Seasonal Bias Research Articles

Spatial patterns of Holocene temperature changes over mid-latitude Eurasia

The Holocene temperature conundrum, the discrepancy between proxy-based Holocene global cooling and simulated global annual warming trends, remains controversial. Meanwhile, reconstructions and simulations show inconsistent spatial patterns of terrestrial temperature changes. Here we report Holocene alkenone records to address spatial patterns over mid-latitude Eurasia. In contrast with long-term cooling trends in warm season temperatures in northeastern China, records from southwestern Siberia are characterized by colder conditions before ~6,000 years ago, thus long-term warming trends. Together with existing records from surrounding regions, we infer that colder airmass might have prevailed in the interior of mid-latitude Eurasian continent during the early to mid-Holocene, perhaps associated with atmospheric response to remnant ice sheets. Our results challenge the proposed seasonality bias in proxies and modeled spatial patterns in study region, highlighting that spatial patterns of Holocene temperature changes should be re-considered in record integrations and model simulations, with important implications for terrestrial hydroclimate changes.

BrGDGT-based seasonal paleotemperature reconstruction for the last 15 000 years from a shallow lake on the eastern Tibetan Plateau

Abstract. Understanding Holocene temperature changes is vital for resolving discrepancies between proxy reconstructions and climate models. The intricate temperature variations across the Tibetan Plateau (TP) add complexity to studying continental climate change during this period. Discrepancies between model-based and proxy-based reconstructions might stem from seasonal biases and environmental uncertainties in the proxies. Employing multiple proxies from a single sediment core for quantitative temperature reconstructions offers an effective method for cross-validation in terrestrial environments. Here, we present an ice-free-season temperature record for the past 15 kyr from a shallow, freshwater lake on the eastern TP, based on brGDGTs (branched glycerol dialkyl glycerol tetraethers). This record shows that the Holocene Thermal Maximum lags the pollen-based July temperature recorded in the same sediment core. We conclude that the mismatch between the brGDGT-based and pollen-based temperatures is primarily the result of seasonal variations in solar irradiance. The overall pattern of temperature changes is supported by other summer temperature records, and the Younger Dryas cold event and the Bølling–Allerød warm period are also detected. A generally warm period occurred during 8–3.5 ka, followed by a cooling trend in the late Holocene. Our findings have implications for understanding the seasonal signal of brGDGTs in shallow lakes and provide critical data for confirming the occurrence of seasonal biases in different proxies from high-elevation lakes. To further investigate the significance of the brGDGTs and temperature patterns on the TP, we examined existing brGDGT-based Holocene temperature records, which interpret these compounds as indicators of mean annual or growing season temperatures. The existing and available temperature records show complicated patterns of variation, some with general warming trends throughout the Holocene, some with cooling trends, and some with a warm middle Holocene. We analyzed the possible reasons for the diverse brGDGTs records on the TP and emphasize the importance of considering lake conditions and modern investigations of brGDGTs in lacustrine systems when using brGDGTs to reconstruct paleoenvironmental conditions.

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

A Comparison of Regional Climate Projections With a Range of Climate Sensitivities

AbstractTo investigate the extent to which differences in regional model projections can be explained by differences in the warming rates of their driving models, we compare projections of temperature and precipitation over the UK from two regional climate ensembles—the EuroCORDEX multi‐model ensemble and UKCP18 perturbed parameter ensemble—along with projections produced by the “parent” GCMs from which boundary conditions were taken. We evaluate the ensembles in terms of their representation of recent climate, then compare the changes simulated between 1981–2010 and 2050–2079. While both ensembles exhibit seasonal biases with similar magnitudes and spatial patterns during the evaluation period, the UKCP18 ensemble exhibits a somewhat stronger change signal in future simulations, due to a combination of higher climate sensitivity of the driving models, variations in the forcings applied, and—in the regional simulations—the inclusion of time‐varying aerosols. In order to reconcile the two sets of projections, we compare two periods corresponding to fixed global warming levels in the driving models, to constrain the variability within and between the ensembles which can be ascribed to differing rates of global warming: the discrepancy between the ensembles is greatly reduced, although some differences in the local response remain, with the UKCP18 runs slightly warmer and drier than the EuroCORDEX runs, particularly in summer. We also highlight potential pitfalls of comparing warming levels with a reference time period, due to uncertainty about the warming that has already occurred in the driving models prior to the reference period.

