Seasonal Bias Research Articles

Past and future changes toward earlier timing of streamflow over Pakistan from bias-corrected regional climate projections (1962–2099)

Pakistan has experienced seasonal changes of streamflow, causing a lack of available water resources for agriculture. However, understanding of future seasonal changes of streamflow over Pakistan remains limited. This study assessed the past and future changes in streamflow timing along the four major rivers of Pakistan (Upper Indus, Kabul, Jhelum, and Chenab River basins), using observational data and bias-corrected hydrological projections. Firstly, the VIC-river routing model was simulated forced by simulated daily surface and base runoff data from six CORDEX-South Asia regional climate models (1962–2099). Secondly, the minimum and seasonality bias in simulated daily streamflow data were corrected based on observational records. To quantify seasonal changes of the hydrologic regime, half of annual cumulative streamflows (HCSs) and center-of-volume dates (CVDs) were computed from observed and bias-corrected simulated streamflow data. Over 1962–2019, observational records showed a significant decreasing trend in CVD (that is, an earlier onset of the wet season) by a range between −4.5 and −12.6 days across the three river basins, except for Chenab River basin. Bias-corrected hydrologic projections showed decreased CVD across the four study river basins by −4.2 to −6.3 days during the record period (1962–2019). The decreased CVDs ranges from −5 to −20 days in the near future (the 2050–2059 average) and −11 days to −37 days in the far future (the 2090–2099 average). This study reported diverse hydrologic responses to a similar magnitude of near-surface temperature in Pakistan, highlighting a need to develop basin-specific water resources mamangement and policies for climate change adaptation.

Impact of radiation variations on temporal upscaling of instantaneous Solar-Induced Chlorophyll Fluorescence

Solar-Induced Chlorophyll Fluorescence (SIF) has been increasingly used as a novel proxy for vegetation productivity. Several space-borne instruments can retrieve SIF at varying overpass time, which complicates the interpretation as SIF is driven by absorbed Photosynthetically Active Radiation (PAR) at the acquisition time. To facilitate comparisons across sensors, satellite-based SIF is upscaled to daily averages with a length-of-day correction factor (DC). In conventional DC calculations, the light intensity over a day is approximated geometrically by the cosine of the Solar Zenith Angle (SZA), neglecting changes in atmospheric extinction and topographic effects. Here, we use reanalysis PAR data for DC calculations to evaluate the impact of atmospheric extinction and diffuse radiation individually. We find that the simple SZA approach is a reliable approximation for flat surfaces, where the overall atmospheric impact on DC is less than 10% as large individual effects on direct and diffuse PAR partially compensate each other. At longer time-scales, a sampling (clear sky) bias might exist due to cloud-filtering of satellite data. We find that in the Amazon the true monthly mean PAR can be 25% lower than the one for cloud-filtered days, potentially inducing seasonal SIF biases on the same order. An additional factor impacting PAR during a day is topography. For complex terrain, direct light in the DC expression requires a correction for surface slopes. For example in the San Gabriel Mountains, California, USA, the modified DC is changed by as much as 500% for strongly tilted surfaces. This modification is especially important for satellite instruments with fine spatial resolutions, where surface slopes are not averaged out and can have a substantial impact on reflectance and SIF. Overall, our refined DC-corrections and averaging strategy can help satellite SIF interpretation as well as intercomparisons over a wide range of spatio-temporal scales and overpass times.

