Global Climate Model Simulations Research Articles

Air quality and public health effects of dairy digesters in California

The dairy industry in California emits large amounts of methane (CH4) that contributes significantly to the state's overall Greenhouse Gas (GHG) budget. Reducing CH4 emissions has become a key priority for dairy farms in support of California's GHG reduction goals. Anaerobic digesters designed to capture CH4 from animal manure present a practical option for reducing CH4 emissions, but a comprehensive evaluation of the local air quality impacts of this technology has not been previously undertaken. The simplest digester configurations decrease local emissions of volatile organic compounds (VOCs) but increase emissions of oxides of nitrogen (NOx), potentially changing local air quality. Here, we evaluate the air quality implications of widespread digester adoption in the year 2050 across the San Joaquin Valley (SJV) in central California, which is home to the highest concentration of dairy farms in the state. Changes to concentrations of air pollutants including ozone (O3), airborne particulate matter with diameter smaller than 2.5 μm (PM2.5), and various PM2.5 chemical components are predicted using the UCD/CIT chemical transport model at 4 km resolution. Dairy digester adoption is evaluated within two regional energy scenarios, including a business as usual (BAU) scenario and an 80% greenhouse gas reduction (GHGAi) scenario, to consider potential changes to the chemical regime that governs formation of secondary air pollution. Concentrations are evaluated across 32 randomly selected weeks over a 10-year period from the year 2046–2055 to establish a long-term average impact in the presence of El Niño Southern Oscillation (ENSO) variability. Regional weather patterns are downscaled from Global Climate Model simulations under the RCP8.5 global scenario. The results indicate that baseline dairy emissions make minor contributions to air pollutant concentrations in 2050. Under a worst-case scenario for digester adoption, PM2.5 concentrations would increase by 0.06 μg/m3 (current standard = 9 μg/m3), and maximum daily 8-h average (MDA8) O3 would change by −1.0 ppb to +0.2 ppb depending on the surrounding regional energy scenario (current standard = 70 ppb). A health impact analysis shows that the widespread use of dairy digesters would result in fewer than 0.1 additional deaths per 100,000 people due to changing air pollution. For comparison, this level of mortality change is more than 100 times smaller than the risk posed by seasonal flu. Further, Environmental Justice analysis indicates that the implementation of digesters will not influence the exposure disparities among different racial groups in either the SJV or the surrounding San Francisco Bay & Sacramento area. These findings suggest that dairy digesters can be widely adopted in central California to reduce GHG emissions with minimal effect on regional air quality and public health.

Powering aircraft with 100 % sustainable aviation fuel reduces ice crystals in contrails

Abstract. Powering aircraft by sustainable aviation fuels (SAFs) is a pathway to reduce the climate impact of aviation by lowering aviation lifecycle CO2 emissions and by reducing ice crystal numbers and radiative forcing from contrails. While the effect of SAF blends on contrails has been measured previously, here we present novel measurements on particle emission and contrails from 100 % SAF combustion. During the ECLIF3 (Emission and CLimate Impact of alternative Fuels) campaign, a collaboration between the Deutsches Zentrum für Luft- und Raumfahrt (DLR), Airbus, Rolls-Royce, and Neste, the DLR Falcon 20 research aircraft performed in situ measurements following an Airbus A350-941 source aircraft powered by Rolls-Royce Trent XWB-84 engines in 1 to 2 min old contrails at cruise altitudes. Apparent ice emission indices of 100 % HEFA-SPK (hydro-processed esters and fatty acids–synthetic paraffinic kerosene) were measured and compared to Jet A-1 fuel contrails at similar engine and ambient ice-supersaturated conditions within a single flight. A 56 % reduction in ice particle numbers per mass of burned fuel was measured for 100 % HEFA-SPK compared to Jet A-1 under engine cruise conditions. The measured 35 % reduction in soot particle numbers suggests reduced ice activation by the low-sulfur HEFA fuel. Contrail properties are consistently modeled with a contrail plume model. Global climate model simulations for the 2018 fleet conservatively estimate a 26 % decrease in contrail radiative forcing and stronger decreases for larger particle reductions. Our results indicate that higher hydrogen content fuels as well as clean engines with low particle emissions may lead to reduced climate forcing from contrails.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

The radiative and dynamical impact of clouds in the atmosphere of the hot Jupiter WASP-43 b

