The moist processes of the Madden-Julian Oscillation (MJO) in the Coupled Model Intercomparison Project Phase 6 models are assessed using moisture mode theory-based diagnostics over the Indian Ocean (10°S-10°N, 75°E-100°E). Results show that no model can capture all the moisture mode properties relative to the reanalysis. Most models satisfy weak temperature gradient balance but have unrealistically fast MJO propagation and a lower moisture-precipitation correlation. Models that satisfy the most moisture mode criteria reliably simulate the background moist static energy (MSE) and low-level zonal winds compared to models that satisfy the least amount of criteria. The MSE budget associated with the MJO is also well-represented in the good models rather than in the poor models. Our results show that capturing the MJO's moisture mode properties over the Indian Ocean is associated with a more realistic representation of the MJO simulation and thus can be employed to diagnose MJO performance.

The Historical Experiment (HE) data in the Intergovernmental Panel on Climate Change (IPCC) Climate Model Intercomparison Project six (CMIP6) should demonstrate that all submitted models accurately simulate the climate of the recent past. I show: none of nine models analysed accurately creates the known occurrence of ENSO events; no model agrees with any other; and average aerosol levels of the South East Asian Plume (SEAP), the South AMerican Plume (SAMP) and the West African Plume (WAP) are too low. Additionally, the SEAP and the SAMP cause the global temperature to rise in all nine models and the WAP in six models. Hence these models and all others which cannot accurately portray the known occurrence of ENSO events should be withdrawn from CMIP6 until they can and use of the IPCC Assessment Report six (AR6) should be paused until the effects of these aerosol plumes on the global temperature is re-evaluated.

This study explores the carbon stability in the Arctic permafrost following the sea level transgression since the Last Glacial Maximum (LGM). Arctic permafrost is a significant natural reservoir of greenhouse gas which is stored in frozen organic carbon, methane hydrates and natural gas reservoirs. Post-LGM sea level transgression resulted in ocean water, which is up to 20 oC warmer compared to the average annual air mass, inundating, and thawing the permafrost. This study develops a one-dimensional multiphase flow, multicomponent transport numerical model and apply it to investigate the coupled thermal, hydrological, microbial, and chemical processes occurring in the thawing permafrost. Results show that microbial methane is produced and vented to the seawater immediately upon the flooding of the Arctic continental shelves. This microbial methane is generated by biodegradation of the previously frozen organic carbon in the thawing permafrost. The maximum seabed methane flux is predicted in the shallow water where the sediment has been warmed up, but the remaining amount of organic carbon is still high. It is less likely to induce seabed methane emission from methane hydrate dissociation. Such situation only happens when there is very shallow (~200 m depth), intra-permafrost methane hydrate, the occurrence of which is limited. This study provides insights into the limits of methane release from the ongoing flooding of the Arctic permafrost, which is critical to understand the role of the Arctic permafrost in the carbon cycle, ocean chemistry and climate change.

The observed partitioning of poleward heat transport between atmospheric and oceanic heat transports (AHT and OHT) is compared to that in coupled climate models. Poleward OHT in the models is biased low in both hemispheres, with the largest biases in the Southern Hemisphere extratropics. Poleward AHT is biased high in the Northern Hemisphere, especially in the vicinity of the peak AHT near 40$^\circ$N. The significant model biases are persistent across three model generations (CMIP3, CMIP5, CMIP6) and are insensitive to the satellite radiation and atmospheric reanalyses products used to derive observational estimates of AHT and OHT. Model biases in heat transport partitioning are consistent with biases in the spatial structure of energy input to the ocean and atmosphere. Specifically, larger than observed model evaporation in the tropics adds excess energy to the atmosphere that drives enhanced poleward AHT at the expense of weaker OHT

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal-mean precipitation-minus-evaporation (P-E) under greenhouse-gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P-E and increase P-E in the deep tropics. The surface-albedo feedback shifts the location of maximum tropical P-E and increases P-E in the polar regions. The intermodel spread in the P-E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse-rate and surface-albedo feedbacks playing important roles in the polar regions. The P-E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet-gets-wetter, dry-gets-drier” paradigm.

A document by Shelley Stall. Click on the document to view its contents.

