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Tropical cyclones (TCs) in the Bay of Bengal (BoB) are among the most devastating events in nature, significantly influencing human life. They affect the region between April and December, being separated into two distinct phases: the pre-monsoon (April-May-June, AMJ) and post-monsoon (October-November-December, OND), with little activity during the monsoon peak. In this article, the influence of the Tropical Easterly Jet (TEJ) on the observed BoB TC activity, and the expected changes under global warming conditions, are investigated using reanalyses and coupled model intercomparison project 6 (CMIP6) models. The general assumption is that TC activity should increase in response to warmer sea surface temperature (SST). However, several dynamical processes are needed to create organized convection, which is a prerequisite for TC development. The results show that the frequency and accumulated energy should decrease in the future (2020-2049), compared to the historical climate (1950-1979), during both phases. The change of the mean TEJ and intermittent shear relaxations in the future climate are found to play a major role in the change of TC activity. During AMJ, a southward shift of the mean TEJ and TEJ intermittent shear relaxations are associated with reduced TC activity. During OND, the decrease of the frequency of TEJ and Subtropical Jet (STJ) intermittent shear relaxations are associated with the reduction of TC activity. The increase in SST in the future seems to play a minor role. This study clarifies the importance of both the mean and intermittency of the TEJ in determining the future of TC activity in the BoB.

Tropical cyclone precipitation (TCP) contributes a significant fraction of total annual rainfall and also is a frequent cause of extreme precipitation in many parts of the tropics. The climatology of TCP in the S2S models is characterized by dry biases in the North Atlantic and wet biases in most other basins, specially in the Southern Indian Ocean and Australia. Biases in total precipitation (P), TCP and their ratio (TCP/P) are mostly positive in the multi-model ensemble mean and change very little with lead time. in these models the frequency biases are the dominant contribution to TCP biases. However, in some models, there are positive biases in average precipitation per each TC which contribute significantly to TCP biases at equatorial latitudes.The prediction skill of these reforecasts is evaluated using skill scores such as the ranked probability skill score for TCP and the Brier Skill score for genesis and occurrence. The implication of these results is discussed for their relevance to mean and extreme precipitation prediction skill using S2S models.

Wind work at the air-sea interface is the transfer of kinetic energy between the ocean and the atmosphere and, as such, is an important part of the ocean-atmosphere coupled system. Wind work is defined as the scalar product of ocean wind stress and surface current, with each of these two variables spanning, in this study, a broad range of spatial and temporal scales, from 10 km to more than 3000 km and hours to months. These characteristics emphasize wind work's multiscale nature. In the absence of appropriate global observations, our study makes use of a new global, coupled ocean-atmosphere simulation, with horizontal grid spacing of 2–5 km for the ocean and 7 km for the atmosphere, analyzed for 12 months. We develop a methodology, both in physical and spectral spaces, to diagnose three different components of wind work that force distinct classes of ocean motions, including high-frequency internal gravity waves, such as near-inertial oscillations, low-frequency currents such as those associated with eddies, and seasonally averaged currents, such as zonal tropical and equatorial jets. The total wind work, integrated globally, has a magnitude close to 5 TW, a value that matches recent estimates. Each of the first two components that force high-frequency and low-frequency currents, accounts for ∼ 28 % of the total wind work and the third one that forces seasonally averaged currents, ∼ 44 %. These three components, when integrated globally, weakly vary with seasons but their spatial distribution over the oceans has strong seasonal and latitudinal variations. In addition, the high-frequency component that forces internal gravity waves, is highly sensitive to the collocation in space and time (at scales of a few hours) of wind stresses and ocean currents. Furthermore, the low-frequency wind work component acts to dampen currents with a size smaller than 250 km and strengthen currents with larger sizes. This emphasizes the need to perform a full kinetic budget involving the wind work and nonlinear advection terms as small and larger-scale low-frequency currents interact through these nonlinear terms. The complex interplay of surface wind stresses and currents revealed by the numerical simulation motivates the need for winds and currents satellite missions to directly observe wind work.

