Sensitivity of amplitude-phase characteristics of the surface air temperature annual cycle to variations in annual mean temperature

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

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Temperature Annual Cycle Variation and Response to Solar Radiation during 1960 to 2016 in China

10.5194/egusphere-egu2020-3777 ◽

2020 ◽

Author(s):

Runze Zhao ◽

Kaicun Wang ◽

Guocan Wu ◽

Chunlue Zhou

Keyword(s):

Solar Radiation ◽

Air Temperature ◽

Annual Cycle ◽

Surface Air Temperature ◽

Seasonal Temperature ◽

Surface Solar Radiation ◽

Seasonal Temperature Variation ◽

Phase Amplitude ◽

Original Time ◽

Effect Of Surface

<p>The change of its annual cycle is extremely important due to global warming. A widely used method to analyze the changes of temperature annual cycle is based on the decomposition to phase, amplitude and baseline terms. Solar radiation as the leading energy source of temperature changes can directly influence temperature annual cycle. In this study, we investigate the phase, amplitude and baseline of temperature and solar radiation annual cycle after Fourier transform during 1960-2016 in China. The results show that annual cycle of maximum, minimum and mean surface air temperature are advancing in time (-0.08, -0.27 and -0.33 days per ten years), decreasing in range (-0.07, -0.25 and -0.18 degrees per ten years) and rising in baseline (0.20, 0.34 and 0.25 degrees per ten years). To further quantify the effect of surface solar radiation to temperature, we remove the effect from its original time series of maximum and mean temperature, based on a linear regression. The compare of raw and adjusted temperature shows that surface solar radiation advancing the time by 0.19 and 0.19 days per ten years, reduces the range by 0.14 and 0.13 degrees per ten years, and reduces the baseline by 0.08 and 0.04 degrees per ten years, for surface maximum and mean daily air temperature. The result can explain parts of seasonal temperature variation. Effect of surface solar radiation is most obvious Yunnan-Guizhou Plateau for maximum phase. The low phase value in this area is corrected and well-match with other same latitude area after adjusted.</p>

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The impact of inter-annual variability of annual cycle on long-term persistence of surface air temperature in long historical records

Climate Dynamics ◽

10.1007/s00382-017-3662-5 ◽

2017 ◽

Vol 50 (3-4) ◽

pp. 1091-1100 ◽

Cited By ~ 10

Author(s):

Qimin Deng ◽

Da Nian ◽

Zuntao Fu

Keyword(s):

Air Temperature ◽

Annual Cycle ◽

Surface Air Temperature ◽

Historical Records ◽

Annual Variability ◽

The Impact

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Amplitude-Phase Characteristics of the Annual Cycle of Surface Air Temperature in the Northern Hemisphere

Advances in Atmospheric Sciences ◽

10.1007/bf03342046 ◽

2003 ◽

Vol 20 (1) ◽

pp. 17-27 ◽

Cited By ~ 1

Author(s):

Alexey V. Eliseev ◽

Igor I. Mokhov

Keyword(s):

Northern Hemisphere ◽

Air Temperature ◽

Annual Cycle ◽

Surface Air Temperature

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The Spatial Structure of the Annual Cycle in Surface Temperature: Amplitude, Phase, and Lagrangian History

Journal of Climate ◽

10.1175/jcli-d-13-00021.1 ◽

2013 ◽

Vol 26 (20) ◽

pp. 7852-7862 ◽

Cited By ~ 26

Author(s):

Karen A. McKinnon ◽

Alexander R. Stine ◽

Peter Huybers

Keyword(s):

Spatial Structure ◽

Air Temperature ◽

Annual Cycle ◽

Surface Air Temperature ◽

Space Time ◽

Physical Models ◽

Balance Model ◽

Phase Lag ◽

Trajectory Model ◽

Time Variance

Abstract The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of the climate system. Here, the authors identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag 1) are broadly consistent with a land and ocean end-member mixing model but 2) exhibit overlap between land and ocean and, despite this overlap, 3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, the authors use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space–time variance in the extratropical annual cycle, as well as features 1 and 2. All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space–time variance of the annual cycle in an illustrative midlatitude zonal band when incorporating the results of the trajectory model. The aforementioned features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

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Effect of atmospheric circulation on surface air temperature trends in years 1979–2018

Climate Dynamics ◽

10.1007/s00382-020-05590-y ◽

2021 ◽

Author(s):

Jouni Räisänen

Keyword(s):

Atmospheric Circulation ◽

Air Temperature ◽

Coupled Model ◽

Surface Air Temperature ◽

The Arctic ◽

Mean Square ◽

Temperature Trends ◽

Mean Temperature ◽

The Mean ◽

Mean Square Difference

AbstractThe effect of atmospheric circulation on monthly, seasonal and annual mean surface air temperature trends in the years 1979–2018 is studied by applying a trajectory-based method on the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data. To the extent that the method captures the effects of atmospheric circulation, the results suggest that circulation trends only had a minor impact on observed annual mean temperature trends in most areas. Exceptions include, for example, a decrease in annual mean warming by about 1 °C in western Siberia, and increased warming in central Europe and the Arctic Ocean. However, the effect of circulation trends on seasonal and particularly monthly temperature trends is more substantial. Subtracting the effect of circulation changes from the ERA5 temperature trends leaves residual trends with a smoother annual cycle than the original trends. The residual monthly mean temperature trends also tend to agree better with the multi-model mean temperature trends from models in the 5th Coupled Model Intercomparison Project (CMIP5) than the original ERA5 trends do, with a 42% decrease in the mean square difference over the global land area. However, the corresponding decrease in the mean square difference of the annual mean temperature trends is only 6%.

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