Duncan, James P C, Elynn Wu, Jean-Christophe Golaz, Peter Caldwell, Oliver Watt-Meyer, and Spencer K Clark, et al., September 2024: Application of the AI2 climate emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity. JGR Machine Learning and Computation, 1(3), DOI:10.1029/2024JH000136. Abstract
Can the current successes of global machine learning-based weather simulators be generalized beyond 2-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10-year simulations with a network trained on output from a physics-based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea-surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10mathematical equation fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine-tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.
Global storm-resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two-year-long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X-SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X-SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X-SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles.
Henn, Brian, Yakelyn R Jauregui, Spencer K Clark, Noah D Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, Andrew G Pauling, and Christopher S Bretherton, March 2024: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation. Journal of Advances in Modeling Earth Systems, 16(3), DOI:10.1029/2023MS003949. Abstract
Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine cloud properties as a function of coarse-grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine-grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened-fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under-dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global-mean top-of-atmosphere (TOA) and surface radiative fluxes. An unbiased global-mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid-cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML-derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse-grid model, as they leverage the accuracy of the fine-grid reference simulation's cloud properties.
McGibbon, Jeremy, Spencer K Clark, Brian Henn, Anna Kwa, Oliver Watt-Meyer, W Andre Perkins, and Christopher S Bretherton, February 2024: Global precipitation correction across a range of climates using CycleGAN. Geophysical Research Letters, 51(4), DOI:10.1029/2023GL105131. Abstract
Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle-generative adversarial network (CycleGAN) to improve global 3-hr-average precipitation fields predicted by a coarse grid (200 km) atmospheric model across a range of climates, morphing them to match their statistical properties with those of reference fine-grid (25 km) simulations. We evaluate its performance on both the target climates and an independent ramped-SST simulation. The translated precipitation fields remove most of the biases simulated by the coarse-grid model in the mean precipitation climatology, the cumulative distribution function of 3-hourly precipitation, and the diurnal cycle of precipitation over land. These results highlight the potential of CycleGAN as a powerful tool for bias correction in climate change simulations, paving the way for more reliable predictions of precipitation patterns across a wide range of climates.
The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research. This GSRM’s climate sensitivity is within the range of conventional climate models, although on the lower end as the result of neutral, rather than amplifying, shortwave feedbacks. Its radiative forcing from carbon dioxide is higher than conventional climate models, and this arises from a bias in climatological clouds and an explicitly simulated high-cloud adjustment. Last, the pattern and magnitude of relative humidity changes, simulated with greater fidelity via explicitly resolving convection, are notably similar to conventional climate models.
Watt-Meyer, Oliver, Noah D Brenowitz, Spencer K Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Lucas Harris, and Christopher S Bretherton, February 2024: Neural network parameterization of subgrid-scale physics from a realistic geography global storm-resolving simulation. Journal of Advances in Modeling Earth Systems, 16(2), DOI:10.1029/2023MS003668. Abstract
Parameterization of subgrid-scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm-resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse-grid (200 km) global atmosphere model, using training data obtained by spatially coarse-graining a 40-day realistic geography global storm-resolving simulation. The training targets are the three-dimensional fields of effective heating and moistening rates, including the effect of grid-scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time-dependent heating and moistening rates in each grid column using the coarse-grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land-sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35-day simulations, with metrics of skill such as the time-mean pattern of near-surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human-designed parameterizations with data-driven ones in a realistic setting.
Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known as k-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations.
We present the global characteristics of rotating convective updrafts in the 2021 version of GFDL's eXperimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), a kilometer-scale global storm resolving model (GSRM). Rotation is quantified using 2–5 km Updraft Helicity (UH) in a year-long integration forced by analyzed SSTs. Updrafts with UH magnitudes above 50 m2 s−2 are common over the mid-latitude continents, where they are associated with severe weather especially in the warm seasons but are also common over most tropical ocean basins. In nearly all areas cyclonically rotating convection dominates, with larger UH values increasingly preferring cyclonic rotation. The ratio of cyclonic to anticyclonic updrafts is largest in the subtropical and mid-latitude oceans and is slightly lower over mid-latitude continents. The ratio of cyclonic to anticyclonic updrafts can be substantively explained by the mean storm-relative helicity (SRH) in convective regions, indicating the importance for environmental controls on the sense of storm rotation, although internal storm dynamics also plays a role in the generation of anticyclonic updrafts.