A case study for best suitable methods of monitoring demographic structure in cervid populations to predict increasing forest damages

The overall increase of ungulate populations in modern Europe has contributed to conflicts in national economies, particularly between game management and the forestry sector. This study assessed damage risks to young pine (Pinus sylvestris L.), spruce (Picea abies (L.) H. Karst.) and aspen (Populus tremula L.) stands at two spatial scales. One level assessed the interaction between sex-age structure of cervid populations, measured by pellet group density, and forest damages, measured as the percentage of heavily browsed trees in 2040 stand surveys distributed proportionally throughout the country. The second level compared pellet counts and trail-camera-based records of moose (Alces alces L.) and red deer (Cervus elaphus L.) presence in a pilot study area. We examined whether 1) there is a correlation between damage amount and ungulate population structure and 2) are the data from trail cameras suitable for wider use in monitoring ungulate population structure. The study confirmed significantly higher moose pellet group densities in pine than in spruce and aspen stands. Pine damages were greater in stands with higher moose pellet group density, especially with female moose prevailing over male moose density index. The red deer pellet group densities were significantly higher in heavily damaged pine and spruce stands, regardless of sex-age structure. In most cases, there were no statistically significant differences between the two survey methods of ungulate population structure by using pellet count transects and trail camera fixations. Both methods provide comparable data on sex-age structure in moose and red deer populations if seasonal and habitat-predicted biases are considered. However, trail cameras are more widely applicable and easier to use by hunters than pellet counts.

Forecasting Precipitation and Temperature Evolution Patterns Under Climate Change Using a Random Forest Approach With Seasonal Bias Correction

Forecasting Precipitation and Temperature Evolution Patterns Under Climate Change Using a Random Forest Approach With Seasonal Bias Correction

Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

Tropical Atlantic rainfall drives bias in extratropical seasonal forecasts

AbstractWe investigate the impact of seasonal forecast biases in the Tropical Atlantic on the North Atlantic. The analysis uses a novel ensemble‐based method to estimate the impact of tropical rainfall bias on forecasts of the Extratropical North Atlantic. The inter‐ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. We argue that Tropical Atlantic rainfall errors explain a significant amount of the bias in seasonal forecasts over the Extratropical North Atlantic.

Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture

Abstract This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics. Significance Statement The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.

Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction

AbstractAlthough groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously‐described process‐guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater‐relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater‐dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground

Abstract. Early versions of satellite nadir-viewing UV SO2 data products did not explicitly account for the effects of snow/ice on retrievals. Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increased uncertainties of satellite emission estimates and potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite SO2 measurements over snow-covered surfaces can be used to improve the annual emissions reported in our SO2 emissions catalogue (version 2; Fioletov et al., 2023). Only 100 out of 759 sources listed in the catalogue have 10 % or more of the observations over snow. However, for 40 high-latitude sources, more than 30 % of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world's largest SO2 point-source, annual emission estimates in the SO2 catalogue were based only on 3–4 summer months, while the addition of data for snow conditions extends that period to 7 months. Emissions in the SO2 catalogue were based on satellite measurements of SO2 slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only clear-sky and snow-free days, (ii) only clear-sky with snow days, and (iii) a merged dataset (snow and snow-free conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20 % for three-quarters of smelters and oil and gas sources and with practically no systematic bias. This is excellent consistency given that there is typically a factor of 3–5 difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25 % higher than for snow-free conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.

Winter determines the annual:seasonal temperature change of Asia temperate monsoon region since mid-Holocene

The evolution of global temperature during Holocene is crucial for understanding the mechanisms of climate change and future climate trends. However, there is a discrepancy between the temperature trends reconstructed from proxy and model, which is attributed to the seasonal biases and the appearance of the Holocene thermal maximum was dominated by the temperature of the warm season. In this paper, fossil charcoal assemblages from the southern Chinese Loess Plateau were analysed to provide quantitative mean annual temperatures (MAT) and four-season temperature records during 6.55–3.45 cal ka BP. The results indicate that both the seasonal temperatures and MAT during the middle Holocene reached warm peaks between 5.55 and 5.35 cal ka BP. The similar trend between the winter temperature (WIT) and MAT denotes the highest contribution of WIT changes to the MAT in the study area since the mid-Holocene.

