Goddard Institute For Space Studies Research Articles

Dynamical and Trace Gas Responses of the Quasi‐Biennial Oscillation to Increased CO2

AbstractLong‐term projections of the Quasi‐Biennial Oscillation (QBO) remain uncertain. Using the high‐top “Middle Atmosphere” (MA) version of the NASA Goddard Institute for Space Studies (GISS) ModelE (E2.2‐AP), we show that increased CO2 reduces the QBO period and weakens its amplitude, consistent with results from recent multimodel studies. The amplitude response is asymmetric, with the easterly phase weakening more than the westerly phase. Results from both coupled atmosphere‐ocean and climatologically fixed sea surface temperature simulations link the period response to changes in parameterized convective gravity wave fluxes, whereas the amplitude changes are more closely tied to changes in tropical upwelling. Comparisons of simulations with and without ozone‐dynamical feedbacks reveal that ozone feedbacks increase the QBO amplitude and period in pre‐industrial control simulations, and reduce the response of the amplitude to increased CO2. The response of stratospheric tracers to projected changes in the QBO is also investigated. Similarity between results using fully interactive (trace gas and aerosol) versus linearized ozone chemical mechanisms suggests that the QBO response to CO2 does not depend on additional chemical feedbacks beyond those associated with ozone, which implies that projected QBO changes may be credibly represented using simplified stratospheric ozone schemes.

Integrated assessment climate policy models have proven useful, with caveats

The headlines are bleak: Regions of our planet becoming uninhabitably hot (1), crippling droughts, wildfires, and floods, collapsing ecosystems. Extreme climate change, models suggest, is likely if nations continue to increase emissions at close to their current rate, with global average temperature rises of at least 1.1 to 3.1 °C by 2100. Integrated assessment models have long made dire predictions about climate change and its myriad impacts. Some researchers would like to see more transparency in how these models are devised. Image credit: ( Clockwise from Top Left ) Shutterstock/ccpixx photography, Zenobillis, Witsawat.S, and Christian Roberts-Olsen. Such warming is probably enough to trigger planetary tipping points, says Gavin Schmidt, director of the NASA Goddard Institute for Space Studies in New York, NY. “We may hit a threshold even with [low emissions],” Schmidt says. “We are at the top of the [ski] mountain and there’s only black runs. And we've got to hope the one that we choose is not the one with the double-black diamond moguls that only a true expert can maneuver.” These projections stem in part from something called integrated assessment models (IAMs). The “process-based” versions of these models are mathematical representations of the world, with modules representing the climate, biosphere, energy, and economy. They’re a popular and extremely important tool, but one that isn’t always well understood. Better explicating their strengths and shortcomings will help refine projections and improve transparency in the years ahead. The conundrum posed by climate change is that policymakers need to act immediately to avoid warming decades hence. IAMs help them to envisage the desired future and the impacts of climate policies. For example, researchers might specify an emissions target and then use an IAM to work out what policies and technologies might be the most cost-effective way to hit that target. These models aren’t …

CMIP6 Historical Simulations (1850–2014) With GISS‐E2.1

AbstractSimulations of the CMIP6 historical period 1850–2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to greenhouse gas forcing than their CMIP5 predecessors (GISS‐E2) but warm less during recent decades due to a forcing reduction that is attributed to greater longwave opacity in the GISS‐E2.1 pre‐industrial simulations. This results in an atmosphere less sensitive to increases in opacity from rising greenhouse gas concentrations, demonstrating the importance of the base climatology to forcing and forced climate trends. Most model versions match observed temperature trends since 1979 from the ocean to the stratosphere. The choice of ocean model is important to the transient climate response, as found previously in CMIP5 GISS‐E2: the model that more efficiently exports heat to the deep ocean shows a smaller rise in tropospheric temperature. Model sea level rise over the historical period is traced to excessive drawdown of aquifers to meet irrigation demand with a smaller contribution from thermal expansion. This shows how fully coupled models can provide indirect observational constraints upon forcing, in this case, constraining irrigation rates with observed sea level changes. The overall agreement of GISS‐E2.1 with observed trends is familiar from evaluation of its predecessors, as is the conclusion that these trends are almost entirely anthropogenic in origin.