Kwa, Anna, Spencer K Clark, Brian Henn, Noah D Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W Andre Perkins, Lucas Harris, and Christopher S Bretherton, May 2023: Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle. Journal of Advances in Modeling Earth Systems, 15(5), DOI:10.1029/2022MS003400. Abstract
One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving model (GSRM). Our past work demonstrating this approach was trained with short (40-day) simulations of GFDL's X-SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year-long GSRM simulation. Our corrective ML models are trained by learning the state-dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200 km grid coarse model, FV3GFS, to the GSRM evolution. Coarse-grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no-ML baseline, the time-mean spatial pattern errors with respect to the fine-grid target are reduced by 6%–26% for land surface temperature and 9%–25% for land surface precipitation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no-ML baseline simulation.
Sanford, Clayton, Anna Kwa, Oliver Watt-Meyer, Spencer K Clark, Noah D Brenowitz, Jeremy McGibbon, and Christopher S Bretherton, November 2023: Improving the reliability of ML-corrected climate models with novelty detection. Journal of Advances in Modeling Earth Systems, 15(11), DOI:10.1029/2023MS003809. Abstract
Using machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate. However, ML corrections often introduce new biases in the upper atmosphere and causes inconsistent model performance across different random seeds. Furthermore, they produce profiles that are outside the distribution of samples used in training, which can interfere with the baseline physics of the atmospheric model and reduce model reliability. This study introduces the use of a novelty detector to mask ML corrections when the atmospheric state is deemed out-of-sample. The novelty detector is trained on profiles of temperature and specific humidity in a semi-supervised fashion using samples from the coarsened reference fine-resolution simulation. The novelty detector responds to particularly biased simulations relative to the reference simulation by categorizing more columns as out-of-sample. Without novelty detection, corrective ML occasionally causes undesirably large climate biases. When coupled to a running year-long coarse-grid simulation, novelty detection deems about 21% of columns to be novelties. This identification reduces the spread in the root-mean-square error (RMSE) of time-mean spatial patterns of surface temperature and precipitation rate across a random seed ensemble. In particular, the random seed with the worst RMSE is improved by up to 60% (depending on the variable) while the best seed maintains its low RMSE. By reducing the variance in quality of ML-corrected climate models, novelty detection offers reliability without compromising prediction quality in atmospheric models.
Bretherton, Christopher S., Brian Henn, Anna Kwa, Noah D Brenowitz, Oliver Watt-Meyer, Jeremy McGibbon, W Andre Perkins, Spencer K Clark, and Lucas Harris, February 2022: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations. Journal of Advances in Modeling Earth Systems, 14(2), DOI:10.1029/2021MS002794. Abstract
Global atmospheric “storm-resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real-geography coarse-grid model (FV3GFS with a 200 km grid) to be closer to those of a 40-day reference simulation using X-SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea-surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse-grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine-grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time-mean precipitation patterns are improved 30% by applying the ML correction. Adding machine-learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time-mean upper tropospheric temperature and zonal wind patterns thereafter.
Intense convection (updrafts exceeding 10 m s−1) plays an essential role in severe weather and Earth's energy balance. Despite its importance, how the global pattern of intense convection changes in response to warmed climates remains unclear, as simulations from traditional climate models are too coarse to simulate intense convection. Here we use a kilometer-scale global storm resolving model (GSRM) and conduct year-long simulations of a control run, forced by analyzed sea surface temperature (SST), and one with a 4 K increase in SST. Comparisons show that the increased SST enhances the frequency of intense convection globally with large spatial and seasonal variations. Changes in the spatial pattern of intense convection are associated with changes in planetary circulation. Increases in the intense convection frequency do not necessarily reflect increases in convective available potential energy. The GSRM results are also compared with previously published traditional climate model projections.
Clark, Spencer K., Noah D Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W Andre Perkins, Oliver Watt-Meyer, Christopher S Bretherton, and Lucas Harris, September 2022: Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations. Journal of Advances in Modeling Earth Systems, 14(9), DOI:10.1029/2022MS003219. Abstract
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine-resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi-year ML-corrected simulations. Here four fine-resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state-dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened state of the fine-resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine-learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ∼200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%–28% and land surface temperature by 19%–25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
McGibbon, Jeremy, Noah D Brenowitz, Mark Cheeseman, Spencer K Clark, Johann P S Dahm, Eddie C Davis, Oliver Elbert, Rhea C George, and Lucas Harris, et al., July 2021: fv3gfs-wrapper: A Python wrapper of the FV3GFS atmospheric model. Geoscientific Model Development, 14(7), DOI:10.5194/gmd-14-4401-20214401-4409. Abstract
Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes hard to understand, hard to modify, and hard to integrate with their analysis tools. fv3gfs-wrapper is an open-source Python-wrapped version of the NOAA (National Oceanic and Atmospheric Administration) FV3GFS (Finite-Volume Cubed-Sphere Global Forecast System) global atmospheric model, which is coded in Fortran. The wrapper provides simple interfaces to progress the Fortran main loop and get or set variables used by the Fortran model. These interfaces enable a wide range of use cases such as modifying the behavior of the model, introducing online analysis code, or saving model variables and reading forcings directly to and from cloud storage. Model performance is identical to the fully compiled Fortran model, unless routines to copy the state in and out of the model are used. This copy overhead is well within an acceptable range of performance and could be avoided with modifications to the Fortran source code. The wrapping approach is outlined and can be applied similarly in other Fortran models to enable more productive scientific workflows.
