Accelerating climate science with AI

* Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay

Climate change is one of the most pressing global challenges, with human emissions now unanimously accepted as the key driver. However, significant scientific uncertainties remain regarding the Earth’s climate sensitivity, which directly translate into uncertainties in the remaining carbon budget on the path to achieving the targets of the Paris Agreement. Current advances in climate science are hindered by the limitations of the methodologies commonly used for scientific data analysis, some of them ill-equipped for capturing complex relationships present in the huge volumes of data now available. At the same time, the field of machine learning has seen a series of dramatic breakthroughs over the past decade, driven by the development of new data-intensive algorithms.

A kick-off event on 7 July 2021 will set the stage for a series of expert talks over the following months which will bring together leading voices from academia, industry and policy to provide a forum to ensure climate science is able to capitalise on the latest advances in AI and machine learning for the benefit of humanity. The outcome will be a whitepaper proposing ways forward.

Panelists
Resources

Keynote Remarks at the ITU AI and Climate Change kick-off event, by Ban Ki-Moon

Introductory remarks on “Accelerating climate science with AI”, by Philip Stier

AI expands scope of climate science; ITU News blog, 13 July 2021

Shownotes:

[01:41] Introduction by Fred Werner (ITU)

[05:21] Introduction by Philip Stier

-Host of the Climate Science series

[05:28] Climate Science

-Climate Change is one of the most pressing global challenges.

-Human emissions now unanimously pressing global challenges

Significant scientific uncertainties remain, in particular regarding the impact on extreme climate events.

[05:47] AI and ML

-AI and machine learning have seen a series of dramatic breakthroughs.

-Applications are transforming scientific areas.

New algorithms

[06:21] Accelerating Climate Science with AI

-Identification and quantification of complex association between features in a vast model and earth observation datasets.

-Climate models are skillful .

-Small dataset of earth has been used.

[07:16] Programme

Mr. Ban Ki Moon

Ms. Valerie Masson Delmotte

Mr. Yoshua Bengio

[08:15] Introduction by Mr. Houlin Zhao

-Applications of AI to accelerate the SDG’s.

-How can we accelerate climate science with AI?

-Introduction to Mr. Ban Ki Moon

-Global impact demands new partnerships and AI for good will works as the platform for this.

-Today's session means an introduction to the new topic on how accelerate climate change

-Introduction to Mr. Bengio

[16:16] Introduction by Mr.Ban Ki-Moon

-How can problems be tackled with the use of AI?

-International community is combating climate change and is looking for effective ways to do it.

-Covid 19 has changed our lives, but climate change is still worsening.

-Highlight the use of technology (AI) to have a brighter future for our humanity.

-How can we mitigate the threats posed by Climate Change

-Global efforts are important through multilateralism and partnerships.

-I have prioritised, as secretary-general, climate change

-Innovation and technology should be in the center of the fight against climate change (global goals: food security, energy, etc).

-Mindset is important to combat climate change.

-Science, innovation and technology is important to achieve SDGs and it is something that the scientific community should have in mind.

[35:45] Mr. Houlin Zhao summary of Mr. Ban Ki-Moon speech

-Support on the use of ICT’s from Mr. Ban Ki-Moon.

-The United Nations system has a framework on using ICTs for the development through the World Summit on the Information Society (WSIS)

-ICT’s have helped societies during COVID-19 situation.

[38:12] Mr. Ban Ki-Moon

- When it comes to climate, there is no time to lose.

-Covid-19 is not an independant situation of climate change.

[42:17] Mr. Philip Stier question

-Advice on how to translate the crisis into specific actions?

We need political leadership. With this, a partnership with civil society the world will have a better response against these crises.

[46:16] Introduction by Valerie Masson-Delmotte

-IPCC reports climate change affects many aspects of life, such as food, security, etc.

-New approaches to identify new climate projects and plan a future answer.

-Clouds play a key role in the response of the climate system.

-Data available is important to use AI to tackle climate problems: quality and quantity.

[52:48] Land

-Land management is important to have food security.

-Climate and biodiversity are very linked. Loss degradation of the ecosystem is made by carbon emissions.

[54:35] Ocean and Cryosphere

-There are no linear responses associated with these two fields.

-Bring together solutions aligned to these is needed.

