Geophysical Fluid Dynamics Laboratory Research Articles

A Study of AR-, TS-, and MCS-Associated Precipitation and Extreme Precipitation in Present and Warmer Climates

Abstract Atmospheric rivers (ARs), tropical storms (TSs), and mesoscale convective systems (MCSs) are important weather phenomena that often threaten society through heavy precipitation and strong winds. Despite their potentially vital role in global and regional hydrological cycles, their contributions to long-term mean and extreme precipitation have not been systematically explored at the global scale. Using observational and reanalysis data, and NOAA’s Geophysical Fluid Dynamics Laboratory’s new high-resolution global climate model, we quantify that despite their occasional (13%) occurrence globally, AR, TS, and MCS days together account for ∼55% of global mean precipitation and ∼75% of extreme precipitation with daily rates exceeding its local 99th percentile. The model reproduces well the observed percentage of mean and extreme precipitation associated with AR, TS, and MCS days. In an idealized global warming simulation with a homogeneous SST increase of 4 K, the modeled changes in global mean and regional distribution of precipitation correspond well with changes in AR/TS/MCS precipitation. Globally, the frequency of AR days increases and migrates toward higher latitudes while the frequency of TS days increases over the central Pacific and part of the south Indian Ocean with a decrease elsewhere. The frequency of MCS days tends to increase over parts of the equatorial western and eastern Pacific warm pools and high latitudes and decreases over most part of the tropics and subtropics. The AR/TS/MCS mean precipitation intensity increases by ∼5% K−1 due primarily to precipitation increases in the top 25% of AR/TS/MCS days with the heaviest precipitation, which are dominated by the thermodynamic component with the dynamic and microphysical components playing a secondary role.

Modeling future asthma attributable to fine particulate matter (PM2.5) in a changing climate: a health impact assessment.

Exposure to fine particulate matter (PM2.5) is associated with asthma development as well as asthma exacerbation in children. PM2.5 can be directly emitted or can form in the atmosphere from pollutant precursors. PM2.5 emitted and formed in the atmosphere is influenced by meteorology; future changes in climate may alter the concentration and distribution of PM2.5. Our aim is to estimate the future burden of climate change and PM2.5 on new and exacerbated cases of childhood asthma. Projected concentrations of PM2.5 are based on the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 climate model, the Representative Concentration Pathway 8.5 greenhouse gas scenario, and two air pollution emissions datasets: a 2011 emissions dataset and a 2040 emissions dataset that reflects substantial reductions in emissions of PM2.5 as compared to the 2011 inventory. We estimate additional PM2.5-attributable asthma as well as PM2.5-attributable albuterol inhaler use for four future years (2030, 2050, 2075, and 2095) relative to the year 2000. Exacerbations, regardless of the trigger, are counted as attributable to PM2.5 if the incident disease is attributable to PM2.5. We project 38 thousand (95% CI 36, 39 thousand) additional PM2.5-attributable incident childhood asthma cases and 29 million (95% CI 27, 31 million) additional PM2.5-attributable albuterol inhaler uses per year in 2030, increasing to 200 thousand (95% CI 190, 210 thousand) additional incident cases and 160 million (95% CI 150, 160 million) inhaler uses per year by 2095 relative to 2000 under the 2011 emissions dataset. These additional PM2.5-attributable incident asthma cases and albuterol inhaler use would cost billions of additional U.S. dollars per year by the late century. These outcomes could be mitigated by reducing air pollution emissions.

