{ "countries": [ { "id": "ABW", "large": false, "name": "Aruba" }, { "id": "AFG", "large": false, "name": "Afghanistan" }, { "id": "AGO", "large": false, "name": "Angola" }, { "id": "AIA", "large": false, "name": "Anguilla" }, { "id": "ALB", "large": false, "name": "Albania" }, { "id": "AND", "large": false, "name": "Andorra" }, { "id": "ARE", "large": false, "name": "United Arab Emirates" }, { "id": "ARG", "large": true, "name": "Argentina" }, { "id": "ARM", "large": false, "name": "Armenia" }, { "id": "ASM", "large": false, "name": "American Samoa" }, { "id": "ATA", "large": true, "name": "Antarctica" }, { "id": "ATF", "large": false, "name": "French Southern Territories" }, { "id": "ATG", "large": false, "name": "Antigua and Barbuda" }, { "id": "AUS", "large": true, "name": "Australia" }, { "id": "AUT", "large": false, "name": "Austria" }, { "id": "AZE", "large": false, "name": "Azerbaijan" }, { "id": "BDI", "large": false, "name": "Burundi" }, { "id": "BEL", "large": false, "name": "Belgium" }, { "id": "BEN", "large": false, "name": "Benin" }, { "id": "BFA", "large": false, "name": "Burkina Faso" }, { "id": "BGD", "large": false, "name": "Bangladesh" }, { "id": "BGR", "large": false, "name": "Bulgaria" }, { "id": "BHR", "large": false, "name": "Bahrain" }, { "id": "BHS", "large": false, "name": "Bahamas" }, { "id": "BIH", "large": false, "name": "Bosnia and Herzegovina" }, { "id": "BLM", "large": false, "name": "Saint Barth\u00e9lemy" }, { "id": "BLR", "large": false, "name": "Belarus" }, { "id": "BLZ", "large": false, "name": "Belize" }, { "id": "BMU", "large": false, "name": "Bermuda" }, { "id": "BOL", "large": false, "name": "Bolivia, Plurinational State of" }, { "id": "BRA", "large": true, "name": "Brazil" }, { "id": "BRB", "large": false, "name": "Barbados" }, { "id": "BRN", "large": false, "name": "Brunei Darussalam" }, { "id": "BTN", "large": false, "name": "Bhutan" }, { "id": "BWA", "large": false, "name": "Botswana" }, { "id": "CAF", "large": false, "name": "Central African Republic" }, { "id": "CAN", "large": true, "name": "Canada" }, { "id": "CHE", "large": false, "name": "Switzerland" }, { "id": "CHL", "large": false, "name": "Chile" }, { "id": "CHN", "large": true, "name": "China" }, { "id": "CIV", "large": false, "name": "C\u00f4te d'Ivoire" }, { "id": "CMR", "large": false, "name": "Cameroon" }, { "id": "COD", "large": false, "name": "Congo, The Democratic Republic of the" }, { "id": "COG", "large": false, "name": "Congo" }, { "id": "COK", "large": false, "name": "Cook Islands" }, { "id": "COL", "large": false, "name": "Colombia" }, { "id": "COM", "large": false, "name": "Comoros" }, { "id": "CPV", "large": false, "name": "Cabo Verde" }, { "id": "CRI", "large": false, "name": "Costa Rica" }, { "id": "CUB", "large": false, "name": "Cuba" }, { "id": "CUW", "large": false, "name": "Cura\u00e7ao" }, { "id": "CYM", "large": false, "name": "Cayman Islands" }, { "id": "CYP", "large": false, "name": "Cyprus" }, { "id": "CZE", "large": false, "name": "Czechia" }, { "id": "DEU", "large": false, "name": "Germany" }, { "id": "DJI", "large": false, "name": "Djibouti" }, { "id": "DMA", "large": false, "name": "Dominica" }, { "id": "DNK", "large": false, "name": "Denmark" }, { "id": "DOM", "large": false, "name": "Dominican Republic" }, { "id": "DZA", "large": false, "name": "Algeria" }, { "id": "ECU", "large": false, "name": "Ecuador" }, { "id": "EGY", "large": false, "name": "Egypt" }, { "id": "ERI", "large": false, "name": "Eritrea" }, { "id": "ESH", "large": false, "name": "Western Sahara" }, { "id": "ESP", "large": false, "name": "Spain" }, { "id": "EST", "large": false, "name": "Estonia" }, { "id": "ETH", "large": false, "name": "Ethiopia" }, { "id": "FIN", "large": false, "name": "Finland" }, { "id": "FJI", "large": false, "name": "Fiji" }, { "id": "FLK", "large": false, "name": "Falkland Islands (Malvinas)" }, { "id": "FRA", "large": false, "name": "France" }, { "id": "FRO", "large": false, "name": "Faroe Islands" }, { "id": "FSM", "large": false, "name": "Micronesia, Federated States of" }, { "id": "GAB", "large": false, "name": "Gabon" }, { "id": "GBR", "large": false, "name": "United Kingdom" }, { "id": "GEO", "large": false, "name": "Georgia" }, { "id": "GGY", "large": false, "name": "Guernsey" }, { "id": "GHA", "large": false, "name": "Ghana" }, { "id": "GIN", "large": false, "name": "Guinea" }, { "id": "GMB", "large": false, "name": "Gambia" }, { "id": "GNB", "large": false, "name": "Guinea-Bissau" }, { "id": "GNQ", "large": false, "name": "Equatorial Guinea" }, { "id": "GRC", "large": false, "name": "Greece" }, { "id": "GRD", "large": false, "name": "Grenada" }, { "id": "GRL", "large": true, "name": "Greenland" }, { "id": "GTM", "large": false, "name": "Guatemala" }, { "id": "GUM", "large": false, "name": "Guam" }, { "id": "GUY", "large": false, "name": "Guyana" }, { "id": "HKG", "large": false, "name": "Hong Kong" }, { "id": "HMD", "large": false, "name": "Heard Island and McDonald Islands" }, { "id": "HND", "large": false, "name": "Honduras" }, { "id": "HRV", "large": false, "name": "Croatia" }, { "id": "HTI", "large": false, "name": "Haiti" }, { "id": "HUN", "large": false, "name": "Hungary" }, { "id": "IDN", "large": true, "name": "Indonesia" }, { "id": "IMN", "large": false, "name": "Isle of Man" }, { "id": "IND", "large": true, "name": "India" }, { "id": "IOT", "large": false, "name": "British Indian Ocean Territory" }, { "id": "IRL", "large": false, "name": "Ireland" }, { "id": "IRN", "large": false, "name": "Iran, Islamic Republic of" }, { "id": "IRQ", "large": false, "name": "Iraq" }, { "id": "ISL", "large": false, "name": "Iceland" }, { "id": "ISR", "large": false, "name": "Israel" }, { "id": "ITA", "large": false, "name": "Italy" }, { "id": "JAM", "large": false, "name": "Jamaica" }, { "id": "JEY", "large": false, "name": "Jersey" }, { "id": "JOR", "large": false, "name": "Jordan" }, { "id": "JPN", "large": false, "name": "Japan" }, { "id": "KAZ", "large": true, "name": "Kazakhstan" }, { "id": "KEN", "large": false, "name": "Kenya" }, { "id": "KGZ", "large": false, "name": "Kyrgyzstan" }, { "id": "KHM", "large": false, "name": "Cambodia" }, { "id": "KIR", "large": false, "name": "Kiribati" }, { "id": "KNA", "large": false, "name": "Saint Kitts and Nevis" }, { "id": "KOR", "large": false, "name": "Korea, Republic of" }, { "id": "KWT", "large": false, "name": "Kuwait" }, { "id": "LAO", "large": false, "name": "Lao People's Democratic Republic" }, { "id": "LBN", "large": false, "name": "Lebanon" }, { "id": "LBR", "large": false, "name": "Liberia" }, { "id": "LBY", "large": false, "name": "Libya" }, { "id": "LCA", "large": false, "name": "Saint Lucia" }, { "id": "LIE", "large": false, "name": "Liechtenstein" }, { "id": "LKA", "large": false, "name": "Sri Lanka" }, { "id": "LSO", "large": false, "name": "Lesotho" }, { "id": "LTU", "large": false, "name": "Lithuania" }, { "id": "LUX", "large": false, "name": "Luxembourg" }, { "id": "LVA", "large": false, "name": "Latvia" }, { "id": "MAC", "large": false, "name": "Macao" }, { "id": "MAF", "large": false, "name": "Saint Martin (French part)" }, { "id": "MAR", "large": false, "name": "Morocco" }, { "id": "MCO", "large": false, "name": "Monaco" }, { "id": "MDA", "large": false, "name": "Moldova, Republic of" }, { "id": "MDG", "large": false, "name": "Madagascar" }, { "id": "MDV", "large": false, "name": "Maldives" }, { "id": "MEX", "large": false, "name": "Mexico" }, { "id": "MHL", "large": false, "name": "Marshall Islands" }, { "id": "MKD", "large": false, "name": "North Macedonia" }, { "id": "MLI", "large": false, "name": "Mali" }, { "id": "MLT", "large": false, "name": "Malta" }, { "id": "MMR", "large": false, "name": "Myanmar" }, { "id": "MNE", "large": false, "name": "Montenegro" }, { "id": "MNG", "large": false, "name": "Mongolia" }, { "id": "MNP", "large": false, "name": "Northern Mariana Islands" }, { "id": "MOZ", "large": false, "name": "Mozambique" }, { "id": "MRT", "large": false, "name": "Mauritania" }, { "id": "MSR", "large": false, "name": "Montserrat" }, { "id": "MUS", "large": false, "name": "Mauritius" }, { "id": "MWI", "large": false, "name": "Malawi" }, { "id": "MYS", "large": false, "name": "Malaysia" }, { "id": "NAM", "large": false, "name": "Namibia" }, { "id": "NCL", "large": false, "name": "New Caledonia" }, { "id": "NER", "large": false, "name": "Niger" }, { "id": "NFK", "large": false, "name": "Norfolk