Reanalysis datasets are widely used to understand atmospheric processes; however, different reanalyses may give very different results for the same diagnostics. The Atmospheric Processes And their Role in Climate (APARC; formerly SPARC) Reanalysis Intercomparison Project, or S(soon to be A)-RIP (https://s-rip.github.io/), is a coordinated activity to compare key diagnostics among atmospheric reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere and above and processes linking these regions to the troposphere and surface. We are broadening our efforts in Phase 2 (S-RIP2), with new directions including studies of the tropospheric circulation, extreme weather events, and their links to the stratosphere, along with evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies and capacity building for Early Career Scientists.

A layer of aerosols has been identified in the upper troposphere and lower stratosphere above the Asian summer monsoon (ASM) region, typically referred to as the Asian Tropopause Aerosol Layer (ATAL). This layer is fed by atmospheric pollutants over southern and eastern Asia lifted to the upper troposphere by deep convection in summer. The radiative effects of this aerosol layer change local temperature, influence thermodynamic stability, and modulate the efficiency of air mass vertical transport near the tropopause. However, quantitative understanding of these effects is still very poor. To estimate aerosol radiative effects in the upper troposphere and above, a set of radiative kernels is constructed for the tropical upper troposphere and stratosphere to reduce the computational expense of decomposing the different contributions of atmospheric components to anomalies in radiative fluxes. The prototype aerosol kernels in this work are among the first to target vertically resolved heating rates, motivated by the linearity and separability of scattering and absorbing aerosol effects in the ATAL. Observationally derived lower boundary conditions and satellite observations of cloud ice within the upper troposphere and stratosphere are included and simplified in our Tropical Upper Troposphere–Stratosphere Model (TUTSM). Separate sets of kernels are derived and tested for the effects of absorbing aerosols, scattering aerosols, and cloud ice particles on both shortwave (solar) and longwave (thermal) radiative fluxes and heating rates. The results indicate that the kernels can reproduce aerosol radiative effects in the ATAL well. Similarly, these aerosol kernels could be used to simulate radiative effects of biomass burning and volcanic eruption above the troposphere. This approach substantially reduces computational expense while achieving good consistency with direct radiative transfer model calculations, and it can be applied to models that do not require high precision but have strict requirements for computing speed and storage space.

Mixed-phase stratocumulus clouds in the polar region affect high-latitude climate in many ways, not least by regulating the boundary layer moisture and energy budgets. Sea ice coverage and thickness are decreasing sharply under global warming, changing the characteristics of the surface underlying much of the polar boundary layer, and the circulation patterns that govern lower tropospheric temperature and humidity inversions may change as well. Given the strength of ocean-ice-atmosphere interactions in the polar boundary layer, it is imperative to understand how different surface and atmospheric inversion conditions affect cloud formation and characteristics from both microphysical and macrophysical perspectives. Stable water isotopes have excellent potential as a tool to study the water cycle in the polar boundary layer, but their applications to understanding mixed-phase clouds in the polar region are limited by the lack of both direct observations and isotope-enabled models at appropriate spatial and temporal scales. Recent observational campaigns such as MOSAiC have observed isotopic composition at and near the Arctic surface under a range of different conditions, creating opportunities to expand the use of isotopes in Arctic water cycle research. Previous research has also established the ability of large-eddy simulations (LESs) to explicitly resolve boundary layer processes in the Arctic region and simulate the sensitivity of Arctic clouds to different atmospheric and surface conditions. To better exploit the potential of recent isotopic observations, we have developed an isotope-enabled large eddy model based on the PyCLES (Python Cloud Large Eddy Simulation) model framework to close some of the gaps between observations and modeling in the study of polar boundary layer clouds. iPyCLES is equipped with a two-moment microphysics scheme and includes representations of all essential isotopic fractionation processes at the surface and within clouds. In this presentation, we briefly introduce a series of sensitivity experiments targeting different surface and tropospheric inversion conditions to evaluate the isotopic signatures of surface-ice-atmosphere interactions within the polar boundary layer. The simulations are based on two well-studied field campaigns conducted near Barrow, Alaska, one in spring and one in autumn. Together with standard metrics of cloud evolution and turbulence mixing, isotope ratios in water vapor, cloud liquid and ice, and snow are tracked during the simulation. Isotopic signatures of each experiment are evaluated for their potential to provide observable constraints on polar clouds and boundary layer processes.