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

A systematic underestimation of subtropical planetary boundary layer (PBL) stratocumulus clouds by the GEOS model has been significantly improved by a new eddy-diffusivity/mass-flux (EDMF) parameterization. The EDMF parameterization represents the subgrid-scale transport in the dry and moist parts of the PBL in a unified manner and it combines an adjusted eddy-diffusivity PBL scheme from GEOS with a stochastic multiplume mass-flux model. The new EDMF version of the GEOS model is first compared against the CONTROL version in a single-column model (SCM) framework for two benchmark cases representing subtropical stratocumulus and shallow cumulus clouds, and validated against large-eddy simulations. Global simulations are performed and compared against observations and reanalysis data. The results show that the EDMF version of the GEOS model produces more realistic subtropical PBL clouds. The EDMF improvements first detected in the SCM framework translate into similar improvements of the global GEOS model.

To predict the future climate on multiyear timescales, it is crucial to understand how the changing external radiative forcing (CO2 and Ozone) drives the climate and impacts the skill of intra-seasonal to multiyear climate prediction. In this study, we use a 1-degree configuration of the GEOS-MITgcm coupled general circulation model to understand the response to different levels of observed external forcing from past decades. We ran `perpetual' experiments for 1992, 2000, and 2020, each with their respective year’s external forcing. Results of the perpetual simulations showed that the Northern Hemisphere polar stratospheric temperature increases from 1992 to 2000, whereas it decreases from 2000 to 2020. We further conducted a 10-member ensemble of transient climate simulations for the 1992--2020 period with observed external forcing and found a similar positive temperature trend from 1992 to 2000 and a negative trend from 2000 to 2020. This is in contrast to the general expectation that the stratosphere cools as CO2 increases. A similar opposing pattern of temperature trends exists in reanalyses and CMIP6 historical simulations with a well-resolved stratosphere. Analysis of the results showed that the external forcing change during the years 1992-2000 increases the tropical diabatic heating, and intensifies the wave activity related meridional heat transport to the Northern Hemisphere mid to high-latitudes, which, in turn, increases the polar stratospheric temperature. In contrast, during the 2000-2020 period the meridional heat transport decreases, contributing to a decrease in the polar stratospheric temperature. The long-wave radiation change in these periods responds to the changing temperature and so doesn't play a significant role in driving them.

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from Ranom=0.13 for snow water equivalent to Ranom=0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

Past studies have shown that accurate soil moisture initialization can contribute significant skill to near-surface air temperature (T2M) forecasts at subseasonal leads. The mechanisms by which soil moisture contributes such skill are examined here with a simple water balance–based model that captures the essence of soil moisture behavior in a state-of-the-art subseasonal-to-seasonal (S2S) forecasting system. The simple model successfully transforms initial soil moisture contents into average “forecast” evapotranspiration (ET) values at 16–30-day lead that agree well, during summer, with the values forecast by the full NASA GEOS S2S system, indicating that soil moisture initialization dominates over forecast meteorological conditions in determining ET fluxes at subseasonal leads. When the simple model’s ET anomalies are interpreted in terms of T2M anomalies, a similar conclusion is reached for T2M: soil moisture initialization explains much (about 50% in the eastern half of the continental United States) of the T2M anomaly values produced by the full GEOS S2S system at 16–30-day lead, and the T2M forecasts produced by the simple model capture about one-half of the skill attained by the full system. The simple model’s framework is particularly conducive to an analysis of uncertainty in forecasts. Drier soils are generally found to induce larger uncertainty in ET (and thus T2M) forecasts, a result linked to the functional form relating ET to soil moisture in the simple model and verified by an analysis of the ensemble spreads within the forecasts produced by the full GEOS S2S system.

