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At least 181 records · Page 10

The GEOS Chemistry Climate Model: Comparisons to Satellite Data

The Goddard Earth Observing System Chemistry Climate Model (GEOS CCM) has been developed by combining the atmospheric chemistry and transport modules developed over the years at Goddard and the GEOS general circulation model, also developed at Goddard. We will compare model simulations of ozone, and the minor constituents that affect ozone, for the period around 1980 with newly released revised data from the Limb Infrared Monitor of the Stratosphere (LIMS) instrument on Nimbus 4. We will also compare model simulations for the period of the early 2000s with the data from the Microwave Limb Sounder (MLS) and the High Resolution Dynamic Limb Sounder (HRDLS) on the Aura satellite. We will use these comparisons to examine the performance of the model for the present atmosphere and for the change that has occurred during the last 2 decades of ozone loss due to chlorine and bromine compounds released from chlorofluorocarbons and halons.

Stolarski, Richard S.↗

Evaluation of Convective Transport in the GEOS-5 Chemistry and Climate Model

The NASA Goddard Earth Observing System (GEOS-5) Chemistry and Climate Model (CCM) consists of a global atmospheric general circulation model and the combined stratospheric and tropospheric chemistry package from the NASA Global Modeling Initiative (GMI) chemical transport model. The subgrid process of convective tracer transport is represented through the Relaxed Arakawa-Schubert parameterization in the GEOS-5 CCM. However, substantial uncertainty for tracer transport is associated with this parameterization, as is the case with all global and regional models. We have designed a project to comprehensively evaluate this parameterization from the point of view of tracer transport, and determine the most appropriate improvements that can be made to the GEOS-5 convection algorithm, allowing improvement in our understanding of the role of convective processes in determining atmospheric composition. We first simulate tracer transport in individual observed convective events with a cloud-resolving model (WRF). Initial condition tracer profiles (CO, CO2, O3) are constructed from aircraft data collected in undisturbed air, and the simulations are evaluated using aircraft data taken in the convective anvils. A single-column (SCM) version of the GEOS-5 GCM with online tracers is then run for the same convective events. SCM output is evaluated based on averaged tracer fields from the cloud-resolving model. Sensitivity simulations with adjusted parameters will be run in the SCM to determine improvements in the representation of convective transport. The focus of the work to date is on tropical continental convective events from the African Monsoon Multidisciplinary Analyses (AMMA) field mission in August 2006 that were extensively sampled by multiple research aircraft.

Pickering, Kenneth E.↗

Evaluation of preindustrial to present-day black carbon and its albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)

As part of the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), we evaluate the historical black carbon (BC) aerosols simulated by 8 ACCMIP models against observations including 12 ice core records, long-term surface mass concentrations, and recent Arctic BC snowpack measurements. We also estimate BC albedo forcing by performing additional simulations using offline models with prescribed meteorology from 1996-2000. We evaluate the vertical profile of BC snow concentrations from these offline simulations using the recent BC snowpack measurements. Despite using the same BC emissions, the global BC burden differs by approximately a factor of 3 among models due to differences in aerosol removal parameterizations and simulated meteorology: 34 Gg to 103 Gg in 1850 and 82 Gg to 315 Gg in 2000. However, the global BC burden from preindustrial to present-day increases by 2.5-3 times with little variation among models, roughly matching the 2.5-fold increase in total BC emissions during the same period.We find a large divergence among models at both Northern Hemisphere (NH) and Southern Hemisphere (SH) high latitude regions for BC burden and at SH high latitude regions for deposition fluxes. The ACCMIP simulations match the observed BC surface mass concentrations well in Europe and North America except at Ispra. However, the models fail to predict the Arctic BC seasonality due to severe underestimations during winter and spring. The simulated vertically resolved BC snow concentrations are, on average, within a factor of 2-3 of the BC snowpack measurements except for Greenland and the Arctic Ocean. For the ice core evaluation, models tend to adequately capture both the observed temporal trends and the magnitudes at Greenland sites. However, models fail to predict the decreasing trend of BC depositions/ice core concentrations from the 1950s to the 1970s in most Tibetan Plateau ice cores. The distinct temporal trend at the Tibetan Plateau ice cores indicates a strong influence from Western Europe, but the modeled BC increases in that period are consistent with the emission changes in Eastern Europe, the Middle East, South and East Asia. At the Alps site, the simulated BC suggests a strong influence from Europe, which agrees with the Alps ice core observations. At Zuoqiupu on the Tibetan Plateau, models successfully simulate the higher BC concentrations observed during the non-monsoon season compared to the monsoon season but overpredict BC in both seasons. Despite a large divergence in BC deposition at two Antarctic ice core sites, some models with a BC lifetime of less than 7 days are able to capture the observed concentrations. In 2000 relative to 1850, globally and annually averaged BC surface albedo forcing from the offline simulations ranges from 0.014 to 0.019Wm−2 among the ACCMIP models. Comparing offline and online BC albedo forcings computed by some of the same models, we find that the global annual mean can vary by up to a factor of two because of different aerosol models or different BC-snow parameterizations and snow cover. The spatial distributions of the offline BC albedo forcing in 2000 show especially high BC forcing (i.e., over 0.1W/sq. m) over Manchuria, Karakoram, and most of the Former USSR. Models predict the highest global annual mean BC forcing in 1980 rather than 2000, mostly driven by the high fossil fuel and biofuel emissions in the Former USSR in 1980.

