Bibliographic record
Abstract
The cryosphere (including, snow, glaciers, permafrost, lake and river ice) is an integral element of high mountain regions, which are home to roughly 10% of the global population.Widespread cryosphere changes affect physical, biological and human systems in the mountains and surrounding lowlands, with impacts evident even in the ocean.Building on the IPCC's 5th Assessment Report (AR5), this chapter assesses new evidence on observed recent and projected changes in the mountain cryosphere as well as associated impacts, risks and adaptation measures related to natural and human systems.Impacts in response to climate changes independently of changes in the cryosphere are not assessed in this chapter.Polar mountains are included in Chapter 3, except those in Alaska and adjacent Yukon, Iceland and Scandinavia, which are included in this chapter. Observations of cryospheric changes, impacts, and adaptation in high mountain areasObservations show general decline in low-elevation snow cover (high confidence 1 ), glaciers (very high confidence) and permafrost (high confidence) due to climate change in recent decades.Snow cover duration has declined in nearly all regions, especially at lower elevations, on average by 5 days per decade, with a likely 2 range from 0-10 days per decade.Low elevation snow depth and extent have declined, although year-to-year variation is high.Mass change of glaciers in all mountain regions (excluding the Canadian and Russian Arctic, Svalbard, Greenland and Antarctica) was very likely -490 ± 100 kg m -2 yr -1 (-123 ± 24 Gt yr -1 ) in 2006-2015.Regionally averaged mass budgets were likely most negative (less than -850 kg m -2 yr -1 ) in the southern Andes, Caucasus and the European Alps/Pyrenees, and least negative in High Mountain Asia (-150 ± 110 kg m -2 yr -1 ) but variations within regions are strong.Between 3.6-5.2 million km 2 are underlain by permafrost in the eleven high mountain regions covered in this chapter corresponding to 27-29% of the global permafrost area (medium confidence).Sparse and unevenly distributed measurements show an increase in permafrost temperature (high confidence), for example, by 0.19ºC ± 0.05ºC on average for about 28 locations in the European Alps, Scandinavia, Canada and Asia during the past decade.Other observations reveal decreasing permafrost thickness and loss of ice in the ground.{2.2.2, 2.2.3, 2.2.4}Glacier, snow and permafrost decline has altered the frequency, magnitude and location of most related natural hazards (high confidence).Exposure of people and infrastructure to natural hazards has increased due to growing population, tourism and 1 In this report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium or high.A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics, for example, medium confidence.For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.9.2 and Figure 1.4 for more details).2In this report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99-100% probability, Very likely 90-100%, Likely 66-100%, About as likely as not 33-66%, Unlikely 0-33%, Very unlikely 0-10% and Exceptionally unlikely 0-1%.Additional terms (Extremely likely: 95-100%, More likely than not >50-100% and Extremely unlikely 0-5%) may also be used when appropriate.Assessed likelihood is typeset in italics, for example, very likely (see Section 1.9.2 and Figure 1.4 for more details).This Report also uses the term 'likely range' to indicate that the assessed likelihood of an outcome lies within the 17-83% probability range. High Mountain Areas Chapter 2 2Enablers and response options to promote adaptation and sustainable development in high mountain areasThe already committed and unavoidable climate change affecting all cryosphere elements, irrespective of the emission scenario, points to integrated adaptation planning to support and enhance water availability, access, and management (medium confidence).Integrated management approaches for water across all scales, in particular for energy, agriculture, ecosystems and drinking water supply, can be effective at dealing with impacts from changes in the cryosphere.These approaches also offer opportunities to support social-ecological systems, through the development and optimisation of storage and the release of water from reservoirs (medium confidence), while being cognisant of potential negative implications for some ecosystems.Success in implementing such management options depends on the participation of relevant stakeholders, including affected communities, diverse knowledge and adequate tools for monitoring and projecting future conditions, and financial and institutional resources to support planning and implementation (medium confidence).{2.3.1, 2.3.3,2.4} Effective governance is a key enabler for reducing disaster risk, considering relevant exposure factors such as planning, zoning, and urbanisation pressures, as well as vulnerability factors such as poverty, which can challenge efforts towards resilience and sustainable development for communities (medium confidence).Reducing losses to disasters depend on integrated and coordinated approaches to account for the hazards concerned, the degree of exposure, and existing vulnerabilities.Diverse knowledge that includes community and multi-stakeholder experience with past impacts complements scientific knowledge to anticipate future risks.{CCB-1, 2.3.2,2.4}International cooperation, treaties and conventions exist for some mountain regions and transboundary river basins with potential to support adaptation action.However, there is limited evidence on the extent to which impacts and losses arising from changes in the cryosphere are specifically monitored and addressed in these frameworks.A wide range of institutional arrangements and practices have emerged over the past three decades that respond to a shared global mountain agenda and specific regional priorities.There is potential to strengthen them to also respond to climate-related cryosphere risks and open opportunities for development through adaptation (limited evidence, high agreement).The Sustainable Development Goals (SDGs), Sendai Framework and Paris Agreement have directed some attention in mountain-specific research and practice towards the monitoring and reporting on targets and indicators specified therein.{2.3.1, 2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.216 | 0.044 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".