Ecosystems at high latitudes are under increasing stress from climate change. To understand changes in carbon fluxes, in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high-latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning-based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter-term measurements functions better in terms of larger-scale representativeness than a network with fewer long-term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. Using a case study in the Canadian Arctic we show several optimization scenarios and compare these to a random site selection among reasonable choices. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

The global distribution of high Remote-sensing reflectance (Rrs) waters visible from satellite, likely associated with coccolithophore blooms, has changed markedly over the past 40 years. Over that period there has globally been an overall decrease in bloom area of 1.15 million km2 but with notable Rrs increases in the Barents Sea and the Antarctic Ocean. The primary drivers of these fundamental changes to ocean biogeochemistry have been investigated using Machine Learning techniques together with contemporaneous global multi-decadal time-series of sea-surface temperature (SST); wind speed and stress; sea level anomaly (SLA); photosynthetically available radiation (PAR) and; mixed layer depth (MLD). When split into ocean provinces different drivers of positive and negative trends in Rrs were found to dominate in different regions, but generally increases were found to coincide with changes to SST, PAR and reductions to wind-speed.

The Paleocene-Eocene Thermal Maximum (PETM) was a transient global warming event recognised in the geologic record by a prolonged negative carbon isotope excursion (CIE). The onset of the CIE was the result of a rapid influx of 13C-depleted carbon into the ocean-atmosphere system. However, the mechanisms required to sustain the negative CIE remains unclear. Previous studies have identified enhanced mobilisation of petrogenic organic carbon (OCpetro) and argued that this was likely oxidised, increasing atmospheric carbon dioxide (CO2) concentrations after the onset of the CIE. With existing evidence limited to the mid-latitudes and subtropics, we determine whether: (i) enhanced mobilisation and subsequent burial of OCpetro in marine sediments was a global phenomenon; and (ii) whether it occurred throughout the PETM. To achieve this, we utilised a lipid biomarker approach to trace and quantify OCpetro burial in a global compilation of PETM-aged shallow marine sites (n = 7, including five new sites). Our results confirm that OCpetro mass accumulation rates (MARs) increased within the subtropics and mid-latitudes during the PETM, consistent with evidence of higher physical erosion rates and intense episodic rainfall events. The high-latitude sites do not exhibit distinct changes in the organic carbon source during the PETM. This may be due to the more stable hydrological regime and/or additional controls. Crucially, we also demonstrate that OCpetro MARs remained elevated during the recovery phase of the PETM. Although OCpetro oxidation was likely an important positive feedback mechanism throughout the PETM, we show that this feedback was both spatially and temporally variable.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

The Yangtze River Valley (YRV) experienced an unprecedented heatwave in midsummer of 2022, but the detailed physical processes involved in the influence of anomalous large-scale atmospheric circulation on the heatwave remain unknown. Here, we show that the positive meridional gradient of anomalous atmospheric moisture at the middle-lower troposphere and associated extreme dry air advection over the YRV are key prerequisites for the formation of the 2022 YRV heatwave. The 2022 YRV heatwave is dominated by the interannual variability, which contributes 72.7% to the total temperature anomalies. Diagnosis of the surface heat budget equation indicates that the surface cloud radiative forcing is the most important process in driving the 2022 YRV heatwave, which is dominated by the positive surface short-wave cloud radiative forcing associated with the suppressed precipitation and the middle-low clouds. The suppressed precipitation is induced by the vertical dynamical processes of anomalous moisture advection caused by the anomalous descending flows over the YRV, which are driven by the negative advection of anomalous latent heat energy by climatological meridional wind (anomalous dry air advection) according to the atmospheric moist static energy equation. Simulations from the Lagrangian model FLEXPART further indicate that the moisture anomaly over the north of YRV is mainly originated from the surface evaporation in the YRV, implying that there is a positive land-air feedback during the life cycle of the YRV heatwave. Our study adds a perspective to the existing mechanism analyses of the 2022 YRV heatwave to serve accurate climate prediction and adaptation planning.

We report an indirect, negative, cloud-mediated, surface radiative effect (RE) of water vapor (IWVE) in certain regions in the tropics, which may be consequential for day-to-day regional heat stress. Using reanalysis and satellite data we show that this effect is marked by a surprisingly dominant positive relationship of cloud RE with near surface and column humidity. These clouds are predominantly low level and altocumuli, previously reported to have a negative surface RE, possibly lending the net negative RE to water vapor. Also reported earlier, these clouds form in the mid-troposphere, as detrainment offshoots of deep convective towers and can be advected away to large distances, hence requiring no local convective triggering in the IWVE regions. Evidently, the IWVE are co-located with the horizontal branch of the Hadley cell, with the lowest vertical forcing in the tropics. Moreover, these are also the transition regions between the highly cloudy and the driest parts of the tropics, with a waning down occurrence of cirrus, deep convective and altostratus clouds, linked with positive RE, corroborating the hypothesis. IWVE regions also show a large temporal variability in humidity possibly providing opportunity for a large variability in cloud fractional coverage, however the mechanism controlling this covariability is not understood. The IWVE is tightly tied with the seasonal cycle of the ITCZ and hence is likely a dominant source of pre-monsoon surface temperature variability and heat stress in the current climate. The evolution of the IWVE under future climate warming needs further investigation.