Island" }, { "id": "NGA", "large": false, "name": "Nigeria" }, { "id": "NIC", "large": false, "name": "Nicaragua" }, { "id": "NIU", "large": false, "name": "Niue" }, { "id": "NLD", "large": false, "name": "Netherlands" }, { "id": "NOR", "large": false, "name": "Norway" }, { "id": "NPL", "large": false, "name": "Nepal" }, { "id": "NRU", "large": false, "name": "Nauru" }, { "id": "NZL", "large": false, "name": "New Zealand" }, { "id": "OMN", "large": false, "name": "Oman" }, { "id": "PAK", "large": false, "name": "Pakistan" }, { "id": "PAN", "large": false, "name": "Panama" }, { "id": "PCN", "large": false, "name": "Pitcairn" }, { "id": "PER", "large": false, "name": "Peru" }, { "id": "PHL", "large": false, "name": "Philippines" }, { "id": "PLW", "large": false, "name": "Palau" }, { "id": "PNG", "large": false, "name": "Papua New Guinea" }, { "id": "POL", "large": false, "name": "Poland" }, { "id": "PRI", "large": false, "name": "Puerto Rico" }, { "id": "PRK", "large": false, "name": "Korea, Democratic People's Republic of" }, { "id": "PRT", "large": false, "name": "Portugal" }, { "id": "PRY", "large": false, "name": "Paraguay" }, { "id": "PSE", "large": false, "name": "Palestine, State of" }, { "id": "PYF", "large": false, "name": "French Polynesia" }, { "id": "QAT", "large": false, "name": "Qatar" }, { "id": "ROU", "large": false, "name": "Romania" }, { "id": "RUS", "large": true, "name": "Russian Federation" }, { "id": "RWA", "large": false, "name": "Rwanda" }, { "id": "SAU", "large": false, "name": "Saudi Arabia" }, { "id": "SDN", "large": false, "name": "Sudan" }, { "id": "SEN", "large": false, "name": "Senegal" }, { "id": "SGP", "large": false, "name": "Singapore" }, { "id": "SGS", "large": false, "name": "South Georgia and the South Sandwich Islands" }, { "id": "SHN", "large": false, "name": "Saint Helena, Ascension and Tristan da Cunha" }, { "id": "SLB", "large": false, "name": "Solomon Islands" }, { "id": "SLE", "large": false, "name": "Sierra Leone" }, { "id": "SLV", "large": false, "name": "El Salvador" }, { "id": "SMR", "large": false, "name": "San Marino" }, { "id": "SOM", "large": false, "name": "Somalia" }, { "id": "SPM", "large": false, "name": "Saint Pierre and Miquelon" }, { "id": "SRB", "large": false, "name": "Serbia" }, { "id": "SSD", "large": false, "name": "South Sudan" }, { "id": "STP", "large": false, "name": "Sao Tome and Principe" }, { "id": "SUR", "large": false, "name": "Suriname" }, { "id": "SVK", "large": false, "name": "Slovakia" }, { "id": "SVN", "large": false, "name": "Slovenia" }, { "id": "SWE", "large": false, "name": "Sweden" }, { "id": "SWZ", "large": false, "name": "Eswatini" }, { "id": "SXM", "large": false, "name": "Sint Maarten (Dutch part)" }, { "id": "SYC", "large": false, "name": "Seychelles" }, { "id": "SYR", "large": false, "name": "Syrian Arab Republic" }, { "id": "TCA", "large": false, "name": "Turks and Caicos Islands" }, { "id": "TCD", "large": false, "name": "Chad" }, { "id": "TGO", "large": false, "name": "Togo" }, { "id": "THA", "large": false, "name": "Thailand" }, { "id": "TJK", "large": false, "name": "Tajikistan" }, { "id": "TKM", "large": false, "name": "Turkmenistan" }, { "id": "TLS", "large": false, "name": "Timor-Leste" }, { "id": "TON", "large": false, "name": "Tonga" }, { "id": "TTO", "large": false, "name": "Trinidad and Tobago" }, { "id": "TUN", "large": false, "name": "Tunisia" }, { "id": "TUR", "large": false, "name": "Turkey" }, { "id": "TWN", "large": false, "name": "Taiwan, Province of China" }, { "id": "TZA", "large": false, "name": "Tanzania, United Republic of" }, { "id": "UGA", "large": false, "name": "Uganda" }, { "id": "UKR", "large": false, "name": "Ukraine" }, { "id": "URY", "large": false, "name": "Uruguay" }, { "id": "USA", "large": true, "name": "United States" }, { "id": "UZB", "large": false, "name": "Uzbekistan" }, { "id": "VAT", "large": false, "name": "Holy See (Vatican City State)" }, { "id": "VCT", "large": false, "name": "Saint Vincent and the Grenadines" }, { "id": "VEN", "large": false, "name": "Venezuela, Bolivarian Republic of" }, { "id": "VGB", "large": false, "name": "Virgin Islands, British" }, { "id": "VIR", "large": false, "name": "Virgin Islands, U.S." }, { "id": "VNM", "large": false, "name": "Viet Nam" }, { "id": "VUT", "large": false, "name": "Vanuatu" }, { "id": "WLF", "large": false, "name": "Wallis and Futuna" }, { "id": "WSM", "large": false, "name": "Samoa" }, { "id": "YEM", "large": false, "name": "Yemen" }, { "id": "ZAF", "large": false, "name": "South Africa" }, { "id": "ZMB", "large": false, "name": "Zambia" }, { "id": "ZWE", "large": false, "name": "Zimbabwe" }, { "id": "AFRICA", "large": true, "name": "Africa" }, { "id": "ASIA", "large": true, "name": "Asia" }, { "id": "EUROPE", "large": true, "name": "Europe" }, { "id": "NORTH_AMERICA", "large": true, "name": "North America and Greenland" }, { "id": "OCEANIA", "large": true, "name": "Oceania" }, { "id": "SOUTH_AMERICA", "large": true, "name": "South america" }, { "id": "GLOBAL", "large": true, "name": "Global" } ], "country_groups": [ { "children": [ "ATA" ], "id": "antarctica", "name": "Antarctica" }, { "children": [ "ATC", "AUS", "NFK", "NZL" ], "id": "australiaandnewzealand", "name": "Australia and New Zealand" }, { "children": [ "ABW", "AIA", "ATG", "BHS", "BLM", "BRB", "CUB", "CUW", "CYM", "DMA", "DOM", "GRD", "HTI", "JAM", "KNA", "LCA", "MAF", "MSR", "PRI", "SXM", "TCA", "TTO", "VCT", "VGB", "VIR" ], "id": "caribbean", "name": "Caribbean" }, { "children": [ "BDI", "CMR", "CAF", "TCD", "COG", "COD", "GNQ", "GAB", "STP" ], "id": "centralafrica", "name": "Central Africa" }, { "children": [ "BLZ", "CRI", "GTM", "HND", "MEX", "NIC", "PAN", "SLV" ], "id": "centralamerica", "name": "Central America" }, { "children": [ "KAZ", "KGZ", "TJK", "TKM", "UZB" ], "id": "centralasia", "name": "Central Asia" }, { "children": [ "COM", "DJI", "ERI", "ETH", "KEN", "MDG", "MUS", "RWA", "SYC", "SOM", "SSD", "SDN", "TZA", "UGA" ], "id": "easternafrica", "name": "Eastern Africa" }, { "children": [ "CHN", "HKG", "JPN", "KOR", "MAC", "MNG", "PRK", "TWN" ], "id": "easternasia", "name": "Eastern Asia" }, { "children": [ "BGR", "BLR", "CZE", "HUN", "MDA", "POL", "ROU", "RUS", "SVK", "UKR" ], "id": "easterneurope", "name": "Eastern Europe" }, { "children": [ "FJI", "NCL", "PNG", "SLB", "VUT" ], "id": "melanesia", "name": "Melanesia" }, { "children": [ "FSM", "GUM", "KIR", "MHL", "MNP", "NRU", "PLW" ], "id": "micronesia", "name": "Micronesia" }, { "children": [ "DZA", "EGY", "LBY", "MRT", "MAR", "ESH", "TUN" ], "id": "northernafrica", "name": "Northern Africa" }, { "children": [ "BMU", "CAN", "GRL", "SPM", "USA" ], "id": "northernamerica", "name": "Northern America" }, { "children": [ "ALD", "DNK", "EST", "FIN", "FRO", "GBR", "GGY", "IMN", "IRL", "ISL", "JEY", "LTU", "LVA", "NOR", "SWE" ], "id": "northerneurope", "name": "Northern Europe" }, { "children": [ "ASM", "COK", "NIU", "PCN", "PYF", "TON", "WLF", "WSM" ], "id": "polynesia", "name": "Polynesia" }, { "children": [ "ATF", "HMD", "IOA", "IOT", "SGS" ], "id": "sevenseas(openocean)", "name": "Seven seas (open ocean)" }, { "children": [ "ARG", "BOL", "BRA", "CHL", "COL", "ECU", "FLK", "GUY", "PER", "PRY", "SUR", "URY", "VEN" ], "id": "southamerica", "name": "South America" }, { "children": [ "BRN", "IDN", "KHM", "LAO", "MMR", "MYS", "PHL", "SGP", "THA", "TLS", "VNM" ], "id": "south-easternasia", "name": "South-Eastern Asia" }, { "children": [ "AGO", "BWA", "SWZ", "LSO", "MWI", "MOZ", "NAM", "ZAF", "ZMB", "ZWE" ], "id": "southernafrica", "name": "Southern Africa" }, { "children": [ "AFG", "BGD", "BTN", "IND", "IRN", "KAS", "LKA", "MDV", "NPL", "PAK" ], "id": "southernasia", "name": "Southern Asia" }, { "children": [ "ALB", "AND", "BIH", "ESP", "GRC", "HRV", "ITA", "KOS", "MKD", "MLT", "MNE", "PRT", "SMR", "SRB", "SVN", "VAT" ], "id": "southerneurope", "name": "Southern Europe" }, { "children": [ "BEN", "BFA", "CPV", "CIV", "GMB", "GHA", "GIN", "GNB", "LBR", "MLI", "NER", "NGA", "SEN", "SLE", "TGO" ], "id": "westernafrica", "name": "Western Africa" }, { "children": [ "ARE", "ARM", "AZE", "BHR", "CYN", "CYP", "GEO", "IRQ", "ISR", "JOR", "KWT", "LBN", "OMN", "PSX", "QAT", "SAU", "SYR", "TUR", "YEM", "PSE" ], "id": "westernasia", "name": "Western Asia" }, { "children": [ "AUT", "BEL", "CHE", "DEU", "FRA", "LIE", "LUX", "MCO", "NLD" ], "id": "westerneurope", "name": "Western Europe" }, { "children": [ "AFRICA", "ASIA", "EUROPE", "NORTH_AMERICA", "OCEANIA", "SOUTH_AMERICA" ], "id": "continents", "name": "Continents" } ], "scenarios": [ { "basescenario": false, "description": "This scenario assumes that only currently implemented climate policies are maintained, with no further strengthening. Global greenhouse gas emissions grow until 2080, leading to about 3\u202f\u00b0C of warming and irreversible changes like higher sea level rise. It was developed for the Network for Greening the Financial System (NGFS). In the NGFS terminology, this is considered a \u201chot house\u201d scenario, characterised by high physical risks, but low transition risks. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "h_cpol", "name": "NGFS current policies", "primary": true }, { "basescenario": false, "description": "This scenario assumes that only currently implemented climate policies are maintained, with no further strengthening. Global greenhouse gas emissions grow until 2080, leading to about 3\u202f\u00b0C of warming and irreversible changes like higher sea level rise. It was developed for the Network for Greening the Financial System (NGFS). In the NGFS terminology, this is considered a \u201chot house\u201d scenario, characterised by high physical risks, but low transition risks. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "h_cpol_high_impact", "name": "NGFS current policies (high climate response)", "primary": true }, { "basescenario": false, "description": "This ambitious scenario, that was developed for the Network for Greening the Financial System (NGFS), limits global warming to 1.5\u202f\u00b0C through immediate introduction of stringent climate policies and innovation, reaching net zero CO\u2082 emissions globally around 2050. Some jurisdictions such as the US, EU and Japan reach net zero for all greenhouse gases by this point. Carbon Dioxide Removal (CDR) is used to accelerate the decarbonisation but kept to the minimum possible and broadly in line with sustainable levels of bioenergy production. Physical risks are relatively low but transition risks are high. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "o_1p5c", "name": "NGFS net-zero 2050", "primary": true }, { "basescenario": false, "description": "This scenario assumes a delayed and divergent climate policy response among countries globally, leading to high physical and transition risks. Countries without zero targets follow current policies, while other countries achieve them only partially (80% of the target). Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "d_strain", "name": "NGFS fragmented world", "primary": true }, { "basescenario": false, "description": "This scenario includes all pledged targets even if not yet backed up by implemented effective policies. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "h_ndc", "name": "NGFS nationally determined contributions", "primary": true }, { "basescenario": false, "description": "This scenario gradually increases the stringency of climate policies, giving a 67% chance of limiting global warming to below 2\u00b0C. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "o_2c", "name": "NGFS below 2 degree", "primary": true }, { "basescenario": false, "description": "This scenario assumes that significant behavioural changes, reducing energy demand, mitigate the pressure on the economic system to reach global net zero CO2 emissions round 2050. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "o_lowdem", "name": "NGFS low demand", "primary": true }, { "basescenario": false, "description": "This scenario was developed for the Network for Greening the Financial System (NGFS), assuming new climate policies are not introduced until 2030 and the level of action differs across countries and regions based on currently implemented policies, leading to a \u201cfossil recovery\u201d out of the economic crisis brought about by COVID-19. The availability of Carbon Dioxide Removal (CDR) technologies is assumed to be low, therefore global emissions decline very rapidly after 2030 to ensure a 67\u202f% chance of limiting global warming to below 2\u202f\u00b0C in 2100. This leads to both higher transition and physical risks than the Net Zero 2050 scenario, but lower physical risk than the Current Policies scenario. Find out more in the Methodology or on the NGFS Scenarios Portal: \"https://www.ngfs.net/ngfs-scenarios-portal/explore/\":https://www.ngfs.net/ngfs-scenarios-portal/explore/", "id": "d_delfrag", "name": "NGFS delayed transition", "primary": true }, { "basescenario": true, "description": "This scenario explores the consequences of continuing along the path of implemented climate policies in 2020, with no further government action. Global emissions remain high until mid-century and slowly decline after that, leading to about 2.7\u00b0C of global warming by 2100 (best estimate). It was developed by an independent scientific analysis called the Climate Action Tracker (CAT), that monitors government climate action of 39 countries representing 85% of global emissions. Find out more in the Methodology or on the Climate Action Tracker website: \"https://climateactiontracker.org/about/\":https://climateactiontracker.org/about/.", "id": "cat_current", "name": "CAT current policies", "primary": true } ], "spatial_weightings": [ { "id": "area", "name": "Area-weighted average" }, { "id": "pop", "name": "Population-weighted average" }, { "id": "gdp", "name": "GDP-weighted average" }, { "id": "harvarea", "name": "Average weighted by agricultural area" }, { "id": "wheat_yield", "name": "Average weighted by observed wheat yield" }, { "id": "maize_yield", "name": "Average weighted by observed maize yield" }, { "id": "soybean_yield", "name": "Average weighted by observed soy yield" }, { "id": "rice_yield", "name": "Average weighted by observed rice yield" }, { "id": "other", "name": "Sum" } ], "temporal_averagings": [ { "id": "annual", "name": "Annual" }, { "id": "MAM", "name": "Mar, April, May" }, { "id": "JJA", "name": "June, July, August" }, { "id": "SON", "name": "September, October, November" }, { "id": "DJF", "name": "December, January, February" } ], "units": [ { "id": "percentagepoints", "latex": "\\text{pp}", "name": "percentage points", "short": "pp" }, { "id": "days", "latex": "\\text{days}", "name": "days", "short": "days" }, { "id": "percent", "latex": "\\%", "name": "percent", "short": "%" }, { "id": "kg_kg-1", "latex": "\\frac{\\text{kg}}{\\text{kg}}", "name": "kilogram per kilogram", "short": "kg kg\u207b\u00b9" }, { "id": "mm_day-1", "latex": "\\frac{\\text{mm}}{\\text{day}}", "name": "millimeter per day", "short": "mm day\u207b\u00b9" }, { "id": "hPa", "latex": "\\text{hPa}", "name": "Hectopascal", "short": "hPa" }, { "id": "W_m-2", "latex": "\\frac{\\text{W}}{\\text{m}^{2}}", "name": "Watt per square metre", "short": "W m\u207b\u00b2" }, { "id": "m_s-1", "latex": "\\frac{\\text{m}}{\\text{s}}", "name": "metre per second", "short": "m s\u207b\u00b9" }, { "id": "degC", "latex": "\\,^{\\circ}\\text{C}", "name": "degrees Celsius", "short": "\u00b0C" }, { "id": "kg_m-2_s-1", "latex": "\\frac{\\text{kg}}{\\text{m}^{2}\\,\\text{s}}", "name": "kilogram per square metre per second", "short": "kg m\u207b\u00b2 s\u207b\u00b9" }, { "id": "m3_s-1", "latex": "\\frac{\\text{m}^{3}}{\\text{s}}", "name": "cubic metres per second", "short": "m3 s\u207b\u00b9" }, { "id": "days", "latex": "\\text{days}", "name": "days", "short": "days" }, { "id": "mm_year-1", "latex": "\\frac{\\text{mm}}{\\text{year}}", "name": "millimeter per year", "short": "mm year\u207b\u00b9" }, { "id": "m3_s-1_day-1", "latex": "\\frac{\\text{m}^{3}}{\\text{s\\,day}}", "name": "qubic meter per second per day", "short": "m3 s\u207b\u00b9 day\u207b\u00b9" }, { "id": "nights", "latex": "\\text{nights}", "name": "nights", "short": "nights" }, { "id": "days_x_degree", "latex": "\\text{days} \\times \\,^{\\circ}\\text{C}", "name": "days times degree", "short": "days x degree" }, { "id": "-", "latex": "-", "name": "-", "short": "\u207b" } ], "variable_groups": [ { "children": [ "psAdjust", "tasmaxAdjust", "tasminAdjust", "rldsAdjust", "tasAdjust", "prAdjust", "number_of_wet_days", "hursAdjust", "prsnAdjust", "hussAdjust", "daily_temperature_variability", "total_annual_precipitation", "sfcWindAdjust" ], "id": "Climate", "name": "Climate", "type": "chronic" }, { "children": [ "spei_gamma_12_min", "extreme_drought", "moderate_drought", "severe_drought", "superextreme_drought", "consecutive_dry_days", "drought_intensity", "spei_gamma_12", "wsi" ], "id": "Drought", "name": "Drought", "type": "chronic" }, { "children": [], "id": "Economic Damages", "name": "Economic damages", "type": "acute" }, { "children": [ "rx5day", "rx1day", "heavy_precipitation_days", "extreme_daily_rainfall" ], "id": "Extreme Precipitation", "name": "Extreme precipitation", "type": "acute" }, { "children": [ "fwils", "fwixd" ], "id": "Fire", "name": "Fire", "type": "chronic" }, { "children": [ "dis", "qs" ], "id": "Freshwater", "name": "Freshwater", "type": "chronic" }, { "children": [ "TXx", "consecutive_tropical_nights", "cooling_degree_days", "wet_bulb_temperature", "HI-danger", "HI-caution", "HI-extreme-danger", "HI-extreme-caution" ], "id": "Heat", "name": "Heat", "type": "acute" }, { "children": [ "labour-productivity-loss" ], "id": "Labour productivity", "name": "Labour productivity", "type": "chronic" }, { "children": [], "id": "Peril-specific hazards", "name": "Peril-specific hazards", "type": "acute" }, { "children": [ "maize_yield", "rice_yield", "soy_yield", "wheat_yield" ], "id": "Agriculture", "name": "Agriculture", "type": "chronic" } ], "vars": [ { "change_type": null, "description": "Relative humidity is defined as the ratio of water vapour in the air to the total amount that could be held at its current temperature (saturation level). Here we consider relative humidity at 2 metres above ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "hursAdjust", "impact_type": "chronic", "level": 0, "name": "Relative Humidity", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "percent" }, { "change_type": null, "description": "Specific humidity is defined as the mass of water vapour contained in each kg of air. Here we consider specific humidity at 2 metres above ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "hussAdjust", "impact_type": "chronic", "level": 0, "name": "Specific Humidity", "orig_unit": "kg_kg-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "kg_kg-1" }, { "change_type": null, "description": "Precipitation is defined as the mass of water (both rainfall and snowfall) falling on the Earth's surface, per unit area and time. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "prAdjust", "impact_type": "chronic", "level": 0, "name": "Precipitation", "orig_unit": "mm_day-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "mm_day-1" }, { "change_type": null, "description": "Snowfall is defined as the mass of water falling on the Earth's surface in the form of snow, per unit area and time. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of random natural climate variability rather than the response to anthropogenic climate change. Small-scale spatial features (locally large increases or decreases) may also reflect the influence of natural climate variability. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "prsnAdjust", "impact_type": "chronic", "level": 0, "name": "Snowfall", "orig_unit": "mm_day-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "mm_day-1" }, { "change_type": null, "description": "Atmospheric pressure quantifies the force exerted by the weight of the column of air situated above a given location, per unit area. Here we consider atmospheric pressure at 2 metres above ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "psAdjust", "impact_type": "chronic", "level": 0, "name": "Atmospheric Pressure (surface)", "orig_unit": "hPa", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "generic", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "hPa" }, { "change_type": null, "description": "Downwelling longwave radiation is defined as the downward energy flux in the form of infrared light that reaches the Earth's surface. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "rldsAdjust", "impact_type": "chronic", "level": 0, "name": "Downwelling Longwave Radiation", "orig_unit": "W_m-2", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "W_m-2" }, { "change_type": null, "description": "Wind speed quantifies the velocity of an air mass. Here we consider the wind speed 10 metres above ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "sfcWindAdjust", "impact_type": "chronic", "level": 0, "name": "Wind Speed", "orig_unit": "m_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "generic", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "m_s-1" }, { "change_type": null, "description": "Mean air temperature refers to the average temperature of air masses near the Earth\u2019s surface (2 metres above the ground in this case). The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or\u00a0GDP\u00a0were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "tasAdjust", "impact_type": "chronic", "level": 0, "name": "Mean Air Temperature", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "degC" }, { "change_type": null, "description": "Daily maximum air temperature is defined as the peak air temperature reached in a day, in this case at 2 metres above the ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "tasmaxAdjust", "impact_type": "acute", "level": 0, "name": "Daily Maximum Air Temperature", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "degC" }, { "change_type": null, "description": "Daily minimum air temperature is defined as the lowest air temperature reached in a day, in this case at 2 metres above the ground. The data used for this variable have undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "tasminAdjust", "impact_type": "acute", "level": 0, "name": "Daily Minimum Air Temperature", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "degC" }, { "change_type": "relative", "description": "Surface runoff (also called overland flow) describes the flow of water occurring on the Earth's surface when excess water, e.g. rainwater, can no longer be absorbed by the soil. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established land surface or hydrological models, which nevertheless depict a simplified, hence imperfect representation of the evolution of surface runoff under climate change. They were forced with a limited number of climate model simulations; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Freshwater", "id": "qs", "impact_type": "chronic", "level": 1, "name": "Surface Runoff", "orig_unit": "kg_m-2_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "percent" }, { "change_type": "relative", "description": "Discharge (also called streamflow) is the volume of water flowing through a river or stream channel. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2020.", "disclaimer": "These results were obtained with established land surface or hydrological models, which nevertheless depict a simplified, hence imperfect representation of the evolution of discharge under climate change. They were forced with a limited number of climate model simulations; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Land-use and irrigation patterns, as well as water use for human activities are assumed to be constant as of 2005. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Freshwater", "id": "dis", "impact_type": "chronic", "level": 1, "name": "River Discharge", "orig_unit": "m3_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "percent" }, { "change_type": "relative", "description": "Annual mean maize yields are derived from the ISIMIP3b crop model ensemble (J\u00e4germeyr et al., 2021) and represent the simulated yieldchange per hectare in each grid cell relative to the 1983-2013 reference period. Two aggregation approaches are used: one averaged over the entire land area and one weighted by the actual harvested area over the reference period for maize in each grid cell. The data reflect differences in yield potential driven solely by climatic conditions, as agricultural management practices and cultivated areas are held constant throughout the 21st century.", "disclaimer": "These results were obtained with established global gridded crop models, which nevertheless depict a simplified, hence imperfect representation of the evolution of crop systems under climate change. In particular, they have not been calibrated for every country. They were forced with a limited number of climate model simulations, therefore short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. CO2 fertilisation is accounted for in the models, but there is considerable uncertainty about its influence on future yields. Projections are shown over areas where maize was grown in year 2010, assuming the same repartition of irrigated and rainfed areas in the future. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Agriculture", "id": "maize_yield", "impact_type": "chronic", "level": 1, "name": "Maize Yields", "orig_unit": "percent", "reference_period": "reference period 1983-2013", "scale_direction": 1, "scale_type": "agriculture", "source": "ISIMIP", "spatial_weighting": [ "area", "maize_yield" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": "relative", "description": "Annual mean rice yields are derived from the ISIMIP3b crop model ensemble (J\u00e4germeyr et al., 2021) and represent the simulated yieldchange per hectare in each grid cell relative to the 1983\u20132013 reference period. Two aggregation approaches are used: one averaged over the entire land area and one weighted by the actual harvested area over the reference period for rice in each grid cell. The data reflect differences in yield potential driven solely by climatic conditions, as agricultural management practices and cultivated areas are held constant throughout the 21st century.", "disclaimer": "These results were obtained with established global gridded crop models, which nevertheless depict a simplified, hence imperfect representation of the evolution of crop systems under climate change. In particular, they have not been calibrated for every country. They were forced with a limited number of climate model simulations, therefore short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. CO2 fertilisation is accounted for in the models, but there is considerable uncertainty about its influence on future yields. Projections are shown over areas where rice was grown in year 2010, assuming the same repartition of irrigated and rainfed areas in the future. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Agriculture", "id": "rice_yield", "impact_type": "chronic", "level": 1, "name": "Rice Yields", "orig_unit": "percent", "reference_period": "reference period 1983-2013", "scale_direction": 1, "scale_type": "agriculture", "source": "ISIMIP", "spatial_weighting": [ "area", "rice_yield" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": "relative", "description": "Annual mean soy yields are derived from the ISIMIP3b crop model ensemble (J\u00e4germeyr et al., 2021) and represent the simulated yieldchange per hectare in each grid cell relative to the 1983\u20132013 reference period. Two aggregation approaches are used: one averaged over the entire land area and one weighted by the actual harvested area over the reference period for soy in each grid cell. The data reflect differences in yield potential driven solely by climatic conditions, as agricultural management practices and cultivated areas are held constant throughout the 21st century.", "disclaimer": "These results were obtained with established global gridded crop models, which nevertheless depict a simplified, hence imperfect representation of the evolution of crop systems under climate change. In particular, they have not been calibrated for every country. They were forced with a limited number of climate model simulations, therefore short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. CO2 fertilisation is accounted for in the models, but there is considerable uncertainty about its influence on future yields. Projections are shown over areas where soy was grown in year 2010, assuming the same repartition of irrigated and rainfed areas in the future. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Agriculture", "id": "soy_yield", "impact_type": "chronic", "level": 1, "name": "Soy Yields", "orig_unit": "percent", "reference_period": "reference period 1983-2013", "scale_direction": 1, "scale_type": "agriculture", "source": "ISIMIP", "spatial_weighting": [ "area", "soybean_yield" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": "relative", "description": "Annual mean wheat yields are derived from the ISIMIP3b crop model ensemble (J\u00e4germeyr et al., 2021) and represent the simulated yieldchange per hectare in each grid cell relative to the 1983\u20132013 reference period. Two aggregation approaches are used: one averaged over the entire land area and one weighted by the actual harvested area over the reference period for wheat in each grid cell. The data reflect differences in yield potential driven solely by climatic conditions, as agricultural management practices and cultivated areas are held constant throughout the 21st century.", "disclaimer": "These results were obtained with established global gridded crop models, which nevertheless depict a simplified, hence imperfect representation of the evolution of crop systems under climate change. In particular, they have not been calibrated for every country. They were forced with a limited number of climate model simulations, therefore short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. CO2 fertilisation is accounted for in the models, but there is considerable uncertainty about its influence on future yields. Projections are shown over areas where wheat was grown in year 2010, assuming the same repartition of irrigated and rainfed areas in the future. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Agriculture", "id": "wheat_yield", "impact_type": "chronic", "level": 1, "name": "Wheat Yields", "orig_unit": "percent", "reference_period": "reference period 1983-2013", "scale_direction": 1, "scale_type": "agriculture", "source": "ISIMIP", "spatial_weighting": [ "area", "wheat_yield" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "Heat stress impact on labour productivity indicates the percentage decrease in efficiency during regular working hours under hot and humid climate conditions, due to the reduced capacity of the human body to perform physical labour.", "disclaimer": "Labour productivity estimates are based on the Heat Index (please find limitations of Heat Index estimates in the respective section) and the peer reviewed damage function from Foster et al. 2022. While damage functions provide a versatile approach for connecting impact drivers with respective impacts, they provide idealized relationships that might miss local specifics. Damage function estimates should be employed under these considerations.", "display_mode": "-", "group": "Labour productivity", "id": "labour-productivity-loss", "impact_type": "chronic", "level": 1, "name": "Labour Productivity Loss due to Heat Stress", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP - Secondary Output", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "percent" }, { "change_type": "relative", "description": "This Extreme Precipitation indicator RX5day is defined as the maximum accumulated mass of water (both rainfall and snowfall) falling on the Earth\u2019s surface over a period of five days, in a given area and year. The precipitation data used to obtain this variable has undergone a bias-adjustment procedure to correct for deviations between modelled and observed values over the time period where they overlap. Projections weighted by population or GDP were calculated assuming that both the size and the repartition of these two parameters would stay constant as of 2005.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Extreme Precipitation", "id": "rx5day", "impact_type": "acute", "level": 0, "name": "Annual Maximum 5-day Precipitation", "orig_unit": "kg_m-2_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP - Secondary Output", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "Temperature Variability, defined as the intra-monthly standard deviation of daily temperature (i.e. deviations from the month-specific daily mean), averaged across months in a given year. Based on Kotz et al. (2024), this indicator captures the year-to-year magnitude of short-term temperature fluctuations rather than shifts in the long-term mean. It is often used to assess the impact of temperature variability on systems sensitive to rapid fluctuations (e.g. ecological, economic, or health impacts).", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "daily_temperature_variability", "impact_type": "chronic", "level": 0, "name": "Temperature Variability", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "degC" }, { "change_type": "relative", "description": "Annual total of extreme rainfall amount, defined as the sum of daily precipitation on days exceeding the 99.9th percentile of daily precipitation in the historical period (1981-2014). Based on Kotz et al. (2024), this indicator identifies the contribution of the most intense precipitation events, and is sensitive to changes in the tail of the precipitation distribution.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Extreme Precipitation", "id": "extreme_daily_rainfall", "impact_type": "chronic", "level": 0, "name": "Total Precipitation from Extreme Precipitation Events", "orig_unit": "kg_m-2_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "Number of days per year on which daily precipitation exceeds 1 mm. Based on Kotz et al. (2024), this indicator captures the frequency of precipitation events rather than their size, and thus reflects changes in how often rainfall occurs rather than just how much.