Best, Martin, Adrian P Lock, Gianpaolo Balsamo, Eric Bazile, Isabelle Beau, Joan Cuxart, Michael Ek, Kirsten L Findell, Ann M Fridlind, Albert A M Holtslag, Wenyan Huang, Maria A Jiménez, Sanjiv Kumar, David Lawrence, and Sergey Malyshev, et al., March 2025: Rolling DICE to advance knowledge of land–atmosphere interactions. Quarterly Journal of the Royal Meteorological Society, DOI:10.1002/qj.4944. Abstract
The Diurnal Land–Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land-surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land–atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a 3-day clear-sky period in the mid-west United States over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within land-surface models needs attention; (2) the height of atmospheric forcing for land-surface models is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land–atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity, and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture, and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary-layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the Global Land–Atmosphere Coupling Experiment.
Catchment-scale representation of the groundwater and its interaction with other parts of the hydrologic cycle is crucial for accurately depicting the land water–energy balance in Earth system models (ESMs). Despite existing efforts to describe the groundwater in the land component of ESMs, most ESMs still need a prognostic framework for describing catchment-scale groundwater based on its emergent properties to understand the implications for the broader Earth system. To fill this gap, we developed a new parameterization scheme to resolve the groundwater and its two-way interactions with the unsaturated soil and stream at the catchment scale. We implemented this new parameterization scheme (SHARC, or the soil–hillslope aquifer–river continuum) in the Geophysical Fluid Dynamics Laboratory (GFDL) land model (i.e., LM4-SHARC) and evaluated its performance. By bridging the gap between hydraulic groundwater theory and ESM land hydrology, the new LM4-SHARC provides a path to learning groundwater emergent properties from available streamflow data (i.e., recession analysis), enhancing the representation of subgrid variability in water–energy states induced by the groundwater. LM4-SHARC has been applied to the Providence headwater catchment at Southern Sierra, NV, and tested against in situ observations. We found that LM4-SHARC leads to noticeable improvements in the representation of key hydrologic variables such as streamflow, near-surface soil moisture, and soil temperature. In addition to enhancing the representation of the water and energy balance, our analysis showed that accounting for groundwater convergence can induce a more significant hydrologic contrast, with higher sensitivity of soil water storage to groundwater properties in the riparian zone. Our findings indicate the feasibility of incorporating two-way interactions among groundwater, unsaturated soil, and streams into the hydrological components of ESMs and show a further need to explore the implications of these interactions in the context of Earth system dynamics.
The deposition of light-absorbing particles (LAPs) leads to a decrease in surface albedo over snow-covered surfaces. This effect, by increasing the energy absorbed by the snowpack, enhances snowmelt and accelerates snow aging, process that in turn are responsible for further decreasing the snow albedo. Capturing this combined process is important in land surface modeling, as the change in surface reflectivity connected with the deposition of LAPs can modulate the time and magnitude of snowmelt and runoff. These processes impact regional water resources and can also lead to relevant feedbacks to the global climate system. We have recently developed a new numerical snowpack model for the Geophysical Fluid Mechanics Laboratory (GFDL) land model (a Global Land Snow Scheme, or GLASS). GLASS provides a detailed description of snow mass and energy balance, as well as the evolution of snow microphysical properties (grain shape and size). We now extend this model to account for the presence of light-absorbing impurities, modeling their dry and wet deposition in the snowpack, the evolution of their vertical distribution in the snow due to precipitation and snowmelt, and the effect of their concentration on snow optical properties. To test the effects of the resulting snow scheme, we force the GFDL land model with deposition of black carbon, mineral dust, and organic carbon obtained from a general circulation model (GFDL AM4.0). We evaluate the new model configuration at a set of instrumented sites, including an alpine site (Col de Porte, France) where in situ observations of snow (including spectral measurements of snow reflectivity and concentration of LAPs) allow for a comprehensive model evaluation. For the Col de Porte site, we show that GLASS reproduces the observed magnitudes of impurity concentrations in the snowpack throughout a winter season. The seasonal evolution of the snow optical diameter is also qualitatively reproduced by the model, although the increase in snow grain diameter during the melt season appears to be underestimated. For a set of instrumented sites spanning a range of climates and LAP deposition rates (the SnowMIP sites), we then evaluate the number of snow days lost due to the deposition of dust and carbonaceous aerosols. We find that this loss ranges between 5 and 24 d depending on the site. The resulting snow model with LAP-aware snow reflectivity shows good agreement with measurements of broadband albedo and seasonal snow water equivalent (SWE) over the study sites.
Abramowitz, Gab, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, and Sergey Malyshev, et al., December 2024: On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results. Biogeosciences, 21(23), DOI:10.5194/bg-21-5517-20245517-5528. Abstract
Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites.
Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models.
In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.
Future socioeconomic climate pathways have regional water-quality consequences whose severity and equity have not yet been fully understood across geographic and economic spectra. We use a process-based, terrestrial-freshwater ecosystem model to project 21st-century river nitrogen loads under these pathways. We find that fertilizer usage is the primary determinant of future river nitrogen loads, changing precipitation and warming have limited impacts, and CO2 fertilization-induced vegetation growth enhancement leads to modest load reductions. Fertilizer applications to produce bioenergy in climate mitigation scenarios cause larger load increases than in the highest emission scenario. Loads generally increase in low-income regions, yet remain stable or decrease in high-income regions where agricultural advances, low food and feed production and waste, and/or well-enforced air pollution policies balance biofuel-associated fertilizer burdens. Consideration of biofuel production options with low fertilizer demand and rapid transfer of agricultural advances from high- to low-income regions may help avoid inequitable water-quality outcomes from climate mitigation.
Lichstein, Jeremy W., Tao Zhang, Ensheng Weng, Caroline E Farrior, Ray Dybzinski, Sergey Malyshev, Elena Shevliakova, Richard A Birdsey, and Stephen W Pacala, November 2024: Effects of water limitation and competition on tree carbon allocation in an Earth system modelling framework. Journal of Ecology, 112(11), DOI:10.1111/1365-2745.14416. Abstract
1. Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality-based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality-based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture.
2. We used two models, LM3-PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3-PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two-dimensional trait space under different precipitation and mortality scenarios.
3. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought-tolerance).
4. Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress.
5. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3-PPA vs. BiomeE) and uncalibrated vsersus calibrated BiomeE versions.
6. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality-based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.
We present a variable-resolution global chemistry-climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban-to-rural chemical regimes, mesoscale convective systems, and land-surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi-decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25-km uniform-resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal-to-diurnal cycles and extremes, notably reducing the central US dry-and-warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process-level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land-biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.
We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon-chemistry-climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi-layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present-day biomass and carbon uptake in comparison to observations.
Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.
Land–atmosphere (L–A) interactions encompass the co-evolution of the land surface and overlying planetary boundary layer, primarily during daylight hours. However, many studies have been conducted using monthly or entire-day mean time series due to the lack of subdaily data. It is unclear whether the inclusion of nighttime data alters the assessment of L–A coupling or obscures L–A interactive processes. To address this question, we generate monthly (M), entire-day mean (E), and daytime-only mean (D) data based on the ERA5 (5th European Centre for Medium-Range Weather Forecasts reanalysis) product and evaluate the strength of L–A coupling through two-legged metrics, which partition the impact of the land states on surface fluxes (the land leg) from the impact of surface fluxes on the atmospheric states (the atmospheric leg). Here we show that the spatial patterns of strong L–A coupling regions among the M-, D-, and E-based diagnoses can differ by more than 80 %. The signal loss from E- to M-based diagnoses is determined by the memory of local L–A states. The differences between E- and D-based diagnoses can be driven by physical mechanisms or averaging algorithms. To improve understanding of L–A interactions, we call attention to the urgent need for more high-frequency data from both simulations and observations for relevant diagnoses. Regarding model outputs, two approaches are proposed to resolve the storage dilemma for high-frequency data: (1) integration of L–A metrics within Earth system models, and (2) producing alternative daily datasets based on different averaging algorithms.
Parameterizing incident solar radiation over complex topography regions in Earth system models (ESMs) remains a challenging task. In ESMs, downward solar radiative fluxes at the surface are typically computed using plane-parallel radiative transfer schemes, which do not explicitly account for the effects of a three-dimensional topography, such as shading and reflections. To improve the representation of these processes, we introduce and test a parameterization of radiation–topography interactions tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) ESM land model. The approach presented here builds on an existing correction scheme for direct, diffuse, and reflected solar irradiance terms over three-dimensional terrain. Here we combine this correction with a novel hierarchical multivariate clustering algorithm that explicitly describes the spatially varying downward irradiance over mountainous terrain. Based on a high-resolution digital elevation model, this combined method first defines a set of sub-grid land units (“tiles”) by clustering together sites characterized by similar terrain–radiation interactions (e.g., areas with similar slope orientation, terrain, and sky view factors). Then, based on terrain parameters characteristic for each tile, correction terms are computed to account for the effects of local 3D topography on shortwave radiation over each land unit. We develop and test this procedure based on a set of Monte Carlo ray-tracing simulations approximating the true radiative transfer process over three-dimensional topography. Domains located in three distinct geographic regions (Alps, Andes, and Himalaya) are included in this study to allow for independent testing of the methodology over surfaces with differing topographic features. We find that accounting for the sub-grid spatial variability of solar irradiance originating from interactions with complex topography is important as these effects led to significant local differences with respect to the plane-parallel case, as well as with respect to grid-cell-scale average topographic corrections. We further quantify the importance of the topographic correction for a varying number of terrain clusters and for different radiation terms (direct, diffuse, and reflected radiative fluxes) in order to inform the application of this methodology in different ESMs with varying sub-grid tile structure. We find that even a limited number of sub-grid units such as 10 can lead to recovering more than 60 % of the spatial variability of solar irradiance over a mountainous area.
Light-absorbing impurities (LAIs) deposited on snow surfaces can accelerate melt by increasing solar radiation absorption through snow darkening and grain metamorphism. To improve the predictive capability of the global impact of LAIs on the surface energy balance, we have developed a simple snow parameterization - Snow LAI Redistribution (SLAIR) to estimate the snow albedo based on the concentration of LAIs and grain size. The parameterization can be run as a standalone model constrained by temperature, snowfall, ablation, and aerosol deposition or be implemented into large scale models. The concentration of LAIs at the snow surface depends on aerosol deposition and vertical redistribution during melt. To represent the uncertainties associated with different melting regimes, two approaches were considered, one assuming all the meltwater is contributed from the top of the snowpack (“surface melt mode”) and one assuming each snow layer contributes the same fraction of the mass of the total melt (“uniform melt mode”). The parameterization is evaluated as a standalone model against publicly available data at
the French Alps using observational inputs. The parameterization captured the temporal variations in grain size but not the detailed variabilities. For concentration of LAIs and visible albedo, both melting modes agree reasonably well with observations during the accumulation phase but only the surface melt model reproduced observations with good agreement. Overall, the simple snow parameterization can estimate the near-surface
concentration of LAIs, grain size and visible albedo within a reasonable range. Further developments are required to minimize uncertainties, especially for relatively warm and humid regions.
