouds play an important role in modifying the surface energy balance. In the Arctic, clouds are thought to influence the underlying sea ice cover through changing downwelling longwave radiative fluxes to the surface and through the selective reflection of the shortwave flux in summer. Atmospheric reanalyses are generally thought to have a poor representation of cloud processes at high latitudes, although the representation of trends over the perennial Arctic sea ice pack is less well known. Here, atmospheric energy fluxes are examined at the top of the atmosphere from contemporary reanalyses in comparison to satellite measurements from the CERES-EBAF version 4.1 product. The principal reanalyses examined are the NASA MERRA-2, the ECMWF ERA5 and ERA-Interim, the JRA-55, and the regional Arctic System Reanalysis version 2. In agreement with previous observation-based studies, changes with time in the shortwave cloud radiative forcing in reanalyses are found to be negligible despite strong trends in the absorbed shortwave. Over the full satellite period, there is large disagreement in the seasonality of longwave cloud forcing trends. These trends are reduced during the CERES-EBAF observing period (2003-present). An examination of these trends with respect to sea ice cover changes in each of the reanalyses is conducted.

Cullather, Richard I.↗

NASA NTRS2019

Numerical Reanalyses as a Gateway to Arctic Synthesis

Reanalyses are regularly gridded, retrospective depictions of the physical earth system, which are produced through the correction of a short-term forecast to available observations. In the Arctic, reanalyses are particularly well suited to marshal the sparse observing network to provide a plausible, multivariate representation of conditions. Atmospheric reanalyses such as MERRA-2 (NASA Modern-Era Retrospective analysis for Research and Applications, version 2) and ocean reanalyses such as SODA3 (Univ. Maryland Simple Ocean Data Assimilation version 3) are widely used in Arctic research for diagnostic studies of circulation, model evaluation, and as boundary conditions for a variety of process models. Here, we provide examples that illustrate the utility of reanalyses for providing information on the spatial and temporal scales of recent, rapid changes in the Arctic. Recent trends in Arctic surface temperatures, surface melt over Greenland and Arctic glaciers, and evolving freshwater conditions in the Arctic Ocean are examples where reanalyses can provide information that cannot easily be obtained via other means. These examples provide information on the scale, magnitude, and the uncertainty of recent Arctic change and provide a context for future scenarios. We further quantify uncertainties in key reanalyses variables and approaches for addressing these issues.

Arctic↗

NASA NTRS2018

The Influence of Prescribed Boundary Conditions on Near-Surface Temperature over the Arctic in the MERRA-2 Atmospheric Model

An accurate historical record of evolving Arctic conditions is integral to furthering our understanding of climate processes and to providing a foundation for predicting future climate scenarios in northern high latitudes. Atmospheric reanalyses are seen as an important source of information on the recent past for the data-sparse Arctic region. An assessment of near-surface Arctic air temperatures finds significant discrepancies among the various modern reanalyses. An important point is the treatment of surface boundary conditions: specifically, the sea ice cover and sea surface temperatures (SSTs) over the Arctic Ocean. Reanalyses use different methodologies and data sources for SSTs and sea ice concentration boundary forcing. Notably, the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) both use boundary forcing derived from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) over an extended, overlapping period of time. This allows for an examination of differences between the two systems while both concurrently employ the same fractional sea ice coverage. To further understand these differences, an ensemble of AMIP-style simulations using the MERRA-2 atmospheric model - but without data assimilation - shows considerable differences in Arctic temperatures as compared to reanalyses, particularly in autumn and winter months. Results from the AMIP simulations suggest that the surface representation over sea ice used in the MERRA-2 model provides an intrinsic warm bias and obfuscates Arctic Amplification, an established feature present in observations and reanalyses. An additional ensemble of AMIP-style simulations using the MERRA-2 atmospheric model was performed using boundary conditions derived from the ERA-Interim reanalysis. An in-depth comparison of surface temperatures over the Arctic from the two reanalyses and two AMIP-style ensembles will be presented, along with an assessment of the effects of the varying Arctic temperature time series on the atmospheric general circulation and energy budget.

