This study aims to enhance the accuracy and reliability of the Global Ensemble Forecast System’s (GEFS) precipitation forecasts over the Indian subcontinent using two post-processing techniques, namely the Analog method (AN) and Logistic Regression method (LR). The post-processing techniques and GEFS Numerical Weather Prediction Model (NWP) outputs were evaluated against the observed dataset using probabilistic and deterministic evaluation metrics. Results found that both the methods considerably improves short range to medium range (1-15 day) precipitation forecasts over India. Overall results showed that both the methods perform poorly during the monsoon seasons compared to other seasons. Basin analysis showed that both the methods underperform in the Western Ghats, while the performance is comparable and decent in other parts of India. Analysis of precipitation at different terciles showed that both the AN and LR methods underperforms at higher terciles compared to the lower ones. This is because the GEFS model itself was performing poorly in detecting the heavy precipitation events. The comparison of logistic regression and analog methods shows that the LR method outperforms the AN method in almost all the locations and lead times.

The choice of reference evapotranspiration (ETo) estimation methods and general circulation model (GCM) are crucial for projecting water deficit under a changing climate. Standardized Precipitation Evapotranspiration Index (SPEI) derived from water deficit also varies with the choice of GCM and ETo estimation methods. In this study a variance-based global sensitivity analysis was used to estimate relative sensitivity of projected changes in future water deficit (P-ETo) and SPEI to the choice of GCM and ETo estimation methods over parts of the Indian subcontinent. For evaluating the change in water deficit and droughts, 7 GCMs and 11 ETo methods were analyzed for two distinct periods i.e. 2030-2060 and 2070-2100 compared to the baseline (1951-1980). The 11 ETo methods were grouped into 4 major categories namely based on temperature, radiation, mass transfer and combination methods. Moreover, based on the ETo categories, a non-parametric Mann-Whitney was performed to quantify robust changes under a warming climate. Results show that changes in future water deficit and droughts varies with regions and seasons. Overall, changes in water deficit droughts are more inclined to the choice of ETo method, while the GCM-ETo interaction effects are more prominent in some regions. Results also showed that within an individual ETo category, individual ETo methods do not necessarily agree on the magnitude/direction of change in projecting water deficit and SPEI for future conditions. This has important implications towards selection of appropriate ETo estimation for drought analysis in data scarce regions under a changing climate. Results of this study indicate, the role of proper ensemble formation of GCMs and ETo estimation methods based on seasons and regions, to develop a robust range of future conditions for water resources planning.

GRACE-FO (GFO) and Swarm are two LEO missions that, among others, provide non-gravitational acceleration measurements required for geopotential model development and modelling of non-gravitational forces acting upon them. Unfortunately, the performance of the accelerometers on board for both missions is not the expected. Measurements from both missions present dominant bias jumps that occur on all accelerometer axes and they have been linked to the satellites’ entrance to and exit from the Earth’s shadow. These jumps are estimated and corrected at Level 1A of GFO C and at Level 2 of Swarm C in an optimal way using Least Squares methodology. The corresponding variances of the jumps are also calculated. Furthermore, the measurements contain spurious signals and dominant spikes mostly connected with thruster activation, mainly in the equatorial region or high temperature sensitivity. These disturbances have a significant impact on the data analysis. We propose an alternative weighting filter methodology to generate Level 1B data from Level 1A for GFO C that includes the attenuated spikes and their corresponding variances and does not involve the removal of the spikes nor does it include any interpolation to fill data gaps. This methodology is used for Swarm C accelerometer Level 2 dataset as well. Using spectral domain methods, we show that the newly generated GFO Level 1B and Swarm Level 2 data are not contaminated by the presence of spikes and data jumps. In the polar regions, mostly at the South pole, spikes in the measurements are connected to magnetic disturbances when the satellites enter these regions. Our proposed methodology contains an optimal and unbiased dataset of non-gravitational acceleration measurements that can be used for the estimation of geopotential models and also for the investigation of the accelerometer’s response to electromagnetic disturbances and the modelling of other non-gravitational accelerations to derive thermospheric neutral densities.