snow cover↗

Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

Climate models↗

A Surface Radiation Balance Data Set from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

A field campaign at Siple Dome in West Antarctica during the austral summer 2019-2020 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-poweredequipment, includes surface energy balance measurements, meteorology and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model's snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-micron atmospheric window reveal cloud phase transitions associated with cloud lifecycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

cloud remote sensing↗

Using Transport Diagnostics to Understand Chemistry Climate Model Ozone Simulations

We demonstrate how observations of N2O and mean age in the tropical and midlatitude lower stratosphere (LS) can be used to identify realistic transport in models. The results are applied to 15 Chemistry Climate Models (CCMs) participating in the 2010 WMO assessment. Comparison of the observed and simulated N2O/mean age relationship identifies models with fast or slow circulations and reveals details of model ascent and tropical isolation. The use of this process-oriented N2O/mean age diagnostic identifies models with compensating transport deficiencies that produce fortuitous agreement with mean age. We compare the diagnosed model transport behavior with a model's ability to produce realistic LS O3 profiles in the tropics and midlatitudes. Models with the greatest tropical transport problems show the poorest agreement with observations. Models with the most realistic LS transport agree more closely with LS observations and each other. We incorporate the results of the chemistry evaluations in the SPARC CCMVal Report (2010) to explain the range of CCM predictions for the return-to-1980 dates for global (60 S-60 N) and Antarctic column ozone. Later (earlier) Antarctic return dates are generally correlated to higher (lower) vortex Cl(sub y) levels in the LS, and vortex Cl(sub y) is generally correlated with the model's circulation although model Cl(sub y) chemistry or Cl(sub y) conservation can have a significant effect. In both regions, models that have good LS transport produce a smaller range of predictions for the return-to-1980 ozone values. This study suggests that the current range of predicted return dates is unnecessarily large due to identifiable model transport deficiencies.

Strahan, S. E.↗

Global Water Cycle Agreement in the Climate Models Assessed in the IPCC AR4

This study examines the fidelity of the global water cycle in the climate model simulations assessed in the IPCC Fourth Assessment Report. The results demonstrate good model agreement in quantities that have had a robust global observational basis and that are physically unambiguous. The worst agreement occurs for quantities that have both poor observational constraints and whose model representations can be physically ambiguous. In addition, components involving water vapor (frozen water) typically exhibit the best (worst) agreement, and fluxes typically exhibit better agreement than reservoirs. These results are discussed in relation to the importance of obtaining accurate model representation of the water cycle and its role in climate change. Recommendations are also given for facilitating the needed model improvements.

climate change↗

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗

Satellite and Ground-based Radiometers Reveal Much Lower Dust Absorption of Sunlight than Used in Climate Models

The ability of dust to absorb solar radiation and heat the atmosphere is one of the main uncertainties in climate modeling and the prediction of climate change. Dust absorption is not well known due to limitations of in situ measurements. New techniques to measure dust absorption are needed in order to assess the impact of dust on climate. Here we report two new independent remote sensing techniques that provide sensitive measurements of dust absorption. Both are based on remote sensing. One uses satellite spectral measurements, the second uses ground based sky measurements from the AERONET network. Both techniques demonstrate that Saharan dust absorption of solar radiation is several times smaller than the current international standards. Dust cooling of the earth system in the solar spectrum is therefore significantly stronger than recent calculations indicate. We shall also address the issue of the effects of dust non-sphericity on the aerosol optical properties.