-How to do the evaluation of the local and regional information.

-Now, climate change events are being presented more repeatedly.

-Coordinate initiatives are needed and it's important to understand the nature of every climate event.

-Science can inform action to address climate change problems.

-Innovative approaches are needed at the international level, to harmonize an action.

[01:00:31] IPCC report

-it’s an online tool to look at past, present and future events.

-75 reviews from the scientific sector, which is exhausting.

-AI techniques can facilitate review of this report.

[1:03:35] Introduction from Philip Stier to Mr. Bengio

[1:04:40] Introduction by Mr. Yoshua Bengio

[1:04:50] AI and fighting climate change

[1:05:21] Environmental Application of AI

-Optimizing energy resources.

-Climate modelling: predicting climate change effects.

Accelerate R&D of new material.

-Visceralization: visualize the future.

[1:06:23] Climate change + AI (CCAI)

-Tackling climate change with machine learning paper.

-Connect xperts outside of AI.

-A grid connects the application of AI in different areas.

[1:07:47] Global Partnership on AI Climate and Biodiversity Committee

-A responsible AI strategy for the environment

-Help identify areas for investment in AI that can contribute to the Paris Accord and to the preservation of biodiversity.

[1:08:43] Synthesizing new material for batteries in carbon

-Areas of application: Synthesizing new materials.

-The process comprises two aspects: Modelling aspect and search aspect.

-Both aspects can be done by using AI and will help to solve problems in their development.

[1:09:26] Learning to explore the space of material

-Exploratory agent -> Imagined experiment -> World model -> learning declarative knowledge -> updated world model

-Mentioned process will be shorter with AI

[1:10:36] Physics-Constrained ML Models for Climate or Material

-Combination of Climate Science, Physics and other fields with ML

-ML approximate physical model dynamics faster to enable greater space-time resolutions.

-Hybrid models: incorporate physical invariantes and equivariance in dynamic models.

-Use deep learning to discover new latent abstract space where dynamics can be run at low resolution

[1:12:02] Why insufficient action? psychological factors?

-Human cognitive biases: we underestimate the threat of climate change.

-Far away in time and space and abstract: we cannot see the future effects of climate change.

-It is hard to compete with short-term economic issues.

-Information bubbles/biases (information filtered).

[1:13:21] This Climate Does not Exist

-Extreme climate events are occurring all around the planet and climate change highlights the contribution to them.

[1:14:01] The Climate Change Visualization Project

-Project: generate images simulating climate change effects.

[1:15:01] Bring Future Societal Impact into Prices Today

-Carbon pricing should work, but governments are not setting the price high enough because of cognitive biases (remote threats vs. immediate loss).

-Defer to the later government to decide what the price should have been.

-Align carbon price with our current best rational bet of what future government will charge, and include the cost of uncertainty.

[1:16:59] Bring the Future Prices Today with Retroactive Carbon Pricing

-Example: Image new law 2022 on retroactively applying carbon price to emission from 2022 onwards.

-2023 If price increases by 10, polluters of 2022 owe 10 extra.

[1:19:52] Bring future societal impact into prices today

-To avoid uncertain cost, companies buy an insurance contract

-The insurance contracts are market-traded: intencetive for honest forecasts.

-Fund used to compensate the underprivileged.

[1:22:03] Closing

[1:22:31] Which areas do you think we can contribute the most? There are particular directions. Spatial models, images, and others.

[1:23:12] What kind of AI exists for the plastic problem in the ocean? I don’t know about this in particular. Collecting data is the most difficult thing, so managing data should be addressed to solve this problem

[1:24:56] Girls and women in AI. How to solve the problem of gender equality in the field of AI? Mentorship at early ages. Emulation and motivation of kids.

[1:27:07] Mindset. Education is important. Travel in time. Go to the past and see the present and the effects of the past to look forward to the future. This is a powerful tool.

[1:29:23] AI for Good next session Duncan

-16 July we will take a deeper dive into the many ways AI can accelerate climate science. This will lead towards the development of a whitepaper.

[1:30:21] Closing remarks by ITU.

