Trends in Atmospheric Humidity and Temperature above Dome C, Antarctica Evaluated from Observations and Reanalyses

The time evolution of humidity and temperature above Dome C (Antarctica) has been investigated by considering data from (1) meteorological radiosondes (2005–2017), (2) the microwave radiometer HAMSTRAD (2012–2017), (3) four modern meteorological reanalyses (1980–2017) and (4) the southern annular mode (SAM) index (1980–2017). From these observations (2005–2017), a significant moistening trend (0.08 ± 0.06 kg m−2 dec−1) is associated with a significant warming trend (1.08 ± 0.55 K dec−1) in summer. Conversely, a significant drying trend of −0.04 ± 0.03 kg m−2 dec−1 (−0.05 ± 0.03 kg m−2 dec−1) is associated with a significant cooling trend of −2.4 ± 1.2 K dec−1 (−5.1 ± 2.0 K dec−1) in autumn (winter), with no significant trends in the spring. We demonstrate that 1) the trends identified in the radiosondes (2005–2017) are also present in the reanalyses and 2) the multidecadal variability of integrated water vapor and near-surface temperature (1980–2017) is strongly influenced by variability in the SAM index for all seasons but spring. Our study suggests that the decadal trends observed in humidity and near-surface temperature at Dome C (2005–2017) reflect the multidecadal variability of the atmosphere, and are not indicative of long-term trends that may be related to global climate change.

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Abrupt changes, multidecadal variability and long-term trends in sea surface temperature and sea level datasets within the southeastern Bay of Biscay

Journal of Marine Systems ◽

10.1016/j.jmarsys.2011.11.014 ◽

2013 ◽

Vol 109-110 ◽

pp. S144-S152 ◽

Cited By ~ 4

Author(s):

Manuel González ◽

Almudena Fontán ◽

Ganix Esnaola ◽

Michael Collins

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Level ◽

Sea Surface ◽

Bay Of Biscay ◽

Multidecadal Variability ◽

Abrupt Changes ◽

Long Term Trends ◽

And Sea Level

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Advances in the HadCRUT5 record of global near-surface temperatures since 1850

10.5194/egusphere-egu21-15931 ◽

2021 ◽

Author(s):

Colin Morice ◽

John Kennedy ◽

Nick Rayner ◽

Jonathan Winn ◽

Emma Hogan ◽

...

Keyword(s):

Surface Temperature ◽

Measurement Errors ◽

Global Climate ◽

The Arctic ◽

Data Sets ◽

Observation Data ◽

Data Set ◽

Near Surface ◽

Temperature Records

<p>The new HadCRUT5 data set combines meteorological station air temperature records with sea-surface temperature measurements in a data set of near-surface temperature anomalies from the year 1850 to present. Major developments in HadCRUT5 include: updates to underpinning observation data holdings; use of an updated assessment of the impacts of changing marine measurement methods; and adoption of a statistical gridding method to extend estimates into sparsely observed regions of the globe, such as the Arctic. The data are presented as a 200-member ensemble that spans the assessed uncertainty associated with adjustments for long-term observational biases, observing platform measurement errors and the interaction of observational sampling with gridding methods. The impacts of methodological changes in HadCRUT5 on diagnostics of the global climate will be discussed and compared to results derived from other state-of-the-art global data sets.</p>

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The influence of low-frequency variability and long-term trends in North Atlantic sea surface temperature on Irish waters

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp062 ◽

2009 ◽

Vol 66 (7) ◽

pp. 1480-1489 ◽

Cited By ~ 33

Author(s):