Kaufman, Y. J.↗

Importance of Sea Ice for Validating Global Climate Models

Reproduction of current day large-scale physical features and processes is a critical test of global climate model performance. Without this benchmark, prognoses of future climate conditions are at best speculation. A fundamental question relevant to this issue is, which processes and observations are both robust and sensitive enough to be used for model validation and furthermore are they also indicators of the problem at hand? In the case of global climate, one of the problems at hand is to distinguish between anthropogenic and naturally occuring climate responses. The polar regions provide an excellent testing ground to examine this problem because few humans make their livelihood there, such that anthropogenic influences in the polar regions usually spawn from global redistribution of a source originating elsewhere. Concomitantly, polar regions are one of the few places where responses to climate are non-anthropogenic. Thus, if an anthropogenic effect has reached the polar regions (e.g. the case of upper atmospheric ozone sensitivity to CFCs), it has most likely had an impact globally but is more difficult to sort out from local effects in areas where anthropogenic activity is high. Within this context, sea ice has served as both a monitoring platform and sensitivity parameter of polar climate response since the time of Fridtjof Nansen. Sea ice resides in the polar regions at the air-sea interface such that changes in either the global atmospheric or oceanic circulation set up complex non-linear responses in sea ice which are uniquely determined. Sea ice currently covers a maximum of about 7% of the earth's surface but was completely absent during the Jurassic Period and far more extensive during the various ice ages. It is also geophysically very thin (typically <10 m in Arctic, <3 m in Antarctic) compared to the troposphere (roughly 10 km) and deep ocean (roughly 3 to 4 km). Because of these unique conditions, polar researchers regard sea ice as one of the more important features to monitor in terms of heat, mass, and momentum transfer between the air and sea and furthermore, the impact of such responses to global climate.

Geiger, Cathleen A.↗

Impacts of Improved Process Representation of Particle Dry Deposition on PM Pollution in a Global Chemistry-Climate Model: Differences Across Regions, Seasons, and PM Sizes

Dry deposition (DD) is a primary removal pathway of particulate matter (PM). The aerosol DD schemes in most global models do not reflect current mechanistic understanding gleaned from observations. The NASA GISS global chemistry-climate model has a new and more dynamic DD scheme that largely captures observed changes in deposition velocities with particle size. We quantify the response of simulated PM to changes in the DD scheme for the Northeast US, Central Europe, North China Plain, and Punjab. Relative to the widely used old scheme, the new scheme shows higher annual PM2.5 for all regions (up to +14%) except C. Europe where there are very small decreases. For PM1, annual increases occur over all regions (up to +20%). For PM10, there are decreases in C. Europe (-8%) and very small decreases in the NE US yet increases (up to +12%) in Punjab and N. China Plain. While there are always increases across seasons for Punjab and N. China Plain, there are both seasonal increases and decreases for the NE US and C. Europe. Given incomplete understanding of observed variations in deposition velocities for a given particle size, we perform sensitivity simulations that perturb the magnitude of the deposition velocities simulated by the new scheme. The annual PM response to increasing DD is similar in magnitude to decreasing DD, implying linearity in the PM sensitivity to DD. The relative annual response to perturbing the DD magnitude is weaker over Punjab and sometimes N. China Plain than the NE US and C. Europe. Higher PM over Punjab and N. China Plain implies a stronger sensitivity to DD when aerosol abundances are low. More mechanistic representation of aerosol DD can sometimes improve or worsen existing model PM biases, which suggests that PM biases due to other processes can be confounded or compounded by biases in DD. Further improvements to DD parameterizations require not only more observational constraints on aerosol deposition velocities but also an advanced understanding of the processes controlling observed variability.

Environmental pollution↗

A Framework for Evaluating Climate Model Performance Metrics

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables?e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Noel C Baker↗

Interannual Variability of Tropical Precipitation: How Well Do Climate Models Agree With Current Satellite Estimates?

