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Atmospheric sciences
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Glossaries

Climate is the long-term weather pattern in a region, typically averaged over 30 years.[1][2] More rigorously, it is the mean and variability of meteorological variables over a time spanning from months to millions of years. Some of the meteorological variables that are commonly measured are temperature, humidity, atmospheric pressure, wind, and precipitation. In a broader sense, climate is the state of the components of the climate system, including the atmosphere, hydrosphere, cryosphere, lithosphere and biosphere and the interactions between them.[1] The climate of a location is affected by its latitude, longitude, terrain, altitude, land use and nearby water bodies and their currents.[3]

Climates can be classified according to the average and typical variables, most commonly temperature and precipitation. The most widely used classification scheme is the Köppen climate classification. The Thornthwaite system,[4] in use since 1948, incorporates evapotranspiration along with temperature and precipitation information and is used in studying biological diversity and how climate change affects it. The major classifications in Thornthwaite's climate classification are microthermal, mesothermal, and megathermal.[5] Finally, the Bergeron and Spatial Synoptic Classification systems focus on the origin of air masses that define the climate of a region.

Paleoclimatology is the study of ancient climates. Paleoclimatologists seek to explain climate variations for all parts of the Earth during any given geologic period, beginning with the time of the Earth's formation.[6] Since very few direct observations of climate were available before the 19th century, paleoclimates are inferred from proxy variables. They include non-biotic evidence—such as sediments found in lake beds and ice cores—and biotic evidence—such as tree rings and coral. Climate models are mathematical models of past, present, and future climates. Climate change may occur over long and short timescales due to various factors. Recent warming is discussed in terms of global warming, which results in redistributions of biota. For example, as climate scientist Lesley Ann Hughes has written: "a 3 °C [5 °F] change in mean annual temperature corresponds to a shift in isotherms of approximately 300–400 km [190–250 mi] in latitude (in the temperate zone) or 500 m [1,600 ft] in elevation. Therefore, species are expected to move upwards in elevation or towards the poles in latitude in response to shifting climate zones."[7][8]

Definition

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Climate (from Ancient Greek κλίμα 'inclination') is commonly defined as the weather averaged over a long period.[9] The standard averaging period is 30 years,[10] but other periods may be used depending on the purpose. Climate also includes statistics other than the average, such as the magnitudes of day-to-day or year-to-year variations. The Intergovernmental Panel on Climate Change (IPCC) 2001 glossary definition is as follows:

"Climate in a narrow sense is usually defined as the "average weather", or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period ranging from months to thousands or millions of years. The classical period is 30 years, as defined by the World Meteorological Organization (WMO). These quantities are most often surface variables such as temperature, precipitation, and wind. Climate in a wider sense is the state, including a statistical description, of the climate system."[11]

The World Meteorological Organization (WMO) describes "climate normals" as "reference points used by climatologists to compare current climatological trends to that of the past or what is considered typical. A climate normal is defined as the arithmetic average of a climate element (e.g. temperature) over a 30-year period. A 30-year period is used as it is long enough to filter out any interannual variation or anomalies such as El Niño–Southern Oscillation, but also short enough to be able to show longer climatic trends."[12]

The WMO originated from the International Meteorological Organization which set up a technical commission for climatology in 1929. At its 1934 Wiesbaden meeting, the technical commission designated the thirty-year period from 1901 to 1930 as the reference time frame for climatological standard normals. In 1982, the WMO agreed to update climate normals, and these were subsequently completed on the basis of climate data from 1 January 1961 to 31 December 1990.[13] The 1961–1990 climate normals serve as the baseline reference period. The next set of climate normals to be published by WMO is from 1991 to 2020.[14] Aside from collecting from the most common atmospheric variables (air temperature, pressure, precipitation and wind), other variables such as humidity, visibility, cloud amount, solar radiation, soil temperature, pan evaporation rate, days with thunder and days with hail are also collected to measure change in climate conditions.[15]

The difference between climate and weather is usefully summarized by the popular phrase "Climate is what you expect, weather is what you get."[16] Over historical time spans, there are a number of nearly constant variables that determine climate, including latitude, altitude, proportion of land to water, and proximity to oceans and mountains. All of these variables change only over periods of millions of years due to processes such as plate tectonics. Other climate determinants are more dynamic: the thermohaline circulation of the ocean leads to a 5 °C (9 °F) warming of the northern Atlantic Ocean compared to other ocean basins.[17] Other ocean currents redistribute heat between land and water on a more regional scale. The density and type of vegetation coverage affects solar heat absorption,[18] water retention, and rainfall on a regional level. Alterations in the quantity of atmospheric greenhouse gases (particularly carbon dioxide and methane) determines the amount of solar energy retained by the planet, leading to global warming or global cooling. The variables which determine climate are numerous and the interactions complex, but there is general agreement that the broad outlines are understood, at least insofar as the determinants of historical climate change are concerned.[19][20]

