Browsing by Author "Kumar, Arjun"

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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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    A Study of Dimensionality Reduction Techniques and its Analysis on Climate Data
    (2015-10) Kumar, Arjun
    Dimensionality reduction is a significant problem across a wide variety of domains such as pattern recognition, data compression, image segmentation and clustering. Different methods exploit different features in the data to reduce dimensionality. Principle component Analysis is one such method that exploits the variance in data to embed data onto a lower dimensional space called the principle component space. These are linear techniques which can be expressed in the form B=TX where T is the transformation matrix that acts on the data matrix X to the reduced dimensionality representation B. Other linear techniques explored are Factor Analysis and Dictionary Learning. In many problems, the observations are high-dimensional but we may have reason to believe that the they lie near a lower-dimensional manifold. In other words, we may believe that high-dimensional data are multiple, indirect measurements of an underlying source, which typically cannot be directly measured. Learning a suitable low-dimensional manifold from high-dimensional data is essentially the same as learning this underlying source. Techniques such as ISOMAP, Locally Linear Embedding, Laplacian EigenMaps (LEMs) and many others try to embed the high-dimensional observations in the non-linear space onto a low dimensional manifold. We will explore these methods making comparative studies and their applications in the domain of climate science.