Assessing stream temperature response and recovery for different harvesting systems in northern hardwood forests using 40 years of spot measurements

AbstractStream temperature is a critical control on aquatic habitat and a key forest management concern in many jurisdictions. Most research on stream temperature response to forest harvesting is from coniferous forests in rain‐dominated watersheds and focused on the first few years following harvesting. In contrast, we know less about the harvesting impacts on stream temperature for silviculture approaches typically used in northern hardwood forests that are influenced by snow. We addressed this knowledge gap by using four decades (1980 to 2020) of spot water temperature measurements recorded at three treatment and two reference catchments (areas 4.5 to 69 ha) as part of a long‐term water quality monitoring programme at the Turkey Lakes Watershed study near the eastern shores of Lake Superior. We were able to control for diel and seasonal biases in the spot temperature measurements and found that clearcut harvesting showed a summer temperature increase that persisted for 5 to 7 years after harvesting. Shelterwood and selection harvest did not exhibit a detectable change in stream temperature. These responses are consistent with observed changes in forest canopy through time and between harvesting approaches. In addition, the stream temperature responses were likely muted due to the streams being short and characterized by intermittent flow conditions, as well as the potential moderating influence of increased subsurface runoff following harvesting. Our results highlight how insights can be extracted from routine water quality programmes that were hitherto unrecognized.

Complex spatio-temporal structure of the Holocene Thermal Maximum

Inconsistencies between Holocene climate reconstructions and numerical model simulations question the robustness of climate models and proxy temperature records. Climate reconstructions suggest an early-middle Holocene Thermal Maximum (HTM) followed by gradual cooling, whereas climate models indicate continuous warming. This discrepancy either implies seasonal biases in proxy-based climate reconstructions, or that the climate model sensitivity to forcings and feedbacks needs to be reevaluated. Here, we analyze a global database of Holocene paleotemperature records to investigate the spatiotemporal structure of the HTM. Continental proxy records at mid and high latitudes of the Northern Hemisphere portray a “classic” HTM (8–4 ka). In contrast, marine proxy records from the same latitudes reveal an earlier HTM (11–7ka), while a clear temperature anomaly is missing in the tropics. The results indicate a heterogeneous response to climate forcing and highlight the lack of globally synchronous HTM.

Meteor Head Echo Analyses From Concurrent Radar Observations at AMISR Resolute Bay, Jicamarca, and Millstone Hill

AbstractOn 10 and 11 October 2019, high‐power radar observations were performed simultaneously for 8 hours at Resolute Bay Incoherent Scatter North (RISR‐N), Jicamarca Radio Observatory (JRO), and Millstone Hill Observatory (MHO). The concurrent observations eliminate diurnal, seasonal, and space weather biases in the meteor head echo populations and elucidate relative sensitivities of each facility and configuration. Each facility observed thousands of head echoes, with JRO observing tens of thousands. An inter‐pulse phase matching technique employs Doppler shifts to determine head echo range rates (velocity component along radar beam) with order‐of‐magnitude greater accuracy versus measuring the Doppler shift at individual pulses, and this technique yields accurate range rates and decelerations for a subset of the head echo population at each facility. Because RISR‐N is at high latitude and points away from the ecliptic plane, it does not observe head echoes with range rates faster than 55 km/s, although its head echo population demonstrates a bias toward larger and faster head echoes. At JRO near the equator, a larger spread of range rates is observed. MHO observes a large spread of range rates at mid‐latitude despite its comparable frequency to RISR‐N, but this occurs because its beam was pointed at a 45° elevation angle unlike RISR‐N and JRO which were pointed near‐zenith. A trend of greater decelerations at lower altitudes is observed at RISR‐N and JRO, with decelerations of up to 60 km/s2, but high‐deceleration events of up to 1,000 km/s2 previously observed in head echo studies are not observed.