Climate classification

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Main article: Climate classification
Map of world dividing climate zones, largely influenced by latitude. The zones, going from the equator upward (and downward) are Tropical, Dry, Moderate, Continental and Polar. There are subzones within these zones.
Worldwide Köppen climate classifications

Climate classifications are systems that categorize the world's climates. A climate classification may correlate closely with a biome classification, as climate is a major influence on life in a region. One of the most used is the Köppen climate classification scheme first developed in 1899.[21]

There are several ways to classify climates into similar regimes. Originally, climes were defined in Ancient Greece to describe the weather depending upon a location's latitude. Modern climate classification methods can be broadly divided into genetic methods, which focus on the causes of climate, and empiric methods, which focus on the effects of climate. Examples of genetic classification include methods based on the relative frequency of different air mass types or locations within synoptic weather disturbances. Examples of empiric classifications include climate zones defined by plant hardiness,[22] evapotranspiration,[23] or more generally the Köppen climate classification which was originally designed to identify the climates associated with certain biomes. A common shortcoming of these classification schemes is that they produce distinct boundaries between the zones they define, rather than the gradual transition of climate properties more common in nature.

Record

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Paleoclimatology

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Main article: Paleoclimatology

Paleoclimatology is the study of past climate over a great period of the Earth's history. It uses evidence with different time scales (from decades to millennia) from ice sheets, tree rings, sediments, pollen, coral, and rocks to determine the past state of the climate. It demonstrates periods of stability and periods of change and can indicate whether changes follow patterns such as regular cycles.[24]

Modern

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See also: Instrumental temperature record and Satellite temperature measurements

Details of the modern climate record are known through the taking of measurements from such weather instruments as thermometers, barometers, and anemometers during the past few centuries. The instruments used to study weather over the modern time scale, their observation frequency, their known error, their immediate environment, and their exposure have changed over the years, which must be considered when studying the climate of centuries past.[25] Long-term modern climate records skew towards population centres and affluent countries.[26] Since the 1960s, the launch of satellites allow records to be gathered on a global scale, including areas with little to no human presence, such as the Arctic region and oceans.

Climate variability

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Main article: Climate variability and change

Climate variability is the term to describe variations in the mean state and other characteristics of climate (such as chances or possibility of extreme weather, etc.) "on all spatial and temporal scales beyond that of individual weather events."[27] Some of the variability does not appear to be caused systematically and occurs at random times. Such variability is called random variability or noise. On the other hand, periodic variability occurs relatively regularly and in distinct modes of variability or climate patterns.[28]

There are close correlations between Earth's climate oscillations and astronomical factors (barycenter changes, solar variation, cosmic ray flux, cloud albedo feedback, Milankovic cycles), and modes of heat distribution between the ocean-atmosphere climate system. In some cases, current, historical and paleoclimatological natural oscillations may be masked by significant volcanic eruptions, impact events, irregularities in climate proxy data, positive feedback processes or anthropogenic emissions of substances such as greenhouse gases.[29]

Over the years, the definitions of climate variability and the related term climate change have shifted. While the term climate change now implies change that is both long-term and of human causation, in the 1960s the word climate change was used for what we now describe as climate variability, that is, climatic inconsistencies and anomalies.[28]

Climate change

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Surface air temperature change over the past 50 years.[30]
Observed temperature from NASA[31] vs the 1850–1900 average used by the IPCC as a pre-industrial baseline.[32] The primary driver for increased global temperatures in the industrial era is human activity, with natural forces adding variability.[33]
Main article: Climate change
See also: Global temperature record, List of weather records, and Extreme event attribution

Climate change is the variation in global or regional climates over time.[34] It reflects changes in the variability or average state of the atmosphere over time scales ranging from decades to millions of years. These changes can be caused by processes internal to the Earth, external forces (e.g. variations in sunlight intensity) or human activities, as found recently.[35][36] Scientists have identified Earth's Energy Imbalance (EEI) to be a fundamental metric of the status of global change.[37]