Estimation of the rice water footprint based on machine learning algorithms

It is essential to investigate the impact of climate change on the water footprint (WF) of rice from both historical simulation and future projections. In this study, four machine learning (ML) models, including random tree (RT), random forest (RF), additive regression (AR) and reduced error pruning tree (REPT) were used to model blue and green water footprint (BWFP and GWFP) for the present and future stages in the Yunnan Province, southwest China. Climate variables of daily precipitation, temperature, solar radiation, sunshine hours, wind speed, relative humidity and vapor pressure deficit data, yield and sown areas of rice were collected form 16 districts from 1990 to 2018. Six different scenarios (Sc1-Sc6) with different combinations of climate variables, crop coefficient and sown areas were used as inputs of the ML models for each of blue and green water footprint. Also, future climate projections of maximum (Tmax) and minimum (Tmin) temperatures, precipitation and sunshine were adopted for two different emission scenarios, RCP 4.5 and 8.5 from 2021to 2050, based on Geophysical Fluid Dynamics Laboratory (GFDL-ESM2M) model. For BWFP, the RT model in Sc1 with inputs of solar radiation, humidity, and vapor pressure deficit, was superior to the other scenarios with root mean square error (RMSE) and mean average percentage error (MAPE) values of 11.82 (m3 ton-1) and 0.5%, respectively. Sc4 (sown area, Tmin, sunshine hours) and Sc5 (sown area, Tmin, Tmax, crop coefficient) were the best two scenarios for all models applied for BWFP, while addition of precipitation to these two scenarios were the best for GWFP. Further, GWFP was highly negative anomaly in 2011 by 46%, 34% and 32% for the districts 5, 1 and 2 respectively, followed by 2010. Predictions of variable trends in the future showed that crop evapotranspiration during the growing season would increase in all the districts, although effective precipitation (Peff) would have both decreasing and increasing trends. Furthermore, BWFP and GWFP will have an upward trend in the future based on RCP4.5 in Sc4. This investigation addresses water footprint prediction which may assist in mitigation plans such as policies for sustainable water-use and development plans for food security.

Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on Dynamical Downscaling

AbstractThis study compares the impacts of global warming and intense urban anthropogenic heat (AH) on extreme hourly precipitation over the Pearl River Delta (PRD) megacity, located in coastal South China. Using the cloud‐resolving Weather Research and Forecasting (WRF) model coupled with the single‐layer urban canopy model (SLUCM), three downscaling experiments were carried out: the first (second) having zero (300 W/m2) as diurnal maximum AH values prescribed over PRD urban grids, under the current climate conditions. The third experiment with diurnal maximum AH of 300 W/m2 under future projected climate representative concentration pathway (RCP) 8.5. Boundary conditions were derived from PRD extreme rainfall episodes, identified from the Geophysical Fluid Dynamics Laboratory Earth System Model (GFDL‐ESM2M) historical and RCP8.5 runs. Global warming forcing leads to ∼20% to more than 100% increase in the probability of hourly precipitation with the magnitude of 20–100 mm/hr over urban locations. The enhancements from intense AH forcing were similar. However, two forcings have distinct signatures in modulating the thermodynamic environment. Warming due to AH is limited to the lowest 1 km above ground, while global warming warms up the whole troposphere. Intense AH results in enhanced convective available potential energy (CAPE) and reduced convective inhibition (CIN) within the megacity, allowing convection to be triggered more easily and with more vigor. On the other hand, global warming enhances CAPE and CIN, over both urban and rural areas. Our results highlight the different mechanisms of AH and global warming in exacerbating extreme urban rainfall, despite the similar impacts on the rainfall intensity.

Dynamical Downscaling of Temperature Variations over the Canadian Prairie Provinces under Climate Change

In this study, variations of daily mean, maximum, and minimum temperature (expressed as Tmean, Tmax, and Tmin) over the Canadian Prairie Provinces were dynamically downscaled through regional climate simulations. How the regional climate would increase in response to global warming was subsequently revealed. Specifically, the Regional Climatic Model (RegCM) was undertaken to downscale the boundary conditions of Geophysical Fluid Dynamics Laboratory Earth System Model Version 2M (GFDL-ESM2M) over the Prairie Provinces. Daily temperatures (i.e., Tmean, Tmax, and Tmin) were subsequently extracted from the historical and future climate simulations. Temperature variations in the two future periods (i.e., 2036 to 2065 and 2065 to 2095) are then investigated relative to the baseline period (i.e., 1985 to 2004). The spatial distributions of temperatures were analyzed to reveal the regional impacts of global warming on the provinces. The results indicated that the projected changes in the annual averages of daily temperatures would be amplified from the southwest in the Rocky Mountain area to the northeast in the prairie region. It was also suggested that the projected temperature averages would be significantly intensified under RCP8.5. The projected temperature variations could provide scientific bases for adaptation and mitigation initiatives on multiple sectors, such as agriculture and economic sectors over the Canadian Prairies.

