Climate
Climate is the long-term average of temperature, precipitation, and other weather variables at a given location, typically assessed over periods of 30 years or more.[1] Unlike weather, which describes short-term atmospheric conditions over hours, days, or weeks, climate captures recurring patterns and variability that shape ecosystems, agriculture, and human adaptations.[2] Key determinants include latitude, which influences solar insolation; altitude, affecting temperature lapse rates; proximity to oceans, moderating extremes via heat capacity; and atmospheric circulation driven by Earth's rotation and tilt.[3] Ocean currents and landforms further modulate regional climates by redistributing heat and moisture.[4] The Köppen-Geiger classification system divides global climates into five main groups—A (tropical), B (dry), C (temperate), D (continental), and E (polar)—based on thresholds of monthly temperature and precipitation to reflect native vegetation and thermal regimes.[5] This framework highlights how equatorial regions sustain high rainfall and warmth, while polar areas endure persistent cold and aridity, with transitional zones exhibiting seasonal contrasts.[6] Empirical records reveal climates have fluctuated naturally over millennia due to orbital variations, volcanic activity, and solar output, though instrumental data since the 19th century document regional shifts amid ongoing debates over anthropogenic influences.[7]Core Concepts
Definition
Climate is the long-term pattern of weather conditions in a specific region, characterized by the average and variability of meteorological variables such as temperature, precipitation, humidity, wind speed and direction, cloud cover, and atmospheric pressure over an extended period, typically at least 30 years.[1][8] This timeframe allows for the identification of statistically significant trends and cycles, distinguishing persistent atmospheric behaviors from transient fluctuations.[9] In contrast to weather, which describes the momentary state of the atmosphere at a particular location—including immediate conditions like rain, temperature readings, or storm events—climate aggregates these elements into a descriptive framework of expected frequencies and intensities.[2][10] For instance, a single heatwave constitutes weather, whereas a sustained rise in average summer temperatures over decades defines a climatic shift.[9] The 30-year standard, established by organizations like the World Meteorological Organization, facilitates consistent global comparisons and periodic updates to climate normals.[1] Climate encompasses not only central tendencies like mean annual temperature but also extremes, seasonality, and probabilistic distributions of events, such as the frequency of droughts or floods.[11] These attributes arise from interactions among solar radiation, atmospheric circulation, oceanic currents, and land surface features, providing a holistic statistical portrait rather than anecdotal observations.[12] Regional climates vary widely, from tropical zones with high year-round precipitation to polar areas with persistent cold and minimal moisture, reflecting latitudinal and topographic influences.[13]Key Elements and Factors
Climate encompasses the average and variability of weather variables such as temperature, precipitation, and wind over extended periods, typically at least 30 years as defined by the World Meteorological Organization for climatological normals.[14] These key elements describe the statistical properties of atmospheric conditions in a region, distinguishing climate from short-term weather fluctuations.[15] Primary elements include air temperature, which influences thermal regimes and evaporation rates; precipitation, encompassing rain, snow, and other forms that determine water availability; and humidity, reflecting moisture content in the air that affects comfort and condensation processes.[16] Wind patterns and atmospheric pressure gradients drive circulation and influence local weather persistence, while cloud cover modulates incoming solar radiation and outgoing longwave radiation.[17] Regional climate variations arise from multiple interacting factors, primarily latitude, which controls the angle and duration of solar insolation, resulting in warmer equatorial zones and cooler polar regions.