A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations; the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve – plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.

Climate models generally project an increase in the winter North Atlantic Oscillation (NAO) index under a future high-emissions scenario, alongside an increase in winter precipitation in northern Europe and a decrease in southern Europe. The extent to which future forced NAO trends are important for European winter precipitation trends and their uncertainty remains unclear. We show using the Multimodel Large Ensemble Archive that the NAO plays a small role in northern European mean winter precipitation projections for 2080-2099. Conversely, half of the model uncertainty in southern European mean winter precipitation projections is potentially reducible through improved understanding of the NAO projections. Extreme positive NAO winters increase in frequency in most models as a consequence of mean NAO changes. These extremes also have more severe future precipitation impacts, largely because of mean precipitation changes. This has implications for future resilience to extreme positive NAO winters, which frequently have severe societal impacts.

We present a Python package geared towards the intuitive analysis and visualization of paleoclimate timeseries, Pyleoclim. The code is open-source, object-oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code’s philosophy, structure and base functionalities, and apply it to three paleoclimate problems: (1) orbital-scale climate variability in a deep-sea core, illustrating spectral, wavelet and coherency analysis in the presence of age uncertainties; (2) correlating a high-resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (3) model-data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting FAIR software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud-executable Jupyter notebooks, to encourage adoption by new users.

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.

Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly-used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that ESM simulated runoff-ratio and soil-moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor-pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.

Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.

In climate reconstructions by data assimilation, the sensitivities to both proxies and prior estimates need to be taken into account because models are uncertain and proxies are limited spatiotemporally. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (corals, ice cores, and tree-ring cellulose). Experiments were conducted based on an offline data assimilation approach. These experiments show annual variations in the global distribution of surface air temperature and precipitation range from 850 to 2000. The results indicate that standard deviations of surface air temperature and precipitation amount during the entire period differ by up to 50% due to prior estimates. Experiments with different types of proxies show that the El Niño-like distribution of positive anomalies in the central to eastern tropical Pacific can be reproduced adequately in experiments with corals, but not in experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55-year Reanalysis (JRA-55) were 0.79 at maximum, while the correlation coefficient between experiments without corals and JRA-55 were 0.20 at maximum.

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

The predicted Antarctic contribution to global-mean sea-level rise is one of the most uncertain among all major sources. Partly this is because of instability mechanisms of the ice flow over deep basins. Errors in bedrock topography can substantially impact the projected resilience of glaciers against such instabilities. Here we analyze the Pine Island Glacier topography to derive a statistical model representation. Our model allows for inhomogeneous and spatially dependent uncertainties and avoids unnecessary smoothing from spatial averaging or interpolation. A set of topography realizations is generated representing our best estimate of the topographic uncertainty in ice sheet model simulations. The bedrock uncertainty alone creates a 5% to 25% uncertainty in the predicted sea level rise contribution at year 2100, depending on friction law and climate forcing. Pine Island Glacier simulations on this new set are consistent with simulations on the BedMachine reference topography but diverge from Bedmap2 simulations.

Increasing ice flux from glaciers retreating over deepening bed topography has been implicated in the recent acceleration of mass loss from the Greenland and Antarctic ice sheets. We show in observations that some glaciers have remained at peaks in bed topography without retreating despite enduring significant changes in climate. Observations also indicate that some glaciers which persist at bed peaks undergo sudden retreat years or decades after the onset of local ocean or atmospheric warming. Using model simulations, we show that glacier persistence may lead to two very different futures: one where glaciers persist at bed peaks indefinitely, and another where glaciers retreat from the bed peak suddenly without a concurrent climate forcing. However, it is difficult to distinguish which of these two futures will occur from current observations. We conclude that inferring glacier stability from observations of persistence obscures our true commitment to future sea-level rise under climate change.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