Martínez Cano, Isabel, Elena Shevliakova, Sergey Malyshev, Jasmin G John, Zoe S Aarons, Yan Yu, Benjamin Smith, and Stephen W Pacala, December 2022: Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios. Proceedings of the National Academy of Sciences, 119(52), DOI:10.1073/pnas.2203200119. Abstract
Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree–grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.
Climate models of varying complexity have been used for decades to investigate the impact of mountains on the atmosphere and surface climate. Here, the impact of removing the continental topography on the present-day ocean climate is investigated using three different climate models spanning multiple generations. An idealized study is performed where all present-day land surface topography is removed and the equilibrium change in the oceanic mean state with and without the mountains is studied. When the mountains are removed, changes found in all three models include a weakening of the Atlantic meridional overturning circulation and associated SST cooling in the subpolar North Atlantic. The SSTs also warm in all the models in the western North Pacific Ocean associated with a northward shift of the atmospheric jet and the Kuroshio. In the ocean interior, the magnitude of the temperature and salinity response to removing the mountains is relatively small and the sign and magnitude of the changes generally vary among the models. These different interior ocean responses are likely related to differences in the mean state of the control integrations due to differences in resolution and associated subgrid-scale mixing parameterizations. Compared to the results from 4xCO2 simulations, the interior ocean temperature changes caused by mountain removal are relatively small; however, the oceanic circulation response and Northern Hemisphere near-surface temperature changes are of a similar magnitude to the response to such radiative forcing changes.
The local climatic impacts of historical expansion of irrigation are substantial, but the distant impacts are poorly understood, and their governing mechanisms generally have not been rigorously analyzed. Our experiments with an earth-system model suggest that irrigation in the Middle East and South Asia may enhance rainfall in a large portion of the Sahel-Sudan Savanna (SSS) to an extent comparable and opposite to its suppression by other anthropogenic climate drivers during the last several decades. The enhancement arises through a reduction in the meridional gradient of moist static energy from the Sahara Desert to the tropical rainforests. An implication of this study is that remote irrigation is a possible factor affecting the risk of drought and famine and, thus, future water security in the SSS region.
Kou-Giesbrecht, Sian, Sergey Malyshev, Isabel Martínez Cano, Stephen W Pacala, and Elena Shevliakova, et al., July 2021: 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). Biogeosciences, 18(13), DOI:10.5194/bg-18-4143-20214143-4183. 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.
Enhanced riverine delivery of terrestrial nitrogen (N) has polluted many freshwater and coastal ecosystems, degrading drinking water and marine resources. An emerging view suggests a contribution of land N memory effects—impacts of antecedent dry conditions on land N accumulation that disproportionately increase subsequent river N loads. To date, however, such effects have only been explored for several relatively small rivers covering a few episodes. Here we introduce an index for quantifying land N memory effects and assess their prevalence using regional observations and global terrestrial-freshwater ecosystem model outputs. Model analyses imply that land N memory effects are globally prevalent but vary widely in strength. Strong effects reflect large soil dissolved inorganic N (DIN) surpluses by the end of dry years. During the subsequent wetter years, the surpluses are augmented by soil net mineralization pulses, which outpace plant uptake and soil denitrification, resulting in disproportionately increased soil leaching and eventual river loads. These mechanisms are most prominent in areas with high hydroclimate variability, warm climates, and ecosystem disturbances. In 48 of the 118 basins analyzed, strong memory effects produce 43% (21%–88%) higher DIN loads following drought years than following average years. Such a marked influence supports close consideration of prevalent land N memory effects in water-pollution management efforts.
Liao, Weilin, Dan Li, Sergey Malyshev, Elena Shevliakova, Honghui Zhang, and Xiaoping Liu, March 2021: Amplified increases of compound hot extremes over urban land in China. Geophysical Research Letters, 48(6), DOI:10.1029/2020GL091252. Abstract
Consecutive hot periods without nighttime heat relief significantly increase mortality and morbidity rates. Using the Geophysical Fluid Dynamics Laboratory land model coupled with a newly developed and validated urban canopy model, changes in three types of summertime hot extremes, that is, independent hot days, independent hot nights, and compound hot events (CHEs), in the 21st century are quantified in China. Results indicate that all three types of hot extremes become more frequent, and CHEs are projected to be the dominant type at the end of this century under the representative concentration pathway 8.5 scenario. Furthermore, compared to the rural land, the urban land experiences even stronger increases in CHEs in terms of frequency, duration, and intensity. These faster increases of CHEs in urban areas highlight the urgency of developing and implementing mitigation and adaptation strategies to combat the adverse health effects of global warming and urban heat islands.
Over the past century, human activities have resulted in substantial global changes that threaten the stability and functionality of coastal habitats. One of these impacts was through nutrient pollution of river runoffs, which have triggered harmful algal blooms and caused low-oxygen conditions in many coastal regions. However, it is challenging for models to simulate coastal impacts of increasing river nutrient loads, especially on a global scale and over a long period of time. Here we take advantage of some recent modeling advances to provide a global perspective on coastal ecosystem responses to increasing river nitrogen loads over the half-century between 1961 and 2010. Overall, we show that the global coastal ocean accumulated more nitrogen over time as river nitrogen loads increased. This caused the primary production of the global coastal system (i.e., the conversion of inorganic to organic materials through photosynthesis) to increase as well. However, we found that the sensitivity of each coastal ecosystem to comparable changes in nitrogen loads varied considerably. This variability was to a large extent related to two factors: the rate of exchange between coastal waters and the adjacent ocean waters, and whether nutrients are limited for phytoplankton to conduct photosynthesis in that system.
Hydrogen (H2) has been proposed as an alternative energy carrier to reduce the carbon footprint and associated radiative forcing of the current energy system. Here, we describe the representation of H2 in the GFDL-AM4.1 model including updated emission inventories and improved representation of H2 soil removal, the dominant sink of H2. The model best captures the overall distribution of surface H2, including regional contrasts between climate zones, when vd(H2) is modulated by soil moisture, temperature, and soil carbon content. We estimate that the soil removal of H2 increases with warming (2–4% per K), with large uncertainties stemming from different regional response of soil moisture and soil carbon. We estimate that H2 causes an indirect radiative forcing of 0.84 mW m−2/(Tg(H2)yr−1) or 0.13 mW m−2 ppbv−1, primarily due to increasing CH4 lifetime and stratospheric water vapor production.
July 2019 saw record-breaking wildfires burning 3,600 km2 in Alaska. The GFDL Earth system model indicates a threefold increased risk of Alaska’s
extreme fires during recent decades due to primarily anthropogenic ignition and secondarily climate-induced biofuel abundance.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
We describe the baseline coupled model configuration and simulation characteristics of GFDL's Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system interactions. ESM4.1 features doubled horizontal resolution of both atmosphere (2° to 1°) and ocean (1° to 0.5°) relative to GFDL's previous‐generation coupled ESM2‐carbon and CM3‐chemistry models. ESM4.1 brings together key representational advances in CM4.0 dynamics and physics along with those in aerosols and their precursor emissions, land ecosystem vegetation and canopy competition, and multiday fire; ocean ecological and biogeochemical interactions, comprehensive land‐atmosphere‐ocean cycling of CO2, dust and iron, and interactive ocean‐atmosphere nitrogen cycling are described in detail across this volume of JAMES and presented here in terms of the overall coupling and resulting fidelity. ESM4.1 provides much improved fidelity in CO2 and chemistry over ESM2 and CM3, captures most of CM4.0's baseline simulations characteristics, and notably improves on CM4.0 in (1) Southern Ocean mode and intermediate water ventilation, (2) Southern Ocean aerosols, and (3) reduced spurious ocean heat uptake. ESM4.1 has reduced transient and equilibrium climate sensitivity compared to CM4.0. Fidelity concerns include (1) moderate degradation in sea surface temperature biases, (2) degradation in aerosols in some regions, and (3) strong centennial scale climate modulation by Southern Ocean convection.
We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 to ~100 km) with revised dynamics and physics from GFDL's previous‐generation AM3 atmospheric chemistry‐climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land‐atmosphere interactions, comprehensive land‐atmosphere‐ocean cycling of dust and iron, and interactive ocean‐atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0's baseline simulations characteristics, and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.
Ito, Akihiko, Tomohiro Hajima, David Lawrence, Victor Brovkin, Christine Delire, Bertrand Guenet, Christopher Jones, Sergey Malyshev, Stefano Materia, Sonali P McDermid, Daniele Peano, Julia Pongratz, Eddy Robertson, and Elena Shevliakova, et al., December 2020: Soil carbon sequestration simulated in CMIP6-LUMIP models: implications for climatic mitigation. Environmental Research Letters, 15, DOI:10.1088/1748-9326/abc912. Abstract
Land-use change affects both the quality and quantity of soil organic carbon (SOC) and leads to changes in ecosystem functions such as productivity and environmental regulation. Future changes in SOC are, however, highly uncertain owing to its heterogeneity and complexity. In this study, we analyzed the outputs of simulations of SOC stock by Earth system models (ESMs), most of which are participants in the Land-Use Model Intercomparison Project. Using a common protocol and the same forcing data, the ESMs simulated SOC distribution patterns and their changes during historical (1850–2014) and future (2015–2100) periods. Total SOC stock increased in many simulations over the historical period (30 ± 67 Pg C) and under future climate and land-use conditions (48 ± 32 Pg C for ssp126 and 49 ± 58 Pg C for ssp370). Land-use experiments indicated that changes in SOC attributable to land-use scenarios were modest at the global scale, in comparison with climatic and rising CO2 impacts, but they were notable in several regions. Future net soil carbon sequestration rates estimated by the ESMs were roughly 0.4‰ yr−1 (0.6 Pg C yr−1). Although there were considerable inter-model differences, the rates are still remarkable in terms of their potential for mitigation of global warming. The disparate results among ESMs imply that key parameters that control processes such as SOC residence time need to be better constrained and that more comprehensive representation of land management impacts on soils remain critical for understanding the long-term potential of soils to sequester carbon.