Comparative analysis of Sub division wise rainfall INSAT-3D vs Ground based observations

Rainfall monitoring during south west monsoon season both are very important ad crucial activity. It is important mainly because it is a boon for agriculture, a mirror for future for both social and economic activities and crucial for its measurements (ground as well as remote). In this current works authors made an attempt to know the performance of recently Space Application Centre (SAC), ISRO developed INSAT-3D improved rainfall algorithms (Hydro Estimator and corrected IMSARA) with actual ground based rainfall data by calculating the bias (Actual –Satellite) for each sub-division. The analysis is done for the southwest monsoon season -2021 in by calculating weekly, monthly and seasonal bias for each subdivisions of Indian domain. It is seen that both the algorithms behave similar fashion (both show increase or decrease, simultaneously) with actual data and mostly satellite overestimate with actual data ranges from ~ 20-40 mm. In some subdivisions bias reached within the range 40 -70 mm (except Konkan & Goa ). Almost 40 % of the subdivisions have bias within 0 to 20 mm range, however the variation on weekly, monthly or seasonal differs subdivision and magnitude-wise. Overall, both the algorithms captures and performance well the trends in weekly, monthly and seasonal accumulated rainfall values. Corrected IMSRA (IMC) algorithm perform slightly better (15-20 %) except heavy rainfall episodes during the monsoon season -2021. In both the heavy and very heavy rainfall cases Hydro Estimators pick up well and performs better (~ 10 -12 %) than IMC algorithm especially over orographic areas. In extremely heavy rainfall cases both the algorithms behave in the same manner and captures the events although it is differing magnitude wise. Seasonal analysis of monsoon 2021 rainfall shows that 8 subdivisions have negative biases in the range of 50-60 mm) and 24 subdivisions have negative biases in the range of 0-20 mm, except Konkan Goa, Coastal Karnataka & A & N Islands have positive biases. Therefore, there is need to strengthen the actual observation rainfall measuring network and re-examine the performance of algorithms with larger data sets so that current algorithms retuned as per changing scenario.

Detection and quantification of temperature sensor drift using probabilistic neural networks

Temperature effects are a major driver of strain and deformations in weather-exposed civil infrastructure, such as bridges and buildings. For such structures, long-term temperature data holds the potential for data-driven prediction of expected structural behavior, which in turn enables the detection of anomalous structural behavior. For this reason, structural health monitoring (SHM) strategies typically employ temperature sensors. However, the success of SHM is contingent on the quantity and quality of the available temperature data. Hence, accurate automatic methods for the detection and quantification of anomalies in temperature data are needed. In particular, gradual temperature sensor drifts are difficult to detect and can introduce errors into the thermal compensation of strain sensors, which can be erroneously confounded with time-dependent structural behavior. Current data-driven methods use air temperature as predictor because it exhibits good correlation with temperatures in the structure. Sensor drift is typically quantified by analysis of the prediction residuals; however, these methods are not robust to outliers and can be affected by seasonal biases. In this work, a probabilistic neural network is used as the nonlinear data-driven temperature prediction model which enables the introduction of a sensible threshold to mitigate seasonal model bias. Furthermore, a novel drift detection method based on the evolution of parameters of a trinomial probability distribution is introduced, together with a robust drift quantification method. The performance of this method is assessed using real temperature data from a pedestrian bridge, spanning over seven years of the structure’s life.

Holocene seasonal temperature evolution and spatial variability over the Northern Hemisphere landmass

The origin of the temperature divergence between Holocene proxy reconstructions and model simulations remains controversial, but it possibly results from potential biases in the seasonality of reconstructions or in the climate sensitivity of models. Here we present an extensive dataset of Holocene seasonal temperatures reconstructed using 1310 pollen records covering the Northern Hemisphere landmass. Our results indicate that both summer and winter temperatures warmed from the early to mid-Holocene (~11–7 ka BP) and then cooled thereafter, but with significant spatial variability. Strong early Holocene warming trend occurred mainly in Europe, eastern North America and northern Asia, which can be generally captured by model simulations and is likely associated with the retreat of continental ice sheets. The subsequent cooling trend is pervasively recorded except for northern Asia and southeastern North America, which may reflect the cross-seasonal impact of the decreasing summer insolation through climatic feedbacks, but the cooling in winter season is not well reproduced by climate models. Our results challenge the proposal that seasonal biases in proxies are the main origin of model–data discrepancies and highlight the critical impact of insolation and associated feedbacks on temperature changes, which warrant closer attention in future climate modelling.