In recent usage, especially in the context of environmental policy, the term "climate change" often refers only to changes in modern climate, including the rise in average surface temperature known as global warming. In some cases, the term is also used with a presumption of human causation, as in the United Nations Framework Convention on Climate Change (UNFCCC). The UNFCCC uses "climate variability" for non-human caused variations.[38]

Earth has undergone periodic climate shifts in the past, including four major ice ages. These consist of glacial periods where conditions are colder than normal, separated by interglacial periods. The accumulation of snow and ice during a glacial period increases the surface albedo, reflecting more of the Sun's energy into space and maintaining a lower atmospheric temperature. Increases in greenhouse gases, such as by volcanic activity, can increase the global temperature and produce an interglacial period. Suggested causes of ice age periods include the positions of the continents, variations in the Earth's orbit, changes in the solar output, and volcanism.[39] However, these naturally caused changes in climate occur on a much slower time scale than the present rate of change which is caused by the emission of greenhouse gases by human activities.[40]

According to the EU's Copernicus Climate Change Service, average global air temperature has passed 1.5C of warming the period from February 2023 to January 2024.[41]

Climate models

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Climate models use quantitative methods to simulate the interactions and transfer of radiative energy between the atmosphere,[42] oceans, land surface and ice through a series of physics equations. They are used for a variety of purposes, from the study of the dynamics of the weather and climate system to projections of future climate. All climate models balance, or very nearly balance, incoming energy as short wave (including visible) electromagnetic radiation to the Earth with outgoing energy as long wave (infrared) electromagnetic radiation from the Earth. Any imbalance results in a change in the average temperature of the Earth.

Climate models are available on different resolutions ranging from >100 km to 1 km. High resolutions in global climate models require significant computational resources, and so only a few global datasets exist. Global climate models can be dynamically or statistically downscaled to regional climate models to analyze impacts of climate change on a local scale. Examples are ICON[43] or mechanistically downscaled data such as CHELSA (Climatologies at high resolution for the earth's land surface areas).[44][45]

The most talked-about applications of these models in recent years have been their use to infer the consequences of increasing greenhouse gases in the atmosphere, primarily carbon dioxide (see greenhouse gas). These models predict an upward trend in the global mean surface temperature, with the most rapid increase in temperature being projected for the higher latitudes of the Northern Hemisphere.

Models can range from relatively simple to quite complex. Simple radiant heat transfer models treat the Earth as a single point and average outgoing energy. This can be expanded vertically (as in radiative-convective models), or horizontally. Finally, more complex (coupled) atmosphere–ocean–sea ice global climate models discretise and solve the full equations for mass and energy transfer and radiant exchange.[46]

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Climate

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Climate is the long-term average of , , and other variables at a given location, typically assessed over periods of 30 years or more. Unlike , which describes short-term atmospheric conditions over hours, days, or weeks, climate captures recurring patterns and variability that shape ecosystems, , and human adaptations. Key determinants include , which influences solar insolation; altitude, affecting temperature lapse rates; proximity to oceans, moderating extremes via ; and driven by and tilt. Ocean currents and landforms further modulate regional climates by redistributing heat and moisture. 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 and to reflect native and thermal regimes. 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. Empirical records reveal climates have fluctuated naturally over millennia due to orbital variations, volcanic activity, and solar output, though instrumental data since the document regional shifts amid ongoing debates over anthropogenic influences.

Core Concepts

Definition

Climate is the long-term pattern of conditions in a specific , characterized by the average and variability of meteorological variables such as , , , and direction, , and over an extended period, typically at least 30 years. This timeframe allows for the identification of statistically significant trends and cycles, distinguishing persistent atmospheric behaviors from transient fluctuations. In contrast to , which describes the momentary state of the atmosphere at a particular location—including immediate conditions like , readings, or events—climate aggregates these elements into a descriptive framework of expected frequencies and intensities. For instance, a single heatwave constitutes , whereas a sustained rise in average summer s over decades defines a climatic shift. The 30-year standard, established by organizations like the , facilitates consistent global comparisons and periodic updates to climate normals. Climate encompasses not only central tendencies like mean annual but also extremes, , and probabilistic distributions of events, such as the frequency of droughts or floods. These attributes arise from interactions among solar radiation, , oceanic currents, and land surface features, providing a holistic statistical portrait rather than anecdotal observations. Regional climates vary widely, from tropical zones with high year-round to polar areas with persistent cold and minimal moisture, reflecting latitudinal and topographic influences.

