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Lauren Andrews

Publications and source records attributed to Lauren Andrews.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Connecting Global Modes of Variability to Climate in High Mountain Asia

Oscillations in global modes of variability (MoV) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Un-derstanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic Oscillation (AO), Eurasian teleconnection, Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (Nino34), with winter and summer climate in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2-m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and also using a lagged correlation analysis to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and Central China in both winter and summer and a positive correlation in Western China in summer. PRECTOT has a positive correlation with all MoV in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, espe-cially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time-windows and specific regions within HMA that exhibit high correlation between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe.

teleconnections↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗
NASA NTRS2022

Historical Context of a Recent Warming Event over Wilkes Land, Antarctica from Numerical Analyses

Recent melt events over polar ice sheets are of interest for identifying regions of vulnerability in a warming climate. On 11-March 2022 a ridging pattern formed in the jet stream near Enderby Land, Antarctica and progressed eastward. The ridge deepened markedly into a classic omega-shaped blocking pattern (Fig. 1), extending into the plateau by 14-March. The blocking pattern is remarkable for the circulation strength – which forced warm air and moisture into the high interior of the continent, its poleward extent, and its persistence. Here, we use GMAO FP, MERRA-2 analyses, conventional observations, and AIRS/Aqua L3 data to characterize the event. We wish to examine, (1) How unusual was the event relative to available analysis records, (2) What were the effects of the event on surface mass balance (SMB)? and (3) Is there a relation to general circulation trends that led to this event?

Richard Cullather↗
NASA NTRS2022

Modeling the Influence of Moulin Shape on Subglacial Hydrology

Subglacial models represent moulins as cylinders or cones, but field observations suggest that the upper part of moulins in the Greenland Ice Sheet has more complex shapes. These more complex shapes should cause englacial water storage within moulins to vary as a function of depth, a relationship not currently accounted for in models. Here, we use a coupled englacial-subglacial channel model to explore how moulin shape affects depth-dependent moulin water storage and water pressure dynamics within a subglacial channel. We simulate seven different moulin shapes across a range of moulin sizes. We find that the englacial storage capacity at the water level is the main control over the daily water level oscillation range and that depth-varying changes in englacial water storage control the temporal shape of this oscillation. Further, the cross-sectional area of the moulin within the daily oscillation range, but not above or below this range, controls pressures within the connected subglacial channel. Specifically, large cross-sectional areas can dampen daily to weekly oscillations that occur in the surface meltwater supply. Our findings suggest that further knowledge of the shape of moulins around the equilibrium water level would improve englacial storage parameterization in subglacial hydrological models and aid predictions of hydrodynamic coupling.

Moulin↗
NASA NTRS2021

Transient evolution of basal drag during glacier slip

Glacier slip is usually described using steady-state sliding laws that relate drag, slip velocity and effective pressure, but where subglacial conditions vary rapidly transient effects may influence slip dynamics. Here we use results from a set of laboratory experiments to examine the transient response of glacier slip over a hard bed to velocity perturbations. The drag and cavity evolution from lab experiments are used to parameterize a rate-and-state drag model that is applied to observations of surface velocity and ice-bed separation from the Greenland ice sheet. The drag model successfully predicts observed lags between changes in ice-bed separation and sliding speed. These lags result from the time (or displacement) required for cavities to evolve from one steady-state condition to another. In comparing drag estimates resulting from applying rate-and-state and steady-state slip laws to transient data, we find the peaks in drag are out of phase. This suggests that in locations where subglacial conditions vary on timescales shorter than those needed for cavity adjustment transient slip processes control basal drag.

Glacier↗
NASA NTRSDate not supplied

Arctic and Boreal Model Development in GMAO

Ongoing research in the GMAO seeks to improve the representation atmospheric, land, and ocean processes in the boreal Arctic and other polar regions. This talk touches on a range of topics, including improvements to sea ice and land ice representation in our subseasonal-to-seasonal prediction system and the initiation of a new collaborative effort with the Cryospheric Sciences Laboratory to develop a high resolution, polar-focused atmospheric reanalysis.

Arctic Boreal↗
NASA NTRSDate not supplied

PolarMERRA: A collaborative effort for NASA’s polar reanalysis

The polar atmosphere is often poorly represented in global models, adversely impacting analyses in these regions and forecasts on much larger scales. PolarMERRA is an initiative that brings together the modeling expertise from NASA’s global modeling and assimilation office (GMAO) and observational expertise from NASA’s Cryospheric sciences lab to assess and improve the representation of polar processes in the NASA’s modelling system in preparation for upcoming and next generation reanalyses. Model improvements in resolution, physics, and data assimilation have been guided by assessment of model skill in the polar regions. Preliminary results from a novel validation framework indicate a reduction in near surface air temperature, turbulent fluxes, and precipitation biases.

Chelsea Parker↗
NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗
NASA NTRSDate not supplied

Improving Modeled Snow Mass in GEOS with Snow Cover Fraction Assimilation

The NASA Goddard Earth Observing System (GEOS) Forward Processing (FP) global weather analysis often considerably underestimates near-surface air temperature during the spring snow melt season, which can be caused by excessive simulated snow cover. To mitigate such errors, we developed an empirical snow cover fraction (SCF) analysis to assimilate SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) into the GEOS Catchment land surface model. Results obtained with a land-only version of the system indicate that the SCF analysis reduces errors in snow mass and snow depth by ~10% when compared to in situ measurements from the Snow Telemetry (SNOTEL), Canadian historical Snow Water Equivalent dataset (CanSWE), and Global Historical Climatology Network - Daily (GHCN-Daily) networks.

Rolf Reichle↗