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Record W3093215296 · doi:10.1017/cbo9781139583817.009

Precipitation, Net Precipitation, and River Discharge

2014· book-chapter· en· W3093215296 on OpenAlexaboutno aff
Mark C. Serreze, Roger G. Barry

Bibliographic record

VenueCambridge University Press eBooks · 2014
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationArcticWater cycleEnvironmental scienceOceanographyArchipelagoSea iceArctic ice packClimatologyDischargeThe arcticSeawaterGeologyGeographyMeteorologyEcologyDrainage basin

Abstract

fetched live from OpenAlex

Overview One of the keys to understanding the Arctic climate system is the determination of freshwater transfers. As introduced in Chapter 2, the Arctic Ocean is characterized by a fairly fresh surface layer, primarily maintained by river discharge, the import of low salinity seawater through the Bering Strait, and net precipitation over the Ocean itself. This fresh surface layer allows sea ice to readily form. In turn, the major exports of freshwater are the ice and water fluxes to the North Atlantic through the Fram Strait and through the Canadian Arctic Archipelago. Following the water through the atmospheric, terrestrial, and oceanic branches of the Arctic hydrologic cycle, and assessing links between these fluxes, freshwater storages, and the global climate system is a vibrant area of research. But the problem cannot be tackled all at once. Here, the focus is on a large, yet digestible piece – precipitation, net precipitation, and river discharge to the Arctic Ocean. Aspects of the ocean branch will be examined as part of Chapter 7. Precipitation is a difficult quantity to accurately measure in the Arctic. Evapotranspiration is even more difficult to obtain. Although annual average precipitation totals over the Arctic range widely, there is also strong seasonality. Precipitation over the Atlantic sector exhibits a general maximum during the winter half of the year, whereas elsewhere a warm season maximum is the rule. Annual net precipitation (precipitation less evapotranspiration, which can be assessed from atmospheric reanalysis data) is typically 150–300 mm over land, 150–200 mm over the central Arctic Ocean and more than 1,000 mm near the Icelandic Low. Although precipitation over much of the land area peaks in summer, evapotranspiration rates are fairly high, such that summer net precipitation is small or even negative in these areas. Over land regions, a considerable fraction of summer precipitation results from regional recycling of water vapor, pointing to the strong effect of the land surface. From autumn through spring, most of the precipitation is stored as snow. Consequently, river discharge to the Arctic Ocean exhibits a pronounced early summer peak as the snow melts. For much of the Arctic, river discharge is strongly impacted by the presence of permafrost which limits infiltration. The bulk of the river discharge to the Arctic Ocean is contributed by four major river systems: the Ob, the Yenisey, the Lena, and the Mackenzie. Discharge from Arctic draining rivers in Eurasia has exhibited a positive trend over the period of record, consistent with increased net precipitation as assessed from atmospheric reanalysis data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.005

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.

Opus teacher head0.011
GPT teacher head0.175
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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