Watt-Meyer, Oliver, Noah D Brenowitz, and Spencer K Clark, et al., August 2021: Correcting weather and climate models by machine learning nudged historical simulations. Geophysical Research Letters, 48(15), DOI:10.1029/2021GL092555. Abstract
Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged toward observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation with moderate skill using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year-long simulations and has significant reductions in short-term forecast error for 500 hPa height, surface pressure and near-surface temperature. Furthermore, the root mean square error of the annual-mean precipitation is reduced by about 20%. Biases of other variables remain similar or in some cases, like upper-atmospheric temperature, increase in the year-long simulations.
In this paper, it is shown that westward-propagating monsoon-low-pressure-system-like disturbances in the South Asian monsoon region can be simulated in an idealized moist general circulation model through the addition of a simplified parameterization of land. Land is parameterized as having one-tenth the heat capacity of the surrounding slab ocean, with evaporation limited by a bucket hydrology model. In this model, the prominent topography of the Tibetan Plateau does not appear to be necessary for these storm systems to form or propagate; therefore focus is placed on the simulation with land but no topography.
The properties of the simulated storms are elucidated using regression analysis and compared to results from composites of storms from comprehensive GCMs in prior literature and reanalysis. The storms share a similar vertical profile in anomalous Ertel potential vorticity to those in reanalysis. Propagation, however, does not seem to be strongly dictated by beta-drift. Rather, it seems to be more closely consistent with linear moisture vortex instability theory, with the exception of the importance of the vertical advection term in the Ertel potential vorticity budget toward the growth and maintenance of disturbances. The results presented here suggest that a simplified GCM configuration might be able to be used to gain a clearer understanding of the sensitivity of monsoon low pressure systems to changes in the mean state climate.
We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.
Many fundamental questions remain about the roles and effects of stationary forcing on atmospheric blocking. As such, this work utilizes an idealized moist general circulation model (GCM) to investigate atmospheric blocking in terms of dynamics, geographical location, and duration. The model is first configured as an aquaplanet, then orography is added in separate integrations. Block-centered composites of wave activity fluxes and height show that blocks in the aquaplanet undergo a realistic dynamical evolution when compared to reanalysis. Blocks in the aquaplanet are also found to have similar life cycles to blocks in model integrations with orography. These results affirm the usefulness of both zonally symmetric and asymmetric idealized model configurations for studying blocking. Adding orography to the model leads to an increase in blocking. This mirrors what is observed when comparing the Northern Hemisphere (NH) and Southern Hemisphere (SH), where the NH contains more orography and thus more blocking. As the prescribed mountain height increases, so do the magnitude and size of climatological stationary waves, resulting in more blocking overall. Increases in blocking, however, are not spatially uniform. Orography is found to induce regions of enhanced block frequency just upstream of mountains, near high pressure anomalies in the stationary waves, which is poleward of climatological minima in upper-level zonal wind, while block frequency minima and jet maxima occur eastward of the wave trough. This result matches what is observed near the Rocky Mountains. Finally, an analysis of block duration suggests blocks generated near stationary wave maxima last slightly longer than blocks that form far from or without orography. Overall, the results of this work help to explain some of the observed similarities and differences in blocking between the NH and SH and emphasize the importance of general circulation features in setting where blocks most frequently occur.
Adames, A F., D Kim, Spencer K Clark, Yi Ming, and Kuniaki Inoue, December 2019: Scale analysis of moist thermodynamics in a simple model and the relationship between moisture modes and gravity waves. Journal of the Atmospheric Sciences, 76(12), DOI:10.1175/JAS-D-19-0121.1. Abstract
Observations and theory of convectively-coupled equatorial waves suggest that they can be categorized into two distinct groups. Moisture modes are waves whose thermodynamics are governed by moisture fluctuations. The thermodynamics of the gravity wave group, on the other hand, are rooted in buoyancy (temperature) fluctuations. On the basis of scale analysis it is found that a simple nondimensional parameter –akin to the Rossby number– can explain the processes that lead to the existence of these two groups. This parameter, defined as Nmode, indicates that moisture modes arise when anomalous convection lasts sufficiently long so that dry gravity waves eliminate the temperature anomalies in the convective region, satisfying weak temperature gradient (WTG) balance. This process causes moisture anomalies to dominate the distribution of moist enthalpy (or moist static energy), and hence the evolution of the wave. Conversely, convectively-coupled gravity waves arise when anomalous convection eliminates the moisture anomalies more rapidly than dry gravity waves can adjust the troposphere towards WTG balance, causing temperature to govern the moist enthalpy distribution and evolution. Spectral analysis of reanalysis data indicates that slowly-propagating waves (cp ~ 3 m s-1) are likely to be moisture modes while fast waves (cp ~ 30 m s-1) exhibit gravity wave behavior, with “mixed moisture-gravity” waves existing in between. While these findings are obtained from a highly idealized framework, it is hypothesized that they can be extended to understand simulations of convectively-coupled waves in GCMs and the thermodynamics of more complex phenomena.
