Surface Temperature Reconstruction Research Articles

Southern Ocean control on atmospheric CO2 changes across late Pliocene Marine Isotope Stage M2

Abstract. During the Pliocene, atmospheric CO2 concentrations (pCO2) were probably sometimes similar to today's, and global average temperature was ∼3 °C higher than preindustrial. However, the relationships and phasing between variability in climate and pCO2 on orbital timescales are not well understood. Specifically, questions remain about the nature of a lag of pCO2 relative to benthic foraminiferal δ18O in late Pliocene Marine Isotope Stage (MIS) M2 (3300 ka), which was longer than during the Pleistocene. Here, we present a multiproxy paleoceanographic reconstruction of the late Pliocene subtropical–subantarctic zone. New dinoflagellate cyst assemblage data are combined with previously published sea surface temperature reconstructions to reveal past surface conditions, including latitudinal migrations of the subtropical front (STF) over the late Pliocene at Ocean Drilling Program (ODP) Site 1168, offshore of western Tasmania. We observe strong oceanographic variability at the STF over glacial–interglacial timescales, especially the interval (3320–3260 ka) across MIS M2. By providing tight and independent age constraints from benthic foraminiferal δ18O, we find that, much more than benthic δ18O or local SST, latitudinal migrations of the STF are tightly coupled to global pCO2 variations across the M2. Specifically, a northerly position of the STF during the MIS M2 deglaciation coincides with generally low pCO2. We postulate that the Southern Ocean CO2 outgassing varied strongly with migrations of the STF and that this in part accounted for the variability in pCO2 across MIS M2.

A hybrid statistical–dynamical framework for compound coastal flooding analysis

Abstract Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic forcings poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple partially correlated drivers, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges, however they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical-dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and a machine learning-based surrogate model. The framework is demonstrated in San Francisco (SF) Bay over the past 500 years with high accuracy and computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and hydrologic model. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding in SF Bay. A process-based hydrodynamic model is built to predict total water levels (TWLs) varying in time and space throughout SF Bay based on the stochastically generated drivers. A machine learning-based surrogate model is then developed from a relatively small library of hydrodynamic model simulations to efficiently predict water levels (WLs) for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical-dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal flooding, along with the relative contributions of the forcing drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.

Silicoflagellate assemblages in the North Pacific surface sediments: an application of the modern analog method to reconstruct the glacial sea surface temperature in the Japan Sea

Recent silicoflagellate distributions have been examined in 195 surface sediment samples from the North Pacific to establish the silicoflagellate assemblage dataset from surface sediments across the North Pacific for paleo sea surface temperature (SST) reconstruction. The relative abundance data of all seven silicoflagellate taxa identified in this study were used for the modern analog method to reconstruct past SST. The SST reconstruction based on the silicoflagellate modern analog technique was applied to the two sediment cores from the Japan Sea. With the development of ice sheets, the Japan Sea experienced a low salinity isolation event during the Last Glacial Maximum (LGM: 19 ka–26.5 ka), which hampers the SST reconstruction employing geochemical proxies such as alkenone and Mg/Ca thermometry. The reconstructed glacial SST between 25 and 15 ka was ~ 5 °C in the northern and southern Japan Sea, indicating significant southward migration of the glacial subpolar front. The timings of the deglacial SST rises were ~ 14 ka at the south core site and ~ 10 ka at the north core site, indicating a latitudinal lag. This lag likely reflects a gradual increase in the fluxes of the Tsushima Warm Current from the East China Sea through the Tsushima Strait. Compilation of the silicoflagellate assemblages in the North Pacific surface sediments used for the modern analog technique to reconstruct past sea surface temperature (SST), revealing the Japan Sea SST during the last glacial maximum ~20 ka ago.