[18] Elevation affects temperature through the environmental lapse rate, approximately 6.5°C decrease per kilometer ascent due to expansion of rising air parcels. Proximity to large bodies of water moderates temperatures via high specific heat capacity of oceans, leading to maritime climates with smaller seasonal ranges compared to continental interiors.[19] Ocean currents redistribute heat globally; for instance, the Gulf Stream transports warm water northward, elevating temperatures along Europe's western coasts by up to 10°C relative to similar latitudes elsewhere. Topography, including mountain ranges, creates rain shadows where leeward sides receive less precipitation due to orographic lift depleting moisture on windward slopes. Prevailing winds and soil moisture further modulate local climates by transporting heat, moisture, and influencing evapotranspiration rates.[18]Classification Systems
Major Schemes
The Köppen-Geiger classification, developed by Wladimir Köppen in 1884 and refined by Rudolf Geiger in the mid-20th century, remains the most widely used system for categorizing global climates. It divides Earth's climates into five principal groups—A (tropical), B (dry), C (mesothermal or temperate), D (continental or microthermal), and E (polar)—based on empirical thresholds of monthly temperature and precipitation, with subdivisions reflecting seasonal patterns and vegetation associations. Group A requires all months above 18°C with no dry season or specific precipitation minima; B identifies arid and semi-arid conditions where potential evapotranspiration exceeds precipitation; C features the coldest month between 0°C and 18°C with at least one month above 10°C; D has the coldest month below 0°C and mean annual temperature below 10°C; and E includes tundra (ET) and ice cap (EF) subtypes with means below 10°C in the warmest month. This scheme correlates climate zones with native vegetation distributions, such as rainforests in A climates and deserts in B.[20] The Trewartha classification, introduced by geographer Glenn Trewartha in 1966, modifies the Köppen system to better account for human perception of climate and thermal regimes, expanding subtropical and boreal categories. It employs seven main groups: A (tropical, all months above 18°C), B (dry, based on aridity index), C (subtropical, humid with hot summers), D (temperate/continental, with winter cold spells), E (boreal, long cold winters), F (polar, very cold), and H (highland, elevation-driven). Unlike Köppen, Trewartha requires at least eight months above 10°C for temperate classification and emphasizes frost-free periods, reducing the extent of humid subtropical zones while enlarging dry and polar areas in global mappings. This adjustment aims to align more closely with agricultural and settlement patterns.[21] Thornthwaite's system, formulated by climatologist C.W. Thornthwaite in 1931 and revised in 1948, focuses on potential evapotranspiration (PET) to quantify thermal efficiency and moisture surplus or deficit, providing a more quantitative approach than Köppen's threshold-based method. The 1948 version uses a Temperature Efficiency Index (derived from monthly means) and a Precipitation-Evapotranspiration Index to delineate provinces such as tropical wet forests (high moisture and heat), mesothermal (moderate), microthermal (cool), taiga, tundra, and perpetual frost, with subtypes for summer-wet or winter-wet regimes. It incorporates seasonality through aridity and humidity indices, enabling assessments of water balance critical for hydrology and ecology, though it requires detailed data on potential evaporation.[22] The Holdridge life zone system, proposed by ecologist L.R. Holdridge in 1947, integrates biotemperature (heat sum excluding frost), annual precipitation, and the ratio of precipitation to potential evapotranspiration on a triangular diagram to predict vegetation formations across 37 life zones from polar deserts to tropical rainforests. This bioclimatic model emphasizes altitudinal and latitudinal gradients, proving useful for tropical and montane regions but less so for oceanic or highly seasonal climates, and has been applied in biodiversity and climate change impact studies.Principles and Critiques