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere—2. Validation of Large‐Scale Transport and Evaluation of Climate Response

AbstractHere we examine the large‐scale transport characteristics of the new “Middle Atmosphere” NASA Goddard Institute for Space Studies (GISS) climate model (E2.2). First, we evaluate the stratospheric transport circulation in historical atmosphere‐only simulations integrated with interactive trace gas and aerosol chemistry. Compared to lower vertical resolution model versions, E2.2 exhibits improved tropical ascent and older stratospheric mean ages that are more consistent with observed values. In the troposphere, poleward transport to the Arctic and interhemispheric mean ages in E2.2 are comparable to models participating in the Chemistry Climate Modeling Initiative. In addition to validating E2.2, we also assess its “transport sensitivity” using the coupled atmosphere‐ocean abrupt 4xCO2 and transient 1%CO2 simulations submitted to the Coupled Model Intercomparison Project, Phase 6, along with a 2xCO2 simulation used to evaluate the linearity of the transport circulation's response to increased CO2. We show that decreases (increases) in a stratospheric mean age (idealized surface loss) tracer scale linearly with increased lower stratospheric upwelling, which also increases linearly with warming tropical sea surface temperatures (SSTs). Abrupt 2xCO2 and 4xCO2 experiments constrained with (fixed) pre‐industrial SSTs are also used to quantify the relative importance of rapid adjustments versus SST feedbacks to the transport circulation responses in the model. Finally, sensitivity experiments are presented to illustrate the impact of changes in the convective parameterization on stratospheric transport.

The interactive global fire module pyrE (v1.0)

Abstract. Fires affect the composition of the atmosphere and Earth's radiation balance by emitting a suite of reactive gases and particles. An interactive fire module in an Earth system model (ESM) allows us to study the natural and anthropogenic drivers, feedbacks, and interactions of open fires. To do so, we have developed pyrE, the NASA GISS (Goddard Institute for Space Studies) interactive fire emissions module. The pyrE module is driven by environmental variables like flammability and cloud-to-ground lightning, calculated by the GISS ModelE ESM, and parameterized by anthropogenic impacts based on population density data. Fire emissions are generated from the flaming phase in pyrE (active fires). Using pyrE, we examine fire occurrence, regional fire suppression, burned area, fire emissions, and how it all affects atmospheric composition. To do so, we evaluate pyrE by comparing it to satellite-based datasets of fire count, burned area, fire emissions, and aerosol optical depth (AOD). We demonstrate pyrE's ability to simulate the daily and seasonal cycles of open fires and resulting emissions. Our results indicate that interactive fire emissions are biased low by 32 %–42 %, depending on emitted species, compared to the GFED4s (Global Fire Emissions Database) inventory. The bias in emissions drives underestimation in column densities, which is diluted by natural and anthropogenic emissions sources and production and loss mechanisms. Regionally, the resulting AOD of a simulation with interactive fire emissions is underestimated mostly over Indonesia compared to a simulation with GFED4s emissions and to MODIS AOD. In other parts of the world pyrE's performance in terms of AOD is marginal to a simulation with prescribed fire emissions.

Time Series, Hidden Variables and Spatio-Temporal Ordinality Networks

In this work, a novel methodology for the modelling and forecasting of time series using higher-dimensional networks in $$\mathbf{R }^{4,1}$$ space is presented. Time series data is partitioned, transformed and mapped into five-dimensional conformal space as a network which we call the ‘Spatio-Temporal Ordinality Network’ (STON). These STONs are characterised using specific Clifford Algebraic multivector functions which are found to be highly effective as featurised variables that forecast future states of the time series. The proposed STONs present as unique mathematical constructions that capture the algebra-geometric inter-dependencies in the historical behaviour of a time series system, thereby presenting governing expressions for forecasting based on historical values. Case studies on the seasonally adjusted Australian unemployment rate and the NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis for Global Land-Ocean Temperature Index are furnished in this work and are compared against multilayer perceptrons (MLP), long short term memory (LSTM) neural networks, ARIMA and Holt-Winters methods. This formulation presents an alternative and effective modelling and forecasting paradigm for time series and multivariate systems with known or hidden variables.