Reducing surface ozone to meet the European Union’s target for human health has proven challenging despite stringent controls on ozone precursor emissions over recent decades. The most extreme ozone pollution episodes are linked to heatwaves and droughts, which are increasing in frequency and intensity over Europe, with severe impacts on natural and human systems. Here, we use observations and Earth system model simulations for the period 1960–2018 to show that ecosystem–atmosphere interactions, especially reduced ozone removal by water-stressed vegetation, exacerbate ozone air pollution over Europe. These vegetation feedbacks worsen peak ozone episodes during European mega-droughts, such as the 2003 event, offsetting much of the air quality improvements gained from regional emissions controls. As the frequency of hot and dry summers is expected to increase over the coming decades, this climate penalty could be severe and therefore needs to be considered when designing clean air policy in the European Union.
Martínez Cano, Isabel, Elena Shevliakova, and Sergey Malyshev, et al., August 2020: Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA‐TV). Global Change Biology, 26(8), DOI:10.1111/gcb.15188. Abstract
Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA‐TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA‐TV drew on extensive datasets on tropical tree traits and long‐term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade‐tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand‐level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand‐level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographical variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA‐TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.
Dust emission is initiated when surface wind velocities exceed the threshold of wind erosion. Most dust models used constant threshold values globally. Here we use satellite products to characterize the frequency of dust events and surface properties. By matching this frequency derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products with surface winds, we are able to retrieve a climatological monthly global distribution of wind erosion threshold (Vthreshold) over dry and sparsely-vegetated surface. This monthly two-dimensional threshold velocity is then implemented into the Geophysical Fluid Dynamics Laboratory coupled land-atmosphere model (AM4.0/LM4.0). It is found that the climatology of dust optical depth (DOD) and total aerosol optical depth, surface PM10 dust concentrations, and seasonal cycle of DOD are better captured over the dust belt (i.e. North Africa and the Middle East) by simulations with the new wind erosion threshold than those using the default globally constant threshold. The most significant improvement is the frequency distribution of dust events, which is generally ignored in model evaluation. By using monthly rather than annual mean Vthreshold, all comparisons with observations are further improved. The monthly global threshold of wind erosion can be retrieved under different spatial resolutions to match the resolution of dust models and thus can help improve the simulations of dust climatology and seasonal cycle as well as dust forecasting.
Many studies have been conducted on the effects of dust on rainfall in the Sahel, and generally show that African dust weakens the West African Monsoon, drying the region. This drying is often assumed to produce a positive dust‐precipitation feedback by reducing vegetation cover for the region. We directly test this relationship for the first time by using a model that explicitly simulates vegetation growth and its impact on dust emission. There are several competing effects of dust that affect plant growth: changes to rainfall, downwelling solar radiation, surface temperature, and resultant changes in surface fluxes. Our model finds that the combined effect of these processes decreases vegetation cover and productivity of the Sahel and West Africa. We determine this by comparing experiments with radiatively active dust to experiments with radiatively invisible dust. In modern conditions, the dust radiative effect decreases leaf area by 12%, productivity by 14%, and increases bare soil area by 3% across the Sahel, and by much higher amounts locally. Experiments where the vegetation experiences preindustrial rather than modern CO2 levels show that without stomatal closure, the reductions would be approximately 20‐40% stronger. In preindustrial conditions the vegetation response is weaker, despite the dust‐induced rainfall and temperature anomalies being similar. We interpret this as the vegetation being less susceptible to drought in a less evaporative climate. These vegetation responses to dust are evidence of a dust‐vegetation feedback loop whose strength varies with the mean state of the climate, and which may grow stronger in the future.
We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
Nitrogen (N) pollution is shaped by multiple processes, the combined effects of which remain uncertain, particularly in the tropics. We use a global land biosphere model to analyze historical terrestrial-freshwater N budgets, considering the effects of anthropogenic N inputs, atmospheric CO2, land use, and climate. We estimate that globally, land currently sequesters 11 (10–13)% of annual N inputs. Some river basins, however, sequester >50% of their N inputs, buffering coastal waters against eutrophication and society against greenhouse gas-induced warming. Other basins, releasing >25% more than they receive, are mostly located in the tropics, where recent deforestation, agricultural intensification, and/or exports of land N storage can create large N pollution sources. The tropics produce 56 ± 6% of global land N pollution despite covering only 34% of global land area and receiving far lower amounts of fertilizers than the extratropics. Tropical land use should thus be thoroughly considered in managing global N pollution.
More than half of the world’s population now live in cities, which are known to be heat islands. While daytime urban heat islands (UHIs) are traditionally thought to be the consequence of less evaporative cooling in cities, recent work sparks new debate, showing that geographic variations of daytime UHI intensity were largely explained by variations in the efficiency with which urban and rural areas convect heat from the land surface to the lower atmosphere. Here, we reconcile this debate by demonstrating that the difference between the recent finding and the traditional paradigm can be explained by the difference in the attribution methods. Using a new attribution method, we find that spatial variations of daytime UHI intensity are more controlled by variations in the capacity of urban and rural areas to evaporate water, suggesting that strategies enhancing the evaporation capability such as green infrastructure are effective ways to mitigate urban heat.
The response of ozone (O3) dry deposition to ecosystem‐atmosphere interactions is poorly understood but is central to determining the potential for extreme pollution events under current and future climate conditions. Using observations and an interactive dry deposition scheme within two dynamic vegetation land models (GFDL LM3.0/LM4.0) driven by observation‐based meteorological forcings over 1948‐2014, we investigate the factors controlling seasonal and interannual variability (IAV) in O3 deposition velocities (Vd,O3). Stomatal activity in this scheme is determined mechanistically, depending on phenology, soil moisture, vapor pressure deficit, and CO2 concentration. Soil moisture plays a key role in modulating the observed and simulated Vd,O3 seasonal changes over evergreen forests in Mediterranean Europe, South Asia, and the Amazon. Analysis of multi‐year observations at forest sites in Europe and North America reveals drought stress to reduce Vd,O3 by ~50%. Both LM3.0 and LM4.0 capture the observed Vd,O3 decreases due to drought; however, IAV is weaker by a factor of two in LM3.0 coupled to atmospheric models, particularly in regions with large precipitation biases. IAV in summertime Vd,O3 to forests, driven primarily by the stomatal pathway, is largest (15‐35%) in semi‐arid regions of western Europe, eastern North America, and northeastern China. Monthly mean Vd,O3 for the highest year is two to four times that of the lowest, with significant implications for surface O3 variability and extreme events. Using Vd,O3 from LM4.0 in an atmospheric chemistry model improves the simulation of surface O3 abundance and spatial variability (reduces mean biases by ~10 ppb) relative to the widely‐used Wesely scheme.
Sulman, Benjamin N., Elena Shevliakova, E R Brzostek, S N Kivlin, and Sergey Malyshev, et al., April 2019: Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Global Biogeochemical Cycles, 33(4), DOI:10.1029/2018GB005973. Abstract
Accurate projections of the terrestrial carbon (C) sink are critical to understanding the future global C cycle and setting CO2 emission reduction goals. Current earth system models (ESMs) and dynamic global vegetation models (DGVMs) with coupled carbon‐nitrogen cycles project that future terrestrial C sequestration will be limited by nitrogen (N) availability, but the magnitude of N limitation remains a critical uncertainty. Plants use multiple symbiotic nutrient acquisition strategies to mitigate N limitation, but current DGVMs omit these mechanisms. Fully coupling N‐acquiring plant‐microbe symbioses to soil organic matter (SOM) cycling within a DGVM for the first time, we show that increases in N acquisition via SOM decomposition and atmospheric N2 fixation could support long‐term enhancement of terrestrial C sequestration at global scales under elevated CO2. The model reproduced elevated CO2 responses from two experiments (Duke and Oak Ridge) representing contrasting N acquisition strategies. N release from enhanced SOM decomposition supported vegetation growth at Duke, while inorganic N depletion limited growth at Oak Ridge. Global simulations reproduced spatial patterns of N‐acquiring symbioses from a novel niche‐based map of mycorrhizal fungi. Under a 100 ppm increase in CO2 concentrations, shifts in N acquisition pathways facilitated 200 Pg C of terrestrial C sequestration over 100 years compared to 50 Pg C for a scenario with static N acquisition pathways. Our results suggest that N acquisition strategies are important determinants of terrestrial C sequestration potential under elevated CO2, and that nitrogen‐enabled DGVMs that omit symbiotic N acquisition may underestimate future terrestrial C uptake.
The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth System models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and the computational constraints of macroscale models, until now these data have been underutilized for global applications. As a proof of concept, this study explores over a 1/4 degree (~ 25 km) grid cell in southeastern California how to effectively and efficiently harness these data in Earth System models. First, a novel hierarchical multivariate clustering approach (HMC) is used to summarize the high dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this data-driven approach to assemble the model tiles impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled by HMC. The results over the 1/4 degree macroscale grid cell and the underlying 30-meter fine-scale grid in southeastern California show that: 1) the observed similarity over the landscape makes it possible to robustly account for the role of multi-scale heterogeneity in the macroscale states and fluxes with around 300 sub-grid land model tiles; 2) assembling the sub-grid tiles from observed data, at times, leads to noticeable differences in the macroscale water, energy, and carbon cycles; for example, explicit subsurface interactions between the tiles leads to a dampening of macroscale extremes; 3) connecting the fine-scale grid to the model tiles via HMC enables circumventing the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth System models.
Mountain snowpack in the western United States provides a natural reservoir for cold season precipitation; variations in snowpack influence warm season water supply, wildfire risk, ecology, and industries like agriculture dependent on snow and downstream water availability. Efforts to understand snowpack variability have predominantly been focused on either weekly (weather) or decadal to centennial (climate variability and change) timescales. We focus on a timescale between these ranges by demonstrating that a global climate model suite can provide snowpack predictions 8 months in advance. The predictions from climate models outperform statistical methods from observations alone. Our results show that seasonal hydroclimate predictions are possible and highlight areas for future prediction system improvements.
