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Joss editorial changes
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34 changes: 17 additions & 17 deletions paper/paper.bib
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Expand Up @@ -10,7 +10,7 @@ @article{tebaldi2022stitches
}

@book{SR15,
title= {IPCC (2018). Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty},
title= {{IPCC (2018). Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty}},
editor = {Masson-Delmotte, V. and Zhai, P. and P\"ortner,H.O and Roberts,D. and Skea, J. and Shukla, P.R. and {et al.}},
year= {2018},
publisher={World Meteorological Organization, Geneva, Switzerland},
Expand All @@ -19,8 +19,8 @@ @book{SR15

@Article{Eyringetal2016,
AUTHOR = {Eyring, V. and Bony, S. and Meehl, G. A. and Senior, C. A. and Stevens, B. and Stouffer, R. J. and Taylor, K. E.},
TITLE = {Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6)
experimental design and organization},
TITLE = {{Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6)
experimental design and organization}},
JOURNAL = {Geoscientific Model Development},
VOLUME = {9},
YEAR = {2016},
Expand All @@ -32,7 +32,7 @@ @Article{Eyringetal2016

@Article{ONeilletal2016,
AUTHOR = {O'Neill, B. C. and Tebaldi, C. and van Vuuren, D. P. and Eyring, V. and Friedlingstein, P. and Hurtt, G. and Knutti, R. and Kriegler, E. and Lamarque, J.-F. and Lowe, J. and Meehl, G. A. and Moss, R. and Riahi, K. and Sanderson, B. M.},
TITLE = {The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6},
TITLE = {{The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6}},
JOURNAL = {Geoscientific Model Development},
VOLUME = {9},
YEAR = {2016},
Expand All @@ -43,7 +43,7 @@ @Article{ONeilletal2016
}

@article{thornton2017biospheric,
title={Biospheric feedback effects in a synchronously coupled model of human and Earth systems},
title={{Biospheric feedback effects in a synchronously coupled model of human and Earth systems}},
author={Thornton, Peter E and Calvin, Katherine and Jones, Andrew D and Di Vittorio, Alan V and Bond-Lamberty, Ben and Chini, Louise and Shi, Xiaoying and Mao, Jiafu and Collins, William D and Edmonds, Jae and others},
journal={Nature Climate Change},
volume={7},
Expand All @@ -57,7 +57,7 @@ @article{thornton2017biospheric


@article{beusch2020emulating,
title={Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land},
title={{Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land}},
author={Beusch, Lea and Gudmundsson, Lukas and Seneviratne, Sonia I},
journal={Earth System Dynamics},
volume={11},
Expand All @@ -70,7 +70,7 @@ @article{beusch2020emulating
}

@article{nath2022mesmer,
title={MESMER-M: an Earth system model emulator for spatially resolved monthly temperature},
title={{MESMER-M: an Earth system model emulator for spatially resolved monthly temperature}},
author={Nath, Shruti and Lejeune, Quentin and Beusch, Lea and Seneviratne, Sonia I and Schleussner, Carl-Friedrich},
journal={Earth System Dynamics},
volume={13},
Expand All @@ -83,7 +83,7 @@ @article{nath2022mesmer
}

@article{quilcaille2022showcasing,
title={Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models},
title={{Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models}},
author={Quilcaille, Yann and Gudmundsson, Lukas and Beusch, Lea and Hauser, Mathias and Seneviratne, Sonia I},
journal={Geophysical Research Letters},
volume={49},
Expand All @@ -96,7 +96,7 @@ @article{quilcaille2022showcasing
}

@article{hartin2015simple,
title={A simple object-oriented and open-source model for scientific and policy analyses of the global climate system--Hector v1. 0},
title={{A simple object-oriented and open-source model for scientific and policy analyses of the global climate system--Hector v1.0}},
author={Hartin, Corinne A and Patel, Pralit and Schwarber, Adria and Link, Robert P and Bond-Lamberty, BP},
journal={Geoscientific Model Development},
volume={8},
Expand All @@ -109,7 +109,7 @@ @article{hartin2015simple
}

@article{meinshausen2011emulating,
title={Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6--Part 1: Model description and calibration},
title={{Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6--Part 1: Model description and calibration}},
author={Meinshausen, Malte and Raper, Sarah CB and Wigley, Tom ML},
journal={Atmospheric Chemistry and Physics},
volume={11},
Expand All @@ -135,7 +135,7 @@ @article{smith2018fair
}

@article{ruane2022climatic,
title={The Climatic Impact-Driver Framework for Assessment of Risk-Relevant Climate Information},
title={{The Climatic Impact-Driver Framework for Assessment of Risk-Relevant Climate Information}},
author={Ruane, Alex C and Vautard, Robert and Ranasinghe, Roshanka and Sillmann, Jana and Coppola, Erika and Arnell, Nigel and Cruz, Faye Abigail and Dessai, Suraje and Iles, Carley E and Islam, AKM Saiful and others},
journal={Earth's Future},
volume={10},
Expand All @@ -161,15 +161,15 @@ @article{james2017characterizing
}

