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When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections. Plain Language Summary: Extreme storm surges, rainfall or river discharge can cause flooding. When these events happen at the same time, even more severe flooding may follow. Climate change could affect the odds that drivers of flooding coincide, potentially leading to larger flood risk. However, few scientists have tried to compute such changes, using only a few different computer models of our climate. Here, we use a much larger set of climate models to compute how the odds that an extreme storm surge coincides with extreme precipitation could change in the future. We find that at the coasts of northwestern Europe, those odds will increase, whereas in southwestern Europe, they will mostly decrease. On average, the changes will be as large as 36%–49% of the current odds, depending on whether the concentration of greenhouse gases in the atmosphere will increase by a medium or a large amount. When we use smaller sets of climate models for our calculations, we get substantially different results in some cases. In conclusion, by using a larger set of climate models than previous studies, we have made more robust computations of how the odds that extreme storm surges and precipitation coincide will change in Europe. Key Points: We project changes in the joint probability of storm surge and precipitation extremes based on a large ensemble of model simulations from the Coupled Model Intercomparison Project 6The joint probability will increase in the northwest and decrease in the southwest of Europe, with an average absolute magnitude of 36%–49%Especially under lower emissions, often more than 5 or 6 climate model simulations are needed to draw robust conclusions on these changes [ABSTRACT FROM AUTHOR]

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea‐level rise. Given the limitations of in‐situ measurements, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16‐day albedo product derived from satellite remote sensing data. Comparison of the results against in‐situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error of the posterior estimates of SMB by 51% and reduces bias by 95%. Plain Language Summary: Greenland ice sheet (GrIS) is losing mass through ice discharge from outlet glaciers and surface processes (e.g., meltwater runoff, sublimation, and evaporation). Recent studies suggest that meltwater runoff will be the dominant mass loss process over the GrIS in the future as it will increase under climate warming. Accurate estimates of the GrIS surface mass balance (SMB) are a critical objective, which, despite its importance, continues to contain large uncertainties from significant errors in forcing data as well as model errors. This work uses a data assimilation framework (which has not been used in estimation of the GrIS SMB) and a satellite‐derived 16‐day albedo product to produce a reanalysis estimates of SMB along the Kangerlussuaq transect (K‐transect) stations in west Greenland. We used the K‐transect in‐situ SMB measurements to validate our results over the 2000–2014 hydrological year. The data assimilation technique (i.e., particle batch smoother) reduces the spatial root‐mean‐square error of SMB over the K‐transect stations by 51% from 858 millimeter water equivalent (mmWE) to 423 mmWE and the bias in the estimates by 95%, from −70 to 3.5 mmWE. It was shown that this methodology has the potential to resolve the spatial variability of the surface processes along the K‐transect stations and in particular of the bare ice surface albedo that is not resolved by the model at a resolution of 25 km (i.e., the model uses a constant bare ice albedo). The results suggest that the methodology can be applied over the entire GrIS using MODIS albedo observations to generate an improved reanalysis of SMB estimates. Key Points: A data assimilation method was used to generate a reanalysis estimates of the surface mass balance of the Greenland ice sheet along the K‐transect stationsA particle batch smoother technique was used to condition the prior estimates of surface mass balance on 16‐day MODIS albedoResults show that the assimilation of albedo reduces the root mean square error of the surface mass balance estimates by 51% [ABSTRACT FROM AUTHOR]

River deltas will likely experience significant land loss because of relative sea‐level rise (RSLR), but predictions have not been tested against observations. Here, we use global data of RSLR and river sediment supply to build a model of delta response to RSLR for 6,402 deltas, representing 86% of global delta land. We validate this model against delta land area change observations from 1985–2015, and project future land area change for IPCC RSLR scenarios. For 2100, we find widely ranging delta scenarios, from +94 ± 125 (2 s.d.) km2 yr−1 for representative concentration pathway (RCP) 2.6 to −1,026 ± 281 km2 yr−1 for RCP8.5. River dams, subsidence, and sea‐level rise have had a comparable influence on reduced delta growth over the past decades, but if we follow RCP8.5 to 2100, more than 85% of delta land loss will be caused by climate‐change driven sea‐level rise, resulting in a loss of ∼5% of global delta land. Plain Language Summary: River deltas can erode and lose land from sea‐level rise. Here we make model predictions of the effects of sea‐level rise for global delta land area change up to 2100. Our model is validated against observations of delta land area change from 1985–2015. For 2100, we find that most climate change scenarios lead to net delta land loss. Worst‐case scenarios for 2100 lead to a global river delta land loss of ∼5% of delta land, at a rate of 1,000 km2 per year. Key Points: Model of river delta response to sea level rise and river sediment applied to 6,402 deltas and tested against land area change observationsProjections suggest net delta land loss for GMSLR >5.5 mm/yr, and for RSLR under IPCC RCP4.5 and RCP8.5 by 2100Climate‐change driven sea‐level rise will likely exceed the effects of dams and subsidence on global delta land loss by 2100 [ABSTRACT FROM AUTHOR]

Abstract: We reanalyze existing paleodata of global mean surface temperature ΔTg and radiative forcing ΔR of CO2 and land ice albedo for the last 800,000 years to show that a state‐dependency in paleoclimate sensitivity S, as previously suggested, is only found if ΔTg is based on reconstructions, and not when ΔTg is based on model simulations. Furthermore, during times of decreasing obliquity (periods of land ice sheet growth and sea level fall) the multimillennial component of reconstructed ΔTg diverges from CO2, while in simulations both variables vary more synchronously, suggesting that the differences during these times are due to relatively low rates of simulated land ice growth and associated cooling. To produce a reconstruction‐based extrapolation of S for the future, we exclude intervals with strong ΔTg‐CO2 divergence and find that S is less state‐dependent, or even constant state‐independent), yielding a mean equilibrium warming of 2–4 K for a doubling of CO2. [ABSTRACT FROM AUTHOR]