Atmospheric & Oceanic Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2747
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Item High Resolution Remote Sensing Observations of Summer Sea Ice(2022) Buckley, Ellen Margaret; Farrell, Sinéad L; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light.Item Seasonal and Interannual Ocean-Atmosphere Variability in the Tropical Atlantic: Observed Structure and Model Representation(2008-08-03) Chang, Ching-Yee; Carton, James A.; Nigam, Sumant; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tropical Atlantic is clamped by the South America (the Amazon) in the west and Africa (the Sahel) in the east. These two regions have been undergoing significant climate/environment changes for decades. In order to use climate models to study the impacts of these changes, climate models need to be able to well simulate the seasonal climate of the tropical Atlantic sector. The first part of this dissertation focuses on the representation of the seasonal cycle in the CCSM3 coupled atmosphere-ocean model. CCSM3 SST has a north-south dipole pattern of bias centered at the latitude of the thermal equator, resembling the observed pattern of interannual climate variability in boreal spring. Along the equator in boreal spring CCSM3 exhibits striking westerly winds at the surface, reminiscent of the pattern of climate variability in boreal summer. The westerly winds cause deepening of the eastern thermocline that keeps the east warm despite enhanced coastal upwelling. Next, a comparison is made with a simulation using historical SST to force the atmospheric model (CAM3) in order to deduce information about the origin of bias in CCSM3. The patterns of bias in CAM3 resemble that in CCSM3, indicating that the source of the bias in CCSM3 may be traced to difficulties in the atmospheric model. The next chapter presents a modeling study of the origin of the westerly wind bias CAM3 by using a steady-state linearized atmospheric model. The results indicate that underestimation of rainfall over the eastern Amazon region can lead to the westerly bias in equatorial Atlantic surface winds. They suggest that efforts to reduce coupled model biases, especially seasonal ones, must target continental biases, even in the deep Tropics where ocean-atmosphere interaction generally rules. The fourth chapter investigates the relationship between the two predominate modes of Tropical Atlantic interannual variability. The leading modes of Tropical Atlantic SST variability in boreal spring and summer are shown to be related, with the spring meridional mode leading into summer equatorial mode. The presence of a meridional mode with warm SST anomalies in the southern tropics in spring leads to a warm phase equatorial mode in summer, and vice-versa. This modal linkage occurs independently of climate variability in other ocean basins (e.g., ENSO). Atmospheric diabatic heating associated with a meridional shift of the Inter-Tropical Convergence Zone plays an important role in this relationship. The identification of this relationship enhances the prospects for prediction of boreal summer rainfall over the Guinea Coast of equatorial Africa.Item ENSEMBLE KALMAN FILTER EXPERIMENTS WITH A PRIMITIVE-EQUATION GLOBAL MODEL(2005-06-30) Miyoshi, Takemasa; Kalnay, Eugenia; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The ultimate goal is to develop a path towards an operational ensemble Kalman filtering (EnKF) system. Several approaches to EnKF for atmospheric systems have been proposed but not systematically compared. The sensitivity of EnKF to the imperfections of forecast models is unclear. This research explores two questions: 1. What are the relative advantages and disadvantages of two promising EnKF methods? 2. How large are the effects of model errors on data assimilation, and can they be reduced by model bias correction? Chapter 2 contains a theoretical review, followed by the FORTRAN development and testing of two EnKF methods: a serial ensemble square root filter (serial EnSRF, Whitaker and Hamill 2002) and a local EnKF (LEKF, Ott et al. 2002; 2004). We reproduced the results obtained by Whitaker and Hamill (2002) and Ott et al. (2004) on the Lorenz (1996) model. If we localize the LEKF error covariance, LEKF outperforms serial EnSRF. We also introduce a method to objectively estimate the optimal covariance inflation. In Chapter 3 we apply the two EnKF methods and the three-dimensional variational method (3DVAR) to the SPEEDY primitive-equation global model (Molteni 2003), a fast but relatively realistic model. Perfect model experiments show that EnKF greatly outperforms 3DVAR. The 2-day forecast "errors of the day" are very similar to the analysis errors, but they are not similar among different methods except in low ensemble dimensional regions. Overall, serial EnSRF outperforms LEKF, but their difference is substantially reduced if we localize the LEKF error covariance or increase the ensemble size. Since LEKF is much more efficient than serial EnSRF when using parallel computers and many observations, LEKF would be the only feasible choice in operations. In Chapter 4 we remove the perfect model assumption using the NCEP/NCAR reanalysis as the "nature" run. The advantage of EnKF to 3DVAR is reduced. When we apply the model bias estimation proposed by Dee and da Silva (1998), we find that the full dimensional model bias estimation fails. However, if instead we assume that the bias is low dimensional, we obtain a substantial improvement in the EnKF analysis.Item BRED VECTORS IN THE NASA NSIPP GLOBAL COUPLED MODEL AND THEIR APPLICATION TO COUPLED ENSEMBLE PREDICTIONS AND DATA ASSIMILATION(2005-04-27) Yang, Shu-Chih; Kalnay, Eugenia; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The theme of my thesis research is to perform breeding experiments with NASA/NSIPP coupled general circulation model (CGCM) in order to obtain ENSO-related growing modes for ensemble perturbations. We show for the first time that the breeding method is an effective diagnostic tool for studying the coupled ENSO-related instabilities in a coupled ocean-atmosphere general circulation model that includes physical and dynamical processes of many different time scales. We also show for the first time that it is feasible to utilize the coupled bred vectors (BV) as a way to construct perturbations for ensemble forecasts for ENSO prediction using an operational coupled climate prediction model. The results of the thesis research show that coupled breeding can detect a precursor signal associated with ENSO events. Bred vectors are characterized by air-sea coupled features and they are very sensitive to ENSO phases and background season. This indicates that bred vectors can effectively project on the seasonal-to-interannual instabilities by growing upon the slowly varying coupled instability. These results are robust: bred vectors obtained from both the NASA and NCEP coupled systems exhibit similarities in many fields, even in atmospheric teleconnected regions. We show that bred vectors have a structure similar to the one-month forecast error (analysis increment). The BV growth rate and the one-month forecast error show similar low frequency variations. Both of their subsurface temperatures have large-scale variability near the depth of thermocline. Evidence shows that bred vectors capture the eastern movement of the analysis increment (one-month forecast error) along the equatorial Pacific during 1997-1998 El Niño evolution. The results suggest that one-month forecast error in NSIPP CGCM is dominated by dynamical errors whose shape can be captured by bred vectors, especially when the BV growth rate is large. These results suggest that bred vectors should be effective coupled perturbations for ensemble ENSO predictions, compensating for the lack of coupled ENSO-related perturbations in current operational ensembles. The similarity between the bred vectors and the one month forecast errors suggests that bred vectors can capture "errors of the month" and could also be applied to improve oceanic data assimilation by providing information on the month-to-month background variability.