Seasonal Bias Research Articles

A nonlinear model for resolving the temperature bias of branched glycerol dialkyl glycerol tetraether (brGDGT) temperature proxies

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a class of ubiquitous, bacteria-derived lipid biomarkers in terrestrial and aquatic environments. Many studies have demonstrated the potential for brGDGTs as paelotemperature proxies, but the observed seasonal bias in brGDGT-inferred temperatures still remains poorly understood, particularly for Arctic or seasonally dry climates. Here we introduce a new physical framework for understanding variations in the methylation index of branched tetraethers (MBT) by explicitly modeling the production and preservation of brGDGTs for a bacterial population with an exponential rate dependence on temperature. The dependence of MBT ratios on temperature is predicted to be nonlinear, and thus has a different form from other empirically defined MBT models. We apply the model to understanding how the methylation index depends on not only the mean annual air temperature (MAAT) but also how it depends on the amplitude of the seasonal temperature cycle and the phase and amplitude of the soil moisture content. We performed mesocosm growth experiments using natural lake waters that confirm that the model correctly accounts for lower brGDGT production rates during cold seasonal temperatures. Comparing the new model predictions with global compilations of MBT’5Me and MBT’ data, we determine a new calibration of best fitting set of model coefficients that more accurately account for the expected physical constraints on MBT-type data. Our new results account naturally for the expected saturation of MBT’5Me and MBT’ at low and high temperatures, a bias of up to 5–7 °C in MAAT for high seasonal temperature fluctuations, and an additional bias of up to 1–3 °C in MAAT for strongly seasonal soil moisture fluctuations. Taken together, the 3 effects explain differences of up to 15 °C in inferred MAAT compared with traditional empirical models for MBT’5Me and MBT’ and results in improved mean squared errors for both. At MAAT above 18 °C, MBT’ temperature errors are lower than those of MBT’5Me, suggesting MBT’ may still be a useful proxy in some situations despite its added complexity. While we apply the framework only to global MBT’5Me and MBT’ proxy data, the physical framework can be adapted to other types of paleoproxies and may therefore be more widely applicable. Preliminary application of our model to the Pliocene North Sea Hank Core yields ∼1 °C cooler temperatures than a previous calibration and application to 130 kyr-long Chinese Loess Plateau records yields ∼4 °C warmer glacial and glacial stadial temperatures compared to previous calibration.

Vestibular Neuritis Following COVID-19 Vaccination: A Retrospective Study.

ObjectiveTo determine if the COVID-19 vaccine can cause vestibular neuritis (VN).DesignRetrospective study.SettingVertigo outpatient clinic of the Department of Otolaryngology JR Tokyo General Hospital.Participants:378 patients who presented at the Vertigo clinic between July 2018 and March 2022Results23 out of 378 cases were diagnosed with vestibular neuritis. There was a significant seasonal bias of the onset of VN in 2021-3Q compared to other seasons. All 7 patients diagnosed with VN whose onset was 2021-3Q and 2021-4Q had received the BNT162b2 (Pfizer-BioNTech) vaccine within the previous 3 months and one patient diagnosed with VN whose onset was 2022-1Q had a history of COVID-19 infection six months earlier.ConclusionsVN should be recognized as one of the side-effects of the BNT162b2 COVID-19 vaccination.

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

AbstractA multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara.

Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

The variability of predicted variables at daily to seasonal scales in coupled models is primarily governed by surface boundary conditions between the ocean and atmosphere, namely, sea surface temperature (SST), turbulent heat, and momentum fluxes. Although efforts have been made to achieve good accuracy in surface fluxes and SST in observation and reanalysis products, less attention has been paid toward achieving improved accuracy in coupled model simulations. Improper diurnal phase and amplitude in intra-daily SST and precipitation are well-known problems in most global coupled general circulation models, including the Climate Forecast System v2 (CFSv2) model. The present study attempts to improve the representation of ocean-atmosphere surface boundary conditions in CFSv2, primarily used for India's operational forecasts at different temporal/spatial scales. In this direction, the diurnal warm layer and cool skin temperature correction scheme are implemented along with the surface flux parameterization scheme following Coupled Ocean-Atmosphere Response Experiment (COARE) v 3.0. The coupled model re-forecasts with a revised flux scheme showed better characteristics in various ocean-atmosphere parameters and processes at diurnal and seasonal time scales. At the diurnal scale, the phase and amplitude of intra-daily SST and mixed layer depth variabilities are improved over most tropical oceans. Improved diurnal SSTs helped in enhancing the diurnal range of precipitation by triggering stronger intra-daily convection. The corrected diurnal ocean-atmospheric boundary state translated into a reduction in seasonal mean dry bias over Indian landmass and the wet bias over tropical oceans. Better simulation of non-linearity associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), ENSO-Indian Summer Monsoon Rainfall (ISMR), and IOD-ISMR relation is among the most critical improvements achieved by revising the turbulent flux parameterization. The revised flux scheme showed enhanced prediction skills for tropical SST indices and ISMR.

An 11-year record of XCO<sub>2</sub> estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

Abstract. The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (<1 ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

A high resolution coupled ocean-atmosphere simulation of the regional climate over Central America

This study analyzes a relatively high resolution (15 km grid spacing), regional coupled ocean–atmosphere simulation configured over Central America. The simulation is conducted with the Regional Spectral Model-Regional Ocean Model System (RSM-ROMS) forced with global atmospheric and oceanic reanalysis for a period of 25 years (1986–2010). The spatial resolution of the RSM-ROMS simulation is unprecedented for the region. The highlights of the RCM simulation include the verifiable seasonal cycle of mesoscale features like the Low-Level Jets (LLJs), the Mid-Summer Drought (MSD), and the seasonal Tropical Cyclone (TC) activity both in the Pacific and in the Atlantic Oceans. However, some of the biases of the RSM-ROMS simulation of the frequency and amplitude of the MSD, the track density of the TCs in the two ocean basins, and the seasonal bias of the LLJs are noted. The seasonal cycle of the robust surface ocean currents in the eastern Pacific and the Costa Rica Dome is also well captured in the RSM-ROMS simulation. The RSM-ROMS simulation also resolves the seasonal cycle of the cyclonic Panama-Colombia Gyre and the anti-cyclonic Gulf of Papagayo-Tehuantepec Gyre. In many instances we find the RSM-ROMS improves upon the global reanalysis forcing the simulation, indicating the potential value of dynamic downscaling. Furthermore, the co-evolving components of the atmosphere and ocean in the RCM is an added benefit to the atmosphere only and ocean only global reanalysis forcing the simulation. However, the RCM displays significant biases that manifest in precipitation, precipitable water, SST, and winds which in the cases of prognostic variables of RSM-ROMS are perpetrated by the biases in the lateral boundary forcing (e.g., precipitable water) and in other instances forced by biases within the RSM-ROMS (e.g., precipitation, SST).

Transpolar Arcs: Seasonal Dependence Identified by an Automated Detection Algorithm

AbstractTranspolar arcs (TPAs) are auroral features that occur polewards of the main auroral oval suggesting that the magnetosphere has acquired a complicated magnetic topology. They are primarily a northward interplanetary magnetic field (IMF) auroral phenomenon, and their formation and evolution have no single explanation that is unanimously agreed upon. An automated detection algorithm has been developed to detect the occurrence of TPAs in UV images captured from the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instrument onboard the Defense Meteorological Satellite Program (DMSP) spacecraft, in order to further study their occurrence. Via this detection algorithm TPAs are identified as a peak in the average radiance intensity poleward of 12.5° colatitude, in two or more of the wavelengths/bands sensed by SSUSI. Using the detection algorithm for the years 2010 to 2016, over 5000 images containing TPAs are identified. The occurrence of these TPAs shows a seasonal dependence, with more arcs being visible in the winter hemisphere. The orbital plane of DMSP has been investigated as a possible explanation of the dependences in the results of the detection algorithm. For the spacecraft of interest this leads to a preferential observation of the northern hemisphere with the detection algorithm missing TPAs in the southern hemisphere around 01–06 UT. No seasonal bias has been found for these spacecraft. We discuss the ramifications of these findings in terms of proposed TPA generation mechanisms and suggest reasons for the seasonal dependence including it being a reflection of probability of seeing TPAs due to visibility.

