Search

Broadband (spectrally‐integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally‐chosen frequencies. The empirically‐optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top‐of‐the‐atmosphere forcing by CO2, in the longwave for clear skies. We evaluate the method against two modern correlated k‐distribution models and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function. Plain Language Summary: Quantifying the way radiation flows through the atmosphere is computationally expensive, and most applications require fast approximation. In this paper, we develop an empirically‐optimized quadrature method that can compute quantities of interest in climate science and weather prediction such as radiative flux throughout the atmosphere, heating rates, and forcing by carbon dioxide. The method uses a simple optimization algorithm, combined with a linear model, to achieve accuracy and computational cost of similar order of magnitude to the modern correlated k‐distribution models. Depending on the focus of the application, the algorithm can easily be adapted to prioritize desired quantities. Key Points: Spectrally integrated radiative quantities can be estimated from a small weighted set of monochromatic calculationsTradeoffs between computational cost and accuracy are commensurate with correlated k‐distribution modelsOptimization priorities can be tailored to a specific applications [ABSTRACT FROM AUTHOR]

Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next‐generation CO2‐observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble‐based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO2 flux parameters. TRACE uses an online full‐physics mesoscale atmospheric model and assimilates observations serially in a coupled atmosphere–carbon ensemble Kalman filter. The data assimilation system was tested in a series of observing system simulation experiments using in situ observations for a regional domain over North America in summer. Under ideal conditions with known prior flux parameter error covariances, TRACE reduced the error in domain‐integrated monthly CO2 fluxes by about 97% relative to the prior flux errors. In a more realistic scenario with unknown prior flux error statistics, the corresponding relative error reductions ranged from 80.6% to 88.5% depending on the specification of prior flux parameter error correlations. For regionally integrated fluxes on a spatial scale of 106 km2, the sum of absolute errors was reduced by 34.5%–50.9% relative to the prior flux errors. Moreover, TRACE produced posterior uncertainty estimates that were consistent with the true errors. These initial experiments show that the ESSPE approach in TRACE provides a promising method for advancing CO2 inversion techniques. Plain Language Summary: To gain a better understanding of the main drivers behind atmospheric carbon dioxide (CO2) variations and trends, it is essential to accurately quantify CO2 exchanges—also known as fluxes—between the atmosphere and other components of the Earth system. It is generally not possible to directly measure surface CO2 fluxes at regional scales; however, fluxes can be inferred from a network of atmospheric CO2 concentration observations combined with atmospheric modeling and inversion methods, which seek to find the most likely fluxes given prior knowledge and observational evidence. This paper presents a new regional inversion framework called TRACE Regional Atmosphere–Carbon Ensemble (TRACE) for deriving CO2 fluxes at high resolution in space and time. One of the major innovations in TRACE is the ensemble‐based dual state and parameter estimation approach, which makes it computationally feasible to ingest large volumes of observations into the system. A series of experiments were carried out in a domain over North America to test the new framework in a controlled setting. In the experiments, we used synthetic tower observations of atmospheric CO2 concentrations to constrain parameters controlling terrestrial biogenic and oceanic CO2 fluxes. The results show that TRACE is capable of accurately estimating both the magnitude and spatial pattern of regional CO2 fluxes. Key Points: An ensemble‐based simultaneous state and parameter estimation approach is introduced for regional CO2 flux inversionsThe new dual‐state TRACE Regional Atmosphere–Carbon Ensemble (TRACE) framework provides a flexible platform for conducting coupled atmosphere–carbon data assimilationControlled experiments show that TRACE is effective at constraining regional CO2 fluxes using in situ CO2 concentration observations [ABSTRACT FROM AUTHOR]