A substantial amount of dust aerosol was transported to the central Himalayas during the multiple dust storm event that occurred from 12 to 15 June 2018 in the Thar desert, northwestern India. In this study, we implemented a cloud-resolving Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to assess the impact of transported Thar-related dust aerosols on precipitation processes over the central Himalayas. We isolated the effect of the transported dust on the precipitation distribution by zeroing out dust aerosols from lateral boundary condition and keeping other species of aerosols. Results show the noticeable impacts of transported dust aerosols on regional precipitation and cloud properties over the central Himalayas. When transported dust is included, spatially averaged (25°N-31°N and 78°E-89°E) AOD increases by 0.36. Over the Himalayas foothills with the dust, precipitation is enhanced by 7.5% (0.63mm), while surface temperature is reduced. The presence of mineral dust aerosols increased the cloud droplets and ice crystal number concentration, which is further facilitated by the mountainous terrain by orographic lifting, leading to enhanced precipitation. We diagnosed the role of cloud microphysics and that of the cloud dynamics in the simulated precipitation sensitivity. This study highlights the effect of remote dust aerosol on the perturbation of cloud microphysical properties, which can significantly influence precipitation over the Himalayas and impact the regional hydrology.

Climate patterns in the agricultural zones of the Indus basin are predicted to undergo undesirable changes in the hydrological cycle. These changes are a threat to the widespread agricultural activity and associated livelihoods of the underlying population. Livestock, an essential sector for human sustenance in the basin, is also a major source of greenhouse gas emissions thereby contributing towards climate change. However, it is also a recipient of climate impacts, thus introducing feedbacks and uncertainties that are further accentuated by the Water-Energy-Food Nexus. Here we model and simulate the farm-level dairy operations of a single dairy farm by introducing informatics-driven precision measurements of water, energy, food, and carbon emissions in a system dynamics framework. We analyze the simulated trajectories for energy, water, and waste fluxes to under different interventive scenarios to identify actions that enhance productivity and minimize environmental impact. The model is constructed based on data gathered from two dairy farms located in rural Punjab, Pakistan. The farms have a livestock capacity of 300 and 134 animals respectively, with data related to water, energy, food, and climate gathered over a duration of two years. The simulated results may be used to uncover structural changes in dairy-farm operations which improve the economic structure of the farm while remining within the thresholds defined by Sustainable Development Goals (SDG) 3, 7 and 13 set by the United Nations. The model itself also helps in unravelling the complex interactions among water-energy-food flows along with their coupling to land-climate interactions in context of the dairy farm operations. Beyond the climate change adaptation measures extracted from this study, the system dynamics model that we construct in the process, can help develop economic tools that leverage the advantages of water/climate informatics driven data services and decisions under large variabilities to devise sound agricultural policy.

Without water no energy, significant trade-offs between carbon and water footprints important for global energy and water policy Winnie Gerbens-Leenes1*, Junguo Liu2 1 Integrated Research on Energy, Environment and Society (IREES), University of Groningen, Nijenborg 6, 9747 AG, Groningen, The Netherlands; [email protected] 2. Southern University of Science and Technology (SUSTech), Shenzhen, China; [email protected] Water and energy are strongly related. Water supply needs energy and energy supply needs water. The focus of the pre-2009 water for energy studies was mainly on the quantification of cooling water use in thermoelectric generation and on water use for transport fuel production. Most of the studies were based on grey literature using data from industry, often from the USA. Water footprint (WF) studies have made it possible to quantify water for bioenergy and hydropower, because the assessments were made based on publically available data, e.g. weather data. WF studies provided new information on the amount of water needed for specific renewable energy types. Energy that originates from photosynthesis (e.g. crops, trees or algae) has relatively large water footprints compared to fossil energy sources. Energy that originates from hydropower also has large average WFs, but variation is large. This paper gives an overview of the contribution of water footprint studies on water for energy relationships. It first explains why water is needed for energy, gives an overview of important water-energy studies until 2009, shows the contribution of WF studies, and indicates how this contribution has supported new research. Finally, it provides knowledge gaps that are relevant for future studies. Energy source categories are: 1. biofuels from sugar, starch and oil crops (food crops); 2. cellulosic feedstocks (residues and energy crops); 3. biofuels from algae; 4. firewood; 5. hydropower and 6. various sources of energy including electricity, heat and transport fuels. Especially category 1, 3, 4, 5 and to a lesser extent 2 have relatively large WFs. This is because the energy source derives from agriculture or forestry, which has a large water use (1,2,4), or has large water use due to evaporation from open water surfaces (3,5). WFs for these categories can be calculated using the WF tool. Category 6 includes fossil fuels and renewables, such as photovoltaics and wind energy and has relatively small WFs. However, information needs to be derived from industry. The policy to decrease carbondioxide emissions has consequences for water. Energy policies need to account for significant trade-offs between carbon, land and water footprints.

