ClimateSet
Release 0.1

Ilija Trajkovic

Mar 14, 2024

CONTENTS

1Contents
1.1 ClimateSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Climate Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3
3
4
11

i

ii

ClimateSet, Release 0.1

ClimateSet: Your gateway to powerful climate insights. Harnessing CMIP6 data, we provide a comprehensive platform
for machine learning advancements in climate science. Explore our user-friendly API and unlock the potential of largescale climate modeling for research and innovation.
Our GitHub: https://github.com/RolnickLab/ClimateSet
Read the ClimateSet section for further information about the project.
Also check out the Climate Context section if you want to learn more about climate models, climate model emulationand other concepts important for understanding ClimateSet.
ClimateSet has its documentation hosted on Read the Docs.
Note: This project is under active development.

CONTENTS

1ClimateSet, Release 0.1

2

CONTENTSCHAPTERONECONTENTS

1.1 ClimateSet
ClimateSet is an initiative aimed at providing a comprehensive and accessible dataset for the intersection of climate
science and machine learning (ML). It is a dataset containing inputs and outputs from 36 different climate models,addressing a crucial need in the ML community to support climate scientists in various tasks such as climate model
emulation, downscaling, and prediction.
The dataset is carefully curated from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Input Datasets
for Model Intercomparison Projects (Input4MIPs). CMIP6 serves as the backbone of ClimateSet, offering climate
model outputs from various sources. Input4MIPs, on the other hand, collect future emission trajectories of climate
forcing agents, crucial for predicting climate model responses. The resulting core dataset encompasses 36 climate
models, including the main greenhouse gases, aerosols, and aerosol precursor emission inputs for five different scenarios.
ClimateSet’s significance lies in its ability to address two key objectives: providing sufficient training data for largescale ML models and capturing the projection uncertainty across different climate models. The need for such adataset arises from the inherent challenges in climate-related tasks, including high dimensionality, low sample size,and distribution shifts within the data. ClimateSet tackles these challenges by offering a consistent and sizabledataset, paving the way for ML models to contribute meaningfully to climate-related modeling tasks.
To facilitate the use of ClimateSet, a modular dataset pipeline is introduced, allowing users to retrieve and preprocess climate model data for ML tasks. In our paper we detail the preprocessing steps, addressing inconsistencies
across different datasets and climate models. The preprocessor, built modularly, checks for corrupt files, variable naming, units, temporal and spatial resolution, and structure. It ensures that the data is ready for use in ML applications bysyncing time-axis, calendars, and height levels, and resolving other relevant issues.
Users can access ClimateSet through the provided website, where both raw and processed data are available. The
dataset can be extended by users who desire additional scenarios, climate forcers, or other variables, provided therequested data is available on the Earth System Grid Federation (ESGF) server. The preprocessing of ClimateSet canalso be accelerated using multi-thread functions and other optimizations.
The heart of ClimateSet’s utility is demonstrated through a benchmarking setup, showcasing its application in climateemulation tasks. ML models, including Convolutional long short-term memory (ConvLSTM), U-Net, and ClimaX,
are trained and evaluated on the dataset. The results reveal insights into model performance across different climate
models and scenarios, emphasising the importance of evaluating ML models on a set of climate models rather than justone.
ClimateSet is a valuable resource that bridges the gap between climate science and ML. It provides a consistent, multiclimate-model dataset along with tools for easy access and preprocessing. Researchers and policymakers can leverage
ClimateSet to enhance ML models’ performance, contribute to climate-related tasks at scale, and ultimately makemeaningful strides in climate policy making.

3

ClimateSet, Release 0.1

1.1.1 Useful links• CMIP6 overview of all available data: https://pcmdi.llnl.gov/CMIP6/ArchiveStatistics/esgf_data_holdings/• IPCC website: https://www.ipcc.ch• ESGF website: https://esgf.llnl.gov/• ClimateSet GitHub: https://github.com/RolnickLab/ClimateSet

