A Perturbed Parameter Ensemble (PPE) with the Community Atmosphere Model version 6 (CAM6) is used to better understand the sensitivity of simulated clouds to both aerosol forcing and cloud feedbacks and the interactions between them. Aerosol forcing through aerosol-cloud interactions is mostly negative (a cooling) due to shortwave radiation, while feedbacks are positive or negative in different regions due to contrasting longwave and shortwave effects. Both forcing and feedbacks are related to the mean climate state. Higher magnitude cloud radiative effects generally mean larger net forcing and larger net feedback. Aerosol forcing is broadly related to the susceptibility of clouds to drop number. Feedbacks are less related to susceptibility, and in different regions. Aerosol forcing and cloud feedbacks are anti-correlated in the CAM6 PPE such that stronger negative forcing is associated with stronger positive feedbacks. Even the processes governing forcing and feedback sensitivity in the PPE are similar. These include the warm rain formation process, ice loss processes and deep convective intensity.

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) photon-counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat-2 measurements. Careful planning and dedicated development were accomplished during the pre-launch phase of the mission in preparation for the 2018 launch. Once on-orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat-2’s contributions to earth observation science initiated by NASA and the ICESat-2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat-2’s data capabilities and allowed for advanced science applications beyond the original mission objectives.

The end-Triassic extinction (ETE) is one of the most severe biotic crises in the Phanerozoic. This event was synchronous with volcanism of the Central Atlantic Magmatic Province (CAMP), the ultimate cause of the extinction and related environmental perturbations. However, the continental weathering response to CAMP-induced warming remains poorly constrained. Strontium isotope stratigraphy is a powerful correlation tool that can also provide insights into the changes in weathering regime but the scarcity of 87Sr/86Sr data across the Triassic-Jurassic boundary (TJB) compromised the use of this method. Here we present new high-resolution 87Sr/86Sr data from bulk carbonates in Csővár, a continuous marine section that spans 2.5 Myrs across the TJB. We document a continuing decrease in 87Sr/86Sr the from the late Rhaetian to the ETE, terminated by a 300 kyr interval of no trend and followed by a transient increase in the early Hettangian that levels off. We suggest that the first in the series of perturbations is linked to the influx of non-radiogenic Sr from the weathering of freshly erupted CAMP basalts, leading to a delay in the radiogenic continental weathering response. The subsequent rise in 87Sr/86Sr after the TJB is explained by intensified continental crustal weathering from elevated CO2 levels and reduced mantle-derived Sr flux. Using Sr flux modeling, we also find support for such multiphase, prolonged continental weathering scenario. Aggregating the new dataset with published records employing an astrochronological age model results in a highly resolved Sr isotope reference curve for an 8.5 Myr interval around the TJB.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their vulnerability to permafrost thawing induced by climate change. An ensemble of six terrestrial biosphere models suggests NHL soil N2O emissions doubled since the preindustrial 1860s, increasing on average by 2.0±1.0 Gg N yr-1 (p<0.01). This trend reversed after the 1980s because of reduced nitrogen fertilizer application in non-permafrost regions and increased plant growth due to CO2 fertilization suppressed emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions.

Air pollution caused by various anthropogenic activities and biomass burning continues to be a major problem in India. To assess the effectiveness of current air pollution mitigation measures, we used a 3D global chemical transport model to analyze the projected optical depth of carbonaceous aerosol (AOD) in India under representative concentration pathways (RCP) 4.5 and 8.5 over the period 2000-2100. Our results show a decrease in future emissions, leading to a decrease in modeled AOD under both RCPs after 2030. The RCP4.5 scenario shows a 48-65% decrease in AOD by the end of the century, with the Indo-Gangetic Plain (IGP) experiencing a maximum change of ~25% by 2030 compared to 2010. Conversely, RCP8.5 showed an increase in AOD of ~29% by 2050 and did not indicate a significant decrease by the end of the century. Our study also highlights that it is likely to take three decades for current policies to be effective for regions heavily polluted by exposure to carbonaceous aerosols, such as the IGP and eastern India. We emphasize the importance of assessing the effectiveness of current policies and highlight the need for continued efforts to address the problem of air pollution from carbonaceous aerosols, both from anthropogenic sources and biomass burning, in India.