Assessment of climate extremes at the regional scale during the last millennium using an annually resolved stalagmite record

In public awareness, extraordinary floods, heat-waves, or drought episodes rank amongst the most tangible indications of a changing climate. Forecasting the magnitude of such extreme events at regional and local scales is a major challenge. Development of reliable small-scale climate models requires testing against past climate records, but these are often incomplete, or cover only partial aspects in a spectrum of environmental parameters, e.g., due to seasonal bias as it is the case for tree-ring records. Here, we study a stalagmite from the “Kleine Teufelshöhle” (KTH) in the Franconian karst (Germany) to obtain independent information on local climate conditions at multi-annual to multi-decadal time scales. Radiocarbon and 230Th/U dating provide constraints for linear growth models, which were then successfully refined by correlating high spatial resolution O isotope data for the KTH stalagmite with a published regional tree-ring width record at annual resolution for the past 1000 years. Besides the availability of the tree-ring record, the KTH site offers the additional advantage that direct comparison between tree-ring and speleothem climate proxies with historical documents is possible due to the proximity of the speleothem and tree-ring sites to the former “Free imperial city” of Nuremberg. Historical documents provide precisely dated records of extreme events, in particular for destructive floods. Higher O isotope values in the stalagmite characterize the “Litte Ice Age" (LIA), an interval coinciding with frequent and devastating floods, sometimes associated with river blockage related to accumulation of ice rafts. Prior to approximately 1500 Common Era (CE), O isotope values were generally lower and only show minor overlap with historical flooding accounts. Conversely, some known drought events for Central Europe as well as the climatic influence of large-volume volcanic eruptions with hemispheric to global impact may be recorded in the speleothem as negative excursions in speleothem O isotopic values. The correlated tree-ring and speleothem data for the first time provide a complementary paleohydrology record that is uniquely suited to resolve extreme short-term climate events at the regional scale.

The value of twinned pollinator-pollen metabarcoding: bumblebee pollination service is weakly partitioned within a UK grassland community

Predicting ecological impact of declining bumblebee (Bombus) populations requires better understanding of interactions between pollinator partitioning of floral resources and plant partitioning of pollinator resources. Here, we combine Cytochrome Oxidase 1 (CO1) barcoding for bumblebee identification and rbcL metabarcoding of pollen carried by bees in three species-rich UK pastures. CO1 barcoding assigned 272 bees to eight species, with 33 individuals belonging to the cryptic Bombus lucorum complex (16 B. lucorum and 17 B. cryptarum). Seasonal bias in capture rates varied by species, with B. pratorum found exclusively in June/July and B. pascuorum more abundant in August. Pollen metabarcoding coupled with PERMANOVA and NMDS analyses revealed all bees carried several local pollen species and evidence of pollen resource partitioning between some species pairings, with Bombus pratorum carrying the most divergent pollen load. There was no evidence of resource partitioning between the two cryptic species present, but significantly divergent capture rates concorded with previous suggestions of separation on the basis of foraging behaviour being shaped by local/temporal differences in climatic conditions. Considering the bee carriage profile of pollen species revealed no significant difference between the nine most widely carried plant species. However, there was a sharp, tipping point change in community pollen carriage across all three sites that occurred during the transition between late July and early August. This transition resulted in a strong divergence in community pollen carriage between the two seasonal periods in both years. We conclude that the combined use of pollen and bee barcoding offers several benefits for further study of plant-pollinator interactions at the landscape scale.

Effect of global and regional SST biases on the East Asian Summer Monsoon in the MetUM GA7 and GC3 configurations

Climate-length experiments of the Met Office Unified Model Global Atmosphere 7.0 (GA7) and Global Coupled 3.0 (GC3) configurations are evaluated against observations and reanalyses for the simulation of the East Asian summer monsoon (EASM). The results show systematic model biases, such as overestimated rainfall over southern China and underestimated rainfall over northern China, suggesting a monsoon that does not penetrate northward enough. We evaluate the effects on the EASM of regional errors in sea-surface temperature (SST) conditions in three regions: the Pacific, the Indian, and the Atlantic Oceans. The global SST biases in GC3 configuration substantially shift the EASM seasonal cycle: a late northward progression of the EASM in the early/mid-monsoon season, and an early retreat of the monsoon that also reduces rainfall over most of northern China. The EASM seasonal rainfall bias in the EASM region is linked to changes in the locations and strength of the western North Pacific subtropical high, which is associated with biases in local evaporation and moisture transport towards South China. GC3 biases in the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) teleconnection pathways also influence the EASM biases. GC3 biases weaken the ENSO teleconnection to the EASM and cause a strong dry bias in southeast China during developing El Niño.