Oceanic heat uptake (OHU) is a significant source of uncertainty in both the transient and equilibrium responses to increasing the planetary radiative forcing. OHU differs among climate models and is related in part to their representation of vertical and lateral mixing. This study examines the role of ocean model formulation – specifically the choice of vertical coordinate and strength of background diapycnal diffusivity (Kd) – in the millennial-scale near-equilibrium climate response to a quadrupling of atmospheric CO2. Using two fully-coupled Earth System Models (ESMs) with nearly identical atmosphere, land, sea ice, and biogeochemical components, it is possible to independently configure their ocean model components with different formulations and produce similar near-equilibrium climate responses. The SST responses are similar between the two models (r2 = 0.75, global average ∼ 4.3 °C) despite their initial pre-industrial climate mean states differing by 0.4 °C globally. The surface and interior responses of temperature and salinity are also similar between the two models. However, the Atlantic Meridional Overturning Circulation (AMOC) responses are different between the two models, and the associated differences in ventilation and deep water formation have an impact on the accumulation of dissolved inorganic carbon in the ocean interior. A parameter sensitivity analysis demonstrates that increasing the amount of Kd produces very different near-equilibrium climate responses within a given model. These results suggest that the impact of the ocean vertical coordinate on the climate response is small relative to the representation of sub-gridscale mixing.
Lee, Minjin, C Jung, Elena Shevliakova, and Sergey Malyshev, et al., October 2018: Control of Nitrogen Exports From River Basins to the Coastal Ocean: Evaluation of Basin Management Strategies for Reducing Coastal Hypoxia. Journal of Geophysical Research: Biogeosciences, 123(10), DOI:10.1029/2018JG004436. Abstract
The spread of coastal hypoxia is a pressing global problem, largely caused by substantial nitrogen (N) exports from river basins to the coastal ocean. Most previous process‐based modeling studies for investigating basin management strategies to reduce river N exports focused on the impacts of different farming practices or land use, used watershed models that simplified many mechanisms that critically affect the state of N storage in land, were limited mainly to fairly small basins, and did not span multiple climate regimes. Here we use a process‐based land‐river model to simulate historical (1999–2010) river flows and nitrate‐N exports throughout the entire drainage network of South Korea (100,210 km2), which encompasses varying climate, land use, and hydrogeological characteristics. Based on projections by using multiple scenarios of N input reductions and climates, we explore the impacts of various ecosystem factors (i.e., N storage in basins, climate and its variability, anthropogenic N inputs, and basin location) on river nitrate‐N exports. Our findings have fundamental implications for reducing coastal hypoxia: (1) a small reduction of N inputs in basins, including intensively utilized human land use, can have a greater improvement on water quality; (2) heightening climate variability may not increase long‐term mean river N exports yet can significantly mask N input reduction effects by producing N export extremes associated with recurring coastal hypoxia; and (3) N exports to the coastal ocean can be most efficiently reduced by decreasing N inputs in subbasins, which are receiving high anthropogenic N inputs and are close to the coast.
Paulot, Fabien, Sergey Malyshev, T B Nguyen, John D Crounse, Elena Shevliakova, and Larry W Horowitz, December 2018: Representing sub-grid scale variations in nitrogen deposition associated with land use in a global Earth System Model: implications for present and future nitrogen deposition fluxes over North America. Atmospheric Chemistry and Physics, 18(24), DOI:10.5194/acp-18-17963-2018. Abstract
Reactive nitrogen (N) emissions have increased over the last 150 years as a result of greater fossil fuel combustion and food production. The resulting increase in N deposition can alter the function of ecosystems, but characterizing its ecological impacts remains challenging, in part because of uncertainties in model-based estimates of N dry deposition. Here, we leverage the tiled structure of the land component (LM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model to represent the impact of physical, hydrological, and ecological heterogeneities on the surface removal of chemical tracers. We show that this framework can be used to estimate N deposition at more ecologically-relevant scales (e.g., natural vegetation, water bodies) than from the coarse-resolution global chemistry–climate model (GFDL-AM3). Focusing on North America, we show that the faster removal of N over forested ecosystems relative to cropland and pasture implies that coarse resolution estimates of N deposition from global models systematically underestimate N deposition to natural vegetation by 10 to 30% in the Central and Eastern US. Neglecting the subgrid scale heterogeneity of dry deposition velocities also results in an underestimate (overestimate) of the amount of reduced (oxidized) nitrogen deposited to water bodies. Overall, changes in land cover associated with human activities are found to slow down the removal of N from the atmosphere, causing a reduction in the dry oxidized, dry reduced, and total N deposition over the contiguous US of 8%, 26%, and 6%, respectively. We also find that the reduction in the overall rate of removal of N associated with land-use change tends to increase N deposition on the remaining natural vegetation and facilitate N export to Canada. We show that subgrid scale differences in the surface removal of oxidized and reduced nitrogen imply that near-term (2010–2050) changes in oxidized (−47%) and reduced (+40%) US N emissions will cause opposite changes in N deposition to water bodies (increase) and natural vegetation (decrease) in the Eastern US, with potential implications for acidification and ecosystems.
Rabin, S, Daniel S Ward, Sergey Malyshev, B I Magi, Elena Shevliakova, and Stephen W Pacala, March 2018: A fire model with distinct crop, pasture, and non-agricultural burning: Use of new data and a model-fitting algorithm for FINALv1. Geoscientific Model Development, 11(2), DOI:10.5194/gmd-11-815-2018. Abstract
This study describes and evaluates the Fire Including Natural & Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land-cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001–2009 (global totals: 0.434 × 106 and 2.02 × 106 km2 yr−1 modeled, 0.454 × 106 and 2.04 × 106 km2 yr−1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.297 PgC yr−1 and 0.712 PgC yr−1 modeled, 0.194 PgC yr−1 and 0.538 PgC yr−1 observed). The non-agricultural fire module underestimates global burned area (1.66 × 106 km2 yr−1 modeled, 2.44 × 106 km2 yr−1 observed) and carbon emissions (1.33 PgC yr−1 modeled, 1.84 PgC yr−1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, central Asia, and Australia, whereas the boreal zone suffers from underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets.
Globally, fires are a major source of carbon from the terrestrial biosphere to the atmosphere, occurring on a seasonal cycle and with substantial interannual variability. To understand past trends and variability in sources and sinks of terrestrial carbon, we need quantitative estimates of global fire distributions. Here we introduce an updated version of the Fire Including Natural and Agricultural Lands model, version 2 (FINAL.2), modified to include multi-day burning and enhanced fire spread rate in forest crowns. We demonstrate that the improved model reproduces the interannual variability and spatial distribution of fire emissions reported in present day remotely sensed inventories. We use FINAL.2 to simulate historical (post-1700) fires and attribute past fire trends and variability to individual drivers: land use and land cover change, population growth, and lightning variability. Global fire emissions of carbon increase by about 10% between 1700 and 1900, reaching a maximum of 3.4 PgC yr-1 in the 1910s, followed by a decrease to about 5% below year 1700 levels by 2010. The decrease in emissions from the 1910s to the present day is driven mainly by land use change, with a smaller contribution from increased fire suppression due to increased human population, and is largest in Sub-Saharan Africa and South Asia. Interannual variability of global fire emissions is similar in the present day as in the early historical period, but present day wildfires would be more variable in the absence of land use change.
In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.
In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.
The ozone (O3) dry depositional sink and its contribution to observed variability in tropospheric O3 are both poorly understood. Distinguishing O3 uptake through plant stomata versus other pathways is relevant for quantifying the O3 influence on carbon and water cycles. We use a decade of O3, carbon, and energy eddy covariance (EC) fluxes at Harvard Forest to investigate interannual variability (IAV) in O3 deposition velocities ( math formula). In each month, monthly mean math formula for the highest year is twice that for the lowest. Two independent stomatal conductance estimates, based on either water vapor EC or gross primary productivity, vary little from year to year relative to canopy conductance. We conclude that nonstomatal deposition controls the substantial observed IAV in summertime math formula during the 1990s over this deciduous forest. The absence of obvious relationships between meteorology and math formula implies a need for additional long-term, high-quality measurements and further investigation of nonstomatal mechanisms.
Land surface processes modulate the severity of heat waves, droughts, and other extreme events. However, models show contrasting effects of land surface changes on extreme temperatures. Here, we use an earth system model from the Geophysical Fluid Dynamics Laboratory to investigate regional impacts of land use and land cover change on combined extremes of temperature and humidity, namely aridity and moist enthalpy, quantities central to human physiological experience of near-surface climate. The model’s near-surface temperature response to deforestation is consistent with recent observations, and conversion of mid-latitude natural forests to cropland and pastures is accompanied by an increase in the occurrence of hot-dry summers from once-in-a-decade to every 2–3 years. In the tropics,long time-scale oceanic variability precludes determination of how much of a small, but significant, increase in moist enthalpy throughout the year stems from the model’s novel representation of historical patterns of wood harvesting, shifting cultivation, and regrowth of secondary vegetation and how much is forced by internal variability within the tropical oceans.
Two state-of-the-art Earth System Models (ESMs) were used in an idealized experiment to explore the role of mountains in shaping Earth’s climate system. Similar to previous studies, removing mountains from both ESMs results in the winds becoming more zonal, and weaker Indian and Asian monsoon circulations. However, there are also broad changes to the Walker circulation and the El Niño Southern Oscillation (ENSO). Without orography, convection moves across the entire equatorial Indo-Pacific basin on interannual timescales. The ENSO has a stronger amplitude, lower frequency and increased regularity. A wider equatorial wind zone and changes to equatorial wind stress curl result in a colder cold tongue and a steeper equatorial thermocline across the Pacific basin during La Niña years. Anomalies associated with ENSO warm events are larger without mountains, and have greater impact on the mean tropical climate than when mountains are present. Without mountains the centennial-mean Pacific Walker circulation weakens in both models by ~45%, but the strength of the mean Hadley circulation changes by <2%. Changes in the Walker circulation in these experiments can be explained by the large spatial excursions of atmospheric deep convection on interannual timescales. These results suggest that mountains are an important control on the large-scale tropical circulation, impacting ENSO dynamics and the Walker circulation, but have little impact on the strength of the Hadley circulation.
Berg, Alexis, Kirsten L Findell, Benjamin R Lintner, A Giannini, Sonia I Seneviratne, Bart van den Hurk, R Lorenz, A J Pitman, S Hagemann, A Meier, F Cheruy, A Ducharne, Sergey Malyshev, and P C D Milly, September 2016: Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nature Climate Change, 6(9), DOI:10.1038/nclimate3029. Abstract
The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies1, 2, 3, 4, 5, 6 indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes4, 5. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment7, 8, 9, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms5 by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.