Assessment of Hydroclimatological Changes in Eastern Himalayan River Catchment of Northeast India

This article focuses on the assessment of climate change impacts on the hydroclimatology of the Subansiri River basin, which is the largest tributary of the Brahmaputra River in the northeastern part of India. Three representative concentration pathway (RCP) emission scenarios, namely, RCP2.6, RCP6.0, and RCP8.5, of three general circulation models (GCM) archived by the Geophysical Fluid Dynamics Laboratory (GFDL), were utilized for the projection of climatic variables (such as precipitation and temperature). Long-term (2011–2100) projections of precipitation and temperature for different emission scenarios were made using the statistical downscaling technique. The soil and water assessment tool (SWAT) hydrological model was used for hydrological modeling of the river basin. The observed streamflow series for the period of 2002–2013 has been utilized for calibration and validation of the hydrological model. Parameterization, uncertainty analysis, and parameter sensitivity analysis of the model were performed using a sequential uncertainty fitting (SUFI2) program. The coefficient of determination (R2) for the calibration and validation of the hydrological model on the monthly streamflow time series was found to be 0.86 and 0.80, respectively. Future projections of the precipitation and temperature suggest an increase in the annual average maximum temperature (Tmax), annual average minimum temperature (Tmin), and annual precipitation of the river basin. These projected climatic variables were used as the primary input in the hydrological model for the projection of the streamflow for the period of 2016–2100. The flow duration curve analysis of streamflow projections reveals an increase in the discharge for a particular percent of the dependable flow in the case of all the RCP scenarios. Water yield analysis also suggests an increase in the annual average water yield in all cases of emission scenarios.

Long-Term Projection of Water Cycle Changes over China Using RegCM

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.

The most sensitive initial error modes modulating intensities of CP- and EP- El Niño events

The two types of El Niño events simulated by the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) model and its “spring predictability barrier” (SPB) associations are examined. By conducting the ensemble hindcast experiments related to the sea temperature on the whole Pacific, both of the predictions for CP- and EP-El Niño show a significant SPB phenomenon, but the CP-El Niño features a much weaker SPB compared to the EP-El Niño. Further analyses revealed that, for CP-El Niño events, the initial sea temperature errors of the North Pacific with triple-like shape, referred to as negative Victoria Mode (VM) induces the largest prediction errors in Niño4 areas and modulates the intensities of CP-El Niño events. While for EP-El Niño events, the initial sea temperature errors in the subsurface layer of the western equatorial Pacific and the upper layer of the Southeast Pacific (15–30ºS) with the meridional mode induce the largest prediction errors in Niño3 areas and modulates the intensities of EP-El Niño events. Obviously, results stress that, in order to reduce final prediction errors and obtain better predictions in terms of intensity on the two types of El Niño events, we should mainly focus on initial sea temperature accuracy in not only the subsurface layer of the west equatorial Pacific but also the surface layer of southeast Pacific and the region covered by the VM-like mode in the North Pacific.

On the Sensitivity of Hurricane Intensity and Structure to Horizontal Tracer Advection Schemes in FV3

AbstractWe investigate the sensitivity of hurricane intensity and structure to the horizontal tracer advection in the Geophysical Fluid Dynamics Laboratory (GFDL) Finite-Volume Cubed-Sphere Dynamical Core (FV3). We compare two schemes, a monotonic scheme and a less diffusive positive-definite scheme. The positive-definite scheme leads to significant improvement in the intensity prediction relative to the monotonic scheme in a suite of 5-day forecasts that mostly consist of rapidly intensifying hurricanes. Notable storm structural differences are present: the radius of maximum wind (RMW) is smaller and eyewall convection occurs farther inside the RMW when the positive-definite scheme is used. Moreover, we find that the horizontal tracer advection scheme affects the eyewall convection location by affecting the moisture distribution in the inner-core region. This study highlights the importance of dynamical core algorithms in hurricane intensity prediction.

A Comparison of Tropical Cyclone Projections in a High-resolution Global Climate Model and from Downscaling by Statistical and Statistical-deterministic Methods

AbstractIn this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Forecast-oriented Low Ocean Resolution (HiFLOR) model, under the Representative Concentration Pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic basin. The downscaled TCs for the historical climate (1986-2005) are compared with those in the mid- (2016-35) and late-twenty-first century (2081-2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that while significantly more storms are detected in HiFLOR towards the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency, and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and Category 5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity while PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity.