Key Elements and Factors

Climate encompasses the average and variability of variables such as , , and wind over extended periods, typically at least 30 years as defined by the for climatological normals. These key elements describe the statistical properties of atmospheric conditions in a , distinguishing climate from short-term fluctuations. Primary elements include air temperature, which influences thermal regimes and rates; , encompassing , , and other forms that determine water availability; and , reflecting moisture content in the air that affects comfort and processes. patterns and gradients drive circulation and influence local weather persistence, while modulates incoming solar radiation and . Regional climate variations arise from multiple interacting factors, primarily , which controls the angle and duration of solar insolation, resulting in warmer equatorial zones and cooler polar regions. affects temperature through the environmental , 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 of oceans, leading to maritime climates with smaller seasonal ranges compared to continental interiors. Ocean currents redistribute heat globally; for instance, the 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 depleting moisture on windward slopes. and further modulate local climates by transporting heat, moisture, and influencing rates.

Classification Systems

Major Schemes

The Köppen-Geiger classification, developed by in 1884 and refined by 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 and , with subdivisions reflecting seasonal patterns and associations. Group A requires all months above 18°C with no or specific minima; B identifies arid and semi-arid conditions where potential evapotranspiration exceeds ; 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 below 10°C; and E includes (ET) and (EF) subtypes with means below 10°C in the warmest month. This scheme correlates climate zones with native distributions, such as rainforests in A climates and deserts in B. 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 ), 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. Thornthwaite's system, formulated by climatologist C.W. Thornthwaite in 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), , , and perpetual frost, with subtypes for summer-wet or winter-wet regimes. It incorporates seasonality through aridity and humidity indices, enabling assessments of critical for and , though it requires detailed data on potential evaporation. 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 and impact studies.

Principles and Critiques

Climate classification systems organize terrestrial regions into categories based on dominant meteorological patterns, primarily monthly and averages, to identify zones with similar ecological potentials. These systems derive principles from empirical observations linking climatic thresholds to distributions, assuming that controls types while modulates . The Köppen-Geiger framework, established by in 1884 and refined by , exemplifies this approach by using formulaic boundaries: for instance, tropical climates (A) require all months above 18°C, dry climates (B) satisfy exceeding via the ratio P<2T+28P < 2T + 28 where PP is annual precipitation in cm and TT is in °C, and polar climates (E) have the warmest month below 10°C. 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. 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. 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.

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. 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. 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. 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. 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.

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. 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. 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. 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. 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. 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. 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. Precipitation records, derived from rain gauges since the 19th century, face additional homogeneity problems from changing gauge types and siting, leading to regional inconsistencies. Overall, instrumental records provide the most direct empirical evidence of recent climate variability but require cautious interpretation due to these methodological limitations.

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. 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. 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. 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. 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. 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. 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.

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. 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. The 1991 eruption of 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. 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. 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. Volcanic CO₂ emissions, while present, are dwarfed by anthropogenic sources and insufficient to drive long-term trends. 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. 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. 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. 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. 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. Empirical reconstructions confirm that internal variability explains less than 10–20% of observed 20th-century warming variance beyond what greenhouse gas forcings predict.

Anthropogenic Influences

Greenhouse Gas Emissions

Anthropogenic 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 (CO2) comprising about 75-80% of total anthropogenic outputs when measured in carbon dioxide equivalents (CO2e). (CH4) and (N2O) contribute the remainder alongside minor , with global totals estimated at around 53 gigatons of CO2e annually in recent inventories. CO2 emissions from human sources reached 37.4 billion metric tons in 2023, predominantly from (about 40%), oil (30%), and (20%) , with 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 and offset declines elsewhere. Atmospheric CO2 concentrations, measured at , 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.
Greenhouse GasAnthropogenic Share of Total Emissions (%)Primary Human SourcesGlobal Emissions (2022, GtCO2e)
CO2~90Fossil fuels, , ~42.3
CH4~60 (, ), fossil fuels, ~8.5
N2O~40-50Agricultural soils, , fertilizers~2.7
Fluorinated gasesNearly 100Industrial , semiconductors~1.2
This table summarizes major anthropogenic contributions based on bottom-up inventories; actual atmospheric impacts vary due to differing lifetimes and radiative forcings. from human activities have risen steadily, totaling about 350-400 million metric tons annually, with accounting for 40%, sector operations ( leaks and venting) for over 30%, and decomposition for 20%. Concentrations have increased from pre-industrial levels of ~700 (ppb) to over 1,900 ppb by 2025, with recent accelerations linked to expanded extraction and herds rather than solely sources. Nitrous oxide emissions, largely from fertilizer application and management in (78% of anthropogenic total), have grown 62% since 1970 to about 7-8 million metric tons of equivalent per year. Atmospheric levels reached 336 ppb in 2025, a 25% rise from pre-industrial eras, driven by intensified global food production without proportional efficiency gains in nitrogen use. , though minor in volume, have potent warming effects and stem almost entirely from human manufacturing, with emissions stable but cumulative due to long persistence. Despite measurement challenges in inventories, which rely on self-reported national data often underestimating emissions, observations and isotopic confirm the dominance of fossil-derived CO2 and anthropogenic CH4 in recent trends. Emissions growth has slowed in advanced economies through fuel switching and , but absolute levels continue upward globally, with contributing over 50% of totals.