Distribution characteristics of terrestrial and marine lipid biomarkers in surface sediment and their implication for the provenance and palaeoceanographic application in the northern South China Sea

Multi biolipids, including plant wax, algal biomarkers, and glycerol dialkyl glycerol tetraethers (GDGTs), were studied in surface sediments of the northern South China Sea (SCS). Unusual elevated contents of odd n-alkanes (C27, C29, C31) and even n-alkanols (C22, C24, C26) derived from terrestrial plant wax in the outer shelf areas indicated the strong influence of deep-water counter current in redistributing and deposition of Taiwan Island sourced organic matter (OM) in the northern SCS. The spatial distribution of dinosterol and brassicasterol (corresponding to dinoflagellates and diatoms of high competitive advantage for nutrients), respectively, displayed a high abundance near the Pearl River Estuary (PRE), southwest off Taiwan Island, and southeast off Hainan Island, in contrast to the relatively high biomass of C37-alkenones, C30-diol/ketol and isoprenoid GDGTs (iGDGTs) in the outer shelf oligotrophic areas, indicating their feasibility in marine productivity reconstruction. The observed high contents of alkenones than brassicasterol in the sediment, albeit the overall dominance of diatoms in the SCS surface water, showed differences in the degradation and/or preservation efficiency of different organic lipids in sediments. The influences of season, source organisms, and different regression methods on the sea surface temperatures (SST) reconstruction were also evaluated in the region extending from shelf to deep water. The reconstructed SSTs based on U37K' mainly reflected spring SSTs, indicating the influence of growing seasons on the source organism. The reconstructed TEX86 SST corresponded more with the mean annual SST in the northern SCS, mainly in the deep-water regions. Changes in the total composition of the archaeal community and population is suggested to be the major factor causing the unusually low and wide range of TEX86 SSTs in the nearshore areas. The combination of the two organic paleothermometers offers complementary information on SST developments in the northern SCS. Multi-biomarkers study in the present work indicates the feasibility of lipids in paleoecology and paleoclimate reconstruction. However, care must be taken in palaeoceanographic reconstruction due to complex hydrological and biological influence, i.e., the deep lateral current on the continental matter transportation, the differential degradation of various organic lipids, and the different seasonal and/or source organism community bias on the organic thermometer in the study area.

The Holocene temperature conundrum answered by mollusk records from East Asia

Seasonal biases (the warm-season contribution) of Holocene mean annual temperature (MAT) reconstructions from geological records were proposed as a possible cause of the mismatch with climate simulated temperature. Here we analyze terrestrial mollusk assemblages that best reflect seasonal signals and provide quantitative MAT and four-season temperature records for northern China during the past 20,000 years. The MAT estimated from the seasonal temperatures of a four-season-mean based on mollusks shows a peak during ~9000–4000 years ago, followed by a cooling trend. In general, the contribution of summer and winter temperature to MAT is significantly greater than that of spring and autumn temperatures. The relative contribution of each season varies over time and corresponds roughly with the seasonal insolation in each season. This independent evidence from mollusk records from the mid-latitudes of East Asia does not support the Holocene long-term warming trend observed in climate simulations and the seasonal bias explanation.

Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data

• SMAP soil moisture (SM) biases are investigated based on core validation sites data. • The p.m. retrievals outperform a.m. retrievals due to better accuracy in clear days. • Dual channel algorithm has the potential to improve SM retrieval in bad weathers. • SM bias correlates negatively with soil temperature and positively with its bias. • Soil wetness, temperature and vegetation parameters jointly contribute to SM biases. The Soil Moisture Active Passive (SMAP) mission provides state-of-the-art global soil moisture (SM) datasets. However, seasonal SM biases and their contributing factors have not be systematically reviewed. This study evaluated the biases of SMAP V6 dual channel algorithm (DCA), single channel algorithm H-pol (SCA-H) and V-pol (SCA-V) SM products based on core validation sites data. All algorithms perform better under clear- than cloudy-sky, and in cloudless daytime than nighttime. Consecutive clear-sky benefits SM retrieval, progressively lowering the uncertainty of SM retrievals while at the cost of dry biases. Cloudy-sky deteriorates the quality of SM retrievals, and wet biases increase with the duration time of cloudy-sky. The modified V7 DCA has a major improvement, owning the potential to provide accurate retrievals under cloudy-sky. SMAP SM biases are co-determined by vegetation index, soil temperature and their biases, and a single factor only explains at most 54% variance in SM biases. Generally, SMAP SM bias is negatively correlated with soil temperature and positively correlated with its bias. SM bias correlates positively with vegetation index and its bias for single channel algorithms, and the underestimation of SM increases with vegetation density for DCA. To get a complete picture of SMAP SM biases, a total differential of radiative transfer equation is recommended for decomposing SMAP SM biases. To this end, more validation sites are required covering diverse land cover types and providing continuous data records.

Evaluating and projecting of climate extremes using a variable-resolution global climate model (VR-CESM)

Increasing risk of many natural hazards including floods, wildfires, and reduction of agricultural production are often due to extreme climate events such as heavy precipitation, heat waves and drought. In this study, the variable-resolution Community Earth system model (VR-CESM) was used to investigate potential changes in extreme climate events at refined-resolution (14 km) in the western U.S. and eastern China. The model performance for simulating climate extremes is evaluated by various extreme metrics and assessed against the PRISM reference dataset in the U.S. VR-CESM is also compared with other models at different resolutions, including the uniform coarser resolution model CESM with the same physical parameterization, and HadGEM2-WRF, one RCM in the CORDEX-NA ensembles with the same SST forcing as the GCMs. Among those precipitation extreme metrics, VR-CESM simulates the short-term daily metrics (e.g., 95th percentile precipitation) in the western U.S. well (with greater uncertainties in the Sierra Nevada and Cascade Mountains), but underestimates the long-term metrics (e.g., number of dry years) during the historical period. For temperature extremes, VR-CESM also overestimates the number of hot temperature extremes in California’s Central Valley and southwestern desert regions; this is consistent with the warm seasonal average JJA biases. In model intercomparisons, VR-CESM has smaller biases than CESM and HadGEM2-WRF for both extreme precipitation and temperature events. Additionally, the Palmer Drought Severity Index (PDSI) for all models is simulated at four watersheds and compared to the PRISM reference dataset with statistical testings for their similarities. Under RCP8.5 through 2050, VR-CESM projects stronger and more frequent extreme precipitation in the mountains and hot events in the deserts of the western U.S., with more wet periods in California as indicated by PDSI. In another VR-CESM refined-resolution region in China, VR-CESM projection indicates an increase in strength and magnitude of extreme precipitation in southern China and hot temperature extremes in eastern China. Geographic heterogeneity in PDSI projections is also evident as the North China Plain is projected to have more dry periods while the Three Gorges Basin is projected to not change much. This study demonstrates that the analysis of multiple extreme metrics is essential to provide an adequate understanding of how extremes may change in the future in a particular place.

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do not. Nevertheless, reliable seasonal climate forecasts can benefit numerous decision-making processes related to agriculture, energy generation, and extreme events mitigation. Thus, given the few works assessing the ECMWF-SEAS5 performance in South America, this study investigated the quality of its seasonal temperature and precipitation predictions over the continent. For this purpose, predictions from all members of the hindcasts (1993–2016) and forecasts (2017–2021) ensemble were used, considering the four yearly seasons. The analyses included seasonal mean fields, bias correction, anomaly correlations, statistical indicators, and seasonality index. The best system’s performance occurred in regions strongly influenced by teleconnection effects, such as northern South America and northeastern Brazil, in which ECMWF-SEAS5 even reproduced the extreme precipitation anomalies that happened in recent decades. Moreover, the system indicated a moderate capability of seasonal predictions in medium and low predictability regions. In summary, the results show that ECMWF-SEAS5 climate forecasts are potentially helpful and should be considered to plan various strategic activities better.