Observations show that Australian dust activity varies by a factor of 4 on decadal timescales. General circulation models, however, typically fail to simulate this variability. Here we introduce a new dust parameterization into the NOAA/Geophysical Fluid Dynamics Laboratory climate model CM3 that represents land surface processes controlling dust sources including soil water and ice, snow cover, vegetation characteristics, and land type. In an additional novel step, we couple this new dust parameterization to the dynamic vegetation model LM3. In Australia, the new parameterization amplifies the magnitude and timescale of dust variability and better simulates the El Niño–Southern Oscillation-dust relationship by more than doubling its strength. We attribute these improvements primarily to the slow response time of vegetation to precipitation anomalies and show that vegetation changes account for approximately 50% of enhanced dust emission during El Niño events. The amplified dust leads to radiative forcing over Australia greater than −1 and −20 W/m2 at top of atmosphere and surface, respectively.
Kanter, D, X Zhang, D L Mauzerall, Sergey Malyshev, and Elena Shevliakova, September 2016: The importance of climate change and nitrogen use efficiency for future nitrous oxide emissions from agriculture. Environmental Research Letters, 11(9), DOI:10.1088/1748-9326/11/9/094003. Abstract
Nitrous oxide (N2O) is an important greenhouse gas and ozone depleting substance. Previous projections of agricultural N2O (the dominant anthropogenic source) show emissions changing in tandem, or at a faster rate than changes in nitrogen (N) consumption. However, recent studies suggest that the carbon dioxide (CO2) fertilization effect may increase plant N uptake, which could decrease soil N losses and dampen increases in N2O. To evaluate this hypothesis at a global scale, we use a process-based land model with a coupled carbon-nitrogen cycle to examine how changes in climatic factors, land-use, and N application rates could affect agricultural N2O emissions by 2050. Assuming little improvement in N use efficiency (NUE), the model projects a 24%–31% increase in global agricultural N2O emissions by 2040–2050 depending on the climate scenario—a relatively moderate increase compared to the projected increases in N inputs (42%–44%) and previously published emissions projections (38%–75%). This occurs largely because the CO2 fertilization effect enhances plant N uptake in several regions, which subsequently dampens N2O emissions. And yet, improvements in NUE could still deliver important environmental benefits by 2050: equivalent to 10 Pg CO2 equivalent and 0.6 Tg ozone depletion potential.
Despite 30 years of basin-wide nutrient-reduction efforts, severe hypoxia continues to be observed in the Chesapeake Bay. Here we demonstrate the critical influence of climate variability, interacting with accumulated nitrogen (N) over multidecades, on Susquehanna River dissolved nitrogen (DN) loads, known precursors of the hypoxia in the Bay. We used the process model LM3-TAN (Terrestrial and Aquatic Nitrogen), which is capable of capturing both seasonal and decadal-to-century changes in vegetation-soil-river N storage, and produced nine scenarios of DN-load distributions under different short-term scenarios of climate variability and extremes. We illustrate that after 1 to 3 yearlong dry spells, the likelihood of exceeding a threshold DN load (56 kt yr−1) increases by 40 to 65% due to flushing of N accumulated throughout the dry spells and altered microbial processes. Our analyses suggest that possible future increases in climate variability/extremes—specifically, high precipitation occurring after multiyear dry spells—could likely lead to high DN-load anomalies and hypoxia.
Li, Dan, Sergey Malyshev, and Elena Shevliakova, June 2016: Exploring historical and future urban climate in the Earth System Modeling framework: 1. Model development and evaluation. Journal of Advances in Modeling Earth Systems, 8(2), DOI:10.1002/2015MS000578. Abstract
A number of recent studies investigated impacts of Land-Use and Land-Cover Changes (LULCC) on climate with global Earth System Models (ESMs). Yet many ESMs are still missing a representation of the most extreme form of natural landscape modification – urban settlements. Moreover, long-term (i.e. decades to century) transitions between build-up and other land cover types due to urbanization and de-urbanization have not been examined in the literature. In this study we evaluate a new urban canopy model (UCM) that characterizes urban physical and biogeochemical processes within the sub-grid tiling framework of the Geophysical Fluid Dynamics Laboratory (GFDL) land model, LM3. The new model LM3-UCM is based on the urban canyon concept and simulates exchange of energy, water (liquid and solid), and carbon between urban land and the atmosphere. LM3-UCM has several unique features, including explicit treatment of vegetation inside the urban canyon and dynamic transition between urban, agricultural and unmanaged tiles. The model is evaluated using observational datasets collected at three urban sites: Marseille in France, Basel in Switzerland and Baltimore in the United States. It is found that LM3-UCM satisfactorily reproduces canyon air temperature, surface temperatures, radiative fluxes, and turbulent heat fluxes at the three urban sites. LM3-UCM can capture urban features in a computationally efficient manner and is incorporated into the land component of GFDL ESMs. This new capability will enable improved understanding of climate change effects on cities and the impacts of urbanization on climate.
Li, Dan, Sergey Malyshev, and Elena Shevliakova, June 2016: Exploring historical and future urban climate in the Earth System Modeling framework: 2. Impact of urban land use over the Continental United States. Journal of Advances in Modeling Earth Systems, 8(2), DOI:10.1002/2015MS000579. Abstract
Using a newly developed urban canopy model coupled to the Geophysical Fluid Dynamics Laboratory (GFDL) land model LM3 (LM3-UCM), this study examines the urban land use impacts over the Continental United States (CONUS) under the present-day climate and two future scenarios. Using natural (undisturbed) vegetation systems as references where no land use has occurred, the LM3-UCM simulations show that the spatial pattern of summer (June, July, and August) temperature differences between urban and natural vegetation systems is primarily controlled by the spatial pattern of differences in evapotranspiration, which further depends on the spatial distribution of precipitation. The magnitude of temperature differences generally increases as the summer precipitation amount increases and then levels off when the total summer precipitation amount exceeds 400 mm, which is broadly consistent with previous studies but with significant variability. In winter (December, January, February), the magnitude of temperature differences is more controlled by the building heating than the precipitation amount. At high latitudes where snow is an important factor in radiative balance, the magnitude is also affected by a larger net shortwave radiation input for urban areas due to the lower albedo of cities. Although both urban and natural vegetation temperatures increase as the climate warms, their increasing rates are different and hence their differences change with time. It is found that the multi-decadal trend of summer temperature difference is negligible. However, the winter temperature differences show a strong negative trend, which is caused by reduced building heating requirements under a warming climate.
Plant photosynthesis and respiration are the largest carbon fluxes between the terrestrial biosphere and the atmosphere1, and their parameterizations represent large sources of uncertainty in projections of land carbon uptake in Earth system models2, 3 (ESMs). The incorporation of temperature acclimation of photosynthesis and foliar respiration, commonly observed processes, into ESMs has been proposed as a way to reduce this uncertainty2. Here we show that, across 15 flux tower sites spanning multiple biomes at various locations worldwide (10° S–67° N), acclimation parameterizations4, 5 improve a model’s ability to reproduce observed net ecosystem exchange of CO2. This improvement is most notable in tropical biomes, where photosynthetic acclimation increased model performance by 36%. The consequences of acclimation for simulated terrestrial carbon uptake depend on the process, region and time period evaluated. Globally, including acclimation has a net effect of increasing carbon assimilation and storage, an effect that diminishes with time, but persists well into the future. Our results suggest that land models omitting foliar temperature acclimation are likely to overestimate the temperature sensitivity of terrestrial carbon exchange, thus biasing projections of future carbon storage and estimates of policy indicators such as the transient climate response to cumulative carbon emissions.
Connections between wildfires and modes of variability in climate are sought as a means for predicting fire activity on interannual to multi-decadal timescales. Several fire drivers, such as temperature and local drought index, have been shown to vary on these timescales, and analysis of tree-ring data suggests covariance between fires and climate oscillation indices in some regions. However, the shortness of the satellite record of global fire events limits investigations on larger spatial scales. Here we explore the interplay between climate variability and wildfire emissions with the preindustrial long control numerical experiments and historical ensembles of CESM1 and the NOAA/GFDL ESM2Mb. We find that interannual variability in fires is underpredicted in both Earth System models (ESMs) compared to present day fire emission inventories. Modeled fire emissions respond to the El Niño/southern oscillation (ENSO) and Pacific decadal oscillation (PDO) with increases in southeast Asia and boreal North America emissions, and decreases in southern North America and Sahel emissions, during the ENSO warm phase in both ESMs, and the PDO warm phase in CESM1. Additionally, CESM1 produces decreases in boreal northern hemisphere fire emissions for the warm phase of the Atlantic Meridional Oscillation. Through analysis of the long control simulations, we show that the 20th century trends in both ESMs are statistically significant, meaning that the signal of anthropogenic activity on fire emissions over this time period is detectable above the annual to decadal timescale noise. However, the trends simulated by the two ESMs are of opposite sign (CESM1 decreasing, ESM2Mb increasing), highlighting the need for improved understanding, proxy observations, and modeling to resolve this discrepancy.
Berg, Alexis, Benjamin R Lintner, Kirsten L Findell, Sonia I Seneviratne, Bart van den Hurk, A Ducharne, F Cheruy, S Hagemann, David Lawrence, and Sergey Malyshev, et al., February 2015: Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. Journal of Climate, 28(3), DOI:10.1175/JCLI-D-14-00324.1. Abstract
Widespread negative correlations between summertime-mean temperatures and precipitation over land regions are a well-known feature of terrestrial climate. This behavior has generally been interpreted in the context of soil moisture-atmosphere coupling, with soil moisture deficits associated with reduced rainfall leading to enhanced surface sensible heating and higher surface temperature. The present study revisits the genesis of these negative temperature-precipitation correlations using simulations from the Global Land-Atmosphere Coupling Experiment - Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5) multi-model experiment. The analyses are based on simulations with 5 climate models, which were integrated with prescribed (non-interactive) and with interactive soil moisture over the period 1950-2100. While the results presented here generally confirm the interpretation that negative correlations between seasonal temperature and precipitation arise through the direct control of soil moisture on surface heat flux partitioning, the presence of widespread negative correlations when soil moisture-atmosphere interactions are artificially removed in at least two out of five models suggests that atmospheric processes, in addition to land surface processes, contribute to the observed negative temperature-precipitation correlation. On longer timescales, the negative correlation between precipitation and temperature is shown to have implications for the projection of climate change impacts on near surface climate: in all models, in the regions of strongest temperature-precipitation anti-correlation on interannual timescales, long-term regional warming is modulated to a large extent by the regional response of precipitation to climate change, with precipitation increases (decreases) being associated with minimum (maximum) warming. This correspondence appears to arise largely as the result of soil-moisture atmosphere interactions.