For important information regarding the classification, please go to the Division’s website and review the last two questions in the Q&A page. Please be advised that the utilization of this list by AI for Good is exclusively for the purpose of ticketing for the 2024 AI for Good Global Summit, unless otherwise specified

Country or Area	ISO-alpha2 Code	ISO-alpha3 Code	Developed / Developing regions
Algeria	DZ	DZA	Developing
Egypt	EG	EGY	Developing
Libya	LY	LBY	Developing
Morocco	MA	MAR	Developing
Sudan	SD	SDN	Developing
Tunisia	TN	TUN	Developing
Western Sahara	EH	ESH	Developing
British Indian Ocean Territory	IO	IOT	Developing
Burundi	BI	BDI	Developing
Comoros	KM	COM	Developing
Djibouti	DJ	DJI	Developing
Eritrea	ER	ERI	Developing
Ethiopia	ET	ETH	Developing
French Southern Territories	TF	ATF	Developing
Kenya	KE	KEN	Developing
Madagascar	MG	MDG	Developing
Malawi	MW	MWI	Developing
Mauritius	MU	MUS	Developing
Mayotte	YT	MYT	Developing
Mozambique	MZ	MOZ	Developing
Réunion	RE	REU	Developing
Rwanda	RW	RWA	Developing
Seychelles	SC	SYC	Developing
Somalia	SO	SOM	Developing
South Sudan	SS	SSD	Developing
Uganda	UG	UGA	Developing
United Republic of Tanzania	TZ	TZA	Developing
Zambia	ZM	ZMB	Developing
Zimbabwe	ZW	ZWE	Developing
Angola	AO	AGO	Developing
Cameroon	CM	CMR	Developing
Central African Republic	CF	CAF	Developing
Chad	TD	TCD	Developing
Congo	CG	COG	Developing
Democratic Republic of the Congo	CD	COD	Developing
Equatorial Guinea	GQ	GNQ	Developing
Gabon	GA	GAB	Developing
Sao Tome and Principe	ST	STP	Developing
Botswana	BW	BWA	Developing
Eswatini	SZ	SWZ	Developing
Lesotho	LS	LSO	Developing
Namibia	NA	NAM	Developing
South Africa	ZA	ZAF	Developing
Benin	BJ	BEN	Developing
Burkina Faso	BF	BFA	Developing
Cabo Verde	CV	CPV	Developing
Côte d’Ivoire	CI	CIV	Developing
Gambia	GM	GMB	Developing
Ghana	GH	GHA	Developing
Guinea	GN	GIN	Developing
Guinea-Bissau	GW	GNB	Developing
Liberia	LR	LBR	Developing
Mali	ML	MLI	Developing
Mauritania	MR	MRT	Developing
Niger	NE	NER	Developing
Nigeria	NG	NGA	Developing
Saint Helena	SH	SHN	Developing
Senegal	SN	SEN	Developing
Sierra Leone	SL	SLE	Developing
Togo	TG	TGO	Developing
Anguilla	AI	AIA	Developing
Antigua and Barbuda	AG	ATG	Developing
Aruba	AW	ABW	Developing
Bahamas	BS	BHS	Developing
Barbados	BB	BRB	Developing
Bonaire, Sint Eustatius and Saba	BQ	BES	Developing
British Virgin Islands	VG	VGB	Developing
Cayman Islands	KY	CYM	Developing
Cuba	CU	CUB	Developing
Curaçao	CW	CUW	Developing
Dominica	DM	DMA	Developing
Dominican Republic	DO	DOM	Developing
Grenada	GD	GRD	Developing
Guadeloupe	GP	GLP	Developing
Haiti	HT	HTI	Developing
Jamaica	JM	JAM	Developing
Martinique	MQ	MTQ	Developing
Montserrat	MS	MSR	Developing
Puerto Rico	PR	PRI	Developing
Saint Barthélemy	BL	BLM	Developing
Saint Kitts and Nevis	KN	KNA	Developing
Saint Lucia	LC	LCA	Developing
Saint Martin (French Part)	MF	MAF	Developing
Saint Vincent and the Grenadines	VC	VCT	Developing
Sint Maarten (Dutch part)	SX	SXM	Developing
Trinidad and Tobago	TT	TTO	Developing
Turks and Caicos Islands	TC	TCA	Developing