Stratospheric temperature adjustment (STA) is often a significant component of greenhouse gas radiative forcing (RF), for both the most widely used definition of RF and effective radiative forcing. It is well established that the magnitude and sign of STA differs among greenhouse gases, being negative (at the tropopause) for CO2 increases (because it induces a cooling of the stratosphere) and positive for many halocarbons (because they induce a warming of the stratosphere); this effect is strongly related to the opacity (and hence the effective emitting temperature) of the troposphere at the wavelengths at which gases absorb. Here the spectral variation of STA is examined for the first time by systematically imposing a weak absorber in each of 300 bands of a narrow‐band radiation code. For this weak absorber, STA is negative for wavelengths greater than about 13 μm and positive in the 8‐ to 13‐μm "window" except in the vicinity of the 9.6‐μm ozone band. By combining narrow‐band and line‐by‐line model results, these findings are used to improve a widely used fast method to estimate radiative efficiency (RE, the RF per unit change in concentration) directly from laboratory measurements or theoretical calculations of halocarbon absorption cross sections; the new method reproduces detailed RE calculations to better than 1.4%. This is a significant improvement over a cruder method used to account for STA in RE tabulations in the Intergovernmental Panel on Climate Change's Fifth Assessment Report, which were used to calculate metrics such as the Global Warming Potential, for halocarbons and related substances. Plain Language Summary: Increased emissions and concentrations of greenhouse gases, due to human activity, are a major driver of climate change. Carbon dioxide is the most important such gas, but many other gases are emitted which, collectively, contribute significantly to climate change. This paper focuses on methods to calculate the climate impact of halocarbons which are used in refrigeration, insulation, air conditioning, etc. This includes chlorofluorocarbons (CFCs), whose usage has been phased out because they deplete the ozone layer; hydrochlorofluorocarbons (HCFCs) which were regarded as transitional substances to replace the CFCs; and hydrofluorocarbons, which are widely used replacements for CFCs and HCFCs. It is important to quantify the climate impacts of halocarbons and to compare the relative impact of different halocarbons; this information is used in international agreements aimed at reducing greenhouse gas emissions. This paper presents an important refinement of a widely used simple method for calculating and comparing the climate impact of different halocarbons. This refinement considers the impact of these gases on temperatures in the stratosphere (i.e., altitudes of about 10–50 km); to achieve this, the paper first develops a general understanding of the way such gases influence stratospheric temperatures. The new technique is a significant improvement over previous methods. Key Points: Stratospheric temperature adjustment is an important component of the effective radiative forcing for many greenhouse gasesThe variation of the magnitude and sign of this adjustment with the wavelength at which greenhouse gases absorb is investigatedThe results are used to enhance a widely used simple method for estimating halocarbon radiative efficiency [ABSTRACT FROM AUTHOR]

Computational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical‐twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental‐scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high‐temporal‐resolution information from the North American tower network, to extract high‐resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high‐resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles. Plain Language Summary: This paper describes a new inverse framework for deriving surface carbon dioxide (CO2) fluxes at high spatial and temporal resolutions from atmospheric mole fraction measurements. We demonstrate the potential of the system over North America by inverting for subdaily biogenic CO2 fluxes while accounting for the complexity of spatio‐temporal structures in flux error covariances in a data‐based manner. Key Points: A new inversion system uses spectral methods for spatial correlations to do a 27 km subdaily inversion over North AmericaThe software package allows simultaneous optimization of multiple prior mean flux estimatesThe system is demonstrated over continental North America for a network of seven towers [ABSTRACT FROM AUTHOR]

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing. Plain Language Summary: The U.S. Department of Energy recently released version two of its Energy Exascale Earth System Model (E3SM). E3SMv2 experienced a significant evolution in many of its model components (most notably the atmosphere and sea ice models), and its supporting software infrastructure. In this work, we document the computational performance of E3SMv2 and analyze its ability to reproduce the observed climate. To accomplish this, we utilize the standard Diagnosis and Evaluation and Characterization of Klima experiments augmented with historical simulations for the period 1850–2015. We find that E3SMv2 is nearly twice as fast as its predecessor and more accurately reproduces the observed climate in a number of metrics, most notably clouds and precipitation. We also find that the model's simulated response to increasing carbon dioxide (the equilibrium climate sensitivity) is much more realistic. Unfortunately, E3SMv2 underestimates the global mean surface temperature compared to observations during the second half of historical period. Using sensitivity experiments, where forcing agents (carbon dioxide, aerosols) are selectively disabled in the model, we determine that correcting this problem would require a strong reduction in the impact of aerosols. Key Points: E3SMv2 is nearly twice as fast as E3SMv1 with a simulated climate that is improved in many metrics (e.g., precipitation and clouds)Climate sensitivity is substantially lower with a more plausible equilibrium climate sensitivity of 4.0 K (compared to an unlikely value of 5.3 K in E3SMv1)E3SMv2 underestimates the warming in the late historical period due to excessive aerosol‐related forcing [ABSTRACT FROM AUTHOR]

