The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Evolution of land surface air temperature trend
Open accessAbstract
The global climate has been experiencing significant warming at an unprecedented pace in the past century(1,2). This warming is spatially and temporally non-uniform, and one needs to understand its evolution to better evaluate its potential societal and economic impact. Here, the evolution of global land surface air temperature trend in the past century is diagnosed using the spatial-temporally multidimensional ensemble empirical mode decomposition method(3). We find that the noticeable warming (>0.5 K) started sporadically over the global land and accelerated until around 1980. Both the warming rate and spatial structure have changed little since. The fastest warming in recent decades (>0.4 K per decade) occurred in northern mid-latitudes. From a zonal average perspective, noticeable warming (>0.2 K since 1900) first took place in the subtropical and subpolar regions of the Northern Hemisphere, followed by subtropical warming in the Southern Hemisphere. The two bands of warming in the Northern Hemisphere expanded from 1950 to 1985 and merged to cover the entire Northern Hemisphere.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
Climate change 2007: the physical science basis
Solomon, S., Qin, D., Manning, M., Marquis, M. C., Averyt, K. B., Tignor, M., Miller, H. L., & Chen, Z. (2007) · Cited by 23,383
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
WU, Z., & HUANG, N. E. (2009) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536909000047 · Cited by 8,715
Self-citationJansen, E. H., Overpeck, J. T., Briffa, K., Duplessy, J., Joos, F., Masson-Delmotte, Olago, D., Otto‐Bliesner, B. L., Peltier, W. R., Rahmstorf, S., et al. (2007) · UEA Digital Repository (University of East Anglia) · Cited by 4,574
Mitchell, T. D., & Jones, P. D. (2005) · International Journal of Climatology · doi:10.1002/joc.1181 · Cited by 3,822
Global surface temperature change
Hansen, J., Ruedy, R., Sato, M., & Lo, K. (2010) · Reviews of Geophysics · doi:10.1029/2010rg000345 · Cited by 3,081
A review on Hilbert‐Huang transform: Method and its applications to geophysical studies
Huang, N. E., & Wu, Z. (2008) · Reviews of Geophysics · doi:10.1029/2007rg000228 · Cited by 1,803
Self-citationAerosols implicated as a prime driver of twentieth-century North Atlantic climate variability
Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T., & Bellouin, N. (2012) · Nature · doi:10.1038/nature10946 · Cited by 1,022
On the trend, detrending, and variability of nonlinear and nonstationary time series
Wu, Z., Huang, N. E., Long, S. R., & Peng, C.-K. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701020104 · Cited by 956
Self-citationWidening of the tropical belt in a changing climate
Seidel, D. J., Fu, Q., Randel, W. J., & Reichler, T. J. (2008) · Nature Geoscience · doi:10.1038/ngeo.2007.38 · Cited by 912
Research works printed but not linked: 7
- Wu, Z., Huang, N. E. & Chen, X. The multi-dimensional ensemble empirical mode decomposition method. Adv. Adapt. Data Anal. 1, 339–372 (2009). · doi:10.1142/s1793536909000187
- Franzke, C. Multi-scale analysis of teleconnection indices: Climate noise and nonlinear trends analysis. Nonlinear Proc. Geoph. 16, 65–76 (2009). · doi:10.5194/npg-16-65-2009
- Ruzmaikin, A. & Feynman, J. Search for climate trends in satellite data. Adv. Adapt. Data Anal. 1, 667–679 (2009). · doi:10.1142/s1793536909000266
- Franzke, C. & Woollings, T. On the persistence and predictability properties of North Atlantic climate variability. J. Clim. 24, 466–472 (2011). · doi:10.1175/2010jcli3739.1
- Hu, Z. Z., Huang, B., Kinter, J. L. III, Wu, Z. & Kumar, A. Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST Part II: Interdecadal variations. Clim. Dynam. 38, 25–43 (2012). · doi:10.1007/s00382-011-1073-6
- Huang, B. et al. Influences of subtropical air–sea interaction on the multidecadal AMOC variability in the NCEP climate forecast system. Clim. Dynam. 39, 631–555 (2012).
- Hudson, R. D., Andrade, M. F., Follette, M. B. & Frolov, A. D. The total ozone field separated into meteorological regimes–Part II: Northern Hemisphere mid-latitude total ozone trends. Atmos. Chem. Phys. 6, 5183–5191 (2006). · doi:10.5194/acp-6-5183-2006
Cited by
The Impact of Climate Change on Mental Health: A Systematic Descriptive Review
Cianconi, P., Betrò, S., & Janiri, L. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00074 · Cited by 1,320
“Moreover, recent studies have highlighted how climate change and global warming are nonlinear trends (118).”
BackgroundReduced Nearshore Warming Associated With Eastern Boundary Upwelling Systems
Seabra, R., Varela, R., Santos, A. M., Gómez-Gesteira, M., Meneghesso, C., Wethey, D. S., & Lima, F. P. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00104 · Cited by 74
Metadata
- Authors
- Ji, F., Wu, Z., Huang, J., & Chassignet, E. P.
- Venue
- Nature Climate Change
- Published
- 2014-05-04
- Volume
- 4
- Issue
- 6
- Pages
- 462-466
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W2083808131
- Cited by
- 601
- References
- 23
- FWCI
- 36.09
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Evolution of land surface air temperature trend. https://app.cochat.ai/papers/10.1038/nclimate2223
- The paper
- Ji, F., Wu, Z., Huang, J., & Chassignet, E. P. (2014). Evolution of land surface air temperature trend. Nature Climate Change. https://doi.org/10.1038/nclimate2223
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.