Evaporation variability and its control factors of Lake Taihu from 1958 to 2017

湖泊蒸发对气候变化非常敏感, 是水文循环响应气候变化的指示因子, 因此研究湖泊蒸发的控制因素, 对于理解区域水文循环有重要意义. 本文利用太湖中尺度涡度通量网避风港站观测数据校正JRA-55再分析资料, 驱动CLM4.0-LISSS模型, 并利用2012—2017年涡度相关通量数据和湖表面温度数据检验模型模拟蒸发结果, 验证了该模型在太湖的适用性; 估算了1958—2017年间太湖的湖面蒸发量, 并利用Manner-Kendall趋势检验分析了湖面蒸发的变化趋势, 寻找太湖实际蒸发的年际变化的主控因子. 结果如下: 校正后的JRA-55再分析资料模拟的太湖蒸发与观测值之间存在季节偏差, 但是季节偏差在年尺度上相互抵消, 再分析资料可用于年际尺度太湖蒸发变化的模拟; 1958—2017年间太湖蒸发量以1977年为界, 先下降(-3.6 mm/a), 后增加(2.3 mm/a); 多元逐步回归结果表明, 向下的短波辐射是太湖1958—2017年间太湖蒸发变化的主控因子, 向下的长波辐射、气温、比湿也对湖泊蒸发年际变化有一定影响, 但是风速对蒸发量的年际变化影响不大.;Lake evaporation is sensitive to climate change and can indicate the response of hydrological cycle to climate change, therefore investigating the mechanism controlling lake evaporation is vital to understand regional hydrological cycle. In this study, evaporation over Lake Taihu was simulated using the CLM4-LISSS model driven by calibrated Japanese 55-year Reanalysis (JRA-55) data. The model was calibrated against the lake evaporation measured directly with the eddy covariance method from 2012 to 2017. The long-term trend of annual evaporation over Lake Taihu was analyzed using the Mann-Kendall trend test, and the dominant factors controlling the interannual variability were extracted using the multiple stepwise regression method. The results indicated obvious seasonal biases between the lake evaporation simulated using the calibrated JRA-55 reanalysis data as inputs, but the bias was offset at the annual time scale, which suggested that the calibrated JRA-55 reanalysis data can be used to simulate the annual lake evaporation over Lake Taihu. Regarding the historical trend of evaporation over Lake Taihu, the year of 1977 can be viewed as a transition year, while annual lake evaporation decreased from 1958 to 1977 (-3.6 mm/a) and increased after then (2.3 mm/a). The results of multiple stepwise regression revealed that solar radiation was the dominant contributor to the interannual variability of lake evaporation over Lake Taihu during 1958-2017, and sky longwave radiation, air temperature and specific humidity also influenced lake evaporation, with limit effect from wind speed.

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIRv), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIRv and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIRv offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIRv and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

Apparent Seasonal Bias in Delta Outflow Estimates as Revealed in the Historical Salinity Record of the San Francisco Estuary: Implications for Delta Net Channel Depletion Estimates

Accurate estimates of freshwater flow to the San Francisco Estuary are important in successfully regulating this water body, in protecting its beneficial uses, and in accurately modeling its hydrodynamic and water-quality transport regime. For regulatory purposes, freshwater flow to the estuary is not directly measured; rather, it is estimated from a daily balance of upstream Delta inflows, exports, and in-Delta water use termed the net Delta outflow index (NDOI). Field research in the 1960s indicated that NDOI estimates are biased low in summer–fall and biased high in winter–spring as a result of conflating Delta island evapotranspiration estimates with the sum of ungauged hydrologic interactions between channels and islands referred to as net channel depletions. In this work, we employed a 50-year observed salinity record along with gauged tidal flows and an ensemble of five empirical flow-salinity (X2) models to test whether a seasonal bias in Delta outflow estimates could be inferred. We accomplished this objective by conducting statistical analyses and evaluating whether model skill could be improved through seasonal NDOI flow adjustments. Assuming that model residuals are associated with channel depletion uncertainty, our findings corroborate the 1960s research and suggest that channel depletions are biased low in winter months (i.e., NDOI is biased high) and biased high in late summer and early fall months (i.e., NDOI is biased low). The magnitude of seasonal bias, which can reach 1,000 cfs, is a small percentage of typical winter outflow but represents a significant percentage of typical summer outflow. Our findings were derived from five independently developed models, and are consistent with the physical understanding of water exchanges on the islands. This work provides motivation for improved characterization of these exchanges to improve Delta outflow estimates, particularly during drought periods when water supplies are scarce and must be carefully managed.

