The atmospheric science community includes both weather and climate scientists. These two groups interact much less than they should, particularly in the United States. The schism is widespread and has persisted for 50 years or more. It is found in academic departments, laboratories, professional societies, and even funding agencies. Mending the schism would promote better, faster science. We sketch the history of the schism and suggest ways to make our community whole.

This paper describes and analyzes the Reed–Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). This intermediate-complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Reference solutions from nine general circulation models (GCMs) with identical simplified physics parameterization packages that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with five of these models also providing solutions at 25 km grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind–pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are presented for all participating GCMs at both horizontal grid spacings. While all TCs undergo a similar evolution process, some reach significantly higher intensities than others, ultimately impacting their horizontal and vertical structures. TCs simulated at 25 km grid spacings retain these differences but reach higher intensities and are more compact than their 50 km counterparts. These results indicate that dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs.

The total poleward energy transport (PET) is set by the top of atmosphere radiation fl ux and is therefore sensitive to any process which can alter those fl uxes, particularly in the shortwave. One example is the direct and indirect effects of anthropogenic aerosols, which increase the local reflection of solar radiation back into space. The historic emission of sulfur dioxide, which peaked in the northern midlatitudes during the 1980s, has been proposed as a primary contributor to historic anomalies in cross-equatorial energy transport, as well as related processes such as a shift in the tropical rainband. In this study, we analyze simulations from the Community Earth System Model, version 2 (CESM2), large ensemble and single-forcing projects to better understand the forced response of PET to historical forcings. First, analysis of the single-forcing project reveals that the position of the intertropical convergence zone (ITCZ) responds in a nonlinear manner to greenhouse gas forcing in CESM2. This type of nonlinearity has been found previously in the context of the aerosol-only simulations in the CESM2 single-forcing project but may be the fi rst identification of a similar effect in the greenhouse gas-only simulations. Second, through analysis of the full CESM2 large ensemble simulations, we fi nd that anomalous heat transport occurred in both the atmosphere (through the mean meridional circulation and atmospheric eddies) and the oceans (through the Atlantic and Indo-Pacific sectors) due to a variety of related processes including the Hadley cells, the midlatitude storm tracks, the Atlantic meridional overturning circulation (AMOC), and the Pacific wind-driven subtropical gyre.

It has been suggested that the Atlantic meridional overturning circulation (AMOC) in many CMIP6 models is overly sensitive to anthropogenic aerosol forcing, and it has been proposed that this is due to the inclusion of aerosol indirect effects for the first time in many CMIP6 models. We analyze the AMOC response in a newly released ensemble of simulations performed with CESM2 forced by the CMIP5 input data sets (CESM2-CMIP5). This AMOC response is then compared to the CMIP5-generation CESM1 large ensemble (CESM1-LE) and the CMIP6-generation CESM2 large ensemble (CESM2-LE). A key conclusion, only made possible by this experimental setup, is that changes in aerosol-indirect effects cannot explain differences in AMOC response between CESM1-LE and CESM2-LE. Instead, we hypothesize that the difference is due to increased interannual variability of anthropogenic emissions. This forcing variability may act through a nonlinear relationship between the surface heat budget of the North Atlantic and the AMOC. The Atlantic meridional overturning circulation (AMOC) is important for the wider climate because it transports a large amount of warm water northward away from the equator. The most recent generation of climate models disagree with the observed behavior of the AMOC over the twentieth century, and it has been suggested that this is due to the inclusion of aerosol-cloud interactions in many of the newest models. Here we look at model simulations of the AMOC in several configurations to show that the disagreement in the past AMOC behavior is instead primarily due to changes in the inputs given to the models, rather than to changes in the models themselves. The CESM2 Atlantic meridional overturning circulation (AMOC) response to aerosols depends on if CMIP5 or CMIP6 emissions are applied Large interannual variability in CMIP6 emissions appears to enhance the interannual variability of north Atlantic turbulent heat fluxes This heat flux variability may drive a non-linear AMOC response to aerosols

