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Evaluation of inter-annual variability and trends of cloud liquid water path in climate models using a multi-decadal record of passive microwave observations

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dc.contributor.authorManaster, Andrew, author
dc.contributor.authorKummerow, Christian, advisor
dc.contributor.authorO'Dell, Christopher W., advisor
dc.contributor.authorRandall, David, committee member
dc.contributor.authorReising, Steven, committee member
dc.date.accessioned2016-07-13T14:50:17Z
dc.date.available2016-07-13T14:50:17Z
dc.date.issued2016
dc.description.abstractLong term satellite records of cloud changes have only been available for the past several decades and have just recently been used to diagnose cloud-climate feedbacks. However, due to issues with satellite drift, calibration, and other artifacts, the validity of these cloud changes has been called into question. It is therefore pertinent that we look for other observational datasets that can help to diagnose changes in variables relevant to cloud-radiation feedbacks. One such dataset is the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP), which blends cloud liquid water path (LWP) observations from 12 different passive microwave sensors over the past 27 years. In this study, observed LWP trends from the MAC-LWP dataset are compared to LWP trends from 16 models in the Coupled Model Intercomparison Project 5 (CMIP5) in order to assess how well the models capture these trends and thus related radiative forcing variables (e.g., cloud radiative forcing). Mean state values of observed LWP are compared to those of previous observed climatologies and are found to have relatively good quantitative and qualitative agreements. Mean state observed LWP variables are compared both qualitatively and quantitatively to our suite of CMIP5 models. These models tend to capture mean state and mean seasonal cycle LWP features, but the magnitudes exhibit large variations from model to model. Several metrics were used to compare observed mean state LWP and mean seasonal cycle amplitude and the mean state LWP and mean seasonal cycle amplitude in each model. However, the models' performance in regards to these metrics is found to not be indicative of their abilities to accurately reproduce trends on a regional or global scale. Global trends in the observations and the model means are compared. It is found that observational trends are roughly 2-3 times larger in magnitude in most regions globally when compared to the model mean although this is thought to be at least partly caused by cancellation effects due to differing inter-annual variability and physics between models. Several regions (e.g., the Southern Ocean) have consistent signs in trends between the observations and the model mean while others do not due to spatial inconsistencies in certain trend features in the model mean relative to the observations. Trends are examined in individual regions. In four of the six regions analyzed, the observational trends are statistically different from zero, while, in most regions, very few models have trends that are statistically significant. In certain regions, the majority of modeled trends are statistically consistent with the observed trends although this is typically due to large estimated errors in the observations and/or models, most likely caused by large inter-annual variability. The Southern Ocean and globally averaged trends show the strongest similarities to the observed trends. Almost all Southern Ocean trends are robustly positive and statistically significant with the majority of models being statistically consistent with the observations. Similarly, the observed and global trends are all positive with the majority being statistically significant and statistically consistent. We discuss why a large positive Southern Ocean trend is unlikely to be due to a trend in cloud phase. CMIP5 model mean and observational LWP trends are compared regionally to Atmospheric Model Intercomparison Project (AMIP) and ERA-interim reanalysis trends. It is found that AMIP model mean and ERA LWPs are better than the CMIP5 model mean at capturing the inter-annual variability in the observed time series in most of the regions examined. The AMIP model mean better replicates the observed trends when the inter-annual variability is better captured. The ERA reanalysis tends to better reproduce the observed inter-annual variability when compared to the AMIP model mean in almost every region, but, surprisingly, it is either worse or roughly the same in regards to matching observed trends. Our results suggest that observed trends are due to a combination of inter-annual and decadal-scale internal variability, in addition to external forced trends due to anthropogenic influences on the climate system. With a record spanning three decades, many modeled trends are statistically consistent with the observed trends, but a true climatically forced signal is not yet apparent in the models that agrees with the observations. The primary exception to this is in the Southern Ocean, where virtually all models and observations indicate an increasing amount of cloud liquid water path.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierManaster_colostate_0053N_13473.pdf
dc.identifier.urihttp://hdl.handle.net/10217/173477
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectinter-annual variability
dc.subjectmodel comparison
dc.subjecttrends
dc.subjectmicrowave
dc.subjectcloud liquid water path
dc.subjectobservations
dc.titleEvaluation of inter-annual variability and trends of cloud liquid water path in climate models using a multi-decadal record of passive microwave observations
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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