1.2 Climate Context
1.2.1 What are climate models?
A climate model is a complex computational representation of the Earth’s climate system. These models simulate theinteractions between various components of the Earth, including the atmosphere, oceans, land surface, ice, and otherfactors, to predict and understand climate patterns and changes over time. Climate models are important tools forstudying the Earth’s climate, making predictions about future climate conditions, and assessing the potential impactsof various factors such as greenhouse gas emissions, land use changes, and atmospheric composition.
What do they include?
Atmospheric Model
Simulates the behaviour of the Earth’s atmosphere, including temperature, pressure, humidity, and wind patterns.
Ocean Model
Represents the behaviour of the world’s oceans, including ocean currents, temperatures, and sea ice.
Land Surface Model
Simulates processes on land, such as vegetation dynamics, soil moisture, and land-atmosphere interactions.
Sea Ice Model
Represents the formation, melting, and movement of sea ice in polar regions.
Biogeochemical Model
Incorporates biological and chemical processes, including the carbon cycle, to simulate interactions between theatmosphere, oceans, and land.
These models use mathematical equations to describe the physical, chemical, and biological processes that occur ineach component of the Earth system. They are run on powerful supercomputers, which still take a long time, sometimes
even several months to simulate climate conditions over time spans ranging from years to centuries and are validated
against historical climate data to ensure their accuracy and reliability.
What is their purpose?
Understanding Climate Processes
Models help scientists understand the fundamental processes driving climate variability and change.
Predicting Future Climate
By inputting different scenarios of human activities and natural processes, models can project future climate
conditions under different circumstances.
Assessing Climate Impact
Models are used to assess the potential impacts of climate change on ecosystems, agriculture, water resources,and human societies.
Policy Decision Support
Climate models provide information to policymakers to make informed decisions about climate mitigation andadaptation strategies.

4

Chapter 1. Contents

ClimateSet, Release 0.1

Types of climate models - ESMs and GCMs
GCMs and ESMs are both types of climate models used in climate research. They stand for General Circulation Models(GCMs) and Earth System Models (ESMs).
GCMs, or Global Climate Models, are complex computer simulations that represent Earth’s climate system. Thesemodels integrate physical, chemical, and biological processes to simulate climate patterns, allowing scientists to study
and make predictions about future climate conditions.
ESMs typically refer to Earth System Models, which are an advanced form of climate models that incorporate not only
the atmosphere but also interactions with oceans, land, ice, and other components of the Earth system. They aim tosimulate a more comprehensive representation of the Earth’s climate. The main difference between GCMs (Global
Climate Models) and ESMs (Earth System Models) lies in their scope. GCMs primarily focus on the atmosphere,
whereas ESMs consider a broader range of components, providing a more holistic understanding of the Earth’s climate
by incorporating interactions between the atmosphere, oceans, land, and other elements. In essence, ESMs build upon
GCMs by including a more integrated representation of the Earth system, which is why we exclusively use ESMs in
ClimateSet.
It’s important to note that while climate models are powerful tools, they have limitations and uncertainties. Improving
the accuracy of models requires ongoing research, refinement, and validation against observed climate data. One of therecent improvements, and the one we focus on in ClimateSet, is using ML algorithms (ClimaX, U-Net, ConvLSTM,etc.) to emulate these models using much less computing power and hopefully achieving better accuracy.

Fig. 1: Grid cells used by climate models and processes calculated in the model for each cell. (Source: NOAA GFDL)

1.2. Climate Context

5

ClimateSet, Release 0.1

1.2.2 What are Forcings?
In climate models, “forcing” refers to external factors that influence the Earth’s energy balance. This includes natural and human-induced changes such as variations in solar radiation, greenhouse gas emissions, aerosols, and landuse. Forcings can lead to warming (positive radiative forcing) or cooling (negative radiative forcing) effects on the climate. Understanding and simulating these forcings help scientists study past climate changes and project future climate
scenarios.
SSP Scenarios
SSP stands for Shared Socioeconomic Pathway, which is a set of scenarios that describe alternative future pathways ofglobal development, particularly focusing on how socioeconomic factors may influence greenhouse gas emissions andother drivers of climate change.
The SSPs are used in conjunction with Representative Concentration Pathways (RCPs) to explore different potential
futures for climate research. While RCPs specify the concentrations of greenhouse gases in the atmosphere, SSPs
provide a narrative and quantitative description of future societal developments, including demographics, economic
structures, energy use, land use, and technological advancements.
The numbers following SSP (e.g., SSP1-1.9, SSP1-2.6) represent the radiative forcing level in watts per square metre(W/m2 ) by the end of the 21st century for the respective scenario. Lower numbers indicate lower radiative forcing,
implying more stringent climate mitigation measures, while higher numbers suggest higher emissions and less stringent
mitigation. For example, SSP1-1.9 corresponds to a scenario aiming to limit global warming to 1.9°C, while SSP1-2.6
targets a 2.6°C limit. These values help quantify the extent of climate change mitigation in each scenario, providing abasis for understanding potential future climate conditions.

