CL5.5 | Climate Data Homogenization and Analysis of Climate Variability, Trends and Extremes
EDI PICO
Climate Data Homogenization and Analysis of Climate Variability, Trends and Extremes
Convener: Lorenzo MinolaECSECS | Co-conveners: Cesar Azorin-Molina, Xiaolan Wang, Rob Roebeling
PICO
| Thu, 27 Apr, 14:00–18:00 (CEST)
 
PICO spot 5
Thu, 14:00
Homogeneous long-term data records (i.e., well calibrated quality-controlled data that are forced to look like a common reference) are essential for researching, monitoring, or attenuating changes in climate, for example to describe the state of climate or to detect climate extremes. Likewise, reanalysis requires harmonized data records (i.e., well calibrated quality-controlled data that maintained the unique nature of each sensor). Climate data records need to be screened and cleared from artificial non-climatic temporal and/or spatial effects, such as gradual degradation of instruments, jumps due to instruments changes, jumps due to observation practices changes, or jumps due to changes of station location and exposure. The magnitude and uncertainty of these gradual and/or abrupt changes determines their suitability for climate trend analyses. Therefore, data intended for applications, such as making a realistic and reliable assessment of historical climate trends and variability, require consistently homogenized and/or harmonized data records including measurement uncertainties.

The above described artificial non-climatic effects influence the quality of different Essential Climate Variables (ECVs), including atmospheric (e.g., air temperature, precipitation, wind speed), oceanic (e.g., sea surface temperature), and terrestrial (e.g., albedo, snow cover) variables. Our session calls for contributions, using data records from i) in-situ observing networks, ii) satellite observing systems, and or iii) climate/earth-system model simulations based data records, on the:

• Calibration, quality control, homogenization/harmonization and validation of either Fundamental Climate Data Records (FCDRs) and/or Essential Climate Variables data records (CDRs).

• Development of new data records and their analysis (spatial and temporal characteristics, particularly of extremes).

• Examination of observed trends and variability, as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, seasonal, monthly, daily, and sub-daily).

• Rescue and analysis of centennial meteorological observations, with focus on data prior to the 1960s, as a unique source to fill in the gap of knowledge of climate variability over century time-scales. In particular, we encourage wind studies dealing with the observed slowdown (termed “stilling”; last 30-50 years) and recent recovery (since ~2013) of near-surface winds.

PICO: Thu, 27 Apr | PICO spot 5

Chairpersons: Lorenzo Minola, Rob Roebeling
14:00–14:05
Dataset homogenization, creation and evaluation
14:05–14:07
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PICO5.1
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EGU23-15322
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On-site presentation
Colin Morice, David Berry, Richard Cornes, Kevin Cowtan, Thomas Cropper, John Kennedy, Elizabeth Kent, Nick Rayner, Timothy Osborn, Michael Taylor, Emily Wallis, and Jonathan Winn

We present a new data set of air temperature change across land and ocean extending back to the late-18th century. This new data set uses marine air temperature observations rather than the sea surface temperature measurements typically used by pre-existing data sets. This allows the new data set to extend further into the past than existing instrumental temperature records, which typically have start dates in the mid-to-late 19th century. The new data set brings together advances in understanding of measurement biases affecting all-day marine air temperature observations with a new assessment of the effects of non-standard thermometer enclosures used at land meteorological stations in the early instrumental record. A further innovation is the use of kriging to obtain localised temperature estimates that allow land air temperature records to be converted into anomalies even for stations without observations during the baseline period. Global and hemispheric series show close agreement with those based on sea-surface temperature for much of the overlapping period of their records, some of the interesting differences will be presented. This data set has been developed under the GloSAT project (https://www.glosat.org/).

How to cite: Morice, C., Berry, D., Cornes, R., Cowtan, K., Cropper, T., Kennedy, J., Kent, E., Rayner, N., Osborn, T., Taylor, M., Wallis, E., and Winn, J.: An observational record of global near surface air temperature change over land and ocean from 1781 to present, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15322, https://doi.org/10.5194/egusphere-egu23-15322, 2023.

14:07–14:09
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PICO5.2
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EGU23-8119
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ECS
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On-site presentation
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Emily Wallis, Timothy Osborn, and Michael Taylor

Exposure biases are non-climatic changes in the surface air temperature record which were introduced due to changes in the way thermometers were protected from solar radiation. The possible presence of the exposure bias in early instrumental temperature datasets is a well-known issue, and the impact of changing thermometer exposures, particularly the transition between historic exposures and the Stevenson screen, has been explored by previous studies. However, despite this, very few adjustments have been made to account for the bias, with the exception of a handful of localised studies. As a result, the exposure bias still accounts for significant uncertainty in global surface air temperature compilations, such as HadCRUT5.

In this work we report an attempt to address the exposure bias for extratropical weather stations in a version of CRUTEM5 that has been extended back in time to 1781 (CRUTEM5_ext). We developed statistical models to predict the bias introduced by transitions from four main categories of historic exposure – open, wall-mounted, intermediate and closed – to the Stevenson screen. The models are based on an empirical analysis of the characteristics of the exposure bias observed in 20 parallel measurement studies, together with the temperature and radiation variables that were a priori expected to influence the magnitude of the bias in mean temperatures on a monthly timescale. Separately, we have compiled a database detailing the historic exposures in use at stations and the timing (or approximate timing when a precise time is not known) of the transition to the Stevenson screen. The statistical models, where robust, are then applied to the individual stations within CRUTEM5_ext to make adjustments for the exposure changes according to the database of historic exposures.

This presentation will outline the model development, give a brief overview of the evolution of thermometer exposures in use in the early instrumental period for extratropical stations, and will illustrate the impact the exposure bias adjustments have on the CRUTEM5_ext data. This work forms part of the NERC-funded GloSAT project (https://www.glosat.org/) which is developing a global surface air temperature dataset starting in 1781. Where appropriate, stations used to create the GloSAT dataset will be adjusted for the exposure bias using the models presented here. 