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

Robertson, Franklin R.↗

Photosynthesis-dependent Isoprene Emission from Leaf to Planet in a Global Carbon-chemistry-climate Model

We describe the implementation of a biochemical model of isoprene emission that depends on the electron requirement for isoprene synthesis into the FarquharBallBerry leaf model of photosynthesis and stomatal conductance that is embedded within a global chemistry-climate simulation framework. The isoprene production is calculated as a function of electron transport-limited photosynthesis, intercellular and atmospheric carbon dioxide concentration, and canopy temperature. The vegetation biophysics module computes the photosynthetic uptake of carbon dioxide coupled with the transpiration of water vapor and the isoprene emission rate at the 30 min physical integration time step of the global chemistry-climate model. In the model, the rate of carbon assimilation provides the dominant control on isoprene emission variability over canopy temperature. A control simulation representative of the present-day climatic state that uses 8 plant functional types (PFTs), prescribed phenology and generic PFT-specific isoprene emission potentials (fraction of electrons available for isoprene synthesis) reproduces 50 of the variability across different ecosystems and seasons in a global database of 28 measured campaign-average fluxes. Compared to time-varying isoprene flux measurements at 9 select sites, the model authentically captures the observed variability in the 30 min average diurnal cycle (R2 6496) and simulates the flux magnitude to within a factor of 2. The control run yields a global isoprene source strength of 451 TgC yr1 that increases by 30 in the artificial absence of plant water stress and by 55 for potential natural vegetation.

photosynthesis↗

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗

Use of North American and European Air Quality Networks to Evaluate Global Chemistry-Climate Modeling of Surface Ozone

We test the current generation of global chemistry-climate models in their ability to simulate observed, present-day surface ozone. Models are evaluated against hourly surface ozone from 4217 stations in North America and Europe that are averaged over 1 degree by 1 degree grid cells, allowing commensurate model-measurement comparison. Models are generally biased high during all hours of the day and in all regions. Most models simulate the shape of regional summertime diurnal and annual cycles well, correctly matching the timing of hourly (approximately 15:00 local time (LT)) and monthly (mid-June) peak surface ozone abundance. The amplitude of these cycles is less successfully matched. The observed summertime diurnal range (25 ppb) is underestimated in all regions by about 7 parts per billion, and the observed seasonal range (approximately 21 parts per billion) is underestimated by about 5 parts per billion except in the most polluted regions, where it is overestimated by about 5 parts per billion. The models generally match the pattern of the observed summertime ozone enhancement, but they overestimate its magnitude in most regions. Most models capture the observed distribution of extreme episode sizes, correctly showing that about 80 percent of individual extreme events occur in large-scale, multi-day episodes of more than 100 grid cells. The models also match the observed linear relationship between episode size and a measure of episode intensity, which shows increases in ozone abundance by up to 6 parts per billion for larger-sized episodes. We conclude that the skill of the models evaluated here provides confidence in their projections of future surface ozone.

diurnal variations↗

Potential Link Between Ice Nucleation and Climate Model Spread in Arctic Amplification

Arctic amplification (AA) is simulated by all global climate models, however the spread in the degree of projected warming is large and the underlying mechanisms driving it are poorly understood. The impact of the temperature dependence of immersion freezing on cloud feedbacks and AA is studied using NASA's GEOS-5 model. Parameterizations that exhibit low ice-nucleating particle (INP) concentrations in the high Arctic during summer are found to weaken the cloud-phase feedback. This allows sunlight to readily melt sea-ice in the summer, which decreases the stability of the lower troposphere, causing a decrease in wintertime cloud fraction over open ocean. Arctic amplification was found to span from ∼1.4 to >2.6, which spans 30% of the spread in AA in the coupled model intercomparison project models, depending on the temperature dependence of immersion freezing. These results suggest that summertime INP concentrations may provide an observational constraint on AA.

Ice Nucleation↗

Multiannual simulations with the Mars climate model

The martian atmospheric circulaton for 10 Mars years was simulated using a simplified three-dimensional climate model that is briefly described below. Analysis of these simulations reveals several interesting behaviors not previously recognized in other three-dimensional models. The first concerns the potential for interannual variability in the martian climate system. It is now known from the aperiodic frequency of planet-encircling storms, as well as differences in atmospheric water vapor from one year to the next, that the real planet exhibits interannual variations. The results of our simulations also show significant interannual variability and, interestingly, that variability maximizes during southern spring, the dust storm season. A second and related finding is that transient eddies in the southern hemisphere during winter are indeed weaker than their northern hemisphere counterparts as was found in more sophisticated models. However, southern hemisphere eddy activity rises dramatically during spring and is greater than that in the northern hemisphere at any season.

Haberle, R. M.↗