International audience; We examine differences among reanalysis high-cloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifth-generation European Centre for Medium-range Weather Forecasts-ECMWF-reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanaly-sis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Cli-mate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanaly-ses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of long-wave, shortwave , and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and shortwave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radia-tive heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERA-Interim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and Published by Copernicus Publications on behalf of the European Geosciences Union. 8990 J. S. Wright et al.: Tropical high clouds in reanalyses feed back to convective behaviour through their relationships with thermodynamic stability.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth System Model (ESM). This estimation approach had previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea-ice, or carbon-cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere-land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea-ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carry out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These control parameters can be used to adjust sea-ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We define eight observational targets: sea-ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

This study examines the veracity of the tropical cyclone (TC) statistics estimated from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global atmospheric reanalysis, focusing on the climatological-mean genesis regions, tracks and their lifetime maximum intensity, as well as the interannual and intraseasonal variations in TC activity. The results are validated against the International Best Track Archive for Climate Stewardship (IBTrACS) data for 1980–2016. MERRA-2 represents the spatial distribution of the TC genesis location and the tracks realistically over all main development regions (MDRs), but the simulated TCs are initiated at lower latitudes closer to the equator compared with the observations. Over the western North Pacific and the North Atlantic, MERRA-2 underestimates recurving TCs steered by background westerlies in the mid-latitudes and thereby exaggerates northwestward moving TCs, resulting in an overestimate of the landfall probability in East Asia and North America. Excessive development of TCs over the Bay of Bengal also tends to exaggerate the landfall probability in India. In spite of the discrepancies in the annual TC number, the seasonal variation of TC genesis is realistic in MERRA-2. MERRA-2 also captures the TC intensity relationship between the minimum pressure and the maximum surface wind speed at the mature stage, although the maximum intensity is weaker than in the observations. While MERRA-2 tends to reproduce the interannual variations of the observed TC number and the power dissipation index (PDI), the level of accuracy varies by each ocean basin. MERRA-2 describes the changes in the TC genesis region and tracks realistically according to the different phases of El Nino and the Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), although it is less realistic over the North Indian Ocean.

We outline a framework for identifying the geographical sources of biases in climate models. By forcing the model with time-averaged short-term analysis increments [tendency bias corrections (TBCs)] over well-defined regions, we can quantify how the associated reduced tendency errors in these regions manifest themselves both locally and remotely through large-scale teleconnections. Companion experiments in which the model is fully corrected [constrained to remain close to the analysis at each time step, termed replay (RPL)] in the various regions provide an upper bound to the local and remote TBC impacts. An example is given based on MERRA-2 and the NASA/GMAO GEOS AGCM used to generate MERRA-2. The results highlight the ability of the approach to isolate the geographical sources of some of the long-standing boreal summer biases of the GEOS model, including a stunted North Pacific summer jet, a dry bias in the U.S. Great Plains, and a warm bias over most of the Northern Hemisphere land. In particular, we show that the TBC over a region that encompasses Tibet has by far the largest impact (compared with all other regions) on the NH summer jets and related variables, leading to significant improvements in the simulation of North American temperature and, to a lesser degree, precipitation. It is further shown that the results of the regional TBC experiments are for the most part linear in the summer hemisphere, allowing a robust interpretation of the results.

The Global Modeling and Assimilation Office (GMAO) is about to release a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐3, that represents an improvement in performance and infrastructure over the previous system, GEOS-S2S-2. The system will be described briefly, highlighting some features unique to GEOS-S2S, such as the coupled interactive aerosol model and ensemble perturbation strategy and size. Results are presented from forecasts and from climate equillibrium simulations. GEOS-S2S-3 will be used to produce a long term weakly coupled reanalysis called MERRA-2 Ocean.The climate or equillibrium state of the atmosphere and ocean shows a reduction in systematic error relative to GEOS‐S2S‐2, attributed in part to an increase in ocean resolution and to the upgrade in the glacier runoff scheme. The forecast skill shows improved prediction of the North Atlantic Oscillation, attributed to the increase in forecast ensemble members. With the release of GEOS-S2S-3 and MERRA-2 Ocean, GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.

