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This article is a Commentary on Robbins et al . (2024), 244 : 2239–2250 .
Abstract The El Niño—Southern Oscillation (ENSO) is an important mode of tropical Pacific atmosphere‐ocean variability that drives teleconnections with weather and climate globally. However, prior studies using state‐of‐the‐art climate models lack consensus regarding future ENSO projections and are often impacted by tropical Pacific sea‐surface temperature (SST) biases. We used 173 simulations from 29 climate models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) to analyze model biases and future ENSO projections. We analyzed two ENSO indices, namely the ENSO Longitude Index (ELI), which measures zonal shifts in tropical Pacific deep convection and accounts for changes in background SST, and the Niño 3.4 index, which measures SST anomalies in the central‐eastern equatorial Pacific. We found that the warm eastern tropical‐subtropical Pacific SST bias typical of previous generations of climate models persists into many of the CMIP6 models. Future projections of ENSO shift toward more El Niño‐like conditions based on ELI in 48% of simulations and 55% of models, in association with a future weakening of the zonal equatorial Pacific SST gradient. On the other hand, none of the models project a significant shift toward La Niña‐like conditions. The standard deviation of the Niño 3.4 index indicates a lack of consensus on whether an increase or decrease in ENSO variability is expected in the future. Finally, we found a possible relationship between historical SST and low‐level cloud cover biases in the ENSO region and future changes in ELI; however, this result may be impacted by limitations in data availability.
Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.
The vertical structure of radiative heating rates over the region of the tropi-cal Indian Ocean associated with the MJO during the DYNAMO/ARM MJO Investigation Experiment is presented. The mean and variability of heating rates during active, suppressed, and disturbed phases are determined from the Paci?c Northwest National Laboratory combined remote sensing retrieval (CombRet) from Gan Island, Maldives (0.69? S, 73.15? E). TOA and surface ?uxes from the CombRet product are compared with collocated three-hourly CERES SYN1deg Ed4A satellite retrievals. The ?uxes are correlated in time with correlation coef?cients around 0.9, yet CombRet time-mean OLR is 15 W m-2 larger. Previous work has suggested that CombRet under-samples high clouds, due to attenuation by low-level clouds and reduced instrument sensitivity with altitude. However, mean OLR differs between CombRet and CERES for all values of OLR, not just the lowest values corresponding to widespread high clouds. The discrepancy peaks for mid-range OLR, sugges-tive of precipitating, towering cumulus convective clouds, rather than strati-form cirrus clouds. Biases in the cloud-top height of thick clouds substantially contribute to the overestimate of OLR by CombRet. CombRet data is used to generate composite shortwave and longwave atmospheric heating rate pro-?les as a function of the local OLR. Though there is considerable variability in CombRet not directly related to OLR, the time-height structure of mean heating rate composites generated using OLR as the interpolant is broadly representative of tropical convective variability on intraseasonal time scales.
Volcanic aerosol forcing can affect global climate, but its role in climate prediction remains poorly understood. We isolate the impact of volcanic eruptions on multiyear-to-decadal climate prediction skill by comparing two suites of initialized decadal hindcasts conducted with and without historical volcanic forcing. Unexpectedly, the inclusion of volcanic forcing in the prediction system significantly degrades the forecast skill of detrended multiyear-to-decadal sea surface temperature (SST) variability in the central-eastern tropical Pacific. The ensemble mean hindcasts produce multiyear-to-decadal tropical Pacific SST cooling in response to large tropical volcanic eruptions through thermodynamic and El Niño–Southern Oscillation (ENSO)–like dynamic processes. However, in observations, these eruptions coincided with tropical Pacific warming, which is well predicted by the no-volcano hindcasts and, hence, is likely related to internal climate variability. Improved model representation of volcanic response and its interaction with internal climate variability is required to advance prediction of tropical Pacific decadal variability and associated global impacts.
Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900-present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO 2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO 2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO 2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land-use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.
Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Furthermore, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks.
Abstract The impact of interbasin linkage on the weather/climate and ecosystems is significantly broader and profounder than that of only appearing in an individual basin. Here, we reveal that a decadal linkage of sea surface temperature (SST) has emerged between western Australian coast and western–central tropical Pacific since 1985, associated with continuous intensification of decadal variabilities (8–16 years). The rapid SST changes in both tropical Indian Ocean and Indo-Pacific warm pool in association to greenhouse gases and volcanoes are emerging factors resulting in enhanced decadal co-variabilities between these two regions since 1985. These SST changes induce enhanced convection variability over the Maritime Continent, leading to stronger easterlies in the western–central tropical Pacific during the warm phase off western Australian coast. The above changes bring about cooling in the western–central tropical Pacific and strengthened Leeuwin Current and anomalous cyclonic wind off western Australian coast, and ultimately resulting in enhanced coupling between these two regions. Our results suggest that enhanced decadal interbasin connections can offer further understanding of decadal changes under future warmer conditions.