This study presents the meso-β/γ scale dynamical features involved in an extreme African dust outbreak, which occurred during 20-21 February 2016 over the Iberian Peninsula (IP), the southwest corner of Europe. During this episode, nearly 90% of the air quality stations in Spain exceeded the European Union’s PM10 daily limit. We used observations and performed nested-grid 2km simulations with the Weather Research and Forecasting model using coupled Chemistry (WRF-Chem) to understand the development of this dust outbreak. The surface observations and the false-color RGB dust product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) revealed that the dust storm was initiated on the southeastern flank of the Saharan Atlas Mountains at two distinct phases of dust emissions. The first dust plume crossed the Saharan Atlas during midday on the 20th, the second one followed in the afternoon of the 21st. The first dust plume was advected towards the Western IP, while the second one towards the Eastern IP. The WRF-Chem simulation results indicated that the phase I dust emission was associated with strong barrier jet (BJ) formation on the southeastern foothills of the Saharan Atlas Mountains. The BJ strengthened just after sunrise on the 20th and emitted a massive amount of dust resulting in the first strong dust storm. In phase II, a long-lived westward propagating mesoscale gravity wave (MGW) was triggered near the northeastern edge of the Tinrhert Plateau in eastern Algeria. When this westward propagating long-lived MGW crossed the Tademaït Plateau, multiple hydraulic jumps were formed on its lee side. The strong winds accompanying these multiple hydraulic jumps emitted and mixed dust aerosols upwards which enabled the second strong dust plume to reach the IP. The lifted dust extended over 2-3 km in altitude in the growing daytime planetary boundary layer (PBL) and was advected poleward by the southerly/southeasterly wind at 700hPa. Our results underline the importance of resolving meso-scale processes to understand dust storm dynamics in detail, which are difficult to represent in coarse-resolution (aerosol-) climate models.

COVID-19 success stories from countries using contact tracing as an intervention tool for the pandemic have motivated US counties to pilot opt-in contact tracing applications. Contact tracing involves identifying individuals who came into physical contact with infected individuals. Recent studies show the effectiveness of contact tracing scales with the number of people using the applications. We hypothesize that the effectiveness of contact tracing also depends on the occupation of the user with a large-scale adoption in certain at risk occupations being particularly valuable for identifying emerging outbreaks. We build on an agent-based epidemiological simulator that resolves spatiotemporal dynamics to model San Francisco, CA, USA. Census, OpenStreetMap, SafeGraph, and Bureau of Labor Statistics data inform the agent dynamics and site characteristics in our simulator. We test different agent occupations that create the contact network, e.g. educators, office workers, restaurant workers, and grocery workers. We use Bayesian Optimization to determine transmission rates in San Francisco, which we validate with transmission rate studies that were recently conducted for COVID-19 in restaurants, homes and grocery stores. Our sensitivity analysis of different sights show that the practices that impact the transmission rate at schools have the greatest impact on the infection rate in San Francisco. The addition of occupation dynamics into our simulator increases the spreading rate of the virus, because each occupation has a different impact on the contact network of a city. We quantify the positive benefits of contact tracing adopted by at risk occupation workers on the community and distinguish the specific benefits on at risk occupation workers. We classify to which degree a certain occupation is at risk by quantifying the impact (a) the number of unique contacts and (b) the total number of contacts an individual has for any given work day on the virus spreading rate. We also attempt to constrain if, when, and for how long certain sites should be shut down once exposed to positive cases. Through our research, we are able to identify the occupations, like educators, that are at greatest risk. We use common geophysical data analysis techniques to bring a different set of insights into COVID-19 and policy research.