During the past few years, the Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology general circulation model (MITgcm) groups have produced, respectively, global atmosphere-only and ocean-only simulations with km-scale grid spacing. These simulations have proved invaluable for process studies and the development of satellite and in-situ sampling strategies. Nevertheless, a key limitation of these simulations is the lack of feedback between the ocean and the atmosphere, limiting their usefulness for studying air-sea interactions and designing observing missions to study these interactions. To remove this limitation, we have coupled the km-scale GEOS atmospheric model with the km-scale MITgcm ocean model. We will present preliminary results from the GEOS-MITgcm contribution to the second phase of the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) initiative.The coupled atmosphere-ocean simulation was integrated using a cubed-sphere-1440 (~6-7 km horizontal grid spacing) configuration of GEOS and a lat-lon-cap-2160 (2–5-km horizontal grid spacing) configuration of MITgcm. We will show results from a preliminary analysis of air-sea interactions between Sea Surface Temperature (SST) and surface winds. In particular, we will discuss non-local atmospheric overturning circulation formed above the Gulf Stream SST front with characteristic sub-mesoscale width. This formation of a secondary circulation above the front suggests that capturing such air-sea interaction phenomena requires high-resolution capabilities in both the models' oceanic and atmospheric components.

We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Sub-seasonal to Seasonal prediction (S2S) system, GEOS-S2S-2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS-S2S-1. The GEOS-S2S-2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along-track Absolute Dynamic Topography. The forecast skill on subseasonal scales showed a much-improved prediction of the Madden-Julian Oscillation in GEOS-S2S-2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS-S2S-1 in the central Pacific. GEOS-S2S-2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS-S2S-1, and showed substantially reduced root mean square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS-S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state of the art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as to evaluate additional sources of predictability in the earth system through the expanded coupling of the earth system model and assimilation components.

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near‐real‐time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)‐based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided., Key Points The GEOS Earth System Model's architecture is based on ESMF and the GMAO's middleware layer called MAPLGEOS uses a common code base of interchangeable chemical components for data assimilation, forecasting, and researchGEOS is applied to atmospheric chemistry topics ranging from diurnal air quality variations to climate change

Offline chemical transport models (CTMs) have traditionally been used to perform studies of atmospheric chemistry in a fixed dynamical environment. An alternative to using CTMs is to constrain the flow in a general circulation model using winds from meteorological analyses. The Goddard Earth Observing System (GEOS) “replay” approach involves reading in analyzed fields every six hours and recomputing the analysis increments, which are applied as a forcing to the meteorology at every model time step. Unlike in CTM, all of the subgrid-scale processes are recalculated on-line so that they are consistent with the large-scale analysis fields, similar in spirit to “nudged” simulations, in which the online meteorology is relaxed to the analysis. Here we compare the transport of idealized tracers in different replay simulations constrained with meteorological fields taken from The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). We show that there are substantial differences in their large-scale stratospheric transport, depending on whether analysis fields or assimilated fields are used. Replay simulations constrained with the instantaneous analysis fields produce stratospheric mean age values that are up to 30\% too young relative to observations; by comparison, simulations constrained with the time-averaged assimilated fields produce more credible stratospheric transport. Our study indicates that care should be taken to correctly configure the model when the replay technique is used to simulate stratospheric composition.

In this study, gravity waves (GWs) in the high-resolution GEOS-5 Nature Run are first evaluated with respect to satellite and other model results. Southern Hemisphere winter sources of non-orographic GWs in the model are then investigated by linking measures of tropospheric non-orographic gravity wave generation tied to precipitation and frontogenesis with absolute gravity wave momentum flux in the lower stratosphere. Finally, non-orographic GW momentum flux is compared to orographic gravity wave momentum flux and compared to previous estimates. The results show that the global patterns in GW amplitude, horizontal wavelength, and propagation direction are realistic compared to observations. However, as in other global models, the amplitudes are weaker and horizontal wavelengths longer than observed. The global patterns in absolute GW momentum flux also agree well with previous model and observational estimates. The evaluation of model non-orographic GW sources in the Southern Hemisphere winter shows that strong intermittent precipitation (greater than 10 mm h−1) is associated with GW momentum flux over the South Pacific, whereas frontogenesis and less intermittent, lower precipitation rates (less than 10 mm h−1) are associated with GW momentum flux near 60°S. In the model, orographic GWs contribute almost exclusively to a peak in zonal mean momentum flux between 70 and 75°S, while non-orographic waves dominate at 60°S, and non-orographic GWs contribute a third to a peak in zonal mean momentum flux between 25 and 30°S.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