The Ming Dynasty Mega-drought (MDMD) (1637-1643) occurred at the end of Ming Dynasty and is the severest drought event in China in the last millennium. This unprecedented drought contributed significantly to the collapse of the Ming Dynasty in 1644, casting profound impacts on Chinese history. Here, the physical mechanism for the MDMD is studied. Based on paleoclimate reconstructions, we hypothesize that this drought was initially triggered by a natural drought event starting in 1637, and was then intensified and extended by the tropical volcanic eruption at Mount Parker in 1641. This hypothesis is supported by the case study of the Community Earth System Model-Last Millennium Experiment archive as well as sensitivity experiments with volcanic forcing superimposed on natural drought events. The volcano-intensified drought was associated with a decreased land-ocean thermal contrast, a negative soil moisture response and a weakening and eastward retreating West Pacific Subtropical High. Plain Language Summary The collapse of Ming Dynasty at 1644, and in turn, the historical transition from Ming Dynasty to Qing Dynasty significantly changed the Chinese history into a long period of conservative policy. The collapse of Ming Dynasty at 1644 is contributed greatly by the Ming Dynasty Mega-drought (MDMD) (1637-1643). In this study, based on paleoclimate reconstructions and climate modelling, we show that the MDMD is triggered by a natural drought event, and is then intensified and extended by the strong volcanic eruption at Mt. Parker in 1641. This “superposition” mechanism of MDMD and the spatiotemporal characteristics of this drought is reproduced by our volcanic sensitivity experiments with volcanic forcing superimposed on natural drought events, and the results demonstrate that the explosion of Mt. Parker at the end of a natural drought event amplified and extended the drought for 3 years, generating the mega-drought. The volcano-prolonged drought is associated with the failure of EASM, which is directly caused by the decreasing of land-ocean thermal contrast after volcanic eruption, and indirectly influenced by negative soil moisture feedback as well as weakening and eastward retreating of West Pacific Subtropical High (WPSH).

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Soft x-ray and EUV radiation from the Sun is absorbed by and ionizes the atmosphere, creating both the ionosphere and thermosphere. Temporal changes in irradiance energy and spectral distribution can have drastic impacts on the ionosphere, impacting technologies such as satellite drag and radio communication. Because of this, it is necessary to estimate and predict changes in Solar EUV spectral irradiance. Ideally, this would be done by direct measurement but the high cost of solar EUV spectrographs makes this prohibitively expensive. Instead, scientists must use data driven models to predict the solar spectrum for a given irradiance measurement. In this study, we further develop the Synthetic Reference Spectral Irradiance Model (SynRef). The SynRef model, which uses broadband EUV irradiance data from EUVM at Mars, was created to mirror the SORCE XPS model which uses data from the TIMED SEE instrument and the SORCE XPS instrument at Earth. Both models superpose theoretical Active Region and Quiet Sun spectra generated by CHIANTI to match daily measured irradiance data, and output a modeled solar EUV spectrum for that day. By adjusting the weighting of Active Region and Quiet Sun spectra, we update the SynRef model to better agree with the FISM model and with spectral data collected from sounding rocket flights. We also use the broadband EUVM measurements to estimate AR temperature. This will allow us to select from a library of AR reference spectra with different temperatures. We present this updated SynRef model to more accurately characterize the Solar EUV and soft x-ray spectra.

We investigate the CO2 flux calculated by the ISBA soil-vegetation-atmosphere transfer model (Noilhan and Planton, 1989)by comparing three different formulations for the plant (dark) respiration scheme applied to a soybean culture. The model includes CO2 flux/photosynthesis based on Jacobs (1994) in a manner similar to Calvet et al. (1998) (ISBA-A-gs). The first respiration scheme (M0) computed the autotrophic respiration Rd similarly to Jacobs (1994) but with an ad-hoc temperature correction calibrated by statistical parameter fitting using measured data. For the second model (M1), we implemented the respiration proposed by Joetzjer et al. (2015). Finally we implemented a third respiration scheme (M2) as in Wang (1996). The three models were calibrated and CO2 fluxes were compared with measurements made over a soybean culture using eddy covariance method between December, 2008 and March, 2009, at a farm near Buenos Aires, Argentina. The total CO2 maximum, minimum and mean measured flux values were respectively 0.9890, -0.2479 and 0.3087 mg m-2 s-1. For the sake of comparison, statistics were computed for the full daily cycle flux (total) and also for nighttime flux, as a means to avoid masking of the results due to the much larger daytime photosynthetic flux. We here present the Nash-Sutcliffe efficiency (NSE) coefficient for each model. M0 gave the best overall performance with 0.7568 for the total daily CO2 flux and 0.0795 for the dark flux. M1 gave similar predictions for the daily CO2 flux with 0.7582, butthe worst result for the nighttime period with -0.4965. M2 gave 0.7424 for the full daily flux and 0.0119 for the night CO2 flux. The results show a seemingly better performance of the models in predicting the total CO2 flux compared to the dark CO2 flux. This is due to several facts such as: respiration is less understood and harder to predict than photosynthesis; measurements are more difficult at nighttime due to the limitations of the eddy-covariance technique in low turbulent activity; in the measured data, it is difficult to identify and separate the portions of CO2 fluxes as soil respiration, autotrophic respiration and photosynthetic flux, without many auxiliary measurements. We also conclude that there is a clear influence of the temperature on the respiration, which can be suitably incorporated in the models.