Climate classification systems organize terrestrial regions into categories based on dominant meteorological patterns, primarily monthly temperature and precipitation averages, to identify zones with similar ecological potentials. These systems derive principles from empirical observations linking climatic thresholds to vegetation distributions, assuming that temperature controls biome types while precipitation modulates aridity. The Köppen-Geiger framework, established by Wladimir Köppen in 1884 and refined by Rudolf Geiger, exemplifies this approach by using formulaic boundaries: for instance, tropical climates (A) require all months above 18°C, dry climates (B) satisfy evaporation exceeding precipitation via the ratio P < 2T + 28 where P is annual precipitation in cm and T is temperature in °C, and polar climates (E) have the warmest month below 10°C.[6][23][24] Subdivisions incorporate seasonality, such as summer precipitation dominance (s) or winter wet seasons (w), to refine types like temperate (C) or continental (D) zones where the coldest month exceeds -3°C but falls below 18°C in the warmest. This vegetation-correlated methodology prioritizes long-term averages over short-term variability, enabling global mapping that aligns broadly with biomes, though it emphasizes thermal and hydric limits over other causal drivers like insolation or topography.[25][26] Critiques highlight the empirical rather than mechanistic foundations, lacking derivation from physical processes like energy balance or atmospheric dynamics, which results in arbitrary thresholds not universally tied to causal factors. Boundaries often fail to resolve gradual transitions or microclimatic variations, leading to classification uncertainties amplified by data resolution differences; for example, grid-based models show discrepancies in up to 15% of zones due to interpolation methods. Systems like Köppen undervalue extremes, humidity, or evapotranspiration, causing mismatches with observed ecosystems, such as arid zones (B) overlooking fog-dependent vegetation in coastal deserts.[27][28] In dynamic contexts like anthropogenic warming, fixed thresholds prove inadequate for tracking zone shifts, as evidenced by projected 20th-century migrations of Köppen types poleward by 50-100 km per decade in some models, necessitating probabilistic or updated mappings rather than static grids. Critics argue for integrating additional variables, such as soil moisture or radiative forcings, to enhance causal realism, though this risks overcomplication without proportional gains in predictive utility.[29][30]Climatic Records
Paleoclimatic Proxies
Paleoclimatic proxies are natural archives that preserve indirect evidence of past climate conditions, allowing reconstruction of variables such as temperature, precipitation, and atmospheric composition before the advent of direct instrumental measurements in the mid-19th century. These proxies include biological, chemical, and physical indicators embedded in materials like ice, sediments, tree rings, and corals, which respond to climatic forcings through measurable properties. By calibrating proxy responses against modern observations, scientists estimate past states, though interpretations require accounting for non-climatic influences and dating uncertainties.[31][32] Ice cores, extracted from Antarctica and Greenland, provide some of the longest continuous records, spanning up to 800,000 years in the EPICA Dome C core. Oxygen isotope ratios (δ¹⁸O) and deuterium (δD) in the ice reflect air temperature at the time of snowfall, with lighter isotopes preferentially evaporating in warmer conditions and precipitating farther from source regions; trapped gas bubbles yield direct measurements of past CO₂ levels, showing concentrations varying between 180 and 300 ppm over glacial-interglacial cycles.[33] Tree rings, analyzed via dendrochronology, offer annual resolution for the past 2,000–12,000 years depending on species and location, with ring width and maximum latewood density serving as temperature proxies in extratropical regions; for example, bristlecone pines in the White Mountains yield records back to 9,000 BCE, though growth can be limited by factors like drought or competition beyond temperature.[32][34] Lake and ocean sediments furnish lower-resolution but globally distributed data, often covering the Holocene (last 11,700 years) and beyond; pollen grains indicate vegetation shifts tied to temperature and rainfall, while oxygen isotopes in foraminiferal calcite (δ¹⁸O) record sea surface temperatures and ice volume, with each 0.22‰ depletion in δ¹⁸O corresponding to roughly 1°C cooling. Coral skeletons provide monthly to annual tropical sea surface temperature records via Sr/Ca ratios or δ¹⁸O, extending 400–500 years in some Pacific atolls, and speleothems (cave deposits) proxy precipitation through dripwater isotopes and growth banding, sensitive to monsoon strength over millennia. Borehole thermometry in permafrost or continental crust infers ground surface temperatures from heat diffusion profiles, revealing warming trends over centuries but with millennial-scale smoothing.