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "number_of_wet_days", "impact_type": "chronic", "level": 0, "name": "Precipitation Days", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "The total annual precipitation is defined as the sum of all precipitation within a year (in mm/year). Based on Kotz et al. (2024), the indicator assess changes in precipitation volume under climate change.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Climate", "id": "total_annual_precipitation", "impact_type": "chronic", "level": 0, "name": "Total Annual Precipitation", "orig_unit": "mm_year-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "mm_year-1" }, { "change_type": null, "description": "Proportion between daily volume deficit of discharge below the 10th percentile daily discharge (Q90) of the reference period (1981 - 2014) and drought event duration. This indicator quantifies the mean severity of hydrological droughts by relating their total volume deficit to their persistence, providing an integrated measure of drought intensity over time.", "disclaimer": "The abstraction of water is related to the current extent of agricultural irrigation for each region. The expected expansion of irrigated areas is projected to cause a further increase in irrigation water demand in some regions (e.g., Africa, South America). Additionally, population growth will result in increased demand for drinking water and industrial activities, leading to a higher water demand. Especially in areas like Africa, the population is projected to increase substantially. The changes in land use could also significantly alter the propagation of drought and hydrological drought characteristics. These projected changes in population and land use are not included, due to low data availability and high projection uncertainty.", "display_mode": "-", "group": "Drought", "id": "drought_intensity", "impact_type": "chronic", "level": 1, "name": "Drought Intensity", "orig_unit": "m3_s-1_day-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "m3_s-1_day-1" }, { "change_type": null, "description": "Maximum number of consecutive days per year with daily precipitation below 1 mm. This indicator captures the duration of dry spells and is widely used to assess drought risk and changes in the frequency of prolonged dry periods.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Drought", "id": "consecutive_dry_days", "impact_type": "acute", "level": 0, "name": "Consecutive Dry Days", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Maximum number of consecutive days per year with minimum daily temperature (tasmin) exceeding 20 \u00b0C. This indicator reflects the persistence of warm nighttime conditions, relevant for assessing heat stress and the compounding effects of reduced nighttime cooling.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Heat", "id": "consecutive_tropical_nights", "impact_type": "acute", "level": 0, "name": "Consecutive Tropical Nights", "orig_unit": "nights", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "nights" }, { "change_type": null, "description": "Annual sum of daily temperature exceedances above a threshold of 26 \u00b0C, calculated as the difference between daily mean temperature (tas) and the 26 \u00b0C baseline, for days exceeding this value. The indicator represents cumulative cooling demand and is commonly used to estimate energy needs for air conditioning and assess population heat exposure.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of the climate systems in response to natural and anthropogenic forcings. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Heat", "id": "cooling_degree_days", "impact_type": "chronic", "level": 0, "name": "Cooling Degree Days", "orig_unit": "days_x_degree", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days_x_degree" }, { "change_type": null, "description": "Length of the fire season, calculated as the number of days in a year where the FWI values are greater than the mid-point value between the maximum and minimum FWI over the historical reference period (1974-2005). The FWI, as defined by the Canadian Forest Fire Weather Index System, integrates temperature, humidity, wind speed, and precipitation to describe potential fire intensity. FWILS quantifies the length of time of fire-conducive conditions, reflecting changes in the duration of the fire-prone period.", "disclaimer": "The Fire Weather Index is an indicator of fire-conducive atmospheric conditions, and does not include any other factors that are typically important for the actual occurrence of wildfire, for example, availability of fuel, likelihood of ignition or fire suppression practices. The Fire Weather Index typically relies on hourly, meteorological data. However, climate models often provide only daily aggregated values, posing a challenge for accurate FWI calculations which can lead to overestimations as recently published by Auroa M. et al. in Nature, 2025.", "display_mode": "-", "group": "Fire", "id": "fwils", "impact_type": "chronic", "level": 1, "name": "Length of the fire season", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Number of days with extreme fire weather, calculated as the local annual number of days above the local threshold. The local thresholds are defined as the 95th percentile of the FWI over the 1974-2005 historical period. The FWI, as defined by the Canadian Forest Fire Weather Index System, integrates temperature, humidity, wind speed, and precipitation to describe potential fire intensity. FWIXD captures the number of days in a year with extreme fire\u2010conducive weather conditions.", "disclaimer": "The Fire Weather Index is an indicator of fire-conducive atmospheric conditions, and does not include any other factors that are typically important for the actual occurrence of wildfire, for example, availability of fuel, likelihood of ignition or fire suppression practices. The Fire Weather Index typically relies on hourly, meteorological data. However, climate models often provide only daily aggregated values, posing a challenge for accurate FWI calculations which can lead to overestimations as recently published by Auroa M. et al. in Nature, 2025.", "display_mode": "-", "group": "Fire", "id": "fwixd", "impact_type": "acute", "level": 1, "name": "Number of days with extreme fire weather", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Annual number of days with daily precipitation exceeding 10 mm. This indicator captures the frequency of moderate to heavy rainfall events and is used to assess changes in precipitation intensity and the occurrence of potentially impactful wet days.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Extreme Precipitation", "id": "heavy_precipitation_days", "impact_type": "chronic", "level": 0, "name": "Heavy Precipitation Days", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": "relative", "description": "Highest daily precipitation total (pr) recorded in a given year in mm. This indicator represents the intensity of single-day extreme rainfall events and is commonly used to assess changes in short-duration precipitation extremes and associated flood risk.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "relative", "group": "Extreme Precipitation", "id": "rx1day", "impact_type": "acute", "level": 0, "name": "Annual Maximum Daily Precipitation", "orig_unit": "kg_m-2_s-1", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. The average annual SPEI is shown here.", "disclaimer": "The SPEI is a widely employed indicator to estimate meteorological drought conditions which is based on precipitation and evapotranspiration. While these variables are well suited to account for local drought drivers, remote drivers from hydrological flows are not taken into account, which could affect the accuracy of this indicator in some cases . The SPEI provides deviations from local climatological values, which makes values sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g. Penman-Monteith equation), which incorporates atmosphere dynamic as well as radiative drivers for the estimation of evapotranspiration. This method, however requires variables that are not readily available in climate models.", "display_mode": "-", "group": "Drought", "id": "spei_gamma_12", "impact_type": "chronic", "level": 0, "name": "Standardized Precipitation Evapotranspiration Index (SPEI)", "orig_unit": "-", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "-" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. The annual minimum SPEI is shown here.", "disclaimer": "The