Western Pacific Warm Pool Warming and Salinity Front Expansion Since 1821 Reconstructed From Paired Coral δ18O, Sr/Ca, and Reconstructed δ18Osw

AbstractThe Southwest Pacific region is of great importance to global climate variability, but instrumental climate observations before the 1980s lack in numbers and quality. Despite efforts in complementing instrumental records with proxy sea surface temperature (SST) and sea surface salinity (SSS) reconstructions based on coral Sr/Ca and δ18Osw records, few of them are longer than a century. This study introduces a northwestern extension to the existing records of South Pacific coral study sites with monthly‐resolved Sr/Ca, δ18O, and δ18Osw reconstructions from Rotuma dating back to 1821. Additionally, we present new monthly‐resolved Sr/Ca and reconstructed δ18Osw from a coral from Tonga dating back to 1848. Results reveal 1.5°C warming in the Western Pacific Warm Pool, while the adjacent coral from Tonga shows 1°C warming over the twentieth century. The Rotuma Sr/Ca record reveals thermal stress events impacting the Sr/Ca‐SST relationship in the following months. Coral δ18Osw results reveal significant freshening of 0.45 Sp (practical salinity unit) in Tonga since the early twentieth century, suggesting the southeastward expansion of the South Pacific Convergence Zone salinity front. The δ18Osw inferred SSS provides a valuable extension into the past considering the short and inconsistent instrumental records available. This study demonstrates the utility of coral‐based reconstructions in capturing long‐term and regional climate variations in the Southwest Pacific and the necessity of expanding replicated studies to other underrepresented areas to enhance our understanding of regional climate dynamics.

Spatial and Temporal Bayesian Hierarchical Model Over Large Domains With Application to Holocene Sea Surface Temperature Reconstruction in the Equatorial Pacific

AbstractWe present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation.

Early-twentieth-century cold bias in ocean surface temperature observations

The observed temperature record, which combines sea surface temperatures with near-surface air temperatures over land, is crucial for understanding climate variability and change1–4. However, early records of global mean surface temperature are uncertain owing to changes in measurement technology and practice, partial documentation5–8, and incomplete spatial coverage9. Here we show that existing estimates of ocean temperatures in the early twentieth century (1900–1930) are too cold, based on independent statistical reconstructions of the global mean surface temperature from either ocean or land data. The ocean-based reconstruction is on average about 0.26 °C colder than the land-based one, despite very high agreement in all other periods. The ocean cold anomaly is unforced, and internal variability in climate models cannot explain the observed land–ocean discrepancy. Several lines of evidence based on attribution, timescale analysis, coastal grid cells and palaeoclimate data support the argument of a substantial cold bias in the observed global sea-surface-temperature record in the early twentieth century. Although estimates of global warming since the mid-nineteenth century are not affected, correcting the ocean cold bias would result in a more modest early-twentieth-century warming trend10, a lower estimate of decadal-scale variability inferred from the instrumental record3, and better agreement between simulated and observed warming than existing datasets suggest2.

Depth Prediction and Reconstruction of Reservoir Surface Temperature based on Multi-data Fusion

Depth Prediction and Reconstruction of Reservoir Surface Temperature based on Multi-data Fusion

Triple oxygen and hydrogen isotopes of glacial diamictites record crustal maturation and changing surface conditions through geologic time