Seasonal Prediction and Predictability of Regional Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

A novel representation of biological nitrogen fixation and competitive dynamics between nitrogen-fixing and non-fixing plants in a land model (GFDL LM4.1-BNF)

Abstract. Representing biological nitrogen fixation (BNF) is an important challenge for coupled carbon (C) and nitrogen (N) land models. Initial representations of BNF in land models applied simplified phenomenological relationships. More recent representations of BNF are mechanistic and include the dynamic response of symbiotic BNF to N limitation of plant growth. However, they generally do not include the competitive dynamics between N-fixing and non-fixing plants, which is a key ecological mechanism that determines ecosystem-scale symbiotic BNF. Furthermore, asymbiotic BNF is generally not included in land models. Here, we present LM4.1-BNF, a novel representation of BNF (asymbiotic and symbiotic) and an updated representation of N cycling in the Geophysical Fluid Dynamics Laboratory Land Model 4.1 (LM4.1). LM4.1-BNF incorporates a mechanistic representation of asymbiotic BNF by soil microbes, a representation of the competitive dynamics between N-fixing and non-fixing plants, and distinct asymbiotic and symbiotic BNF temperature responses derived from corresponding observations. LM4.1-BNF makes reasonable estimations of major carbon (C) and N pools and fluxes and their temporal dynamics, in comparison to the previous version of LM4.1 with N cycling (LM3-SNAP) and to previous representations of BNF in land models generally (phenomenological representations and those without competitive dynamics between N-fixing and non-fixing plants and/or asymbiotic BNF) at a temperate forest site. LM4.1-BNF effectively reproduces asymbiotic BNF rate (13 kgNha-1yr-1) in comparison to observations (11 kgNha-1yr-1). LM4.1-BNF effectively reproduces the temporal dynamics of symbiotic BNF rate: LM4.1-BNF simulates a symbiotic BNF pulse in early succession that reaches 73 kgNha-1yr-1 at 15 years and then declines to ∼0 kgNha-1yr-1 at 300 years, similarly to observed symbiotic BNF, which reaches 75 kgNha-1yr-1 at 17 years and then declines to ∼0 kgNha-1yr-1 in late successional forests. As such, LM4.1-BNF can be applied to project the dynamic response of vegetation to N limitation of plant growth and the degree to which this will constrain the terrestrial C sink under elevated atmospheric CO2 concentration and other global change factors.

CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model

Abstract. The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics but also changes land–atmosphere feedbacks. Specifically, changes in land cover affect biophysical feedbacks of water and energy, thereby contributing to climate change. In this study, we couple the well-established and comprehensively validated dynamic global vegetation model LPJmL5 (Lund–Potsdam–Jena managed Land) to the coupled climate model CM2Mc, the latter of which is based on the atmosphere model AM2 and the ocean model MOM5 (Modular Ocean Model 5), and name it CM2Mc-LPJmL. In CM2Mc, we replace the simple land-surface model LaD (Land Dynamics; where vegetation is static and prescribed) with LPJmL5, and we fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, conductance of the soil evaporation and plant interception, canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy-layer temperature within LPJmL5. Exchanging LaD with LPJmL5 and, therefore, switching from a static and prescribed vegetation to a dynamic vegetation allows us to model important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases to the original CM2Mc model with LaD. The performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historical global mean temperature evolution of our model setup is within the range of CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation–climate feedbacks with a state-of-the-art and process-based dynamic vegetation model.

Future scenarios of risk of Vibrio infections in a warming planet: a global mapping study

Infections caused by non-cholera Vibrio species have undergone a global expansion over the past few decades reaching new areas of the world that were previously considered adverse for these organisms. The geographical extent of the expansion has not been uniform, and some areas have shown a rapid increase in infections. We applied a new generation of models combining climate, population, and socioeconomic projections to map future scenarios of distribution and season suitability for pathogenic Vibrio. We used the Coupled Model Intercomparison Project 6 framework. Three datasets were used: Geophysical Fluid Dynamics Laboratory's CM4.0 sea surface temperature and sea surface salinity; the coastline length dataset from the World Resources Institute; and Inter-Sectoral Impact Model Intercomparison Project 2b annual global population data. Future projections were used up to the year 2100 and historical simulations from 1850 to 2014. We also project human population at risk under different shared socioeconomic pathways worldwide. Projections showed that coastal areas suitable for Vibrio could cover 38 000 km of new coastal areas by 2100 under the most unfavourable scenario with an expansion rate of season suitability in these regions of around 1 month every 30 years. Population at risk in suitable regions almost doubled from 1980 to 2020 (from 610 million to 1100 million under the scenario of medium challenges to mitigation and adaptation, shared socioeconomic pathway 2-4.5), although the increment will be more moderate in the future and stabilises after 2050 at 1300 million. Finally, we provide the first global estimate for Vibrio infections, with values around half a million of cases worldwide in 2020. Our projections anticipated an expansion of both the temporal and spatial disease burden for Vibrio infections, in particular at high latitudes of the northern hemisphere. However, the largest extent occurred from 1980 to 2020 and a more moderate increase is expected for the future. The most positive outcome is that the projections showed that Vibrio morbidity will remain relatively stable over the coming decades. NOAA Atlantic Oceanographic and Meteorological Laboratory and NOAA OceanWatch, and by the University of Miami's Cooperative Institute for Marine and Atmospheric Studies.

