scholarly journals 25 years of Cloud Base Height Measurements by Ceilometer in Ny Ålesund, Svalbard

2018 ◽  
Author(s):  
Marion Maturilli ◽  
Kerstin Ebell
Keyword(s):  
Time Series ◽  
Cloud Cover ◽  
The Arctic ◽  
Cloud Base ◽  
High Temporal Resolution ◽  
Radiation Balance ◽  
Essential Information ◽  
Data Record ◽  
Cloud Base Height

Abstract. Clouds are a key factor for the Arctic Amplification of global warming, but their actual appearance and distribution is still afflicted with large uncertainty. On the Arctic wide scale, large discrepancies are found between the various reanalyses and satellite products, respectively. Although ground-based observations by remote sensing are limited to point measurements, they have the advantage to obtain extended time series of vertically resolved cloud properties. Here, we present a 25-year data record of cloud base height measured by ceilometer at the Arctic site Ny-Ålesund, Svalbard. Linked to cyclonic activity, the cloud base height provides essential information for the interpretation of the surface radiation balance and contributes to the understanding meteorological processes. Furthermore, it is a useful auxiliary component for the analysis of advanced technologies that provide insight to cloud microphysical properties, like the cloud radar. The long-term time series also allows deriving an annual cycle of the cloud occurrence frequency, revealing the more frequent cloud cover in summer and the lowest cloud cover amount in April. However, as the use of different ceilometer instruments over the years potentially imposed inhomogeneities to the data record, any long-term trend analysis should be avoided. The Ny-Ålesund cloud base height data from August 1992 to July 2017 are provided in high temporal resolution of 5 minutes (1 minute) before (after) July 1998, respectively, at the PANGAEA repository (https://doi.org/10.1594/PANGAEA.880300).

scholarly journals Twenty-five years of cloud base height measurements by ceilometer in Ny-Ålesund, Svalbard

2018 ◽  
Vol 10 (3) ◽  
pp. 1451-1456 ◽  
Author(s):  
Marion Maturilli ◽  
Kerstin Ebell
Keyword(s):  
Time Series ◽  
Cloud Cover ◽  
The Arctic ◽  
Cloud Base ◽  
High Temporal Resolution ◽  
Data Set ◽  
Essential Information ◽  
Data Record ◽  
Cloud Base Height

Abstract. Clouds are a key factor for the Arctic amplification of global warming, but their actual appearance and distribution are still afflicted by large uncertainty. On the Arctic-wide scale, large discrepancies are found between the various reanalyses and satellite products, respectively. Although ground-based observations by remote sensing are limited to point measurements, they have the advantage of obtaining extended time series of vertically resolved cloud properties. Here, we present a 25-year data record of cloud base height measured by ceilometer at the Ny-Ålesund, Svalbard, Arctic site. We explain the composition of the three sub-periods with different instrumentation contributing to the data set, and show examples of potential application areas. Linked to cyclonic activity, the cloud base height provides essential information for the interpretation of the surface radiation balance and contributes to the understanding of meteorological processes. Furthermore, it is a useful auxiliary component for the analysis of advanced technologies that provide insight into cloud microphysical properties, like the cloud radar. The long-term time series also allows derivation of an annual cycle of the cloud occurrence frequency, revealing the more frequent cloud cover in summer and the lowest cloud cover amount in April. However, as the use of different ceilometer instruments over the years potentially imposed inhomogeneities onto the data record, any long-term trend analysis should be avoided. The Ny-Ålesund cloud base height data from August 1992 to July 2017 are provided in a high temporal resolution of 5 min (1 min) before (after) July 1998, respectively, at the PANGAEA repository (https://doi.org/10.1594/PANGAEA.880300).

scholarly journals The influence of the lunar nodal cycle on Arctic climate

2006 ◽  
Vol 63 (3) ◽  
pp. 401-420 ◽  
Author(s):  
Harald Yndestad
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Phase Relationship ◽  
Harmonic Spectrum ◽  
Arctic Climate ◽  
The Arctic ◽  
Major Influence ◽  
Sea Temperature ◽  
Arctic Ice