We examine the hypothesis that the observed connection between the stratospheric quasi-biennial oscillation (QBO) and the strength of the Madden-Julian oscillation (MJO) is modulated by the sea surface temperature (SST)}for example, by El Nino-Southern Oscillation (ENSO). A composite analysis shows that, globally, La Nina SSTs are remarkably similar to those that occur during the easterly phase of the QBO. A maximum covariance analysis suggests that MJO power and SST are strongly linked on both the ENSO time scale and the QBO time scale. We analyze simulations with a modified configuration of version 2 of the Community Earth System Model, with a high top and fine vertical resolution. The model is able to simulate ENSO, the QBO, and the MJO. The ocean-coupled version of the model simulates the QBO, ENSO, and MJO, but does not simulate the observed QBO-MJO connection. When driven with prescribed observed SST anomalies based on composites for QBO east and QBO west (QBOE and QBOW), however, the same atmospheric model produces a modest enhancement of MJO power during QBOE relative to QBOW, as observed. We explore the possibility that the SST anomalies are forced by the QBO itself. Indeed, composite Hovm oller diagrams based on observations show the propagation of QBO zonal wind anomalies all the way from the upper stratosphere to the surface. Also, subsurface ocean temperature composites reveal a similarity between the western Pacific and Indian Ocean subsurface signal between La Nina and QBOE.

Abstract Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

Syukuro (Suki) Manabe, Klaus Hasselmann, and Georgio Parisi were awarded the 2021 Nobel Prize in Physics. At first blush, this prize appears to recognize unrelated work in disparate fields. Even one of the Nobel Committee members acknowledged in the postannouncement press conference that it looks like a “split prize.” Upon reflection, however, there is a common thread running through the works of the awardees. Simply put, this year’s prize in physics acknowledges that disordered systems are predictable and that systems that behave chaotically can respond predictably to changes in external parameters. Earth’s climate is one such complex system, and it is of great importance to humanity. Its future must be predicted to guide policy. The prize also acknowledges that changes in the climate, like the properties of disordered condensed matter, are predictable using methods grounded in sound physics. Therefore, it is very appropriate to jointly recognize the foundational work done by these three visionary researchers with the Nobel Prize in Physics. This Nobel Prize in Physics is very different from the earlier Nobel Peace Prize awarded in 2007 to former Vice President Al Gore and the Intergovernmental Panel on Climate Change (IPCC), which recognized the importance of human-induced climate change and the actions needed to understand and mitigate it. It is also different from the 1995 Nobel Prize in Chemistry to Profs. Rowland, Molina, and Crutzen for their work on a global environmental problem arising from ozone depletion by manufactured chemicals, some of which were also major greenhouse gases. In contrast to those two earlier awards, the 2021 Nobel Prize in Physics recognizes the fundamental scientific basis of climate predictability, detection of change due to known external forcings, and the predictability of disordered condensed matter. Syukuro Manabe. Image credit: Denise Applewhite (Princeton University, Princeton, NJ). Climate scientists Syukuro Manabe and … [↵][1]1To whom correspondence may be addressed. Email: a.r.ravishankara{at}colostate.edu. [1]: #xref-corresp-1-1

Studies in recent decades have demonstrated a robust relationship between tropical precipitation and column relative humidity (CRH). The present study identifies a similar relationship between CRH and the atmospheric cloud radiative effect (ACRE) calculated from satellite observations. Like precipitation, the ACRE begins to increase rapidly when CRH exceeds a critical value near 70%. We show that the ACRE can be estimated from CRH, similar to the way that CRH has been used to estimate precipitation. Our method reproduces the annual mean spatial structure of the ACRE in the tropics, and skillfully estimates the mean ACRE on monthly and daily time scales in six regions of the tropics. We propose that the exponential dependence of precipitation on CRH may be partially explained by cloud‐longwave feedbacks, which facilitate a shift from convective to stratiform conditions.

