Diagnosis of Global Vegetation-Atmosphere Interactions at the Interannual Time Scale
Bibliographic record
Abstract
Time-lag correlation between monthly leaf area index(LAI) derived from satellite remote sensing by NOAA Advanced Very High Resolution Radiometer(AVHRR),2-m air temperature from ERA40 reanalysis and precipitation from Climate Prediction Center Merged Analysis of Precipitation(CMAP) during the 19-year period(1982-2000) is analyzed for the whole year and four seasons.Self-correlations of LAI,temperature and precipita-tion are calculated.Feedback of LAI on later time temperature and precipitation is studied based on linear theory.By comparing the simultaneous LAI-temperature and LAI-precipitation correlations,it is found that LAI is more closely related to concurrent temperature than concurrent precipitation around most parts of the global continents,with generally positive(negative) LAI-temperature correlation coefficients to the north(south) of 30°N.As for the LAI-temperature or LAI-precipitation relationship with LAI lagging one month,LAI is more closely and positively correlated with antecedent precipitation in most parts of the mid-low latitudes of the Northern Hemisphere and southward of 20°S;whereas LAI is more closely connected with one-month earlier temperature in the eastern mid-latitudes of the Northern Hemisphere and in the southern tropics,with positive correlation in the mid-latitudes of the Northern Hemisphere and southern tropical Africa and negative correlation in eastern Amazon and northern Australia.The aforementioned LAI-temperature correlations are most significant during spring and autumn,whereas LAI is more closely correlated with one-month earlier precipitation during rainy seasons,i.e.JJA(June,July,August) for the Northern Hemisphere and DJF(December,January,February) for the Southern Hemisphere.Significant positive feedback of LAI on later surface temperature exists in the mid-high latitudes of North America,West Europe,and eastern Eurasian continent;whereas significant negative feedback on temperature occurs around most parts to the south of 20°S.The explained variance by the feedback of LAI on temperature can be as large as 20% of the total variance poleward of 20°S and 20°N.Significant positive feedback of LAI on precipitation occurs in northwestern Canada,southern East European plain,northeastern Eurasian continent,northern part of the Indo-China Peninsula,and coastal areas of the tropics,such as eastern Amazon plain,to the east of the East African Plateau and northwestern Australia;whereas most of the negative feedback of LAI on precipitation is not statistically significant.The explained variance by the feedback of LAI on precipitation can be as large as 20% of the total variance poleward of 20°N.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".