A Survey of X2 Isohaline Empirical Models for the San Francisco Estuary

This work surveys the performance of several empirical models, all recalibrated to a common data set, that were developed over the past 25 years to relate freshwater flow and salinity in the San Francisco Estuary (estuary). The estuary’s salinity regime—broadly regulated to meet urban, agricultural, and ecosystem beneficial uses—is managed in spring and certain fall months to meet ecosystem objectives by controlling the 2 parts per thousand bottom salinity isohaline position (referred to as X2). We tested five empirical models for accuracy, mean, and transient behavior. We included a sixth model, employing a machine learning framework and variables other than outflow, in this survey to compare fitting skill, but did not subject it to the full suite of tests applied to the other five empirical models. Model performance was observed to vary with hydrology, year, and season, and in some cases exhibited unique limitations as a result of mathematical formulation. However, no single model formulation was found to be consistently superior across a wide range of tests and applications. One test revealed that the models performed equally well when recalibrated to a uniformly perturbed input time-series. Thus, while the models may be used to identify anomalies or seasonal biases (the latter being the subject of a companion paper), their use as inverse models to infer freshwater outflow to the estuary from salinity observations is not expected to improve upon the absolute accuracy of existing outflow estimates. This survey suggests that, for analyses that span a long hydrologic record, an ensemble approach—rather than the use of any individual model on its own—may be preferable to exploit the strengths of individual models.

Deciphering Temperature Seasonality in Earth's Ancient Oceans

Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. ▪ The seasonal cycle defines climate and its constraints on biology, both today and in the deep past. ▪ Paleoseasonal data improve proxy-based estimates of mean annual temperature and validate Earth System Model simulations. ▪ Large, internally consistent data sets can reveal robust spatiotemporal climate patterns on the ancient Earth and how they change with pCO2. ▪ Computational tools enable rigorous numerical analysis of paleoseasonal data for comparison with other paleoclimate data and model output.

Forecast Skill for California Heavy Precipitation Periods from the High-Resolution Rapid Refresh Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System

Abstract Precipitation forecasts from the High-Resolution Rapid Refresh model (HRRR) of the National Centers for Environmental Prediction (NCEP) and the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) are examined during heavy precipitation periods in California. Precipitation forecast discrepancies between the two models are examined during a recent heavy winter precipitation episode in California from 6 to 8 December 2019. The skill of initial 12-h precipitation forecasts is examined objectively from 1 December 2018 to 28 February 2019 from the HRRR, COAMPS, and NCEP’s North American Mesoscale Forecast System (NAM-3km). The HRRR exhibited lower seasonal biases and higher skill based on several metrics applied to a sample of 48 12-h periods during California’s second wettest winter season during the past 20 years. Overall, the NAM-3km and COAMPS exhibited a large wet bias over the interior mountain regions while the HRRR model indicated a dry bias along the northern coastal region. All models tended to underestimate precipitation along the coastal mountains of Northern California. To highlight the regional and localized nature of forecast skill, the fraction skill score (FSS) metric is applied across ranges of spatial scales and precipitation values. For the domain as a whole, the HRRR had higher precipitation forecast skill compared to the other two models, particularly within radial distances of 20–30 km and moderate (10–50 mm) precipitation totals. FSS computed locally highlights the HRRR’s overall higher skill as well as enhanced skill in the southern half of the state.

