Improved Sea Ice Fraction Characterization for L-Band Observations by the Aquarius Radiometers.
ABSTRACT: Radiometers operating at L-band (1.4 GHz) are used to retrieve sea surface salinity over ice-free oceans and have been used recently to study the cryosphere. One hindrance of their use in the high latitudes is the preponderance of mixed scenes, where seawater and sea ice are both present in the sensor's field of view (FOV). Accurately characterizing the scene is crucial for oceanographic and cryospheric applications. To that end, a sea ice fraction model, composed of passive microwave sea ice concentration retrievals and an instrument simulator that integrates radiative power coming from all around the antenna, is used. We investigate the model currently used operationally to derive the ice fraction affecting the Aquarius observations and show that it can be significantly improved. On the one hand, the current model tends to overestimate sea ice fraction in the marginal ice zone where observations are used for salinity retrievals. On the other hand, the current model underestimates ice fraction within the ice pack where observations are used to derive sea ice properties. For the northern hemisphere, we also find evidence of the sea ice type impact on L-band radiometric observations. We present a model to derive sea ice fractions that are in better agreement with Aquarius radiometric observations using the Advanced Microwave Scanning Radiometer 2 Bootstrap algorithm for sea ice concentration and using high-resolution integration over the sensor's FOV.
Project description:Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500-1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500-1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500-1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.
Project description:Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the F score, which balances precision and recall, to determine overall product performance (F = 85%, 82(82)%, 81%, 58%, 46(54)% for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.
Project description:Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979-2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January-September (but not October-December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.
Project description:The Antarctic sea ice area expanded significantly during 1979-2015. This is at odds with state-of-the-art climate models, which typically simulate a receding Antarctic sea ice cover in response to increasing greenhouse forcing. Here, we investigate the hypothesis that this discrepancy between models and observations occurs due to simulation biases in the sea ice drift velocity. As a control we use the Community Earth System Model (CESM) Large Ensemble, which has 40 realizations of past and future climate change that all undergo Antarctic sea ice retreat during recent decades. We modify CESM to replace the simulated sea ice velocity field with a satellite-derived estimate of the observed sea ice motion, and we simulate 3 realizations of recent climate change. We find that the Antarctic sea ice expands in all 3 of these realizations, with the simulated spatial structure of the expansion bearing resemblance to observations. The results suggest that the reason CESM has failed to capture the observed Antarctic sea ice expansion is due to simulation biases in the sea ice drift velocity, implying that an improved representation of sea ice motion is crucial for more accurate sea ice projections.
Project description:During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR.
Project description:The Arctic has warmed significantly since the early 1980s and much of this warming can be attributed to the surface albedo feedback. In this study, satellite observations reveal a 1.25 to 1.51% per decade absolute reduction in the Arctic mean surface albedo in spring and summer during 1982 to 2014. Results from a global model and reanalysis data are used to unravel the causes of this albedo reduction. We find that reductions of terrestrial snow cover, snow cover fraction over sea ice, and sea ice extent appear to contribute equally to the Arctic albedo decline. We show that the decrease in snow cover fraction is primarily driven by the increase in surface air temperature, followed by declining snowfall. Although the total precipitation has increased as the Arctic warms, Arctic snowfall is reduced substantially in all analyzed data sets. Light-absorbing soot in snow has been decreasing in past decades over the Arctic, indicating that soot heating has not been the driver of changes in the Arctic snow cover, ice cover, and surface albedo since the 1980s.
Project description:In recent years, solar-induced chlorophyll fluorescence (SIF) retrieved from space borne spectrometers has been extensively used as a proxy for terrestrial photosynthesis at relatively sparse temporal and spatial scales. The near-infrared band of the recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF in a spectral range devoid of atmospheric absorption features. We find that initial TROPOMI spectra meet high expectations for a substantially improved spatio-temporal resolution (up to 7 km × 3.5 km pixels with daily revisit), representing a step change in SIF remote sensing capabilities. However, interpretation requires caution, as the broad range of viewing-illumination geometries covered by TROPOMI's 2600 km wide swath needs to be taken into account. A first inter-sensor comparison with OCO-2 (Orbiting Carbon Observatory-2) SIF shows excellent agreement, underscoring the high quality of TROPOMI's SIF retrievals and the notable radiometric performance of the instrument. Plain Language Summary:Photosynthesis is the most essential process for life on Earth, but gradually changing environmental conditions such as increasing concentrations of atmospheric trace gases, rising temperatures or reduced water availability could adversely affect the photosynthetic productivity. The recently launched TROPOspheric Monitoring Instrument (TROPOMI) is designed to monitor atmospheric trace gases and air pollutants with an unprecedented resolution in space and time, while its radiometric performance also permits us to see a weak electromagnetic signal emitted by photosynthetically active vegetation - solar induced chlorophyll fluorescence (SIF). Mounting evidence suggests that SIF observations from satellite instruments augment our abilities to track the photosynthetic performance and carbon uptake of terrestrial vegetation. In this study, we present the first TROPOMI SIF retrievals, largely outperforming previous and existing capabilities for a spatial continuous monitoring of SIF from space.
Project description:Aerosol-cloud interactions represent one of the largest uncertainties in external forcings on our climate system. Compared with liquid clouds, the observational evidence for the aerosol impact on ice clouds is much more limited and shows conflicting results, partly because the distinct features of different ice cloud and aerosol types were seldom considered. Using 9-year satellite retrievals, we find that, for convection-generated (anvil) ice clouds, cloud optical thickness, cloud thickness, and cloud fraction increase with small-to-moderate aerosol loadings (<0.3 aerosol optical depth) and decrease with further aerosol increase. For in situ formed ice clouds, however, these cloud properties increase monotonically and more sharply with aerosol loadings. An increase in loading of smoke aerosols generally reduces cloud optical thickness of convection-generated ice clouds, while the reverse is true for dust and anthropogenic pollution aerosols. These relationships between different cloud/aerosol types provide valuable constraints on the modeling assessment of aerosol-ice cloud radiative forcing.
Project description:Recent rapid Arctic sea-ice reduction has been well documented in observations, reconstructions and model simulations. However, the rate of sea ice loss is highly variable in both time and space. The western Arctic has seen the fastest sea-ice decline, with substantial interannual and decadal variability, but the underlying mechanism remains unclear. Here we demonstrate, through both observations and model simulations, that the Pacific North American (PNA) pattern is an important driver of western Arctic sea-ice variability, accounting for more than 25% of the interannual variance. Our results suggest that the recent persistent positive PNA pattern has led to increased heat and moisture fluxes from local processes and from advection of North Pacific airmasses into the western Arctic. These changes have increased lower-tropospheric temperature, humidity and downwelling longwave radiation in the western Arctic, accelerating sea-ice decline. Our results indicate that the PNA pattern is important for projections of Arctic climate changes, and that greenhouse warming and the resultant persistent positive PNA trend is likely to increase Arctic sea-ice loss.
Project description:The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor's actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.