The Atlantic Meridional Overturning Circulation (AMOC) plays a crucial role in regulating global climate. Although subpolar sea surface temperature (SST) covaries with recent AMOC variability, the relatively short timescales considered by previous studies leave room for doubt on whether subpolar SST reliably represents AMOC state. The same doubt arises for the sea surface salinity (SSS), though freshwater flux into the subpolar North Atlantic (SPNA) affects AMOC stability by regulating salinity. Here, we investigate the relationships of SST and SSS with the AMOC mean states in model simulations conducted for paleoclimate modeling. SPNA SSS aligns well with changes in the AMOC mean state under these scenarios, while SST does not. Notably, climate experiments simulating an abrupt quadrupling of CO2 demonstrate a significant correlation between SPNA SSS and transient AMOC strength. The absence of significant SPNA freshening over the past several decades may imply the AMOC is less fragile than previously postulated, but data remain insufficient to predict its long-term stability.

Marine heatwaves are devastating to regional ecosystems and often evolve under the influence of persistent atmospheric weather patterns. However, the relative importance of synoptic atmospheric processes in the development of these events remains unclear, as previous studies have predominantly focused on quasi‐stationary systems. Here, we demonstrate that a severe marine heatwave (MHW) over the Southwest Pacific unfolded through two distinct warming stages followed by a cooling phase, all dominated by recurrent synoptic Rossby wave packets propagating through the upper troposphere. During each warming phase, these wave packets organized near‐surface circulation anomalies that inhibited wind‐driven mixing, causing unusually shallow mixed‐layer depths. The resultant shoaling amplified surface warming by intensifying the effects of increased solar radiation, ultimately triggering the extreme MHW. Conversely, during the intermediate cooling phase, the mixed layer returned to its climatological state, allowing the heatwave conditions to dissipate. Strong quasi‐stationary patterns emerged only near the ends of both intensification and decay, downstream of the recurring wave events. These blocking patterns appear to have helped terminate the recurrence by causing potential instability to build up to a point of more widespread release. A diagnostic analysis of the lower tropospheric vorticity budget highlights the central role of dynamical vorticity production tied to the anomalous atmospheric circulation forced by upper‐level waves.

Several recent studies have highlighted differences in simulated properties of El Nino-Southern Oscillation (ENSO) under transient and equilibrium responses to increasing CO2. However, the reasons behind these disparate responses and the extent to which they are robust to different scales of CO2 forcing remain unclear. In this study, we adopt a climate system model with reduced SST bias in the eastern tropical Pacific and incrementally apply abrupt increases in CO2, analyzing outputs after each simulation reaches quasi-equilibrium with the imposed forcing. The results suggest that ENSO activity under quasi-equilibrium first increases and then decreases with increasing CO2, peaking in simulations with CO2 concentrations similar to the present day. Bjerknes-Jin stability analysis indicates that changes in the ENSO growth rate result primarily from changes in the thermocline feedback and thermodynamic damping terms. While thermodynamic damping increases monotonically with increasing CO2, the positive thermocline feedback varies within the range of internal variability up to twice the preindustrial value of CO2 and then weakens sharply with further increases. The mechanisms behind these changes include weaker mean ocean upwelling and weaker dynamical coupling between the atmosphere and subsurface ocean associated with substantial near-surface freshening at higher levels of CO2. These changes steepen the thermodynamic barrier to mixing between the surface and subsurface, weakening the east-west temperature gradient in the mean state and suppressing variability in the cold tongue. Analysis of similar model simulations from the Coupled Model Intercomparison Project (CMIP6) archive indicates that changes in the Bjerknes-Jin stability index are robust but do not establish a consensus as to the mechanisms behind them.