Fig. 2: Different SSP Scenarios. (Source: Sfdiversity, CC BY-SA 4.0, via Wikimedia Commons)
IPCC Assessment Reports
The IPCC Assessment Reports are comprehensive scientific evaluations of climate change, produced by thousandsof experts. They cover the physical science basis, impacts on ecosystems and societies, and options for mitigation.
The reports provide policymakers with crucial information for international climate negotiations and decisions. The

6

Chapter 1. Contents

ClimateSet, Release 0.1

assessment process includes consensus-building and is regularly updated to reflect the latest scientific knowledge, themost recent one being IPCC AR 6 from July 2023.

Fig. 3: The first four IPCC assessment reports and the resolutions used in them. (Source: IPCC AR4, Fig 1.2)

1.2.3 Inputs and Outputs of a Climate Model
Climate models take a range of inputs and produce various outputs to simulate and predict the behaviour of the Earth’s
climate system.
Inputs
The inputs of a climate model comprise crucial elements defining the Earth’s climate system’s initial state and external
influences. Key components in ClimateSet’s context include:
Initial Conditions
The starting state of the atmosphere, oceans, land, and ice components.
Emission Scenarios
Future projections of greenhouse gas emissions, land use changes, and human activities.
Observational Data
Real-world observations assimilated to refine model simulations.
Forcing Data
External data influencing the model, e.g., observational datasets.
Computational Grid
Spatial and temporal resolution, determining simulation detail.

1.2. Climate Context

7

ClimateSet, Release 0.1

Model Parameters
Values defining model components like cloud physics, ocean circulation, and vegetation properties.

Fig. 4: Factors influencing the climate which are used in climate models. (Image courtesy of MetEd, The COMET
Program, UCAR.)
Outputs
The output of a climate model comprises a diverse set of information representing the simulated behaviour of the
Earth’s climate system. Key components of climate model outputs include:
Climate Variables• Temperature, precipitation, humidity, wind speed, and other atmospheric variables.
• Oceanic variables, including sea surface temperature, ocean currents, and salinity.
• Land surface variables, such as soil moisture, snow cover, and vegetation.
Uncertainty Estimates
Models often provide uncertainty ranges for various variables to account for the inherent uncertainties in climate
predictions.
Model outputs are often used in conjunction with observational data to validate and improve the models, ensuring they
provide reliable and actionable information.
Dimensionalities

8

Chapter 1. Contents

ClimateSet, Release 0.1

In the context of climate modelling, “dimensionalities” refer to the diverse aspects and variables considered in model
simulations. This encompasses factors like spatial and temporal resolutions, climate variables, radiative forcing, sealevel change, extreme events, carbon cycle dynamics, feedback mechanisms, uncertainty estimates, and more. Managing these dimensionalities is crucial for comprehensive climate modelling, ensuring simulations capture the complexities of Earth’s climate system. It involves representing various dimensions of information to provide accurate andmeaningful outputs for understanding climate processes and predicting future conditions.
Types of experiments on climate models
Scientists run various types of experiments on climate models to study different aspects of the Earth’s climate system
and assess the potential impacts of various factors. Some common types of experiments include:
Historical Simulations
Models are run using observed historical data for atmospheric composition, solar radiation, and other relevant
variables to simulate past climate conditions.
Future Projections
Models simulate future climate conditions under different scenarios of greenhouse gas emissions, land usechanges, and other human activities.
Sensitivity Experiments
Scientists systematically vary specific model parameters or initial conditions to assess the sensitivity of the climate system to changes in those factors.
Emission Scenarios
Models are used to project future climate conditions based on different scenarios of future greenhouse gas emissions.
Impact Assessments
Models are employed to assess the potential impacts of climate change on ecosystems, agriculture, water resources, and human societies.
Paleoclimate Simulations
Models are run to simulate past climates, including periods with different concentrations of greenhouse gases,
ice ages, and warm intervals.

1.2.4 Data Sources
CMIP6
CMIP6 (Coupled Model Intercomparison Project Phase 6) is a collaborative effort for climate modelling. ClimateSet
utilises CMIP6, presenting a dataset with outputs from 36 climate models. It addresses the need for large, consistentdatasets in machine learning (ML) for climate-related tasks. CMIP6 models inform the IPCC reports, and ClimateSet’s
modular pipeline fetches and preprocesses CMIP6 data for ML applications. The dataset’s value lies in its ability to
train ML models at scale, enabling the community to contribute to climate tasks.
Input4Mips
Input4MIPs refers to Input Datasets for Model Intercomparison Projects. It collects future emission trajectories ofclimate-forcing agents used as input for climate models. Endorsed by CMIP6, it aligns with ClimateSet’s CMIP6 data,
providing essential climate model input. Input4MIPs encompass different climate-forcing trajectories based on SSP
scenarios, crucial for understanding future climate changes. ClimateSet specifically selects four main SSP scenariosand four climate forcers from Input4MIPs, emphasising the importance of these trajectories in training machine learningmodels for climate emulation tasks.
ESGF (Earth System Grid Federation)
The Earth System Grid Federation (ESGF) is an organisation that serves as the primary source for climate model data
retrieval in ClimateSet. It enables the download of diverse climate datasets from various sources, including the above-