Robustness of BSISO and air-sea interactions in the CMIP (Phase-6) models over the North Indian Ocean

Historical runs of 30 Coupled Model Intercomparison Project Phase-6 (CMIP6) General circulation models (GCMs) are evaluated for the representation of Boreal Summer Intraseasonal Oscillations (BSISO). Several statistical metrics were developed to evaluate the characteristic features of BSISO, such as propagation, phase speed, and exchange of air-sea fluxes at the air-sea interface over the major regions of the Indian Summer Monsoon (ISM). The mean state of the monsoon precipitation in the CMIP6 models is evaluated by using seasonal mean bias, pattern correlation, and root mean square error. The majority of CMIP6 models underestimate the precipitation over central India and overestimate the precipitation over the eastern equatorial region. Multi-model mean (MME) of the models shows good agreement of precipitation pattern with the observations. In the observations, the precipitation anomalies propagate northward from the equatorial latitudes to the northern latitudes over the ISM region (60°E-100°E longitudes). However, the initiation of northward propagating convection shows a significant variation with time in the CMIP6 models. Most of the models well simulated the BSISO propagation over the Bay of Bengal (BoB) and the Indian subcontinent. The majority of the models underestimate the phase speed of BSISO over the Arabian Sea (AS), and easterlies from the western north Pacific, which led to the failure of models in representing the northwest-southeast tilt of convection. Surface turbulent fluxes and zonal winds lag the deep convection over the North Indian Ocean on intraseasonal timescales. However, misrepresentation of air-sea fluxes in the CMIP6 models leads to the significant biases of intraseasonal variances. This study examines the simulation characteristic features of BSISO by CMIP6 models and is mainly attributes them to the atmospheric internal dynamics and air-sea interactions. The present study further suggests that improving atmospheric-oceanic feedback mechanisms, specific humidity, and low-level winds in the CMIP6 models is necessary to accurately predict the ISM intraseasonal variability.

Gas isotope thermometry in the South Pole and Dome Fuji ice cores provides evidence for seasonal rectification of ice core gas records

Abstract. Gas isotope thermometry using the isotopes of molecular nitrogen and argon has been used extensively to reconstruct past surface temperature change from Greenland ice cores. The gas isotope ratios δ15N and δ40Ar in the ice core are each set by the amount of gravitational and thermal fractionation in the firn. The gravitational component of fractionation is proportional to the firn thickness, and the thermal component is proportional to the temperature difference between the top and bottom of the firn column, which can be related to surface temperature change. Compared to Greenland, Antarctic climate change is typically more gradual and smaller in magnitude, which results in smaller thermal fractionation signals that are harder to detect. This has hampered application of gas isotope thermometry to Antarctic ice cores. Here, we present an analytical method for measuring δ15N and δ40Ar with a precision of 0.002 ‰ per atomic mass unit, a two-fold improvement on previous work. This allows us to reconstruct changes in firn thickness and temperature difference at the South Pole between 30 and 5 kyr BP. We find that variability in firn thickness is controlled in part by changes in snow accumulation rate, which is, in turn, influenced strongly by the along-flowline topography upstream of the ice core site. Variability in our firn temperature difference record cannot be explained by annual-mean processes. We therefore propose that the ice core gas isotopes contain a seasonal bias due to rectification of seasonal signals in the upper firn. The strength of the rectification also appears to be linked to fluctuations in the upstream topography. As further evidence for the existence of rectification, we present new data from the Dome Fuji ice core that are also consistent with a seasonal bias throughout the Holocene. Our findings have important implications for the interpretation of ice core gas records. For example, we show that the effects of upstream topography on ice core records can be significant at flank sites like the South Pole – they are responsible for some of the largest signals in our record. Presumably upstream signals impact other flank-flow ice cores such as EDML, Vostok, and EGRIP similarly. Additionally, future work is required to confirm the existence of seasonal rectification in polar firn, to determine how spatially and temporally widespread rectifier effects are, and to incorporate the relevant physics into firn air models.

