Browsing by Author "Liess, Stefan"

Now showing 1 - 10 of 10
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    A Cautionary Note on Decadal Sea Level Pressure Predictions from GCMs
    (2018-02-06) Liess, Stefan; Snyder, Peter K.; Kumar, Arjun; Kumar, Vipin
    Decadal prediction of sea level pressure (SLP) plays an important role in regional climate prediction, because it shows changes in atmospheric behavior on time scales that are relevant for policy makers. These changes consist of a combination of externally forced and internally driven climate system characteristics. A comparison of SLP trends in a subset of seven Coupled Model Intercomparison Project (CMIP) phase 5 general circulation models (GCM) during the satellite-era to their CMIP3 counterparts reveals an unrealistically strong forecast skill in CMIP3 models for trend predictions for 2001-2011 when using the 1979-2000 period to train the forecast.   Boreal-winter SLP trends over five high-, mid-, and low-latitude zones were calculated over a two-decade initialization period for each ensemble member and then ranked based on their performance relative to observations in all five zones over the same time period.  The same method is used to rank the ensemble members during the following decade. In CMIP3, 17 out of 38 ensemble members retain their rank in the 2001-2011 hindcast period and 3 retain the neighboring rank. However, these numbers are much lower in more recent CMIP5 decadal predictions over a similar period with the same number of ensembles. The conclusion to consider the forecast skill in CMIP3 predictions during the 2001-2011 as unrealistic is corroborated by comparisons to earlier periods from the 1960s to the 1980s in both CMIP3 and CMIP5 simulations. Thus, although the 2001-2011 CMIP3 predictions show statistically significant forecast skill, this skill should be treated as a spurious result that is unlikely to be reproduced by newer more accurate GCMs.
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    A New Teleconnection : The Australian Southern Oscillation
    (2012-09-21) Kumar, Arjun; Liess, Stefan; Kawale, Jaya; Ormsby, Dominick; Steinhaeuser, Karsten; Kumar, Vipin
    A possibly new teleconnection has been discovered off the east coast of Australia in the region around Tasman sea and Southern Ocean. Found in pressure anomalies using a novel graph based approach called shared reciprocal nearest neighbors, this dipole appears in reanalysis datasets such as NCEP, JRA, ERA and MERRA. The HadSLP2 observation data shows the new dipole, despite of limited observations in the Tasman Sea. Tests are performed in order to understand the uniqueness of the dipole and its relationship to existing well known phenomena. The dipole index is correlated with known dipole indices such as the SO (Southern Oscillation), AAO (Antarctic Oscillation) with which it shares a marginally higher correlation of less than 0.4 and other northern teleconnections with which it is shown to have a poor relationship. We limit further analysis with only the AAO and SO indices as these are spatially close, have a higher correlation with the new index and tend to influence it in one or more seasons. Seasonal analysis is done to look at the variation in strength as well as its influence on other variables such as TAS (Temperature at Surface), OLR (Outgoing Longwave Radiation), Precipitation etc. We also look at composite maps and do significance tests to determine the significant regions in these maps. We also determine regions that are influenced by the new dipole index alone and are not influenced by other dipoles namely the SO and AAO by looking at difference maps. We discover the dipole at different geopotential heights - 700 hPa, 500 hPa and 50 hPa (Sea Level Pressure is 1013 hPa)- and determine if the dipole is a sea surface phenomenon such as the SO or an upper atmospheric phenomenon such as the AAO. Our tests have shown that we may indeed be looking at a new phenomenon and further tests are being conducted to confirm that.
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    Climate modeling: An introductory primer for practitioners
    (2023) Clark, Suzanna; Roop, Heidi A; Meyer, Nathan; Liess, Stefan; Mosel, Jamie; Hoppe, Brenda; Farris, Amanda
    This document is intended to provide an introductory overview to climate models and their function. It explains the basics of how a climate model works, how data can be transformed from a global to a regional scale, and the constraints placed on modeling as a result of computational power. It also explains the Coupled Model Intercomparison Project (CMIP) and modeling scenarios established by CMIP. It is not an exhaustive overview, nor is it intended to replace a formal modeling course for those wishing to run climate models. The intended audience includes those who would like to understand or are interested in using model output.
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    Dynamically downscaled CMIP5 climate projection data for Minnesota
    (2022-01-25) Liess, Stefan; Twine, Tracy E; Snyder, Peter K; Hutchison, William D; Konar-Steenberg, Gabriel; Keeler, Bonnie L; Brauman, Kate A; liess@umn.edu; Liess, Stefan
    This dataset contains climate projections over Minnesota at 10 km horizontal resolution. Eight CMIP5 global climate models have been dynamically downscaled with the regional WRF model for the periods 1980-1999, 2040-2059, and 2080-2099, with the latter being represented as a moderate (RCP4.5) and also as an extreme "business as usual" scenario (RCP8.5). The projections suggest ongoing warming in all seasons, especially in winter, as well as reduced snow depth and fewer days with snow cover. Significant increases in spring and early summer heavy precipitation events are expected. The other variables in this dataset are daily max. and min. temperatures, relative humidity, latent heat flux (as proxy for evaporation), sensible heat flux, ground heat flux, incoming solar radiation, total radiation, snow depth, and wind speed. Temperatures, precipitation, and snow depth are also available as bias adjusted. Results indicate a climate near the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6C and 4C warmer, respectively, and spring precipitation may increase by more than 1 mm per day over northern Minnesota. Winter snow depth is projected to decrease by more than 12 cm and the number of days per year with snow depth of more than 2.54 cm (one inch) is expected to decrease by up to 55. These results are expected to influence regional decision-making related to agriculture, infrastructure, water resources, and other sectors.