This dataset contains data that can be used to reproduce the figures in Jenney et al., (2021) "Drivers of uncertainty in future projections of MJO teleconnections". Most of the files are Matlab structures as described below I. Output from linear baroclinic model (LBM): Geopotential height at 500 hPa description: LBM output for experiments with control MJO and varied mean states name: "z500_.mat" format: matlab structure "out" with fields: ThVh (historical dry static energy & historical winds) ThVs (historical dry static energy & future winds) TsVh (future dry static energy & historical winds) TsVs (future dry static energy & future winds) dimensions: [30 x 128 x 64 x 59] = [ensemble member x lon x lat x simulation day] II. Output from linear baroclinic model (LBM) for MJO perturbed experiments name: "_z500_.mat" experiments: EAST20: 20-degree eastward shifted thermal forcing MEDIUM: lower bound of increased prop. speed experiment FAST: upper bound of increased prop. speed experiment WAVENO: perturbed wavenumber experiment (decreased wavenumber) format, fields, & dimensions: files with format identical to I III. Linearized Rossby wave source name: "rws.mat" format: matlab structure "rws" fields & dimensions: these are described and listed in the structure IV. Stationary Rossby wave number description: K_s computed for individual CMIP6 model mean states and for the multi-model mean wind name: "Ks.mat" format: matlab structure "Ks_struct" fields & dimensions: described and listed in the structure V. Model global mean surface temperature description: global mean surface temperature for individual CMIP6 models for the historical and future periods name: "mmGlobalSfcTemp.nc" format: netcdf file VI. Tropical mean temperature profile description: Tropical mean temperature profile (20S to 20N) for individual CMIP6 models name: "tropical_mean_temp.mat" format: Matlab structure "trop_mean_temp" fields & dimensions: described and listed in the structure VII. Propagating forcing description: Propagating MJO-like idealized thermal forcing used to force the LBM. name: "movT_weak.grd": control forcing "movT_weak_.grd": perturbed MJO cases, naming convention described in II format: binary (.grd file) notes: Use Matlab script "read_forcing.m", also included, to read and plot the files.

The intertropical convergence zone (ITCZ) exports energy and imports moisture. This has been understood for decades. By analyzing a set of uniform, nonrotating aquaplanet simulations, we show that energy export and moisture convergence are general characteristics of warm humid regions, and not just of the ITCZ. Using an analysis method based on the column relative humidity, we find that the absorption of longwave radiation by clouds supplies the energy that is exported from humid regions. The longwave absorption also induces a thermally direct circulation that lifts water vapor and converges moisture into regions that are already quite humid. An additional set of simulations shows that strong atmospheric energy convergence is absent when radiation is homogenized across the domain.

Teleconnections from the Madden–Julian Oscillation (MJO) are a key source of predictability of weather on the extended timescale of about 10–40 d. The MJO teleconnection is sensitive to a number of factors, including the mean dry static stability, the mean flow, and the propagation and intensity characteristics of the MJO, which are traditionally difficult to separate across models. Each of these factors may evolve in response to increasing greenhouse gas emissions, which will impact MJO teleconnections and potentially impact predictability on extended timescales. Current state-of-the-art climate models do not agree on how MJO teleconnections over central and eastern North America will change in a future climate. Here, we use results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical and SSP585 experiments in concert with a linear baroclinic model (LBM) to separate and investigate alternate mechanisms explaining why and how boreal winter (January) MJO teleconnections over the North Pacific and North America may change in a future climate and to identify key sources of inter-model uncertainty. LBM simulations suggest that a weakening teleconnection due to increases in tropical dry static stability alone is robust across CMIP6 models and that uncertainty in mean state winds is a key driver of uncertainty in future MJO teleconnections. Uncertainty in future changes to the MJO's intensity, eastward propagation speed, zonal wavenumber, and eastward propagation extent are other important sources of uncertainty in future MJO teleconnections. We find no systematic relationship between future changes in the Rossby wave source and the MJO teleconnection or between changes to the zonal wind or stationary Rossby wave number and the MJO teleconnection over the North Pacific and North America. LBM simulations suggest a reduction of the boreal winter MJO teleconnection over the North Pacific and an uncertain change over North America, with large spread over both regions that lends to weak confidence in the overall outlook. While quantitatively determining the relative importance of MJO versus mean state uncertainties in determining future teleconnections remains a challenge, the LBM simulations suggest that uncertainty in the mean state winds is a larger contributor to the uncertainty in future projections of the MJO teleconnection than the MJO.