Grassland ecosystems cover one-fourth of the global land area and harbor over 30% of the global carbon stored in soils. However, grasslands are subjected to extensive and intensive land degradation, which threatens biodiversity, the well-being and food-security of millions of people, and poses challenges for climate change mitigation. The question is where grasslands have degraded and where long-term greening is taking place. Time series of satellite data can be used for trend analyses, but when testing for statistical significance, it is important to account for temporal and spatial autocorrelation. Here we present our new statistical method to analyze long-term trends in grasslands based on physically-based Cumulative Endmember Fractions (annual sums of monthly ground cover fractions). Our trend analysis incorporates two steps: first we apply an autoregressive time series to each pixel to obtain a slope estimate while accounting for temporal autocorrelation. Second, we apply a general least-square regression to the slope estimates, in which we incorporate spatial covariance structure, as well as explanatory variables. We tested our approach mapping long-term trends in grasslands in Central Asia using MODSI 2001 2019 time series, which we regressed against meteorological measurements. Our results showed long term changes of both, positive (i.e., revegetation; e.g., east part of Central Asia) and negative trajectories (i.e., desiccation; e.g., north-west part of the Central Asia). Importantly, our method is scalable and transferable to other time series of satellite data and regions, and can be implemented in any computational environment, assuring accessibility and reproducibility.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov), as the non-solar NASA Heliophysics active final archive, works with current operating missions and the heliophysics community to ingest, preserve and serve a wide range of past and current public science-quality data from the mesosphere into the furthest reach of deep-space exploration. SPDF facilitates scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of the many missions. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Parker Solar Probe, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, IBEX, Voyager, Geotail, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions, and is expecting Solar Orbiter and ICON data. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and OMNIWeb and COHOWeb for near-Earth and deep-space solar wind plasma, magnetic field, and energetic particle database, respectively.

Space traffic management is difficult. Monitoring and predicting the accurate, real-time position of satellites for collision avoidance in low earth orbit is an engineering challenge. The dominant source of error in satellite prediction tracking models is in the determination of space weather driven atmospheric drag. The High Accuracy Satellite Drag Model (HASDM) (Storz et al., 2005) was developed by the U.S. Air Force Space Command (AFSPC) between 2000–2005 to help solve this problem. It is a data assimilative modeling system using the JB2008 thermospheric density model (Bowman et al., 2008) plus continuously derived densities from several dozens of calibration satellites to achieve <5% density uncertainty at most epochs. The HASDM data is being made available to the community of scientists and operators for the first time. Under authority from the AFSPC, Space Environment Technologies (SET) has extracted two solar cycles of operational High Accuracy Satellite Drag Model (HASDM) data for scientific use and this is called the SET HASDM database. Navigating and extracting information from this database quickly and efficiently to manage satellite space traffic is currently complex and tedious. We present the development of a data-driven model for the HASDM mass density using Machine Learning and an attempt to quantify the associated uncertainties.

The increase of vegetation greenness in the Northern latitudes suggests a rise in the fixation of CO2 by photosynthesis, but the observed upward trends in respiration could compensate for elevated uptake by photosynthesis, necessitating the monitoring of variation in vegetation structure and carbon (C) storage at very high spatio-temporal resolution. Compared to passive optical remote sensing, Light Detection and Ranging (Lidar) scanners may improve the quantification of C sink by providing 3D information of plant structures without apparent sign of saturation of spectral response over dense canopies. We evaluate a novel approach to precisely map C sequestration and key metrics describing the 3D canopy structure of a temperate agricultural expanse by implementing drone-borne Lidar scanner technology and deep learning (DL) architectures potentially capable of detecting individual plants and associated geometrical properties while deriving their above ground biomass (AGB) from point cloud datasets originating from the scanner. An intensive aerial and field campaign was carried out over an Integrated Carbon Observation System (ICOS) class 1 station site (60 ha) in Denmark to remotely measure the horizontal and vertical canopy structure at 15-day intervals during the vegetation growing period, and to collect ground truth data of crop growth in terms of height, density, AGB and green area index of more than 1200 plants. The point cloud data are processed using pattern recognition tools to remove noise and classify them to ground and non-ground points. Two DL models specifically designed to handle the irregular structure of raw point clouds are trained to extract features of vegetation by labeling the processed point cloud data; DL’s suitability for assigning semantic information on 3D data representing cropland is assessed by validating them with the field-based observations. In combination with tower-based flux data, the application of Lidar and DL technologies appear to offer a characterization of the dynamic interaction between climatic conditions, vegetation growth, C sink, water and CO2 fluxes suitable to the challenge of assessing the rapidly changing northern landscapes.