How to cite: Wallis, E., Osborn, T., and Taylor, M.: Estimating exposure biases in early instrumental land surface temperature data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8119, https://doi.org/10.5194/egusphere-egu23-8119, 2023.

14:09–14:11
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PICO5.3
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EGU23-12790
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Virtual presentation
ricardo vasquez, Enric Aguilar, and Claudia Villarroel

In Chile, in the last 20 years, significant changes have led to a notoriously warmer climate, which translates into meteorological records being recorded every year. For this reason, it is increasingly necessary for the National Meteorological Service to have reliable data to respond to the information needs of society and stakeholders.

Changes and modifications in meteorological stations, such as relocations, instruments, and environmental changes, can alter the estimation of means and long-term trends. Data homogenization consists of correcting these abrupt changes that are not associated with climate through different statistical methods to obtain reliable estimates. The most widely used homogenization methods are the relative ones, consisting of creating reference series from nearby stations, assuming they have the same long-term climatic signal.

In this work, the changing points of daily series of extreme temperatures were evaluated for 17 meteorological stations in Chile in the period 1961-2021. The segmentation method available in the R GNSSfast library was applied to the difference between a candidate series and a reference series created from neighboring stations. The correction of the inhomogeneous series was done through the difference in the daily median of the different segments, taking the most current period as a reference.

A total of 15 changepoints were detected in both minimum and maximum temperatures for 11 measurement stations, where 3 and 4 points were explained with dates of changes in the metadata, respectively. The most significant differences in the trend estimation with and without homogenization were observed in the weather stations of the country's central zone.

How to cite: vasquez, R., Aguilar, E., and Villarroel, C.: Homogenization of extreme temperatures in Chile with GNSSfast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12790, https://doi.org/10.5194/egusphere-egu23-12790, 2023.

14:11–14:13
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PICO5.4
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EGU23-3906
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On-site presentation
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Leopold Haimberger, Federico Ambrogi, and Ulrich Voggenberger

In preparation for the next generation Copernicus reanalysis ERA6, we aim at augmenting the global insitu upper-air dataset with additional data and metadata, in order to reduce observation and representation errors in those data. 

For all available data, the balloon drift is calculated from the available wind data information. Also the actual launch time is supplied for as many ascents as possible, to reduce representation errors. Results indicate that background departures (from ERA5) are almost uniformly reduced for temperature, humidity and wind, with strongest reductions near the extratropical jet streams, if balloon drift is taken into account. 

The data set also contains background departures, calculated offline, for data not assimilated in ERA5, which helps spotting spurious data episodes. Background departures were calculated with respect to ERA5, before 1940 a bias-adjusted version of the NOAA/CIRES/DOE reanalysis v3 was used as reference. For the first time, we also include ascents launched from ships. 

The background departures form the basis for comprehensive statistics-based adjustment of biases in temperature, wind direction and also humidity, using the RAOBCORE/RICH method. The corresponding software has been completely rewritten in python and has an improved handling of data gaps. 

Results from bias-adjusted records indicate realistic spatial trend heterogeneity and very good fit to reprocessed satellite data products. Background departures from ERA5 increase substantially when going back to the early 1950s and 1940s, even after bias adjustments,  but are still better than departures found when comparing to the 20th century reanalysis. 

 

How to cite: Haimberger, L., Ambrogi, F., and Voggenberger, U.: Reducing biases and representation errors in the global historical insitu upper-air network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3906, https://doi.org/10.5194/egusphere-egu23-3906, 2023.

14:13–14:15
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PICO5.5
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EGU23-9774
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On-site presentation
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Joaquin Munoz-Sabater, Gianpaolo Balsamo, Carlo Buontempo, Samantha Burgess, Hans Hersbach, John Hodkinson, Anna Mueller-Quintino, Raluca Radu, Iryna Rozum, and Sebastien Villaume

Reanalysis is a key activity within the Copernicus Climate Change Service (C3S), which is funded by the European Union Copernicus programme and implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF). Currently, the latest generation of European Reanalysis, ERA5 and ERA5-Land, are available through the C3S Climate Data Store (https://cds.climate.copernicus.eu/). ERA5 provides hourly snapshots of the Earth's atmosphere, land surface and ocean waves from 1950 to present, thus providing a global view of the evolution of the Earth’s climate without temporal or spatial gaps for the last seven decades. The land surface component, although part of the ERA5 portfolio, contains few inconsistencies. For instance, significant regional steps between different production segments are present, compromising the reliability of long-term trends. In addition, ERA5 does not provide sufficient resolution for a large and growing number of land applications.

ERA5-Land was designed to overcome the above-mentioned shortcomings of the land branch of reanalysis. For instance, steps in the seam between production segments may be avoided by applying a long spin-up strategy for the initialization of each production segment. ERA5-Land is a unique dataset of its kind, providing a global scale description of the continental water and energy cycles through a series of 50 key surface variables, hourly at a spatial resolution of 9 km, from 1950 to present. ERA5-Land is driven by the near-surface meteorology of ERA5, and temperature is adjusted by considering the orographic differences between ERA5 and ERA5-Land numerical grids. The fidelity of ERA5-Land was assessed by comparing the main fields to a large number of available in-situ observations distributed along the world from 2000 onwards. The variables under analysis were soil moisture, snow depth, lake surface water temperature, river discharge, surface latent and sensible heat fluxes, and skin temperature. The results of the evaluation analysis suggested significant improvements of the ERA5-Land hydrological cycle in comparison to those of ERA5 and ERA-Interim.

The number of ERA5-Land users is counted in thousands. Very recently and similarly to ERA5T, the ERA5-Land-T facility was enabled, which means that preliminary updates are made available daily with only 5-days delay with respect to real time. The final quality-checked product is published with 2-3 month delay with respect to real time. ERA5-Land-T is the result of a requirement of users needing more recent data and opens the door to new applications such as flood forecasting or biomass monitoring.

In this paper the main characteristics of ERA5-Land dataset will be highlighted, its main strengths and weaknesses, as well as the current status.