Abstract The Arctic Ocean is a substantial energy sink for the northern hemisphere. Fluctuations in its energy budget will have a major influence on the Arctic climate. The paper presents an analysis of the time-series for the polar position, the extent of Arctic ice, sea level at Hammerfest, Kola section sea temperature, Røst winter air temperature, and the NAO winter index as a way to identify a source of dominant cycles. The investigation uses wavelet transformation to identify the period and the phase in these Arctic time-series. System dynamics are identified by studying the phase relationship between the dominant cycles in all time-series. A harmonic spectrum from the 18.6-year lunar nodal cycle in the Arctic time-series has been identified. The cycles in this harmonic spectrum have a stationary period, but not stationary amplitude and phase. A sub-harmonic cycle of about 74 years may introduce a phase reversal of the 18.6-year cycle. The signal-to-noise ratio between the lunar nodal spectrum and other sources changes from 1.6 to 3.2. A lunar nodal cycle in all time-series indicates that there is a forced Arctic oscillating system controlled by the pull of gravity from the moon, a system that influences long-term fluctuations in the extent of Arctic ice. The phase relation between the identified cycles indicates a possible chain of events from lunar nodal gravity cycles, to long-term tides, polar motions, Arctic ice extent, the NAO winter index, weather, and climate.

A Validation of CloudSat and CALIPSO's Temperature, Humidity, Cloud Detection, and Cloud Base Height over the Arctic Marine Cryosphere

2013 ◽  
Vol 51 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Lauren M. Candlish ◽  
Richard L. Raddatz ◽  
Geoffrey G. Gunn ◽  
Matthew G. Asplin ◽  
David G. Barber
Keyword(s):  
The Arctic ◽  
Cloud Base ◽  
Cloud Detection ◽  
Cloud Base Height

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

scholarly journals Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

2021 ◽  
Vol 21 (5) ◽  
pp. 4079-4101
Author(s):  
Julia Maillard ◽  
François Ravetta ◽  
Jean-Christophe Raut ◽  
Vincent Mariage ◽  
Jacques Pelon
Keyword(s):  
Field Experiment ◽  
Cloud Cover ◽  
Great Majority ◽  
Cloud Base ◽  
The North ◽  
Low Clouds ◽  
Radiative Impact ◽  
Low Cloud ◽  
Ocean Observing ◽  
Cloud Base Height

Abstract. The Ice, Atmosphere, Arctic Ocean Observing System (IAOOS) field experiment took place from 2014 to 2019. Over this period, more than 20 instrumented buoys were deployed at the North Pole. Once locked into the ice, the buoys drifted for periods of a month to more than a year. Some of these buoys were equipped with 808 nm wavelength lidars which acquired a total of 1777 profiles over the course of the campaign. This IAOOS lidar dataset is exploited to establish a novel statistic of cloud cover and of the geometrical and optical characteristics of the lowest cloud layer. The average cloud frequency from April to December over the course of the campaign was 75 %. Cloud occurrence frequencies were above 85 % from May to October. Single layers are thickest in October/November and thinnest in the summer. Meanwhile, their optical depth is maximum in October. On the whole, the cloud base height is very low, with the great majority of first layer bases beneath 120 m. In April and October, surface temperatures are markedly warmer when the IAOOS profile contains at least one low cloud than when it does not. This temperature difference is statistically insignificant in the summer months. Indeed, summer clouds have a shortwave cooling effect which can reach −60 W m−2 and balance out their longwave warming effect.

scholarly journals Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982–2015 Time Series

2018 ◽  
Vol 10 (6) ◽  
pp. 940 ◽  
Author(s):  
José García-Lázaro ◽  
José Moreno-Ruiz ◽  
David Riaño ◽  
Manuel Arbelo
Keyword(s):  
Time Series ◽  
Boreal Forest ◽  
Burned Area ◽  
Data Record ◽  
Term Data

scholarly journals Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

2018 ◽  
Vol 11 (5) ◽  
pp. 2949-2965 ◽  
Author(s):  
Dunya Alraddawi ◽  
Alain Sarkissian ◽  
Philippe Keckhut ◽  
Olivier Bock ◽  
Stefan Noël ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Satellite Data ◽  
Cloud Cover ◽  
Surface Albedo ◽  
Hydrological Cycle ◽  
Radiation Budget ◽  
The Arctic ◽  
Gnss Measurements ◽  
Gnss Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001–2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

scholarly journals Long-term time series of Arctic tropospheric BrO derived from UV–VIS satellite remote sensing and its relation to first-year sea ice