[34][35] Despite their value, paleoclimatic proxies face inherent limitations: many respond to multiple covariates (e.g., tree rings to CO₂ fertilization alongside temperature), necessitating statistical models like principal component analysis for disentangling signals, which amplify uncertainties estimated at ±0.2–0.5°C for hemispheric reconstructions over the last millennium. Spatial coverage is biased toward landmasses and polar regions, underrepresenting oceans and tropics, and temporal resolution degrades in older records due to bioturbation or erosion. Divergences among proxy ensembles, such as cooler Medieval estimates in some tree-ring networks versus warmer in others, highlight methodological sensitivities and calibration endpoints, underscoring the need for multi-proxy convergence rather than single-indicator reliance. Peer-reviewed syntheses emphasize that while proxies constrain long-term trends—like orbital-driven insolation changes—they cannot resolve sub-decadal variability without instrumental analogs, and over-reliance on homogenized datasets risks overlooking regional heterogeneities.[36][37]Instrumental Observations
![Change in Average Temperature With Fahrenheit.svg.png][float-right] Instrumental observations of climate refer to direct measurements of meteorological variables using scientific instruments, beginning in the 17th century with localized records such as the Central England Temperature series initiated in 1659.[38] Systematic global coverage emerged around 1850, enabled by expanding networks of weather stations and ship-based marine observations, though early data were predominantly from the Northern Hemisphere land areas.[39] By the late 19th century, datasets like those compiled by the Hadley Centre provide estimates starting from 1850, incorporating land air temperatures and sea surface temperatures (SSTs) measured via buckets from ships.[40] Key modern datasets include NASA's GISS Surface Temperature Analysis (GISTEMP v4), which estimates global surface temperature anomalies from 1880 onward using over 6,000 weather stations and SST data; NOAA's GlobalTemp; the UK Met Office's HadCRUT5; and Berkeley Earth's surface temperature record, all showing broad agreement on a warming trend of approximately 1.1°C from pre-industrial baselines to the present, though with variations in exact magnitudes due to methodological differences.[41][42] These records rely on homogenization techniques to adjust for non-climatic influences, such as station relocations or instrument changes, but critics argue that such adjustments, often increasing past cooling and thus recent warming, may introduce biases, particularly given institutional incentives in climate science.[43] Challenges to data quality include sparse early coverage, especially in the Southern Hemisphere and polar regions, where interpolation fills gaps and amplifies uncertainties estimated at ±0.05°C per decade pre-1950.[39] The urban heat island (UHI) effect, where urban stations record higher temperatures due to impervious surfaces and human activity, poses another issue; while datasets apply corrections, analyses indicate uncorrected UHI can contribute up to 0.05–0.1°C to apparent 20th-century warming in some regions, with ongoing debate over the completeness of adjustments.[44] Precipitation records, derived from rain gauges since the 19th century, face additional homogeneity problems from changing gauge types and siting, leading to regional inconsistencies.[45] Overall, instrumental records provide the most direct empirical evidence of recent climate variability but require cautious interpretation due to these methodological limitations.[46]Natural Drivers
Orbital and Solar Forcings