1.2. Climate Context

9ClimateSet, Release 0.1

mentioned Input4Mips and CMIP6 datasets, which facilitates the creation of a consistent and large-scale dataset formachine learning applications in climate science.

1.2.5 Different Tasks with Climate Models
Climate projection
Climate projection involves forecasting future climate conditions based on various scenarios. It employs climate models to simulate the Earth’s response to different greenhouse gas emissions, aerosols, and other influencing factors.
These models project changes in temperature, precipitation, wind patterns, and more, providing insights into potential
future climatic conditions. Climate projections are vital for policymakers, allowing them to anticipate and plan forpotential impacts on ecosystems, societies, and economies. In ClimateSet, the core dataset utilises climate models to
capture projection uncertainties, which is essential for training machine learning models and informing climate-related
decision-making.
Downscaling
Downscaling in climate science refers to the process of refining climate model outputs to a finer spatial resolution.
Global Climate Models (GCMs) often have coarse resolutions, making them less suitable for regional-scale analyses.
Downscaling involves using statistical or dynamical techniques to generate higher-resolution climate projections. ClimateSet may implement downscaling methods to enhance the spatial precision of its dataset, providing more detailed
information about local climate impacts. Downscaled data allows researchers to better understand regional variationsin climate patterns, essential for addressing localised impacts of climate change and supporting more accurate decisionmaking in areas such as agriculture, water resources, and infrastructure planning.
In general, increasing the spatial resolution of a model by a factor of two will require around 10 times the computing
power to run in the same amount of time.1

1.2.6 Climate Model Emulation
Climate emulation involves the development of machine learning models to simulate climate model outputs. The goal
is to create emulators that can predict climate variables with greater efficiency than traditional climate models during
inference. In this context, emulators receive input data such as greenhouse gas emission trajectories and generate
climate projections. ClimateSet serves as a valuable resource for large-scale climate emulation tasks by providing acomprehensive dataset derived from 36 climate models.
Emulation is crucial for handling the computational intensity of climate modelling, enabling faster simulations while
maintaining accuracy. Two types of emulators are available in ClimateSet: Single Emulators, trained on individual
climate models, and Super Emulators, capable of projecting responses from multiple models. Climate emulation playsa pivotal role in advancing climate research, facilitating tasks like predicting temperature and precipitation patterns. It
is important to evaluate emulators across diverse climate models to ensure robust performance and generalisation.
Metrics
Climate model emulation metrics are quantitative measures used to assess the accuracy and performance of machinelearning models in simulating climate variables. In ClimateSet, the latitude-longitude weighted root mean squared error(RMSE) is a primary evaluation metric for assessing the performance of emulators. This metric quantifies the difference
between predicted and observed values, providing insights into the model’s ability to replicate climate model outputs.
Robust evaluation metrics are crucial for determining the reliability and generalisation capabilities of emulators acrossdiverse climate models.
1 https://scied.ucar.edu/longcontent/climate-modeling

10

Chapter 1. Contents

ClimateSet, Release 0.1

1.2.7 Additional
Accuracy of climate model projections of temperature
Climate models provide accurate projections of the overall trend and patterns of global temperature changes over thelong term. They capture the fundamental warming trend associated with increased greenhouse gas concentrations.
However, uncertainties exist in predicting specific regional variations, short-term fluctuations, and the exact magnitudeof temperature changes. Ongoing advancements in model development and increased understanding of key processes
aim to reduce uncertainties and enhance the accuracy of temperature projections. Evaluation against observed data andcontinuous model refinement contribute to improving the reliability of climate model temperature predictions.
Main limitations in climate modelling
Current limitations in climate modelling include finite spatial resolution, challenges in accurately representing cloud
processes, uncertainties in feedback, incomplete understanding of biogeochemical processes, difficulties in simulating
past climates and ice sheet dynamics, and challenges in predicting extreme events. Ocean circulation complexities,
uncertainty quantification, and the need for substantial computational resources also pose challenges. Ongoing researchaims to address these limitations and improve the accuracy of climate models for more reliable future projections andimpact assessments.