As a critical regulator of the global climate system, the Atlantic Meridional Overturning Circulation (AMOC) has attracted huge attention since its bimodal state change could abruptly alter climate1–5. The subpolar sea surface temperature (SST) has been used as a fingerprint to predict the state change of AMOC3,6–8. Although the subpolar SST agrees well with the variability of AMOC in recent years, it is under debate whether the subpolar SST can represent the abrupt state change of AMOC since its physical role remains unclear9. In contrast, it is well-known that the freshwater flux in high latitudes is a key to AMOC stability10–13. To foresee the AMOC collapse based on salinity, we investigate the relationship of SST and SSS with various AMOC mean states from the Paleoclimate Modeling Intercomparison Project phase 4 (PMIP4). It is found that SST does not agree with changes in AMOC mean state under various climate conditions, while the sea surface salinity (SSS) within the subpolar North Atlantic (SPNA) does. Our results indicate that about 3.5 PSU drop in SPNA SSS is required for AMOC collapse to totally shut down. It's worth noting that no significant salinity trend was observed in the SPNA, implying the stability of deep water formation in recent decades. More attention should be paid to monitoring salinity variations in the SPNA and studying the anthropogenic-induced ice melting and alterations in the water cycle in high latitudes.

Climate models have long-standing difficulties simulating the South Pacific Convergence Zone (SPCZ) and its variability. For example, the default Zhang-McFarlane (ZM) convection scheme in the Community Atmosphere Model version 5 (CAM5) produces too much light precipitation and too little heavy precipitation in the SPCZ, with this bias toward light precipitation even more pronounced in the SPCZ than in the tropics as a whole. Here, we show that implementing a recently developed convection scheme in the CAM5 yields significant improvements in the simulated SPCZ during austral summer and discuss the reasons behind these improvements. In addition to intensifying both mean rainfall and its variability in the SPCZ, the new scheme produces a larger heavy rainfall fraction that is more consistent with observations and state-of-the-art reanalyses. This shift toward heavier, more variable rainfall increases both the magnitude and altitude of diabatic heating associated with convective precipitation, intensifying lower tropospheric convergence and increasing the influence of convection on the upper-level circulation. Increased diabatic production of potential vorticity in the upper troposphere intensifies the distortion effect exerted by convection on transient Rossby waves that pass through the SPCZ. Weaker distortion effects in simulations using the ZM scheme allow waves to propagate continuously through the region rather than dissipating locally, further reducing updrafts and weakening convection in the SPCZ. Our results outline a dynamical framework for evaluating model representations of tropical–extratropical interactions within the SPCZ and clarify why convective parameterizations that produce ‘top-heavy’ profiles of deep convective heating better represent the SPCZ and its variability.

A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework.

Sea surface temperature (SST) is a vital oceanic parameter that significantly influences air-sea heat flux and momentum exchange. SST datasets are crucial for identifying and describing both short-term and long-term climate perturbations in the ocean. This article focuses on cloud detection and SST retrievals in the Western Pacific Ocean, using observations obtained by the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C satellite. To distinguish between clear-sky and overcast regions, reflectance after sun glint correction and brightness temperature are used as inputs for an alternative decision tree (ADTree). The accuracy of cloud detection is 93.85% for daytime and 91.98% for nighttime, respectively. Application of the cloud detection algorithm improves the accuracy and data availability (spatiotemporal coverage) of SST retrievals. We implement a nonlinear algorithm to retrieve the SST and validate these retrieved values against buoy measurements of SST. Comparisons are conducted for measurements within +/- 1 h and 0.01 degrees x 0.01 degrees of the retrieval. During the day, the bias and standard deviation (SD) are -0.01 degrees C and 0.63 degrees C, respectively, while at night, they stand at -0.08 degrees C and 0.71 degrees C, respectively. Furthermore, the intercomparison between the SST products derived from the moderate-resolution imaging spectroradiometer (MODIS) onboard Terra and the results are conducted. During the day, the bias and SD are 0.03 degrees C and 0.42 degrees C, respectively, whereas at night, they are 0.25 degrees C and 0.76 degrees C, respectively. This article improves the accuracy and applicability of the SST retrieved from the COCTS thermal infrared channels.

A generalized probability density function (pdf) is introduced to enhance sea surface slope modeling for remote sensing applications. This new pdf, which incorporates the anisotropy index to better capture the direction and tilt of surface waves relative to the classical Cox and Munk model proposed 70 years ago, is then applied to sun glint correction in satellite imagery. Sixteen different mean square slope (MSS) models are reviewed to establish both the strengths and limitations of the classical model and the stability and adaptability of the anisotropy index. The new sea surface model applies to a wider range of sea surface states, including those in coastal environments, and provides a stable quantitative description of sea surface topography. Application of the generalized pdf to sun glint correction in satellite imagery demonstrates its overall accuracy and improved efficacy compared to the Cox and Munk model, particularly in maintaining the integrity of sea surface and cloud features in complex weather environments. This initial study provides a promising approach to improve the accuracy and reliability of sun glint correction in remote sensing of water surfaces, with applications to improving both historical and future satellite-based climate data records.