Heather Cannaby ◽

Y. Sinan Hüsrevoğlu

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

North Atlantic ◽

Low Frequency ◽

Warming Trend ◽

Sea Surface ◽

Low Frequency Variability ◽

Warm Anomaly ◽

Long Term Trends

Abstract Cannaby, H., and Hüsrevoğlu, Y. S. 2009. The influence of low-frequency variability and long-term trends in North Atlantic sea surface temperature on Irish waters. – ICES Journal of Marine Science, 66: 1480–1489. Sea surface temperature (SST) time-series collected in Irish waters between 1850 and 2007 exhibit a warming trend averaging 0.3°C. The strongest warming has occurred since 1994, with the warmest years in the record being 2005, 2006, and 2007. The warming trend is superimposed on significant interannual to multidecadal-scale variability, linked to basin-scale oscillations of the ocean–atmosphere system. The dominant modes of low-frequency variability in North Atlantic SST records, investigated using an empirical orthogonal function (EOF) analysis, correspond to the Atlantic Multidecadal Oscillation (AMO), the East Atlantic Pattern (EAP), and the North Atlantic Oscillation (NAO) index, respectively, accounting for 23, 16, and 9% of the total variance in the dataset. Interannual variability in Irish SST records is dominated by the AMO, which, currently in its warm phase, explains approximately half of the current warm anomaly in the record. The EAP and the NAO influence variability in Irish SST time-series on a smaller scale, with the EAP also contributing to the current warm anomaly. After resolving the prevalent oscillatory modes of variability in the SST record, the underlying warming trend compares well with the global greenhouse effect warming trend. The anthropogenic contribution to the current warm anomaly in Irish SSTs was estimated at 0.41°C for 2006, and this is predicted to increase annually.

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A decline of observed daily peak wind gusts with distinct seasonality in Australia, 1941-2016

Journal of Climate ◽

10.1175/jcli-d-20-0590.1 ◽

2021 ◽

pp. 1-63

Author(s):

Cesar Azorin-Molina ◽

Tim R. McVicar ◽

Jose A. Guijarro ◽

Blair Trewin ◽

Andrew J. Frost ◽

...

Keyword(s):

Wind Speed ◽

Decadal Variability ◽

Southern Annular Mode ◽

Near Surface ◽

Wind Gusts ◽

Decadal Scale ◽

Mean Wind Speed ◽

Long Term Trends ◽

The Indian Ocean

AbstractWind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, as examples: human safety; maritime-terrestrial-aviation activities; engineering and insurance applications; and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed “stilling”, but a lack of knowledge on the multi-decadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for the period 1941-2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer-spring-autumn and weak negative or non-trending (even positive) trends in winter. We demonstrate that ocean-atmosphere oscillations such as the Indian Ocean Dipole and the Southern Annular Mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the “stilling” phenomenon, suggesting that global warming may have reduced Australian wind gusts.

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Correction of Inhomogeneities in Observed Land Surface Temperatures over China

Journal of Climate ◽

10.1175/jcli-d-19-0521.1 ◽

2020 ◽

Vol 33 (20) ◽

pp. 8885-8902

Author(s):

Jizeng Du ◽

Kaicun Wang ◽

Baoshan Cui ◽

Shaojing Jiang

Keyword(s):

Climate Change ◽

Land Surface ◽

Surface Air Temperature ◽

Warming Trend ◽

Cmip5 Models ◽

Near Surface ◽

Land Surface Temperatures ◽

Long Term Trends ◽

The Difference

AbstractLand surface temperature Ts and near-surface air temperature Ta are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003–05, mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21°, 0.34°, and 0.25°C decade−1, respectively, during the 1960–2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960–2014. The Ts minus Ta (Ts − Ta) trend is from −0.004° to 0.009°C decade−1, accounting for from −3.19% to 5.93% (from −3.09% to 6.39%) of the absolute warming trend of Ts (Ta).

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Analysis of variability and long-term trends of sea surface temperature over the China Seas derived from a newly merged regional data set

Climate Research ◽

10.3354/cr01471 ◽

2017 ◽

Vol 73 (3) ◽

pp. 217-231 ◽

Cited By ~ 2

Author(s):

Y Li ◽

L Mu ◽

Y Liu ◽

G Wang ◽

D Zhang ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

China Seas ◽

Regional Data ◽

Data Set ◽

Long Term Trends ◽

The China Seas

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Cities Exacerbate Climate Warming

Urban Science ◽

10.3390/urbansci5010027 ◽

2021 ◽

Vol 5 (1) ◽

pp. 27

Author(s):

Lahouari Bounoua ◽

Kurtis Thome ◽

Joseph Nigro

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Climate Models ◽

Warming Trend ◽

Climate Mitigation ◽

Temperature Changes ◽

Surface Warming ◽

The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

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Long term trends in the sea surface temperature of the Black Sea

Ocean Science ◽

10.5194/os-6-491-2010 ◽

2010 ◽

Vol 6 (2) ◽

pp. 491-501 ◽

Cited By ~ 26

Author(s):

G. I. Shapiro ◽

D. L. Aleynik ◽

L. D. Mee

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Black Sea ◽

Sea Surface ◽

The Black Sea ◽

The North ◽

Sst Variability ◽

Long Term Trends ◽

Cooling Trend

Abstract. There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.

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