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    Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience
    (2018-02-12) Agrawal, Saurabh; Steinbach, Michael; Boley, Daniel; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin; Atluri, Gowtham
    In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets, and lead to novel domain insights.
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    MAP Inference on Million Node Graphical Models: KL-divergence based Alternating Directions Method
    (2012-02-27) Fu, Qiang; Wang, Huahua; Banerjee, Arindam; Liess, Stefan; Snyder, Peter K.
    Motivated by a problem in large scale climate data analysis, we consider the problem of maximum a posteriori (MAP) inference in graphical models with millions of nodes. While progress has been made in recent years, existing MAP inference algorithms are inherently sequential and hence do not scale well. In this paper, we present a parallel MAP inference algorithm called KL-ADM based on two ideas: tree-decomposition of a graph, and the alternating directions method (ADM). However, unlike standard ADM, we use an inexact ADM augmented with a Kullback-Leibler (KL) divergence based regularization. The unusual modification leads to an efficient iterative algorithm while avoiding double-loops. We rigorously prove global convergence of KL-ADM. We illustrate the effectiveness of KL-ADM through extensive experiments on large synthetic and real datasets. The application on real world precipitation data finds all major droughts in the last century.
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    Spatial synoptic classifications projected for the 21st century in Minneapolis-St Paul, Minnesota
    (2022-10-17) Twine, Tracy, E; Birkel, Jonathan F H; Liess, Stefan; Kalkstein, Larry S; Sheridan, Scott; twine@umn.edu; Twine, Tracy E; University of Minnesota Climate Adaptation Partnership
    This dataset consists of (1) CMIP5 climate projections from 8 global climate models through the 21st century, downscaled with the Weather Research Forecast model, output into time series for the model grid cell containing the Minneapolis-St Paul International airport, and (2) spatial synoptic classifications for this climate dataset, under RCP4.5 and RCP8.5 emissions scenarios.
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    Temperature Observations of the Twin Cities Canopy-Layer Urban Heat Island
    (2024-10-10) Smoliak, Brian V; Snyder, Peter K; Twine, Tracy E; Mykleby, Phillip M; Hertel, William F; Liess, Stefan; liess@umn.edu; Liess, Stefan; Department of Soil, Water, and Climate
    Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1-km x 1-km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land-atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.
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    Tripoles: A New Class of Climate Teleconnections
    (2015-12-11) Agrawal, Saurabh; Atluri, Gowtham; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin
    Teleconnections in climate represent a persistent and large-scale temporal connection in a given climate variable between two distant geographical regions. They are known to impact and explain the variability in climate of many regions across the globe and have been a subject of interest to climatologists. Traditionally, climate teleconnections have been studied as a persistent relationship between a pair of geographical regions (e.g. North Atlantic Oscillation (NAO), and El-Nino Southern Oscillation (ENSO)). In this report, we define a new class of climate teleconnections which we refer to as tripoles that capture climatic relationships between three regions, in contrast to teleconnections that are traditionally defined using only two regions. We further provide a categorization of tripoles based on pairwise relationships between the three participating regions and propose a shared nearest neighbor (SNN) graph-based approach to find tripoles in a given spatio-temporal dataset.
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    Understanding the Sea SurfaceTemperature - Tropical Cyclone Relationship: A Data Driven Approach
    (2012-04-26) Faghmous, James H; Liess, Stefan; Ganguly, Auroop; Steinbach, Michael; Semazzi, Fred; Kumar, Vipin
    The majority of works investigating future Atlantic tropical cyclone (TC) frequency in a warming environment employ a simulation-based approach. Observational data-driven analyses meanwhile, have focused on basin-wide trends to conclude that no clear relation can be attributed to the recent increase in Atlantic sea surface temperature (SST) and Atlantic TC frequency. We present a data-driven analysis that monitors smaller regions in the Atlantic and is able to capture connections between the warming of certain regions in the Atlantic ocean and Atlantic TC frequency better than the previously used basin-wide analyses. Our results suggest that the warming of the Atlantic off the West African coast near 20?- 30?N prior to the TC season, as well as the warming westward of 10? ? 20?N between 18? and 60?W during the TC season have a pronounced effect on TC formation. Furthermore, we propose that, unlike other basins, the recent increase in TC activity can be linked to Atlantic SST.