We present a new data set of air temperature change across land and ocean extending back to the late-18^th 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 19^th 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.

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.

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.

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 19^th 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.

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.

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.

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.

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.

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.

 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.

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.

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.

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.

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.

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.