Context. Hot Jupiters are tidally locked gaseous exoplanets that exhibit large day-night temperature contrasts. Their cooler nightsides are thought to host clouds, as has been suggested by numerous observations. However, the exact nature of these clouds, their spatial distribution, and their impact on atmospheric dynamics, thermal structure, and spectra is still unclear. Aims. We investigate the atmosphere of WASP-43 b, a short period hot Jupiter recently observed with James Webb Space Telescope (JWST), to understand the radiative and dynamical impact of clouds on the atmospheric circulation and thermal structure. We aim to understand the impact of different kinds of condensates potentially forming in WASP-43 b, with various sizes and atmospheric metallicities. Methods. We used a 3D global climate model (GCM) with a new temperature-dependent cloud model that includes radiative feedbacks coupled with hydrodynamical integrations to study the atmospheric properties of WASP-43 b. We produced observables from our GCM simulations and compared them to spectral phase curves from various observations to derive constraints on the atmospheric properties. Results. We show that clouds have a net warming effect, meaning that the greenhouse effect caused by clouds is stronger than the albedo cooling effect. We show that the radiative effect of clouds has various impacts on the dynamical and thermal structure of WASP-43 b. Depending on the type of condensates and their sizes, the radiative-dynamical feedback will modify the horizontal and vertical temperature gradient and reduce the wind speed. For super-solar metallicity atmospheres, fewer clouds form in the atmosphere, leading to a weaker feedback. Comparisons with spectral phase curves observed with HST, Spitzer, and JWST indicate that WASP-43 b's nightside is cloudy and rule out sub-micron Mg2SiO4 cloud particles as the main opacity source. Distinguishing between cloudy solarand cloudy super-solar-metallicity atmospheres is not straightforward, and further observations of both reflected light and thermal emission are needed.

Contribution of Anthropogenic Activities to the Intensification of Heat Index‐Based Spatiotemporally Contiguous Heatwave Events in China

AbstractIn this study, we identified heat index‐based spatiotemporally contiguous heatwaves (HI‐STHWs) in China based on meteorological observations and Coupled Model Intercomparison Project Phase 6 global climate model simulations. We analyzed the spatiotemporal patterns of changes in HI‐STHWs in the past and future and quantitatively attributed these changes to anthropogenic activities. The results show that the duration, severity, average, maximum, and total impacted area of the annual strongest HI‐STHWs during the present period of 1991–2014 are 1.77, 2.0, 1.05, 1.14, and 1.89 times the historical period of 1961–1990, respectively. In the fingerprint results, the anthropogenic greenhouse gases (GHG) signal is significantly detected, while the aerosol (AER) and natural (NAT) signals are not. GHG is the primary factor driving the intensification of HI‐STHWs, which alone explains about 130%, 122%, 112%, 111%, and 114% of the above changes. The reason for GHG contribution exceeding 100% is that AER might have a negative contribution although nonsignificant. In the future warming climate, anthropogenic activities are projected to lead to more unprecedented HI‐STHWs. Under the high emissions scenario of SSP585, by 2100, the annual strongest HI‐STHW in China is projected to last almost the whole year and influence 96% regions of China in the most serious day. Meanwhile, its duration and total impacted area are 24.5 [17.2, 31.6] (90% confidence interval) and 107.2 [70, 129.9] times the preindustrial period. However, if the warming level could be limited to 2/1.5°C, those values would be 3.4/5.4 and 8.2/16.2 times smaller than that under the SSP585 scenario by 2100.

Resilient Antarctic monsoonal climate prevented ice growth during the Eocene

Abstract. Understanding the extreme greenhouse of the Eocene (56–34 Ma) is key to anticipating potential future conditions. While providing an end member towards a distant high-emission scenario, the Eocene climate also challenges the different tools at hand to reconstruct such conditions. Besides remaining uncertainty regarding the conditions under which the large-scale glaciation of Antarctica took place, there is poor understanding of how most of the continent remained ice free throughout the Eocene across a wide range of global temperatures. Seemingly contradictory indications of ice and thriving vegetation complicate efforts to explain the Antarctic Eocene climate. We use global climate model simulations to show that extreme seasonality mostly limited ice growth, mainly through high summer temperatures. Without ice sheets, much of the Antarctic continent had monsoonal conditions. Perennially mild and wet conditions along Antarctic coastlines are consistent with vegetation reconstructions, while extreme seasonality over the continental interior promoted intense weathering shown in proxy records. The results can thus explain the coexistence of warm and wet conditions in some regions, with small ice caps forming near the coast. The resilience of the climate regimes seen in these simulations agrees with the longevity of warm Antarctic conditions during the Eocene but also challenges our view of glacial inception.

Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High‐Resolution Global Climate Model Simulations

AbstractThe increase in water holding capacity of the atmosphere with temperature, given by the Clausius‐Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation‐scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present‐day climate simulation and a time‐slice future climate change projection experiments of a high‐resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of global climate model (GCM) simulations with two scenario analyses on climate change detection and indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens, rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.

Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Diagnosing Mechanisms of Hydrologic Change under Global Warming in the CESM1 Large Ensemble

Abstract Global warming is expected to cause significant changes in the pattern of precipitation minus evaporation (P − E), which represents the net flux of water from the atmosphere to the surface or, equivalently, the convergence of moisture transport within the atmosphere. In most global climate model simulations, the pattern of P − E change resembles an amplification of the historical pattern—a tendency known as “wet gets wetter, dry gets drier.” However, models also predict significant departures from this approximation that are not well understood. Here, we introduce a new method of decomposing the pattern of P − E change into contributions from various dynamic and thermodynamic mechanisms and use it to investigate the response of P − E to global warming within the CESM1 Large Ensemble. In contrast to previous decompositions of P − E change, ours incorporates changes not only in the monthly means of atmospheric winds and moisture, but also in their temporal variability, allowing us to isolate the hydrologic impacts of changes in the mean circulation, transient eddies, relative humidity, and the spatial and temporal distributions of temperature. In general, we find that changes in the mean circulation primarily control the P − E response in the tropics, while temperature changes dominate at higher latitudes. Although the relative importance of specific mechanisms varies by region, at the global scale departures from the wet-gets-wetter approximation over land are primarily due to changes in the temperature lapse rate, while changes in the mean circulation, relative humidity, and horizontal temperature gradients play a secondary role.

Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions

Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.

Incorporating Weather Attribution to Future Water Budget Projections

Weather attribution is a scientific study that estimates the relative likelihood of an observed weather event occurring under different climate regimes. Water budget models are widely used tools that can estimate future water resource management and conservation conditions using daily weather forcing. A stochastic weather generator (WG) is a statistical model of daily weather sequences designed to simulate or represent a climate description. A WG provides a means to generate stochastic, future weather forcing to drive a water budget model to produce future water resource projections. Observed drought magnitude and human-induced climate change likelihood from a weather attribution study provide targets for WG calibration. The attribution-constrained WG approximately reproduces the five-fold increase in probability attributed to observed drought magnitude under climate change. A future (2031–2060) climate description produced by the calibrated WG is significantly hotter, with lower expected soil moisture than the future description obtained from global climate model (GCM) simulation results. The attribution-constrained WG describes future conditions where historical extreme and severe droughts are significantly more likely to occur.

Can Sub‐Daily Multivariate Bias Correction of Regional Climate Model Boundary Conditions Improve Simulation of the Diurnal Precipitation Cycle?

AbstractThe diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias‐corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias‐corrected lateral and lower boundary conditions. Results show that the RCMs with bias‐corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub‐daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short‐term intense precipitation occurs.

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

AbstractIndian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results.

Emerging extreme heat conditions as part of the new climate normal

As a result of global warming, extreme heat conditions have become more frequent and severe. This will likely continue or accelerate in the future, particularly under high radiative forcing scenarios. In the present study, based on an ensemble of global climate model simulations, we identify the absolute historical extremes expressed by several temperature indices. Considering projections under two future pathways (SSP1-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}2.6, SSP5-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}8.5), we investigate to what extent extreme heat conditions will become predominant during the rest of the century. The timing of a transition to prevailing hot weather extremes is critical for the development of mitigation and adaptation strategies; therefore, we also identify the projected first year of such a transition, as well as the persistence in subsequent decades. Different aspects of heat extremes are investigated, including both maximum and minimum temperature. For some climate zones, our results highlight that regardless of mitigation efforts, hot weather conditions will be at least similar but likely more harsh compared to historical extremes within the following decades. By the end of the century, under a business-as-usual pathway, successive years will be much more extreme than the most severe conditions in the recent past virtually everywhere.