Orbital forcings arise from periodic variations in Earth's orbital parameters, which alter the distribution of solar insolation across latitudes and seasons, thereby influencing global climate on timescales of tens to hundreds of thousands of years. These variations, formalized in Milankovitch theory, include changes in orbital eccentricity (cycle period approximately 100,000 years), which modulates the distance from the Sun and seasonal insolation contrast; obliquity (axial tilt, cycle ~41,000 years), affecting high-latitude summer insolation; and precession (cycle ~23,000 years), shifting the timing of perihelion relative to seasons.[47][48] Collectively, these cycles can produce insolation changes of up to 25% at certain latitudes over their periods, pacing glacial-interglacial transitions evident in paleoclimate records like ice cores and marine sediments.[47] In the current interglacial Holocene epoch, orbital configurations yield a net insolation forcing that favors gradual cooling over the next 50,000 years, with minimal short-term influence on centennial-scale climate variability. Empirical reconstructions from deep-sea sediments and speleothems confirm that orbital forcings dominate Pleistocene climate oscillations but operate too slowly to account for rapid 20th- or 21st-century temperature shifts, where changes in annual global insolation are on the order of 0.01 W/m² or less per century.[49][50] Solar forcings stem from fluctuations in total solar irradiance (TSI), the primary energy input to Earth's climate system, with variations reconstructed from sunspot records, cosmogenic isotopes like ¹⁴C and ¹⁰Be, and direct satellite measurements since 1978. The dominant 11-year Schwabe cycle induces TSI swings of about 1 W/m² (0.1% of mean TSI ~1361 W/m²), while longer-term modulations, such as the ~80-90 year Gleissberg cycle, have contributed to historical minima like the Maunder Minimum (1645–1715), associated with cooler European temperatures during the Little Ice Age.[51][52] Proxy-based TSI series indicate a rise of ~0.2–0.4 W/m² from the late 19th to mid-20th century, correlating with early 20th-century warming phases, but satellite data reveal a slight decline or stasis since the 1980s amid rising global temperatures.[53][54][55] Quantitatively, solar forcing's radiative impact is small relative to other drivers; the net 20th-century solar contribution to global temperature is estimated at 0.05–0.1°C, primarily in the early period, with no sustained positive trend post-1950 to explain observed warming of ~0.8°C since then.[56][57] This decoupling underscores that while solar variability amplifies internal climate modes like the North Atlantic Oscillation, it does not drive the bulk of recent anthropogenic-era changes, as confirmed by attribution studies isolating forcings via climate models.[58][59]Volcanic and Internal Variability
Volcanic eruptions exert a short-term cooling influence on global climate through the injection of sulfur dioxide (SO₂) into the stratosphere, where it oxidizes to form sulfate aerosols that reflect incoming solar radiation.[60] This radiative forcing typically peaks within months of a major eruption and dissipates over 1–3 years as aerosols settle or are removed by precipitation.[61] [62] The 1991 eruption of Mount Pinatubo in the Philippines released approximately 15–17 million tons of SO₂, resulting in a global surface temperature drop of about 0.5°C that persisted from 1991 to 1993.[62] [63] Similarly, the 1815 Tambora eruption contributed to the "year without a summer" in 1816, with hemispheric cooling on the order of 0.2–0.5°C.[64] Over the 20th century, multiple such events produced transient declines in average global surface temperature up to 0.5°F (0.3°C), but their sporadic nature yields no sustained net warming; instead, volcanic activity has imposed a minor net cooling relative to baseline conditions.[65] [66] Volcanic CO₂ emissions, while present, are dwarfed by anthropogenic sources and insufficient to drive long-term trends.[64] Internal variability refers to fluctuations in the climate system arising from chaotic interactions among its components, such as the atmosphere, oceans, and cryosphere, without external forcings.[67] These modes redistribute heat internally rather than adding or removing energy from the Earth system, leading to multiannual to multidecadal oscillations that superimpose on forced trends.[67] The El Niño-Southern Oscillation (ENSO), with a periodicity of 2–7 years, exemplifies this: during El Niño phases, enhanced equatorial Pacific warmth releases stored ocean heat to the atmosphere, elevating global surface temperatures by approximately 0.1–0.2°C temporarily; La Niña phases produce the opposite effect.