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Record W2474067854 · doi:10.7850/jkso.2016.21.2.49

Regional Characteristics of Global Warming: Linear Projection for the Timing of Unprecedented Climate

2016· article· en· W2474067854 on OpenAlexaboutno aff
Ho‐Jeong Shin, Chan Joo Jang

Bibliographic record

VenueThe Sea · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologyGlobal warmingClimate changeLatitudeGlobal temperatureClimate modelForcing (mathematics)Environmental sciencePeninsulaMagnitude (astronomy)Abrupt climate changeGeographyEffects of global warmingGeology

Abstract

fetched live from OpenAlex

기후변화를 일으키는 외부강제력이 전지구적으로 동일하게 주어지더라도 그에 따른 기후변화와 되먹임 효과는 지역마다 다르게 나타난다. 따라서 기후변화에 나타난 내부변동성 및 다른 잡음 효과로부터 지구온난화 신호를 구별하기 위한 기후변화 탐지는 전구평균뿐만 아니라 지역규모에서도 이뤄져 왔다. 본 논문은 지구온난화로 인해 미래에 전례 없는 기후가 나타나는 시기를 추정하고 그 지역적 차이를 분석함이 목적이며 이를 위해, 기후모형 자료를 이용한 기존 연구와는 달리, 관측 자료를 이용하여 내부변동성을 추정하고 미래 온도변화를 전망하였다. 전례 없는 기후 시기는 미래에 예측된 지표 온도가 과거 관측 기록에 나타난 온도 범위를 벗어나 전례 없이 따뜻한 기후가 이후로도 지속되는 시점으로 정의하였다. 1880년부터 2014년까지 관측된 지표온도 아노말리의 연평균 시계열을 이용하여 온난화 선형추세를 계산하였고, 이 추세로부터 벗어난 최대 변이 값을 내부변동성의 크기로 간주하였다. 관측 자료로 구한 온난화 선형추세와 내부변동성의 크기가 미래에도 유지된다고 전제하고 계산한 결과에 따르면, 육지에서 전례없는 기후는, 아프리카는 서쪽에서, 유라시아는 인도와 아라비아 반도 남부 등 저위도에서, 북아메리카는 캐나다 중서부와 그린란드 등 고위도에서, 남아메리카는 아마존을 포함하는 저위도에서, 남극대륙은 로스해 주변지역에서 향후 200년 이내에 비교적 빨리 나타나며, 우리나라를 포함한 동아시아 일부 지역에서도 200년 이내로 빨리 나타난다. 반면에 북유럽을 포함하는 고위도 유라시아 지역과 미국과 멕시코를 포함하는 북아메리카 중남부에서는 400년 이후에 나타난다. 해양에서는 전례 없는 기후가 인도양, 중위도 북대서양과 남대서양, 남극해 일부 해역과 남극 로스해, 북극해 일부 해역에서 200년 이내로 비교적 빨리 나타나는 반면, 내부변동성이 큰 동적도태평양, 중위도 북태평양 등의 일부 해역에서는 수천 년이 지나야 오는 곳도 있다. 즉, 전례 없는 기후시기는 육지에서는 대륙마다 서로 다른 양상을 보이고 해양에서는 온난화 추세가 큰 고위도 해역을 제외하면 내부변동성의 영향을 많이 받는다. 결론적으로 지구온난화로 인한 전례 없는 기후는 특정 시기에 공통적으로 나타나는 것이 아니라 지역에 따라 시기적으로 상당한 차이가 있다. 따라서 기후변화 대응책을 마련할 때 온난화 추세뿐만 아니라 내부변동성의 크기도 함께 고려할 필요가 있다. Even if an external forcing that will drive a climate change is given uniformly over the globe, the corresponding climate change and the feedbacks by the climate system differ by region. Thus the detection of global warming signal has been made on a regional scale as well as on a global average against the internal variabilities and other noises involved in the climate change. The purpose of this study is to estimate a timing of unprecedented climate due to global warming and to analyze the regional differences in the estimated results. For this purpose, unlike previous studies that used climate simulation data, we used an observational dataset to estimate a magnitude of internal variability and a future temperature change. We calculated a linear trend in surface temperature using a historical temperature record from 1880 to 2014 and a magnitude of internal variability as the largest temperature displacement from the linear trend. A timing of unprecedented climate was defined as the first year when a predicted minimum temperature exceeds the maximum temperature record in a historical data and remains as such since then. Presumed that the linear trend and the maximum displacement will be maintained in the future, an unprecedented climate over the land would come within 200 years from now in the western area of Africa, the low latitudes including India and the southern part of Arabian Peninsula in Eurasia, the high latitudes including Greenland and the mid-western part of Canada in North America, the low latitudes including Amazon in South America, the areas surrounding the Ross Sea in Antarctica, and parts of East Asia including Korean Peninsula. On the other hand, an unprecedented climate would come later after 400 years in the high latitudes of Eurasia including the northern Europe, the middle and southern parts of North America including the U.S.A. and Mexico. For the ocean, an unprecedented climate would come within 200 years over the Indian Ocean, the middle latitudes of the North Atlantic and the South Atlantic, parts of the Southern Ocean, the Antarctic Ross Sea, and parts of the Arctic Sea. In the meantime, an unprecedented climate would come even after thousands of years over some other regions of ocean including the eastern tropical Pacific and the North Pacific middle latitudes where an internal variability is large. In summary, spatial pattern in timing of unprecedented climate are different for each continent. For the ocean, it is highly affected by large internal variability except for the high-latitude regions with a significant warming trend. As such, a timing of an unprecedented climate would not be uniform over the globe but considerably different by region. Our results suggest that it is necessary to consider an internal variability as well as a regional warming rate when planning a climate change mitigation and adaption policy.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.051
GPT teacher head0.293
Teacher spread0.241 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations4
Published2016
Admission routes1
Has abstractyes

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