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS-E2.1-G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. In this ensemble there are detectable differences in zonal-mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model’s representation of historical changes, including reversing the forced trends in Southern Ocean surface temperature and Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that these drivers should be included in all-forcing historical simulations in coupled model intercomparisons.

The Earth System is warming due to anthropogenic greenhouse gas emissions which increases the risk of passing a tipping point in the Earth System, such as a collapse of the Atlantic Meridional Overturning Circulation (AMOC). An AMOC weakening can have large climate impacts which influences the marine and terrestrial carbon cycle and hence atmospheric pCO2. However, the sign and mechanism of this response are subject to uncertainty. Here, we use a state-of-the-art Earth System Model, the Community Earth System Model v2 (CESM2), to study the atmospheric pCO2 response to an AMOC weakening under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios. A freshwater flux anomaly in the North Atlantic strongly weakens the AMOC, and we simulate a weak positive pCO2 response of 0.45 and 1.3 ppm increase per AMOC decrease in Sv for SSP1-2.6 and SSP5-8.5, respectively. For SSP1-2.6 this response is driven by both the oceanic and terrestrial carbon cycles, whereas in SSP5-8.5 it is solely the ocean that drives the response. However, the spatial patterns of both the climate and carbon cycle response are similar in both emission scenarios over the course of the simulation period (2015-2100), showing that the response pattern is not dependent on cumulative CO2 emissions up to 2100. Though the global atmospheric pCO2 response might be small, locally large changes in both the carbon cycle and the climate system occur due to the AMOC weakening, which can have large detrimental effects on ecosystems and society.

The U.S. rice paddy systems play an increasingly vital role in ensuring food security, which also contribute massive anthropogenic non-CO2 (CH4 and N2O) greenhouse gas (GHG) emissions with expanding cultivation area. Yet, the full assessment of GHG balance, considering trade-offs between soil organic carbon (SOC) sequestration and non-CO2 GHG emissions, is lacking. Integrating an improved agricultural ecosystem model with a meta-analysis of multiple field studies, we found that U.S. rice paddy was a rapidly growing net GHG emission source, increased 138% to 8.88 ± 2.65 Tg CO2eq yr-1 in the 2010s. CH4 emission made the most significant contribution (10.12 ± 2.28 Tg CO2eq yr-1) to this increase in net GHG emissions in the 2010s, but increasing N2O emissions, accounting for ~2.4% (0.21 ± 0.03 Tg CO2eq yr-1), cannot be ignored. SOC sequestration could offset about 14.0% (1.45 ± 0.46 Tg CO2eq yr1) of the climate-warming effects of soil non-CO2 GHG emissions in the 2010s. The aggravation of net GHG emissions stemmed from intensified land use/cover changes, rising atmospheric CO2, and heightened synthetic N fertilizer and manure application. Climate change exacerbated around ~21% of soil N2O emissions and ~10% of soil CO2 release in the 2010s. Nonetheless, adopting no/reduced tillage resulted in a substantial decrease of ~10 % in net soil GHG emissions, and non-continuous irrigation exhibited the potential to mitigate around 39% of soil non-CO2 GHG emissions. Great potential for emissions reduction in the mid-South U.S. by optimizing synthetic N fertilizer and manure ratios, reducing tillage, and implementing non-continuous irrigation.

A document by Evi Wubben. Click on the document to view its contents.

A document by Bianca Robin Spiering. Click on the document to view its contents.

As sea ice disappears, the emergence of open ocean deep convection in the Arctic has been suggested. Here, using 36 state-of-the-art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare even under the strongest warming scenario. Only 5 models have somewhat permanent convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the Eurasian basin, by St Anna Trough. When the models convect, that region undergoes a salinification and increasing wind speeds; it is freshening otherwise. We discuss the causality and potential reasons for the opposite trends. Given the model’s different parameterisations, and given that the ensemble members that convect the deepest, most often, are those with the strongest sensitivity, we conclude that differences in deep convection are most likely linked to the model formulation.

A document by Daniel Michael Gilford, PhD. Click on the document to view its contents.

Cloud microphysics is a critical aspect of the Earth’s climate system, which involves processes at the nano- and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm-rain bulk schemes using high-fidelity super-droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including ensemble Kalman inversion (EKI) and unscented Kalman inversion (UKI) to calibrate bulk microphysics schemes with probabilistic super-droplet simulations. We demonstrate the framework’s effectiveness by calibrating a single-moment bulk scheme, resulting in a reduction of data-model mismatch by more than $75\%$ compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.