The shape of ice-shelf cavities are a major source of uncertainty in understanding ice-ocean interactions. This limits assessments of the response of the Antarctic ice sheets to climate change. Here we use vibroseis seismic reflection surveys to map the bathymetry beneath the Ekström Ice Shelf, Dronning Maud Land. The new bathymetry reveals an inland-sloping trough, reaching depths of 1100 m below sea level, near the current grounding line, which we attribute to erosion by palaeo-ice streams. The trough does not cross-cut the outer parts of the continental shelf. Conductivity-temperature-depth profiles within the ice-shelf cavity reveal the presence of cold water at shallower depths and tidal mixing at the ice-shelf margins. It is unknown if warm water can access the trough. The new bathymetry is thought to be representative of many ice shelves in Dronning Maud Land, which together regulate the ice loss from a substantial area of East Antarctica.

Coral reefs are rapidly declining due to local environmental degradation and global climate change. In particular, corals are vulnerable to ocean heating. Anomalously hot sea surface temperatures (SSTs) create conditions for severe bleaching or direct thermal death. We use SST observations and CMIP6 model SST to project thermal conditions at reef locations at a resolution of 1 km, a 16-fold improvement over prior studies, under four climate emissions scenarios. We use a novel statistical downscaling method which is significantly more skillful than the standard method, especially at near-coastal pixels where many reefs are found. For each location we present projections of thermal departure (TD, the date after which a location with steadily increasing heat exceeds a given thermal metric) for severe bleaching recurs every 5 years (TD5Y) and every 10 years (TD10Y), accounting for a range of post-bleaching reef recovery/degradation. As of 2021, we find that over 91% and 79% of 1 km reefs have exceeded TD10Y and TD5Y, respectively, suggesting that widespread long-term coral degradation is no longer avoidable. We project 99% of reefs to exceed TD5Y by 2034, 2036, and 2040 under SSP5-8.5, SSP3-7.0, and SSP2-4.5 respectively. We project that 2%-5% of reef locations remain below TD5Y at 1.5 degrees Celsius of mean global heating, but 0% remain at 2.0 degrees Celsius. These results demonstrate the importance of further improving ecological projection capacity for climate-vulnerable marine and terrestrial species and ecosystems, including identifying refugia and guiding conservation efforts. Ultimately, saving coral reefs will require rapidly reducing and eliminating greenhouse gas emissions.

Aura Microwave Limb Sounder (MLS) measurements show that chemical processing was critical to the observed record-low Arctic stratospheric ozone in spring 2020. The 16-year MLS record indicates more denitrification and dehydration in 2019/2020 than in any Arctic winter except 2015/2016. Chlorine activation and ozone depletion began earlier than in any previously observed winter, with evidence of chemical ozone loss starting in November. Active chlorine then persisted as late into spring as it did in 2011. Empirical estimates suggest maximum chemical ozone losses near 2.8 ppmv by late March in both 2011 and 2020. However, peak chlorine activation, and thus peak ozone loss, occurred at lower altitudes in 2020 than in 2011, leading to the lowest Arctic ozone values ever observed at potential temperature levels from ~400–480 K, with similar ozone values to those in 2011 at higher levels.