The ocean reduces human impacts on global climate by absorbing and sequestering CO2 from the atmosphere. To quantify global, time‐resolved air‐sea CO2 fluxes, surface ocean pCO2 is needed. A common approach for estimating full‐coverage pCO2 is to train a machine learning algorithm on sparse in situ pCO2 data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO2, it is often unclear how they drive pCO2 outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO2. First, we apply pre‐processing to the pCO2 data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO2 in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO2. The final pCO2 reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO2 and impacts on the estimated CO2 fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO2 fluxes from observation‐based products. Our reconstructed CO2 fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005. We estimate an air‐sea flux of −1.8 PgC/yr (anthropogenic flux of −2.3 ± 0.5 PgC/yr) for 1990–2019, agreeing with other data products and the Global Carbon Budget 2020 (−2.3 ± 0.4 PgC/yr). Plain Language Summary: The ocean absorbs carbon dioxide from the atmosphere, moderating the human impact on Earth's climate. To quantify how much carbon dioxide is removed from the atmosphere each year, we must know how much gas is exchanged at each location across the ocean over time. The observations necessary to quantify this gas exchange are very sparse and require gap‐filling in both space and time. Because of the heterogeneity of this gas exchange, complex relationships between the ocean observations with near global coverage and ocean carbon are determined using machine learning algorithms and other statistical techniques. A concern is that these statistical algorithms do not require inputs to be linked to outputs in a manner consistent with ocean carbon cycle process understanding. Here, we develop a novel machine learning approach that starts by removing known physical signals from the data to create a cleaner signal for the computer algorithm to learn. Additional analysis demonstrates appropriate mechanistic links between algorithm inputs and outputs. Key Points: A new approach for pCO2 reconstruction applies pre‐processing to remove the direct effect of temperature, simplifying the target variable for machine learningReconstructed pCO2 captures independent data more closely than most existing productsEstimated ocean carbon uptake has a trend since 2005 (−0.05 PgC/yr2) that is on the lower end of previous observation‐based estimates [ABSTRACT FROM AUTHOR]

Empirical studies have shown that plant photosynthetic responses to environmental change can vary over time due to acclimation, but acclimation responses are often not included in Earth System Models. Photosynthetic least cost theory can be used to develop models of photosynthetic acclimation that are simple and testable. The theory is based on the idea that plants will acclimate to minimize the ratio of carbon costs to photosynthetic assimilation rate (Prentice et al., 2014, https://doi.org/10.1111/ele.12211). Formulations of this theory have been developed for C3 plants, but not C4 plants, which account for over 20% of global photosynthesis and are over‐represented among widely grown crops. Here, we use photosynthetic least cost theory to derive a model for C4 photosynthetic acclimation to above‐ground abiotic conditions. We then compare our model's responses to a similar model of C3 photosynthetic acclimation and find that C4 photosynthesis has the highest simulated advantage over C3 photosynthesis in hot, dry, and low CO2 environments. We find that this advantage predicts C4 abundance globally, but that the shallower CO2 response of C4 as compared to C3 photosynthesis will reduce C4 plant competitiveness under future conditions, despite higher temperatures. We also show that an acclimated model predicts similar or faster rates of C4 under all conditions than a model that does not consider acclimation, suggesting that Earth System Models (ESMs) are underestimating future C4 carbon uptake by not including acclimation. Our model is designed for easy incorporation into such ESMs. Plain Language Summary: Plants change their rate of photosynthesis in response to their environment. Their photosynthetic rates can change minute to minute based on the quick changes in their environment, but they can also change over much longer timescales as the plants become accustomed to a new environmental condition. Long‐term (days to weeks) regulation of photosynthesis is termed acclimation. When we predict how plants will behave in the future, we must take acclimation into account so that we can more accurately predict the future carbon, water, and nutrient cycles. Previous studies have developed mathematical models of photosynthetic acclimation for some, but not all, plants. One understudied group of plants that lacked an acclimation model were the C4 species, a subtype of plants often found in deserts and other arid environments, but one that also includes important agricultural crops such as maize. In this study, we develop a theoretical model of photosynthetic acclimation for C4 species and show that the model yields expected results based on where C4 plants currently grow. Our model can improve the predictions of carbon, water, and nutrient cycling in larger Earth System Models. Key Points: A parameter‐sparse model of C4 photosynthetic acclimation to changes in environmental conditions based on photosynthetic least cost theoryIncluding acclimation will improve simulations of C4 carbon assimilation and water/nutrient‐use efficiencies under present/future conditionsIn simulated competition experiments with a similar C3 model, C4 photosynthesis becomes less advantageous under increased carbon dioxide [ABSTRACT FROM AUTHOR]