Reliable air temperature forecasts are necessary for mitigating the effects of droughts and Heatwaves. The numerical weather prediction(NWP) model forecasts have significant biases associated and therefore need post-processing. Post-processing of temperature forecasts using probabilistic approaches are lacking in India. In this study, we post-process the Global Ensemble Forecast System (GEFS) and EuropeanCentre for Medium Range Weather Forecasts (ECMWF) NWP model temperature forecasts for short to medium range time scales (1-7 days)using two probabilistic techniques, namely, Bayesian model averaging(BMA) and Nonhomogeneous gaussian regression (NGR). The post-processing techniques are evaluated for temperature (maximum and minimum) predictions across the Indian region. Results show that the probabilistic approaches considerably enhance the temperature predictions across India except the Himalayan regions. These techniques also comprehensively outperform the traditional post-processing techniques such as the running mean and simple linear regression. The NGR performs better than the BMA across all regions and is able to provide highly skillful temperature forecasts at higher lead times as well. Further, the study also assesses the implication of probabilistic post-processing Tmax forecast towards forecast enhancement of heatwaves (HW) in India. Post-processed Tmax forecasts revealed that the NGR approach considerably enhanced the HW prediction skill in India, especially in the northwestern and central Indian regions, considered highly prone to HW. The findings of this study will be useful in developing enhanced HW early warning and prediction systems in India.

Arctic permafrost thaw holds the potential to drastically alter the Earth’s surface in Northern high latitudes. We utilize high-resolution Large Eddy Simulations to investigate the impact of the changing surfaces onto the neutrally stratified Atmospheric Boundary Layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length.

There is an urgent need for models that can robustly detect past and project future ecosystem changes and risks to the services that they provide to people. The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) was established to develop model ensembles for projecting long-term impacts of climate change on fisheries and marine ecosystems while informing policy at spatio-temporal scales relevant to the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. While contributing FishMIP models have improved over time, large uncertainties in projections remain, particularly in coastal and shelf seas where most of the world’s fisheries occur. Furthermore, previous FishMIP climate impact projections have mostly ignored fishing activity due to a lack of standardized historical and scenario-based human activity forcing and uneven capabilities to dynamically model fisheries across the FishMIP community. This, in addition to underrepresentation of coastal processes, has limited the ability to evaluate the FishMIP ensemble’s ability to adequately capture past states - a crucial step for building confidence in future projections. To address these issues, we have developed two parallel simulation experiments (FishMIP 2.0) on: 1) model evaluation and detection of past changes and 2) future scenarios and projections. Key advances include historical climate forcing, that captures oceanographic features not previously resolved, and standardized fishing forcing to systematically test fishing effects across models. FishMIP 2.0 is a key step towards a detection and attribution framework for marine ecosystem change at regional and global scales, and towards enhanced policy relevance through increased confidence in future ensemble projections.

Global temperature responses from different abrupt CO2 change experiments participating in Coupled Model Intercomparison Project Phase 6 (CMIP6) and LongRunMIP are systematically compared in order to study the linearity of the responses. For CMIP6 models, abrupt-4xCO2 experiments warm on average 2.2 times more than abrupt-2xCO2 experiments. A factor of about 2 can be attributed to the differences in forcing, and the rest is likely due to nonlinear responses. Abrupt-0p5xCO2 responses are weaker than abrupt-2xCO2, mostly because of weaker forcing. CMIP6 abrupt CO2 change experiments respond linearly enough to well reconstruct responses to other experiments, such as 1pctCO2, but uncertainties in the forcing can give uncertain responses. We derive also a generalised energy balance box model that includes the possibility of having oscillations in the global temperature responses. Oscillations are found in some models, and are connected to changes in ocean circulation and sea ice. Oscillating components connected to a cooling in the North Atlantic can counteract the long-term warming for decades or centuries and cause pauses in global temperature increase.