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Abstract. Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.5) air pollution but has sparse regulatory air monitoring. Challenges for low-cost PM2.5 sensors in India include high-aerosol mass concentrations and pronounced regional and seasonal gradients in aerosol composition. Here, we report on a detailed long-time performance evaluation of a popular sensor, the Purple Air PA-II, at multiple sites in India. We established three distinct sites in India across land use categories and population density extremes (in urban Delhi and rural Hamirpur in north India and urban Bengaluru in south India), where we collocated the PA-II model with reference beta attenuation monitors. We evaluated the performance of uncalibrated sensor data, and then developed, optimized, and evaluated calibration models using a comprehensive feature selection process with a view to reproducibility in the Indian context. We assessed the seasonal and spatial transferability of sensor calibration schemes, which is especially important in India because of the paucity of reference instrumentation. Without calibration, the PA-II was moderately correlated with the reference signal (R2 = 0.55–0.74) but was inaccurate (NRMSE ≥ 40 %). Relative to uncalibrated data, parsimonious annual calibration models improved the PurpleAir (PA) model performance at all sites (cross-validated NRMSE 20 %–30 %; R2 = 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed among our sites, suggesting that local calibrations are desirable when possible. Using a moving-window calibration, we found that using seasonally specific information improves performance relative to a static annual calibration model, while a short-term calibration model generally does not transfer reliably to other seasons. Overall, we find that the PA-II model can provide reliable PM2.5 data with better than ±25 % precision and accuracy when paired with a rigorous calibration scheme that accounts for seasonality and local aerosol composition.

Evaluation of Seasonal Differences among Three NOAA Climate Data Records of Precipitation

Abstract Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.

Sources of seasonal sea-ice bias for CMIP6 models in the Hudson Bay Complex

AbstractThe seasonal ice-free period in the Hudson Bay Complex (HBC) has grown longer in recent decades in response to warming, both from progressively earlier sea-ice retreat in summer and later sea-ice advance in fall. Such changes disrupt the HBC ecosystem and ice-based human activities. In this study, we compare 102 simulations from 37 models participating in phase 6 of the Coupled Model Intercomparison Project to the satellite passive microwave record and atmospheric reanalyses. We show that, throughout the HBC, models simulate an ice-free period that averages 30 d longer than in satellite observations. This occurs because seasonal sea-ice advance is unrealistically late and seasonal sea-ice retreat is unrealistically early. We find that much of the ice-season bias can be linked to a warm bias in the atmosphere that is associated with a southerly wind bias, especially in summer. Many models also exhibit an easterly wind bias during winter and spring, which reduces sea-ice convergence on the east side of Hudson Bay and impacts the spatial patterns of summer sea-ice retreat. These results suggest that, for many models, more realistic simulation of atmospheric circulation would improve their simulation of HBC sea ice.

A Comparison Study of Mg/Ca‐, Alkenone‐, and TEX86‐Derived Temperatures for the Brazilian Margin

AbstractThe reconstruction of accurate sea‐surface temperatures (SST) is of utmost importance due to the ocean's central role in the global climate system. Yet, a number of environmental processes might bias reliable SST estimations. Here, we investigate the fidelity of SST reconstructions for the western tropical South Atlantic (WTSA) for the interval covered by Marine Isotope Stages (MIS) 6–5, utilizing a core collected off eastern Brazil at ∼20°S. This interval was selected as previous SST estimates based on Mg/Ca ratios of planktic foraminifera suggested a peculiar pooling of warm surface waters in the WTSA during MIS 6 despite glacial boundary conditions. To ground‐truth the Mg/Ca‐based SST data we generated SST reconstructions on the same core material using the alkenone and TEX86 paleothermometers. Comparison with alkenone‐based temperature estimates corroborate the previous Mg/Ca‐based SST reconstructions, supporting the suggestion of a warm‐water anomaly during MIS 6. In contrast, TEX86‐derived temperatures, albeit representing annual mean SST in recent core top samples, are up to 6°C colder than Mg/Ca‐ and alkenone‐based SST reconstructions. We interpret the periods of anomalously cold TEX86‐temperatures as a result of a vertical migration of the TEX86 producers (heterotrophic marine Thaumarchaeota) toward greater water depths, following food availability during phases of enhanced fluvial suspension input. Likewise, the data suggest that alkenone‐based SST are, albeit to a minor degree when compared to TEX86, affected by river run‐off and/or a seasonal bias in the growth season of haptophyte algae.

Novel application of a process convolution approach for calibrating output from numerical models

Output from numerical models at high spatial and temporal resolutions is critical for modeling applications in a variety of disciplines. Prior to its use in modeling, output from climate models must be brought to a finer spatial resolution and calibrated with respect to observations. The calibration of model output, referred to as bias‐correction, poses many statistical challenges. Here, we develop a bias‐correction method in which systematic biases in the mean and standard deviation of model output are corrected. In addition, we employ a novel process convolution approach to correct bias in temporal dependence. We apply this approach to temperature simulations generated by a regional climate model over the Northeastern USA. The goal of this study was to correct systematic bias in model simulations over historical (1976–2005) and future (2006–2099) time periods while simultaneously preserving future trends resulting from carbon emissions scenarios. We compare the proposed method to a quantile mapping method (empirical quantile mapping, EQM). The proposed method resulted in a more effective correction of seasonal biases and temporal dependence compared to EQM.