[68] Decadal modes like the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) further modulate regional and global patterns; the PDO influences North Pacific sea surface temperatures and can amplify or dampen ENSO impacts, while the AMO drives multidecadal North Atlantic variability affecting drought and rainfall.[69] [70] Over the instrumental record, these oscillations account for year-to-year and decadal-scale deviations, such as the early 21st-century warming slowdown partly linked to a negative PDO phase and La Niña dominance, but they exhibit zero mean forcing on centennial timescales.[71] Empirical reconstructions confirm that internal variability explains less than 10–20% of observed 20th-century warming variance beyond what greenhouse gas forcings predict.[67]Anthropogenic Influences
Greenhouse Gas Emissions
Anthropogenic greenhouse gas emissions primarily stem from fossil fuel combustion for energy production and transportation, agricultural activities including livestock digestion and fertilizer use, land-use changes such as deforestation, and industrial processes. These emissions have driven the accumulation of long-lived gases in the atmosphere, with carbon dioxide (CO2) comprising about 75-80% of total anthropogenic greenhouse gas outputs when measured in carbon dioxide equivalents (CO2e). Methane (CH4) and nitrous oxide (N2O) contribute the remainder alongside minor fluorinated gases, with global totals estimated at around 53 gigatons of CO2e annually in recent inventories.[72][73] CO2 emissions from human sources reached 37.4 billion metric tons in 2023, predominantly from coal (about 40%), oil (30%), and natural gas (20%) combustion, with cement production and land-use changes adding the rest. Fossil fuel-related emissions alone totaled 36.8 billion metric tons, marking a 1.1% increase from 2022 despite expansions in renewables, as demand in developing economies like China and India offset declines elsewhere. Atmospheric CO2 concentrations, measured at Mauna Loa Observatory, stood at 426.06 parts per million (ppm) as of October 24, 2025, up from 315 ppm in 1958 and pre-industrial levels of approximately 280 ppm, reflecting cumulative anthropogenic additions exceeding natural variability and sinks.[74][75]| Greenhouse Gas | Anthropogenic Share of Total Emissions (%) | Primary Human Sources | Global Emissions (2022, GtCO2e) |
|---|---|---|---|
| CO2 | ~90 | Fossil fuels, cement, deforestation | ~42.3 |
| CH4 | ~60 | Agriculture (livestock, rice), fossil fuels, waste | ~8.5 |
| N2O | ~40-50 | Agricultural soils, manure, fertilizers | ~2.7 |
| Fluorinated gases | Nearly 100 | Industrial refrigeration, semiconductors | ~1.2 |
Land Use and Aerosols
Anthropogenic land use changes, including deforestation, agricultural expansion, and urbanization, alter surface albedo, evapotranspiration, and carbon storage, exerting both biogeophysical and biogeochemical influences on climate. In tropical regions, deforestation typically reduces forest canopy cover, increasing surface albedo as darker vegetation is replaced by lighter soils or grasslands, which reflects more solar radiation and induces a cooling effect; however, this is often outweighed by decreased evapotranspiration (reducing latent heat cooling) and carbon emissions from biomass loss, resulting in net warming.[82] [83] Global modeling estimates suggest that full-scale deforestation could yield a net radiative forcing equivalent to 0.8 K of warming after 100 years when combining CO2, albedo, and short-lived climate forcer effects.[82] In contrast, boreal deforestation increases albedo over snow-covered ground, potentially causing regional cooling that dominates over carbon release in high latitudes.[84] Agricultural practices, such as irrigation and tillage, further modify soil moisture and roughness, influencing local temperature and precipitation patterns, with studies attributing up to 40% of present-day anthropogenic radiative forcing to land use and land cover changes (LULCC) when including emissions of reactive gases and aerosols from land management.[85] Urbanization contributes to the urban heat island (UHI) effect, where impervious surfaces and reduced vegetation elevate local temperatures by 1–3°C on average compared to rural surroundings, primarily through decreased albedo and increased anthropogenic heat emissions; however, this effect is localized and does not significantly bias global temperature trends after site adjustments, as rural stations show similar warming patterns.