As oceanic moisture evaporates, it leaves a signature on sea surface salinity. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in published literature to assess the role of sea surface salinity for improved predictions on low-skill summertime subseasonal timescales for terrestrial precipitation predictions. Neural networks are trained with the CESM2 Large Ensemble using North Atlantic salinity anomalies to quantify predictability of U.S. Midwest summertime heavy rainfall events at 0 to 56-day leads. Using explainable artificial intelligence, salinity anomalies in the Caribbean Sea and Gulf of Mexico are found to provide skill for subseasonal forecasts of opportunity, e.g. confident and correct predictions. Further, a moisture-tracking algorithm applied to reanalysis data demonstrates that the regions of evaporation identified by neural networks directly provide moisture that precipitates in the Midwest.

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions.  Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL’s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 – 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 – 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 142-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

Estimation of extreme climate trends is a crucial, influential, and also controversial step in long-term water resources planning studies. One of the main approaches to capturing the variability of climate trends is to use a diverse set of General Circulation Models (GCMs). As climate change models refine following deepening climate knowledge, utilizing updated models is unavoidable. The California Central Valley (CCV), a key agricultural zone in the western U.S., derives the bulk of its surface water from the Sacramento and San Joaquin rivers. Moreover, this area serves as a water source for several megacities, including Los Angeles, San Francisco, San Diego, and Sacramento. On average, over 80% of the total Sacramento-San Joaquin Delta outflow comes from the north and eastern upgradient regions (called rim watersheds) surrounding the valley. In this study, the effect of climate change on extreme trends in precipitation and temperature is evaluated for 12 CCV rim watersheds using downscaled CMIP6 data. Downscaled data are derived from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), which were downscaled using the Bias-Correction Spatial Disaggregation (BCSD) statistical method. Based on the availability of precipitation and temperature data from historical and future time spans, 21 models were selected out of 35 available models. For comparison and consistency with previous studies, 1980–2010 is selected to represent the base period, and 2040–2070 is selected to represent the future period. Average daily temperature and precipitation are calculated for each period under historical and SSP126, SSP245, SSP370, and SSP585 scenarios at each grid point lying inside the rim watershed boundaries. Figure 1 shows the average changes in temperature and precipitation for each GCM and SSP scenario during the historical period. As shown in Figure 1, which is an average across all specified rim watersheds, extreme trends show a maximum of 10.75% decrease to a maximum of 28.25% increase in precipitation and a minimum of 0.7°C increase to a maximum of 5°C increase in temperature. The previous study, conducted using CMIP5 by Schwarz et al. in 2019, revealed that the changes in precipitation and temperature would range approximately from -13% to +25% and +0.6°C to +3.9°C, respectively. These findings show more severe temperature extremes when using CMIP6 compared to CMIP5. On the other hand, extreme precipitation trends were not significantly influenced by changing model generation and scenarios. These findings suggest that using the latest CMIP generation would take a more diverse set of climatological uncertainties into account. Another analysis was conducted by examining each of the 12 rim watersheds separately. The results of this section show that the temperature and precipitation extremes did not change significantly compared to those from the holistic analysis. Thus, it seems that a holistic analysis of all 12 rim watersheds could properly represent precipitation and temperature extreme trends for each of the rim watersheds. 

The impact of carbon fluxes on soil organic carbon (SOC) remains underexplored. We employed machine learning to model SOC dynamics. Our findings project an increase in China’s SOC through to the year 2100 across various Shared Socioeconomic Pathways. Sensitivity analyses have identified carbon fluxes as the main drivers for this projected rise, followed by climate and land use. Further examination using an explainable artificial intelligence method, Shapley Additive Explanations, has uncovered both spatial and temporal variations in how gross primary production (GPP) influences SOC levels. Notably, GPP’s contribution on SOC is initially negative at low levels, turning positive once a threshold of approximately 3 gC m-2d-1 is surpassed. Beyond a GPP of about 7 gC m-2d-1, its positive contribution to SOC plateaus. Critical zones for soil carbon sequestration are located around 400 mm annual precipitation line.

