\vspace{-22pt}
{\section*{\textit{DataDiagnostics} class}}

\vspace{-2mm}
\noindent Diagnostics of \textbf{ensemble} simulations for a given variable:

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1. \textbf{Time series}: multiple simulations + observations

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2. \textbf{Spatial plots}: absolute difference + effect size (Cohen's \textit{d}) 

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\noindent Usage examples employing \href{https://www.cesm.ucar.edu/community-projects/lens2}{CESM2 LENS2 data}:

% NOTE on LENS2: Biomass burning (BMB) emissions in CMIP6 utilize remote sensing data during 1997-2014, which contain more year-to-year variability compared to the data sources 
% used before and after that time (Fasullo et al., 2022; DeRepentigny et al., 2022). In CESM2, some aspects of the extratropical Northern Hemisphere climate response (in particular, 
% air temperature and sea ice) are sensitive to such a “discontinuity” in BMB variability due to non-linear aerosol-cloud interactions, the details of which are still under investigation.

\vspace{-2mm}
\begin{minted}[fontsize=\small, frame=lines, framesep=5mm, baselinestretch=1.2, linenos, rulecolor=\color{gray}]{python}
diags = pyhanami.DataDiagnostics(lens2_cmip6)
diags.time_series_plot('tas', start_year=1990, end_year=2014, obs=True, plot_ens=True)
diags.time_series_plot('tas', start_year=1990, end_year=2014, time_freq='monthly')
\end{minted}

\vspace{2mm}
\begin{center}
\includegraphics[width=0.48\textwidth]{time_series_yearly.pdf}
\includegraphics[width=0.48\textwidth]{time_series_monthly.pdf}
\end{center}


\begin{minted}[fontsize=\small, frame=lines, framesep=5mm, baselinestretch=1.2, linenos, rulecolor=\color{gray}]{python}
diags.add_dataset(lens2_smbb)
diags.abs_diff_plot('tas', start_year=1990, end_year=2014)
diags.eff_size_plot('tas', start_year=1990, end_year=2014, clon=180)
\end{minted}

\vspace{2mm}
\begin{center}
\includegraphics[width=0.49\textwidth]{abs_diff.pdf}
\includegraphics[width=0.49\textwidth]{eff_size.pdf}
\end{center}