scholarly journals Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations

2021 ◽  
Author(s):  
Nicola Scafetta

AbstractThe 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = TMax − TMin trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, TMax globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, TMean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed.

2010 ◽  
Vol 23 (3) ◽  
pp. 485-503 ◽  
Author(s):  
Kirsten L. Findell ◽  
Thomas L. Delworth

Abstract Climate model simulations run as part of the Climate Variability and Predictability (CLIVAR) Drought Working Group initiative were analyzed to determine the impact of three patterns of sea surface temperature (SST) anomalies on drought and pluvial frequency and intensity around the world. The three SST forcing patterns include a global pattern similar to the background warming trend, a pattern in the Pacific, and a pattern in the Atlantic. Five different global atmospheric models were forced by fixed SSTs to test the impact of these SST anomalies on droughts and pluvials relative to a climatologically forced control run. The five models generally yield similar results in the locations of drought and pluvial frequency changes throughout the annual cycle in response to each given SST pattern. In all of the simulations, areas with an increase in the mean drought (pluvial) conditions tend to also show an increase in the frequency of drought (pluvial) events. Additionally, areas with more frequent extreme events also tend to show higher intensity extremes. The cold Pacific anomaly increases drought occurrence in the United States and southern South America and increases pluvials in Central America and northern and central South America. The cold Atlantic anomaly increases drought occurrence in southern Central America, northern South America, and central Africa and increases pluvials in central South America. The warm Pacific and Atlantic anomalies generally lead to reversals of the drought and pluvial increases described with the corresponding cold anomalies. More modest impacts are seen in other parts of the world. The impact of the trend pattern is generally more modest than that of the two other anomaly patterns.


2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Marcos Paulo Santos Pereira ◽  
Marcos Heil Costa ◽  
Flavio Justino ◽  
Ana Cláudia Mendes Malhado

Global warming in the first half of the 21st century is likely to have profound influences on South American vegetation and climate. Although coupled atmosphere-biosphere models have been widely used to forecast future vegetation patterns under various scenarios of global warming, they have not been used to assess the potentially critical role of variations in sea surface temperature (SST) in modifying the climate-vegetation interactions. Here, we use monthly output of a 100-year coupled model run to investigate the relationship between SST, precipitation, and productivity of vegetation. Specifically, we assess statistical correlations between SST variability and vegetation in six different South America regions: Northern South America, Western Amazonia, Eastern Amazonia, Northeast Brazil, Central Brazil, and Patagonia. Our model robustly simulates changes in mean precipitation, net primary production (NPP), upper canopy leaf area index (LAI), and lower canopy LAI under warming and nonwarming scenarios. Most significantly, we demonstrate that spatial-temporal variability in SST exerts a strong influence over the vegetation dynamics in all six South American regions.


2012 ◽  
Vol 27 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Erin L. McClymont ◽  
Raja S. Ganeshram ◽  
Laetitia E. Pichevin ◽  
Helen M. Talbot ◽  
Bart E. van Dongen ◽  
...  

2022 ◽  
Author(s):  
Hector Luis D’Antoni ◽  
Lidia Susana Burry ◽  
Patricia Irene Palacio ◽  
Matilde Elena Trivi ◽  
Mariano Somoza

2019 ◽  
Vol 32 (19) ◽  
pp. 6271-6284 ◽  
Author(s):  
Xiaofan Li ◽  
Zeng-Zhen Hu ◽  
Ping Liang ◽  
Jieshun Zhu

Abstract In this work, the roles of El Niño–Southern Oscillation (ENSO) in the variability and predictability of the Pacific–North American (PNA) pattern and precipitation in North America in winter are examined. It is noted that statistically about 29% of the variance of PNA is linearly linked to ENSO, while the remaining 71% of the variance of PNA might be explained by other processes, including atmospheric internal dynamics and sea surface temperature variations in the North Pacific. The ENSO impact is mainly meridional from the tropics to the mid–high latitudes, while a major fraction of the non-ENSO variability associated with PNA is confined in the zonal direction from the North Pacific to the North American continent. Such interferential connection on PNA as well as on North American climate variability may reflect a competition between local internal dynamical processes (unpredictable fraction) and remote forcing (predictable fraction). Model responses to observed sea surface temperature and model forecasts confirm that the remote forcing is mainly associated with ENSO and it is the major source of predictability of PNA and winter precipitation in North America.


1999 ◽  
pp. 33-55 ◽  
Author(s):  
Ralph R. Schneider ◽  
Peter J. Müller ◽  
Ruth Acheson

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