How to cite: Munoz-Sabater, J., Balsamo, G., Buontempo, C., Burgess, S., Hersbach, H., Hodkinson, J., Mueller-Quintino, A., Radu, R., Rozum, I., and Villaume, S.: ERA5-Land: More than 7 decades of land surface consistency with timely updates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9774, https://doi.org/10.5194/egusphere-egu23-9774, 2023.

14:15–14:17
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PICO5.6
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EGU23-12325
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ECS
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On-site presentation
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Ruben Urraca and Nadine Gobron

Reanalysis products are becoming an increasingly popular option for monitoring climate due to (1) their comprehensive assessment of Earth processes (providing multiple variables of different domains), (2) their absence of gaps, (3) their global spatial coverage, and (4) their long temporal coverage, with some products extending back to the 1950s. However, reanalysis estimates have larger uncertainties than satellite products and may also present stability issues, so the fitness of each reanalysis variable for monitoring a particular climate application needs to be assessed. Reanalysis datasets combine numerical models and observations with a data assimilation scheme that controls the observations assimilated and their weight in the model. Both uncertainty and stability of reanalysis datasets strongly depend on this assimilation scheme and the number of observations available.  

Producing a data assimilation scheme that optimizes both uncertainty and stability is not straightforward as both properties have opposing requirements. On the one hand, the uncertainty of reanalysis estimates is reduced by increasing the number (and weight) of observations assimilated. This is typically achieved in most recent years due to the abundance of satellite and in-situ observations. On the other hand, the former approach creates a stability challenge. Both the satellite and in-situ observations available exponentially increase in time, so artificial trends and discontinuities can be potentially introduced in the climate data record. The magnitude of these trends/discontinuities depends on the variable evaluated (observations are assimilated only for some variables), the change in the number of observations in time, the spatial region (some observations are unevenly distributed), and the weight given to the observations. All these factors need to be evaluated to determine whether the uncertainty, and particularly the stability, of a reanalysis product is adequate for monitoring a specific climate variable based on the requirements established by GCOS. 

In this study, we evaluate the data assimilation scheme of the most well-known atmospheric reanalyses (ERA5, JRA-55, MERRA-2) and the land component of ERA5 (ERA5-Land). We focus on two variables with different assimilation schemes: snow cover, a variable with direct assimilation of in-situ and satellite observations, and snow albedo, a variable without direct assimilation of observations that depends strongly on the snow data assimilated. The products evaluated also present different assimilation schemes: ERA5 and JRA-55 assimilate an increasing number of snow observations in time, ERA5-Land does not directly assimilate observations but is indirectly forced by ERA5 fields, and MERRA-2 assimilates precipitation observations but not snow observations. We evaluate their fitness for climate monitoring by using in-situ snow depth observations as a reference to quantify their uncertainty and stability. We also inter-compare their global/regional snow cover and snow albedo trends to evaluate their stability globally. 

How to cite: Urraca, R. and Gobron, N.: How much is the stability of reanalysis products affected by the increasing number of observations assimilated?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12325, https://doi.org/10.5194/egusphere-egu23-12325, 2023.

14:17–14:19
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PICO5.7
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EGU23-3571
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ECS
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On-site presentation
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Andrew Clelland, Gareth Marshall, and Robert Baxter

Reanalysis data provide a complete picture of the past climate by re-running previous forecasts using modern methods and assimilating with observations. In this work three reanalysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA-Interim, ERA5 and ERA5-Land, were validated against data from 29 meteorological stations across Siberia and the Russian Far East. ERA5 offers improved spatial and temporal resolutions compared to ERA-Interim, as well as starting twenty years earlier in 1959 and being continually updated to the present day with little delay. ERA5-Land replays the land component of ERA5 over an improved 9km spatial resolution and the dataset begins in 1950. The validation was conducted at daily, monthly, seasonal and annual timescales for seven climate variables first to 1979, then additionally to 1959 for ERA5 and ERA5-Land. We found that the snow depth values in ERA5 are only assimilated with meteorological station data from 1992 onwards, leading to significant and inhomogeneous overestimations before this time. As ERA5-Land uses the ERA5 values as its boundary conditions, the snow depth values in this dataset are further from the observations. The mean sea level pressure in the reanalyses is closest to those from the meteorological stations. The daily minimum 2-metre air temperature is noticeably weak during the summer months, however on broader timescales the reanalyses perform very well for the minimum, average and maximum temperatures. Total precipitation and wind speed consistently have the lowest correlations at all temporal and spatial resolutions. Despite the increased spatial resolution, we found no improvement to using ERA5-Land over ERA5, however we would recommend using ERA5 over ERA-Interim due to the larger amount of data available.

How to cite: Clelland, A., Marshall, G., and Baxter, R.: Evaluation of Climate Data from ECMWF Reanalyses over Siberia and the Russian Far East, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3571, https://doi.org/10.5194/egusphere-egu23-3571, 2023.

14:19–14:21
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PICO5.8
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EGU23-9977
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ECS
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On-site presentation
Nathan Lenssen, Gavin Schmidt, Michael Hendrickson, Peter Jacobs, Matthew Menne, and Reto Ruedy

The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly means, where station sparsity and other systematic issues contribute to greater uncertainty.

This work details a gridded uncertainty ensemble of historical temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature product (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty in gridded historical temperature, enabling proper uncertainty propagation for climate and social science at regional and monthly scales. This work details the methodology for generating the uncertainty ensemble, key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble are in good agreement with production GISTEMP.

Two applications of the uncertainty ensemble are also presented. First, the warmest year on record is shown to most likely be 2016 with a 53.2% chance and 2020 as the second most likely with a 44.4% chance. Second, it is shown that the arctic is warming 2.5 - 5 times faster than the globe, significantly faster than the regularly quoted twice as fast.

How to cite: Lenssen, N., Schmidt, G., Hendrickson, M., Jacobs, P., Menne, M., and Ruedy, R.: A NASA GISTEMP Observational Uncertainty Ensemble: Regional and Monthly Uncertainty, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9977, https://doi.org/10.5194/egusphere-egu23-9977, 2023.