The eastern subpolar North Atlantic upper ocean salinity undergoes decadal fluctuations. A large fresh anomaly event occurred during 2012--2016. Using the ECCO state estimate, we diagnose and compare mechanisms of this low salinity event with that of the 1990s fresh anomaly event. To avoid erroneous interpretations of physical mechanisms due to reference salinity values in the freshwater budget, we perform a salt mass content budget analysis of the eastern subpolar North Atlantic. It shows that the recent fresh anomaly occurs due to the circulation of anomalous salinity by mean currents entering the eastern subpolar basin from its western boundary via the North Atlantic Current. This is in contrast to the early 1990s, when the dominant mechanism governing the fresh anomaly was the transport of the mean salinity field by anomalous currents across the southern boundary of the subpolar North Atlantic.

Using model projections to study the emergence of observable climate signals presumes omniscient knowledge about the climate system. In reality, observational knowledge suffers from data quality and availability issues. Overlooking such deficiencies leads to misrepresentations of the time of emergence (ToE). We introduce a new definition of ToE that accounts for observational limitations (e.g., data gaps, gridding, changes in instrumentation, retrieval algorithms, etc), and show the potential for significant corrections to achieve the same statistical confidence as would be afforded by omniscient knowledge. We also show how our method can inform future observational needs and observing systems design.

Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long-term hazard of rare high-impact storm events. Existing downscaling approaches rely on statistical or statistical-deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two-way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post-processing to introduce asymmetries in the simulated surface wind. In this study, we present an innovative data-driven approach for TC synthetic downscaling. Using a machine learning-based high-resolution global weather model (ML-GWM), our approach is able to simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two-way interactions between the storm and its environment. This approach consists of multiple components: a data-driven model for generating synthetic TC seeds, a blending method that seamlessly integrate storm seeds into the surrounding while maintain the seed structure, and a recurrent neural network-based model for correcting the biases in maximum wind speed. Compared to observations and synthetic storms simulated using existing statistical-deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Taking advantage of the computational efficiency of ML-GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

Unprecedented warming of Arctic–boreal regions (ABR) has poorly understood consequences on carbon cycle processes. Uncertainties in annual methane (CH4) budgets partly arise because of limited data availability during winter. In this study, winter CH4 flux measurements were conducted using the snowpack diffusion gradient method over five ABR ecosystem types in Canada and Finland: closed–crown and open–crown coniferous boreal forest, boreal wetland and erect–shrub and prostrate–shrub tundra. Boreal forest uplands acted as net CH4 sinks, while the boreal wetland acted as net CH4 source during winter. We identified several wetland tundra CH4 emission hotspots and large spatial variability in boreal wetland CH4 emissions. In the boreal forest uplands, soil liquid water content was identified as an important environmental control of winter CH4 fluxes. Our results indicate non–negligible winter CH4 flux, which must be accounted for in annual carbon balance and terrestrial biosphere models over ABR.

