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Richard I Cullather

Publications and source records attributed to Richard I Cullather.

15 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Sea-Level Rise Projections for Maryland 2023

This report provides updated projections of sea-level rise in the State of Maryland, as required at least every five years by the Maryland Commission on Climate Change Act of 2015. The Sixth Assessment Report (AR6) for the Intergovernmental Panel on Climate Change (IPCC) produced a database of probabilistic projections of relative sea-level rise for tide-gauge locations around the world that reflect differences caused by thermal expansion, glacier and polar ice sheet melting, winds and currents, and vertical land motion. These are available online for seven locations in Maryland and the District of Columbia. Considered together with extrapolations of tide gauge and satellite observations, these projections indicate that sea level rise will likely be between 0.3 m (1 ft) and 0.5 m (1.6 ft) by 2050 (from a 2005 starting point). Beyond the middle of the century, the pathway of greenhouse gas emissions increasingly matters. Under the Current Commitments scenario, the best estimate of sea-level rise in 2100 is 0.8 m (2.7 ft) and sea level will likely rise between 0.6 m (2.0 ft) and 1.1 m (3.5 ft), barring unexpected processes driving rapid ice-sheet melt. Even with exceptionally rapid ice loss, it is very unlikely that it would exceed 1.5 m (4.9 ft).

Donald F Boesch↗
NASA NTRS2020

Intercomparison of Precipitation Estimates Over the Southern Ocean from Atmospheric Reanalyses

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea-ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from 8 global reanalyses produce similar spatial patterns between 2000-2010, although their annual means vary by about 250 mm yr -1 (or 26 percent of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat-derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr -1 resulting in an accumulation of about 4.5 mm yr -1 . While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Linette N. Boisvert↗
NASA NTRS2020

Anomalous Circulation in July 2019 Resulting in Mass Loss on the Greenland Ice Sheet

Current mass loss on the Greenland Ice Sheet (GrIS) includes a significant contribution from surface runoff. The circumstances associated with melt events are important for understanding the global sea level contribution of the GrIS. In late July 2019, surface melt occurred over 62% of the GrIS, including Summit Station. The general circulation leading to the event is found to be dissimilar to 2012 and other events documented in the 21st century, with warm air associated with remote atmospheric blocking over western Europe eventually transiting west to the GrIS. Gravimetric data indicate that the 2019 summer mass loss was 137 Gt more than the 2004–2010 median, or about 92% of the 2012 record. Mass loss during the event was significant in GrIS northeastern regions in 2019. As compared to 2012, the southwest did not fully participate. Similar circulation patterns have not previously been associated with significant melt.

Richard I Cullather↗
NASA NTRS2019

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

The inter-relationship between subtropical western–central Pacific sea surface temperatures (STWCPSST), sea ice concentrations in the Beaufort Sea (SICBS), and the North Atlantic Oscillation (NAO) in summer are investigated over the period 1980–2016. It is shown that the Arctic response to the remote impact of the Pacific SST is more dominant in recent summers, leading to a frequent occurrence of the negative phase of the NAO following the STWCPSST increase. Lag–correlations of STWCPSST positive (negative) anomalies in spring with the negative (positive) NAO and SICBS loss (recovery) in summer have increased over the last two decades, reaching r = 0.4–0.5 with significance at the 5 percent level. Both observations and the atmospheric general circulation model experiments suggest that the positive STWCPSST anomaly and subsequent planetary-scale wave propagation act to increase the Arctic upper-level geopotential heights and temperatures in the following season. This response extends to Greenland, providing favorable conditions for developing the negative phase of the NAO. Connected with this atmospheric response, SIC and surface albedo decrease with an increase in the surface net shortwave flux over the Beaufort Sea. Examination of the surface energy balance (radiative and turbulent fluxes) reveals that surplus energy that can heat the surface increases over the Arctic, enhancing the SIC reduction.