Land Use and Aerosols

Anthropogenic land use changes, including , agricultural expansion, and , alter surface , , and carbon storage, exerting both biogeophysical and biogeochemical influences on climate. In tropical regions, typically reduces forest canopy cover, increasing surface as darker is replaced by lighter soils or grasslands, which reflects more solar radiation and induces a cooling effect; however, this is often outweighed by decreased (reducing cooling) and carbon emissions from loss, resulting in net warming. Global modeling estimates suggest that full-scale could yield a net equivalent to 0.8 K of warming after 100 years when combining CO2, , and short-lived climate forcer effects. In contrast, boreal increases over snow-covered ground, potentially causing regional cooling that dominates over carbon release in high latitudes. Agricultural practices, such as and , further modify and roughness, influencing local and precipitation patterns, with studies attributing up to 40% of present-day anthropogenic to and changes (LULCC) when including emissions of reactive gases and aerosols from . 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 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. Projected urban expansion could amplify global warming via reductions, with one analysis estimating contributions from future equivalent to additional without . Overall, LULCC 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 may partially offset biogeochemical warming from emissions. Anthropogenic aerosols, primarily sulfates from combustion, from burning, and nitrates from , exert a net negative 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 . This cooling masks an estimated 0.4–0.9°C of gas-induced warming, as aerosols' short atmospheric lifetimes (days to weeks) contrast with long-lived CO2. Recent clean air regulations have reduced emissions, particularly in and 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 . Uncertainty in aerosol ERF remains high (±0.5 W m⁻²), driven by variability in burning, distributions, and natural emission interactions, with some studies highlighting overestimation in models due to underrepresented regional heterogeneity. , conversely, warms by absorbing radiation, but its global forcing (+0.1 to +0.3 W m⁻²) is smaller than cooling, yielding a net effect that tempers observed warming but complicates attribution.

Empirical Observations

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 , 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. Since the satellite era began in , microwave sounding unit (MSU) and advanced MSU (AMSU) instruments have provided lower tropospheric temperature anomalies, as analyzed in datasets like UAH and . The UAH version 6.1 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. 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. A notable slowdown in surface warming occurred from 1998 to 2013, following the strong 1997-1998 El Niño, during which trends were near zero in several datasets, attributed to internal variability like La Niña dominance, enhanced , and volcanic influences rather than cessation of . This "hiatus" prompted reevaluation of model projections, which had overestimated warming rates in that interval. 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 cooling from shipping regulations and possibly low stratospheric . 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.
DatasetPeriodTrend (°C/decade)Source
HadCRUT5 (surface)1850-2024~0.09 (long-term)
GISTEMP (surface)1880-2024~0.08 (long-term); 0.19 (post-1979)
UAH v6.1 (lower )1979-Sep 20250.16