This study presents an overview of the Late Cenozoic evolution of the West African Monsoon (WAM), and the associated changes in atmospheric dynamics and oxygen isotopic composition of precipitation (δ18Op). This evolution is established by using the high-resolution isotope-enabled GCM ECHAM5-wiso to simulate the climatic responses to paleoenvironmental changes during the Mid-Holocene (MH), Last Glacial Maximum (LGM), and Mid-Pliocene (MP). The simulated responses are compared to a set of GCM outputs from Paleoclimate Model Intercomparison Project phase 4 (PMIP4) to assess the added value of a high resolution and model consistency across different time periods. Results show WAM magnitudes and pattern changes that are consistent with PMIP4 models and proxy reconstructions. ECHAM5-wiso estimates the highest WAM intensification in the MH, with a precipitation increase of up to 150 mm/month reaching 25°N during the monsoon season. The WAM intensification in the MP estimated by ECHAM5-wiso (up to 80 mm/month) aligns with the mid-range of the PMIP4 estimates, while the LGM dryness magnitude matches most of the models. Despite an enhanced hydrological cycle in MP, MH simulations indicate a ~50% precipitation increase and a greater northward extent of WAM than the MP simulations. Strengthened conditions of the WAM in the MH and MP result from a pronounced meridional temperature gradient driving low-level westerly, Sahel-Sahara vegetation expansion, and a northward shift of the Africa Easterly Jet. The simulated δ18Op values patterns and their relationship with temperature and precipitation are non-stationarity over time, emphasising the implications of assuming stationarity in proxy reconstruction transfer functions.

A document by Cassandra D W Rogers. Click on the document to view its contents.

Tipping points have gained substantial traction in climate change discourses, both as representing the possibility of catastrophic and irreversible physical and societal impacts and as a way to set in motion positive, rapid and self-sustaining responses, such as the adoption of new technologies, practices, and behaviors. As such, tipping points appear ubiquitous in natural and social systems. Here, we critique 'tipping point' framings, specifically their insufficiency for describing the diverse dynamics of complex systems; their reductionist view of individuals, their agency and their aspirations; and their tendency to convey urgency without fostering a meaningful basis for climate action. We argue for clarifying the scientific discussion of the phenomena lumped under the 'tipping point' umbrella by using more specific language to capture relevant aspects (e.g., irreversibility, abruptness, self-amplification, potential surprise) and for the critical evaluation of whether, how and why the different framings can support accurate scientific understanding and effective climate risk management. Multiple social scientific frameworks suggest that deep uncertainty and perceived abstractness associated with many proposed Earth system 'tipping points' make them both unlikely to provoke effective action and not helpful for setting governance goals that must be sensitive to multiple constraints. The mental model of a 'tipping point' does not align with the multifaceted nature of social change; a broader focus on the dynamics of social transformation is more useful. Temperature-based benchmarks originating in a broad portfolio of concerns already provide a suitable guide for global mitigation policy targets and should not be confused with physical thresholds of the climate system.

A document by Yuan-Jen Lin. Click on the document to view its contents.

The ocean oxygen (O2) inventory has declined in recent decades but the estimates of O2 trend is uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed for the North Atlantic basin using machine learning techniques and biogeochemical Argo array. The source data includes 159 thousand historical shipboard (bottle and CTD-O2) profiles from 1965 to 2020 and 17 thousand Argo O2 profiles after 2005. Neural network and random forest algorithms were trained using 80% of this data using different hyperparameters and predictor variable sets. From a total of 240 trained algorithms, 12 high performing algorithms were selected based on their ability to accurately predict the 20% of oxygen data withheld from training. The final product includes gridded monthly O2 ensembles with similar skills (mean bias < 1mol/kg and R2 > 0.95). The reconstruction of basin-scale oxygen inventory shows a moderate increase before 1980 and steep decline after 1990 in agreement with a previous estimate using an optimal interpolation method. However, significant differences exist between reconstructions trained with only shipboard data and with both shipboard and Argo data. The gridded oxygen datasets using only shipboard measurements resulted in a wide spread of deoxygenation trends (0.8-2.7% per decade) during 1990-2010. When both shipboard and Argo were used, the resulting deoxygenation trends converged within a smaller spread (1.4-2.0% per decade). This study demonstrates the importance of new biogeochemical Argo arrays in combination with applications of machine learning techniques.

We use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature anomalies on interannual (1-3 year) and decadal (1-5 and 3-7 year) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate datasets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

Climate change is an alarming global threat to individual and public health. This commentary addresses how the field of climate health informatics can spearhead research and innovation and guide climate adaptation and mitigation efforts.