The triple oxygen and hydrogen stable isotopic compositions of 24 glacial diamictite composites with depositional ages from 2.9 to 0.3 Ga are used to reconstruct the evolution of the continental crust and surface environments. The δ18O of the diamictite composites increases from the Archean to the Phanerozoic by ∼4 ‰ (3–5 ‰ in Mesoarchean to 13.5 ‰ in Neoproterozoic), a trend that is comparable to those seen in shales and granites, although the diamictites plot on the lower δ18O end of the range seen in shales. The Δ’17O0.528 decreases from −0.04 to −0.15 ‰, overlapping with shales and granites. Nine zircon separates extracted from individual diamictites also show a ∼3 ‰ increase in δ18O from 3.75 to 4.5 ‰ to 7 ‰ between 2.9 and 0.6 Ga. The δ18O of diamictites and zircon separates negatively correlates with ɛNd(i) of the diamictites, suggesting that the longer the rocks reside in the upper continental crust (the lower the ɛNd), the more they are subjected to repeated weathering cycles, thus acquiring a higher δ18O and lower Δ’17O0.528 during each cycle. On a triple O isotope plot (δ17O vs δ18O), diamictites define an array with a slope of 0.523, analogous to granites and shales.The δD values of nearly all diamictites overlap with the typical mantle and crustal ranges of −58 ± 12 ‰ and − 69 ± 24 ‰, respectively, but are on the lower end of coeval shale values. Water, contained primarily in sheet silicates, ranges from 2.85 wt% (pre-2.4 Ga) and 2.35 wt% (post-2.4 Ga), showing no temporal change, likely reflecting post-depositional reset. The youngest diamictites (0.3 Ga Dwyka Group) exhibit low δD values of −105 to −111 ‰, lower than the typical crustal values, potentially reflecting the near-polar position of South Africa at the time of their deposition, which is corroborated by the reconstructed δ18O value of −26 ‰ based on triple oxygen isotopic systematics.The chemical index of alteration (CIA) allows inverting measured δ18O and Δ’17O values of diamictite composites into a pure weathering product (wp) end member by subtracting values of coeval unweathered crust based on tzircon δ18O. This reveals that the δ18Owp also increases with time, suggesting that the increase in δ18O (decrease in Δ’17O) of the maturing crust is insufficient to fully explain the trends. Therefore, at least part of the increasing δ18Owp likely reflects increasing δ18O of the hydrosphere. Reconstructed surface temperatures around the time of diamictite deposition using δ18Owp and Δ’17Owp display a general decrease since the Archean. Given the diagenetic and post-diagenetic history experienced by the diamictites, the temporal and global trends should be interpreted with caution. Nonetheless, reconstructed δ18Owater shows an increase from −21 ‰ at 2.9 Ga to −11 ‰ at 0.6 Ga. Thus, diamictites, like shales, broadly monitor the evolution of surface conditions on Earth from warmer to colder and from lower to higher δ18Owater values of the hydrosphere.

High-resolution UK’37 sea surface temperature reconstruction in the southwestern Atlantic Ocean during the Holocene

Modern sea surface temperatures (SST) were assessed using the alkenone calibration index (UK′37; SST-UK′37) values from twenty-one core-top sediment samples collected along the Southwestern Atlantic Ocean and validated by comparison with satellite-derived SSTs. The SST-UK′37 values revealed an annual signal between 28 and 30°S and a winter signal (83% of the region samples) between 30 and 34° S, with deviations falling within the UK′37 calibration error range. The paleotemperature (SST-UK′37) reconstruction through the Holocene was performed using two high-resolution marine sediment cores from 33 to 34°S latitude band. Millennial-scale variations dominate most of the SST-UK′37 results with changes up to ∼6 °C. Considering the modern ocean circulation dynamics, the SST-UK’37 in our records likely stems from the combined influence of the Subtropical Shelf Front (STSF) position and the interaction between the warm, salty, and nutrient-poor Subtropical Shelf Water (STSW) and the cold, fresh, and nutrient-rich Subantarctic Shelf Water (SASW). Our SST-UK’37 results also suggest that the northernmost position limit of the STSF reached around 33°S during the mid-to-late Holocene, while after ∼5500 cal yr BP the Plata Plume Water became the dominant influence on the regional SST-UK’37 signal. Spectral analyses revealed statistically significant oscillations of ∼8600 and ∼ 2250 years in record MDBT 557, and ∼ 5300 and ∼ 2600 years in record MDBT 561 which suggests the influence of Southern Westerly Winds (SWW) on the SST-UK’37, and therefore, on the STSF dynamic through the Holocene.

Coral Sr/Ca-SST reconstruction from Fiji extending to ~1370 CE reveals insights into the Interdecadal Pacific Oscillation.

