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<a class="post-title-link" href="/2019/09/18/xMCA/" itemprop="url">xMCA</a></h1>
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2019-09-18
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<p><a href="https://xmca.readthedocs.io/en/latest/?badge=latest" target="_blank" rel="noopener"><img src="https://readthedocs.org/projects/xmca/badge/?version=latest" alt="Documentation Status"></a></p>
<h1 id="About-xMCA"><a href="#About-xMCA" class="headerlink" title="About xMCA"></a>About xMCA</h1><p>xMCA, inspired from EOFs <a href="https://github.com/ajdawson/eofs">https://github.com/ajdawson/eofs</a>, is Maximum Covariance Analysis (sometimes also called SVD) in xarray. </p>
<p>API Documentation: <a href="http://xmca.readthedocs.io/" target="_blank" rel="noopener">http://xmca.readthedocs.io/</a><br>
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<a class="post-title-link" href="/2018/12/18/BMA/" itemprop="url">Bayesian model averaging to predict global wamring at the end of 21st century</a></h1>
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2018-12-18
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<h1 id="How-warm-the-future-will-be-and-how-certain-we-are"><a href="#How-warm-the-future-will-be-and-how-certain-we-are" class="headerlink" title="How warm the future will be and how certain we are"></a>How warm the future will be and how certain we are</h1><p>The ongoing warming is a well-accepted feature of the near future climate change, however how warm it will be and how certain we are remain debatable and unclear, even though significant progress has been made in qualifying the increase in the temperature by averaging an bunch of start-of-the-art climate models (e.g. the <a href="https://esgf-node.llnl.gov/projects/cmip5/" target="_blank" rel="noopener">CMIP5</a>) or an ensemble of member forecasts from a single model (e.g. <a href="http://www.cesm.ucar.edu/projects/community-projects/LENS/" target="_blank" rel="noopener">CESM-LEN</a>). Here I provide modeling evidence that reveals the increase in temperature and its uncertainty based on Bayesian Inference. Basically, members from an ensemble model prediction is exxtracted randomly, then they are regressed on historical observation data using Bayesian linear regression. Finally these Bayesian models are averaged by stacking the predictive distributions. The results show that the stacking model can largely remove the error in the testing period and predict that the global mean surface air temperature at the end of this century is about 3 oC warmer in RCP8.5 scenario, not as intense as that of the ensemble mean, which warms about 4 oC. The warming mostly occurs at high latitudes but with large certainty which shall be taken into serious consideration by policy makers. </p>
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2017-12-25
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<h1 id="About-xcesm"><a href="#About-xcesm" class="headerlink" title="About xcesm"></a>About xcesm</h1><p>Xcesm tries to provide an easy-to-use plugin for xarray to better handle CESM output in python. </p>
<h1 id="Features"><a href="#Features" class="headerlink" title="Features"></a>Features</h1><p>Xcesm is still in developing, right now it has the following features:</p>
<ul>
<li>quick plot on global map (quickmap)</li>
<li>regrid pop output to linear grids (regrid, nearest interpolation)</li>
<li>compute global mean (gbmean, gbmeanpop)</li>
<li>diagnose AMOC, PRECP, d18O(only support for iCESM), Heat transport etc.</li>
<li>truncate ocean as several main basins (ocean_region)</li>
</ul>
<p>More feature will be added in the future.<br>
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<a class="post-title-link" href="/2017/12/25/GCM-Averager/" itemprop="url">GCM Averager</a></h1>
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<h1 id="About-GCMAverager"><a href="#About-GCMAverager" class="headerlink" title="About GCMAverager"></a>About GCMAverager</h1><p>GCMAverger is a lightweight post process package designed for large amount general circulation model(GCM) outputs using parallel computing.<br>It was originally designed to post process a long run from the community earth system model (CESM), who genreated hundreds of TBs data.<br>NCAR has developed two fancy tools: pyAverager and pyReshaper, which do the samething as GAMAverager, however, they are pooly maintained and not easy to use.</p>
<p>GCMAverager is a <a href="https://github.com/pydata/xarray">xarray-based</a> project, therefore python 2.7, 3.4, 3.5, and 3.6 are supported.<br>Right now, GCMAverager primarily can </p>
<ul>
<li>extract variables from time slice history files into time series files;</li>
<li>compute annual (decadal) and seasonal mean for model outputs from time slice or time series files. </li>
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