14:21–14:23
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PICO5.9
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EGU23-5860
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ECS
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On-site presentation
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Nuria P. Plaza Martin, Makki Khorchani, Cesar Azorin-Molina, Lihong Zhou, Zhenzhong Zeng, Borja Latorre, Sergio M. Vicente Serrano, Tim R. McVicar, Deliang Chen, and Jose A. Guijarro

Historical near-surface wind speed (NSWS; ~10 m above the ground) measurements from terrestrial weather stations are crucial for assessing NSWS changes and variability and its implications for various socioeconomic and environmental issues, such as wind energy. However, currently there is no all-Spain gridded NSWS observation product with higher spatial coverage than station-based wind series. A new methodological approach based on image reconstruction using artificial intelligence could help to solve this limitation. We use a partial convolutional neural network (PCNN) and station-based NSWS series from the Spanish Meteorological Agency (AEMET) to create a 0.1º daily gridded wind speed observation product over Spain for 1961-2021. The deep neural network is trained with wind data from the ERA5-Land reanalysis (at 9-km grid-spacing, ECMWF), and a mask where grid points with historical wind observations are identified. Thus, the 0.1º resolution wind distribution grid is treated as the pixel values of an image with the masked grid points being pixels to be reconstructed. The training process allows the PCNN model to learn the physical laws, such as momentum conservation, present as internal relationships between pixels in the reanalysis data. The learned laws were then implemented to estimate the wind speed of the masked grid points. During the training process, the PCNN model predictions are iteratively compared to the reanalysis data and improved according to the error (i.e., MAE or RMSE) between prediction and the original reanalysis data. Once trained, the model is applied to NSWS measurements in the target domain to predict wind at locations with no observations. The gridded NSWS product provides a high-resolution wind speed data for whole Spain that respects the available observations and reliably predict wind speed in unsampled places, which is useful to many applications requiring wind information.

How to cite: Plaza Martin, N. P., Khorchani, M., Azorin-Molina, C., Zhou, L., Zeng, Z., Latorre, B., Vicente Serrano, S. M., McVicar, T. R., Chen, D., and Guijarro, J. A.: Development of a daily gridded wind speed observation product using artificial intelligence in Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5860, https://doi.org/10.5194/egusphere-egu23-5860, 2023.

Climate variablity
14:23–14:25
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PICO5.10
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EGU23-4090
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Highlight
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On-site presentation
Caroline Ummenhofer, Timothy Walker, Bastian Münch, Neele Sander, Tyson George, and Milon Miah

Maritime weather data contained in U.S. whaling ship logbooks are used to assess historical changes in global wind patterns. We focus on unexploited caches of archival documentation, namely U.S. whaling logbooks of voyages spanning the period 1785 to 1910 from New England archives housed by the New Bedford Whaling Museum, Nantucket Historical Association, and Providence Public Library. The logbooks, often covering multi-year voyages around the globe, contain systematic weather observations (e.g., wind strength/direction, sea state, precipitation) at daily to sub-daily temporal resolution. The qualitative, descriptive wind recordings are quantified and compared with reanalysis products where applicable. They are also employed to help address contemporary questions in climate science, such as long-term shifts in position and strength of the Southern Hemisphere westerlies since the late 1700s, changes in characteristics of the subtropical high pressure systems (e.g., Azores High, Mascarene High) and associated circulation regimes in the 19th century, as well as changes in South Asian monsoon characteristics.

The historical records provide an important long-term context for changing maritime wind patterns in remote ocean regions lacking high-quality observational records. The project is predicated on historical climate data rescue and recovery through the extraction of data from under-utilised archived documentation, and advocating and facilitating the digitisation of such materials.

How to cite: Ummenhofer, C., Walker, T., Münch, B., Sander, N., George, T., and Miah, M.: Changes in global wind patterns since the late 1700s from American whaling ship logbooks and reanalyses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4090, https://doi.org/10.5194/egusphere-egu23-4090, 2023.

14:25–14:27
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PICO5.11
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EGU23-11545
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ECS
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On-site presentation
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Shalenys Bedoya-Valestt, Cesar Azorin-Molina, Lorenzo Minola, Luis Gimeno, and Miguel Andres-Martin

Western Mediterranean sea-breezes are becoming more frequent during winter and less frequent in summer. Further, observed trends in the mean wind speed show a weakening of the sea-breezes in all time scales. These changes could have direct implications for the air pollution dispersion in winter, or for the hydrologic cycle and desertification due to the sea-breeze thunderstorm losses in summer; among other environmental effects.  The drivers and physical mechanisms that underpin long-term sea-breeze changes are yet to be understood, but the response of the atmospheric circulation patterns to the anthropogenic driven warming might be one of the main triggers for the increased occurrence over the Western Mediterranean basin. Recent studies focused on the Eastern Iberian Peninsula suggest that more frequent anticyclonic circulations might be behind the increase of sea-breeze occurrence in winter. This work aims to advance on the likely causes driving sea-breeze changes by investigating their relationship with the Jenkinson and Collison weather type classification. To do so, we will analyze homogenized hourly wind speed data from 40 weather stations across the Western Mediterranean basin (i.e., Spain, France, Italy, Tunisia and Algeria) for 1981-2021. Sea-breeze episodes will be identified by applying a robust automated algorithm based on objective criteria considering the large- and local-scale conditions. The sign, magnitude and statistical significance of the trends in the occurrence, wind speed and gusts of Western Mediterranean sea-breezes will be quantified, as well as for the Jenkinson and Collison weather type regimes. Finally, we will estimate the relationship between these sea-breeze parameters and the synoptic weather classification. This study will provide new knowledge about the historical changes and multidecadal variability of sea-breezes across the Western Mediterranean basin and its response to global atmospheric circulation changes.

How to cite: Bedoya-Valestt, S., Azorin-Molina, C., Minola, L., Gimeno, L., and Andres-Martin, M.: Trends of sea breezes over the Western Mediterranean basin,1981-2021: are they affected by large-scale atmosphericcirculation changes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11545, https://doi.org/10.5194/egusphere-egu23-11545, 2023.