Methods for assessing seasonal and annual trends in wasting in Indian surveys (NFHS-3, 4, RSOC & CNNS)

Wasting in children under-five is a form of acute malnutrition, a predictor of under-five child mortality and of increased risk of future episodes of stunting and/or wasting. In India, national estimates of wasting are high compared to international standards with one in five children found to be wasted. National surveys are complex logistical operations and most often not planned or implemented in a manner to control for seasonality. Collection of survey data across differing months across states introduces seasonal bias. Cross-sectional surveys are not designed to collect data on seasonality, thus special methods are needed to analyse the effect of data collection by month. We developed regression models to estimate the mean weight for height (WHZ), prevalence of wasting for every month of the year for an average year and an overall weighted survey estimates controlling for the socio-demographic variation of data collection across states and populations over time. National level analyses show the mean WHZ starts at its highest in January, falls to the lowest in June/August and returns towards peak at year end. The prevalence of wasting is lowest in January and doubles by June/August. After accounting for seasonal patterns in data collection across surveys, the trends are significantly different and indicate a stagnant period followed by a decline in wasting. To avoid biased estimates, direct comparisons of acute malnutrition across surveys should not be made unless seasonality bias is appropriately addressed in planning, implementation or analysis. Eliminating the seasonal variation in wasting would reduce the prevalence by half and provide guidance towards further reduction in acute malnutrition.

Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

Aerosol Populations, Processes, and Ages in Bulk Deposition: Insights From a 9‐Year Study of 7Be, 210Pb, Sulfate, and Major/Trace Elements

AbstractWe report fallout radionuclide (FRN) and major/trace element (MTE) contributions to bulk atmospheric deposition in Hanover, NH, USA (43.7022°N, 72.2896°W). Deposition of 7Be, 210Pb, and SO4 covary [R2 > 0.4, n = 461] but are discriminated by production sources, depositional mechanisms, meteorological controls, MTE associations, and seasonal biases. 7Be is dominated by rainout (78% of total deposition), recharged by long‐range transport (+23% over mean, o.m.), influenced by stratosphere‐troposphere exchange (+9% o.m.), and solar activity (−2% per doubling of sunspot count). Correlation with particulate nitrogen (+9% per doubling of N) indicates 7Be affinity for biogenic aerosols. 210Pb is dominated by dry + washout deposition (54% of total) and convective storms (+107% o.m), depleted in marine moisture sources (−133% o.m.), correlated with S (+9% per doubling of S), and biased to autumn with Mn, Hg, and V (+7% o.m.). Coincident long‐term declines in S and 210Pb (−14%, −4% per year) suggest co‐scavenging by PbSO4. 7Be:210Pb ratios increase asymptotically with precipitation through the dry‐washout‐rainout transition and recharge of 7Be. At the global scale, 7Be:210Pb increases with precipitation for North American/European sites due to recharge of 7Be in midlatitude storm belts [R2 = 0.64, n = 31]. Conversely, 7Be:210Pb is independent of precipitation for Southeast/East Asian sites where 7Be recharge is low [R2 = 0.01, n = 40]. Globally, 7Be:210Pb ratios in dry deposition reflect resuspended aerosols with mean age of ca. 200 days, contributing <5% of 210Pb deposition. Different aerosol populations contributing to FRN deposition across spatial and temporal scales should emerge as a focus in terrestrial 7Be, 210Pb, and 10Be tracer applications.

Abnormally warm-saline alkenone signature in Lake Tuosu in centennial cold-wet periods over the late Holocene: An exceptional case for alkenone application