This study investigates how environmental factors influence the level of maximum detrainment (LMD) in deep convective clouds. Through a novel application of the Linear Non‐Gaussian Acyclic Model (LiNGAM), we discover causal structures between environmental variables and LMD, observed at six tropical sites operated by the Atmospheric Radiation Measurement (ARM) user facility. LiNGAM effectively identifies causal directions among variables of interest, revealing robust relationships such as those among the lifting condensation level (LCL), level of free convection (LFC), and convective inhibition (CIN), aligning with prior knowledge. Relative humidity is shown to directly influence LMD; however, this relationship exhibits strong nonlinearity and becomes difficult to detect when the contrast between oceanic and continental environments is excluded from the analysis. This study highlights the importance of establishing causal relationships before performing statistical inference.
The terrestrial carbon sink slows the accumulation of carbon dioxide (CO 2 ) in the atmosphere by absorbing roughly 30% of anthropogenic CO 2 emissions, but varies greatly from year to year. The resulting variations in the atmospheric CO 2 growth rate (CGR) have been related to tropical temperature and water availability. The apparent sensitivity of CGR to tropical temperature ( ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ ) has changed markedly over the past six decades, however, the drivers of the observation to date remains unidentified. Here, we use atmospheric observations, multiple global vegetation models and machine learning products to analyze the cause of the sensitivity change. We found that a threefold increase in ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ emerged due to the long-term changes in the magnitude of CGR variability (i.e., indicated by one standard deviation of CGR; STD CGR ), which increased 34.7% from 1960-1979 to 1985-2004 and subsequently decreased 14.4% in 1997-2016. We found a close relationship (r 2 = 0.75, p < 0.01) between STD CGR and the tropical vegetated area (23°S – 23°N) affected by extreme droughts, which influenced 6-9% of the tropical vegetated surface. A 1% increase in the tropical area affected by extreme droughts led to about 0.14 Pg C yr -1 increase in STD CGR . The historical changes in STD CGR were dominated by extreme drought-affected areas in tropical Africa and Asia, and semi-arid ecosystems. The outsized influence of extreme droughts over a small fraction of vegetated surface amplified the interannual variability in CGR and explained the observed long-term dynamics of ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ .
The tropics are a dynamic component of the global climate system and exert a profound influence on modern climate variability. Yet the role of the tropics in past climate changes, for example during the last glacial termination, is uncertain. This uncertainty is due in part to the relative paucity of terrestrial temperature records from the tropics, which hinders efforts to understand the mechanisms that influenced deglacial warming and abrupt climate events. Tropical glaciers are sensitive to changes in climate, and mapping and dating past tropical glacial fluctuations provides a valuable record of terrestrial climate changes in the low latitudes. We report a chronology of past glacial extents in the Rwenzori Mountains of Uganda (0.3°N, 29.9°E) based on 51 beryllium-10 ( 10 Be) surface-exposure ages from three separate glacial catchments. Results indicate that Rwenzori glaciers retreated considerably during Heinrich Stadial 1. The rate of net glacial recession slowed between ~15 and 11 ka, although glaciers continued to retreat during this period. Rwenzori glaciers then retreated more rapidly during the early Holocene. The Rwenzori glacial chronology is broadly similar to glacial chronologies reconstructed elsewhere in East Africa and in tropical South America. We suggest that this similarity may reflect coherent, tropics-wide temperature fluctuations during the last deglaciation. We report that the Rwenzori glacial chronology provides crucial information on the global footprint of deglacial warming and abrupt climate events and, thus, the potential mechanisms that influenced tropical climate during the last glacial termination.
Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.
Abstract The Pacific Decadal Oscillation (PDO), the leading mode of Pacific decadal sea surface temperature variability, arises mainly from combinations of regional air-sea interaction within the North Pacific Ocean and remote forcing, such as from the tropical Pacific and the Atlantic. Because of such a combination of mechanisms, a question remains as to how much PDO variability originates from these regions. To better understand PDO variability, the equatorial Pacific and the Atlantic impacts on the PDO are examined using several 3-dimensional partial ocean data assimilation experiments conducted with two global climate models: the CESM1.0 and MIROC3.2m. In these partial assimilation experiments, the climate models are constrained by observed temperature and salinity anomalies, one solely in the Atlantic basin and the other solely in the equatorial Pacific basin, but are allowed to evolve freely in other regions. These experiments demonstrate that, in addition to the tropical Pacific’s role in driving PDO variability, the Atlantic can affect PDO variability by modulating the tropical Pacific climate through two proposed processes. One is the equatorial pathway, in which tropical Atlantic sea surface temperature (SST) variability causes an El Niño-like SST response in the equatorial Pacific through the reorganization of the global Walker circulation. The other is the north tropical pathway, where low-frequency SST variability associated with the Atlantic Multidecadal Oscillation induces a Matsuno-Gill type atmospheric response in the tropical Atlantic-Pacific sectors north of the equator. These results provide a quantitative assessment suggesting that 12–29% of PDO variance originates from the Atlantic Ocean and 40–44% from the tropical Pacific. The remaining 27–48% of the variance is inferred to arise from other processes such as regional ocean-atmosphere interactions in the North Pacific and possibly teleconnections from the Indian Ocean.
Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.
Abstract Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model, version 2 (CESM2), preindustrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows a significant poleward migration of tropical cyclone LMI even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regard to projected changes to the equatorial zonal sea surface temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones. Significance Statement Observations in the northwest Pacific basin show that the latitude at which tropical cyclones are at their most intense has been trending northward in the recent half century. These changes are important since tropical cyclones could bring hazardous weather to coastal areas that are poorly equipped to handle them. Here, we show that natural variations in Earth’s climate are very unlikely to explain the observed poleward trend in the latitude that tropical cyclone reach their maximum intensity. We find that it is much more likely that the observed trend is forced by human-related emissions, though the spatial pattern of warming in response to greenhouse emissions can have significant impacts on the magnitude of the trend.
We propose a protocol to evaluate and analyze year-long simulations of global storm-resolving models (GSRMs). The proposed protocol complements an earlier 40-day simulation protocol under the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) project to allow the analysis of the seasonal cycle and associated climatic relevant phenomena. This intercomparison aims to reveal how GSRMs, which can simulate mesoscale convective systems (MCSs) in the global domain, reproduce atmospheric large-scale structures related to convection beyond month-long simulations. The intercomparison for one-year simulations is conducted by either atmosphere-only models or atmosphere–ocean coupled models with atmospheric horizontal mesh sizes less than 5 km. We recommend the continuous four seasons from March 2020 to February 2021 as a target period for the intercomparison but with options for many groups to join more flexibly. The output variables are collected at 0.25° resolution, and archives of a small set of native grid variables are encouraged to analyze tropical cyclones and MCSs. Through the proposed global storm-resolving simulation, we will evaluate the climatological distributions of the atmospheric large-scale circulations, such as the Intertropical Convergence Zone (ITCZ), monsoon, midlatitude jets, their time evolution, and the upscale impacts on them. We present sample analyses from a one-year simulation using the 3.5 km mesh Nonhydrostatic Icosahedral Atmospheric Model (NICAM), revealing the realistic zonal contrast of tropical precipitation, no double ITCZ structure, the reasonable midlatitude jet position and intensity but a weak bias of storm track activities, and a warm bias over the Eurasia during boreal winter. We also clarify the cross-scale interaction, such as the effects of cold pools on mean precipitation over the Maritime Continent through the precipitation diurnal cycle and the effects of resolved gravity waves on midlatitude mean flows. The proposed one-year simulation protocol is referred to as the “Sendai Protocol.” This protocol is not unique or definite for evaluating GSRMs; we prospect a hierarchical set of experiments from short-term to multi-year simulations as GSRM intercomparisons.
Fire emissions from the Maritime Continent (MC) over the western tropical Pacific are strongly influenced by El Niño–Southern Oscillation (ENSO), posing various climate effects to the Earth system. Here, in this study, we show that the historical biomass burning emissions of black carbon (BC bb ) aerosol in the dry season from the MC are strengthened in El Niño years due to the dry conditions. The eastern Pacific type of El Niño exerts a stronger modulation in BC bb emissions over the MC region than the central Pacific type of El Niño. Based on simulations using the fully coupled Community Earth System Model (CESM), the impacts of increased BC bb emissions on ENSO variability and frequency are also investigated in this study. With BC bb emissions from the MC scaled up by a factor of 10, which enables the identification of climate response from the internal variability, the increased BC bb heats the local atmosphere and changes land–sea thermal contrast, which suppresses the westward transport of the eastern Pacific surface water. It leads to an increase in sea surface temperature in the eastern tropical Pacific, which further enhances ENSO variability and increases the frequency of extreme El Niño and La Niña events. This study highlights the potential role of BC bb emissions on extreme ENSO frequency, and this role may be increasingly important in the warming future with higher wildfire risks.
Microbially-explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (RH), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7,821 daily site-scale RH measurements, to evaluate the spatio-temporal performance of one first-order decay model (CASA-CNP) and two microbially-explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different climate datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the meteorological input data used to drive them. Spatial RH variability was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise, but also needs to be improved for realistic spatio-temporal variability of RH. Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.