14:27–14:29
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PICO5.12
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EGU23-162
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ECS
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On-site presentation
Cheng Shen, Jinlin Zha, Zhibo Li, Cesar Azorin-Molina, Lorenzo Minola, and Deliang Chen

We evaluate the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed global terrestrial near-surface wind speed (NSWS) and project its future changes under three different Shared Socioeconomic Pathways (SSPs). Results show that the CESM2 has the best ability in reproducing the observed NSWS trends, although all models examined are generally not doing well. Based on projections of CESM2, the global NSWS will decrease from 2021 to 2100 under all three SSPs. The projected NSWS declines significantly over the north of 20°N, especially across North America, Europe, and the mid-to-high latitudes of Asia; meanwhile, it increases over the south of 20°N. Under SSP585, there would be more light-windy days and fewer strong-windy days than those under SSP245, which leads to a significant global NSWS decline. Robust hemispheric-asymmetric changes in the NSWS could be due to the temperature gradient in the two hemispheres under global warming, with −1.2%, −3.5%, and −4.1% in the Northern Hemisphere, and 0.8%, 1.0%, and 1.5% in the Southern Hemisphere, for the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100), respectively.

How to cite: Shen, C., Zha, J., Li, Z., Azorin-Molina, C., Minola, L., and Chen, D.: Evaluation and projection of global terrestrial near-surface wind speed based on CMIP6 GCMs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-162, https://doi.org/10.5194/egusphere-egu23-162, 2023.

14:29–14:31
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PICO5.13
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EGU23-3507
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On-site presentation
Radan Huth and Tomáš Krauskopf

An important aspect of temperature variability is day-to-day temperature difference. Only a few previous studies have looked into its climatology. It is known that the distribution of day-to-day temperature changes in central Europe is asymmetrical, with large temperature increases prevailing over large decreases in winter, and vice versa in summer. A detailed study into the climatology of day-to-day temperature change and its properties is still missing, however.

This study attempts to fill in this knowledge gap. We present climatology of the mean absolute value of day-to-day temperature changes, its standard deviation, values of extreme quantiles, and skewness for Europe in winter and summer. We examine several datasets, including station data (from ECA&D database), an interpolated gridded dataset (E-OBS), and reanalyses (NCEP-NCAR, JRA55, and 20CR). The comparison of various types of datasets allows us to identify their specific behaviour, pointing to potential errors and biases.

This contribution is a part of our efforts to describe and understand long-term changes in short-term (intraseasonal) atmospheric variability and their mechanisms.

How to cite: Huth, R. and Krauskopf, T.: Climatology of day-to-day temperature changes in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3507, https://doi.org/10.5194/egusphere-egu23-3507, 2023.

14:31–14:33
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PICO5.14
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EGU23-15301
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ECS
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On-site presentation
Hyebin Song, Yong-Sang Choi, and Hyoji Kang

Clouds play a significant role in our climate system by influencing radiative balances. Global clouds, by changing the amount and optical property, alter the amount of the net radiation by trapping outgoing longwave radiation or reflecting the incoming solar radiation. Therefore, under global warming, the future cloud change and its impact on the radiation budget is a question at issue. This study examined the trend in cloud radiative effect in current models and satellite observations. We targeted Total Cloud Fraction (TCF) and net radiation using the Coupled Model Intercomparison Project-6 (CMIP6) for 1950-2100 over the globe. We utilized historical data (1950-2014) and the shared socio-economic pathway (SSP) (2015-2100) data. This 1950-2100 period spans a period from the rapid growth point in global surface temperature relative to 1850-1900 according to the IPCC AR6 to the future climate provided by the SSP scenarios. The modeled trends of TCF and net radiation are calculated based on linear regression analysis. As a result, net radiation increases by 0.16 W/m2/decade with −0.14 %/decade changing TCF over the globe. In more detail, TCF changes an average of −0.15±0.8 %/decade and −0.20±1.6 %/decade in low and middle latitudes, whereas TCF increases by 0.08±2.5 %/decade in high latitudes for both hemispheres. On the other hand, the net radiation increases an average of 0.1±0.9 W/m2/decade, 0.22±1.06 W/m2/decade, and 0.22±1.38 W/m2/decade in low, middle, and high latitudes for both hemispheres. Therefore, the decrease in TCF may have allowed more solar radiation into the earth, contributing to surface warming in mid and low latitudes. The clear cloud radiative effects will be investigated by the difference between cloudy and clear-sky radiation trends. In high latitudes where surface albedo is high, the increase in TCF does not necessarily mean a decrease in net radiation although increased clouds reflect more incident solar radiation. This is because the reduction of sea ice albedo has a larger effect on the net radiation than the cloud increase. These model results were validated by the Clouds and the Earth’s Radiant Energy System (CERES) satellite data for 2001-2021. The correlation coefficients of TCF between CMIP6 and CERES are an average of −0.05, 0.32, and 0.2 in low, middle, and high latitudes for both hemispheres. Net radiation shows the correlation coefficients as an average of −0.18, 0.41, and 0.26 in low, middle, and high latitudes for both hemispheres. We will show cloud trends for each level (low, middle, and high) using CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data during 2007-2015 in order to investigate the contribution of vertical clouds to each zonal mean net radiation trend. This study would contribute to the enhancement of cloud parameterization in climate models.

How to cite: Song, H., Choi, Y.-S., and Kang, H.: Impact of Cloud Changes on the Radiation Budget Under Global Warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15301, https://doi.org/10.5194/egusphere-egu23-15301, 2023.