Hydrological and Cryospheric Changes

Arctic sea ice extent has declined since satellite observations began in 1979, with the September minimum extent averaging about 4.5 million square kilometers in recent decades compared to over 7 million in the , driven primarily by summer melt and reduced winter growth. The 2024 September extent ranked as the sixth lowest on record, part of a linear trend of approximately 13% per decade loss in minimum extent. In contrast, extent showed an overall increase of about 1% per decade from 1979 to 2014, though it has experienced record lows since 2016, with the 2024 winter maximum at 17.16 million square kilometers, the second lowest observed. The has lost mass consistently since 2002, with GRACE satellite measurements indicating an average loss of around 200 gigatons per year from 2002 to 2023, accelerating in recent years due to enhanced surface melting and calving. The has also contributed to net mass loss, shedding approximately 150 gigatons per year over the same period, primarily from , though has shown periods of mass gain that partially offset losses. Global glacier mass loss has accelerated, with an estimated annual loss of 273 ± 16 gigatons from 2000 to 2023, equivalent to about 0.75 millimeters per year of sea-level rise contribution, based on intercomparison of satellite and ground observations; losses were particularly pronounced in 2023 at over 300 gigatons. Northern Hemisphere snow cover extent has decreased in spring and summer since 1967, with a statistically significant decline of about 2,000 square kilometers per year in North America from 1972 to 2023, though winter extents show less consistent trends amid natural variability. Permafrost in the Arctic is thawing, with the active layer (seasonally thawed upper soil) deepening by 10-20 centimeters per decade in many regions since the 1980s, leading to ground subsidence, thermokarst lake formation, and release of stored carbon, though the total permafrost extent remains vast at over 15 million square kilometers. Global land has shown a slight positive trend since 1900, with variability increasing by about 1.2% per decade, particularly in wetter regions like parts of and , while some subtropical areas experience drying. The frequency and intensity of heavy events (exceeding the 99th percentile daily amounts) have increased in many regions since the mid-20th century, with a global uptick in the proportion of annual from such events rising from about 10% to 12% in the United States, though trends vary regionally and are influenced by natural oscillations like the El Niño-Southern Oscillation.

Climate Modeling

Model Frameworks

Climate model frameworks encompass the computational architectures and numerical methods employed to simulate the Earth's , primarily through solving systems of partial differential equations that represent fundamental physical processes such as , , and . These frameworks discretize the global domain into three-dimensional grids, with typical horizontal resolutions ranging from 250 to 600 kilometers and 10 to 20 vertical layers in atmospheric components, enabling simulations of large-scale circulations while parameterizing unresolved sub-grid processes like formation and . The foundational framework is the general circulation model (GCM), which couples modules for the atmosphere, , surface, and to compute interactions of , momentum, and moisture across these components using derived from the Navier-Stokes equations, continuity, and state equations. GCMs rely on explicit dynamical cores for grid-scale and , with parameterizations for processes below the grid scale, such as schemes based on moist static or boundary-layer turbulence models. Extensions to this framework include Earth system models (ESMs), which augment GCMs with interactive biogeochemical modules, such as carbon cycle representations involving terrestrial vegetation dynamics and ocean alkalinity feedbacks, to simulate coupled physical-biogeochemical responses over centennial timescales. ESMs, exemplified by frameworks like the Community Earth System Model (CESM), facilitate modular coupling of components through standardized interfaces for flux exchanges, allowing investigations of processes like nutrient cycling and aerosol-climate interactions. For applications requiring computational efficiency, such as millennial-scale paleoclimate reconstructions, Earth system models of intermediate complexity (EMICs) adopt simplified or zonally averaged representations of dynamics, reducing and omitting fine-scale eddies while preserving key feedbacks like ice-albedo effects. EMICs often employ energy balance or quasi-geostrophic approximations rather than full , enabling ensemble simulations that explore uncertainty in long-term forcings. Regional (RCM) frameworks nest high-resolution domains (typically 10-50 km grids) within GCM boundary conditions, using limited-area dynamical cores to downscale global outputs while applying similar parameterization suites tailored to orographic and convective enhancements in specific domains. Emerging hybrid frameworks integrate emulators for sub-grid parameterizations or as neural general circulation models, which approximate GCM outputs through data-driven architectures trained on high- simulations to accelerate projections without sacrificing fidelity in representation.

Performance and Limitations

![Change in average temperature observations versus climate model projections][float-right] Climate models in ensembles like CMIP6 demonstrate reasonable fidelity in hindcasting large-scale features such as global mean surface temperature increases over the instrumental record, with multi-model means aligning closely with observed trends when evaluated against selected projections from earlier phases. However, detailed assessments reveal persistent systematic biases, including overestimation of tropospheric warming rates, particularly in the tropical mid-to-upper layers, where CMIP6 simulations exceed and observations by factors of two or more over 1979–2014. These discrepancies persist across 38 CMIP6 models, indicating structural issues in representing amplification of warming aloft. Projections of surface warming have similarly shown a tendency to run hot relative to empirical data, with CMIP5 and CMIP6 ensembles forecasting global temperature rises exceeding realized changes by approximately 0.19°C per decade since 1970 in aggregate analyses. Evaluations of (ECS) in these models span 1.8°C to 5.6°C, driven largely by divergent responses, yet observational constraints suggest many high-ECS models overestimate feedbacks. Regional performance lags further, with biases in extremes, storm tracks, and concentration undermining downscaled applications. Fundamental limitations stem from the necessity to parameterize unresolved subgrid-scale processes, such as and microphysics, which introduce irreducible uncertainties in feedbacks and balance. feedbacks, the dominant source of ECS spread, exhibit state-dependence and regional variability not fully captured, leading to compensating errors like overestimated positive low- feedback in the . Computational constraints limit resolution to ~10–100 km grids, precluding explicit simulation of mesoscale dynamics critical for extremes. Moreover, models often fail to conserve precisely or replicate observed decadal variability without adjustments, highlighting gaps in causal process representation. These shortcomings necessitate caution in using model outputs for attribution, favoring empirical validation over ensemble averages.