Although links between the atmospheric convergence zone and the local ocean dipole in the South Atlantic are well established, relationships between the South Pacific convergence zone (SPCZ) and the South Pacific quadrupole (SPQ) remain largely unexplored. Based on maximum covariance analysis applied to a 110-yr monthly coupled atmosphere- ocean reanalysis, we describe a coupled quadrupole mode (CQM) that connects the SPCZ and SPQ during austral summer [December-February (DJF)]. The CQM is linked to the "enhanced SPCZ" mode in the atmosphere and the SPQ in the ocean, with the atmospheric signal leading the ocean signal by about 1 month. This coupled mode essentially represents the atmospheric and oceanic responses to a stationary Rossby wave train that propagates from low- to high latitudes before reflecting back toward lower latitudes around 150 degrees E. Coupled atmosphere-ocean feedbacks help to maintain anomalous convective activity in the SPCZ and related circulation anomalies. The stationary waves that organize the CQM are often rooted in anomalous convection over the Maritime Continent and have close connections with the atmospheric wavenumber-4 mode in the midlatitude Southern Hemisphere.

A unified clustering framework based on pattern correlation is used to identify eight blocking regimes over Eurasia and surrounding oceans during the winter months (December-March) of 1948-2021. The regimes are labeled based on their centers of action, which are located over the West Atlantic, Greenland, the East Atlantic, Scandinavia, the Ural Mountains and Siberia, the Okhotsk Sea, the Bering Strait, and the North Pacific. It should be noted that the classification is almost insensitive to the time period but mainly depends on the percentage of different blocking events. The spatial distributions of cold surges differ substantially among these eight regimes. Due mainly to the cold advection downstream of the centers of blocking activities, cold surges can be observed over parts of the Eurasian continent. Possible relationships between the eight blocking regimes and large-scale modes of climate variability, including the Arctic Oscillation, the North Atlantic Oscillation, and the El Ni & ntilde;o-Southern Oscillation, are explored. Not only contemporary connections but also the predictive value of large-scale modes is discussed. Atmospheric blocking, an important circulation system in middle and high latitudes, has significant local and upstream/downstream impacts on weather and short-term climate. Since blockings with diverse occurrence locations affect different regions, it is necessary to classify them by a unified framework. In this study, a clustering method based on pattern correlations is adopted to identify wintertime blocking regimes over Eurasia and surrounding oceans to establish their connections with the corresponding spatial distribution of cold surges. The clustering result is almost insensitive to the time period, which mainly depends on the percentage of blocking events located at different regions. We also examine the large-scale climate background for each regime to provide a reference for the cold surge forecast over the Eurasian continent. Eight wintertime blocking regimes over Eurasia and surrounding oceans are revealed by a clustering method Blocking activities induce cold surges over parts of the Eurasian continent due to downstream cold advection The cluster regimes are almost insensitive to the time period