Variability in the position and strength of the subtropical jet (STJ) and polar front jet (PFJ) streams has important implications for global and regional climate. Previous studies have related the position and strength of the STJ to tropical thermodynamic processes, whereas the position and strength of the PFJ are more associated with mid-latitude eddies. These conclusions have largely resulted from studies using idealized models. In this study, ERA-Interim reanalysis and CMIP6 global climate models are used to examine month-to-month and interannual variability of the wintertime Northern Hemisphere (NH) STJ and PFJ. This study particularly focuses on the regional characteristics of the jet variability, extending previous studies on zonal-mean jet streams. Consistent with idealized modeling studies, a close relationship is found between tropical outgoing longwave radiation (OLR) and the STJ, and between mid-latitude surface temperature gradients and the PFJ. Variations of both jets are also linked to well-known teleconnection patterns. Variations in tropical convection over the Pacific Ocean are associated with variations of the NH STJ at most longitudes, with different phases of the El Niño-Southern Oscillation (ENSO) associated with the shift and strengthening of the STJ in different regions. CMIP6 models generally capture these relationships, but the models’ tropical convection is often displaced westward when compared to observations, reflecting a climatological bias in OLR in the western tropical Pacific Ocean in many models. The displaced tropical convection in models excites different paths of Rossby wave propagation, resulting in different ENSO teleconnections on the STJ over North America and Europe.

Hydroperiod, or the amount of time a lentic waterbody contains water, shapes communities of aquatic organisms. Precise measurement of hydroperiod features such as inundation timing and duration can help predict community dynamics and ecosystem stability. In areas defined by high spatial and temporal variability, fine-scale temporal variation in inundation timing and duration may drive community structure, but that variation may not be captured using common approaches including remote sensing technology. Here, we provide methods to accurately capture inundation timing by fitting hidden Markov models to measurements of daily temperature standard deviation collected from temperature loggers. We describe a rugged housing design to protect loggers from physical damage and apply our methods to a group of intermittent ponds in southeastern Arizona, showing that initial pond inundation timing is highly variable across a small geographic scale (~50km2). We also compare a 1-logger (pond only) and 2-logger (pond + control) design and show that, although a single logger may be sufficient to capture inundation timing in most cases, a 2-logger design can increase confidence in results. These methods are cost-effective and show promise in capturing variation in intraregional inundation timing that may have profound effects on aquatic communities, with implications for how these communities may respond to hydroperiod alteration from a changing climate.

Changes in the concentration of greenhouse gases within the atmosphere lead to changes in radiative fluxes throughout the atmosphere. The value of this change, called the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models, and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. The clear-sky instantaneous forcing by greenhouse gases to which the world has been subject is computed using a set of 100 profiles, selected from a re-analysis of present-day conditions, that represent the global annual mean forcing with sampling errors of less than 0.01 \si{\watt\per\square\meter}. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 \si{\watt\per\square\meter} while even recently-developed parameterizations have typical errors four or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily resolved. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are roughly estimated, as are adjustments due to stratospheric temperature change. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest non-linearity.

Previous studies have interpreted Last Interglacial (LIG; ~129-116 ka) sea-level estimates in multiple different ways to calibrate projections of future Antarctic ice-sheet (AIS) mass loss and associated sea-level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea-level rise. We develop a Gaussian process emulator of an ice-sheet model to produce continuous probabilistic projections of Antarctic sea-level contributions over the LIG and a future high-emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice-sheet instabilities and projections of future sea-level rise through 2150. Although best-available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (+/-5 cm), indicating there is a limit to how informative the LIG could be for ice-sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea-level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea-level projections, highlighting the importance of continued observational efforts.

In this paper, we leverage open data and open-source software to develop flexible, probabilistic monthly and seasonal (three-month) precipitation forecasts for the Pacific region. We use data from a Multi-Model Ensemble (MME), i.e. a large ensemble of state-of-the-art General Circulation Models (GCMs) and make use of recent developments in the Python open-source software ecosystem allowing the processing of large datasets on standard consumer grade laptops or desktop computers, of particular relevance in the Pacific context. The validation of the deterministic MME forecasts against reanalysis and observational products shows good performance, and confirms that an MME outperforms even the best single GCM. We show that the MME’s forecast performance is modulated by the phases and characteristics of the El Nino Southern Oscillation (ENSO), with the longitude of the maximum Sea Surface Temperature anomalies playing a major role. We suggest that these findings could be used to provide additional confidence information along with the operational MME forecasts. Validation metrics for the probability of drought conditions, alternatively defined as seasonal rainfall accumulations below the climatological 1 tercile (percentile 33) or 1st quartile (percentile 25) show that the MME forecasts are reliable enough for most of the region. We provide an example of how this probabilistic forecast information can be integrated with real-time rainfall monitoring, in order to highlight areas in the tropical Pacific region which are at risk of water stress (i.e., where rainfall has recently been in deficit and forecasts indicate a high likelihood of dry conditions to persist or worsen).