14:33–15:45
Chairpersons: Lorenzo Minola, Rob Roebeling
Remote sensing studies
16:15–16:17
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PICO5.1
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EGU23-15951
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On-site presentation
Rob Roebeling, Viju John, Joerg Schulz, Jaap Onderwaater, Oliver Sus, Ken R. Knapp, Andrew Heidinger, Tasuku Tabata, Arata Okuyama, Frank Ruethrich, Paul Poli, Mike Grant, Roope Tervo, and Timo Hanschmann

The utilisation of observations of past, present, and future geostationary satellites for climate monitoring is a challenge. Since the late 1970s, space agencies operated up to 50 geostationary satellite missions with a variety of instrumentation. Merging these observations in a quasi-global geostationary 'ring' data record is essential for the provision of satellite-based data records of Essential Climate Variables (ECVs). EUMETSAT is engaged in data rescue, uncertainty characterisation, recalibration, and harmonisation of these observations and aims at the provision of the data to users on its joint EUMETSAT-ECMWF cloud infrastructure the so called European Weather Cloud and the EUMETSAT Data Store. The process of preparing satellite data for climate monitoring and analysis - such as undertaken by WCRP’s project GEWEX - is tedious and only recently being recognised as fundamental first step in preparing records ECVs from these data. 

Past and present geostationary data come with the possibility of unforeseen radiometric, geometric, and metadata anomalies. These anomalies may be related to the instrument or the data processing. EUMETSAT developed a system that performs an automatic anomaly analysis to the observations of past and present Meteosat and JMA satellites. The system is able to detect the most common types of anomalies with a high probability of detection and low false alarm rate. The anomalies are stored in a data base so as to inform downstream processing. As the anomalies are flagged on a pixel-by-pixel basis the loss of data is kept to a minimum.

EUMETSAT recalibrated its anomaly screened infrared channel observations from MVIRI on Meteosat First Generation (MFG) and SEVIRI on Meteosat Second Generation (MSG) measurements against IASI, AIRS, and HIRS measurements. The recalibration improved the radiometric accuracy of MVIRI and SEVIR to less than 0.5 K. Such improvements allow the seamless use of these observations for the retrievals of ECVs data records from geostationary orbit covering more than 40 years. Similarly, EUMETSAT applied its recalibration approach to the instruments operated on JMA’s geostationary satellites, resulting in similar improvements as made for the Meteosat satellites. Regarding satellite data quality, first steps have been made to provide recalibrated data with quantitative uncertainty estimates, as developed in the framework of the EU-H2020 FIDUCEO project. Such estimates add another dimension of quality information that is essential to make a data record a true climate data record.  With the aim to close the geostationary 'ring', EUMETSAT and NOAA now started applying the methods presented above to the US geostationary sensor data as well.

Once available, the individual time-series of recalibrated geostationary satellite data of the three collaborating organisations (EUMETSAT, JMA, and NOAA) will be quality controlled, cross-calibrated and merged into a single geostationary ‘ring’ product. Hereto the methods developed by the ISCPP-NG will be used. The collaborating organisations plan to use the cloud computing infrastructure to work on the data that are distributed over three continents.

How to cite: Roebeling, R., John, V., Schulz, J., Onderwaater, J., Sus, O., Knapp, K. R., Heidinger, A., Tabata, T., Okuyama, A., Ruethrich, F., Poli, P., Grant, M., Tervo, R., and Hanschmann, T.: Development of a Quasi-Global Fundamental Climate Data Record for Observations from Geostationary Satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15951, https://doi.org/10.5194/egusphere-egu23-15951, 2023.

16:17–16:19
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PICO5.2
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EGU23-14403
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On-site presentation
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Shengli Wu, Ling Sun, Fenglin Sun, Songyan Gu, and Peng Zhang

A long time series fundamental climate data record (FCDR) have been developed using operational L0 data from Microwave Radiation Imager (MWRI) onboard FengYun-3 series satellites. This FCDR contains re-calibrated brightness temperature data from three MWRIs mounted on FY-3B/3C/3D satellites from 2010 to 2021. Corresponding to the very unique on-orbit calibration system design of MWRI, the calibration error of the sensors may come from the hot-load reflector back-lobe, hot-load reflector emissivity, hot-load efficiency, cold reflector RFI, and receiver non-linearity. Therefore, a comprehensive error analysis is performed by using the double-difference (DD) method. GPM Microwave Imager (GMI) data are also used as a reference sensor. Simultaneous Conical Overpassing (SCO) method is selected to sample the paired data from two satellites. Based on temporal and spatial SCO rules, the required DD thresholds over a stable ocean surface or rain forest is determined and the optimized re-calibration parameters are derived from the multiple iterations. After reprocessing, the obvious improvement for all channels from 10GHz to 89GHz is shown in the root mean squared error (RMSE) of MWRI with respect to GMI which is significantly reduced from 5K to 1K. The RMSE of all 3 instruments and all channels is decreased to less than 1.5K. Most of the channels' RMSE is around 1K or even less. The bias time series between MWRI and GMI also shows very stable from 10GHz/18GHz.

Recently, some typical climate parameters retrieval using FCDR of MWRI have been done by different researchers, including sea ice concerntration, soil moisture, soil frozen and thaw detection, land surface microwave emissivity. The results are very encourage compared with operational MWRI datasets.

How to cite: Wu, S., Sun, L., Sun, F., Gu, S., and Zhang, P.: Progress on FCDR of MWRI onboard FY-3 Series Satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14403, https://doi.org/10.5194/egusphere-egu23-14403, 2023.

16:19–16:21
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PICO5.3
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EGU23-1729
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On-site presentation
Ling Sun, Peng Zhang, Shengli Wu, Hong Qiu, Lin Chen, Yang Guo, Na Xu, Dawei An, and Chengli Qi

NSMC/CMA has been continuously pushing forward the reprocessing of Fengyun series dataset for better quality, especially L1 product. A comprehensive Fengyun historical data rescue and reprocessing task has been completed. At present, the early historical data of FY-1 archive was rescued and L1 data was reproduced providing full data record since 1988. The long-term L1 data reprocessing includes 7 instrument series on 13 satellites in FY-1, FY-2 and FY-3 missions. There are 3 optical imagers with 2 in polar orbit and 1 in geostationary orbit, 1 infrared sounder, 1 microwave imager, and 2 microwave sounders. After reprocessing, the sensor related instability issues caused by instrument degradation and instrument status changes are solved. The inter-platform consistency is also improved by radiometric reference transfer correction in each instrument series. The Fengyun fundamental climate data records (FCDRs) for 7 instruments have been evaluated by radiometric comparison with reference instruments or RTM simulations. The reprocessed L1 dataset has also supported long-term dataset generation for several geophysical parameters. This paper gives an overview of the data reprocessing efforts. 