SST↗
NASA NTRSDate not supplied

The Land Ice Representation in the NASA Goddard Seasonal Forecasting System

Models used in seasonal forecasting systems have aimed for a level of sophistication comparable to Earth system models by incorporating complex physical processes, including those relevant to the polar regions. Here, we examine the polar surface climate in version 3 of the NASA Goddard Earth Observing System Sub-seasonal to Seasonal prediction system (GEOS S2S v3). The model is composed of the ⅟₂̊ resolution Jason4.0version of the GEOS AGCM and the MOM5 ocean model at ⅟₄̊ resolution. The GEOSS2S model incorporates interactive aerosols and two-moment cloud microphysics. As compared to version 2, the model incorporates improved radiative transfer and are designed diurnal cycle atmosphere-ocean interface layer. This study aims to understand how the version 3 model compares with version 2, and with contemporary Earth system models.

Richard I Cullather↗
NASA NTRSDate not supplied

PolarMERRA: A Polar-Focused Global Reanalysis Project for Scientific and Stakeholder Needs

The adequate modeling of physical processes in the Arctic and Antarctic is vital for developing prediction capabilities and for obtaining an understanding of rapidly evolving polar conditions, including their potential global impacts. These processes are often poorly represented in global models and reanalyses, owing to a legacy modeling focus on midlatitude processes, as well as a scarcity of observations needed for model development in polar regions. The polarMERRA initiative, a joint effort between NASA’s Cryospheric Sciences and Modeling and Prediction programs, seeks to improve the representation of cryospheric and polar atmospheric processes and to develop an open-source framework for a quantitively evaluation of polar-relevant variables against current and future satellite and in-situ observations, models, and reanalyses. Here, we evaluate the impacts of spatial resolution and modifications to sea ice, ice sheet, and atmospheric parameterizations on the representation of high latitude conditions by using a quantitative scorecard approach that leverages NASA’s extensive satellite record. Investigations are conducted using the NASA Goddard Earth Observing System model (GEOS) and its data assimilation system (GEOS DAS). Through a quantitative identification of process deficiencies, bias reductions in key surface variables, including temperature and precipitation over cryospheric surfaces, may be achieved. The polarMERRA project additionally seeks to identify additional data sources for use in the GEOS DAS, and to incorporate data and parameterization improvements into future model and reanalysis products for scientific and stakeholder use.

Lauren C Andrews↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
NASA NTRSDate not supplied

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗
NASA NTRSDate not supplied

Supporting Arctic Research, Engagement, and Policy With GMAO’s Next Generation Reanalysis & Prediction Systems

Modeling efforts at NASA aim to advance scientific understanding of the Earth system and its response to natural and human-induced changes and to improve our ability to predict climate, weather, and natural hazards. These efforts are particularly relevant for communities, scientists and stakeholders working and living in the Arctic. Here, we highlight current and planned Earth system prediction and data assimilation products from the Global Modeling and Assimilation Office (GMAO). We invite feedback and discussion on GMAO’s model and reanalysis development in support of Arctic research, engagement, and policy.

Lauren C Andrews↗
NASA NTRSDate not supplied

The Impact of Ice Sheet Surface Representation on Surface Mass Balance in the Goddard Earth Observing System

Surface conditions and their impacts on surface mass balance (SMB) play important roles in ice sheet dynamics and ice sheet interactions with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) – an ecosystem of models and reanalyses – represent ice sheet and glacier SMB components, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. We find the successful representation of ice sheet surface mass balance in MERRA-2, and similar systems, is partly due to compensating biases in surface energy budget. In the atmospheric system, subsequent changes to cloud microphysics and in the longwave radiative transfer model are found to have reduced biases in surface net longwave radiation fluxes while exacerbating surface net shortwave radiation biases, producing erroneously high near-surface temperatures and surface melt in summer months. These issues can be exacerbated by poor experiment initialization and the use of two-moment cloud microphysics within the GEOS ocean-atmosphere coupled system. Here, we document the spatial and temporal extent and causes of these biases in the ice sheet surface energy budget across GEOS systems and implement a range of model improvements to mitigate these issues. We examine the combined impact on these changes on regional energy budget and SMB using both free running and replay experiments (to simulate the impact in reanalyses). Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet SMB in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