Collow, Allison B. Marquardt↗

NASA NTRS2018

Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the ‘‘new Arctic’’). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

Mcpartland, Linette N.↗

NASA NTRS2017

Inter-Relationship Between Subtropical Pacific Sea Surface Temperature, Arctic Sea Ice Concentration, and the North Atlantic Oscillation in Recent Summers and Winters

The inter-relationship between subtropical western-central Pacific sea surface temperatures (STWCPSST), sea ice concentration in the Beaufort Sea (SICBS), and the North Atlantic Oscillation (NAO) are investigated for the last 37 summers and winters (1980-2016). Lag-correlation of the STWCPSST×(-1) in spring with the NAO phase and SICBS in summer increases over the last two decades, reaching r = 0.4-0.5 with significance at 5 percent, while winter has strong correlations in approximately 1985-2005. Observational analysis and the atmospheric general circulation model experiments both suggest that STWCPSST warming acts to increase the Arctic geopotential height and temperature in the following season. This atmospheric response extends to Greenland, providing favorable conditions for developing the negative phase of the NAO. SIC and surface albedo tend to decrease over the Beaufort Sea in summer, linked to the positive surface net shortwave flux. Energy balance considering radiative and turbulent fluxes reveal that available energy that can heat surface is larger over the Arctic and Greenland and smaller over the south of Greenland, in response to the STWCPSST warming in spring. XXXX Arctic & Atlantic: Positive upper-level height/T anomaly over the Arctic and Greenland, and a negative anomaly over the central-eastern Atlantic, resembling the (-) phase of the NAO. Pacific: The negative height/T anomaly over the mid-latitudes, along with the positive anomaly over the STWCP, where 1degC warming above climatology is prescribed. Discussion: It is likely that the Arctic gets warm and the NAO is in the negative phase in response to the STWCP warming. But, there are other factors (e.g., internal variability) that contribute to determination of the NAO phase: not always the negative phase of the NAO in the event of STWCP warming (e.g.: recent winters and near neutral NAO in 2017 summer).

Lim, Young-Kwon↗

NASA NTRS2017

An Intercomparison of Changes Associated with Earth's Lower Tropospheric Temperature Using Traditional and AMIP-Style Reanalyses

Reanalyses have become an integral tool for evaluating regional and global climate variations, and an important component of this is modifications to the energy budget. Reductions in Arctic Sea ice extent has induced an albedo feedback, causing the Arctic to warm more rapidly than anywhere else in the world, referred to as "Arctic Amplification." This has been demonstrated by observations and numerous reanalyses, including the Modern Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). However, the Arctic Amplification signal is non-existent in a ten member ensemble of the MERRA-2 Atmospheric Model Intercomparison Project (M2AMIP) simulations, using the same prescribed climate forcing, including Sea Surface Temperature (SST) and ice. An evaluation of the temperature tendency within the lower troposphere due to radiation, moisture, and dynamics as well as the surface energy budget in MERRA-2 and M2AMIP will demonstrate that despite identical prescribed SSTs and sea ice in both versions, enhanced warming in the Arctic in MERRA-2 is in response to the analysis increment tendency due to temperature observations. Furthermore, the role of boundary conditions, model biases and changes in observing systems on the Arctic Amplification signal will be assessed. Literature on the topic of Arctic Amplification demonstrates that the enhanced warming begins in the mid-1990s. Anomalously warm Arctic SST in the early time period of MERRA-2 can mute the trend in Arctic lower troposphere temperature without the constraint of observations in M2AMIP. Additionally, MERRA-2 uses three distinct datasets of SST and sea ice concentration, which also plays a role.