Computational and Statistical Analysis of Surface Temperature over Inhambane-Mozambique from 1931-2015: The Future Implications

The danger of the El Nino southern oscillation (ENSO) over tropical pacific might be heading for an all-time high. Eighty-five years (1931-2015) surface temperature dataset from Goddard Institute for Space Studies (GISS) over Inhambane-Mozambique were used for this study. The dataset was analyzed using the computational and statistical technique. The maximum monthly surface temperature that was recorded within eighty-five years was given as 16 oC (January - 1990), 21.4 oC (February - 2006), 26.3 oC (March - 2010), 32.7 oC (April - 2010), 35.2 oC (May - 1978), 36.2 oC (June - 2012), 34.8 oC (July - 2002), 32.4 oC (August - 1987), 31.2 oC (September - 1987), 28.7 oC (October - 1951), 22.1 oC (November – 1977 & 2011) and 17.6 oC (December - 2013). From the study, it was discovered that droughts over Mozambique and parts of Southern Africa were not caused by certain extreme ENSO. Rather, the drought was caused by series of continuous processes (extreme weather conditions) that influence certain atmospheric conditions.

A General View of TOPSIS Method Involving Multi-Attribute Decision Making Problems

The TOPSIS method, commonly known as the technique for order performance by similarity to ideal solutions, is one of the most popular approaches used in the multi-attribute decision making (MADM). In this method has some advantages are simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. In the last four decades world is getting warmer. It is whether the cause of human activity or natural variability. The thermometer reading all round the world have risen steadily since the beginning of the industrial revolution. According to the ongoing temperature analysis conducted by scientist at NASA’s Goddard Institute for Space Studies (GISS), the average global temperature on the Earth has increased about 0.80 Celsius since 1880. So, applying this method to determines the best solution from a set of alternatives with certain attributes. The best alternative is chosen based on its Euclidean distance from the ideal solution. The aim of this paper to systematizes the knowledge within the scope of techniques of decision taking with the use of the TOPSIS method. Simple numerical examples that reference real situations show practical applications of different aspects of this method.

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013

The global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods.

Peatlands may hold double the carbon

Peatlands are the planet’s biggest terrestrial carbon sink, and understanding how they store and cycle carbon is critical for understanding climate change. But Jonathan Nichols, a biogeochemist at Columbia University’s Lamont-Doherty Earth Observatory, noticed a pattern when reading about peatlands: everyone cites the same statistics over and over again. He began to wonder whether those numbers were “the best we can do,” he says. Nichols and coauthor Dorothy Peteet of NASA Goddard Institute for Space Studies created a probabilistic distribution that included data from peatlands that had been left out of previous studies because their data were too sparse. The new approach nearly doubled the estimated amount of carbon held in northern peatlands (Nat. Geosci. 2019, DOI: 10.1038/s41561-019-0454-z). The study also has implications for paleoclimatology; it suggests that the expansion of peatlands and their carbon accumulation rate increased much more quickly after the end of the last ice age than

Teaching anthropogenic global climate change (AGCC) using climate models

ABSTRACTGeography educators have long taught anthropogenic global climate change (AGCC) using geospatial data, visualizations, analysis tools and models. We examined the attitudes of teachers at secondary schools, research institutions, colleges and universities tasked with teaching using one such technology. We designed a seventeen-question online survey about the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) Educational Global Climate Model (EdGCM) that was completed by 115 respondents. Researchers and educators at more than 150 research and educational institutions internationally have utilized EdGCM since 2005. There are numerous reasons why teaching AGCC with a global climate model (GCM) is attractive, and this study examined the successes and challenges teachers confront when implementing such educational technologies. Those in our sample provided insight into what methods and approaches proved effective in reinforcing conceptual understandings of the geographic processes that shape the Earth system. Our findings show educators are willing to use complex scientific models in their classroom but prefer those that enable genuine student research. We also found that more easily accessible online technologies pose fewer technical challenges and often are preferred by teachers to more complex climate education technologies.