Climate change will affect both the mean state and seasonality of marine physical and biogeochemical properties, with important implications for the oceanic sink of atmospheric CO2. Here, we investigate the seasonal cycle of the air‐sea exchange of CO2 and pCO2,sw (surface seawater pCO2) and their long term changes using the CMIP6 submission of the NASA‐GISS modelE (GISS‐E2.1‐G). In comparison to the CMIP5 submission (GISS‐E2‐R), we find that on the global scale, the seasonal cycles of the CO2 flux and NPP have improved, while the seasonal cycles of dissolved inorganic carbon (DIC), alkalinity, and macronutrients have deteriorated. Moreover, for all ocean biogeochemistry fields, changes in skill between E2.1‐G and E2‐R display large regional variability. For E2.1‐G, we find similar modeled and observed CO2 flux seasonal cycles in the subtropical gyres, where seasonal anomalies of pCO2,sw and the flux are temperature‐driven, and the Southern Ocean, where anomalies are DIC‐driven. Biases in these seasonal cycles are largest in the subpolar and equatorial regions, driven by a combination of biases in temperature, DIC, alkalinity, and wind speed. When comparing the historical simulation to a simulation with an idealized increase in atmospheric pCO2, we find that the seasonal amplitudes of the CO2 flux and pCO2,sw generally increase. These changes are produced by increases in the sensitivity of pCO2,sw to its respective drivers. These findings are consistent with the notion that the seasonality of pCO2,sw is expected to increase due to the increase of atmospheric pCO2, with changes in the seasonality of temperature, DIC, and alkalinity having secondary influences. Plain Language Summary: The ocean plays an important role in removing human CO2 emissions from the atmosphere. The removal varies seasonally, and this variability is expected to change as the ocean's carbon content increases. To predict these changes, models need to accurately simulate seasonal changes of the air‐sea exchange of CO2. In this study, we examine the seasonal cycle of the air‐sea exchange of CO2 in the CMIP6 version of the NASA‐GISS modelE (GISS‐E2.1‐G). We find good agreement between the seasonal cycles in the model and observations in the subtropical latitudes, where seasonal changes are mainly caused by temperature changes, and in the Southern Ocean, where seasonal changes are mainly caused by changes in dissolved inorganic carbon (DIC). Agreement is much poorer in high latitudes and tropical waters, where discrepancies in model wind speed, temperature, DIC, and alkalinity all contribute to differences between the modeled and observed seasonal cycle of air‐sea CO2 exchange. We further find, under future atmospheric CO2 increase, that seasonal extremes in the air‐sea exchange of CO2 increase in most of the ocean. Our findings support the idea that, under increased CO2 levels, the change in the ocean's ability to take CO2 from the atmosphere will be seasonally dependent. Key Points: The seasonality of the CO2 flux, pCO2, and their drivers in the NASA‐GISS model are evaluated using a suite of monthly climatologiesThe seasonal cycles of the CO2 flux and pCO2, sw are temperature‐driven in the subtropics and DIC‐driven in the Southern OceanIn an idealized future forcing scenario, changes in the flux are largely driven by changes in the sensitivity of pCO2 to its drivers [ABSTRACT FROM AUTHOR]

We examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly—2.1 K in CESM1 and 2.0 K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab‐ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully coupled Earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model. Plain Language Summary: Computer models of the Earth's climate system are complex. Our best guess scenarios for how the climate system will change due to human activity over the next century are also complex. They include estimates of changing greenhouse gas (e.g., CO2) levels in the atmosphere, aerosol (e.g., smog and haze) emissions, and land use changes (e.g., deforestation and urbanization). To help understand this complex system, the climate modeling community has designed two simplified experiments: abrupt CO2 quadrupling (4xCO2) and 1% annual CO2 increase (1%CO2). In these experiments all human‐induced factors in the climate system are held constant (at preindustrial levels) except for CO2 in the atmosphere. Results of these experiments from different climate models can be compared to gain insight into the climate system. We look at two versions of the Community Earth System Model (CESM1 and CESM2). The warming simulated in the 4xCO2 experiment (climate sensitivity) has increased substantially in CESM2. This is related to changes in clouds over the Southern Ocean and tropics. At the same time warming in in the 1%CO2 experiment has not increased. This is related to differences in how CESM1 and CESM2 simulate northern oceans (Arctic, North Atlantic, and North Pacific). Key Points: Climate sensitivity has increased from 4 K to over 5 K in CESM2 compared to CESM1Shortwave radiation feedbacks over the Southern Ocean play a key role in determining the response of CESM to increasing CO2Various measures of climate response, including equilibrium climate sensitivity (ECS) and transient climate response (TCR), are not simply related in CESM [ABSTRACT FROM AUTHOR]

Ecosystem function (particularly CO2 fluxes and the subsequent atmospheric transport), synoptic‐scale weather (e.g., midlatitude cyclones), and interactions between ecosystems and the atmosphere can be investigated using a weather‐biosphere‐online‐coupled model. The Vegetation Photosynthesis and Respiration Model (VPRM) was coupled with the Weather Research and Forecasting (WRF) model in 2008 to simulate "weather‐aware" biospheric CO2 fluxes and subsequent transport and dispersion. The ability of the coupled WRF‐VPRM modeling system to simulate the CO2 structures within midlatitude cyclones, however, has not been evaluated due to the lack of data within these weather systems. In this study, VPRM parameters previously calibrated off‐line using eddy covariance tower data over North America are implemented into WRF‐VPRM. The updated WRF‐VPRM is then used to simulate spatiotemporal variations of CO2 over the contiguous United States at a horizontal grid spacing of 12 km for 2016 using an optimized downscaling configuration. The downscaled fields are evaluated using remotely sensed data from the Orbiting Carbon Observatory‐2, Total Carbon Column Observing Network, and in situ aircraft measurements from Atmospheric Carbon and Transport‐America missions. Evaluations show that WRF‐VPRM captures the monthly variation of column‐averaged CO2 concentrations (XCO2) and episodic variations associated with frontal passages. The downscaling also successfully captures the horizontal CO2 gradients across fronts and vertical CO2 contrast between the boundary layer and the free troposphere. WRF‐VPRM modeling results indicate that from May to September, biogenic fluxes dominate variability in XCO2 over most of the contiguous United States, except over a few metropolitan areas such as Los Angeles. Plain Language Summary: Global warming due to increase in greenhouse gases, particularly CO2, is well known as a critical issue facing humanity. CO2 concentration increased quickly over the past two centuries, with the overall trend largely due to fossil fuel emissions. Year‐to‐year variations in CO2 growth rate are not well understood, which is partially due to uncertainty in terrestrial CO2 fluxes. Accurate estimation of terrestrial CO2 fluxes is limited by land cover and land use changes, drought, and weather influences. These factors/processes and their impact on CO2 fluxes and atmospheric mole fractions can be investigated using a weather‐biosphere‐online‐coupled model. The Vegetation Photosynthesis and Respiration Model (VPRM) coupled with the Weather Research and Forecasting (WRF) model (referred to as WRF‐VPRM) is one such tool. In this study, optimal VPRM parameters are implemented into WRF‐VPRM. The updated WRF‐VPRM is then used to simulate CO2 mole fractions over the United States for 2016. The simulation is evaluated using aircraft measurements and remote sensing data. Evaluations show that WRF‐VPRM captures the temporal variation of CO2 concentrations, as well as the horizontal CO2 gradients across fronts and vertical CO2 contrast in the low troposphere. Simulations using this modeling system can be used to help understand regional to global CO2 budgets. Key Points: The simulation is evaluated using CONUS‐scale, long‐term data, and in situ measurements that resolve the structures of midlatitude cyclonesWRF‐VPRM is shown to reproduce the structures in CO2 mole fractions observed within midlatitude cyclonesFrom May to September, biogenic fluxes dominate variability in XCO2 over most of the CONUS except around a few metropolitan areas such as LA [ABSTRACT FROM AUTHOR]