West African precipitation is characterized by a strong variability from intra-seasonal to climatic scales. In this region, it typically rains from March to October with differences in the annual cycle of precipitation (e.g. Sow et al., Atmosphere 2020). Studies based on the Coupled Model Inter-comparison Project (CMIP) phase 3 and 5 show that the representation of the West African Monsoon (WAM) mean state and its response to global warming, are both highly model-dependent (Roehrig et al., J. Atmos. Sci. 2013). CMIP5 simulations display large inter-model spread in both the mean precipitation climatology and higher-frequency indices by overestimating the frequency of wet days (Sow et al, 2020). Likewise, strong uncertainties are found in the simulation of the consecutive wet and dry days (CWD and CDD respectively) over west Africa. In this study we analyse the added-value of the new generation models available from the CMIP6 archive in simulating precipitation and its extreme indices over West Africa. The same group of CMIP5 and CMIP6 models is evaluated with CRU and REGEN datasets for a long time period (more than 30 years) as well as more than four other observational datasets in a shorter time period (15 years). Results show that the inter-model spread as well as the overestimation of these indices slightly decreases in most CMIP6 historical simulations compared to their CMIP5 counterpart. When considering experiments where sea surface temperature (SST) is prescribed over a long period (1870-2014), performed in the Global Monsoons Model Intercomparison Project (GMMIP), we find more improvement in the annual structure of precipitation over the Guinean region with less inter-model spread compared to observations. The inter-annual variability of precipitation is then relatively well captured by all CMIP6 models with significant correlations. Almazroui et al. (2020) found that precipitation in CMIP6 models will increase in the central part and decrease in the western part of west Africa during the twenty-first century under Shared Socio-economic Pathways (SSP). In this study we further analyse how extreme precipitation indices are represented in SSP5-8.5 over West Africa.

Magnetohydrodynamic (MHD) models have been used for nearly four decades to study the dynamics of magnetospheric substorms. However, until recently no demonstration has been made that MHD models can consistently reproduce substorm onset times in a statistical sense. To test whether MHD can reproduce observed substorm onset times, we developed a procedure for identifying substorm onsets that can be applied both to observational data and to MHD output. Our substorm identification procedure aims to improve upon existing methods of substorm identification by using multiple types of observations to corroborate each identified substorm. Using this procedure, we identified over 100 substorms from the period 1-31 January 2005. Using this list of substorm onset times, we show that the MHD model has weak, but statistically significant skill in predicting substorm onset times. We explore paths to improving the ability of the MHD model to predict substorm dynamics by testing different configurations of the MHD model.

The advancement of seismic attributes and visualization techniques has allowed the study of seismic geomorphology from 3D reflection data. The study of deepwater deposits denes and characterizes architectural elements depending on their genesis, morphology, and position along the slope and basin floor. However, every individual basin’s geological configuration determines the dimensions, morphology, and lithological composition of its architectural elements. To understand how seismic attributes help characterize geological settings, we employ multiple datasets with variable qualities since few studies elaborate on compiling and discussing the differences between basins. We explore and compare the use of seismic attributes to highlight deepwater architectural elements in three different basins around the world: The Ceará Basin in Equatorial Brazil, The Taranaki Basin in New Zealand, and The North Carnarvon Basin in Australia, focusing on the deepwater sedimentary section in each case. Although the first two datasets are examples of siliciclastic environments and the North Carnarvon, a mixed carbonate-siliciclastic exponent, the architectural elements identified in all the datasets are similar, as well as their attribute response. The results show that the most robust attributes to characterize deepwater elements such as incised channels, channel-levee systems, and lobes are a combination of geometric, amplitude derived, frequency, and textural attributes. These seismic attributes indicate morphological, lithological, bed stacking, and help to define the stratigraphic architecture. Moreover, we found that the co-rendering of RMS (lithology-proxy), coherence (morphology indicator), and curvature attributes help to define the internal configuration for most of the deepwater architectural elements. While each basin is unique, our results and comparisons serve as a guide for seismic interpreters to use in deepwater seismic geomorphology characterization.