Simulated Greenland Surface Mass Balance in the GISS ModelE2 GCM: Role of the Ice Sheet Surface

AbstractThe rate of growth or retreat of the Greenland and Antarctic ice sheets remains a highly uncertain component of future sea level change. Here we examine the simulation of Greenland ice sheet surface mass balance (GrIS SMB) in a development branch of the ModelE2 version of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM). GCMs are often limited in their ability to represent SMB compared with polar region regional climate models. We compare ModelE2‐simulated GrIS SMB for present‐day (1996–2005) simulations with fixed ocean conditions, at a spatial resolution of 2° latitude by 2.5° longitude (~200 km), with SMB simulated by the Modèle Atmosphérique Régionale (MAR) regional climate model (1996–2005 at a 25‐km resolution). ModelE2 SMB agrees well with MAR SMB on the whole, but there are distinct spatial patterns of differences and large differences in some SMB components. The impacts of changes to the ModelE2 surface are tested, including a subgrid‐scale representation of SMB with surface elevation classes. This has a minimal effect on ice sheet‐wide SMB but corrects local biases. Replacing fixed surface albedo with satellite‐derived values and an age‐dependent scheme has a larger impact, increasing simulated melt by 60%–100%. We also find that lower surface albedo can enhance the effects of elevation classes. Reducing ModelE2 surface roughness length to values closer to MAR reduces sublimation by ~50%. Further work is required to account for meltwater refreezing in ModelE2 and to understand how differences in atmospheric processes and model resolution influence simulated SMB.

Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model

In this study, the sliced functional time series (SFTS) model is applied to the Global, Northern and Southern temperature anomalies. We obtained the combined land-surface air and sea-surface water temperature from Goddard Institute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere mean and Southern Hemisphere means (monthly, quarterly and annual) since 1880 to present (updated through March 2019). We analyze the global surface temperature change, compare alternative analyses, and address the questions about the reality of global warming. We detected the outliers during the last century not only in global temperature series but also in northern and southern hemisphere series. The forecasts for the next twenty years are obtained using SFTS models. These forecasts are compared with ARIMA, Random Walk with drift and Exponential Smoothing State Space (ETS) models. The comparison is made on the basis of root mean square error (RMSE), mean absolute percentage error (MAPE) and the length of prediction intervals.

The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle

Abstract. In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the “ocean carbon states”, as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical–subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air–sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in the Southern Ocean flux bias. All data and analysis scripts are available at https://data.giss.nasa.gov/oceans/carbonstates/ (DOI: https://doi.org/10.5281/zenodo.996891).

Coupled Retrieval of Liquid Water Cloud and Above‐Cloud Aerosol Properties Using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)