In this study we explore effects of land-use and land-cover change (LULCC) on surface climate using two ensembles of numerical experiments with the Geophysical Fluid Dynamics Laboratory (GFDL) comprehensive Earth System Model ESM2Mb. The experiments simulate historical climate with two different assumptions about LULCC: (1) no land use change with potential vegetation (PV) and (2) with the CMIP5 historical reconstruction of LULCC (LU). We used two different approached in the analysis: (1) we compare differences in LU and PV climates to evaluate the regional and global effects of LULCC, and (2) we characterize sub-grid climate differences among different land-use tiles within each grid cell in the LU experiment. Using the first method, we estimate the magnitude of LULCC effect to be similar to some previous studies. Using the second method we found a pronounced sub-grid signal of LULCC in near-surface temperature over majority of areas affected by LULCC. The signal is strongest on croplands, where it is detectable with 95% confidence over 68.5% of all non-glaciated land grid cells in June-July-August, compared to 8.3% in the first method. In agricultural areas, the sub-grid signal tends to be stronger than LU-PV signal by a factor of 1.3 in tropics in both summer and winter and by 1.5 in extra-tropics in winter. Our analysis for the first time demonstrates and quantifies the local, sub-grid scale LULCC effects with a comprehensive ESM and compares it to previous global and regional approaches.
Weng, E S., Sergey Malyshev, Jeremy W Lichstein, Caroline E Farrior, Ray Dybzinski, T Zhang, Elena Shevliakova, and Stephen W Pacala, May 2015: Scaling from individuals to ecosystems in an Earth System Model using a mathematically tractable model of height-structured competition for light. Biogeosciences, 12(9), DOI:10.5194/bg-12-2655-2015. Abstract
The long-term and large scale dynamics of ecosystems are in large part determined by the performances of individual plants in competition with one another for light, water and nutrients. Woody biomass, a pool of carbon (C) larger than 50% of atmospheric CO2, exists because of height-structured competition for light. However, most of the current Earth System Models that predict climate change and C cycle feedbacks lack both a mechanistic formulation for height-structured competition for light and an explicit scaling from individual plants to the globe. In this study, we incorporate height-structured competition and explicit scaling from individuals to ecosystems into the land model (LM3) currently used in the Earth System Models developed by the Geophysical Fluid Dynamics Laboratory (GFDL). The height-structured formulation is based on the Perfect Plasticity Approximation (PPA), which has been shown to accurately scale from individual-level plant competition for light, water and nutrients to the dynamics of whole communities. Because of the tractability of the PPA, the coupled LM3–PPA model is able to include a large number of phenomena across a range of spatial and temporal scales, and still retain computational tractability, as well as close linkages to mathematically tractable forms of the model. We test a range of predictions against data from temperate broadleaved forests in the northern USA. The results show the model predictions agree with diurnal and annual C fluxes, growth rates of individual trees in the canopy and understory, tree size distributions, and species-level population dynamics during succession. We also show how the competitively optimal allocation strategy – the strategy that can competitively exclude all others – shifts as a function of the atmospheric CO2 concentration. This strategy is referred as an evolutionary stable strategy (ESS) in the ecological literature and is typically not the same as a productivity- or growth-maximizing strategy. Model simulations predict that C sinks caused by CO2 fertilization in forests limited by light and water will be down-regulated if allocation tracks changes in the competitive optimum. The implementation of the model in this paper is for temperate broadleaved forest trees, but the formulation of the model is general. It can be expanded to include other growth forms and physiologies simply by altering parameter values.
Understanding how different physical processes can shape the probability distribution function (pdf) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture-atmosphere interactions to surface temperature pdfs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back on near-surface climate, in particular temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) earth system model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ’hotspots’ of land-atmosphere coupling. Moreover, higher-order distribution moments such as skewness and kurtosis are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature pdf. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model.
The high mountains of Asia, including the Karakoram, Himalayas and Tibetan Plateau, combine to form a region of perplexing hydroclimate changes. Glaciers have exhibited mass stability or even expansion in the Karakoram region1, 2, 3, contrasting with glacial mass loss across the nearby Himalayas and Tibetan Plateau1, 4, a pattern that has been termed the Karakoram anomaly. However, the remote location, complex terrain and multi-country fabric of high-mountain Asia have made it difficult to maintain longer-term monitoring systems of the meteorological components that may have influenced glacial change. Here we compare a set of high-resolution climate model simulations from 1861 to 2100 with the latest available observations to focus on the distinct seasonal cycles and resulting climate change signatures of Asia’s high-mountain ranges. We find that the Karakoram seasonal cycle is dominated by non-monsoonal winter precipitation, which uniquely protects it from reductions in annual snowfall under climate warming over the twenty-first century. The simulations show that climate change signals are detectable only with long and continuous records, and at specific elevations. Our findings suggest a meteorological mechanism for regional differences in the glacier response to climate warming.
We developed a~process model LM3-TAN to assess the combined effects of direct human influences and climate change on Terrestrial and Aquatic Nitrogen (TAN) cycling. The model was developed by expanding NOAA's Geophysical Fluid Dynamics Laboratory land model LM3V-N of coupled terrestrial carbon and nitrogen (C-N) cycling and including new N cycling processes and inputs such as a~soil denitrification, point N sources to streams (i.e. sewage), and stream transport and microbial processes. Because the model integrates ecological, hydrological, and biogeochemical processes, it captures key controls of transport and fate of N in the vegetation-soil-river system in a comprehensive and consistent framework which is responsive to climatic variations and land use changes. We applied the model at 1/8° resolution for a study of the Susquehanna River basin. We simulated with LM3-TAN stream dissolved organic-N, ammonium-N, and nitrate-N loads throughout the river network, and we evaluated the modeled loads for 1986–2005 using data from 15 monitoring stations as well as a reported budget for the entire basin. By accounting for inter-annual hydrologic variability, the model was able to capture inter-annual variations of stream N loadings. While the model was calibrated with the stream N loads only at the last downstream station Marietta (40.02° N, 76.32° W), it captured the N loads well at multiple locations within the basin with different climate regimes, land use types, and associated N sources and transformations in the sub-basins. Furthermore, the calculated and previously reported N budgets agreed well at the level of the whole Susquehanna watershed. Here we illustrate how point and non-point N sources contribute to the various ecosystems are stored, lost, and exported via the river. Local analysis for 6 sub-basins showed combined effects of land use and climate on the soil denitrification rates, with the highest rates in the Lower Susquehanna sub-basin (extensive agriculture; Atlantic coastal climate) and the lowest rates in the West Branch Susquehanna sub-basin (mostly forest; Great Lakes and Midwest climate). In the re-growing secondary forests, most of the N from non-point sources was stored in the vegetation and soil, but in the agricultural lands most N inputs were removed by soil denitrification indicating that anthropogenic N applications could drive substantial increase of N2O emission, an intermediate of the denitrification process.
Lichstein, Jeremy W., Ni-Zhang Golaz, Sergey Malyshev, Elena Shevliakova, Tao Zhang, Justin Sheffield, Richard A Birdsey, Jorge L Sarmiento, and Stephen W Pacala, June 2014: Confronting terrestrial biosphere models with forest inventory data. Ecological Applications, 24(4), DOI:10.1890/13-0600.1. Abstract
Efforts to test and improve terrestrial biosphere models (TBMs) using a variety of data sources have become increasingly common. However, geographically extensive forest inventories have been under-exploited in previous model-data fusion efforts. Inventory observations of forest growth, mortality, and biomass integrate processes across a range of time scales, including slow time-scale processes such as species turnover, that are likely to have important effects on ecosystem responses to environmental variation. However, the large number (thousands) of inventory plots precludes detailed measurements at each location, so that uncertainty in climate, soil properties, and other environmental drivers may be large. Errors in driver variables, if ignored, introduce bias into model-data fusion. We estimated errors in climate and soil drivers at U.S. Forest Inventory and Analysis (FIA) plots, and we explored the effects of these errors on model-data fusion with the Geophysical Fluid Dynamics Laboratory LM3V dynamic global vegetation model. When driver errors were ignored or assumed small at FIA plots, responses of biomass production in LM3V to precipitation and soil available water capacity appeared steeper than the corresponding responses estimated from FIA data. These differences became non-significant if driver errors at FIA plots were assumed large. Ignoring driver errors when optimizing LM3V parameter values yielded estimates for fine-root allocation that were larger than biometric estimates, which is consistent with the expected direction of bias. To explore if complications posed by driver errors could be circumvented by relying on intensive study sites where driver errors are small, we performed a power analysis. To accurately quantify the response of biomass production to spatial variation in mean annual precipitation within the eastern U.S. would require at least 40 intensive study sites, which is larger than the number of sites typically available for individual biomes in existing plot networks. Driver errors may be accommodated by several existing model-data fusion approaches, including hierarchical Bayesian methods and ensemble filtering methods; however, these methods are computationally expensive. We propose a new approach, in which the TBM functional response is fit directly to the driver-error-corrected functional response estimated from data, rather than to the raw observations.
“LM3” is a new model of terrestrial water, energy, and carbon, intended for use in global hydrologic analyses and as a component of earth-system and physical-climate models. It is designed to improve upon the performance and extend the scope of the predecessor Land Dynamics (LaD) and LM3V models, by quantifying better the physical controls of climate and biogeochemistry and by relating more directly to components of the global water system that touch human concerns. LM3 includes multi-layer representations of temperature, liquid-water content, and ice content of both snow pack and macroporous soil/bedrock; topography-based description of saturated area and groundwater discharge; and transport of runoff to the ocean via a global river and lake network. Sensible heat transport by water mass is accounted throughout for a complete energy balance. Carbon and vegetation dynamics and biophysics are represented as in the model LM3V. In numerical experiments, LM3 avoids some of the limitations of the LaD model and provides qualitatively (though not always quantitatively) reasonable estimates, from a global perspective, of observed spatial and/or temporal variations of vegetation density, albedo, streamflow, water-table depth, permafrost, and lake levels. Amplitude and phase of annual cycle of total water storage are simulated well. Realism of modeled lake levels varies widely. The water table tends to be consistently too shallow in humid regions. Biophysical properties have an artificial step-wise spatial structure, and equilibrium vegetation is sensitive to initial conditions. Explicit resolution of thick (>100 m) unsaturated zones and permafrost is possible, but only at the cost of long (>>300 y) model spin-up times.