Debates and Attribution

Evidence for Natural Dominance

Analyses of rural-only temperature records, which minimize effects, reveal a strong between solar activity proxies and Northern Hemisphere land surface temperature trends from to the present, with solar variability explaining up to 100% of the observed warming when using low-variability total reconstructions. This contrasts with urban-inclusive datasets that show weaker solar correlations and greater apparent anthropogenic influence, highlighting potential biases in homogenized records that amplify recent warming signals. Connolly et al. (2015) argue that such rural data better reflect true climatic changes, attributing minimal role to forcings after accounting for solar and related natural drivers like geomagnetic activity. Multi-proxy reconstructions of solar activity further support a dominant natural role, estimating that solar forcing accounts for 50-80% of global surface rise since 1900, including amplified effects through uptake and atmospheric dynamics. Scafetta (2023) demonstrates coherence between balanced solar records—incorporating numbers, heliospheric , and cosmogenic isotopes—and anomalies, with planetary-induced solar oscillations providing a mechanistic link via tidal-gravitational influences on and flux. These findings challenge IPCC attribution by showing that recent solar minima (e.g., post-2005 decline) align with expected lagged responses, rather than requiring unforced anthropogenic dominance. Decadal ocean-atmosphere oscillations, such as the (PDO) and (AMO), exhibit positive phases since the mid-1970s and 1995, respectively, correlating with accelerated global warming rates of approximately 0.15-0.20°C per decade during these periods. These modes redistribute heat internally, with PDO warm phases enhancing Pacific trade wind weakening and AMO positivity boosting Atlantic heat release, together explaining multidecadal temperature variance that aligns with observed trends without invoking primary CO₂ forcing. For instance, the 1998-2013 warming hiatus coincided with a negative PDO shift, underscoring natural variability's capacity to mask or mimic long-term signals. Additional evidence includes reduced volcanic aerosol loading post-1991 Pinatubo eruption, which contributed to transient cooling of 0.5°C globally; the subsequent absence of comparable events has permitted rebound warming consistent with natural recovery cycles rather than escalating anthropogenic effects. Galactic cosmic ray modulation, via Svensmark's hypothesis, links solar magnetic field strength to cloud formation: higher solar activity reduces cosmic ray penetration, decreasing low-level cloud cover and amplifying surface warming by up to 1-2 W/m² in radiative forcing. Empirical satellite data from 1983-2010 show inverse correlations between cosmic ray flux and cloudiness, supporting a natural amplifier for solar-driven climate shifts. Collectively, these factors suggest internal and external natural processes suffice to explain recent observations, with critiques of anthropogenic models noting their underestimation of variability and overreliance on adjusted data.