The southwestern tropical Pacific is a key center for the Interdecadal Pacific Oscillation (IPO), which regulates global climate. This study introduces a groundbreaking 627-year coral Sr/Ca sea surface temperature reconstruction from Fiji, representing the IPO's southwestern pole. Merging this record with other Fiji and central tropical Pacific records, we reconstruct the SST gradient between the southwestern and central Pacific (SWCP), providing a reliable proxy for IPO variability from 1370 to 1997. This reconstruction reveals distinct centennial-scale temperature trends and insights into Pacific-wide climate impacts and teleconnections. Notably, the 20th century conditions, marked by simultaneous basin-scale warming and weak tropical Pacific zonal-meridional gradients, deviate from trends observed during the past six centuries. Combined with model simulations, our findings reveal that a weak SWCP gradient most markedly affects IPO-related rainfall patterns in the equatorial Pacific. Persistent synchronous western and central Pacific warming rates could lead to further drying climate across the Coral Sea region, adversely affecting Pacific Island nations.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale.

Robust Reconstruction of Historical Climate Change From Permafrost Boreholes

AbstractReconstructing historical climate change from deep ground temperature measurements in cold regions is often complicated by the presence of permafrost. Existing methods are typically unable to account for latent heat effects due to the freezing and thawing of the active layer. In this work, we propose a novel method for reconstructing historical ground surface temperature (GST) from borehole temperature measurements that accounts for seasonal thawing and refreezing of the active layer. Our method couples a recently developed fast numerical modeling scheme for two‐phase heat transport in permafrost soils with an ensemble‐based method for approximate Bayesian inference. We evaluate our method on two synthetic test cases covering both cold and warm permafrost conditions as well as using real data from a 100 m deep borehole on Sardakh Island in northeastern Siberia. Our analysis of the Sardakh Island borehole data confirms previous findings that GST in the region have likely risen by 5–9°C between the pre‐industrial period of 1750–1855 and 2012. We also show that latent heat effects due to seasonal freeze‐thaw have a substantial impact on the resulting reconstructed surface temperatures. We find that neglecting the thermal dynamics of the active layer can result in biases of roughly −1°C in cold conditions (i.e., mean annual ground temperature below −5°C) and as much as −2.6°C in warmer conditions where substantial active layer thickening (>200 cm) has occurred. Our results highlight the importance of considering seasonal freeze‐thaw in GST reconstructions from permafrost boreholes.

Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Utilising a multi-proxy to model comparison to constrain the season and regionally heterogeneous impacts of the Mt Samalas 1257 eruption

Abstract. The Mt Samalas eruption, thought to have occurred in summer 1257, ranks as one of the most explosive sulfur-rich eruptions of the Common Era. Despite recent convergence, several dates have been proposed for the eruption ranging between 1256–1258, with, as of yet, no single combination of evidence that has been able to robustly distinguish between and exclude the other dates proposed for the Mt Samalas eruption. Widespread surface cooling and hydroclimate perturbations following the eruption have been invoked as contributing to a host of 13th century social and economic crises, although regional-scale variability in the post-eruption climate response remains uncertain. In this study we run ensemble simulations using the UK Earth System Model (UKESM1) with a range of eruption scenarios and initial conditions in order to compare our simulations with the most complete globally resolved multi-proxy database for the Mt Samalas eruption to date, incorporating tree rings, ice cores, and historical records. This allows more precise constraints to be placed on the year and season of the Mt Samalas eruption, as well as an investigation into the regionally heterogeneous post-eruption climate response. Using a multi-proxy to model comparison, we are able to robustly distinguish between July 1257 and January 1258 eruption scenarios, where the July 1257 ensemble simulation achieves considerably better agreement with spatially averaged and regionally resolved proxy surface temperature reconstructions. These reconstructions suggest the onset of significant cooling across Asia and Europe in 1258 and thus support the plausibility of previously inferred historical connections. Model-simulated temperature anomalies also point to severe surface cooling across the Southern Hemisphere with as of yet unexplored historical implications for impacted civilisations. Model simulations of polar sulfate deposition also reveal distinct differences in the timing of ice sheet deposition between the two simulated eruption dates, although comparison of the magnitude or asymmetric deposition of sulfate aerosol remains limited by large inter-model differences and complex intra-model dependencies. Overall, the multi-proxy to model comparison employed in this study has strong potential in constraining similar uncertainties in eruption source parameters for other historical eruptions for which sufficient coincident proxy records are available, although care is needed to avoid the pitfalls of model–multi-proxy comparison.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