1.2.8 Sources
Watson-Parris, D. (2021). Machine learning for weather and climate are worlds apart. Philosophical Transactions of
the Royal Society A, 379(2194), 20200098. https://doi.org/10.1098/rsta.2020.0098
McSweeney, Robert. “Q&A: How Do Climate Models Work?” Carbon Brief, July 20, 2022. https://www.carbonbrief.
org/qa-how-do-climate-models-work/
IPCC, 2018: Annex I: Glossary [Matthews, J.B.R. (ed.)]. In: Global Warming of 1.5°C. An IPCC Special Report on theimpacts 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 [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W.
Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T.
Maycock, M. Tignor, and T. Waterfield (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA,pp. 541-562, doi:10.1017/9781009157940.008.

1.3 Glossary
Aerosols
Tiny particles or droplets suspended in the atmosphere, often originating from natural sources or human activities,
influencing climate by scattering or absorbing sunlight and affecting cloud formation.
Aerosol Precursors
Substances that contribute to the formation of aerosols in the atmosphere
Biomass Burning Data
Part of Input4MIPs, representing emissions from open biomass burning, used as input for climate models.
Climate Emulation
The development of machine learning models to simulate climate model outputs.
Climate Model
A mathematical representation of the Earth’s climate system used for predicting future climate conditions.
Climate Projection
A prediction of future climate conditions based on climate model simulations.

1.3. Glossary11ClimateSet, Release 0.1

Climate Scenario
A set of conditions used in climate models to project possible future climate states.
Climate Variables
Parameters such as temperature, precipitation, and wind velocity used in climate models.
ClimateSet
The dataset introduced here, providing climate model outputs and emission inputs for use in large-scale machinelearning models.
ClimateSet Data Pipeline
A modular pipeline for retrieving and preprocessing climate model data for ML tasks.
CMIP6 (Coupled Model Intercomparison Project Phase 6)
An archive uniting climate model outputs from various sources.
CMIP6 (Coupled Model Intercomparison Project Phase 6)
A project that collects climate model outputs from various sources, providing a comprehensive archive forclimate-related research.
Dimension Reduction
Techniques to streamline large datasets by reducing the number of variables while retaining essential information.
Downscaling
A process of generating high-resolution climate predictions from lower-resolution climate models.
ESGF (Earth System Grid Federation)
A system for managing and distributing climate model data.
Emulation
In the context of ClimateSet, it involves developing machine learning models to simulate climate model outputs,
providing faster predictions for climate variables based on input data.
Forcings
External factors influencing the Earth’s energy balance, such as variations in solar radiation, greenhouse gas
emissions, aerosols, and land use.
GCMs (Global Climate Models)Complex simulations representing Earth’s climate system, focusing primarily on the atmosphere.
GHG (Greenhouse Gases)
Gases like CO2 and CH4 that trap heat in the Earth’s atmosphere.
Grid
Spatial and temporal framework dividing the Earth’s surface and atmosphere into discrete cells, facilitating therepresentation of physical and environmental variables at specific locations and time intervals for simulation andanalysis.
IPCC (Intergovernmental Panel on Climate Change)
An international body assessing climate science.
IPCC Assessment Reports
Comprehensive scientific evaluations of climate change, informing policymakers and based on consensusbuilding.
HPC (High-Performance Compute) Cluster
A computing cluster designed for tasks requiring substantial processing power, beneficial for extending ClimateSet with additional climate models.
Input4MIPs
Datasets collecting future emission trajectories of climate-forcing agents used as input for climate models.

12

Chapter 1. Contents

ClimateSet, Release 0.1

Model Outputs
The diverse information generated by climate models, including climate variables, radiative forcing, sea levelchange, and more.
Preprocessing
The process of preparing raw climate data for machine learning tasks by handling inconsistencies, syncing parameters, and adjusting resolutions.
Projection Uncertainties
Variabilities in climate model projections arising from differences in model formulations (inter-model variability)
and initializations (intra-model variability).
RMSE (Root Mean Squared Error)
An evaluation metric used to assess the accuracy of climate emulators.
Single Emulators and Super Emulators
ML models trained on a single climate model vs. those trained on a set of climate models for broader applications.
Spatial and Temporal Resolution
The granularity of spatial and temporal dimensions in climate data.
SSP (Shared Socioeconomic Pathways)
Scenarios within ScenarioMIP representing different socioeconomic development pathways that influence greenhouse gas emissions.
Weighting of Climate Models
Assigning different weights to climate models to prevent over or under-representation.

1.3. Glossary

13