Important factors in the tracking of tropical cyclones in operational models

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

Two‐Moment Bulk Cloud Microphysics With Prognostic Precipitation in GFDL's Atmosphere Model AM4.0: Configuration and Performance

AbstractA two‐moment Morrison‐Gettelman bulk cloud microphysics with prognostic precipitation (MG2), together with a mineral dust and temperature‐dependent ice nucleation scheme, have been implemented into the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4.0 (AM4.0). We refer to this configuration as AM4‐MG2. This paper describes the configuration of AM4‐MG2, evaluates its performance, and compares it with AM4.0. It is shown that the global simulations with AM4‐MG2 compare favorably with observations and reanalyses. The model skill scores are close to AM4.0. Compared to AM4.0, improvements in AM4‐MG2 include (a) better coastal marine stratocumulus and seasonal cycles, (b) more realistic ice fraction, and (c) dominant accretion over autoconversion. Sensitivity tests indicate that nucleation and sedimentation schemes have significant impacts on cloud liquid and ice water fields, but higher horizontal resolution (about 50 km instead of 100 km) does not.

A mechanistic analysis of tropical Pacific dynamic sea level in GFDL-OM4 under OMIP-I and OMIP-II forcings

Abstract. The sea level over the tropical Pacific is a key indicator reflecting vertically integrated heat distribution over the ocean. Here, we use the Geophysical Fluid Dynamics Laboratory global ocean–sea ice model (GFDL-OM4) forced by both the Coordinated Ocean-Ice Reference Experiment (CORE) and Japanese 55-year Reanalysis (JRA-55)-based surface dataset for driving ocean–sea ice models (JRA55-do) atmospheric states (Ocean Model Intercomparison Project (OMIP) versions I and II) to evaluate the model performance and biases compared against available observations. We find persisting mean state dynamic sea level (DSL) bias along 9∘ N even with updated wind forcing in JRA55-do relative to CORE. The mean state bias is related to biases in wind stress forcing and geostrophic currents in the 4 to 9∘ N latitudinal band. The simulation forced by JRA55-do significantly reduces the bias in DSL trend over the northern tropical Pacific relative to CORE. In the CORE forcing, the anomalous westerly wind trend in the eastern tropical Pacific causes an underestimated DSL trend across the entire Pacific basin along 10∘ N. The simulation forced by JRA55-do significantly reduces the bias in DSL trend over the northern tropical Pacific relative to CORE. We also identify a bias in the easterly wind trend along 20∘ N in both JRA55-do and CORE, thus motivating future improvement. In JRA55-do, an accurate Rossby wave initiated in the eastern tropical Pacific at seasonal timescale corrects a biased seasonal variability of the northern equatorial countercurrent in the CORE simulation. Both CORE and JRA55-do generate realistic DSL variation during El Niño. We find an asymmetry in the DSL pattern on two sides of the Equator is strongly related to wind stress curl that follows the sea level pressure evolution during El Niño.