Anthropogenic Claims and Critiques

The Intergovernmental Panel on Climate Change's Sixth Assessment Report concludes that anthropogenic have caused approximately 1.1°C of global warming since 1850–1900, with detection and attribution methods indicating a dominant influence on observed large-scale changes. These claims rely on climate models that simulate from CO₂ and other gases as the primary mechanism outweighing natural factors, supported by fingerprinting techniques matching observed warming patterns to simulated anthropogenic signals. Critiques of these attribution efforts emphasize persistent empirical discrepancies between model projections and observations, where coupled general circulation models have systematically overestimated tropospheric and surface warming rates since the late . For example, over the 1970–2020 period, observed global surface warming trends have fallen below the median projections of the Phase 5 ensemble, with root-mean-square errors in simulating key variables like and mid-tropospheric temperatures highlighting unresolved model limitations in feedbacks and ocean heat uptake. Natural internal variability, including modes like the Atlantic Multidecadal Oscillation and , has been shown in peer-reviewed reconstructions to explain substantial fractions of 20th-century warming without requiring dominant anthropogenic forcings, as evidenced by non-monotonic temperature patterns in paleoclimate proxies that align more closely with multidecadal ocean-atmosphere cycles than steady radiative trends. The 1998–2013 "hiatus" in surface warming, despite rising CO₂, further illustrates how such variability can mask or amplify trends, with studies attributing up to 60% of the pause to enhanced Pacific redistributing heat subsurface. Urban heat island (UHI) effects introduce upward biases in land-station records, particularly as station siting has shifted toward developed areas; analyses of pairwise rural-urban station comparisons reveal UHI contributions of 0.05–0.1°C per to continental trends, potentially inflating global land averages by 20–50% before homogenization adjustments, which themselves remain contested for introducing artificial warming signals. Reassessments of solar forcing challenge claims of negligible solar influence, with empirical models incorporating total and geomagnetic activity showing correlations exceeding 0.7 with global temperatures over 1880–2020, suggesting amplified effects via cosmic ray modulation of that models underrepresent. These critiques, drawn from independent peer-reviewed analyses, underscore that while anthropogenic emissions contribute to warming, overreliance on model-based attribution may undervalue natural drivers, especially given institutional incentives in academia toward emphasizing human causation.

Consensus Versus Empirical Discrepancies

The purported scientific consensus on anthropogenic global warming (AGW) is frequently summarized by the figure that 97% of climate scientists or peer-reviewed papers endorse the view that humans are causing most observed warming, a claim traced primarily to a 2013 study by Cook et al. analyzing 11,944 abstracts from 1991–2011, where 97.1% of those expressing a position on AGW supported it. However, this assessment has faced substantial methodological critiques, including reliance on abstract ratings rather than full-text analysis, subjective classifications lumping neutral or implicit mentions with explicit endorsements, and exclusion of papers not endorsing AGW from the denominator in key calculations. A re-examination of the same dataset by Legates, Soon, and Briggs in 2015, published after peer review, found that only 41 papers (0.3% of the total) explicitly quantified human contributions as over 50% of warming, with just 0.3% meeting strict criteria for consensus-level endorsement of catastrophic AGW. Surveys of active climate researchers reveal fractures in agreement beyond basic warming trends, particularly on attribution magnitude, , and policy implications; for instance, a 2012 poll of 1,868 scientists by the indicated only 52% viewed AGW as the primary driver, with significant dissent on projected extremes. Institutional processes like those of the IPCC, which require consensus among lead authors for high-confidence statements, have been accused of diluting uncertainties and sidelining dissenting empirical analyses to maintain narrative cohesion, as evidenced by leaked emails from Climategate (2009) and subsequent reviews highlighting suppression of minority reports. This dynamic contributes to issues, where academia's funding dependencies and publication biases—systematically favoring AGW-aligned research—may inflate perceived unanimity, as non-endorsing studies face higher rejection rates in major journals. Empirical observations diverge from consensus-driven model projections in several domains, underscoring attribution challenges. Global surface temperature trends since 1970 average 0.18°C per decade, yet (CMIP6) ensembles, underpinning IPCC AR6, overestimate this by 0.2–0.5°C per decade in hindcasts when tuned to observed forcings, largely due to inflated equilibrium climate sensitivity (ECS) estimates of 2.5–5.0°C versus observationally constrained values around 1.5–2.5°C. High-sensitivity ("hot") models fail to replicate historical patterns, wind trends, and precipitation variability, with errors persisting even in short-term seasonal forecasts. Cloud feedback discrepancies are pronounced: observations indicate a decrease in high-cloud fraction amid warming, contradicting model predictions of amplification, which overstates positive feedbacks. Further mismatches include the absence of predicted tropical tropospheric amplification (hotspot) in radiosonde and data, and regional Holocene reconstructions showing model underestimation of natural variability in tropical mountains, where proxies reveal cooler peaks than simulated. These gaps suggest overreliance on parameterized processes in models, potentially magnifying anthropogenic signals while underweighting solar, oceanic cycles (e.g., AMO, PDO), and influences, as natural forcings alone explain much unforced variability in paleoclimate records. A 2025 reassessment argues the AGW hypothesis lacks robust empirical validation for dominance, with natural drivers like and geothermal heat fluxes providing stronger causal fits to 20th-century warming phases. Such discrepancies erode confidence in consensus projections of extreme futures, prompting calls for model weighting by observational fidelity over ensemble averaging.

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