Arcti↗

NASA NTRS2017

Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Arctic↗

NASA NTRS2017

Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office

Sea Ice↗

NASA NTRS2016

Analysis of the Warmest Arctic Winter, 2015-2016

December through February 2015-2016 defines the warmest winter season over the Arctic in the observational record. Positive 2m temperature anomalies were focused over regions of reduced sea ice cover in the Kara and Barents Seas and southwestern Alaska. A third region is found over the ice-covered central Arctic Ocean. The period is marked by a strong synoptic pattern which produced melting temperatures in close proximity to the North Pole in late December and anomalous high pressure near the Taymyr Peninsula. Atmospheric teleconnections from the Atlantic contributed to warming over Eurasian high-latitude land surfaces, and El Niño-related teleconnections explain warming over southwestern Alaska and British Columbia, while warm anomalies over the central Arctic are associated with physical processes including the presence of enhanced atmospheric water vapor and an increased downwelling longwave radiative flux. Preconditioning of sea ice conditions by warm temperatures affected the ensuing spring extent.

Barents and Kara Seas↗

NASA NTRS2015

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

SIPN↗

NASA NTRS2014

Evaluation of the Surface Representation of the Greenland Ice Sheet in a General Circulation Model

Simulated surface conditions of the Goddard Earth Observing System model, version 5 (GEOS 5) atmospheric general circulation model (AGCM) are examined for the contemporary Greenland Ice Sheet (GrIS). A surface parameterization that explicitly models surface processes including snow compaction, meltwater percolation and refreezing, and surface albedo is found to remedy an erroneous deficit in the annual net surface energy flux and provide an adequate representation of surface mass balance (SMB) in an evaluation using simulations at two spatial resolutions. The simulated 1980-2008 GrIS SMB average is 24.7+/-4.5 cm yr(- 1) water-equivalent (w.e.) at.5 degree model grid spacing, and 18.2+/-3.3 cm yr(- 1) w.e. for 2 degree grid spacing. The spatial variability and seasonal cycle of the simulation compare favorably to recent studies using regional climate models, while results from 2 degree integrations reproduce the primary features of the SMB field. In comparison to historical glaciological observations, the coarser resolution model overestimates accumulation in the southern areas of the GrIS, while the overall SMB is underestimated. These changes relate to the sensitivity of accumulation and melt to the resolution of topography. The GEOS-5 SMB fields contrast with available corresponding atmospheric models simulations from the Coupled Model Intercomparison Project (CMIP5). It is found that only a few of the CMIP5 AGCMs examined provide significant summertime runoff, a dominant feature of the GrIS seasonal cycle. This is a condition that will need to be remedied if potential contributions to future eustatic change from polar ice sheets are to be examined with GCMs.

AGCM↗

NASA NTRS2010

The Energy Budget of the Polar Atmosphere in MERRA

Components of the atmospheric energy budget from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are evaluated in polar regions for the period 1979-2005 and compared with previous estimates, in situ observations, and contemporary reanalyses. Closure of the energy budget is reflected by the analysis increments term, which results from virtual enthalpy and latent heating contributions and averages -11 W/sq m over the north polar cap and -22 W/sq m over the south polar cap. Total energy tendency and energy convergence terms from MERRA agree closely with previous study for northern high latitudes but convergence exceeds previous estimates for the south polar cap by 46 percent. Discrepancies with the Southern Hemisphere transport are largest in autumn and may be related to differences in topography with earlier reanalyses. For the Arctic, differences between MERRA and other sources in TOA and surface radiative fluxes maximize in May. These differences are concurrent with the largest discrepancies between MERRA parameterized and observed surface albedo. For May, in situ observations of the upwelling shortwave flux in the Arctic are 80 W/sq m larger than MERRA, while the MERRA downwelling longwave flux is underestimated by 12 W/sq m throughout the year. Over grounded ice sheets, the annual mean net surface energy flux in MERRA is erroneously non-zero. Contemporary reanalyses from the Climate Forecast Center (CFSR) and the Interim Re-Analyses of the European Centre for Medium Range Weather Forecasts (ERA-I) are found to have better surface parameterizations, however these collections are also found to have significant discrepancies with observed surface and TOA energy fluxes. Discrepancies among available reanalyses underscore the challenge of reproducing credible estimates of the atmospheric energy budget in polar regions.

Cullather, Richard I.↗