Parsons, L A., Jianjun Yin, J T Overpeck, Ronald J Stouffer, and Sergey Malyshev, January 2014: Influence of the Atlantic Meridional Overturning Circulation on the Monsoon Rainfall and Carbon Balance of the American Tropics. Geophysical Research Letters, 41, DOI:10.1002/2013GL058454. Abstract
We examine the response of the American Tropics to changes in Atlantic Meridional Overturning Circulation (AMOC) strength using a set of water-hosing experiments with an Earth system model that explicitly simulates the global and regional carbon cycle. We find that a moderate weakening (27%) of the AMOC, induced by a 0.1 Sv (1 Sv ≡ 106 m3 s-1) freshwater addition in the northern North Atlantic, drives small but statistically significant drying in the South American monsoon region. By contrast, a complete shutdown of the AMOC, induced by a 1.0 Sv freshwater addition, acts to considerably shift the ITCZ southward, which changes the seasonal cycle of precipitation over Amazonia. Our results indicate that AMOC weakening can have a significant impact on the terrestrial primary productivity and carbon storage of the American Tropics.
We describe carbon system formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate good climate fidelity as described in Part I while incorporating explicit and consistent carbon dynamics. The two models differ almost exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. On land, both ESMs include a revised land model to simulate competitive vegetation distributions and functioning, including carbon cycling among vegetation, soil and atmosphere. In the ocean, both models include new biogeochemical algorithms including phytoplankton functional group dynamics with flexible stoichiometry. Preindustrial simulations are spun up to give stable, realistic carbon cycle means and variability. Significant differences in simulation characteristics of these two models are described. Due to differences in oceanic ventilation rates (Part I) ESM2M has a stronger biological carbon pump but weaker northward implied atmospheric CO2 transport than ESM2G. The major advantages of ESM2G over ESM2M are: improved representation of surface chlorophyll in the Atlantic and Indian Oceans and thermocline nutrients and oxygen in the North Pacific. Improved tree mortality parameters in ESM2G produced more realistic carbon accumulation in vegetation pools. The major advantages of ESM2M over ESM2G are reduced nutrient and oxygen biases in the Southern and Tropical Oceans.
Jeong, S-J, David Medvigy, Elena Shevliakova, and Sergey Malyshev, January 2013: Predicting changes in temperate forest budburst using continental-scale observations and models. Geophysical Research Letters, 40(2), DOI:10.1029/2012GL054431. Abstract
A new framework for understanding the macro-scale variations in spring phenology is developed by using new data from the USA National Phenology Network. Changes in spring budburst for the U.S. are predicted by using Coupled Model Intercomparison Project phase 5 outputs. Macro-scale budburst simulations for the coming century indicate that projected warming leads to earlier budburst by up to 17 days. The latitudinal gradient of budburst becomes less pronounced due to spatially-varying sensitivity of budburst to climate change, even in the most conservative emissions scenarios. Currently existing inter-species differences in budburst date are predicted to become smaller, indicating the potential for secondary impacts at the ecosystem level. We expect that these climate-driven changes in phenology will have large effects on the carbon budget of U.S. forests and these controls should be included in dynamic global vegetation models.
Seneviratne, Sonia I., Alexis Berg, Kirsten L Findell, and Sergey Malyshev, et al., October 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophysical Research Letters, 40(19), DOI:10.1002/grl.50956. Abstract
GLACE-CMIP5 is a multi-model experiment investigating the impact of soil moisture-climate feedbacks in CMIP5 projections. We present here first GLACE-CMIP5 results based on five Earth System Models, focusing on impacts of projected changes in regional soil moisture dryness (mostly increases) on late 21st-century climate. Projected soil moisture changes substantially impact climate in several regions in both boreal and austral summer. Strong and consistent effects are found on temperature, especially for extremes (about 1–1.5 K for mean temperature and 2–2.5 K for extreme daytime temperature). In the Northern Hemisphere, effects on mean and heavy precipitation are also found in most models, but the results are less consistent than for temperature. A direct scaling between soil moisture-induced changes in evaporative cooling and resulting changes in temperature mean and extremes is found in the simulations. In the Mediterranean region, the projected soil moisture changes affect about 25% of the projected changes in extreme temperature.
Previous studies have demonstrated the importance of enhanced
vegetation growth under future elevated atmospheric CO2 for
21st century climate warming. Surprisingly no study has completed
an analogous assessment for the historical period, during
which emissions of greenhouse gases increased rapidly and landuse
changes (LUC) dramatically altered terrestrial carbon sources
and sinks. Using the Geophysical Fluid Dynamics Laboratory comprehensive
Earth System Model ESM2G and a reconstruction of
the LUC, we estimate that enhanced vegetation growth has lowered
the historical atmospheric CO2 concentration by 85 ppm,
avoiding an additional 0.31 ± 0.06 °C warming. We demonstrate
that without enhanced vegetation growth the total residual terrestrial
carbon flux (i.e., the net land flux minus LUC flux) would be
a source of 65–82 Gt of carbon (GtC) to atmosphere instead of the
historical residual carbon sink of 186–192 GtC, a carbon saving of
251–274 GtC.
We describe the physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous CM2.1 climate model while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in the El Niño-Southern Oscillation being overly strong in ESM2M and overly weak ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to: total heat content variability given its lack of long term drift, gyre circulation and ventilation in the North Pacific, tropical Atlantic and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to: surface circulation given its superior surface temperature, salinity and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. Our overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords us the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.
Jeong, S-J, David Medvigy, Elena Shevliakova, and Sergey Malyshev, March 2012: Uncertainties in terrestrial carbon budgets related to spring phenology. Journal of Geophysical Research: Biogeosciences, 117, G01030, DOI:10.1029/2011JG001868. Abstract
In temperate regions, the budburst date of deciduous trees is mainly regulated by temperature variation, but the exact nature of the temperature dependence has been a matter of debate. One hypothesis is that budburst date depends purely on the accumulation of warm temperature; a competing hypothesis states that exposure to cold temperatures is also important for budburst. In this study, variability in budburst is evaluated using 15 years of budburst data for 17 tree species at Harvard Forest. We compare two budburst hypotheses through reversible jump Markov Chain Monte Carlo. Then, we investigate how uncertainties in budburst date mapped into uncertainties in ecosystem carbon using GFDL's LM3 land model. For 15 of 17 species, we find that more complicated budburst models that account for a chilling period are favored over simpler models that do not include such dependence. LM3 simulations show that the choice of budburst model induces differences in the timing of carbon uptake commencement of ~11 days, in the magnitude of April-May carbon uptake of ~1.03 g C m-2 day-1, and in total ecosystem carbon stocks of ~2 kg C m-2. While the choice of whether to include a chilling period in the budburst model strongly contributes to this variability, another important factor is how the species-dependent field data gets mapped into LM3's single deciduous plant functional type (PFT). We conclude budburst timing has a strong impact on simulated CO2 fluxes, and uncertainty in the fluxes can be substantially reduced by improving the model's representation of PFT diversity.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
Koster, Randal D., C Tony Gordon, and Sergey Malyshev, et al., October 2011: The second phase of the global land-atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. Journal of Hydrometeorology, 12(5), DOI:10.1175/2011JHM1365.1. Abstract
The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multi-model “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.
The dynamic vegetation and carbon cycling component, LM3V, of the Geophysical Fluid Dynamics Laboratory (GFDL) prototype Earth System Model (ESM2.1), has been designed to simulate the effects of land use on terrestrial carbon pools, including secondary vegetation regrowth. Because of the long time scales associated with the carbon adjustment, special consideration is required when initializing the Earth System Model (ESM) when “historical” simulations are conducted. Starting from an equilibrated, preindustrial climate and potential vegetation state in an “offline” land only model (LM3V), estimates of historical land use are instantaneously applied in five experiments beginning in calendar years: 1500, 1600, 1700, 1750 and 1800. This application results in the land carbon pools experiencing an abrupt change – a “carbon shock”- and the secondary vegetation needs time to regrow into consistency with the harvesting history. We find that it takes approximately 100 years for the vegetation to recover from the carbon shock, while soils take at least 150 years to recover. The vegetation carbon response is driven primarily by land-use history, while the soil carbon response is affected by both land-use history and the geographic pattern of soil respiration rates. Based on these results, we recommend the application of historical land-use scenarios in 1700 to provide sufficient time for the land carbon in ESMs with secondary vegetation to equilibrate to adequately simulate carbon stores at the start of the historical integrations (i.e., 1860) in a computationally efficient manner.
Koster, Randal D., C Tony Gordon, and Sergey Malyshev, et al., January 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophysical Research Letters, 37, L02402, DOI:10.1029/2009GL041677. Abstract
The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is aimed at quantifying, with a suite of long-range forecast systems, the degree to which realistic land surface initialization contributes to the skill of subseasonal precipitation and air temperature forecasts. Results, which focus here on North America, show significant contributions to temperature prediction skill out to two months across large portions of the continent. For precipitation forecasts, contributions to skill are much weaker but are still significant out to 45 days in some locations. Skill levels increase markedly when calculations are conditioned on the magnitude of the initial soil moisture anomaly.
Lichstein, Jeremy W., Ni-Zhang Golaz, Sergey Malyshev, Elena Shevliakova, Tao Zhang, Justin Sheffield, Richard A Birdsey, Jorge L Sarmiento, and Stephen W Pacala, April 2010: Confronting terrestrial biosphere models with forest inventory data. Ecological Applications, 20(3), DOI:10.1890/13-0600.1. Abstract
Efforts to test and improve terrestrial biosphere models (TBMs) using a variety of data sources have become increasingly common. However, geographically extensive forest inventories have been under-exploited in previous model-data fusion efforts. Inventory observations of forest growth, mortality, and biomass integrate processes across a range of time scales, including slow time-scale processes such as species turnover, that are likely to have important effects on ecosystem responses to environmental variation. However, the large number (thousands) of inventory plots precludes detailed measurements at each location, so that uncertainty in climate, soil properties, and other environmental drivers may be large. Errors in driver variables, if ignored, introduce bias into model-data fusion. We estimated errors in climate and soil drivers at U.S. Forest Inventory and Analysis (FIA) plots, and we explored the effects of these errors on model-data fusion with the Geophysical Fluid Dynamics Laboratory LM3V dynamic global vegetation model. When driver errors were ignored or assumed small at FIA plots, responses of biomass production in LM3V to precipitation and soil available water capacity appeared steeper than the corresponding responses estimated from FIA data. These differences became non-significant if driver errors at FIA plots were assumed large. Ignoring driver errors when optimizing LM3V parameter values yielded estimates for fine-root allocation that were larger than biometric estimates, which is consistent with the expected direction of bias. To explore if complications posed by driver errors could be circumvented by relying on intensive study sites where driver errors are small, we performed a power analysis. To accurately quantify the response of biomass production to spatial variation in mean annual precipitation within the eastern U.S. would require at least 40 intensive study sites, which is larger than the number of sites typically available for individual biomes in existing plot networks. Driver errors may be accommodated by several existing model-data fusion approaches, including hierarchical Bayesian methods and ensemble filtering methods; however, these methods are computationally expensive. We propose a new approach, in which the TBM functional response is fit directly to the driver-error-corrected functional response estimated from data, rather than to the raw observations.