Direct radiative forcing is a major impact of atmospheric dust aerosols. Mineral dust is an important aerosol component that interacts with both incoming and outgoing radiation, modulating radiative fluxes on Earth and its atmosphere. Dust radiative impact directly depends on the particles physico-chemical characteristics (e.g. mineralogy, shape, size) which are the main source of uncertainties. Spectral signatures of dust particles in the visible/short-wave infrared (V/SWIR) and long-wave infrared (LWIR) are linked to mineral composition and variability. Obtaining the precise spectral signature and size distribution of dust particles can result in accurate derivation of refractive indices which are used as inputs to model radiative forcing. In this work, V/SWIR and LWIR reflectance spectra of heterogeneous dust samples from the United States, East Asia, and Middle East were analyzed for their mineral abundances. For this study we used global well-characterized soil samples with comparable mineral compositions to windblown dust. The soil samples cover a wide range of mineral compositions and represent both arid and semi-arid regions [J P Engelbrecht et al., 2016]. Our preliminary analysis used linear spectral mixing for both V/SWIR and LWIR reflectance spectra. This approach is the simplest method to determine mineral abundances from reflectance spectra. While this resulted in a very low RMSE for the fit between the sample and modeled spectra, modeled spectra did not match band centers and strengths for all features. We also converted reflectance spectra to continuum removed (CR) and mean optical path (MOPL) which have the potential to eliminate nonlinear effects (e.g. multiple scattering) in spectral mixing. These approaches, which modify the reflectance hull, significantly weakened or removed the calcite absorption features at 2375 nm. Because these samples are very fine grained (< 38 µm [J P Engelbrecht et al., 2016]) multiple scattering effects are expected to be important for both V/SWIR and LWIR spectral ranges and as our initial results show linear mixing is insufficient to produce reasonable mineral abundances. Our next efforts will include full radiative transfer models of the measured spectra which we will present at the meeting.

The sun is a plasma threaded variously by magnetic fields that stretch from the deep interior to the heliosphere. These fields can couple various layers together and transfer momentum between different parts of the solar interior. The sun is not a rigid body and there is no requirement from conservation of angular momentum that the overall solar rotation rate as measured at the photosphere need remain constant. At the 150-foot solar tower telescope on Mt. Wilson the Doppler shift, magnetic fields and line intensity of the solar photosphere have been measured as often as possible beginning in 1965 (1965 and 1966 were lost in a data handling mistake). The overall rotation rate is determined for each observation by fitting the observed photospheric velocities to the function ωsid(φ) =A+Bsin2(φ) +Csin4(φ) where φ is latitude, to determine what is known as the A coefficient. We are currently re-reducing all the data from the 150-foot tower system. Differential rotation is described by the B and C coefficients which we are holding constant with average values. The velocities come from Doppler shifts which with a Babcock magnetograph come mostly from the displacement of the moving sampling stage which balances the intensity in the wings of the spectral line. Line shape calibration uncertainties do not influence this shift. We find variations in the global rotation rate which are larger than the shifts known as the torsional oscillations. If the B and C coefficients are fitted to each Dopplergram the torsional oscillations become evident. Instrument changes of the exit slit system and spectrograph grating do not introduce jumps in the A coefficient. Restriction of observations to those when the sun is within 40 degrees of local noon leaves the result essentially unchanged. There may be a solar cycle influence but, the resulting pattern shown in the attached figure is more complex than that. Data from before 1983 has a scatter about 3 times larger than what is shown here with an average consistent with these results. However, the larger scatter prevents the variability from being evident.

String based sea ice mass balance array (SIMBA) buoy is a good data source to figure out the temporal variations of sea ice freeboard or thickness over the polar oceans. In particular, SIMBA buoys operated as a part of MOSAiC expedition provide relatively homogeneous measurements of snow depth and ice thickness nearby MOSAiC distributed regional network. Therefore, by using 10 SIMBA buoys from MOSAiC expedition, we assess the ability of CryoSat-2 and ICESat-2 in estimating the temporal variations of the sea ice freeboard over the freezing season. We first calculate the ice freeboard and total freeboard at each SIMBA buoy from the temperature profiles of the buoy. Then the mean, median, and lognormal mode of the CryoSat-2 ice freeboard and ICESat-2 total freeboard are calculated within 20 km buffer from the buoy. CryoSat-2 ice freeboard shows a good correspondence with the buoy ice freeboard: increase rate of ~2 cm/month, correlation coefficient (R) greater than 0.7 (P < 0.001), and RMSE of 3-4 cm. Meanwhile, ICESat-2 also shows a significant correlation: increase rate of 2-4 cm/month, R > 0.7, and RMSE of 8-12 cm. CryoSat-2 generally overestimates the ice freeboard and the lognormal estimations show the least biases. On the contrary, ICESat-2 underestimates the total freeboard and the mean estimations show the least biases. This result should be associated with various sources of uncertainties: formation of snow ice at the buoys, variations of snow/ice density, freeboard retrieval algorithms of the satellite altimeters, and spatial/temporal variations between buoy and satellite data.