AbstractAn optimization algorithm is developed to retrieve liquid water cloud properties including cloud optical depth (COD), droplet size distribution and cloud top height (CTH), and above‐cloud aerosol properties including aerosol optical depth (AOD), single‐scattering albedo, and microphysical properties from sweep‐mode observations by Jet Propulsion Laboratory's Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) instrument. The retrieval is composed of three major steps: (1) initial estimate of the mean droplet size distribution across the entire image of 80–100 km along track by 10–25 km across track from polarimetric cloudbow observations, (2) coupled retrieval of image‐scale cloud and above‐cloud aerosol properties by fitting the polarimetric data at all observation angles, and (3) iterative retrieval of 1‐D radiative transfer‐based COD and droplet size distribution at pixel scale (25 m) by establishing relationships between COD and droplet size and fitting the total radiance measurements. Our retrieval is tested using 134 AirMSPI data sets acquired during the National Aeronautics and Space Administration (NASA) field campaign ObseRvations of Aerosols above CLouds and their intEractionS. The retrieved above‐cloud AOD and CTH are compared to coincident HSRL‐2 (HSRL‐2, NASA Langley Research Center) data, and COD and droplet size distribution parameters (effective radius reff and effective variance veff) are compared to coincident Research Scanning Polarimeter (RSP) (NASA Goddard Institute for Space Studies) data. Mean absolute differences between AirMSPI and HSRL‐2 retrievals of above‐cloud AOD at 532 nm and CTH are 0.03 and <0.5 km, respectively. At RSP's footprint scale (~ 323 m), mean absolute differences between RSP and AirMSPI retrievals of COD, reff, and veff in the cloudbow area are 2.33, 0.69 μm, and 0.020, respectively. Neglect of smoke aerosols above cloud leads to an underestimate of image‐averaged COD by ~15%.

Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) 1.0: A General Circulation Model for Simulating the Climates of Rocky Planets

Abstract Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) is a three-dimensional General Circulation Model (GCM) developed at the NASA Goddard Institute for Space Studies for the modeling of atmospheres of solar system and exoplanetary terrestrial planets. Its parent model, known as ModelE2, is used to simulate modern Earth and near-term paleo-Earth climates. ROCKE-3D is an ongoing effort to expand the capabilities of ModelE2 to handle a broader range of atmospheric conditions, including higher and lower atmospheric pressures, more diverse chemistries and compositions, larger and smaller planet radii and gravity, different rotation rates (from slower to more rapid than modern Earth’s, including synchronous rotation), diverse ocean and land distributions and topographies, and potential basic biosphere functions. The first aim of ROCKE-3D is to model planetary atmospheres on terrestrial worlds within the solar system such as paleo-Earth, modern and paleo-Mars, paleo-Venus, and Saturn’s moon Titan. By validating the model for a broad range of temperatures, pressures, and atmospheric constituents, we can then further expand its capabilities to those exoplanetary rocky worlds that have been discovered in the past, as well as those to be discovered in the future. We also discuss the current and near-future capabilities of ROCKE-3D as a community model for studying planetary and exoplanetary atmospheres.

Interannual variation, decadal trend, and future change in ozone outflow from East Asia

Abstract. We examine the past and future changes in the O3 outflow from East Asia using a global 3-D chemical transport model, GEOS-Chem. The simulations of Asian O3 outflow for 1986–2006 are driven by the assimilated GEOS-4 meteorological fields, and those for 2000–2050 are driven by the meteorological fields archived by the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) 3 under the IPCC SRES A1B scenario. The evaluation of the model results against measurements shows that the GEOS-Chem model captures the seasonal cycles and interannual variations of tropospheric O3 concentrations fairly well with high correlation coefficients of 0.82–0.93 at four ground-based sites and 0.55–0.88 at two ozonesonde sites where observations are available. The increasing trends in surface-layer O3 concentrations in East Asia over the past 2 decades are captured by the model, although the modeled O3 trends have low biases. Sensitivity studies are conducted to examine the respective impacts of meteorological parameters and emissions on the variations in the outflow flux of O3. When both meteorological parameters and anthropogenic emissions varied from 1986–2006, the simulated Asian O3 outflow fluxes exhibited a statistically insignificant decadal trend; however, they showed large interannual variations (IAVs) with seasonal values of 4–9 % for the absolute percent departure from the mean (APDM) and an annual APDM value of 3.3 %. The sensitivity simulations indicated that the large IAVs in O3 outflow fluxes were mainly caused by variations in the meteorological conditions. The variations in meteorological parameters drove the IAVs in O3 outflow fluxes by altering the O3 concentrations over East Asia and by altering the zonal winds; the latter was identified to be the key factor, since the O3 outflow was highly correlated with zonal winds from 1986–2006. The simulations of the 2000–2050 changes show that the annual outflow flux of O3 will increase by 2.0, 7.9, and 12.2 % owing to climate change alone, emissions change alone, and changes in both climate and emissions, respectively. Therefore, climate change will aggravate the effects of the increases in anthropogenic emissions on future changes in the Asian O3 outflow. Future climate change is predicted to greatly increase the Asian O3 outflow in the spring and summer seasons as a result of the projected increases in zonal winds. The findings from the present study help us to understand the variations in tropospheric O3 in the downwind regions of East Asia on different timescales and have important implications for long-term air quality planning in the regions downwind of China, such as Japan and the US.