Assessment of Porites microatolls for paleothermometry: Calibration for French Polynesia

Massive dome-shaped coral Porites are the predominant choice for paleoclimate studies due to their consistent and reliable growth. When growing close to sea level, they become limited in their vertical growth and form so-called ‘microatolls’. Microatolls have not yet been extensively explored for paleoclimate reconstruction. Here, we investigate how reliable modern Porites microatolls are against empirical sea-surface temperature using Sr/Ca, δ18O, Li/Mg and SrU paleothermometry methods on samples from the Society Islands, French Polynesia. Our results show Sr/Ca ratios have the lowest Standard Error of the Inverse Prediction (SEIP) at 0.415 °C (N = 41) with a calibration of Sr/Ca (mmol mol−1) = −0.082 ± 0.006 SST (°C) + 11.256 ± 0.170 and with high reproducibility across multiple corals. The reproducibility of δ18O was less good, with SEIP increasing to 0.829 °C (N = 41). Considering methods directly from the literature, Li/Mg ratio empirically corrected for Sr/Ca had the best balance between bias and precision where no local calibration could be available. This study independently evaluates and confirms the suitability of Porites microatolls from well-flushed environments for paleoclimate studies. Fossil dome-shaped Porites grow anywhere between near-surface and roughly 20 m depths which inherently incorporates uncertainty into any sea surface temperature reconstruction. This uncertainty is significantly reduced for microatolls due to their well-constrained bathymetry. The study represents a fundamental step in paleoclimate research targeting consistently near the water-air interface bringing reliability and, especially when combined with their ability to reconstruct past sea-level changes, microatolls have the potential to be central for future paleoenvironmental studies.

North Atlantic Temperature Change Across the Eocene‐Oligocene Transition From Clumped Isotopes

AbstractThe Eocene‐Oligocene transition (EOT) (∼34 Ma) is marked by the rapid development of a semi‐permanent Antarctic ice‐sheet, as indicated by ice‐rafted debris and a 1–1.5‰ increase in deep sea δ18O. Proxy reconstructions indicate a drop in atmospheric CO2 and global cooling. How these changes affected surface ocean temperatures in the North Atlantic and ocean water stratification remains poorly constrained. In this study, we apply clumped‐isotope thermometry to well‐preserved planktonic foraminifera, that are associated with lower mixed‐layer to subthermocline dwelling depths from the drift sediments at international ocean discovery program Site 1411, Newfoundland, across four intervals bracketing the EOT. The thermocline/lower mixed‐layer dwelling foraminifera record a cooling of 1.9 ± 3.5 K (mean ± 95% CI) across the EOT. While the cooling amplitude is similar to previous sea surface temperature (SST) reconstructions, absolute temperatures (Eocene 20.0 ± 2.9°C, Oligocene 18.0 ± 2.2°C) appear colder than previous organic proxy reconstructions for the northernmost Atlantic extrapolated to this location. We discuss seasonal bias, recording depth, and appropriate consideration of paleolatitudes, all of which complicate the comparison between SST reconstructions and model output. Our subthermocline dwelling foraminifera record a larger cooling across the EOT (Eocene 19.0 ± 3.5°C, Oligocene 13.0 ± 3.2°C, cooling of 5.5 ± 4.6 K) than foraminifera from the thermocline/lower mixed‐layer, consistent with global cooling and an increase in ocean stratification which may be related to the onset or intensification of the Atlantic meridional overturning circulation.