Impact of regional Northern Hemisphere mid-latitude anthropogenic sulfur dioxide emissions on local and remote tropospheric oxidants

Abstract. The unintended consequences of reductions in regional anthropogenic sulfur dioxide (SO2) emissions implemented to protect human health are poorly understood. SO2 decreases began in the 1970s in the US and Europe and are expected to continue into the future, while recent emissions decreases in China are also projected to continue. In addition to the well-documented climate effects (warming) from reducing aerosols, tropospheric oxidation is impacted via aerosol modification of photolysis rates and radical sinks. Impacts on the hydroxyl radical and other trace constituents directly affect climate and air quality metrics such as surface ozone levels. We use the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 nudged towards National Centers for Environmental Prediction (NCEP) reanalysis wind velocities to estimate the impact of SO2 emissions from the US, Europe, and China by differencing a control simulation with an otherwise identical simulation in which 2015 anthropogenic SO2 emissions are set to zero over one of the regions. Springtime sulfate aerosol changes occur both locally to the emission region and also throughout the Northern Hemispheric troposphere, including remote oceanic regions and the Arctic. Hydroperoxy (HO2) radicals are directly removed via heterogeneous chemistry on aerosol surfaces, including sulfate, in the model, and we find that sulfate aerosol produced by SO2 emissions from the three individual northern mid-latitude regions strongly reduces both HO2 and hydroxyl (OH) by up to 10 % year-round throughout most of the troposphere north of 30∘ N latitude. Regional SO2 emissions significantly increase nitrogen oxides (NOx) by about 5 %–8 % throughout most of the free troposphere in the Northern Hemisphere by increasing the NOx lifetime as the heterogeneous sink of HO2 on sulfate aerosol declines. Despite the NOx increases, tropospheric ozone decreases at northern mid-latitudes by 1 %–4 % zonally averaged and by up to 5 ppbv in summertime surface air over China, where the decreases in HO2 and OH suppress O3 production. Since 2015 anthropogenic SO2 emissions in China exceed those in the US or Europe, the oxidative response is greatest for the China perturbation simulation. Chemical effects of aerosols on oxidation (reactive uptake) dominate over radiative effects (photolysis rates), the latter of which are only statistically significant locally for the large perturbation over China. We find that the SO2 emissions decrease in China, which has yet to be fully realized, will have the largest impact on oxidants and related species in the Northern Hemisphere free troposphere compared to future decreases in Europe or the US. Our results bolster previous calls for a multipollutant strategy for air pollution mitigation to avoid the unintended consequence of aerosol removal leading to surface ozone increases that offset or mask surface ozone gains achieved by regulation of other pollutants, especially in countries where current usage of high-sulfur emitting fuels may be phased out in the future.

Representation of Tropical Mesoscale Convective Systems in a General Circulation Model: Climatology and Response to Global Warming

AbstractThe characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50-km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures (SST) and sea-ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean.

A Next Generation Ocean Carbon Isotope Model for Climate Studies I: Steady State Controls on Ocean 13C

AbstractThe 13C/12C of dissolved inorganic carbon (δ13CDIC) carries valuable information on ocean biological C‐cycling, air‐sea CO2 exchange, and circulation. Paleo‐reconstructions of oceanic 13C from sediment cores provide key insights into past as changes in these three drivers. As a step toward full inclusion of 13C in the next generation of Earth system models, we implemented 13C‐cycling in a 1° lateral resolution ocean‐ice‐biogeochemistry Geophysical Fluid Dynamics Laboratory (GFDL) model driven by Common Ocean Reference Experiment perpetual year forcing. The model improved the mean of modern δ13CDIC over coarser resolution GFDL‐model implementations, capturing the Southern Ocean decline in surface δ13CDIC that propagates to the deep sea via deep water formation. Controls on δ13CDIC of the deep‐sea are quantified using both observations and model output. The biological control is estimated from the relationship between deep‐sea Pacific δ13CDIC and phosphate (PO4). The δ13CDIC:PO4 slope from observations is revised to a value of 1.01 ± 0.02‰ (μmol kg−1)−1, consistent with a carbon to phosphate ratio of organic matter (C:Porg) of 124 ± 10. Model output yields a lower δ13CDIC:PO4 than observed due to too low C:Porg. The ocean circulation impacts deep modern δ13CDIC in two ways, via the relative proportion of Southern Ocean and North Atlantic deep water masses, and via the preindustrial δ13CDIC of these water mass endmembers. The δ13CDIC of the endmembers ventilating the deep sea are shown to be highly sensitive to the wind speed dependence of air‐sea CO2 gas exchange. Reducing the coefficient for air‐sea gas exchange following OMIP‐CMIP6 protocols improves significantly surface δ13CDIC relative to previous gas exchange parameterizations.