Successful trial of mist nets and whoosh nets to catch non-breeding European Shags Gulosus aristotelis at a daytime roost

SUMMARY During field studies, seabirds are typically caught in the breeding season because they are attached to a nesting site, which allows much closer approach by researchers. This contributes to seasonal and age-class bias in bio-logging studies because only breeding birds are caught. Some species such as the European Shag Gulosus aristotelis present opportunities to capture them outside of the breeding season. We trialed the use of mist and whoosh nets to capture European Shags in Shetland, UK, in September 2021. A total of 16 individuals were caught using both methods, although the whoosh net was more efficient than the mist net. We showed that the capture of non-breeding European Shags is possible outside the breeding season and that such studies could provide valuable data from understudied age classes and seasons.

Snow Representation over Siberia in Operational Seasonal Forecasting Systems

Seasonal forecasting systems still have difficulties predicting temperature over continental regions, while their performance is better over some maritime regions. On the other hand, the land surface is a substantial source of (sub-)seasonal predictability. A crucial land surface component in focus here is the snow cover, which stores water and modulates the surface radiation balance. This paper’s goal is to attribute snow cover seasonal forecasting biases and lack of skill to either initialization or parameterization errors. For this purpose, we compare the snow representation in five seasonal forecasting systems (from DWD, ECMWF, Météo-France, CMCC, and ECCC) and their performances in predicting snow and 2-m temperature over a Siberian region against ERA5 reanalysis and station data. Although all systems use similar atmospheric and land initialization approaches and data, their snow and temperature biases differ in sign and amplitude. Too-large initial snow biases persist over the forecast period, delaying and prolonging the melting phase. The simplest snow scheme (used in DWD’s system) shows too-early and fast melting in spring. However, systems including multi-layer snow schemes (Météo-France and CMCC) do not necessarily perform better. Both initialization and parameterization are causes of snow biases, but, depending on the system, one can be more dominant.

Coupled Coccolith-Based Temperature and Productivity High-Resolution Reconstructions in the Eastern Equatorial Pacific During the Last Deglaciation and the Holocene

We present a new high-resolution reconstruction of annual sea-surface temperatures (SSTa) and net primary productivity (NPP) using novel coccolithophore-based models developed for the Eastern Equatorial Pacific (EEP). We combined published coccolithophore census counts from core-tops in the Eastern Pacific with 32 new samples from the Equatorial region, to derive a new statistical model to reconstruct SSTa. Results show that the addition of the new EEP samples improves existing coccolithophore-based SST-calibrations, and allow reconstructing SSTa in the EEP with higher confidence. We also merged the relative abundance of deep-photic species Florisphaera profunda in the same surface sediment samples with existing calibration datasets for tropical regions, to reconstruct annual NPP. Both temperature and productivity calibrations were successfully applied to fossil coccolith data from Ocean Drilling Project Site 1240, in the EEP. The coccolith-based SSTa estimates show a cooling during the Last Glacial Maximum (LGM) and the Younger Dryas, and warming at the start of the Holocene. This pattern differs in the timing and magnitude of the temperature changes from other available SST-reconstructions based on biogeochemical and faunal proxies. We discuss these discrepancies to be the result of different proxy sensitivities to insolation forcing, seasonal bias, and/or preservation artifacts. Reconstructed annual NPP shows a general decreasing trend from the late last glacial period to recent times, which we relate to the weakening of wind-driven equatorial upwelling towards the Holocene. We also calculated carbon export using our SSTa and NPP reconstructions, and compared to other geochemical-based reconstructions for the same location. Our coupled SSTa-NPP reconstruction provides key data to more fully assess the evolution of primary and export productivity as well as organic carbon burial in the EEP, with implications for its role in global biogeochemical cycles across glacial terminations.