SPEI is a widely employed indicator to estimate meteorological drought conditions which is based on precipitation and evapotranspiration. While these variables are well suited to account for local drought drivers, remote drivers from hydrological flows are not taken into account, which could affect the accuracy of this indicator in some cases . The SPEI provides deviations from local climatological values, which makes values sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g.Penman-Monteith equation), which incorporates atmosphere dynamic as well as radiative drivers for the estimation of evapotranspiration. This method, however requires variables that are not readily available in climate models.", "display_mode": "-", "group": "Drought", "id": "spei_gamma_12_min", "impact_type": "acute", "level": 0, "name": "Annual Minimum SPEI", "orig_unit": "-", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "-" }, { "change_type": null, "description": "Highest daily maximum temperature (tasmax) in a given year in \u00b0C. This indicator represents the annual peak of daytime heat and is widely used to assess changes in extreme temperature events and the intensity of heatwaves.", "disclaimer": "These results were obtained with established climate models, which nevertheless depict a simplified, hence imperfect representation of the evolution of temperature and relative humidity under climate change. A limited number of climate model simulations were used to derive them; therefore despite our efforts to account for this while pre-processing the data, short-term fluctuations can reflect the influence of natural climate variability rather than the response to anthropogenic climate change. Our confidence in the results decreases for high warming levels, which have been attained in a smaller number of the climate model simulations underlying these results, and especially as of 2.5-3\u00b0C of global warming.", "display_mode": "-", "group": "Heat", "id": "TXx", "impact_type": "acute", "level": 0, "name": "Annual Maximum Daily Temperature", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "degC" }, { "change_type": null, "description": "Daily maximum Wet Buld Temperature, representing the combined effects of temperature and humidity. It is derived from daily maximum temperature (tasmax) and relative humidity (hurs) and indicates the lowest temperature achievable through evaporative cooling, providing a direct measure of heat stress conditions.", "disclaimer": "Daily maximum Wet Bulb Temperature (WBT) values ideally require temperature and humidity conditions during the hottest hour of the day \u2013 which are not available in the ISIMIP3 model environment. Here, WBT is calculated directly from daily maximum temperature and daily mean humidity data from an approximation published by Stull et al in 2011, without applying additional sub-daily approximations or bias correction procedures. As a result, model biases in temperature and humidity may propagate into the WBT estimates, and the use of daily mean relative humidity may overestimate the relative humidity at the hottest hour of the day and the subsequent WBT.", "display_mode": "-", "group": "Heat", "id": "wet_bulb_temperature", "impact_type": "acute", "level": 0, "name": "Daily Maximum Wet Bulb Temperature", "orig_unit": "degC", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "good_bad", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual", "MAM", "JJA", "SON", "DJF" ], "unit": "degC" }, { "change_type": null, "description": "Water stress index measures the fraction between net human demands (domestic, industrial, irrigation) and renewable surface water availability. In this case it is also the same as the withdrawal to availability ratio. The Water stress index compares water demands to available water supply. High water stress, > 0.4, means that a high proportion of the available water is being used.", "disclaimer": "The index presented here does not account for seasonal and regional variations in water availability and water use. It fails to take into account whether the water resources are readily accessible or safe to use for the intended purpose and depends on historic data availability or future estimations, which, especially in less-developed regions, is often not available or lacks quality.", "display_mode": "-", "group": "Drought", "id": "wsi", "impact_type": "chronic", "level": 1, "name": "Water Stress Index", "orig_unit": "-", "reference_period": "reference period 1995-2014", "scale_direction": -1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "-" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. A SPEI below -1 is classified as moderate drought while a value around 0 represents near-normal conditions. This indicator shows the area affected by an index below -1 in at least one month of the year.", "disclaimer": "While the SPEI is a widely employed indicator to estimate drought conditions, users should be aware that hydrological drought conditions associated with region specific water flows and run-off are not always accurately captured. This is due to the fact that the SPEI is based on precipitation and evapotranspiration only and does not take into account local hydrological flows. Further, SPEI values are sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g., Penman-Monteith equation), which incorporates aerodynamic as well as radiative drivers for the estimation of evapotranspiration. However, the Penman-Monteith method was not applied because reliable and internally consistent projection data for all required variables were unavailable, and using such inconsistent data would have considerably increased the uncertainty of the SPEI-12 drought indicator.", "display_mode": "-", "group": "Drought", "id": "moderate_drought", "impact_type": "acute", "level": 0, "name": "Area under moderate drought (SPEI < -1)", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": -1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp", "harvarea" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. A SPEI below -1.5 is classified as severe drought while a value around 0 represents near-normal conditions. This indicator shows the area affected by an index below -1.5 in at least one month of the year.", "disclaimer": "While the SPEI is a widely employed indicator to estimate drought conditions, users should be aware that hydrological drought conditions associated with region specific water flows and run-off are not always accurately captured. This is due to the fact that the SPEI is based on precipitation and evapotranspiration only and does not take into account local hydrological flows. Further, SPEI values are sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g., Penman-Monteith equation), which incorporates aerodynamic as well as radiative drivers for the estimation of evapotranspiration. However, the Penman-Monteith method was not applied because reliable and internally consistent projection data for all required variables were unavailable, and using such inconsistent data would have considerably increased the uncertainty of the SPEI-12 drought indicator.", "display_mode": "-", "group": "Drought", "id": "severe_drought", "impact_type": "acute", "level": 0, "name": "Area under severe drought (SPEI < -1.5)", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": -1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp", "harvarea" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. A SPEI below -2 is classified as extreme drought while a value around 0 represents near-normal conditions. This indicator shows the area affected by an index below -2 in at least one month of the year.", "disclaimer": "While the SPEI is a widely employed indicator to estimate drought conditions, users should be aware that hydrological drought conditions associated with region specific water flows and run-off are not always accurately captured. This is due to the fact that the SPEI is based on precipitation and evapotranspiration only and does not take into account local hydrological flows. Further, SPEI values are sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g., Penman-Monteith equation), which incorporates aerodynamic as well as radiative drivers for the estimation of evapotranspiration. However, the Penman-Monteith method was not applied because reliable and internally consistent projection data for all required variables were unavailable, and using such inconsistent data would have considerably increased the uncertainty of the SPEI-12 drought indicator.", "display_mode": "-", "group": "Drought", "id": "extreme_drought", "impact_type": "acute", "level": 