[86] [87] Projected urban expansion could amplify global warming via albedo reductions, with one analysis estimating contributions from future urbanization equivalent to additional radiative forcing without mitigation.[88] Overall, LULCC radiative forcing is estimated at -0.2 to 0.2 W m⁻² since pre-industrial times, with high uncertainty due to regional variability and interactions with vegetation dynamics, underscoring that biogeophysical cooling from albedo may partially offset biogeochemical warming from emissions.[85] Anthropogenic aerosols, primarily sulfates from fossil fuel combustion, black carbon from biomass burning, and nitrates from agriculture, exert a net negative radiative forcing through direct scattering of sunlight and indirect enhancement of cloud reflectivity. Effective radiative forcing (ERF) from anthropogenic aerosols is quantified at approximately -1.0 to -0.5 W m⁻² globally since 1750, with sulfates dominating the cooling via increased planetary albedo.[89] This cooling masks an estimated 0.4–0.9°C of greenhouse gas-induced warming, as aerosols' short atmospheric lifetimes (days to weeks) contrast with long-lived CO2.[91] Recent clean air regulations have reduced sulfate emissions, particularly in Europe and North America since the 1980s, unmasking warming at a rate of 0.2 ± 0.1 W m⁻² per decade from 2001–2020 and contributing to accelerated temperature rise in the early 21st century.[92] [93] Uncertainty in aerosol ERF remains high (±0.5 W m⁻²), driven by variability in biomass burning, particle size distributions, and natural emission interactions, with some studies highlighting overestimation in models due to underrepresented regional heterogeneity.[94] Black carbon, conversely, warms by absorbing radiation, but its global forcing (+0.1 to +0.3 W m⁻²) is smaller than sulfate cooling, yielding a net aerosol effect that tempers observed warming but complicates attribution.Empirical Observations
Global Temperature Trends
Instrumental measurements of global surface air temperatures began in the mid-19th century, with systematic records compiled from land stations and sea surface temperatures. Major datasets, including HadCRUT5 from the UK Met Office and University of East Anglia, NASA's GISTEMP, NOAA's GlobalTemp, and Berkeley Earth's surface temperature series, indicate an overall warming trend of approximately 1.1°C from 1850 to 2020, with 2024 marking the warmest year on record at about 1.55°C above the 1850-1900 pre-industrial baseline according to HadCRUT5 and Copernicus analyses.[96][97][98] Since the satellite era began in 1979, microwave sounding unit (MSU) and advanced MSU (AMSU) instruments have provided lower tropospheric temperature anomalies, as analyzed in datasets like UAH and RSS. The UAH version 6.1 dataset reports a linear trend of +0.16°C per decade from January 1979 through September 2025, lower than many surface-based estimates for the same period, which range from 0.18°C to 0.20°C per decade.[99][41] Discrepancies arise partly from surface datasets incorporating adjustments for station changes, urban heat island effects, and sparse coverage in polar regions, which critics argue can inflate recent warming; for instance, rural-only subsets show reduced trends compared to all-station data.[100] A notable slowdown in surface warming occurred from 1998 to 2013, following the strong 1997-1998 El Niño, during which global surface temperature trends were near zero in several datasets, attributed to internal variability like La Niña dominance, enhanced trade winds, and volcanic influences rather than cessation of radiative forcing.[101][102] This "hiatus" prompted reevaluation of model projections, which had overestimated warming rates in that interval.[103] The years 2023 and 2024 exhibited a sharp temperature spike, with global means rising 0.27-0.29°C from 2022 to 2023, driven primarily by a strong El Niño event peaking in early 2024, compounded by reduced aerosol cooling from shipping regulations and possibly low stratospheric water vapor.[104][105] Post-El Niño, as of September 2025, tropospheric anomalies moderated to +0.53°C in UAH relative to the 1991-2020 baseline, suggesting a return toward multi-decadal trends without sustained acceleration beyond historical variability.[99]| Dataset | Period | Trend (°C/decade) | Source |
|---|---|---|---|
| HadCRUT5 (surface) | 1850-2024 | ~0.09 (long-term) | [96] |
| GISTEMP (surface) | 1880-2024 | ~0.08 (long-term); 0.19 (post-1979) | [41] |
| UAH v6.1 (lower troposphere) | 1979-Sep 2025 | 0.16 | [99] |