Paleoclimatic reconstructions on the arid/semi-arid northern Tibetan Plateau are important for understanding the complex interactions between the mid-latitude westerly and subtropical Asian monsoon circulations. The development of paleoclimatic reconstructions largely relies on the invention and application of proper quantitative/semi-quantitative temperature and salinity proxies. Over the last few decades, alkenones have shown great potential as indicators of the past temperatures and salinities of lakes on the northern Tibetan Plateau. Several alkenone-based temperature and salinity records have confirmed centennial warm-dry and cold-wet variations during the Holocene on the northern Tibetan Plateau. Herein, we present alkenone-based records from Lake Tuosu in the Qaidam Basin over the last ∼1200 years. The alkenone signals show similar characteristics to published alkenone records in the nearby region during centennial warm periods (the Medieval Warm Period and Current Warm Period). However, the alkenones from Lake Tuosu demonstrate abnormally warm and saline signals during centennial cold periods (mainly during the Little Ice Age), in contrast to the widely recognized cold-wet conditions in the Qaidam Basin. Accordingly, the alkenone records from Lake Tuosu provide an exceptional case for the application of alkenones. The abnormally warm and saline alkenone signals from Lake Tuosu during centennial cold periods cannot be explained by either the presence of the C37:3 alkenone isomer or seasonal bias in alkenone production. Changes in species composition might be an alternative reason for this, which requires a further investigation of the sources of alkenones in lakes in the future.

Impact of parameterizing the turbulent orographic form drag on convection-permitting simulations of winds and precipitation over South China during the 2019 pre-summer rainy season

A turbulent orographic form drag (TOFD) parameterization, which accounts for the unresolved drag induced by the subgrid orographic variance, is implemented in the Weather Research and Forecasting (WRF) model with a horizontal grid spacing of 3 km. Its impact on the surface wind and precipitation forecasts over South China during a pre-summer rainy season (April–June 2019) is evaluated based on 3-hourly observations from more than 2500 stations, by comparing the results from two series of parallel simulation with the TOFD parameterization turned on or off, respectively. Results show that the seasonal mean root mean square error (RMSE) and bias of the surface wind have been significantly reduced by more than 7% and 5%, respectively. The low-level wind is also shown improved by comparing the two series of simulations with 12-hourly observations from 26 sounding stations. Consequently, the equitable threat score (ETS) is increased by the TOFD parameterization for most rainfall-intensity categories, and the six-category mean ETS shows an improvement of more than 3%. The mechanism of the TOFD impact on low-level wind and precipitation is investigated as well. The low-level southwesterlies from the South China Sea flow over the rough land in South China are found to be weakened, which leads to the low-level wind convergence and more precipitation over those regions. Moreover, this effect is more apparent during the daytime.

Seasonal bias in global ocean color observations.

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (Rrs(λ);sr-1) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs(λ), and we recommend more simulations to discern these influence Rrs(λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

ISODRIP, a model to transfer the δ18O signal of precipitation to drip water — Implementation of the model for Eagle Cave (central Spain)

The isotope signature of cave waters provides an excellent opportunity to better understand the recharge in karst regions and the complexity of drainage systems in the vadose zone. We have developed a cave isotope hydrological model (ISODRIP) that requires entering basic hydrometeorological information and a precipitation δ18O record to simulate the discharge and δ18O signals of different drip sites. The model includes four different modules to simulate various flow route regimes: continuous and discontinuous drips under diffuse or preferential flows. We use precipitation and cave water δ18O records that were obtained in Eagle Cave (central Spain) during a 5-year period to test our model and to better understand the dynamics of karst aquifers. Eagle Cave waters do not record evaporation. The δ18O signals do not have seasonality, although they record intra-annual and inter-annual variability. Additionally, cave water δ18O signal falls within the range of the annual average weighted isotope composition of precipitation. Well-mixed cave waters, that characterize diffuse flows, record 1‰ δ18O variability, whereas partially-mixed waters, that flow along preferential drainage routes, have up to 3‰ δ18O variability. The results suggest that precipitation takes on average 15 months to reach the cave through the diffuse flow network, whereas under preferential flow the transit time is highly variable depending on the previous condition of the system. ISODRIP includes a soil layer above the vadose zone that controls large recharge events, together with direct recharge components that bypass the soil layer enabling at least some recharge all year round. Thus, the simulations reproduce the observed lack of seasonal bias in the cave water δ18O composition in relation to the average weighted isotope composition of precipitation. This research highlights the importance of understanding recharge dynamics and the configuration of particular drips sites to properly interpret speleothem δ18O records.