Effects of wave‐driven Langmuir turbulence on the air‐sea flux of carbon dioxide (CO2) are examined using large eddy simulations featuring actively reacting carbonate chemistry in the ocean mixed layer at small scales. Four strengths of Langmuir turbulence are examined with three types of carbonate chemistry: time‐dependent chemistry, instantaneous equilibrium chemistry, and no reactions. The time‐dependent model is obtained by reducing a detailed eight‐species chemical mechanism using computational singular perturbation analysis, resulting in a quasi steady state approximation for hydrogen ion (H+); that is, fixed pH. The reduced mechanism is then integrated in two half‐time steps before and after the advection solve using a Runge‐Kutta‐Chebyshev scheme that is robust for stiff systems of differential equations. The simulations show that as the strength of Langmuir turbulence increases, CO2 fluxes are enhanced by rapid overturning of the near‐surface layer, which rivals the removal rate of CO2 by time‐dependent reactions. Equilibrium chemistry and nonreactive models are found to bring more and less carbon, respectively, into the ocean as compared to the more realistic time‐dependent model. These results have implications for Earth system models that either neglect Langmuir turbulence or use equilibrium, instead of time‐dependent, chemical mechanisms. Key Points: Detailed carbonate chemistry is solved in large eddy simulations of upper ocean turbulenceLangmuir turbulence increases the air‐sea flux of CO2, resulting in increased dissolved inorganic carbonEquilibrium chemistry leads to overpredicted fluxes of CO2 into the upper ocean [ABSTRACT FROM AUTHOR]

Changes in the Madden-Julian Oscillation (MJO) with increasing CO2 concentrations are examined using the Goddard Institute for Space Studies Global Climate Model (GCM). Four simulationsperformed with fixed CO2 concentrations of 0.5, 1, 2, and 4 times preindustrial levels using the GCM coupledwith a mixed layer ocean model are analyzed in terms of the basic state, rainfall, moisture and zonal windvariability, and the structure and propagation of the MJO. The GCM simulates basic state changes associatedwith increasing CO2 that are consistent with results from earlier studies: column water vapor increases at~7.1% K-1, precipitation also increases but at a lower rate (~3% K-1), and column relative humidity showslittle change. Moisture and rainfall variability intensify with warming while zonal wind variability shows littlechange. Total moisture and rainfall variability increases at a rate this is similar to that of the mean statechange. The intensification is faster in the MJO-related anomalies than in the total anomalies, though theratio of the MJO band variability to its westward counterpart increases at a much slower rate. On the basisof linear regression analysis and space-time spectral analysis, it is found that the MJO exhibits faster eastwardpropagation, faster westward energy dispersion, a larger zonal scale, and deeper vertical structure inwarmer climates. [ABSTRACT FROM AUTHOR]