Analysis of CO<sub>2</sub>, CH<sub>4</sub>, and CO surface and column concentrations observed at Réunion Island by assessing WRF-Chem simulations

Abstract. Réunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4, and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight to the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied. A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics, and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a Pearson correlation coefficient of 0.90. These observations represent different air masses to those near the surface; they are influenced by processes from Madagascar, Africa, and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Réunion Island. Furthermore, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Furthermore, WRF-GHG is capable of simulating CO levels on Réunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column Fourier transform infrared (FTIR) observations on the other hand, a model resolution of 50 km might already be sufficient.

Plio-Pleistocene Perth Basin water temperatures and Leeuwin Current dynamics (Indian Ocean) derived from oxygen and clumped-isotope paleothermometry

Abstract. The Pliocene sedimentary record provides a window into Earth's climate dynamics under warmer-than-present boundary conditions. However, the Pliocene cannot be considered a stable warm climate that constitutes a solid baseline for middle-of-the-road future climate projections. The increasing availability of time-continuous sedimentary archives (e.g., marine sediment cores) reveals complex temporal and spatial patterns of Pliocene ocean and climate variability on astronomical timescales. The Perth Basin is particularly interesting in that respect because it remains unclear if and how the Leeuwin Current sustained the comparably wet Pliocene climate in Western Australia, as well as how it influenced Southern Hemisphere paleoclimate variability. To constrain Leeuwin Current dynamics in time and space, this project obtained eight clumped-isotope Δ47 paleotemperatures and constructed a new orbitally resolved planktonic foraminifera (Trilobatus sacculifer) stable isotope record (δ18O) for the Plio-Pleistocene (4–2 Ma) interval of International Ocean Discovery Program (IODP) Site U1459. These new data complement an existing TEX86 record from the same site and similar planktonic isotope records from the Northern Carnarvon Basin (Ocean Drilling Program (ODP) Site 763 and IODP Site U1463). The comparison of TEX86 and Δ47 paleothermometers reveals that TEX86 likely reflects sea surface temperatures (SSTs) with a seasonal warm bias (23.8–28.9 ∘C), whereas T. sacculifer Δ47 calcification temperatures probably echo mixed-layer temperatures at the studied Site U1459 (18.9–23.2 ∘C). The isotopic δ18O gradient along a 19–29∘ S latitudinal transect, between 3.9 and 2.2 Ma, displays large variability, ranging between 0.5 ‰ and 2.0 ‰. We use the latitudinal δ18O gradient as a proxy for Leeuwin Current strength, with an inverse relationship between both. The new results challenge the interpretation that suggested a tectonic event in the Indonesian Throughflow as the cause for the rapid steepening of the isotopic gradient (0.9 ‰ to 1.5 ‰) around 3.7 Ma. The tectonic interpretation appears obsolete as it is now clear that the 3.7 Ma steepening of the isotopic gradient is intermittent, with flat latitudinal gradients (∼0.5 ‰) restored in the latest Pliocene (2.9–2.6 Ma). Still, the new analysis affirms that a combination of astronomical forcing of wind patterns and eustatic sea level controlled Leeuwin Current intensity. On secular timescales, a period of relatively weak Leeuwin Current is observed between 3.7 and 3.1 Ma. Notably, this interval is marked by cooler conditions throughout the Southern Hemisphere. In conclusion, the intensity of the Leeuwin Current and the latitudinal position of the subtropical front are both long-range effects of the same forcing: heat transport through the Indonesian Throughflow (ITF) valve and its propagation through Indian Ocean poleward heat transport. The common ITF forcing explains the observed coherence of Southern Hemisphere ocean and climate records.