Abstract. Recent field campaigns and advances in observational techniques have yielded a wealth of observations of stable water isotopes in the atmosphere, but the heirarchy of isotope-enabled models is not well-placed to leverage these observation for improving constraints on parameterized physics in global models. Here, we introduce the isotope-enabled Python Cloud Large-Eddy Simulation model (iPyCLES) for mixed-phase clouds. Isotopic tracers are implemented in a parallel passive water cycle and experience all processes and phase changes that affect the model's prognostic total water variable. Isotopic fractionation occurs during cloud and precipitation processes as well as surface evaporation, with facilities for applying external forcing. In addition to isotopic tracers, we extend the two-moment warm cloud microphysics scheme to enable prognostic simulation of cloud liquid water and ice while eliminating dependence on saturation adjustment. Relative to a one-moment mixed-phase scheme with saturation adjustment, the new microphysical scheme yields substantial benefits in simulating phase partitioning and isotopic exchange in mixed-phase regions. The LES model is based on an energetically-consistent implementation of the anelastic equations and employs high-order weighted, essentially non-oscillatory numerics, and is therefore theoretically suitable for simulations spanning the gray zone of the convective spectrum. In this initial evaluation, we present the results of test cases for non-precipitating subtropical shallow cumulus, precipitating subtropical shallow cumulus, and precipitating Arctic mixed-phase stratocumulus clouds. The iPyCLES simulations agree well with available observations and previous model simulations in all three cases, with distinct signatures among the cases that highlight the added potential of isotopic tracers. The benefits of the revised microphysics scheme are especially evident in the Arctic mixed-phase cloud test case, with vapor-liquid-ice exchange within the cloud producing a conspicuous peak in deuterium excess near the top of the cloud. As an idealized testbed, the iPyCLES model can bridge gaps between cloud chamber experiments, real-world observations, and global and regional models, allowing information provided by water isotopes to be translated more effectively into observational constraints for cloud and boundary layer parameterizations.

Circulation patterns linked to the East Asian winter monsoon (EAWM) affect precipitation, surface temperature, and air quality extremes over East Asia. These circulation patterns can in turn be influenced by aerosol radiative and microphysical effects through diabatic heating and its impacts on atmospheric vorticity. Using global model simulations, we investigate the effects of anthropogenic aerosol emissions and concentration changes on the intensity and variability of the EAWM. Comparison with reanalysis products indicates that the model captures the mean state of the EAWM well. The experiments indicate that anthropogenic aerosol emissions strengthen the Siberian High but weaken the East Asian jet stream, making the land areas of East Asia colder, drier, and snowier. Aerosols reduce mean surface air temperatures by approximately 1.5°C, comparable to about half of the difference between strong and weak EAWM episodes in the control simulation. The mechanisms behind these changes are evaluated by analyzing differences in the potential vorticity budget. Anthropogenic aerosol effects on diabatic heating strengthen anomalous subsidence over southern East Asia, establishing an anticyclonic circulation anomaly that suppresses deep convection and precipitation. Aerosol effects on cloud cover and cloud longwave radiative heating weaken stability over the eastern flank of the Tibetan Plateau, intensifying upslope flow along the western side of the anticyclone. Both circulation anomalies contribute to reducing surface air temperatures through regional impacts on thermal advection and the atmospheric radiative balance.

A novel multivariable prediction system based on a deep learning (DL) algorithm, i.e., the residual neural network and pure observations, was developed to improve the prediction of the El Nino-Southern Oscillation (ENSO). Optimal predictors are automatically determined using the maximal information for spatial filtering and the Taylor diagram criteria, enabling the best prediction skills at lead times of eight months compared with most operational prediction models. The hindcast skill for the most challenging decade (2011-18) outperforms the multi-model ensemble operational forecasts. At the six-month lead, the correlation (COEF) skill of the DL model reaches 0.82 with a normalized root-mean-square error (RMSE) of 0.58 & DEG;C, which is significantly better than the average multi-model performance (COEF = 0.70 and RMSE = 0.73 & DEG;C). DL prediction can effectively alleviate the long-standing spring predictability barrier problem. The automatically selected optimal precursors can explain well the typical ENSO evolution driven by both tropical dynamics and extratropical impacts.

Stratospheric water vapor increases are expected in response to greenhouse gas-forced climate warming, and these changes act as a positive feedback to surface climate. Previous efforts at inferring trends from the 3–4 decade-long observational stratospheric water vapor record have yielded conflicting results. Here we show that a robust multi-decadal variation of water vapor concentrations exists in most parts of the stratosphere based on satellite observations and atmospheric model simulations, which clearly divides the past 40 years into two wet decades (1986–1997; 2010–2020) and one dry decade (1998–2009). This multi-decadal variation, especially pronounced in the lower to middle stratosphere and in the northern hemisphere, is associated with decadal temperature anomalies (±0.2 K) at the cold point tropopause and a hemispheric asymmetry in changes of the Brewer-Dobson circulation modulating methane oxidation. Multi-decadal variability must be taken into account when evaluating stratospheric water vapor trends over recent decades.

Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth’s water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended.

Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land-atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands. Significance StatementThe eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land-atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land-atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.