In comprehensive and idealized general circulation models, hemispherically asymmetric forcings lead to shifts in the latitude of the Intertropical Convergence Zone (ITCZ). Prior studies using comprehensive GCMs (with complicated parameterizations of radiation, clouds, and convection) suggest that the water vapor feedback tends to amplify the movement of the ITCZ in response to a given hemispherically asymmetric forcing, but this effect has yet to be elucidated in isolation. This study uses an idealized moist model, coupled to a full radiative transfer code, but without clouds, to examine the role of the water vapor feedback in a targeted manner.
In experiments with interactive water vapor and radiation, the ITCZ latitude shifts roughly twice as much off the equator as in cases with the water vapor field seen by the radiation code prescribed to a static hemisperically-symmetric control distribution. Using energy flux equator theory for the latitude of the ITCZ, the amplification of the ITCZ shift is attributed primarily to the longwave water vapor absorption associated with the movement of the ITCZ into the warmer hemisphere, further increasing the net column heating asymmetry. Local amplification of the imposed forcing by the shortwave water vapor feedback plays a secondary role. Experiments varying the convective relaxation time, an important parameter in the convection scheme used in the idealized moist model, yield qualitatively similar results, suggesting some degree of robustness to the model physics; however, the sensitivity experiments do not preclude that more extreme modifications to the convection scheme could lead to qualitatively different behavior.
Clark, Spencer K., Daniel S Ward, and Natalie M. Mahowald, March 2017: Parameterization-based uncertainty in future lightning flash density. Geophysical Research Letters, 44(6), DOI:10.1002/2017GL073017. Abstract
In this study we implement eight lightning parameterizations in the Community Atmospheric Model (CAM5), evaluate the performance of the parameterizations in the present climate, and test the sensitivity of future lightning activity to the choice of parameterization. In the present-day, the annual mean lightning flash densities in simulations constrained by reanalysis data show the highest spatial correlation to satellite observations for parameterizations based either on cloud top height (0.83) or cold cloud depth (0.80). Under future scenarios using representative concentration pathways, changes in global mean lightning flash density are highly sensitive to the parameterization chosen, with cloud top height schemes, a cold cloud depth scheme, and a scheme based on convective mass flux projecting large increases (36% to 45%), a mild increase (12.6%), and a decrease (-6.7%) in lightning flash density respectively under the RCP8.5 scenario, which causes a 3.4 K warming between 1996-2005 and 2079-2088.
Clark, Spencer K., Daniel S Ward, and Natalie M. Mahowald, November 2015: The sensitivity of global climate to the episodicity of fire aerosol emissions. Journal of Geophysical Research: Atmospheres, 120(22), DOI:10.1002/2015JD024068. Abstract
Here we explore the sensitivity of the global radiative forcing and climate response to the episodicity of fire emissions. We compare the standard approach used in present day and future climate modeling studies, in which emissions are not episodic but smoothly interpolated between monthly mean values and that contrast to the response when fires are represented using a range of approximations of episodicity. The range includes cases with episodicity levels matching observed fire day and fire event counts, as well as cases with extreme episodicity. We compare the different emissions schemes in a set of Community Atmosphere Model (CAM5) simulations forced with reanalysis meteorology and a set of simulations with online dynamics designed to calculate aerosol indirect effect radiative forcings. We find that using climatologically observed fire frequency improves model estimates of cloud properties over the standard scheme, particularly in boreal regions, when both are compared to a simulation with meteorologically synchronized emissions. Using these emissions schemes leads to a range in global indirect effect radiative forcing of fire aerosols between −1.1 and −1.3 W m−2. In cases with extreme episodicity, we see increased transport of aerosols vertically, leading to longer lifetimes and less negative indirect effect radiative forcings. In general, the range in climate impacts that results from the different realistic fire emissions schemes is smaller than the uncertainty in climate impacts due to other aspects of modeling fire emissions.
