A review of the results for GFDL models from the Computational Performance Model Intercomparison Project (CPMIP) raises the question of why the coupling costs appear so extreme, especially in light of tests demonstrating minimal cost for the FMS coupling framework. This technical memorandum seeks to explain the seemingly high cost associated with the FMS coupler in pre-industrial control (piControl) simulations using the GFDL ESM4 Earth system model and two configurations of the CM4 climate model1. While this technical note presents results for three specific piControl simulations, the findings are assumed to be general and applicable to the full range of configurations and experiments.
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.
Simulation of coupled carbon‐climate requires representation of ocean carbon cycling, but the computational burden of simulating the dozens of prognostic tracers in state‐of‐the‐art biogeochemistry ecosystem models can be prohibitive. We describe a six‐tracer biogeochemistry module of steady‐state phytoplankton and zooplankton dynamics in Biogeochemistry with Light, Iron, Nutrients and Gas (BLING version 2) with particular emphasis on enhancements relative to the previous version and evaluate its implementation in Geophysical Fluid Dynamics Laboratory's (GFDL) fourth‐generation climate model (CM4.0) with ¼° ocean. Major geographical and vertical patterns in chlorophyll, phosphorus, alkalinity, inorganic and organic carbon, and oxygen are well represented. Major biases in BLINGv2 include overly intensified production in high‐productivity regions at the expense of productivity in the oligotrophic oceans, overly zonal structure in tropical phosphorus, and intensified hypoxia in the eastern ocean basins as is typical in climate models. Overall, while BLINGv2 structural limitations prevent sophisticated application to plankton physiology, ecology, or biodiversity, its ability to represent major organic, inorganic, and solubility pumps makes it suitable for many coupled carbon‐climate and biogeochemistry studies including eddy interactions in the ocean interior. We further overview the biogeochemistry and circulation mechanisms that shape carbon uptake over the historical period. As an initial analysis of model historical and idealized response, we show that CM4.0 takes up slightly more anthropogenic carbon than previous models in part due to enhanced ventilation in the absence of an eddy parameterization. The CM4.0 biogeochemistry response to CO2 doubling highlights a mix of large declines and moderate increases consistent with previous models.
The imprint of anthropogenic activities on the marine nitrogen (N) cycle remains challenging to represent in global models, in part because of uncertainties regarding the source of marine N to the atmosphere. While N inputs of terrestrial origin present a truly external perturbation, a significant fraction of N deposition over the ocean arises from oceanic ammonia (NH3) outgassing that is subsequently deposited in other ocean regions. Here, we describe advances in the Geophysical Fluid Dynamics Laboratory's (GFDL) Earth System Model 4 (ESM4.1) aimed at improving the representation of the exchange of N between atmosphere and ocean and its response to changes in ocean acidity and N deposition. We find that the simulated present‐day NH3 outgassing (3.1 TgN yr−1) is 7% lower than under preindustrial conditions, which reflects the compensating effects of increasing CO2 (−16%) and N enrichment of ocean waters (+9%). This change is spatially heterogeneous, with decreases in the open ocean (−13%) and increases in coastal regions (+15%) dominated by coastal N enrichment. The ocean outgassing of ammonia is shown to increase the transport of N from N‐rich to N‐poor ocean regions, where carbon export at 100 m increases by 0.5%. The implications of the strong response of NH3 ocean outgassing to CO2 for the budget of NH3 in the remote marine atmosphere and its imprint in ice cores are discussed.
This contribution describes the ocean biogeochemical component of the Geophysical Fluid Dynamics Laboratory's Earth System Model 4.1 (GFDL‐ESM4.1), assesses GFDL‐ESM4.1's capacity to capture observed ocean biogeochemical patterns, and documents its response to increasing atmospheric CO2. Notable differences relative to the previous generation of GFDL ESM's include enhanced resolution of plankton food web dynamics, refined particle remineralization, and a larger number of exchanges of nutrients across Earth system components. During model spin‐up, the carbon drift rapidly fell below the 10 Pg C per century equilibration criterion established by the Coupled Climate‐Carbon Cycle Model Intercomparison Project (C4MIP). Simulations robustly captured large‐scale observed nutrient distributions, plankton dynamics, and characteristics of the biological pump. The model overexpressed phosphate limitation and open ocean hypoxia in some areas but still yielded realistic surface and deep carbon system properties, including cumulative carbon uptake since preindustrial times and over the last decades that is consistent with observation‐based estimates. The model's response to the direct and radiative effects of a 200% atmospheric CO2 increase from preindustrial conditions (i.e., years 101–120 of a 1% CO2 yr−1 simulation) included (a) a weakened, shoaling organic carbon pump leading to a 38% reduction in the sinking flux at 2,000 m; (b) a two‐thirds reduction in the calcium carbonate pump that nonetheless generated only weak calcite compensation on century time‐scales; and, in contrast to previous GFDL ESMs, (c) a moderate reduction in global net primary production that was amplified at higher trophic levels. We conclude with a discussion of model limitations and priority developments.
We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea‐ice model. OM4 serves as the ocean/sea‐ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project (CMIP6/OMIP). The ocean component of OM4 uses version 6 of the Modular Ocean Model (MOM6) and the sea‐ice component uses version 2 of the Sea Ice Simulator (SIS2), which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments (CORE) protocol to assess simulation quality across a broad suite of climate relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization.
MOM6 makes use of a vertical Lagrangian‐remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth‐isopycnal coordinate reduces the mid‐depth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution.
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.
A climate model represents a multitude of processes on a variety of time and space scales; a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory bound. Such weak-scaling, I/O and memory-bound multi-physics codes present particular challenges to computational performance.
Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models.
We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth System) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform, and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering.
We present results for these measures for a diverse suite of models from several modeling centres, and propose to use these measures as a basis for a CPMIP, a computational performance MIP.
Theurich, G, C DeLuca, T Campbell, F Liu, K Saint, M Vertenstein, J Chen, R C Oehmke, James D Doyle, T Whitcomb, A Wallcraft, M Iredell, T Black, A Da Silva, T Clune, R Ferraro, P Li, M Kelley, I Aleinov, V Balaji, and Niki Zadeh, et al., July 2016: The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability. Bulletin of the American Meteorological Society, 97(7), DOI:10.1175/BAMS-D-14-00164.1. Abstract
The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open source terms or to credentialed users.
The ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the U.S. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. This shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multi-agency development of coupled modeling systems, controlled experimentation and testing, and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NavGEM), HYbrid Coordinate Ocean Model (HYCOM), and Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS®); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and GEOS-5 atmospheric general circulation model.
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.
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.
This paper documents time mean simulation characteristics from the ocean and sea ice components in a new coupled climate model developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The climate model, known as CM3, is formulated with effectively the same ocean and sea ice components as the earlier GFDL climate model, CM2.1, yet with extensive developments made to the atmosphere and land model components. Both CM2.1 and CM3 show stable mean climate indices, such as large scale circulation and sea surface temperatures (SSTs). There are notable improvements in the CM3 climate simulation relative to CM2.1, including a modified SST bias pattern and reduced biases in the Arctic sea ice cover. We anticipate SST differences between CM2.1 and CM3 in lower latitudes through analysis of the atmospheric fluxes at the ocean surface in corresponding Atmospheric Model Intercomparison Project (AMIP) simulations. In contrast, SST changes in the high latitudes are dominated by ocean and sea ice effects absent in AMIP simulations. The ocean interior simulation in CM3 is generally warmer than CM2.1, which adversely impacts the interior biases.