A near-global aquaplanet cloud-resolving model (NGAqua) with fixed meridionally varying seasurface temperature (SST) is used to investigate cloud feedbacks due to three climate perturbations: a uniform 4 K SST increase, a quadrupled-CO2 concentration, and both combined. NGAqua has a horizontal resolution of 4 km with no cumulus parameterization. Its domain is a zonally periodic 20,480 km-long tropical channel, spanning 46°S-N. It produces plausible mean distributions of clouds, rainfall, and winds. After spinup, 80 days are analyzed for the control and increased-SST simulations, and 40 days for those with quadrupled CO2. The Intertropical Convergence Zone width and tropical cloud cover are not strongly affected by SST warming or CO2 increase, except for the expected upward shift in high clouds with warming, but both perturbations weaken the Hadley circulation. Increased SST induces a statistically significant increase in subtropical low cloud fraction and in-cloud liquid water content but decreases midlatitude cloud, yielding slightly positive domain-mean shortwave cloud feedbacks. CO2 quadrupling causes a slight shallowing and a statistically insignificant reduction of subtropical low cloud fraction. Warming-induced low cloud changes are strongly correlated with changes in estimated inversion strength, which increases modestly in the subtropics but decreases in the midlatitudes. Enhanced clear-sky boundary layer radiative cooling in the warmer climate accompanies the robust subtropical low cloud increase. The probability distribution of column relative humidity across the tropics and subtropics is compared between the control and increased- SST simulations. It shows no evidence of bimodality or increased convective aggregation in a warmer climate. [ABSTRACT FROM AUTHOR]

The response of global tropical cyclone (TC) frequency to solar and carbon dioxide radiative forcing is examined in TC-permitting aquaplanet general circulation model simulations. With an energetically consistent slab ocean lower boundary condition, the simulations show a larger response to positive radiative forcing from increased carbon dioxide than a solar constant increase with a comparable global-mean radiative forcing. Prescribed sea surface temperature (SST) simulations reveal that both the direct response to radiative forcing (radiative forcing with unchanged SST) and the patterned-SST response vary between forcing agents. The forcing-agent dependence of the patterned-SST response of TC frequency can be accounted for by the variation in simulated intertropical convergence zone shifts. The forcing-agent dependence of the direct response of TC frequency to radiative forcing can be accounted for by the variation in direct circulation changes and in normalized moist static energy deficit changes. That the direct TC response differs across forcing agents suggests that solar radiation manipulation geoengineering schemes will not return TC frequency to that of an unperturbed climate. [ABSTRACT FROM AUTHOR]

Phase 1 of the CGILS large-eddy simulation (LES) intercomparison is extended to understand if subtropical marine boundary-layer clouds respond to idealized climate perturbations consistently in six LES models. Here the responses to quadrupled carbon dioxide ( 'fast adjustment ') and to a composite climate perturbation representative of CMIP3 multimodel mean 2×CO2 near-equilibrium conditions are analyzed. As in Phase 1, the LES is run to equilibrium using specified steady summertime forcings representative of three locations in the Northeast Pacific Ocean in shallow well-mixed stratocumulus, decoupled stratocumulus, and shallow cumulus cloud regimes. The results are generally consistent with a single-LES study of Bretherton et al. () on which this intercomparison was based. Both quadrupled CO2 and the composite climate perturbation result in less cloud and a shallower boundary layer for all models in well-mixed stratocumulus and for all but a single LES in decoupled stratocumulus and shallow cumulus, corroborating similar findings from global climate models (GCMs). For both perturbations, the amount of cloud reduction varies across the models, but there is less intermodel scatter than in GCMs. The cloud radiative effect changes are much larger in the stratocumulus-capped regimes than in the shallow cumulus regime, for which precipitation buffering may damp the cloud response. In the decoupled stratocumulus and cumulus regimes, both the CO2 increase and CMIP3 perturbations reduce boundary-layer decoupling, due to the shallowing of inversion height. [ABSTRACT FROM AUTHOR]

The Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) is a dynamic vegetation model that incorporates photosynthesis and respiration submodules among many other physiological processes of the terrestrial biosphere. While the photosynthesis and leaf respiration submodules of CTEM operate at a time step of 30 min, other respiratory fluxes are estimated at a daily time step using the daily-averaged values of canopy and soil temperature, and soil moisture content. Here we modify CTEM to be able to simulate the diurnal variation of ecosystem respiratory fluxes caused by the diurnal variation in the driving climate data. Simulating respiration at a 30 min time step changed the equilibrium states of primary carbon pools and fluxes. This required changes to some of the model's parameters in order to realistically simulate the equilibrium values of carbon pools and fluxes. The resulting estimated daily and annual carbon fluxes from the modified and original CTEM are similar with small relative differences. The similar annual cycles of daily values of primary CO2 fluxes confirm that modeling the respiratory fluxes at a subdaily time step does not affect the annual cycles of these fluxes. We also demonstrate that the modified version of the model is able to simulate the diurnal cycle of net ecosystem exchange of CO2 fluxes that are broadly comparable to observation-based estimates at two flux tower sites and exhibit realistic seasonal patterns. Limitations remain in the modeled diurnal patterns of primary land-atmosphere CO2 fluxes which will form the basis of further model improvements. [ABSTRACT FROM AUTHOR]

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions. [ABSTRACT FROM AUTHOR]

Earth System Models increasingly include ocean biogeochemistry models in order to predict changes in ocean carbon storage, hypoxia, and biological productivity under climate change. However, state-of-the-art ocean biogeochemical models include many advected tracers, that significantly increase the computational resources required, forcing a trade-off with spatial resolution. Here, we compare a state-of-the art model with 30 prognostic tracers (TOPAZ) with two reduced-tracer models, one with 6 tracers (BLING), and the other with 3 tracers (miniBLING). The reduced-tracer models employ parameterized, implicit biological functions, which nonetheless capture many of the most important processes resolved by TOPAZ. All three are embedded in the same coupled climate model. Despite the large difference in tracer number, the absence of tracers for living organic matter is shown to have a minimal impact on the transport of nutrient elements, and the three models produce similar mean annual preindustrial distributions of macronutrients, oxygen, and carbon. Significant differences do exist among the models, in particular the seasonal cycle of biomass and export production, but it does not appear that these are necessary consequences of the reduced tracer number. With increasing CO2, changes in dissolved oxygen and anthropogenic carbon uptake are very similar across the different models. Thus, while the reduced-tracer models do not explicitly resolve the diversity and internal dynamics of marine ecosystems, we demonstrate that such models are applicable to a broad suite of major biogeochemical concerns, including anthropogenic change. These results are very promising for the further development and application of reduced-tracer biogeochemical models that incorporate 'sub-ecosystem-scale' parameterizations. [ABSTRACT FROM AUTHOR]

We here present, document and validate a new atmospheric component of the Community Earth System Model (CESM1): the Specified Chemistry Whole Atmosphere Community Climate Model (SC-WACCM). As the name implies, SC-WACCM is a middle atmosphere-resolving model with prescribed, rather than interactive chemistry. Ozone concentrations are specified throughout the atmosphere, using zonal and monthly mean climatologies computed by a companion integration with the Whole Atmosphere Community Climate Model (WACCM). Above 65 km, in addition, the climatological chemical and shortwave heating, nitrogen oxide, atomic and molecular oxygen and carbon dioxide are also prescribed from the companion WACCM integration. We carefully compare the climatology and the climate variability of preindustrial integrations of SC-WACCM and WACCM each coupled with active land, ocean and sea-ice components. We note some differences in upper stratospheric and lower mesospheric temperature, just below the 65 km transition level, due to the diurnal ozone cycle that is not captured when monthly mean ozone is used. Nonetheless, we find that the climatology and variability of the stratosphere, the troposphere and surface climate are nearly identical in SC-WACCM and WACCM. Notably, the frequency and amplitude of Northern Hemisphere stratospheric sudden warmings in the two integrations are not significantly different. Also, we compare WACCM and SC-WACCM to CCSM4, the 'low-top' version of CESM1, and we find very significant differences in the stratospheric climatology and variability. The removal of the chemistry reduces the computational cost of SC-WACCM to approximately one half of WACCM: in fact, SC-WACCM is only 2.5 times more expensive than CCSM4 at the same horizontal resolution. This considerable reduction in computational cost makes the new SC-WACCM component of CESM1 ideally suited for studies of stratosphere-troposphere dynamical coupling and, more generally, the role of the stratosphere in the climate system. [ABSTRACT FROM AUTHOR]