Closing and balancing Earth’s global water cycle remains a challenge for the climate community. Observations are limited in duration, global coverage, and frequency, and not all water cycle terms are adequately observed. Reanalyses aim to fill the gaps through the assimilation of as many atmospheric water vapor observations as possible. Former generations of reanalyses have demonstrated a number of systematic problems that have limited their use in climate studies, especially regarding low-frequency trends. This study characterizes the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) water cycle relative to contemporary reanalyses and observations. MERRA-2 includes measures intended to minimize the spurious global variations related to inhomogeneity in the observational record. The global balance and cycling of water from ocean to land is presented, with special attention given to the water vapor analysis increment and the effects of the changing observing system. While some systematic regional biases can be identified, MERRA-2 produces temporally consistent time series of total column water and transport of water from ocean to land. However, the interannual variability of ocean evaporation is affected by the changing surface-wind-observing system, and precipitation variability is closely related to the evaporation. The surface energy budget is also strongly influenced by the interannual variability of the ocean evaporation. Furthermore, evaluating the relationship of temperature and water vapor indicates that the variations of water vapor with temperature are weaker in satellite data reanalyses, not just MERRA-2, than determined by observations, atmospheric models, or reanalyses without water vapor assimilation.

This study investigates tropical waves and their role in driving a quasi-biennial oscillation (QBO)-like signal in stratospheric winds in a global 7-km-horizontal-resolution atmospheric general circulation model. The Nature Run (NR) is a 2-yr global mesoscale simulation of the Goddard Earth Observing System Model, version 5 (GEOS-5). In the tropics, there is evidence that the NR supports a broad range of convectively generated waves. The NR precipitation spectrum resembles the observed spectrum in many aspects, including the preference for westward-propagating waves. However, even with very high horizontal resolution and a healthy population of resolved waves, the zonal force provided by the resolved waves is still too low in the QBO region and parameterized gravity wave drag is the main driver of the NR QBO-like oscillation (NR-QBO). The authors suggest that causes include coarse vertical resolution and excessive dissipation. Nevertheless, the very-high-resolution NR provides an opportunity to analyze the resolved wave forcing of the NR-QBO. In agreement with previous studies, large-scale Kelvin and small-scale waves contribute to the NR-QBO driving in eastward shear zones and small-scale waves dominate the NR-QBO driving in westward shear zones. Waves with zonal wavelength < 1000 km account for up to half of the small-scale (

The structure, dynamics, and ozone signal of the quasi-biennial oscillation (QBO) produced by the 35-yr NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are examined based on monthly mean output. Along with the analysis of the QBO in assimilation winds and ozone, the QBO forcings created by assimilated observations, dynamics, parameterized gravity wave drag (GWD), and ozone chemistry parameterization are examined and compared with the original MERRA system. Results show that MERRA-2 produces a realistic QBO in the zonal winds, mean meridional circulation, and ozone over the 1980–2015 time period. In particular, the MERRA-2 zonal winds show improved representation of the QBO 50-hPa westerly phase amplitude at Singapore when compared to MERRA. The use of limb ozone observations creates improved vertical structure and realistic downward propagation of the ozone QBO signal during times when the MLS ozone limb observations are available (from October 2004 to present). The increased equatorial GWD in MERRA-2 has reduced the zonal wind data analysis contribution compared to MERRA so that the QBO mean meridional circulation can be expected to be more physically forced and therefore more physically consistent. This can be important for applications in which MERRA-2 winds are used to drive transport experiments.