0, "name": "Area under extreme drought (SPEI < -2)", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": -1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp", "harvarea" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "The Standardized Precipitation Evapotranspiration Index (SPEI) is an indicator of meteorological drought. It is designed to take into account both precipitation and potential evapotranspiration in determining droughts. A SPEI below -2.5 is classified as exceptional drought while a value around 0 represents near-normal conditions. This indicator shows the area affected by an index below -2.5 in at least one month of the year.", "disclaimer": "While the SPEI is a widely employed indicator to estimate drought conditions, users should be aware that hydrological drought conditions associated with region specific water flows and run-off are not always accurately captured. This is due to the fact that the SPEI is based on precipitation and evapotranspiration only and does not take into account local hydrological flows. Further, SPEI values are sensitive to the selected baseline period and provide drought severity estimates relative to what is considered \u2018normal\u2019 in a specific area. We also caution that our use of the Thornthwaite method to estimate evapotranspiration, which relies solely on air temperature, may lead to overestimations in both magnitude and temporal trends. This limitation is likely to be amplified under substantial global warming. The IPCC AR6 recommends the use of physically-based models (e.g., Penman-Monteith equation), which incorporates aerodynamic as well as radiative drivers for the estimation of evapotranspiration. However, the Penman-Monteith method was not applied because reliable and internally consistent projection data for all required variables were unavailable, and using such inconsistent data would have considerably increased the uncertainty of the SPEI-12 drought indicator.", "display_mode": "-", "group": "Drought", "id": "superextreme_drought", "impact_type": "acute", "level": 0, "name": "Area under very extreme drought (SPEI < -2.5)", "orig_unit": "percent", "reference_period": "reference period 1995-2014", "scale_direction": -1, "scale_type": "water_related", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp", "harvarea" ], "temporal_averaging": [ "annual" ], "unit": "percent" }, { "change_type": null, "description": "Number of days per year at which the daily maximum Heat Index exceeds the \u2018caution\u2019 threshold of 26.7 \u00b0C. The Heat Index, is based on a definition by NOAA and relies on daily maximum temperature (tasmax) and relative humidity (hurs), and is widely used for heat risk warnings.", "disclaimer": "Daily maximum HI values require relative humidity values during the hottest hour of the day - which are not available in ISIMIP3 model environment. To account for this we employ a physics based approximation, which makes use of relationships between daily mean temperature, saturation vapor pressure, daily mean vapor pressure and specific humidity. To adjust remaining model biases, a grid-point wise quantile mapping was applied to HI projections using distributions from historical ISIMIP3 simulations and ERA5 reanalysis data for the period 1980-2010. This standard approach adjusts simulated HI data to fit the distribution of observed HI values. While this approach avoids unrealistically high HI values, it also assumes that the shape of future HI distributions remain unchanged, possibly underestimating effects from local dynamical changes that may act on the extreme tails exclusively.", "display_mode": "-", "group": "Heat", "id": "HI-caution", "impact_type": "acute", "level": 0, "name": "Days per year with emerging heat risk (HI > 26.6 \u00b0C)", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "heat_days", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Number of days per year at which the daily maximum Heat Index exceeds the \u2018extreme caution\u2019 threshold of 32.2 \u00b0C. The Heat Index, is based on a definition by NOAA and relies on daily maximum temperature (tasmax) and relative humidity (hurs), and is widely used for heat risk warnings.", "disclaimer": "Daily maximum HI values require relative humidity values during the hottest hour of the day - which are not available in ISIMIP3 model environment. To account for this we employ a physics based approximation, which makes use of relationships between daily mean temperature, saturation vapor pressure, daily mean vapor pressure and specific humidity. To adjust remaining model biases, a grid-point wise quantile mapping was applied to HI projections using distributions from historical ISIMIP3 simulations and ERA5 reanalysis data for the period 1980-2010. This standard approach adjusts simulated HI data to fit the distribution of observed HI values. While this approach avoids unrealistically high HI values, it also assumes that the shape of future HI distributions remain unchanged, possibly underestimating effects from local dynamical changes that may act on the extreme tails exclusively.", "display_mode": "-", "group": "Heat", "id": "HI-extreme-caution", "impact_type": "acute", "level": 0, "name": "Days per year with high heat risk (HI > 32.2 \u00b0C)", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "heat_days", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Number of days per year at which the daily maximum Heat Index exceeds the \u2018danger\u2019 threshold of 40 \u00b0C. The Heat Index, is based on a definition by NOAA and relies on daily maximum temperature (tasmax) and relative humidity (hurs), and is widely used for heat risk warnings.", "disclaimer": "Daily maximum HI values require relative humidity values during the hottest hour of the day - which are not available in ISIMIP3 model environment. To account for this we employ a physics based approximation, which makes use of relationships between daily mean temperature, saturation vapor pressure, daily mean vapor pressure and specific humidity. To adjust remaining model biases, a grid-point wise quantile mapping was applied to HI projections using distributions from historical ISIMIP3 simulations and ERA5 reanalysis data for the period 1980-2010. This standard approach adjusts simulated HI data to fit the distribution of observed HI values. While this approach avoids unrealistically high HI values, it also assumes that the shape of future HI distributions remain unchanged, possibly underestimating effects from local dynamical changes that may act on the extreme tails exclusively.", "display_mode": "-", "group": "Heat", "id": "HI-danger", "impact_type": "acute", "level": 0, "name": "Days per year with dangerous heat risk (HI > 40 \u00b0C)", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "heat_days", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" }, { "change_type": null, "description": "Number of days per year at which the daily maximum Heat Index exceeds \u2018extreme danger\u2019 threshold of 51.7 \u00b0C. The Heat Index, is based on a definition by NOAA and relies on daily maximum temperature (tasmax) and relative humidity (hurs), and is widely used for heat risk warnings.", "disclaimer": "Daily maximum HI values require relative humidity values during the hottest hour of the day - which are not available in ISIMIP3 model environment. To account for this we employ a physics based approximation, which makes use of relationships between daily mean temperature, saturation vapor pressure, daily mean vapor pressure and specific humidity. To adjust remaining model biases, a grid-point wise quantile mapping was applied to HI projections using distributions from historical ISIMIP3 simulations and ERA5 reanalysis data for the period 1980-2010. This standard approach adjusts simulated HI data to fit the distribution of observed HI values. While this approach avoids unrealistically high HI values, it also assumes that the shape of future HI distributions remain unchanged, possibly underestimating effects from local dynamical changes that may act on the extreme tails exclusively.", "display_mode": "-", "group": "Heat", "id": "HI-extreme-danger", "impact_type": "acute", "level": 0, "name": "Days per year with extremely dangerous heat risk (HI > 51.6 \u00b0C)", "orig_unit": "days", "reference_period": "reference period 1995-2014", "scale_direction": 1, "scale_type": "heat_days", "source": "ISIMIP", "spatial_weighting": [ "area", "pop", "gdp" ], "temporal_averaging": [ "annual" ], "unit": "days" } ] }