Evaluation of NASA GISS post-CMIP5 single column model simulated clouds and precipitation using ARM Southern Great Plains observations

The planetary boundary layer turbulence and moist convection parameterizations have been modified recently in the NASA Goddard Institute for Space Studies (GISS) Model E2 atmospheric general circulation model (GCM; post-CMIP5, hereafter P5). In this study, single column model (SCM P5) simulated cloud fractions (CFs), cloud liquid water paths (LWPs) and precipitation were compared with Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) groundbased observations made during the period 2002–08. CMIP5 SCM simulations and GCM outputs over the ARM SGP region were also used in the comparison to identify whether the causes of cloud and precipitation biases resulted from either the physical parameterization or the dynamic scheme. The comparison showed that the CMIP5 SCM has difficulties in simulating the vertical structure and seasonal variation of low-level clouds. The new scheme implemented in the turbulence parameterization led to significantly improved cloud simulations in P5. It was found that the SCM is sensitive to the relaxation time scale. When the relaxation time increased from 3 to 24 h, SCM P5-simulated CFs and LWPs showed a moderate increase (10%–20%) but precipitation increased significantly (56%), which agreed better with observations despite the less accurate atmospheric state. Annual averages among the GCM and SCM simulations were almost the same, but their respective seasonal variations were out of phase. This suggests that the same physical cloud parameterization can generate similar statistical results over a long time period, but different dynamics drive the differences in seasonal variations. This study can potentially provide guidance for the further development of the GISS model.

Climate responses to SATIRE and SIM-based spectral solar forcing in a 3D atmosphere-ocean coupled GCM

We apply two reconstructed spectral solar forcing scenarios, one SIM (Spectral Irradiance Monitor) based, the other the SATIRE (Spectral And Total Irradiance REconstruction) modeled, as inputs to the GISS (Goddard Institute for Space Studies) GCMAM (Global Climate Middle Atmosphere Model) to examine climate responses on decadal to centennial time scales, focusing on quantifying the difference of climate response between the two solar forcing scenarios. We run the GCMAM for about 400 years with present day trace gas and aerosol for the two solar forcing inputs. We find that the SIM-based solar forcing induces much larger long-term response and 11-year variation in global averaged stratospheric temperature and column ozone. We find significant decreasing trends of planetary albedo for both forcing scenarios in the 400-year model runs. However the mechanisms for the decrease are very different. For SATIRE solar forcing, the decreasing trend of planetary albedo is associated with changes in cloud cover. For SIM-based solar forcing, without significant change in cloud cover on centennial and longer time scales, the apparent decreasing trend of planetary albedo is mainly due to out-of-phase variation in shortwave radiative forcing proxy (downwelling flux for wavelength >330 nm) and total solar irradiance (TSI). From the Maunder Minimum to present, global averaged annual mean surface air temperature has a response of ~0.1 °C to SATIRE solar forcing compared to ~0.04 °C to SIM-based solar forcing. For 11-year solar cycle, the global surface air temperature response has 3-year lagged response to either forcing scenario. The global surface air 11-year temperature response to SATIRE forcing is about 0.12 °C, similar to recent multi-model estimates, and comparable to the observational-based evidence. However, the global surface air temperature response to 11-year SIM-based solar forcing is insignificant and inconsistent with observation-based evidence.