United States Virgin Islands	VI	VIR	Developing
Belize	BZ	BLZ	Developing
Costa Rica	CR	CRI	Developing
El Salvador	SV	SLV	Developing
Guatemala	GT	GTM	Developing
Honduras	HN	HND	Developing
Mexico	MX	MEX	Developing
Nicaragua	NI	NIC	Developing
Panama	PA	PAN	Developing
Argentina	AR	ARG	Developing
Bolivia (Plurinational State of)	BO	BOL	Developing
Bouvet Island	BV	BVT	Developing
Brazil	BR	BRA	Developing
Chile	CL	CHL	Developing
Colombia	CO	COL	Developing
Ecuador	EC	ECU	Developing
Falkland Islands (Malvinas)	FK	FLK	Developing
French Guiana	GF	GUF	Developing
Guyana	GY	GUY	Developing
Paraguay	PY	PRY	Developing
Peru	PE	PER	Developing
South Georgia and the South Sandwich Islands	GS	SGS	Developing
Suriname	SR	SUR	Developing
Uruguay	UY	URY	Developing
Venezuela (Bolivarian Republic of)	VE	VEN	Developing
Kazakhstan	KZ	KAZ	Developing
Kyrgyzstan	KG	KGZ	Developing
Tajikistan	TJ	TJK	Developing
Turkmenistan	TM	TKM	Developing
Uzbekistan	UZ	UZB	Developing
China	CN	CHN	Developing
China, Hong Kong Special Administrative Region	HK	HKG	Developing
China, Macao Special Administrative Region	MO	MAC	Developing
Democratic People’s Republic of Korea	KP	PRK	Developing
Mongolia	MN	MNG	Developing
Brunei Darussalam	BN	BRN	Developing
Cambodia	KH	KHM	Developing
Indonesia	ID	IDN	Developing
Lao People’s Democratic Republic	LA	LAO	Developing
Malaysia	MY	MYS	Developing
Myanmar	MM	MMR	Developing
Philippines	PH	PHL	Developing
Singapore	SG	SGP	Developing
Thailand	TH	THA	Developing
Timor-Leste	TL	TLS	Developing
Viet Nam	VN	VNM	Developing
Afghanistan	AF	AFG	Developing
Bangladesh	BD	BGD	Developing
Bhutan	BT	BTN	Developing
India	IN	IND	Developing
Iran (Islamic Republic of)	IR	IRN	Developing
Maldives	MV	MDV	Developing
Nepal	NP	NPL	Developing
Pakistan	PK	PAK	Developing
Sri Lanka	LK	LKA	Developing
Armenia	AM	ARM	Developing
Azerbaijan	AZ	AZE	Developing
Bahrain	BH	BHR	Developing
Georgia	GE	GEO	Developing
Iraq	IQ	IRQ	Developing
Jordan	JO	JOR	Developing
Kuwait	KW	KWT	Developing
Lebanon	LB	LBN	Developing
Oman	OM	OMN	Developing
Qatar	QA	QAT	Developing
Saudi Arabia	SA	SAU	Developing
State of Palestine	PS	PSE	Developing
Syrian Arab Republic	SY	SYR	Developing
Turkey	TR	TUR	Developing
United Arab Emirates	AE	ARE	Developing
Yemen	YE	YEM	Developing
Fiji	FJ	FJI	Developing
New Caledonia	NC	NCL	Developing
Papua New Guinea	PG	PNG	Developing
Solomon Islands	SB	SLB	Developing
Vanuatu	VU	VUT	Developing
Guam	GU	GUM	Developing
Kiribati	KI	KIR	Developing
Marshall Islands	MH	MHL	Developing
Micronesia (Federated States of)	FM	FSM	Developing
Nauru	NR	NRU	Developing
Northern Mariana Islands	MP	MNP	Developing
Palau	PW	PLW	Developing
United States Minor Outlying Islands	UM	UMI	Developing
American Samoa	AS	ASM	Developing
Cook Islands	CK	COK	Developing
French Polynesia	PF	PYF	Developing
Niue	NU	NIU	Developing
Pitcairn	PN	PCN	Developing
Samoa	WS	WSM	Developing
Tokelau	TK	TKL	Developing
Tonga	TO	TON	Developing
Tuvalu	TV	TUV	Developing
Wallis and Futuna Islands	WF	WLF	Developing

Accelerating climate science with AI