We present a dynamically consistent gridded data set of the global, monthly mean oxygen isotope ratio of seawater ( ). The data set was created from an optimized simulation of an ocean general circulation model constrained by global monthly data collected from 1950 to 2011 and climatological salinity and temperature data collected from 1951 to 1980. The optimization was obtained using the adjoint method for variational data assimilation, which yields a simulation that is consistent with the observational data and the physical laws embedded in the model. Our data set performs equally well as a previous data set in terms of model‐data misfit but brings an improvement in terms of the seasonal cycle and physical consistency. As a result the data set does not show any sharp transitions between water masses or in areas where the data coverage is low. The data assimilation method shows high potential for interpolating sparse data sets in a physically meaningful way. Comparatively big errors, however, are found in our data set in the surface levels in the Arctic Ocean mainly because the influence of isotopically highly depleted precipitation is not preserved in the sea ice model, and the low model resolution of about 285 km horizontally. The data set is publicly available, and it is anticipated to be useful for a large range of applications in (paleo‐) oceanographic studies.

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Combining ocean general circulation models with proxy data via data assimilation is a means to obtain estimates of past ocean states that are consistent with model physics as well as with proxy data. The climate during the Last Glacial Maximum (LGM, 19–23 ka) was substantially different from today. Even though boundary conditions are comparatively well known, the large-scale patterns of the ocean circulation during this time remain uncertain. Previous efforts to combine ocean models with proxy data have shown dissimilar results regarding the state of the ocean, in particular of the Atlantic Meridional Overturning Circulation. Here, we present a new LGM ocean state estimate that extents previous estimates by using global benthic as well as planktic data on the oxygen isotopic composition of calcite. It is further constrained by global seasonal and annual sea surface temperature (SST) reconstructions. The estimate shows an Atlantic Ocean that is similar to the Late Holocene Atlantic Ocean but with a reduced formation of Antarctic Bottom Water, in contrast to results of previous studies. The results indicate that SST and oxygen isotopic data alone do not require the presence of a shallower North Atlantic Deep Water and a more extensive Antarctic Bottom Water, and highlight the need for more proxy data of different types to obtain reliable ocean state estimates. Additional adjoint sensitivity experiments reveal that data from the deep North Atlantic and from the global deep Southern Ocean are most important to constrain the Atlantic Meridional Overturning Circulation.

We present a dynamically consistent gridded data set of the global, monthly-mean oxygen isotope ratio of seawater (δ¹⁸Osw). The data set is created from an optimized simulation of an ocean general circulation model constrained by global monthly δ¹⁸Osw data collected from 1950 until 2011 and climatological salinity and temperature data collected from 1951 to 1980. The optimization was obtained using the adjoint method for variational data assimilation, which yields a simulation that is consistent with the observational data and the physical laws incorporated in the model. Our data set performs equally well as a previous data set in terms of model-data misfit and brings an improvement in terms of physical consistency and a seasonal cycle. The data assimilation method shows high potential for interpolating sparse data sets in a physical meaningful way. Comparatively big errors, however, are found in our data set in the surface levels in the Arctic Ocean mainly because there is no influence of isotopically highly depleted precipitation on the ocean in areas with sea-ice, and because of the low model resolution. The data set is the 100-year monthly-mean of the optimized 400-year equilibrium model simulation. It includes simulated δ¹⁸Osw, potential temperature, and salinity on the model grid. The model uses a cubed-sphere grid with a horizontal resolution of 2.8° and 15 vertical levels. We additionally provide the data interpolated onto a 1° lat-lon grid. Values at the edge of the ocean, which could not be interpolated, are set to the respective values in the raw data set on the model grid.

Combining ocean models and proxy data via data assimilation is a powerful means to obtain more reliable estimates of past ocean states, but studies using data assimilation for paleo-ocean state estimation are rare. A few studies used the adjoint method, also called 4D-Var, to estimate the state of the ocean during the Last Glacial Maximum (LGM). The adjoint method, however, requires the adjoint of the model code, which is not easily obtained for most models. The method is computationally very demanding and does not readily provide uncertainty estimates. Here, we present a new and computationally very efficient technique to obtain ocean state estimates. We applied a state reduction approach in conjunction with a finite difference sensitivity-iterative Kalman smoother (FDS-IKS) to estimate spatially varying atmospheric forcing fields and to obtain an equilibrium model simulation in consistency with proxy data. We tested the method in synthetic pseudo-proxy data experiments. The method is capable of very efficiently estimating 16 control variables and reconstructing a target ocean circulation from sea surface temperature (SST) and oxygen isotopic composition of seawater data at LGM coverage. The method is advantageous over the adjoint method regarding that it is very easy to implement, it requires substantially less computing time and provides an uncertainty estimate of the estimated control variables. The computing time, however, depends linearly on the size of the control space limiting the number of control variables that can be estimated. We used the method to investigate the constraint of data outside of the Atlantic Ocean on the Atlantic overturning circulation. Our results indicate that while data from the Pacific or Indian Ocean aid in correctly estimating the Atlantic overturning circulation, they are not as crucial as the Atlantic data. We additionally applied the method to estimate the LGM ocean state constrained by a global SST reconstruction and data on the oxygen isotopic composition of calcite from fossil benthic and planktic foraminifera. The LGM estimate shows a large improvement compared to our first guess, but model-data misfits remain after the optimization due to model errors that cannot be corrected by the control variables. The estimate shows a shallower North Atlantic Deep Water and a weaker Atlantic overturning circulation compared to today in consistency with previous studies. The combination of the FDS-IKS and the state reduction approach is a step forward in making ocean state estimation and data assimilation applicable for complex and computationally expensive models and to models where the adjoint is not available.