@article{arias2021climate,
title={Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary},
title={{Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary}},
author={Arias, Paola and Bellouin, Nicolas and Coppola, Erika and Jones, Richard and Krinner, Gerhard and Marotzke, Jochem and Naik, Vaishali and Palmer, Matthew and Plattner, G-K and Rogelj, Joeri and others},
year={2021},
url = {https://www.ipcc.ch/report/ar6/wg1/},
doi = {10.1017/9781009157896.002}
}

@article{masson2021ipcc,
title={Ipcc, 2021: Summary for policymakers. in: Climate change 2021: The physical science basis. contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change},
title={{IPCC, 2021: Summary for Policymakers. in: Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change}},
author={Masson-Delmotte, VP and Zhai, Panmao and Pirani, SL and Connors, C and P{\'e}an, S and Berger, N and Caud, Y and Chen, L and Goldfarb, MI and Scheel Monteiro, Pedro M},
year={2021},
publisher={Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA},
Expand All @@ -179,8 +179,8 @@ @article{masson2021ipcc


@article{core2023ipcc,
title={IPCC, 2023: Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to
the Sixth Assessment Report of the Intergovernmental Panel on Climate Change},
title={{IPCC, 2023: Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to
the Sixth Assessment Report of the Intergovernmental Panel on Climate Change}},
author={Core Writing Team, H. Lee and J. Romero (eds.)},
year={2023},
doi={10.59327/IPCC/AR6-9789291691647.001},
Expand All @@ -203,7 +203,7 @@ @article{harris2020array
}

@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
title={Scikit-learn: {M}achine {L}earning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
Expand All @@ -215,7 +215,7 @@ @article{scikit-learn

@software{reback2020pandas,
author= {The pandas development team},
title={pandas-dev/pandas: Pandas},
title={pandas-dev/pandas: {P}andas},
month=feb,
year=2020,
publisher={Zenodo},
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49 changes: 24 additions & 25 deletions paper/paper.md
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Expand Up @@ -39,7 +39,7 @@ spatially resolved and often multiple variables representing climatic impact-dri
of detailed, computationally expensive Earth System Models (ESMs) run according
to a standard, limited set of future scenarios, the latest being the SSP-RCPs
run under CMIP6-ScenarioMIP [@Eyringetal2016;@ONeilletal2016]. At the time of
writing, O'Neill et al. has been cited more than 1750 times and Eyring et al.
writing, @ONeilletal2016 has been cited more than 1750 times and @Eyringetal2016
more than 5000 times, highlighting the broad, general applications of this data.


Expand All @@ -59,13 +59,13 @@ simplified, computationally tractable representation of ESM behavior in a
coupled human-Earth system modeling framework. We proposed a new,
comprehensive approach to such emulation of gridded, multivariate ESM
outputs for novel scenarios without the computational cost of a full ESM,
STITCHES [@tebaldi2022stitches]. The approach outlined in Tebaldi et al. should
be extensible to future CMIP eras, although the `stitches` software at present
STITCHES [@tebaldi2022stitches]. The approach outlined in @tebaldi2022stitches should
be extensible to future CMIP eras, although the `STITCHES` software at present
is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo
(https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/).


The corresponding `stitches` Python package uses existing archives of ESMs’
The corresponding `STITCHES` Python package uses existing archives of ESMs’
scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate
realizations of new scenarios provided by reduced complexity climate models
[@hartin2015simple;@meinshausen2011emulating;@smith2018fair], or to
Expand All @@ -74,7 +74,7 @@ characteristics as the emulated ESM output: multivariate (spanning
potentially all variables that the ESM has saved), spatially resolved (down to
the native grid of the ESM), and preserving the same high frequency as the original data.
A new realization of multiple variables can be generated on the order of minutes with
`stitches`, rather than the hours or sometimes days that ESMs require.
`STITCHES`, rather than the hours or sometimes days that ESMs require.



Expand All @@ -90,66 +90,65 @@ details of some statistical process (or, more recently, a machine learning algo
able to generate new realizations with the same spatiotemporal behavior of the original
ESM outputs, using as input in the generative phase only large scale information,
like global average temperature (GSAT), that can be generated by a reduced complexity
model, such as Hector, Magicc, or FAIR
model, such as Hector, MAGICC, or FAIR
[@hartin2015simple;@meinshausen2011emulating;@smith2018fair].