Irrigated cultivation, as a prevalent anthropogenic activity, exerts a significant influence on land use and land cover, resulting in notable modifications to land-atmosphere interaction and the hydrological cycle. Given the extensive cropland, high productivity, compact rotation, semi-arid climate, intense irrigation, and groundwater depletion in the North China Plain (NCP), the development of a comprehensive crop-irrigation-groundwater model becomes imperative for understanding agricultural-induced climate response in this region. This study presents an integrated crop model explicitly tailored to the NCP, which incorporates double-cropping rotation, irrigation practice, and groundwater interactions into the regional climate model. The modifications are implemented to: (1) enable a seamless transition from field scale application to regional scale application, facilitating the incorporation of spatial variability, (2) capture the distinctive attributes of the NCP region, ensuring the model accurately reflects its unique characteristics, and (3) reinforce the direct interaction among crop-related variables, thereby enhancing the model’s capacity to simulate their dynamic behaviors. The integrated crop modeling system demonstrates a commendable performance in crop simulations using climatic conditions, which is substantiated by its identification of crop stages, estimation of field biomass, prediction of crop yield, and finally the projection of monthly leaf area index. In our next phase, this integrated crop modeling system will be employed in long-term simulations to enhance our understanding of the intricate relationship between agricultural development and climate change.

The study and simulation of the Urban Heat Island (UHI) and Heat Index (HI) effects in the Houston-Galveston metropolitan area demand special attention, particularly in considering moist processes aloft. During the warm season, the afternoon sea breeze phenomenon in this coastal city acts as a natural air conditioner for city residents, facilitating the dispersion of pollutants, moisture, and heat. To delve into the intricate relationships among urbanization, clouds, and land-sea interaction, we conducted cloud- and urban-resolving simulations at a 900 m grid resolution. Results show that urbanization correlates with the presence of shallower cumulus clouds, cloud bases at higher altitudes, and increased cloud duration over the Galveston-Houston region compared to rural areas. These urban clouds benefit from the enhanced sensible heat and dynamic drag imparted by the urban landscape, thereby intensifying vertical mixing and moisture flux convergence. This dynamic interplay uplifts heat and moisture convergence, contributing to the enhancement of moist static energy that sustains the additional urban convection. Interestingly, our findings suggest that urbanization augments the mean HI while mitigating its afternoon high. An urban circulation dome emerges, overpowering the influence of land-sea circulations. Contrary to expectations, urbanization doesn’t seem to promote a stronger sea breeze that would favor moist and cooler air mass to the city. Instead, the influence of urbanization on cloud enhancement emerges as a crucial pathway responsible for reducing the afternoon HI values. Moreover, uncertainties in SSTs are closely linked to the sensitivities of land-sea circulations, which in turn modulate UHI and extreme heat indicators.

The water-energy-food nexus has emerged as a critical research interest to support integrated resource planning, management, and security. For this reason, many tools have been developed recently to evaluate the WEF nexus security and monitor progress towards the WEF-related sustainable development goals. Among these, the calculation of the WEF composite index model is critical since it can provide a quantitative approach to demonstrate the WEF nexus security status. However, the current WEF nexus index model framework needs to include the incorporation of governance indicators, neglecting the importance of governance in the WEF nexus framework. Thus, this article develops a new WEF nexus composite index model that incorporates governance indicators in each subpillar is developed. The principal component analysis (PCA) is adopted to reduce the variables’ collinearity and the model’s dimensionality. A quasi-Monte Carlo based uncertainty and global sensitivity analysis are applied to the index model to assess its effectiveness. Finally, the new WEF index model is applied on the 16 South African Development Community (SADC) countries as a case study. A critical synergy effect within the WEF nexus framework is identified that nations with better WEF governance ability tend to perform better in improving the WEF accessibility capability, suggesting the importance of the governance in the WEF nexus security framework.

The Atlantic meridional overturning circulation (AMOC) in many CMIP6 models has been shown to be overly-sensitive to anthropogenic aerosol forcing, and it has been speculated that this is due to the inclusion of aerosol indirect effects for the first time in many models of that generation. We analyze the AMOC response in a newly-released ensemble of historic simulations performed with CESM2 and forced by the older CMIP5 input datasets (CESM2-CMIP5). This AMOC response is then compared to the CESM1 large ensemble (CESM1-LE, forced by the older CMIP5 inputs) and the CESM2 large ensemble (CESM2-LE, forced by the newer CMIP6 inputs). A key conclusion, only made possible by this experimental setup, is that changes in modeled aerosol-indirect effects cannot explain the differences in turbulent fluxes between CESM1-LE and CESM2-LE. Instead, differences in surface turbulent heat fluxes from changes in model inputs likely drive the different AMOC responses.