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Abstract Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

AbstractRadiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.

MJO in Different Orbital Regimes: Role of the Mean State in the MJO’s Amplitude during Boreal Winter

Abstract The Madden–Julian oscillation (MJO) exhibits pronounced seasonality, with one of the key unanswered questions being the following: what controls the maximum in MJO precipitation variance in the Southern Hemisphere during boreal winter? In this study, we examine a set of global climate model simulations in which the eccentricity and precession of Earth’s orbit are altered to change the boreal winter mean state in an attempt to reveal the processes that are responsible for the MJO’s amplitude in the boreal winter. In response to the forced insolation changes, the north–south asymmetry in sea surface temperature is amplified in boreal fall, which intensifies the Hadley circulation in boreal winter. The stronger Hadley circulation yields higher mean precipitation and stronger mean lower-tropospheric westerlies in the southern part of the Indo-Pacific warm pool. The MJO precipitation variability increases significantly where the mean precipitation and lower-tropospheric westerlies strengthen. In the column-integrated moisture budget of the simulated MJO, only surface latent heat flux feedback shows a trend that is consistent with the MJO’s amplitude, suggesting an important role for the surface latent heat flux feedback in the MJO’s amplitude during the boreal winter. An analysis of the moisture–precipitation relationship in the simulations shows that the increase in the mean precipitation lowers the convective moisture adjustment time scale, leading to the increase in precipitation variance. Our results suggest that the mean-state precipitation plays a critical role in the maintenance mechanism of the MJO.

More Than Half of Emitted Black Carbon Is Missing in Marine Sediments

Marine sediments are the ultimate reservoir for black carbon (BC) preservation, and BC burial in sediment/soils is an efficient method for carbon sequestration to mitigate CO2 emissions. A portion of soil charcoal and atmospheric BC is dissolved in inland and oceanic water, but the amount of BC in the ocean remains unclear. We analyzed multi-sediment cores from the northwestern Pacific Ocean and lakes in China and reconstructed the timeline of BC deposition from 1860 to ~2012. The lacustrine sediment cores showed an increase in BC deposition by a factor of 4–7 during the industrialization period in China compared to the years 1860–1950 (reference level). Such increasing trends in BC have also been reproduced by ten global climate model simulations. However, the marine sediment cores did not retain these significant increases in BC deposition. Meanwhile, the model simulations predicted increased trends compared to the observed flat trends of BC deposition in marine sediments. The discrepancy suggests a large amount of BC, i.e., 65 (±11)%, is missing in marine sediment sinks. Thus, since more than half of emitted BC has dissolved into oceanic water, the dissolved BC and carbon cycle should be reconsidered in the global carbon budget.

Evaluation of CMIP6 HighResMIP Models and ERA5 Reanalysis in Simulating Summer Precipitation over the Tibetan Plateau

The High Resolution Model Intercomparison Project (HighResMIP) experiment within the Coupled Model Intercomparison Project Phase 6 (CMIP6) has enabled the evaluation of the performance of climate models over complex terrain for the first time. The study aims to evaluate summer (June to August) precipitation characteristics over the Tibetan Plateau (TP). Precipitation derived from HighResMIP models and ERA5 are compared against the China Merged Precipitation Analysis (CMPA). The nineteen models that participated in HighResMIP are classified into three categories based on their horizontal resolution: high resolution (HR), middle resolution (MR), and low resolution (LR). The multimodel ensemble means (MMEs) of the three categories of models are evaluated. The spatial distribution and elevation dependency of the hourly precipitation characteristics, which include the diurnal peak hour, diurnal variation amplitude, and frequency–intensity structure, are our main focus. The MME-HR and ERA5 both show comparable ability in simulating precipitation in the TP. The MME-HR has a smaller deviation in the precipitation amount and diurnal variation at various altitudes. The ERA5 can better simulate the elevation dependence of the frequency–intensity structure, but its elevation dependence of diurnal variation shows a trend opposite to the observations. Although the MME-HR produces the best simulation results among the three MMEs, the simulation effects of HighResMIP’s precipitation in the TP do not necessarily improve with increasing the horizontal resolution from LR to MR. The finer model resolution has a small impact on the simulation effect of precipitation intensity, but the coarser model resolution will limit the generation of heavy precipitation. These findings give intensive measures for evaluating precipitation in complex terrain and can help us in comprehending rainfall biases in global climate model simulation.