Abstract. Teleconnections from the Madden-Julian Oscillation (MJO) are a key source of predictability of weather on the extended time scale of about 10–40 days. The MJO teleconnection is sensitive to a number of factors, including the mean state dry static stability, the mean flow, and the propagation and intensity characteristics of the MJO itself, which are traditionally difficult to separate across models. Each of these factors may evolve in response to increasing greenhouse gas emissions, which will impact MJO teleconnections and potentially impact potential predictability on extended time scales. Current state-of-the-art climate models do not agree on how MJO teleconnections will change in a future climate. Here, we use results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical and SSP585 experiments in concert with a linear baroclinic model to separate and investigate alternate mechanisms explaining why and how MJO teleconnections over the North Pacific and North America will change in a future climate, and to identify key sources of inter-model uncertainty. We find that decreases to the MJO teleconnection due to increases in tropical dry static stability alone are robust, and that uncertainty in mean state winds are a key driver of uncertainty in future MJO teleconnections. We find no systematic relationship between changes in Rossby wave excitation and the MJO teleconnection. However, we find that models that predict increases (decreases) in the stationary Rossby wave number over the gulf of Alaska also predict stronger (weaker) teleconnections over North America. Uncertainty in future changes to the MJO's intensity, eastward propagation speed, and eastward propagation extent are other important sources of uncertainty in future MJO teleconnections, although to a lesser degree than uncertainty in the mean state. The overall outlook is a reduction of the boreal winter MJO teleconnection across the vast majority of CMIP6 models, especially over the North Pacific, but with some nuance over North America due to larger sensitivity to expansion of the MJO's eastward extent.

Idealized simulations of the tropical atmosphere have predicted that clouds can spontaneously clump together in space, despite perfectly homogeneous settings. This phenomenon has been called self-aggregation, and it results in a state where a moist cloudy region with intense deep convectivestorms is surrounded by extremely dry subsiding air devoid of deep clouds. We review here the main findings from theoretical work and idealized models of this phenomenon, highlighting the physical processes believed to play a key role in convective self-aggregation. We also review the growing literature on the importance and implications of this phenomenon for the tropical atmosphere, notably, for the hydrological cycle and for precipitation extremes, in our current and in a warming climate.

Abstract Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set of idealized simulations with specified surface warming and superparameterized convection, we find support for these previous conclusions. We use an energy and mass balance framework to develop a simple diagnostic that links the fractional area covered by the region of upward motion to the strength of the mean circulation. We demonstrate that the diagnostic works well for our idealized simulations and use it to understand how changes in tropical ascent area and the strength of the mean circulation relate to changes in heating in the ascending and descending regions. We show that the decrease in the strength of the mean circulation can be explained by the relatively slow rate at which atmospheric radiative cooling intensifies with warming. In our simulations, decreases in tropical ascent area are balanced by increases in nonradiative heating in convective regions. Consistent with previous work, we find a warming‐induced decrease in the mean convective mass flux. However, when we condition by the sign of the mean vertical motion, the warming‐induced changes in the convective mass flux are nonmonotonic and opposite between the ascending and descending regions.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.