How to cite: Sun, L., Zhang, P., Wu, S., Qiu, H., Chen, L., Guo, Y., Xu, N., An, D., and Qi, C.: Progress of reprocessed long-term data records from Fengyun satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1729, https://doi.org/10.5194/egusphere-egu23-1729, 2023.

16:21–16:23
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PICO5.4
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EGU23-10640
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ECS
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On-site presentation
Jongho Woo, Kyung-soo Han, Sungwon Choi, Noh-hun Seong, Daeseong Jung, Suyoung Sim, Nayeon Kim, and Eunha Sohn

 Satellite-based solar radiation data is widely used to monitor global climate and environmental changes and is also actively used to analyze weather data and predict particulate matter. Korea can continuously retrieval solar radiation in the observation area due to the generational shift of COMS (Communication, Ocean and Meteorological Satellite)/MI (Meteorological Imager sensor) and GK-2A (GEO-KOMPSAT-2A)/AMI(Advanced Meteorological Imager sensor). However, The quality of each solar radiation output is different due to difference between the algorithms, input data and resolution. Therefore, it is possible to produce a climate resource map for the Korean Peninsula for continuous climate change monitoring by analyzing the error characteristics between the solar radiation of COMS/MI and GK-2A/AMI and expanding the retrieval period through correction between the two products. In this study. We analyzed the error characteristics of the two satellites compare to the meteorological observation data of Korea and the satellite CERES solar radiation data in overlapping periods. As a result of error analysis, the RMSE of COMS/MI was 85.6 (W/m2), lower than the RMSE of GK-2A/AMI, 95.6 (W/m2). Considering the solar radiation data error characteristics of these satellites, a correction model based on machine learning techniques was created to secure the consistency of solar radiation data. When this was verified with in situ data for a period of 10 years, RMSE was 89.21 (W/m2) and Bias was 17.39 (W/m2), which was stable in the temporal consistency test, and the annual increase in solar radiation on the Korean Peninsula was confirmed.

 

※ This work was supported by the "Graduate school of Particulate matter specialization." of Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment, Republic of Korea.

How to cite: Woo, J., Han, K., Choi, S., Seong, N., Jung, D., Sim, S., Kim, N., and Sohn, E.: Secure Consistency of COMS/MI and GK-2A/AMI Shortwave Radiation product based on machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10640, https://doi.org/10.5194/egusphere-egu23-10640, 2023.

16:23–16:25
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PICO5.5
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EGU23-11968
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Virtual presentation
Melanie Coldewey-Egbers, Diego Loyola, Klaus-Peter Heue, Christophe Lerot, Michel van Roozendael, Richard Siddans, Barry Latter, and Brian Kerridge

In this study, we analyze global and regional patterns of total and height-resolved ozone trends 1995-2021 based on two data records: (1) the GOME-type Total Ozone Essential Climate Variable (GTO-ECV) generated in the framework of the European Union project Copernicus Climate Change Service (C3S) and (2) the GOME-type Ozone Profile Essential Climate Variable (GOP-ECV) developed in the framework of the European Space Agency’s Climate Change Initiative (ESA-CCI) ozone project. Both GTO-ECV and GOP-ECV combine measurements from a series of nadir-viewing ultra-violet satellite sensors of the GOME-type including GOME, SCIAMACHY, OMI, GOME-2A, and GOME-2B. On top of that, GTO-ECV incorporates also GOME-2C and TROPOMI/Sentinel-5P measurements. For the retrieval of the total columns the GOME Direct Fitting version 4 (GODFIT_V4) algorithm is used, and for the retrieval of ozone profiles the Rutherford Appleton Laboratory (RAL) scheme is applied. Both the total columns and the profiles from the single sensors are merged into two homogeneous long-term gridded level-3 data records, carefully taking into account and reducing inter-sensor differences. As a final step, the ozone profile record is homogenized with respect to the well-established GTO-ECV total column record in order to achieve consistency between both products. The homogenization relies on an altitude-dependent scaling of the profiles in order to match the total column product. We apply a standard multiple linear least-squares regression to both longitudinally-resolved data records and present estimates of the long-term trend and the correlations with explanatory variables such as the Quasi-Biennial Oscillation, the solar cycle, or the El Nino-Southern Oscillation index. Of particular interest are the search for signs of ozone recovery related to decreasing amounts of Ozone Depleting Substances, the evaluation of the long-term evolution in the lower stratosphere, and the investigation of regional structures in the observed trend patterns.

How to cite: Coldewey-Egbers, M., Loyola, D., Heue, K.-P., Lerot, C., van Roozendael, M., Siddans, R., Latter, B., and Kerridge, B.: Using the GTO-ECV total ozone and GOP-ECV ozone profile climate data records for analyzing global and regional trend patterns 1995-2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11968, https://doi.org/10.5194/egusphere-egu23-11968, 2023.

16:25–16:27
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PICO5.6
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EGU23-2465
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Virtual presentation
Wanchun Zhang

Outgoing longwave radiation (OLR) of the atmosphere top (TOA) is an important component of the radiation energy balance of the TOA. The Medium Resolution Imaging Spectrometer (MERSI) carried by the FY-3D and FY-3E satellites can be used for OLR calculation. This paper described the algorithm of MERSI OLR, and the consistency and accuracy of FY-3D MERSI and FY-3E MERSI instantaneous OLR were evaluated based on AQUA CERES OLR. The inspection results showed that the FY-3D and FY-3E MERSI OLR accuracy were basically consistent. And there was uniformly negative deviation compared AQUA CERES instantaneous OLR, the average deviation was about -3 W m-2, and the RMSE was about 6-7 W m-2. On this basis, the ability of daily mean OLR retrieved from FY-3D MERSI and FY-3E MERSI was discussed. The results showed that the accuracy of OLR from four-time observation per day was higher than that from two-time observation per day. The average OLR of the four-time observation per day could be better represent the diurnal variation of that, and the diurnal variation characteristics of OLR also was provided with seasonal changes. In general, the OLR retrieval capabilities of FY-3D and FY-3E satellites are equivalent, and the joint use of them is better on daily mean OLR retrieval. This study can provide a basis for users to analyze OLR data of Fengyun satellites.