We have developed a dynamic land model (LM3V) able to simulate ecosystem dynamics and exchanges of water, energy, and CO2 between land and atmosphere. LM3V is specifically designed to address the consequences of land use and land management changes including cropland and pasture dynamics, shifting cultivation, logging, fire, and resulting patterns of secondary regrowth. Here we analyze the behavior of LM3V, forced with the output from the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model AM2, observed precipitation data, and four historic scenarios of land use change for 1700–2000. Our analysis suggests a net terrestrial carbon source due to land use activities from 1.1 to 1.3 GtC/a during the 1990s, where the range is due to the difference in the historic cropland distribution. This magnitude is substantially smaller than previous estimates from other models, largely due to our estimates of a secondary vegetation sink of 0.35 to 0.6 GtC/a in the 1990s and decelerating agricultural land clearing since the 1960s. For the 1990s, our estimates for the pastures' carbon flux vary from a source of 0.37 to a sink of 0.15 GtC/a, and for the croplands our model shows a carbon source of 0.6 to 0.9 GtC/a. Our process-based model suggests a smaller net deforestation source than earlier bookkeeping models because it accounts for decelerated net conversion of primary forest to agriculture and for stronger secondary vegetation regrowth in tropical regions. The overall uncertainty is likely to be higher than the range reported here because of uncertainty in the biomass recovery under changing ambient conditions, including atmospheric CO2 concentration, nutrients availability, and climate.
We
present
a
mechanism
for
exchange
of
quantities
between
components
of
a
coupled
Earth
system
model,
where
each
component
is
independently
discretized.
The
exchange
grid
is
formed
by
overlaying
two
grids,
such
that
each
exchange
grid
cell
has
a
unique
parent
cell
on
each
of
its
antecedent
grids.
In
Earth
System
models
in
particular,
processes
occurring
near
component
surfaces
require
special
surface
boundary
layer
physical
processes
to
be
represented
on
the
exchange
grid.
The
exchange
grid
is
thus
more
than
just
a
stage
in
a
sequence
of
regrid-
ding
between
component
grids.
We
present
the
design
and
use
of
a
2-dimensional
exchange
grid
on
a
horizontal
planetary
surface
in
the
GFDL
Flexible
Modeling
System
(FMS),
highlighting
issues
of
parallelism
and
performance
The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.
Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.
The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.
Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).
Manuscript received 8 December 2004, in final form 18 March 2005
Guo, Zhichang, Paul A Dirmeyer, Randal D Koster, Gordon Bonan, Edmond Chan, Peter Cox, C Tony Gordon, Shinjiro Kanae, Eva Kowalczyk, David Lawrence, P Liu, Cheng-Hsuan Lu, Sergey Malyshev, B McAveney, J L McGregor, K Mitchell, D Mocko, T Oki, K W Oleson, A J Pitman, Y C Sud, C M Taylor, D Verseghy, R Vasic, Y Xue, and T Yamada, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. Journal of Hydrometeorology, 7(4), DOI:10.1175/JHM511.1. Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment
(GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing
commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations
within a given model and the model-to-model differences—are addressed. The ability of soil moisture to
affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation,
and the ability of evaporation to affect precipitation. Most of the differences between the models and within
a given model are found to be associated with the first stage—an evaporation rate that varies strongly and
consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect
identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–
precipitation connection, however, also play a key role.
Hurtt, George C., S Frolking, M G Fearon, B Moore, Elena Shevliakova, Sergey Malyshev, Stephen W Pacala, and R A Houghton, 2006: The underpinnings of land-use history: three centuries of global gridded land-use transitions, wood-harvest activity, and resulting secondary lands. Global Change Biology, 12(7), DOI:10.1111/j.1365-2486.2006.01150.x. Abstract
To accurately assess the impacts of human land use on the Earth system, information is needed on the current and historical patterns of land-use activities. Previous global studies have focused on developing reconstructions of the spatial patterns of agriculture. Here, we provide the first global gridded estimates of the underlying land conversions (land-use transitions), wood harvesting, and resulting secondary lands annually, for the period 1700–2000. Using data-based historical cases, our results suggest that 42–68% of the land surface was impacted by land-use activities (crop, pasture, wood harvest) during this period, some multiple times. Secondary land area increased 10–44 × 106 km2; about half of this was forested. Wood harvest and shifting cultivation generated 70–90% of the secondary land by 2000; permanent abandonment and relocation of agricultural land accounted for the rest. This study provides important new estimates of globally gridded land-use activities for studies attempting to assess the consequences of anthropogenic changes to the Earth's surface over time.
Klein, Stephen A., Xianan Jiang, J S Boyle, Sergey Malyshev, and Shang-Ping Xie, 2006: Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach. Geophysical Research Letters, 33, L18805, DOI:10.1029/2006GL027567. Abstract
Weather forecasts started from realistic initial conditions are used to diagnose the large warm and dry bias over the United States Southern Great Plains simulated by the GFDL climate model. The forecasts exhibit biases in surface air temperature and precipitation within 3 days which appear to be similar to the climate bias. With the model simulating realistic evaporation but underestimated precipitation, a deficit in soil moisture results which amplifies the initial temperature bias through feedbacks with the land surface. The underestimate of precipitation may be associated with an inability of the model to simulate the eastward propagation of convection from the front-range of the Rocky Mountains and is insensitive to an increase of horizontal resolution from 2° to 0.5° latitude.
Koster, Randal D., Zhichang Guo, Paul A Dirmeyer, Gordon Bonan, Edmond Chan, Peter Cox, H Davies, C Tony Gordon, and Sergey Malyshev, et al., 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. Journal of Hydrometeorology, 7(4), DOI:10.1175/JHM510.1. Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Yin, Jianjun, Michael E Schlesinger, N Andronova, Sergey Malyshev, and B Li, 2006: Is a shutdown of the thermohaline circulation irreversible?Journal of Geophysical Research, 111, D12104, DOI:10.1029/2005JD006562. Abstract
The Atlantic thermohaline circulation (THC) plays a vital role in explaining past abrupt climate changes and in maintaining the current climate. Its remarkable nonlinear dynamics, first demonstrated by H. M. Stommel, have been supported by various types of climate models. This has led to severe concerns that global warming may shut down the THC irreversibly, with consequent catastrophic climate changes, particularly for Europe. Here we use an uncoupled ocean general circulation model (OGCM) and a coupled atmosphere-ocean general circulation model (AOGCM) to investigate the nonlinear response of the THC to freshwater perturbations in the northern North Atlantic. We find that the THC shuts down irreversibly in the uncoupled OGCM simulations but reversibly in the coupled AOGCM simulations. This occurs because of different feedback processes operating in the uncoupled OGCM and AOGCM. The reversal of the THC in the uncoupled OGCM tends to stabilize the “off” mode of the THC by decreasing the mean salinity of the Atlantic, whereas a crucial negative feedback in the AOGCM helps the THC recover. This negative feedback results from complex air-sea interactions, and its operation needs the full participation of the atmosphere. Thus given the more realistic simulation by the AOGCM, the irreversible shutdown of the THC caused by freshwater addition appears to be an artifact of the uncoupled OGCM rather than a likely outcome of global warming.
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
Keith, David, Joseph DeCarolis, David Denkenberger, Donald H Lenschow, Sergey Malyshev, Stephen W Pacala, and Philip J Rasch, November 2004: The influence of large-scale wind power on global climate. Proceedings of the National Academy of Sciences, 101(46), DOI:10.1073/pnas.0406930101. Abstract
Large-scale use of wind power can alter local and global climate by extracting kinetic energy and altering turbulent transport in the atmospheric boundary layer. We report climate-model simulations that address the possible climatic impacts of wind power at regional to global scales by using two general circulation models and several parameterizations of the interaction of wind turbines with the boundary layer. We find that very large amounts of wind power can produce nonnegligible climatic change at continental scales. Although large-scale effects are observed, wind power has a negligible effect on global-mean surface temperature, and it would deliver enormous global benefits by reducing emissions of CO2 and air pollutants. Our results may enable a comparison between the climate impacts due to wind power and the reduction in climatic impacts achieved by the substitution of wind for fossil fuels.
Koster, Randal D., Paul A Dirmeyer, Zhichang Guo, Gordon Bonan, Edmond Chan, Peter Cox, C Tony Gordon, Shinjiro Kanae, Eva Kowalczyk, David Lawrence, Ping Liu, Cheng-Hsuan Lu, and Sergey Malyshev, et al., August 2004: Regions of Strong Coupling Between Soil Moisture and Precipitation. Science, 305(5687), DOI:10.1126/science.11002171138-1140. Abstract
Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.
Rozanov, Eugene, Michael E Schlesinger, Natasha G Andronova, F Yang, and Sergey Malyshev, November 2002: Climate//chemistry effects of the Pinatubo volcanic eruption simulated by the UIUC stratosphere//troposphere GCM with interactive photochemistry. Journal of Geophysical Research, 107(D21), DOI:10.1029/2001JD000974. Abstract
The influence of the sulfate aerosol formed following the massive Pinatubo volcanic eruption in June 1991 on the chemical composition, temperature, and dynamics of the atmosphere has been investigated with the University of Illinois at Urbana‐Champaign (UIUC) stratosphere–troposphere General Circulation Model (GCM) with interactive photochemistry (ST‐GCM/PC). Ensembles of five runs have been performed for the unperturbed (control) and perturbed (experiment) conditions. The simulated repartitioning within the chlorine and nitrogen groups, as well as the ozone changes, are in reasonable quantitative agreement with observations and theoretical expectations. The simulated ozone changes in the tropics reveal the ozone mixing ratio decreases below 28 km and increases in the stratosphere above this level. However, these changes are not statistically significant in the lowermost stratosphere. The simulated total ozone loss reached 15% over the northern middle and high latitudes in winter and early spring. However, the simulated changes are statistically significant only during early winter. The magnitude of the simulated total ozone depletion is generally less than that observed, but some members of the experiment ensemble are in better agreement with the observed ozone anomalies. The model simulates a pronounced stratospheric warming in the tropics, which exceeds the warming derived from observations by 1–2 K. The model matches well the intensification of the polar‐night jet (PNJ) in December 1991 and 1992, the statistically significant cooling of the lower stratosphere and warming of the surface air in boreal winter over the United States, northern Europe, and Russia, and the cooling over Greenland, Alaska, and Central Asia.