The STITCHES approach instead takes a top-down approach inspired by the warming-level
style of analyses used by past Intergovernmental Panel on Climate Change
reports [@SR15;@arias2021climate;@masson2021ipcc;@core2023ipcc]. Specifically,
`stitches` takes existing ESM output and intelligently recombines time windows
`STITCHES` takes existing ESM output and intelligently recombines time windows
of these gridded, multivariate outputs into new instances of transient, 21st
century trajectories by stitching them together on the basis of a target GSAT
trajectory. The latter can represent an existing scenario (i.e., one that the
ESM has run) or a new one that a simple model can produce, as long as the latter
is intermediate to existing ones in forcing levels/GSAT. We encourage users to
see the flowchart included in the `stitches`
see the flowchart included in the `STITCHES`
[quickstart notebook](https://github.com/JGCRI/stitches/blob/main/notebooks/stitches-quickstart.ipynb)
and [website](https://jgcri.github.io/stitches/), as well as in Tebaldi et al., for a visual example of this process.
Tebaldi et al. of course contains the full details as well as more illustrative figures.
and [website](https://jgcri.github.io/stitches/), as well as in @tebaldi2022stitches, for a visual example of this process.
@tebaldi2022stitches of course contains the full details as well as more illustrative figures.


Research from the climate science community has indicated that many ESM output
variables are tightly dependent upon the GSAT trajectory and thus scenario
independent (see [@SR15] and citations therein, in particular James et al.
[@james2017characterizing]), justifying our approach. Thus, the statistical
characteristics of ESM output are preserved by the construction process `stitches`
implements, as outlined in Tebaldi et al. One of the major benefits of this
independent (see [@SR15] and citations therein, in particular @james2017characterizing), justifying our approach. Thus, the statistical
characteristics of ESM output are preserved by the construction process `STITCHES`
implements, as outlined in @tebaldi2022stitches. One of the major benefits of this
top-down approach is that it jointly emulates outputs of multiple ESM variables,
maintaining by construction the joint behavior of the original ESM output,
something not presently available in other packages to our knowledge. Most
impact-relevant atmospheric variables such as temperature, precipitation, relative
humidity, and sea level pressure can be emulated by `stitches` as they are
humidity, and sea level pressure can be emulated by `STITCHES` as they are
scenario-independent and have a short memory (compared to the window used by
‘stitches’, presently set to nine (9) years). Any variable that the ESM has archived can
‘STITCHES’, presently set to nine (9) years). Any variable that the ESM has archived can
be emulated jointly. Variables that represent the cumulative effect of warming,
such as sea level rise, or that have a long memory, like glacier mass loss or
mega-drought, cannot be emulated with `stitches`. `stitches` can produce new
mega-drought, cannot be emulated with `STITCHES`. `STITCHES` can produce new
realizations for variables archived by the ESM, but it can produce only finitely
many new realizations, the maximum number depending on the number of runs
archived by each ESM. Currently, new realizations from `stitches` can be
archived by each ESM. Currently, new realizations from `STITCHES` can be
appended to archived ESM realizations to result in double to triple the number
of runs available; this is arguably one of the main differences from the above-mentioned
bottom-up approaches, which can generate infinite new realizations
once an accurate statistical process is estimated from existing data. We see
this as a source of complementarity between these two emulation approaches.

The `stitches` Python package currently relies on close integration with the
The `STITCHES` Python package currently relies on close integration with the
Pangeo Cloud catalog of CMIP6 ESM outputs (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/).
Thanks to this integration, users are not required to pre-download the entire
CMIP6-ScenarioMIP archive of ESM outputs, and can quickly and flexibly
emulate variables from any of the 40 ESMs participating in ScenarioMIP.
In addition to the requirements for working with Pangeo in Python, `stitches`
In addition to the requirements for working with Pangeo in Python, `STITCHES`
relies only on a few common scientific Python packages, namely `xarray`, `numpy`,
`pandas`, `scikit-learn`
[@Hoyer_xarray_N-D_labeled_2017;@harris2020array;@reback2020pandas;@scikit-learn],
which are specified required dependencies in the package.
Finally, because `stitches` is intended for use by impact modelers, the new
realizations generated by `stitches` are NetCDF files with the same dimension
Finally, because `STITCHES` is intended for use by impact modelers, the new
realizations generated by `STITCHES` are NetCDF files with the same dimension
information and generally identical structure to the original CMIP6 ESM outputs.
These outputs from `stitches` can then serve as inputs to impact models with
These outputs from `STITCHES` can then serve as inputs to impact models with
little to no code changes in the impact models. It may also be possible to
endogenize climate impacts in scenario construction by coupling `stitches`
endogenize climate impacts in scenario construction by coupling `STITCHES`
with impact models for multiple sectors and a reduced complexity climate model
such as Hector, MAGICC, or FAIR
[@hartin2015simple;@meinshausen2011emulating;@smith2018fair].
Expand All @@ -162,7 +161,7 @@ off-the-shelf ESM scenarios alone.

# Code availability

The `stitches` GitHub repository (https://github.com/JGCRI/stitches) provides
The `STITCHES` GitHub repository (https://github.com/JGCRI/stitches) provides
installation instructions.

Also included is a [quickstart notebook](https://github.com/JGCRI/stitches/blob/main/notebooks/stitches-quickstart.ipynb) that serves as a tutorial for using the package.
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