2020 ◽  
Vol 20 (20) ◽  
pp. 11869-11892
Author(s):  
Ilias Bougoudis ◽  
Anne-Marlene Blechschmidt ◽  
Andreas Richter ◽  
Sora Seo ◽  
John Philip Burrows ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
First Year ◽  
Vertical Column ◽  
Chain Reactions ◽  
Heterogeneous Chain ◽  
Good Agreement

Abstract. Every polar spring, phenomena called bromine explosions occur over sea ice. These bromine explosions comprise photochemical heterogeneous chain reactions that release bromine molecules, Br2, to the troposphere and lead to tropospheric plumes of bromine monoxide, BrO. This autocatalytic mechanism depletes ozone, O3, in the boundary layer and troposphere and thereby changes the oxidizing capacity of the atmosphere. The phenomenon also leads to accelerated deposition of metals (e.g., Hg). In this study, we present a 22-year (1996 to 2017) consolidated and consistent tropospheric BrO dataset north of 70∘ N, derived from four different ultraviolet–visible (UV–VIS) satellite instruments (GOME, SCIAMACHY, GOME-2A and GOME-2B). The retrieval data products from the different sensors are compared during periods of overlap and show good agreement (correlations of 0.82–0.98 between the sensors). From our merged time series of tropospheric BrO vertical column densities (VCDs), we infer changes in the bromine explosions and thus an increase in the extent and magnitude of tropospheric BrO plumes during the period of Arctic warming. We determined an increasing trend of about 1.5 % of the tropospheric BrO VCDs per year during polar springs, while the size of the areas where enhanced tropospheric BrO VCDs can be found has increased about 896 km2 yr−1. We infer from comparisons and correlations with sea ice age data that the reported changes in the extent and magnitude of tropospheric BrO VCDs are moderately related to the increase in first-year ice extent in the Arctic north of 70∘ N, both temporally and spatially, with a correlation coefficient of 0.32. However, the BrO plumes and thus bromine explosions show significant variability, which also depends, apart from sea ice, on meteorological conditions.

scholarly journals Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

2021 ◽  
Vol 13 (18) ◽  
pp. 3618
Author(s):  
Stefan Dech ◽  
Stefanie Holzwarth ◽  
Sarah Asam ◽  
Thorsten Andresen ◽  
Martin Bachmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Observation Time ◽  
Sensor Data ◽  
High Temporal Resolution ◽  
Statistical Parameters ◽  
Medium Resolution ◽  
Long Time ◽  
Earth Observation Satellite

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

Arctic Cloud Changes from Surface and Satellite Observations

2010 ◽  
Vol 23 (15) ◽  
pp. 4233-4242 ◽  
Author(s):  
Ryan Eastman ◽  
Stephen G. Warren
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Cloud Cover ◽  
The Arctic ◽  
Interannual Variations ◽  
Infrared Observation ◽  
Cloud Data ◽  
Surface Data ◽  
Avhrr Data ◽  
Very High

Abstract Visual cloud reports from land and ocean regions of the Arctic are analyzed for total cloud cover. Trends and interannual variations in surface cloud data are compared to those obtained from Advanced Very High Resolution Radiometer (AVHRR) and Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) satellite data. Over the Arctic as a whole, trends and interannual variations show little agreement with those from satellite data. The interannual variations from AVHRR are larger in the dark seasons than in the sunlit seasons (6% in winter, 2% in summer); however, in the surface observations, the interannual variations for all seasons are only 1%–2%. A large negative trend for winter found in the AVHRR data is not seen in the surface data. At smaller geographic scales, time series of surface- and satellite-observed cloud cover show some agreement except over sea ice during winter. During the winter months, time series of satellite-observed clouds in numerous grid boxes show variations that are strangely coherent throughout the entire Arctic.

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