 

How to cite: Zhang, W.: Evaluation of daily average OLR retrieved jointly by FY-3 multiple satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2465, https://doi.org/10.5194/egusphere-egu23-2465, 2023.

16:27–16:29
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PICO5.7
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EGU23-3666
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On-site presentation
Xu Liu, wan Wu, liqiao lei, Xiaozhen Xiong, and Qiguang yang

Climate products are typically derived by performing spatial and temporal averaging of level-2 products. It is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation.  Additionally, differences in level-2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. We have developed a Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly.  The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. It eliminates or reduces the errors due to inconsistent L2 algorithms. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature.  The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

How to cite: Liu, X., Wu, W., lei, L., Xiong, X., and yang, Q.: Climate Data Record Derived from Hyperspectral Sounders on AQUA, S-NPP and NOAA 20, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3666, https://doi.org/10.5194/egusphere-egu23-3666, 2023.

16:29–16:31
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PICO5.8
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EGU23-2966
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ECS
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On-site presentation
Katherine Wentz, Lucrezia Ricciardulli, Thomas Meissner, and Frank Wentz

Remote Sensing Systems (RSS) provides a global community of researchers and decision makers with inter-calibrated microwave radiances and Air-Sea Essential Climate Variable (AS-ECV) geophysical retrievals originating from passive spaceborne sensors. The geophysical retrievals include: sea-surface temperature, near-surface ocean wind speed and direction, columnar atmospheric water vapor, columnar cloud liquid water, and sea-surface rain rate. In total, RSS generates microwave radiance and AS-ECV data from 14 microwave radiometers that span a time period of 35+ years. Consistent calibration procedures and retrieval methods have been applied during the data processing to ensure these datasets are suitable for climate research. Geophysical retrievals from two new sensors will be added to the RSS data repository in the 2023 to 2024 timeframe: GOSAT-GW AMSR3 and WSF-M MWI. The addition of these two microwave sensors, with their excellent spatial and temporal coverage, will extend the microwave AS-ECV climate data record (CDR) by up to ten years. In this presentation, we will present the intercalibration framework for integrating AS-ECVs from AMSR3 and MWI into the current CDR.

Historically, the RSS Radiative Transfer Model (RTM) developed for the SSMI sensor was the primary standard for inter-calibrating passive microwave radiances ranging from 6 to 89 GHz. Now, the calibration is tied to GMI onboard GPM. GMI’s unique design enabled absolute calibration of its antenna temperatures and brightness temperatures using various known calibration parameters, including hot load and cold sky antenna temperature tie points, coefficients that describe the non-linearity between sensor counts and antenna temperatures, and cold space spillover. In this presentation, we will describe how the GMI radiances will be used to calibrate AMSR3 and MWI. GMI’s 65-degree inclined orbit is not sun-synchronous, and it allows tight collocation windows with other satellite sensors. This is particularly useful for calibrating AMSR3 and MWI day and night observations. For a set of collocations, a double difference method will be used to find the AMSR3 and MWI calibration parameters that best match the GMI absolutely-calibrated radiances. We will present the inter-calibration process in detail, including the optimization of calibration parameters, the AS-ECVs used in the RTM in order to correct for small differences between sensors, and cases where the sensor radiance channel is not present in GMI.

How to cite: Wentz, K., Ricciardulli, L., Meissner, T., and Wentz, F.: Extending the Air-Sea Essential Climate Variables CDR with AMSR3 and MWI Microwave Radiances, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2966, https://doi.org/10.5194/egusphere-egu23-2966, 2023.

16:31–16:33
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PICO5.9
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EGU23-16371
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On-site presentation
Can we measure surface temperature change over Europe and Africa using the EUMETSAT CM SAF Land Surface Temperature CDR?
(withdrawn)
Anke Duguay-Tetzlaff, Quentin Bourgeois, Reto Stöckli, Viju John, Rainer Hollmann, Marc Schroeder, and Isabel Trigo
16:33–16:35
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PICO5.10
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EGU23-16205
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On-site presentation
Sebastian Hahn, Wolfgang Wagner, Oto Alves, Pavan Muguda Sanjeevamurthy, Mariette Vreugdenhil, and Thomas Melzer

The Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) is providing surface soil moisture data record products based on a change detection technique applied to the Advanced Scatterometer (ASCAT) on-board the series of Metop satellites. At the moment two of the three Metop satellites are still operational (Metop-B and Metop-C), while the first satellite (Metop-A), launched in 2007, completed its mission in November 2021. Thus, the latest ASCAT surface soil moisture data record product covers a period of more than 15 years (2007-2022).

First analysis of long-term trends in the H SAF ASCAT surface soil moisture data record product have indicated strong anomalies for specific regions around the globe. Trend similarities have been found compared to other data sets such as soil moisture information provided by the ERA5 land surface model. However, certain soil moisture anomaly pattern did not match spatially or in their trend direction. It has been observed that land cover changes contribute to the overall ASCAT backscatter signal with a noticeable impact on the retrieved soil moisture information especially over longer time periods (>10 years). Most notably are areas with slowly changing ground conditions such as growing cities or regions suffering deforestation.

In this study we want to present a new method to mitigate the effects of long-term land cover changes based on a regular re-calibration of the dry and wet backscatter reference. It is important to address and remove this non-climatic effects from the surface soil moisture data record products to correctly detect and monitor climate extremes.

How to cite: Hahn, S., Wagner, W., Alves, O., Muguda Sanjeevamurthy, P., Vreugdenhil, M., and Melzer, T.: Metop ASCAT soil moisture trends: Mitigating the effects of long-term land cover changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16205, https://doi.org/10.5194/egusphere-egu23-16205, 2023.

16:35–18:00